One important step towards achieving FAIR data is the development and improvement of data repositories to be findable, accessible, and interoperable. The recent recommendation from the Research Data Alliance, Common Descriptive Attributes of Research Data Repositories, provides guidance to enable well-described repositories to support researchers, funders, publishers, repository developers and managers, registries, and other stakeholders, including both users and user agents.

The FAIR Principles have succeeded to unite stakeholders worldwide behind a common concept: good data management under common standards. However, FAIR is aspirational, and the narrative principles are insufficient to circumscribe the valid mechanisms to achieve the behaviours they describe. This presentation provides an overview of a community-driven resource, and a community task force showing how they contribute to enabling FAIR compliance and turn FAIR into reality.

Created by data managers, professionals in academia, (bio)pharmaceutical companies, and information service industries, the FAIR Cookbook is an online resource of hands-on recipes that guides researchers and data stewards in their FAIRification journey. It also provides policy-makers and trainers with practical examples to recommend in their guidance and use in their educational material. Part of the ELIXIR ecosystem, this resource is open to contributions of new recipes.

Ensuring that data are FAIR is nowadays a clear expectation across all science domains, as a result of many years of global efforts. Research software, has only just started to receive the same level of attention in recent years, with targeted actions towards the definition of the FAIR principles as applied to research software, as well as concerted efforts around reproducibility, quality, and sustainability. Given the rapid rise of ML as a key technology across all science domains, it is important to build on our collective experience, and already start addressing the challenges ahead of us, towards making ML FAIR.

Research software such as data analysis tools and AI models have become an essential part of biomedical research. Making them FAIR is therefore critical to enable the reproducibility of research results, prevent duplicate efforts, and ultimately increase the pace of discoveries. In this talk, we discuss what it means to make software FAIR and present the FAIR Biomedical Research Software (FAIR-BioRS) guidelines, which are actionable guidelines for making software FAIR.

The Alliance of Genome Resources is a consortium of model organism knowledgebases (MODs) and the Gene Ontology Consortium whose goals are to use biocuration coupled with AI/ML to make information computable and FAIR, to support comparative genomics, and to promote sustainability of core community data resources. We develop widely-used ontologies, make shared instances of common tools and processes, and present both integrative and organism-centric views of genetic and genomic data. Our progress and challenges in making complete data lifecycle FAIR will be discussed. 

Figshare is a flexible generalist repository that allows researchers to FAIR-ly share any research output in a trusted repository so it is discoverable and reusable. As part of the NIH Generalist Repository Ecosystem Initiative (GREI), Figshare has been working together with other repositories to enhance its interoperable metadata, use of persistent identifiers, user interface, search capabilities, and metrics reporting to support both the sharing and discovery of FAIR open data.

The explosion of the amount of data coming off instruments, new research data sharing policy requirements for publication of scientific data, and the availability of a wide diversity of storage systems contribute to the increased demands on system administrators in research computing. Globus (globus.org) is a comprehensive platform for research IT which includes data description and discovery, protected data management, and automation. The platform balances findability and privacy, and improves the users’ experience through service offerings which abstract away system complexities and reduce the obstacles in data management, while enabling access to remote computing.

Making data and analytics FAIR has transformative potential within organizations to build on existing knowledge. FAIR resources also democratize access to information and tools in underserved communities. Global standards and analysis platforms provide strong foundational elements. However, FAIRness across time and different sectors of the biomedical workforce presents challenges. Here we summarize how platforms make data and analysis FAIR today and what we see as key areas of future focus.

As the scientific community strives for FAIR data, good research data management (RDM) is becoming crucial. The ELIXIR RDMkit, is a framework for best practices for RDM, acting as a hub of RDM information. It offers links to registries for tools, training materials, standards, and databases, as well as to services offering deeper knowledge for FAIRification practices. Guided by a community-driven model, RDMkit has contributions from data stewards, researchers, and RDM experts from all areas of biomedical sciences. In this talk, we will present the RDMkit—its aims, content, community, and potential prospects for the RDMkit Alliance, a global cooperation.

The Cambridge Crystallographic Data Centre (CCDC) maintains the Cambridge Structural Database (CSD), a trusted repository of 1.2M+ experimental 3D structures used by academics and researchers in pharmaceutical, agrochemical, and fine chemical industries. FAIR data concepts have driven our approach to sustaining this critical global resource. We will share our FAIR journey and reflect on the value and importance of the FAIR data principles to the life sciences industry at large.