Abstracts
Résumé
Cet article poursuit trois objectifs. Dans un premier temps, nous cherchons à positionner la gestion des données de recherche (GDR) dans le grand mouvement de la science ouverte. Ensuite, nous voulons démontrer comment le bibliothécaire universitaire pourrait être appelé à être un précieux allié dans l’écosystème scientifique afin de mieux saisir les multiples enjeux de la gestion des données de recherche et ainsi mieux soutenir le corps professoral et les autres professionnels qui gravitent autour de ces pratiques. Finalement, nous voulons également mettre en lumière certaines particularités induites par la GDR pour les disciplines qui utilisent plus largement les méthodologies qualitatives afin de produire des données.
Abstract
This article has three objectives. First, we seek to position research data management (RDM) within the broader open science movement. Secondly, we want to demonstrate how the academic librarian could be called upon to be a valuable ally in the scientific ecosystem to better grasp the multiple issues involved in research data management and thus better support faculty and other professionals who gravitate around these practices. Finally, we would also like to highlight some of the particularities induced by MDM for disciplines that make greater use of qualitative methodologies to produce data.
Appendices
Bibliographie
- Aspesi, C., Allen, N. S., Crow, R., Daugherty, S., Joseph, H., McArthur, J. T., & Shockey, N. (2019, 29 mars). SPARC Landscape Analysis. https://doi.org/10.31229/osf.io/58yhb
- Belli, S., Mugnaini, R., Baltà, J., & Abadal, E. (2020). Coronavirus mapping in scientific publications : When science advances rapidly and collectively, is access to this knowledge open to society ? Scientometrics, 124(3), 2661-2685.
- Berends, Hans. (17 novembre 2022). Open Science Webinar S1E5 : Facilitating the sharing and reuse of qualitative data.Youtube. https://www.youtube.com/watch?v=VjznmX5pHYM
- Bilder G, Lin J, Neylon C (2020), The Principles of Open Scholarly Infrastructure.https://doi.org/10.24343/C34W2H
- Boyadjian, J. & Velcin, J. (2017). L’analyse quantitative des médias sociaux, une alternative aux enquêtes déclaratives ? : La mesure de la popularité des personnalités politiques sur Twitter. Questions de communication, 31, 111135. https://doi.org/10.4000/questionsdecommunication.11078
- COPIM. (2021). COPIM statement on the corporate acquisition of OA infrastructure. https://copim.pubpub.org/pub/copim-statement-corporate-acquisition-oa-infra/release/1
- Cuocolo. L. (2019). L’utilizzo dei dati della P.A. : dall’accesso « civico » all’accesso « commerciale ». [résumé] Diritto pubblico comparato ed europeo. Numero speciale. 163-190. DOI : 10.17394/93046 https://www.rivisteweb.it/doi/10.17394/93046#
- Dominik, M., Nzweundji, J.G., Ahmed, N., Carnicelli, S., Mat Jalaluddin, N.S., Fernandez Rivas, D., Narita, V., Enany, S. and Rios Rojas, C., 2022. Open Science – For Whom ?. Data Science Journal, 21(1) http://doi.org/10.5334/dsj-2022-001
- Duan, Y., Luo, J., Zhao, L., Zhang, X., Miao, J., Moher, D., & Bian, Z. (2022). Reporting and data sharing level for COVID-19 vaccine trials : A cross-sectional study. EBioMedicine, 78 doi :10.1016/j.ebiom.2022.103962
- Fecher, B. et Friesike, S. (2014). Open Science : One Term, Five Schools of Thought. Dans Bartling S., Friesike S. (dir.) Opening Science (p. 17-47). Springer. https://doi.org/10.1007/978-3-319-00026-8_2
- Federer, L. M., Belter, C. W., Joubert, D. J., Livinski, A., Lu, Y. L., Snyders, L. N., & Thompson, H. (2018). Data sharing in PLOS ONE : an analysis of data availability statements. PloS one, 13(5), e0194768.
- Fortin, M.-F. et Gagnon, J. (2022). Fondements et étapes du processus de recherche : méthodes quantitatives et qualitatives (4e édition). Chenelière éducation.
- Gilbert J-P, Ng V, Niu J, Rees EE. Un appel à un cadre éthique lors de l’utilisation des données des médias sociaux pour des applications d’intelligence artificielle dans la recherche en santé publique. Relevé des maladies transmissibles au Canada 2020 ; 46(6) :191–6. https://doi.org/10.14745/ccdr.v46i06a03f
- Gingras, Y. (2013). Sociologie des sciences (1re éd, Ser. Que sais-je ?, 3950). Presses universitaires de France.
- Hesse, A., Glenna, L., Hinrichs, C., Chiles, R., & Sachs, C. (2019). Qualitative Research Ethics in the Big Data Era. American Behavioral Scientist, 63(5), 560–583. https://doi.org/10.1177/0002764218805806
- Karcher, Sebastian et Lafferty-Hess, Sophia. (2019). An epic journey in sharing : The story of a young researcher’s journey to share her data and the information professionals who tried to help. IASSIST Quarterly, 43(1), 1‑9.
- Iwasin´ski, Łukasz. (2020). Theoretical Bases of Critical Data Studies. Teoretyczne podstawy critical data studies., 115A(1A), 96‑109.
- Max Planck Digital Library. (2018). Final Conference Statement : 14th Berlin Open Access Conference. https://oa2020.org/b14-conference/final-statement/
- McKeown, M. (2022). The View from Below : How the Neoliberal Academy Is Shaping Contemporary Political Theory. Society, 59(2), 99-109.
- Merton, R. K. (1973). The sociology of science : Theoretical and empirical investigations. Chicago and London : Chicago Univeristy Press.
- Metcalf, J. et Crawford, K. (2016). Where are human subjects in Big Data research ? The emerging ethics divide. Big Data & Society, 3(1). https://doi.org/10.1177/2053951716650211
- Mirowski, P. (2018). The future(s) of open science. Social Studies of Science, 48(2), 171203.
- Miyakawa, T. (2020). No raw data, no science : another possible source of the reproducibility crisis. Molecular brain, 13(1), 1-6.
- Morais, R., Saenen, B., Garbuglia, F., Berghmans, S. et Gaillard, V. (2021). From Principles to Practices : Open Science at Europe’sUniversities. 2020-2021 EUA Open Science Survey Results. European University Association.
- Neff G, Tanweer A, Fiore-Gartland B, Osburn L. (2017). Critique and Contribute : A Practice-Based Framework for Improving Critical Data Studies and Data Science. Big Data. 5(2) :85-97. doi : 10.1089/big.2016.0050.
- Organisation de coopération et de développement économique. (2007). Principes et lignes directrices pour l’accès aux données de la recherche financée sur fonds publics. https://www.oecd.org/fr/science/inno/38500823.pdf
- Ortega-Rodríguez C, Licerán-Gutiérrez A, Moreno-Albarracín AL. Transparency as a Key Element in Accountability in Non-Profit Organizations : A Systematic Literature Review. Sustainability. 2020 ; 12(14) :5834. https://doi.org/10.3390/su12145834
- Read, K. B., Ganshorn, H., Rutley, S., & Scott, D. R. (2021, October 14). Surveying the landscape of CIHR-funded research data sharing practices : An analysis of the published literature. [prépublication] https://doi.org/10.31219/osf.io/h7byr
- Schonfeld, R. C. (2017). Who Owns Digital Science ? Scholarly Kitchen. https://scholarlykitchen.sspnet.org/2017/10/23/ownership-digital-science/
- Smale, N, Unsworth, K., Denyer, G. et Magatova, E. (2020), A Review of the History, Advocacy and Effcacy of Data Management Plans. International Journal of Digital Curation, 15(1). https://doi.org/10.2218/ijdc.v15i1.525
- Wilkins, J. S. (2008). The roles, reasons and restrictions of science blogs. Trends in ecology & evolution, 23(8), 411-413.
- Wilkinson, M. D., Dumontier, M., . Aalbersberg, I. J., Appleton, G., Axton,M. Baak, A., Bomberg, N., Boiten, J.-W., … Mons, B. (2016) The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3 :article no 160018 doi : 10.1038/sdata.2016.18 (2016).
- Weinhardt M. Big Data : Some Ethical Concerns for the Social Sciences. Social Sciences. 2021 ; 10(2) :36. https://doi.org/10.3390/socsci10020036
- Zhang, L., Wei, Y., Huang, Y., & Sivertsen, G. (2022). Should open access lead to closed research ? The trends towards paying to perform research. Scientometrics, 1-27.