Operationalizing and evaluating the FAIRness concept for a good quality of data sharing in Research: the RDA-SHARC-IG (SHAring Rewards and Credit Interest Group)

The RDA-SHARC (SHAring Reward & Credit) interest group is an interdisciplinary volunteer member-based group set up as part of RDA (Research Data Alliance) to unpack and improve crediting and rewarding mechanisms in the sharing process throughout the data life cycle. Background and objectives of this group are reported here. Notably, one of the objectives is to promote the inclusion of data sharing activities in the research (& researchers) assessment scheme at national and European levels. To this aim, the RDA-SHARC-IG is developing two assessment grids using criteria to establish if data are compliant to the F.A.I.R principles (findable /accessible / interoperable / reusable) based on previous works on FAIR data management (Reymonet et al., 2018; Wilkinson et al., 2018; and E.U.Guidelines*): 1/ The self-assessment grid to be used by a scientist as a ‘checklist’ to identify her/his own activities and to pinpoint the hurdles that hinder efficient sharing and reuse of his/her data by all potential users. 2/ The two-level grid (quick/extensive) to be used by the evaluator to assess the quality of the researcher/scientist sharing practice, over a given period, taking into account the means & support available over that period. Assessment criteria are classified according their importance with regards to FAIRness (essential / recommended / desirable) meanwhile good practices are recommended for critical steps. To implement a highly fair assessment of the sharing process, appropriate criteria must be selected in order to design optimal generic assessment grids. This process requires participation, time and input from volunteer scientists data producers/users from various fields.


G.D.R. MaDICS Meeting -20 -21 november 2018
Operationalizing and evaluating the FAIRness concept for a good quality of data sharing in Research: the RDA-SHARC-IG (SHAring Rewards and Credit Interest Group) Romain David, Laurence Mabile, Mohamed Yahia, Anne Cambon-Thomsen, Anne-Sophie Archambeau, Louise Bezuidenhout, Sofie Bekaert, Gabrielle Bertier, Elena Bravo, Jane Carpenter, Anna Cohen-Nabeiro, Aurélie Delavaud, Michele De Rosa, Laurent Dollé, Florencia Grattarola, Fiona Murphy, Sophie Pamerlon, Alison Specht, Anne-Marie Tassé, Mogens Thomsen, Martina Zilioli, and the RDA-SHARC Interest Group.GRAMINEES members have been participating actively in the SHARC Interest Group from the beginning.As the aim of GRAMINEES action is to develop analyses based on the graph theory with an interdisciplinary approach, FAIRness of data sharing is a sine qua non condition to permit transdisciplinary work without misunderstanding as well as preserving consistency of aggregated data.

* MORE INFORMATION AT:
• FAIR principles: FORCE 11: ○ https://www.force11.org/• SHARC-IG at RDA Plenary 9: ○ https://www.rd-alliance.org/how-give-credit-scientists-their-involvement-making-data-samples-available-sharing-rda-9th-plenary).Data sharing statements and promotion is a strong reality but challenging, especially when considering the many obstacles that remain on several fronts.Among these obstacles is the lack of relevant and recognized rewarding mechanisms for the very specific efforts required to share organized datasets and physical resources (Cambon-Thomsen et al., 2011;Mabile et al., 2016) RDA provides a neutral space where its members can come together through focused global Working and Interest Groups.SHARC-IG works through virtual web meetings & tools as often as necessary, with a minimum of once a month for a regular update and face to face meetings when possible at each RDA plenary conference every 6 months.When needed, task sub-groups are formed to work on specific issues.Regular information and feedback is relayed via the RDA SHARC's mailing list and webpage towards RDA members.
Ongoing background paper available on the RDA SHARC's webpage.

Please JOIN US! FOLLOW our work! and if interested, PARTICIPATE!! BUILDING FAIR-BASED ASSESSMENT GRIDS
To be generic and trans-disciplinary, assessment grids should be understandable by all scientist including ones who are not expert in data science.
The two grids are based on previous works on FAIR data management (Reymonet et al., 2018;Wilkinson et al., 2018; and E.U.Guidelines*): 1/ The self-assessment grid to be used by a scientist as a 'checklist' to identify her/his own activities and to pinpoint the hurdles that hinder efficient sharing and reuse of his/her data by all potential users.
2/ The two-level grid (quick/extensive) to be used by the evaluator to assess the quality of the researcher/scientist sharing practice, over a given period, taking into account the means & support available over that period.Assessment criteria are classified according to their level of stringency for FAIRness (mandatory / recommended / optional).

ASSESSING DATA SHARING ACTIONS TO BUILD CREDITING/REWARDING PROCESSES INPUT NEEDED FROM RESEARCH COMMUNITIES
To implement a highly fair assessment of the sharing process, appropriate criteria must be selected in order to design optimal generic assessment grids.This process requires participation, time and input from volunteer scientists data producers/users from various fields.Since the beginning of this task group in RDA-SHARC-IG on sept 4th, 2018, about two teleconferences per week have been held as a working subgroup, to enable exchanges between participants on this specific issue.The aim was to get feedback from a larger community regarding the validity of the criteria over various fields.The assessment grids will circulate within the RDA community as an online questionnaire before end of 2018.
Are you a scientist producing or using data?Please participate to the development of the FAIRness assessment grids by completing the questionnaire.It will help you get credit back for your efforts!HOW?
. The RDA-SHARC interest group is an interdisciplinary volunteer member-based group set up to unpack and improve crediting and rewarding mechanisms in the data/resources sharing process.The objectives are: 1/ To review the existing rewarding mechanisms and their limits in various scientific communities, and to identify key factors to improve the process and optimize the sharing of datasets and bioresources; i.e. data and physical samples (ex: tools, incentives, requirements…).2/ To develop processes for stepwise adoption of principles and implementation measures tuned to national, local and institutional contexts.3/ To rely upon this analysis to encourage the inclusion of physical bioresources sharing-related criteria in the research evaluation process at the European and national institutional levels; 4/ To disseminate information and findings to diverse communities of stakeholders; As a way to foster data sharing, the RDA-SHARC-IG is developing two assessment grids using criteria to establish if data are compliant to the F.A.I.R principles (findable /accessible / interoperable / reusable) from FORCE 11*, and on the Open Science Career Assessment Matrix designed by the EC Working group on Rewards under Open science* SHARC'S MECHANISM SHARC-IG is a recognised and endorsed interest group (40 pers., oct 2018) within RDA (Research Data Alliance).RDA is a community-driven organisation that aims to enable open sharing of data worldwide.