The primary aim of this session is to identify the requirements for FAIR literacy in support of the emerging practices around the FAIRification of data and services(a).
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.
The optimisation of data reuse, the reproducibility of research and the openness of research results (if possible) are integral parts of research integrity. This has profound ethical roots that needs to be part of FAIR literacy and training must be emphasized in an international context.
The choice of communication channel or ‘vector’ is here defined as:
(i) the form of communication chosen to carry a ‘message’,
(ii) attributes of the communication including the tools used to carry the message,
(iii) acknowledgement from the recipients that the message has been received and understood, with feedbacks or other impacts demonstrated.
The choice of vector (i and ii above) needs to be sensitive to 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. Optimally each type of communication used should be chosen according to the audience’s role in FAIRification in the short, medium and long term.
Task 1 Definition and description of stakeholders. Using the stakeholders described by the EU Commission in 2018 (4) as a basis, create a suite of personas for each according to their role in FAIRification, their needs according to their FAIR interaction, responsibilities, and their likely or preferred vector of communication / literacy,
Task 2 Reflect on the form that rewards / credit could take in the framework of training on FAIR (i.e. for trainers and trainees), identify what already exists and to consider their creation if necessary,
Task 3 (As a work to initiate) Define where and how ethical considerations and requirements have a role in FAIRification.
Perspective: Consider the possibility for a new RDA interest group focused on literacy vectors of FAIRification (in line with existing groups e.g. “Education and Training on handling of research data IG”, BoF Professionalizing Data Stewards).
The targeted outputs of the session (FAIR literacy vector typology / rewards and credits mechanisms linked to each vector type (as far as possible during the session) / ethical considerations and precautions for use for some vectors) will fuel the work of several projects and could be adapted by others such as::
TRAIN group within the ANR project FooSIN (FR) whose goal is to sketch the frame of a future MOOC FAIR or other training format (beginning of March 2020);
Inter CATI project aimed in particular to sensitize and train FAIR relay/support persons acting closer to the communities in a concerted way, by rationalizing efforts, for example on the watch or the creation of training materials.
ecoinfoFAIR project, first practical action combining introductory training and educational vector of FAIRisation with the ecology community taking the scientific software development orientation.
Belmont-PARSEC project where an optimal data sharing model is being elaborated at a global scale in the biodiversity community.
“Pôle national de données de Biodiversité” PNDB French research e-infrastructure dedicated to biodiversity (in-situ) communities
Food System IN in GO FAIR to gather or build, and disseminate tutorials on specific skills and tools related to semantic resources and data.
The BONSAI network, working on open data for product footprinting (https://bonsai.uno/)
See work in vocabulary and ontology development around FAIR in general https://osf.io/8eqb5/wiki/home/
ENVRI-FAIR project (ESFRI cluster project) aims at developing materials for training & skills building on technological solutions that support FAIR data management at the research infrastructure level. Target groups are data centre/IT staff, end user communities. (See https://envri.eu/wp-content/uploads/2019/10/ENVRI-FAIR_D_6-1.pdf)