Abstract
Objective. To research scientific production at the thematic intersection of Data Science (DS) and Information Science (IS). Method. Informetric, descriptive study, and of first incursion, in the analysis of the written discourse of the academic texts included in the Web of Science (WoS), period 1900 to November 6, 2020, and coverage in the databases: SCI-Expanded, SSCI, A & HCI, ESCI, CPCI-S, CPCI-SSH, BKCI-S and BKCI-SSH. Results. 49 documents represented in 38 articles, 7 conference memoires, 2 book chapters and 2 editorial materials were retrieved and analyzed. The set of investigations that deal with the subject of DS and IS added 128 citations, 2.6 citations per document and H index: 7. Discussion. Conceptually, it was found that for DS and IS their origin is the “data” and that both disciplines are predominantly practical. In those investigations with greater visibility there are more than an author. DS and IS are recent areas of knowledge in which information technologies are indispensable for the analysis of large amounts of data and information. Conclusions. DS and IS have an intra- and multi and transdisciplinary character and they are characterized by the use of information technologies for the analysis of large amounts of data and information.
References
Almind, T.C. e Ingwersen, P. (1997). Informetric Analyses on the World Wide Web: Methodological Approaches to Webometrics. Journal of documentation, 53(4), 404-426.
Arnaboldi, M., y Azzone, G. (2020). Data Science in the Design of Public Policies: Dispelling the Obscurity in Matching Policy Demand and Data Offer. Heliyon, 6(6), 13, e04300. DOI: https://doi.org/10.1016/j.heliyon.2020.e04300
Bicalho, L., y Oliveira, M. D. (2011). A teoria e a prática da interdisciplinaridade em Ciência da Informação. Perspectivas em Ciência da Informação, 16(3), 47-74. Recuperado de https://www.scielo.br/pdf/pci/v16n3/04.pdf
Borko, H. (1968). Information Science: What is it? American Documentation, 19(1), 3-5.
Breunig, M., Bradley, P. E., Jahn, M., Kuper, P., Mazroob, N., Rosch, N., Al-Doori, M., Stefanakis, E., y Jadidi, M. (2020). Geospatial Data Management Research: Progress and Future Directions. ISPRS International Journal of Geo-Information, 9(2), 20, 95. DOI: https://doi.org/10.3390/ijgi9020095
Califf, R. M. (2018). Future of Personalized Cardiovascular Medicine JACC State-of-the-Art Review. Journal of the American College of Cardiology, 72(25), 3301-3309. DOI: https://doi.org/10.1016/j.jacc.2018.09.079
Casarosa, V., Ruggieri, S., Salvatori, E., Simi, M., & Turbanti, S. (2020). Educational Ecosystems for Information Science: the Case of the University of Pisa. Education for Information, 36(2), 119-138. DOI: https://doi.org/10.3233/efi-190330
Cervone, H. F. (2016). Informatics and Data Science: An Overview for the Information Professional. Digital Library Perspectives, 32(1), 7-10. DOI: https://doi.org/10.1108/dlp-10-2015-0022
Cho, J. (2019). Subject Analysis of LIS Data Archived in a Figshare Using Co-occurrence Analysis. Online Information Review, 43(2), 256-264. DOI: https://doi.org/10.1108/oir-12-2017-0369
Da Sylva, L. (2017). Les données et leurs impacts théoriques et pratiques sur lesprofessionnels de l’information [The Theoretical and Practical Impact of Data on Information Professionals]. Documentation Et Bibliotheques, 63(4), 5-34. DOI: https://doi.org/10.7202/1042308ar
Figuerola, C. G., Marco, F. J. G., & Pinto, M. (2017). Mapping the Evolution of Library and Information Science (1978–2014) Using Topic Modeling on LISA. Scientometrics, 112(3), 1507-1535. DOI: https://doi.org/10.1007/s11192-017-2432-9
Freire, G. H. D., y Freire, I. M. (2019). Ciência de Dados e Ciência da Informação [Data Science and Information Science]. Informação & Sociedade: Estudos, 29(3), 3-4. Recuperado de https://brapci.inf.br/index.php/res/download/147968
Grimaldi, D., Diaz, J., Arboleda, H., y Fernandez, V. (2019). Data Maturity Analysis and Business Performance. A Colombian Case Study. Heliyon, 5(8), 9, e02195. DOI: https://doi.org/10.1016/j.heliyon.2019.e02195
Hagen, L. (2020). Teaching Undergraduate Data Science for Information Schools. Education for Information, 36(2), 109-117. DOI: https://doi.org/10.3233/efi-200372
Haghighatlari, M., Vishwakarma, G., Altarawy, D., Subramanian, R., Kota, B. U., Sonpal, A., Setlur, S., y Hachmann, J. (2020). ChemML: A Machine Learning and Informatics Program Package for the Analysis, Mining, and Modeling of Chemical and Materials Data. Wiley Interdisciplinary Reviews-Computational Molecular Science, 10(4), 10. DOI: https://doi.org/10.1002/wcms.1458
Hirsch, J. E., y Buela-Casal, G. (2014). The Meaning of the H-index. International Journal of Clinical and Health Psychology, 14(2), 161-164. DOI: https://doi.org/10.1016/S1697-2600(14)70050-X
Kazakci, A. O. (2015). Data Science as a New Frontier for Design. En C. Weber, S. Husung, M. Cantamessa, G. Cascini, D. Marjanovic, & S. Venkataraman (Eds.), Design (ICED 15) Vol 10: Design Information and Knowledge Management Milan, Italy, 27-30.07.15 . Congreso organizado por Mines ParisTech, Milan, Italia. Recuperado de https://www.designsociety.org/download-publication/37969/DATA+SCIENCE+AS+A+NEW+FRONTIER+FOR+DESIGN
Maghsoodi, A. I., Kavian, A., Khalilzadeh, M., & Brauers, W. K. M. (2018). CLUS-MCDA: A Novel Framework Based on Cluster Analysis and Multiple Criteria Decision Theory in a Supplier Selection Problem. Computers & Industrial Engineering, 118, 409-422. DOI: https://doi.org/10.1016/j.cie.2018.03.011
Martínez Musiño, C. (2012). La ciencia de la información como plataforma para potenciar el estudio de los flujos de la información en las organizaciones. e-Ciencias de la Información, 2(1), 1-14. DOI: https://DOI 10.15517/ECI.V2I1.1212
Miller, H. J. (2017). Geographic Information Science I: Geographic Information Observatories and Opportunistic GIScience. Progress in Human Geography, 41(4), 489-500. DOI: https://doi.org/10.1177/0309132517710741
Mochizuki, M. (2020). Exploration of Education and Research on Drug Informatics. Yakugaku Zasshi-Journal of the Pharmaceutical Society of Japan, 140(4), 543-554. DOI: https://doi.org/10.1248/yakushi.19-00250
Morriello, R. (2020). Birth and Development of Data Librarianship. Jlis.It, 11(3), 1-15. DOI: https://doi.org/10.4403/jlis.it-12653
Nüst, D., Granell, C., Hofer, B., Konkol, M., Ostermann, F. O., Sileryte, R., y Cerutti, V. (2018). Reproducible Research and GIScience: An Evaluation Using AGILE Conference Papers. Peerj, 6(23), e5072. DOI: https://doi.org/10.7717/peerj.5072
Ortiz-Repiso, V., Greenberg, J., y Calzada-Prado, J. (2018). A Cross-Institutional Analysis of Data-Related Curricula in Information Science Programmes: A Focused Look at the iSchools. Journal of Information Science, 44(6), 768-784. DOI: https://doi.org/10.1177/0165551517748149
Paul, P., Bhuimali, A., y Aithal, P. S. (2017). Information Science: Science or Social Science? International Journal on Recent Researches in Science, Engineering & Technology, 5(9), 54-65. Recuperado de https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3042150
Paul, P. K. (2018). The Context of IST for Solid Information Retrieval and Infrastructure Building: Study of Developing Country. International Journal of Information Retrieval Research, 8(1), 86-100. DOI: https://doi.org/10.4018/ijirr.2018010106
Paul, P. K. y Dey, J. L. (2017). Data Science Vis-a-Vis Efficient Healthcare and Medical Systems: A Techno-Managerial Perspective. En 2017 Innovations in Power and Advanced Computing Technologies (i-PACT), 2017, Congreso organizado por IEEE, Vellore, India. DOI: 10.1109/IPACT.2017.8245148.
Saracevic, T. (1999). Information Science. Journal of the American Society for Information Science, 50(12), 1051-1063. DOI: https://doi.org/10.1002/(SICI)1097-4571(1999)50:12<1051::AID-ASI2>3.0.CO;2-Z
Saracevic, T. (1996). Ciência de Informação: Origem, evolução e relações. Perspectivas em Ciência da Informação, Belo Horizonte, 1(1), 41-46. Recuperado de http://portaldeperiodicos.eci.ufmg.br/index.php/pci/article/view/235/22
Saracevic, T. (1995). Interdisciplinary Nature of Information Science. Ciência da informação, 24(1), 36-41. Recuperado de https://brapci.inf.br/_repositorio/2010/03/pdf_dd085d2c4b_0008887.pdf
Sheble, L. (2016). Research Synthesis Methods and Library and Information Science: Shared Problems, Limited Diffusion. Journal of the Association for Information Science and Technology, 67(8), 1990-2008. DOI: https://doi.org/10.1002/asi.23499
Tague-Sutcliffe, J. 1994. Introducción a la informetría. Acimed, 2(3), 26-35.
Thill, J. C. (2018). Innovations in GIS&T, Spatial Analysis, and Location Modeling. En J. C. Thill (Ed.), Spatial Analysis and Location Modeling in Urban and Regional Systems (pp. 1-6). Alemania: Springer-Verlag. DOI: https://doi.org/10.1007/978-3-642-37896-6_1
Tucker, E. C., Capps, C. J., y Shamir, L. (2020). A Data Science Approach to 138 Years of Congressional Speeches. Heliyon, 6(8), 8, e04417. DOI: https://doi.org/10.1016/j.heliyon.2020.e04417
Virkus, S., y Garoufallou, E. (2020). Data Science and Its Relationship to Library and Information Science: A Content Analysis. Data Technologies and Applications, 21. DOI: https://doi.org/10.1108/dta-07-2020-0167
Virkus, S., y Garoufallou, E. (2019). Data Science from a Library and Information Science Perspective. Data Technologies and Applications, 53(4), 422-441. DOI: https://doi.org/10.1108/dta-05-2019-00760
Wang, L. (2018). Twinning Data Science with Information Science in Schools of Library and Information Science. Journal of Documentation, 74(6), 1243-1257. DOI: https://doi.org/10.1108/JD-02-2018-0036
Zhou, X., Yu, X. y Zhang, L. (2020). The Definitions of Information Science in China. En Multidisciplinary Digital Publishing Institute Proceedings, 47(1). DOI: https://doi.org/10.3390/proceedings2020047045
Zins, C. (2007). Conceptions of Information Science. Journal of the American Society for Information Science and Technology, 58(3), 335-350.