The RDA-SHARC (SHAring Reward & Credit) interest group is working to improve crediting and rewarding mechanisms for scientists who share their data for potential reuse. In this perspective, assessing compliance with FAIR practice and increasing understandability of FAIRness criteria for everyone (data scientists AND non specialists, particularly for researchers and evaluators of projects and careers) are critical steps.
To that aim, a FAIR criteria assessment survey is being undertaken to obtain feedback from the scientific community on intelligible, realistic and human-readable assessment criteria that could help guide
- the scientist to follow FAIR practicesas much as possible, and
- the evaluator to achieve his/her task.
This feedback survey is now online and we invite you to participate.
This survey is extensive. We are very much aware that completing it is time-consuming.
We have divided the survey into 5 parts that can be completed independently (links below). The estimated time of completion of each part is 20 minutes; replies can be recorded incrementally using the 'resume later' tab at the top right of the page:
- 1/ FAIR- Self-assessment survey and global feedback <https://sondage.osupytheas.fr/index.php/115136> ;
- 2/ FINDABLE- feedback survey <https://sondage.osupytheas.fr/index.php/922722>;
- 3/ ACCESSIBLE- feedback survey <https://sondage.osupytheas.fr/index.php/213283>;
- 4/ INTEROPERABLE- feedback survey <https://sondage.osupytheas.fr/index.php/323172> and
- 5/ REUSABLE- feedback survey <https://sondage.osupytheas.fr/index.php/417435>
Each participant who completes the 5 parts of the survey will have the possibility to be a co-author of the publication that will come out from this work. If you would like to contribute but are not able to complete all parts, please be sure to complete at least part 1 (FAIR self-assessment and general feedback).
Your experience and competence are invaluable! Help constructing an evaluation tool of FAIR practices will enable to consider the data sharing activities as a valuable research output and will foster this activity by promoting fairness literacy and dedicated support for data management.Your detailed comments and suggestions will be most appreciated.
Many thanks in advance,
Looking forward to your participation *_before end of May_*.
The SHARC’s survey team.
P.S: You are very welcome to forward this mail to other colleagues who could be possibly interested.