FAIRsharing is a community-driven resource with a growing number of users, adopters, collaborators and activities, all working to enable the FAIR Principles and to make Standards, Knowledgebases, Repositories and Data Policies FAIR.
Cite us and learn more about FAIRsharing:
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”.|
Learn more about the FAIRsharing community, and please do not hesitate to contact us if you are interested in working with us.
Anyone can use FAIRsharing. Adopters, however, use FAIRsharing specifically to:
- Educate their users/community on the variety of existing standards, repositories and policies, and actively encourage them to submit/claim records, where relevant;
- Create Recommendations by registering their data policy, and then link it to standards and/or databases recommended in the policy; and/or
- Create a Collection by pulling together a list of standards and/or databases around a given domain of interest relevant to them.
If you wish to create a new metadata record on FAIRsharing, you can find instructions here.
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 display a FAIRsharing logo on their websites with a link from their website to our homepage.
We cannot list all of our adopters, but we've listed here those publishers that use FAIRsharing to define and refine their data policy.
|Adopter||Associated FAIRsharing Record|
Faculty of 1000
Public Library of Science (PLOS)
Springer Nature BioMed Central
Springer Nature Scientific Data
Taylor and Francis
Wellcome Open Research
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 or connect them in/to the FAIRsharing resource itself.
Some of these activities are part of funded projects and of national or international consortia, while others are volunteer efforts that fall under a variety of umbrella organisations, such as working groups (WG) and learned societies.
Our activities are classified using the three GO-FAIR pillar structures (change, build, train) and 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, Hindawi, 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.
In addition, please see the following blogs that discuss this work:
Your feedback on these criteria is welcomed. Please use the following form no later than January 31st January 2020.
|FAIRsharing team and Datacite|
|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|
|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|
|5. UK Reproducibility Network||The UK Reproducibility Network (UKRN) is a peer-led consortium that aims to ensure the UK retains its place as a centre for world-leading research. This will be done by investigating the factors that contribute to robust research, promoting training activities, and disseminating best practice, and working with stakeholders to ensure coordination of efforts across the sector. UKRN works across disciplines, ranging from the arts and humanities to the physical sciences, with a particular focus on the biomedical sciences. FAIRsharing is a key stakeholder of UKRN and Reproducible Research Oxford (RROx), the Oxford local network of UKRN||UKRN and RROx|
|BUILD - focussing on the technology needed to enable FAIR|
|6. 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|
|7. 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|
|8. 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|
|9. 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|
|10. 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|
|11. 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|
|12. 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|
|13. 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; terms4FAIRskills|
- Varsha Khodiyar, Springer Nature, UK
- David Carr, The Wellcome Trust and Wellcome Open Research, UK
- Chris Graf, Wiley, UK
- Marta Teperek, Data Stewardship Coordinator, TUDelft, The Netherlands
- Robert Hanisch, Director of the Office of Data and Informatics, National Institute of Standards and Technology (NIST), USA
- Emma Ganley, Director of Strategic Initiatives, Protocols.io, UK
- Michael Ball, The Economic and Social Research Council (ESRC), UK
- Theo Bloom, British Medical Journal (BMJ), UK
- Matthew Cannon, Taylor and Francis, UK
- Wei-Mun Chan, eLife, UK
- Geraldine Clement-Stoneham, Head of Knowledge Management and Scholarly Communication, Medical Research Council, UK
- Helena Cousijn, Datacite, The Netherlands
- Scott Edmunds, GigaScience, BGI, China
- Dominic Fripp, JISC, UK
- Simon Hodson, CODATA, France
- Iain Hrynaszkiewicz, PLoS, UK
- Mike Huerta, Coordinator of Data & and Open Science Initiative, Associate Director for Programme Development at the NIH National Library of Medicine, USA
- Amye Kenall, VP of Publishing and Product, Research Square, UK
- Adam Leary, Oxford University Press, UK
- Thomas Lemberger, EMBO Press, UK
- Luiz Olavo Bonino, GO-FAIR, The Netherlands
- Kiera McNiece, Cambridge University Press, UK
- Dagmar Meyer, European Research Council (ERC), Executive Agency, Belgium
- Hollydawn Murray Head of Data Publishing, F1000, UK
- Marina Soares E Silva, Elsevier, The Netherlands
- Imma Subirats, Information Management Officer, FAO of the United Nations, Italy
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.