FAIR in action - a flexible framework to guide FAIRification

Abstract

The COVID-19 pandemic has highlighted the need for FAIR (Findable, Accessible, Interoperable, and Reusable) data more than any other scientific challenge to date. We developed a flexible, multi-level, domain-agnostic FAIRification framework, providing practical guidance to improve the FAIRness for both existing and future clinical and molecular datasets. We validated the framework in collaboration with several major public-private partnership projects, demonstrating and delivering improvements across all aspects of FAIR and across a variety of datasets and their contexts. We therefore managed to establish the reproducibility and far-reaching applicability of our approach to FAIRification tasks.

The FAIR Cookbook - the essential resource for and by FAIR doers

“…These recipes describe real life FAIRification processes in a number of IMI/IHI research projects. If users need generic guidance on the FAIRification processes, we describe in a sister paper 15 , a framework that helps them to: (1) set realistic and practical goals; (2) examine data, capability and resource requirements; (3) assess, design, implement and reiterate; (4) review against the initial goals. This FAIRification process is also outlined in a recipe ( https://w3id.org/faircookbook/FCB079 ).…”

“…In the latter, the content was triaged for prioritization based on the needs collected from the IMI/IHI data-producing projects and the (bio)pharmaceutical companies. The triage worked by mapping the use cases along the FAIRification path 15 , and then the procedures were broken down into the smallest possible units to create recipes, which were revised and reviewed. Fortnightly one-hour calls were held to track progress and raise issues, while monthly content creation events, known as ‘book dashes’, were aligned with GitHub milestones to ensure a steady pace of the development.…”

Cartographic Metadata for Improving Accessibility and Facilitating Knowledge Extraction and Validation in Planetary Mapping Based on Remote-Sensing Observations

“…Published use cases, experiences, and discussions of data FAIRification processes will help to better understand and adjust specific procedures. While the majority of discussions about such processes in the research literature are related to health data, they do share some of the complexity with compound map data [98,99,[101][102][103][104].…”

“…Our first objective is to better contextualize the need for stewardship, interoperability, and the concept of transparency by visiting the FAIR principles [95] and the related indicator framework [96,97]. The concepts of making data assets findable (F), accessible (A), interoperable (I) and reusable (R) are specifically addressed in a community-driven initiative that brought the FAIR principles to wide attention [97][98][99]. This initiative is based on a seminal paper in which the principles of Findability, Accessibility, Interoperability, and Reuse (FAIR) of data and the need to improve data infrastructures are emphasized, in particular, for academic purposes [95].…”

“…Lastly, we will discuss the process of turning maps into FAIR data assets. In this process of FAIRification [98,99] we will discuss how various principles could be implemented. This will ultimately lead to a number of recommendations for the researchers and mappers regarding the transfer of their products to FAIR research data.…”