Have a personal or library account? Click to login

References

  1. Curty, RG, Crowston, K, Specht, A, et al. 2017. Attitudes and norms affecting scientists’ data reuse. PLOS ONE, 12: e0189288. DOI: 10.1371/journal.pone.0189288
  2. David, R, Mabile, L, Specht, A, et al. 2020. Templates for FAIRness evaluation criteria – RDA-SHARC ig (Version 1.1) [Data set]. Zenodo. DOI: 10.5281/zenodo.3922069
  3. de Miranda Azevedo, R and Dumontier, M. 2019. Considerations for the Conduction and Interpretation of FAIRness Evaluations. Data Intelligence, 285292. DOI: 10.1162/dint_a_00051
  4. Doorn, P and Science Europe. 2018. Science Europe Guidance. Presenting a Framework for Discipline-specific Research Data Management. [WWW Document]. URL http://www.scienceeurope.org/wp-content/uploads/2018/01/SE_Guidance_Document_RDMPs.pdf [Accessed January 07, 2020].
  5. Doorn, P and Timmermann, M. 2018. Towards Domain Protocols for Research Data Management (IG Domain Repositories RDA 9th Plenary meeting Community-driven Research Data Management). Paper presented at the 9. Plenary meeting Community-driven Research Data Management, Barcelona. https://www.rd-alliance.org/sites/default/files/attachment/RDA%20DRIG%20Domain%20Protocols%20V3%20Barcelona%20April%202017%20-%20DoornAerts.pptx [Accessed January 07, 2020].
  6. Erdmann, C, Simons, N, Otsuji, R, et al. 2019. Top 10 FAIR Data & Software Things. [WWW Document]. [Accessed January 07, 2020]. DOI: 10.5281/zenodo.2555498
  7. European Commission Directorate General for Research and Innovation (EC DGRI). 2016. E.U. H2020 Programme Guidelines on FAIR Data Management in Horizon 2020, Version 3.0. Luxembourg: Publications Office of the EU. [WWW Document], URL https://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oa-data-mgt_en.pdf [Accessed January 07, 2020].
  8. European Commission Directorate General for Research and Innovation (EC DGRI). 2017. Evaluation of Research Careers fully acknowledging Open Science Practices; Rewards, incentives and/or recognition for researchers practicing Open Science. Luxembourg: Publications Office of the EU. [WWW Document]. URL https://ec.europa.eu/research/openscience/pdf/os_rewards_wgreport_final.pdf [Accessed January 07, 2020].
  9. European Commission Directorate General for Research and Innovation (EC DGRI). 2018. Turning FAIR into reality: final report and action plan from the European Commission expert group on FAIR data. Luxembourg: Publications Office of the EU. [WWW Document]. URL https://op.europa.eu:443/en/publication-detail/-/publication/7769a148-f1f6-11e8-9982-01aa75ed71a1/language-en/format-PDF [Accessed January 07, 2020].
  10. Federer, LM, Belter, CW, Joubert, DJ, et al. 2018. Data sharing in PLOS ONE: An analysis of Data Availability Statements. PLOS ONE, 13: e0194768. DOI: 10.1371/journal.pone.0194768
  11. Hansen, KK, Buss, M and Sztuk Haahr, L. 2018. A FAIRy tale. Zenodo. [WWW Document]. DOI: 10.5281/zenodo.2248200
  12. Herschel, M, Diestelkämper, R and Ben Lahmar, H. 2017. A survey on provenance: What for? What form? What from? The VLDB Journal, 26: 881906. DOI: 10.1007/s00778-017-0486-1
  13. Jacobsen, A, de Miranda Azevedo, R, Juty, N, et al. 2019. FAIR Principles: Interpretations and Implementation Considerations. Data Intelligence, 1029. DOI: 10.1162/dint_r_00024
  14. Jones, S, Pergl, R, Hooft, R, et al. 2019. Data Management Planning: How Requirements and Solutions are Beginning to Converge. Data Intelligence, 208219. DOI: 10.1162/dint_a_00043
  15. Landi, A, Thompson, M, Giannuzzi, V, et al. 2019. The “A” of FAIR – As Open as Possible, as Closed as Necessary. Data Intelligence, 4755. DOI: 10.1162/dint_a_00027
  16. Lannom, L, Koureas, D and Hardisty, AR. 2019. FAIR Data and Services in Biodiversity Science and Geoscience. Data Intelligence, 122130. DOI: 10.1162/dint_a_00034
  17. Mabile, L, De Castro, P, Bravo, E, et al. 2016. Towards new tools for bioresource use and sharing. Information Services & Use, 36: 133146. DOI: 10.3233/ISU-160811
  18. McQuilton, P, Batista, D, Beyan, O, et al. 2019. Helping the Consumers and Producers of Standards, Repositories and Policies to Enable FAIR Data. Data Intelligence, 151157. DOI: 10.1162/dint_a_00037
  19. Mons, B, Neylon, C, Velterop, J, et al. 2017. Cloudy, increasingly FAIR; revisiting the FAIR Data guiding principles for the European Open Science Cloud. Information Services & Use, 37: 4956. DOI: 10.3233/ISU-170824
  20. Reymonet, N, Moysan, M, Cartier, A, et al. 2018. Réaliser un plan de gestion de données «FAIR»: modèle. [WWW Document]. URL https://archivesic.ccsd.cnrs.fr/sic_01690547/document [Accessed January 10, 2020].
  21. Sansone, S-A, McQuilton, P, Rocca-Serra, P, et al. 2019. FAIRsharing as a community approach to standards, repositories and policies. Nat Biotechnol, 37: 358367. DOI: 10.1038/s41587-019-0080-8
  22. Schultes, E, Strawn, G and Mons, B. 2018. Ready, Set, GO FAIR: Accelerating Convergence to an Internet of FAIR Data and Services. DAMDID/RCDL. http://ceur-ws.org/Vol-2277/paper07.pdf.
  23. Schwardmann, U. 2020. Digital Objects – FAIR Digital Objects: Which Services Are Required? Data Science Journal, 19(1): 15. DOI: 10.5334/dsj-2020-015
  24. Stall, S, Cruse, P, Cousijn, H, et al. 2018. Data Sharing and Citations: New Author Guidelines Promoting Open and FAIR Data in the Earth, Space, and Environmental Sciences. Science Editor, 41: 8387. https://www.csescienceeditor.org/wp-content/uploads/2018/11/CSEv41n3_text_83-87.pdf [Accessed January 07, 2020].
  25. Sustkova, HP, Hettne, KM, Wittenburg, P, et al. 2019. FAIR Convergence Matrix: Optimizing the Reuse of Existing FAIR-Related Resources. Data Intelligence, 158170. DOI: 10.1162/dint_a_00038
  26. Thompson, M, Burger, K, Kaliyaperumal, R, et al. 2019. Making FAIR Easy with FAIR Tools: From Creolization to Convergence. Data Intelligence, 8795. DOI: 10.1162/dint_a_00031
  27. Wilkinson, MD, Dumontier, M, Aalbersberg, IjJ, et al. 2016. The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3: 160018. DOI: 10.1038/sdata.2016.18
  28. Wilkinson, MD, Dumontier, M, Sansone, S-A, et al. 2019. Evaluating FAIR maturity through a scalable, automated, community-governed framework. Scientific Data, 6: 112. DOI: 10.1038/s41597-019-0184-5
  29. Wilkinson, MD, Sansone, S-A, Schultes, E, et al. 2018. A design framework and exemplar metrics for FAIRness. Scientific Data 5. DOI: 10.1038/sdata.2018.118
  30. Wilkinson, MD, Verborgh, R, da Silva Santos, LOB, et al. 2017. Interoperability and FAIRness through a novel combination of Web technologies (No. e2522v2). PeerJ Inc. DOI: 10.7287/peerj.preprints.2522v2
  31. Wittenburg, P, Sustkova, HP, Montesanti, A, et al. 2019. The FAIR Funder pilot programme to make it easy for funders to require and for grantees to produce FAIR Data. [WWW Document]. URL https://arxiv.org/abs/1902.11162? [Accessed January 07, 2020].
Language: English
Submitted on: Feb 3, 2020
|
Accepted on: Jul 27, 2020
|
Published on: Aug 11, 2020
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2020 Romain David, Laurence Mabile, Alison Specht, Sarah Stryeck, Mogens Thomsen, Mohamed Yahia, Clement Jonquet, Laurent Dollé, Daniel Jacob, Daniele Bailo, Elena Bravo, Sophie Gachet, Hannah Gunderman, Jean-Eudes Hollebecq, Vassilios Ioannidis, Yvan Le Bras, Emilie Lerigoleur, Anne Cambon-Thomsen, The Research Data Alliance – SHAring Reward and Credit (SHARC) Interest Group, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.