COLLNET 2011 Proceedings

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Oct 30, 2006 - (1989), D. Kaser (1989), Margaret Beckham (1991), Frederick Wilfrid Lancaster. (1994), Philip Bryant ...... Nicholas, David" (with 38 collaborated ...
COLLNET 2011 Proceedings 7. International Conference on Webometrics, Informetrics and Scientometrics (WIS) &

12. COLLNET Meeting September 20-23, Istanbul Bilgi University, Turkey

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Table of Contents A CENTURY OF CITATION INDEXING.........................................................................................................................5 INVITED TALKS............................................................................................................................................................ 11 QUANTITY IS O NLY O NE OF THE QUALITIES................................................................................................................... 12 THE ROLE OF CORE DOCUMENTS IN BIBLIOMETRIC NETWORK ANALYSIS AND THEIR RELATION W ITH H- TYPE I NDICES..... 19 GENDER EFFECTS OF EVALUATION: ARE MEN MORE PRODUCTIVE AND MORE CITED THAN W OMEN?........................... 22 PRODUCTIVITY AND CITEDNESS OF A GERMAN MEDICAL RESEARCH I NSTITUTION........................................................... 39 RELATIONS AMONG THE NUMBER OF CITATIONS, REFERENCES AND AUTHORS: REVISITED ............................................. 55 FESTSCHRIFTEN IN THE I NFORMATION SCIENCES, WITH SPECIAL ATTENTION TO EUGENE GARFIELD’S FESTSCHRIFT “THE W EB OF KNOWLEDGE” ................................................................................................................................................ 67 ORAL PRESEN TATIONS ............................................................................................................................................. 76 LAWS OF SCATTERING.................................................................................................................................................. 77 W EBSITES OF THE CENTRAL UNIVERSITIES OF I NDIA: A W EBOMETRIC STUDY ................................................................. 87 THE DIFFERENT FLAVOURS OF RESEARCH COLLABORATION: A C ASE STUDY OF THEIR I NFLUENCE ON UNIVERSITY EXCELLENCE IN FOUR W ORLD REGIONS........................................................................................................................ 98 THE RELATIONSHIP BETWEEN QUALITY AND QUANTITY I NDICATORS OF SCIENTIFIC PUBLICATIONS OF I RANIAN MEDICAL SCIENCES UNIVERSITIES (1999–2008)...................................................................................................................... 109 A BIBLIOMETRIC ANALYSIS OF SCIENCE COOPERATION GROUP SIZE ............................................................................. 114 CORRELATION BETWEEN SCIENTIFIC O UTPUT AND COLLABORATION AMONG LIS SCHOLARS AROUND THE W ORLD....... 123 ASSESSING THE DIFFUSION OF NANOTECHNOLOGY IN TURKEY: A SOCIAL N ETWORK ANALYSIS APPROACH.................... 133 A COMPARATIVE CITATION ANALYSIS OF I RANIAN & TURKISH I NVENTOR'S PATENTS I NDEXED IN USPTO DURING 1988 TO 2010........................................................................................................................................................................ 141 CITING TO HIGHLY-CITED RESEARCHERS BY THEIR CO-AUTHORS AND THEIR S ELF-CITATIONS: HOW D O THESE AFFECT HIGHLY-CITED RESEARCHERS’ H-I NDEX IN SCOPUS ..................................................................................................... 153 THE ASSOCİATİON B ETWEEN FOUR CİTATİON METRİCS AND PEER RANKİNGS OF RESEARCH I NFLUENCE İN SİX FİELDS OF AUSTRALİAN PUBLİC HEALTH ..................................................................................................................................... 166 AUTHOR PRODUCTIVITY LAW: A C ASE S TUDY OF THE RESEARCHERS ............................................................................ 177 PUBLICATION AND PATENT ANALYSIS OF EUROPEAN RESEARCHERS IN THE FIELD OF PRODUCTION TECHNOLOGY AND MANUFACTURING SYSTEMS ....................................................................................................................................... 184 EFFECTS OF COMMUNICATION VARIETY ON PERFORMANCE IN COLLABORATIVE SOFTWARE DEVELOPMENT .................. 195 CO-AUTHORSHIP AND CO-CITATION IN NANOTECHNOLOGY: A SOCIAL NETWORK APPROACH....................................... 204 I MPACT AND ROLE OF DIGITAL LIBRARIES ON POPULARIZATION OF SCIENCE: A C ASE FOR DIGITAL PARLIAMENTARY LIBRARY OF I RAN .................................................................................................................................................................... 215 SUBJECT SPECIALIZATION AND SUBJECT DISPERSION IN HISTORICAL SCHOLARSHIP : QUALITATIVE PRELIMINARY EXPLORATIONS IN DISCIPLINARY CULTURES................................................................................................................. 224 DEVELOPMENT OF T ECHNOLOGY-DRIVEN TECHNOLOGY ROADMAP FOR I DENTIFYING NEW BUSINESS O PPORTUNITIES BASED ON PATENT I NFORMATION .............................................................................................................................. 235 TRACING THE W IDER I MPACTS OF BIOMEDICAL RESEARCH: A LITERATURE SEARCH TO DEVELOP A NOVEL CITATION CATEGORISATION TECHNIQUE .................................................................................................................................... 247 COLLABORATION STUDIES IN TURKEY: THE C ASE OF “POLYMER SCIENCE”.................................................................... 259 SCIONTOMETRIC ANALYSIS OF STEM CELL RESEARCH: A COMPARATIVE STUDY OF I NDIA AND O THER ........................... 268 SUCCESS FACTORS FOR I NNOVATION OF GLOBAL LEADING SMES ................................................................................ 280 A NEW APPROACH FOR AUTOMATIZING THE ANALYSIS OF RESEARCH TOPICS DYNAMICS: APPLICATION TO OPTOELECTRONICS RESEARCH.................................................................................................................................... 292 TO W HAT EXTEND ARE FUNDED ARTICLES MORE HIGHLY CITED THAN UNFUNDED ARTICLES? .................................... 304 HAVE SOUTH AFRICAN NON-W HITE SCIENTISTS PROSPERED SINCE THE ENDING OF APARTHEID?................................. 309 THE EVALUATION OF I NDIAN CANCER RESEARCH, 1990-2010 ................................................................................... 317 COLLABORATION PATTERNS OF TAIWAN SCIENTIFIC PUBLICATIONS IN DIFFERENT RESEARCH AREAS ............................. 329 2

NANOTECHNOLOGY DEVELOPMENT IN I NDIA: QUANTITATIVE ANALYSIS....................................................................... 338 MATTHEW, M ATILDA AND THE O THERS ..................................................................................................................... 348 THE TOP 10 ALTERNATIVE S EARCH ENGINES (ASE) W ITHIN SELECTED CATEGORIES RANKED BY W EBOMETRIC I NDICATORS ................................................................................................................................................................................. 362 THE COLLABORATION ON SCHOLARLY DISCOURSES: COLLABORATIVE AND SEMANTIC ASPECTS ON THE CONSTRUCTION OF SCIENTIFIC CONCEPTS................................................................................................................................................ 373 BARRIERS TO SCIENTIFIC COLLABORATION: I RANIAN CASE STUDY IN SCIENTIFIC SOCIAL NETWORK CHARACTERIZATION BASED ON CO-AUTHORSHIP ....................................................................................................................................... 383 SCIENTIFIC PRODUCTIVITY IN THE FIELD OF PHYSICAL CHEMISTRY FOR SELECTED COUNTRIES ........................................ 388 COLLABORATION RATE A MONG ENGINEERS IN I NTERNATIONAL CONFERENCE ON EARTHQUAKE ENGINEERING AND SEISMOLOGY (ICEES) IN I RAN DURING 1991-2011 .................................................................................................. 397 FACTORS THAT AFFECT SCIENTIST’ BEHAVIOR TO SHARE SCIENTIFIC KNOWLEDGE ........................................................ 412 BIBLIOMETRIC STUDY OF IPR LITERATURE AS REFLECTED IN JOURNAL OF INTELLECTUAL PROPERTY RIGHTS .................. 424 PROMOTING AGRICULTURE KNOWLEDGE VIA PUBLIC W EB SEARCH ENGINES AN EXPERIENCE BY AN I RANIAN LIBRARIAN IN RESPONSE TO AGRICULTURAL QUERIES ...................................................................................................................... 435 CO-LINK & CO-AUTHORSHIP : CONFLUENCE OF W EBOMETRICS & SCIENTOMETRICS ..................................................... 443 COMPARATIVE ANALYSIS OF SCIENTIFIC PRODUCTIONS OF 5 TOP I NDUSTRIAL UNIVERSITIES OF I RAN IN W EB OF SCIENCE DURING 2000-2009 ................................................................................................................................................ 448 SCIENTOMETRIC SECRETS OF EFFICIENT COUNTRIES: TURKEY, GREECE, POLAND, AND SLOVAKIA .................................. 458 "CO- AUTHORSHIP " STUDIES:FROM 1986-2010: A R EVIEW OF I RANIAN AND NON-I RANIAN STUDIES......................... 466 I NDIA IN PARTNERSHIP TO FIGHT HIV/AIDS .............................................................................................................. 478 SCIENCE IN SOUTH BRAZIL : O UTPUT O VERVIEW BETWEEN 2000 AND 2009............................................................... 489 CAHIT ARF: EXPLORING HIS SCIENTIFIC I NFLUENCE USING SOCIAL NETWORK ANALYSIS, AUTHOR CO -CITATION MAPS AND SINGLE PUBLICATION H I NDEX.................................................................................................................................... 499 COLLABORATION OF TURKISH SCHOLARS: LOCAL OR GLOBAL ?..................................................................................... 522 EXPLORING ALTERNATIVES TO W EB CO-LINK ANALYSIS: A W EB CO-WORD STUDY OF HETEROGENEOUS I NDUSTRIES.... 533 CITATION ANALYSIS OF DOCTORAL DISSERTATIONS IN THE SUBJECT OF M ATHEMATICS SUBMITTED TO PT. RAVISHANKAR SHUKLA UNIVERSITY .................................................................................................................................................. 545 MEASURING TAIWANESE PUBLIC –PRIVATE COLLABORATION IN SCIENCE PARKS.......................................................... 555 THE CHANGE OF CITATION RANKS IN HIGHLY CITED PAPERS O VER TIME ...................................................................... 561 COLLABORATIVE BRIDGE BETWEEN ACADEMIC RESEARCH AND I NDUSTRY: A C ASE STUDY OF THE UNIVERSITY OF YAOUNDE I IN CAMEROON......................................................................................................................................................... 566 AN I NVESTIGATION OF CITATION PATTERN TO I RANIAN'S ENGLISH JOURNALS USING GOOGLE SCHOLAR & ISI: DO THEY DIFFER? .................................................................................................................................................................... 574 POSTER PRESENTATIONS ....................................................................................................................................... 583 EVALUATING SCIENTIFIC COLLABORATION AND PERFORMANCE RELATIONSHIP ............................................................. 584 I RANIAN SCIENTIFIC PULICATIONS IN MIDDLE EAST COUNTRIES: CASE STUDY OF DENTISTRY FIELDS 1996- 2009......... 594 FREQUENCY IN CITATION ANALYSIS : S TUDY OF R ELATION BETWEEN CITATION FREQUENCY AND JOURNAL I MPACT FACTORS ................................................................................................................................................................................. 597 A BIBLIOMETRIC STUDY OF SCIENTIFIC O UTPUT OF MIDDLE EAST COUNTRIES IN PSYCHOLOGY (1996-2010).............. 601 E-LEARNING SYSTEM FOR GENERATING C................................................................................................................... 604 PROCEEDING PAPERS OR JOURNAL ARTICLES? A COMPARATIVE ANALYSIS ON COMPUTER SCIENCE VERSUS ELECTRICAL & ELECTRONIC ENGINEERING......................................................................................................................................... 610 A SURVEY OF SCIENTIFIC PRODUCTION AND COLLABORATION RATE AMONG OF LIBRARY AND I NFORMATION SCIENCE IN PHILOSOPHY AND THEORETICAL BASES OF LIBRARY AND I NFORMATION SCIENCE IN ISI AND SCOPUS DATABASES DURING 2001-2010 ............................................................................................................................................................. 612 SCIENTOMETRICS STUDY OF HUMAN-PAPILLOMAVIRUS IN MEDLINE 2005-2009..................................................... 622 KNOWLEDGE SHARING FOR I MPROVING THE EFFECTIVENESS OF UNIVERSITY –I NDUSTRY COLLABORATION..................... 625 COMPARATIVE ANALYSIS OF SCIENTIFIC PRODUCTIONS OF I RAN AND TURKEY IN W EB OF SCIENCE FOR THE PERIOD 20052010........................................................................................................................................................................ 632 AUTHORSHIP PATTERN AND PROMINENT AUTHORS IN STRATEGIC MANAGEMENT : A BIBLIOMETRIC STUDY.................. 641 W EB USABILITY EVALUATION OF I RAN NATIONAL LIBRARY W EBSITE ............................................................................ 645 APPLYING SOCIAL NETWORK ANALYSIS FOR KNOWLEDGE MANAGEMENT IN I NTER-ORGANIZATIONAL NETWORKS ........ 658 3

AUTHOR PRODUCTIVITY LAW: A C ASE S TUDY OF THE RESEARCHERS OF I SFAHAN MEDICAL UNIVERSITY ........................ 672 A STUDY OF THEORETICAL FOUNDATIONS OF LIBRARY AND I NFORMATION SCIENCE ABSTRACTS IN ISI AND SCOPUS DATABASES DURING 2001-2010 TO PROVIDE MAJOR AND MINOR COMPONENTS IN THIS AREA ................................ 680 THE SURVEY OF SCIENTIFIC PRODUCTIONS IN FIELDS OF SCIENTOMETRICS AND W EBOMETRICS IN W EB OF SCIENCE (WOS) ................................................................................................................................................................................. 691 AUTHOR INDEX ........................................................................................................................................................ 699

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A Century of Citation Indexing Keynote Address Eugene Garfield [email protected] Chairman Emeritus, Thomson Reuters - Professional (formerly The Institute for Scientific Information) 1500 Spring Garden Street, Philadelphia, PA 19130 http://eugenegarfield.org/

It has been 60 years since I began my career in information science. Since that time there have been huge changes in technology that are reflected in the way that scientific and scholarly information is disseminated, retrieved, and evaluated. The younger generation of scientists in Turkey and elsewhere will not even remember Current Contents or the huge printed volumes of the Science Citation Index. In its first generation of life, Current Contents became the compulsory reading habit of scientists throughout the world. Then in its second generation of life, we began to publish in Current Contents the series called This week's Citation Classic. Over the next 15 years, we asked thousands of authors to write commentaries on their highly cited papers. Four thousand of these one-page commentaries can be found at (http://www.citationclassics.org/). Quite often authors would say that these were not necessarily their most important papers. So when I was asked to write a commentary about my 1955 paper in Science1 I had to point out that this primordial paper on Citation Indexing was neither my most cited work nor my most significant. My 1972 paper in Science,2 on using citation analysis to evaluate journals, has attracted much more attention. My most-cited work is in fact my 1979 book, Citation Indexing.3 Books, like review papers, are easy ways to refer to an author‘s work in a general way. However, tracing the genealogy of cita tions to my 1955 paper reveals the evolution of the concept of citation indexing -- from a system for information retrieval to a tool for research evaluation. In a paper that I had prepared at the request of the then editor of Science in 1995, I suggested that the tail was now wagging the dog. 4 I will say more about this later. Indeed, this was the theme of my Keynote Address at the last COLLNET conference in Dalian, China. I feel honored to have been invited to COLLNET again. In the first few decades after the appearance of my 1955 paper, and its 1964 successor 5 , most of the papers citing them were concerned with the pros and cons of citation indexing for information retrieval. In those days librarians and researchers were preoccupied with traditional controlled vocabulary-based indexing or cataloging. So we created the Permuterm Subject Index as a natural language supplement to the Citation Index. Henry Small, much later, would formalize the role of citations as concept symbols.6 5

An early portent of the use of citations for evaluating science was the 1967 paper by the Australian Joel Margolis.7 By that time Irving Sher and I had already done the simplistic exercise of sorting the Science Citation Index to produce a list of the 50 most cited authors. About one-third of these proved to be Nobel Prize winners and almost all were authors of Nobel Class.8 When my 1955 paper was published, there were no computers. Punched-card methods were considered revolutionary. Even 10 years later, when we launched the SCI, punched-cards were used as input to the first primitive IBM computers to produce the printed SCI. In those days, Vannevar Bush's concept of Memex was as close as we came to thinking about the idea of an internet.9 But the topographical and network properties of citations were fully recognized and given formal descriptions by my brother Ralph Garner 10 and Derek Price.11 The idea of mapping science, based on the linking properties of citations, was well understood and used to explore the historiography of DNA.12 Early on a small group of people saw in the SCI its significant potential for bibliometric evaluations. It would be tempting to outline the various significant papers and reports that have eventually made the SCI and its successor Web of Science a standard tool in the hands of science policy analysts and others interested in research evaluation. And it is also the basis for annual predictions or forecasts of the Nobel Prizes by David Pendlebury. However, the Web of Science is now not only considered essential in libraries and elsewhere but also sufficiently popular to engender competition from Google Scholar, Scopus and others. Indeed it should be noted that citation linking is at the heart of Google's success as a search engine, based as it is on the page ranking process derived originally from the Journal Citation Reports. Reading my 1955 paper once again reminds me of the inspiration that the concept had from my early interest in encyclopedism. In 1970, Professor Manfred Kochen (University of Michigan) commented on its role in the worldwide encyclopedic movement.13 Today the Internet has enabled the development of Wikipedia and other grand schemes that will make the H.G. Wells dream of a World Brain a reality. I have sometimes called this ―Bibliographic Nirvana‖. I should point out that H.G. Wells was probably inspired by the Belgian documentalist Paul Otlet. The relatively low memory capacity of computers in the early days would prevent their application for these uses for three or more decades. Since then the network properties of citation indexes have been explored by numerous investigators. These were pioneered by Derek deSola Price in his 1965 network 11 paper. It appeared shortly after my 1964 paper in Science5 which described the SCI not only as a new dimension in indexing but pointed to its use in science evaluation. It is easy to forget today that even ten years after the first printed SCI annual was published, libraries hotly debated whether to purchase it to supplement, or even replace, a combination of traditional indexing services. Eventually the basic conservatism of scientists and librarians was overcome. This evolution paralleled the growth in computer memory capacity -- from the 16K memory of the IBM 1401 computer we used in those days, to the gigabyte or even terabyte capacity we take for granted today. It was even more accepted when ISI added author abstracts to the database. When that occurred, many libraries no longer felt the need to retain the traditional abstracting services. 6

While the Web of Science is now routinely used in industry for alerting purposes, one of my greatest disappointments has been the failure of most scholars to use it as an alerting tool, i.e. for selective dissemination of information or SDI. Today SDI is performed by weekly or daily citation alerts, but the first such service, the Automatic Subject Citation Alert (ASCA),14 was started in 1965, a year after we started SCI. It is still difficult for most users to develop citation consciousness, which is another way of saying they resist the preparation of search profiles that include cited authors or references as well as keywords. The younger generation uses Google alerts, unaware that electronic alerting services have been available for over 45 years. I think there is more than enough evidence to indicate that quantitative studies of science and scholarship have come of age. Fifty years after the launch of the Science Citation Index covering 1961 literature it is relevant to ask whether the tail of scientometrics is wagging the dog of information retrieval. 15 The new generation needs to be regularly reminded that the Science Citation Index (SCI) was neither created to conduct quantitative studies, nor to calculate journal impact factors, nor to facilitate the study of the history of science. A professor at Cornell University mistakenly made that claim in a paper entitled ―Reward or Persuasion? The battle to define the meaning of a citation.‖ 16 In an e-mail message Prof. Michael Koenig reminded him that the Science Citation Index was not developed as a tool to study the history of science but as an aid to information retrieval. 17 This generational amnesia is not unusual. That same week, in a posting to Steve Harnad‘s Open Access listserv, readers had to be reminded that the ASCA (now known as Research Alert) personal profiling system based on citation indexing and keywords had been in existence at the Institute for Scientific Information since 1965. So, to expand the historical record, SCI and citation indexing were meant not only to aid information retrieval, but also to facilitate SDI, i.e. Selective Dissemination of Information as described earlier. SDI eventually evolved into the Personal Alert. And, of course, this is a part of the Web of Science system as well. Google Citation Alerts are nothing new. It is not entirely surprising that some people assume that the SCI was created as a tool for historians. Since the early 1960‘s I was associated with Robert K. Merton, Derek de Solla Price, V.V. Nalimov, J. D. Bernal among others whose work I have frequently cited. Bernal was the progenitor of the ―Science of Science,‖ which later evolved to Scientometrics, who laid the foundation for this field. I described all this history at the 11th ISSI conference in Madrid several years ago. 18, 19 This page from a HistCite collection of papers citing his 1939 book ― The Social Function of Science‖ illustrates Bernal‘s impact on our progenitors. http://garfield.library.upenn.edu/histcomp/index-bernal.html The association of the SCI with the history of science was perhaps further conflated when Irv Sher and I published our 1964 report on the use of citation analysis for writing the history of science. That project ultimately led to the creation, with Alexander Pudovkin and Vladimir Istomin, of the HistCite software for creating historiographs as a by-product of searches in the Web of Science ." 20

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Please refer to my web site (http://garfield.library.upenn.edu/histcomp/) for dozens of other mini- histories of science that are illuminated by chronological historiographs for Nobel class scientists like Albert Einstein, J. D. Watson, J. H. Hubbell, Werner Heisenberg, Max Planck, Joshua Lederberg, Raold Hoffmann among many others. And I might add that the expanded Web of Science makes these historical analyses possible since we have gone back in time to process the significant literature of the twentieth century. In short, I have always believed that tracing the history and evolution of topics is intimately related to the task of documenting not only new scholarly journal articles and books but also tracing the origins of technologies in patent citation analysis. Many scientists and editors often make the even more outrageous error of assuming, that the SCI was created just to produce its by-product database called Journal Citation Reports (JCR). That database now reports the impact factors for about 15,000 journals each year . Considering the diverse range of editorial commentaries arguing for and against journal impact factors, it is understandable that science editors and administrators new to the field of bibliometrics assume that SCI and its successor, Web of Science, was created to calculate journal impact factors. The growth of COLLNET and ISSI and this symposium demonstrate that it is appropriate to say of Citationology or citation analysis, that not only is the tail wagging the dog, but the tail has become a huge animal which is rapidly mutating into a multiplegic monster. Indeed, research evaluation and the creation of research indicators is an industry that is driving the modern R&D enterprise through its influence on administrators and policy makers. Whether it is the NSF Biennial Science & Engineering Indicators report in the US, the annual JCR reports from ThomsonReuters or the Research Assessment Exercise (RAE) in the U.K. or its equivalents in other countries, I think there is no turning back. Considering the impact of publication studies on individual careers and funding decisions, it is ever more critical that the scientific community and especially journal editors adopt the ―Gentle art of citation.‖20 We cannot allow the Google generation of researchers to repeat what Ginsburg called ―the disregard syndrome‖. 21 Each author, like each potential patentee, must insure that the appropriate historic precedents are cited with precision to assure accuracy. When that happens, perhaps we will approach bibliographical nirvana. If time allows. Appendix: I would like to repeat the sage remarks of Peter Swart and Paul Carling concerning the ethical and critical role of citation as published in Sedimentology, a journal most of us do not read. Their comments state the case very well and I will, therefore, quote directly from their abovementioned editorial... ―The citations which authors use reveal a significant amount in r elation to what the authors understand about a subject, how the paper will fare with reviewers and, ultimately, the number of times the paper itself will be cited. The first problem, which we see repeatedly as editors, relates to the citation of a primary reference for a particular 8

phenomenon. Frequently, authors cite the most recent paper rather than the original work.‖ ―Some authors do not want to do the hard work of unearthing who first came up with a particular idea, so they use the dreaded et al to include a collective group of papers. This is a ―cover your backside‖ citation so you can give credit to multiple persons including those who maybe had nothing to do with creating the concept in the first place. This problem is particularly bad in some papers in which every single citation is accompanied by a list of papers, making the citation essentially useless. Finally, we have the problem of the excessive self citation, people who feel that they are not cited enough and insist on squeezing as many of the ir papers into the reference list as possible. It is natural to cite oneself in a paper, usually for quite valid reasons, but when the self-citation quotient becomes excessive it reflects poorly on the paper, the author and the journal. We have been attempting to correct these problems but it is still up to the authors, who know their fields better than anyone, to consider carefully the insertion of a reference. Find out who was originally responsible for an idea and do not add references indiscriminately. The appropriate use of citations will benefit everyone and delay what is probably inevitable, citation inflation.‖ ____________________________________________________________

References: 1.

Garfield E. "Citation indexes for science: a new dimension in documentation through association of ideas" Science 1955;122:108-11. Available at: http://www.garfield.library.upenn.edu/papers/science_v122v3159p108y1955.html (Reprinted Int J Epidemiol 2006;35:1123-27.) 2. Garfield E. "Citation analysis as a tool in journal evaluation" Science 1972;178:471-479. Reprinted in Current Contents, 1973; Reprinted in Essays of an Information Scientist, Volume 1, pp. 528-44. Philadelphia: ISI Press, 1977. http://www.garfield.library.upenn.edu/essays/V1p527y1962-73.pdf 3. Garfield E. "Citation Indexing: Its Theory and Application in Science, Technology, and Humanities" Philadelphia: ISI Press, 1977, p. 79. http://www.garfield.library.upenn.edu/ci/title.pdf 4. Garfield E. "From citation indexes to informetrics: Is the tail wagging the dog?" Libri 1998;48:67-80. http://www.garfield.library.upenn.edu/papers/libriv48(2)p67-80y1998.pdf 5. Garfield E. "Science Citation Index : a new dimension in indexing" Science 1964;144:649-54. http://www.garfield.library.upenn.edu/essays/v7p525y1984.pdf 6. Small HG. "Cited documents as concept symbols" Soc Stud Sci 1978;8:327-340. http://www.garfield.library.upenn.edu/small/hsmallsocstudsciv8y1978.pdf 7. Margolis J. "Citation indexing and evaluation of scientific papers" Science 1967;155:1213 http://www.sciencemag.org/cgi/content/abstract/155/3767/1213 8. Garfield E. "Citation Indexing for studying science" Nature 1977;227:669-671. Reprinted in Current Contents, 1970; Essays of an Information Scientist; Volume 1, pp. 13238. Philadelphia: ISI Press, 1970. http://garfield.library.upenn.edu/papers/naturev227p669y1970.pdf

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9.

Bush V. "As We May Think" Atlantic Monthly 1945;176:101-08. Available at: http://www.theatlantic.com/doc/194507/bush 10. Garner R. "A Computer Oriented, Graph Theoretic Analysis of Citation Index Structures" Philadelphia: Drexel University Press, 1967. http://www.garfield.library.upenn.edu/rgarner.pdf 11. Price DJD. "Networks of scientific papers: the pattern of bibliographic references indicates the nature of the scientific research front" Science 1965;149:510-15. http://garfield.library.upenn.edu/papers/pricenetworks1965.pdf 12. Garfield E, Sher IH, Torpie RJ. "The Use of Citation Data in Writing the History of Science" Philadelphia: Institute for Scientific Information, 1964, p. 75. http://www.garfield.library.upenn.edu/papers/useofcitdatawritinghistofsci.pdf 13. Kochen M. "WISE: world information synthesis and encyclopedia" J Document 1972;28:322-343. 14. Garfield E, Sher IH. "ISI's experiences with ASCA: a selective dissemination system" J Chem Document 1967;7:147-53. Reprinted in Essays of an Information Scientist, Volume 6, p. 533 Philadelphia: ISI Press, 1984. http://www.garfield.library.upenn.edu/essays/v6p533y1983.pdf 15. Garfield, E. "From Citation Indexes to Informetrics: Is the tail wagging the dog?" Libri,48(2), p.67-80, June 1998. http://www.garfield.library.upenn.edu/papers/libriv48(2)p67-80y1998.pdf 16. Davis PM. ―Reward or persuasion? The battle to define the meaning of a citation‖ Learned Publishing, 22(1):5-11, January 2009. 17. Koenig M. e-mail message dated February 9, 2009. 18. Garfield E. "From the Science of Science to Scientometrics : Visualizing the History of Science with HistCite software". Presented at the 11th ISSI International Conference, Madrid, Spain - June 25, 2007. http://garfield.library.upenn.edu/papers/issispain2007.pdf 19. Garfield E. "From the Science of Science to Scientometrics : Visualizing the History of Science with HistCite software" Presented at the 11th ISSI International Conference, Madrid, Spain - June 25, 2007. Reprinted in special issue of Journal of Informetrics 3 (2009) 173-179. 20. Swart P and Carling P. Sedimentology 55:115-116, October 2008. 21. Ginsburg I. ―The Disregard Syndrome: A menace to honest science?‖ The Scientist 15(24):51, December 10, 2001.

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Invited Talks

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Quantity is Only One of the Qualities Donald deB. Beaver [email protected] Williams College, 117 Bronfman Science Center, 18 Hoxsey St. Williamstown, MA 01267, USA

Abstract: As our fields have become more sophisticated, complex, and specialized, we deal with ever larger masses of data, and our quantitative results have become more detailed and esoteric, and difficult to interpret. Because our methods are predominantly quantitative, we tend to overlook or underemphasize the qualitative judgments that enter at every stage of our work, and to forget that quantity is only one of the qualities. As in our world today, where we face a flood of factoids and quantitative data stripped of context, and struggle to evaluate it, to give it meaning, and make it into information, so ought we qualitatively to acknowledge and contextualize our research results, not only to make them more relevant, meaningful, and useful to the larger world, but to give our work greater impact and value.

Introduction My thanks to Hildrun Kretschmer and Bülent Özel for their kind invitation to address this Twelfth COLLNET Meeting and 7 th International Conference on Webometrics, Informetrics, and Scientometrics. Most of this summer I have been at a loss in thinking of what I might say to you, my fellow colleagues in the quantitative studies of science and technology. But finally I think I‘ve come up with a few remarks appropriate from a member of COLLNET and a long time student of collaboration in science. As some of you know, I am primarily an historian of science who teaches at a small private liberal arts college, Williams College, in the northwestern corner of the state of Massachusetts in the United States of America. Over the past fifty years bibliometric and scientometric studies of scientific activity, especially collaboration, have constituted the major thread of my research career. On the other hand, my research has played very little role in my teaching, which is predominantly qualitative, not quantitative. It is that distinction that I would like to discuss with you today.

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On quantity and quality One of the articles I like to use in teaching an introduction to science and technology studies is ―Technology Assessment from the Stance of a Medieval Historian,‖ by the late Lynn White, jr., his presidential address to the American Historical Society in December 1973. (White, 1974) To illustrate the complexities of technological assessment, White presents examples of the unpredictable and unexpected consequences of a number of important medieval inventions, including distilled alcohol, gunpowder and cannons, eyeglasses, the spinning wheel, paper, and printing with moveable metal type. He concludes that ―technology assessment, if it is not to be dangerously misleading, must be based as much, if not more, on careful discussion of the imponderables in a total situation as upon the measurable elements.‖(White, 1974, 13) That conclusion echoes his earlier key characterization of a defect in the art of technology assessment, that of its nearly exclusive focus on quantitative measures. He notes that Descartes‘ dualism between the measurable res extensa and the incommensurable res cogitans, so fundamental to Western metaphysics, must, in the long run, ―be abandoned for recognition that quantity is only one of the qualities and that all decisions, including the quantitative, are inherently qualitative.‖(White, 1974, 3) Now, of course we are all aware of that, and we acknowledge as much in our research, when we discuss our methodology. Most recently, I have been taking data for a study of collaboration in 14 journals of women‘s or gender studies, along with Hildrun and Theo Kretschmer, and Ramesh Kundra. In the course of doing so, we have tried to restrict our data to research articles only, but the process of determining which items are ―research‖ in the conventional scholarly sense has not been easy. We have had to make decisions about what to exclude and have a list of 42 items, some of which, like cartoons, fiction, poems, lyrics, and errata and corrections, biographical sketches and obituaries are easy to identify and exclude. Descriptive reports, summaries, editorials, introductions, interviews, comments, and discussions are a little more problematic, but also justifiably not included. Developing policy and community organizing, we took to be on the fringes of research, more concerned with methodology. And, finally, the most difficult of all, we established an article length cutoff of 4 pages or less: any ―research‖ item that was only 1, 2, 3, or 4 pages long. It‘s possible that some 4 page contribut ions really do reflect valuable research, but as almost all items specifically denoted as ―research articles‖ are much longer, up to 25 or 30 pages, we thought that eliminating the very short entries a justifiable procedure. The point is that we made qualitative decisions about what constituted data and what did not. Our quantitative research rests at bottom on qualitative decisions. In fact, the very selection of which journals of gender studies to include was qualitative, as there are many more such ―journals‖ than 14, although most of those would be considered marginal from a scholarly research perspective. One of the consequences is that we miss some of the spirit of gender studies journals in the early days (70s, 80s), where songs, poems, short stories, fiction, sketches, and cartoons are a regular part of the journals‘ offerings. However necessary our quantitative focus, it comes at a cost.

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On quantitative studies One of the characteristics of our research activities is the growing number, sophistication, and complexity of useful quantitative indexes and methods of graphical display. We have added to the relatively simple impact and immediacy measures, the tools of relative citation rates, Co-word analysis, clustering techniques, network analysis, the h- index, Google PageRank, etc. We have become increasingly sophisticated mathematically in the different ways we have of obtaining and analyzing data, and our tool kit of quantitative measures continues to grow. Such growth has the advantage that quantitative methods confer a measure of scientific authority and legitimacy to our research results. They also run the risk of ―scientism,‖ or appearing to imitate the methods of science in order to cloak our results with the objectivity of the methods of the natural sciences. In our paper on gender studies‘ journals, we use three levels of ―new‖ indicators, from the relatively simple to the mathematically complex. The first three are relatively simple and were developed by Naldi and Parenti (Naldi and Pare nti, 2002): Participation

counts the number of publications with at least one author of a given gender

Contribution

measures the involvement of each gender in the production of a publication assuming that each author contributed the same amount

Number of authors

total count of the authors of a given gender in each publication

In our study of 14 Journals of Gender Studies, women‘s participation was 91.6% (the percentage of 8649 papers with at least one female author), whereas men‘s participation was 17.3% (the percentage of 8649 papers with at least one male author). Women‘s contribution, 87.5% and the number of female authors expressed in percent, 85.6% were approximately equal, and nearly the same as their participation. By contrast, Naldi and Parent‘s study of 10,000 papers in different fields of the natural sciences showed men‘s participation to be 94.7% (9470 of 10,000 papers had at least one male author) with women‘s participation fairly substantial at 45.8% (4580 of 10,000 papers had at least one female author). But despite substantial participation, women‘s contribution and number were much less, being approximately equal at 19.5% and 22.2%. Overall, participation is greater than contribution, but the largest discrepancy is in the ratio for women in the natural sciences, 45.8 to 19.5, or more than 2 to 1. How to interpret these data is the question these data raise. It would seem that in the natural sciences, although women‘s participation is relatively large, nearly half of the papers having at least one woman author, their contribution is about equal to their number, both around 20%. Men‘s participation of 94.7% outstrips their contribution and number, both again nearly equal, at 80.5% and 77.8%. Their participation, however, does not exceed their contribution by nearly proportionally as much as women‘s. These results are nearly the mirror images of the results for the field of gender studies. Do the patterns reflect the stereotypical attributions of the natural sciences being more masculine than those of the social sciences and humanities? It

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would be interesting to know how the figures might change if the single authored papers were removed from the samples. A second level of indexes contains a pair of concentration measures. The concentration of females (COF) and the concentration of males (COM) was used to test for the presence of bias in the order of authors by gender. The COF is the ratio of the percentage of females in position x (x = 1,2,3,4 or more) in the byline, to the percentage of the total number of females; the COM similarly as the ratio of the percentage of males in position x (x = 1,2,3,4 or more) to the percentge of the total number of males. In our journals of gender studies, females as first (or sole) authors tended disproportionately to be first authors, with males as first (or only) authors underrepresented. For subsequent positions, however, males were overrepresented, almost as if in compensation. Again, these data probably reflect that the subject field and predo minant authorship are more aligned with women‘s interests. A third and final graphical representation of some quantitative data rests upon a complex mathematical calculation, whose initial description and results have been published before by Hildrun and Theo Kretschmer and with which many of you are familiar. (Kretschmer and Kretschmer, 2007) The following is taken from the forthcoming paper by Hildrun and Theo Kretschmer, Ramesh Kundra, and me:

R2 =0.998

R2 =0.998

15

R2 =0.996

Fig.1: Well-ordered distributions of co-author pairs‟ frequencies (z-axis: log N‟ij ) in dependence on the productivities of the co-authors (The logarithmic binning procedure is used: x-axis: log i‟ and y-axis: log j‟). The leftmost patterns in each row are rotated clockwise as viewed from the top 90 degrees two times, resulting in three patterns per row. First row: Distribution is based on data obtained from the journal PNAS (1980-1998). Total number of co-author pairs (N)=634,014; Authors: 80,058; Articles: 32,486 Regression analysis: R2= 0.998 Second row: Journal “Psychology of Women Quarterly” (1976-2011). N=4,342; Authors: 2,569; Articles: 1,146; R2 = 0.998 Third row: Mixture of 14 journals in women‟s and gender studies (1976-2011); N=11,996; Authors: 16,493; Articles: 5,990; R2 = 0.996 The lines of the theoretical patterns are obtained by the mathematical model. The points where two of the lines intersect are the theoretical predictions for the empirical values at those coordinates. The goodness-of-fit is the greatest in the case where the empirical values correspond exactly to these points obtained from the theoretical model. The empirical values are presented here in the form of dots.

What has always intrigued me about these graphical three dimensional views is that they are easy to grasp, but difficult to interpret. It seems as if the natural sciences usually form convex upward surfaces, and that the social sciences and/or humanities concave upward surfaces. The former pattern reflects associations between aut hors of roughly the same productivity, or peers. The latter pattern reflects associations between authors of different productivity, or ―masters‖ and ―apprentices‖. It would be interesting to extend the analysis further, to see whether such a characterization holds, and what it might imply about the patterns of authorship in the different areas, and what those patterns might imply about the role of collaboration. Although our work is predominantly quantitative, specialized, and complex, it is directed to providing important and essential observations useful for drawing qualitative conclusions, illuminating qualitative questions, and helping to improve science and technology policy. We should not lose sight of the larger context within which our research takes its place and justification. On Seeing the Trees and Not the Forest Our fields enjoy increasing prestige and productivity, and it is amazing how rapidly they have grown. It is almost overwhelming to keep up with the literature, and the threshold for acquiring research competence is continually becoming more difficult to cross. Our fields mirror several increasing trends in contemporary society, whose direction is potentially disturbing. One is the growing deluge of ―information,‖ by which we are 16

daily flooded, and in which we are slowly drowning. We no longer share a sense of a coherent world view by which to place and evaluate the growing flow of information exchange. As Neil Postman remarked, "...Everything from telegraphy and photography in the 19th century to the silicon chip in the twentieth has amplified the din of information, until matters have reached such proportions today that for the average person, information no longer has any relation to the solution of problems."(Postman, 1990) Postman went on to claim that "Information is now a commodity that can be bought and sold, or used as a form of entertainment, or worn like a garment to enhance one's status. It comes indiscriminately, directed at no one in particular, disconnected from usefulness; we are glutted with information, drowning in information, have no control over it, don't know what to do with it."(Postman, 1990) It is becoming increasingly difficult to separate the significant from the trivial, to separate the gold from the dross. Too often we resort to unattributed authority, as when in conversation we remark, ―I read it somewhere.‖ Is there not a world of difference between JASIS, the New York Times, Reader‘s Digest, Facebook, and Twitter? We might similarly say ―I saw it somewhere‖ (You Tube?) or ―I heard it somewhere‖ (TV, rumor) Just a few weeks ago, Neal Gabler discussed a tangent on the same theme in the New York Times Sunday Review (Gabler, 2011). Gabler laments that we seem no longer to have great thinkers like Marx, Freud, Einstein, and McLuhan, but instead we live increasingly in a Post- idea world. Our world used to collect information, not simply to know facts, but also to make of them something larger, ideas that explained the information, made it comprehensible. He argues that there is ―an informational Gresham‘s Law in which information, trivial or not, pushes out ideas‖ because ―we prefer knowing to thinking.‖(Gabler, 2011, 6) Like Postman, Gabler notes that ―we are inundated with so much information that we wouldn‘t have time to process it even if we wanted to and most of us don‘t want to.‖(Gabler, 2011, 6) Reflect for a while on the popularity of Power Point presentations, where the slides too often overemphasize bullet points and summary outlines, instead of a carefully developed argument. Presenters are frequently tempted in their remarks to constrain them to reading the slides with little supporting contextual detail. Permit me an anecdotal observation along these lines: in my 2001 paper in Scientometrics, ―Reflections on Scientific collaboration (and its study): Past, present, and future‖ I listed 18 reasons why scientists collaborate in Table 1. (Beaver, 2001) It is my impression that those 18 numbered points have attracted more attention from citing articles than the general context of the article. The task we face is making our work relevant in a wider sense, in connecting it to the world beyond our relatively narrow specializations. And that task is a very difficult one, too easily relegated to the pressing demands of research and project deadlines.

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Conclusion Do I have a solution? No. That is because there are no acknowledged experts in determining relevance and wider significance, and no algorithms for the individual to follow. Should that mean we should not try? No. The lack of expertise is an advantage, because it means that we can each contribute to an ongoing discussion of the significance of our work to the larger global society. The National Science Foundation has a grant funding criterion which every research project must try to satisfy. It is known as Broader Impact: How will the proposed research have a broader impact (than that on the researcher‘s specialty)? To the extent that we incorporate within our publications our own qualitative visions of the significance of our research, it may help us each appreciate more the value of each other‘s work, and thus bring us closer, as we have come together this week. References Beaver, D. deB., Reflections on scientific collaboration (and its study): Past, present, and future, Scientometrics, 52(3), 2001, 365-377 [web of science 79 citations; http://www.Ingentaconnect.com 169 citations] Gabler, Neal, The Elusive Big Idea, Sunday Review, The New York Times, August 14, 2011, 1,6-7 Kretschmer, H. & T. Kretschmer, Lotka‘s Distribution and Distribution of Co-Author Pairs‘ Frequencies, Journal of Informetrics 1 (2007) 308-377 Naldi, F., Parenti, I.V. (2002). Scientific and Technological Performance by Gender: a feasibility study on Patent and Bibliometric Indicators. Vol. II: Methodological Report. European Commission Research, EUR 20309 Postman, Neil, Informing Ourselves to Death, German Informatics Society (Gesellschaft für Informatik), October 11, 1990, Stuttgart White, Lynn, jr., Technology Assessment from the Stance of a Medieval Historian, The American Historical Review, vol. 9, No. 1, Feb. 1974, pp. 1-13

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The Role of Core Documents in Bibliometric Network Analysis and Their Relation With H-type Indices Wolfgang Glänzel [email protected] Centre for R&D Monitoring (ECOOM), K.U. Leuven, Leuven (Belgium) Institute for Research Policy Studies (IRPS), Hungarian Academy of Sciences, Budapest (Hungary)

Introduction The notion of a ‗core‘ of literature has its roots in co-citation analysis. ‗Core documents‘ have originally introduced in the sense of co-cited core papers, i.e., papers in a core formed by the most cited documents (Small, 1973). The term core documents has later been re- introduced by Glänzel and Czerwon (1996) in the context of bibliographic coupling. The core was defined as those publications that are strongly linked with at least a given number of other documents based on similarity measures derived from bibliographic coupling. Core documents proved useful in many respects, above all 1. for the identification of central documents representing research topics, 2. as tool for the retrieval of related papers, and 3. for reducing the dimensionality of large document spaces. Core documents were, among others, used and studied in cluster analysis based on bibliographic coupling (Jarneving, 2007a,b). An extension has been made by Glänzel and Thijs (2011a,b) by defining core documents on hybrid textual–citation link structures. It was shown that these documents can be used in conjunction with cluster analysis and but can also be uncoupled from clustering for topic representation. Core documents should ideally represent about 0.1%–1.0% of the complete network, dependently of its size. Nevertheless, the choice of the two parameters (strength of links and number of nodes) always remained somewhat arbitrary and rather a matter of experience than of theoretical consideration. The following section will be devoted to the attempt to overcome this arbitrariness. H-index and characteristic scores and scales in networks The h-index (Hirsch 2005) was originally designed as a measure of the research performance of individual scientists. However, the publication output and its citations form a network with papers as nodes and citation links as their edges. In the case of a scale- free network all results obtained from the analysis of the properties of the h- index (e.g., Glänzel, 2006, Barcza and Telcs, 2009) can be applied to the degree distribution of the nodes in this network. One can even go a step further. The h-index can be using for characterising networks in general. For instance, a node‘s h- index can be defined as h if not more than h of its neighbours have a degree not less than h (Korn, et al., 2009 and a network‘s h- index is h if not more than h of its nodes have a degree not less than h (Schubert et al., 2009). This approach works well with directed and undirected graphs representing networks with binary links, that is, in cases if the links among documents 19

do not have different weights. The h-core of a citation network can this way be considered a set of core documents obtained by self-adjusting algorithm. The number of core documents in a network‘s h-core can readily be estimated based on the happroximation formula (cf. Glänzel, 2006). In contrast to binary citation networks, graphs representing structures based on bibliographic coupling, co-citations or textual links and their combination are usually undirected and have weighted edges. In such cases an additional threshold for the strength of links might be required. Then the h-approach alone is not sufficient. Furthermore, there is no algorithm for applying h-related indices to similarity measures. To solve these problems, an alternative method can be applied to similarities measures and even to the determination of the core nodes of the network or of parts of that. The close relationship of the slightly forgotten characteristic scores and scales (CSS) with Hirsch-related statistics (Glänzel, 2007, 2010, Egghe, 2010a,b) might serve as the groundwork for the possible application of this method to network analysis as well. Examples Examples from several disciplines in the sciences and social sciences illustrate how these core nodes of networks can be determined using he different approaches described above, and visualise how core documents are applied to represent the internal structure of the complete network or of parts of those. One special focus is the detection of new emerging topics in the sciences and social sciences. References Barcza, K., Telcs, A. (2009), Paretian publication patterns imply Paretian Hirsch index. Scientometrics, 81 (2), 513–519. Egghe, L. (2010), Characteristic scores and scales based on h-type indices. Journal of Informetrics, 4(1), 14–22. Egghe, L. (2010), Characteristic scores and scales in a Lotkaian framework. Scientometrics, 83(2), 455–462. Glänzel, W., Czerwon, H.J. (1996), A new methodological approach to bibliographic coupling and its application to the national, regional and institutional level. Scientometrics, 37, 195– 221. Glänzel, W. (2007), Characteristic scores and scales: A bibliometric analysis of subject characteristics based on long-term citation observation. Journal of Informetrics, 1(1), 92– 102. Glänzel, W., The role of the h-index and the characteristic scores and scales in testing the tail properties of scientometric distributions. Scientometrics, 83 (3), 2010, 697–709. Glänzel, W., Thijs, B. (2011a), Using `core documents' for the representation of clusters and topics. Scientometrics, DOI: 10.1007/s11192-011-0347-4. Glänzel, W., Thijs, B. (2011b), Using ‗core documents‘ for detecting new emerging topics. Proceedings of ISSI 2011 – the 13th International Conference on Scientometrics and Informetrics, Durban, South Africa, 2011, to be published. Hirsch, J. E. (2005), An index to quantify an individual's scientific research output. Proceedings of the National Academy of Sciences of the United States of America, 102 (46), 16569– 16572. Jarneving, B. (2007a), Bibliographic coupling and its application to research-front and other core documents. Journal of Informetrics, 1 (4), 287–307.

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Jarneving, B. (2007b), Complete graphs and bibliographic coupling: A test of the applicability of bibliographic coupling for the identification of cognitive cores on the field level. Journal of Informetrics, 1 (4), 338–356. Korn, A., Schubert, A., Telcs, A. (2009), Lobby index in networks, Physica A, 388, 2221–2226. Small, H. (1973), Cocitation in scientific literature - new measure of relationship between 2 documents. Journal of the American Society for Information Science, 24 (4), 265–269. Schubert, A., Korn, A., Telcs, A. (2009), Hirsch-type indices for characterizing networks. Scientometrics, 78 (2), 375–382.

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Gender Effects of Evaluation: Are Men More P roductive and More Cited Than Women? Hildrun Kretschmer [email protected] Faculty of Business Administration/Business Computing, University of Applied Sciences, Bahnhofstrasse, D-15745 Wildau

Alexander Pudovkin [email protected] Institute of Marine Biology, Far East Branch, Russian Academy of Sciences, Vladivostok 690041

Johannes Stegmann [email protected] Berlin, Germany

Abstract: Productivity and citedness of the staff of a German medical research institution are analyzed. It was found in our previous study (Pudovkin & AL. 2011) that male scientists are more prolific and cited more often than female scientists. We try to explain in our present study one of the possible causes for obtaining this result with reference to Abramo & AL. (2009) who have verified star or ―high -end‖ scientists play a preponderant role in determining higher performance among males and they found a higher performance of male star scientists with respect to female star scientists, but in the complementary subpopulation the performance gap between the two sexes is truly marginal. According to this idea the authors of the present paper have classified the staff into a subgroup of star scientists (25% of the staff) and in the remaining complementary subgroup (75% of the staff). In agreement with Abramo & Al. a higher performance of male star scientists with respect to female star scientists was found but vice versa a slightly (not significant) higher performance of female scientists with respect to male scientists was identified in the complementary subgroup. Moreover, the differences between male and female scientists both in the staff as well as in the subgroup of star scientists are significant in indexes related to the number of papers (Category 1 indexes), while values of indexes characterizing the quality of papers (Category 2 indexes: average citation rate per paper and similar indexes) are not substantially different among the sexes compared.

Introduction Although participation of women in science is increasing in some countries and fields, the low participation rate of women in research activities has stimulated studies of the barriers faced by women in academia (Vetter, 1981; Moore, 1987; Leta & Lewison, 2003). In recent years, increasing attention has been drawn to gender issues in academia (Butterwick & Dawson, 2005). The recent issue of She Figures 2009 (published by the European Commission) indicates the urgency of the problem: averaged over all fields, despite the fact that more than half the European student population is female only 30% of European researchers and 18% of full professors are women. The more senior the position, the lower the presence of women (ETAN, 2000; Hullmann, 2001; Naldi & Parenti, 2002; Naldi et al., 2004). Therefore,

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studying of the implications of evaluation parameters for the career development of women researchers is requested. One of the goals of our previous study (Pudovkin A., Kretschmer H., Stegmann J., Garfield E. 2011) was related to gender differences obtaining the following results: men are more productive than women the parameters in which the differences between these groups are pronounced are the number of papers, sum of impact factor values of the journals in which the papers are published, cumulative number of cites to these papers, H- index and some other indicators. These indexes are characterizing either productivity or both productivity and quality while values of indexes characterizing the quality of papers (average citation rate per paper and similar indexes) are not substantially different among female and male scientists In our present study we intend to extend these findings from another point of view, i.e. we tr y to explain one of the possible causes for obtaining our former results with reference to Abramo & AL. (2009). These authors have studied gender differences in research productivity of the entire population of research personnel in the scientific –technological disciplines of Italian university system. In particular the contribution of ―star scientists‖ to overall sex differences in research productivity was analyzed. Abramo et.al could find out star, or ―highend‖, scientists play a preponderant role in determining higher performance among males (The term ―star scientist‖ was coinded by Zucker and Darby (1996)). Abramo & AL., identified the star (or high-end) scientists as those located in the top 10% of the rankings of scientific performance. These authors have verified (cf. Abramo &A L. 2009, page 821):

a) there is a higher concentration of men among star scientists, and b) additionally, there is a higher performance of male star scientists with respect to female star scientists in this subpopulation c) the performance gap between male and female star scientists is greater than for the complementary subpopulation d) in this complementary subpopulation the performance gap between the two sexes can be even seen as truly marginal. Two evaluation parameters for scientific performance are used, both in our previous study (Pudovkin & AL., 2011) and by Abramo & AL. (2009): - the number of papers (called ―Output‖ by Abramo & AL.) - sum of impact factor values of the journals in which the papers are published (called ―Scientific Strength‖ by Abramo & AL.) On the other hand there are differences existing in data sizes, in performance indexes and in methodology: Whereas Abramo & AL. have studied 29,036 researchers from different scientific disciplines and universities in Italy, the authors of the previous and present papers created a small data set only of research staff (30 female and 32 male scientists) of one institution (Deutsches Rheuma-Forschungszentrum: DRFZ).

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Whereas the study by Abramo & AL. is based on indexes characterizing either productivity or both productivity and quality of this productivity, the authors of the previous and present papers have additionally used: *indexes characterizing citations *indexes characterizing only quality but not quantity In our previous and present studies the size of the differences between the compared groups are estimated by the difference index (DI): DI = (x1 – x2 ) / SD1&2 , x1 and x2 being the means in the compared groups 1 and 2, SD1&2 being the averaged standard deviation. Statistical significance of the differences was estimated by the Student ttest. Abramo & AL. have applied other statistical tests. Our present study has five goals: to deliver similar verifications for the productivity patterns as done by Abramo & A L. (cf. a, b, c, d above) although different sample sizes and methods are used. This is a kind of a ―double‖ proof of the original approach by Abramo & AL. moreover, to deliver similar verifications for citation patterns and other indexes characterizing either productivity or both productivity and quality contrasting we will show there are minimum gender differences existing regarding values of indexes characterizing the quality of papers only. That means, the special role of the star or (high-end) scientists is rather diminished in this case. to deliver methods for visualization and comparison of the contrasting profiles of gender distributions in dependence on * Indexes of Category 1: indexes characterizing either productivity or both productivity and quality * Indexes of Category 2: indexes characterizing quality of publications independent on quantity to discuss the question: ―Are men more productive and more cited than women?‖ on the basis of the new empirical results. Data In a previous study (Pudovkin A., Kretschmer H., Stegmann J. Garfield E., 2011) entitled ―Productivity and citedness of the staff of a German medical research institution‖ the authors performed an exercise in using different performance indexes (cf. Table 1) in an attemp t to see which of them are more informative in telling the difference in performance of male and female researchers and of other groups. Two indexes, e- index and g-index are added for the present study but ASI99 and ASI95 are not included based on the spec ialties of these indexes. The authors created a small data set of publications produced by the Deutsches RheumaForschungszentrum (DRFZ) in 2004-2008. They extracted from the Web of Science (Thomson/Reuters [ http://science.thomsonreuters.com/isi/]) all the publications by the staff of this institution and citation numbers to these publications. Consulting the web-site of this institution we identified the authors, their position and gender. There are 313 papers in the data base, authored and co-authored by 66 scientists of the DRFZ. The 313 papers were published in 96 journals, domestic and international. We identified 30 female and 32 scientists.

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The citation-based indexes we used are presented in Table 1. Some of these only characterize productivity of an author (NumP), some correlate with the quality of publications (avC, avIF, avPRI, avPRI*IF, av%75), while others depend on both productivity and quality (H- index, sumIF, sumPRI, sumPRI*IF, ASI99, ASI95, ASI75, ASI50) Table 1. Characters and their abbreviations No 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Abbreviation ASI99 ASI95 ASI75 ASI50 Nu mP sumC avC sumIF avIF avPRI H-index av%75 avPRI*IF sumC/y avC/y

16 avNumAuth 17 sumPRI 18 sumPRI*IF 19 e-index 20 g-index * see Appendix

Meaning Author Superority Index* at 99th percentile Author Superority Index* at 95th percentile* Author Superority Index* at 75th percentile* Author Superority Index* at 50th percentile* Nu mber of papers (per author) Sum of cites to papers (per author) Average numer of citations (per paper, per author) Sum of IF values of journals where author‘s papers are published Average journal IF (per paper, per author) Average Percentile Rank Index* (per paper, per author) H-index (after Hirsch, 2005), per author Average percentage of papers with PRI* ≥ 75 (per author) Average product of PRI* by journal IF (per author) Sum of age-corrected citations (per author) Average number of citations divided by the years after publication (per paper per author) Average number of authors (per paper, per author) Sum of PRIs * of papers (per author) Sum of products of PRI* by journal IF (per author) e-index (after Zhang) g-index (after Egghe, 2006)

For the present study we have classified these indexes into two categories: Category 1: Indexes characterizing either productivity or both productivity and quality (NumP, sumIF, sumC, sumC/y, H- index, sumPRI, sumPRI*IF, G, E, ASI50, ASI75, ASI95, ASI99) Category 2: Indexes characterizing quality of publications independent on quantity (avIF, avC, avPRI, avPRI*IF, av%75, avC/y, avNumAuth) Furthermore, new tables are produced for characterizing each index as follows: (Remarks: The cumulative frequencies are calculated from down (corresponding to Vmax ) to up (corresponding to Vmin ), i.e. in the opposite direction as usual.) -

First column: index values (V)

-

Second column: frequencies of female scientists (fe)

-

Third column: cumulative frequencies of females (CF)

-

Fourth column: frequencies of males (ma)

-

Fifth column: cumulative frequencies of males (CM)

-

Sixth column: frequencies of scientists in total (tot)

-

Seventh column: cumulative frequencies of scientists in total (CT)

-

Eighth column: Female%=100*CF/CF max =PF

-

Ninth column: Male%=100*CM/CMmax = PM 25

-

Tenth column: Scientists%= 100*CT/CTmax=PT

-

Eleventh column: Concentration of females=PF/PT=COF

-

Twelfth column: Concentration of males=PM/PT=COM

PF, PM and PT are called as relative cu mu lative frequencies.

In analogy to Abramo & AL. (2009), the concentration of females (COF) is defined here as the ratio between the percentage of females in the subgroup of scientists with higher indicator values (100*CF/CT) and the percentage of females in the population (100*CF max /CTmax ). The concentration of males (COM) is the ratio between the percentage of males in the subgroup of scientists with higher indicator values (100*CM/CT) and the percentage of males in the population (100*CMmax /CTmax ), i.e. COM=PM/PT and COF=PF/PT Table 2: NumP V 1

fe 7

CF 30

ma 7

CM 32

tot 14

CT 62

2 3 4 5 6 7 8 9 10 12 14 16 17 20 22 24 27 28 40 41 42 52 64

3 2 1 4 3 2 1 1 1 1 1 1 0 1 0 0 0 1 0 0 0 0 0

23 20 18 17 13 10 8 7 6 5 4 3 2 2 1 1 1 1

3 3 4 0 1 1 1 0 0 0 2 0 1 0 1 1 1 1 1 1 1 1 1

25 22 19 15 15 14 13 12 12 12 12 10 10 9 9 8 7 6 5 4 3 2 1

6 5 5 4 4 3 2 1 1 1 3 1 1 1 1 1 1 2 1 1 1 1 1

48 42 37 32 28 24 21 19 18 17 16 13 12 11 10 9 8 7 5 4 3 2 1

PF 100.0 0 76.67 66.67 60.00 56.67 43.33 33.33 26.67 23.33 20.00 16.67 13.33 10.00 6.67 6.67 3.33 3.33 3.33 3.33

PM 100.0 0 78.13 68.75 59.38 46.88 46.88 43.75 40.63 37.50 37.50 37.50 37.50 31.25 31.25 28.13 28.13 25.00 21.88 18.75 15.63 12.50 9.38 6.25 3.13

PT 100.0 0 77.42 67.74 59.68 51.61 45.16 38.71 33.87 30.65 29.03 27.42 25.81 20.97 19.35 17.74 16.13 14.52 12.90 11.29 8.06 6.45 4.84 3.23 1.61

COF 1.00

COM 1.00

0.99 0.98 1.01 1.10 0.96 0.86 0.79 0.76 0.69 0.61 0.52 0.48 0.34 0.38 0.21 0.23 0.26 0.30 0.00 0.00 0.00 0.00 0.00

1.01 1.01 0.99 0.91 1.04 1.13 1.20 1.22 1.29 1.37 1.45 1.49 1.61 1.59 1.74 1.72 1.70 1.66 1.94 1.94 1.94 1.94 1.94

Methods Three methods are applied: Classification of the population (staff) into a subgroup of ―high-end‖ scientists and the complementary subgroup Visualization and comparison of the contrasting profiles of gender distributions in dependence on Category 1 or Category 2 indexes Statistical tests of gender differences Classification of the population (staff) into a subgroup of ―high-end‖ scientists and the complementary subgroup: 26

As mentioned above, Abramo & AL. identified the star (or high-end) scientists as those located in the top 10% of the rankings of scientific performance. However, because of our small sample size (30 female and 32 male scientists) it is not useful to identify the subgroup of high-end scientists as those located in the top 10% (i.e. 6-7 scientists only). Inspired by the Pareto principle (known as the 80/20 rule: for many events, roughly 80% of the effects are coming from 20% of the causes), we propose in our case, to divide the whole population (62 scientists) per performance index slightly modified: into a subgroup of scientists located in the top about 25% of the rankings of scientific performance (called high-end scientists in our present paper) and in the remaining complementary subgroup (about 75% of the population). Additionally, a method of visualization of gender distributions in the population is applied to identify if there are pronounced visible differences between men and women located in the field of high-end scientists. If this is the case, the population can be divided as mentioned above. Visualization and comparison of the contrasting profiles of gender distributions in dependence on Category 1 or Category 2 indexes We will visualize the contrasting profiles of gender distributions in dependence on the different categories: Category 1 and Category 2 with help of the SYSTAT graphic program: *per index, per category *per category Statistical tests of gender differences: First: We will check in analogy to Abramo & AL. (2009) if there is a higher concentrat ion of men among high-end scientists using the concentration indexes COF and COM. We will study separately: *Category 1 indexes characterizing either productivity or both productivity and quality *Category 2 indexes characterizing quality of publications independently on quantity Second: We will test some verified assumptions by Abramo et.al (2009) but with other methods: if there is a higher performance of male high-end scientists with respect to female high-end scientists and if the performance gap between male and female star scientists is greater than for the complementary subpopulation if in this complementary subpopulation the performance gap between the two sexes can be even seen as truly marginal In our previous study (Pudovkin & AL. 2011) the size of the differences between the compared groups were estimated by the difference index (DI): DI = (x1 – x2 ) / SD1&2 , x1 and x2 being the means in the compared groups 1 and 2, SD1&2 being the averaged standard deviation. Statistical significance of the differences was estimated by the Student t-test. We are using the same method here for comparing male and female high-end scientists and comparing male and female scientists in the complementary subpopulation. 27

We will study separately again: *Category 1 indexes characterizing either productivity or both productivity and quality *Category 2 indexes characterizing quality of publications independently on quantity Results Visualization of some characteristic differences of gender distributions using Category 1 or Category 2 indexes Per index, per category: Depending on indexes, two examples are selected for presentation, one for Category 1, and another for Category 2. Example for Category 1, index NumP: Whereas the relative male cumulative frequency (PM) regarding the highest index values (Distance from V=NumP=40 up to V=NumP=64) is containing rather 8% of the male scientists the relative cumulative female frequency (PF) is equal to zero, i.e. the distributions of male and female cumulative frequencies are different from each other, cf. Figure 1 (left pattern: The cumulative frequencies are counted from the right side of the abscissa to the left). The relative cumulative frequency distributions (PF dots in red and PM dots in blue) are presented in dependence on the index‘ values V=NumP. Example for Category 2, index avIF: In opposite, we could find other examples, the distributions for males and females are rather the same, cf. an example in Fig. 1 (right pattern) with the index avIF. Whereas the relative male cumulative frequency (PM) regarding the highest index values (Distance from V=avIF=12.3 up to V=avIF=21.2) is containing rather the same percentage of the male scientists as in NumP (about 9%) the relative cumulative female frequency (PF) is about 7%, i.e. the distributions of male and female cumulative frequencies are similar to each other, cf. Figure 1 (right pattern). Concluding, the different distributions between males and females in Category 1 indexes are rather diminished in the Category 2 indexes. 100

100 80

PF (red), PM (blue)

PF (red), PM (blue)

80 60 40 20 0 0

60 40 20

10

20

30

40

NumP

50

60

70

NUMP7 NUMP8

0 0

5

10

avIF

15

20

25

AVIF7 AVIF8

Fig. 1: Distributions of the relative male cumulative frequency (PM, in blue) and the relative female cumulative frequency (PF, in red) in dependence on the indexes. The left pattern shows the distributions in dependence on NumP (Category 1 index) and the right the distributions in dependence on avIF (Category 2 index)

Whereas in Fig. 1 the distributions of PM and PF are presented in dependence on different indexes, this abscissa is replaced by the reversed scale of PT (scientists %), cf. Fig. 2. The patterns on the first row show the distributions of PM and PF in dependence on the index values as in Fig. 1. Three corresponding specified percentages of the staff (From right to left: PT=10%, PT=20% and PT=40%) are marked with help of vertical spikes. The same is done with the patterns on the second row. Comparing the patterns on the first row with the patterns on the second, the replacement is producing a visual shifting of the relative cumulative 28

frequencies of scientists (PT) but it does not influence any relation between the corresponding PM and PF values. For example, in both patterns on the first column PM is equal to 19% and PF is equal to 3% in dependence on PT=10%. In analogy the patterns on the second column can be considered. This procedure is done regarding all of the indexes producing a unique abscissa independent from the specialties of the indexes. Thus, this replacement is an option for better comparison of the distributions related to different indexes.

80

80 PF (red), PM (blue)

100

PF (red), PM (blue)

100

60 40 20 0 0

16 40%

20%

33 NumP

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65

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0 0

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avIF

40% 20% 10%

10%

100

100

80 PF (red), PM (blue)

80

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40 20

19% 3%

60

60 40 20 0 100 90 80 70 60 50 40 30 20 10 0 20% 10% PT 40%

NumP

40 20

19%

3%

60

NUMP7 NUMP8

12.5% 7%

0 100 90 80 70 60 50 40 30 20 10 0 PT 40% 20%10% avIF

AVIF7 AVIF8

Fig.2: Replacement of the index values on the abscissa (patterns on the first row) by the reversed scale of PT (scientists %), resulting in patterns on the second row. This replacement is producing a visual shifting of the relative cumulative frequencies of scientists but it does not influence any relation between the corresponding PM and PF values. (Compare both NumP patterns, first column, first and second rows: Whereas the values on the ordinate are constant, the PT values (10%, 20%, 40%) are shifted. The same with the avIF patterns on the second column, first and second rows)

In Fig. 3 the distributions of the male and female concentrations (COM and COF) are shown in dependence on PT. The left pattern shows the distributions based on the index NumP (Category 1 index) and the right the distributions in dependence on the index avIF (Category 2 index).

29

2.5

2.0

2.0

COF (red), COM (blue)

COF (red), COM (blue)

2.5

1.5 1.0 0.5 0.0 100

80

60

40

20

0

NUMP12 NUMP13

1.5 1.0 0.5 0.0 100

80

PT

60

40

20

AVIF12 AVIF13

0

PT

NumP

avIF

Fig. 3: Distributions of the male and female concentrations (COM in blue and COF in red) in dependence on PT. The left pattern shows the distributions based on the index NumP (Category 1 index) and the right the distributions in dependence on the index avIF (Category 2 index).

Per category: Four profiles of gender distributions in dependence on scientist‘s% (PT) will be es tablished by overlay of all of the corresponding graphs into a single frame for: *the distributions of the relative male cumulative frequency (PM, in blue) and the relative female cumulative frequency (PF, in red), Category 1 indexes (Fig. 4, first row, first column) * the distributions of the relative male cumulative frequency (PM, in blue) and the relative female cumulative frequency (PF, in red), Category 2 indexes (Fig. 4, first row, second column) * the distributions of the male and female concentrations (COM in blue and COF in red), Category 1 indexes (Fig. 4, second row, first column)

100

100

80

80 PF (red), PM (blue)

PF (red), PM (blue)

* the distributions of the male and female concentrations (COM in blue and COF in red), Category 2 indexes (Fig. 4, second row, second column)

60 40

40 20

20 0 100

60

75

50 25 PT, Category 1

0

CAT1FALLPLUS CAT1ALLMPLUS

0 100

75

50 25 PT, Category 2

0

CAT2FALL CAT2MALL

30

2.5

COF (red), COM (blue)

COF (red), COM (blue)

2.5

2.0 1.5 1.0 0.5 0.0 100

75

50 25 PT, Category 1

0

FECO MCO

2.0 1.5 1.0 0.5 0.0 100

75

50 25 PT, Category 2

0

FECO2 MCO2

Fig. 4: Four profiles of gender distributions in dependence on scientist‟s% (PT). The profiles on the first column are related to the Category 1 indexes and the profiles on the second column to the Category 2 indexes. The first row profiles show distributions of the relative male cumulative frequencies (PM, in blue) and the relative female cumulative frequencies (PF, in red). The second row profiles show distributions of the concentrations (COM in blue and COF in red)

This method of visualization of gender distributions in the population has confirmed there are pronounced visible differences between men and women, especially located in the field of high-end scientists (PT up to 25%). Thus, the population can be divided for further studies into a subgroup of the 25% high-end scientists and the complementary subgroup of the remaining 75 % of the staff. We could identify the profiles of the Category 1 indexes (the two graphs, first column) are very different from the profiles of the Category 2 inde xes (the two graphs, second column), especially pronounced in the field with PT up to 25% (high-end scientists): -

Category 1 (Profiles, first column): male scientists are overrepresented in the field with the highest indicator values, i.e. there are visible gender differences of distributions both of the relative cumulative frequencies (PM and PF) and of the concentration measures (COM and COF) in favour of men.

-

Category 2 (Profiles, second column): the relative cumulative frequency distribution of female scientists (PF) is rather equal to the male cumulative frequency distribution (PM) up to PT=25% and there are similar distributions of the concentration measures COF and COM, i.e. the gender differences of distributions are rather diminished compared with the indicators in Category 1.

Concluding, the method of visualization of gender distributions in the population has shown for the Category 1 indexes, there are pronouncedly visible differences between men and women located in the field of high-end scientists (25% of the staff). Thus, the population can be divided for the following studies as mentioned above. However, this kind of visible differences cannot be found in Category 2 indexes. There is the question for further studies if this result is a specialty of the studied small German medical research institution. Nevertheless, for comparison Category 1 indexes with Category 2 indexes: The same method of division the population (25%/75%) is applied. Statistical tests of gender differences: In correspondence with Abramo & AL. (2009) we have verified in our study, there is a higher concentration of men among high-end scientists (cf. Tables 3 and 4) with exception of the average journal impact factor (avIF, Table 4).

31

But the mean concentration of males is higher for Category 1 indexes (COMmean =1.457 in relation to women COFmean =0.512) than for Category 2 indexes (COMmean =1.107 in relation to women COFmean =0.886). The Tables 5 and 6 give the average values for the groups ―Male‖ - M and ―Female‖– F and the difference indexes (DI). These groups with the difference indexes (Statistical significance of the differences was estimated by the Student t-test) can be found under: Population (Staff: These values were already found and published in our previous paper) High-End Subgroup Complementary Subgroup Table 5 gives the values for the Category 1 indexes. One can see that the largest average contrast is observed between ―Males‖ and ―Females‖ in the high-end subgroup (DI=0.99) and the smallest in the complementary subgroup (DI= - 0.34). Moreover, the last shows even a slightly (not significant) higher performance of female scientists with respect to male scientists (DI is equal to minus 0.34). The average contrast between ―Males‖ and Females‖ in the whole population (staff) is smaller (DI=0.61) than the contrast in the high-end subgroup but very much larger than in the complementary subgroup. The most expressed differences between ―Males‖ and ―Females‖ are seen in the high-end subgroup, in the following characteristics: sumC/y (DI=1.39, p 0) is the shape parameter, a (a >0) is the scale parameter, and (here equal to 0) is the location parameter of the distribution. Its complementary cumulative distribution function is a stretched exponential function. The Weibull distribution is related to a number of other probability distributions, in particular, it interpolates between the exponential distribution and the Rayleigh distribution. In the physics model of Weibull distribution, a chain linked by several loops of similar type and both of the ends are pulled by an equal and opposite force x. represents a limited force to keep the chain not to be broken. Obviously, as long as there is a loop is broken by force x, the whole chain will be tensile failure. The probability of chain failure is F(x)=P( 0)

Xmax(Pmax) x

0 . 098482

0 . 693

x x

0 . 641

17 . 1909

e

97 . 995 x

0 . 048886

x

0 . 2 40

e

2

Papers amount

1 . 693

e

x

0 . 026949

R

3.1661(0.1454) 0.923

1208

4.7393(0.1860) 0.984

3817

2 . 641

2 . 240

4 5 . 8 20 7

4.2394(0.1683) 0.992 10241

Because Kernel Density Estimation can relax assumptions on the underlying distribution and can model any distribution to higher levels of accuracy as we mentioned above, we applied this method in modeling the same dataset NANOSCIENCE & NANOTECHNOLOGY. Fig.4 shows the Kernel Density Estimation curve fits the Weibull Probability Density curve very well. Accordingly, it is reasonable to use KDE to analyse the scientific growth and collaboration research group size.

118

0. 18

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(a)

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(c)

Figure 4. Kernel Density Estimation vs Weibull Probability Density distribution in 2000, 2005, and 2010 Table.5 The peak value in Weibull Probalility Density distribution curve and in Kernel Density Estimation curve Kernel Weibull

Size/Density(a) 3.8795/0.1423 3.1661/0.1454

Size/Density (b) 3.8861/0.2376 4.7393/0.1860

Size/Density (c) 4.0542/0.1695 4.2354/0.1683

We made an empirical analysis by Kernel Density Estimation on Chinese cooperation in Biology subcategory(Fig.5)(Zhang, 2009). Fig.5 shows the number of co-authors in most of papers in the Molecular Biology field concentrated in the interval from 2 to 7 in 2001, the distribution curve made a slight movement to right in 2004, the interval change from 2 to 8. In 2004, Kernel curve made a relatively substantially movement to right Kernel curve move to right, and the peak obviously decrease, by original steep curve became immensely gently. This apparent change illustrates the number of coauthors in Molecular Biology field has greatly increased, the vast majority of papers coauthored by 4 to 15, the number of paper co-authored by more than 10 increased noticeably and became very popular. 0.18

Density

.24

.20

Nucleus physics 2010 Physics 2010 Microbiology 2010 Chemistry 2010 Nano 2010

0.16

0.14

BIO_2001 BIO_2004 BIO_2007

.16

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.08

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0.04

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Figure 5. KDE of coauthorship in the publish year of 2001,2004and 2007 in the field of Molecular biology

0

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45

Figure 6. The comparison of five subject categories in cooperation group size

Scientific cooperation group size, involves best size and reasonable size(Fig.2). The best size refers to the co-author size opposite to the peak, and the reasonable size is decided by ―20/80 law‖,

P(

x

)

f ( x)dx

119

50

(x means the cooperation group

size), if P ( x ) 8 0 % , then [ , ] refers reasonable size(Fig.5). We take the data from 5 subject categories in 2009 and 2010 as example, calculate the best size and reasonable size in KDE model(Tab.2). Table 2. Best size and reasonable size in 5 subject categories Physics, Nuclear Physics, Applied Nanoscience & Nanotechnology Chemistry, Applied Microbiology

Best size (Density) 3.5886 (0.1413) 4.0342 (0.1546) 4.0542 (0.1695) 3.393 (0.1141) 3.8307 (0.1197)

Reasonable size 3.5886-7.9373 0.7167-7.3518 1.5317-7.5857 2.3255-7.6628 1.2736-9.5842

Range 4.3487 6.6351 6.054 5.3373 8.3106

Proportion 82.9% 80.5% 81.3% 84.9% 82.5%

The ranking is Nano, physics/applied, microbiology, nuclear physics and chemistry/applied respectively according to best size. From Fig.6 and Tab.2, It is difficult to find strong correlation between collaboration group size and discipline biology connotation. It should be discuss more in detail in future. Conclusion and prospects The above results give responses to the three questions mentioned in introduction. - The Weibull distribution is suitable to cooperation group size distribution. - Kernel Density Estimation curve fits with the Weibull Density Distribution curve perfectly(R2>0.9) . As a comparatively convenient function, Kernel Density Estimation can be used as a model to study the relationship between scientific productivity and group size. - We calculated the best group size and reasonable group size according to ―20/80 law‖, the result didn‘t show correlation between collaboration group size and d iscipline biology connotation obviously. We just selected the data from the top 20 journals of each among five chosen subcategories respectively, which is limited to reflect the conclusions comprehensively. More datasets in diverse scientific fields will be used in the future to implement the approaches put forward and answer the questions proposed above, especially for the third one. Acknowledgme nts Thanks for Dr. Liang Liming, Dr.Zhang Dongling, Dr. Xie Caixia, this paper is inspired by their previous work mostly. Thanks for Dr.H.Kretschmer, she send her relevant research results to us for reference timely. Thanks for Dr. Guo Hanning, she made contribution to paper‘s English translation.

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References Crane, D. (1969). Social structure in a group of scientists: A test of the" invisible college" hypothesis. American Sociological Review 34, 335-352. Goffman, W. & Warren, K. (1980). Scientific information systems and the principle of selectivity: Praeger New York. Kretschmer, H. (1985). Cooperation structure, group size and productivity in research groups. Scientometrics 7, 39-53. Kretschmer, H. & Kretschmer, T. (2007). Lotka's distribution and distribution of co-author pairs' frequencies. Journal of Informetrics 1, 308-337. Lawani, S. (1986). Some bibliometric correlates of quality in scientific research. Scientometrics 9, 13-25. Liang, L., Kretschmer, H., Guo, Y. & deB. Beaver, D. (2001). Age structures of scientific collaboration in Chinese computer science. Scientometrics 52, 471-486. Lotka, A. (1926). The frequency distribution of scientific productivity. Journal of the Washington Academy of Sciences 16, 317-323. Needham, J. & Gwei-Djen, L. (1986). Science and civilisation in China: Biology and biological technology. Botany. Cambridge: Cambridge University Press. Scott, D. (1992). Multivariate density estimation: theory, practice, and visualization. New York: Wiley-Interscience. Seglen, P. & Aksnes, D. (2000). Scientific productivity and group size: A bibliometric analysis of Norwegian microbiological research. Scientometrics 49, 125-143. Stankiewicz, R. (1979). The size and age of Swedish academic research groups and their scientific performance. In: Andrews, F. M. (ed.) Scientific Productivity: The Effectiveness of Research Groups in Six Countries. Cambridge: Cambridge University Press, 191. Wallmark, J., Eckerstein, S., Langered, B. & Holmqvist, H. (1973). The increase in efficiency with size of research teams. IEEE Transactions on Engineering Management 20, 80-86. Xie, C. (2006). Scientometric Study on Scientific Collaboration Pattern and Its Funcation. Dalian: Dalian University of Technology. Zhang, D. (2009). Bibliometric Analysis of Output and Collabration of China's Scientific Literature. Dalian: Dalian University of Technology. Appendix Table 1. Data source list Physics, Nuclear Journals Annu Rev Nucl Part S Prog Part Nucl Phys Atom Data Nucl Data

Physics, Applied 5-Y IF Journals

5-Y IF

11.551 Nat Mater

28.507

3.33 3.27

Nat 23.215 Photonics Mat Sci Eng 23.095 R

Nucl Fusion 3.195 Adv Mater Phys Rev C

3.178

9.836

Adv Funct 8.46 Mater

Plas ma Phys 2.493 Small Contr F

7.235

Energ Convers

5.978

2.465 Mrs Bull

Nanoscience & Chemistry, Nanotechnology Applied Journals 5-Y IF Journals 5-Y IF Journals 5-Y IF Nat Rev Adv Synth 19.049 Nat Nanotechnol 27.67 5.477 Microbiol Catal Microbiol Mol 17.669 Nano Today 12.269 Catal Today 3.659 Biol R Clin M icrobiol 17.471 Nano Lett 11.52 Food Chem 3.606 Rev Food Annu Rev 15.672 Adv Mater 9.836 Hydrocolloi 3.556 Microbiol d Annu Rev Carbohyd 13.021 Adv Funct Mater 8.46 3.469 Microbiol Poly m Micropor Fems 10.829 Acs Nano 7.496 Mesopor 3.168 Microbiol Rev Mat Dyes Plos Pathog 9.705 Small 7.235 3.075 Pig ments Microbiology

121

Manage Laser J Phys G Adv Microb 1.949 Photonics 5.814 8.174 Nucl Partic Physiol Rev Curr Opin Curr Op in Nucl Phys A 1.858 5.18 7.931 Solid St M Microbiol Nucl Data 1.746 Nano Res 4.389 Clin Infect Dis 7.552 Sheets Eur Phys J A 1.576 Org Electron 3.805 Phys Rev 1.47 Spec Top-Ac

Trends Microbiol

Int J Mod Nanoscale 0.649 Phys E Res Lett

2.942

J Infect Dis

J Agr Food 3.051 Chem

Nanomed icineUk

6.19

J Co mb 2.99 Chem

Nanotoxicology 5.791 Biosens Bioelectron

5.698 J Phys Chem C

5.48

4.389 4.229

Mol Microbiol 5.602 Plas monics

3.75

Environ Microbiol

5.278

3.689

Antimicrob Agents Ch

4.819 Nanotechnology 3.574

J Appl Environ Synchrotron 2.829 Microb Radiat Chinese Phys Phys Status Appl Environ 0.256 2.565 C Solidi-R Microb Ieee T J Antimicrob Nukleonika 0.254 2.562 Electron Dev Chemoth High Energ J Clin 0.186 Eur Phys J E 2.334 Phys Nuc Microbiol Atom

6.859

Prog 3.797 Cell Microb iol 5.779 Nano Res Photovoltaics

Nucl Instrum Appl Phys 1.078 3.78 Meth B Lett Plas ma Mod Phys 1.071 Process 3.58 Lett A Poly m Int J Mod Nanotechnol 0.797 3.574 Phys A ogy

Phys Nucl

7.546

Lab Chip

0.48

122

Microfluid Nanofluid

4.516

Bio med Microdevices

3.514

4.407

Micropor Mesopor Mat

3.168

4.294 Scripta Mater

3.073

4.126 J Nanopart Res

3.027

Top Catal

2.971

J Food Co mpos 2.913 Anal Fuel Process 2.849 Technol Sep Purif 2.688 Rev React Funct 2.626 Poly m Food Addit 2.527 Contam A Co mb Chem High 2.248 T Scr Plant Food 2.178 Hu m Nutr Carbohyd 2.166 Res Org Process 2.061 Res Dev Prog Org 2.029 Coat

Correlation Between Scientific Output and Collaboration Among LIS Scholars Around the World

Farshid Danesh

Amir Hussein Abdulmajid

[email protected]

MSc, Library and Information Science, Islamic Parliament of I.R. Iran, Tehran, Iran.

PhD candidate of Library and Information Sciences. Dept. of Library and Information Sciences. School of Education & Psychology. Ferdowsi University of Mashhad. Mashhad. Iran

Mansour Mirzaee

Mohsen Haji Zeinolabedini

MSc, Library and Information Science, Islamic Parliament of I.R. Iran, Tehran, Iran.

. Ph.D. Faculty member of ASIDC.

Abdolrassol Khosravi Ph.D. candidate. Dept. of Library and Information Sciences. School of Education & Psychology. Ferdowsi University of Mashhad. Iran.

Abstract: The purpose of this paper is determination of correlation between scientific production and collaboration rate of LIS scientists and proliferated authors, countries, universities and journals. In this descriptive survey, scientometric methods were utilized. All articles of the journals in LIS field in Emerald from beginning 2003 to the end of 2008 were considered. The data were gathered via check list and analyzed by SPSS. The correlation between scientific output and collaboration rate of LIS scientists measured by Pearson correlation. " Hannabuss, Stuart "," Duckett, Bob "and" James, Stuart "were the most proliferated authors." Nicholas, David "," Huntington, Paul "&" Oppenheim, Charles "had the most collaboration with other scientists."Reference Reviews", "Library Reviews" and "The Electronic Library" were the most proliferated journals. UK, USA and Australia were the most hard – working countries in scientific production in the field of LIS. This survey showed that in spite of the importance of collaborated works among LIS scientists, there was a low level of collaboration rate in their articles. This could be a warning for researchers in order to improve next researches. This is the first survey on determination of correlation between scientific production and collaboration rate. The paper identified the most proliferated authors, journals, countries & organizations; then stated some suggestions and strategies to achieve more collaboration rate. Key words: LIS, collaboration rate, scientific production, Emerald.

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Introduction Recently, increasing of interdisciplinary areas has caused the science to be more dynamic and improve better than before in the world. Scientists almost are not able to research individually and team working in research leads to sharing knowledge of two or more scientists in order to utilize the expertise of each other (Osareh, 2005 b). It is found and proved that researchers tend to working solely less than previous years (Katz & Martin, 1997). Decreasing of the numbers of single author articles and increasing of collaborated papers have been mentioned simultaneously in many fields of science, so that most publications are the result of team working in most disciplines. Authorities of universities, and researchers specially should pay attention to collaboration and scientific team working. Russell (2001) has declared the ability of scientists in communication even internationally have lead to increasing of joint research activities and consequently development of world science with scientist of all over the world today. The increasing of the number of collaborated articles at the end of the last century indicates this fact. Collaboration among researchers can lead to a common goal in different fields. For example the discovery of human genome map couldn' t be done without the cooperation of computer and biology scientists (Russell, 2001). Articles are the most important document to kno wledge improvement in every field. Generally the last findings are published in scientific articles. The correlation between scientific productions and author collaboration in emerald hasn' t been studied specifically yet, and because of the importance of the subject, this paper deals with it and answers weather there is any significant relationship between the scientific production and author collaboration in LIS researchers also, the collaboration rate was calculated. Some other questions of the survey were: what are the most prolific authors, journals countries and research centers and institutions or universities? What is the frequency of distribution in LIS author collaboration in Emerald during 2003-2008? Review of lite rature Osareh (2005 b) has studied the cooperation of Iran in scientific world based on research findings in an article entitled "Higher Education Research collaboration between Iran &UK". The findings indicated that scientific cooperation of Iran has increased daring past 5 years compared with previous decades and UK was the second country which has the scientific collaboration with Iran during 1985-2003. Marshakova & shaikevich (2006) Studied SSCI database in order to analyzed scientific collaboration among ten European countries. The results declared that there was a small collaboration both among themselves and among other European countries. Harirchi, Melin & Etemad (2007) found that Iranian scientist‘s immigration has a big portion in scientific collaboration and bounding with international research communities. Hou, Kretchmer & liu (2008) studied the structure of scientific collaboration networks in "scientometrics" by using bibliographic data of all papers published in the international journal Scientometrics retrieved from the Sc ience Citation Index of the years 1978-2004. Combined analysis of social network analysis, co-occurrence analysis, cluster analysis and frequency analysis of words is explored to reveal: (1) The 124

microstructure of the collaboration network on scientists' aspects of scientometrics; (2) The major collaborative fields of the whole network and of different collaborative subnetworks; (3) The collaborative center of the collaboration network in scientometrics. Jonker & Tijssen(2008) debated on the relationship between scientific mobility and international collaboration. This case study deals with leading Chinese researchers in the field of plant molecular life sciences who returned to their home country. A correlation analysis of their mobility history, publication output, and international copublication data, showed the relationship between scientific output, levels of international collaboration and various individual characteristics of returned researchers. Navaro & Martin (2008) studied the scientific production and collaboration in the field of epidemiology and public health. The results showed that 49% of the articles were produced in USA, 13.3 in UK and 6.8% in Canada. Danesh et al (2009) in a research entitled" Survey on collaboration rate among researchers in Medical Sciences" stated 80% of the research projects were performed by 3 or 4 researcher. The mean collaboration rate among medical researchers during the period of study was 0.35. Sooryamoorthy (2009) indicated that the number of citations received by a publication varies not only according to the collaboration but also to the types of collaboration of the authors who are involved in its production. Furthermore, it emerges that the impact of citations on publications differs from discipline to discipline, and affiliating sector to sector, regardless of collaboration. According to the mentioned literature in can be concluded that scientific collaboration has been useful in most cases and regarding today's information community situation, specialists have no choice except collaboration, because this way they can achieve knowledge, skills, sources and facilities which can't be gotten easily without team working. If researchers recognize the advantages of cooperation and adapt with the patterns, methods and steps of collaboration, this can lead to improvement of their scientific production and consequently development of the knowledge in general. The high number of studies in this area recently can be an evidence of the necessity and need of scientific community to know the importance of collaboration in scientific world. This paper has shown the mentioned subjects in LIS area. Methodology This is a descriptive survey and scientometrics methods were used to analyze data. The research population was all journals in LIS area in Emerald from the beginning of 2003 till the end of 2008. The number of articles was 8320 and 10760 authors have participated in papers. All types of articles have been considered except editorials. A check list consisting of the following information was provided for gathering data: article code, author's name, journal's name, author's education, country, affiliation, collaboration rate and number of papers, then the home pages of journals were observed and each article downloaded and reviewed in order to filling the form. After completing all forms, data was analyzed by SPSS to determine the correlation between the number articles and collaboration rate. Pearson correlation was used. Finally collaboration rate was calculated with the following formula:

125

k

cc

1

1

* j

j

1

Fj

N

Fj= the number of articles with ―j‖ author J= articles (one author, two authors, three authors…) N= the whole number of authors in an article (Ajiferuke& Burell, 1988) Findings The answers to the research questions follow as results consequently. "Hannabuss, Stuart "(with 247 articles), "Duckett, Bob" (with 183 articles) and " James, Stuart "(with 138 articles) were the most proliferated authors." Nicholas, David" (with 38 collaborated articles), Huntington, Paul" (with 30 collaborated articles) and "Oppenheim, Charles" (with 19 collaborated articles) had the most collaboration with other researchers. Table 1 – Distribution of the most proliferated authors and the most collaboration Number of articles Author‟s name

One author

Two authors

Three authors

Four authors

>four authors

total

Hannabuss, Stuart

247

247

Duckett, Bob

183

183

James, Stuart

138

138

Guha, M artin

136

136

Fraser, K.C.

132

132

Harrison, David

90

0

Chalcraft, Anthony

0

0

0

90

88

0

0

0

0

88

Nicholas, David

1

7

12

12

7

39

Huntington, Paul

0

4

8

10

8

30

Oppenheim, Charles

6

10

5

3

1

25

The second question was about the most proliferated journals deal with the number of papers and collaboration. "Reference Reviews" with 2634 papers, "Library Reviews" with 705 papers and "The Electronic Library" with 594 papers were the most proliferated among LIS journals in Emerald. About collaboration, "The Electronic Library" with 180 articles, "Online Information Review" with 146 articles and "References Services Review" with 140 articles have the most collaborated articles as it is presented in table 2.

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Table 2– Distribution of the most proliferated journals based on the number of articles and collaboration Number of articles Journal name

One

Two

Three

Four

>four

author

authors

authors

authors

authors

sum

percent age

Reference Reviews

2633

1

0

0

0

2634

31.66

Library Review

597

73

28

6

1

705

8.47

The Electronic Library

414

113

48

14

5

594

7.14

Online Information Review

405

74

55

9

8

551

6.62

Library M anagement

406

70

28

6

2

512

6.15

New Library World

358

90

18

7

3

476

Program

318

48

16

5

3

390

4.69

Journal of Documentation

247

73

27

21

2

370

4.45

Library Hi Tech

207

88

27

6

7

335

4.03

Library Hi Tech News

227

76

17

6

5

331

3.98

References Services Review

168

99

31

6

4

308

3.7

Aslib Proceeding

117

65

25

8

11

226

2.72

OCLC System & Services

145

46

19

8

4

222

2.67

Collection Building

161

35

5

2

0

203

2.44

127

53

12

5

5

202

2.43

Interlending &Document

Supply

The Bottom line: managing library finances Performance

1.72 130

M easurement

5.72

9

3

1

0

143

and

1.42

M etrics

71

20

16

9

2

118

Total

6731

1033

275

119

62

8320

100

In responding to the third question about the most proliferated countries in terms of the number of papers and collaboration rate, results showed that UK (with 3384 articles), USA (with 2303 articles) and Australia (with 283 articles) were the most proliferated countries, researchers of USA (with 488 collaborated articles), UK (with 310 collaborated articles) and Canada (with 50 collaborated articles) had the most collaboration among a the countries. (table3)

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Table 3–The distribution of the most proliferated countries based on the number of articles and collaboration Number of articles Country

One author

Two authors

Three authors

Four authors

>four authors

total

UK

3076

177

84

36

15

3384

USA

1815

337

95

34

22

2303

Australia

236

36

8

3

2

283

New Zealand

216

19

1

0

0

236

South Africa

154

20

4

0

2

180

Canada

112

32

14

4

0

162

Nigeria

62

39

8

0

0

109

India

57

30

15

1

0

103

Germany

58

28

3

0

0

89

Denmark

54

14

11

0

0

79

In responding to the forth question about the most proliferated universities in terms of the number of papers and collaboration rate, results showed that "University of Stratchlyde, UK" (with 210 articles), "Victoria University of Wellington" (with 194 articles) and "Washington State University" (with 169 articles) were the most proliferated universities. "Loughborough University, UK" (with 41 collaborated articles), "University of California" (with 31collaborated articles), "City University of London" and "University of Stratchlyde, UK" (with 29 collaborated articles) had the most collaboration among the universities (table 4). Table 4– Distribution of the most proliferated institutions and research centers based on the number of articles and collaboration Number of articles institutions and research centers University of Stratchlyde, UK Victoria University of Wellington Washington State University Aberdeen Business School Kings College of London University of Paisley, UK St Andrews University, UK Loughborough University, UK

One author

Two authors

Three authors

Four authors

>four authors

total

181

17

12

0

0

210

191

2

1

0

0

194

165

4

0

0

0

169

164

0

0

0

0

164

159

2

0

0

0

161

133

0

0

0

0

133

133

0

0

0

0

133

78

20

20

0

1

119

128

University of California York St John University, UK City University of London

73

23

3

3

1

103

98

0

0

0

0

98

67

12

10

4

3

96

Another question of research was the collaboration rate among researchers in LIS field in Emerald 2003-2008. According to table 5 from 8320 articles, 6731 articles (80.9%) had one author, 1033 articles (12.42%)had two authors, 375 articles (4.51%)had three authors , 119 (1.43%) had four authors and 62 articles (0.75) had more than four authors. So it is shown that 1589 articles (19.01%) of total articles have written with collaboration. Table 5 - Distribution of collaboration based on the year of publication Number of articles year

One author

T wo authors

Three authors

Four authors

sum

>four authors

2003

1075

116

44

13

9

1257

2004

1123

149

47

18

8

1345

2005

1105

158

70

18

9

1360

2006

1061

180

66

15

12

1334

2007

1300

242

90

31

9

1672

2008

1067

188

58

24

15

1352

sum

6731

1033

375

119

62

8320

percentage

80.9

12.42

4.51

1.43

0.75

100

Another important question was the collaboration rate of articles. It is calculated through the mentioned formula (Ajiferuke & Burell, 1988). The following table shows the amount during different years as it is shown, the average of collaboration rate is increasing from 2003 (0.06) to 2007 (0.1) but the year 2008 has been different. The mean collaboration rate in six years was 0.08. Table 6 - collaboration rate in articles written by authors Year collaboration rate 2003

0.06

2004

0.07

2005

0.08

2006

0.09

2007

0.1

2008 mean

0.09 0.08

129

The last question was about correlation between the number of articles and collaboration rate. Pearson correlation between these two variables is – 0.187 with the P=0.15. So there is no significant relationship between them. (Table7). Table7- Correlation coefficient between number of articles and collaboration rate Collaboratio n rate

Collaboration Rate

No. art icles

1

-0.187

Pearson correlation Sig. (2-tailed) N

0.153 60

60

Conclusion: It is attempted to have a general image of science production and collaboration rate in LIS researchers in Emerald during 2003-2008 and the correlation between them. Findings showed that 8320 articles are published from the beginning of 2003, to the end of 2008.Written by 10760 authors. The results revealed that most of articles had been written by just one author so that 80.9% of all articles were single – author. Just 19.1% of articles had been written by two or more than two authors, this indicated the low level of collaboration among them. Comparing with previous works, Farajpahlou(2004) found that just 23 articles from 168 articles were the result of team working (14%) that means also the low level of tendency to collaboration. Biglou (1996) showed in his research about the scientific production of faculty members in "Tabriz University" that 69.3% of papers were done by just one author. Abam (2000) surveyed about collaboration rate in "Chahid Chamran University of Ahwaz" faculty members. Almost 66.35% of articles were written by one author. Maghsoodi (2002) studied the articles of four journals in the field of LIS. His results revealed that almost 92% of articles were written by just one author. Nouruzi & Ali Mohammadi (2007) also stated that 36 papers from 47 ones was single- author and just 11 papers were collaborated. The results of recent research also are coordinated with the other ones and indicated the low participation of researchers in collaborated articles. The mean of LIS authors' collaboration rate was 0.08. Compare with the previous work it is not an acceptable range. Danesh et al (2009) studied about the collaboration rate in researchers of IUMS and found that was 0.35. Ghahnavieh & Danesh (2009) also studied the papers in conferences and found the collaboration rate 0.22. Osareh (2005a) found in her research about collaboration rate in astronomy articles in Science Direct this rate was 0.494. As it mentioned before compared with other fields collaboration in LIS is not in a good level. Recent paper showed that there is no significant relationship between scientific production and collaboration. Reviewing results indicated that in spite of the importance of collaboration in research projects and writing articles which lead to higher quality of paper, there is a low level of tendency to working together and collaboration in the field of LIS. Although librarians are responsible for the distribution of knowledge, it is a big question why there is a low collaboration among them. As the collaboration can lead to higher quality of papers in all fields of sciences, authorities should pay attention to it more than before and have strategic planning to improve team working. Undoubtedly there must be policies and ideas for improving scientific production in today's society otherwise 130

there is the possibility of staying beyond the scie ntific and advanced world. With the expansion of the culture of team working and collaboration especially among scientists in universities and research centers, big steps can be taken to development and growth of science in different countries. References Abam, Z. (2000)," Survey on scientific production of Shahid Chamran University of Ahvaz's academic members 1979-1999." [Dissertation], faculty of psychology. shahid Chamran University of Ahvaz. Ajiferuke, I., Q. Burell, J. T. (1988). "Collaborative Coefficient: A Single Measure of the Degree of Collaboration in Research". Scientometrics Vol.14, No.5-6, pp: 421-433. Biglou, M.H. (1996)," Survey on scientific production of Tabriz University of Medical science's academic members 1987-1995" [dissertation] faculty of literature and humanity. Tarbiat modarres University. Danesh, F. et al, (2009) "Collaboration Rate among Researchers in Research Center of IUMS in Carrying out Research Projects", Iranian Journal of Health Information Management.Vol6 No.1:pp.43-52. Farajpahlou A. H. (2004). Collaboration among Library and Information experts vs. scientist. In the Proceedings of International workshop on Webometrics, Informetrics and Scientometrics. 2-5 March. Roorkee, India. Ghahnavieh, H., Danesh, F. (2009). " Survey on scientific collaboration and article producing in national conferences of medical and paramedical sciences of IUMS during 2001-2006) [Research Project] Research center of Information technology in health sciences. IUMS. Harirchi, G.; Melin, G.; Etemad Sh. (2007). "An exploratory study of the feature of Iranian coauthorships in biology, chemistry and physics ". Scintometircs. Vol. 72 No.1, pp: 11-24. Hou, H.; Kretchmer, H.; Liu, Z. (2008). The structure of scientific collaboration networks in Scientometrics. Scintometircs. Vol.75, No.2, pp: 189-202. Jonkers K, Tijssen R. (2008)." Chinese researchers returning home: Impacts of international mobility on research collaboration and scientific productivity". Scintometircs. Vol. 77 No.2, pp: 309-333. Katz J.S., Martin B.R. (1995). What is research collaboration? London: ERSRC center for science, Technology, Energy and environment. Maghsoudi Dorrieh,R. (1999), " A comparative- citation analysis of master dissertations in faculty of psychology of Shahid Chamran University of Ahvaz and Shiraz University during 1993-1997",[dissertation], faculty of psychology. Shahid Chamran University of Ahvaz. Marshakova-Shaikevich I. (2006). ”Scientific collaborati on of new 10 EU countries in the fiel d of social sciences". Information Processing and Management. Vol. 42, No. 6, pp: 1592-1598. Navaro A. Martin M. (2008). "Scientific production and collaboration in Epidemiology and Public Health, 1997–2002". Scintometircs. Vol. 76 No.2, pp: 299-313. Nurozi. A., Alimohammadi D. "Scientific collaboration of Iranian LIS professionals across the world with emphasis on citation indexes 1971-2006." Iranian Journal of Informologhy.Vol3 No.3-4, pp: 181-195. Osareh, F. (2005a). Collaboration in Astronomy knowledge production: a case study in Science Direct from 2000-2004. In Proceedings of 10 th International Conference on Scientometrics and Informetric., 24-28 July in Stockholm-Sweden. Osareh, F (2005b). Higher Education Research collaboration between Iran &UK. In Proceedings of COLLNET Meeting Extra Session in conjunction with 10th ISSI Conference, 28th July in Stockholm-Sweden. Russel, J. M. (2001)." Scientific collaboration at the beginning of the 21st." . ISSJ. Vol.16, No.3, pp: 271-279.

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Rahimi, M.; Fattahi, R. (2007) "collaboration and science production: A glance on concepts and common paradigms" ,Iranian journal of Faslnameh-ye ketab, Vol.18 No.3: pp.235-240 Rahimi, M.,Fattahi, R. (2008), " Survey on scientific collaboration and science production in faculty members of four branches in Ferdowsi University of Mashhad" , Iranian Journal of Ketabdary va Ettela resani, Vol.11 No 2, pp.95-121 Sooryamoorthy, R. (2009). "Do types of collaboration change citation? Collaboration and citation patterns of South African science publications." Scientometrics, Vol. 8, No., pp: 1177–1193

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Assessing the Diffusion of Nanotechnology in Turkey: A Social Network Analysis Approach Hamid Darvish [email protected] Department of Informatics, Çankaya University, 06530 Balgat, Ankara, TR Abstract: According to the United Nations, nanotechnology is one of the emerging research fields which will have a positive impact on both developing and developed countries. For example, nanotechnology is used for water purification in poor countries. On the other hand, it is used in developed countries (rich) to manufacture better computer devices. Nanotechnology is a part of the strategic plan for each developing country. For example, the 7th European Union Scientific Framework (EUSF) has invested tremendously in nanotechnology since 2000. The Turkish government also has adopted a new approach by becoming part of the EUSF and has invested heavily in research and development. Focusing on an invisible college in the scientific community in Turkey, this study aims to evaluate the diffusion of nanotechnology among scientists. Using social network analysis metrics, we identified and analyzed the role and status of scientists in the two most prominent universities (Hacettepe University & Bilkent University) regarding nano-related technologies diffusion in Turkey. We found that co-authorship across the two universities is minimal, even in subject area research in both universities. However, the number of nano-related articles have increased exponentially in past decade in Turkey. Furthermore, a co-word analysis revealed the scientific trends in both universities. Keywords description revealed that the scientific trends in Hacettepe University are mostly nano biomedical research oriented whereas in Bilkent University scientific trends are metallurgical oriented in nano-related technologies. Data were derived from Thomson Reuters‘ Web of Science (WoS) version 4 database and CiteSpace was used to map the co author network structure. Keywords: Social network analysis, diffusion, nanotechnology, CiteSpace.

Introduction Mehta stated (2002) that nanoscience is about creation and manipulation of information and nanotechnology is an application which is based on nanoscience principles. The term ―nano‖ refers to particles as small as molecules in a size of 10-9 of a meter. The term nano derived from micro with the advancement of technology, specially the World Wide Web, since 1990 (Leydesdorff & Rafols, 2010). Nanotechnology was accepted by the United Nations as one of the important recent breakthroughs in science. In this respect, Turkey has adopted the same approach by investing in nano-related technology. In addition, Turkey included nanotechnology in its national 2023 Technology Foresight Program ( Saritaş et al., 2007). Hence, the number of publications has increased exponentially. We found that the term nano-related technology is a broad name which can be used interchangeably instead of nanotechnology.

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Private and state universities have participated in nano-related research since 2005 actively in Turkey. For example, the first nanotechnology conference was held at Bilkent University in 2005; this was followed by the establishment of a nanotechnology centre in Bilkent University in Ankara. UNAM (National Nanotechnology Research Center) which incubates several research centers and has 25 scientists who gained 13 patents in the year 2010, is considered the leading center in nanotechnology research and development in Turkey. On the other hand, Hacettepe University is a well known state university that has contributed to the field of nano-biomaterials, nano-polymers and nanocomposites (Dumanlı & Yürüm, 2005). According to Dumanlı and Yürüm (2005) there are several cutting-edge nano-related technologies which have gained special attention from academia and industry in Turkey:  Nanophotonics-nanoelectronics-nanomagnetism, where Turkey hopes to become an international production center for integrated circuit systems with nanostructures.  Nanomaterials, with the amim of producing advanced nanocomposite materials, bio- inspired materials and catalysts and production of nanoelectronic and nanomechanical devices by self- assembling methods.  Fuel cells and energy, where there is a plan to produce fuel cells with high efficiency.  Nanocharacterization, where scientists aim to improve scanning probe microscopes and atomic force microscopes.  Nanofabrication, where the aim is to produce nanostructures and integrated circuit systems with competency.  Nanosized quantum information processing, with the aim of high quality designing, simulating and producing of nanoscale units.  Nanobiotecnology, which is designed to improve DNA diagnosis However, there are obstacles preventing nanotechnology development fro reaching its potential. For example, lack of patents and copyrights, skilled labour and fullyoperational industry regarding nanotechnology are some of the barriers for nanotechnology development (Duda & Şener, 2010). In this paper, we try to find the position of individuals and study their roles in the diffusion of nano-related technologies in two prominent universities using social network metrics. Lite rature review De Solla Price (1965) argued in his paper published in Science magazine that patterns of bibliometric data in scientific communication indicate the scientific development in different research domains. However, citation analysis alone does not suffice to explain the structure of the network of collaboration among scientists. Therefore, there is a need for a new technique. Sociometry is derived from social network analysis which is rooted in psychology, mathematics and sociology. Information scientists utilize sociometric methods for measuring relational patterns among members of social systems. Social network analysis is a paradigm (Wellman, 2009), which is based on a theoretical premise which studies humans‘ relations in a structural manner. Gestalt theory was instrumental for shaping social network analysis among sociologists in the early 1920s. 134

Jacob Moreno, Kurt Lewin were the first to utilize the social network analysis in social sciences. Lewin (1951) worked on group behaviour; he argued that a person‘s attitude or behaviour is influenced by his/her position in the social group. In addition, they integrate mathematical formulas from graph theory into social network analysis. Moreno (1943) used network analysis to show social configuration among school children. His work resulted in the publication of the journal Sociometry in 1937. Theoretically and practically, psychologists and anthropologists, among others have employed social network analysis to assess organizational settings. Their work stimulated other scientists to follow this up, and incorporated the application of social network analysis in their research (Cartwright & Harry, 1977). Rogers (2003) pioneered and studied innovation diffusion in several fields. He defines the diffusion of an innovation as "the process by which an innovation is communicated through certain channels over time among the members of a social system." Information scientists have used social network analysis measures to study the relationships among people in organizational settings. For example, Kretschmer (2004) found that the most productive scientists in a co-authorship network structure have shorter paths (―geodesic‖) to other authors. Using social network theory, we can examine the characteristics of the network such as: (a) ties (b) structure-connectedness, its cohesion and shape (i.e., at the node level and structural level). According to Freeman (1997) centrality measure plays an important role in any organization with social networks, especially ―betweenness‖ centrality. Freeman (1978) discussed particularly three degree centralities: centrality in terms of ―degrees:‖ in- and outgoing information flows from each node as a center; …Centrality in terms of ―closeness,‖ that is, the distance of an actor from all other actors in a network. Centrality in terms of ―betweenness,‖ that is, the extent that the actor is positioned on the shortest path (―geodesic‖) between other pairs of actors in the network. One can compare measures of centrality and their impact on knowledge diffusion in social network structures. Moody (2004) found that social scientists who work on quantitative research topics are rather willing to collaborate with each other. Moreover, Burt (2004) coined the term ―structural ho le‖. He argued that structural holes in social networks are crucial for connecting clusters in a network structure, resulting in diffusion of knowledge in the network. Chen et al. (2009) stated that scientific discovery comes with a group of specialist, who attend, read and cite same body of literature and attends same conference. Moreover, analysing a co-word map reveals the scientific trends in the network structure. Co-word maps depict the semantic relations in scientific literature. Callon et al. (1983) developed co-word occurrence maps to show trends in temporal scientific knowledge domains. Leydesdorff and Welbers (2011) argued that semantic analysis of the words in the discourse is an important factor in content analysis. Data and methods To single out the relevant nano-related technologies, we set our search strategy as:

TS = ((( NANOPARTICLE* OR NANOTUB* OR NANOSTRUCTURE* OR NANOCOMPOSITE* OR NANOWIRE* OR NANOCRYSTAL* OR NANOFIBER* OR NANOFIBRE* OR NANOSPHERE* OR NANOROD* OR NANOTECHNOLOG* OR NANOCLUSTER* OR NANOCAPSULE* OR 135

NANOMATERIAL* OR NANOFABRICAT* OR NANOPOR* OR NANOPARTICULATE* OR NANOPHASE* OR NANOPOWDER* OR NANOLITHOGRAPHY* OR NANO-PARTICLE* OR NANODEVICE* OR NANODOT* OR NANOINDENT* OR NANOLAYER* OR NANOSCIENCE* ORNANOSIZE* OR NANOSCALE* OR QUANTUMDOT* OR QUANTUM WIRE* OR NANOELECTROSPRAY* OR NANOBIO* OR NANOMOLECULAR*) AND AD= TURKEY) NOT ( nanomet* OR nano2 OR nano3 OR nano4 OR nano5 OR nanosecon* OR nano secon* OR nanomet* scale* OR nanometerscale* OR nanometer length OR nano meter length)), where * stands for a wildcard. A wildcard is used because the nano-related technologies span an inter-disciplinary domain. We retrieved 167 and 230 bibliographic data for Hacettepe and Bilkent Universities during 20002010. Ucinet (Borgatti, Everett, & Freeman, 2002) is used to calculate the statistical data: betweenness, degree centrality. CiteSpace, developed by Dr. Chen is used to map the development of co-authorship of scientists who work on nano-related technologies in Hacettepe and Bilkent University. Using the above metrics, we try to tackle the following research questions: (1) who is the productive author in each social structure network? (2) to what extend do scientists in each network structure co-operate in scientific activities? (3) what are the research topics studied in each network structure? Results and discussion Data revealed that the subject area in traditional science domains: Physics, Chemistry followed by Engineering, Material Science, Nanoscience and Nanotechnology and Optics are important nodes which have the highest degree centrality in the network structure. Each node with a purple ring indicates the highest centrality in knowledge space. Moreover, the authors‘ productivity fits Lotka‘s law, which means that a few prolific authors produce more papers than the rest. The Kamada-Kawai algorithm (Kamada & Kawai, 1989) embedded in CiteSpace (Chen, 2006) is used to map the coauthorship network structure in Bilkent University. Each cluster consists of prolific authors. Ucinet is used to calculate the betweenness and degree centrality of the network structure. The former shows the flow of knowledge in the social network structure whereas the latter shows the importance (location) of the node in the network structure. The most productive authors in Bilkent University are the following: Demir H.V., Ciraci S., Nezamoglu S., Mutlugun E. and Ozel T. have the highest degree centrality. They hold important position in the network structure. However, Gulseren O., Demir H.V. and Aydinli A. have the highest betweenness centrality. They are instrumental in on diffusion of nano-related technologies in the network structure. In another words, the tree ring nodes colored red show the authors where there are structural holes that stimulate the diffusion process. For example, Ciraci S., Gulseren O., Yildirim T., Uyar T., Demir H.V., and Nizamoglu S. have red tree ring nodes (see Fig., 1).

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Figure 1. Network of co-authorship in Bilkent University including its subject categories

In contrast, in Hacettepe University, Biotechnology and Applied Microbiology, Pharmacology and Pharmacy, Polymer Science and Engineering are subject nodes that mostly occupy the network structure. However, Chemistry and P hysics still appear at the edge of the network. All nodes mentioned above are purple colorr in their outer most rings, indicating a high degree of centrality. According to Chen (2009), ―The thickness of the purple ring is proportional to the degree of the centrality‖. Denizli A., Aktas Y., Hincal A.A., Piskin E. have the highest degree centralities and they are important nodes in the network structure. Piskin E ,Denizli A, Hincal AA, and Guven G have the highest betweenness centralities. According to C hen (2004), nodes with high betweenness centrality act as pivotal points in co-citation clusters. For example, Denizli A. connects research activities in Cell Biology with Polymer Science. Hence, they boost the diffusion process in the network structure. Furthermore, the Denizli, Piskin and Hincal nodes not only act as pivotal points but also they demonstrate temporal properties too. For example, the red ring node in the network structure depicts the fact that the above authors enclose structural and temporal properties (see Fig., 2).

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Figure 2. Network of co-authorship in Hacettepe University including its subject categories

Co-occurrence of the keywords (descriptions) reveals the evolution of scientific research in both universities. CiteSpace mapped the intellectual structure in Bilkent University from 2000 to 2010. 17 nodes with a high degree of centrality were detected. Almost all of the nodes have a purple ring in their outermost ring. This means that each cluster contains a specific research activity. The network of the frequency of keywords is dense. The keywords quantum dots, nanocrystals, and atoms have the highest betweenness centrality (see Fig., 3).

Figure 3. Network of co-word frequency in Bilkent University

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Nanoparticles (indegree 20 and outdegree 10) have the highest degree centrality in the network of co-word frequency of the keywords in Hacettepe University. The term nanoparticles is very widely used among the generic terms in nano-related technologies. However, the term Chitosan (indegree 3 and outdegree 25) has attracted more attention from other nodes in the network. Hence, it is instrumental in the diffusion process. The terms delivery, nanospheres and adsorption and membranes shows that nano-related technologies in Hacettepe University is nano-biomedical technology oriented (see Fig., 4).

Figure 4. Network of co-word frequency in Hacettepe University

Conclusion We mapped the structural and temporal evolution of nano-related technologies in two prominent universities (Hacettepe & Bilkent) during 2000-2010 in Ankara, Turkey. In addition, we identified the productive authors in each co-authorship network structure. Ucinet was used to calculate the betweenness and degree centrality of the social network analysis. Results indicate nodes having higher betweenness centrality boost the diffusion process of nano-related technologies in the network structure whereas nods with higher degree of centrality do not have as much strong effect as in diffusion processes in social network structure. The co-word frequency analysis revealed mostly, scientists in Bilkent University have published articles in material science. However, scientists in Hacettepe University not only have published papers in material science, but also they have published heavily in biomedical technologies. There are almost no shared research activity between scientists in two universities. In order to see a better picture of diffusion of nano-related technologies in Turkey, it would be a valuable research task to include all universities where nano-related technologies have become research priorities. 139

References Borgatti, S. P., Everret, M. G., & Freeman, L.C. (2002). UCINET 6 for windows: Software for social network analysis. Harvard: Analytic Technologies. Burt, 1992 R.S. (1992). Structural holes: The social structure of competition, Cambridge, MA; Harvard University Press. Callon, M., Courtial, J.-P., Turner, W. A., & Bauin, S. (1983). From Translations to Problematic Networks: An Introduction to Co-word Analysis. Social Science Information 22, 191-235. Cartwright, D., Harary, F. (1977). A graph theoretic approach to the investigation of systemenvironment relationships. Journal of Mathematical Sociology, (5), 87-111. Chen, C. (2006). CiteSpaceII : Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359-377. Chen, et el. (2009). Towards an explanatory and computational theory of scientific discovery, Journal of Informetrics, 3(3), pp191-209. Duda, N. A. & Şener, I.. (2010). Nanotechnology and Microelectronics: Global Diffusion, Economics and Policy: IGI Global Dumanlı, A. G., & Yürüm, Y. (2007). Nanoetchnology in Turkey. Retrieved May 1, 2010 from: http://digital.sabanciuniv.edu/elitfulltext/3011800000123.pdf , Freeman, L. C. (1978/1979). Centrality in Social Networks: Conceptual Clarification. Social Networks, 1, 215-239. Freeman, L. C. (1997). A Set of Measures of Centrality Based on Betweenness. Sociometry, 40(1), 35-41. Kamada, T., & Kawai, S. (1989). An algorithm for drawing general undirected graphs. Information processing letters, 31(1), 7-15. Kretschmer, H., & Aguillo, I.F. (2004). Visibility of collaboration on the Web. Scientometrics, Vol.61 No:3 pp405-426. Lewin, K. (1951). Field theory in social science. New York: Harper Leydesdorff, L. & Rafols, I. (2011). How do emerging technologies conquer the world? An exploration of patterns of diffusion and network formation. Retrieved June. 1, 2011 from: http://www.leydesdorff.net/list.htm, Leydesdorff, L., & Welbers, K. (2011). The semantic mapping of words and co-words in contexts, Journal of Informetrics, 5(3), 469-475. Mehta, M. (2002). Nanoscience and nanotechnology: Assessing the nature of innovation in these fields. Bulletin of Science, Technology and Society, 22(4b), 269-273. Moody, J. (2004). The structure of a social science collaboration network: Disciplinary cohesion from 1963 to 1999, American Sociological Review,69(2), 213-238. Moreno, J. (1934). Who shall survive? Sociometry, 6( 3). 206-213, American Sociological Association Price, D.J. de Solla. (1965). Networks of scientific papers. Science 49(3683), pp 510-515. Rogers, E. M. (2003). Diffusion of Innovations, 5th Edition, New York: The Free press Saritaş, O., Yilmaz, E., & Turgut, T. (2007). Vision 2023: Turkey‘s national Technology Foresight Program: A contextualise analysis and discussion. Technological Forecasting and Social Change, 74(8) 1374-1393.

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A Comparative Citation Analysis of Iranian & Turkish Inventor's Patents Indexed in USPTO During 1988 to 2010 Mozhdeh Dehghani

Elaheh Hasanzadeh

[email protected] Member of the Young Researchers Club, Islamic Azad University, Tehran North Branch, Iran,

[email protected] M.A in Medical Library and Information Science

Abstract: Patents are one of important outputs of research and development process and a key performance indicator of science and technology system. By review and analysis of changes rate in the number of patents and frequency of their citing, the effectiveness of expenses on research and development could be evaluated to some extent. The objective of this research is to do a comparative citation analysis of Iranian and Turkish inventor's patents, in order to determine the technological effectiveness of their registered inventions in the US patent office. This research has been done using citation analysis method. First, the Iranian and Turkish patents in USPTO were retrieved and then reviewed. Findings of this research were then reviewed by descriptive statistics using excel software. Findings of this research showed the number of patents in USPTO between 1988 to 2010 for Iranian and Turkish inventors as 72 and 480 respectively with IR suffix for Iran and TR for Turkey. There were total of 74.82 % backward citations of Iranian inventors and 77.28 % backward citation of Turkish inventors to patents; and 25.17 % backward citations of Iranian inventors and 22.71 % backward citations of Turkish inventors to NPRs3 . The highest number of citation by Turkish inventors with 472 citations referred to G subject area (physics) vs. 119 citations in E subject area (fixed structures) by Iranians. Research found that the highest number of patents for Iranian inventors‘ collaboration corresponds to subject area C (Chemistry and Metallurgy) with 26.38 % and highest number of patents for Turkish inventors‘ collaboration corresponds to subject area G (Physics) by 18.54 %; while the highest number of citation to Iranian patents refer to section E (fixed structures) with 119 citation vs. 472 citation for Turkish patents in section G (Physics). Keywords: Patent, Iranian inventors, Turkish inventors, united states patent and trademark office, citation analysis, patent references, non-patent references

4

. Non-Patent Referenc es (NPRs)

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Introduction In recent years, identification, strengthening and providing required basis for production and application of science and technology have been the major priorities for each country‘s officials and policy makers. Science production is one of the basic pillars of knowledge development. Only knowledge- and science-based development could be a stable development. Science production paves the way for creation of technology and consequently the creation of jobs and wealth in the society. Apart from science products, another issue which has attracted attention in the world of science and technology is the ―innovation in technology or inventions and initiatives‖. Technology production, which results from application of science and innovation process in the area of practice, is also the applicability of the results of scientific products. Registered inventions are important outputs of research and development process and are considered to be major performance indicators of science and technology system. By reviewing and analyzing the rate of changes in the number of patents and frequency of their citing, the effectiveness of expenses on research and development could be evaluated to some extent. Case Expression In the context of scientific researches, citation indicators are considered as the tools for hypotheses testing or for reviewing the general relatio ns between variables, involved in a theoretical framework. In policy- making, citation indicators could be used to make a policy-based decision. This decision could be related not only to a person, but also to a group of people: like research groups, institutions or scientific disciplines. Citation analysis Outputs have scientific outcomes for researchers and the institutions they‘re working with (moed, 2008). Since patents and scientific products are the outputs of scientific and industrial community of the country, and since the science and technology are infrastructures for a stable development in each country, their evaluation in the international level has been highlighted in recent years and currently is being done regularly in most of industrial countries by institutions in public or private sectors. Hence, science and technology system of each country could be described, its science and technology structure could be identified and its weaknesses and strengths could be determined, so as to achieve a stable development by creating regular and accurate strategies and putting them into practice (khaleghi, 2007). In case the invention is globally registered and is put to technical and scientific tests, it can be considered as science production indicator (zolfigoli, bakhtiyari, 2007). A patent, offered by international organizations, has two citations in its text: one refers to the prior knowledge in that field and is known as ―backward citation‖ and the other is citing to the patent and shows the rate of patent‘s citation in other technologies- known as ―forward citation‖. In fact, patents cited more frequently, have more effect in the technology cycle. On the other hand, considering the fact that there‘s a close relationship between the growth rate of scientific productions and certain conditions in each country- like social, economical, political, cultural characteristics and …, accurate and broad study of scientific production growth rate can determine the ups and downs of the issue in each country and guide science and technology policy makers towards planning more correct paths. Comprehensive study of Iran‘s science production status and comparing it with 142

countries in better position- with regards to science products- can pave the way for investigating the reasons of promotion or demotion of their science production and provide country‘s research managers with required awareness. It‘s a priority to study the status of countries close to Iran geographically or regionally; countries that have something in common with Iran with regards to the development rate. Meanwhile, due to scientific production, some countries may have passed Iran or may have lagged behind Iran at some points. One of the countries the study of which is necessary is Turkey - due geographical and regional location and also due to its nonEnglish official language. Since Turkey is an Islamic neighboring country, and sometimes considered a competitor neighbor, the study of its science production status could provide more results (Norozi chakoli, Hassanzadeh, Nourmohammadi, 2008). Since patents are symbols of science and technology production, their citation analysis reveals how inventors and companies- requesting for invention- behave while using information resources. Determining those technology areas with the maximum citation, we can show the country‘s role in global technology. This will help countries when allocating investment on production and application of science and technology, so that governments apply major assets in sectors related to industry in order to increase the science production of that country. Considering all above said, this research uses citation analysis to study sources cited by patents and the technological effects of patents in the science and technology cycle of Iranian and Turkish inventors in USPTO 4 . All backward and forward citations of Iranian and Turkish patents, mentioned in electronic format at patent‘s first page, during 1988-2010 were reviewed using citation analysis. The aim was determining the rate of forward citation, backward citation, the highest-rated technology subject areas and … so as to specify the status of Iranian and Turkish inventors in application of information resources and other inventions and also, to show the technological effect of invent ion in various fields in international levels. General Goal of Research The general goal of this research is the comparative citation analysis of patents by Iranian and Turkish inventors in USPTO during 1988-2010. Importance and necessity of research It‘s important to review the references and resources used in patents as intellectual and scientific assets and to review patents‘ forward citations which show the technological role and importance of a certain invention. Findings of such studies reveal the pattern of scientists and inventors behavior; by showing the weaknesses and strengths, creation of the basis for country‘s investment towards cooperation and advancement in global science production becomes possible.

4

. United States Patent and Trademark Office

143

Research Methodology This research has been done using the method of citation analysis. First, patents in USPTO were searched and retrieved from PATFT: Issued Patents section. Extracted data were then transferred to an excel file with certain fields: invention name, patent number, Issue date, inventor‘s name, applicant name, international patent classification, backward citation (citation to patent references and citation to non-patent references) and forward citations. Data Extraction Method from USPTO A. Iran To make sure of existence of Recall and precision in collected data from USPTO, two below methods were used: a. In the first method below command was used in advanced search section: i. ICN/IR With this command, all documents containing IR were retrieved, many of which were not related to Iran. To resolve the problem, another keyword and command was used. In retrieving the patents based on ICN/IR from advanced search of USPTO, all records must be reviewed one by one because due to some weaknesses in indexing system of the database, some irrelevant 5 records (patents) are retrieved which should be deleted at the first stage to facilitate the extraction and analysis of real data in the next stages of research. b. In this command, names of country‘s provinces was searched separately and retrieved results were compared with those retrieved by country‘s name (Method a) and were used to increase the recall of search results. In this method, below command was used: a. IC/City Where, city stands for capitals of Iranian provinces. For instance, search command of ―IC/Shiraz‖ retrieves patents which contain Shiraz and are also related to Iran. Eventually, results of both methods were combined and the final file was extracted. B. Turkey For Turkey, there was no problem (like the one for Iran); so, below command was used and all retrieved records were relevant to turkey ICN/TR

5

. Of irrelevant retrieved records we can point to abbreviated signs (IR) Iran and Ireland and in some cases Israel; after searching and first review, half of retrieved records were from Ireland and Israel which were r eviewed, identified and deleted from statistical community

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Research Society Statistical community of this research is the 552 patents of Iranian and Turkish inventors, registered during 1988 to 2010 in USPTO. Out of this number 72 patents come from Iranian inventors while 480 patents belong to Turkish inventors. It should be noted that Iranian and Turkish inventors were searched and retrieved by IR and TR suffixes, therefore those Iranian and Turkish inventors- nationals of the two countrieswho didn‘t have IR and TR in their specific fields were not included in the statistical community. Research questions 1. How many patents by Iranian and Turkish inventors were indexed in USPTO during 1988 to 2010? 2. How is the subject trend of Iranian and Turkish inventors from 1988 to 2010 in the studied database? 3. What is the rate of backward citation by Iranian and Turkish inventors? 4. What is the forward citation rate by Iranian and Turkish inventors? 5. Who are the most cited Iranian and Turkish inventors? 6. Which technological field receives the highest rate of citation for Iranian and Turkish inventors? Lite rature review In a research titled ―Review: the Status of Patenting and scientific article publishing in the Islamic Republic of Iran‖ by The Board of Supervision and Assessment of Cultural and Scientific Affairs in 2006, a report was submitted in two separate parts; the first part studies the country‘s position with regards to the status of patenting in national and international levels, and the second part studies and analyzes the status of publishing articles and rate of their changes in different scientific groups in national and international levels. The research shows that in recent years, patents have increased particularly in the country, although this increase has been very low compared to the increase of scientific production. Both parts of the report contain concluding sections, which come after the findings section (The Board of Supervision and Assessment of Cultural and Scientific Affairs, 2006). In his research ―Study: relation of Iranian Inventors‘ Patents and their Scientific products‖, Alaei Arani (2009) used data in USPTO, Europe Patent Office, Japan Patent office and World Intellectual Property Organization to review the status of Iranian inventors among these groups. The rate of science production by Iranian inventors (whose names have been extracted from these groups) was evaluated, using the Thompson Citation Index (ISI) in WOS 6 database. This research is mainly aimed at the evaluation of relation between the number of patents and number of inventors‘ scientific products. In the national level, it studies the ratio of patents to whole indexed Iranian scientific products in WOS. The research was done by a combination of library studies, 6

. Web of Science

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bibliometrics, citation analysis and content analysis. The findings of research eventually passed correlation analysis: for the rate of scientific products and patents at the level of 95 %, the correlation coefficient of 0.039 was calculated, which shows no meaningful relation between the two variables. The study of citations showed that citation behavior is different in various technological areas. Highly cited inventions as well as highly active inventors-authors were determined. Lo (2007) did his research ―Patent Analysis of genetic engineering research in Japan, Korea and Taiwan‖, using bibliometric approaches, with the goal of finding rate of research distribution of researches‘ productivity and effect of genetic engineering research in Japan, Korea and Taiwan. He used quantitative bibliometric methods to analyze patents of Japan, Korea and Taiwan in USPTO during 1991 to 2002. In addition to citation counting of patents, Bradford law was used to identify the core applicants in genetic engineering. In this research, 13055 genetic engineering patents given during 1991 to 2002 were reviewed out of which, 841 patents in the field of genetic engineering belonged to Japan, Korea and Taiwan. There were 270 applicants for getting 841 patents, of whom 16 core applicants were identified by Bradford law. Results showed that Japan had a better position in productivity and research effect than the other two countries and core applicants were also from Japanese institutions. Alcacer, Gittlelman and Sampat(2009) In their research ―Applicant and examiner citations in U.S. patents: An overview and analysis‖, stated that citing to prior art is a good benchmark to evaluate the quality of patents and the flow of knowledge among the companies. They believe that interpretation of such evaluations is complicated as the citation to prior art is added by examiners and applicants. They analyzed all prior arts of patents given by USPTO during 2001 to 2003 and found that examiner has an important role in identifying prior art. It means around 63 percent of patent citation is done by examiners. Using multi- variable regression, they found that foreign applicants have a high share in examiners‘ citations. Applicants with high number of patents have a bigger share of examiners‘ citations. They found that from technological viewpoint, examiners‘ share is much higher in electronics, communications and computer-related fields. Studying research-related histories shows those previous researches performed, examined- among science and technology indicators- mostly ―indicators for relating science to technology‖ and ―Patent Citation of Scientific Papers‖ and that ―indicator of patent citation of earlier patents‖ and rate of patent forward citation in previous researches were not examined exclusively. Studying these indicators in foreign researches has been done mostly among countries and in one technological subject area. In national records, such a research has not been performed. This research covers all technological subject areas common between two countries. Two competitive and Muslim countries have been studied in sense of global science production with partnership of authors from other countries; but research about two competitive and Muslim countries in global technology production with the partnership of other countries‘ inventors has not been performed yet; which makes this research different from previous ones.

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Data analysis Table 1. Share of Iranian and Turkish inventors in USPTO‟s Patents Country

Frequency

percentage

Iran

72

13.04

Turkey

480

86.95

Total

552

100

In reply to the 1st question of this research: How many patents by Iranian and Turkish inventors were indexed in USPTO during 1988 to 2010? According to table 1, research findings showed tha t in a studied period of 22 years, in USPTO, total of 552 patents from Iranian and Turkish inventors have been indexed. Out of this, 480 patents belong to Turkish inventors and 72 patents belong to Iranian inventors. In fact, Iranian inventors have registered share of 13.04 % inventions in USPTO and the remaining 86.95 % inventions belong to Turkish inventors. Result: the highest number of inventions in USPTO comes from Turkish inventors. Table 2. Growth rate of Iranian and Turkish inventors‟ collaboration in USPTO Country‘s Name Year 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Colu mn total

Iran frequency Percentage 2 1 2 2 1 2 3 3 0 2 3 3 3 4 1 0 0 4 7 4 2 10 13 72

2.7 1.38 2.7 2.7 1.38 2.7 4.16 4.16 0 2.7 4.16 4.16 4.16 5.55 1.38 0 0 5.55 9.72 5.55 2.7 13.88 18.05 1000

Total

Turkey frequency Percentage 2 3 3 2 5 3 4 5 7 10 9 14 12 30 24 48 24 21 37 38 55 57 70 480 147

0.41 0.62 0.62 0.41 1.04 0.62 0.83 1.04 1.45 2.08 1.87 2.91 2.5 6.25 5 10 5 4.37 7.70 7.91 11.45 11.87 14.58 100

frequency

Percentage

4 4 5 4 6 5 7 8 7 12 12 17 15 34 25 48 24 25 44 42 57 67 83 552

0.72 0.72 0.90 0.72 1.08 0.90 1.26 1.44 1.26 2.17 2.17 3.07 2.71 6.15 4.52 8.69 4.34 4.52 7.97 7.60 10.32 12.13 15.03 100

According to above table, number of collaborations by each inventor and USPTO‘s share of Iranian and Turkish inventors from 1988 to 2010 have been mentioned separately. For more clarification, growth trend in different years have been s hown separately in diagram 1. According to table 2, growth of Turkish inventors in USPTO has an ordered trend and increases regularly during the studied period, but growth trend for Iranian inventors is disordered. Considering the findings of table 2, it can be argued that biggest difference between the collaboration of Iranian and Turkish inventors in USPTO refers to the final years of the studied time period. 90 80 70 60

50 40

turkye

30

Iran

20

10 0 2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

1988

Figure 1. Growth trend of collaboration by Iranian and Turkish inventors in USPTO

Table 2. Subject categories of patent requests by Iranian and Turkish inventors, based on IPC7 Country Subject Area A B C D E F G 7

.

Iran frequency Percentage 8 11.11 6 8.33 19 26.38 2 2.7 6 8.33 2 2.7 13 18.05

Total

Turkey frequency Percentage 62 12.91 36 7.5 76 15.83 19 3.95 5 1.04 53 11.04 89 18.54

frequency 70 42 95 21 11 55 102

: http://www.wipo.int/classifications/ipc/ipc /?lang=en

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Percentage 12.68 7.60 17.21 3.80 1.99 9.96 18.47

H Other Total

15 1 72

20.83 1.38 100

51 89 480

10.62 18.54 100

66 90 552

11.95 16.30 100

Answering the second question: How is the subject trend of Iranian and Turkish inventors from 1988 to 2010 in the studied database? Table 2 shows that 26.38 % of patents by Iranian inventors‘ collaboration belong to subject section C (Chemistry and Metallurgy). This is followed by subject section H (electricity) with 20.83 %. Subject section G (physics) with 18.05 % stands at the third place. Section A (Human necessities: essential products, agricultural [items], food industries, household goods, hygiene and entertainment) with 11.11 % occupy the 4th place Sections E (fixed constructions) and B (performing operation; transporting) jointly with 8.33 % and sections F (Mechanics engineering, lighting, heating, weaponry and explosion) and D (textiles and paper) jointly with 2.7 % occupy the next p laces. Also, 18.54 % of the patents with Turkish inventors‘ collaboration belong to Category G (Physics), which is followed by category C (Chemistry and metallurgy) with 15.83 %. Category A (essential products, agricultural [items], food industries, house hold goods, hygiene and entertainment) occupies third place with 12.91 %. Fourth place belongs to category F (Mechanics engineering, lighting, heating, weaponry and explosion) with 11.04 %, followed by category H (electricity) with 10.62 %, category B (technology of operational performance) with 7.5 %, category D (Textiles and paper) with 3.95 % and E (fixed structures) with 1.04 % . Table 3. Rate of backward citations to Iranian and Turkish patents in USPTO

Country name

Citation to patents Nu mber

Total

Citation to NPRS

Percentage

Number

Percentage

Number

Percentage

Iran

832

74.82

280

25.17

1112

100

Turkey

6662

77.28

1958

22.71

8620

100

Answering the third question: What is the rate of backward citation by Iranian and Turkish inventors? According to table 3, Iranian inventors have had 1112 backward citations in creation of 72 patents and Turkish inventors had 8620 backward citations to create 480 patents. It should be noted that these citations also include test-based citation. As it could be seen in table 3, Iranian inventors have 832 backward citations (74.82 %) to patent references and 280 backward citations (25.17 %) to non-patent references. In USPTO, there are 6662 backward citations (77.28 %) to patent references and 1958 backward citations to non-patent references by Turkish inventors. Above findings show that most of

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backward citations in USPTO for Iranian and Turkish patents are citations to patent reference and, considering the nature of patent, one could expect such result. Answering below questions: What is the forward citation rate by Iranian and Turkish inventors? Who are the most cited Iranian and Turkish inventors? Which technological field receives the highest rate of citation for Iranian and Turkish inventors? It should be noted that forward citations in USPTO are listed under the title of ―Reference Cited [Referenced By]‖. During the studied period of time, this field was reviewed for Iranian and Turkish patents. Table 4. Rate of forward citations by Iranian and Turkish inventors in USPTO by separate fields of technology Non-Patent References

Patent references Technology filed

Total

Nu mbe r

Percentag e

Nu mbe r

Percentag e

Nu mbe r

Percentag e

114

7.77

40

10.61

154

8.35

B. Operational Performances

258

17.58

9

2.38

267

14.47

C. Chemistry and metallurgy

233

15.88

32

8.48

265

14.37

31

2.11

0

0

31

1.68

2

0.13

119

31.56

121

6.56

207

14.11

8

2.12

472

32.17

130

34.48

602

32.64

150

1022

39

1034

189

10.24

1467

100

377

100

1844

100

A. Essential Items

D. Textiles and paper E.

Fixed Structures

F.

Mechanics Engineering

G. Physics H. Electrical engineering Total

215

11.65

Number of forward citations change with time. Table 4 shows the number of forward citations by Iranian and Turkish inventors in separate fields of technology. According to this table‘s data, the total number of forward citations of Turkish inventors in the studied period of time in USPTO was 1467, of which the highest forward citation is 472 citations in section G (Physics). The invention of Burduroglu; Mehmet (Yenikoy) takes the first place of Turkish patents with 109 forward citations in section B (operational performance). Also, the total number of Iranian inventors‘ forward citations in the studied time period was 377 citations, of which the highest rate of Iranian inventors‘ citation was 119 citations to Category E (fixed structures). Regarding the results of research on Iranian 150

inventors‘ patents, the invention of Ahmad Masoudi , in fixed structures field, stands at the top with 103 citations. Discussion and conclusion In a 22-year period of time which was studied in the research, total of 522 patents of Iranian and Turkish inventors have been indexed in USPTO, of which 480 patents belong to Turkish inventors and 72 patents belong to Iranian inventors. The highest number of patents for Iranian inventors is in category of chemistry and Turkish inventors have record in Physics category. According to findings of Moein 8 (2005), Osareh9 (2002) and Mousavi (2005), Iranian authors in category of chemistry had the highest rate of collaboration in science production. So, one can conclude that science and technology production with Iranian inventors‘ collaboration goes with one parallel subject. Also, in their backward citations, Iranian and Turkish inventors cite patents, which- considering the nature of patent- is expected. They rarely use articles, books and …. The highest number of patents with Iranian inventors‘ collaboration belongs to section of Chemistry, while same for Turkish patents refers to section G (physics). The most forward citation to Iranian patents belongs to section E (fixed structures) and similar number for Turkish patents are in section G (physics). In fact, effective inventions of Turkish inventors in global technology belong to a subject area where they have more activity in, while Iranian inventors‘ effective inventions in global technology are in a subjective field where Iranian inventors have less activity. Studies show that in sense of efficient workforce, which is the most important asset in knowledge-based development, there‘s high potential among Iranians; these potentials can become de facto with accurate planning and full support for Iranian technological and research Development Document Outlines 20- Year Outlook‖ and ―Fo ‖ which are knowledge-based and to reach the first place in southwest Asia (including central Asia, Caucasus, Middle East and neighboring countries) economically, technically and scientifically: 1. Identify the current and suitable status, 2. Plan to move from current status to suitable status, To identify the current status and determine the suitable situation, methods of citation analysis and science policy in science and technology field and comparison with competitive developing countries are very helpful. Of course, it should be noted that when comparing Iran with Turkey, all conditions should be considered; for instance share of research in GNI (Gross National Income) as an indicator for investment in researches, number of scientists in each million people of the population, number of articles, number of patents, research records, formation of scientific abilities and the knowledge of performing researches, are important research experiences. After

8

. Scientific output of Iran at the thr eshold of the 21st century

9

. Collaboration in Iranian Scientific Publications

151

identifying the current and proper status, the government should consider the situation and submit proper plans for each sector by logical and suitable policies. References Alaei Arani, Mohammad(2009). Study the relationship between patents and scientific products, Iranian inventors, MA theses, Tehran University. Alcacer,J., Gittelman,M. , Sampat, B.(2009)." Applicant and Eaxaminer Citations in U.S.Patent: An Overview and Analysis", Research Policy, 38(2), 415-427. Khaleghi, Narges( 2007). Indicators of Science and Technology. Faslnameh Ketab, 18, no.3.p.104. LO, Szu-Chia(2007). "Patent Analysis of genetic engineering research in japan, korea and Taiwan", Scientometrics, 70(1), 183-200. Moed, Henk.F (2005). Citation Analysis in Research Evaluation. Abbas Mirzaie, Heidar Mokhtar, translators; Javad Ghazi, Mirsaees, editor. Tehran: chapar. P.30. Moin, Mostafa, Mahmoudi, Maryam, Rezaei, Nima(2005)." Scientific output of Iran at the threshold of the 21st century", Scientometrics, 62(2), 239-248. Mosavi, Mirfazlolah(2005). Taking first scientific place in region" . Rahyaft, no.35, p.45-59. Noroozi chakoli, Abdoreza;Hassanzadeh, Mohammad; Nourmohammadi, Hamzehali( 2008). An Analytical view on the Dissemination of Iranian Knowledge in the world(1993-2007). Tehran: National Research Institute for Science Policy. Osareh, F., Wilson, C.S.(2002)." Collaboration in Iranian Scientific Publications", Libri , 52, 88-89. The Board of Supervision and Assessment of Cultural and Scientific Affairs ( 2006). Assessment of Patents and papers in the Islamic Republic of Iran. Tehran: The Board of Supervision and Assessment of Cultural and Scientific Affairs. Zolfigoli, Mohammad Ali; bakhtiyari, Abolfazl( 2007). "Example of scientific output: selection criteria". Available: http://www.korsi.ir/default.aspx?page=NewsItemShow&app=News&PageNumber=&docPar Id= &docId=

152

Citing to Highly-Cited Researchers by Their Co-authors and Their Self-Citations: How Do These Affect Highly-Cited Researchers‟ H-Index in Scopus Leila Dehghani

Reza Basirian Jahromi

[email protected] Department of Medical LIS- Faculty Member of Bushehr University of Medical Sciences Bushehr, Iran

[email protected] Department of Medical LIS- Faculty Member of Bushehr University of Medical Sciences Bushehr, Iran and

Mazyar Ganjoo [email protected] Department of Computer Science, Bushehr Branch, Islamic Azad University Bushehr, Iran

Abstract: The h-index was introduced by Hirsch to quantify an individual‘s scientific research output. It has been widely used in different fields to show the relevance of the research work of prominent scientists. But, does h-index really show this relevance? This research is to investigate citing to highly-cited researchers by their co-authors, as well as, highly-cited researchers‘ selfcitation in three disciplines of knowledge: clinical medicine, computer science, and economics & business; then, we want to evaluate the impact of these two elements on their h-index in Scopus database. In this research, highly-cited researchers were selected, in three disciplines, from http://www.isihighlycited.com. Finally 999 highly-cited researchers in those three disciplines were selected (280 authors in clinical medicine, 373 authors in computer science, and 346 authors in economics & business) as our population. After using Excel for primitive calculating and descriptive statistics, SPSS 16.0 was used for inferential statistics; these tests included: Ttest, ANOVA test, Correlation test, and Regression test. The results show that the average of self -citation and co-author citation in clinical medicine is more than computer science, and in computer science is more than economics & business. In these three disciplines, the results show that there is a positive correlation between self-citation and total citations (P. Acesso em: 08 jul. 2011 Moura, Ana Maria Mielniczuk de. A interação entre artigos e patentes : um estudo cientométrico da comunicação científica e tecnológica em biotecnologia. 2009. 269 f. : il. Tese (doutorado) - Universidade Federal do Rio Grande do Sul. Faculdade de Biblioteconomia e Comunicação. Programa de Pós-Graduação em Comunicação e Informação, Porto Alegre, BR-RS, 2009 Moura, Ana Maria Mielniczuk de; CAREGNATO, Sonia Elisa (2010). Co-classificação entre Artigos e Patentes: um estudo da interação entre C&T na Biotecnologia Brasileira. Informação e Sociedade, 20, 2. Penteado Filho, R. de C.; Avila, A. F. D. (2009). Embrapa Brasil: análise bibliométrica dos artigos na Web of Science: 1977/2006. Brasília: Embrapa Informação Tecnológica. Rocha, Elisa Maria Pinto; Ferreira, Marta Araújo Tavares (2004). Indicadores de ciência, tecnologia e inovação: mensuração dos sistemas de CTeI nos estados brasileiros. Ciência da Informação, 33, 3, 61-68. SCImago. SJR — SCImago Journal & Country Rank. (2011). Disponível em: . Acesso em: 27 jul. 2011. Tancredi, Leticia. Produção científica brasileira é a 15ª em todo o mundo. 8 jul. 2008. Disponível em: . Acesso em: 07 out. 2008. Vanz, Samile Andrea de Souza. As redes de colaboração científica no Brasil : (2004-2006). 2009. 204 f. : il. Tese (doutorado) - Universidade Federal do Rio Grande do Sul. Faculdade de Biblioteconomia e Comunicação. Programa de Pós-Graduação em Comunicação e Informação, Porto Alegre, BR-RS, 2009. Velho, L. Publicação científica e avaliação nas Ciências Agrárias: pontos para discussão (2008). Boletim Informativo da Sociedade Brasileira de Ciência do solo, set./out, 22-24.

498

Cahit Arf: Exploring His Scientific Influence Using Social Network Analysis, Author Co-citation Maps and Single Publication h Index Yaşar Tonta

A. Esra Özkan Çelik

[email protected] Department of Information Management, Faculty of Letters, Hacettepe University, 06800 Beytepe, Ankara, TR

[email protected] Registrar's Office, Hacettepe University, 06800 Beytepe, Ankara, TR

Abstract: Cahit Arf (1910-1997), a famous Turkish scientist whose picture is depicted in one of the Turkish banknotes, is a well known figure in mathematics with his discoveries named after him (e.g., Arf invariant, Arf rings, the Hasse-Arf theorem). Although Arf may not be considered as a prolific scientist in terms of number of papers (he authored a total of 23 papers), his influence on mathematics and related disciplines was profound. As he was active before, during and after the World War II, Arf‘s contributions were not properly listed in citation indexes and thus did not generate that many citations even though several papers with ―Arf‖ in their titles appeared in the literature. This paper traces the influence of Arf in scientific world using citation analysis techniques first. It reviews the scientific impact of Arf by analyzing both the papers authored by Arf and papers whose titles or keywords contain various combinations of ―Arf invariant‖, ―Arf rings‖, and so on. The paper then goes on to study Arf‘s contributions using social network analysis and author co -citation analysis techniques. CiteSpace and pennant diagrams are used to explore the scientific impact of Arf by mapping his cited references derived from Thomson Reuters‘ Web of Science (WoS) database. The direct and indirect influences of Arf‘s highly cited paper on Arf variants are assessed through author co-citation analysis and single publication h index, respectively. The paper ends with a discussion of whether data analysis techniques used in this study can be useful to study the scientific impact of researchers retrospectively. Keywords: Cahit Arf, author co-citation analysis, social network analysis, pennant diagrams, single publication h index, CiteSpace.

Introduction Cahit Arf is a world-renowned Turkish mathematician who had had significant inventions to his credit, which still are in use today such as ―Arf invariant‖, ―Arf rings‖ and ―Arf closure‖. He was born in Selanik (Thessaloniki) on February 18, 1910. With the outbreak of the Balkan Wars, his family migrated to Istanbul in 1912, then to Ankara, and finally settled in Izmir. As a brilliant student, Arf had successfully completed the École Normale Supérieure in Paris in two years a nd then worked as a teacher at Galatasaray High School for a year with great willingness. In 1933, he joined the Mathematics Department of Istanbul University as an assistant professor and finally began to work as a mathematician for academic purposes. Starting from 1933, Istanbul University became home to many German émigré scientists who fled the Nazi regime in Germany. Among them were distinguished mathematicians such as Richard von Mises,

499

William Prager and Hilda Geiringer (Terzioğlu & Yılmaz, 2005, p. 58; Reisman, 2007, p. 13).10 In 1937, Arf went to Göttingen to have his PhD degree under the supervision of Professor Helmut Hasse. He completed his doctoral studies in one and a half years (1938); the Hasse-Arf theorem was an outcome of his doctoral thesis. After completing his PhD, he stayed in Göttingen one more year at the request of Professor Hasse (O'Connor & Robertson, 1998) and began to study on the quadratic forms over a field of characteristic two to improve the theory established by Ernst Witt (1937). In 1941, Arf published the results of his study and completed the theoretical gap by introducing an important invariant of quadratic forms over a field of characteristic two (Arf, 1941). The invariant is well known as Arf invariant and is the key for the solution of several classical and fundamental problems about the topology of manifolds and finally introduced him to the world (Ikeda, 1998; Önder, 1990). In 1948, Arf published another significant contribution in the Proceedings of the London Mathematical Society (Arf, 1948). The most significant follow up to Arf‘s paper came from Lipman (1971) who was the first mathematician to mention ―Arf rings" in the literature (Sertöz, 1997). Besides his many significant inventions in mathematics, Professor Arf worked in Istanbul University until his involvement, upon invitation, in the foundation of the Scientific and Technological Research Council of Turkey (TUBITAK). He served as the head of TUBITAK‘s Science Council between 1963 and 1967. In 1963, he joined the Mathematics Department of Robert College in Istanbul and worked at the Institute for Advanced Studies in Princeton, New Jersey, for two years. While at Princeton, he was invited to spend a year at the University of California at Berkeley as a visiting scholar. Then he made his final return to Turkey, joined the Mathematics Department of the Middle East Technical University and continued his studies there until his retirement in 1980. He received the prestigious Inonu Award in 1948, TUBITAK Science Award in 1974, and Commandeur des Palmes Académiques (France) in 1994. Arf was a member of the Mainz Academy and the Turkish Academy of Sciences. He served as the president of the Turkish Mathematical Society between 1985 and 1989. After his many contributions to the international mathematics society and the Turkish scientific environment, he passed away on December 26, 1997 in Bebek, Istanbul, at the age of 87 (Terzioğlu & Yılmaz, 2005). 11 In 2009, the banknote of 10 Turkish Lira was issued with Professor Cahit Arf's portrait depicted on it. 12 The two libraries of TUBITAK bear Cahit Arf‘s name: ―Cahit Arf Information Center‖ in Ankara (http://www.ulakbim.gov.tr/eng/cabim/) and ―Cahit Arf Library‖ in Kocaeli nearby Istanbul (http://www.mam.gov.tr/kutuphane/). Within his 87 years life time, Arf was well known for the Arf invariant, Arf rings, Arf semi groups and the Hasse–Arf theorem, among others. This paper aims to study the 10

For more information on Turkey‘s modern izat ion of its higher education system in 1933 and the involvement of so me 190 German scientists in this process, see Reisman (2006). 11

See also: O'Connor & Robertson, http://en.wikipedia.org/wiki/Cahit_Arf. 12

http://www.tcmb.gov.tr/yeni/banknote/E9/10tle.ht m

500

1998,

and

the

Wikipedia

article

at

influence of Cahit Arf‘s papers retrospectively by means of a combination of citat ion and author co-citation analysis, social network analysis and single publication h index. Papers that referred to Arf‘s contributions in their titles and topics were identified and an author co-citation analysis was carried out. CiteSpace was used to find out Arf‘s place in mathematics and his impact on the basis of bibliometric analysis, author cocitation analysis and pennant diagrams (White, 2007a, 2009). The indirect influence of Arf‘s seminal paper was calculated using Schubert‘s single publicatio n h index (Schubert, 2009). We discuss the consequences of such an approach and conclude that author co-citation analysis, pennant diagrams and single publication h index can shed further light on the influence of authors and help put their works in full perspective. Lite rature Review Social network analysis (SNA) is used to study and visualize the structures of social networks. Based on graph theory, SNA has been widely used to reveal the relationships among documents, journals and authors (Otte & Ro usseau, 2002). Scientific and intellectual ties and collaboration between researchers can be identified using bibliometric data (e.g., citations). The structure of scientific disciplines as social networks can be mapped by means of visualization software such as CiteSpace (Chen, 2006). Author co-citation analysis (ACA) was first introduced by White and Griffith (1981). ACA assumes that the two researchers being cited together in the scientific literature are likely to share the same research interests and work in the same field. Researchers working in the same domain get clustered through ACA, thereby facilitating the discovery of social structure among researchers as well as among research domains. The outcome of ACA studies were used to map and visualize the structure of several scientific disciplines including information science and macroeconomics (see, for example, McCain, 1984, 1986, 1990; McCain, Verner, Hislop, Evanco, & Cole, 2005; White & McCain, 1998). White recently combined author co-citation analysis with information retrieval (IR) and relevance theory (RT) to study the influence of a seed work, author or paper (White, 2007a, 2007b, 2009). He used the weights of term frequency (tf) and inverse document frequency (idf) formula to draw pennant diagrams for works co-cited with a seed work, the authors co-cited with a seed author or articles and books co-cited with a seed article. Originally, tf values are used in IR to determine the relevance of a term within a given document (the more a term is used in a single document, the higher its relevance to a given query) while idf values are used to determine its relevance within the entire document collection (the more a term is used in different documents in a collection, the less discriminatory -and therefore topically less relevant- it becomes). Sparck Jones (1972) created idf as a measure for weighting the ―statistical specificity‖ of terms. The idf measure pushes the related term‘s weight down in the rankings so that terms that occur relatively frequently in the document collection are considered less relevant to a query. Bibliometric data and IR techniques are used in pennant diagrams ―to mimic a relevance theoretic model of cognition on the user side‖ (White, 2007a, p. 537). Pennant diagrams are scatterplots of tf values representing ―cognitive effects‖ of works/authors/articles in the context of a seed work/author/article, and idf values 501

representing the ―processing effort‖ of the user. Cognitive effects (tf) and ease of processing (idf) of works, authors or articles determine their relevance to a seed work, seed author or seed article (White, 2007a, p. 550). Whereas tf*idf formula multiplies the two to come up with a single score of relevance, pennant diagrams plot tf values on the x axis and idf values on the y axis without multiplication (White, 2007a, p. 541). 13 White used pennant diagrams innovatively to study the influence of a work (Moby Dick), an author (Howard D. White) and a paper (Stephen Harter‘s ―Psychological Relevance and Information Science‖) and interpreted his findings based on ACA, IR and RT (White, 2007a). Pennant diagrams proved to be useful in discovering new relationships between works, authors and papers. Metrics such as citation indicators and h index originally proposed by Hirsch (2005) measure the direct influence of authors and papers as well as journals, institutions and so on but usually ignore their indirect influences. Schubert (2009) proposed the single publication h index for highly cited papers, taking their indirect influence into account through citing papers. The higher the h index of the set of papers citing a particular paper in question, the higher its single publication h index score becomes even though the work in question is not cited directly. Recently, Thor and Bornmann (2011) developed a web application14 to calculate the single publication h index of a paper using Schubert‘s definition and Google Scholar data. 15 It is fitting to review works on Cahit Arf in this study briefly. A detailed Turkish biography including his memoirs as well as his colleagues‘ views on his work and personality was written by Terzioğlu and Yılmaz and published by the Turkish Academy of Sciences in 2005 (Terzioğlu & Yılmaz, 2005). The book also includes some of his papers on mathematics as well as his writings on more general issues such as education. A scientific biography of Arf was written by Sertöz (2011), who also maintains a web site with pointers to papers (mostly in Turkish) about Arf as well as about other famous Turkish mathematicians. 16 A special issue on Arf was published after his death by TUBITAK‘s science and technology magazine, Bilim ve Teknik (1998). The special issue comprises several articles about Arf by some of his colleagues who met him or worked with him during his long career. To honor Arf‘s 80th birthday, a monograph including all of his papers (along with four papers written by his colleagues about his works) was published by the Turkish Mathematical Society (Arf, 1990). The Mathematics Department of the Middle East Technical University organizes ―Cahit Arf Lectures‖ annually. 17 13

See White (2007a) for a mo re comp rehensive discussion of theoretical foundation of biblio metrics, IR and RT along with their use in three different types of pennant diagrams for a seed work, seed author and seed article. 14

Available freely at http://labs.dbs.uni-leip zig.de/gsh.

15

http://scholar.google.com

16

http://www.b ilkent.edu.tr/~sertoz/turkler.ht m.

17

http://www3.iam.metu.edu.tr/matemat ikvakfi/arf.ht ml.

502

As for books and papers based on Cahit Arf‘s works, they are too numerous to review in this paper. For instance, there are books that are entirely or in large part on Arf invariant and its generalizations (e.g., Snaith, 2009; Klaus, 1995; Scorpan, 2005; Kirby, 1989; Browder, 1987). Recently, Lorenz and Roquette (2011) investigated the Arf invariant in its historical context based on letters exchanged between Cahit Arf and his advisor, Professor Helmut Hasse, after Arf got his PhD from Göttingen University. Interested readers can find further information on similar works by consulting the basic reference sources or databases some of which are readily a vailable through the Web. 18 The present study is an attempt to explore the scientific legacy of a world-famous Turkish mathematician, Cahit Arf. Since the scientific impact of Arf‘s works not be measured readily by using citation indexes, we use both soc ial network analysis and White‘s approach of author co-citation analysis coupled with pennant diagrams. The indirect influence of Arf‘s highly cited paper, ―Untersuchungen über quadratische Formen in Körpern der Charakteristik 2‖ (Arf, 1941) is also assessed through its single publication h index. Results of SNA, author co-citation analysis and single publication h index are discussed along with the implications of using these methods and metrics to study the scientific impact of authors directly, indirec tly and retrospectively. Method We think the scientific legacy of Cahit Arf is underrepresented in Thomson Reuters‘ (formerly ISI‘s) citation indexes. Hence, we decided to address several research questions to paint a better picture of his accomplishments as a mathematician. For example, who were the authors being co-cited most often with Arf? How high were their h index scores? Can we trace the scientific influence of Arf through paper titles and topics that contain the terms ―Arf invariant‖, ―Arf rings‖ and so on? Which paper of Arf received the highest number of citations? Will the pennant diagram of Arf‘s most frequently cited paper provide further insight into his influence in mathematics as well as in other disciplines? What is the single publication h index of his most significant work? To address these research questions, we first searched for bibliographic records for Cahit Arf (―Arf C*‖) in the Web of Science (WoS). Search results were in no way satisfactory (for the reasons explained before) to study the influence of Arf‘s contributions. We then searched for records having ―Arf*‖ in their titles and/or topics and analyzed them using CiteSpace, which is a freely available application developed by Dr. Chen to analyze the scientific literature and visualize the trends and patterns in the data. 19 We studied the distribution of citations on the co-citation network derived from CiteSpace by time-slicing them in 10-year intervals to identify other influential mathematicians who were co-cited with Arf. 18

See, for example, Wikipedia articles at: http://en.wikipedia.org/wiki/ Cahit_Arf; http://en.wikipedia.org/wiki/Arf_ invariant; and http://en.wikipedia.org/wiki/Arf-Kervaire_ invariant.

19

http://cluster.cis.drexel.edu/~cchen/citespace/

503

We searched for cited references of Arf from WoS and found that his paper on what is later called ―Arf invariant‖ were cited a total of 100 times (Arf, 1941). Then, using Arf (1941) as the seed work, we mapped author co-citation analysis results onto a pennant diagram using White‘s approach (White, 2007a, 2009). To create Arf‘s pennant diagram, we used the most highly cited 20% of references contained in 100 papers citing Arf (1941). Using Arf‘s pennant diagram, we identified other influential authors in the author co-citation network whose work was most relevant to those of Arf and, more specifically, to Arf‘s seed work (White, 2007a). Finally, we calculated the single publication h index of Arf‘s highly cited work (1941) on the basis o f Google Scholar data and tried to ascertain its indirect influence based on its citing papers (Schubert, 2009; Thor & Bornmann, 2011). We used WoS to identify the top 50 mathematics papers that received the highest number of citations and compared their single publication h indexes with that of Arf (1941). Findings and Discussion In this section we first present the findings of citation analysis of Arf‘s papers using Thomson Reuters‘ citation indexes. Our analysis includes the results of both cited reference search under ―Arf C*‖ and of an advanced search for papers that included ―Arf‖ in their titles and keywords. We then present the findings of author co-citation analysis of papers of the latter group by means of an author co-citation map, displaying pivotal authors whose work was co-cited along with papers that indirectly cited Arf‘s papers. Our analysis also includes Arf‘s seminal paper on quadratic forms over a field of characteristic two (Arf invariant). We trace the direct influence his paper by means of a pennant diagram based on author co-citation analysis. Finally, we trace the indirect influence of Arf‘s paper by calculating its single publication h index based on its citing papers and discuss our findings. Citation Analysis Arf published a total of 23 papers between 1939 and 1966 (Arf, 1990). The distribution of these papers by language is as follows: 12 in French, 6 in German, 4 in English and 1 in Italian. The two of his English papers were listed in Thomson Reuters‘ citation indexes along with two English abstracts (Table 1). They were cited a total of four times, thereby making Arf‘s h index score 1. This is by no means commensurate with his fame, however. In spite of his many prominent papers with significant inventions named after him, Arf‘s bibliometric profile is almost nonexistent. None of Arf‘s now-classic papers that influenced advanced mathematics profoundly was indexed in Thomson Reuters‘ citation indexes. Uncitedness among mathematicians with Fields Medal (cons idered to be the Nobel Prize of mathematics) or even Nobel Laureates is not uncommon (Egghe, Guns & Rousseau, 2011). Yet, this is not the case for Arf. Even though his works were not listed in citation indexes properly, a cited reference search under ―Ar f C*‖ produced a total of 146 citations. Compare this with a total of four citations that Thomson Reuters used to calculate Arf‘s h index. Arf‘s one paper (1941) alone received a total of 100 citations (which is not listed in ISI indexes). 20 Similarly, Lipman wrote a paper entitled 20

American Mathematical Society‘s Mathematical Reviews (M R) Citation Database on the Web (http://www.ams.org/mathscinet/) provides 31 citations to Arf‘s six papers including 20 citations to Arf 504

―Stable ideals and Arf rings‖ in 1971, which were cited a total of 94 times (Lipman, 1971). Lipman was referring to Arf‘s original paper that appeared in the Proceedings of the London Mathematical Society in 1948, which is not listed in ISI indexes, either (Arf, 1948). None of these direct or indirect citations to Arf‘s papers (1941, 1948) contributed to Arf‘s h index score. This is mainly due to the fact that Arf‘s significant contributions were published before the then ISI‘s citation indexes came into being and a great majority of them were not written in English language journals and therefore not indexed by ISI. One could only speculate as to how many citations Arf‘s original papers would have generated had they been listed in ISI indexes. Table 1. Arf‟s papers and abstracts listed in Thomson Reuters‟ citation indexes 1.

Arf, C., Imre, K. & Ozizmir, E. (1965). On algebraic structure of cluster expansion in statistical mechanics. Journal of Mathematical Physics, 6(8): 1179-&. (Times cited: 3) Arf, C. (1952). On methods of Rayleigh-Rit z-Weinstein. Proceedings of the American Mathematical Society, 3(2): 223-232. (Times cited: 1)

2. 3.

Arf, C. (1951). On Rayleigh-Rit z-Weinstein method. Bulletin of the American Mathematical Society, 57(4): 269-270. (Times cited: 0)

4.

Arf, C. (1951). On a free boundary problem in elasticity. Bulletin of the American Mathematical Society, 57(2): 136-136. (Times cited: 0)

Note: The query ―Arf C*‖ was run on Thomson Reuters‘ databases SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH using the all years time span (on July 25, 2011). Although Arf‘s papers were not properly listed in ISI‘s citation indexes and citations to them therefore did not count towards Arf‘s h index score, Arf‘s influence can be observed further through paper titles that contain various references to Arf‘s works (e.g., ―Arf invariant‖, ―Arf rings‖, ―Hasse- Arf theorem‖ and so on). We performed an advanced search in Thomson Reuters‘ citation indexes 21 and found a total of 43 papers (38 articles, 4 proceedings paper, and 1 correction) with ―Arf*‖ in their titles. 22 Note that not all 43 papers contained references to Arf‘s works. In fact, only 15 of them did (a total of 16 citations). Arf‘s classic papers (1941, 1948) received 8 and 5 citations, respectively. These 43 papers were mainly classified under Mathematics and were cited a total of 279 times (h index 9, max. citation per item: 95, avg. citation per item: 6.49). We performed a topical search (TS) in Thomson Reuters‘ citation indexes and found an additional 52 papers with ―Arf*‖ in their keywords (i.e., topics). 23 Note that only 9 out (1941). The MR database covers relatively current citations (year (http://www.ams.org/mathscinet/help/citation_database_understanding.html).

2000

to

present)

21

We used the following query: TI=(arf theorem) OR TI=( arf invariant*) OR TI=(arf ring*) OR TI=( arf propert*) OR TI=(arf filtrat ion*) OR TI=(arf semigroup * ) OR TI=(arf singularit*) OR TI=(arf equivalence ) OR TI=(arf closure*). Irrelevant items were discarded. 22

The terms used in the titles of these papers are as follows: Arf invariant* (the most common one) or Arf‘s invariant, Hasse-Arf filt rations, Arf rings, Arf nu merical semigroups, Arf semigroups, Arf functions, Arf characteristics of singularit ies, Arf closure, Arf equiva lence, Arf-Kervaire invariant, HasseArf theorem, and Hasse-Arf property. 23

Note that TS gets the keywords fro m tit les, abstracts, and the author-assigned keywords. Therefore, we excluded the titles to find the unique items that would be retrieved only through keywords that come fro m 505

of 52 papers cited Arf‘s two papers (1941, 1948). These 52 papers received a sum of 208 citations (h index 8, max. citation per item: 25, avg. citation per item: 4.00). Altogether, 95 papers with ―Arf*‖ in their titles or topics (e.g., abstract keywords or keywords given by their authors) published between 1965 and 2011 were cited a total of 487 times (h index 11, avg. max. citation per item: 95, avg. citation per item: 5.13). It should be noted that 24 authors cited Arf‘s works 25 times but the great majority of authors (69 out of 95) who referred to Arf‘s works in titles or keywords of their papers did not necessarily give due credit to Arf in their reference lists by properly citing Arf‘s papers. Apparently, they were either unaware of the existence of Arf‘s papers or they did not cite them because Arf‘s papers became a part of ―regular scientific discourse‖ (Tonta & Darvish, 2010, p. 169). Indeed, as Terzioğlu (1998) points out, Arf‘s name is so intertwined with mathematics one needs to work hard to find citations to Arf‘s papers. Some authors using Arf invariant in their works seem to have referred to it as a mathematical symbol or notation without, perhaps, thinking that these three characters are actually the last name of a Turkish mathematician (Tosun Terzioğlu, personal communication, August 16, 2011). Papers on geometric, algebraic or differential topology frequently refer to Arf invariant as Arf(X) or, Arf(M), (here X or M stands for a manifold), Arf(K) for a knot and Arf(q) for a quadratic form. These various forms of use of Arf invariant in relevant papers are hardly reflected in papers‘ titles, keywords or reference lists (Turgut Önder, personal communication, August 24, 2011). 24 We know that ISI indexers tended to make ―implicit‖ citations to works of art or musical scores within the arts and humanities papers ―explicit‖ by indexing them accordingly (Al, Şahiner & Tonta, 2006, p. 1012; Garfield, 1980; Stern, 1983). Such citations count towards one‘s cited references and possibly towards his/her h index score. Yet, we are not aware of any Thomson Reuters convention that makes implicit references in paper titles or topics explicit, thereby giving credit to those whose works get cited tacitly. An implicit reference made explicit by indexers within a paper is equal to one citation. But what about an implicit reference found in the title or topic of a paper? It would certainly be worth more than one citation. For instance, Sertöz (1990) in his article on Arf rings cites no fewer than eight papers that were clearly based, at least in part, on Arf‘s work and the two of those papers have ―Arf*‖ in their titles (excluding Lipman‘s oft-cited paper that we mentioned earlier). Suppose that we decided to find out what would Arf‘s total influence be if we weighted the papers with ―Arf*‖ in their titles (43) and keywords (52). Further suppose that each paper with ―Arf*‖ in its title and keywords list is worth 10 and 5 citations, respectively. This would give us a total of 690 citations to Arf‘s works. One would safely claim that Arf‘s h index score would have been much higher if his works were listed in Thomson

abstracts or author-assigned keywords. In other words, these 52 papers do not overlap with the previous 43 papers that we identified through title search. 24

We are most grateful to Professor Turgut Önder of the Middle East Technical University (Ankara, TR) who generously shared his knowledge of Arf invariant and references to it in various forms in the mathematics literature and provided pointers to the relevant monographs mentioned earlier in the Literature Review.

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Reuters indexes and citations to his works in paper titles and keywords were made explicit. 25 Author Co-citation Analysis We further analyzed the papers with ―Arf*‖ in their titles and topics (i.e., keywords) using CiteSpace. We selected the most frequently cited 20% of those papers and used author co-citation analysis to visualize them in 10-year time slices starting from 1966 (Fig. 1).Clearly, Arf C is the centroid of such an author co-citation network. Kaufman LH, Robertello RA, Milnor J, Serre JP, Kervaire MA, Browder W, Bass H, Adams JF, and Bourbaki N are the pivotal nodes in the network as the purple rings indicate. Pivotal nodes are strategically important in pulling together other nodes and therefore their betweenness centrality scores 26 are higher (Chen, Song, Yuan & Zhang, 2008). These pivotal nodes and many light colored ones not labeled represent very important mathematicians. Orange colored clusters of the network indicate works that are published most recently (from 2006 to 2011). They make up a considerable part of the full network in proportion. If we add yellow colored parts of the network (representing the time span 1996 to 2005) to orange colored ones, we could obtain almost the whole network. In other words, Cahit Arf is still cited heavily in papers with ―Arf*‖ in their titles or keywords, along with the great mathematicians of today. As we indicated earlier, a cited reference search for ―Arf C*‖ generated 146 results. Whereas his official h index is 1, his h index based on citations to his works that are not listed in citation indexes would be 5. Table 2 provides the names of 10 famous mathematicians who were co-cited frequently with Arf along with their h index scores. Names in Table 2 come from the most frequently cited 20% of Arf‘s co-cited authors.27 For example, an award-winning mathematician Jean-Pierre Serre is on the top of the cocited authors list with an h index of 23. He won Abel Prize (2003), Fields Medal (1954), Wolf Prize in Mathematics (2000) and Balzan Prize (1985). 28 He also gave a speech about Cahit Arf in ―Cahit Arf Seminars‖ in 2006 at the Middle East Technical University in Ankara, Turkey. Milnor J has an h index of 29 and appeared in Arf‘s cocited authors list, too. Witt and Kervaire are also on the above list. Witt, too, wo rked in Göttingen and is still being cited with his works on Witt algebra, Witt decomposition, Witt design (Witt geometry), Witt group, Witt index, Witt polynomial, Witt ring, Witt scheme, Witt's theorem, Witt vector, Bourbaki–Witt theorem, and Shirshov–Witt 25

For comparison, J. Lip man wrote 34 papers (including the one on Arf rings receiving 94 citations) and were cited 622 times (h index 12). 26

Betweenness centrality score measures how nodes facilitate the flow in the network (Otte & Rousseau, 2002, pp. 442– 443). 27

It should be noted that ―Nicolas Bourbaki‖ (Bourbaki N) is actually a pseudonym referring to more than one authors. For more in formation, see the Wikipedia article at http://en.wikipedia.org/wiki/Nicolas_Bourbaki. 28

Wikipedia: http://en.wikipedia.org/wiki/Jean-Pierre_Serre

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theorem. 29 However, he has no record with his name in citation indexes, either. Due to Kervaire‘s later work (1960), the Arf invariant is sometimes referred to as the ArfKervaire invariant because ―the Kervaire invariant is the Arf invariant of a quadra tic form‖. 30 Incidentally, the Arf-Kervaire invariant, a longstanding problem in algebraic topology, has been solved by three mathematicians in April 2009 (Hill, Hopkins & Ravenel, 2010). 31 Neither Arf nor Kervaire (who died in 2007) has lived long enough to see the Arf-Kervaire invariant‘s solution. A group of 20 th century mathematicians published nine important books under the pseudonym of Nicolas Bourbaki, 32 presenting an exposition of modern advanced mathematics and receiving hundreds of citations to them. (Serre JP, Dieudonne J and Omeara OT also wrote influential textbooks.) It should be pointed out that names that appeared in author co-citation network and the top co-cited author distributions such as Serre, Milnor, Dieudonne, Kervaire, Kneser, Bass, Adams and Witt are all well known mathematicians.

29

Wikipedia: http://en.wikipedia.org/wiki/ Ernst_Witt

30

Wikipedia: http://en.wikipedia.org/wiki/Arf -Kervaire_ invariant

31

We thank Professor Turgut Önder for drawing our attention to the solution of the Arf-Kervaire invariant. Professor Önder points out that the solution of this almost 50 -year old problem was one of the few outstanding developments in the mathemat ics world in 2009 (Turgut Önder, personal communicat ion, August 24, 2011). For mo re on this, see Douglas Ravenel‘s web site at http://www.math.rochester.edu/u/faculty/doug/kervaire.ht ml. 32

Wikipedia: http://en.wikipedia.org/wiki/Nicolas_Bourbaki

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Fig. 1. Author co-citation network (co-cited with authors of papers with “Arf*” in their titles/keywords) Table 2. Co-cited author distributions

Co-cited Authors ARF C

Frequencies 146

h Index Scores 1

30 23 21 20 20 20 18 18 18 14

23 (No ISI records under his name) (No ISI records under his name) 29 11 14 9 (with respect to 10 papers) 11 25 8

SERRE JP WITT E BOURBAKI N MILNOR J DIEUDONNE J SAH CH KERVAIRE MA OMEARA OT WALL CTC KNESER M

We used co-citation statistics to draw a pennant diagram of Cahit Arf to trace his scientific influence further. What follows is a discussion of the influence of Cahit Arf 509

based on the pennant diagram of his most significant work, ―Untersuchungen über quadratische Formen in Körpern der Charakteristik 2. (Teil I).‖ (Arf, 1941). The outcome of the cited reference search under ―Arf C*‖ (as of July 25, 2011) showed that his paper in which he first described the ―Arf invariant‖ (Arf, 1941) were cited 100 times, constituting more than two thirds of all citations to his works. We used Arf (1941) as the seed paper and identified the most highly cited 20% of references contained in those 100 papers citing Arf (1941). We found that 234 papers were cocited with Arf‘s seminal work (1941) at least once. To refine the resultant pennant diagram (otherwise it would be difficult to read the labels of nodes), we used 34 (out of 234) papers which were co-cited with Arf (1941) at least four times. Searching citation indexes, we found the tf (Items Ranked) and df (Items in File) values and used the following tf*idf formula (Manning & Schütze, 1999) weight (i,j) = (1 + log(tfi,j))log(N/dfi ) (1) where all term counts are greater than or equal to 1, logarithms are based 10, and N is the total number of items in the Thomson Reuters‘ data collection 33 (White, 2007a). Values for x and y axes in the pennant diagram in (Fig. 2) were calculated according to (1). (Appendix 1 gives the tf and idf values sorted by dfs and tfs.) Arf with his most significant paper (1941) appears at the tip of the pennant (Fig. 2) at the right hand side as the seed term (White, 2007a, p. 541). (The years next to authors‘ names indicate publication years of relevant papers.) The seed term generates a bibliometric distribution which predicts the relevance of any associated term with itself. Higher tf and idf scores of any associated term will produce greater predicted relevance to the seed. While the pennant diagram narrows through the right hand side, papers represented by points in Figure 2 become increasingly more relevant to that of Arf (1941). Authors in the left- most column were co-cited at least four times with Arf‘s paper (1941) while the ones to its right were co-cited progressively more. For instance, Witt‘s 1937 paper were co-cited 12 times with that of Arf (1941). (Figures in the x and y axes are both logged.) This is not a coincidence as Arf developed and completed Witt‘s work, as we explained earlier. Similarly, O‘Meara‘s 1963 textbook Introduction to Quadratic Forms is the second highest co-cited work with Arf (1941) because it builds, at least in part, upon Arf‘s seminal work on the subject. 34 Thus, it is relatively easier to discern the increasing relevance of the papers as we move to the tip of the pennant.

33

The number o f items in ISI file is assumed to be 5 million.

34

Note that references to authors and their wo rks in the pennant diagram can be found in Appendix 1.

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Figure 2. Pennant diagram of items co-cited with Arf‟s “Untersuchungen über quadratische Formen in Körpern der Charakteristik 2” (Arf, 1941)

Note: Only the first author‘s name is given in the pennant diagram for co-authored works. White divided the pennant diagram into three (sections A, B, and C) and interpreted the results from various different angles such as the specificity of works (i.e., topicality), ease of processing, age and authority, fame and so on (White, 2007a). For instance, cocited authors at the top (section A) of the pennant are topically more specific and therefore more relevant to that of Arf (1941) compared to the ones at the middle (section B) or bottom (section C) of the pennant. Similarly, co-cited authors in section A are juniors of Arf while the ones in Section B and C are his peers and seniors, respectively. We interpret Arf‘s pennant diagram based on figures given in Table 3, which s how how the idf portion of the formula effects the ranking of term distribution. Thomson Reuters‘ cited reference strings combine both cited works and cited authors, we can use 511

them to make judgments about papers, works and their authors (White, 2007a). Based on these judgments, we draw dividers between sections A, B, and C. Table 3. Ranks of the first 10 co-cited items with Arf (1941) based on tf and idf Freq REFERENCE

tf

Sector %

df

N

(tf/df)*100

tf*idf

log(1+tf) log(5mil/df)

ARF C,1943

5

5 5E+06

28.316

10.194

1.699

6.000

RIEHM CR,1965

5

6 5E+06

28.694

10.059

1.699

5.921

KLINGENBERG W,1954

5

7 5E+06

29.023

9.946

1.699

5.854

WITT E,1954

5

7 5E+06

29.023

9.946

1.699

5.854

SAH CH,1960

9 13 5E+06

34.991

10.914

1.954

5.585

TROJAN A,1966

4 16 5E+06

29.156

8.803

1.602

5.495

RIEHM C,1964

5 33 5E+06

32.796

8.801

1.699

5.180

SPRINGER TA,1955

4 37 5E+06

31.225

8.220

1.602

5.131

SAH C,1972

7 40 5E+06

36.200

9.404

1.845

5.097

CAPPELL SE,1974

4 40 5E+06

31.432

8.166

1.602

5.097

Note that all but one (Cappell SE) authors referred to in Table 3 are p laced in section A of Arf‘s pennant diagram depicted in Figure 2. They have higher tf and idf values in that they are both topically relevant to that of Arf (1941) and their relevance can easily be discerned. Take, for instance, Arf‘s own work published in 1943 (―Untersuchungen über quadratische Formen in Körpern der Charakteristik 2. II. Über aritmetische Äquivalenz quadratischer Formen in Potenzreihenkörpern über einem vollkommenen Körper der Charakteristik 2‖). It is shown at the top of the pennant diagram because it is a sequel of and complements Arf‘s original 1941 paper. Papers by Riehm CR, Klingenberg W, Witt E and Sah CH at the top of section A are specifically about quadratic forms over a field of characteristic two, which is exactly the subject of Arf‘s paper. 35 The topics of papers by the remaining four authors (namely, Trojan A, Riehm C, Springer TA, and Sah C) in section A are all about quadratic forms (but not necessarily quadratic forms over a field of characteristic two) and therefore placed in the relatively lower parts of section A. 36 Note that Chih-Han Sah (entered in two different forms in citation indexes as Sah CH 1960 and Sah C 1972 but corrected in the pennant diagram) is placed higher in the top of the pennant diagram with his spec ific paper on quadratic forms over a field of characteristic two whereas he is placed relatively lower with his more general paper on symmetric bilinear forms and quadratic forms. So, all nine papers (including Arf‘s sequel in 1943) in section A are highly relevant to Arf‘s original paper (Hsia, 1968). 35

The titles of their papers are: ―Integral representations of quadratic forms in characteristic 2‖ by Rieh m CR (1965); ―Über die Arfsche Invariante quadratischer Formen mod 2‖ by Klingenberg W (1954); ―Über eine Invariante quadratischer Formen mod 2‖ by Witt E. (1954); and ―Quadratic forms over fields of characteristic-2‖ by Sah CH (1960). 36

The titles of their papers are: ―Integral extension of isometries of quadratic forms over local fields‖ by Trojan A (1966); ―On integral representations of quadratic forms over local fields‖ by Rieh m C (1964); ―Quadratic forms over fields with a discrete valuation‖ by Springer TA, and ―Sy mmetric bilinear forms and quadratic forms‖ by Sah C (1972).

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One can easily see that authors in section A are juniors of Arf (not by age, perhaps, but by the topic of their papers) in that they built on or they did further research on the Arf invariant. Note that Witt whose 1937 paper was developed by Arf (1941) wrote a specific paper on Arf invariant later in 1954. As we move down from section A to section B, the difference between the subject of Arf‘s paper and those in section B gets more difficult to discern because papers are no longer specifically on quadratic forms. For instance, Cappell‘s 1974 paper is not on quadratic forms (―Unitary Nilpotent Groups and Hermitian K-Theory. 1‖). We therefore drew the line between section A and B just above Cappell SE 1974 in Figure 2. In general, the relationship between Arf‘s original paper and those in sector B are not necessarily obvious. Yet, authors in section B can be considered peers of Arf in mathematics and they were usually co-cited with Arf (see Table 2). We have already mentioned Witt E 1937 and Kervaire M 1960 earlier. Many of Arf‘s peers in section B are considered top mathematicians, some with high index scores, 37 some (just like Arf himself) not properly represented in indexes (e.g., Kervaire M) 38 or not represented at all (e.g., Witt E). White (2007a, p. 556) considers cited authors in section C as ―seniors, culture heroes‖, cited works as serials, generic titles, and world classics, and cited references as books and classic articles. The contributions of authors in section C of Arf‘s pennant diagram validate White‘s prediction in that they consist mostly of classic mathematics texts. Corps Locaux by Serre JP (1962), Algebre by Bourbaki N (1959), Linear Groups with an Exposition of the Galois Field Theory by Dickson LE (1958), Introduction to Quadratic Forms over Fields by Lam TY (1973), Introduction to Quadratic Forms by O‘Meara (1963), La Géométrie Des Groupes Classiques by Dieudonne (1955), Algebraic Theory SPI by Chevalley C (1954) are all placed at the bottom of Arf‘s pennant diagram along with a highly cited article (cited a total of 449 times as of July 25th , 2011) by Kervaire MA (and Milnor J) entitled ―Groups of Homotopy Spheres: I‖. These authors can be considered seniors of Arf as they authored highly regarded textbooks in mathematics. Single Publication h Index After the assessment of the direct influence of Arf‘s seed work on Arf invariants (Arf, 1941) based on author co-citation analysis, we wanted to ascertain the indirect influence of the same work based on its citing papers using Schubert‘s single publication h index (defined as ―the set of papers citing the work in question‖) (Schubert, 2009, p. 559). We used Thor and Bornmann‘s (2011) readily available web application to calculate the h index of Arf‘s paper. The application retrieved a total of 99 papers from Google Scholar citing Arf (1941). Figure 3 provides the partial list of citing papers with their bibliographic information (author, title, and the publication year) and the number of times they were cited. Citing papers were cited between 827 and 0 times. Thirty-two 37

For example, Atiyah MF with h index 34, M ilnor J with 29, Wall CTC with 25, Cappell SE with 17, and Frohlich A with 16. 38

Kervaire M has an h index score of 9 with 10 of his papers being listed in citation indexes.

513

out of 99 citing papers (32.3%) were never cited at all. The rest (67) were cited a total of 4,075 times. The most current paper citing Arf (1941) is dated 2008. 39 It is interesting to note that all citing papers were written in German, indicating the international coverage of Google Scholar database. It should be noted that not all papers citing Arf (1941) are correctly identified by Google Scholar. Fifteen papers in the list citing Arf (1941) were published before 1941. An additional 5 papers lacked publication year information one of which was published before 1941. These 16 papers were cited a total of 1,303 times (almost one-third of all indirect citations). We have not checked the papers with publication year information furnished to see if they are correctly identified by Google Scholar matching algorithms. 40

Figure 3. Search results for Arf‟s seminal paper, “Untersuchungen über quadratische Formen in Körpern der Charakteristik 2. (Teil 1).” based on Google Scholar data (Aug. 25, 2011).

Notwithstanding the limitations of Google Scholar, the single publication h index of Arf‘s paper (1941) was calculated as 24 by Thor and Bornmann‘s (2011) web application. Figure 4 provides citation distribution graph of 100 papers citing Arf (1941) excluding self-citations. 39

Professor Turgut Önder points out that the solution of the Arf-Kervaire invariant has brought the Arf invariant into the fore once again and will likely increase citations to Arf‘s original 1941 paper as well as to current papers discussing the solution, thereby increasing Arf‘s original paper‘s single publication h index (Turgut Önder, personal co mmunication, August 24, 2011) . 40

The shortcomings of Google Scholar‘s matching algorith ms are well documented in the literature. See, for examp le, Jacsó (2008) and Bar-Ilan (2008).

514

The graph plots the citing papers sorted by the number of times they were each cited in the x axis and the number of citations in the y axis. Note that the area plotted is divided into three sections (h2 upper, h2 center, and h2 lower) proposed by Bornmann, Mutz and Daniel (2010). These metrics and what they represent are delineated in the single publication h index web application site (http://labs.dbs.uni- leipzig.de/gsh/) as follows: They allow quantification of three areas within a distribution of citing publications of one single publication: the low impact area (h 2 lower), the area captured by the h index (h 2 center), and the area of publications with the highest visibility (h 2 upper). The citing publications of one single publication (with the same h index) may be do minated by low-impact cit ing publications (reflected by a high percentage for h 2 lower) or by highimpact citing publications (reflected by a high percentage for h2 upper). The m index is the median number of citations received by the citing publications in the Hirsch core; this is the citing publications ranking s maller than or equal to h (Born mann, Mutz and Daniel, 2008).

Figure 4. Single publication h index for Arf‟s seminal paper, “Untersuchungen über quadratische Formen in Körpern der Charakteristik 2. (Teil 1).‖ (Aug. 25, 2011).

515

The upper section of h graph of Arf‘s paper comprises 67.4% of the whole area, indicating that citing publications of Arf (1941) ―are dominated by publications with high citation counts‖ (Thor & Bornmann, 2011, p. 293). The percentage of low- impact citing publications of Arf‘s paper is relatively low (16.6%). To put the h index of 24 of Arf‘s seminal paper (1941) into perspective within mathematics papers, we carried out a simple search on Thomson Reuters‘ Web of Science (Aug. 25, 2011). We identified a total of 33,398 papers listed under the topic ―Mathematics‖. Papers listed under this topic are not necessarily on ―Mathematics‖ because WoS topical search also retrieves papers that contain the word ―mathematics‖ used in an ordinary manner in their abstracts (e.g., ―mathematics students scored higher than . . .‖). Therefore, we refined the search to the WoS categories of ―Mathematics‖ and ―Mathematics Applied‖, reducing the retrieval set to 5,653 papers. 41 We then sorted them by the number of times they were cited using the ―times cited‖ option. Out of 5,653 papers, only 18 papers were cited more than 100 times (the highest being 1.027). Recall that Arf‘s paper was cited 100 times in the reference lists of papers indexed in the WoS database even though Arf‘s original paper was not listed in citation indexes. According to American Mathematical Society‘s MathSciNet, Arf‘s paper is the 36th among highly cited papers published in 1941. Note that the coverage of the MathSciNet citation database is as yet limited, as it comprises citations from reference lists of journals published during the period 2000-present only. 42 We then calculated the single publication h index for the first 50 papers with the highest number of citations (ranging between 1,027 and 56). We located only 15 papers whose single publication h index scores were higher than that of Arf‘s 1941 paper (i.e., 24). 43 The number of citations measures the direct influence of his paper while the single publication h index measures its indirect influence. This small scale experiment shows that Arf‘s paper has been among the top 15 or 20 papers in terms of both the total number of citations it generated and its single publication h index score. This is a remarkable achievement for a scientist, especially when one considers the fact that Arf‘s paper has never been listed in Thomson Reuters‘ citation indexes.

41

We are well aware of the fact that papers listed in WoS is just a subset of all papers published (primarily in English) in the world on mathematics. For instance, American Mathematical So ciety‘s MathScieNet (Mathematical Reviews on the Web) database contains a total of 2,700,607 records as of August 25, 2011. The number of MathSciNet records with the word ―mathematics‖ appearing anywhere in the designated fields is 1,103,552. 42

http://www.ams.org/ mathscinet/citations.html. See the note entitled ―Understanding the MR Citation Database‖ at http://www.ams.org/mathscinet/help/citation_database_understanding.html. 43

Note that the rest of the papers are highly unlikely to produce single publication h index scores over 24.

516

Conclusions and Further Research Cahit Arf‘s contributions were not properly listed in citation indexes and his h index score or any other bibliometric indicator cannot therefore be calculated properly. In this study, we used SNA and author co-citation network of Arf to study his overall scientific influence retrospectively. Using White‘s approach, we drew Arf‘s pennant diagram on the basis of his author co-citation map to reveal his scientific impact (despite the foggy retrospective data that could be gathered from Thomson Reuters). We also calculated the single publication h index score for his most significant paper (Arf, 1941). Arf‘s cited references and paper titles or keywords with Arf‘s contributions to mathematics (e.g., ―Arf invariant‖ or ―Arf rings‖) indicate that Arf is still being cited heavily, despite the fact that his last contribution was in 1960s. This is further confirmed by the results of SNA (1966-2011) as Arf continues to play a prominent role in mathematics. Moreover, the pennant diagram and the single publication h index score based on Arf‘s seminal paper (Arf, 1941) clearly show his overarching influence on generations of mathematicians. His paper on Arf invariants has been a mong the top 20 papers having both direct and indirect influence in mathematics and related fields such as knot theory. Findings obtained through SNA and the pennant diagram seemed to be similar in that some pivotal authors on the co-citation network appeared as peers of Arf in the pennant diagram. However, further work is needed to compare the results of the two methods more comprehensively. As Arf‘s pennant diagram is based on his single work, we may be missing some crucial authors or works. Further work is also needed to find out if Arf‘s implicit citations (from titles and keywords of papers) can be incorporated in such analyses using a somewhat different approach. This may provide a fuller picture of Arf‘s scientific influence. This study clearly shows that White‘s (2007a) approach and pennant diagrams can be used to study the impact of authors who are no longer active or their h index scores cannot be calculated on the basis of available data. Once a proper method is developed in the future to incorporate implicit citations to pennant diagrams as co-citations, they can be used to calculate authors‘ retrospective h index scores. One can conjecture that explicit citations in reference lists of papers would be placed in section A of a pennant diagram, while implicit citations contained in paper titles and keywords be placed in section B and section C, respectively. Further research is needed to validate this conjecture. Similarly, Schubert‘s single publication h index (2009) can be used to study the indirect influence of individual papers. Yet, we need cleaner data and better matching algorithms to attribute the citations correctly to the seed paper and to improve the accuracy of the scores of single publication h index. Simple checks like the comparison of publication years will certainly be of help in this respect. Furthermore, the calculation of the indirect influence of a paper can further be refined by assigning weights to indirect citations, as suggested by Rousseau (1987) long before h index came into being.

517

Acknowledge ments We are grateful to Professors Tosun Terzioğlu of Sabancı University (İstanbul, TR) and Turgut Önder of the Middle East Technical University (Ankara, TR) for their generous comments on an earlier draft of this paper. We also thank Dr. Umut Al and Güleda Düzyol of Hacettepe University (Ankara, TR) for carefully reading its first draft. References Al, U., Şahiner, M. & Tonta, Y. (2006). Arts and Humanities literature: Bibliometric characteristics of contributions by Turkish authors. Journal of the American Society for Information Science & Technology, 57, 1011-1022. Arf, C. (1941). Untersuchungen über quadratische Formen in Körpern der Charakteristik 2. (Teil I.) Journal für die Reine und Angewandte Mathematik , 183, 148-167. Arf, C. (1948). Une interpretation a lgébrique de la suite des ordres de multiplicité d'une branche algébrique. Proceedings of the London Mathematical Society, Series 2, 50, 256-287. Arf, C. (1990). The Collected Papers of Cahit Arf. İstanbul: Türk Matematik Derneği. Bar-Ilan, J. (2008). Which h-index? A comparison of WoS, Scopus and Google Scholar. Scientometrics, 74(2): 257-271. Bilim ve Teknik. (1998 February). Special issue on Cahit Arf. No. 363. Retrieved, August 25, 2011, from http://www.biltek.tubitak.gov.tr/bdergi/ozel/arf/default.html. Bornmann, L., Mutz, R. & Daniel, H-D. (2010). The h index research output measurement: Two approaches to enhance its accuracy. Journal of Informetrics, 4, 407-414. Bornmann, L., Mutz, R. & Daniel, H-D. (2008). Are there better indices for evaluation purposes than the h index? A comparison of nine different variants of the h index using data from biomedicine. Journal of the American Society for Information Science & Technology, 59, 830-837. Browder, W., ed (1987). Algebraic topology and algebraic K-theory: Proceedings of a Symposium in Honor of John C. Moore. Princeton, NJ: Princeton University Press. Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57, 359–377. Chen, C., Song, I.Y., Yuan, X. & Zhang, J. (2008). The thematic and citation landscape of Data and Knowledge Engineering (1985‐2007). Data and Knowledge Engineering, 67, 234‐259. Egghe, L., Guns, R. & Rousseau, R. (2011). Thoughts on uncitedness: Nobel laureates and Fields medalists as case studies. Journal of the American Society for Information Science and Technology, 62, 1637–1644. Garfield, E. (1980). Is information retrieval in the arts and humanities inherently different from that in science? The effect that ISI‘s citation index for the arts and humanities is expected to have on future scholarship. Library Quarterly, 50, 40–57. Hill M.A., Hopkins, M.J. & Ravenel,D.C.. (2010). On the non-existence of elements of Kervaire invariant one. Retrieved, August 26, 2011, from http://arxiv.org/PS_cache/arxiv/pdf/0908/0908.3724v2.pdf. Hirsch, J.E. (2005, November 15). An index to quantify an individual‘s scientific research output. Proceedings of the National Academy of Sciences of the United States of America , 102(46), 16569–16572. Hsia, J.S. (1968). A note on the integral equivalence of vectors in characteristic 2. Mathematische Annalen, 179, 63–69. Ikeda, M.G. (1998). Cahit Arf's contribution to algebraic number theory and related fields. Turkish Journal of Mathematics, 22, 1-14.

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Jacsó, P. (2008). The pros and cons of computing the h-index using Google Scholar. Online Information Review, 32, 437-452. Kervaire, M. (1960). A manifold which does not admit any differentiable structure. Commentarii Mathematici,Helvetici, 34, 257–270. Kirby, R.C. (1989). The topology of 4-manifolds. Berlin: Springer-Verlag. Klaus, S. (1995). Brown-Kervaire Invariants. PhD Thesis. Universitäty Mainz. Aachen: Shaker. Lorenz, F. & Roquette, P. (2010). On the Arf invariant in historical perspective. Mathematische Semesterberichte, 57(1), 73–102. Lipman, J. (1971). Stable ideals and Arf rings. American Journal of Mathematics, 93, 649-685. McCain, K.W. (1984). Longitudinal author cocitation mapping: the changing structure of macroeconomics. Journal of the American Society for Information Science, 35, 351-359. McCain, K.W. (1986). Cocited author mapping as a valid representation of intellectual structure. Journal of the American Society for Information Science, 37, 111-122. McCain, K.W. (1990). Mapping authors in intellectual space. A technical overview. Journal of the American Society for Information Science, 41, 433-443. McCain, K.W., Verner, J.M., Hislop, G.W, Evanco, W. & Cole, V. (2005). The use of bibliometric and knowledge elicitation techniques to map a knowledge domain: Software Engineering in the 1990s. Scientometrics, 65, 131-144. Manning, C.D., & Schütze, H. (2000). Foundations of statistical natural language processing. Cambridge, MA: MIT Press. O'Connor, J. J., & Robertson, E.F. (1998). Arf biography, MacTutor History of Mathematics archive. Retrieved, July 18, 2011, from http://www-history.mcs.standrews.ac.uk/Biographies/Arf.html. Otte, E., & Rousseau, R. (2002). Social network analysis: A powerful strategy, also for the information sciences. Journal of information Science, 28, 443–455. Önder, T. (1990). Arf invariant and its applications in topology. In Arf, C. The Collected Papers of Cahit Arf (pp. 413-415). İstanbul: Türk Matematik Derneği. Reisman, A. (2006). Turkey‘s modernization: Refugees from Nazism and Atatürk‘s vision. Washington, DC: New Academia Publishers. Reisman, A. (2007, June 12), Turkey's invitations to Nazi persecuted intellectuals circa 1933: A bibiliographic essay on history's blind spot. Retrieved, August 30, 2011, from http://ssrn.com/abstract=993310 Rousseau, R. (1987). The Gozinto theorem: Using citations to determine influences on a scientific publication. Scientometrics, 11(3-4): 217-229. Schubert, A. (2009). Using the h-index for assessing single publications. Scientometrics, 78(3): 559-65. Scorpan, A. (2005). The wild world of 4-manifolds. Providence, RI: American Mathematical Society. Sertöz, A.S. (2011). A scientific biography of Cahit Arf (1910–1997). (Unpublished manuscript). Sertöz, S. (1990). On Arf rings. In Arf, C. The Collected Papers of Cahit Arf (pp. 416-419). İstanbul: Türk Matematik Derneği. Sertöz, S. (1997). Arf rings and characters. Note di Matematica, 14, 251-261.Sparck Jones, K. (1972). A statistical interpretation of term specificity and its application to retrieval. Journal of Documentation, 28, 11-21. Snaith, V.P. (2009). Stable homotopy around the Arf-Kervaire invariant. Basel:

Birkhäuser. Stern, M. (1983). Characteristics of the literature of literary scholarship. College & Research Libraries, 44, 199–209. Terzioğlu, T. (1998 February). Sunuş yerine (Introduction to the special issue on Cahit Arf). Bilim ve Teknik, No. 363. Retrieved, August 25, 2011, from http://www.biltek.tubitak.gov.tr/bdergi/ozel/arf/sunus.html. 519

Terzioğlu, T. & Yılmaz, A. (2005). Anlamak tutkunu bir matematikçi: Cahit Arf. Ankara: TÜBA. Thor, A., & Bornmann, L. (2011) The calculation of the single publication h index and related performance measures: A web application based on Google Scholar data. Online Information Review, 35(2): 291 – 300. Tonta, Y., & Darvish, H.R. (2010). Diffusion of Latent Semantic Analysis as a research tool: A social network analysis approach. Journal of Informetrics, 4, 166-174. White, H.D. (2007a). Combining bibliometrics, information retrieval, and relevance theory. Part 1: First examples of a synthesis. Journal of the American Society for Information Science and Technology, 58, 536-559. White, H.D. (2007b). Combining bibliometrics, information retrieval, and relevance theory. Part 2: Some implications for information science. Journal of the American Society for Information Science and Technology, 58, 583-605. White, H. D. (2009). Pennants for Strindberg and Persson. In Celebrating scholarly communication studies: A festschrift for Olle Persson at his 60th birthday. Special Volume of the E-Newsletter of the International Society for Scientometrics and Informetrics, v. 5-S: 71-83. White, H.D. (2010). Some new tests of relevance theory in information science. Scientometrics, 83:653–667. White, H.D., & Griffith, B.C. (1981). Author co-citation: a literature measure of intellectual structure. Journal of the American Society for Information Science, 32,163-171. White, H.D., & McCain, K.W. (1998). Visualizing a discipline: An author co-citation analysis of information science, 1972-1995. Journal of the American Society forInformation Science, 59, 2146-2155. Witt, E. (1937). Theorie der quadratischen Formen in beliebigen Körpern. Journal für die Reine und Angewandte Mathematik , 176, 31–44.

520

APPENDIX 1: tf and i df values for authors in Arf‟s pennant diagram Note: For papers having more than one authors, only the first author‘s name is provided below in the first column. weight

log(tf)

log(idf)

tf*idf

log(1+tf)

log(5mil/df)

14.076

2.996

4.699

ARF C,1943,RFSUI A,V8,P297

10.194

1.699

6.000

RIEHM CR,1965,AM J MATH,V87,P32

ARF C,1941,J REINE ANGEW MATH,V183,P148 SECTOR A

10.059

1.699

5.921

KLINGENBERG W,1954,J REINE ANGEW M ATH,V193,P121

9.946

1.699

5.854

WITT E,1954,J REINE ANGEW MATH,V193,P119

9.946

1.699

5.854

10.914

1.954

5.585

TROJAN A,1966,CANADIAN J M ATH,V18,P920

8.803

1.602

5.495

RIEHM C,1964,AM J MATH,V86,P25

8.801

1.699

5.180

SPRINGER TA,1955,INDAG MATH,V17,P352

8.220

1.602

5.131

SAH C,1972,J ALGEBRA,V20,P144

9.404

1.845

5.097

CAPPELL SE,1974,B AM M ATH SOC,V80,P1117

8.166

1.602

5.097

JOHNSON D,1980,J LOND M ATH SOC,V22,P365

8.525

1.699

5.018

ROURKE CP,1971,ANN MATH,V94,P397

8.024

1.602

5.009

KERVAIRE M ,1960,COMM ENT MATH HELV,V34,P257

8.803

1.778

4.951

TITS J,1968,INVENT MATH,V5,P19

8.790

1.778

4.943

WALL CTC,1970,PROC CAM B PHILOS S-M ,V67,P243

7.919

1.602

4.943

FROHLICH A,1969,J ALGEBRA,V12,P79

7.839

1.602

4.893

ROBERTEL.RA,1965,COMMUN PUR APPL M ATH,V18,P543

8.676

1.778

4.879

BROWN EH,1972,ANN MATH,V95,P368

8.588

1.778

4.830

BAEZA R,1976,LECT NOTES M ATH,V655,P

7.683

1.602

4.796

ATIYAH MF,1971,ANN SCI ECOLE NORM S,V4,P47

8.340

1.778

4.690

BROWDER W,1969,ANN M ATH,V90,P157

7.437

1.602

4.642

MUM FORD D,1971,ANN SCI ECOLE NORM S,V4,P181

7.351

1.602

4.588

WITT E,1937,J REINE ANGEW MATH,V176,P31

9.738

2.146

4.538

EICHLER M ,1952,QUADRATISCHE FORM EN,V,P

7.201

1.602

4.495

M ILNOR J,1973,SYMM ETRIC BILINEAR F,V,P

6.768

1.602

4.225

CHEVALLEY CC,1954,ALGEBRAIC THEORY SPI,V,P

7.260

1.778

4.083

KERVAIRE M A,1963,ANN MATH,V77,P504

7.908

1.954

4.047

DIEUDONNE J,1955,GEOM ETRIE GROUPES CL,V,P

7.303

1.845

3.958

OM EARA OT,1963,INTRO QUADRATIC FORM,V,P

8.191

2.079

3.939

LAM TY,1973,ALGEBRAIC THEORY QUA,V,P

7.244

1.845

3.926

DICKSON LE,1958,LINEAR GROUPS EXPOSI,V,P

6.749

1.778

3.795

BOURBAKI N,1959,ALGEBRE,pCH9,P

7.021

1.954

3.593

SERRE JP,1962,CORPS LOCAUX,V,P

6.492

1.845

3.518

SAH CH,1960,AM J MATH,V82,P812

SECTOR B

SECTOR C

521

Collaboration of Turkish Scholars: Local or Global? Umut Al

Zehra Taşkın

[email protected] İrem Soydal [email protected]

[email protected] Güleda Düzyol [email protected]

Hacettepe University Department of Information Management 06800 Beytepe, Ankara, Turkey

Umut Sezen [email protected] Hacettepe University Department of Electrical and Electronics Engineering 06800 Beytepe, Ankara, Turkey Abstract: Collaboration patterns of scholars have been the subject of many studies. This paper investigates the collaboration patterns of the Turkish scholars‘ publications within the citation indexes. Turkey‘s contribution to the world‘s scientific literature has increased significantly during the recent years. It is important to understand the collaboration types in scholarly communication in order to derive a legitimate scientific publication policy in Turkey. In this context, the following research questions have been addressed: 1. Does the multiple authorship prevalent in the Turkish publications? 2. Does the collaboration rate change by year? 3. What is the distribution of collaboration types (intranational/international) authored by Turkish scholars? 4. Does the rate and type of collaboration differ across the disciplines? 5. Which countries are the most important collaborative partners of Turkish scholars? Based on the analysis of findings, we found that Turkish scholars generally collaborate intranationally.

Introduction Scholarly collaboration can be simply defined as two or more researchers working together. Bozeman and Corley (2004) review and explain the main reasons for scholarly collaboration as: accessing the expertise, equipment or resources one does not have, encouraging crossfertilization across disciplines, improving the access to funds, obtaining prestige or visibility, learning tacit knowledge about a technique, pooling knowledge for tackling large and complex problems, enhancing productivity, educating a student, and so on. Whatever the reasons for collaboration, it is a known fact that multiple authorship is increasingly dominating the scholarly communication. Globalization affects scientific world as well as politics or economics. Especially technological innovations (Internet, mobile phones, etc.) and the increasing opportunities for the mobility of researchers remove the boundaries among countries. This fortifies the concept of multiple authorship in scholarly communication. Multiple authorship can be defined not only as two or more people from the same institution working together, but this concept may also indicate the collaboration of two or more researchers living in different continents.

522

Various bibliometric studies answering the common questions that were related to scholarly collaboration such as ―what is research collaboration?‖, ―how much is a collaboration worth?‖, ―when do researchers collaborate?‖ addressed in the literature (Katz & Martin, 2007; Katz & Hicks, 1997; Birnholtz, 2007). In our study, we tried to answer the question: Do Turkish scholars collaborate intranationally or internationally? Our main goal is to identify the collaboration rate of Turkey‘s publications by year and to reveal detailed collaboration patterns. The findings of this study can be helpful for science policy makers. Multiple authorship Researchers usually focus on two basic points while setting the trends on multiple authorship concept. These are determining the possible differences between the number of single and multi-authored publications in years and determining the changes in the average number of authors per publication. These numbers may vary according to different fields, yet it is observed that multiple authorship is becoming dominant in scientific communication over the years. It has been observed from the ratio of multiple authorship and the number of authors per publication that the most rapid changes are in the medicine field. Some studies that had been carried out by different researchers in different time periods showed that there has been a significant increase in the average number of authors per article in medical journals (Cronin, 2001; Glynn, Kerin, & Sweeney, 2010). In an evaluation study of the journal called British Journal of Medicine, it was found out that the average number of authors per article was raised from 3.9 to 4.5 between the years 1985-1995 (Drenth, 2001). In another study, which evaluated the same journal, it was revealed that in 1975 the average number of authors per article was 3.2 (Drenth, 1998, 219). Similarly, radiology journals were evaluated (Mussurakis, 1993) and it was found out that in 1991, the average number of authors per article increased two- fold when compared to the numbers in 1966. Occasionally, some extreme examples of multiple authorship can also be observed. For example, an article that was published in 1993 in New England Journal of Medicine has 972 authors. In this article it was observed that the average number of words per author was two (Liu, 2003, 890). In the field of medicine, the cooperation between sub-areas has become increasingly imperative. Besides, large number of clinical researches are being conducted and many projects are carried out by professors with the help of their assistants. These can be counted as the main reasons of multiple authorship in the field of medicine (Bennett & Taylor, 2003, 264). Other than medicine, multiple authorship tendency is rising in the fields such as sociology (Hunter & Leahey, 2008), psychology (Kliegl & Bates, 2011) and agriculture (Farahat, 2002) over the years. It is impossible to list here all the disciplines which have the tendency of multiple authorship, yet it is likely to generalize that the multiple authorship tendency is intensively observed in medicine and health sciences, followed by the basic sciences, engineering and social sciences respectively. In arts and humanities, studies with single author are still more commonly observed. Especially in the last 25-30 years, it is seen that the number of authors per publication is rising significantly. The main reason of this is, the large-scaled researches are being conducted and to be able to conclude these researches, some other contributors such as data-cleaners, field researchers, research designers are needed. On the other hand, some other factors such as increased telecommunication facilities, co-operation between researchers and new opportunities for authors to produce more publications, also contribute the emergence of multiple authorship. Sometimes, non- ―real authors‖ can be added to the authors list of an 523

article, in such a case, the contributions of all the authors of a publication can also be questioned. Scholarly collaboration The scholarly collaboration has grown rapidly since the late 1960s (Bordons & Gómez, 2000). Many studies (Sonnenwald, 2007; Wagner & Leydesdorff, 2005) examined the growth of international scholarly collaboration and tried to understand the patterns of it. It is observed in the literature that researchers investigate different dimensions of scholarly collaboration such as, different countries (Anuradha & Urs, 2007; Kim, 2005; Perianes-Rodríguez, OlmedaGómez, Antonia Ovalle-Perandones, Chinchilla-Rodríguez, & Moya-Anegón, 2011), specific fields (Ma & Guan, 2005; Yan, Ding, & Zhu, 2010), and impact of the publications (Persson, Glänzel, & Danell, 2004; Sooryamoorthy, 2009). There are also some studies that examined these dimensions together (Arunachalam, 2000; Zhao & Guan, 2011). In these studies, bibliometricians concentrated on the scientists‘ collaboration motives, structure of collaboration networks, international or intranational cooperation levels. Scholarly collaboration emerges from the structure of relationships between scientific actors. Nevertheless, the number of studies on intranational scholarly collaboration is fewer than the number of international collaboration researches. One of the first examples of intranational scholarly collaboration studies was conducted by Katz (1992). Katz‘s research reported the characteristics of intranational collaboration within the United Kingdom, Canada and Australia. Furthermore, the study revealed that collaborations most frequently occurred between geographically close partners. Method This study aims to investigate the collaboration patterns of the Turkish scholars‘ publications within the citation indexes and the following research questions were addressed: Does the multiple authorship prevalent in the Turkish publications? Does the collaboration rate change by year? What is the distribution of collaboration types (intranational/international) authored by Turkish scholars? Does the rate and type of collaboration differ across the disciplines? Which countries are the most important collaborative partners of Turkish scholars? The Thomson Reuters‘ Web of Science (WoS), was used to identify Turkish scholars‘ publications. WoS covers five different databases, namely, Science Citation Index, Social Sciences Citation Index, Arts & Humanities Citation Index, Conference Proceedings Citation Index (Science), Conference Proceedings Citation Index (Social Science & Humanities). However Conference Proceedings Citation Index (Science) and Conference Proceedings Citation Index (Social Science & Humanities) are excluded in our study, and the term ―publication‖ is defined, unless otherwise indicated, as journal articles, meeting abstracts, notes, and etc. which were authored by the scholars affiliated with Turkish institutions and included in the citation indexes. To identify the publications within these databases, an online search was performed on March 10, 2011, by using the ―address‖ field. To obtain reliable data, different forms of Turkish addresses in different languages (e.g., ―Turkey,‖ ―Turkiye‖, ―Turkei,‖ ―Turquie‖) were entered in the address field. After the data cleaning process, a total of 198,687 publications were identified. In this study we excluded the publications, which 524

were published before 1970, since the total number (92) is too small to make analysis by year. Thus, our data set decreased to 198,595 records. Each record provided information about the author name(s), author address(es), publication year, language, document type, number of references included in the publication, and the number of citations that the publication received as of March 10, 2011. Separate files were created for publication year s, subject categories, and author addresses for all of the 198,595 publications. The affiliation addresses of the first authors and joint authors were counted separately, and the countries were credited accordingly. Findings As mentioned earlier, there were 198,595 publications that were indexed within the citation indexes between the years 1970-2009. The data represented in Figure 1 covers a 40 years period, however 80% of these publications belong to the year 2000 and onwards. In a previously conducted study (Al, 2008) which was covering a 33 years period in Science Citation Index, it was mentioned that half of these publications were belong to the years 20012004. In our research, a similar trend was emerged. The number of publications that were produced by Turkey between the years 2005-2009 (106,232 publications) is greater than the remaining 35 years (1970-2004) of publication productivity (92,363 publications). 27000 Number of publications

24000 21000 18000 15000 12000 9000 6000 3000 0

1970

1975

1980

1985

1990

1995

2000

2005

2010

Publication years

Figure 1. Number of Turkey addressed publications by year within the citation indexes (N=198,595).

The number of authors was analyzed to identify the collaboration patterns in our study. The great majority (88%) of publications had multiple authorship (174,145). Figure 2 shows the gradual increase of multiple authorship rate in 10- year periods. Multiple authorship has reached to a top rate of 89% within the last ten years. Examining the last ten years data closer, it is seen that the ratio of multiple authorship for all the publications was not less than 88% in each and every year (Table 1).

525

100

89

86

90 80

71

70

60

%

60 50

40

single authorship

40

29

30

multiple authorship

20

14

11

10 0

1970-1979 1980-1989 1990-1999 2000-2009

periods Figure 2. Distribution of publications by authorship

Table 1. Distribution of publications by authorship (2000-2009) Year 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000

Single N % 3,142 12.4 2,696 11.6 2,443 11.1 1,894 10.0 1,616 9.7 1,603 10.3 1,386 11.1 1,165 11.3 798 10.2 713 11.1

Multiple N % 22,228 87.6 20,513 88.4 19,549 88.9 17,045 90.0 15,106 90.3 13,892 89.7 11,053 88.9 9,141 88.7 7,008 89.8 5,713 88.9

Total 25,370 23,209 21,992 18,939 16,722 15,495 12,439 10,306 7,806 6,426

A significant trend is clearly observed for the number of authors per publication over the years (Figure 3). It is seen that the average number of authors per publication was less than two during 1970-1972 time period, whereas it increased to over three in 1991. In the last six years (2004-2009) -which are included within the scope of our study- it is observed that the average number of authors per publication did not drop below four.

526

Number of authors (mean)

5

R² = 0.941

4.5

4 3.5 3 2.5 2 1.5

1 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009

Publication years Figure 3. Average number of authors per publication

Collaboration types Although citation indexes have usually American and British based scientific publications, they still have an international character. The concept of internationality for citation indexes can be explained by the countries that contribute to or that benefit from the context of citation indexes. However, when it comes to scientific publications, the language of the publication, contributions of researchers from different countries, the distribution of citations according to countries, can be counted as some of the indicators of internationality. From the bibliometric perspective, when compared to international publications, domestic publications known to have relatively low impact. In this study, to what extent was the production of publications carried out locally or internationally was investigated. Within the scope of our study, the number of publications with multiple authorship was 174,145, where 140,956 (80.9%) of these publications were produced by the cooperation of Turkey addressed authors. The intensity of cooperation among the Turkey addressed authors indicates that Turkey addressed publications, at a certain point, have local features. Figure 4 reveals the increase of collaboration of Turkey addressed authors for the publications produced in Turkey over time. On the one hand, Turkey addressed journals that were recently included in the citation indexes increase the number of publications originated in Turkey, and it seems like they also inflated the local collaborative environment of Turkey addressed authors. It is a known fact that these journals usually include Turkey addressed researchers' publications and their language is generally Turkish (Al & Soydal, 2011, 13).

527

90.0

R² = 0.710

85.0

80.0

%

75.0 70.0 65.0

60.0 55.0 50.0 1980

1985

1990

1995

2000

2005

2010

Publication years Figure 4. Percentage of Turkey‟s intranational publications by year (1980-2009)

Collaboration in different disciplines We will also look into the collaboration patterns of Turkish scholars in different disciplines. It is important to understand the collaboration types in scholarly communication in order to derive a legitimate science policy in a country. According to Thomson Reuters' classification, Turkey addressed researchers have made publications that belong to 247 different fields. Table 2 shows the top ten fields which Turkey had produced publications most frequently and the number of their authors. According to the table, the first five fields that have the greatest number of publications are Surgery (14,365), Pediatrics (9,142), Clinical Neurology (7,748), Pharmacology & Pharmacy (7,404) and Cardiac & Cardiovascular Systems (6,119). Multiple authorship tendency is dominant for the publications that were in the top five fields. However, the ratio of single authorship is higher for Engineering, Chemical and Environmental Sciences when compared with others. The highest multiple authorship ratio for Turkey addressed journals is for Genetics & Heredity (98%). The multi-disciplinary characteristic of this field and the quality of published works makes producing publications with single author almost impossible when it co mes to accrediting the completed product. Table 2. Authorship distribution by disciplines Single Multiple Disciplines N % N % Surgery 790 5.5 13,575 94.5 Pediatrics 550 6.0 8,592 94.0 Clinical Neurology 426 5.5 7,322 94.5 Pharmacology & Pharmacy 356 4.8 7,048 95.2 Cardiac & Cardiovascular Systems 246 4.0 5,873 96.0 Engineering, Chemical 1,006 16.7 5,021 83.3 Environmental Sciences 930 16.0 4,883 84.0 Biochemistry & Molecular Biology 313 5.8 5,122 94.2 Oncology 213 4.0 5,137 96.0 Radiology, Nuclear Medicine & Medical Imaging 329 6.2 4,999 93.8

528

Total 14,365 9,142 7,748 7,404 6,119 6,027 5,813 5,435 5,350 5,328

Although multiple authorship predominates the most productive ones, there are still some differences among the whole fields. The evaluation of Turkey addressed publications on the basis of each field reveals that the publications of 41 fields (among the total of 247) have single author with a rate of 50% and above. Philosophy (98%), Literature (97%), Folklore (94%), History (94%), Religion (93%) are among those fields. These ratios reflect the characteristics of researchers working in those fields and the working characteristics of the disciplines. When examined by the disciplines, it is seen that Turkey addressed publications c reated in 210 different fields were conducted by an intranational collaboration with a ratio of 50%. In this evaluation, it is also important to note that the number of fields with the publications written by multiple authors has decreased from 247 to 240. On the other hand, for the fields that have publications written by internationally collaborated authors with a ratio of more than 50% (30 fields), it should be considered that the total number of publications are relatively less than other fields. For example for the Law field, there are eight Turkey addressed publications and three of them have intranationally-collaborated authors where five of them have internationally-collaborated authors. The collaboration in the most productive fields is shown in Table 3 and it is clearly seen that the number of publications that were created by international collaboration is significantly low. Even for Environmental Sciences field, which has the lowest ratio for intranational collaboration, every three of four publications have intranational structure. The intranational collaboration tendencies of Turkish researchers can be a problem in terms of international visibility. Table 3. Types of collaboration by disciplines Intranational International Disciplines N % N % Surgery 12,720 93.7 855 6.3 Pediatrics 8,120 94.5 472 5.5 Clinical Neurology 6,390 87.3 932 12.7 Pharmacology & Pharmacy 5,849 83.0 1,199 17.0 Cardiac & Cardiovascular Systems 5,464 93.0 409 7.0 Engineering, Chemical 4,428 86.2 709 13.8 Environmental Sciences 3,851 75.2 1,271 24.8 Biochemistry & Molecular Biology 4,645 91.9 410 8.1 Oncology 4,127 82.2 894 17.8 Radiology, Nuclear Medicine & Medical Imaging 4,502 90.1 497 9.9

Total 13,575 8,592 7,322 7,048 5,873 5,021 4,883 5,122 5,137 4,999

Collaborative partners of Turkey Researchers in Turkey have carried out joint studies with the researchers from 160 different countries. When the publications were evaluated entirely it can be seen that Turkey addressed researchers collaborated most frequently with the authors from United States of America, England, Germany, Italy, France, Canada, Japan, Netherlands, Switzerland and Spain respectively. It is a known fact that the USA has the highest number of scientific publications when compared to other countries. This is the main reason why the USA regarded as being well ahead among the countries that Turkey addressed researchers collaborated in the scientific publishing. In Table 4, the two other countries (England and Germany), ranking right after the USA, differ from the others in terms of the number of publications. 529

In this study, the ratio of articles within the whole publications was also investigated. It was found out that, the England has the highest (78.9%) and Italy has the lowest (68.3%) rate of publications among the 10 countries that Turkey has collaborated most frequently. The reason why the People's Republic of China, Russia and Australia did not rank among the countries listed in Table 4, where frequently collaborated countries were listed, should be questioned, since they normally take place among (usually in the top 10) countries that has the highest number of publications. For instance, although People's Republic of China was the second most productive country in the world according to the data obtained from Essential Science Indicators, it ranked 23rd among the countries that Turkey has collaborated in terms of scientific publications. In our view, countries such as People's Republic of China and Russia were not fully able to keep pace with the globalized academic world. Table 4. The most collaborative countries with Turkey Countries USA England Germany Italy France Canada Japan Netherlands Switzerland Spain

# of publications 13,911 4,298 3,997 2,176 2,141 1,531 1,415 1,290 1,045 985

# of articles 10,610 3,392 3,011 1,486 1,555 1,168 1,082 928 732 697

articles (%) 76.3 78.9 75.3 68.3 75.6 76.3 76.5 71.9 70.0 70.8

Almost in all fields, the researchers that Turkey has collaborated most frequently were located in the USA. The countries that Turkey has collaborated most frequently other than the USA are Germany, England, Italy and France. It is observed that such countries as, Azerbaijan, Ukraine or Pakistan ranked unexpectedly top in some disciplines. For example, Azerbaijan is the fourth most frequently collaborated country of Turkey in Physics, Condensed Matter field. Similarly, in the Engineering, Electrical & Electronic field, Ukraine is the fifth most frequently collaborated country of Turkey. To determine the countries and the fields that were being collaborated and its scientific consequences may have an impact on the preferences of the scientists for their future cooperation decisions. Table 5. The most collaborative countries with Turkey by disciplines (first five countries) Disciplines Surgery Pediatrics Clin ical Neuro logy Pharmacology & Pharmacy Card iac & Cardiovascular Systems Engineering, Chemical Environmental Sciences

1 (N) USA (549) USA (225) USA (531) USA (405) USA (184) USA (283) USA 530

2 (N) Japan (68) Germany (80) Germany (136) Germany (133) Russia (84) England (154) England

3 (N) Germany (63) England (50) Italy (105) Japan (112) Germany (39) Germany (90) Germany

4 (N) Italy (41) France (44) England (103) England (109) England (39) Japan (42) Italy

5 (N) England (38) Italy (39) France (87) France (108) Italy (32) Canada (35) England

Biochemistry & Molecular Biology Oncology Radio logy, Nuclear Medicine & Medical Imaging

(283) USA (495) USA (384) USA (319)

(154) Germany (161) Italy (128) France (58)

(90) England (159) France (108) England (56)

(41) (38) Italy France (95) (89) England Germany (97) (88) Italy Germany (48) (43)

Conclusion Today, it is observed that researchers from various fields contribute to the production of publications that has scope concerning multiple disciplines. The necessity for interdisciplinary researches and accordingly, the needs of researcher's from different disciplines for undertaking roles in collaborative studies play an important role in the growing number of publications written by multiple authors. These kinds of partnerships have an important impact on the development of science. As a result of this study it could be said that the increasing trend of multiple authorship in the global scientific literature is also observed in the production of Turkey addressed publications. The study reveals that Turkey addressed publications were produced mostly with the collaboration of multiple authors which were generally limited in the country. Due to these reasons, most of the Turkey addressed scientific works could be counted as domestic publications. Having examined the scientific fields individually it was also observed that, in general, most of the fields also exhibit a tendency towards multiple authorship. In the scholarly communication process, multiple authorship not only helps scientists to think with different perspectives, but also could decrease the errors that could be arisen during the research and report phases which may be overlooked by one person. It is ob vious that, to cooperate with specialists and research groups will be much easier with the globalization of the scientific world. This will improve the visibility and quality of the publications. It is also thought that, collaborations with both national and international researchers would especially help to the inexperienced researchers to increase their research abilities and to produce more qualified publications. Acknowledgme nts This study is supported by a research grant (no: 110K044) of the Turkish Scientific and Technological Research Center (TÜBİTAK). References Al, U. (2008). Türkiye‘nin bilimsel yayın politikası: Atıf dizinlerine dayalı bibliyometrik bir yaklaşım. [Scientific publication policy of Turkey: A bibliometric approach based on citation indexes]. Unpublished Ph.D. dissertation. Hacettepe University. Al, U. & Soydal, İ. (2011). Atıf dizinlerindeki Türkiye adresli dergiler üzerine bir değerlendirme. [An evaluation on Turkey addressed journals in citation]. Bilgi Dünyası, 12(1), 13-29. Anuradha, K.T. & Urs, S.R. (2007). Bibliometric indicators of Indian research collaboration patterns: A correspondence analysis. Scientometrics, 71(2), 179-189. Arunachalam, S. (2000). International collaboration in science: The case of India and China. In B. Cronin and H.B. Atkins (Eds.), The Web of Knowledge: A festschrift in honor of Eugene Garfield (pp. 215-231). New Jersey: Information Today. Bennett, D.M. & Taylor, D. (2003). Unethical practices in authorship of scientific papers, Emergency Medicine, 15(3), 263-270. 531

Birnholtz, J.P. (2007). When do researchers collaborate? Toward a model of collaboration propensity. Journal of the American Society for Information Science and Technology, 58(14), 2226-2239. Bordons, M. & Gómez, I. (2000). Collaboration networks in science. In B. Cronin and H.B. Atkins (Eds.), The Web of Knowledge: A festschrift in honor of Eugene Garfield (pp. 197-213). New Jersey: Information Today. Bozeman, B. & Corley, E. (2004). Scientists‘ collaboration strategies: implications for scientific and technical human capital. Research Policy, 33(4), 599-616. Cronin, B. (2001). Hyperauthorship: A postmodern perversion or evidence of a structural shift in scholarly communication practices?, Journal of the American Society for Information Science & Technology, 52(7), 558-569. Drenth, J.P.H. (1998). Multiple authorship: The contribution of senior authors, Journal of the American Medical Association, 280(3), 219-221. Drenth, J.P.H. (2001). Professors responsible for increasing in authorship. International Con gress on Biomedical Peer Review and Scientific Publication. Retrieved, July 8, 2011 from http://www.ama assn.org/public/peer/prau.htm. Essential Science Indicators. (2011). Country/territory rankings in (all fields). Retrieved, July 8, 2011 from http://esi.isiknowledge.com/rankdatapage.cgi. Farahat, H. (2002). Authorship patterns in agricultural sciences in Egypt, Scientometrics, 55(2), 157170. Glynn, R.W., Kerin, M.J., & Sweeney, K.J. (2010). Authorship trends in the surgical literature. British Journal of Surgery, 97, 1304-1308. Hunter, L. & Leahey, E. (2008). Collaborative research in sociology: Trends and contributing factors. The American Sociologist, 39(4), 290-306. Katz, J.S. (1992). Bibliometric assessment of intranational university-university collaboration. Unpublished Ph.D. dissertation. University of Sussex. Retrieved, July 8, 2011 from http://www.sussex.ac.uk/Users/sylvank/pubs/JSKatz-Thesis-1992.pdf. Katz, J.S. & Hicks D. (1997). How much is a collaboration worth? A calibrated bibliometric mode l. Scientometrics, 40(3), 541-554. Katz, J.S. & Martin, B.R. (2007). What is research collaboration? Research Policy, 26(1), 1-18. Kim, M.J. (2005). Korean science and international collaboration, 1995-2000. Scientometrics, 63(2), 321-339. Kliegl, R. & Bates, D. (2011). International collaboration in psychology is on the rise. Scientometrics, 87(1), 149-158. Liu, Z. (2003). Trends in transforming scholarly communication and their implications, Information Processing & Management, 39(6), 889-898. Ma, N. & Guan, J. (2005). An exploratory study on collaboration profiles of Chinese publications in Molecular Biology. Scientometrics, 65(3), 343-355. Mussurakis, S. (1993). Coauthorship trends in the leading radiological journals, Acta Radiologica, 34(4), 316-320. Perianes-Rodríguez, A., Olmeda-Gómez, C., Antonia Ovalle-Perandones, M.A., ChinchillaRodríguez, Z. & Moya-Anegón, F. (2011). R&D collaboration in 50 major Spanish companies. Aslib Proceedings, 63(1), 5-27. Persson, O., Glänzel, W., & Danell, R. (2004). Inf lationary bibliometrics values: The role of scientific collaboration and the need for relative indicators in evaluative studies. Scientometrics, 60(3), 421432. Sonnenwald, D.H. (2007). Scientific collaboration. Annual Review of Information Science and Technology, 41, 643-681. Sooryamoorthy, R. (2009). Do types of collaboration change citation? Collaboration and citation patterns of South African science publications. Scientometrics, 81(1), 177-193. Wagner, C.S. & Leydesdorff, L. (2005). Network structure, self-organization, and the growth of international collaboration in science. Research Policy, 34(10), 1608-1618. Yan, E., Ding, Y., Zhu, Q. (2010). Mapping library and information science in China: A coauthorship network analysis. Scientometrics, 83(1), 115-131. Zhao, Q. & Guan, J. (2011). International collaboration of three 'giants' with the G7 countries in emerging nanobiopharmaceuticals. Scientometrics, 87(1), 159-170. 532

Exploring Alternatives to Web Co-link Analysis: A Web Co-word Study of Heterogeneous Industries

Liwen Vaughan

Esteban Romero-Frías

[email protected] Faculty of Information and Media Studies, University of Western Ontario, London, Ontario, Canada

[email protected] Department of Accounting and Finance, University of Granada, Granada, Spain

Abstract: Co-link analysis has been used to analyze relationships among various types of organizations such as universities, businesses, and political parties. However, currently Yahoo! is the only commercial search engine that provides inlink search needed for Webometrics study. This situation calls for studies to find alternative methods and data sources. Vaughan and You (2010) proposed and tested the feasibility of using Web co-word analysis to analyze relationships among organizations. The current study extends the previous study by testing the Web co word method in a heterogeneous environment, specifically the five different industries in the United States. Inlink and co-link data were collected from Yahoo! while keyword and co-word data were collected from Google and Google Blog. All co-link and co-word data were normalized with Jaccard Index and then analyzed with multidimensional scaling (MDS). Correlations between inlink counts and keyword counts were high and statistically significantly. Findings from the study suggest that the co-word method has the potential of supplementing the co-link method.

Introduction Numerous studies have been carried out to analyze Web hyperlinks since the early days of Webometrics. Co-link analysis has been used to analyze relationships among various types of organizations such as universities, businesses, and political parties. However, sources for collecting link data has become fewer in recent years. Currently Yahoo! is t he only commercial search engine that provides inlink search needed for Webometrics study. This situation calls for studies to find alternative methods and data sources. Toward this end, an earlier study (Vaughan & You, 2010) proposed and tested the feasib ility of using co-word analysis to analyze relationships among organizations. Specifically, the study used co-word analysis to map business competition scenes and found that the method is promising, particularly when co-word data were collected from blogs rather than general Websites. That study was carried out in a group of companies within the telecommunication industry. The current study extends the previous from a single industry to multiple industries to find out if and how the method works in a heterogeneous environment. Specifically, we selected five diverse American industries that have a broad range of economic features and different degrees of exposure on the Internet. In this line, Romero-Frías and Vaughan (2010b) used colink to study companies belonging to different industries that were included in some of the most relevant stock markets indexes. The results showed how industries developing activities more centered in information formed particular clusters. Our study has two main purposes. First, to test the validity of co-word method in heterogeneous industries as an alternative to co- link analysis. Second, to verify if inlink count and keyword count are correlated. 533

Methodology Selection of companies to study Five diverse US industries were selected for the study: information technology, media, heavy construction and engineering, mining, and banking. The selection intended to include companies with a broad range of economic features and different degrees of exposure on the Internet. The companies belong to industries that range from more traditional industries (i.e., mining or heavy construction) to more information-centered industries (i.e. IT and media). This characterisation is supported by their presence on the Web (Section ―Correlation between Web inlink and keyword search counts‖). To make an objective selection of companies within each industry, we consulted industry reports produced by Mergent (http://www.mergentonline.com), a reputable business database. Mergent reports list top companies (usually nine to ten) for each industry. All companies listed were included in the study. All the reports are dated in 2010 (Mergent, 2010a, 2010b, 2010c, 2010d) except one in 2009 (Mergent, 2009), corresponding the heavy construction sector. By the time the reports were consulted (September 14, 2010), this report was the most recent for the industry in the Mergent database. Nevertheless, for the purpose of the study, the selection of relevant companies in each sector is not affected. A total of 49 companies, including top companies in each industry, were included in the study. The complete list of all companies, including labels used in the analysis, URLs, company acronyms and inlink and keyword counts, is shown in Appendix 1. The Website address of each of these banks was collected using the Google Search Engine and then manually checked to ensure that is correct. The vast majority of companies in the study have only one URL for their Websites. When a company had more than one relevant URL in the form of alias or redirect, we checked each URL to find out which one has more inlinks and then we used that one for collecting data. Yahoo!, the search engine used for data collection, cannot handle the complex query syntax for collecting co-link data using two URLs and therefore we could not include alternative URLs in data collection. For each company, the acronym of the company name was determined based on common use by users and then checked by search it in Google and Google Blog to ensure the accuracy of the most relevant results retreived. Company acronyms were used in collecting the co- link and co-word data. For example, Intel is the acronym for Intel Corp. and Cisco is the acronym for Cisco Systems Inc. If the acronym had more than one word, it is searched as a phrase by using quotation marks around the term. For example, the acronym of Time Warner Inc is ―Time Warner‖. Collecting Web link data Of the three major search engines, Google, Bing and Yahoo!, only Yahoo! could be used for inlink data collection for the study. Google‘s inlink search only returns a sample of all inlinks that the Google database records (Google, 2009). In addition, Google does not allow to combine different operators to design the queries and therefore to filter out internal inlinks (inlinks originated from within the Website itself such as ―back to home‖ type of links). In our study, only external inlink counts are significant, because internal inlinks could be biased as long as they are created by the entity itself. MSN Live Search (Bing, at this moment) used to offer inlink search functions until March 2007, when the service was turned off. At the time of data collection (October 5, 2010), Yahoo! is the only option available for collecting the inlink data required by the study. 534

Regarding search engines functioning, it is worth mentioning that their country versions may have databases that favour Websites of the host countries. In our case, as long as all the companies belong to the United States, we used the Yahoo! API global ver sion for data collection. Yahoo! has two inlink search operators: link and linkdomain. The ―link‖ operator finds links to a particular page (e.g. link:http://www.website1.com finds links to the homepage of www.website1.com) while the linkdomain operator retrieves all links that point to all pages of a particular Website or domain including the homepage. In the study, we used the linkdomain operator because all links to the Website or domain of the company are relevant to understand the presence and connections of that entity with other businesses. The query syntax for inlink data is: linkdomain:website1.com –site:website1.com; whereas the query syntax to collect co- link data is: (linkdomain:website1.com –site:website1.com) (linkdomain:website2.com –site:website2.com). We truncated the www portion of the URLs in the queries in order to capture links to all subdomains (i.e. to mail.website1.com). The ―site:website1.com‖ part of the query let us filter out internal links coming from within the domain of abc.com itself. The co- link data are collected in the form of a symmetrical matrix with row x and column y of the matrix representing the number of co- links between URL x and URL y. Collecting Web Keyword Data Two types of information sources were used for the study: a general search engine and a specialised blog search engine. In order to compare the information gathered from both sources, we looked for an Internet agent that provides both services. Following the same logic as Vaughan and You (2010), we selected Google and Google Blogs for two reasons: (1) Google is the most popular search engine on the Web and offers the biggest coverage of websites, and (2) Yahoo! and MSN Bing did not have blog search functions, at the time of the study. The keywords used as queries in both Google Search Engine and Google Blogs, are the company acronyms (see Appendix 1). Considering that the acronyms of two companies are website1 and website2, the search we did to obtain the keyword count was website1 and to collect the co-word count website1 website2. We omitted the Boolean operator AND between the two words because it is the default operator in Google. Data from Google Search Engine was collected on October 6, 2011, and from Google Blog on October 11, 2011. Methods of data analysis Co-link and co-word matrices were analyzed using multidimensional scaling (MDS) to generate a MDS map. MDS uses a heuristic method to place companies with higher co- link or co-word counts closer in the resulting MDS map. In our analylis we assume that co- links are created by individual meaningful decisions that, once aggregated on a macro level, can reflect general patterns that are helpful to understand the business context. Co- link and co-word counts constitute a measure of similarity or relatedness. Companies sharing the same activities are more likely to be co- linked or mentioned together. Therefore, the more co-links or co-words between two companies, the more similar or related they would be.

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We hoped that the MDS map would reflect homogeneo us clusters of companies belonging to the same industry. Co-link is a source of information that has proved to be useful in many studies applied to business, universities or political parties (Romero-Frías & Vaughan, 2010). Nevertheless as this type of data is more difficult to obtain and could suffer restrictions by Yahoo!, due to its merge with Bing, we also examined the effectiveness of using co-words as an alternative source of information. If the MDS maps based on co- links and on co-words are similar, then we could justify the use of co-word analysis if co- link data are not available in the future. Co-word is a measure of similarity analogous to co- link. The logic behind is that, if two entities are related, their acronyms are likely to be mentioned together on Webpages. Link information is based on the structure of the Web, this is, on the relationships between Websites established by hyperlinks, whereas keyword information is based on the content of the Websites. Link data are more precise, whereas keyword searches are subject to ambiguities. This study intend to check if we can extract valid knowledge using both methods. The raw co- link and co-word counts needed to be normalized to obtain a relative measure of the relatedness of companies. The co- link and co-word matrices normalized by Jaccard index were fed into SPSS for MDS analysis. The stress values of MDS analysis are 0.049 for the colink data, 0.035 for the Google Search Engine co-word data and 0.029 for the Google Blog co-word data. These stress values are all low, which indicates a good fit between the data and the MDS map positions. Results Correlation between Web inlink and keyword search counts Firstly, we carried out descriptive statistical analysis for inlink and keyword search data (Table 1). The frequency distribution of the three sets of data (inlink, Google count and Google Blog count) are very skewed. Therefore, the Spearman correlation test rather than the Pearson correlation test was used to explore the relationships between the var iables. We expected that industries that are more information-centered would receive more inlinks or have more Webpages that mention their company acronyms. This is confirmed by the data. Concerning inlink and Google count, the industries with the highest median are: IT, Media, Banking, Construction and Mining. Concerning Google Blog count the ranking is: IT, Banking, Media, Mining and Construction.

Table 1. Descriptives for inlink and keyword search data. Industry All industries (n=49) Banking (n=10) IT (n=9) Media (n=10)

Mean Median Std. Deviat ion Mean Median Std. Deviat ion Mean Median Std. Deviat ion Mean Median Std. Deviat ion

Inlink count 4,236,745.9 27,900 1.727E7 159,600 81,550 154,879.04 20,740,788.89 3,780,000 3.739E7 1,923,758 459,500 3,418,389.89 536

Google count 68,784,616.33 778,000 2.360E8 4,625,550 2,620,000 5,513,953.93 3.51E8 1.29E8 4.732E8 15,680,220 3,715,000 2.219E7

Google Blog count 4,742,513.9 55,359 1.381E7 4,245,574.9 354,673.5 1.157E7 20,424,257.11 6,743,219 2.527E7 556,630.3 305,259 635,183.39

Industry Mining (n=10) Construction (n=10)

Mean Median Std. Deviat ion Mean Median Std. Deviat ion

Inlink count 2,640.3 1,945 2,684.45 7,346.6 5,405 7,231.33

Google count 423,590 106,800 894,511.36 647,670 255,000 1,092,584.89

Google Blog count 29,095.2 14,525 35,901.45 25,186.3 6,030 54,915.8

The correlation coefficients between the inlink count and Google keyword count was 0.805 and between inlink count and Google Blog count was 0.807. Both correlation coefficients are significant at the 0.01 level (2-tailed) and fairly high. As long as inlink count has proved to be a useful indicator of the relevance of a company (Vaughan & Romero Frías, 2010), these results provide evidence of usefulness of Google and Google Blog to evaluate the impact of business. Co-link and co-word analysis Three MDS maps are presented in this section. The companies in the map are represented by dots accompanied by labels (made of letters identifying the industry and a number). Industrial belonging of the companies are represented by letters and co loured circles. The key used to facilitate the interpretation of the maps are: Banking (red and letters ―Ba‖), Construction (green; ―Co‖), Media (yellow; ―Me‖), Mining (grey; ―Mi‖) and IT (blue; ―IT‖). MDS map in Figure 1 shows the relative positions of the companies in the study based on colinks. As expected, we can observe several clusters in terms of industrial affiliation. The clusters are particularly clear for the Banking, Mining and Constructions industry. All IT companies appear in a well defined cluster, except for companies Ingram Micro (IT8) and Tech Data (IT9). This is explained by the smaller size of the companies in relation to the rest of the IT companies in the study and also for the different nature of their activities. Media companies are scattered in the right part of the map, showing that the connections between them are weaker than the relations with companies in other industries. This could be explained by its nature as mediators in the process of diseminating information in society, including companies such as News Corp, a leading company in the media industry. Manufacturing industries such as mining and construction appear to be located in the left part of the map, whereas more information-centered industries, such as media and IT are concentrated in the right side. Banking is placed in a central position, showing its role as an intermediate in economic terms. A red line shows this division in the map

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Figure 1. MDS map based on co-link data.

The MDS map reflecting co-word collected from Google Blog is shown in Figure 2. Five clusters are drawn in the map with few exceptions. IT and Media companies, together with Banking are located in the left part of the map, whereas Mining and Construction occupy the right part. Media companies constitute a well defined cluster. Mining and Construction companies have some overlap. Again IT8 and IT9 are located in positions away from the IT cluster. The portrait of the industries in this map is coherent with the map created from colink data, only with few differences. It is worth noting how both sources of data seem to distinguish industries that are more technological than those more traditional.

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Figure 2. MDS map based on co-word data collected from Google Blog

Finally we added Figure 3 that shows the MDS map created using the Google Search Engine data. The portrait that we observe is more confused and less defined than in the previous maps. Only the IT cluster is well defined, with the recurrent exception of IT8 and IT9. The line divides companies beloging to more traditional industries versus more technological industries. However, differ from Figure 1 and 2, no other clusters are defined.

Figure 3. MDS map based on co-word data collected from Google Search Engine.

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Conclusions and future research The original purpose of this study was to test the validity of using new data sources for mapping business relationships. In line with Vaughan and You (2010), who tested the use of co-word analysis based on Google and Google Blog data in a particular industry, we examined the feasibility of this method to distinguish different industries on the level of presence in the Web. Our results reinforce the evidence found in that study concluding that co-word data could potentially supplement or be an alternative to co- link data. As shown in the co-word maps, Google Blog seems to be a better source than Google for co-word data collection, although the keyword counts from both services are highly correlated with inlink count. Google Blog seems to be a more selective context to mine than Google itself that potentially index the entire Web. More studies in other areas are needed to test the feasibility of co-word analysis as a substitute of co- link analysis. If so, a significant advantage would be the variety o f data sources that, in order to collect inlink data is currently limited to Yahoo!. Keyword search has some inconveniences. For example, if the acronym of the entity name is a common word the results are exposed to noise. It is important to select unique names as acronyms or combined acronyms. However, this technique should pay attention to user practices in the way that they refer to a particular entity. User behaviour is relevant in designing a succesful research. Future research will focus on monitoring the changes of the MDS maps by collecting and analyzing data over time. Also, although original Webometric techniques are quantitative, research could be complemented by a content analysis to validate the findings. Acknowledgme nts The first author is supported by a research grant from the Social Sciences and Humanities Research Council of Canada (SSHRC) for the research program of Web data mining for business intelligence. References Google (2009). Links to your site. Retrieved July 14, 2011, from http://www.google.com/support/webmasters/bin/answer.py?hl=en&answer=55281 Mergent (2009) North America – Heavy Construction Sectors. March 2009, available at http://webreports.mergent.com (accessed 14 September 2010). Mergent (2010a,b,c,d) North America – (Banking Sectors/ IT & Technology Sectors/ Media Sectors/ Mining Sectors). April 2010, available at http://webreports.mergent.com (accessed 14 September 2010). Romero-Frías, E. & Vaughan, L. (2010a). European Political Trends Viewed Through Patterns of Web Linking. Journal of the American Society for Information Science and Technology, 61(10), 21092121. Romero-Frías, E. & Vaughan, L. (2010b). Patterns of Web Linking to Heterogeneous Groups of Companies: The Case of Stock Exchange Indexes. Aslib Proceedings, 62(2), 144-164. Vaughan, L. & Romero-Frías, E. (2010). Web hyperlink patterns and the financial variables of the global banking industry. Journal of Information Science, 36(4), 530–541. Vaughan, L. & You, J. (2010). Word co-occurrences on Webpages as a measure of the relatedness of organizations: a new Webometrics concept. Journal of Informetrics, 4(4), 483–491.

540

541

Apenddix 1. Companies included in the study.

label in MDS Company map

Industry

Domain

Ba1

Bank of America Corp

Banking

https://www.bankofamerica ―Bank of .com/ America‖

442,000

16,200,000

1,840,007

Ba2

JPMorgan Chase & Co

Banking

https://www.chase.com/

jpmorgan

348,000

4,200,000

559,427

Ba3

Citigroup Inc

Banking

http://www.citigroup.com/ citigroup

61,600

6,270,000

1,248,905

Ba4

Wells Fargo & Co

Banking

https://www.wellsfargo.co "wells fargo" m/

339,000

12,300,000

1,326,229

Ba5

US Bancorp (DE)

Banking

http://www.usbank.com/

us bancorp

130,000

716,000

51,216

Ba6

PNC Financial Services Banking Group

https://www.pnc.com/

"pnc financial services"

86,200

325,000

31,463

Ba7

BB&T Corp

Banking

http://www.bbt.com/

bb&t

52,100

906,000

37,127,851

Ba8

Suntrust Banks Inc

Banking

https://www.suntrust.com/ suntrust

48,200

1,690,000

149,920

Ba9

Fifth Third Bancorp

Banking

https://www.53.com/

76,900

3,550,000

107,845

Ba10

State Street Corp

Banking

http://www.statestreet.com/

12,000

98,500

12,886

IT1

Hewlett-Packard Co

IT Tech

http://www.hp.com/

4,250,000

34,800,000

1,426,990

IT2

International Business Machines Corp

IT Tech

http://www.ibm.com/

ibm

3,780,000

129,000,000

6,743,219

IT3

Dell Inc

IT Tech

http://www.dell.com/

dell

4,220,000

1,430,000,000

37,027,949

IT4

Microsoft Corporation

IT Tech

http://www.microsoft.com/ Microsoft

66,200,000

599,000,000

50,939,791

IT5

Intel Corp

IT Tech

http://www.intel.com/

intel

2,380,000

219,000,000

16,611,752

IT6

Apple Inc

IT Tech

http://www.apple.com/

apple

103,000,000

648,000,000

67,526,194

IT7

Cisco Systems Inc

IT Tech

http://www.cisco.com/

cisco

2,790,000

96,400,000

3,497,152

IT8

Ingram Micro Inc

IT Tech

http://www.ingrammicro.co "ingram micro" m/

27,900

438,000

43,724

IT9

Tech Data Corp

IT Tech

http://www.techdata.com/

19,200

37,900

1,543

Company acronym

542

―Fifth Third‖ "State Street Corp" "Hewlett Packard"

"tech data corp"

Inlink count

Google Google serch count blog count

Me1

Time Warner Inc

Media

http://www.timewarner.com "time warner" /

Me2

Comcast Corp

Media

http://www.comcast.com/

Me3

News Corp

Media

Me4

Dish Network Corp

Me5

98,100

6,960,000

1,094,944

2,430,000

38,000,000

2,008,371

http://www.newscorp.com/ "news corp"

774,000

1,750,000

513,680

Media

http://www.dishnetwork.co "dish network" m/

105,000

4,690,000

994,722

Liberty Global Inc

Media

http://www.lgi.com/

"liberty global"

3,280

91,200

20,002

Me6

Gannett Co Inc

Media

http://www.gannett.com/

gannett

11,200,000

38,800,000

229,000

Me7

McGraw-Hill Cos Inc

Media

145,000

62,100,000

180,300

Me8

Discovery Communications Inc

Media

2,580,000

720,000

106,950

Me9

McClatchy Co

Media

http://www.mcclatchy.com/ McClatchy

1,880,000

2,740,000

381,518

Me10

IAC/InterActiveCorp

Media

http://www.iac.com/

22,200

951,000

36,816

Mi1

Freeport-McMoRan Copper & Gold

Mining

http://www.fcx.com/

9,290

333,000

22,518

Mi2

Peabody Energy Corp

Mining

5,220

203,000

29,363

Mi3

Southern Copper Corp

Mining

1,930

50,100

13,085

Mi4

Massey Energy Co

Mining

2,340

323,000

55,359

Mi5

Arch Coal Inc

Mining

Mi6 Mi7

Alpha Natural Resources Mining Inc Cliffs Natural Resources Mining Inc

Comcast

http://www.mcgrawhill.com "mcgraw hill" / "discovery http://www.discovery.com/ communications "

InterActiveCorp

"Freeport McMoRan" http://www.peabodyenergy. "Peabody com/ Energy" http://www.southernperu.co "Southern m/ Copper" http://www.masseyenergyc "Massey o.com/ Energy" http://www.archcoal.com/

"Arch Coal"

2,070

115,000

15,965

http://www.alphanr.com/

"Alpha Natural"

1,300

89,700

10,059

http://www.cliffsnaturalreso "Cliffs Natural" urces.com/

1,960

98,600

11,714

360

52,700

8,722

985

20,800

2,157

948

2,950,000

122,010

15,600

146,000

5,326

Mi8

Patriot Coal Corp

Mining

http://www.patriotcoal.com/ "patriot coal"

Mi9

Alliance Resource Partners

Mining

http://www.arlp.com/

Mi10

Headwaters Inc

Mining

http://www.headwaters.com Headwaters /

Co1

Fluor Corp

Construction http://www.fluor.com/

543

"Alliance Resource Partners"

"Fluor Corp"

Co2

Jacobs Engineering Group Inc

Construction http://www.jacobs.com/

"Jacobs Engineering"

8,560

421,000

12,748

Co3

KBR Inc

Construction http://www.kbr.com/

KBR

9,760

3,690,000

180,781

Co4

URS Corp

Construction http://www.urscorp.com/

"urs corp"

23,000

81,700

4,407

Co5

Foster Wheeler Ltd

6,010

579,000

19,604

2,490

133,000

2,551

4,800

185,000

6,435

1,570

778,000

10,783

496

325,000

3,603

1,180

138,000

5,625

Co6 Co7 Co8

"Foster Wheeler" MDU Resources Group "MDU Construction http://www.mdu.com/ Inc Resources" http://www.graniteconstruct "Granite Granite Construction Inc Construction ion.com/ Construction" Martin Marietta http://www.martinmarietta. "Martin Construction Materials In com/ Marietta" Construction http://www.fwc.com/

Co9

Dycom Industries Inc

Construction http://www.dycomind.com/ dycom

Co10

MasTec Inc

Construction http://www.mastec.com/

544

mastec

Citation Analysis of Doctoral Dissertations in the Subject of Mathematics Submitted to Pt. Ravishankar Shukla University Maya Verma

Jharna Soni

[email protected] Lecturer, SoS in Library and Information Science, Pt. Ravishankar Shukla University, Raipur, Chhattisgarh,INDIA

Research Scholar, SoS in Library and Information Science, Pt. Ravishankar Shukla University, Raipur, Chhattisgarh,INDIA

Abstract: Bibliometric study of 30 doctoral dissertations in the area of Mathematics awarded during 1970-2008 and available in Pt. Ravishankar Shukla University Library has been carried out to determine the use pattern of literature in the area. A total of 4389 references are analyzed to identify their bibliographic form, authorship pattern, ranking of journals, ranking of countries and collaboration of author. Keywords: Bibliometric Study, Authorship Pattern, Ranking, Citation

Introduction Citation analysis is a set of methods used to study or measure texts and information. Citation analysis has established itself as a variable and distinctive research technique for measurement of science based on citation data. As citations are the formal explicit linkage between scientific communications that have particular points in common, it is one of the most important bibliometric techniques which involve analysis of references forming part of primary communication. Citation analysis is used to identify the pattern of publication, authorship, and ranking. The part of literature which is cited most and in which part of globe such relevant works are going on can be judged through citation study which provides helpful guidance in the process of collection development of the library. Objectives The objectives of the study are: To identify the forms of documents mostly used in theses; To identify the year-wise and subject-wise research trend in India in the subject of mathematics; To find out the authorship pattern in the field of mathematics; To find out a ranking list of highly cited journals; To find out a ranking list of countries; and To identify the value of degree of collaboration 545

Methodology and Sample The total samples for this study are 4389 citations that figured in 30 doctoral dissertations accepted by the Pt. Ravishankar Shukla University from 1970 till the end of 2008. These citations pertained to journals, books, conference-proceedings, and theses. Accordingly the bibliographic references cited at the end of doctoral dissertations are taken as the source data for the study and the cited documents are recorded on catalogue cards which are tabulated and analyzed. Trend of Research in the Subject of Mathe matics in India To know the trend of research in subject of mathematics in India the Bibliography Doctoral Dissertations from the year 1857 to 1993 and the University News from the year 1994 to 2008 have been taken in the present study. To know the trend of research in the subjects of mathematics in India subject-wise and year-wise study has been done in Table 1. It shows that in the subject of mathematics research has been done in Mathematical Physics in total no. of which is 1910 (27.73%), after it Analysis comes which is 1500(21.77%). Total number of research work done in the subject of mathematics in India from the year 1857 to 2008 is 6889 out of which more research has been done in the year between 1986-90 which is 1060 (15.39%) and less in the year between 1996-2006 which is 320 (4.65%). Table - 1

S. No. 1

BRANCHES

1857- 1971 - 1976- 1981- 1986- 1991- 1996- 2001- 2006- TOTAL 1970 1975 1980 1985 1990 1995 2000 2005 2008 Percentage 34

28

54

99

61

-

-

-

-

276

4.01

2

GENERAL MATHEMATICS ALGEBRA

63

60

95

95

81

52

43

49

57

595

8.64

3

ANALYSIS

343 84

269

292 224

141 46

53

48

1500

21.77

4

ARITHMATIC

5

3

10

3

3

2

4

-

30

0.44

5

GEOMETRY

58

24

58

115 40

29

25

37

17

403

5.85

6

MATHEMATICAL PHYSICS TOPOLOGY

402 265

263

377 241

226 26

45

65

1910

27.73

7

25

36

41

22

45

16

19

24

235

3.41

APPLIED MATHEMATICS 9 MATHEMATICSSTATISTICS 10 STATISTICS

-

14

19

-

109

15

27

24

23

231

3.35

-

86

156

291 205

4

7

8

3

760

11.03

-

-

27

-

36

160 -

-

-

223

3.24

11 MATHEMATICAL ENGINEERING 12 MATHEMATICAL ASTRONOMY

-

-

-

-

3

3

5

6

7

24

0.35

-

-

-

-

4

2

4

9

11

30

0.44

7 8

-

546

13 APPLIED NUMERICAL ANALYSIS 14 APPROXIMATION

-

-

-

-

7

28

9

3

8

55

0.80

-

-

-

-

7

19

9

24

13

72

1.05

15 BIOMATHEMATICS -

-

-

-

1

22

8

11

7

49

0.71

16 GRAPH THEORY

-

-

-

-

1

11

11

12

13

48

0.70

17 GROUP THEORY

-

-

-

-

1

23

10

5

3

42

0.61

18 MATHEMATICAL LOGIC 19 OPERATION RESEARCH 20 PROGRAMMING

-

-

-

-

-

40

2

11

53

0.77

-

-

-

-

7

50

12

23

7

99

1.44

-

-

-

-

1

18

11

12

12

54

0.78

21 SET THEORY

-

-

-

-

9

-

2

1

-

12

0.17

22 GEOMATHEMATICS -

-

-

-

-

6

-

5

3

14

0.20

23 MATHEMATICS MODELS 24 MACHANICS

-

-

-

-

-

11

18

17

16

62

0.90

-

-

-

-

-

8

10

17

2

37

0.54

25 FUZZY THEORY

-

-

-

-

-

-

9

20

15

44

0.64

26 WAVE LETS

-

-

-

-

-

3

7

9

5

24

0.35

27 NUMBER THEORY

-

-

-

-

-

2

1

3

1

7

0.10

1313 1060 921 320 427 360 6889

TOTAL

912 589

987

PERCENTAGE

13.24 8.55

14.33 19.06 15.39 13.37 4.65 6.20 5.23

100

6. Trend of Research in the Subject of Mathe matics in Pt. Ravishankar Shukla University In the present study the trend in research in the subject of mathematics in Pt.Ravishankar Shukla University has also been carried out for which 30 doctoral dissertations available from the year 1970 to 2005 has been taken. It is presented in Table 2. Table - 2 1970-2008 S. No.

BRANCHES

1

GENERA L MATHEMATICS

2

ALGEBRA

3

ANALYSIS

4

Percentage 1

3.33

12

40

ARITHMATIC

-

-

5

GEOM ETRY

-

-

6

MATHEMATICA L PHYSICS

-

-

7

TOPOLOGY

16

53.33

547

It that in subject

8

APPLIED MATHEMATICS

1

3.33

9

MATHEMATICS-STATISTICS

-

-

10

STATISTICS

-

-

11

MATHEMATICA L ENGINEERING

-

-

12

MATHEMATICA L ASTRONOM Y

-

-

13

APPLIED NUM ERICAL ANA LYSIS

-

-

14

APPROXIMATION

-

-

15

BIOMATHEMATICS

-

-

16

GRA PH THEORY

-

-

17

GROUP THEORY

-

-

18

MATHEMATICA L LOGIC

-

-

19

OPERATION RESEA RCH

-

-

20

PROGRAMMING

-

-

21

SET THEORY

-

-

22

GEOMATHEMATICS

-

-

23

MATHEMATICS M ODELS

-

-

24

MACHANICS

-

-

25

FUZZY THEORY

-

-

26

WAVE LETS

-

-

27

NUM BER THEORY

-

-

TOTAL

shows the of

30

mathematics research has been done in only four branches i.e. Topology in total no. of which is 16 (53.33%) after it Analysis comes which is 12 (40%). Types of Documents The following table presents data on different types of documents cited by the researchers of mathematics in their doctoral dissertations: Table - 3 S.N.

Nature of Documents

Total

%

1

Books

612

13.94

2

Journals

2769

63.08

3

Theses

54

1.24

4

Conference Proceedings

669

15.24

5

Seminar-Notes

144

3.28

548

6

Others

141

3.22

Total

4389

100

Types of Document 144

669

141

612

54

2769

Books

Journals

Thesis

Conference Proceedings

Seminar Notes

Others

The analysis of data in Table 3 shows that out of 4389 citations, 2769 (63.08%) are from journals. It shows that the researchers in the field of Mathematics are mainly used journals for collecting the information. Conference-proceedings occupied the second place with 15.24% followed by books (13.94%), Seminar-Notes (3.28%), and theses (1.54%) with low percentage. 7. Ranking of Cited Journals Ranking of the journals has been prepared on the basis of total citation frequency received by each journal. The titles have been arranged in decreasing order of the number of citations. It is given in the Table 4 with their rank and percentage of citations of contribution. The rest of the journals having less than 20 citations (1095) are given as the last rank as single group. In the rank- list of journals Indian Journal of Pure and Applied Mathematics occupies the first rank, accounting to 8.75% of total citations followed by Journal of Mathematical Analysis and Application (7.22%) and Mathematica Japonica (4.98%). Table - 4 No.of S.No. Rank Name of Journals Citation 1 1 Indian Journal of Pure and Applied Mathematics 242 2 2 Journal of Mathematical Analysis and Application 200 3 3 Mathematica Japonica 138 International Journal o f Mathemat ics and Mathematics 4 4 Sciences 72 5 5 Pacific Journal of Mathematics 69 549

Percentage 8.75 7.22 4.98 2.6 2.49

6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

6 7 8 9 10 11 12 13 14 14 14 15 16 17 17 18 19 19 19 19 20 20 20 21 22 23

Mathematische Nachrichten Bulletin of Calcutta Mathematics Society Bulletin of A merican Mathematical Society American Mathemat ical Monthly Transactions of American Mathemat ical Society Nonlinear Analysis Journal of London Maths Society Publications de l‘Institut Mathematique Bulletin of the Australian Mathematical Society Ganita Quarterly Journal of Mathematics Studia Mathematica Annals of Mathematics Journal of Ind ian Mathematical Society Bulletin of Un ione Mathematica Italiana Acta Cinencia Indica Mathematische Zeitschrift SIAM Review Jnanadhara Duke Mathematical Journal Fundamenta Mathematicae Indian Journal of Mathematics Co mptes Rendus-Academie Bulgarie des Sciences Canadian Mathematical Bu llet in Pure and Applied Mathematics Series Bulletin of Institute Mathematics Academia Sinica Other Journals having less than 20 citations Total

65 64 57 56 55 53 51 47 41 41 41 37 34 28 28 27 24 24 24 24 23 23 23 22 21 20 1095 2679

2.35 2.31 2.06 2.02 1.99 1.92 1.84 1.70 1.48 1.48 1.48 1.34 1.23 1.01 1.01 0.98 0.86 0.86 0.86 0.86 0.83 0.83 0.83 0.80 0.76 0.72 39.54 100.00

8. Country-wise Citation Ranking of the countries has been prepared on the basis of total citation frequency received by each journal. The countries have been arranged in decreasing order of the number of jo urnal and citations. It is given in the Table 5. Table - 5 S.No. 1 2 3 4 5 6 7 8 9 10

Country USA IND JAP DEU GBR POL UK AU Italy Bulgaria

No. of J ournal 11 07 01 03 02 02 01 01 01 01 550

No. of Citati on 632 449 138 136 94 60 51 41 28 23

11 12

CAN Other many countries

01 ----

22 1095

Total

2769

Country-Wise Citation 12 11 10 8

7 6 No. of Journal

4

3

2

2

2 1

1

1

1

1

1

0

In the ranklist of countries out of total 2769 citations USA occupies the first rank with 632 citation and IND is second with 449 citations. It is observed that Indian scholars of mathematics prefer USA journals and due to easy availability of Indian journals it comes in second position in comparison to other countries. Japan and Germany occopied third and fourth position respectively. 9. Authorship Pattern The study has also analyzed the citations by of number of authors to assess the pattern of authorship in the literature of Mathematics. Table - 6 Author Single Author Two Authors Three Authors More than Three Authors Total

Citation of Books 374

Percentage

Percentage

62

Citation of Proceedings 424

201

32.78

869

31.39

237

35.43

08

1.3

153

5.53

08

1.19

30

4.90

30

1.08

0

00.00

613

100.00

2769

100.00

669

100.00

61.01

Citation of Journals 1717

551

Percentage

63.38

869

Authorship Pattern 900

Citation of Books

800

Citation of Journals Citation of Proceedings

8

100

30

200

30

153

201

171

300

237

400

8

500

424

600

374

No. of Citations

700

0 Single Author

Two Authors

Three Authors

More than Three authors

No. of Authors

It is clear from Table 6 that single author (63.38%) are highest percentage in conference-proceedings, two authors (35.43%) have maximum percentage in conferenceproceedings. Three authors (5.48%) have maximum percentage in journals and more than three authors (4.90%) have highest percentage in books. 10. Degree of Collaboration Such type of study by which it is possible to know the inter-relationship between the number of single authors and the number of multi-author is called the degree of collaboration. To know the degree of collaboration the following formula has been given by K. Subramanian :

i.e. DC = Degree of Collaboration NM = Number of multi author NS = Number of single author 10.1 Degree of Collaboration in Books The formula given by the K. Subramanian is used to know the degree of collaboration in cited books in doctoral dissertations in the subject of mathematics.

Table - 7 552

S.N. 1. 2.

= = =

Author Single Author Multi Author Total

Citation 374 239 613

Percentage 61.01 38.99 100

239/ 239+374 239/ 613 0.389%

The value of degree of collaboration in cited books in doctoral dissertations in the subject of mathematics is 0.389%. 10.2 Degree of Collaboration in Journal The formula given by the K. Subramanian is used to know the degree of collaboration in cited journals in doctoral dissertations in the subject of mathematics. Table - 8 S.N. 1. 2.

Author Single Author Multi Author Total

Citation 1717 1052 2769

Percentage 62 38 100

= 1052/1052+1717 = 1052/2769 = 0.379%

The value of degree of collaboration in cited journals in doctoral dissertations in the subject of mathematics is 0.379%. 11. Conclusion The study reveals that journal is the most preferred by the researchers of Pt. Ravishankar Shukla University in the field of Mathematics for accounting to 43.89% of total citations. Indian Journal of Pure and Applied Mathematics is occupied first position in the ranking of cited journals and Mathematical Analysis is in second position. 553

In the ranklist of countriesout of total 2769 citation USA occupies the first rank with 632 citation and IND is second with 449 citations. The study of authorship pattern reveals that majority of the contribution are from s ingle author. The value of degree of collaboration in cited books and journal in doctoral dissertations in mathematics is 0.389% and 0.379% respectively. It is conclused that in rank list of journals Indian journals occupies the first rank but in the ranklist of countries USA occupies the first rank. References Ahuja, Ram. 2001. Research Methods. Jaipur : Rawat Publication.P 25. Verma, A.K.1986. Citation pattern of documents analysis of the literature. Ph.D.Thesis, Pt. Ravishankar Shukla University, Raipur. Humayan, Kabir S. 1995. Bibliometrics of bibliometrics. Library Science with a Slant to Documentation and Information Studies. 32:165-171. Dutta, B. and B.K. 2001. Indian journal of chemistry-section A : an analysis of citation pattern. Annals of Library and Information Studies. 48 (3):121-134. SenGupta, I. N. 1985. Bibliometrics : a Birds Eye View. IASLIC Bulletin. 30:167-174.

554

Measuring Taiwanese Public –Private Collaboration in Science Parks Wen-chi Hung [email protected] Science and Technology Policy Research and Information Center, National Applied Research Laboratories, 14 F, No. 106, Sec. 2, He-Ping E. Rd., Taipei, 10636, Taiwan. Abstract: This work takes a bibliometric approach, based on co-authorship of scientific publications, to measure the performance of universities and public research institutes in collaboration with the domestic private sectors, especially in the areas of science parks. The result shows there is thriving enterprise demand for collaboration with public sectors in science park area, especially in the ICT related sectors.

Introduction It is widely recognized that collaboration between the public and private research sectors is a method to promote innovation and regional development. Universities and research institutions are important sources of new knowledge, especially in the areas of science and technology (e.g. Rosenberg, 1990; Nelson and Rosenberg, 1994; O‘Shea et al., 2005). A number of concepts have been proposed for modeling the transformation processes in university- industry relations, for example, national systems of innovation (Lundvall, 1992; Nelson, 1993) and regional innovation systems (e.g., Cooke, 1992; Gulbrandsen, 1997). Two main kinds of characteristics of universities could allow firms reach various kinds of information. The gatekeeper characteristic of university enables firms to access to a wide range of industries, learn the different knowledge from many industries and link knowledge across industries and sectors (e.g. Saxenian, 1994; Varga, 2000; Adams, 2005). Besides, interaction with universities is a symbol of utilizing the result of public R&D, bring the explorative innovation from basic research to applied research. Science parks policy instruments, aimed at promoting research-based industrial and innovative activity and private-public collaboration, is now attracting interest throughout the world. In Taiwan, considerable resources are being devoted to science parks. Hsinchu Science Park (HSP), built in 1980, is one of the world's most significant areas for semiconductor manufacturing. More than 400 high–tech companies, mainly involved in the semiconductor, computer, telecommunication, and optoelectronics industries, have been established in the park. The science park model has proved to be a highly successful approach for the development of high– tech industries. With the Hsinchu Science Park now basically saturated, the government in recent years has been extending the concept to other parts of Taiwan. The Southern Taiw an Science Park, with a main base in Tainan County, was established in 1996, while the Central Taiwan Science Park, with a main base in Taichung, was followed in 2001 (Figure 1). This work takes a bibliometric approach, based on co-authorship of scientific publications, to measure the performance of universities and public research institutes in collaboration with the domestic private sectors, especially in the areas of science parks. The aim of this study is to analyze whether the science parks instruments will facilitate the public–private research

555

collaboration. In addition, this study will present the patterns of intra-regional and extra-regional collaboration of universities/public research institutes and private sectors in Taiwan. Methodology The data are from Science Citation Index Expended and Social Science Citation Index, produced by Thomson Reuters. The first step is to retrieve publications produced by Taiwanese enterprises (the enterprises those are located in Taiwan), and the next step is to select those publications coauthored by universities or public research institutes located in Taiwan. The ‗‗public –private collaboration‘‘ in this work means a research collaboration between a public sector and an enterprise that has resulted in one co-authored publication. A publication produced by m public sectors and n enterprises will be counted as m*n public–private collaborations. The location of each university and enterprise is identified in this study. This study allocates papers‘ affiliations into regions according to the locations of enterprises. The public–private collaboration in paper publication could thus be analyzed at the regional level, thereby regarding the effects of geographic proximity. Results The regional distributions of public–private collaborations in 2000 and 2007 are presented in Table 1. The table shows that there is a bunch of collaborations in Hsinchu, Taoyuan, and Taipei, indicated by the public–private co-authorships by enterprises in these regions. There exists thriving enterprise demand for collaboration with public sectors in these regions. The Hsinchu region is the main area of Hsinchu Science Park which contains almost half of the public–private co-authorships. The growth rate of public–private co-authorships from 2000 to 2007 reaches 122%, indicating an increasing tendency of public–private interaction. Tainan and Taichung, two main regions of Southern Taiwan Science Park and Central Taiwan Science Park, respectively, represent highest growth rate during this period. In addition, the regions with science parks represent higher rate of intra-region collaboration, showing an effect of science park instrument on strengthening university- industry relation and promoting innovation.

556

Figure 1. Main science park areas in Taiwan Table 1 Public – private co-authorships in Taiwan, by region Nu m. of publ.-priv. Growth Region of collaboration rate enterprise 2007 2000

Nu m. of intra-reg ion publ.-priv co llaboration 2007 2000

% of intra-region collaboration 2007 2000

Hsinchu*

162

88

84%

74

58

46%

66%

Taoyuan

58

35

66%

12

3

21%

9%

Taipei

45

17

165%

14

5

31%

29%

Kaohsiung

33

13

154%

6

2

18%

15%

Miaoli

21

Tainan*

21

2

950%

12

1

57%

50%

Taichung*

14

3

367%

8

1

57%

33%

Chiay i

10

6

67%

1

0

10%

0%

70

35%

43%

0

0%

Total 364 164 122% 127 *The region where science parks are mainly located

557

The public–private collaboration details of enterprises in Hsinchu Science Park are showed in Table 2. National Chiao-Tung University and National Tsing- Hua University, both located in the heart of the Hsinchu Science Park, are the primary partners for firms in Hsinchu Science Park and indicate the effects of geographic proximity and science parks instruments. Table 3 shows the distribution of industrial partners of universities and research institutions. Most of the universities and institutions located in north part tend to collaborate with firms which are also located in north Taiwan, implying a phenomenon of geographic proximity. However, in addition to collaborate with firms in the same broad region, the inter-regional collaboration between universities in central and south parts of Taiwan and firms in north part of Taiwan is popular. Table 2 The Public Partners of enterprises in Hsinchu Science Park Broad Region

University/ Public research institution

Nu m. of Co llaboration with enterprises in Hsinchu Science Park (2007)

% of total

North

Natl Ch iao Tung Univ

40

25%

North

Natl Tsing Hua Un iv

27

17%

South

Natl Cheng Kung Univ

21

13%

North

Natl Taiwan Un iv

20

12%

North

Natl Nano Device Labs

6

4%

South

Natl Sun Yat Sen Univ

6

4%

North

Natl Cent Un iv

4

3%

Central

Natl Chung Hsing Univ

4

3%

South

Natl Univ Kaohsiung

4

3%

Table 3 The distribution of industrial partners of universities and research institutions by broad region. North North

95%

0%

5%

3 Natl Taiwan Un iv

84%

10%

6%

4 Natl Tsing Hua Un iv

100%

0%

0%

6 Natl Cent Un iv

100%

0%

0%

88%

0%

13%

100%

0%

0%

9 Acad Sinica

83%

0%

17%

9 Natl Taiwan Ocean Univ

67%

0%

33%

9 Natl Nano Device Labs

100%

0%

0%

0%

100%

0%

83%

17%

0%

40%

60%

0%

51%

4%

45%

8 Chang Gung Univ

7 China Med Univ 9 Natl Chung Hsing Univ 10 Natl Yunlin Un iv Sci & Technol

South

South

1 Natl Ch iao Tung Univ

7 Natl Taipei Un iv Technol

Central

Central

2 Natl Cheng Kung Univ 558

5 Natl Sun Yat Sen Univ

71%

0%

29%

7 Natl Chung Cheng Univ

75%

0%

25%

Table 4 shows the top 10 fields in public –private collaboration. JCR journal classification is applied to capture the field of publication. The top 3 fields are electrical & electronic engineering, applied physics, and optics, respectively. It shows a strong concentration in engineering fields, especially in the ICT- related areas, reflecting the industrial development in Taiwan. Table 4 The top 10 fields in public – private collaboration Field

share

ENGINEERING, ELECTR CA L & ELECTRONIC

23.24%

PHYSICS, APPLIED

11.22%

OPTICS

5.00%

MATERIA LS SCIENCE, MULTIDISCIPLINARY

4.68%

ELECTROCHEMISTRY

4.04%

NANOSCIENCE & NANOTECHNOLOGY

3.39%

POLYM ER SCIENCE

2.58%

TELECOMMUNICATIONS

2.58%

GEOSCIENCES, INTERDISCIPLINARY

2.42%

TRANSPORTATION SCIENCE & TECHNOLOGY

2.42%

Conclusion This study takes a bibliometric approach to measure the trend of public–private interaction, as well as the effect of science parks instruments on public–private collaboration in Taiwan on the basis of regional innovation viewpoint. The results from this study should be of interest to policy makers, for formulation of policies and stimuli initiatives towards greater public–private integration, and to university management, for evaluating the performance of research collaboration with industries. References Adams, S.B. (2005): Stanford and Silicon Valley: lessons on becoming a high-tech region, in: California Management Review, 48, pp. 29-51. Cooke, P. (1992). Regional Innovation Systems: Competitive Regulation in the New Europe. Geoforum, 23, 365–382. Gulbrandsen, M. (1997). Universities and industrial competitive advantage. In: Etzkowitz, H. and Leydesdorff, L. (Eds.), Universities and the Global Knowledge Economy. Printer, London, pp. 121-31. Lundvall, B.A. (Ed.) (1992). National Systems of Innovation. London: Printer. Nelson, R.R. (Ed.) (1993). National Innovation Systems. Oxford: Oxford University Press. O‘Shea, R.P., Allen, T.J., Chevalier, A, & Roche, F. (2005). Entrepreneurial orientation, technology transfer and spinoff performance of U.S. universities. Research Policy, 34, 994–1009. Rosenberg, N.(1990). Why do firms do basic research (with their own money)? Research Policy, 19, 165– 174. Rosenberg, N., & Nelson, R. (1994). American universities and technical advance in industry. Research Policy, 23, 323–348. 559

Saxenian, A. (1994). Regional Advantage: Culture and Competition in Silicon Valley and Route 128. Cambridge, MA: Harvard University Press. Varga, A. (2000). Local academic knowledge spillovers and the concentration of economic activity. Journal of Regional Science, 40, 289–309.

560

The Change of Citation Ranks in Highly Cited Papers Over Time Wen-chi Hung [email protected] Science and Technology Policy Research and Information Center, National Applied Research Laboratories, 14 F, No. 106, Sec. 2, He-Ping E. Rd., Taipei, 10636, Taiwan.

Abstract: This study analyzes what an acceptable length for the ‗citation window‘ for assessing highly cited papers is. It analyzes whether the citation patterns of highly cited papers will be diverse by number of co-authors, international collaboration, and journal impacts. The result shows that the highly cited papers published in high-impact journals tend to get high citation immediately, while the papers published in poorly cited journals takes longer time to become highly cited papers.

Introduction The highly cited papers, such as top1%, top5%, and top10% of the worldwide citation impact distribution of all relevant fields are widely used as an indicator of scientific excellence (Noyons et al 2003). Tijssen et al (2002) concluded that highly cited research papers do represent useful indicators for identifying ‗worldclass‘ research. It provides a better statistical measure that takes into account the skewing of the citation distribution than those based on mean values. For identifying emerging field or research front, the first step is to find the most-cited papers across multiple disciplines over a period of time. However, the importance of a publication does not necessarily appear immediately and that identification of quality would take a considerable time. The time lag may cause a substantial problem in measuring scientific excellence. Thus, the criteria of choosing highly cited papers may not be appropriate for newly published papers. The aim of this study is to analyze whether the citation patterns of highly cited papers will be diverse by number of co-authors, international collaboration, and journal impacts. Methods We collect citation data from the Science Citation Index (SCI) and the Social Sciences Citation Index (SSCI) which maintain citation databases covering thousands of academic journals. This study adopts fixed windows, which spans a fixed 10 years. The citations of papers published in 1998 would be counted from 1999-2008. A relative standard of what counts as a highly cited paper is used in this study. A paper has been considered as highly cited if it‘s citations from 1999 to 2008 ranked top 1% in its field. Thus, the concept of highly cited paper is field specific. Then, this study addresses the curve of citation rank changes over time by ranking each paper according to its accumulated citation times per year. The patterns of citation rank change over time are used to answer whether the highly-cited papers are highly-cited at the beginning. If not, when will the citation rank distribution converge to top 1%? In addition, the number of coauthors, international collaboration, and journal impacts, and the impact of the journals in which these highly-cited papers are published will be taken into account to see if the citation rank distribution of highly- cited papers will be moderated by these factors. This study uses latent growth modeling (Curran, 2003)to characterize accumulated intraindividual growth (within-paper) change over time as well as examine interindividual 561

(between-paper) difference by means of a random intercept and random slopes. The within-paper errors over time and the between-paper errors (representing random effects for the intercept and slopes) are conventionally referred to as level-1 and level-2 errors, respectively. The first- level submodel, describing individual change, is given by

(1)

Y it

0i

1i

T IM E t

it

, t

1, 2 , ..., T ,

where T is the total number of time points, TIMEt represents a particular time point t and serves as an explanatory variable, time points are usually equally spaced, set as 0, 1, …, T–1, Yit is the level of accumulated citation ranking percentage of paper i at time t, and i t the corresponding error. 0 i and 1 i denote, respectively, the intercept (initial status) and slope (rate of change) of the linear growth trajectory for paper i. They are random because firms differ in their initial patent levels and linear trajectory. The model in eq. (1) indicates the accumulated citation ranking percentage (Yit) can be depicted as a linear function of time, and i t , called the first level error associated with paper i at time t (intrapaper), reflects departure from the growth trajectory for paper i. i t ‘s are serially correlated for paper i. The structure of autocovariance of i t , assumed to be identical for all papers, needs to be identified. The second- level submodel, describing inter-paper differences in initial and speed of citation accumulation, is given by

0i

00

01

i

,

0i

(2) 1i

where

00

and

10

01

0i

i

1i

,

represent, respectively, the level-2 intercept and slope of

the initial status, and accumulation.

11

and

10

1i

and

11

with respect to

represent those with respect to the growth of citation

are level-2 errors. They make the same assumptions and interpretation

as those for eq. (3) except controlling for predictor . In particular, 1 1 is the difference of the average citation accumulation by a unit increase of . The growth parameters in this case include 0 0 , 0 1 , 1 0 , and 1 1 .In this study, three level-2 predictors, number of co-authors, number of collaborating countries, and journal impacts are taken into consideration. Results The results show the average citation rank changes over time by fie lds (Figure 1). The average citation rank of each field is fluctuated in the beginning and gradually converges to top 1% stable status. Figure 1 shows the variations among fields. In telecommunication, the average citation rank rapidly rises to top 1% in 4 years, while in some field, such as mathematics and material sciences, composites, the average citation rank rapidly rises to top 1% in more than 6 years. 562

Possibly, the rapid rising fields are in the areas that the research interests are fast-moving. On the contrast, the delayed rising fields present research on long term interests. The highly cited papers of each field are divided into 2 groups, the IF ranking >=50% and the IF ranking 0.046). In terms of comparison between 2 lines we have got a logic reason to recalculate impact factors or other citation analysis including ―frequency‖ in the future .Evaluating of citations to investigate their influence in other works is considerable. This studies are essential in order to discover the motivations behind each citation, their influences, location or frequency of occurrence in an article (Hanney et al,2005). So it is better to go far from just citation counting in our evaluation studies. many citation context analysis and new indicators can be developed for getting more accurate and reliable data.

References Bornmann, Lutz; Daniel, Hans-Dietrer (2007). ―Functional use of frequently and infrequently cited articles in citing publications: A content analysis of citations to articles with low and high citation counts‖. [online]. Available: http://www.lutzbornmann.de/icons/BornmannDanielShort.pdf. Bornmann, Lutz; Daniel, Hans-Dietrer (2008).‖ What do citation counts measure? A review of studies on citing behavior‖. Journal of documentation. 64 (1). [online]. Available:http://www.emeraldinsight.com/Insight/viewPDF.jsp?contentType=Article&Filename=h tml/Output/Published/EmeraldFullTextArticle/Pdf/2780640103.pdf. Garfield, Eugene (1999). Journal impact factor :a brief review. Canadian Association Journal, 161 (8), 979-980. Glanzel, Wolfang (2003).Bibliometrics as a research field: a course on theory and application of bibliometric indicators, Course handouts. Hanney et al (2005)‘ Categorising citations to trace research impact‖. Proceedings of ISSI : the 10‘th International Conference of the International Society for Scientometrics and Informetrics‖.edited by Peter Ingwersen and Birger Larsen. Stockholm: Karolinska university press Horri,A; Azizkhani,Z (2009).‖Introduction to the citation frequency as a new approach of citation counting in research evaluation‖.etelashenasi.6(1) [in persian]. 599

Lowe, M. Sara (2003). ― Reference analysis of the American historical review‖. Collection building. 22 (1). [online]. Available: http://www.emeraldinsight.com/Insight/viewPDF.jsp?Filename=html/Output/Published/EmeraldF ULLTextArticle/pdf/1710220103.pdf Moed,H.F.(2005), Citation analysis in research evaluation.Springer Osareh, Farideh; McCain, Katherine W (2008). ―The structure of Iranian chemistry research, 19902006 : An author cocitation analysis‖. Journal of the American Society for Information Science and Technology. 59 (13): 2146-2155. ). [online]. Available: http://www3.interscience.wiley.com/cgi-bin/fulltext/120780769/PDFSTART Thelwall, Mike (2004). Link analysis: An information science approach. San Dego: Academic press.

600

A Bibliometric Study of Scientific Output of Middle East Countries in Psychology (1996-2010) Mohammad Hossein Biglu

N. Chakhmachi

[email protected] Paramedical Faculty, Tabriz University of Medical Sciences, Tabriz, Iran

[email protected] Paramedical Faculty, Tabriz University of Medical Sciences, Tabriz, Iran

Abstract: A bibliometric analysis was conducted to explore the scientific activities in the field of psychology from Middle East countries in contrast to the leading countries in the world throughout 1996-2010. All row data was extracted from the SCImago. The SCImago Journal & Country Rank is a portal that includes the journals and country scientific indicators developed from the information contained in the Scopus database. The study showed that a total number of 5,186 citable publications in the field of psychology came from the Middle East countries. Israel with producing 82.2% of total citable publications is the most productive country in the region. The following countries are Iran (5.5%), Kuwait (3.0%), Lebanon (2.4%), and United Arab Emirates (2.3%). The fraction of other countries (Iraq, Jordan, Oman, Qatar, Saudi Arabia, Syria, and Yemen) in the region was 4.3%. The most majority of publications came from North America (48%) and Western Europe (32%). Regarding to the subject categories 47% of total publications was in the subject area of miscellaneous Psychology, 12% in Neuropsychology and Physiological Psychology, 10% in Clinical Psychology, 10% in Experimental and Cognitive Psychology, 8% in Developmental and Educational Psychology, 7% in Social Psychology and 6% in Applied Psychology. Keywords: Scientometrics, Bibliometrics, Scientific Profile, Psychology, Middle East

Introduction ―Psychology is both an applied and academic field that studies the human mind and behavior. Research in psychology seeks to understand and explain thought, emotion and behavior. Applications of psychology include mental health treatment, performance enhancement, selfhelp, ergonomics and many other areas affecting health and daily life. Psychology evolved out of both philosophy and biology. Such discussions of the two subjects date as far back as the early Greek thinkers such as Aristotle and Socrates. The word psychology is derived from the Greek word psyche, meaning 'soul' or 'mind.' The field and study of psychology was truly born when Wilhelm Wundt established the first psychology lab in Leipzig, Germany.‖[1] The psychological health is the most important thing in human‘s life. That is why the policymakers in developed countries dedicate an appropriate budget for psychological health care of countries inhabitant. As the scientific output in subject areas can mirror the research activity of scientists in that subject fields; therefore in this paper we attempt to investigate the trend of scientific activities in the field of psychology in different categories during a period of fifteen years.

601

Methodology The web portal of SCImago was used to obtain row data about literature of science in psychology during a period of fifteen years. The obtained data was categorized in different disciplines of psychology. We took only the citable publications under consideration. ‖The SCImago Journal & Country Rank is a portal that includes the journals and country scientific indicators developed from the information contained in the Scopus® database (Elsevier B.V.). These indicators can be used to assess and analyze scientific domains. This platform takes its name from the SCImago Journal Rank (SJR) indicator, developed by SCImago from the widely known algorithm Google PageRank™. This indicator shows the visibility of the journals contained in the Scopus® database from 1996.‖[2] Results Analysis of data showed that a total number of 289,594 papers in the field of psychology was distributed during the period of study. The number of publications has been increased in a power law relationship. It reached from 15,061 papers in 1996 into 26032 in 2010, an increase of 73%. The study furthermore indicated that the most majority of publications was in miscellaneous psychology (47%) followed by Neuropsychology and Physiological Psychology (12%), Clinical Psychology (10%), Experimental and Cognitive Psychology (10%), Developmental and Educational Psychology (8%), Social Psychology (7%) and Applied Psychology (6%). The USA contributing of 42% (115,366 papers) of world‘s publications was the most productive country in the world. Only seven countries distributed equal or more than 3% of world‘s publications throughout the time and they are: U.K. 11% (28,785 papers), Canada 7% ( 18,131 papers), Germany 5% ( 14,757), Australia 4% ( 10,592), Netherlands 4% ( 9,748), France 3% ( 8,771) and Spain 3% ( 7,103 ). In the case of regions a total number of 5,186 citable papers came from the Middle East. Israel contributing of 4,265 papers was the most prolific country in the region. The following countries are Iran (258 papers), Kuwait (158), Lebanon (125), Saudi Arabia (72), Jordan (46), Qatar (16), Iraq (11), Syria (5) and Yemen 1 paper.

Conclusion Analysis of data indicated that the number of publications has been increased in a power law relationship during the period of study. The number of publications in the field of psychology in 2010 was 73% greater than in 1996. The most majority of publications in the field came from North America (48%) and Western Europe (32%). A total number of 5,186 papers (1.9% 602

of world‘s publication) came from the Middle East. Israel sharing 82.2% of total publications is the most prolific country in this region. The following productive countries are Iran 5.5%, Kuwait 3.0% and Lebanon 2.4%. References 1. http://psychology.about.com/od/psychology101/f/psychfaq.htm 2. SJR -SCImago Journal & Country Rank. Retrieved July 26, 2011, from http://www.scimagojr.com 3. S. Biglu, M.H. Biglu and C. Falk (2011). Scientometric study of scientific production in psyciatry. European Psychiatry, Volume 26, Supplement 1, 2011, Page 515 4. M.H. Biglu and A. Asgharzadeh (2011). The prevalence of stress factors among medicine students in Iran. European Psychiatry, Volume 26, Supplement 1, 2011, Page 1577

603

E-Learning System for Generating C

Youness Boukouchi

Abdelaziz Marzak

[email protected] Ben M‘Sik Faculty of science, University Hassan II, PB 7955 Casablanca, Morocco

[email protected] Ben M‘Sik Faculty of science, University Hassan II, PB 7955 Casablanca, MoroccoHicham Behja National School of Arts and Trades, PB 4024, Beni Mohammed, Meknes, Morocco

Abstract: This study is the projection on a main actor of the stage of Web 2.0 and social web, is Generation C, who born and developed under the information technology and communication that draws on any environment open and rich of Web 2.0 technologies and social services. This study propose main criteria to develop a new generation of e-Learning who compliance the trends of generation C

Introduction: This study is the projection on a main actor of the stage of Web 2.0 and social web is the C generation, who born and developed under the information technology and communication, this is a generation that specific trends that will be raised in this study. On the other hand, this study offers quality criteria to develop e-Learning systems meeting the trends of the C generation; in same time; the study is a starting point to develop a quality charter for e- learning system. Generation C under the microscope Generation C After Generation X, made up of people now aged between 36 and 45, and those of Generation Y who have 25 to 35 years ago, today it is Generation C (for Communication, Collaboration, Connection and Creativity), composed of young people between 10 and 24, who were born and grew up in a context of extremely rapid development of information technology and communications, including Web 2.0 and collaborative tools (Facebook, MySpace, Twitter, YouTube, etc. ...). It is a hyper-connected generation that is not limited to developing his personality by the intervention of Web 2.0 technologies and social media, it is always permanently connected to the computer or phone (home, transportation, school, or work), it manages contacts, sharing his albums and develop social networks in a natural way, this has become a daily activity and primordial in its life. Generation C occupies an important space in the demographic structure of each country (Table 1), it has an important influence in society as citizens, students, consumers, workers. Today, a large proportion of them are active in the labor market and many are still in school or university. That young are actively involved as social actors to address major challenges of society, is a revolutionary generation, is the case with Generation C in Tunisia, Egypt, Syria, Yemen, etc. 604

Table 1: percentage of generation C in different countries

Generation C

Population

Morocco (2009)

29,80%

31,5 millions

Egypte (2010)

28,94%

80,5 millions

Tunisia (2010)

25,98%

10,5 millions

USA (2010)

20,60%

310,2 millions

France (2010)

18,60%

64,7 millions

Quebec (2010)

17,90%

7,9 millions

Trends in Generation C Investigations into Generation C have shown that young people are heavy users of information technology. A Quebec study on over 2000 young of generatio n C showed that almost all young people (95%) use the Internet as part of their studies for the realization of their work, only 6% of young people relate primarily to paper to carry out work, only (35%) of students believe that most of their teachers are able to help them acquire the desired knowledge in technology, 72% said they would vote more often if they could do on the Internet with a computer or cell phone. These young people are difficult to attract to a poor environment in daily practice because they use more of the Web to communicate and access their network of contacts. If they realize they can‘t use at work which is often for them a real professional tool in addition to being a tool of socialization, they may decide to work elsewhere. They feel that with the Web, we are moving towards a world of increasingly open, so they will not want a career in an environment they consider closed. They attract each other in open space and high technology of Web 2.0 and social services, an environment that guarantees freedom of expression and communication simple Social networks, a favorite choice for generating C! Statistics of social networks Social networks have invaded the world like an epidemic, people spend more time online than ever, and these statistics will show how social media has become a social practice, especially young generation C (Table 2).

605

Table 2: percentage of members of Generation C using Facebook

(CheckFacebook.com), Facebook members on 19/04/2011 are: 656,870,980 Country

Morocco Egypte 3,282,980 6,810,180

Tunisia

France

2,408,540 21,825,880

USA 155,235,060

Facebook members (0.50%)

Users in each country

12%

Generation C (14 to 24 years)

63,40%

(1.04%)

8% 56,20%

(0.37%)

(3.32%)

(23.63%)

23%

34%

50%

57,20%

40,70%

38,40%

Australians are the most prolific users of social media in the world, Facebook has over 500 million users, if Facebook were a country it would be the world‘s 3rd largest, more than 200 million users access their Facebook account from a mobile device, mobile Twitter usage rose 347% in the past year, roughly 1 billion pieces of content are shared on Facebook daily, 2 hours of video are uploaded to YouTube every second. In 2011 users are making nearly 100 million tweets per day, the average Australian Facebook session is 29 minutes. That‘s longer than the average Australian‘s commute to work. In the time it takes to read this article, users will upload and share around 25000 photos to Flickr (Source of those statics is Box Hill Institute Social Media Video 2011). What is the secret behind these statistics? Social networks are not just a social instrument of communication that allows people to share ideas and thoughts with others, but they can be understood as open spaces and models that emerge daily practices of people, because they were developed to manage the complexity of human social behavior. Danah Boyd (2008) has identified in his thesis (Taken Out of Context American Teen Sociality in Networked Publics) the sociological profile of the young digital natives: « As social network sites like MySpace and Facebook emerged, American teenagers began adopting them as spaces to mark identity and socialize with peers. Teens leveraged these sites for a wide array of everyday social practices—gossiping, flirting, joking around, sharing information, and simply hanging out—… While teenagers primarily leverage social network sites to engage in common practices, the properties of these sites configured their practices and teens were forced to contend with the resultant dynamics. Often, in doing so, they reworked the technology for their purposes. As teenagers learned to navigate social network sites, they developed potent strategies for managing the complexities of and social awkwardness incurred by these sites. Their strategies reveal how new forms of social media are incorporated into everyday life, complicating some practices and reinforcing others. New technologies reshape public life, but teens‘ engagement also reconfigures the technology itself. ". The technology has been reconfigured to meet the social trends of Generation C, it has forced companies to invest in Web social to rebuild media by incorporating the daily practices. Today, It speak on Facebook and Twitter with words, on YouTube and

606

Dailymotion with videos, on Picasa with photos, this will give us a complete profile of the individual's interests, thoughts, feelings, etc.. What e-Learning System for Generation C? In this section we propose quality criteria to meet to develop e-Learning systems, systems following the trends of Generation C and ensuring effective learning. Criteria on the form of resources If the forms of information are diverse they are intended for any type of human intelligence (Linguistic, Logical, Spatial, etc. ...). Creating rich teaching resources ( videos, graphs, diagrams, sounds, text, etc .) will mobilize the mental sense of learning in these young, they learn while having fun, and creating in them the desire to reach all educational objectives. Criteria on the learning sequences Few are the sites that attract young people to visit their pages every day, and few people monopolize their attention for long on those sites. The leaders of these sites are social networks (Facebook, Twitter, YouTube ...) in which young people spend 18 minutes to 32 minutes each day (Table 3). Table 3: The duration of the connection on the top 10 most visited sites in the world (source: Alexa site)

qq.com

10

Twitter.com

9

Live.com

8

Wikipédia

7

Baidu.com

6

Bloger.com

5

Yahoo.com

4

YouTube.com

3

Facebook.com

2

Google.com

1

Site (Top 10)

Ordre

minutes

13

32

18

9

7

13

5

5

7

14

Then we must segment the learning process - which is usually long - in small sequences, what we call the micro- learning, they vary between 1 minute and 20 minutes. These micro-learning will ensure, from one side, continuity of the process without being cut each time by a zapping, obviously causing the loss of information and concentration, on the other hand, they will motivate young people to do more of these micro- learning and achieve the objectives of the sequence Criteria on Web 2.0 technologies Web 2.0 is not a revolution but an evolution of Internet users in the use of the Internet, an use based on mature technologies like HTML, CSS, JavaScript, XML, RSS, Ajax, and others, that take their potential to make web pages much lighter and enriched. Hence the need to improve the interactivity and dynamism of e- learning system and provide many functions relevant to young people to simplify their task of learning.

607

Criteria on design When performing an e-Learning system should develop rich and simple designs that are consistent with the trends of young people, and at the same time, is to involve youth in the personalization of interfaces and enrichment by appropriate functions, which gives the user the possibility to choose the colors, fonts, size, etc. Criteria on the knowledge extracted It is very important to leverage the full potential of systems to extract knowledge from database (KDD: Knowledge Discovery in Databases) to give life to the e-Learning. Storage and analysis of youth behavior during the learning process is an essential action, initially it allows the use of traces etched in the database to better understand the behavior of these young people and their ability to learn, finally, is to provide learning scenarios appropriate to their skills and help them overcome their difficulties. Criteria of learning practices What distinguishes social networks is their ability to embody the sensations and feelings of young people, that's why the e-Learning needs to develop and adapt these social practices, including learning practices (I knew, good course, need help, write a teacher ...), it is the time to model the relationships to overcome the complexities of formal and informal learning. Criteria on social services Exploit the wealth of communities to develop the social side of the platform e- learning, young people will converge more and more towards e- learning if they find social functions, then it is imperative to enhance the platform through extens ions social to services such as: YouTube, Facebook, etc. Conclusion Technology continues to grow exponentially with a need for young people, a need that is an extraordinary share of the billions of information using the social web. So, is it not time now to build e-Learning systems of quality that makes the most out of social networks? Should we rush to pass a "Social Web" to "Learning Web" that promotes lifelong learning? References Danah Boyd. (2008). Taken Out of Context: American Teen Sociality in Networked Publics. Boyd & Ellison (2007). Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication. Danah Boyd (2009), Streams of Content, Limited Attention: The Flow of Information through Social Media, Web2.0 Expo New York, NY, 17 November 2009. Tim O'Reilly (2005). What Is Web 2.0: Design Patterns and Business Models for the Next Generation of Software. Eric van der Vlist & Dyomedea (2005). Web 2.0 : mythe et réalité, www.xmlfr.org, Friday 2 decembre 2005. Eric van der Vlist & Dyomedea (2006). Web 2.0 : risques et perspectives. www.xmlfr.org, Friday 1st December 2006.

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Frederic Cavazza (2005). Web 2.0 : la révolution par les usages, www.Lejournaldunet.com, 19 Decembre 2005. Sorav Jain (2010). Fascinating Social Media Facts of Year 2010. www.socialmediatoday.com, 16 November 2010. Cyril Fievet (2005). Le renouveau de l‘interface web, www.internetactu.net, 29 september 2005. Didier Frochot & Fabrice Molinaro (2006). Regards sur le web 2.0, www.les-infostrateges.com decembre 2006. Fabrice Molinaro (2006). Le web 2.0 au service de la veille et de la recherche d'information, www.les-infostrateges.com, decembre 2006. NetTendance (2010) Internet : source d‘information et modes de communication, Volume1, N°4. NetTendance (2010). Divertissement en ligne: place aux jeux sociaux, Volume1, N°2. NetTendance (2010). L‘explosion des médias sociaux au Quebec, Volume1, N°1. CEFRIO (2011). Les C en tant que consommateurs, volume1, N°3, March 2011. CEFRIO (2011). Les C en tant que travailleurs, volume1, N°2, February 2011. CEFRIO (2011). Les C en tant que citoyens, volume1, N°1, January 2011. Philippe Aubé (2009). Enquête Génération C : que doit-on retenir ? Monday, December 14, 2009, www.generationc.cefrio.qc.ca Tendance Veille (2011) .Quelles tendances pour la veille en 2011 ? La vision d‘experts sur la question, March 2011.

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Proceeding Papers or Journal Articles? A Comparative Analysis on Computer Science Versus Electrical & Electronic Engineering Lin Zhang

Wolfgang Glänzel

Centre for R&D Monitoring (ECOOM)

Dept. MSI, K.U. Leuven, Leuven (Belgium)

Introduction Recently Gonzalez-Albo and Bordons (2011) have published an interesting study of the proceedings literature published in journals in the field of Library and Information Science. Zhang and Glänzel (2011) further extended the research to a larger scope of fields in the science and social sciences. The current study will zoom in two sub- fields in engineering, and the comparative exploration is two- folds: proceeding papers vs. ―regular‖ journal articles, and computer science/information technology vs. electrical & electronic engineering. Data and Methods More than 500000 publications of the document type ―articles‖ and ―proceedings paper‖ respectively assigned to the field of ―Computer science/information technology‖ and ―electrical & electronic engineering‖ according to the ECOOM classification scheme were downloaded from web of science (1999-2008). A fixed three- year citation window was used for each publication, beginning with the publication year till 2010. Results Almost half of the publications (47.74%) in computer science/information technology is proceeding papers, which is much more than that in electrical & electronic engineering (26.98%). Table 1 presents the related data of proceedings and citations in the two fields under study. Table 1. The citation impact of proceedings and articles in computer science/information technology and electrical & electronic engineering Field

P roceedings

Articles

Mean Citations of P roceedings

Mean Citations of Articles

Computer science/information technology Electrical & electronic engineering

157602

172536

1.22

2.42

59064

159833

2.00

2.02

The evolutional trend of proceedings proportion, and the citation impact of proceedings and articles in the two fields is presented in Figure 1. Two quite different typologies are observed in the two fields.

610

Figure 1. Evolution of proceedings proportion, and citation impact of proceedings and articles in computer science/information technology (E1) and electrical & electronic engineering (E2).

Conclusions Though proceeding papers occupied a large share of publica tions in computer science/information technology, they didn‘t succeed in gaining the equivalent citation impact as the ―regular‖ journal articles in general. Comparatively, the proportion of proceedings in electrical & electronic engineering is much smaller, however, with an almost equal citation impact of ―regular‖ journal publications. Two different evolutional typologies are observed in the two fields. References Gonzalez-Albo, B., Bordons, M. (2011), Articles vs. proceedings papers: Do they differ in research relevance and impact? A case study in the library and information science field. Journal of Informetrics, 5 (3), 369–381. Zhang, L., Glänzel, W. (2011), Proceeding papers in journals versus the ―regular‖ journal publications. Journal of Informetrics, in press.

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A Survey of Scientific Production and Collaboration Rate among of Library and Information Science in Philosophy and Theoretical Bases of Library and Information Science in ISI and SCOPUS Databases during 2001-2010 Parisa Malekahmadi

Nayere Sadat Soleimanzade Najafi

MA students of medical Library

Information Science in Isfahan University of Medical Sciences [email protected]

Abstract: Research is essential for development. In other words, scientific development of each country can be evaluated by researcher's scientific production. Understanding and assessing the activities of researchers for planning and policy making is essential. The significance of collaboration in the production of scientific publications in today's complex world where technology is everything is very apparent. Scientists realized that in order to get their work wildly used and cited to by experts, they must collaborate. The collaboration among researchers result in the development of scientific knowledge and hence attainment of wider information. The main objective of this research is to survey Scientific Production and Collaboration Rate among of Library and Information Science in Philosophy, and Theoretical bases of Library and Information Science in ISI and SCOPUS Databases during 2001 2010. This is a descriptive survey and scientometrics methods were used for this research. Then data gathered via check list and analyzed by the SPSS software. Collaboration rate was calculated according to the formula. Among the 294 related abstracts about Philosophy, and Theoretical bases of Library and Information Science in ISI and SCOPUS Databases during 2001-2010, the year 2007 with 45 articles has the most and the year 2003 with 16 articles has the least number of related collaborative articles in this scope. "B. Hjorland" with 8 collaborative articles had the most one among LIS professionals in ISI and SCOPUS. Journal of Documentation with 29 articles and 12 collaborative articles had the most related articles. Library and Information Science challenges with 150 articles had first place in number of articles. Results also show that the most elaborative country in terms of collaboration point of view and number of articles was US. "University of Washington" and "University Western Ontario"are the most elaborative affiliation from a collaboration point. The average collaboration rate between researchers in this field during the years studied is 0.25.The most completive reviewed articles are single authors that included %60.54 of the whole articles. Only %30.46of articles was provided with two or more than two authors. Keywords: collaboration, Library and Information Science, ISI Databases, SCOPUS Databases, Scientometrics, collaboration rate.

612

Introduction One of the most important aspects of development in each country is production of scientific information. Information is power and countries were powerful that have developed the production of scientific information. Indeed, this development influenced the other aspects of development including economic, social and culturaldevelopment. Abdul Salam said that the "standard of living of a nation depends on the nation's science and technology" (1969 Abdul Salam, a cantaloupe in 1381). There is also a direct relationship between production of scientific and economic development. Indeed, it is clear that the classification of countries based on economic development. in fact,classification of countries based on their level of participation in science.Increasing interdisciplinary fields of science makes the science of the world even more dynamic in recent decades. Therefore, researchers in any scientific field do not have the ability to perform other activities, so they tends tocooperation to use each other's expertise (Osareh 2005b). One of the observations in the field of scientific communication has been well documented is less tends to individually work on their research activities (1997 ,10-18Katz and Martin). Decreased activities of individual scientific works and increasing group papers that widely used in many scientific disciplines has been mentioned, so that the group works in many scientific fieldsmake up the majority of publications. In the world of science, articles published in international journals in every field are the most important ways of informing and growth of knowledge. Professional journal articles reflect the views and the latest scientific achievements in any field.From this perspective, the scientific cooperation is not the research quality, but quality is a means to achieve it. The main purpose of this study is to determine the most important authors, magazines, countries, institutions ,research centers and collaboration rate in Philosophy and Theoretical bases of Library and Information Science in ISI and SCOPUS Databases during 2001-2010 Research questions The research questions are: 1. What are the most important authors of the papersas the number of articles and the collaborative worksin Philosophy and Theoretical bases of Library and Information Science in ISI and SCOPUS Databases during 2001-2010? 2. What are the most important journals as the number of articles and the collaborative works in the scope of Philosophy and Theoretical bases of Library and Information Science in ISI and SCOPUS Databases during 2001-2010? 3. What are the most important countries as the number of articles and the collaborative works in the scope of Philosophy and Theoretical bases of Library and Information Science in ISI and SCOPUS Databases during 2001-2010?

613

4. What are the most important institutions and affiliations as the number of articles and the collaborative works in the scope of Philosophy and Theoretical bases of Library and Information Science in ISI and SCOPUS Databases during 2001-2010? 5. How is the cooperative group of writers in the scope of Philosophy and Theoretical bases of Library and Information Science in ISI and SCOPUS Databases during 20012010in different years distributed? 6. What is the Collaboration Rate among in the scope of Philosophy and Theoretical bases of Library and Information Science in ISI and SCOPUS Databases during 20012010? Background Research Sengupta studies about scientific production in the scope of nerve sciences. Subjects in this study were based on the 5785 journals cited by the Journal "an annual study of nerve" as a core journal. The results showed that, despite the close relationship between biomedical field with the field of neurological sciences, biochemistry topic in neuroscience research has fewer partnership (8. 8). Bradford distribution rule in this study has been approved (Sengupta 1989). Gomez and colleagues in a paper entitled "Patterns of cooperation in the fields of Spanish scientific publications on various topics" are trying to find about these patterns. Exploring 43,402 Spanish papers Published in 1990 and 1993 for each of the scientific fields a range of indicators such as the internationalization of scientific research, collaboration, organizational level, the amount of co-writing were measured. The results

indicate the pattern withthe (Gomez et al 1995).

High dispersion in indicators

Osareh and Wilson's research, entitled "Cooperation in the scientific publications of Iran" explores the international cooperation to investigate scientific publications in Science Citation Index during the years 1995 - 1999. The results show the highest scientific cooperation with the USA. The busiest author with 94 (8 / 18%) articles and the most cited author in with 290 citations (44 / 6). University of Shiraz, Tehran, Sharif industrial have the most articles and chemical Topics are the highest produced articles with 393 events (71 / 9%) , the next highest number of articles and topics are in analytical chemistry, chemistry and chemical engineering in rank (Osareh and Wilson 2002). 614

Wilson and Osarehin in the other research entitled "Science and Research in Iran: A Scientometric study " has done Science and Technology Publishing Scientometric analysis of 7-year period in four periods 1975-1981, 1988-1982, 1995-1989, 1996-2002 in Science Citation Index.The growing trend of publications from 0 / 02% in 1985 to 0 / 23% in 2002 (with an approximate growth) is reached from the research. In all four courses, the United States has the highest rating in the collaboration in the first, about topic rankings veterinary medicine, pharmacology and pharmaceutics, chemistry and organic chemistry are the highest share (Wilson and Osareh 2003). Belinchon and others in the research" The contribution of countries in producing scientific articles on skin diseases during the years 2000-1987" have been using the Medline database. In all 19,255 articles in 32 journals the countries which produced the largest share belonged to England (7 / 26%), Germany (7 / 16%), Italy (5 / 11%) and France (2 / 9%) respectively. In proportion to the population of articles published per country per one million population, average 9 / 51 article published reiterated that the European countries, Denmark (0 / 164 articles per million inhabitants), Sweden (7 / 127) and Finland (6 / 119) respectively have the highest rank. In total, 9 / 53% of articles have been published in six of the British Journal of skin diseases with a 2779 article (4 / 14%) has the highest number of article sites (Belinchon et al 2004). Marshkova-shaikevich using Social Sciences Citation Index database (MS. MS. Thirty.) Is collaborating in 2002 Bibliometric Analysis of 10 ca ndidate countries for EU membership. More publications in the field of economics, business, sociology, psychology, psychiatry (social aspects) and political science estimated (Marshkovashaikevich 2006). Ho's research is based on SCI data on issues of environmental engineering, environmental science and water resources in the period 2004-1991 was performed. However, the results show that 5.7% of articles were not never invoked. 9 Article of 20 cited articles belonging to the Water Research (Ho 2008). "Osareh" in an article entitled "Cooperation between Iran and the UK higher education and research" investigated the cooperation brtween Iran based on the findings of previous research.Years 1985 and 2003, United state was the first and Britain was the second most of any major country has had scientific cooperation with Iran (Osareh 2005b). " Jonkers" and "Tijssen" began to investigate the scientific and international cooperation. Research. The natural scienticts from China who were studying abroad and returned to their country is the research commuinity. They also examines the date of departure of this research, joint scientific output and international cooperation were examined. They believe that a significant relationship between scientific output, levels of international cooperation, and personality characteristics of this group of researchers. They also find that the Chinese scientists that return home had not have the motivation research that they have before (Jonkers and Tijssen 2008). Research methodology and data collection This is a descriptive survey with scientometrics methods that used for this research. Data gathered via check list and analyzed by the SPSS software, among the 294 related abstracts about Philosophy and Theoretical bases of Library and Information Science in 615

ISI and SCOPUS databases during 2001-2010.Check list validity is confirmed by experts of library and information sciences. For gathering information, the checklist with the following items was prepared: The article, authors, journal name, author studies, country name, organizational affiliation, participation rates, and the number of articles. 8 scopes of Philosophy and Theoretical bases of Library and Information Science prepared by the library and information science experts and add to the phrase "library and information science", then limited to the years 2001-2010, limit the document type to journal articles, and finally the results are analyzed.

In this study, descriptive statistics was used. The collaboration rate calculated as below:

In this formula: Fj= number of articles written by j Authors j = Articles written by (1 author, 2 authors, 3 authors etc.) N = the total number of published writtenarticles K= the largest number of authors in a paper Findings In this part we answered the research questions. Table 1 shows the frequency distribution of the busiest writers and editors working with the highest collaboration in Philosophy and Theoretical bases of Library and Information Science in ISI and SCOPUS databases during 2001-2010. HJorland B. and Buschman J. with 8 and 5 articles are the most active authers in the field of Philosophy, and Theoretical bases of Library and Information Science in ISI and SCOPUS databases during 2001-2010. Table 1. The frequency distribution of the busiest writers and editors working with the highest collaboration in of Philosophy and Theoretical bases of Library and Information Science in ISI and SCOPUS databases during 2001-2010 Number of written articles Name of author

1 author

2authors

3authors

4authors

More than 4 authors

HJorland B.

8

-

-

-

-

Buschman J.

5

-

-

-

-

Ocholla D.N.

2

1

1

-

-

Wiston MD.

1

2

-

-

-

Overal P.M .

1

2

-

-

-

Virkus S.

1

-

-

-

1

Blu mel L.

-

2

-

-

-

Onyancha OB.

-

1

1

-

-

616

About the second question, Journal of Documentation , Library Trends and Library Quarterly are the most active journals in number of articlesin the field of Philosophy, and Theoretical bases of Library and Information Science in ISI and SCOPUS databases during 2001-2010. Table 2. Distribution and percentage of the busiest journals in Philosophyand Theoretical bases of Library and Information Science in ISI and SCOPUS databases during 2001 -2010 Number of written articles 1

2authors

3authors

More than 4 authors

sum

percentage

3

3

29

9.8

2

1

-

24

8.1

2

2

1

-

20

6.8

2

6

1

2

-

11

3.7

2

1

1

1

-

5

1.7

New library world

-

3

1

-

1

5

1.7

Journal of med ical library

-

-

1

2

1

4

1.3

-

2

1

-

-

3

1

-

3

-

-

-

3

1

Journal name

author

Journal of Documentation

17

4

2

Library Trends

13

8

Library Quarterly

15

Information Research Library and informat ion

4authors

science research

association Medical Reference Services Quarterly Library Hi Tech

About the collaboration between authors in the articles Journal of Documentation , Library Trends and New library world are top journals about collaboration in the field of Philosophy, and Theoretical bases of Library and Information Science in ISI and SCOPUS databases during 2001-2010. About the third question, United State of America , United Kingdom and Australia are the most active countries in number of articlesin the field of Philosophy, and Theoretical bases of Library and Information Science in ISI and SCOPUS databases during 2001-2010. United State of America , Australia and Canada are the most active countries about collaboration in this field of study.

617

Table 3. The frequency distribution of the busiest states in terms of number of papers and participation in Philosophy and Theoretical bases of Library and Information Science in ISI and SCOPUS databases during 2001-2010 Number of written articles Country name

1 author

2authors

3authors

4authors

More than 4 authors

sum

United State of America

53

23

8

8

6

100

United Kingdom

12

3

3

-

-

18

Australia

6

3

4

4

1

18

South Africa

7

2

2

1

-

12

Canada

5

2

2

1

1

11

England

3

-

1

2

1

7

Spain

1

2

-

2

-

5

Germany

-

1

1

-

1

3

Iran

-

1

2

-

-

3

Sweden

-

2

-

1

-

3

About the most active institutions and affiliations in the field of Philosophy, and Theoretical bases of Library and Information Science in ISI and SCOPUS databases during 2001-2010, as the number of articles University of Washington , University Western Ontario and University of Pittsburgh are the most active affiliations and as the collaboration are University of Washington , University Western Ontario and University of Pittsburgh . Table 4. The frequency distribution of the busiest affiliations in terms of number of papers and participation in Philosophy and Theoretical bases of Library and Information Science in ISI and SCOPUS databases during 2001-2010 Number of written articles affiliations

1 author

2authors

3authors

4authors

More than 4 authors

sum

University of Washington

2

3

1

-

-

6

University Western Ontario

-

-

1

1

1

3

University of Pittsburgh

-

2

-

-

1

3

University New S Wales

-

2

-

-

-

2

Florida state University

1

-

1

-

-

2

618

University Maryland

-

-

1

-

1

2

University of Zu luland

-

1

1

-

-

2

TBI

-

2

-

-

-

2

Depart ment of Po lit ical

2

-

-

-

-

2

Science

About the other question, between 294 articles, 178 articles(%60.54) are written by single authors and 117 articles ( %30.46) are written collaboratively. Tendency to written articles with 1, 2, 3, 4 and more than 4 authors vacillate during different years but the tendency in writing articles with 2 and 3 authors is relatively enhanced through 2001 to 2011. Table 5. The frequency distribution of participation in Philosophy and Theoretical bases of Library and Information Science in ISI and SCOPUS databases during 2001-2010 Number of written articles affiliations

1 author

2001

2

-

2

2002

1

3

2003

1

2004

2authors

3authors

4authors

More than 4 authors

sum

4

15

23

1

5

9

19

1

-

4

12

16

1

2

2

3

13

21

2005

3

-

1

5

24

33

2006

2

-

1

4

23

30

2007

-

-

5

9

31

45

2008

-

4

4

11

22

41

2009

6

2

8

6

14

36

2010

-

2

3

9

15

29

sum

16

14

27

60

178

294

percentage

5.4

4.7

9.1

20

60

100

About the collaboration rate between authors with the collaboration rate formula, the table 6 shows that the collaboration rate between authors has a vacillation process during the years. The most collaboration rate is in 2009 (%0.33) and the least collaboration rate is in 2004 and 2006 ( %0.16). The total collaboration rate is 0.25.

619

Table 6.The collaboration rate between authors in the articles of Philosophy, and Theoretical bases of Library and Information Science in ISI and SCOPUS databases during 2001 -2010 year

Collaboration rate

2001 2002

0.30

2003

0.20

2004

0.16

2005

0.20

2006

0.16

2007

0.27

2008

0.17

2009

0.33

2010

0.19

average

0.25

0.70

Conclusion This research gives a general schema of science production and collaboration between authors in the scope of Philosophy and Theoretical bases of Library and Information Science in ISI and SCOPUS databases during 2001-2010. Between the 294 articles, 851 authors worked together. HJorland B. , uschman J. and Ocholla D.N. are top three authors. United State of America , United Kingdom and Australia are the most active countries in number of articles in the field of Philosophy, and Theoretical bases of Library and Information Science in ISI and SCOPUS databases during 20012010. United State of America , Australia and Canada are the most active countries about collaboration in this field of study. About the most active institutions and affiliations in the field of Philosophy, and Theoretical bases of Library and Information Science in ISI and SCOPUS databases during 2001-2010, as the number of articles University of Washington , University Western Ontario and University of Pittsburgh are the most active affiliations and as the collaboration are University of Washington , University Western Ontario and University of Pittsburgh . Journal of Documentation , Library Trends and Library Quarterly are the most active journals in number of articles in the field of Philosophy, and Theoretical bases of Library and Information Science in ISI and SCOPUS databases during 2001-2010. About the collaboration between authors in the articles Journal of Documentation , Library Trends and New library world are top journals about collaboration in the field of Philosophy, and Theoretical bases of Library and Information Science in ISI and SCOPUS databases during 2001-2010. About 60 percentage of articles are written by only one author and the remain are written by two or more than two authors. About the collaboration rate between authors with the collaboration rate formula, the table 6 shows that the collaboration rate between authors has a vacillation process during the years. The most collaboration rate is in 2009 (%0.33) and the least collaboration rate is in 2004 and 2006 ( %0.16). The total collaboration rate is 0.25.

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References 1. Beaver, D.; Rosen, R. 1978. Studies in scientific collaboration.Part 1.the professional origins ofscientific co – authorship. Scientometrics.1(1): 65-84. 2. Farajpahlou A. H. 2004. Collaboration among Library and Information experts vs. scientist.Inthe Proceedings of International workshop on Webometrics, Informetrics andScientometrics.2-5 March.Roorkee, India. 3. Harirchi, G.; Melin, G.; Etemad Sh. 2007. An exploratory study of the feature of Iranian coauthorshipsin biology, chemistry and physics.Scintometircs. 72(1): 11-24. 4.Jonkers K, Tijssen R. 2008. Chinese researchers returning home: Impacts of international mobility on research collaboration and scientific productivity. Scintometircs. 77(2): 309333. 5. Katz J.S., Martin B.R. 1995. What is research collaboration? London: ERSRC center for science, Technology, Energy and environment. 6. Marshakova-Shaikevich I. 2006. Scientific collaboration of new 10 EU countries in the field ofsocial sciences.Information Processing and Management. 42 (6): 1592-1598. 7. Osareh, F. 2005a. Collaboration in Astronomy knowledge production: a case study in Science 8. Direct from 2000-2004. In Proceedings of 10th International Conference on Scientometrics andInformetric., 24-28 July in Stockholm-Sweden

9. Osareh, F. 2005b.Higher Education Research collaboration between Iran &UK. In Proceedingsof COLLNET Meeting Extra Session in conjuction with 10th ISSI Conference, 28th July inStockholm-Sweden. 10. Russel, J. M. 2001. Scientific collaboration at the beginning of the 21st. ISSJ. 16(3): 271-279. 11. Belinchon, Isabel; Jose Manuel Ramos; Evaristo Sanchez-Yus; and Isabel Betlloch. 2004. Dermatologicalscientific production from Eurooean Union authors. Scientometrics (61)2: 271-281. 12. Garfield Eugene; Soren W. Paris; and Wolfgang Stock. 2006. HistCited™: A software tool for informaticsanalysis of citation linkage. Information Wissenschaft und Praxis(57): 391-400 13. Gomez, I.; T. M. Fernández; and A. Méndez. 1995. Collaboration patterns of Spanish scientificpublication in different research areas and disciplines. Presented of Proceeding of the fifth biennialinternational conference of the international society for scientometrics and infometrics learnedinformation. Medford NJ, ETATS-UNIS. 14. Ho, Y. S. 2008. Bibliometric analysis of biosorptiontechonology in water treatment and from 1991 to2004. International Journal of Environment and pollution 34(1-4): 1-13. 15. Marshakova-shaikevich, Irhna.2006. Scientific collaboration of new 10 EU countries in the field of socialsciences. Information processing and management42: 1592-1598. 16. Mehrdad, Morteza; Akbar Heidari; MohmmadNabiSarbolouki; and ShapourEtemad,. 2004. Basicscience in the Islamic Republic of Iran. scientometrics (61)1: 79-88 17. Osareh, F; and C. S. Wilson. 2002. Collaboration in Iranian Scientific publications. Libri 52: 88-98. 18. Sengupta, I. N. 1989. The growth of knowledge and literature in neuroscience.Scientometrics 17 (3-4:253-288. 19. Wilson, C. S; and F. Osareh. 2003. Science and research in Iran: a scientometrics study. InterdisciplinaryScience Reviews 28(1): 26-37. 621

Scientometrics Study of Human-Papillomavirus in MEDLINE 2005-2009 N. Navali

M.H. Biglu

[email protected] Medicine Faculty, Tabriz University of Medical Sciences, Tabriz, Iran

[email protected] Paramedical Faculty, Tabriz University of Medical Sciences, Tabriz, Iran

Abstract: Human papillomavirus (HPV) is an affiliate of papillomavirus family that is capable of infecting humans. More than 30 types of HPV are typically transmitted through sexual contact and infect the anogenital region. The objective of current study is to measure the scientific production of leading countries in the field of HPV for a period of five years in MEDLINE. On December 12th 2010 we extracted all documents indexed as a major main heading of human papillomavirus from PubMed. We restricted the extraction of data to MEDLINE by selecting MEDLINE from the sub setting list. Findings showed that a total number of 3,157 scientific productions originated from 43 countries which were indexed under the ―major main heading of human-papillomavirus‖ in MEDLINE during a period of 5 years. The USA with contributing ~31% of world‘s production in the field of Papillomavirus is the most productive country. The following countries are U.K (10.8%), China (6.1%), Italy (5.3%), Germany (4.9%), France (3.8%), Brazil (3.5%) and Netherlands (3.3%). The International Journal of cancer contributing a total number of 127 scientific publications is the most prolific journal followed by Journal of virological methods (119), Gynecol Onco (96), J Clin Viro (89), Virology (89) and J Clin Microbio (80). Analysis of study indicated that the policymakers in Iran and Turkey should pay more attention on this basis of mortality among young women. In addition high consideration of WHO is essential and vital in these two neighboring countries.

Introduction HPV is the abbreviation for the Human Papillomavirus. It is a group of more than 100 different kinds of viruses. A number of HPV cause warts on the hands and feet and some of them cause genital warts and cervical cancer. HPV is known as the most common sexually transmitted disease and more than 30 are sexually transmitted and considered as genital HPV. HPV has been proposed as the first identified, ―necessary cause‖ of a human cancer. Strong clinical, epidemiological and molecular biological evidence indicates that HPV is the central causal factor in at least 95% of invasive cervical cancer [1 ]. Within the last decade, research on HPV has become one of the most interesting scientific activities among scientists. Based on findings, the number of publications related to HPV and cervical cancer has been increased from 333 in 2001 into 926 in 2010, an increase of 178% [Fig.2 ]. There are some methods for detecting HPV, among them the DNA tests for HPV has earned its place as an accepted method for detection the early cervical cancer, but it is not recommended for screening women younger than age 30 because HPV infections are relatively common in this age group and often resolve itself without treatment or complications. However, it may be used as a follow-up test in women who are 21 years or older and who have certain abnormal 622

results on a Pap-smear known as "atypical squamous cells of undetermined significance (ASC-US) to determine the need for a colposcopy, a procedure that allows a doctor to visually inspect the vagina and cervix under magnification for the presence of abnormal cells. [2] Methodology On December 12th, 2010, we extracted all documents indexed as a major main heading of the Human Papillomavirus from PubMed. We restricted the extraction of data to MEDLINE by selecting MEDLINE from the sub setting list. World Health Organization reports were used as key statistics of selected countries. Results The number of scientific publication in the field of the Human Papillomavirus has been increased through the period of study. The USA sharing 30.8% of total world profiles in the field of HPV is the most prolific country followed by UK (10.8%), China (6.1%), Italy (5.3%) and Germany (4.9%) [ Fig 1]. A total number of 3,465 scientific documents published in 742 journals, the journal of (Int J Cance) distributing 3.7% of total papers is the most prolific journal, followed by J Viro 3.4%, Gynecol Onco 2.8%, J Clin Viro 2.6% and Virolog 2.6%. The number of scientific output per population at risk of developing cervical cancer in Germany is 42 per 10,000 people whereas this number in Turkey and Iran is 7 and 5 per 10,000 people, respectively. It means the number of scientific activities in Germany is 6 times greater than in Turkey and 8.4 times greater than in Iran. Although the number of population at risk of developing cervical cancer among women older than 15 in Turkey is approximately likewise in Iran, but the number of scientific activity by Turkish scientists in the field of HPV is greater than Iranian scientists. The number of published papers in the field can mirror the quantity of research activities in the countries. This is an indicative that the policymakers in Iran should pay more attention on this basis of mortality among young women. High consideration of WHO seems to be essential and vital in Iran as well as in Turkey. Conclusion We should bear in mind that the diagnosed rate as well as the death rate among women in Iran and Turkish seems be greater than the calculated rate in this ppaper, we may refer to the study of Gita Esmaeli et al [5 ] which showed among 70 clinical study patients in Iran 49% were positive for HPV. The study of Polat Dursun et al for detecting the presence of HPV types in cervicovaginal samples obtained from 403 Turkish women during gynecologic examination showed that 23% of them were HPV positive [6]. Because of many social restrictions, such as social behavior, that women are not screened regularly in these countries, and women do not see a doctor until they become aware of any symptoms. HPV vaccines that prevent HPV infection are now available and have the potential to reduce the incidence of cervical and other anogenital cancers; hence it is strongly recommended the WHO to consider it for women in Iran and Turkey.

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References 1. Bosch FX, Lorincz A, Munoz N, Meijer CJ, Shah KV. The Causal relation between human papillomavirus and cervical cancer. J Clin pathol 2002; 55:244-65 2. http://www.labtestsonline.org/understanding/analytes/hpv/test.html 3. WHO/ICO Information Centre on HPV and Cervical Cancer (HPV Information Centre). Human Papillomavirus and Related Cancers in Germany. Summary Report 2010. Available at : http://apps.who.int/hpvcentre/statistics/dynamic/ico/SummaryReportsSelect.cfm 4. http://www.who.int/hpvcentre 5. Eslami et al: PCR detection and high risk typing of HPV DNA in cervical cancer Iranian patients (2008). Cancer Therapy Vol. 6, p. 361-366. 6. Polat Dursun, Süheyla S Senger, Hande Arslan, Esra Kuşçu and Ali Ayhan (2009). Human papillomavirus (HPV) prevalence and types among Turkish women at a gynecology outpatient unit. BMC Infectious Diseases. Retrieved Jult 8. 2011 from: http://www.biomedcentral.com/1471-2334/9/191 7. Biglu M.H. and Umstätter W. (2007). The editorial policy of languages is being changed in Medline. Acimed 16(3). Available at: http://bvs.sld.cu/revistas/aci/vol16_3_07/aci06907.html

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Knowledge Sharing for Improving the Effectiveness of University–Industry Collaboration Leila Nemati Anaraki

Azadeh Heidari

[email protected] Young Researchers Club, Science and Research Branch, Islamic Azad University, Tehran, Iran

[email protected] PhD student of Library and Information Science, Department of Library and Information Science, Science and Research Branch, Islamic Azad University, Tehran, Iran

Abstract: Nowadays knowledge is one of the most important strategic resources in organizations. Knowledge sharing is a mechanism for people to capture, disseminate, transfer and apply useful knowledge. In this regard knowledge sharing has become a strategic issue: as a source of funding for university research and as a policy tool for economic development. So collaboration between universities and industrial organizations can play an important role in the field of knowledge sharing. There are many factors which can affect collaboration with industrial firms and universities. Therefore this paper begins with the description of the term knowledge. It then discusses knowledge sharing and scientific collaboration between universities & Industrial organizations, and then different ways of this process like communicational channels, especially ICTs and the importance of university libraries are discussed. After all, this article tries to propose a conceptual model for the scientific communication and collaboration system between the scholars, researchers and scientific societies of universities and indu strial organizations. Keywords: Knowledge Sharing- Scientific Communication- University- Industry

Introduction In modern knowledge economies, science is becoming increasingly more important in realizing economic growth(Coriat & Weinstein, 2001; OECD, 2002). Structural economic growth can only exist if the knowledge-based society and production of knowledge increase. Universities are places for science. However, for playing an important role in the economy, it is inevitable that the new knowledge is not only created at universities, but also transferred from universities to society, or more precisely to industry. In this regard communication and knowledge sharing play an important role in the scientific endeavor. Scientific/scholarly communication means the study of how scholars in any field use and disseminate information through formal and informal channels (Khosrowjerdi, 2011). As expected, industry–university interaction is found to be more important in science-based technologies (Schartinger, Rammera, hlich, 2002). Thus, our study begins with defining the word knowledge then after discussing about the importance of organizational knowledge sharing, scientific collaboration and communicational channels between University and Industry, the important role of ICTs and scientific role of university libraries in knowledge sharing are mentioned. Finally, the entities of the proposed model are explained. 625

What is Knowledge? It is usually agreed that no standard definitio n of knowledge exists. One of the most referenced definitions in the literature is provided by Davenport & Prusak (1998): ―Knowledge is a fluid mix of framed experience, values, contextual information, expert insight and grounded intuition that provides an environment and framework for evaluating and incorporating new experiences and information. It originates and is applied in the minds of individuals. In organizations, it often becomes embedded not only in documents or repositories but also in organizatio nal routines, processes, practices, and norms‖(Davenport & Prusak, 1998). Historically, the concept knowledge has been defined in many ways. Recently, prominent authors have defined it as a meaningful resource that makes a new society unique(Drucker, 1993). He argued that knowledge has been the basis of capitalist society, which is highly specialized. ―Toffler saw knowledge as the essence of power in information age. This is the source of the highest-quality power and the key to the power shift that lies ahead. For sharing and transferring knowledge between universities and industries it is essential to realize what knowledge sharing and scientific collaboration are‖(Toffler, 1990). Knowledge Sharing Knowledge sharing is the process of being aware of knowledge needs and making knowledge available to others by constructing and providing technical and systematic infrastructure. Numerous studies have addressed issues related to knowledge sharing at various levels within organizations and between types of organizations (Kim & Ju, 2008). The effectiveness of knowledge sharing in organizations can be a significant factor to successful organizational management. ―Dixon viewed knowledge sharing as the flow of knowledge from someone who has it to someone who wants it‖(Dixon, 2000). In other words knowledge sharing is the process of exchanging and communicating knowledge between employees in an organization. Effectively sharing knowledge increases the accumulation of organizational knowledge and develops the capability of its employees for better performing their jobs. Such a process of sharing organizational knowledge, facilitates the exchange of working experiences, technical know-how and individual insights between and among individuals (Xiong & Deng, 2008). Scientific Collaboration between Universities and Industries There are some Benefits of collaboration: sharing of valuable knowledge, avoid reinventing the wheel, reducing redundant work, reduce the cost for inventions, creation of knowledge with the help of experts and experienced persons and so on. For policy makers, it is very much essential to invent new ways to establish a proper knowledge sharing system. Collaboration between universities and industrial organizations can play an important role in the field of knowledge sharing (Parekh, 2009). By collaborations, the firms will inform universities and universities will frame the research work as per the needs to fulfill the aim and any kind of the problem raised will be solved at the primary level which will save the time, money and man power (Parekh, 2009). It is very much essential to apply knowledge on practical ground. For that, collaboration of 626

universities and industrial firms is a must (van Zyl, Amadi- Echendu, & Bothma, 2007). By collaborating with universities, firms may reduce uncerta inty inherent from the innovation process, as well as expand their markets, access to new or complementary resources and skills, keep up with evolution of scientific knowledge, and create new technological learning options on future technologies (Hagedoorn, 2001). Communicational Channels Firms that operate in different industrial sectors seem to make use of diverse types of technological and market knowledge; they also seem to attribute different levels of importance to interact and access knowledge developed by the universities (Bekkers, Maria, & Freitas, 2008). Given the diversity of knowledge and the way it interacts with economic processes, it is not surprising that there is also a variety of potential channels through which knowledge is transferred. Perhaps one of the most arc hetypical ways of knowledge transfer is publication of research. By writing down and publicizing research, knowledge becomes public and accessible for many people. However, due to the nature of publications, only explicit knowledge can be transferred. Along with publicizing, academic researchers are often encouraged to visit conferences and workshops. It offers the researchers the advantage to be able to communicate directly with many (international) specialists. When speaking at a conference, scholars rece ive direct feedback from those specialists, enhancing the quality of their work. Moreover, conferences and workshops can also be very important in creating social networks of people within a certain field of science especially at industries with other scie ntific institutes, like Universities. Many contacts between industry and universities seem to be informal(OECD, 2002). A well-known form of knowledge sharing on an informal basis is the flow of information via social networks (blogs, wikis, web 2.0 etc.)(Bongers, den Hertog, & Vandeberg, 2003). Cooperation in R&D is typified by the common formulation of the targets of the research and the long-term cooperation that is established. Only a flow of money from industry to university and a flow of knowledge to the other direction are not enough to be called cooperation in R&D. Some mutual benefits have to occur to establish a long-term relationship. Industry and university can transfer knowledge by cooperating in education. Since education is one of the corebusinesses of the academy, it can also be used to educate employees of the industry (Brennenraedts, Bekkers, & Verspagen, 2006). As we mentioned, there are various scientific communicational channels between universities and industries, there are many other communicational channels with the aim for improving these scientific collaborations. Open-door university day for industries for visiting laboratories and new equipments and their applications, preparing research missions for motivated human resources at industrial organizations, and even researchers changes with some scientific institutes like universities for transmitting new scientific research results and ideas through organizing workshops, conferences, disseminating pamphlets, job training courses with getting support from scientific centers and government in funding, planning, policy making and etc. are some practical ways. On the other hand, nowadays, modern information and communication technologies (ICTs) are powerful medium which facilitating and accelerating these scientific collaborations. In addition, libraries and especially digital libraries, as knowledge centers play important role in developing and improving the scientific knowledge 627

diffusion between organizations. So university libraries have effective role in strengthening the scientific level of any kind of organizations, especially industries. In this regard we concentrate on the role of ICT and university libraries in knowledge sharing. Role of IT and ICT in Knowledge Sharing One of the most powerful forms of informal networks is the new ICTs (discussion forums, e-scholarly societies and etc.). ICTs employment in the universities and industries allows the communication between all the persons and also inter or intra relationship between organizations. These technologies have the potential to eliminate significant barriers to the communication. The influence of the ICT in knowledge sharing has been investigated very much lately by many researchers. The technological hardware is applicable for supporters of the knowledge sharing, because the efficacy of the transference of the knowledge can be improved to increase the transfer and diminish the costs due to the time and at the distance (Albino, Garavelli, & Gorgogline, 2004). So ICTs have been accelerated the scientific communication between universities and industries. Today, Cyber scientific communication has been utilized from this modern technology. E-learning courses, e-publication of recent research results, and other eservices are some of ICTs services which are now available. Digital libraries with preparing e-books, e-journals, databases and various useful services are proper platforms in order to constructing digital scientific collaboration between universityindustry, and other organizations which are interested in knowledge sharing. Role of Unive rsity Libraries and Librarians in Knowledge Sharing With the advent of ICT, there are now drastic changes almost in every sphere of life. Within a decade, it has absolutely changed the working of traditional libraries. Now the libraries specifically, academic libraries have to start changing their role in knowledge management. By collaboration with industrial firms for knowledge sharing, academic libraries can play an important role in the field of knowledge creation and knowledge sharing(Parekh, 2009). The role of university library is much more important in this type of collaboration. The university librarian will be overall in charge of the project. He will act as the link between the university departments and the industrial firms. The librarian will be responsible for maintaining the research works in proper classified way and in recent years change them in digital formats to allow anyone to access them from library webpage and portals. All university libraries should be connected with each other through networks. With such collaborations, government, industry and universities, all three parties can sharpen their entrepreneurial skills to effectuate transformation of nation‘s science and technology landscape. So, libraries are heart of the organizations due to their information resources, and librarians are knowledge administrators who know the importance of information, knowledge transfer and science popularization. Today, organizations should empower their libraries not only for themselves but also for promoting inter- library services, for transferring and diffusion of knowledge. It should be mentioned that it is necessary for all institutes to have a rich library and skillful librarians if they want to have access to scientific resources. In addition, digital libraries in preparing knowledge-based society and distribution of

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information through communities with transmission of knowledge in the digital environment (with utilizing IT and ICT infrastructure) are appropriate options. Conceptual Model University

Industry

Library

Digital Library

E-Learning E-Conference E-Workshop E-Publishing Social Networks Etc.

Ejournals, Ebooks, databeses,...

Library

Digital Library

IT and ICT infrastructure

Figure18. Conceptual Model of University-Industry Knowledge SharingKS

As we mentioned before, IT and ICTs development have been accelerating and promoting communicational channels especially for scientific collaboration. In this conceptual model, industry and university, make benefit from mutual scientific cooperation which is embedded in IT and ICT sphere. This mutual collaboration in cyber space (equipped with IT and ICT facilities) is formed in various communicational channels and in different shapes. As universities are knowledge institutes, so industries can make benefit from their recent research results and make use of them in their organizational operations and processes with utilizing IT and ICT infrastructures. Eservices, like e- learning courses, e-publishing, organizing e-conferences and eworkshops…and getting feedback and informing about industrial organization information needs, through social networks and any other services offered in this cyber space. On the other hand, university libraries and librarians have important role in diffusion of knowledge in both inter and intra organizational environments, but along with IT and ICT development, digital libraries at universities, facilitate knowledge sharing through delivering e-resources like e-books, e-journals, e- information services and even various related databases. Although digital libraries in scientific (like universities), industrial, cultural and any kind of organizations rectify inter organizational information needs, it also promote intra scientific cooperation with other institutes. So in this model, IT and ICT infrastructure strengthen the process of knowledge sharing, especially scientific communicational channels between university and industry, and university library (preferably digital library), can play an important role in knowledge sharing process. 629

Conclusion Knowledge sharing appears to work best when it is seen not so much as a relay race, but as a team sport. It is ‗a game during which the ball moves continuously between the players and in which all players have to collaborate and share resources to win‘. One significant point of this paper is that there is a distinct need to explore the knowledge lying in university research resources. By involving the industrial organizations, centering university library in collaborations, a knowledge life cycle can be moved on and on. Library is the heart of the university. University libraries are the treasure house of knowledge. By involving enthusiastic, fresh and intelligent youth in res earch works, universities and enterprises can contribute in economic, scientific, technical and social development of the country. Government will have to do more to support industryuniversity collaboration. Industries will have to learn how to exploit the innovative ideas that are being developed in the university sector. In general, collaboration with universities influences the decision making procedures in industry firms. Many universities established technology transfer offices (TTOs) to manage and protect their intellectual property. The role of the TTO (sometimes referred to as the Technology Licensing Office) is to facilitate commercial knowledge transfers (or technological diffusion) through the licensing to industry of inventions or other forms of intellectual property resulting from university research. Thus university library would accelerate diffusion of knowledge. All institutes should make use of libraries. With taking advantages from recent ICTs developments, digital libraries could facilitate and accelerate knowledge sharing from universities to industries and vice versa, and even between all institutes not only at national but also at international scale. In addition, government supporting from this important project, funding, policy making, planning, monitoring and implementation strategies could play critical role in betterment of knowledge sharing in all kinds of organizations, especially between universities and industries.

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References Albino, V., Garavelli, A. C., & Go rgogline, M. (2004). Organizat ion and technology in knowledge transfer. Benchmarking an International Journal, 11(6). Bekkers, R., Maria, I., & Freitas, B. (2008). Analysing knowledge transfer channels between universities and industry: To what degree do sectors also matter? Research Policy, 37, 1837– 1853.

Bongers, F., den Hertog, P., & Vandeberg, R. (2003). Naar een meetlat voor wisselwerking. Verkenning van de mogelijkheden voor meting van kennisuitwisseling tussen publieke kennisinstellingen en bedrijven/maatschappelijke organisaties. Den Haag: AWT. Brennenraedts, R., Bekkers, R., & Verspagen, B. (2006). The different channels of university industry knowledge transfer: Emp irical evidence fro m Bio med ical Eng ineering. EC IS. Coriat, B., & Weinstein, O. (2001). The organisation of R&D and the dynamics of innovation. A ―Sectoral‖ view. Sectoral Systems of Innovation. Concept, Issues and Analyses of Six Major Sectors in Europe. Camb ridge: Camb ridge University Press. Davenport, T., & Prusak, L. (1998). Working Knowledge: How organizations manage what they know: Havard Business school press. Dixon, N. (2000). Common knowledge: How companies thrive by sharing what they know . Boston: Harvard Business School Press. Drucker, P. (1993). Post-capitalist society. Oxford, UK: Butterworth Heinemann. Hagedoorn, L. (2001). University-Industry Collaboration and the Development of High -Technology Sectors in Brazil. Paper presented at the Prime-Latin A merica Conference. Retrieved fro m www.prime_ mexico2008.xoc.uam.mx Khosrowjerdi, M. (2011). Designing a viable scientific commun ication model: VSM approach. Library Hi Tech, 29(2), 359-372. Kim, S., & Ju, B. (2008). An analysis of faculty perceptions: Attitudes toward knowledg e sharing and collaboration in an academic institution. Library & Information Science Research, 30, 282–290. OECD. (2002). Benchmarking Industry-Science Relationships. Paris. Parekh, R. A. (2009). Knowledge Sharing : Collaboration between Universities and Industrial Organisations. Paper presented at the International Conference on Academic Libraries (ICA L2009). Schartinger, D., Rammera, C., Fisc hlich, J. (2002). Knowledge interactions between universities and industry in Austria: sectoral patterns and determinants. Research Policy, 31, 303–328. Toffler, A. (1990). Powershift: Knowledge, wealth and violence at the edge of the 21st century. New Yo rk: Bantam Books. van Zyl, A., Amadi-Echendu, J., & Both ma, T. J. D. (2007). Nine drivers of knowledge transfer between universities and industry R&D partners in South Africa. South African Journal of Information Management, 9(1). Xiong, S., & Deng, H. (2008). Critical Success Factors for Effective Knowledge Sharing in Chinese Joint Ventures. Paper presented at the 19th Australasian Conference on Informat ion Systems Knowledge Sharing in Ch inese.

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Comparative Analysis of Scientific P roductions of Iran and Turkey in Web of Science for the Period 2005-2010 Roghayeh Ghorbani Bousari

Shahrzad Sharifi

[email protected]

[email protected]

Mohaddeseh Taghipour Moazzen- Langaroudi [email protected]

Department of library and information science, Tonekabon branch, Islamic Azad University, Tonekabon, Iran

Abstract : Surveying the status of scientific productions of countries in valid citation databases and the rate of their collaboration is considered as one of the most important evaluation indicators in Scientometrics. The aim of this paper is to compare and analyze the scientific productions of Turkey and Iran in the ―Web of Science‖ (WOS) during 2005-2010. The results indicated that Turkey had high productions than Iran. Therefore, Turkey ranked above Iran qualitatively. Most scientific productions of the two countries published in English language and in article format. Information was separately collected and analyzed in aspects of Source titles, publication yea r, subject areas, institutions, authors and counties who have most scientific productions of Iran and Turkey in WOS. Although scientific productions of top Iranian author were fewer than Turkish counterpart, his H-index was higher. The rate of scientific collaboration of Iran and Turkey has increased for the period of 2005-2010. In common scientific publications, subject areas, top authors analyzed. They had scientific cooperation with 144 countries, of which the highest share of the cooperation is with USA. The results of the study showed that although Iran and Turkey are neighboring countries, the rate of their scientific cooperation was very low. Following the result of this

research, scientific policymakers should invest in the top scientific fields and common scientific productions of either country in the future. Introduction Scientometrics is one of the most efficient ways to evaluate and assess research situation. Hence in recent decades, it is one of the important fields in recent studies. To survey countries‘ scientific productions in valid citation databases and analyze coauthorship among their researchers are the most important indicators in Scintometrics studies. In last two decades, Turkey, as one of the 57 developing countries, not only has increased their researchers and its research institutions, but also it has had more scientific productions indexed in valid databases. Turkey has had high potential to 632

produce scientific productions and co-authorship in national and international levels too. In recent years Iran, as one of the developing countries, the growth rate of its productions in international journals has gained the highest rank globally. A recent policy of government officials to increase participation of Iranian researchers in universal scientific publications has substantially increased the number of Iranian scientific publications in international journals. Increasing science in these two countries may have different effects on their scientific outputs. Hence, this article tries to evaluate and analyze scientific productions of Turkey and Iran to recognize their place in scientific publications, Co-authorship and their trends in scientific collaboration during 2005-2010 in universal level base on Web Of Science Database. Methodology This article is descriptive-analytical research that surveys scientific publications of Iran and Turkey by using the indicators of WOS Database during 2005-2010. This study was conducted through library method to collect data, and the data was analyzed via comparative method. The data collected from WOS by searching this formula in advanced search‖ CU=IRAN‖. Then, we limited the results to 2005-2010 years. By using the software of ISI database, we analyzed these data base on the number of papers, document types, language, publication year , subject areas, institutions, authors and Co-authorship. This method was used to collect scientific productions of Turkey too. The data in this account was collected from WOS during March 6 to 16, 2011. Purpose of the Study The most important purpose of this research is the study and comparison of the situation of papers of Turkey and Iran published in Web Of Science database during 2005-2010. It was also tended to analyze different fields of them according to the numbe r of papers, document types, language, subject areas, institutions, authors, Co-authorship and publication year. Moreover, the analysis and comparison of their scientific productions by each field are also considered as the objectives of this research. Significance of the Study A comparative study of the situation of Iranian and Turkish papers can be influential in leading the major research plans of Iran to achieve its regional goals and outpace the countries in the region; however, it must be taken into consideration that useful information can also be obtained from the study which have had considerable growth according to many indicators of research and development. Some of the most important benefits of this study are: to recognize the priority of subject areas, institutions, authors, Source titles, document types in each country to use them in later researches which would deal with scientific developments of these countries. Iran and Turkey have been chosen in this study because they are developing countries and their population is near together. These countries as two Asian countries have had considerable improvements considering various indicators of sciences and technology.

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Results Analysis Table 1. Comparison of scientific productions of Iran and Turkey in WOS by publication year during 2005-2010 Year

Turkey

Growth

Growth

Iran

2005 2006 2007 2008 2009 2010 Total

17725 17912 21924 23211 25366 23247 129385

25.54 1.06 22.4 5.87 9.28 -8.35

49.15 14.36 45.03 26.26 20.2 1.85

6442 7367 10684 13492 16314 16615 70914

In table 1, the number of scientific productions of Iran and Turkey indexed in WOS and the rate of their growth in each year (from 2005 to 2009) can be compared. Ranking countries according to ―total number of citations‖ and ―to tal number of productions‖ can be considered as the qualitative and quantitative indicators of papers which show their impact and usage. Although the table indicates that Turkey had the most number of scientific productions in comparison with Iran, Iran had the most number of citations in comparison with Iran during 2005-2010 in WOS. The results showed that the growth of Iranian scientific productions was in two years, 2005 and 2007 with 49.15 % and 45.3 % respectively. But the growth of Turkish scientific productions belonged to 2005 and 2007, with 25.54 % and 22.40 % respectively. The lowest rate of scientific production‘ growth in Turkey has seen in 2010 with negative growth of 8.35 %. Table 2. Comparison of scientific productions of Iran and Turkey in WOS by document type during 2005-2010 Document Type

Turkey

Iran

Article Meeting Abstract

78427 10221

58891 6954

Proceedings Paper

3237

1917

Letter

3679

1147

Review

1944

958

Ed itorial Material

1818

707

Correct ion

271

232

Book Review

294

60

News Item

28

27

58

18

Art Exhib it Review

---

1

Reprint Software Rev iew

7 3

1 1

Film Rev iew

4

---

Theater Review Bibliography

4 3

-----

Biographical-Item

634

Music Performance Review

1

---

TV Review, Rad io Review

1

---

Comparison of the countries under the study on the basis of the data at WOS by document types showed that most of their scientific productions have been published in form of article. Turkish scientific productions had more variety than Iranian ones. Base on 34 document types of WOS, all scientific production of Turkey and Iran have been published in 18 and 13 formats respectively. Results also showed that Turkey published some articles in Art Exhibit Review art forms, including film review, theatre review, bibliography, music performance reviews, TV and radio review but these forms had no place in Iranian scientific productions. Table3. The comparison of scientific production of Iran and Turkey in WOS during 2005 2010 by language Language

Turkey

Iran

language

Turkey

Iran

Ru manian

4

1

English

95078

70770

Russian

42

1

Arabic

----

61

Serbian

1

1

Persian

-----

25

Ukrain ian

1

---

French

42

16

Croatian

15

----

German

105

10

Portuguese Czech Slovak

7 3 3

------------

Spanish Turkish Chinese

32 4651 3

8 7 6

Slovene

3

-----

Italian

2

3

Japanese

2

---

Polish

2

2

Catalan Greek

1 1

---------

Welsh Danish

----1

2 1

Furthermore, table 3 shows that total number of scientific productions of Iran indexed WOS during 2005-2010 were in 15 languages, and most of scientific productions were in English and other scientific productions have published in other languages (0.019 %). Meanwhile, there‘s a wider variety of languages in scientific productions of Turkey. Total scientific productions of Turkey have published in 25 languages. Most of scientific productions were in English (4651 papers).

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Table4. Comparison of Iranian and Turkish scientific productions in WOS during 2005 -2010 base on subject area Country

Subject area

Record

% of

Iran

Chemistry, Multidisciplinary Materials Science, Mult idisciplinary Engineering, Electrical & Electronic

4477 3265 2940

6.30% 4.62% 4.19%

Engineering, Chemical Chemistry, Physical Surgery Clin ical Neuro logy

2705 2666 5623 3782

3.80% 3.70% 5.60% 3.70%

Pediatrics

3686

3.60%

Medicine, General & Internal Environmental Sciences

3664 3491

3.60% 3.40%

Turkey

The countries under the study were evaluated according to the subject areas which are mentioned in table 4. This table reveals the most produced papers in 5 subject areas of Iran and Turkey indexed in WOS. According to the table, Chemistry, Multidisciplinary had higher papers (4477); and ―materials science and metallurgy, electrical engineering and chemistry engineering had respectively more papers. Meanwhile, this situation varied from Iran. Surgery had higher papers (5623) and it‘s following, Turkish scientists were also interested to produce respectively some papers in Clinical, Pediatrics, Neurology, Medicine, General and Internal‘ areas. We can conclude that researches priorities in Turkey and Iran are mostly from Medical and engineering sciences respectively. Table5. Comparison of scientific productions of Iran and Turkey in WOS during 2005 to 2010 by cooperating countries country

Record

% of

USA Canada England

2986 2000 1800

4.21% 2.82% 2.54%

Germany

1169

1.65%

Iran

Australia

1005

1.4172 %

Turkey

USA Germany England Italy France

6460 2138 1731 1304 1228

6.46% 2.14% 1.73% 1.30% 1.23%

According to the Table5, both countries had scientific cooperation with 144 countries, of which their highest rate of cooperation goes to the US. Iran has produced respectively 636

2986, 2000, 1800, 1196, 1005 co-authorship with USA, Canada, England, Germany and Australia. Turkey, in its turn, cooperated with USA, Germany, England, Italy and France with. 6464, 2138, 1731, 1304, 1228 respectively. The country under the study had 14120 and 15937 co-authorship with other countries. Generally, considering the global ranks of these countries basis in their co-authorship in WOS, they placed at average level of scientific cooperation with other countries. So, they can expand it and increase the number of their scientific productions together. Table6. Comparison of scientific productions of Iran and Turkey in WOS during 2005- 2010 by cooperating organizations Institutions

Record

% of

h-index

Times Cited

UNIV Tehran UNIV Tehran M ED SCI Islamic Azad UNIV

7939 5727 5183

11.20% 8.07% 7.31%

41 35 33

24,173 16,056 10,969

Sharif UNIV Technol

4327

6.10%

30

13,820

Tarbiat Modares UNIV Hacettepe UNIV Istanbul UNIV Ankara UNIV GAZIUNIV EGE UNIV

3822 5962 5555 5478 5035 4449

5.39% 5.97% 5.55% 5.48% 5.03% 4.44%

27 28 28 28 22 29

10,043 11,379 9,230 9,437 8,601 9,922

According to the 70914 Iranian‘scientific productions in WOS during 2005-2010, Tarbiat Modares had 7939 papers and it ranked above. Tehran Medical Science University, Islamic Azad University, Sharif University Technicol, Tarbiat Modares University have ranked the following the Tarbiat Modares with 5727, 5183, 3822, 4327, 3822 papers respectively. Generally, these five universities produced 26998 papers that were equaled with (38.07 %) of total Iranian scientific productions (129385). The top five Turkish institutions with highest rate of scientific productions in WOS has produced 26479 (20.47 %) of Turkey‘s scientific productions. Hacettepe University ranked in first place with 5962 papers, and Istanbul University, Ankara University Gazi University and Ege University with 5555, 5478, 5035, and 4449 have ranked papers respectively. Table 8. Comparison of top 5 Iranian and Turkish‟ authors base on Qualitative and quantitative dimensions in WOS during 2005-2010 Author

h-index

Record

34

Times Cited 4,530

Ganjali, M R

370

0.52%

Heravi, MM

25

2,403

303

Norouzi, P Larijani B Dehghan, M Yilmaz, M Yilmaz, S

32 15 23 14 10

3,578 1,025 1,995 1,175 595

299 278 272 455 398

0.4273 % 0.42% 0.39% 0.38% 0.46% 0.40%

637

% of

Buyukgungor, O

10

670

362

Haberal, M Yilmaz, O

8 10

454 426

306 298

0.3620 % 0.31% 0.30%

The table revealed that top five Iranian and Turkish authors who ranked in the first places base on their scientific productions indexed in WOS, separated by number of scientific productions, number of citations, and percentage of the ir papers. Furthermore, table 10 shows that GANJALI has placed in the first with 370 papers among other Iranian authors. So, he had higher scientific productions in Iran. HERAVI, MM, NOROUZI, P, M have placed in next places with 303 and 299 papers respectively. YILMAZ, M, YILMAZ, S , and BUYUKGUNGOR, O with 455, 398 and 362 papers had more higher scientific productions among other Turkish authors respectively Although Turkish authors had more scientific productions in comparison with Iranian authors, Iranian authors had more higher citations and H-Index. Although the comparison of most produced authors of Iran and Turkey in WOS during 2005-2010 show that most produced authors of Iran had lower scientific productions than his Turkish counterpart, they had higher citations and hi- index. GANJALI, MR had most cited papers (4530). He achieved the high indicator among Iranian and Turkish scientists with H-index of 34. In addition, we surveyed first five ranking source title in two countries. The results indicated that the most scientific productions of Iran was ASIAN Journal of Chemistry; Applied Mathematics And Computation; ACTA Crystallographica Section E-Structure Reports Online; International Journal of Psychology; and Journal of Applied Polymer Science had 797, 562, 546, 408, 400 papers respectively. In other words, these sources have produced 1.12% of total scientific productions of Iran. Most of Turkey‘s scientific productions (941papers) were published in TURKIYE KLINIKLERI TIP BILIMLERI DERGISI. Asian Journal OF Chemistry; FEBS Journal; Journal of Animal and Veterinary advances; ANADOLU KARDIYOLOJI DERGISI-The Anatolian Journal Of Cardiology with 867 , 865, 704, 601 respectively. Table 9. Co-authorship of Iran and Turkey in WOS during 2005-2010 based on publication year year

2010

2009

2008

2007

2006

2005

Total 313

93

78

81

50

11

----

Table 9 showed that scientific collaboration of them was 313 papers, with highest cooperation in 2010 (93 papers) and lowest collaboration in 2005 with no scientific production. One of the important points in this regard is that the rate of their coauthorship has been increasing during the study time period.

638

Table 10. Iran and Turkey‟s co-authorship during 2005-2010 in WOS, based on subject areas Subject area

Record

Subject area

Record

CRYSTA LLOGRAPHY ENVIRONM ENTA L SCIENCES

25 21

ENGINEERING, CIVIL CHEMISTRY, MULTIDISCIPLINARY

16 13

PHYSICS, PARTICLES & FIELDS

21

MATHEMATICS, APPLIED

13

PHYSICS, MULTIDISCIPLINARY

20

SURGERY

13

ENGINEERING, ENVIRONM ENTA L

17

CHEMISTRY, INORGA NIC &

12

NUCLEA R

The results showed that Iran and Turkey had most collaboration in CRYSTALLOGRAPHY area with 25 papers. in the next places, ENVIRONMENTAL SCIENCE; PHYSICS, PARTICLES & FIELDS; PHYSICS, MULTIDISCIP LINARY ENGINEERING, ENVIRONMENTAL; ENGINEERING, CIVIL had 21, 21, 20, 17 and 16 papers respectively. Most of their co-authorship belonged to engineering areas. Most of Turkey‘s scientific productions belonged to medical sciences and revealed their scientists‘ interest to the mentioned subject area. Iranians had a good chance to increase their collaborations with Turkish scientists in the mentioned subject areas. Table 11. Top ten Iranian and Turkish authors in co-authorship during 2005-2010 in WOS Author

Record

Author

Record

Ghademi, M

28

Akchurin, N

14

Soylak, M Buyukgungor, O Akkurt, M Shokrollahi, A

28 24 20 17

Akgun, U Banerjee, S Bencze, G Bodek, A

14 14 14 14

Table 11 showed that top 10 Iranian and Turkish authors in co-authorship during 20052010. According to the findings, GHAEDI, M from Iran and SOYLAK, M from Turkey had 28 cooperation with the highest number of co-authorship. Conclusion Findings showed that Turkey had more scientific productions in comparison with Iran in WOS during 2005-2010. However, most of their collaboration was with UAS, Turkey has cooperated 46% more than Iran with it. Moreover, except of USA, Iran had more collaboration with Germany, England and Italy; and Turkey with Canada, England respectively. Their total co-authorship was 313 papers and the rate of their scientific collaboration has increased for the period of 2005-2010. Authors who had most co639

authorship in two countries were Ghademi and Soylak with 28 papers. We analyzed authors who had most scientific productions of Iran and Turkey respectively too. The results showed that Iranian authors have had high H- index in comparison with Turkey authors. But in quality criteria, Turkey‘ authors have ranked better. The data regarding subject areas, Chemistry, Multidisciplinary and Materials Science areas have had more scientific productions in Iran. So, they have gained 4477 and 3265 papers respectively. This survey was done about Turkey, and findings showed that Surgery and Clinical Neurology areas with 5623, 3782 papers have had more scientific productions respectively. Chemistry areas had 52% of scientific productions in Iran. But medical areas had 66% of scientific productions in Turkey. In fact, Iran in chemistry areas and turkey in medical areas had most scientific productions. Most of scientific productions of Turkey and Iran were in English language. But the varieties of turkey productions were more than Iran (25%). Most co-authorship of them was in Crystallography and Environmental Sciences areas with 25 and 21 papers respectively. In addition, scientific productions of Turkey and Iran analyzed base on publications year and results showed that turkey‘ scientific productions has increased during 2005-2009. But their productions have reduced in 2010 about 119 papers. While Iranian‘ scientific productions have increased during 2005-2010. In ranking Iranian Institutions found that Tehran University has had 11.2% of its total scientific productions with 7939 papers and h- index 14. Hacettepe, Istanbul and Ankara universities have had 5962, 5555 5478 papers with 28 h- index in turkey respectively. These universities have had about 6% of turkey productions. At the end, findings indicated that two countries had most scientific productions in article form. Hauptman (2005) stated that scientific collaboration and having co-authorship are the important steps for producing articles in different journals. Liao (2011) indicated that scientific collaboration show scientists‘ levels and their science networking relations in national and international levels. Generally, According to the findings of this article, scientific collaboration of two countries was very low and their co-authorship was very fewer than total of their scientific productions. Intrinsic and extrinsic factors to the research process can be useful for encouraging Iranian and Turkish scientists. Policymakers should present the initiative for collaboration of Iranian and Turkish scientists in each country (Gupta, Dhawan, 2003). We hope that Turkey and Iran expand their scientific productions in different fields in Web Of Science. So, it is necessary to held workshops, educational programs to increase scientific productions of scientists and encourage them with rewards systems to cooperate scientists together. References Gupta, B. M., Dhawan, S. M. (2003). India‘s collaboration with People‘s Republic of China in Science and Technology: A Scientometric analysis of coauthored papered during 1994-1999, Scientometrics, 57(1), 59-74. Huuptman, R. (2005). How to be a successful scholar: publish efficiency. Journal of Scholarly Publishing, 36(2), 115-119. Liao, C. H. How to improve research quality? Examining the impacts of collaboration intensity and member diversity in collaboration networks, Scientometrics, 86, 747-761.

640

Authorship Pattern and Prominent Authors in Strategic Management: A Bibliometric Study Sada Bihari Sahu [email protected] Manager(Library), Central Library, Small Industries Development Bank of India(SIDBI), Lucknow-226001, UP, INDIA

Introduction: Studies related to authorship trend and collaborative researches are considered as an important facet of modern science. There is no dearth of literature for such studies. It is well established fact that more and more research reported in the literature is of collaborative in nature. Owing to the pressures of information explosion, author of these days do not hesitate to cooperate and conduct collaborative research. In this paper, the investigator has made an attempt to study the bibliographical forms of documents, authorship pattern and prominent authors in the field of strategic management. Objective: 1. Growth of literature in the field of Strategic Management during the period of 2002-2006 2. The most popular bibliographical forms of documents 3. The authorship pattern of publications; 4. The most popular author who has significant contribution to the field; Methodology: The methodology chosen for this work is based on the analysis of footnotes and the bibliographic references cited in each chapter of the Strategic Management Journal, Vol. 23-27, 2002-2006. The reference given at the end of each chapter found from the publisher John Wiley‘s website of strategic management journal, Vol. 23-27, i.e. http://www3.interscience.wiley.com/cgi-bin . Each citation is then transfer to MSExcel data. Each citation will be analyzed to ascertain the various bibliographical forms of the document like books, periodicals, conference proceedings, workshop, theses etc. Other aspects of study like authorship pattern, ranking of authors etc. The study is based mainly on the quantity rather than the quality of the documents. Analysis: Ranking of Documents:

From the table 1 and figure no.1, it has been observed that, there are a total of 22409 citations in the Strategic Management Journal from 2002 to 2006, the period we have chosen for our study. Out of the total, 16196 citations are from journals accounting 72.27 per cent of total citations, which holds the first position among the bibliographical forms. Books occupy second position with 5668 citations accounting 25.29 per cent. 641

The third position goes to working papers only with 254 citations accounting 1.13 per cent. we can conclude that journal is the most popular form of documents among the researchers in the Strategic Management with second most popular form is books. Table-1 (Ranking of Documents)

Sl. No 01 02 03 04 05

Rank 01 02 03 04 08

Bibliographic Form Journals Books Working papers Theses Others Total

No.of citations 16196 5668 254 74 217 22409

Percentage Cumulative Cumulative of citations citations percentage 72.27453 16969 72.27453 25.29341 21864 97.56794 1.133473 22118 98.70141 0.330224 22192 99.03164 0.968360 22409 100 100

R a n k in g o f D o c u m e n t s

O th e rs 2%

0%

Books 25%

J o u rn a ls

Books

0% O th e rs

J o u rn a ls 73%

Figure-1 (Ranking of Documents) Authorship Pattern of Journals:

Studies of the authorship are varied and many. First among them is to study the nature of authorship pattern, which will indicate whether the research outputs are carried by a single person or in a collaborative manner. So the study is concerned with the investigation on solo-authorship or collaborative authorship in the specific subject. Table-2 (Authorship Pattern of Journals)

Sl. No 01 02 03 04 05

Rank 01 02 03 05 04

Authorship Patte rn Joint Single Three Multi Corporate Total

No. of Citations 7221 6107 2273 520 75 16196

Percentage Cumulative Cumulative of Citations Citations Percentage 44.58508 7221 44.58508 37.70684 13328 82.29192 14.03433 15601 96.32625 3.210669 16121 99.53692 0.463077 16196 100 100

642

A u th o r s h ip P a tte r n o f J o u r n a ls

N o o f C it a t io n s

8000 6000 4000

N o . o f C it a t io n s

2000 0 J o in t

S in g le

T h re e

M u lt i

C o r p o r a te

T y p e s o f A u th o r

Figure-2 (Authorship Pattern of Journals)

Ranking of Authors (Journals): Table-3 (Authorship Pattern of Journals)

Name of Author

01 02 03 04 05 06 07 =07 =07 08

Kogut B Barney JB Teece DJ Levinthal DA Eisenhardt KM Porter ME Gulati R Henderson RM Hitt MA Hoskisson RE Others less than 132 citation

No. of Citatio ns 230 226 171 168 149 143 136 136 136 132

Cumulati ve Citations 230 456 627 795 944 1087 1223 1359 1495 1627

17569

19196

Percentage of citations

Cumulative percentage

1.420104 1.395406 1.055816 1.037293 0.91998 0.882934 0.839714 0.839714 0.839714 0.815016

1.420104 2.81551027 3.87132652 4.90861968 5.82859992 6.71153398 7.55124749 8.390961 9.23067451 10.0456906

91.524275

100

R a n k in g o f A u th o rs in J o u rn a ls

250 200 150

N o . o f C ita tio n s

100 50

H itt M A

G u la ti R

KM

E is e n h a r d t

Teece D J

0

Kogut B

01 02 03 04 05 6 07 08 09 10

Rank

N o . o f C it a t io n s

Sl.No .

N a m e o f A u th o r

Figure-3 (Authorship Pattern of Journals)

643

Conclusion: Inferences obtained from this analysis are as follows: 1. Researchers used highest number of journals i.e 16196 (72%) and followed by books i.e. 5668 (25%). So emphasis should be given to journals and books. 2. Researchers prefer to write jointly in journals and it indicates there is collaboration or team research in journals. 3. It is found that the top five authors in journals are Kogut B, Barney J B, Teece DJ, Levinthal DA and Eisenhardt KM accounting for more than five per cent of total citations. 4. This study shows the trends of research in the field of Strategic management and prominent contributors in this field.

644

Web Usability Evaluation of Iran National Library Website Sedigheh Mohamadesmaeil Somaye Kazemi Koohbanani

[email protected] Assistant Professor of Department of Library and Information Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran.

Department of Library and Information Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran

Abstract: It is obvious that each information service exists for the sole purpose of satisfying its users. All its activities, services and products, ie., the totality of its functions are geared towards this purpose. How well this purpose is served is a measure of the effectiveness and usability of that information service. According to the value place of National Library as the golden gateway to access quick easy and unlimited information, considering its web usability can be useful. This paper aims to provide knowledge about the metrics of web usability evaluation in Iran National Library Website. Taking a critical approach, this article examines Iran National Library Web Site, in order to conduct a reliable usability assessment. The tools for data collection was an explanatory checklist, developed by the researchers based on the review of the literatures - a practical way with providing appropriate solutions deals - consisted of 11 log evaluation criteria and 160 components. desired website was measured by it. In order to analyze research findings,

descriptive statistics (frequency and percentage) used for this purpose. In general, the results showed that the web usability evaluation of Iran National Library Website (In comparison with the overall assessment of

compliance of all criteria, in index website, with 663 points) is 594 points (88/5 percent). While the design of National Library Websites should be completely based on functions that support of National Library‘s major aims, especially in information retrieving as well as rendering public information services. Identifying the advantages and disadvantages of the method and employing it in real research environments to assess the usability of national library web site can not only be a useful exercise to measure the extent to which the studied website had the desired elements and features, but can also be a starting point for further discussions on the reliability of usability evaluation method which can also be applied to other types of web site. Keywords - Iran, Web Usability, Evaluation, Checklist, National Library, Web site, Information searches, Information retrieval.

Introduction The World Wide Web (www) is one of the most important information service providers on the internet and one that is vastly growing compared to other facilities and services offered by the internet. The advent of the World Wide Web brought with itself a revolution in providing of information services to users across the globe such that 645

today an enormous amount of information is available on the Net. On the other hand, its capability to present colorful graphic images, audio and video along with text and links to other contents can be thought of as the most important reason behind the everexpanding use of the internet.(Hassanzadeh, 2010) Library is an organism which applies any modern technology for the purpose of dissemination of information and provision of services to given users. In the modern era, the use of web and related technologies has become widespread and libraries have been no exception, using the phenomenon with a view to remove the obstacles facing the libraries and enhance the ways of quick and easy information retrieval, having no need to physical presence of users in the library. It is the necessary for the web users to assess consistently the levels of usability of any resources available on such websites (Mohamadesmaeil, 2004). The present research was conducted in 2010, mainly aims to conduct Web Usability Evaluation of Iran National Library Website. The goal is to determine whether and how the national library of Iran is usable, how is met the criteria and standards set and defined in this regard and how website design standards have been met in their design. Research methodology The present research applies library (attributive) and evaluative survey method. Put it another way, the attributive method was applied to develop the checklist of 11 criteria s and 160 components and features (through a study of domain related texts and resources) and the evaluative survey method was applied to assess the usability of the website (according to existing criteria s in the checklist). Nevertheless, for further assurances about the content validity, Cronbach's Alpha Coefficient, a renowned method of assessment of internal consistency of measurement instrument was applied to test the reliability. The obtained alpha coefficient was 0.96, pointing to a strong reliability. Descriptive statistics (frequency, percentage and mean) was applied in order to analyze the research findings and describe the situation of the websites in question. The checklist in question applies the double scales of existent and non-existent (Yes □/ No –) and the points given were Yes=1 and No=0. Findings To answer the research questions, it is first necessary to extract the points of the effective criterias and features concerning the website usability from the model website. As noted earlier, the present research follows an imaginary model website which has incorporated all the 160 features so that it could be applied as a yardstick for assessment of other websites n the population under study. The research finds that according to the model website, the total points for observance of all criteria s and features of website usability are 663 of which 107 points (16 percent) belong to data credibility, 32 points (5 percent) belong to data accuracy, 26 points (4 percent) belong to currency, 21 points (3 percent) belong to surface of coverage and special audiences, 31 points (5 percent) belongs to interactive and interchangeable views, 34 points (5 percent) belong data objectivity, 166 points belong to general criteria of website navigation (25 percent overall), consisting of 9 points (1 percent) for features of explorer's title, 22 points (3 percent) for features of the page's title, 92 points (14 percent) for textual and metatextual links, 6 points (1 percent) for internet logo, 25 points (4 percent) for site map 646

and profiles, 12 points (2 percent) for internal search engine, 64 points (10 percent) for non textual views, 77 (12 percent) points for accessibility, 99 points (15 percent) for efficiency and 6 points (1 percent) for appearance. non textual views 10%

site map internal search 4% engine internet logo 2% 1%

accessibility 12%

textual and meta-textual links 14% page's title 3% interactive and objectivity interchangeable 5% explorer's accuracy title views currency 5% 1% 5% 4%

efficiency 15% credibility 16% surface of coverage and special appearance audiences 3% 1%

Diagram 1- Distribution of points (at percent) concerning the levels of observance of the criteria s and features inviolved in te model website's (separated by every criteria )

Findings The findings, concerning all data criteria s and features of website are shown in table 1. Table 1- Checklist of web usability of national library website of the Iran

     

Data credibility

Features

Frequency

Author's name mentioned Author's details (qualifications, famousness, credibility, etc) mentioned Contact of authority or organization in charge of website content Authority or organization in charge of website content holding official accreditation Details of authority or organization in charge of website content mentioned List of major organizers and their details available Contact of page author mentioned (including postal address, telephone number, email, etc) Way to verify author's details available (his/her experiences in a special domain, membership in professional organizations, etc) 647

Ite ms

Iran

National Library

1 2 3 4 5 6 7 8

            88

Title of organization in charge of website content mentioned Sponsor's nature stated Organization's establishment date mentioned Copyright of information resources offered in website stated (name mentioned) Title of organization behind website mentioned Checklist of organizations and websites endorsing the website mentioned Aim of design and publication of page stated List of names and details of individuals in charge of supervision over the organization available List of print sources published by the organization available Title and logo of the website organization put on every page Logo of organization visibly and distinctively shown Title of organization put on top Name of copyright holder stated National flag put on website 107

Iran

National Library

9 10 11 12 13 14 15 16 17 18 19 20 21 Total points Data accuracy

Features

Items



Frequency



Features



Date of latest revision of page content stated The first date an information source (at any format whatsoever) is put on the web page stated Time intervals time-sensitive information is updated stated



23 24 25 9,

26

Total points Data currency Item s

Iran

No misspellings or grammatical errors Book title and bibliographical details of main  source stated Reference available to confirm the editor checks  data accuracy in sources review process Visible and clear-cut titling f graphs, diagrams  or tables in a page 32 32 National Library

Frequency

648

27 14

28 40

29

Statistical information available on page Date of statistics collection stated Dates presented at an international format 26

30 31 32

Features

Fequency

Title and type of existing sources on the page specified  Specific audiences of the page stated Estimated date of completion of a page under construction stated 17 21 National Library Ira n



  

    25

Iran    

34 35 Total points Inte ractive and interchangeable views

Features

Frequency

Clear-cut system available for user feedback Clear-cut system available for users to demand more information from organization Necessary waiting time before reception of organization's response stated Membership capabilities available Clear-cut system available for user membership Users aware about cookies mechanism in website FAQ system answers user's questions quickly and accurately Capabilities available to answer user's questions Users know when to receive answers 31

36 40

37 38 39 40

34

41 42 43 44 Total points Data objectivity

National Library



33

Ite ms

Iran

National Library

Ite ms

Total points Surface of coverage and specific audience

Features

Frequency

Relationship between author and organization or individual in charge of website known Author's opinion clear Opinion of individual or organization in charge of data provision known Page free of ads Content available about aims of individual or organization collecting the information Explanations available about information on page unrelated to the organization's services 649

Item s

   21

1,10,15,16,23,43

45

1,15,16,4,23

46

1,8,15,4,38

47

1,10,15,18

48

1,6,17,15,27,30

49

1,15

50

28

27

52

40,39

53

Total points Navigation

A: features of explorer's title

   

Stating name of organization or individual in charge of the website content Designating a page as homepage Explorer's title being short Explorer's title being exclusive to the website

9

9

Total points

B: features of the page's title

Frequency

Ira n

Iran

National Library

51

  

Frequency

Designating the page as homepage Designating a page as belonging t a specific website (at least by means of single logo) Page's title being short

1,1,21

54

1,15,26 1,15 15

55 56 57

1,10,34,15,16,21

58

1,10,45,15,26

59

15,33

60

34,40,23,15

61

22,27

62



Key words used in titles



Appropriate title used for precise introduction of content

18

22

Total points

Iran

Page's title being exclusive to the website

C: textual and meta-textual links

Frequency

 

Repeated links having a coherent appearance Easy navigation between pages possible Internal direct links laid out harmoniously in the page Link's title consistent with what it leads to Links keep operating consistent with preset goals Users find website/ page's structure comprehensible Pages' layout consistent with one anther Shortcuts available in homepage for easy an effective access of users to most visited pages Access possible from homepage to main sections of website

      

650

Ite ms



15,39,27

Ite ms



Information content distinctive from content for fun Information not resembling ads in terms of type of design Page free from irrelevant concepts or information 34

27,29,22

63

Ite ms



1,6,10,4,15,38,25,26 27,30,24,1,6,10,4,15,21,25

64 65

1,6,30,10,4,15,21,26

66

1,15,17,21,25,26,35

67

1,15,16,17,18,26

68

27,40,32,1,6,4,15,21,26

69

27,1,10,15,21,25

70

1,15,21,26,38

71

1,10,39,15,21,26

72

89

92

Total points

D: internet logo of page

Frequency

Internet logo put on the page's main body Short internet logo No change in internet logo Users admire internet logo

1,15,21 27 27 30 Total points Frequency

89 90 91 92

  

     

Ira n



Ira n

   3



Iran

   25

6 E: site map or profiles Site map or profiles available on the homepage or link pages Site map showing main subjects of website Profiles or site map easily readable Profiles or site map logically organized 25 F: internal search engine

 

Internal search engine available Information retrieved by the internal search engine are relevant 651

73 74

1,15,19

75

15,26

76

30

77

27

78

40

79

27,32

80

42 30 24,27

81 82 83

24

84

22,30 45

85 86

22

87

40

88

27,39,1,6,15,21,26,37

93

1,6,15,21,26,37 1,6,15,21,37 1,27,6,15,21,37 Total points Frequency

94 95 96 Ite ms



1,10,15,21 15,21,25

Ite ms



No underlined texts used beside links Bookmarks entitled appropriately Intended information items can be chosen from a table of content instead of necessarily typing them Website's logo link put on the homepage Links identified through underlining or use of a special color Page's title consistent with link Visited links distinguished from unvisited links by a change of color Homepage link laid out in order to identify the homepage Number of links logical Image linked to related page Quick identification of links possible Appropriate expression used for the links (expression like 'click here' or 'more' not used) Textual link put in the beginning of paragraph Page's title consistent with link Visual signs like color and size used for signifying a relation between the links Sufficient texts used to explain a link

Ite ms

 

1,4,15,21,25,28

97

1,4,15,21,25,28

98

Total points

12

 



       51

Non textual vie ws

Features

Frequency

No use of irrelevant animations Graphic images and audiovisual files used to enhance website usability Essential software and how to access them stated Replacement available for files in need of essential software for access of all users to information Essential explorer software, or a specific version of it, identified for access to web pages (if necessary) Textual replacement available for all users for the existing images and graphics on the page No use of flash technology (flashing signs) User aware that a sizeable file would open by following a specific link Graphic images relevant to content of text Text added to images for further comprehension Appropriate ALT tag for images Graphic images follow a specific goal 64

Features





Accessible through Internet Explorer 6.0 software Accessible through Netscape Navigator 6.5 software Accessible through Mozilla Bird 0.7 software Accessible through Opera 7.2 software Accessible through general search engines Page size less than 50 kb Support possible for user's operational platform (Windows 2000, XP, ME, AND Linux REDHAT Standard fonts used



All parts of website are visible

     

27,39,1,6,15,21,26,35,42,37

99

22,40,1,10,15,21,26,37,35

100

1,10,15,21,26,37,35

101

1,10,15,21,26,37,35

102

1,10,15,26,35,37

103

1,10,15,27,21,26,37,35

104

1,10,30,45,35,37,42

105

1,15,21,26,35,37

106

27 27 30 32 Total points

107 108 109 110

Accessibility

Iran

National Library

Item s

Iran

National Library

Frequency

652

Item s

12

1,6,10,15,16,25,41,37,43,35

111

1,6,10,15,16,26,41,37,43,35

112

1,6,10,15,16,26,41, 37,43,35 1,6,10,15,16,26,41, 37,43,35 1,15,16,17,43,25,26 1,4,39,26,35,38,41

113 114 115 116

10,6,15,26

117

4,15,35,38,27,30,24,43,32,34,41

118

15,38

119

 

User gets access to essential resources in less than three clicks Information laid out in inverted pyramid (for quicker access to more important information) Website easy to use for all users (nascent and professional alike)

 77

Iran                   -

120

45

121

27,24

122

39,30 Total points

77

National Library



27

Efficiency

Features

Frequency

Standard color used in page design Appropriate titling of website/ page capabilities Website identifiable based on domain title Website language consistent with user culture and mood Marginal definitions available to describe existing information items on the page User aware about an operation underway Website's information consistent with mission statement All website capability operable in the website Capability available to cancel operation in the website User's position obvious in the website User's mental record taken into consideration Website proves attractive Tools considered to assist the user All major headwords used in the pages stated Easy scrolling of homepage Information printable without any changes necessary in the computer system regulations Number of visitors in a specified period of time stated Short expressions used for explaining the items of page User's attention attracted through design type (color, font, etc) Content of page not tightly put together rather spaced in between Easy access to Help section Prices offered to users in case of for-profit websites 653

42,37,35,16,21,10,1,34,32,39,24,27 1,15,21,17, 25,26 15,28,25,19,35 1,15,39,26,28,38 1,45,26,38 30,22,1,15,26,38 1,15,26,38 45,26,37 10,15,25 1,15,26 1,37,39,26 17,26 1,15,26,38 15,34,25,30 6,42 25,15 15 27,43,26 27,34 27,39,40 39 27

Ite ms



124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145

Loading speed high

     

29 34 45 39 34,43 Total points Appearance

Iran

Searching function available Navigation possible through flipping Navigation possible through searching Website enjoying an exclusive policy Material put in less than 3 pages Content chronologically put in pages (based on date, title, etc)  Numbers used to show figures (2 instead of two) No use of PDF files to avoid lesser attractiveness (excluding for documents)  Alert messages are visible  Download time is low 93 98 National Library

30,27 27,39,34,22 29 29,27 27,33 24

Features

Frequency

    6

Appropriate page layout Designs consistent with pages Colors consistent with writing style Texts have margins around 6

22,34,40 27 30 24 Total points

Conclusion However, the design of National Library Websites should be based on functions that support major aims of National Library especially in rendering public information services, but there are some problems and drawbacks in this connection, particularly in the theoretical approaches of it. So, the aim of this study is to identify the elements that are important in the design of National Library Websites at one hand, and help to the users for using good websites and specialist for designing and implementing high qualified websites for National Libraries on the other hand. At the end, some of the recommendations have been suggested for the improving of the present situation of National Library Website in Iran. Based on the research findings, the lowest amount of the Usability was 6 points for Appearance and the highest one was 156 points for Navigation. The figures are shown in diagram 2.

654

146 147 148 149 150 151 152 153 154 155 156

Item s



157 158 159 160

200 150 100 50 0

Series1

Diagram 2- Distribution of points concerning the levels of observance of the criteria s and features

References Asqari Pouda, Ahmad-Reza (2001), Review of Elements and Features Involved in Design of University Libraries Websites. MA thesis in Librarianship and Information, Faculty of Educational Sciences and Psychology, Mashhad Ferdowsi University Alexander, Janet (2004). Web knowledge: How to Assess Existing Information on the Web and Develop such Pages. Translated by Sedighe Mohamadesmaeil. Tehran: Dabizesh Alexander, J. E. ,Tate, M. A. (1999). Web Wisdom: who to evaluate and create information quality on the web. London, Mahwah, NewJersey.LEA: (Lawrence Erlbaum Associates). Back, S. E. (1997). Evaluation criteria: the good, The Bad and The Ugly: Or, Why It‘s a Good Idea to Evaluate Web Sources‖.[on-line] Available: http:lib.nmsu.edu/instruction/evalcrit.html Barker. J. (2004) Finding Information on the Internet: A Tutorial University of California. [online].Available: www.lib.berkeley.edu/TeachingLib/Guides/Internet/Evaluate.html Barker.J. (2003). Evaluating Web Pages: Techniques to Apply & Questions to Ask.‖ VC Berkeley – Teaching Library Internet Workshops, 12sep.2003.[on-line]. Available: http://www.Lib.berkeley.edu/Teaching Lib/Guides/Internet/Evaluated.html Berger. P. (1999) Web Evaluation Guide: Tramline, Incorporated. [on-line]. Available: http://www.infosearcher.com/cybertours/tours/touro4/-tourlaunch1.html Bertot J.C. et al. (2006) Functionality, usability and accessibility: Interactive user-centered evaluation strategies for digital libraries", Performance Management and Metrics, Vol. 7 No. 1, pp. 17-28. Braynik. G. (2003). Atomatic Web Usability Evaluation: What Need to be done?.(29jan.2003): 1-16.[on-line].Available: http://Usable.binghamton.edu.Atomatic Thesis.html Clay . E. S. (2003) content management and library web site . Public library . 42( 5 ) . Sep/Oct 2003 . 278-279. Engle. M. (1996). Evaluating Websites: Criteria and Tools. New York Library Association Conference, Saratoga Springs, Ny. (October 1996): 1-3.[on-line]. Available: http://www.Library.Cornel.edu/Okuref/research/Webeval.html The Essential Web Site Usability Checklist. (2007). [on-line].Available: www.dailybits.com. Fogg, B.J.et al(2001). What Makes Websites Credible? A report on a large quantitative study. CHI, vol.3, no.1 655

Haji Zeinolabedini, Mohsen, Leila Maktabifard, and Farida Osara (2005). Analysis of World National Libraries Websites' Links. Journal of Educational Sciences and Psychology, Vol. 7, No. 1, pp 173-193 Hassanzadeh, Mohammad, Navidi, Fatemeh(2010).Web site accessibility evaluation methods in action : A comparative approach for ministerial web sites in Iran .Journal of the Electronic Library, vol. 28, No. 6, pp789-803. Hupp.J.(2008). Test Your Web Site : A 57- Point Checklist for Maximum Usability .[ononline].Available: www.virtualhosting.com. Jafari. A. Optimizing Campus Web Sites. EDUCASE QUARTERLY, No.2(2000):56-58. Johnson, Steve (2003). Pages for Offer and Reception of Library Resources Materials on the Web. Translated by Sediqa Mohamadesmaeil. Ettela' Shenasi, 1 (autumn): 169-187 Khansari, Jiran. Development of Successful Websites for Small University Libraries. San'at-e Barq, 54 (Nov.), pp 24-28 Leggett. D. Quick Usability Checklist.2009, [on-line]. Available: www.uxbooth.com. Mazinani, Ali (2002). Library and Librarianship. Tehran: Organization of Study and Development of university Textbooks in Humanities (SAMT) Meyers. P. J. 25-Point Web Site Usability Checklist, 2008. [on-line]. Available: www.usereffect.com Mohamadesmaeil, Sediqheh (2004). Assessment of Usability of Websites of National Industrial Universities. Doctoral thesis in Librarianship and Information. Tehran: Islamic Azad University, Olum and Tahqiqat branch Mohamadesmaeil, Sediqheh (2005). Assessment of Usability of Websites of National Industrial Universities. Book Quarterly, Vol. 16, No. 1 Nadler.D.M. and Furman.V.M. (2001). Access board issues final standards for disabled access under Section 508 of Rehabilitation Act, Government Contract Litigation Reporter, Vol. 14 No. 19, p. 14. National Science foundation(NSF).Universal Design of College Algebra,2008.[on-line]. Available: www.usablealgebra.landmark.edu Navidi, Fatema (2007). Assessment of Accessibility of Websites of Ministries of the IRI Government. MA Thesis in Librarianship and Information, Tarbi'at-e Modarres University Nielsen.J.Coyne, K, Tahir, Marie, (2001). Make It Usable-Web Site UsabilityMagazine ,(6 feb.2001): 1-5.[on-line].Available: http://www.Pcmag.com/article2/0,4149,33821,00.asp,23jan.2003. Nowrouzi, Ali-Reza (2006), Review of Participation of Iran in Web. Book Quarterly, winter 2006 Osara, Farida and Ali Moradmand (2005). Identification of Major Feature in Design of Websites of World National Libraries with a View to Presenting an Appropriate Model for Quality promotion of Website of IRI's National Library. Information Quarterly, autumn and winter, No. 1, 2, pp 170-190 Osara, Farida (2002). Criteria s for Assessment of Internet Sources. Book Quarterly, Vol. 13, No. 2 (summer), pp 61-73 Reza'I Sharifabadi, Saeed and Noushin Foroudi (2002), Assessment of Web Pages of Iran's University Libraries with a Model Presented. Book Quarterly, Vol. 13, No. 4 (winter), pp 1219 Sandberg, Robert.(2009) ― Assessment and Usability Test of Company Specific Hardware Configuration Tool‖. University essay from linkÖpings Universitet/Instituation fÖr datavetenskap. The SEO File: Usability Checklist 2009.[on-line]. Available: www.theseofiles.co.uk.

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Sloan, John. Rampo College Web Design Standards. Rampo College of New yersey Statements and policies.2001.[on-line].Available: http://www.guide.rampo.edu 1/content/Webstandards.html Stueart . R.D. Library and Information Center management .fifth edition . Endewood , Colorado : Libraries Unlimited ;INC ,1998. Sullivan, T. Matson R. Barriers to use: Usability and Content Accessibility on the Web‘s Most Popular Sites. Interdisciplinary ph.D. program in information Science, University of North Texas, Denton, 2000. Tungar.M. N. Heuristic Evaluation.2002.[on-line].Available: http://www.cc.gatech.edu/~manas/cs8803a/Heuristic.pdf. Usability Guidelines Information Science & Technology,2004.[on-line].Available: www.web.mit.edu. ― Web Usability Checklist.The College of New Jersey. 2008. [on-line]. Available: www.tcng.edu. Weibel.S.L. The World Wide Web and Emerging Internet Resource Discovery Standards for Scholarly Litrature. Library Trends.vol.43,No.4(1995):627-664. Wilson, S.( 1995)World Wide Web Design Guide. Indianapolis. IN: Hayden Books,1995. Zaphiris. P. Darin, Ellis, R. Website Usability and Content Accessibility of The Top USA Universities (2001). Dertoit, MI: Institute of Gerontology and Dept of Industrial and Manufacturing Engineering Wayne State University, 2001. 50 Web Usability Tips to Attract and Retain Web Visitors (2009),:[on-line].Available: www.doshdosh.com.

657

Applying Social Network Analysis for Knowledge Management in Inter-organizational Networks Seyed Hossein Hosseini

Peyman Akhavan

[email protected] Department of Industrial Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran.

[email protected] Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.

Mostafa Jafari

Mostafa Ghanbari

[email protected] Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.

[email protected] Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.

Abstract: The main objective of this paper is to investigate the role of Social Network Analysis (SNA) in the evaluation of inter-organizational networks of knowledge perspective and to outline potential applications of SNA in both organizational and inter-organizational networks. Based on review of literature, this conceptual paper investigates how SNA can help knowledge management in inter-organizational networks. Finally, we use Soft System Methodology (SSM) to show how our conceptual model is validated. This study opens up new lines of research and highlights implications of SNA in inter-organizational networks‘ knowledge management. The paper also provides a framework of knowledge evaluation in interorganizational networks. Keywords: knowledge management, inter-organizational networks, social network analysis, knowledge-evaluation

1. Introduction Knowledge has become the driving force in current economy, and it is considered the essential source of competitive advantage. Drucker (1995) argued that knowledge is displacing natural resources, capital, and labor as the basic economic resource in the ‗new economy‘. Davenport and Prusak (2000) defined knowledge as ‗‗a fluid mix of framed experiences, values, contextual information, and expert insight that provides a framework for evaluating and incorporating new experiences and information‘‘. Knowledge is originated and is applied in the mind of individuals, whereas in organizations it can be embedded in routines, processes, practices, and norms (Davenport and Prusak, 2000). It actively enables performance, problem solving, decision making, learning and teaching by integrating ideas, experience, intuition, and skills, to create value for employees, the organization, its customers, and shareholders (Liebowitz, 2000; Probst et al., 2000). In today‘s knowledge-based economy, greater emphasis is being placed on managing organizational intangible knowledge assets. The importance of knowledge management (KM) 658

is succinctly provided in an article titled ‗‗If Only We Knew What We Know‘‘ (O‘Dell and Grayson, 1998). Knowledge management is a deliberate, systematic business optimization strategy that selects, stores, organizes, packages, and communicates information essentials to the business of a company in a manner that improves employee performance and corporate competitiveness (Bergeron, 2003). Nonaka said: ‗When markets shift, technologies proliferate, competitors multiply, and/or products become obsolete almost overnight, successful companies are those that constantly create new knowledge, disseminate it widely throughout the organization, and quickly embody it in new technologies and products‘ (Nonaka, 1991). KM, as a discipline, is designed to provide strategy, process, and technology to increase organizational learning (Satyadas et al., 2001). The process of developing competencies and capacities to the benefit of the organization has been divided into four areas of organizational activity (Leonard and Barton, 1995): -

Shared creative problem solving;

-

Integrating and implementing new methods and adopting new tools;

-

Formal and informal experimentation; and

-

Learning outside of the organization.

Regarding the importance of the ‗Learning outside of the organization‘ due to the increasingly growth of inter-organizational networks, this paper, at first, reviews knowledge management in inter-organizational networks as an important source for competitive advantage (Gulati et al., 2000), Then, after introducing social network analysis (SNA), we will show the role of SNA in analyze, evaluation and the improvement of these knowledge networks. 2. Knowledge management in inter-organizational networks Carlsson (2003) argued that the different views of knowledge lead to different conceptualizations of knowledge management and then he defined ‗knowledge as resource‘ because this view can be used to address the links between knowledge, knowledge management, and firm performance. He debated about what ‗knowledge as resource‘ means and then argued that it is not knowledge per se that should be in focus, but ‗knowing‘. This means an emphasis on the context where knowledge is created, shared, integrated and put to use that has primarily a process and flow view which means that the design and structuring of knowledge processes and flows form the basis for achieving competitive advantage. Hence, our focus is a firm‘s ability, through inter-organizational network-based knowledge processes and flows, to create new knowledge and to share and employ existing knowledge to solve problems, make decisions, and take actions. Thorelli (1986) stressed the importance of networks and the need for research on networks. Thorelli used the construct ‗network‘ to refer to relationships between two or more organizations and argued that networks are hybrid intermediate forms and alternatives to markets and hierarchies. Laumann et al. (1978) defined a social network as ‗a set of nodes (e.g. persons, organizations) linked by a set of social relationships (e.g. friendship, transfer of funds, overlapping membership) of a specified type‘. A network is ―generally defined as a specific type of relation linking a defined set of persons, objects, or events‖ (Mitchell, 1969). Inter-organizational networks differ in their importance and criticality. Here our focus is on ‗strategic networks‘ that . . . encompass a firm‘s set of relationships, both horizontal and vertical, with other organizations— be they suppliers, customers, or other entities—including relationships across industries. These strategic networks are composed of inter-organizational ties that 659

are enduring, are of strategic significance for the firms entering them, and include strategic alliances, joint- ventures, long-term buyer–supplier partnerships, and a host of similar ties. (Gulati et al., 2000) Both executives and academics have identified organizatio nal learning as perhaps the key factor in achieving sustainable competitive advantage (Cohen and Levinthal, 1990; Teece, Pisano, and Shuen, 1997; Kogut and Zander, 1992; Spender, 1996; Grant, 1996). There is increasing evidence which suggests that a ‗network‘ of firms may be a critical, but less understood, unit of analysis for understanding firm- level learning (Powell, Koput, and SmithDoerr, 1996; Dyer and Singh, 1998). For example, von Hippel (1988) found that a firm‘s customers and suppliers were its primary sources of innovative ideas. He argues that a production network with superior knowledge transfer mechanisms among users, suppliers, and manufacturers will be able to ‗out-innovate‘ networks with less effective knowledgesharing routines. Dyer and Nobeoka (2000) argued that Toyota‘s ‗network‘ appears to be highly effective at facilitating inter- firm knowledge transfer that lead to a high performance for Toyota, because the cost and quality of a vehicle are a function of the productivity of a network of firms working in collaboration. A network can be more effective than a firm at the generation, transfer, and recombination of knowledge, because each firm had excelled in their development and use of different processes. Hence inter-firm sharing has seen as an opportunity for mutual benefit. According to the resource-based view (RBV) of the firm, competitive advantage is based on valuable and unique internal resources and capabilities that are costly for competitors to imitate (Barney, 1991; Wernerfelt, 1984). Resources are assets available in the firm or which the firm can acquire. Capabilities are developed by combining and using resources; these resources can be capabilities. The knowledge-based view of the firm states that these resources and capabilities are knowledge-related and knowledge- intensive resources and capabilities (Grant 1996, 1997). The RBV argues that competitive advantage is an outcome of resources and capabilities residing within the firm, but these capabilities can be ‗directed‘ towards the environment of the firm(Eisenhardt and Schoonhoven, 1996; Choudhury and Xia, 1999). An important source that can be used to create, acquire, and integrate knowledge in knowledge- intensive processes that can lead to competitive advantage lies in an organization‘s networks of external relationships (Gulati et al., 2000; Nohria and Ghoshal, 1997; Kale et al., 2001; Carlsson, 2003). Altogether there are Rationales for inter-firm sharing of knowledge (Bell et al., 2002): -

Amortizing costs across multiple extended enterprises

-

Accessing state-of-the-art learning technologies

-

Accessing world-class knowledge of product development processes

Knowledge management has to become network- focused if knowledge- intensive organizations are to gain and sustain competitive advantage from knowledge management (Carlsson, 2003). For example, knowledge sharing within a supply chain has become a common practice because it promises to enhance the competitive advantage of the supply chain as a whole; that is, the benefit of the cooperation is mutual (Holland, 1995). A crucial capability of innovative firms is the ability to find new external information and knowledge and through processes apply it to commercial ends (Carlsson, 2003). Dyer and Nobeoka (2000) showed how Toyota established highly effective inter- firm knowledge transfer network within its supply chain. They found that a successful knowledge660

sharing network must devise methods to (1) motivate members to participate and openly share valuable knowledge (while preventing undesirable spillovers to competitors), (2) prevent free riders, and (3) reduce the costs associated with finding and accessing different types of valuable knowledge. They claimed that three institutional innovations have played an important role in the creation of the network and in facilitating inter-organizational learning. These innovations were: (1) the supplier association, (2) the knowledge transfer consultants, and (3) small- group learning teams. They showed the Evolution of knowledge-sharing network that found it in Toyota case as shown in figure 1. Phase 1: Developing weak ties

Phase 2: Developing strong ties with Toyota

Phase 3: Developing strong ties among suppliers

Dimension

Key Characteristics

Key Characteristics

Network Structure:

One large network with core firm as hub Bilateral relationships with core firm Weak ties among most members

Large network plus multiple ―nested networks‖ Multi-lateral relationships Strong/embedded ties in nested networks and with core firms

Numerous structural holes Explicit knowledge Demonstrate commitment to core firm

Few structural holes Both explicit and tacit knowledge Lean faster than competitors (benefits of participation far outweigh isolation); reciprocity

Type of Knowledge: Member Motivation: (to participate)

Figure 19- The Evolution of knowledge-sharing network in Toyota case (Dyer and Nobeoka, 2000)

Over time, firms develop the capabilities and processes necessary to facilitate knowledge flows across firm borders (Kale, Singh and Perlmutter, 2000). Rowley, Behrens, and Krackhardt‘s (2000) stated that a highly interconnected, strong tied network is well suited for the diffusion (exploitation) of existing knowledge rather than exploration for new knowledge (which is the strength of a ‗weak tied‘ network). Authors believe that in current knowledge economy to effectively achieve competitive advantage the inter-organizational networks should be considered comprehensively that these networks include suppliers, retailers and partners as shown in figure 2.

Figure 20- The comprehensive Inter-organizational network

If inter-organizational networks are considered comprehensively the information and knowledge that acquired would be extensive and includes, for example, as follows: 661

-

Retailers: information and knowledge about customers (needs, classification, etc.), market (the forecasting of sale, patterns and trends, etc.), the feedback of new product etc.

-

Suppliers: technical information and knowledge etc.

-

Partners: work procedures and routines, technologies etc.

Effectively managing knowledge in these networks can have great impact on organization performance and success in current competitive environment. Inter-organizational networks can be of different types. Carlsson (2003) based on the possibility of an organization to design and govern a network (designed and governed by the firm vs. not designed and governed by the firm) as well as the openness of a network (open vs. closed networks), defines three different types of inter-organizational networks for knowledge management: (1) extra-networks; (2) inter-networks; and (3) open networks. He argued that an extra- network is a network that is designed and governed by the firm; And, Participation in such a network is restricted (closed network). The network is a gated community, meaning that only specific nodes (individuals and organizations) are allowed to participate. An inter-network is also a network that is designed and governed by the firm, but participation in the network is not restricted. This type of network is open to anyone who wants to join and participate. An open network is a network open for anyone interested and willing to participate in knowledge creation and sharing and is not designed and governed by the firm. Extra- and inter-networks are designed and governed by firms in order to use the external environment to create new knowledge, assimilate it, and apply it to commercial ends and considered in KM. 3. Social Network Analysis and Knowle dge Management SNA has a history beginning in the early 1930s across many disciplines, including anthropology, economics, transportation planning, and business (Durland and Fredericks, 2005a).The volume of social network research in management has increased radically in recent years, as it has in many disciplines. Indeed, the network literature is growing exponentially as shown in Figure 3. The boom in network research is part of a general shift, beginning in the second half of the 20th century, away from individualist, essentialist and atomistic explanations toward more relational, contextual and systemic understandings (Borgatti and Foster, 2003).

662

Figure 21- Exponential growth of publications indexed by Sociological Abstracts containing “social network” in the abstract or title (Borgatti and Foster, 2003)

There are some factors that have led to this increased interest in and use of SNA. First, practical applications have conceptualized new understandings of interactions. The second factor for the increased interest in the application of SNA, particularly at the corporate and business level, is its development and focus on understanding complexities and systems. A system is a group of interacting, interrelated, or interdependent elements forming a complex whole. Structural properties of networks are systemic elements and the interactions of structural elements, such as found in informal communication patterns, work group interactions, and leadership paths, which are features for understanding complexity and system properties. The third factor for the increased interest in networks, which underlies all other indicators of SNA growth, is the availability of software programs that facilitate the analysis of data and the creation of sociograms (Durland and Fredericks, 2005a). Cross et al. (2002) argued that another factor is rapid growth in close cooperative relationships across organizational boundaries—outsourcing, joint ventures, alliances, multiorganizational project work, and so on. Companies have lamented the need to understand what they know as an organization, who knows it, where it resides, and how it is communicated to members (O‘Dell and Grayson, 1998). Some researchers argued that industry and business leaders need tools to make knowledge capacity visible (Holtshouse, 1998; Rulke, Zaheer, and Anderson, 2000). Social network analysis is an approach to the study of human social interactions. SNA is the study of relationships within the context of social situations. It contains a set of measures and analysis tools that are used to describe and understand relational data. Relational data indicate whether a relationship between two components or actors exists and also indicate the value of that relationship. SNA is not an organizational outline, a logic model of relationships, a conceptual framework, or a relational database. SNA is a methodology for understanding the capacity of an organization to engage in its activities based on its organizatio n structure, operationally defined or not, both informally and formally (Durland and Fredericks, 2005a). SNA can be used to investigate kinship patterns, community structure, or the organization of other formal and informal social networks (Scott, 2000; Wasserman and Faust, 1994). SNA 663

allows one to ―see the forest and the trees and how they are related‖ (Krebs, 1998). The application of SNA appears particularly appropriate to several types of evaluation practice, for example the evaluation of collaboration and communities of practice and participatory evaluation. For example SNA would provide a snapshot of each work team and the structure of communication patterns within teams. The structure of this team provides an indication of its capacity to engage in collaborative activities that are designed to achieve defined goals (Durland and Fredericks, 2005a). A significant, yet often overlooked component of people‘s information environments are composed of the relationships that they use for information and knowledge capture (Granovetter, 1973; Burt, 1987 and 1992; Rogers, 1995; Szulanski, 1996; Shah, 1998). One study demonstrated that people are roughly five times more likely to turn to friends or colleagues for answers than other sources of information such as a database or file cabinet (Allen, 1977). Social networks, whether supported by relationships established through computer environments or not, serve as a base for communities of practice. Communities of practice (Brown & Duguid 1991, Lave & Wenger 1991), in turn, serve as a base for knowledge management (Brown & Duguid 2000). Three important characteristics underscore and provide a framework for doing SNA: a relationship should be defined, a network need be identified that the relationship can be placed on, and the relationship can be measured. The relationships concern communication and knowledge relationships and include communication about work over a specific period of time in a specified manner, organizational connections, and knowledge experts; Who talks to whom, who works with whom, and who do you know who is an expert on a specific topic. A network defines the boundaries within which a relationship will be measured. After defining the relationships and identifying the network, the next step is to select measures that align to the specific relationship (Durland, 2005). Some of these measures are: Indegree centrality: For indegree centrality, central means the most popular person, that is, the person who got the most selections. Centrality is an indicator of who can control a network and how he or she might be able to do this. Betweenness centrality: Betweenness centrality means that an individual may not be the most popular but is indirectly connected to others. A person with a high betweenness score is under the surface of a network. These people can get to others indirectly through the connections that they have; they are on paths that provide opportunities to others, even if they are not directly connected to those others. They hold power through influe nce. Clique analysis: Cliques are clusters or subsets of the network in which every member connects with the other. As Wasserman and Faust (1994) said that, there are usually three distinct but related theoretical constructs that form the tools for a network approach to analysis: graph theory, the use of matrices, and sociograms. Graphs are not the charts that are used to illustrate data in traditional research and evaluation analysis; rather, SNA graphs illustrate the characteristics of the relationship between two or more actors. The matrix or matrices place the network relationships into a format for matrix and algebraic manipulation, and sociograms have traditionally been used to represent or depict the network relationships resulting from both the graphs and the products of matrix manipulations (Durland, 2005). Sociograms provide a picture of the general pattern of communication or relationship studied. The interpretation of SNA data and sociograms is not a linear process. Rather, it is much like the systems it explores: complex and systemic, with 664

the ability to explore the individual, groups, and the complete network (Durland and Fredericks, 2005b). The resulting sociograms clearly highlight business-relevant features and positions occupied by individuals in the program in easy-to-understand pictures of the network structure (Speel and others, 1999). Managers are able to see at a glance those whom the program depends on and how much. SNA can reveal who program experts are and who may be bottlenecks because too many people go to them, identify experts who are close to retirement, and make the lack of necessary connections between individuals within a program painfully clear (Patton, 2005). Cross et al. (2002) found that four dimensions tended to be critica l for a relationship to be effective, in terms of knowledge creation and use: • Knowing what someone knows. • Gaining timely access to that person. • Creating viable knowledge through cognitive engagement. • Learning from a safe relationship. Regarding all previously mentioned, SNA can help managers effectively to analyze and evaluate this four dimensions. Therefore SNA is a good tool to characterize expertise and to capture the connections between individuals and their sources of experts because it makes t he unseen and unknown visible and concrete (Birk, 2005). Social Network Analysis allows managers to visualize and understand the myriad of relationships that can either facilitate or impede knowledge creation and transfer (Cross et al., 2002). Managers can map, oversee, and influence social networks (Krackhardt and Hanson, 2000) through the use of SNA. Organizations increasingly use knowledge of social networks to identify opportunities for internal collaboration (McGregor, 2006). Hence, regarding the importance of inter-organizational networks in competition and the role of knowledge interaction in the performance of such network, we should evaluate them to measure performance and then improving them. 4. Analyze, evaluation, and improvement of knowledge sharing, creation, and dissemination in inte r-organizational networks The establishing of inter-organizational networks is not the end of the work. Organizations after establishing such networks should analyze, evaluate and then improve the sharing, creation and the dissemination of knowledge to achieve their goals of establishing these networks. After establishing such networks some questions arise: -

If network operates effectively and efficiently? (Insufficient or excessive links between organizations that must coordinate effectively)

-

Knowledge flows in what paths and where requires some supports?

-

Which organization has expertise in specific knowledge?

-

Which organization does not have knowledge and otherwise why does not share that knowledge?

-

Where are subgroups or cliques?

-

Where are bottlenecks? (Central organizations that provide the only connection between different parts of the network) 665

-

Network‘s organizations achieve knowledge from which external sources?

By knowing which organizations perceived as the initiative experts, where they were, and how organizations were connected to each other, the evaluative data would be exist that could help making future decisions about inter-organizational network structure, retention, adding external organization to network etc. We can design programs to enhance inter-organizational network learning, knowledge transfer or innovation but it is often difficult to understand the impact of such interventions. A method to answer these questions is applying SNA to the inter-organizational networks. Therefore in the use of SNA for inter-organizational networks, organizations (Partners, suppliers and retailers) considered as nodes and we try to analyze their knowledge relationships. First we should collect data about relationships. As with traditional data collection, SNA data can be collected through observations, interviews, surveys, artifacts, documents, and records (Durland, 2005). Then the network will be made as mentioned in pervious section. The relationships can be measured and evaluated. After analyze, we can find some answers to questions above and then find some solutions: Which organization has expertise and in what field. Where the knowledge transfers paths are and whether we can do something to minimize time and path length to reach experts for better knowledge acquiring and transfer. Which organizations are isolated and whether the problem can be solved. Subgroups or cliques might be discovered and whether we can extend their tight relations into others. Bottlenecks might be discovered and some other connections might be created for the better cohesive of network. Find the external sources of knowledge and if needed, invite them to contribute in network. Usually, the problem of managers is that they don‘t know where the problem is to solve it. In inter-organizational networks, also, this problem is existent and therefore an approach should be existent to evaluate some issues such knowledge sharing and accessibility of it and guarantee that it occurs. As argued previously, a source for knowledge is the knowledge relation of members in an inter-organizational network. SNA can help to analyze and evaluate network relationships effectively and thus can help to managers to find problems and solve them. Also, SNA via visualization can give a great help to have comprehensible and explicable results for managers which help them to decision making. Altogether, SNA can be used as a useful tool for knowledge management in inter-organizational networks. Following, after reviewing SSM methodology, we use SSM to create a conceptual model for the purpose of this paper. Soft systems methodology (SSM) (Checkland, 1988; Checkland and Scholes, 1990) can be described as an iterative methodology that focuses and accommodates various stakeholders‘ perspective in the design of the solution. It can also involve learning about a complex problematical human situation, and lead to finding accommodations and taking purposeful action in the situation aimed at improvement. Figure 4 shows the ‗7-step‘ SSM methodology. Among these stages, stages (1), (2), (5), (6) and (7) are about working in real world, whereas stages (3) and (4) are considered to be systems thinking about the real world.

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Figure 22- The early „7-step‟ representation of SSM (Checkland 2001, p. 71)

Stages (1) and (2) are the start of a dialogue on this problem area that would lead into the development of a shared language. The authors conducted deep interviews and used the appreciative influence diagrams as rich pictures (Avison and Wood-Harper, 1990; Checkland and Scholes, 1990). The results allowed commentaries and discuss to be made allowing the problem situation to become more structured. In Stage (3), ―Root definitions‖ are constructed for the relevant systems identified in the previous stages. It should include the emergent properties of the system in question. In order to define these properties, CATWOE is used as follow: C: customer (These are the immediate beneficiaries or victims of what the system does. It can be an individual, several people, a group or groups); A: actor (In any system there are people who carry out one or more of the activities in the system, these are the actors); T: transformation (This is the core of the system in which some definite inp ut is converted into some output and then passed on to the customers); W: Weltanschauung (This is the often taken- for-granted outlook or worldview that makes sense of the root definition being developed); O: ownership (This is the individual or group respo nsible for the proposed system in the sense that they have the power to modify it or even to close it down); E: environment (All systems operate within some constraints imposed by their external environment); In Stage (4) conceptual models are drawn based on ―root definitions‖. The conceptual model must be derived from the root definition alone. It is an intellectual model and must not be affected by knowledge of the ―real‖ world. Stages (5) and (6) identify which activities need to be included in that particular situation. Finally, stage (7) is the implementation phase. The related CATWOE for the purpose of this paper depicted in Table 1.

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Table 1-CATWOE Analysis CATWOE Customers Actors Transformati on Weltanschauung Ownershi p Environmental constraints

In the Context of knowledge management in inter-organizati onal network All of members in the inter-organizat ional network Center co mpany or a team which co mposed of organizations‘ agent Information about knowledge relat ions to knowledge about it and then finding improvements The network needs to be supplied with the most effective understanding of its knowledge relations to improve it Center co mpany There are some limitations to find knowledge relat ions

Alternatively, we might capture this in the following sentences: A system owned by the center company and managed by center company or a team which composed of organizations‘ agent to transfer Information about knowledge relations to knowledge about it and then finding improvements. It must be effective in the real world ambiguity about knowledge relations. The system is needed so that the network can be supplied with the most effective understanding of its knowledge relations to improve it. After formulating root definition of relevant systems, in order to avoid purely problemstructuring, a conceptual model (Wilson, 2001) is intended to be developed. This conceptual model depicts what a system must include in order to meet root definitions. Regarding all discussed in this section, we create a conceptual model of applying SNA to knowledge management in inter-organizational networks which sowed in figure 5. In the context of Resource Based View of network the model shows how SNA can help these networks, which composed of suppliers, partners, and retailers, to improve their knowledge relations in order to higher competitiveness. Knowledge

Management Operating Improvements

Analyze and Knowledge evaluation The use of SNA: I. Defining knowledgerelationships II. Identifying network III. Measuring relationships (e.g.

Finding Improvements

Indegree centrality , Betweenness centrality , Clique analy sis)

Inter-organizational networks Suppliers

Partners

Retailers

Resource Based View Figure 5 - The role of SNA in knowledge management of Inter-organizational networks

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5. Conclusion Nowadays, global markets seeing fast and unpredictable changes. These changes can be caused by changes and development in technology, wo rk procedures and rules, customer needs and more competitive markets. Hence, organizations can not respond solely to these needs and changes quickly, which finally result in failure. Being more competitive and successful, organizations come to collective action in the manner of inter-organizational networks. In inter-organizational networks the success of whole is not only a function of each member success and some essential departure should be done to having a good alliance. In today knowledge economy, the key factor for success is knowledge management and therefore inter-organizational networks need knowledge management for success. One of tools that can be useful in this way is SNA. With the help of SNA we can analyze and evaluate a knowledge network and investigate that whether it works effectively and efficiently from knowledge perspective. Then we can find some solutions for barriers and facilitate knowledge sharing and creation on the network. Developing these networks from knowledge perspective, the network would be beneficial that finally results in the success of each member. The implementation of introduced framework for analyzing and evaluation of an interorganizational network is the constraint of this conceptual paper and can be done to examine the application of SNA as argued, and to reveal hidden application of SNA in the knowledge management of inter-organizational networks. References: Allen, T., 1977. Managing the Flow of Technology. Cambridge, MA: MIT Press. Avison, D.E. and Wood-Harper, A.T. (1990), Multiview: an Exploration in Information Systems Development, Blackwell Scientific Publications, Oxford. Barney, JB., 1991. Firm resources and sustained competitive advantage. Journal of Management 17(1): 99–120. Bell, D. G., Giordano, R. and Putz, P., 2002. Inter-firm Sharing of Process Knowledge: Exploring Knowledge Markets. Knowledge and Process Management, Vol. 19, No. 1, pp 12–22. Bergeron, B., 2003. Essentials of Knowledge Management. John Wiley & Sons, New York, NY. Borgatti, S.P., Foster, P.C., 2003. The Network Paradigm in Organizational Research: A Review and Typology. Journal of Management, 29(6), pp. 991–1013. Birk, S.M., 2005. Application of Network Analysis in Evaluating Knowledge Capacity. The chapter6 of ―NEW DIRECTIONS FOR EVALUATION‖, published online in Wiley InterScience (www.interscience.wiley.com), DOI: 10.1002/ev.162. Brown, J.S. and Duguid, P., 1991. Organizational Learning and Communities of Practice: Toward a Unified View of Working, Learning and Innovation. Organizational Science 2(1), pp. 40–57. Brown, J.S. and Duguid, P., 2000. Balancing Act: How to Capture Knowledge Without Killing it. Harvard Business Review 78(3), pp. 73–79. Burt, R., 1987. Social Contagion and Innovation: Cohesion versus Structural Equivalence. American Journal of Sociology 92, pp. 1287-1335. Burt, R., 1992. Structural Holes. Cambridge, MA: Harvard University Press. Carlsson, S. A., 2003. Knowledge Managing and Knowledge Management Systems in Interorganizational Networks. Knowledge and Process Management, Vol. 10, No. 3, pp. 194–206. Checkland, P., 1988. Information systems and systems thinking: time to unite?. International Journal of Information Management, Vol. 8. Checkland, P. and Scholes, J., 1990. Soft Systems Methodology in Action. Wiley, Chichester. Cohen, W. M. and Levinthal, D. A., 1990. Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35, pp. 128–152. Cross, R., Parker, A. and Borgatti, S.P., 2002. A bird‘s-eye view: Using social network analysis to improve knowledge creation and sharing. IBM Institute for Knowledge-Based Organizations. 669

Davenport, T. and Prusak, L., 2000. Working Knowledge. Harvard Business School Press, Boston, MA. Drucker, P., 1995. The Post-Capitalist Society. Butterworth- Heinemann: Oxford, UK. Durland, M.M., 2005. Exploring and Understanding Relationships. The chapter3 of ―NEW DIRECTIONS FOR EVALUATION‖, published online in Wiley InterScience (www.interscience.wiley.com), DOI: 10.1002/ev.159. Durland, M.M. and Fredericks, K.A., 2005a. An Introduction to Social Network Analysis. The chapter1 of ―NEW DIRECTIONS FOR EVALUATION‖, published online in Wiley InterScience (www.interscience.wiley.com), DOI: 10.1002/ev.157. Durland, M.M. and Fredericks, K.A., 2005b. Next Steps for Nodes, Nets, and SNA Analysis in Evaluation. The chapter 9 of ―NEW DIRECTIONS FOR EVALUATION‖, published online in Wiley InterScience (www.interscience.wiley.com), DOI: 10.1002/ev.165. Dyer, J. H. and Singh, H., 1998. The relational view: Cooperative strategy and sources of interorganizational competitive advantage. Academy of Management Review, 23(4), pp. 660–679. Dyer, J. H. and Nobeoka, K., 2000. Creating and managing a high-performance knowledge-sharing network: the Toyota case. Strategic Management Journal, 21, pp. 345–367. Granovetter, M., 1973. The Strength of Weak Ties. American Journal of Sociology 78, pp. 1360-1380. Grant RM., 1996. Toward a knowledge-based theory of the firm. Strategic Management Journal 17(Winter Special Issue), pp. 109–122. Grant, RM., 1996. Prospering in dynamically competitive environments: Organizational capability as knowledge integration. Organization Science, 7(4), pp. 375–387. Grant RM., 1997. The knowledge-based view of the firm: implications for management practice. Long Range Planning 30(3), pp. 450–454. Gulati, R., Nohria, N. and Zaheer, A., 2000. ―Strategic networks‖, Strategic Management Journal 21(3), pp. 203–215. Holland, CP., 1995. Cooperative supply chain management: the impact of inter-organizational information systems. Journal of Strategic Information Systems 4(2), pp. 117–133. Holtshouse, D., 1998. Knowledge Research Issues. California Management Review, 40(3), pp. 277– 280. Kale, P., Singh, H. and Perlmutter, H., 2000. Learning and protection of proprietary assets in strategic alliances: Building relational capital. Strategic Management Journal Special Issue 21, pp. 217–237. Kale, P., Dyer, J. and Singh, H., 2001. Value creation and success in strategic alliances: alliancing skills and the role of alliance structure and systems. European Management Journal 19(5), pp. 463– 471. Kogut, B. and Zander, U., 1992. Knowledge of the firm, combinative capabilities, and the replication of technology. Organization Science, 3(3), pp. 383– 397. Krackhardt, D. and Hanson, J.R., 2000. Informal Networks: The Company Behind the Chart. Harvard Business Review, Vol. 71, No. 4, pp. 104-111. Krebs, V., 1998. Knowledge Networks: Mapping and Measuring Knowledge Creation. Reuse, and Flow. Retrieved Aug. 12, 2004, from http: //www.orgnet.com/IHRIM.html. Lave, J. and Wenger, E., 1991. Situated Learning: Legitimate Perpheral Participation. Cambridge: Cambridge University Press. Laumann, EO., Galskeiwicz, L. and Marsden, PV., 1978. Community structure as inter-organizational linkages. Annual Review of Sociology 4, pp. 455–484. Leonard, D.A. and Barton, D., 1995. Wellsprings of Knowledge: Building and Sustaining the Sources of Innovation. Harvard Business School Press, Boston. Liebowitz, J. (Ed.), 2000. The Knowledge Management Handbook. CRC Press, Boca Raton, FL. McGregor, J., 2006. The Office Chart that Really Counts. Business Week, No. 3974 (February 27), pp. 48-49. Mitchell, J.C., 1969. The Concept and Use of Social Networks. in J. C. Mitchell, ed., ―Social Networks in Urban Situations‖, Manchester University Press, pp. 1–50. Nohria, N. and Ghoshal, S., 1997. The Differentiated Network. Jossey-Bass: San Francisco, CA. Nonaka I., 1991. The knowledge-creating company. Harvard Business Review 69(6), pp. 96–104. O‘Dell, C. and Grayson, C.J., 1998. If only we knew what we know: identification and transfer of internal best practices. California Management Review, Vol. 40 No. 3, pp. 154-74. 670

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Author Productivity Law: A Case Study of the Researchers of Isfahan Medical University Faramarz Soheili

Farshid Danesh

Faculty Member of Payamnoor Univ Iran

Faculty Member of the Islamic Science Citation Center (ISC) & PhD Candidate of LIS, Ferdowsi Univ Mashhad. Iran

The main purpose of the present study is evaluating Bradford‘s Scattering and Lotka‘s Productivity Law with regard to scientific productions of the Researchers of Isfahan University of Medical Sciences (IUMS) from 1992 to 2008, in WOS Database. To carry out this research, different scientometric indicators were applied to evaluate and analyze a sample population of 802 documents. Results revealed that the distribution of the scientific productions follows Lotka‘s Productivity Law; that is the majority of scientific outputs of this university are in fact produced by just a limited number of authors affiliated to that university. Moreover, the normal distribution of the journals published by IUMS Press follows Bradford‘s scattering Law. With respect to collaboration with other authors, the major collaboration of the researchers of this university has been with authors from United States of America, Canada, and England. Results also proved that in carrying out their research projects, the researchers of IUMS have a high level of collaboration with each other. In fact, the research history of this university reveals that the collaboration rate at this university is 0.967, which is a rather significant record.

Abstract:

Key Words: Lotka‘s Productivity Law, Braford‘s Scattering Law, collaboration Rate, Scientometrics, Isfahan University of Medical Sciences(IUMS) This article is part of a research project entitled " A survey on scientific production and collaboration rate among scholars from Isfahan University of Medical Sciences in WOS with utilizing HistCite software during 1992-2008 ” No.287286, approved by the Center of Health Research and Information Technology of Isfahan University of Medical Sciences.

Introduction Scientific information Production is one of the substantial aspects of stable development at any country. In other words, powerful countries are the developed ones in terms of scientific information production. In fact, scientific development guarantees other aspects of progress ( economical, social ,cultural and etc.). It is an accepted fact that indexing articles in ISI or other accredited citation institutions of the world is the only way to internationally introduce those scientific products. The reason of competition over publishing scientific articles in international journals is that, the articles published in international journals are indexable in ISI or any other accredited international citation centers around the world. Moreover, the rank of those journals is internationally identifiable (Mosavi et al., 2003) The articles, indexed at accredited citation databases, reveal the contribution of scientists to knowledge development and are considered as a criterion for evaluating their efforts. The number of times an article has been cited indicates the degree of its acceptance in the scientific society (Talebi, 2003). With regard to the significance and 672

role of scientific articles in science development, the current citations in WOS database provide the suitable grounds for presenting the bibliographic information of key and scientifically outstanding articles (Osareh & Farsi, 2002). In this research also WOS has been applied to determine the level of scientific collaboration of the researchers of Isfahan University of Medical Sciences (IUMS), and also testing Lotka‘s Productivity and Braford‘s Scattering Law. Review of Literature Belinchon et al. (2004), via Medline database, investigated the contribution of European countries' researchers to scientific articles' production in the field o f skin diseases from 1987 to 2000. On the whole, the authors of these countries, except Luxembourg, have published 19,255 articles in 32 journals, the majority of which belongs to England (26.7%), Germany (16.7%), Italy (11.5%), and France (9.2%). Calvino (2006) investigated the scientific productions of Iberian-American (IA) countries in Food Science and Technology field during three four-year periods respectively 1992-1995, 1996-1999, 2000-2003, via WOS database. The research results revealed significant changes in collaborative pattern and frequency of citations in scientific productions of these countries during these periods. Jacobs and Pichappan (2006) studied the scientific productions of a selection of South African Universities which had been published in ISI or South African Studies Databases, from 1994 to 2003.Eventually, it turned out that the majority of those scientific productions (29.51%) belong to psychological sciences field. Meng,Hu&Liu(2006) evaluated the basic researches in China. Via WOS database, the impact of investing on high- quality scientific productions, at international level from 1991 to 2000, was investigated. The results revealed that during this period, the investing process has had two significant bounces. Talebi (1999) in a research entitled ―Scientific collaboration of Iran's research centers with the domestic and foreign ones according to SCI data" examined the collaboration rate of Iran's university and non-university research centers with each other and with that of foreign countries. It was proved that, except the field of medicine, the required specialists of non-university research centers are often chosen from the native researchers. Applying WOS data, Iran‘s scientific productions in year 2000 were extensively examined (Ensafi & Gharib, 2002). Based on that database's three indicators, Iran's scientific production rate in that year has respectively been 96.9% (SCI-938 records), 2.89 %( SSCI-28 records), and 0.21% (A and HCI-2records). Moreover, it was found that the majority of documents (90.8%) are in article format and 99.7% are in English language. Saboori and Poor Sasan(2006) studied the contribution of the collaboration of Iranian authors to science production of the world in 2005 , and according to citation indicators of ISI. The total number of Iran's indexed documents in this year is 5,578, which forms 0.36% of the indexed documents of that institute. The results showed that 30% of those published documents (5,578) belong to Chemistry Field and the contribut ion of the Field of Medicine to Iran's science production in that year has been 28%. 673

Noroozi Cahkoli et al. (2007) evaluated Iran's scientific productions in 2005 and 2006. It was revealed that in 2006, compared to the previous year, Iran‘s science product ion in SCI had witnessed a 21% growth, while in SSCI; it showed a 13.83 % negative growth. Furthermore, the mean of the number of citations to each of Iran's scientific products was 2.93 which had resulted in Iran's 135 third-place ranking worldwide. In general, researches conducted in scientiometrics area are so extensive and multidimensional that although all of the above-mentioned researches are considered scientometric ones, the factors under study are quite diverse and different from each other. Research Objectives The main purpose of this survey is examining Lotka's Productivity Law with regard to scientific products of the researchers of IUMS in WOS database from 1992 to 2008. Other objectives of the study are the application of Bradford's Law of Scattering (BLS) for determining the subject scattering of core document sets created by Iranian authors; discovering the scattering pattern of those foreign authors that have the highest scientific collaboration with the researchers of IUMS, and finally determining the collaboration rate of authors affiliated to IUMS. To do so, first the following questions have to be answered: 1) what is the scattering pattern of collaborators affiliated to IUMS for science production, in WOS database from 1992 to 2008? 2)How is the subject scattering of core document sets created by Iranian authors, in WOS database from 1992 to 2008? 3. What is the scattering pattern of those foreign authors that have the highest scientific collaboration with the researchers of IUMS, in WOS database from 1992 to 2008? 4. Based on the documents indexed in WOS database from 1992 to 2008, what is the collaboration rate of authors affiliated to IUMS? Methodology It is an applied research that has been carried out using scientometric indicato rs. This research studied a sample population of 802 documents, created by authors affiliated to IUMS, which have been indexed in the WOS database from 1992 to 2008. The data were compiled, organized and analyzed at two stages and via two research instrume nts. In the first place, the data were extracted from WOS database and saved in Plain Text file format. At the second stage, through ISI.exe software, those data were identified, analyzed and imported into Excel ;then in order to test Lotka's Productivity Law , the following equation was applied: Xn .y=C, which means that on a given subject ,there is a reverse relation between the total number of authors(y) each with X publications and the number of publications(x) powered by n X= number of publications, Y= the number of authors with X publications, n= a fixed figure, and C=a fixed figure With regard to scientific subjects, n is approximately equal to 2, therefore:

X2 .y=C

As a result, in bibliometric assessment, it can be expected that a limited number of core authors produce the majority of publications at any field. Similarly, to determine the 674

number of core journals, Bradford's Scattering law is applied. And finally calculating the collaboration rate is carried out via the following formula: k

cc

1

1

* j

1

j

Fj

N

In which, Fj

j

The number of authored articles with j authors Authored articles (1 author, 2authors, 3authors, etc)

N

The total number of published authored articles

k

The maximum number of authors of an article(Ajiferuke& Burell, 1988)

Data Analysis The scattering pattern of collaborators affiliated to IUMS in science production, via examining Lotka's productivity Law: Analysis of the scattering pattern of articles belonging to IUMS authors revealed that the pattern follows Lotka's Productivity Law (see Table1 and Figure 1). With regard to scientific products of IUMS in ISI and during the given period, Lotka's law proved right. That is according to this law; the result of dividing the number of authors with n documents by n documents is equal to Lotka's figure. Table 1. The Comparison of Published Documents by IUMS authors in ISI Web of Science the number of documents(n)

the number of authors with n documents

Lotka's figure

1

1094

2

275

273

3

119

121

4

67

68

5

40

43

675

100.00% 95.00% 90.00% 85.00% 80.00% 75.00% 70.00% 65.00% 60.00% 55.00% 50.00% 45.00% 40.00% 35.00% 30.00% 25.00% 20.00% 15.00% 10.00% 5.00% 0.00%

y = 1.842x-1.98 R² = 0.976

0

10

20

30

40

50

60

Figure 1.IUMS authors‟ scattering according to Lotka's Scattering Law

Determining the scattering pattern of core documents produced by IUMS researchers, applying Bradford's Scattering Law One of the major aims of librarians is applying different rules and methods to p rovide the users with more and more knowledge and information, while economizing on time and the number of selected journals. One of such laws is Bradford's. To determine the core journals of IUMS, first a list of journal titles was prepared and arranged based on the number of citations received and also in a decreasing order. Then, the number of citations present in the collection was calculated. It was revealed that 3.256 of the total citations (22.026) were journals' citation. For classifying the journals, Bradford's law was applied: Based on this rule, the number of journals in each group should be more z

328

2 than half of the journals that have each been cited once 2 . Therefore the journals were classified into five groups (Table 2) and Bradord factor calculated as 1.69. Note that the table is summarized and the unnecessary data omitted.

As you see in table 2, the number of documents on the first category are a=29, in the second a=82 and in the third a=231. The organization note of Table 2 reveals that these figures are so similar to each other; in other words, Bradord's law is verified in this survey. It can be concluded that the 29 documents of the first category form the core documents, which have published the majority of articles.

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Table 2. The Scattering Pattern of the Journals that have published IUMS Authors' Articles ROW

the number of the number published documents published articles

cumulat ive frequency(proportion )

1

29

277

277

2

82

250

527

3

231

275

802

342

802

--

T otal

of

mu ltip lier k)

Organization note 1*a (a=29)

2 .827 2 .827

1*a*k (k=2.827)=81.98 1*a*k2 231.8

-

-

Table 3. The Scattering Pattern of the Collaboration of IUMS Scientists with other Countries the number of documents 792 33 24 8 7 5 4 3 2 1

country

Iran U.S.A Canada, England Australia, Germany Taiwan Holland Israel, Italy, Japan, China, Sweden India, Turkey Algeria, Denmark, New Island Finland, Hungary, Mozambique, Nigeria, Russia, Thailand,

No. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

Table 3 reveals the fact that the major collaboration of IUMS authors is respectively with the authors of U.S.A, Canada, and England (see the rest of countries in the same table). Collaboration Rate of IUMS Authors in W.O.S Database As a part of this project, the collaboration rate of the authors of these articles was investigated and the articles were ranked based on the number of their authors. According to the formula provided in ―Methodology” section, the collaboration rate of the authors is equal to 0.967. In fact, the collaboration rate of the authors is a figure between zero and one. The bigger this rate is, the more collaboration exists between the authors; and the closer it gets to zero, the weaker this collaboration grows. The research history of this university reveals that the collaboration rate at this university is 0.967, which is a rather significant record. 677

=

Discussion and conclusion The scattering pattern of IUMS authors' articles followed Lotka's Productivity Law that is the majority of scientific productions are published by a limited number of authors. In other words, a large number of authors produce just a very small portion of scientific products. Moreover, the scattering pattern of journals published by IUMS Press, conformed to Bradord's Scattering Law; that is a limited number of journals are the publisher of the majority of scientific documents .Due to the increasing growth of publications in the world, journals' price increase, and the reduction of the budgets dedicated to librarians, the collection developments departments of the libraries order the journals just after a meticulous study of the above- mentioned factors. On one hand, Journals contain the most recent information, and on the other hand, ordering them is so costly; therefore ordering a wrong journal is wasting the libraries budget for one year subscription to that journal. Therefore, librarians can choose and buy the journal suggested as the core journals in this article. In this waye, by spending less, they will be able to provide users with vaster knowledge and information. The data analysis proved that the major collaboration of the researchers of this university is with authors of U.S.A, Canada and England. This result is in line with some other research results in this domain (Talebi, 1999; Soheili & Osareh, 2009). Moreover, it was revealed that IUMS scientists have a great collaboration rate in carrying out their research projects and producing their articles ; they have taken advantage of different skills and various fields of knowledge. This fact proved that fields of knowledge are moving towards becoming interdisciplinary and also more specialized. In general, the research history of this university reveals that the collaboration rate at this university is 0.967, which is a rather significant record. However, the results of this survey is not in line with those of Danesh et al. (2009) and also Ghahnaviyeh and Danesh(2009). References Ajiferuke I. Q, Burell J. T.(1988). Collaborative Coefficient: A Single Measure of the Degree of Collaboration in Research. Scientometrics. 14: (5-6): 421-433. Ansafi, S; Gharib, H.(2002). Iran knowledge in International Level. Tehran: Irandoc. Belinchon I, Ramos Jose, Sanchez-Yus, Betlloch I.(2004) Dermatological scientific production from European Union authors. Scientometrics. 2 (61): 271-281. Calvino Amlia M. (2006) Assessment of research performancein food science and technology: publication behavior of five Iberian-American Countries (1992-2003). Scientometrics (2006); 1 (69): 103-116. Danesh, F. etal. (2009). Correlation between Scientific Output and Collaboration among LIS Scholars around the World [as Reflected in Emerald Database]. Journal of Information science & Technology. 25(1): 5-22. Ghnavyeh, H. Danesh, F. (2009). Survey on IUMS researchers‘ collaboration rate in national medical and Para medical seminars. [Research project]. Isfahan: IUMS Vice Chancellery. 678

Jacobs D., Pichappan P. Research Collaborations and Scientific productivity among the Research Universities in South Africa (2006). In Proceedings of International Workshop on Webometrics, Informetrics and Scientometrics & Seventh COLLNET Meeting, Nancy; France. Meng, W., Hu, Z., Liu, W. Evaluation of basic research in China. (69): 85-101.

Scientometrics (2006); 1

Mosavi movahedi, A.K.; Keyani bakhteyari, A.; Khanchamni, J. (2003). The methods of producing and disseminating scientific results. Rahyaft.21: 5-19. Norozichakoli, A.R. etal.(2007). Science production in Iran 2005, 2006: Based on statistics of WOS. Faslnamyeh Ketab. 71:71-90. Osareh, F.; Farsi, G.(2002). SCI: structure and applications. Rahyaft. 27:226-235. Soheili, F.; Osareh, F. (2009). Survey on Razi universities‘ researchers‘ scientific production in WOS 1992-2008. [Research project]. Kermanshah: Razi Univ. vice Chancellery. Sabori, A.K.; Poorsasan, N. (2006). Scientific production in Iran 2005. Rahyaft. 27:49-52. Talebi, M. (1999).Collaboration rate of research centers in Iran and International level via SCI. Rahyaft. 21:112-122. Talebi, M. (2003). Survey on effective factors of producing and publishing scientific articles.

Rahyaft. 27:184-196.

679

A Study of Theoretical Foundations of Library and Information SCIENCE Abstracts in ISI and Scopus Databases During 2001-2010 to Provide Major and Minor Components in This Area

Nayere Soleimanzade

Parisa Malekahmadi

[email protected] M.S. student of library and Information Science Isfahan University of Medical Sciences, Isfahan, Iran

M.S. student of library and Information Science Isfahan University of Medical Sciences, Isfahan, Iran

Hasan Ashrafi Rizi Assistant Professor of library and Information Science Isfahan University of Medical Sciences, Isfahan, Iran

Abstract: It is obvious for library and Information science professionals that theoretical foundations of library and Information science is weak, So professionals and researchers should have more attention to research in this area. In order to accomplish it, they need to identify the components in this area that are disregarded. By doing this, they will achieve agreement about the Limits, scope and nature of this area. So in this research we determine theoretical foundations of library and Information science abstracts in ISI and Scopus databases during 2001-2010 to provide major and minor components in this area. We used a bibliometrics and content analysis method. The needed data has collected via direct refer to articles. The accumulating tool for collecting data from articles was a researcher made checklist, which it‘s validity concluded by Library and Information science professionals. The sample and society of this paper are the same, means that all of the theoretical foundations of library and Information SCIENCE abstracts (N=321) were investigated. After consulting with professionals, 8 major components in this area were identified. Then researchers start to search in ISI and Scopus databases. After refining data, they were entered to the software. At the end of this stage, researchers answered some of research questions. For answering last question, they analyze extracted abstracts with content analysis method. Collected data were analyzed by SPSS, and descriptive statistics (frequency and percentage) were used for data describing. The findings showed that during 2001-2010, in theoretical foundations of library and Information SCIENCE, 321 articles in ISI and Scopus databases were published. The most productive year is 2007 with 53 articles and the least one is 2003 with 17 related articles. The most productive author is ―B. Hjorland‖ with 8 articles. 47.57% of extracted articles were published in 10 main Journals. The ―Journal of Documentation‖ with 29 articles is the most productive Journal in this area. "Challenges in Library and Information Science" with 150 articles has the most frequency and "Library and 680

Information Science and methodological pluralism" with 1 article has the least frequency among the 8 major components. This research showed that during 2001-2010, 321 articles in theoretical foundations of library and Information science were published in these databases. According to extracted articles, 8 major components and some minor components were identified. Among the 8 major components, component of Theoretical Challenge with 51% of articles has allocated highest rate of articles in this area. Other components remain separated and it is witness to this claim. Perhaps pay more attention to other components of this area decrease disagreements comments on definitions and principles of it. Keywords: theoretical foundations, Library and Information Science, ISI Database, Scopus Database

Introduction Among the social sciences, there are rare numbers of sciences similar to ―library and information science‖ in which to be exposed to quarrels and disagreements about its definitions, principles and foundations; in other way librarians and researchers of this field of study have different trends (Rahadoust, 2006). So necessity dictates that philosophical concepts be extracted from dispersed and incoherent concepts and perceptions and beliefs. Because theories in this field are also incoherent and there isn‘t any enough research on theoretical foundations, we can‘t remark the ―library and information science philosophy‖ as an independent and coherent branch such as ―science philosophy‖, ―history philosophy‖ (Rahadoust, 2005). Theoretical and philosophical foundations are approximately synonymous and there is a dainty difference between them. It means that theoretical foundations are the infrastructure of a scientific field so there is no scientific field without theoretical foundations; but the philosophical foundations bring rank and status for a scientific field of study. As a whole the incoherent in definitions of theoretical and philosophical foundations of ―library and information science‖ in Persian text, impel researchers to clarify definitions, principles and foundations of ―library and information science‖ by analyzing the content of abstracts which were published in ISI and Scopus databases during 2001- 2010. The main objective of this article is to determine theoretical foundations of library and Information Science abstracts in ISI and Scopus databases during 2001-2010 to provide major and minor components in this area. The auxiliary goals are assignment of frequency distribution of related abstracts with this area by separation of year; determination of productive authors in this area; assignment of core journal and determination of minor components of theoretical foundations of library and Information Science.

681

Lite rature Review Ashrafi Rizi & colleagues (2011) in their article which the title was ―A survey of related abstracts with information economy in ISI and Scopus in 2000-2010 to propose course headline for information economy‖ determine 12 major components. According to findings in 2000-2010, 799 related articles in these databases were published. The most productive year was 2008 by 148 article and the less productive year was 2003 by 27 related articles. The most productive author was ―Ghosh M.‖ with 6 articles. About 44 percent of articles were published in 13 core journals. The journal of ―librarian Serials‖ with 53 articles have had the most related articles. The component of ―information marketing‖ with 218 articles has the most articles and the component of ―political economy‖ with 3 articles have had the less articles. Zare Farashbandi & kokabi(2007) in their article which named ―Information management in librarianship: a quantitative survey of related abstracts with Information management in LISA in 1990- 2005‖ study all documents which were indexed is LISA during 1990-2005 and have had the term ―information management‖ in their keywords or title. The researchers presented an international definition of information management and said that it involves concepts such as providing, evaluation, storage, organization, modifying, distribution, retrieval and use of information in information systems. The productive authors were ―T.D. Willson‖ with 5 and ―F. Greene‖ with 3 articles. The most productive years were 1998 and 2003 with 21 and 19 related articles. Sotoude and Didgah (2010) in their articles which named ―A survey of relationship of library and information science with other disciplines by using citation analysis of journals‖ performed researched in JCR. They found that library and information science was cited by 36 disciplines and was citing to the same 36 disciplines. Abdkhoda, Mohammadi and Bigdeli(2010) in their article which named ―A survey of the ration of interdisciplinary researches among scientific products of library and information science in LISA and LISTA during 1999-2009‖ by bibliometric analysis investigate all published articles. They found that rate of scientific of Iran had a noticeable increase during years and in 2008 was the most. Male‘s ration was 75 percent and 56 percent of articles were published by single author. The most relationship of Iranian authors was with English and Australian authors. Most of published articles in journal of ―the electronic library‖ were Iranian articles. 46 percent of articles were in interdisciplinary researches. Methods We used a bibliometric and content analysis method. The needed data has collected via direct refer to articles. The accumulating tool for collecting data from articles was a research made checklist, which it‘s validity confirmed by Library and Information Science professionals. The sample and society of this paper are the same, means that all of the theoretical foundations of library and Information Science abst racts (N=321) were investigated. After consulting with professionals, 8 major components in this area were identified. Then researchers start to search in ISI and Scopus databases. For search in ISI, researchers combine each of main components with ―librar y and information science‖ by using ―And‖ operator. For example for searching challenges of library and information science, they search as follow: 682

(Challenges) AND (―library and information science") AND (PY= 2001-2010) They restricted document type to ―journal‘s article‖ and limited the search phrase to article‘s title. Same search strategy was performed in Scopus, but the search phrase was limited to ―abstract, keyword, title‖. After refining data, they were entered to the software. At the end of this stage, researchers answered some of research questions. For answering last question, they analyze extracted abstracts with content analyzing method. Collected data were analyzed by SPSS, and descriptive statistics (frequency and percentage) were used for data describing. Findings In this part we answer and analyzing main questions of research separately. Table 1. 8 main components and subjective subgroups of main component Main components

Subjective subgroups of main co mponent

Challenges of library

Challenges of information management, library and digital library

and

development, instruction of library and informat ion science, librarianship,

code 1

information

science

service providing, research methods, publishing, health librarianship, technology development, name of library and information science, all types of libraries

2

Professional ethic of

Instruction of information ethic, ethic issue in protecting websites, station

lib rary

of ethic in library and informat ion science, ethic principle for lib rarianship

and

informat ion science 3

Theory-making lib rary

4

in

Information visibility, social network‘s services, theory in library and

and

informat ion science, social aspect of information, critical theory,

informat ion science

informat ion literacy

Meta

Data mining, semantic in formation theory

theory

lib rary

in and

informat ion science 5

6

Information

Knowledge organization, info rmation behavior, structure of anthologies,

organization

systematic theory of organization

Identity of library

Relevance, scientific identity of journals, crit ical theory and legislat ion in

and

lib rary and information science, information literacy, annually ranking of

information

science

lib raries, social and national and personal identity of library and informat ion science

7

Pluralism in library and

Data mining, knowledge discovery

information

science

683

8

Philosophy lib rary

of

Mixed research methods, information organization, library performance,

and

philosophy of informat ion science, crit ical realism, citation an alysis,

informat ion science

epistemology in library and information science, biblio metric analysis

Table2. Frequency distribution of “library and information science challenges” in theoretical and philosophical foundations of library and information science by separation of year in ISI and Scopus

option

percentage frequencies

Year Scopus

ISI

Scopus

ISI

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 sum

Table 2 shows Frequency distribution of ―library and information science challenges‖ in theoretical and philosophical foundations of library and information science by separation of year in ISI and Scopus. The years 2009-2010 with 23 articles have the most articles in ―library and information science challenges‖. In 2002-2004 the fewer articles were published.

684

Table3. Frequency distribution of “professional ethic of library and information science” in theoretical and philosophical foundations of library and information science by separation of year in ISI and Scopus

option

percentage frequencies

Year Scopus

ISI

Scopus

ISI

2002

-

-

-

-

2003

-

2001

-

2004 2005 2006 2007 2008 -

2009

-

-

2010 sum

Table3 shows Frequency distribution of ―professional ethic of library and information science‖ in theoretical and philosophical foundations of library and information science by separation of year in ISI and Scopus. The most productive years are 2005-2006 with 4 articles. In the year 2002 no article was published in ―professional ethic of library and information science‖. Table4. Frequency distribution of “pluralism of library and information science” in theoretical and philosophical foundations of library and information science by separation of year in ISI and Scopus

option

percentage frequencies

Year Scopus

ISI

2001

-

sum

-

Scopus

ISI -

1

-

Table4 shows Frequency distribution of ―p luralism of library and information science‖ in theoretical and philosophical foundations of library and information science by separation of year in ISI and Scopus. In ―pluralism of library and information science‖ just one article was published in Scopus in 2001.

685

Table5. Frequency distribution of “theory-making of library and information science” in theoretical and philosophical foundations of library and information science by separation of year in ISI and Scopus

option

percentage frequencies

Year Scopus

ISI

Scopus

2001

-

-

2002

-

-

2003

-

-

2004

-

-

2005

-

-

2006

-

-

2007

-

-

2008

-

-

2009

-

-

2010

-

-

sum

-

-

ISI

Table 5 shows Frequency distribution of ―theory- making of library and information science‖ in theoretical and philosophical foundations of library and information science by separation of year in ISI and Scopus. It presents that Scopus has no articles in t his component, the most productive year is 2007 with 16 article and the less ones are 2004 and 2010 with 1 article. Table6. Frequency distribution of “information organization” in theoretical and philosophical foundations of library and information science by separation of year in ISI and Scopus

option

percentage frequencies

Year 2001

Scopus

ISI

Scopus

ISI

-

-

-

-

2002 -

2003

-

2004 2005 2006 2007 2008 2009 2010

686

-

-

-

sum

Table6 shows Frequency distribution of ―information organization‖ in theoretical and philosophical foundations of library and information science by separation of year in ISI and Scopus. In 2001 and 2010 there are no related articles in these databases. The most productive year is 2008 with 9 related articles. Table7. Frequency distribution of “meta-theory of library and information science” in theoretical and philosophical foundations of library and information science by separation of year in ISI and Scopus

option

percentage frequencies

Year 2001

Scopus

ISI

Scopus

-

0

-

-

2008 -

2010

0

ISI -

-

sum

Table7 shows Frequency distribution of ―meta-theory of library and information science‖ in theoretical and philosophical foundations of library and information science by separation of year in ISI and Scopus. In this area just in 3 years 2001, 2008, 2010 and in each year only 1 article was published. Table8. Frequency distribution of “philosophy of library and information science” in theoretical and philosophical foundations of library and information science by separation of year in ISI and Scopus

option

percentage frequencies

Year Scopus 2001

ISI

Scopus

ISI

-

-

-

-

2002 2003 2004 2005 2006 2007 2008 2009 2010 sum 687

Table 8 shows Frequency distribution of ―philosophy of library and information science‖ in theoretical and philosophical foundations of library and information science by separation of year in ISI and Scopus. It presents that the 2005 and 2008 with 8 articles are the most productive years in this component. The less productive year in this component is 2002 with just 2 articles. Table9. Frequency distribution of “identity of library and information science” in theoretical and philosophical foundations of library and information science by separation of year in ISI and Scopus

option

percentage frequencies

Year Scopus

ISI

Scopus

ISI

2001 2002

-

2003

-

-

-

-

2004 2005

-

-

-

-

2006

-

-

-

2007 2008 2009 2010 sum

Table9 shows Frequency distribution of ―identity of library and information sc ience‖ in theoretical and philosophical foundations of library and information science by separation of year in ISI and Scopus. The most productive year is 2007 with 8 articles. No article was published in this component in 2003 and 2006.

Table10. Frequency distribution of “productive authors” in theoretical and philosophical foundations of library and information science during 2001-2010

name of Author

Number of published articles

688

HJorland B. Buschman J. Ocholla D.N. Overal P.M . Wiston MD. Blu mel L. Onyancha OB. Virkus S. Pirro G.

Table10 shows Frequency distribution of ―productive authors‖ in theoretical and philosophical foundations of library and information science during 2001-2010. It presents that ―HJorland B.‖ is an outstanding author with 8 articles in theoretical and philosophical foundations of library and information science during 2001-2010. After him ―Buschman J.‖ with 5 articles ranked in second place. It is noticeable that the names which mentioned in the table aren‘t just the first author (entry) but also it involves co-authors. Table 11. Frequency distribution of “core journals” in theoretical and philosophical foundations of library and information science during 2001-2010

Frequency code

Option journal title

ISI

Scopus

Journal of Documentation Library Quarterly Library Trends Science & Technology libraries Library and Info rmation science Research Canadian Journal of Library and In formation Science Library Hi Tech -

New library world International Informat ion and Library Rev iew

-

Education for Informat ion(4)

Table 11 shows Frequency distribution of ―core journals‖ in theoretical and philosophical foundations of library and information science during 2001-2010. It 689

presents that about 47.57 percent of articles (N=321) were published in 10 core journals. The journal of documentation with 29 articles has the most number of articles in theoretical and philosophical foundations of library and information science. Conclusion This research showed that during 2001-2010, 321 articles in theoretical foundations of library and Information science were published in these databases. According to extracted articles, 8 major components and some minor components were identified. Among the 8 major components, component of Theoretical Challenge with 51% of articles has allocated highest rate of articles in this area. Other components remain separated and it is witness to this claim, perhaps paying more attention to other components of this area decrease disagreements comments on definitions and principles of it. References Ashrafi Rizi, Hasan; Kazempour, Zahra; Bahrami, Sousan; Nouri, Rasoul (2011). A survey of related abstracts with information economy in ISI and Scopus in 2000-2010 to propose course headline for information economy. Abstracts of 1th National congress of ―Health information systems and knowledge management‖. 8-10 march. Iran. Isfahan. Isfahan University of Medical Sciences. Zare Farashbandi, Firooze; kokabi, morteza (2007). Information management in librarianship: a quantitative survey of related abstracts with Information management in LISA in 19902005. Library and information science quarterly. 39(3). V. 10. Abdkhoda,Hiva; Mohammadi, Leila; Bigdeli, Abdolhosein (2010). A survey of the ration of interdisciplinary researches among scientific products of library and information science in LISA and LISTA during 1999-2009. Abstracts of 3th congress of Iranian association of library and information science. Iran. Shiraz. Shiraz University. Sotoude, Hajar; Didgah, Fereshte (2010). A survey of relationship of library and information science with other disciplines by using citation analysis of journals. Abstracts of 3th congress of Iranian association of library and information science. Iran. Shiraz. Shiraz University. Rahadoust, Fateme(2006). Philosophy of library and information science. Iran. Tehran: Ketabdar. Rahadoust, Fateme(2005). Philosophy of library and information science. Iranian Library and Information Science Encyclopedia. V.2. p. 1224

690

The Survey of Scientific Productions in Fields of Scientometrics and Webometrics in Web of Science (WOS) Hajar Zarei

Roghayeh Ghorbani Bousari

hajar_zarei@ toniau.ac.ir Faculty member o f Library & Info rmation science, Tonekabon branch, Islamic Azad University, Tonekabon, Iran

Depart ment of library and informat ion science, Tonekabon branch, Islamic Azad University, Tonekabon, Iran

[email protected]

Abstract : The main purpose of this study is to recognize the most scientific productions in both Scientometrics and Webometrics fields in WOS database based on some indicators like Authors, Document types, Publication year, Subject Areas, the rate of Citations, Language, the status of Congress and Conference productions, and collaborative Countries. This article was descriptive- analytical research that surveyed scientific productions of Scientometrics and Webometrics by using the indicators of WOS Database. This study was conducted through library method to collect data, and the data was evaluated via analytical method. Findings showed that the most proportion of scientific productions indexed in Web Of Science database in two fields were related to the articles which have been produced by USA in English between 2008-2010. These two fields had the most scientific productions in one specific language(English), subject areas, conference, source title, institution and publication year in respectively. Following the results of this research,

scientific policymakers should invest in the top scientific fields and common scientific productions in each country in the future. Introduction The post–structuralistic approach shadowing on art, literature, and science, since 1960 has created new perspective in which literature, work, and context have been affected. In this decade, the citation analysis would basically be post–structuralistic following the first citation indices (Horri,2009). Citation analysis has been widely used to evaluate research and to identify the impact of scientific work in many areas of science (Moed, 2005). All assessments known as bibliometrics, Informetrics, Scientometrics , and Webometrics have suggested a relatively modern theme based on partially thematic and mostly citation analyses. In the post–structuralistic approach to Scientometrics no themes could be assessed without a relationship with prior themes. Scientomertics has been employed as a study of quantitative features in scientific communications, research-development operations, and it means analysis of science and a basis for measuring knowledge too. this term is mainly used for the study of all aspects of the literature of science and technology (Hood & Wilson,2001). Scientometrics term was developed in Russian in 1969 (Sengupta, 1992), but there is a controversy over who first constituted it . Webometrics has been also defined as the quantitative study of productions features and using information resources, structures and technologies in web site by using theoretical principles of Scientometrics and Informetrics, of course through this definition, there is a difference between it and other metrics in spite of 691

some similarities. Obviously, the study upon webometrisc has been developed since 1996. Considering the concepts and the importance of citation indices to develop citation analysis ideas, this study will review Scie ntometrics , Webometrics history in one of the most important citation indices. Lite rature review Literature review in the field of webometrics, Scientometrics and science productions indicates that many researches have been done in these fields. Most researches trends take place in the following three classes: 1. The researches that their scientific productions have considered the fields of publication year, institution type, language and emphasize on scientific collaboration too. These studies have rarely considered subject areas; a research focusing on the country field has been done by Bakri & Willet ( 2011). A research has been done in the field of scientific collaboration and co-authorship by Niehaves & Plattfaut(2011), and Aharony(2010) research in subject areas has focused on information literacy. Maurer & Salman khan (2010) research studied electronics learning in 2003-2008, and Bonilla-Calero(2008) surveyed institution fields in 2007 and 2008 respectively. 2. The comparison of scientific productions in different countries and various citation indexes ; such as, Jacsó (2009) to discuss the results of recent experiments in determining the h-index at the country level for the 10 IberoAmerican countries of South America, and Gavel & iselid (2008) to provide the scientific community with some quantitative data of relevance to the evaluation of two major citation databases, and Bakri & Willett(2011) to analyze the publications of, and the citations to, the current staff of 19 departments of computer science in Malaysian universities 3. Researches which study literature review, history, concepts and the models related to scientific productions, Scientometrics and Webometrics with evaluation of different websites with various viewpoints; such as, research of Davarpanah (2010) to construct a model for measuring the strength and weakness of individual disciplines, and Ramirez AMU, Fernandez M, Ortega JL, et al.(2009) to Ranking the world's Web of hospital, Aminpour F, Kabiri P, Otroj Z, et al.(2009), and Faba-Perez, Guerrero-Bote, De Moya-Anegon (2004) to Methods for analysing web citations Nowadays, studies show that the importance of two research methods of Scientometrics and Webometrics in the field of library and information science, and they are used to evaluate scientific outputs of universities, institutions, countries, subject areas and etc. Most researches in Scientometrics are related to scientific productions survey in different databases especially Scopus and Google Scholar that are used as the most important tools to measure scientific productions in different fields. Web Of Science is one of the ISI database to cover citation indexes such as Science Citation Index, Social Sciences Citation Index/ Art & Humanities Citation Index.

692

Purpose of the study The main purpose of this study is to recognize the most scientific productions in both Scientometrics and Webometrics fields in WOS database based on some indicators like authors, types of documents, publication year, citation rates, documents types(the status of congress and conference productions ) collaborative countries and language. Methodology This article is descriptive- analytical research that surveys scientific publications of Scientometrics and Webometrics by using the indicators of WOS Database. This study was conducted through library method to collect data, and the data was analyzed via analytical method. The data collected from WOS by searching Scientometric* and Webometric* in topic respectively. By using the software of ISI database, we analyzed these data base on above indicators. The data in this account was collected from WOS during March 1 to 10, 2011. We moved them to Excel files and analyzed them. Then, the results have showed in the statistical tables. Findings Data was collected through two fields of Scientometrics and Webomerics in different aspects in WOS database. The first document in the fie ld of Scientometrics was related to three articles in 1976, and in the field of Webometrics belonged to an article in 1997. Data was analyzed in different areas of these fields from the mentioned years to the beginning of March 2001. Table 1. Three first ranking of different field base on Scientometrics in Web Of Science fields

Fr.

%Fr

Authors

Schubert, A Glan zel, W Braun, T

37 34 28

4 3.5 3

Country

USA Hungary India

138 88 80

14.6 9.3 8.4

Information science & library science Co mputer science, interdisciplinary applicat ions Co mputer science, info rmation systems

603 401 147

Most documents are indexed in these three subject areas

10 th International conference of the international-societyfor-scientometrics-and-informet rics 11 th International conference of the international-societyfor-scientometrics-and-informet rics 8 th International conference on scientometrics and informetrics

16

1.7

12

1.3

12

1.3

Scientometrics Information processing & management Journal of informat ion science

376 21 20

39.8 2.2 2.1

Subject Areas

Conference Titles

Source Titles

Three first ranking

693

Document Types

ARTICLE Article Proceeding paper editorial material

616 155 46

65 16.4 4.9

2010 2009 2008

116 103 64

12.2 10.9 6.8

Instituti ons

HUNGA RIAN A CAD SCI Hungarian A CAD SCI Natl inst sci technol & dev studiesuniv Amsterdam

49 31 23

4 1.4 11

languages

English Spanish German

863 22 20

91.1 2.32 2.2

Publicati on Years

Ti me cited

Schubert A, Glan zel W, Braun T.(1989). Scientometric 160 datafiles- A co mprehensive set of indicators on 2649 journals and 96 countries in all major science fields and subfields 1981-1985. Scientometrics. Vol16(1-6)p :3-& Rip A, Court ial JP.(1982). Co-woed maps of biotechnology-an examp le of cognitive scientometrices. 63 Scientometrics Vol6(6) p :381-400 Haitun SD.(1982). Stationary scientometric distribution.2.non-gaussian nature of scienrific activities. Scientometrics Vol4(2) p:89-104 60

According to Table 1, the most prolific authors are Schubert,Al with 37 documents(4%), Glanzel,W with 34 documents (3.5%) and Braun,T with 28 documents(3%). The most documents in the country field are related to three countries: USA with 138 documents (14.6%), Hungry with 88 documents (8.4%) and India with 80 documents (8.4%). According to subject areas, the most subjects are in the field of Information Science & Library Science with 603 documents. After it, two fields of Computer Science, Interdisciplinary Applications Computer Science and Information Systems allocated most scientific productions to themselves with 401 and 147 documents respectively. Findings of the conferences field indicates that both of 10th International,conference of the international– society- for– scientometric– and Informetrics with 16 documents(1.7%) and & 11th &8th International conference on Scientometrics and Informetrics with 12 (1.3%) allocated the most scientific productions of Scientometrics in Web Of Science respectively. In the field of source type, the most scientific productions are related to three Journals of Scientometrics (39.8%), with 376 documents; Information processing & management(2.2%) with 21 documents and Journal of Information Science(2.1%) with 20 documents. 694

Based on the findings of document type, the results show that Article with 616 documents (65%), Proceeding Paper with 155 documents (16.4%) and Editorial Material with 46 documents (4.9%) have the most scientific productions in Web Of Science. According to publication year, the most scientific productions indexed in Web Of Science database between 2008 to 2010 with 116, 103 and 64 documents respectively. In field of language type, the most scientific productions are in English with 863 documents (91.1%), Spanish with 22 documents (2.32%) and German with 20 documents (2.2%). Findings in institutions fields show that Hungarian acad Sci, Natl inst Sci tecnol & Dev studiesuniv Amsterdam have allocated the most scientific productions in Web Of Science during 2000-2009. Also, the most citations belong to the article of Schubert A, Glanzel W, Braun T; and the article of Rip A, Courtial JP and Haitun SD with 160, 63 and 60 citations respectively. Table 2. Three first ranking of different fields base on Webometrics in Web Of Science fields

Three First ranking

Fr.

%Fr

authors

Thelwall, M Ortega, Jl Aguillo, If

39 15 12

26.3 10.1 8.1

country

Eng land Spain USA

44 29 13

29.7 19.6 8.8

Information science & library science Co mputer science, info rmation systems Co mputer science, interdisciplinary applicat ions

127 62 37

Most documents are indexed in these three subject areas

10 th International conference of the international-societyfor-scientometrics-and-informet rics 11 th International conference of the international-societyfor-scientometrics-and-informet rics 12 th International conference of the international-societyfor-scientometrics-and-informet rics

7

4.7

6

4

5

3.4

Scientometrics Journal of the American society for information science and technology Journal of informat ion science

30 15

20.3 10.1

12

8.1

Document Types

Article Rev iew Ed itorial material

49 6 4

33.1 4 2.7

Publicati on Years

2009 2008 2010

24 18 18

16.2 12.2 12.2

Wolverhampton Univ CSIC Univ western Ontario

37 13 9

25 8.8 6

Eng lish

136

91.9

Subject Areas

Conference Titles

Source Titles

Instituti ons

695

languages

Spanish Portuguese

11 1

Almind TC, Ingwersen P.(1997). Info rmetric analyses on the World Wide Web: Methodological approaches to ‗webo metrics‘. Journal of documentation. Vo l 53(4). P: 404-426

146

Ti me cited

7.4 0.7

84 Bjorneborn L, Ingwersen P.(2001). Perspectives of webometrics. Sciento metrics Vol 50(1). P65-82 Vaughan L, Thelwall M(2003). Scholarly use of the Web: What are the key inducers of links to journal Web sites?. Journal of the American society for informat ion science and Technology Vol 54(1), P:

61

Findings in table 2 show that the most scientific productions in author field are related to Thelwall,M with 39 documents (26.3%), Ortega, JL with 15 documents (10.1%) and Agullo, IF with 12 documents (8.1%) which were the most prolific authors. In country field, the most documents were related to three countries of England with 44 documents (29.7%), Spanish with 29 documents (19.6%) and USA with 13 documents (8.8%) that have had the most scientific productions in Web Of Science database. According to subject areas, the most scientific productions indexed in Information Science & Library Science with 127 documents. Then, two fields of Computer Science, Information Systems and Computer Science, Interdisciplinary Applications have the most scientific productions with 62 and 37 documents respectively. Findings in conferences field indicate that both of 10 th & 11 th International conference of the international –society- for –Scientometric – and Informetrics have 13 documents (8.7%) and 12th international conference of the international – society- forScientometrics – and- Informetrics has 5 documents (3.4%). So, they have produced the most scientific productions in Webometrics. According to source title, the most scientific productions are related to three Journals: Scientometrics (20.3%), Journal of the American Society for Information Science and Technology with 15 documents (10.1) and Journal of Information Science12 documents (6.1%). Furthermore, table 2 shows that in regard to documents type, Articles (33.1%) with 49 documents; Review (4%) with 6 documents; and Editorial Materials (2.7) with 4 documents have more scientific productions respectively. Regarding the publication year, the most scientific productions (60 documents) have been produced in 2008 to 2010 ( 40.6%). Findings of language field indicates the most scientific productions are in English (91.9) with 136 documents; Spanish (7.4%) with 11 documents and Portuguese (0.7%) with one document. Surveing the cooperating institutions shows that the most scientific productions are related to Wolverhampton university (11%) with 37 documents; CSIC (1.4) with 13 documents and Western Ontario(11%) with 9 documents. Also, the most citations are related to the articles of Almind TC, Ingwersen P with 146 citations. Then, the articles 696

of Bjorneborn L, Ingwersen P and Vaughan L, Thelwall M have 84 and 61citations respectively. Conclusion and Discussion Findings showed that 947 and 147 out of 1094 retrieved documents are related to Scientometrics and Webometrics respectively. Most documents have been recorded as articles in both fields Scientometrics (616 documents), Webometrics (95 documents) at WOS database. Among authors, the most documents and citations in scientometrics field have been related to A.Schubert (37 documents) and the articles of T.Braun, W.Glanzel, A.Schubert (160 citations) respectively.They have been published in the Scientometrics Journal in 1989 titled "Scientometric datafiles- A comprehensive set of indicators on 2649 journals and 96 countries in all major science fields and subfields 1981-1985‖. In Webometrics field, the most documents and citations have been related to M.Thelwall ( 34 documents ) and the article of P.Ingwersen , Tc .Almind (146 citations ) respectively which have been published in Journal of documentation in 1997. Its title was ―Informetric analyses on the World Wide Web: Methodological approaches to 'Webometrics'. Among journals, the most scientific productions in both scientometrics and Webometrics fields have been related to Scientometrics journal (367documents in Scientometrics and 30 documents in Webometrics). Among institute, the most scientific productions have been related to Hungrain ACAD SCI institute (49 documents) in Scientometrics and Wolverhampton UNIV (37 documents) in Webometrics fields respectively. Among countries, the most scientific productions have been related to the USA (138 documents) in Scientometrics and England (44 documents) in Webometrics fild respectively. Among languages, most scientific productions have been published in English language in both Scientometrics(863 documents) and Webometrics fields (136 documents) respectively. Among several conferences, most scientific production have been published in WOS database have been related to 12th International conference of international society for Scientometrics and Informetrics (16 documents) in the Scientometrics fild, and 10th international conference of the international society for Scientometrics and Informetrics (7documents) in the Webometrics field respectively. The study of scientific productions indexed in citation indexes can be an indicator to evaluate the rate of scientific productions and the degree of their usefulness in different fields. The results showed that scientific productions of Scientometrics and Webometrics have had noticeable growth in their different fields in recent years, and allocated about high percent of published scientific productions to themselves. Generally, the most proportion of scientific productions indexed in Web Of Science database in two fields were related to the articles which have produced by USA in English between 2008-2010. Also, the results showed that the most of their scientific productions have produced and indexed in one specific language(English), subject areas, conferences, source or Journal, institution and publication year in its field respectively.

697

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Faba-Perez C, Guerrero-Bote VP, De Moya-Anegon F(2004). Methods for analysing web citations: A study of web-coupling in a closed environment. Libri. Vol:54(1), Pages: 43-53 Gavel Ylva, Iselid Lars, (2008) "Web of Science and Scopus: a journal title overlap study", Online Information Review, Vol. 32 Iss: 1, pp.8 – 21. doi: 10.1108/14684520810865958 Hood William W., Wilson Concepcion S.(2001). The literature of bibliometrics, scientometrics, and informetrics. Scientometrics.,Vol. 52(2) P: 291–314 Horri,A.(2008). Introduction in Osare and et al. From bibliometric to webometric. (p:13-17). Tehran: arjang. Jacsó Péter (2009) "The h-index for countries in Web of Science and Scopus", Online Information Review, Vol. 33 Iss: 4, pp.831 – 837. doi: 10.1108/14684520910985756

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698

Author Index G. Mahesh ........................................................... 386 Grant Lewison ........................................... 320, 328 Gü leda Dü zyol.................................................... 555 Gyung mi Jin........................................................ 247

A A. Esra Özkan Çelik .......................................... 532 A.P.Jayanti .......................................................... 362 A.S. Haynes......................................................... 166 Abdelaziz Marzak .............................................. 637 Abdolreza Noroozi Chakoli.............................. 481 Alexander Pudovkin ......................................22, 39 Alizera Abbasi .................................................... 617 Amir Hussein Abdulmajid ................................ 123 Amirreza Asnafi ................................................. 430 Amjad Ali .............................................................. 87 Ana Maria Mielniczuk de Moura .................... 522 Arif Riahi ............................................................. 416 Azadeh Heidari .......................................... 214, 658

H Hajar Zarei .......................................................... 726 Hamid Darvish ................................................... 133 Hanney, Steve..................................................... 260 Hasan Ashrafi Rizi............................................. 715 Hassan B. Ali ...................................................... 491 Hicham Beh ja ..................................................... 637 Hildrun Kretschmer ....................................... 22, 39 Hsuan-I Liu ......................................................... 340 I

B

I K Ravichandra Rao ............................................55 Ida Reg ina Ch ittó Stumpf ................................. 522

B. Ramesh Babu ................................................. 280 Bastian Eine ........................................................ 395 Bernd Markscheffel ........................................... 395 Bhati Madhulika ................................................. 362 Bi-Chun Chang ................................................... 340 Burcu Umut Zan ................................................. 272 Bülent Karasözen ............................................... 271 Byungun Yoon........................................... 247, 292

İ İrem Soydal......................................................... 555 J Jean-Charles Lamirel......................................... 304 Jean-Pierre V. M. Hérubel................................ 224 Jharna Soni.......................................................... 577 Jianhua Hou ........................................................ 235 Jie Pang................................................................ 235 Johannes Stegmann ....................................... 22, 39 Jonathan M. Lev itt ............................................. 315 Jones, Teresa H .................................................. 259 Juan Go rraiz...........................................................98

C Christian Gu mpenberger ..................................... 98 D Daisy Jacobs........................................................ 320 Deming Lin ......................................................... 114 Dilruba Mahbuba................................................ 372 Divya Srivastava ................................................ 511 Do menico Maisano ............................................ 184 Donald deB. Beaver............................................. 12 Donovan, Claire .................................................. 259

K Kee-eun Lee ........................................................ 292 Kuan-Ch ia Chen................................................. 340 L

E

Leila Dehghani ................................................... 153 Leila Nemati Anaraki ........................................ 658 Li Liu ................................................................... 350 Lin Zhang ............................................................ 643 Liwen Vaughan .................................................. 566

Elaheh Hasanzadeh ............................................ 141 Elisa Turina ......................................................... 184 Esteban Ro mero-Frías ....................................... 566 Et ienne Zé Amvela. ........................................... 599 Eugene Garfield ................................................5, 39

M

F

M. Natarajan .............................................. 280, 421 M.H. Biglu .......................................................... 655 Mansour Mirzaee ............................................... 123 Mansoureh Serati Sh irazi ................................. 607 Maria Aparecida Moura .................................... 406 Maria Benavent-Pérez..........................................98 Maryam Pakdaman Naeini ............................... 430 Maya Verma ....................................................... 577 Mazyar Ganjoo ................................................... 153 Mehmet Gençer .................................................. 195

Fahime Abbasi ........................................... 109, 627 Fahimeh Babalhavaeji ...................................... 499 Faramarz Soheili........................................ 177, 707 Farshid Danesh ................................. 123, 177, 707 Félix de Moya-Anegón........................................ 98 Fioren zo Franceschini ....................................... 184 G G. E. Derrick ....................................................... 166 699

Mohaddeseh Taghipour Moazzen- Langaroudi ........................................................................... 665 Mohammad Hassanzadeh ........................ 203, 445 Mohammad Hossein Biglu ...................... 109, 634 Mohammed Imt iaz Ah med ................................. 77 Mohsen Haji Zeinolabedin i .............................. 123 Mostafa Ghanbari............................................... 693 Mostafa Jafari ..................................................... 693 Mozhdeh Dehghani............................................ 141

S. Gopalakrishnan.............................................. 280 Saeed Ghaffari .................................................... 468 Samile Andréa de Sou za Van z ........................ 522 Sandhya Diwakar ............................................... 511 Sanjayku mar Baburao Burde ........................... 457 Sedigheh Mohamadesmaeil .................... 468, 678 Seyed Hossein Hosseini.................................... 693 Shadi Tabatabaei far .......................................... 630 Shadi Tabatabaeifar ........................................... 476 Shahrzad Sharifi ................................................. 665 Sharhzad Sharifi ................................................. 481 Shilpa Chaudhury .............................................. 362 Solmaz Zardary .................................................. 627 Somaye Kazemi Koohbanani........................... 678 Somayyeh Joolaei .............................................. 499 Sônia Elisa Caregnato ....................................... 522 Sujit Bhattacharya.............................................. 362

N N K Wadhwa....................................................... 386 N. Chakh mach i ................................................... 634 N. K. Wadhwa .................................................... 457 N. Navali.............................................................. 655 Nader Naghshineh.............................................. 416 Nayere Sadat So leimanzade Najafi ................. 645 Nayere So leimanzade ........................................ 715 Negin Nikoo manzari.......................................... 430

U

Ö

Umut Al ............................................................... 555 Umut Sezen ......................................................... 555

Özlem Bayram .................................................... 271

W

P

W.D. Hall ............................................................ 166 Wen-chi Hung ........................................... 588, 594 Wolfgang Glän zel ........................................19, 643

Parisa malekahmad i ........................................... 645 Parisa Malekah madi........................................... 715 Peyman A khavan................................................ 693 Philip Roe ............................................................ 328

X Xian wen Wang ................................................... 114

R

Y

R. D. Shelton....................................................... 491 R. K. Verma ........................................................ 457 R. Karpagam ....................................................... 280 Reza Basirian Jahro mi....................................... 153 Reza Khodadust.................................................. 203 Roghayeh Ghorbani Bousari ................... 445, 726 Roghayeh Ghorbani Bousari r.ghorbani.b@g mail.co m .............................. 665 Ronald Rousseau ......................................... 67, 372 Ruihao Wang ...................................................... 350

Yang Yangand .................................................... 350 Yaşar Tonta......................................................... 532 Youness Boukouchi........................................... 637 Yue Chen ............................................................. 114 Yu xian Liu .......................................................... 350 Z Zahra Azizkhani ........................................ 476, 630 Zehra Taşkın ....................................................... 555 Zeyuan Liu .......................................................... 114 Zizil Arled i Glienke Nunes .............................. 522 Zohreh Zahedi .................................................... 607

S S. Chap man ......................................................... 166

700