Published Versions 2 Vol 2 (1) : 1-9 2019
Download
The FAIR Principles: First Generation Implementation Choices and Challenges
285 41 0
Abstract & Keywords
Abstract: 
Keywords: 
Acknowledgments
We are very grateful for so many authors contributing their early FAIR related experiences, choices, perceived challenges, views and practices to this special issue. We hope this is the first in a series of publications in which community-emerging and increasingly endorsed community practices in the FAIR realm will be published at regular intervals to serve conversion and the rapid realization of the Internet of FAIR Data and Services.
[1]
Directorate-General for Research and Innovation (European Commission). First report and recommendationsof the Commission high level expert group on the European Open Science Cloud. Available at: https://publications.europa.eu/en/publication-detail/-/publication/2ec2eced-9ac5-11e6-868c-01aa75ed71a1/language-en. doi:10.2777/940154.
[2] M.D. Wilkinson, M. Dumontier, I.J. Aalbersberg, G. Appleton, M. Axton, A. Baak, ... & B. Mons. The FAIRGuiding Principles for scientific data management and stewardship. Scientific Data 3(2016), Article No.160018. doi: 10.1038/sdata.
[3] L. Lannom, D. Koureas & A.R. Hardisty. FAIR data and services in biodiversity science and geoscience. DataIntelligence 2(2020), 122–130. doi: 10.1162/dint_a_00034.
[4] P. Wittenburg, F. de Jong, D. van Uytvanck, M. Cocco, K. Jeffery, M. Lautenschlager, ... & P. Holub. State ofFAIRness in ESFRI projects. Data Intelligence 2(2020), 230–237. doi: 10.1162/dint_a_00045.
[5] P. Wittenburg, M. Lautenschlager, H. Thiemann, C. Baldauf & P. Trilsbeek. FAIR practices in Europe. Data Intelligence 2(2020), 257–263. doi: 10.1162/dint_a_00048.
[6] R. Dekker. Social data: CESSDA best practices. Data Intelligence 2(2020), 220–229. doi: 10.1162/dint_a_00044.
[7] M. Bloemers & A. Montesanti. 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.
[8] M. Hahnel & D. Valen. How to (easily) extend the FAIRness of existing repositories. Data Intelligence 2(2020), 192–198. doi: 10.1162/dint_a_00041.
[9] M. Van Reisen, M. Stokmans, M. Mawere, M. Basajja, A. O. Ong’ayo, P. Nakazibwe, C. Kirkpatrick & K. Chindoza. FAIR Practices in Africa. Data Intelligence 2(2020), 246–256. doi: 10.1162/dint_a_00047.
[10] L. Sales, P. Henning, V. Veiga, M.M. Costa, L.F. Sayão, L.O. Bonino da Silva Santos, ... & L.F. Pires. GO FAIR Brazil: A challenge for Brazilian data science. Data Intelligence 2(2020), 238–245. doi: 10.1162/dint_a_00046.
[11] Chinese Scientific Data. Available at: http://www.sciencedb.cn.
[12] Global Change Research Data Publishing & Repository. Available at: http://www.geodoi.ac.cn/WebEn/Default.aspx.
[13] B2SHARE. Available at: 10.23728/b2share.4e8ac36c0dd343da81fd9e83e72805a0.
[14] M. van Reisen, M. Stokmans, M. Basajja, A. Ong’ayo, C. Kirkpatrick & B. Mons. Towards the tipping point of FAIR implementation. Data Intelligence 2(2020), 264–275. doi: 10.1162/dint_a_00049.
[15] S. Jones, R. Pergl, R. Hooft, T. Miksa, R. Samors, J. Ungvari, R.I. Davis & T. Lee. Data management planning: How requirements and solutions are beginning to converge. Data Intelligence 2(2020), 208–219. doi: 10.1162/dint_a_00043.
[16] S. Stall, L. McEwen, L. Wyborn, N. Hoebelheinrich & I. Bruno. Growing the FAIR community at the intersec-tion of the geosciences and pure and applied chemistry. Data Intelligence 2(2020), 139–150. doi: 10.1162/dint_a_00036.
[17] H. van Vlijmen, A. Mons, A. Waalkens, W. Franke, A. Baak, G. Ruiter, ... & J.-M. Neefs. The need of Industry to go FAIR. Data Intelligence 2(2020), 276–284. doi: 10.1162/dint_a_00050.
[18] S.J. Coles, J.G. Frey, E.L. Willighagen & S.J. Chalk. Taking FAIR on the ChIN. Data Intelligence 2(2020), 131–138. doi: 10.1162/dint_a_00035.
[19] A. Jacobsen, R. de Miranda Azevedo, N. Juty, D. Batista, S. Coles, R. Cornet, ... & E. Schultes. FAIR principles: Interpretations and implementation considerations. Data Intelligence 2(2020), 10–29. doi: 10.1162/dint_r_00024.
[20] R. de Miranda Azevedo, M. Wilkinson & M. Dumontier. Considerations for the conduction and interpretation of FAIRness evaluations. Data Intelligence 2(2020), 285–292. doi: 10.1162/dint_a_00051.
[21] J. Hendler & A. Mulvehill. Social machines: The coming collision of artificial intelligence, social networking, and humanity. New York: Apress, 2016. isbn: 978-1-4842-1156-4.
[22] G. Guizzardi. Ontology, ontologies and the “I” of FAIR. Data Intelligence 2(2020), 181–191. doi: 10.1162/dint_a_00040.
[23] B. Mons. Data stewardship for open science. Chapman and Hall/CRC, 2018. isbn: 9780815348184.
[24] S. Rikacovs & J. Barzdins. Export of relational databases to RDF databases: A case study. In: International Conference on Business Informatics Research. BIR 2010: Perspectives in Business Informatics Research, 2010, pp. 203–211. doi: 10.1007/978-3-642-16101-8_16.
[25] M. Grieves & J. Vickers. Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In: FJ. Kahlen, S. Flumerfelt & A. Alves (eds) Transdisciplinary Perspectives on Complex Systems. Cham: Springer, 2017, pp 85–113. doi: 10.1007/978-3-319-38756-7_4.
[26]
N. Juty, S.M. Wimalaratne, S. Soiland-Reyes, J. Kunze, C.A. Goble & T. Clark. Unique, persistent, resolvable: Identifiers as the foundation of FAIR. Data Intelligence 2(2020), 30–39. doi: 10.1162/dint_a_00025.
[27] T. Weigel, U. Schwardmann, J. Klump, S. Bendoukha & R. Quick. Making data and workflows findable for machines. Data Intelligence 2(2020), 40–46. doi: 10.1162/dint_a_00026.
[28] O. Beyan, A. Choudhury, J van Soest, O. Kohlbacher, L. Zimmermann, H. Stenzhorn, Md. R. Karim, M. Dumontier, S. Decker, L.O. Bonino da Silva Santos & A. Dekker. Distributed analytics on sensitive medical data: The Personal Health Train. Data Intelligence 2(2020), 96–107. doi: 10.1162/dint_a_00032.
[29] A. Landi, M. Thompson, V. Giannuzzi, F. Bonifazi, I. Labastida, L.O. Bonino da Silva Santos & M. Roos. The “A” of FAIR – as open as possible, as closed as necessary. Data Intelligence 2(2020), 47–55. doi: 10.1162/dint_a_00027.
[30] C. Brewster, B. Nouwt, S. Raaijmakers & J. Verhoosel. Ontology-based access control for FAIR data. Data Intelligence 2(2020), 66–77. doi: 10.1162/dint_a_00029.
[31] I. Labastida & T. Margoni. Licensing FAIR data for reuse. Data Intelligence 2(2020), 199–207. doi: 10.1162/dint_a_00042.
[32] H. van Vlijmen, A. Mons, A. Waalkens, W. Franke, A. Baak, G. Ruiter, ... & J.-M. Neefs. The need of Industry to go FAIR. Data Intelligence 2(2020), 276–284. doi: 10.1162/dint_a_00050.
[33] P. Groth, H. Cousijn, T. Clark & C. Goble. FAIR data reuse – the path through data citation. Data Intelligence 2(2020), 78–86. doi: 10.1162/dint_a_00030.
[34] A. Jacobsen, R. Kaliyaperumal, L.O. Bonino da Silva Santos, B. Mons, E. Schultes, M. Roos & M. Thompson. A generic workflow for the data FAIRification process. Data Intelligence 2(2020), 56–65. doi: 10.1162/dint_a_00028.
[35] M. Thompson, K. Burger, R. Kaliyaperumal, M. Roos & L.O. Bonino da Silva Santos. Making FAIR easy with FAIR tools: From creolization to convergence. Data Intelligence 2(2020), 87–95. doi: 10.1162/dint_a_00031.
[36] P. McQuilton, D. Batista, O. Beyan, R. Granell, S. Coles, M. Izzo, ... & S.-A. Sansone. Helping the consumers and producers of standards, repositories and policies to enable FAIR data. Data Intelligence 2(2020), 151–157. doi: 10.1162/dint_a_00037.
[37] C. Goble, S. Cohen-Boulakia, S. Soiland-Reyes, D. Garijo, Y. Gil, M.R. Crusoe, K. Peters & D. Schober. FAIR computational workflows. Data Intelligence 2(2020), 108–121. doi: 10.1162/dint_a_00033.
[38] I. Labastida & T. Margoni. Licensing FAIR data for reuse. Data Intelligence 2(2020), 199–207. doi: 10.1162/dint_a_00042.
[39] H.P. Sustkova, K.M. Hettne, P. Wittenburg, A. Jacobsen, T. Kuhn, R. Pergl,... & E. Schultes. FAIR convergence matrix: Optimizing the reuse of existing FAIR-related resources. Data Intelligence 2(2020), 158–170. doi: 10.1162/dint_a_00038.
Article and author information
Cite As
B. Mons, E. Schultes, F.H. Liu & A. Jacobsen. The FAIR principles: First generation implementation choices and challenges. Data Intelligence 2(2020), 1–9. doi: 10.1162/dint_e_00023
Barend Mons
barend.mons@go-fair.org
Barend Mons is Professor of BioSemantics at the Human Genetics Department of Leiden University Medical Center and founder of the BioSemantics group. He was elected CODATA President in 2018. Next to his leading role in the research of the group, Barend plays a leading role in the international development of “data stewardship” for biomedical data. For instance, he was head-of-node of ELIXIR-NL at the Dutch Techcentre for Life Sciences (until 2015), is Integrator Life Sciences at the Netherlands eScience Center, and board member of the Leiden Center of Data Science. In 2014, Barend initiated the FAIR data initiative and in 2015, he was appointed Chair of the European Commission’s High Level Expert Group for the “European Open Science Cloud”, from which he retired by the end of 2016. Presently, Barend is co-leading the GO FAIR initiative, an initiative to kick start dvelopments towards the Internet of FAIR data and services, which will also contribute to the implementation of components of the European Open Science Cloud. The focus of the contribution of the BioSemantics group is on developing an interoperability backbone for biomedical applications in general and rare disease in particular.
0000-0003-3934-0072
Erik Schultes
Erik Schultes is International Science Coordinator at the GO FAIR International Support and Coordination Office where he has been working with a diverse community of stakeholders to develop FAIR data and services. Erik is also a member of the Leiden Center for Data Science at Leiden University. Erik is an evolutionary biologist with long standing interests in data-intensive research. In addition to private consulting, he has held previous academic appointments at the University of California, Los Angeles, The Whitehead Institute for Biomedical Research at the Massachusetts Institute of Technology, Duke University, and The Santa Fe Institute.
0000-0001-8888-635X
Fenghong Liu
Fenghong Liu an Associate Professor at the National Science Library, Chinese Academy of Sciences (NSLC) and School of Economics and Management, University of Chinese Academy of Sciences. She received PhD degree at the Institute of Botany, CAS (IBCAS) in 2006. Before joining in NSLC in 2015, she had been the head of the library of IBCAS for four years. She is currently the Managing Editor of Data Intelligence (DI) Journal. Her research interests focus on data journals, especially whether and how data journals foster and promote data sharing awareness and culture across communities.
0000-0002-3633-1464
Annika Jacobsen
Annika Jacobsen is a postdoctoral researcher at the BioSemantics group, Human Genetics Department, Leiden University Medical Center, The Netherlands. She obtained her Bachelor and Master degrees at the Technical University of Denmark in 2009 and 2012, and her PhD degree at the Vrije Universiteit Amsterdam in 2019. Her research interests are to create interoperable FAIR rare disease data with the aim to learn more about cause, diagnosis and treatment.
0000-0003-4818-2360
Publication records
Published: None (Versions2
References
Data Intelligence