FAIRsharing is a community-driven resource and has a growing number of users, adopters, collaborators and activities working to enable the FAIR Principles.


Cite us and learn more about FAIRsharing:


FAIRsharing paper

“FAIRsharing as a community approach to standards, repositories and policies” OPEN CC-BY.

Authored by 68 international authors representing different stakeholder groups: (i) researchers in academia, industry and government, (ii) scholarly publishers, (iii) funders and other data policy makers, (iv) research data facilitators, librarians and trainers, (v) infrastructure providers, developers and curators of resources; and (vi) learned societies, unions and associations.

We have come together as a community, representing the core adopters, advisory board members, and/or key collaborators of FAIRsharing to present its mission and work, and show the role FAIRsharing plays in informing and educating each stakeholder group to maximize the visibility and adoption of standards, databases and repositories within their community and in data policies.


A selection of funder-driven policies and reports recommending FAIRsharing:


EU European Open Science Cloud (EOSC) “Turning FAIR into Reality” . European Research Council (ERC) “Open Research Data and Data Management Plans” . UK Jisc “FAIR in Practice” . Science Europe “Framework for Discipline-specific Research Data Management”.
Turning FAIR into reality ERC FAIR in practice Science Europe Guidance Document

Learn more about the FAIRsharing community, and please do not hesitate to contact us if you are interested in working with us.




 Adopters

Lighthouse stakeholders from our user base.



 Activities

Guidance and tools we lead on or contribute to.



 Governance

Our international Advisory Board and Team.






Adopters

Anyone can use FAIRsharing. Adopters, however, use FAIRsharing specifically to:

  1. Educate their users/community on the variety of existing standards, repositories and policies, and actively encourage them to submit/claim records, where relevant;
  2. Create Recommendations by registering their data policy, and then link it to standards and/or databases recommended in the policy; and/or
  3. Create a Collection by pulling together a list of standards and/or databases around a given domain of interest relevant to them.

Here are the instructions for record creation.

Adopters are generally representatives of institutions, libraries, journal publishers, infrastructure programmes, societies and other organizations or projects that in turn serve and guide individual researchers or other stakeholders on research data management matters.

Adopters have a FAIRsharing logo on their websites with a link from their website to our homepage.

We cannot list all of our adopters, but list here those publishers that use FAIRsharing to define and refine their data policy.






Global Organisations

Logo Name

RDA

GO-FAIR

FORCE11






Activities

FAIRsharing is not just a registry. The team behind FAIRsharing is involved in a number of FAIR-enabling activities, delivering guidance, tools and services with and for a variety of stakeholders. As these activities mature, we will implement them in, or connect them to, the FAIRsharing resource itself.

Some of these activities are part of funded projects andas part of national or international consortia, while others are volunteer efforts that fall under a variety of umbrella organisations, e.g. working groups (WG) and learned societies.

Our activities, classified using the three GO-FAIR pillar structures (change, build, train), are outlined here.

Activities Brief description and links Umbrella organisation Related funded projects
CHANGE - focusing on priorities, policies and incentives for implementing FAIR
1. FAIR maturity indicators, metrics and models A core set of 14 universal machine-actionable measurable FAIR Metrics covering the FAIR principles, a questionnaire for manual assessment, and a template form to create new metrics. Publication on these metrics and the FAIRsharing Collection of metrics . GO-FAIR OPEDAS IN; GO-FAIR StRePo; FAIR Metrics WG
Discussion forum to improve the interoperability of existing and emerging FAIR assessment methodologies. RDA FAIR Maturity Model WG bringing together other RDA groups, including the RDA FAIRsharing WG.
2. Cross-publishers common criteria for repository selection Through a collaboration with Datacite, we are working with a number of journal publishers (PLOS, Springer Nature, F1000, Wiley, Taylor and Francis, Elsevier, EMBO Press, eLife, GigaScience and Cambridge University Press) to identify a common set of criteria for selecting and recommending data repositories (and associated standards) that will be implemented in FAIRsharing. Read our pre-print providing a summary of the work. FAIRsharing team and Datacite   Memorandum of Understanding
3. Journal data policies and the TOP guidelines We are working with Jisc and the Center for Open Science (COS) to disseminate information about open science policies (including preprints & open data journal/funder policies) and to standardize classification of these policies in the hope of encouraging change. FAIRsharing team, COS   Memorandum of Understanding
4. Standardized templates for journal data policies Working within the RDA community and collaborating with a number of journal publishers to help define common frameworks for publisher data policies and increase adoption of (standardized) research data policies by all stakeholders and in particular journal publishers. RDA Standardisation and Implementation IG and RDA FAIRsharing WG
BUILD - focussing on the technology needed to enable FAIR
5. Domain and subject terminologies for data classification We have developed and maintain two terminologies: the Subject Resource Application Ontology (SRAO) describing subject areas / academic disciplines and the Domain Resource Application Ontology (DRAO) describing cross-discipline research domains. These are used by the FAIRsharing curators and the users to describe and classify standards, repositories and polices. FAIRsharing team Wellcome Trust
6. Future-proofing the FAIRsharing technical architecture To adapt and improve the FAIRsharing data model to accurate reflect and respond to community requirements. To update and refactor FAIRsharing code to facilitate improved data visualisation and access and to respond to user requirements. FAIRsharing team and International Advisory Board Wellcome Trust
7. FAIR assessment tools FAIRshake: a prototype software to assess the FAIRness of bioinformatics tools, analyses, and biological datasets against a variety of different metrics that can be uploaded in the tool. FAIRsharing is a core element of this work. FAIRsharing team and the NIH Data Commons teams NIH FAIR Data Commons Consortium
FAIR Evaluator: a software to register and execute tests of compliance with the published FAIR Metrics. FAIRsharing is a core element of this work. Pre-print of this work. GO-FAIR OPEDAS IN; GO-FAIR StRePo IN; FAIR Metrics WG
8. Connecting FAIRsharing to data stewardship and data management plans tools We have a MoU with the Data Stewardship Wizard to provide metadata information on databases, standards and data policies to inform and drive instances of the Data Stewardship Wizard. GO-FAIR Build; GO-FAIR StRePo IN
9. Data FAIRification We are developing a FAIR Cookbook, a process with examples of methods and tools needed to increase the level of FAIRness of biomedical datasets, as part of a public-private consortium under the Innovative Medicine Initiative (IMI) programme. Details of this work and participants here. FAIRsharing team IMI FAIRplus
10. Metadata standards for machines We are investigating how to maximize the ‘computability’ of these data/metadata standards , which are essential to measure the level of compliance of a given dataset (or other digital object) against the relevant metadata descriptors. These machine-readable standards will provide the necessary quantitative and verifiable measures of the degree by which a digital object meet these reporting guidelines. Our work will also feed into a larger GO-FAIR driven effort, as described in this preprint. FAIRsharing team, GO-FAIR StRePo IN; GO-FAIR OPEDAS IN (partly) NIH Data Commons
TRAIN - focussing on FAIR awareness and skills development
11. Guidance to stakeholders We are developing FAIRassist, a tool to navigate and select standards, repositories and other digital objects to guide researchers, data managers and other data producers and consumers to improve the FAIRness of their data. FAIRsharing team EU INFRA EOSC-Life
12. FAIR competencies and curricula Working with the community, including GO-FAIR, CODATA, the RDA and others, we are building infrastructure in training and teaching to enable both a competency or skills framework and a generic teaching curriculum. GO-FAIR Training; GO-FAIR StRePo IN; CODATA, FAIRsFAIR





Governance

Advisory Board

See also the FAIRsharing RDA and Force11 WG webpages. DOI generation for each record is kindly provided through a collaboration with the Bodleian Library at the University of Oxford.

  • Emma Ganley (PLOS) Co-chair
  • Varsha Khodiyar (NPG) Co-chair
  • Michael Ball (ESRC)
  • Theo Bloom (BMJ)
  • Jennifer Boyd (OUP)
  • Dave Carr (The Wellcome Trust and Wellcome Open Research)
  • Helena Cousijn (Datacite)
  • Scott Edmunds (GigaScience, BGI)
  • Dominic Fripp (JISC)
  • Chris Graf (Wiley)
  • Simon Hodson (CODATA), Co-Chair of the RDA/Force11 WG
  • Mike Huerta (Coordinator of Data & and Open Science Initiative, Associate Director for Programme Development at the NIH National Library of Medicine)
  • Amye Kenall (BMC)
  • Rebecca Lawrence (F1000), Co-Chair of the RDA/Force11 WG
  • Thomas Lemberger (EMBO Press)
  • Jennifer Lin (CrossRef)
  • Luiz Olavo Bonino (GO-FAIR)
  • Gabriella Rustici (ELIXIR UK Node, University of Cambridge, UK)
  • Marina Soares E Silva (Elsevier)
  • Imma Subirats (Information Management Officer, FAO of the United Nations, Italy)
  • Marta Teperek (Data Stewardship Coordinator, TUDelft, The Netherlands)

Meet The Team

Operating since 2011, and born from an early community-driven portal we launched in 2008 (MIBBI), FAIRsharing has become a sustainable service, hosted at the University of Oxford, and run by the Data Readiness Group funded from a portfolio of infrastructure grants (to Prof. Sansone, a permanent faculty member) that will ensure ongoing management and curation of this invaluable resource.


Principal Investigator and Founder
Project Coordinator
Lead Research Software Engineer
Knowledge Engineer
Research Software Engineer
Research Software & Knowledge Engineer
Research Software & Knowledge Engineer
Curator (Contract)