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Link-based approach to study scientific software usage: the case of VOSviewer

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Link-based approach to study scientific software usage: the case of VOSviewer

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Orduña Malea, E.; Costas, R. (2021). Link-based approach to study scientific software usage: the case of VOSviewer. Scientometrics. 126(9):8153-8186. https://doi.org/10.1007/s11192-021-04082-y

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Título: Link-based approach to study scientific software usage: the case of VOSviewer
Autor: Orduña Malea, Enrique Costas, Rodrigo
Entidad UPV: Universitat Politècnica de València. Facultad de Bellas Artes - Facultat de Belles Arts
Fecha difusión:
Resumen:
[EN] Scientific software is a fundamental player in modern science, participating in all stages of scientific knowledge production. Software occasionally supports the development of trivial tasks, while at other instances ...[+]
Palabras clave: Scientific software , Link analysis , Informetrics , Webometrics , Scholarly communication , Social media metrics , VOSviewer
Derechos de uso: Reconocimiento (by)
Fuente:
Scientometrics. (issn: 0138-9130 )
DOI: 10.1007/s11192-021-04082-y
Editorial:
Springer-Verlag
Versión del editor: https://doi.org/10.1007/s11192-021-04082-y
Agradecimientos:
RC was partially funded by the South African DST-NRF Center of Excellence in Scientometrics and Science, Technology, and Innovation Policy (SciSTIP).
Tipo: Artículo

References

Bruns, A., Weller, K., Zimmer, M., & Proferes, N. J. (2014). A topology of Twitter research: Disciplines, methods, and ethics. Aslib Journal of Information Management, 66(3), 250–261.

Cronin, B., Snyder, H. W., Rosenbaum, H., Martinson, A., & Callahan, E. (1998). Invoked on the Web. Journal of the American Society for Information Science, 49(14), 1319–1328.

Delgado López-Cózar, E., Orduna-Malea, E., & Martín-Martín, A. (2019). Google Scholar as a data source for research assessment. In W. Glänzel, H. Moed, U. Schmoch, & M. Thelwall (Eds.), Springer handbook of science and technology indicators (pp. 95–127). Springer. [+]
Bruns, A., Weller, K., Zimmer, M., & Proferes, N. J. (2014). A topology of Twitter research: Disciplines, methods, and ethics. Aslib Journal of Information Management, 66(3), 250–261.

Cronin, B., Snyder, H. W., Rosenbaum, H., Martinson, A., & Callahan, E. (1998). Invoked on the Web. Journal of the American Society for Information Science, 49(14), 1319–1328.

Delgado López-Cózar, E., Orduna-Malea, E., & Martín-Martín, A. (2019). Google Scholar as a data source for research assessment. In W. Glänzel, H. Moed, U. Schmoch, & M. Thelwall (Eds.), Springer handbook of science and technology indicators (pp. 95–127). Springer.

Delgado López-Cózar, E., Orduna-Malea, E., Martín-Martín, A., & Ayllón, J. M. (2017). Google Scholar: The big data bibliographic tool. In F. J. Cantú-Ortiz (Ed.), Research analytics: Boosting university productivity and competitiveness through scientometrics (pp. 59–80). Taylor and Francis.

Díaz-Faes, A., Bowman, T. D., & Costas, R. (2019). Towards a second generation of ‘social media metrics’: Characterizing Twitter communities of attention around science. PLoS ONE, 14(5), e0216408. https://doi.org/10.1371/journal.pone.0216408

Du, C., Cohoon, J., Lopez, P., & Howison, J. (2021). Softcite dataset: A dataset of software mentions in biomedical and economic research publications. Journal of the Association for Information Science and Technology. https://doi.org/10.1002/asi.24454

Gusenbauer, M. (2019). Google Scholar to overshadow them all? Comparing the sizes of 12 academic search engines and bibliographic databases. Scientometrics, 118(1), 177–214.

Hafer, L., & Kirkpatrick, A. E. (2009). Assessing open source software as a scholarly contribution. Communications of the ACM, 52(12), 126–129.

Halavais, A. (2008). The hyperlink as organizing principle. In J. Turow & L. Lokman (Eds.), The hyperlinked Society: Questioning connections in the digital age (pp. 39–55). The University of Michigan Press.

Hannay, J. E., MacLeod, C., Singer, J., Langtangen, H. P., Pfahl, D., & Wilson, G. (2009). How do scientists develop and use scientific software? Proceedings of the 2009 ICSE workshop on software engineering for computational science and engineering, SECSE 2009, 1–8. https://ieeexplore.ieee.org/abstract/document/5069155.

Haustein, S., Bowman, T. D., & Costas, R. (2016). Interpreting “altmetrics”: Viewing acts on social media through the lens of citation and social theories. In C. Sugimoto (Ed.), Theories of informetrics and scholarly communication (pp. 372–406). De Gruyter Saur.

Hey, T., Tansley, S., & Tolle, K.M. (Ed.) (2009). The fourth paradigm: data-intensive scientific discovery. Redmond, WA: Microsoft research. https://www.microsoft.com/en-us/research/wp-content/uploads/2009/10/Fourth_Paradigm.pdf.

Howison, J., & Bullard, J. (2016). Software in the scientific literature: Problems with seeing, finding, and using software mentioned in the biology literature. Journal of the Association for Information Science and Technology, 67(9), 2137–2155.

Howison, J., & Herbsleb, J. D. (2011). Scientific software production: incentives and collaboration. Proceedings of the ACM 2011 conference on computer supported cooperative work –CSCW ’11, 513–522. https://doi.org/10.1145/1958824.1958904

Howison, J., Deelman, E., McLennan, M. J. M., Da Silva, R. F., & Herbsleb, J. D. (2015). Understanding the scientific software ecosystem and its impact: Current and future measures. Research Evaluation, 24(4), 454–470.

Jansen, B. J., Jung, S.G., & Salminen, J. (2020). Data Quality in Website Traffic Metrics: A Comparison of 86 Websites Using Two Popular Analytics Services. http://www.bernardjjansen.com/uploads/2/4/1/8/24188166/traffic_analytics_comparison.pdf.

Jones, D. (2012). Flow Metrics™ will change the way you look at links. Majestic Blog. https://blog.majestic.com/development/flow-metrics.

Katz D. S., Choi S-. C. T., Niemeyer, K. E. et al. (2016). Report on the third workshop on sustainable software for science: practice and experiences (WSSSPE3). https://arxiv.org/abs/1602.02296.

Li, K., Chen, P. Y., & Yan, E. (2019). Challenges of measuring software impact through citations: An examination of the lme4 R package. Journal of Informetrics, 13(1), 449–461.

Li, K., & Yan, E. (2018). Co-mention network of R packages: Scientific impact and clustering structure. Journal of Informetrics, 12(1), 87–100.

Li, K., Yan, E., & Feng, Y. (2017). How is R cited in research outputs? Structure, impacts, and citation standard. Journal of Informetrics, 11(4), 989–1002.

Lepori, B., Aguillo, I. F., & Seeber, M. (2014). Size of web domains and interlinking behavior of higher education institutions in Europe. Scientometrics, 100(2), 497–518.

Niemeyer, K. E., Smith, A. M., & Katz, D. S. (2016). The challenge and promise of software citation for credit, identification, discovery, and reuse. Journal of Data and Information Quality, 7(4), 1–5.

Orduna-Malea, E. (2021). Dot-Science Top Level Domain: Academic websites or dumpsites? Scientometrics, 126(4), 3565–3591. https://doi.org/10.1007/s11192-020-03832-8

Orduna-Malea, E. (2020). Investigando con Twitter: una mirada según el Reglamento General de Protección de Datos. In Francisca Ramón-Fernández (Ed.). Marco jurídico de la ciencia de datos (pp. 331–378). Valencia: Tirant lo Blanch.

Orduna-Malea, E., & Alonso-Arroyo, A. (2017). Cybermetric techniques to evaluate organizations using web-based data. Chandos Publishing.

Orduna-Malea, E., Ayllón, J. M., Martín-Martín, A., & Delgado López-Cózar, E. (2015). Methods for estimating the size of Google Scholar. Scientometrics, 104(3), 931–949.

Orduna Malea, E., Martín-Martín, A., & Delgado-López-Cózar, E. (2017). Google Scholar as a source for scholarly evaluation: A bibliographic review of database errors. Revista Española De Documentación Científica, 40(4), 1–33.

Orduna-Malea, E., & Regazzi, J. J. (2014). US academic libraries: Understanding their web presence and their relationship with economic indicators. Scientometrics, 98(1), 315–336.

Ortega, J. L. (2014). Academic search engines: A quantitative outlook. Elsevier.

Ovadia, S. (2009). Exploring the potential of Twitter as a research tool. Behavioral & Social Sciences Librarian, 28(4), 202–205.

Pan, X., Cui, M., Yu, X., & Hua, W. (2017). How is CiteSpace used and cited in the literature? An analysis of the articles published in English and Chinese core journals. ISSI 2017–16th International conference on Scientometrics and Informetrics. http://issi-society.org/proceedings/issi_2017/2017ISSI%20Conference%20Proceedings.pdf.

Pan, X., Yan, E., & Hua, W. (2016). Disciplinary differences of software use and impact in scientific literature. Scientometrics, 109(3), 1–18.

Pan, X., Yan, E., Cui, M., & Hua, W. (2018). Examining the usage, citation, and diffusion patterns of bibliometric mapping software: A comparative study of three tools. Journal of Informetrics, 12(2), 481–493.

Pan, X., Yan, E., Cui, M., & Hua, W. (2019). How important is software to library and information science research? A content analysis of full-text publications. Journal of Informetrics, 13(1), 397–406.

Pan, X., Yan, E., Wang, Q., & Hua, W. (2015). Assessing the impact of software on science: A bootstrapped learning of software entities in full-text papers. Journal of Informetrics, 9(4), 860–871.

Park, H. W., & Thelwall, M. (2003). Hyperlink analyses of the World Wide Web: A review. Journal of computer-mediated communication. https://doi.org/10.1111/j.1083-6101.2003.tb00223.x

Park, H., & Wolfram, D. (2019). Research software citation in the Data Citation Index: Current practices and implications for research software sharing and reuse. Journal of Informetrics, 13(2), 574–582.

Pia, M. G., Basaglia, T., Bell, Z. W., & Dressendorfer, P. V. (2009). Geant4 in scientific literature. IEEE Nuclear Science Symposium Conference Record, 189–194. https://ieeexplore.ieee.org/document/5401810.

Piwowar, H. A. (2013). Value all research products. Nature, 493, 159.

Pradal, C., Varoquaux, G., & Langtangen, H. P. (2013). Publishing scientific software matters. Journal of Computational Science, 4(5), 311–312.

Smith, K. (2020). 58 Incredible and Interesting Twitter Stats and Statistics. Brandwatch. https://www.brandwatch.com/blog/twitter-stats-and-statistics.

Smith, A. M., Katz, D. S., & Niemeyer, K. E. (2016). Software citation principles. PeerJ Computer Science, 2, e86. https://peerj.com/articles/cs-86/.

Soito, L., & Hwang, L. J. (2016). Citations for Software: Providing identification, access and recognition for research software. IJDC, 11(2), 48–63.

Stewart, B. (2017). Twitter as method: Using Twitter as a tool to conduct research. L. Sloan, & A. Quan-Haase, Social Media Research Methods, 251–266.

Thelwall, M. (2004). Link Analysis: An information science approach. Elsevier.

Thelwall, M. (2006). Interpreting social science link analysis research: A theoretical framework. Journal of the American Society for Information Science and Technology, 57(1), 60–68.

Thelwall, M., & Kousha, K. (2016). Academic software downloads from google code. Information Research, 21(1). http://informationr.net/ir/21-1/paper709.html#.XzelJ-gzbIU.

Van Eck, N., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538.

Williams, S. A., Terras, M. M., & Warwick, C. (2013). What do people study when they study Twitter? Classifying Twitter related academic papers. Journal of Documentation, 69(3), 384–410.

Wouters, P., Zahedi, Z., & Costas, R. (2019). Social media metrics for new research evaluation. In W. Glänze, H. F. Moed, U. Schmoch, & M. Thelwall (Eds.), Springer handbook of science and technology indicators (pp. 687–713). Springer.

Yang, B., Rousseau, R., Wang, X., & Huang, S. (2018). How important is scientific software in bioinformatics research? A comparative study between international and Chinese research communities. Journal of the Association for Information Science and Technology, 69(9), 1122–1133.

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