Whether you’re new to the conversation about open science or a longtime supporter of sharing and reusing research data, the FAIR guidelines for making data findable, accessible, interoperable, and reusable establish a basic set of principles for all practitioners who wish to make their research more reproducible. The “how” of doing so varies greatly among fields, modes of research, and investigators’ goals, however, so figuring out the first actions to take to make your research products more FAIR can pose a challenge. A new online tool from the FAIRsFAIR project aims to help researchers think through each FAIR principle and demystify related jargon with FAIR-Aware, a self-guided questionnaire with extensive explanatory guidance for the concrete steps involved in making data FAIR.
When I teach workshops on research data management, I encourage participants to tackle the R in FAIR (for reuse) by imagining that they have come across their own research data like buried treasure. What would they need to know in order to make it useful? The FAIR-Aware assessment breaks this question down into specific components. For example, one sub-question asks whether the practitioner is aware that provenance information about data generation or collection should be included in the metadata. The linked guidance provides examples of provenance information, like the instrument used for data capture. At the same time, it notes that the information important to determining provenance varies widely among data types and research domains, and so the guidance refrains from offering an exhaustive list. Throughout the roughly 30-minute questionnaire, FAIR-Aware balances specificity with room for interpretation, allowing it to apply to an international community working in diverse research settings.
Although FAIR-Aware was developed to make the FAIR principles more approachable, some of the tool’s text seems aimed more at data curators than at working researchers (for example, assuming familiarity with the Semantic Web for producing linked-data metadata). The Data Services team at HSLS is available to discuss any of these concepts in more detail, including how to make them work for your particular research and situation. Simply send us an email at HSLSDATA@pitt.edu and we will arrange a consultation via e-mail, phone, or Zoom.