The Average Data Scientist is an Outlier Achiever in an Information Science Department
DOI:
https://doi.org/10.21900/j.alise.2024.1689Keywords:
Data science, Information science, Publications, CitationsAbstract
The aim of this study was to test whether data science and information science have similar or different publication and citation standards. In order to test this, we applied serpAPI to compare one hundred random Google Scholar data science profiles to their equivalents – one hundred information science profiles. The results indicate that: (a) The yearly average number of data scientist publications (15.70) was over three times higher than for information scientist (4.26). (b) The average citation per paper for data scientists (23.06) was over 4 times higher than the average for information scientists (5.70). (c) The total number of citations of papers published in 2021 for data scientists (334.54) was over 14 times higher than for information scientists (23.38). These results clearly indicate that when making academic career decisions, data scientists should be evaluated according to data science standards which are very different from information science standards.
References
Avnoon, N. (2021). Data scientists’ identity work: Omnivorous symbolic boundaries in skills acquisition. Work, Employment and Society, 35(2), 332-349.
Buckland, M. K., & Liu, Z. (1995). History of information science. Annual review of information science and technology, 30, 385-416.
Fred, Y. Y., & Ma, F. C. (2023). An essay on the differences and linkages between data science and information science. Data and Information Management, 7(1), 100032.
Lillquist, E., & Green, S. (2010). The discipline dependence of citation statistics. Scientometrics, 84(3), 749-762.
Marchionini, G. (2016). Information science roles in the emerging field of data science. Journal of Data and Information Science, 1(2), 1-6.
Mazzocchi, F. (2015). Could Big Data be the end of theory in science? A few remarks on the epistemology of data‐driven science. EMBO reports, 16(10), 1250-1255.
Patil, D. J. (2011) Building Data Science Teams. Beijing; Cambridge; Farnham; Koln; Sebastopol, CA; Tokyo: O’Reilly.
Podlubny, I. (2005). Comparison of scientific impact expressed by the number of citations in different fields of science. Scientometrics, 64, 95-99.
Priestley, J. L., & McGrath, R. J. (2019). The evolution of data science: A new mode of knowledge production. International Journal of Knowledge Management (IJKM), 15(2), 97-109.
Rayward, W. B. (1996). The history and historiography of information science: some reflections. Information processing & management, 32(1), 3-17.
Stock, W. G., & Stock, M. (2013). Handbook of information science. Walter de Gruyter.
Van Der Aalst, W., & van der Aalst, W. (2016). Process Mining - Data science in action. Springer.
Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77-84.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Ofer Bergman, Noa Gradovitch, Tamar Israeli
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.