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These papers are an important guide for other researchers. However, the increasing amount of digital data with the development of information technologies makes it difficult to reach the desired information. Recommendation systems play an important role in facilitating researchers' access to studies on their subjects. It provides faster and easier access to papers on the desired subject. Recommendation systems are developed according to the user profile or subject. In this paper, a novel hybrid paper recommendation system based on deep learning is proposed. The method uses a combination of document similarity, hierarchical clustering, and keyword extraction. Our aim is to group papers in different fields such as computer science, economics, medicine, or in a specific field, according to their subjects, and to present papers with high semantic similarity to the user according to the query entered. The study has been applied on real dataset containing papers from different categories such as machine learning, artificial intelligence, human–computer interaction in computer science. The success of each stage of the study has been evaluated separately. However, looking at the system as a whole, the overall performance of the proposed approach is 80%. Papers having high similarity with their queries have been recommended to users. Thus, access to the studies on the desired subject in the huge amount of papers has been made faster and easier.",{"EN":261,"VI":262},"A novel hybrid paper recommendation system using deep learning","Hệ thống gợi ý bài báo khoa học lai ghép mới sử dụng học sâu",{"VOID":264},"Ariff, N. M., Bakar, M. A. A., & Rahmad, M. I. (2018). Comparative study of document clustering algorithms. International Journal of Engineering and Technology (UAE), 7(4), 246–251.\nBai, X., Wang, M., Lee, I., Yang, Z., Kong, X., & Xia, F. (2019). Scientific paper recommendation: A survey. Ieee Access, 7, 9324–9339.\nBancu, C., Dagadita, M., Dascalu, M., Dobre, C., Trausan-Matu, S., & Florea, A. M. (2012). ARSYS—Article Recommender System. In 2012 14th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (pp. 349–355). IEEE.\nBharti, S. K., Babu, K. S. (2017). Automatic keyword extraction for text summarization: A survey. arXiv preprint arXiv:1704.03242.\nBulut, B., Gündoğan, E., Kaya, B., Alhajj, R., Kaya, M. (2020). User’s research interests based paper recommendation system: A deep learning approach. In Putting Social Media and Networking Data in Practice for Education, Planning, Prediction and Recommendation (pp. 117–130). Springer, Cham.\nBütün, E., & Kaya, M. (2019). Predicting citation count of scientists as a link prediction problem. IEEE Transactions on Cybernetics, 50(10), 4518–4529.\nBütün, E., Kaya, M., & Alhajj, R. (2018). Extension of neighbor-based link prediction methods for directed, weighted and temporal social networks. Information Sciences, 463, 152–165.\nDai, A. M., Olah, C., Le, Q. V. (2015). Document embedding with paragraph vectors. arXiv preprint arXiv:1507.07998.\nFiroozeh, N., Nazarenko, A., Alizon, F., & Daille, B. (2020). Keyword extraction: Issues and methods. Natural Language Engineering, 26(3), 259–291.\nGündoğan, E., & Kaya, M. (2019). Creating special issues automatically for papers accepted in journals. In 2019 1st International Informatics and Software Engineering Conference (UBMYK) (pp. 1–4). IEEE.\nGündoğan, E., & Kaya, M. (2020). Research paper classification based on Word2vec and community discovery. In 2020 International Conference on Decision Aid Sciences and Application (DASA) (pp. 1032–1036). IEEE.\nLau, J. H., & Baldwin, T. (2016). An empirical evaluation of doc2vec with practical insights into document embedding generation. arXiv preprint arXiv:1607.05368.\nLe, Q., & Mikolov, T. (2014). Distributed representations of sentences and documents. In International Conference on Machine Learning (pp. 1188–1196). PMLR.\nLee, Y. C., Yeom, J., Song, K., Ha, J., Lee, K., Yeo, J., & Kim, S. W. (2016b). Recommendation of research papers in DBpia: A Hybrid approach exploiting content and collaborative data. In 2016b IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 002966–002971). IEEE.\nLee, Y. C., Yeom, J., Song, K., Ha, J., Lee, K., Yeo, J., & Kim, S. W. (2016a). Recommendation of research papers in DBpia: A Hybrid approach exploiting content and collaborative data. In 2016a IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 002966–002971). IEEE.\nLiu, H., Kou, H., Yan, C., & Qi, L. (2020). Keywords-driven and popularity-aware paper recommendation based on undirected paper citation graph. Complexity, 2020, 1–15. https:\u002F\u002Fdoi.org\u002F10.1155\u002F2020\u002F2085638\nLorbeer, B., Kosareva, A., Deva, B., Softić, D., Ruppel, P., & Küpper, A. (2018). Variations on the clustering algorithm BIRCH. Big Data Research, 11, 44–53.\nMa, L., Zhang, Y. (2015, October). Using Word2Vec to process big text data. In 2015 IEEE International Conference on Big Data (Big Data) (pp. 2895–2897). IEEE.\nPan, L., Dai, X., Huang, S., & Chen, J. (2015). Academic paper recommendation based on heterogeneous graph. In Chinese computational linguistics and natural language processing based on naturally annotated big data (pp. 381–392). Springer, Cham.\nPan, S., Li, Z., & Dai, J. (2019). An improved TextRank keywords extraction algorithm. In Proceedings of the ACM Turing Celebration Conference-China (pp. 1–7).\nPera, M. S., & Ng, Y. K. (2014). Exploiting the wisdom of social connections to make personalized recommendations on scholarly articles. Journal of Intelligent Information Systems, 42(3), 371–391.\nQaiser, S., & Ali, R. (2018). Text mining: Use of TF-IDF to examine the relevance of words to documents. International Journal of Computer Applications, 181(1), 25–29.\nQingyun, Z., Yuansheng, F., Zhenlei, S., & Wanli, Z. (2020). Keyword extraction method for complex nodes based on TextRank algorithm. In 2020 International Conference on Computer Engineering and Application (ICCEA) (pp. 359–363). IEEE.\nRamadhani, F., Zarlis, M., & Suwilo, S. (2020). Improve BIRCH algorithm for big data clustering. IOP Conference Series: Materials Science and Engineering, 725(1), 012090.\nShirkhorshidi, A. S., Aghabozorgi, S., Wah, T. Y., & Herawan, T. (2014). Big data clustering: A review. In International conference on computational science and its applications (pp. 707–720). Springer, Cham.\nSon, J., & Kim, S. B. (2018). Academic paper recommender system using multilevel simultaneous citation networks. Decision Support Systems, 105, 24–33.\nSteinert, L., & Hoppe, H. U. (2016). A comparative analysis of network-based similarity measures for scientific paper recommendations. In 2016 Third European Network Intelligence Conference (ENIC) (pp. 17–24). IEEE.\nSugiyama, K., & Kan, M. Y. (2013). Exploiting potential citation papers in scholarly paper recommendation. In Proceedings of the 13th ACM\u002FIEEE-CS Joint Conference on Digital Libraries (pp. 153–162).\nSun, J., Ma, J., Liu, Z., & Miao, Y. (2014). Leveraging content and connections for scientific article recommendation in social computing contexts. The Computer Journal, 57(9), 1331–1342.\nWang, G., Zhang, X., Wang, H., Chu, Y., & Shao, Z. (2021). Group-oriented paper recommendation with probabilistic matrix factorization and evidential reasoning in scientific social network. IEEE Transactions on Systems, Man, and Cybernetics: Systems.\nWang, G., He, X., & Ishuga, C. I. (2018). HAR-SI: A novel hybrid article recommendation approach integrating with social information in scientific social network. Knowledge-Based Systems, 148, 85–99.\nWang, H., Ye, J., Yu, Z., Wang, J., & Mao, C. (2020). Unsupervised keyword extraction methods based on a word graph network. International Journal of Ambient Computing and Intelligence (IJACI), 11(2), 68–79.\nWen, Y., Yuan, H., & Zhang, P. (2016). Research on keyword extraction based on word2vec weighted textrank. In 2016 2nd IEEE International Conference on Computer and Communications (ICCC) (pp. 2109–2113). IEEE.\nWest, J. D., Wesley-Smith, I., & Bergstrom, C. T. (2016). A recommendation system based on hierarchical clustering of an article-level citation network. IEEE Transactions on Big Data, 2(2), 113–123.\nXia, X. (2020). Clustering Analysis of Interactive Learning Activities Based on Improved BIRCH Algorithm. arXiv preprint arXiv:2010.03821.\nXia, F., Liu, H., Lee, I., & Cao, L. (2016). Scientific article recommendation: Exploiting common author relations and historical preferences. IEEE Transactions on Big Data, 2(2), 101–112.\nXia, F., Wang, W., Bekele, T. M., & Liu, H. (2017). Big scholarly data: A survey. IEEE Transactions on Big Data, 3(1), 18–35.\nZhang, Z., Petrak, J., & Maynard, D. (2018). Adapted textrank for term extraction: A generic method of improving automatic term extraction algorithms. Procedia Computer Science, 137, 102–108.\nZhao, W., Wu, R., & Liu, H. (2016). Paper recommendation based on the knowledge gap between a researcher’s background knowledge and research target. Information Processing & Management, 52(5), 976–988.",{"VOID":266},"10.1007\u002Fs11192-022-04420-8","PUBLICATION","VERIFIED","2025-02-24T02:49:27.665+00:00","Auto Verify",[272],"VI","https:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs11192-022-04420-8",[275,291],{"id":276,"sortIndex":18,"researcher":17,"roles":277,"affiliations":279,"properties":288,"displayName":290,"givenName":17,"familyName":17},"8791bb96-a379-4501-806a-32ea29fec595",[278],"AUTHOR",[280],{"id":281,"sortIndex":18,"affiliation":282,"properties":17},"561e4b50-0d94-4778-a4e6-a85a8bbc9965",{"id":281,"createTime":17,"updateTime":17,"relativeEntities":283,"slug":17,"properties":284,"entityType":17,"verifyStatus":17,"verifyTime":17,"verifyNote":17,"languages":17,"translateLanguages":17,"viewCount":17,"url":17,"parentIds":287,"statistic":17},[],{"title":285},{"VI":286},"Department of Computer Engineering, Firat University, Elazig, Turkey",[],{"title":289},{"VI":290},"Esra 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and representativeness are two enriching components of an academic field and for quite some time, Management Information Systems (MIS) researchers have been advocating for more studies on them. The objective of this study is to investigate the extent to which survey-based research articles reflect diversity in MIS research and to evaluate representativeness in journals in the MIS field. Diversity is a multifaceted concept that can be delineated along several constructs such as diversity in research approach, diversity in theories, and diversity in research methods. Survey research is the dominant research method in the MIS field accounting for 32–41% of published empirical research studies. In this study, diversity is conceptualized and explored through six factors that are easily identifiable in a survey-based research article namely the unit of analysis, research topics, type of respondents, number of authors, number of schools of authors, and number of country of authors. The study makes use of a set of 1466 survey-based research articles published over 21 years in 15 mainstream MIS journals in Europe and the USA to study diversity and representativeness using Tsallis entropy and Chi-Square statistics. The results present an overview of the state of the MIS survey-based research in Europe and the USA while also revealing that MIS researchers are predominantly gathering survey data from business executives at the firm and individual units of analysis in both regions. Overall, we find dominant categories (top 2) emerge for each factor as they relate to diversity both in Europe and the USA with three factors having the same dominant categories indicating that diversity continues to elude the MIS research field. The results have implications for both research and practice in the MIS field.",{"EN":382,"VI":383},"What does MIS survey research reveal about diversity and representativeness in the MIS field? A content analysis approach","Nghiên cứu khảo sát MIS tiết lộ điều gì về tính đa dạng và tính đại diện trong lĩnh vực MIS? Một cách tiếp cận phân tích nội dung",{"VOID":385},"Acedo, F. J., Barroso, C., Casanueva, C., & Galán, J. L. (2006). Co-authorship in management and organizational studies: An empirical and network analysis. Journal of Management Studies,43(5), 957–983.\nAlavi, M., & Carlson, P. (1992). A review of MIS research and disciplinary development. Journal of Management Information Systems,8(4), 45–62.\nAyanso, A., Lertwachara, K., & Vachon, F. (2007). Diversity or identity crisis? An examination of leading IS journals. Communications of the Association for Information Systems,20(1), 660.\nBaccini, A., & Barabesi, L. (2010). Interlocking editorship: A network analysis of the links between economic journals. Scientometrics,82(2), 365–389.\nBaker, M. (2016). 1500 Scientists lift the lid on reproducibility. Nature News,533(7604), 452.\nBandodkar, N., & Grover, V. (2016). 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ACM Transactions Computer–Human Interaction,1(3), 55–107.",{"VOID":387},"10.1007\u002Fs11192-019-03331-5","2025-02-25T13:14:43.824+00:00",[272],"https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11192-019-03331-5",[392,407,420],{"id":393,"sortIndex":18,"researcher":17,"roles":394,"affiliations":395,"properties":404,"displayName":406,"givenName":17,"familyName":17},"f0bc0134-a221-4f84-ad1b-ed2b03ab5335",[278],[396],{"id":397,"sortIndex":18,"affiliation":398,"properties":17},"13aea2d9-ca13-49f4-9f76-ab1dda92529d",{"id":397,"createTime":17,"updateTime":17,"relativeEntities":399,"slug":17,"properties":400,"entityType":17,"verifyStatus":17,"verifyTime":17,"verifyNote":17,"languages":17,"translateLanguages":17,"viewCount":17,"url":17,"parentIds":403,"statistic":17},[],{"title":401},{"VI":402},"School of Business - Computer Information Systems Department, North Carolina Central University, Durham, USA",[],{"title":405},{"VI":406},"Erastus 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Sampling can be done in the sources or in the items. The simplest sampling technique is the systematic one where every k\u003CSuperscript>th\u003C\u002FSuperscript> source or k\u003CSuperscript>th\u003C\u002FSuperscript> item is taken (alternatively: deleted) (k&Icirc;&ucirc;). In this paper we give a definition of systematic sampling in items and sources in the framework of an IPP in which we have continuous variables. We prove the theorem that in such IPPs we have a Lotkaian size-frequency function (i.e. a decreasing power function) if and only if systematic sampling in sources is the same as systematic sampling in items. In this proof we use the well-known characterization of power functions as scale-free functions.",{"EN":771},"World Flash on Basic Research",{"VOID":773},"[\"1636941633412748200\",\"15785754688090094670\"]",{"VOID":775},"10.1007\u002Fs11192-005-0023-7","2024-05-04T00:41:17.801+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11192-005-0023-7",[779,796],{"id":780,"sortIndex":18,"researcher":17,"roles":781,"affiliations":782,"properties":791,"displayName":793,"givenName":17,"familyName":17},"e2e9c6de-6181-4309-ad59-10006185dd66",[278],[783],{"id":784,"sortIndex":18,"affiliation":785,"properties":17},"36d8d09a-42c8-4091-b896-2027d1d823d7",{"id":784,"createTime":17,"updateTime":17,"relativeEntities":786,"slug":17,"properties":787,"entityType":17,"verifyStatus":17,"verifyTime":17,"verifyNote":17,"languages":17,"translateLanguages":17,"viewCount":17,"url":17,"parentIds":790,"statistic":17},[],{"title":788},{"VI":789},"Information Science and Scientometrics Research Unit (ISSRU), Institute for Research Policy Studies, Hungarian Academy of Sciences",[],{"title":792,"gsAuthor":794},{"VI":793},"Tibor Braun",{"VOID":795},"[\"jfM8cPAAAAAJ\"]",{"id":797,"sortIndex":293,"researcher":17,"roles":798,"affiliations":799,"properties":806,"displayName":808,"givenName":17,"familyName":17},"fa570ca7-07ad-49f9-9001-87fa172a0b51",[278],[800],{"id":784,"sortIndex":18,"affiliation":801,"properties":17},{"id":784,"createTime":17,"updateTime":17,"relativeEntities":802,"slug":17,"properties":803,"entityType":17,"verifyStatus":17,"verifyTime":17,"verifyNote":17,"languages":17,"translateLanguages":17,"viewCount":17,"url":17,"parentIds":805,"statistic":17},[],{"title":804},{"VI":789},[],{"title":807},{"VI":808},"Ildikó 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speaking, citation relationship among authors can be divided into 3 types: co-citation, coupling and cross-citation. Since author co-citation analysis was first introduced in 1982, it has been widely applied to study discipline structure, research state and research trends. Afterwards, conception of author bibliographic-coupling analysis was put forward and related empirical studies provided a method for mapping active authors in a research field for a more realistic picture of the current state of its research activities. Additionally, if one of author A’s papers has a citation from one of author B’s, there is cross-citation relationship between A and B. However, studies based on author cross-citation relationship mainly describe citation behaviors themselves using citation identity and citation image; they rarely involve any implicit knowledge communication, author research correlation or discovering academic communities. Author cross-citation analysis infers to both citing and cited phenomenon, which can be roughly correspond to citation identity and citation image. The study will further explore the author cross-citation relationship with core authors in scientometrics field as study object in order to provide reference for development of scientometrics field and in-depth application of citation analysis.",{"EN":888},"Research on the cross-citation relationship of core authors in scientometrics",{"VOID":515},{"VOID":891},"Blaise, C., & Debora, S. (2002). Identity-creators and image-makers: Using citation analysis and thick description to put authors in their place. Scientometrics, 54(1), 31–49.\nHair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis (5th ed.). Upper Saddle River, NJ: Prentice Hall.\nHou, H., Kretschmer, H., & Liu, Z. (2008). The structure of scientific collaboration networks in Scientometrics. Scientometrics, 75(2), 189–202.\nKessler, M. M. (1963). Bibliographic coupling between scientific papers. Journal of the American Society for Information Science and Technology, 14(1), 10–25.\nMccain, K. W. (1990). Mapping authors in intellectual space: A technical overview. Journal of the American Society for Information Science, 41(1), 433–443.\nQiu, J. P. (2007). Informetrics (pp. 192–195). Wuhan: Wuhan University Press.\nSchoepflin, U., & Glanzel, W. (2001). Two decades of “scientometrics”—an interdisciplinary field represented by its leading journal. Scientometrics, 50(2), 301–312.\nSchubert, A. (2002). The web of scientometrics: A statistical overview of the first 50 volumes of the journal. Scientometrics, 53, 3–20.\nSchubert, A., & Maczelka, H. (1993). Cognitive changes in scientometrics during the 1980s, as reflected by the reference patterns of its core journal. Social Studies of Science, 23(3), 571–581.\nSmall, H. (1973). Cocitation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24, 265–269.\nWang, F. (2010). Comparative analysis of author co-citation and author bibliographic-coupling relationship in scientometrics. Wuhan Daxue Xuebao (Xinxi Kexue Ban), 35(Special issue 1), 123–128.\nWhite, H. D. (2001). Authors as citers over time. Journal of the American Society for Information Science and Technology, 52(2), 87–108.\nWhite, H. D. (2004). Reward, Persuasion and the sokal hoax: A study in citation identities. Scientometrics, 60(1), 93–120.\nWhite, H. D., & Griffith, B. C. (1981). Author co-citation: A literature measure of intellectual structure. Journal of the American Society for Information Science, 32(3), 163–171.\nWhite, H. D., & McCain, K. W. (1998). Visualizing a discipline: An author co-citation analysis of information science 1972–1995. Journal of the American Society for Information Science, 49(4), 327–355.\nZhao, D., & Strotmann, A. (2008a). Information science during the first decade of the web: An enriched author cocitation analysis. Journal of the American Society for Information Science and Technology, 59(6), 916–937.\nZhao, D., & Strotmann, A. (2008b). Evolution of research activities and intellectual influences in information science 1996–2005: Introducing author bibliographic-coupling analysis. Journal of the American Society for Information Science and Technology, 59(13), 2070–2086.",{"VOID":893},"10.1007\u002Fs11192-012-0621-0","2024-09-05T01:30:59.718+00:00","http:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs11192-012-0621-0",[897,912,927],{"id":898,"sortIndex":18,"researcher":17,"roles":899,"affiliations":900,"properties":909,"displayName":911,"givenName":17,"familyName":17},"13e33095-582c-4394-9653-cba3d38a2600",[278],[901],{"id":902,"sortIndex":18,"affiliation":903,"properties":17},"594b9b37-c88a-4d8c-856b-06bba13253c3",{"id":902,"createTime":17,"updateTime":17,"relativeEntities":904,"slug":17,"properties":905,"entityType":17,"verifyStatus":17,"verifyTime":17,"verifyNote":17,"languages":17,"translateLanguages":17,"viewCount":17,"url":17,"parentIds":908,"statistic":17},[],{"title":906},{"VI":907},"School of Information Management, Wuhan University, Wuhan, China",[],{"title":910},{"VI":911},"Feifei 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Qiu",{"id":928,"sortIndex":422,"researcher":17,"roles":929,"affiliations":930,"properties":937,"displayName":939,"givenName":17,"familyName":17},"8c994488-3009-4243-8992-80a3da17c026",[278],[931],{"id":902,"sortIndex":18,"affiliation":932,"properties":17},{"id":902,"createTime":17,"updateTime":17,"relativeEntities":933,"slug":17,"properties":934,"entityType":17,"verifyStatus":17,"verifyTime":17,"verifyNote":17,"languages":17,"translateLanguages":17,"viewCount":17,"url":17,"parentIds":936,"statistic":17},[],{"title":935},{"VI":907},[],{"title":938},{"VI":939},"Houqiang 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susceptibility parameter called the “intelligence constant” by which it is possible to assess the complexity of scientific research in the different periods in history is suggested. In scientific sense, the intelligence constant measures creative energy expended in the achievement of a major scientific result. It is demonstrated that the sudden change of intelligence constant signalizes a scientific revolution and so the law of intelligence constant change might provide a particular method to forecast scientific revolutions in the future.",{"EN":1016},"An intelligence constant of scientific work",{"VOID":1018},"[\"14632994713296838642\"]",{"VOID":1020},"D. PRICE, The productivity of Research Scientists,Encyclopedia Britannica, 1974, 409–421.\nW. DENNIS, Creative productivity between the ages of 20 and 80 years,Journal of Gerontology, 21 (1966) 1–8.\nB. L. GEANZBURK,Nature (in Russian), 6 (1976) 73.\nT. KUHN,The Structure of scientific revolutions (2nd ed.), University of Chicago Press, Chicago, 1970.\nH. C. LEHMAN,Age and achievement, Princeton University Press, Princeton, N. J. 1953.\nD. PRICE,Science Since Babylon, New Haven, 1961, p. 96.\nD. PRICE, Regular Patterns in the Organization of Science,Organon, No. 2 (1965) 244–245.\nD. PRICE,Little Science, Big Science, Columbia University Press, New York, 1963.\nD. PRICE,Ups and downs in the pulse of science and technology, The Sociology of Science, Ed. J. GASTON, Jossey-Bass, San Francisco, 1978, 162–171.\nD. K. SIMONTON, Times series analysis of literary creativity: a potential paradigm,Poetics, 7 (1978) 249–259.\nD. K. SIMONTON, Creative productivity, age, and stress: a biographical time-series analysis of 10 classical composers,Journal of Personality and Social Psychology, 35 (1977) no. 11. 791–804.\nM. YUASA, Japanese Studies in the History of Science, 1 (1962) 57.\nM. YUASA, The shifting center of scientific activity in the West. in: S. NAKAYAMA and other (Eds),Science and Society in Modern Japan, University of Tokyo Press, Tokyo, 1974.\nH. ZUCKERMAN,Scientific Elite, Free Press, New York, 1977.\nZHAO HONG-ZHOU,Journal of Dialectics of Nature (in Chinese), No. 4. (1979).\nG. L' E. TURNER,The Patronage of Science in the Nineteenth Century, Noordhoff International Publishing, 1976, p. 7.",{"VOID":1022},"10.1007\u002FBF02020109","2024-06-24T02:18:51.288+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002FBF02020109",[1026],{"id":1027,"sortIndex":18,"researcher":17,"roles":1028,"affiliations":1029,"properties":1038,"displayName":1040,"givenName":17,"familyName":17},"e0c2ec91-ff26-4c9b-8c0c-2d87d0ddf2a7",[278],[1030],{"id":1031,"sortIndex":18,"affiliation":1032,"properties":17},"991dde18-63b5-44f9-9323-725e18f98efc",{"id":1031,"createTime":17,"updateTime":17,"relativeEntities":1033,"slug":17,"properties":1034,"entityType":17,"verifyStatus":17,"verifyTime":17,"verifyNote":17,"languages":17,"translateLanguages":17,"viewCount":17,"url":17,"parentIds":1037,"statistic":17},[],{"title":1035},{"VI":1036},"Institute of Physics, Academia Sinica, Beijing, China",[],{"title":1039},{"VI":1040},"Zhao 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major difficulty with bibliometric measures of departmental research contributions based upon publications counts has concerned the summing of publications of different types. An attempt is made in this paper to bypass this aggregation problem by appeal to the methods of Data Envelopment Analysis (DEA). In this way we investigate the technical efficiency of UK university departments of economics as producers of research. The data set used is an extended version of the one which informed the recent Universities Funding Council peer review, and the results obtained here are compared with those obtained by the Council. We conclude that, although due caution is needed in the interpretation of results, DEA has a positive contribution to make in the development of meaningful indicators of university performance.",{"EN":1119},"Apples and oranges: The aggregation problem in publications analysis",{"VOID":515},{"VOID":1122},"B.R. Martin, J. Irvine, Assessing basic research,Research Policy, 12 (1983) 61–90.\nG. Johnes, Research performance indicators in the university sector,Higher Education Quarterly, 42 (1988) 54–71.\nG. Johnes, Ranking university departments: problems and opportunities,Politics, 9 (2) (1989) 16–22.\nB. Meltzer, The productivity of social scientists,American Journal of Sociology, 55 (1949) 25–29.\nD. Crane, Scientists at major and minor universities: a study of productivity and recognition,American Sociological Review, 30 (1965) 699–714.\nA. Schubert, T. Braun, Some scientometric measures of publishing performance for 85 Hungarian research institutes,Scientometrics, 3 (1981) 379–388.\nG. Johnes, Measures of research output: university departments of economics in the UK, 1984–88,Economic Journal, 100 (1990) 556–560.\nG.B. Dantzig, Maximization of a linear function of variables subject to linear inequalities, in:T.C. Koopmans, (Ed.)Activity Analysis of Production and Allocation, Wiley, New York, 1951.\nM.J. Farrell, The measurement of productive efficiency,Journal of the Royal Statistical Society, Series A, 120 (1957) 253–290.\nA. Charnes, W.W. Cooper, E. Rhodes, Measuring the efficiency of decision making units,European Journal of Operational Research, 2 (1978) 429–444.\nT.R. Sexton, The methodology of data envelopment analysis, in:R.H. Silkman, (Ed.)Measuring Efficiency: An Assessment of Data Envelopment Analysis, Jossey-Bass, San Francisco, 1986.\nA. Boussofiane, R.E. Dyson, E. Thanassoulis, Applied Data Envelopment Analysis,European Journal of Operational Research, 52 (1991) 1–15.\nC. Tomkins, R.H. Green, An experiment in the use of data envelopment analysis for evaluating the efficiency of UK university departments of accounting,Financial Accountability and Management, 4 (1988) 147–164.\nJ.E. Beasley, Comparing university departments,Omega, 18 (1990) 171–183.\nG. Johnes, J. Johnes, Measuring the research performance of UK economics departments: an application of Data Envelopment Analysis,Oxford Economic Papers, forthcoming.\nE. Thanassoulis, R.G. Dyson, M.G. Foster, Relative efficiency assessments using Data Envelopment Analysis: an application to data on rates departments,Journal of the Operational Research Society, 38 (1987) 397–491.\nH.D. Sherman, Managing productivity of health care organizations, in:R.H. Silkman, (Ed.),Measuring Efficiency: An Assessment of Data Envelopment Analysis, Jossey-Bass, San Francisco, 1986.\nA. Charnes, T. Clark, W.W. Cooper, B. Golany, A developmental study of Data Envelopment Analysis in measuring the efficiency of maintenance units is the US air force,Annals of Operational Research, 2 (1985) 95–112.\nJ. Flemming, The use of assessments of British university teaching, and especially research, for the allocation of resources: a personal view,European Economic Review, 35 (1991) 612–618.\nA.M. Diamond, The core journals of economics,Current Contents, 21 (1) (1989) 4–11.",{"VOID":1124},"10.1007\u002FBF02028091","2024-09-04T21:58:10.229+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002FBF02028091",[1128,1143],{"id":1129,"sortIndex":18,"researcher":17,"roles":1130,"affiliations":1131,"properties":1140,"displayName":1142,"givenName":17,"familyName":17},"2302f596-1978-4500-84d5-c8043ac7abe1",[278],[1132],{"id":1133,"sortIndex":18,"affiliation":1134,"properties":17},"637cce65-26a4-48d3-8af6-4744046ededb",{"id":1133,"createTime":17,"updateTime":17,"relativeEntities":1135,"slug":17,"properties":1136,"entityType":17,"verifyStatus":17,"verifyTime":17,"verifyNote":17,"languages":17,"translateLanguages":17,"viewCount":17,"url":17,"parentIds":1139,"statistic":17},[],{"title":1137},{"VI":1138},"Department of Economics, The Management School, Lancaster University, Lancaster, UK",[],{"title":1141},{"VI":1142},"Geraint 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The f-value considers all information present in a Citation Graph in order to produce a ranking of the articles. Apart from the mathematical equation that calculates the f-value, we also present the corresponding algorithm with its implementation, plus an experimental comparison of f-value with two known indicators of an article’s scientific importance, namely, the number of citations and the Page Rank for citation analysis. 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K. S., & Killworth P. D. (2008) Beyond the durfee square: Enhancing the h-index to score total publication output. Scientometrics 76(3), 577–588. doi:10.1007\u002Fs11192-007-2071-2.","https:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs11192-007-2071-2",{"doi":1375},"10.1007\u002Fs11192-007-2071-2",{"id":17,"text":1377,"url":1378,"identifiers":1379},"Austin, D. (2006). How Google finds your needle in the web’s haystack. http:\u002F\u002Fwww.ams.org\u002Ffeaturecolumn\u002Farchive\u002Fpagerank.html.","http:\u002F\u002Fwww.ams.org\u002Ffeaturecolumn\u002Farchive\u002Fpagerank.html",{},{"id":1381,"text":1382,"url":1383,"identifiers":1384},"8d6f429a-9bd8-4136-adfe-d208d8cd1084","Boldi, P., Santini, M., & Vigna, S. (2009). Pagerank: Functional dependencies. ACM Transactions on Information Systems 27(4), 1–23. doi:10.1145\u002F1629096.1629097.","https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F1629096.1629097",{"doi":1385},"10.1145\u002F1629096.1629097",{"id":17,"text":1387,"url":17,"identifiers":1388},"Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems, 30, 107–117.",{},{"id":17,"text":1390,"url":1391,"identifiers":1392},"Citeseer. (1997). http:\u002F\u002Fwww.citeseer.ist.psu.edu.","http:\u002F\u002Fwww.citeseer.ist.psu.edu",{},{"id":1394,"text":1395,"url":1396,"identifiers":1397},"4c68646b-0035-4279-8000-0006b275d4fa","Dervos, D., & Kalkanis, T. (2005). cc-IFF: A cascading citations impact factor framework for the automatic rankings of research publications. In 3rd IEEE international workshop on intelligent data acquisition and advanced computer systems: technology and applications (IDAACS 2005), Sofia, Bulgaria.","https:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs10440-022-00541-7",{"doi":1398},"10.1007\u002Fs10440-022-00541-7",{"id":1394,"text":1400,"url":1396,"identifiers":1401},"Dervos, D., & Klimis, L. (2008). Exploiting cascading citations for retrieval. In Proceeding of the ASSIST 2008 annual meeting.",{"doi":1398},{"id":1394,"text":1403,"url":1396,"identifiers":1404},"Dervos, D., Samaras, N., Evangelidis, G., & Folias, T. (2006). A new framework for the citation indexing paradigm. In Proceedings of the ASSIST 2006 annual meeting, Austin, Texas, USA.",{"doi":1398},{"id":1394,"text":1406,"url":1396,"identifiers":1407},"Egghe, L. (2006). Theory and practise of the g-index. Scientometrics, 69(1), 131–152.",{"doi":1398},{"id":17,"text":1409,"url":1410,"identifiers":1411},"Fragkiadaki, E., Evangelidis, G., Samaras, N., & Dervos, D. (2009). Cascading citations indexing framework algorithm implementation and testing. Informatics, Panhellenic conference on Informatics, 70–74. doi:10.1109\u002FPCI.2009.30.","https:\u002F\u002Fdoi.org\u002F10.1109\u002Fpci.2009.30",{"mag":1412,"openalex":1413,"doi":1414},"2101666541","W2101666541","10.1109\u002Fpci.2009.30",{"id":1394,"text":1416,"url":1396,"identifiers":1417},"Garfield, E. (1955). Citation indexes for science. A new dimension in documentation through association of ideas. Science 122, 1123–1127.",{"doi":1398},{"id":1394,"text":1419,"url":1396,"identifiers":1420},"Garfield, E. (1999). Journal impact factor: A brief review. CMAJ 161(8), 979–980.",{"doi":1398},{"id":17,"text":1422,"url":17,"identifiers":1423},"Garfield, E. (2005). The agony and the ecstasy—the history and meaning of the journal impact factor. In International Congress on Peer Review And Biomedical Publication.",{},{"id":17,"text":1425,"url":17,"identifiers":1426},"Giles, C. L., Bollacker, K. D., & Lawrence, S. (1998). Citeseer: An automatic citation indexing system (pp. 89–98). New York: ACM Press.",{},{"id":1428,"text":1429,"url":1430,"identifiers":1431},"b7985a16-bda5-4d6b-a190-6c9c86ce6756","Guns, R., & Rousseau, R. (2009). Real and rational variants of the h-index and the g-index. Journal of Informetrics, 3(1): 64–71. doi:10.1016\u002Fj.joi.2008.11.004.","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1751157708000692",{"doi":1432},"10.1016\u002Fj.joi.2008.11.004",{"id":1434,"text":1435,"url":1436,"identifiers":1437},"c87303a2-79b5-48d0-849a-04fca1cfc03b","Hirsch, J. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences, 102, 16569–16572.","https:\u002F\u002Fpnas.org\u002Fdoi\u002Ffull\u002F10.1073\u002Fpnas.0507655102",{"doi":1438},"10.1073\u002Fpnas.0507655102",{"id":1440,"text":1441,"url":1442,"identifiers":1443},"207fc78e-3bb1-416b-a3c8-7c91b9a995b4","Jin, B., Liang, L., Rousseau, R., & Egghe, L. (2007). The R- and AR-indices: Complementing the h-index. Chinese Science Bulletin, 52(6), 855–863. doi:10.1007\u002Fs11434-007-0145-9.","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11434-007-0145-9",{"doi":1444},"10.1007\u002Fs11434-007-0145-9",{"id":1394,"text":1446,"url":1396,"identifiers":1447},"Katsaros, D., Sidiropoulos, A., & Manopoulos, Y. (2007). Age decaying h-index for social network of citations. In SAW proceedings of the BIS 2007 workshop on social aspects of the web, Poznan, Poland, April 27, 2007, CEUR-WS.org. CEUR workshop proceedings. 245.",{"doi":1398},{"id":17,"text":1449,"url":1450,"identifiers":1451},"Kräutler, V. (2006). The Google pagerank algorithm in 126 lines of Python. http:\u002F\u002Fwww.kraeutler.net\u002Fvincent\u002Fessays\u002Fgooglepagerankinpython.","http:\u002F\u002Fwww.kraeutler.net\u002Fvincent\u002Fessays\u002Fgooglepagerankinpython",{},{"id":1453,"text":1454,"url":1455,"identifiers":1456},"63e15b7f-b744-438e-a7fe-697d3cb16266","Ma, N., Guan, J., & Zhao, Y. (2008). Bringing pagerank to the citation analysis. Information Processing and Management, 44(2): 800–810. doi:10.1016\u002Fj.ipm.2007.06.006.","https:\u002F\u002Flinkinghub.elsevier.com\u002Fretrieve\u002Fpii\u002FS0306457307001203",{"doi":1457},"10.1016\u002Fj.ipm.2007.06.006",{"id":1394,"text":1459,"url":1396,"identifiers":1460},"Rousseau, R. (1987). The Gozinto theorem: Using citations to determine influences on a scientific publication. Scientometrics, 11(3–4): 217–229.",{"doi":1398},{"id":1462,"text":1463,"url":1464,"identifiers":1465},"1d23324c-0761-4644-a91c-ae9b05f6297c","Sidiropoulos, A., Katsaros, D., & Manolopoulos, Y. (2007). Generalized hirsch h-index for disclosing latent facts in citation networks. Scientometrics, 72(2), 253–280 doi:10.1007\u002Fs11192-007-1722-z.","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11192-007-1722-z",{"doi":1466},"10.1007\u002Fs11192-007-1722-z",{"id":1468,"createTime":1469,"updateTime":1470,"relativeEntities":1471,"slug":1472,"properties":1473,"entityType":267,"verifyStatus":268,"verifyTime":1482,"verifyNote":270,"languages":17,"translateLanguages":17,"viewCount":18,"primaryUrl":1483,"fullTextUrl":17,"authors":1484,"publicationType":305,"publisherRelationship":1564,"citationCount":18,"citationInfo":1626,"publishDate":1628,"publishYear":1003,"citationAnalyzeStatus":1629,"lastCitationAnalyze":1470,"indexDatabases":1630,"openAccess":17,"references":1631,"isForceReanalyzing":371},"4f1972be-102d-49dc-8013-231fdf0bbb6e","2023-12-01T00:33:02.298+00:00","2026-07-30T00:19:17.760+00:00",[],"Dentistry-scientometric-analysis-a-comparative-study-between-Brazil-and-other-most-productive-countries-in-the-area",{"abstract":1474,"title":1476,"gsPaper":1478,"doi":1480},{"EN":1475},"This research aims at performing a comparative study between the Brazilian scientific production in Dentistry, from 2000 to 2009 and countries that contribute with at least 2 % of the world’s scientific production indexed in the Scopus database. More specifically, we intend to assess the annual Brazilian scientific production by comparing it to the other countries’, analyze the Brazilian and other countries’ publications in journals with higher impact factors, as well as to highlight the scientific production from these countries and its international visibility, measured by its total and by its average of citations and normalized citation index per year, by comparing the countries, and to compare the index h of such countries. As work procedure, the SCImago Journal and Country Rank was used as source, identifying the group of producing countries in the Dentistry area from 1996 to 2009. From a total of 136 countries, 13 were highlighted as the most productive, each one of them accounting for at least 2 % the worldwide scientific production in the area. The following indicators were raised for each country: number of produced documents, total of citations, self-citations, average of citations per document and index h. We verified that Brazil is the only country in Latin America that is pictured among the most productive ones in the Dentistry area. We observed that Brazil presents a growing visibility and impact in the international scenery, what suggests that its production is constantly consolidating, with Brazilian scientific recognition in the main vehicles of dissemination in the area.",{"EN":1477},"Dentistry scientometric analysis: a comparative study between Brazil and other most productive countries in the 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