Opening Research Data: What Does It Mean for Social Sciences?
Abstract
Recent international trends demonstrate multilevel efforts to ‘open’ science across its whole ecosystem and lifecycle – from capturing research data through to publishing results. In social sciences, the publication process is already largely ‘open access’ or transitioning toward it. However, opening research data raises specific issues and concerns for the field. Here, we set out to understand what open research data mean for social sciences, and if, why, and how data should be made open. We argue that while the ecosystem of actors, infrastructures, standards, and principles is starting to take structure in France and abroad, there are several barriers to the process of opening data in social sciences: (1) a misperception of the motivations for opening data (i.e., focusing on risks of exercising control over researchers and their academic freedom and overlooking motivations like data patrimonialization, pooling and potential synergies, trust-building, and broader engagement), (2) a system based on competition and the dominant process of ‘starification’ in research, (3) a lack of resources and capabilities that might further exacerbate inequalities among genders, communities, institutions, and countries, and (4) the potential risks inherent to opening data and the specific constraints posed by social science data. Against this backdrop, we investigate several ways forward to operationalize not only FAIR (Findable, Accessible, Interoperable and Reusable) but also CARE (Collective benefit, Authority to control, Responsibility, Ethics) principles for open data in social sciences, before going on to present M@n@gement’s new open data policy.
Downloads
References
Austin, C. C., Bloom, T., Dallmeier-Tiessen, S., Khodiyar, V. et al. (2015). Key components of data publishing: Using current best practices to develop a reference model for data publishing. International Journal on Digital Libraries, 18, 77–92. doi: 10.5281/zenodo.34542
Berkowitz, H. & Delacour, H. (2020). Sustainable academia: Open, engaged, and slow science. M@n@gement, 23(1), 1–3. doi: 10.37725/mgmt.v23.4474
Bonneville, A., Tucci, I., Vion, A. & Giglio, L. (2021). Données de la recherche: Pratiques et besoins dans un laboratoire pluridisciplinaire SHS [PhD thesis]. Laboratoire d’Economie et Sociologie du Travail (LEST).
Borgerud, C., & Borglund, E. (2020). Open research data, an archival challenge? Archival Science, 20(3), 279–302. doi: 10.1007/s10502-020-09330-3
Burgelman, J.-C., Pascu, C., Szkuta, K., Von Schomberg, R. et al. (2019). Open science, open data, and open scholarship: European policies to make science fit for the twenty-first century. Frontiers in Big Data, 2, 43.
Carroll, S. R., Herczog, E., Hudson, M., Russell, K. et al. (2021). Operationalizing the CARE and FAIR principles for indigenous data futures. Scientific Data, 8(1), 108. doi: 10.1038/s41597-021-00892-0
Fabre, C. & Gouzi, F. C. (2020). Proposition de modèle de politique pour les revues et éditeurs quant aux données de la recherche. Retrieved from https://hal-univ-tlse2.archives-ouvertes.fr/hal-03026731
Feret, R., Gouzi, F. C., Guigonis, S., Jouguet, H. et al. (2021). Guidelines for journals that wish to establish a ‘data policy’ related to their publications (10 p.) [Report, Comité pour la science ouverte]. doi: 10.52949/19
Gauquelin, C., Lutoff, C. & La Branche, S. (2017). Quelle place pour les données issues des sciences humaines et sociales dans le développement de services climatiques régionaux? VertigO, 17(3). doi: 10.4000/vertigo.18891
Hrynaszkiewicz, I., Simons, N., Hussain, A., Grant, R. et al. (2020). Developing a research data policy framework for all journals and publishers. Data Science Journal, 19(1), 5. doi: 10.5334/dsj-2020-005
Longley Arthur, P. & Hearn, L. (2021). Toward open research: A narrative review of the challenges and opportunities for open humanities. Journal of Communication, 71(5), 827–853. doi: 10.1093/joc/jqab028
Marlet, O., Baudoin, B., Bernard, L., Bézard, L. et al. (2022). Le livre blanc du consortium ‘Mémoires des archéologues et des sites archéologiques’: Guide des bonnes pratiques numériques en archéologie. Consortium MASA ‹halshs-03561376v2›, 39.
Martone M. (Ed.). (2014). Data Citation Synthesis Group: Joint Declaration of Data Citation Principles. FORCE11. doi: 10.25490/a97f-egyk
Millerand, F. & Bowker, G. C. (2008). Metadata, trajectoires et ‘énaction’. In C. Rosental (Ed.), La cognition au prisme des sciences sociales (pp. 277–303). Editions des Archives Contemporaines.
OECD (2016). Research ethics and new forms of data for social and economic research. OECD Science, Technology and Industry Policy Papers n° 34. doi: 10.1787/5jln7vnpxs32-en
Osterloh, M. & Frey, B. S. (2015). Ranking games. Evaluation Review, 39(1), 102–129. doi: 10.1177/0193841X14524957
Peng, G., Lacagnina, C., Ivánová, I., Downs, R. R. et al. (2021). International community guidelines for sharing and reusing quality information of individual earth science datasets. Open Science Framework Preprints. doi: 10.31219/osf.io/xsu4p
Roche, D. G., Kruuk, L. E. B., Lanfear, R. & Binning, S. A. (2015). Public data archiving in ecology and evolution: How well are we doing? PLoS Biology, 13(11), e1002295. doi: 10.1371/journal.pbio.1002295
Science Europe. (2021). Practical guide to the international alignment of research data Management. Retrieved from https://www.scienceeurope.org/media/4brkxxe5/se_rdm_practical_guide_extended_final.pdf
Stats NZ. (2020). Ngā Tikanga Paihere: A framework guiding ethical and culturally appropriate data use. Retrieved from www.data.govt.nz; https://data.govt.nz/assets/data-ethics/Nga-Tikanga/Nga-Tikanga-Paihere-Guidelines-December-2020.pdf
The FAIR Data Principles – FORCE11. (2014, September 3). Retrieved from https://force11.org/info/the-fair-data-principles/
Vijay, D. (2021a). Vulnerability as praxis in studying social suffering. In E. Bell & S. Sengupta (Eds.), Empowering methodologies in organisational and social research (pp. 55–74.). Routledge. doi: 10.4324/9780429352492-4
Vijay, D. (2021b). Settled knowledge practices, truncated imaginations. Organization, 0(0), onlinefirst. doi: 10.1177/13505084211057261
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G. et al. (2016). The FAIR guiding principles for scientific data management and stewardship. Scientific Data, 3(1), 160018. doi: 10.1038/sdata.2016.18
Willinsky, J. (2005). The access principle: The case for open access to research and scholarship. MIT Press.
Woolston, C. (2021). Impact factor abandoned by Dutch university in hiring and promotion decisions. Nature, 595(7867), 462–462. doi: 10.1038/d41586-021-01759-5
Xu, F., Wu, L. & Evans, J. (2022). Flat teams drive scientific innovation. PNAS, 119(23), e2200927119. doi: 10.1073/pnas.2200927119
Copyright (c) 2022 Héloïse Berkowitz, Hélène Delacour
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors retain copyright of their work, with first publication rights granted to the AIMS.
Published by 