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Bibliographie

[1] A Persistent Identifier (PID) policy for the European Open Science Cloud, European Commission, Directorate-General for Research and Innovation, 2020. doi: 10.2777/926037

[2] Bloemers M, Montesanti A. “The FAIR funding model: Providing a framework for research funders to drive the transition toward FAIR data management and stewardship practices”, Data Intelligence 2(2020), 171–180. doi: 10.1162/dint_a_00039

[3] David R, et al. “FAIRness Literacy: The Achilles’ Heel of Applying FAIR Principles”, Data Science Journal. 2020;19(1):32. doi: 10.5334/dsj-2020-032

[4] FAIR Data Maturity Model: specification and guidelines, RDA FAIR Data Maturity Model Working Group, 2020. doi: 10.15497/rda00045

[5] Guidelines on FAIR Data Management in Horizon 2020, Version 3.0, 2016. En ligne

[6] Handbook of Metadata, Semantics and Ontologies, éd. Sicilia M.-A., World Scientific Publishing, 2014. doi: 10.1142/7077

[7] Harjes J. et al. “FAIR digital objects in environmental and life sciences should comprise workflow operation design data and method information for repeatability of study setups and reproducibility of results”, Database: the journal of biological databases and curation (2020). doi: 10.1093/database/baaa059

[8] Hay D. C., Data model patterns: a metadata map, Elsevier, 2006. doi: 10.1016/B978-0-12-088798-9.X5000-1

[9] Jacobsen A. et al. “A Generic Workflow for the Data FAIRification Process.” Data Intelligence 2 (2020): 56-65. doi: 10.1162/dint_a_00028

[10] Jacobsen A. et al. “FAIR principles: Interpretations and implementation considerations”, Data Intelligence. 2020;2 (1-2) :10-29. doi: 10.1162/dint_r_00024

[11] Landi A. et al. “The “A” of FAIR – as open as possible, as closed as necessary”, Data Intelligence 2(2020), 47–55. doi: 10.1162/dint_a_00027

[12] de Miranda Azevedo R., Dumontier M., “Considerations for the Conduction and Interpretation of FAIRness Evaluations”, Data Intelligence. 2020 Jan 1;2(1-2):285-292. doi: 10.1162/dint_a_00051

[13] Pergl R., et al. 2019. “Data Stewardship Wizard”: A Tool Bringing Together Researchers, Data Stewards, and Data Experts around Data Management Planning”, Data Science Journal, 18: 59, pp. 1–8. doi: 10.5334/dsj-2019-059

[14] Pomerantz J., Metadata, MIT Press, 2015. doi: 10.7551/mitpress/10237.001.0001

[15] Schwardmann U., “Digital Objects – FAIR Digital Objects: Which Services Are Required?”, Data Science Journal, 19(1) 2020. doi: 10.5334/dsj-2020-015

[16] Thompson M., et al. “Making FAIR Easy with FAIR Tools: From Creolization to Convergence.” Data Intelligence 2 (2020): 87-95. doi: 10.1162/dint_a_00031

[17] Turning FAIR into reality. Final report and action plan from the European Commission expert group on FAIR data. Luxembourg: Publications Office of the European Union, 2018 doi: 10.2777/1524

[18] Wilkinson M., et al. “The FAIR Guiding Principles for scientific data management and stewardship”, Science Data 3, 160018 (2016). doi: 10.1038/sdata.2016.18

[19] Wilkinson M., et al. “A design framework and exemplar metrics for FAIRness”, Science Data 5, (2018), 1–4. doi: 10.1038/sdata.2018.118

[20] Wise J., et al. “Implementation and relevance of FAIR data principles in biopharmaceutical R&D”, Drug Discovery Today, Volume 24/4 April 2019. doi: 10.1016/j.drudis.2019.01.008


À suivre: Annexe 1. Principes FAIR


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