Shelley Stall1, Romain David2,3,4, Laurence Mabile5,6, Anne-Sophie Archambeau7,8,9, Sophie Aubin, Michele De Rosa11, Xavier Engels12, Yvan Le Bras14, Maggie Hellström13, Hana Pergl15, Erik Schultes15, Ben Schaap16, Alison Specht17, Sarah Stryeck18, Mogens Thomsen5,6, Silvia Wissel19, Mohamed Yahia20, Anne Cambon-Thomsen5,6
1American Geophysical Union, Washington, United States, 2U.M.R. MISTEA, INRAE, Montpellier, France, 3Montpellier SupAgro, Montpellier, France, 4Université de Montpellier, Montpellier, France, 5INSERM, Toulouse, France, 6Université Paul Sabatier Toulouse III, Toulouse, France, 7IRD, Paris, France, 8UMS PatriNat, Paris, France, 9GBIF France, Paris, France, 10DIST, INRAE, Versailles, France, 11BONSAI, Aalborg, Danemark, 12ANR, Boulogne Billancourt, France, 13ICOS Carbon Portal, Lund University, , Sweden, 14PNDB, MNHN, , France, 15GO FAIR International Support and Coordination Office, Leiden, The Netherlands, 16GODAN, WUR, Wageningen, Netherlands , 17SEES-TERN, the University of Queensland, St Lucia South, Australia, 18Graz University of Technology, Institute for Interactive Systems and Data Science , Graz, Austria, 19GO FAIR Initiative, ZBW – Leibniz Information Centre for Economics, Leibniz , Germany, 20INIST-CNRS, France
Introduction: The optimisation of data reuse, the reproducibility of research and the openness of research results (if possible) are inseparable parts of research integrity. This has profound ethical roots that need to be part of FAIR literacy and training and emphasized in an international context.
Identifying the requirements for FAIR literacy in support of the emerging practices around the FAIRification of research data and services is a key step for open science development. By better defining the literacy of FAIR, it will be possible to bestow rewards and credits incentivizing FAIR skills such as accreditation of competence, awards for support-person recognition, for conference organisers, for trainers, diplomas for trainees and so on.
Method: This poster presents the method for identifying communication channels – ‘the vectors’ and associated rewards proposed by the RDA-SHARC and RDA-GOFAIR interest groups as:
(i) form of communication for the ‘message’,
(ii) attributes of the communication, E.g. tools,
(iii) acknowledgement that the message has been received and understood, with appropriate feedback.
Result: The choice of vector needs to be aligned with the preferences of the target audience receiving the message…and provide options for feedback to facilitate the adoption of FAIR practices. Each type of communication (letter, action sheet, MOOC, conference, practicals, continuous education, success stories, experience sharing …) and the method of sending it should be adaptable to different levels of skill sets and needs.
Conclusion: Optimally each type of communication used should be a community approved process in FAIRification in the short, medium and long term.
Shelley Stall is the Senior Director for the American Geophysical Union’s Data Leadership Program. She works with AGU’s members, their organizations, and the broader research community to improve data and digital object practices with the ultimate goal of elevating how research data is managed and valued. Better data management results in better science. Shelley’s diverse experience working as a program and project manager, software architect, database architect, performance and optimization analyst, data product provider, and data integration architect for international communities, both nonprofit and commercial, provides her with a core capability to guide development of practical and sustainable data policies and practices ready for adoption and adapting by the broad research community.