Special Issue on Emerging FAIR Practices Published in Data Intelligence

A special issue on The FAIR Principles: First Generation Implementation Choices and Challenges, has been recently published in Data Intelligence. This special issue, organized by Prof. Dr. Barend Mons, the senior author of the foundational paper on FAIR Principles and the President of CODATA, Dr. Erik Schultes and Dr. Annika Jacobsen, contains 28 articles authored by 135 experts from 14 countries/territories worldwide.

In this special issue, the original conception of the FAIR principles and what they are intended to cover is explained in detail (see https://www.mitpressjournals.org/doi/abs/10.1162/dint_r_00024), and the prototype of FAIR Implementation Profile and the FAIR convergence Matrix which aims to coordinate a broadly accepted, widely used FAIR implementation approaches is presented (see https://www.mitpressjournals.org/doi/abs/10.1162/dint_a_00038). The first 16 articles are bundled as a relevant set of “first generation” implementations and emerging practices in the context of FAIR and the last 12 articles focus more on gaps in existing technology and practices encountered or envisioned and offer opinions and propose directional solutions for the relevant communities to develop FAIR guided approaches. Although this special issue only has covered a limited number of all early endeavors, “it will likely inspire other efforts to bundle and expose useful and hopefully reusable solutions”, as stated by Prof. Dr. Rianne Letschert in her brief introductory comment to this special issue. 

The TOC of the issue is listed below. To go to a full-text page, please just click on the title.


Editorial: The FAIR Principles: First Generation Implementation Choices and Challenges

Barend Mons, Erik Schultes, Fenghong Liu & Annika Jacobsen

1 FAIR Principles: Interpretations and Implementation Considerations

  1. Jacobsen, R. de Miranda Azevedo, N. Juty, D. Batista, S. Coles, R. Cornet, … & E. Schultes.

2 Unique, Persistent, Resolvable: Identifiers as the Foundation of FAIR

Nick Juty, Sarala M. Wimalaratne, Stian Soiland-Reyes, John Kunze, Carole A. Goble & Tim Clark

3 Making Data and Workflows Findable for Machines

Tobias Weigel, Ulrich Schwardmann, Jens Klump, Sofiane Bendoukha & Robert Quick

4 The “A” of FAIR – As Open as Possible, as Closed as Necessary

Annalisa Landi, Mark Thompson, Viviana Giannuzzi, Fedele Bonifazi, Ignasi Labastida, Luiz Olavo Bonino da Silva Santos & Marco Roos

5 A Generic Workflow for the Data FAIRification Process

Annika Jacobsen, Rajaram Kaliyaperumal, Luiz Olavo Bonino da Silva Santos, Barend Mons, Erik Schultes, Marco Roos & Mark Thompson

6 Ontology-based Access Control for FAIR Data

Christopher Brewster, Barry Nouwt, Stephan Raaijmakers & Jack Verhoosel

7 FAIR Data Reuse – the Path through Data Citation

Paul Groth, Helena Cousijn, Tim Clark & Carole Goble

8 Making FAIR Easy with FAIR Tools: From Creolization to Convergence

Mark Thompson, Kees Burger, Rajaram Kaliyaperumal, Marco Roos & Luiz Olavo Bonino da Silva Santos

9 Distributed Analytics on Sensitive Medical Data: The Personal Health Train

Oya Beyan, Ananya Choudhury, Johan van Soest, Oliver Kohlbacher5, Lukas Zimmermann, Holger Stenzhorn, Md. Rezaul Karim, Michel Dumontier, Stefan Decker, Luiz Olavo Bonino da Silva Santos & Andre Dekker 

10 FAIR Computational Workflows

Carole Goble, Sarah Cohen-Boulakia, Stian Soiland-Reyes, Daniel Garijo,Yolanda Gil, Michael R. Crusoe, Kristian Peters & Daniel Schober

11 FAIR Data and Services in Biodiversity Science and Geoscience

Larry Lannom, Dimitris Koureas & Alex R. Hardisty

12 Taking FAIR on the ChIN: The Chemistry Implementation Network

Simon J. Coles, Jeremy G. Frey, Egon L. Willighagen & Stuart J. Chalk

13 Growing the FAIR Community at the Intersection of the Geosciences and Pure and Applied Chemistry

Shelley Stall, Leah McEwen, Lesley Wyborn, Nancy Hoebelheinrich & Ian Bruno

14 Helping the Consumers and Producers of Standards, Repositories and Policies to Enable FAIR Data

Peter McQuilton, Dominique Batista, Oya Beyan, Ramon Granell, Simon Coles, Massimiliano Izzo … & Susanna-Assunta Sansone

15 FAIR Convergence Matrix: Optimizing the Reuse of Existing FAIR-Related Resources

Hana Perg Sustkova, Kristina Maria Hettne, Peter Wittenburg, Annika Jacobsen, Tobias Kuhn … & Erik Schultes

16 The FAIR Funding Model: Providing a Framework for Research Funders to Drive the Transition toward FAIR Data Management and Stewardship Practices

Margreet Bloemers & Annalisa Montesanti

17 Ontology, Ontologies and the “I” of FAIR

Giancarlo Guizzardi

18 How to (Easily) Extend the FAIRness of Existing Repositories

Mark Hahnel & Dan Valen

19 Licensing FAIR Data for Reuse

Ignasi Labastida1 & Thomas Margoni

20 Data Management Planning: How Requirements and Solutions are Beginning to Converge

Sarah Jones, Robert Pergl, Rob Hooft, Tomasz Miksa, Robert Samors, Judit Ungvari … & Tina Lee 

21 Social Data: CESSDA Best Practices

Ron Dekker

22 State of FAIRness in ESFRI Projects

Peter Wittenburg, Franciska de Jong, Dieter van Uytvanck, Massimo Cocco, Keith Jeffery, Michael Lautenschlager … & Petr Holub

23 GO FAIR Brazil: A Challenge for Brazilian Data Science

Luana Sales, Patrícia Henning, Viviane Veiga, Maira Murrieta Costa, Luís Fernando Sayão, Luiz Olavo Bonino da Silva Santos & Luís Ferreira Pires

24 FAIR Practices in Africa

Mirjam van Reisen, Mia Stokmans, Munyaradzi Mawere, Mariam Basajja, Antony Otieno Ong’ayo, Primrose Nakazibwe, Christine Kirkpatrick & Kudakwashe Chindoza

25 FAIR Practices in Europe

Peter Wittenburg, Michael Lautenschlager, Hannes Thiemann, Carsten Baldauf & Paul Trilsbeek

26 Towards the Tipping Point for FAIR Implementation

Mirjam Van Reisen, Mia Stokmans, Mariam Basajja, Antony Otieno Ong’ayo, Christine Kirkpatrick & Barend Mons

27 The Need of Industry to Go FAIR

Herman van Vlijmen, Albert Mons, Arne Waalkens, Wouter Franke, Arie Baak, Gerbrand Ruiter … & Jean-Marc Neefs.

28 Considerations for the Conduction and Interpretation of FAIRness Evaluations

Ricardo de Miranda Azevedo, Mark Wilkinson & Michel Dumontier


About Data Intelligence

Data Intelligence (DI) journal, a new publication jointly launched by the MIT Press and Chinese Academy of Sciences. It is chaired by Prof. Barend Mons. Co-Editors in Chief of the journal are Prof. Jim Hendler at Rensselaer Polytechnic Institute, USA , Prof Huizhou Liu, the Chinese Academy of Sciences and Prof. Ying Ding at University of Texas at Austin.