How Social and Humanities Researchers Conduct Scientometric Studies? Methods, Databases, and Tools

Öz

The evaluation and measurement of scientific output have become vital for research fields and institutions that shape the realm of science. In scientific assessment and appraisal, it is crucial to recognize contemporary approaches, instruments, and resources and utilize them precisely and dependably. As a specialized area of research, Scientometrics adopts efficient analytical methods and is extensively used to gauge scientific productivity, remaining pertinent in today's world. Nevertheless, the execution of scientometric studies in social sciences and humanities is affected by specific factors inherent to these domains. This study presents a thorough and current insight into the conceptual framework, methodologies, tools, and data sources that can be employed for conducting scientometric research in the social sciences and humanities based on a literature review. The literature review examines performance metrics and analytical methods concerning the software's features. This investigation explores scientometric analyses' methods, tools, and software. Seven free software were evaluated for the design of the scientometric inquiry, in addition to two R programming language packages and three Python programming language packages designed for the same objective. The research emphasizes scientometric analysis techniques, bibliographic databases, and concepts, providing scholars in the social sciences and humanities with a comprehensive understanding of these methods

Anahtar Kelimeler

• Scientometrics
• Review
• Bibliographic databases
• Scientometric softwares

Sosyal ve Beşerî Bilimler Araştırmacıları Bilimetrik Çalışmaları Nasıl Yürütür? Yöntemler, Veri tabanları ve Araçlar

Öz

Bilimsel çıktıların değerlendirilmesi ve ölçülmesi, bilim dünyasını şekillendiren araştırma alanları ve kurumlar için hayati önem taşımaktadır. Bilimsel ölçme ve değerlendirmede çağdaş yaklaşımları, araçları ve kaynakları tanımak ve bunları doğru ve güvenilir bir şekilde kullanmak çok önemlidir. Özel bir araştırma alanı olarak bilimetri, etkili analitik yöntemleri benimser ve günümüz dünyasında geçerliliğini koruyan bilimsel üretkenliği ölçmek için yaygın olarak kullanılır. Bununla birlikte sosyal ve beşeri bilimlerdeki bilimetrik çalışmaların yürütülmesi, bu alanlara özgü faktörlerden etkilenmektedir. Bu çalışma, literatür taramasına dayalı olarak sosyal ve beşeri bilimlerde bilimetrik araştırma yürütmek için kullanılabilecek kavramsal çerçeve, metodolojiler, araçlar ve veri kaynakları hakkında kapsamlı ve güncel bir bakış açısı sunmaktadır. Literatür taraması, yazılımın özelliklerine ilişkin performans ölçütlerini ve analitik yöntemleri incelemektedir. Bu araştırma, bilimetrik analiz yöntemlerini, araçlarını ve yazılımlarını incelemektedir. Aynı amaç için tasarlanmış iki R programlama dili paketi ve üç Python programlama dili paketine ek olarak, yedi ücretsiz yazılım scientometric araştırmanın tasarımı için değerlendirilmiştir. Araştırma, sosyal ve beşeri bilimlerdeki akademisyenlerin bu yöntemleri kapsamlı bir şekilde anlamalarını sağlamak için scientometrik analiz tekniklerini, bibliyografik veri tabanlarını ve kavramları vurgulamaktadır.

Anahtar Kelimeler

• bilimetri
• derleme
• bibliyografik veri tabanları
• bilimetrik yazılımlar

Kaynakça

1. Acedo, F. J., Barroso, C. ve Galan, J. L. (2006). The Resource-Based Theory: Dissemination and Main Trends. Strategic Management Journal, 27(7), 621-636. https://doi.org/10.1002/smj.532
2. Aksnes, D. W., Langfeldt, L. ve Wouters, P. (2019). Citations, citation indicators, and research quality: An overview of basic concepts and theories. Sage Open, 9(1). https://doi.org/10.1177/2158244019829575
3. Aksnes, D. W. ve Taxt, R. E. (2004). Peer reviews and bibliometric indicators: A comparative study at a norwegian university. Research Evaluation, 13(1), 33-41. https://doi.org/10.3152/147154404781776563
4. Alonso, S., Cabrerizo, F., Herrera-Viedma, E. ve Herrera, F. (2010). hg-index: A new index to characterize the scientific output of researchers based on the h-and g-indices. Scientometrics, 82(2), 391-400. https://doi.org/10.1007/s11192-009-0047-5
5. Aria, M. ve Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007
6. Baas, J., Schotten, M., Plume, A., Côté, G. ve Karimi, R. (2020). Scopus as a curated, high-quality bibliometric data source for academic research in quantitative science studies. Quantitative science studies, 1(1), 377-386. https://doi.org/10.1162/qss_a_00019
7. Bar-Ilan, J. (2008). Informetrics at the beginning of the 21st century—A review. Journal of Informetrics, 2(1), 1-52. https://doi.org/10.1016/j.joi.2007.11.001
8. Bettencourt, L. M. ve Kaur, J. (2011). Evolution and structure of sustainability science. Proceedings of the National Academy of Sciences, 108(49), 19540-19545. https://doi.org/10.1073/pnas.1102712108

9. Björneborn, L. ve Ingwersen, P. (2004). Toward a basic framework for webometrics. Journal of the American society for information science and technology, 55(14), 1216-1227. https://doi.org/10.1002/asi.20077
10. Blackert, L. ve Siegel, K. (1979). Ist in der wissenschaftlich-technischen information platz für die informetrie. Wissenschaftliche Zeitschrift der Technischen Hochschule Ilmenau, 25(6), 187-199.
11. Bonaccorsi, A., Daraio, C., Fantoni, S., Folli, V., Leonetti, M. ve Ruocco, G. (2017). Do social sciences and humanities behave like life and hard sciences? Scientometrics, 112(1), 607-653. https://doi.org/10.1007/s11192-017-2384-0
12. Bornmann, L., Mutz, R. ve 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 and Technology, 59(5), 830-837. https://doi.org/10.1002/asi.20806
13. Bornmann, L., Mutz, R., Hug, S. E. ve Daniel, H.-D. (2011). A Multilevel Meta-Analysis of Studies Reporting Correlations Between the h Index and 37 Different h Index Variants. Journal of Informetrics, 5(3), 346-359. https://doi.org/10.1016/j.joi.2011.01.006
14. Borrego, Á., Ardanuy, J. ve Arguimbau, L. (2023). Crossref as a bibliographic discovery tool in the arts and humanities. Quantitative Science Studies, 4(1), 91-104. https://doi.org/10.1162/qss_a_00240
15. Boyack, K. W. ve Klavans, R. (2010). Co-Citation Analysis, Bibliographic Coupling, and Direct Citation: Which Citation Approach Represents the Research Front Most Accurately? Journal of the American Society for information Science and Technology, 61(12), 2389-2404. https://doi.org/10.1002/asi.21419
16. Bozeman, B. ve Sarewitz, D. (2011). Public value mapping and science policy evaluation. Minerva, 49(1), 1-23. https://doi.org/10.1007/s11024-011-9161-7
17. Brown, R. J. (2009). A Simple Method for Excluding Self-Citation from the h-Index: The b-Index. Online Information Review, 33(6), 1129-1136. https://doi.org/10.1108/14684520911011043
18. Callon, M., Courtial, J.-P. ve Laville, F. (1991). Co-Word Analysis as a Tool for Describing the Network of Interactions Between Basic and Technological Research: The Case of Polymer Chemsitry. Scientometrics, 22(1), 155-205. https://doi.org/10.1007/BF02019280
19. Chen, C. (2004). Searching for Intellectual Turning Points: Progressive Knowledge Domain Visualization. Proceedings of the National Academy of Sciences, 101(suppl_1), 5303-5310. https://doi.org/10.1073/pnas.0307513100
20. 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(3), 359-377. https://doi.org/10.1002/asi.20317
21. Chen, C. (2012). Predictive Effects of Structural Variation on Citation Counts. Journal of the American Society for Information Science and Technology, 63(3), 431-449. https://doi.org/10.1002/asi.21694
22. Chen, C. (2016). CiteSpace: A practical guide for mapping scientific literature. Nova Science Publishers.
23. Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E. ve Herrera, F. (2011). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for information Science and Technology, 62(7), 1382-1402. https://doi.org/10.1002/asi.21525
24. Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., ve Herrera, F. (2012). SciMAT: A new science mapping analysis software tool. Journal of the American Society for Information Science and Technology, 63(8), 1609-1630. https://doi.org/10.1002/asi.22688
25. Coccia, M. (2005). Technometrics: Origins, historical evolution and new directions. Technological Forecasting and Social Change, 72(8), 944-979. https://doi.org/10.1016/j.techfore.2005.05.011
26. Comins, J. A. ve Hussey, T. W. (2015). Compressing Multiple Scales of Impact Detection by Reference Publication Year Spectroscopy. Journal of Informetrics, 9(3), 449-454. https://doi.org/10.1016/j.joi.2015.03.003
27. Cronin, B. (2013). From signtometrics to scientometrics: A cautionary tale of our times. Journal of Information Science Theory and Practice, 1(4), 6-11. https://doi.org/10.1633/JISTaP.2013.1.4.1
28. da Silva, J. T., Dobránszki, J., Al-Khatib, A. ve Tsigaris, P. (2018). Challenges facing the DOAJ (Directory of Open Access Journals) as a reliable source of open access publishing venuess. Journal of Educational Media & Library Sciences, 55(3), 349-358. https://doi.org/10.6120/JoEMLS.201811_55(3).e003.BC.BE
29. De Bellis, N. (2009). Bibliometrics and citation analysis: From the science citation index to cybermetrics. Scarecrow Press.
30. Düzyol, G. (2011). Türkiye kütüphanecilik ve bilgibilim literatürünün entellektüel haritasının çıkarılması: Bir yazar ortak atıf analizi çalışması [Yüksek Lisans Tezi]. Hacettepe Üniversitesi.
31. Egghe, L. (2005). Expansion of the Field of Informetrics: Origins and Consequences. Information Processing & Management, 41(6), 1311-1316. https://doi.org/10.1016/j.ipm.2005.03.011
32. Egghe, L. (2006). Theory and Practise of the g-Index. Scientometrics, 69(1), 131-152. https://doi.org/10.1007/s11192-006-0144-7
33. Fischer, A. R., Tobi, H. ve Ronteltap, A. (2011). When natural met social: A review of collaboration between the natural and social sciences. Interdisciplinary Science Reviews, 36(4), 341-358. https://doi.org/10.1179/030801811X13160755918688
34. Franssen, T. ve Wouters, P. (2019). Science and its significant other: Representing the humanities in bibliometric scholarship. Journal of the Association for Information Science and Technology, 70(10), 1124-1137. https://doi.org/10.1002/asi.24206
35. Fricke, S. (2018). Semantic Scholar. Journal of the Medical Library Association: JMLA, 106(1), 145. https://doi.org/10.5195/jmla.2018.280
36. Gagolewski, M. (2011). Bibliometric Impact Assessment with R and the CITAN Package. Journal of Informetrics, 5(4), 678-692. https://doi.org/10.1016/j.joi.2011.06.006
37. Garfield, E. (1964). “Science Citation Index”—A new dimension in indexing: This unique approach underlies versatile bibliographic systems for communicating and evaluating information. Science, 144(3619), 649-654. https://doi.org/10.1126/science.144.3619.649
38. Garfield, E. (1979). Is citation analysis a legitimate evaluation tool? Scientometrics, 1, 359-375. https://doi.org/10.1007/BF02019306
39. Georgas, H. (2014). Google vs. The library (part ii): Student search patterns and behaviors when using google and a federated search tool. portal: Libraries and the Academy, 14(4), 503-532. https://doi.org/10.1353/pla.2014.0034
40. Gingras, Y. (2016). Bibliometrics and research evaluation: Uses and abuses. MIT Press. Godin, B. (2006). On the origins of bibliometrics. Scientometrics, 68(1), 109-133. https://doi.org/10.1007/s11192-006-0086-0
41. Grauwin, S. (2023). BiblioTools / BiblioMaps [Personal Webpage]. http://www.sebastian-grauwin.com/bibliomaps/index.html
42. Grauwin, S. ve Jensen, P. (2011). Mapping scientific institutions. Scientometrics, 89(3), 943-954. https://doi.org/10.1007/s11192-011-0482-y
43. Grauwin, S. ve Sperano, I. (2018). Bibliomaps-a software to create web-based interactive maps of science: The case of UX map. Proceedings of the Association for Information Science and Technology, 55(1), 815-816. https://doi.org/10.1002/pra2.2018.14505501129
44. Harzing, A.-W. (2019). Two new kids on the block: How do crossref and dimensions compare with google scholar, microsoft academic, scopus and the web of science? Scientometrics, 120(1), 341-349. https://doi.org/10.1007/s11192-019-03114-y
45. Harzing, A.-W. ve Alakangas, S. (2017). Microsoft academic is one year old: The phoenix is ready to leave the nest. Scientometrics, 112(3), 1887-1894. https://doi.org/10.1007/s11192-017-2454-3
46. Haunschild, R. ve Bornmann, L. (2022). Reference publication year spectroscopy (RPYS) in practice: A software tutorial. Scientometrics, 1-19.
47. Hellqvist, B. (2010). Referencing in the humanities and its implications for citation analysis. Journal of the American Society for Information Science and Technology, 61(2), 310-318.
48. Hendricks, G., Tkaczyk, D., Lin, J. ve Feeney, P. (2020). Crossref: The sustainable source of community-owned scholarly metadata. Quantitative Science Studies, 1(1), 414-427. https://doi.org/10.1162/qss_a_00022
49. Herzog, C., Hook, D. ve Konkiel, S. (2020). Dimensions: Bringing down barriers between scientometricians and data. Quantitative Science Studies, 1(1), 387-395. https://doi.org/10.1162/qss_a_00020
50. Hess, D. J. (1997). Science studies: An advanced introduction. NYU Press.
51. Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences, 102(46), 16569-16572. https://doi.org/10.1073/pnas.0507655102
52. Hood, W. W. ve Wilson, C. S. (2001). The Literature of Bibliometrics, Scientometrics, and Informetrics. Scientometrics, 52(2), 291-314. https://doi.org/10.1023/A:1017919924342
53. Hook, D. W., Porter, S. J. ve Herzog, C. (2018). Dimensions: Building Context for Search and Evaluation. Frontiers in Research Metrics and Analytics, 3, 23. https://doi.org/10.3389/frma.2018.00023
54. Jefferson, O. A., Koellhofer, D., Warren, B. ve Jefferson, R. (2019). The Lens MetaRecord and LensID: An open identifier system for aggregated metadata and versioning of knowledge artefacts. LIS Scholarship Archive. https://doi.org/10.31229/osf.io/t56yh
55. Jefferson, O. A., Lang, S., Williams, K., Koellhofer, D., Ballagh, A., Warren, B., Schellberg, B., Sharma, R. ve Jefferson, R. (2021). Mapping CRISPR-Cas9 public and commercial innovation using The Lens institutional toolkit. Transgenic Research, 30(4), 585-599. https://doi.org/10.1007/s11248-021-00237-y
56. Jin, B., Liang, L., Rousseau, R. ve Egghe, L. (2007). The R-and AR-Indices: Complementing the h-Index. Chinese Science Bulletin, 52(6), 855-863. https://doi.org/10.1007/s11434-007-0145-9
57. Klavans, R. ve Boyack, K. W. (2017). Which type of citation analysis generates the most accurate taxonomy of scientific and technical knowledge? Journal of the Association for Information Science and Technology, 68(4), 984-998. https://doi.org/10.1002/asi.23734
58. Lavik, G. A. V. ve Sivertsen, G. (2017). ERIH PLUS–Making the SSH visible, searchable and available. Procedia Computer Science, 106, 61-65. https://doi.org/10.1016/j.procs.2017.03.035
59. López-Cózar, E. D., Orduña-Malea, E., Martín-Martín, A. ve Ayllón, J. M. (2017a). Google Scholar: The big data bibliographic tool. F. J. Cantu-Ortiz (Ed.), Research Analytics içinde (ss. 59-80). Auerbach Publications. https://doi.org/10.1201/9781315155890
60. López-Cózar, E. D., Orduña-Malea, E., Martín-Martín, A. ve Ayllón, J. M. (2017b). Google scholar: The big data bibliographic tool. Research Analytics içinde (ss. 59-80). Auerbach Publications.
61. Lund, B. (2022). Bibliometrics Is Valuable Science. Why do some journals seem to oppose it? Journal of Data and Information Science, 7(3), 4-7. https://doi.org/10.2478/jdis-2022-0012
62. Mählck, P. ve Persson, O. (2000). Socio-bibliometric mapping of intra-departmental networks. Scientometrics, 49(1), 81-91. https://doi.org/10.1023/A:1005661208810
63. Martín-Martín, A., Orduña-Malea, E., Ayllón, J. M. ve Delgado López-Cózar, E. (2016). Back to the past: On the shoulders of an academic search engine giant. Scientometrics, 107, 1477-1487. https://doi.org/10.1007/s11192-016-1917-2
64. Marx, W. ve Bornmann, L. (2014). Tracing the origin of a scientific legend by reference publication year spectroscopy (RPYS): The legend of the Darwin finches. Scientometrics, 99, 839-844. https://doi.org/10.1007/s11192-013-1200-8
65. Marx, W., Bornmann, L., Barth, A. ve Leydesdorff, L. (2014). Detecting the historical roots of research fields by reference publication year spectroscopy (RPYS). Journal of the Association for Information Science and Technology, 65(4), 751-764. https://doi.org/10.1002/asi.23089
66. McLevey, J. ve McIlroy-Young, R. (2017). Introducing Metaknowledge: Software for computational research in information science, network analysis, and science of science. Journal of Informetrics, 11(1), 176-197. https://doi.org/10.1016/j.joi.2016.12.005
67. Meagher, L., Lyall, C. ve Nutley, S. (2008). Flows of knowledge, expertise and influence: A method for assessing policy and practice impacts from social science research. Research Evaluation, 17(3), 163-173. https://doi.org/10.3152/095820208x331720
68. Moral-Muñoz, J. A., Herrera-Viedma, E., Santisteban-Espejo, A. ve Cobo, M. J. (2020). Software tools for conducting bibliometric analysis in science: An up-to-date review. Profesional de la Información, 29(1). https://doi.org/10.3145/epi.2020.ene.03
69. Morrison, H. (2017). Directory of Open Access Journals (DOAJ). The Charleston Advisor, 18(3), 25-28. https://doi.org/10.5260/chara.18.3.25
70. Mouratidis, R. W. (2019). Dimensions. Journal of the Medical Library Association: JMLA, 107(3), 459. https://doi.org/10.5195/jmla.2019.695
71. Nacke, O. (1979). Informetrie: Ein neuer name für eine neue disziplin. Begriffsbestimmung, wissensstand und entwicklungsprinzipien.
72. Nalimov, V. ve Mulcjenko, B. (1971). Measurement of Science. Study of the Development of Science as an Information Process. https://eric.ed.gov/?id=ED065286
73. Nerur, S. P., Rasheed, A. A. ve Natarajan, V. (2008). The intellectual structure of the strategic management field: An author co-citation analysis. Strategic Management Journal, 29(3), 319-336. https://doi.org/10.1002/smj.659
74. Ochsner, M., Hug, S. ve Galleron, I. (2017). The future of research assessment in the humanities: Bottom-up assessment procedures. Palgrave Communications, 3(1), 1-12. https://doi.org/10.1057/palcomms.2017.20
75. Penfold, R. (2020). Using the Lens database for staff publications. Journal of the Medical Library Association: JMLA, 108(2), 341. https://doi.org/10.5195/jmla.2020.918
76. Persson, O., Danell, R. ve Schneider, J. W. (2009). How to use bibexcel for various types of bibliometric analysis. F. Åström, R. Danell, B. Larsen, ve J. Schneider (Ed.), Celebrating Scholarly Communication Studies: A Festschrift for Olle Persson at his 60th Birthday içinde (ss. 9-24). International Society for Scientometrics and Informetrics.
77. Persson, O., Stern, P. ve Holmberg, K. (1992). BIBMAP: a toolbox for mapping the structure of scientific literature (P. Weingart, R. Sehringer, ve M. Winterhager, Ed.; ss. 189-199). DSWO Press.
78. Pranckutė, R. (2021). Web of Science (WoS) and Scopus: The titans of bibliographic information in today’s academic world. Publications, 9(1), 12. https://doi.org/10.3390/publications9010012
79. Price, J. D. de S. (1963). Little science, big science. Columbia University Press.
80. Priem, J., Groth, P. ve Taraborelli, D. (2012). The altmetrics collection. PLoS ONE, 7(11): e48753. https://doi.org/10.1371/journal.pone.0048753
81. Priem, J., Taraborelli, D., Groth, P. ve Neylon, C. (2010, October 26). Altmetrics: A manifesto. http://altmetrics.org/manifesto
82. Pritchard, A. (1969). Statistical bibliography or bibliometrics. Journal of Documentation, 25(4), 348-349. Proctor, R. W. ve Capaldi, E. (2006). The new means of understanding science. Why Science Matters: Understanding the Methods of Psychological Research içinde (ss. 96-118). Blackwell Publishing.
83. Retzer, V. ve Jurasinski, G. (2009). Towards objectivity in research evaluation using bibliometric indicators–a protocol for incorporating complexity. Basic and Applied Ecology, 10(5), 393-400. https://doi.org/10.1016/j.baae.2008.09.001
84. Rousseau, R. (2006). New Developments Related to the Hirsch Index. Science Focus, 4(1), 23-25.
85. Rousseau, R. (2014). Forgotten founder of bibliometrics. Nature, 510(7504), 218-218. https://doi.org/10.1038/510218e
86. Rousseau, R., Egghe, L. ve Guns, R. (2018). Becoming metric-wise: A bibliometric guide for researchers. Chandos Publishing.
87. Ruiz-Rosero, J., Ramírez-González, G. ve Viveros-Delgado, J. (2019). Software survey: Scientopy, a scientometric tool for topics trend analysis in scientific publications. Scientometrics, 121(2), 1165-1188. https://doi.org/10.1007/s11192-019-03213-w
88. Scheidsteger, T. ve Haunschild, R. (2023). Which of the metadata with relevance for bibliometrics are the same and which are different when switching from Microsoft Academic Graph to OpenAlex? Profesional de la información, 32(2). https://doi.org/10.3145/epi.2023.mar.09
89. Sci2 Team, S. (2009). Science of science studies: Sci2 Tool. Indiana University and SciTech Strategies. https://sci2.cns.iu.edu.
90. Skupin, A. (2014). Making a mark: A computational and visual analysis of one researcher’s intellectual domain. International Journal of Geographical Information Science, 28(6), 1209-1232. https://doi.org/10.1080/13658816.2014.906040
91. Slaughter, S. A. ve Leslie, L. E. (1997). Academic capitalism. Politics, policies and the entrepreneurial university. John Hopkins University Press.
92. Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for information Science, 24(4), 265-269. https://doi.org/10.1002/asi.4630240406
93. Sooryamoorthy, R. (2020). Scientometrics for the humanities and social sciences. Routledge. Stenson, L. (2012). Why all these directories? An introduction to DOAJ and DOAB. Insights, 25(3). https://doi.org/10.1629/2048-7754.25.3.251
94. Tague-Sutcliffe, J. (1992). An Introduction to informetrics. Information Processing & Management, 28(1), 1-3. https://doi.org/10.1016/0306-4573(92)90087-G
95. Tay, A., Hug, S. E. ve Martin-Martin, A. (2021, May 27). Goodbye, Microsoft Academic–hello, open research infrastructure? https://blogs.lse.ac.uk/impactofsocialsciences/2021/05/27/goodbye-microsoft-academic-hello-open-research-infrastructure/
96. Thelwall, M. (2018). Dimensions: A competitor to Scopus and the Web of Science? Journal of Informetrics, 12(2), 430-435. https://doi.org/10.1016/j.joi.2018.03.006
97. Thelwall, M., Vaughan, L. ve Björneborn, L. (2005). Webometrics. Annual Review of Information Science and Technology, 39(1), 81-135. https://doi.org/10.1002/aris.1440390110
98. Thor, A., Bornmann, L., Marx, W. ve Mutz, R. (2018). Identifying single influential publications in a research field: New analysis opportunities of the CRExplorer. Scientometrics, 116(1), 591-608. https://doi.org/10.1007/s11192-018-2733-7
99. Trujillo, C. M. ve Long, T. M. (2018). Document co-citation analysis to enhance transdisciplinary research. Science Advances, 4(1). https://doi.org/10.1126/sciadv.1701130
100. Van Eck, N. J. ve Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538. https://doi.org/10.1007/s11192-009-0146-3
101. Van Eck, N. J. ve Waltman, L. (2014a). CitNetExplorer: A new software tool for analyzing and visualizing citation networks. Journal of Informetrics, 8(4), 802-823. https://doi.org/10.1016/j.joi.2014.07.006
102. Van Eck, N. J. ve Waltman, L. (2014b). Visualizing bibliometric networks. Y. Ding, R. E. Rousseau, ve D. Wolfram (Ed.), Measuring scholarly impact: Methods and practice içine (ss. 285-320). Springer. https://doi.org/10.1007/978-3-319-10377-8_13
103. Van Eck, N. J. ve Waltman, L. (2017). Citation-based clustering of publications using CitNetExplorer and VOSviewer. Scientometrics, 111, 1053-1070. https://doi.org/10.1007/s11192-017-2300-7
104. Van Eck, N. J. ve Waltman, L. (2023). VOSViewer Manual. CWTS. https://www.vosviewer.com/documentation/Manual_VOSviewer_1.6.19.pdf
105. Van Raan, A. F. (2004). Measuring science. U. Schmoch, W. Glänzel ve H. F. Moed (Ed.), Handbook of Quantitative Science and Technology Research içinde (ss. 19-50). Springer Dordrecht. https://doi.org/10.1007/1-4020-2755-9_2
106. Vinkler, P. (2009). The π-Index: A new indicator for assessing scientific impact. Journal of Information Science, 35(5), 602-612. https://doi.org/10.1177/0165551509103601
107. Visser, M., van Eck, N. J. ve Waltman, L. (2021). Large-scale comparison of bibliographic data sources: Scopus, web of science, dimensions, crossref, and microsoft academic. Quantitative Science Studies, 2(1), 20-41. https://doi.org/10.1162/qss_a_00112
108. Wang, K., Shen, Z., Huang, C., Wu, C.-H., Dong, Y. ve Kanakia, A. (2020). Microsoft academic graph: When experts are not enough. Quantitative Science Studies, 1(1), 396-413. https://doi.org/10.1162/qss_a_00021
109. White, H. D. (1981). Cocited author retrieval online: An experiment with the social indicators literature. Journal of the American Society for Information Science, 32(1), 16-21. https://doi.org/10.1002/asi.4630320103
110. White, H. D. (2001). Authors as citers over time. Journal of the American Society for Information Science and Technology, 52(2), 87-108. https://doi.org/10.1002/1097-4571(2000)9999:9999<::aid-asi1542>3.0.co;2-t
111. White, H. D. (2003). Author cocitation analysis and pearson’s r. Journal of the American Society for Information Science and Technology, 54(13), 1250-1259. https://doi.org/10.1002/asi.10325
112. White, H. D. ve Griffith, B. C. (1981). Author cocitation: A literature measure of intellectual structure. Journal of the American Society for Information Science, 32(3), 163-171. https://doi.org/10.1002/asi.4630320302
113. Wildgaard, L. (2015). A comparison of 17 author-level bibliometric indicators for researchers in astronomy, environmental science, philosophy and public health in Web of Science and Google Scholar. Scientometrics, 104(3), 873-906. https://doi.org/10.1007/s11192-015-1608-4
114. Wildgaard, L., Schneider, J. W. ve Larsen, B. (2014). A review of the characteristics of 108 author-level bibliometric indicators. Scientometrics, 101(1), 125-158. https://doi.org/10.1007/s11192-014-1423-3
115. Wu, Q. (2010). The w-Index: A measure to assess scientific impact by focusing on widely cited papers. Journal of the American Society for Information Science and Technology, 61(3), 609-614. https://doi.org/10.1002/asi.21276
116. Zan, B. U. (2019). Doğrudan atıf, ortak atıf ve bibliyografik eşleşme yaklaşımlarına dayalı olarak araştırma alanlarının değerlendirilmesi. Sosyal Bilimler Araştırmaları Dergisi, 14(2), 501-516.
117. Zhang, H., Li, D., Gu, X. ve Chen, N. (2022). Three decades of topic evolution, hot spot mining and prospect in ccus studies based on CitNetExplorer. Chinese Journal of Population, Resources and Environment, 20(1), 91-104. https://doi.org/10.1016/j.cjpre.2022.03.010
118. Zhang, L., Thijs, B. ve Glänzel, W. (2013). What does scientometrics share with other “metrics” sciences? Journal of the American Society for Information Science and Technology, 64(7), 1515-1518. https://doi.org/10.1002/asi.22834