Give Early Career Researchers (ECR’s) the foundational skills in Data Science to work with their data. | Capacity building/ incentivisation | institutions, data stewards | A curriculum for foundational Research Data Science skills for Early Career Researchers |
1. All datasets intended for citation must have a globally unique persistent identifier that can be expressed as an unambiguous URL. | Discovery/ indexing/ search, Interlinking/ interoperability | data service providers | A data citation roadmap for scholarly data repositories |
10. Content negotiation for schema.org/JSON-LD and other content types may be supported so that the persistent identifier expressed as URL resolves directly to machine-readable metadata. | Metadata richness/ ingest/ submission, Machine-actionability | data service providers | A data citation roadmap for scholarly data repositories |
11. HTTP link headers may be supported to advertise content negotiation options. | Metadata richness/ ingest/ submission, Machine-actionability | data service providers | A data citation roadmap for scholarly data repositories |
2. Persistent identifiers for datasets must support multiple levels of granularity, where appropriate. | Discovery/ indexing/ search, Interlinking/ interoperability | data service providers | A data citation roadmap for scholarly data repositories |
3. The persistent identifier expressed as an URL must resolve to a landing page specific for that dataset, and that landing page must contain metadata describing the dataset. | Discovery/ indexing/ search, Interlinking/ interoperability | data service providers | A data citation roadmap for scholarly data repositories |
4. The persistent identifier must be embedded in the landing page in machine-readable format. | Discovery/ indexing/ search, Interlinking/ interoperability, Machine-actionability | data service providers | A data citation roadmap for scholarly data repositories |
5. The repository must provide documentation and support for data citation. | Metadata richness/ ingest/ submission | data service providers | A data citation roadmap for scholarly data repositories |
6. The landing page should include metadata required for citation, and ideally also metadata facilitating discovery, in human-readable and machine-readable format. | Metadata richness/ ingest/ submission, Machine-actionability | data service providers | A data citation roadmap for scholarly data repositories |
7. The machine-readable metadata should use schema.org markup in JSON-LD format. | Metadata richness/ ingest/ submission, Machine-actionability | data service providers | A data citation roadmap for scholarly data repositories |
8. Metadata should be made available via HTML meta tags to facilitate use by reference managers. | Metadata richness/ ingest/ submission, Machine-actionability | data service providers | A data citation roadmap for scholarly data repositories |
9. Metadata should be made available for download in BibTeX and/or another standard bibliographic format. | Metadata richness/ ingest/ submission | data service providers | A data citation roadmap for scholarly data repositories |
ORCID-Integration für Publikationsplattformen und -dienste | Interlinking/ interoperability | data service providers | Autorenidentifikation anhand der Ooen Researcher and Contributor ID (ORCID) |
Adress the challenges of curating for reproducible and FAIR research output | Quality control/ curation | data stewards, research community | Challenges of Curating for Reproducible and FAIR Research Output |
Implement data versioning practices | Quality control/ curation | data service providers, data stewards | Compilation of Data Versioning Use cases from the RDA Data Versioning Working Group |
R1. The repository has an explicit mission to provide access to and preserve data in its domain. | Policy | data service providers | Core Trustworthy Data Repositories Requirements v01.00 |
R10. The repository assumes responsibility for long -term preservation and manages this function in a planned and documented way. | Sustainability/ funding/ business-model | data service providers | Core Trustworthy Data Repositories Requirements v01.00 |
R11. The repository has appropriate expertise to address technical data and metadata quality and ensures that sufficient information is available for end users to make quality-related evaluations. | Quality control/ curation, Metadata richness/ ingest/ submission | data service providers, data stewards | Core Trustworthy Data Repositories Requirements v01.00 |
R12. Archiving takes place according to defined workflows from ingest to dissemination. | Access/ integration, Metadata richness/ ingest/ submission | data service providers | Core Trustworthy Data Repositories Requirements v01.00 |
R13. The repository enables users to discover the data and refer to them in a persistent way through proper citation. | Discovery/ indexing/ search, Interlinking/ interoperability | data service providers | Core Trustworthy Data Repositories Requirements v01.00 |
R14. The repository enables reuse of the data over time, ensuring that appropriate metadata are available to support the understanding and use of the data. | Metadata richness/ ingest/ submission | data service providers | Core Trustworthy Data Repositories Requirements v01.00 |
R15. The repository functions on well-supported operating systems and other core infrastructural software and is using hardware and software technologies appropriate to the services it provides to its Designated Community. | Access/ integration | data service providers | Core Trustworthy Data Repositories Requirements v01.00 |
R16. The technical infrastructure of the repository provides for protection of the facility and its data, products, services, and users. | Certification/ evaluation | data service providers | Core Trustworthy Data Repositories Requirements v01.00 |
R2. The repository maintains all applicable licenses covering data access and use and monitors compliance. | Quality control/ curation | data service providers | Core Trustworthy Data Repositories Requirements v01.00 |
R3. The repository has a continuity plan to ensure ongoing access to and preservation of its holdings. | Sustainability/ funding/ business-model | data service providers | Core Trustworthy Data Repositories Requirements v01.00 |
R4. The repository ensures, to the extent possible, that data are created, curated, accessed, and used in compliance with disciplinary and ethical norms. | Quality control/ curation | data service providers | Core Trustworthy Data Repositories Requirements v01.00 |
R5. The repository has adequate funding and sufficient numbers of qualified staff managed through a clear system of governance to effectively carry out the mission. | Sustainability/ funding/ business-model | data service providers, research funders, institutions | Core Trustworthy Data Repositories Requirements v01.00 |
R6. The repository adopts mechanism(s) to secure ongoing expert guidance and feedback (either in-house, or external, including scientific guidance, if relevant). | Certification/ evaluation | data service providers | Core Trustworthy Data Repositories Requirements v01.00 |
R7. The repository guarantees the integrity and authenticity of the data. | Quality control/ curation | data service providers | Core Trustworthy Data Repositories Requirements v01.00 |
R8. The repository accepts data and metadata based on defined criteria to ensure relevance and understandability for data users. | Policy | data service providers | Core Trustworthy Data Repositories Requirements v01.00 |
R9. The repository applies documented processes and procedures in managing archival storage of the data. | Quality control/ curation | data service providers | Core Trustworthy Data Repositories Requirements v01.00 |
R1 – Data Versioning: Apply versioning to ensure earlier states of data sets can be retrieved. | Quality control/ curation | data service providers | Data Citation of Evolving Data |
R10 – Automated Citation Texts: Generate citation texts in the format prevalent in the designated community for lowering the barrier for citing the data. Include the PID into the citation text snippet. | Metadata richness/ ingest/ submission | data service providers | Data Citation of Evolving Data |
R11 – Landing Page: Make the PIDs resolve to a human readable landing page that provides the data (via query re-execution) and metadata, including a link to the superset (PID of the data source) and citation text snippet. | Discovery/ indexing/ search | data service providers | Data Citation of Evolving Data |
R12 – Machine Actionability: Provide an API / machine actionable landing page to access metadata and data via query re-execution. | Machine-actionability | data service providers | Data Citation of Evolving Data |
R13 – Technology Migration: When data is migrated to a new representation (e.g. new database system, a new schema or a completely different technology), migrate also the queries and associated fixity information. | Sustainability/ funding/ business-model, Quality control/ curation | data service providers | Data Citation of Evolving Data |
R14 – Migration Verification: Verify successful data and query migration, ensuring that queries can be re-executed correctly. | Sustainability/ funding/ business-model, Quality control/ curation | data service providers | Data Citation of Evolving Data |
R2 – Timestamping: Ensure that operations on data are timestamped, i.e. any additions, deletions are marked with a timestamp. | Quality control/ curation | data service providers | Data Citation of Evolving Data |
R3 – Query Store Facilities : Provide means for storing queries and the associated metadata in order to re-execute them in the future. | Discovery/ indexing/ search | data service providers | Data Citation of Evolving Data |
R4 – Query Uniqueness : Re-write the query to a normalised form so that identical queries can be detected. Compute a checksum of the normalized query to efficiently detect identical queries. | Discovery/ indexing/ search | data service providers | Data Citation of Evolving Data |
R5 – Stable Sorting : Ensure that the sorting of the records in the data set is unambiguous and reproducible | Discovery/ indexing/ search | data service providers | Data Citation of Evolving Data |
R6 – Result Set Verification: Compute fixity information (checksum) of the query result set to enable verification of the correctness of a result upon re-execution. | Discovery/ indexing/ search | data service providers | Data Citation of Evolving Data |
R7 – Query Timestamping: Assign a timestamp to the query based on the last update to the entire database (or the last update to the selection of data affected by the query or the query execution time). This allows retrieving the data as it existed at the time a user issued a query. | Discovery/ indexing/ search | data service providers | Data Citation of Evolving Data |
R8 – Query PID : Assign a new PID to the query if either the query is new or if the result set returned from an earlier identical query is different due to changes in the data. Otherwise, return the existing PID. | Discovery/ indexing/ search | data service providers | Data Citation of Evolving Data |
R9 – Store Query: Store query and metadata (e.g. PID, original and normalized query, query & result set checksum, timestamp, superset PID, data set description, and other) in the query store. | Discovery/ indexing/ search | data service providers | Data Citation of Evolving Data |
Provide researchers with a mechanism to connect datasets in different data repositories based on various models such as co-authorship, joint funding, grants, etc. | Interlinking/ interoperability | data service providers, coordination fora | Data Description Registry Interoperability WG: Interlinking Method and Specification of Cross-Platform Discovery |
1. Provide a range of query interfaces to accommodate various data search behaviours. | Discovery/ indexing/ search | data service providers | Data Discovery Paradigms: User Requirements and Recommendations for Data Repositories |
10. Follow API search standards and community adopted vocabularies for interoperability. | Access/ integration, Metadata richness/ ingest/ submission | data service providers | Data Discovery Paradigms: User Requirements and Recommendations for Data Repositories |
2. Provide multiple access points to find data. | Discovery/ indexing/ search, Access/ integration | data service providers | Data Discovery Paradigms: User Requirements and Recommendations for Data Repositories |
3. Make it easier for researchers to judge relevance, accessibility and reusability of a data collection from a search summary. | Discovery/ indexing/ search, Access/ integration | data service providers | Data Discovery Paradigms: User Requirements and Recommendations for Data Repositories |
4. Make individual metadata records readable and analysable. | Machine-actionability, Access/ integration | data service providers | Data Discovery Paradigms: User Requirements and Recommendations for Data Repositories |
5. Enable sharing and downloading of bibliographic references. | Metadata richness/ ingest/ submission | data service providers | Data Discovery Paradigms: User Requirements and Recommendations for Data Repositories |
6. Expose data usage statistics. | Metadata richness/ ingest/ submission | data service providers | Data Discovery Paradigms: User Requirements and Recommendations for Data Repositories |
7. Strive for consistency with other repositories. | Sustainability/ funding/ business-model | data service providers | Data Discovery Paradigms: User Requirements and Recommendations for Data Repositories |
8. Identify and aggregate metadata records that describe the same data object. | Quality control/ curation, Metadata richness/ ingest/ submission | data service providers | Data Discovery Paradigms: User Requirements and Recommendations for Data Repositories |
9. Make metadata records easily indexed and searchable by major web search engines. | Discovery/ indexing/ search | data service providers | Data Discovery Paradigms: User Requirements and Recommendations for Data Repositories |
Ensure researchers apply a common core data model when organising their data and thus making data accessible and re-usable. | Metadata richness/ ingest/ submission | data service providers, data stewards | Data Foundation and Terminology Work Group Products |
Guidelines (Policy): M.2-1 The operators publicly provide a policy that describes the services. | Policy | data service providers | DINI Certificate for Open Access Publication Services 2019 |
M.4-1 The legal relationship between author(s) and publisher(s) (rights holders) and the operating institution is governed in a formal agreement (granting of rights). | Access/ integration | data service providers | DINI Certificate for Open Access Publication Services 2019 |
M.4-10 The operators document the legal attributes of the published documents in their resp. metadata to make them accessible for machine-reading. | Metadata richness/ ingest/ submission, Machine-actionability | data service providers | DINI Certificate for Open Access Publication Services 2019 |
M.4-11 The legal attributes of the documents are available in machine-readable form on the web frontend to make them accessible for users. | Access/ integration, Machine-actionability | data service providers | DINI Certificate for Open Access Publication Services 2019 |
M.4-2 The operators publish the deposit license(s) in the country’s official language(s) where the service is based | Access/ integration | data service providers | DINI Certificate for Open Access Publication Services 2019 |
M.4-3 The right to store the publication electronically and to make the publication available to the public | Access/ integration | data service providers | DINI Certificate for Open Access Publication Services 2019 |
M.4-4 The right to notify and transfer the document to third parties, e.g. within the framework of national collection mandates, especially for the purpose of long-term archiving. | Access/ integration | data service providers | DINI Certificate for Open Access Publication Services 2019 |
M.4-5 The right to copy and to convert the document for archiving purposes into additional, different electronic or physical formats while retaining the content’s integrity | Access/ integration | data service providers | DINI Certificate for Open Access Publication Services 2019 |
M.4-6 When registering a document, the author has the option of selecting a license that defines user rights. A preselection takes stan-dardized license models into account; licenses conforming to the Open Definition9are encouraged. | Access/ integration | data service providers | DINI Certificate for Open Access Publication Services 2019 |
M.4-8 The copyright holders assure the operators that no third-party rights will be violated by publishing the document or parts thereof. Should third-party rights be asserted following publication, the copy-right holders warrant that they will immediately inform the operators thereof. | Access/ integration | data service providers | DINI Certificate for Open Access Publication Services 2019 |
M.4-9 A legal notice is published on the website that meets legal requirements. | Access/ integration | data service providers | DINI Certificate for Open Access Publication Services 2019 |
M.5-1 A security concept exists for the technical system that forms the basis for the service. | Quality control/ curation, Sustainability/ funding/ business-model | data service providers | DINI Certificate for Open Access Publication Services 2019 |
M.5-10 Data exchange between web servers and users’ web browsers during login and the publication process takes place using current TSL technologies, e.g. SSL. | Quality control/ curation | data service providers | DINI Certificate for Open Access Publication Services 2019 |
M.5-2 There is an operations concept in place that includes a technical maintenance plan | Sustainability/ funding/ business-model | data service providers | DINI Certificate for Open Access Publication Services 2019 |
M.5-3 There is written documentation available on the technical system and all of its components needed for the operation of the system. | Quality control/ curation, Sustainability/ funding/ business-model | data service providers | DINI Certificate for Open Access Publication Services 2019 |
M.5-4 All data and documents are regularly saved in a back-up procedure. | Quality control/ curation, Sustainability/ funding/ business-model | data service providers | DINI Certificate for Open Access Publication Services 2019 |
M.5-5 Autonomous software regularly monitors the availability of the servers that are necessary for the service’s operation. | Quality control/ curation, Sustainability/ funding/ business-model | data service providers | DINI Certificate for Open Access Publication Services 2019 |
M.5-6 Documents uploaded to the publication service will not be al-tered. | Sustainability/ funding/ business-model | data service providers | DINI Certificate for Open Access Publication Services 2019 |
M.5-7 Every document (and every version) uploaded to and published by the publication service is assigned a Persistent Identifier (PI) | Interlinking/ interoperability | data service providers | DINI Certificate for Open Access Publication Services 2019 |
M.5-8 Persistent identifiers are provided on the service’s website and in the exported metadata as primary identifiers in the form of an operable URL. | Interlinking/ interoperability | data service providers | DINI Certificate for Open Access Publication Services 2019 |
M.5-9 Documents are only deleted as an exception and subsequently documented publicly under the persistent URL of the original document. | Sustainability/ funding/ business-model | data service providers | DINI Certificate for Open Access Publication Services 2019 |
M.6-1 There is a written policy containing the indexing regulations for documents that is available online to users. | Policy, Discovery/ indexing/ search | data service providers | DINI Certificate for Open Access Publication Services 2019 |
M.6-2 Every document is represented in an indexed form that employs the means and methods of the Dublin Core element set. | Discovery/ indexing/ search | data service providers | DINI Certificate for Open Access Publication Services 2019 |
M.6-3 All documents are classified using the Dewey Decimal Classi-fication (DDC), at least in accordance with the German National Bibliography’s subject headings. | Metadata richness/ ingest/ submission, Interlinking/ interoperability | data service providers | DINI Certificate for Open Access Publication Services 2019 |
M.6-4 All documents are assigned document or publication type descriptions following DINI’s recommendations in Common Vocabulary for Publication and Document Types (Gemeinsames Vokabular für Publikations- und Dokumenttypen). | Metadata richness/ ingest/ submission | data service providers | DINI Certificate for Open Access Publication Services 2019 |
M.6-5 There is a web interface allowing users to access all published documents and their respective metadata. | Access/ integration | data service providers | DINI Certificate for Open Access Publication Services 2019 |
M.6-6 An OAI interface is integrated that complies with the requirements of OAI-PMH 2.0 and of the DINI OAI Guidelines. | Metadata richness/ ingest/ submission, Access/ integration | data service providers | DINI Certificate for Open Access Publication Services 2019 |
M.6-7 A direct export of individual metadata records resp. of search results in at least one suitable data format is available on the website. | Access/ integration | data service providers | DINI Certificate for Open Access Publication Services 2019 |
M.7-1 The service keeps a consistent access log as per legal regulations. | Access/ integration | data service providers | DINI Certificate for Open Access Publication Services 2019 |
M.7-2 Web-server logs are anonymized or pseudonymized for long-term storage. | Access/ integration | data service providers | DINI Certificate for Open Access Publication Services 2019 |
M.7-3 Automatic access is not logged for usage statistics of individual documents or data. | Access/ integration | data service providers | DINI Certificate for Open Access Publication Services 2019 |
M.7-4 There is publicly available documentation describing the criteria and standard applied to create the statistics | Policy | data service providers | DINI Certificate for Open Access Publication Services 2019 |
M.8-1 A minimum time span of no less than five years is defined for the availability of documents and their resp. metadata published via the service. | Access/ integration | data service providers | DINI Certificate for Open Access Publication Services 2019 |
M.8-2 The original files and possible additional archive copies are free of any technical protection. | Access/ integration | data service providers | DINI Certificate for Open Access Publication Services 2019 |
M.8-3 There are regulations in place for the deletion of documents | Policy | data service providers | DINI Certificate for Open Access Publication Services 2019 |
R.3-1 At least one of the following APIs is integrated to support clarification of rights | Machine-actionability, Interlinking/ interoperability | data service providers | DINI Certificate for Open Access Publication Services 2019 |
R.3-2 The embedding of freely available bibliographical sources supports the upload of secondary publications. | Interlinking/ interoperability | data service providers | DINI Certificate for Open Access Publication Services 2019 |
R.3-3 To facilitate author identification, the entry of an Open Researcher and Contributor ID (ORCID) is optionally offered during the upload to allow authors to be linked to their ORCID | Interlinking/ interoperability | data service providers | DINI Certificate for Open Access Publication Services 2019 |
R.3-4 As an alternative to independent uploads by the authors/publishers, a central institution offers an upload service to authors/publishers. Information about this should be provided on the service’s website. | Metadata richness/ ingest/ submission | data service providers | DINI Certificate for Open Access Publication Services 2019 |
R.3-5 Workflow systems are offered to assist publishers with extensive publication projects. Information about this should be provided on the service’s website. | Metadata richness/ ingest/ submission | data service providers | DINI Certificate for Open Access Publication Services 2019 |
R.3-6 Specific information is offered about citation of the offered elec-tronic documents. | Capacity building/ incentivisation | data service providers | DINI Certificate for Open Access Publication Services 2019 |
R.3-7 The operating institution offers information about the Open Researcher and Contributor ID (ORCID) and about other author identification standards. | Capacity building/ incentivisation | data service providers | DINI Certificate for Open Access Publication Services 2019 |
R.3-8 The available information or parts thereof are also provided in English | Access/ integration | data service providers | DINI Certificate for Open Access Publication Services 2019 |
R.4-3 If a deposit license is used to grant rights, it should additionally be available online in English. | Access/ integration | data service providers | DINI Certificate for Open Access Publication Services 2019 |
R.4-4 The operators are allowed to transfer rights granted in the deposit license in full or in part, to third parties and to transfer non-exclusive copyrights to other repositories without the specific consent of the copyright holders. | Sustainability/ funding/ business-model | data service providers | DINI Certificate for Open Access Publication Services 2019 |
R.4-5 The operators license the documents’ metadata under CC0 or the Open Data Commons Open Database License (ODbL) | Access/ integration | data service providers | DINI Certificate for Open Access Publication Services 2019 |
R.5-1 The individual document’s integrity is regularly verified through internal processesusing a hash value. | Quality control/ curation | data service providers | DINI Certificate for Open Access Publication Services 2019 |
R.5-2 Upon publication of a new version of a document, the older version is marked as not current and links to the new version. | Quality control/ curation | data service providers | DINI Certificate for Open Access Publication Services 2019 |
R.6-1 In addition to the German National Bibliography’s subject headings, verbal (uncontrolled keywords) or an (interdisciplinary or intradisciplinary) classificatory subject indexing is performed. | Metadata richness/ ingest/ submission, Interlinking/ interoperability, Discovery/ indexing/ search | data service providers | DINI Certificate for Open Access Publication Services 2019 |
R.6-2 English keywords are assigned to all metadata sets. | Metadata richness/ ingest/ submission, Discovery/ indexing/ search | data service providers | DINI Certificate for Open Access Publication Services 2019 |
R.6-3 Short summaries or abstracts in English and German are offered in all metadata sets | Metadata richness/ ingest/ submission, Access/ integration | data service providers | DINI Certificate for Open Access Publication Services 2019 |
R.6-4 The metadata (e.g. of parts of the holdings) are provided in additional metadata formats and are available via the OAI interface. | Access/ integration, Machine-actionability | data service providers | DINI Certificate for Open Access Publication Services 2019 |
R.6-5 Metadata are made publicly available via additional interfaces | Access/ integration, Machine-actionability | data service providers | DINI Certificate for Open Access Publication Services 2019 |
R.6-6 Authors’ names are linked to norm data. | Interlinking/ interoperability | data service providers | DINI Certificate for Open Access Publication Services 2019 |
R.6-7 A SWORD API is used for the (semi-)automated import of data into the publication service. | Interlinking/ interoperability | data service providers | DINI Certificate for Open Access Publication Services 2019 |
R.7-1 Access statistics are listed with every document as dynamic metadata and are publicly available | Access/ integration | data service providers | DINI Certificate for Open Access Publication Services 2019 |
R.7-2 Access to individual documents is counted using the COUNTER standard. | Access/ integration | data service providers | DINI Certificate for Open Access Publication Services 2019 |
R.7-3 Data transfer to a service provider such as OpenAIRE is sup-ported. | Interlinking/ interoperability | data service providers | DINI Certificate for Open Access Publication Services 2019 |
R.7-4 Alternative metrics on the documents are provided. In most cases, this requires a DOI. | Interlinking/ interoperability | data service providers | DINI Certificate for Open Access Publication Services 2019 |
R.7-5 Citation figures are displayed for the documents. In most cases, this requires a DOI. | Access/ integration | data service providers | DINI Certificate for Open Access Publication Services 2019 |
R.8-1 Long-term availability of the documents is ensured. | Sustainability/ funding/ business-model, Access/ integration | data service providers | DINI Certificate for Open Access Publication Services 2019 |
R.8-2 Open file formats facilitating long-term availability are used to store documents | Sustainability/ funding/ business-model, Access/ integration | data service providers | DINI Certificate for Open Access Publication Services 2019 |
R.8-3 A basic metadata set is kept for deleted documents | Sustainability/ funding/ business-model | data service providers | DINI Certificate for Open Access Publication Services 2019 |
Support of Authors and Publishers: M.3-1 A contact and advisory service is accessible via the website | Capacity building/ incentivisation | data service providers | DINI Certificate for Open Access Publication Services 2019 |
Support of Authors and Publishers: M.3-2 Authors have the option to upload their documents intended for publication directly to the repository (e.g. via a web form) or to use other ways to add documents to the repository. | Metadata richness/ ingest/ submission | data service providers | DINI Certificate for Open Access Publication Services 2019 |
Support of Authors and Publishers: M.3-3 Information on the relevant technical questions on electronic publishing is provided or linked to. | Capacity building/ incentivisation | data service providers | DINI Certificate for Open Access Publication Services 2019 |
Visibility of the Service: M.1-1 The entire range of services must be available online on the www. | Access/ integration | data service providers | DINI Certificate for Open Access Publication Services 2019 |
Visibility of the Service: M.1-2 The service’s homepage must be referenced in a central location on the institution’s homepage | Access/ integration | data service providers | DINI Certificate for Open Access Publication Services 2019 |
Visibility of the Service: M.1-3 Open Access publications are clearly marked on the website. | Discovery/ indexing/ search | data service providers | DINI Certificate for Open Access Publication Services 2019 |
Visibility of the Service: R.1-1 The service and, where applicable, its OAI interface are listed with current data in at least one additional registry. | Discovery/ indexing/ search | data service providers | DINI Certificate for Open Access Publication Services 2019 |
Visibility of the Service: R.1-2 All documents published with the service are available via links. | Interlinking/ interoperability | data service providers | DINI Certificate for Open Access Publication Services 2019 |
Visibility of the Service: R.1-3 Links to social media are offered on the landing page of each individual publication. | Capacity building/ incentivisation | data service providers | DINI Certificate for Open Access Publication Services 2019 |
Visibility of the Service: R.1-4 The service supports search engine optimization (SEO). | Discovery/ indexing/ search | data service providers | DINI Certificate for Open Access Publication Services 2019 |
Beschreibung des Access-Status die Verwendung des von der Confederation of Open Access Repositories (COAR) entwickelten Controlled Vocabulary for Access Rights. | Access/ integration, Interlinking/ interoperability | data service providers, data stewards | Empfehlungen für Rechteinformationen in Metadaten |
Beschreibung von Dokumenten, für die institutionsspezifische Nutzungshinweise gelten, mit dem Element Lokaler Nutzungshinweis und der zusätzlichen Angabe von Access Status und Rechtehinweis. | Access/ integration | data service providers, data stewards | Empfehlungen für Rechteinformationen in Metadaten |
Entscheidend für die Wahl der Lizenz beziehungsweise des Rechtehinweises ist, dass es sich um einen Wert aus einem möglichst breit genutzten Vokabular handelt. | Access/ integration, Interlinking/ interoperability | data service providers | Empfehlungen für Rechteinformationen in Metadaten |
Legal: A clear list of EOSC-recommended licenses and their compatibility with Member States’ recommended licenses. | Quality control/ curation | data service providers, data stewards | EOSC Interoperability Framework |
Legal: Additional restrictions on access and use of data only applied in cases of applicable legislation or legitimate reasons. | Access/ integration | data service providers | EOSC Interoperability Framework |
Legal: Alignment between Member States national legislations and EOSC. | Policy | policy makers, data service providers | EOSC Interoperability Framework |
Legal: Clearly marked instances of expired or inexistent copyright, as well as for orphan data. | Quality control/ curation | data service providers, data stewards | EOSC Interoperability Framework |
Legal: GDPR-compliance for personal data. | Quality control/ curation | data service providers, data stewards | EOSC Interoperability Framework |
Legal: Harmonised policy and guidance to dealing with cases where patent filing or trade secrets may be compromised by disclosure. | Policy | policy makers, data service providers | EOSC Interoperability Framework |
Legal: Harmonised terms of use across repositories | Policy | policy makers, data service providers | EOSC Interoperability Framework |
Legal: Identification of different parts of a dataset with different licenses. | Quality control/ curation | data service providers, data stewards | EOSC Interoperability Framework |
Legal: Permissive licenses for metadata (and preferably for data, whenever possible). And CC0 preferred over CC BY 4.0. | Quality control/ curation | data service providers, data stewards | EOSC Interoperability Framework |
Legal: Standardised human and machine-readable licenses, with a centralised source of knowledge and support on copyright and licenses. | Capacity building/ incentivisation, Quality control/ curation, Machine-actionability | data service providers, data stewards | EOSC Interoperability Framework |
Legal: Tracking of license evolution over time for datasets. | Quality control/ curation | data service providers, data stewards | EOSC Interoperability Framework |
Organisational: A clear management of permanent organisation names and functions. | Quality control/ curation | data service providers, data stewards | EOSC Interoperability Framework |
Organisational: Interoperability-focused rules of participation recommendations | Interlinking/ interoperability | data service providers, data stewards | EOSC Interoperability Framework |
Organisational: Usage recommendations of standardised data formats and/or vocabularies, and with their corresponding metadata. | Capacity building/ incentivisation, Quality control/ curation | data service providers, data stewards | EOSC Interoperability Framework |
Semantic: A minimum metadata model (and crosswalks) to ease discovery over existing federated research data and metadata. | Metadata richness/ ingest/ submission | data service providers, data stewards | EOSC Interoperability Framework |
Semantic: Associated documentation for semantic artefacts. | Interlinking/ interoperability, Metadata richness/ ingest/ submission | data service providers, data stewards | EOSC Interoperability Framework |
Semantic: Clear and precise, publicly-available definitions for all concepts, metadata and data schemas. | Interlinking/ interoperability, Metadata richness/ ingest/ submission | data service providers, data stewards | EOSC Interoperability Framework |
Semantic: Clear protocols and building blocks for the federation/harvesting of semantic artefacts catalogues. | Access/ integration, Interlinking/ interoperability | data service providers, data stewards | EOSC Interoperability Framework |
Semantic: Extensibility options to allow for disciplinary metadata. | Metadata richness/ ingest/ submission | data service providers, data stewards | EOSC Interoperability Framework |
Semantic: Repositories of semantic artefacts, rules with a clear governance framework. | Sustainability/ funding/ business-model
| data service providers, data stewards | EOSC Interoperability Framework |
Semantic: Semantic artefacts preferably with open licenses. | Metadata richness/ ingest/ submission | data service providers, data stewards | EOSC Interoperability Framework |
Technical: A clear EOSC PID policy. | Policy | policy makers | EOSC Interoperability Framework |
Technical: A common security and privacy framework (including Authorisation and Authentication Infrastructure). | Access/ integration | data service providers | EOSC Interoperability Framework |
Technical: Coarse-grained and fine-grained dataset (and other research object) search tools. | Discovery/ indexing/ search | data service providers | EOSC Interoperability Framework |
Technical: Easy access to data sources available in different formats. | Access/ integration | data service providers | EOSC Interoperability Framework |
Technical: Easy-to-understand Service-Level Agreements for all EOSC resource providers. | Sustainability/ funding/ business-model | data service providers | EOSC Interoperability Framework |
Technical: Open Specifications for EOSC Services | Access/ integration | data service providers | EOSC Interoperability Framework |
RDA-A1-01M: Metadata contains information to enable th e user to get access to the data | Machine-actionability, Access/ integration | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-A1-02D: Data can be accessed manually (i.e. with h uman intervention) | Access/ integration | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-A1-02M: Metadata can be accessed manually (i.e. with human intervention) | Access/ integration | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-A1-03D: Data identifier resolves to a digital object | Access/ integration | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-A1-03M: Metadata identifier resolves to a metadata record | Access/ integration | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-A1-04D: Data is accessible through standardised protocol | Access/ integration, Machine-actionability | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-A1-04M: Metadata is accessed through standardised protocol | Access/ integration, Machine-actionability | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-A1-05D: Data can be accessed automatically (i.e. by a computer program) | Access/ integration, Machine-actionability | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-A1.1-01D: Data is accessible through a free access protocol | Access/ integration | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-A1.1-01M: Metadata is accessible through a free access protocol | Access/ integration | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-A1.2-01D: Data is accessible through an access protocol that supports authentication and authorisation | Access/ integration | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-A2-01M: Metadata is guaranteed to remain available after data is no longer available | Sustainability/ funding/ business-model | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-F1-01D: Data is identified by a persistent identifier | Discovery/ indexing/ search, Interlinking/ interoperability | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-F1-01M: Metadata is identified by a persistent identifier | Discovery/ indexing/ search, Interlinking/ interoperability | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-F1-02D: Data is identified by a globally unique identifier | Discovery/ indexing/ search, Interlinking/ interoperability | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-F1-02M: Metadata is identified by a globally unique identifier | Discovery/ indexing/ search, Interlinking/ interoperability | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-F2-01M: Rich metadata is provided to allow discovery | Metadata richness/ ingest/ submission, Discovery/ indexing/ search | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-F3-01M: Metadata includes the identifier for the data | Metadata richness/ ingest/ submission, Interlinking/ interoperability | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-F4-01M: Metadata is offered in such a way that it can be harvested and indexed | Metadata richness/ ingest/ submission, Discovery/ indexing/ search | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-I1-01D: Data uses knowledge representation expressed in standardised format | Interlinking/ interoperability | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-I1-01M: Metadata uses knowledge representation expressed in standardised format | Metadata richness/ ingest/ submission, Interlinking/ interoperability | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-I1-02D: Data uses machine-understandable knowledge representation | Interlinking/ interoperability, Machine-actionability | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-I1-02M: Metadata uses machine -understandable knowledge representation | Interlinking/ interoperability, Machine-actionability | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-I2-01D: Data uses FAIR-compliant vocabularies | Interlinking/ interoperability | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-I2-01M: Metadata uses FAIR-compliant vocabularies | Metadata richness/ ingest/ submission, Interlinking/ interoperability | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-I3-01D: Data includes references to other data | Interlinking/ interoperability | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-I3-01M: Metadata includes references to other metadata | Metadata richness/ ingest/ submission, Interlinking/ interoperability | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-I3-02D: Data includes qualified references to other data | Interlinking/ interoperability | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-I3-02M: Metadata includes references to other data | Metadata richness/ ingest/ submission, Interlinking/ interoperability | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-I3-03M: Metadata includes qualified references to other metadata | Metadata richness/ ingest/ submission, Interlinking/ interoperability | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-I3-04M: Metadata include qualified references to other data | Metadata richness/ ingest/ submission, Interlinking/ interoperability | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-R1-01M: Plurality of accurate and relevant attributes are provided to allow reuse | Metadata richness/ ingest/ submission | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-R1.1-01M: Metadata includes information about the licence under which the data can be reused | Metadata richness/ ingest/ submission | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-R1.1-02M: Metadata refers to a standard reuse licence | Metadata richness/ ingest/ submission | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-R1.1-03M: Metadata refers to a machine -understandable reuse licence | Metadata richness/ ingest/ submission, Machine-actionability | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-R1.2-01M: Metadata includes provenance information according to community -specific standards | Metadata richness/ ingest/ submission | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-R1.2-02M: Metadata includes provenance information according to a cross-community language | Metadata richness/ ingest/ submission | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-R1.3-01D: Data complies with a community standard | Quality control/ curation | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-R1.3-01M: Metadata complies with a community standard | Metadata richness/ ingest/ submission | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-R1.3-02D: Data is expressed in compliance with a machine-understandable community standard | Quality control/ curation, Machine-actionability | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
RDA-R1.3-02M: Metadata is expressed in compliance with a machine -understandable community standard | Metadata richness/ ingest/ submission, Machine-actionability | data service providers, data stewards | FAIR Data Maturity Model, Specification and Guidelines 2020 |
Use indicators to assess the FAIRness of research data. | Quality control/ curation | data service providers, data stewards | FAIR Data Maturity Model: specifcation and guidelines |
FsF-A1-01M Metadata contains access level and access conditions of the data. | Metadata richness/ ingest/ submission, Access/ integration | data service providers, research community, data stewards | FAIRsFAIR Data Object Assessment Metrics |
FsF-A1-02M Metadata is accessible through a standardized communication protocol | Machine-actionability, Access/ integration | data service providers | FAIRsFAIR Data Object Assessment Metrics |
FsF-A1-03D Data is accessible through a standardized communication protocol | Machine-actionability, Access/ integration | data service providers | FAIRsFAIR Data Object Assessment Metrics |
FsF-A2-01M Metadata remains available, even if the data is no longer available. | Access/ integration, Sustainability/ funding/ business-model | data service providers, research funders, institutions, policy makers | FAIRsFAIR Data Object Assessment Metrics |
FsF-F1-01D Data is assigned a globally unique identifier. | Discovery/ indexing/ search, Interlinking/ interoperability | data service providers, research community, data stewards | FAIRsFAIR Data Object Assessment Metrics |
FsF-F1-02D Data is assigned a persistent identifier. | Discovery/ indexing/ search, Interlinking/ interoperability | data service providers, research community, data stewards | FAIRsFAIR Data Object Assessment Metrics |
FsF-F2-01M Metadata includes descriptive core elements (creator, title, data identifier, publisher, publication date, summary and keywords) to support data findability. | Metadata richness/ ingest/ submission, Discovery/ indexing/ search | data service providers, research community, data stewards | FAIRsFAIR Data Object Assessment Metrics |
FsF-F3-01M Metadata includes the identifier of the data it describes. | Interlinking/ interoperability | data stewards | FAIRsFAIR Data Object Assessment Metrics |
FsF-F4-01M Metadata is offered in such a way that it can be retrieved by machines. | Machine-actionability | data service providers | FAIRsFAIR Data Object Assessment Metrics |
FsF-I1-01M Metadata is represented using a formal knowledge representation language. | Access/ integration, Interlinking/ interoperability | data service providers | FAIRsFAIR Data Object Assessment Metrics |
FsF-I1-02M Metadata uses semantic resources. | Metadata richness/ ingest/ submission, Interlinking/ interoperability | data service providers | FAIRsFAIR Data Object Assessment Metrics |
FsF-I3-01M Metadata includes links between the data and its related entities. | Metadata richness/ ingest/ submission, Interlinking/ interoperability | data service providers, research community, data stewards | FAIRsFAIR Data Object Assessment Metrics |
FsF-R1-01MD Metadata specifies the content of the data. | Metadata richness/ ingest/ submission | data service providers, research community, data stewards | FAIRsFAIR Data Object Assessment Metrics |
FsF-R1.1-01M Metadata includes license information under which data can be reused. | Metadata richness/ ingest/ submission | data service providers, research community, data stewards | FAIRsFAIR Data Object Assessment Metrics |
FsF-R1.2-01M Metadata includes provenance information about data creation or generation. | Metadata richness/ ingest/ submission | data service providers, research community, data stewards | FAIRsFAIR Data Object Assessment Metrics |
FsF-R1.3-01M Metadata follows a standard recommended by the target research community of the data. | Interlinking/ interoperability | data service providers, research community, coordination fora | FAIRsFAIR Data Object Assessment Metrics |
FsF-R1.3-02D Data is available in a file format recommended by the target research community. | Machine-actionability | data service providers, research community, data stewards | FAIRsFAIR Data Object Assessment Metrics |
R 2: Identify what resources are to be marked up with structured data | Discovery/ indexing/ search, Interlinking/ interoperability, Machine-actionability | data service providers, data stewards | Guidelines for publishing structured metadata on the Web V3.0 |
R 3: Adopt or develop a crosswalk from your repository schema to Schema.org | Discovery/ indexing/ search, Interlinking/ interoperability | data service providers | Guidelines for publishing structured metadata on the Web V3.0 |
R 4: Incorporate external vocabularies as necessary | Interlinking/ interoperability | data service providers, data stewards | Guidelines for publishing structured metadata on the Web V3.0 |
R 5: Implement markup syntax consistently by following community practices | Interlinking/ interoperability | data service providers | Guidelines for publishing structured metadata on the Web V3.0 |
R 6: Be friendly to web crawlers | Discovery/ indexing/ search | data service providers | Guidelines for publishing structured metadata on the Web V3.0 |
R 7: Utilise tools that can help you (customise, create, mark up, validate, extract) | Discovery/ indexing/ search, Quality control/ curation | data service providers | Guidelines for publishing structured metadata on the Web V3.0 |
R 8: Document and share the whole process | Capacity building/ incentivisation | data service providers | Guidelines for publishing structured metadata on the Web V3.0 |
R 9: Identify and join a community, and follow their established practices | Sustainability/ funding/ business-model | data service providers, coordination fora | Guidelines for publishing structured metadata on the Web V3.0 |
R1. Clarify the purpose(s) of your markup (or why you want to markup your data) | Discovery/ indexing/ search, Interlinking/ interoperability | data service providers, data stewards | Guidelines for publishing structured metadata on the Web V3.0 |
Consider alternative cost recovery options and a diversification of revenue streams for repositories | Sustainability/ funding/ business-model
| data service providers | Income Streams for Data Repositories |
Share information about the links between the literature and research data. | Interlinking/ interoperability | data service providers | Interoperability Framework Recommendations |
Data repositories need to publish their policies & procedures machine-actionable to build trust in their operation | Machine-actionability, Policy | data service providers | Machine Actionable Policy Templates - Practical Policy WG Recommendations |
Allow content to be marked for deletion by authorized users | Quality control/ curation | data service providers | Matrix of use cases and functional requirements for research data repository platforms |
Allow data annotation by the data owner, by other (authorized) people or by automatic (metadata) extraction tools | Metadata richness/ ingest/ submission, Quality control/ curation | data service providers | Matrix of use cases and functional requirements for research data repository platforms |
Allow data providers to choose the level of access to data (e.g. Open Access). | Access/ integration, Quality control/ curation | data service providers, data stewards | Matrix of use cases and functional requirements for research data repository platforms |
Allow local download of a selected set of information | Access/ integration | data service providers | Matrix of use cases and functional requirements for research data repository platforms |
Allow product developers to update product information | Quality control/ curation | data service providers | Matrix of use cases and functional requirements for research data repository platforms |
Allow the (automated) use of data policies | Policy, Machine-actionability | data service providers | Matrix of use cases and functional requirements for research data repository platforms |
Allow the creation of special collection views or digital exhibitions | Discovery/ indexing/ search | data service providers, data stewards | Matrix of use cases and functional requirements for research data repository platforms |
Assignment of PID / DOI | Discovery/ indexing/ search, Interlinking/ interoperability | data service providers | Matrix of use cases and functional requirements for research data repository platforms |
Capture "degree of confidence" on each data item | Metadata richness/ ingest/ submission, Quality control/ curation | data stewards | Matrix of use cases and functional requirements for research data repository platforms |
Collection virtualization / logical naming | Metadata richness/ ingest/ submission, Quality control/ curation | data service providers | Matrix of use cases and functional requirements for research data repository platforms |
Data and metadata collection with mobile devices | Metadata richness/ ingest/ submission | data service providers | Matrix of use cases and functional requirements for research data repository platforms |
Definable submission/ingest workflow | Metadata richness/ ingest/ submission | data service providers | Matrix of use cases and functional requirements for research data repository platforms |
Display bibliographic citation for data; ideally, also allow the export of bibliographic data to citation software. | Access/ integration, Metadata richness/ ingest/ submission | data service providers | Matrix of use cases and functional requirements for research data repository platforms |
Easy to use ingest process with few barriers to participation | Metadata richness/ ingest/ submission | data service providers | Matrix of use cases and functional requirements for research data repository platforms |
Embargo date selection for data-depositing user | Quality control/ curation | data service providers | Matrix of use cases and functional requirements for research data repository platforms |
Fast data transfer, ingest and export | Access/ integration | data service providers | Matrix of use cases and functional requirements for research data repository platforms |
Federation | Access/ integration, Interlinking/ interoperability | data service providers | Matrix of use cases and functional requirements for research data repository platforms |
Files need to be converted to the most accessible formats; use of proprietary and legacy types are possible, but may be reviewed on a case by case basis. | Sustainability/ funding/ business-model, Access/ integration | data service providers | Matrix of use cases and functional requirements for research data repository platforms |
Fine grained authentication and authorization. Allow the integration or the import from external authentication/authorizsation systems. | Access/ integration | data service providers | Matrix of use cases and functional requirements for research data repository platforms |
Integration of PIDs into data management | Interlinking/ interoperability, Metadata richness/ ingest/ submission | data service providers, data stewards | Matrix of use cases and functional requirements for research data repository platforms |
Maintain a permanent history of versions for all data | Quality control/ curation | data service providers, data stewards | Matrix of use cases and functional requirements for research data repository platforms |
Maintain citations linked to the data (e.g. experiments or simulations) | Interlinking/ interoperability | data service providers | Matrix of use cases and functional requirements for research data repository platforms |
Metadata quality evaluation | Metadata richness/ ingest/ submission, Quality control/ curation | data service providers | Matrix of use cases and functional requirements for research data repository platforms |
Micro-services | Access/ integration | data service providers | Matrix of use cases and functional requirements for research data repository platforms |
Policy enforcement points | Policy | data service providers | Matrix of use cases and functional requirements for research data repository platforms |
Provide (authorized) users access to versions of data (e.g. different simulation runs) | Quality control/ curation | data service providers | Matrix of use cases and functional requirements for research data repository platforms |
Provide both single and batch ingest paths | Metadata richness/ ingest/ submission | data service providers | Matrix of use cases and functional requirements for research data repository platforms |
Provide data access statistics either by the use of external analytics services or internal monitoring of user activity. | Certification/ evaluation | data service providers | Matrix of use cases and functional requirements for research data repository platforms |
Provide integrity and qiality control mechanisms for data and metadata | Quality control/ curation | data service providers | Matrix of use cases and functional requirements for research data repository platforms |
Provide interfaces (APIs) for the automated execution of tasks, e.g. to ingest data or to integrate data analysis tools and other external applications | Access/ integration, Machine-actionability | data service providers | Matrix of use cases and functional requirements for research data repository platforms |
Provide Single-Sign-On and/or support for different authentication methods | Access/ integration | data service providers | Matrix of use cases and functional requirements for research data repository platforms |
Provide state-of-the-art user interfaces and clients over the life time of a repository platform | Certification/ evaluation, Access/ integration | data service providers | Matrix of use cases and functional requirements for research data repository platforms |
Record audit trails | Quality control/ curation | data service providers | Matrix of use cases and functional requirements for research data repository platforms |
Remote Access Management | Access/ integration, Quality control/ curation | data service providers | Matrix of use cases and functional requirements for research data repository platforms |
Require all data to be attributed with handling requirements | Metadata richness/ ingest/ submission | data service providers, data stewards | Matrix of use cases and functional requirements for research data repository platforms |
Seamless integration of data and other research outputs into a coherent and consistent discovery and access solution | Access/ integration, Discovery/ indexing/ search | data service providers | Matrix of use cases and functional requirements for research data repository platforms |
Sophisticated search capabilities for metadata and data both for humans and computers | Discovery/ indexing/ search, Machine-actionability | data service providers | Matrix of use cases and functional requirements for research data repository platforms |
Storage drivers | Access/ integration | data service providers | Matrix of use cases and functional requirements for research data repository platforms |
Support for different metadata (schemas), including domain-specificity and interoperability | Metadata richness/ ingest/ submission | data service providers | Matrix of use cases and functional requirements for research data repository platforms |
Support Staged Content | Metadata richness/ ingest/ submission, Quality control/ curation | data service providers | Matrix of use cases and functional requirements for research data repository platforms |
The repository must be scalable regarding the amount of data | Sustainability/ funding/ business-model | data service providers | Matrix of use cases and functional requirements for research data repository platforms |
Tight integration with (near) data processing. | Access/ integration | data service providers | Matrix of use cases and functional requirements for research data repository platforms |
Vocabulary Service | Metadata richness/ ingest/ submission, Quality control/ curation | data service providers, data stewards | Matrix of use cases and functional requirements for research data repository platforms |
Workflows | Access/ integration | data service providers | Matrix of use cases and functional requirements for research data repository platforms |
Implement globally unique, persistent and resolvable identification of instruments | Discovery/ indexing/ search, Interlinking/ interoperability | data service providers | Persistent Identification of Instruments |
Define standard core PID information types to enable simplified verification of data identity and integrity | Machine-actionability, Quality control/ curation | coordination fora, data service providers | PID Information Types WG final deliverable |
RDI actively develops and applies modern, strategic technologies (AI, blockchain, and so on) for better data management and re-use possibilities, possibly in collaboration with other RDIs. | Sustainability/ funding/ business-model | data service providers, institutions | Practical Guide to Sustainable Research Data |
RDI aligns on RDM at (inter)national level through collaboration between RPOs, RFOs, RDIs, and research communities. | Sustainability/ funding/ business-model | data service providers, institutions | Practical Guide to Sustainable Research Data |
RDI collaborates with research communities/stakeholders with well-developed links for feedback to improve policies. | Policy | policy makers, institutions | Practical Guide to Sustainable Research Data |
RDI has a sustainable source of both long- and short-term financing. | Sustainability/ funding/ business-model | data service providers, institutions | Practical Guide to Sustainable Research Data |
RDI has complementary services to other RDIs to ensure cost efficiency. | Sustainability/ funding/ business-model | data service providers, institutions | Practical Guide to Sustainable Research Data |
RDI has complementary services to other RDIs. | Sustainability/ funding/ business-model | data service providers | Practical Guide to Sustainable Research Data |
RDI has policies aligned with those other RDIs of related RPOs and/or RFOs and other RDIs, as well as in a wider | Policy | policy makers, institutions | Practical Guide to Sustainable Research Data |
RDI has training requirements aligned with (inter)national policies. | Capacity building/ incentivisation | data service providers, institutions | Practical Guide to Sustainable Research Data |
RDI is an active member of a national RDI network, exchanging experience on technology and aiming to ensure best practice for sustainability. | Sustainability/ funding/ business-model | data service providers, institutions | Practical Guide to Sustainable Research Data |
RDI is connected to, and where applicable co-operating with, other relevant (inter)national RDM initiatives. | Sustainability/ funding/ business-model | data service providers, institutions | Practical Guide to Sustainable Research Data |
RDI is federated to EOSC or other relevant organisations to help improve access to funding | Sustainability/ funding/ business-model | data service providers, institutions | Practical Guide to Sustainable Research Data |
RDI offers specialist training activities (for example, on FAIR data, technical and IT skills, and financial and legal skills for data management services). | Capacity building/ incentivisation | data service providers, institutions | Practical Guide to Sustainable Research Data |
RDI participates in (inter)national consortia or organisations of computational centres where relevant. | Sustainability/ funding/ business-model | data service providers, institutions | Practical Guide to Sustainable Research Data |
RDI provides a portfolio of solutions (such as training, documentation). | Capacity building/ incentivisation | data service providers, institutions | Practical Guide to Sustainable Research Data |
RDI provides co-ordinated procedures with other RDI to develop long-term sustainability and exit scenarios, for example through seamless exchange of data holdings. | Sustainability/ funding/ business-model | data service providers, institutions | Practical Guide to Sustainable Research Data |
RDI provides co-ordinated procedures with other RDIs to swap data holdings potentially seamlessly if necessary. | Interlinking/ interoperability, Sustainability/ funding/ business-model | data service providers | Practical Guide to Sustainable Research Data |
RDI provides targeted services (such as data curation, long- term preservation, data storage, computing facilities) to research groups or institutions. | Sustainability/ funding/ business-model | data service providers | Practical Guide to Sustainable Research Data |
RDI uses appropriate communications channels to present examples of successful use of research data (best practice examples). | Capacity building/ incentivisation | data service providers | Practical Guide to Sustainable Research Data |
RDI’s policies are an integrated part of the total (inter)national RDM policy environment. | Policy | policy makers, institutions | Practical Guide to Sustainable Research Data |
Ensure that wide distributed and diverse long-tail data is discoverable and stored in appropriate formats to facilitate reuse. | Discovery/ indexing/ search | data service providers, data stewards | Precise Data Identification Services for Long Tail Research Data |
Changes in the Metadata | Quality control/ curation | data stewards | Principles and best practices in data versioning for all datasets big and small |
Identification of data collections | Policy, Quality control/ curation | data stewards, policy makers | Principles and best practices in data versioning for all datasets big and small |
Identifiers for dataset revisions | Quality control/ curation | data service providers, data stewards | Principles and best practices in data versioning for all datasets big and small |
Identifying manifestations of datasets | Quality control/ curation | data stewards | Principles and best practices in data versioning for all datasets big and small |
Identifying releases of data products | Quality control/ curation | data stewards | Principles and best practices in data versioning for all datasets big and small |
Provenance of datasets | Quality control/ curation | data stewards | Principles and best practices in data versioning for all datasets big and small |
Requirements for Data Citation | Quality control/ curation | data stewards | Principles and best practices in data versioning for all datasets big and small |
Version control and revisions | Quality control/ curation | data service providers, data stewards | Principles and best practices in data versioning for all datasets big and small |
Ensure data producers classify their data sets in standard data types, allowing data users to automatically identify instruments to process and visualise the data | Machine-actionability, Interlinking/ interoperability | data service providers | RDA Data Type Registries Working Group Output |
Implement a common standard for machine-actionable data management plans | Machine-actionability | data service providers | RDA DMP Common Standard for Machine-actionable Data Management Plans |
Make data collections actionable by automated processes | Machine-actionability, Interlinking/ interoperability | data service providers | RDA Research Data Collections WG Recommendations |
Increase professional reward for curation, maintainance or collection with propper metadata attribution using the PROV standard | Metadata richness/ ingest/ submission | data service providers, data stewards | RDA/TDWG Attribution Metadata Working Group: Final Recommendations |
Provide elementary kernel information for each PID record for scalable middleware infrastructure and automated processes. | Machine-actionability, Interlinking/ interoperability | data service providers | Recommendation on PID Kernel Information |
Build services on top of Crossref Event Data | Interlinking/ interoperability, Discovery/ indexing/ search | data service providers, publishers | recommendations from the dfg *metrics project |
Improve referenceability of scholarly outputs | Interlinking/ interoperability | data service providers, publishers | recommendations from the dfg *metrics project |
R 1: The definition of metrics should be a continuous process, taking in particular feedback from implementation into account. We recommend that the metrics are reviewed after two years initially, then every three years. | Certification/ evaluation | data service providers | Recommendations on FAIR Metrics for EOSC |
R 2: Inclusiveness should be a key attribute. | Certification/ evaluation | data service providers | Recommendations on FAIR Metrics for EOSC |
R 2.1: Diversity, especially across communities, should be taken into account. Priorities on criteria should be adopted to the specific case. | Certification/ evaluation | data service providers | Recommendations on FAIR Metrics for EOSC |
R 2.2: FAIR should be considered as a journey. Gradual implementation is to be expected in general, and evaluation tools should enable one to measure progress. | Certification/ evaluation | data service providers | Recommendations on FAIR Metrics for EOSC |
R 2.3: Resources should be able to interface with EOSC with a minimal overhead, and the existing data and functionalities should remain available. | Certification/ evaluation, Interlinking/ interoperability | data service providers | Recommendations on FAIR Metrics for EOSC |
R 3: Do not reinvent the wheel: the starting point for FAIR data metrics at this stage should be the criteria defined by the RDA FAIR Data Maturity Model Working Group. Groups defining metrics for their own use case should start with these criteria, and then liaise with the RDA Maintenance Group to provide feedback and participate in discussions on possible updates of the criteria and on priorities. | Certification/ evaluation | data service providers | Recommendations on FAIR Metrics for EOSC |
R 4: Evaluation methods and tools should be thoroughly assessed in a variety of contexts with broad consultation, in particular in different domains to ensure they scale and meet diverse community FAIR practices. | Certification/ evaluation | data service providers | Recommendations on FAIR Metrics for EOSC |
R 5: FAIR metrics should be developed for digital objects other than data, which may require that the FAIR guidelines be translated to suit these objects, particularly software source code. | Certification/ evaluation | data service providers | Recommendations on FAIR Metrics for EOSC |
R 6: Guidance should be provided from and to communities for evaluation and implementation. | Certification/ evaluation, Capacity building/ incentivisation | data service providers | Recommendations on FAIR Metrics for EOSC |
R 7: Cross-domain usage should be developed in a pragmatic way based on use-cases, and metrics should be carefully tailored in that respect. | Certification/ evaluation | data service providers | Recommendations on FAIR Metrics for EOSC |
Implement interoperable packaging and exchange formats/standards for digital content to enhance cross-repository platform interoperability. | Interlinking/ interoperability | data service providers | Research Data Repository Interoperability WG Final Recommendations |
Implement a metadata schema for exchanging information about the links between scholarly literature and data | Interlinking/ interoperability | data service providers, publishers | Scholix Metadata Schema for Exchange of Scholarly Communication Links |
Register metadata standards, databases, policies that are implemented in a repository in the FAIRsharing registry | Interlinking/ interoperability | data service providers | The FAIRsharing Registry and Recommendations: Interlinking Standards, Databases and Data Policies |
Action 1.1: Additional concepts and policies should be refined that make explicit that data selection, long-term stewardship, assessability, legal interoperability and timeliness of sharing are necessary for the implementation of FAIR. | Policy, Capacity building/ incentivisation | research funders, coordination fora, policy makers, institutions | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 1.2: The term FAIR is widely-used and effective so should not be extended with additional letters. | Policy | policy makers, standards bodies | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 1.3: The relationship between FAIR and Open should be clarified and well-articulated as the concepts are often wrongly conflated. FAIR does not mean Open. However, in the context of the EOSC and global drive towards Open Science, making FAIR data a reality should be supported by policies requiring appropriate Openness and protection, which can be expressed as ‘as Open as possible, as closed as necessary’. | Policy, Capacity building/ incentivisation | research funders, coordination fora, policy makers, institutions | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 10.1: Key data roles need to be recognised and rewarded, in particular, the data scientists who will assist research design and data analysis, visualisation and modelling; and data stewards who will inform the process of data curation and take responsibility for data management. | Capacity building/ incentivisation, Sustainability/ funding/ business-model | data stewards, institutions, research funders | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 10.2: Formal career pathways must be implemented to demonstrate the value of these roles and retain such professionalised roles in support of research teams. | Capacity building/ incentivisation | institutions | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 10.3: Professional bodies for these roles should be created, consolidated when they exist, and promoted. Accreditation should be developed for training and qualifications for these roles. | Capacity building/ incentivisation | institutions, coordination fora | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 10.4: Data skills, including an appropriate foundational level in data science and data stewardship, should be included in undergraduate and postgraduate training across disciplines, and in the provision of continuing professional development (CPD) credits for researchers. | Capacity building/ incentivisation | institutions | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 11.1: Curriculum frameworks for data science and data stewardship should be made available and be easily adaptable and reusable. | Capacity building/ incentivisation | coordination fora, institutions | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 11.2: Sharing and reuse of Open Educational Resources and reusable materials for data science and data stewardship programmes should be encouraged and facilitated. | Capacity building/ incentivisation | coordination fora, institutions, policy makers | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 11.3: Practical, on-the-job methods of training such as fellowships and staff exchanges should be supported, as well as Train-the-Trainer programmes so the body of data professionals can rapidly scale. | Capacity building/ incentivisation | coordination fora, institutions | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 11.4: A programme of certification and endorsement should be developed for organisations and programmes delivering train-the-trainer and/or informal data science and data stewardship training. As a first step, a lightweight peer-reviewed self-assessment would be a means of accelerating the development and implementation of quality training. | Capacity building/ incentivisation | coordination fora | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 12.1: A core set of metrics for FAIR Digital Objects should be defined to apply globally across research domains. More specific metrics should be defined at the community level to reflect the needs and practices of different domains and what it means to be FAIR for that type of research. | Certification/ evaluation | coordination fora | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 12.2: Convergence should be sought between the efforts by many groups to define FAIR assessment. The European Commission should support a project to coordinate activities in defining FAIR metrics and ensure these are created in a standardised way to enable future monitoring. | Certification/ evaluation | coordination fora | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 12.3: The process of developing, approving and implementing FAIR metrics should follow a consultative methodology with research communities, including scenario planning to minimise any unintended consequences and counter-productive gaming that may result. Metrics need to be regularly reviewed and updated to ensure they remain fit-for-purpose. | Certification/ evaluation | coordination fora, research community | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 13.1: Where existing frameworks exist to certify data services, these should be reviewed and adjusted to align with FAIR. The language of the CTS requirements should be adapted to reference the FAIR data principles more explicitly (e.g. in sections on levels of curation, discoverability, accessibility, standards and reuse). | Certification/ evaluation | coordination fora | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 13.2: New certification schemes should be developed and refined by the community where needed to assess and certify core components in the FAIR data ecosystem such as identifier services, standards and vocabularies. | Certification/ evaluation | coordination fora, research community, data service providers | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 13.3: Formal registries of certified components are needed. These must be maintained primarily by the certifying organisation but should also be communicated in community discovery registries such as Re3data and FAIRsharing. | Certification/ evaluation, Discovery/ indexing/ search | coordination fora, data service providers | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 13.4: Steps need to be taken to ensure that the organisations overseeing certification schemes are independent, trusted, sustainable and scalable. | Certification/ evaluation | coordination fora | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 14.1: Funding decisions for new and existing services to implement FAIR should be tied to evidence, community-approved metrics and certification schemes that validate service delivery. | Sustainability/ funding/ business-model | research funders | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 14.2: Investment in new tools, services and components of the FAIR data ecosystem must be made strategically in order to leverage existing investments and ensure services are sustainable. | Sustainability/ funding/ business-model | data service providers, institutions, research funders, coordination fora | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 14.3: Effective guidance and procedures need to be established and implemented for retiring services that are no longer required or cannot justifiably be sustained. | Sustainability/ funding/ business-model | coordination fora, data service providers, policy makers | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 15.1: Criteria for service acceptance and operation quality, including certification standards, need to be derived and applied with the aim to foster a systematic and systemic approach. | Certification/ evaluation | data service providers | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 15.2: Regular evaluation of the relevance and quality of all services needed to support FAIR should be performed. Adoption and acceptance by the research community is paramount; cost-benefit analyses should also be considered. | Certification/ evaluation, Sustainability/ funding/ business-model | data service providers, research community | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 15.3: Examples of different business models should be shared, and data services given time and support to trial approaches to test the most viable sustainability paths. | Sustainability/ funding/ business-model | data service providers | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 16.1: Policies must assert that the FAIR principles should be applied to research data, to metadata, to code, to DMPs and to other relevant digital objects, as well as to policies themselves. | Policy | policy makers | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 16.2: The FAIR data principles and this Action Plan must be tailored for specific contexts - in particular to the relevant research field - and the precise application nuanced, while respecting the objective of maximising data accessibility and reuse. | Policy | coordination fora | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 16.3: Guidelines for the implementation of FAIR in relation to research data, to metadata, to code, to DMPs and to other relevant digital objects should be developed and followed. | Policy | coordination fora, institutions, data stewards | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 16.4: Examples and case studies of implementation should be collated so that other communities, organisations and individuals can learn from good practice. | Capacity building/ incentivisation | coordination fora | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 17.1: The greatest potential reuse comes when data are both FAIR and Open. Steps should be taken to ensure coherence across data policy, emphasising both concepts and issuing collective statements of intent wherever possible. | Policy | policy makers | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 17.2: A funders' forum and other coordinating bodies at European and global level should do concrete work to align policies, reducing divergence, inconsistencies and contradictions. Requirements for DMPs and principles governing recognition and rewards should also be coordinated. | Policy | coordination fora, research funders | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 17.3: Policies should be versioned, indexed and semantically annotated in a policy registry to enable broad reuse within the FAIR data ecosystem. Resources mandated by policies (e.g. consent forms) should be treated the same way. | Policy | policy makers, data service providers | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 17.4: Data and other FAIR Digital Objects (e.g. code, models) that directly underpin, and provide evidence for, the findings articulated in published research must also be published unless there are legitimate reasons for protecting and restricting access. | Policy | policy makers, institutions, publishers | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 17.5: For data created by publicly funded research projects, initiatives and infrastructures, and where action 17.4 does not apply, the default should be to make the data available as soon as possible. However, policies may explicitly allow a reasonable embargo period to facilitate the right of first use of the data creators. Embargoes should be short (e.g. c. six months to two years) based on the prevailing culture in the given research community. | Policy | policy makers, institutions, publishers | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 17.6: Policies should require an explicit and justified statement when (publicly-funded) data cannot be Open and a proportionate and discriminating course of action should be followed to ensure maximum appropriate data accessibility, rather than allowing a wholesale opt-out from the mandate for Open data. | Policy | policy makers, institutions, publishers | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 17.7: Sustained work is needed to clarify in more detail the appropriate boundaries of Open and robust processes for secure data handling. Information on exceptions should be captured and fed into a body of knowledge that can inform future policy guidance and practice. | Policy | policy makers, coordination fora | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 17.8: Concrete and accessible guidance should be provided to researchers to find the optimal balance between sharing whilst also safeguarding privacy. There are many exemplars of good practice in providing managed access to sensitive data on which researchers can draw. | Capacity building/ incentivisation | data stewards | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 18.1: Questions about the costs of data management, curation and publication should be included in all DMP templates. Information from existing and completed projects should be used to retrospectively identify costs and develop examples and guidelines based on these. Funders, institutions and data services should collaborate on retrospective analysis, including the cost of long-term curation. | Sustainability/ funding/ business-model | research funders, institutions, data stewards | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 18.2: Research institutions and research projects need to take data management seriously and provide sufficient resources to implement the actions required in DMPs, while ensuring that financial resources are written into proposals as eligible costs. | Sustainability/ funding/ business-model | data stewards, research community, institutions | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 18.3: Guidelines should be provided for researchers and reviewers to raise awareness of eligible costs and reinforce the view that data management, long term curation and data publication should be included in project proposals. Funders should collaborate to enhance guidance. | Sustainability/ funding/ business-model | research funders, data stewards | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 18.4: Funders should trial different mechanisms for supporting the costs of FAIR data management and stewardship, such as having a separate dedicated budget in the grant scheme. Apportioning specific costs for FAIR data should help to encourage researchers to budget for these and not fear their proposals will be uncompetitive. | Sustainability/ funding/ business-model | research funders | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 19.1: Research communities should be encouraged and funded to make concerted efforts to develop and refine appraisal and selection criteria and to improve guidance and processes on what to keep and make FAIR and what not to keep. | Capacity building/ incentivisation | data stewards, research funders | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 19.2: The appraisal and selection of research outputs that are likely to have future research value and significance should reference current and past activities and emergent priorities. Established archival principles and the importance of unrepeatable observations of natural and human phenomena should be taken into account. | Policy | research community, data stewards, policy makers, institutions | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 19.3: When data are to be deleted as part of selection and prioritisation efforts, metadata about the data and about the deletion decision should be kept. If data deletion is carried out routinely, the underlying protocols for selection and prioritisation need to be made FAIR. | Policy | research community, data stewards, policy makers, institutions | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 2.1: The universal use of appropriate PIDs for FAIR Digital Objects needs to be facilitated and implemented. | Interlinking/ interoperability, Discovery/ indexing/ search, Access/ integration | data service providers | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 2.2a: Educational programmes are needed to raise awareness, understanding and use of relevant standards; | Capacity building/ incentivisation | institutions, research community, coordination fora | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 2.2b: tools are needed to facilitate the routine capture of metadata during the research process. | Metadata richness/ ingest/ submission | data service providers, data stewards, research community | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 2.3: Systems must be refined and implemented to make automatic checks on the existence and accessibility of PIDs, metadata, a licence or waiver, and code, and to test the validity of the links between them. | Quality control/ curation | data service providers, data stewards | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 20.1: Policy should require data deposit in certified repositories and specify support mechanisms (e.g. incentives, structural funding and/or funding for deposit fees, and training) to enable compliance. | Policy, Capacity building/ incentivisation | policy makers, institutions | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 20.2: Mechanisms need to be established to support research communities to determine the optimal data repositories and services for a given discipline or data type. | Capacity building/ incentivisation, Discovery/ indexing/ search | data service providers, coordination fora | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 20.3: Concrete steps need to be taken to ensure the development of domain repositories and data services for interdisciplinary research communities so the needs of all researchers are covered. | Discovery/ indexing/ search | coordination fora, data service providers | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 20.4: Outreach is required via scholarly societies, scientific unions and domain conferences so researchers in each field are aware of the relevant disciplinary repositories. | Capacity building/ incentivisation | publishers, research community, coordination fora | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 21.1: Researchers - including graduate students - should be required to demonstrate in research proposals and in DMPs that existing FAIR data resources have been consulted and used where appropriate, before proposing the creation of new data. | Sustainability/ funding/ business-model | research funders, institutions | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 21.2: Research funders and the academic reward system should ensure that research that reuses data and other outputs is valued as highly as research that creates new content. | Sustainability/ funding/ business-model, Capacity building/ incentivisation | research funders, institutions | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 21.3: Appropriate levels of funding should be dedicated to reusing existing FAIR outputs by initiating schemes that incentivise and stimulate reuse of data and code. | Sustainability/ funding/ business-model, Capacity building/ incentivisation | research funders | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 22.1: DMPs should be explicitly referenced in systems containing information about research projects and their outputs. Relevant standards and metadata profiles in such systems should consider adaptations to include DMPs as a specific project output entity (rather than inclusion in the general category of research products). This is to allow them to be more easily accessed and used as project outputs, including by machines. The same should apply to all types of FAIR Digital Objects. | Metadata richness/ ingest/ submission, Interlinking/ interoperability | data service providers, institutions | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 22.2: A DMP standard should be developed that is extensible (e.g. Dublin Core) by discipline (e.g. Darwin Core) or by the characteristics of the data (e.g. scale, sensitivity), or the data type (specific characteristics and requirements of the encoding). | Metadata richness/ ingest/ submission | standards bodies, coordination fora | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 22.3: Work is necessary to make DMPs machine-readable and actionable. This includes the development of concepts and tools to support the creation of useful and usable data management plans tied to actual research workflows. | Machine-actionability | data service providers | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 22.4: DMPs themselves should conform to FAIR principles and be Open where possible. | Discovery/ indexing/ search, Access/ integration | research community, data stewards | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 22.5: Information gathered from the process of implementing and evaluating DMPs relating to conformity, challenges and good practices should be used to improve practice. | Capacity building/ incentivisation | coordination fora, data stewards | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 23.1: The development of FAIR-compliant components needs to involve research communities, technical experts and other stakeholders. They should be provided with a forum for the exchange of views. | Capacity building/ incentivisation | data service providers, research community, coordination fora | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 23.2: Engagement of the necessary stakeholders and experts needs to be facilitated with appropriate funding, support, incentives and training. | Capacity building/ incentivisation | research funders, research community, coordination fora | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 23.3: FAIR components will need regular iteration cycles and evaluation processes to ensure that they are fit for purpose and meet community needs. | Certification/ evaluation | data service providers, research community, coordination fora | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 24.1: The metrics and criteria by which research infrastructures are assessed should reference the FAIR principles, incorporating language and concepts as appropriate, in order to align policy with implementation and to avoid confusion and dispersion of effort. | Certification/ evaluation | coordination fora, policy makers, standards bodies | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 24.2: The cost of providing FAIR services should be covered sustainably in the budgets for research infrastructures. | Sustainability/ funding/ business-model | data service providers, research funders, institutions | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 24.3: A set of case study examples of FAIR data provision should be developed and provided to research facilities. | Capacity building/ incentivisation | data service providers, coordination fora | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 24.4: Investment in new tools, services and components of the FAIR data ecosystem must be made strategically in order to leverage existing investments and ensure services are sustainable. | Sustainability/ funding/ business-model | data service providers, research funders, institutions | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 25.1: Convergence should be sought between the efforts by many groups to define FAIR assessments. | Certification/ evaluation | coordination fora | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 25.2: Funders should publish statistics on the outcome of all investments to report on levels of FAIR data and certified services. Specifically, funders should assess how FAIR the research objects are that have been produced and to what extent the funded infrastructures are certified and supportive of FAIR. | Sustainability/ funding/ business-model, Certification/ evaluation | research funders | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 25.3: Repositories should publish assessments of the FAIRness of data sets, where practical, based on community review and the judgement of data stewards. Methodologies for assessing FAIR data need to be piloted and developed into automated tools before they can be applied systematically and in a standardised way by repositories. | Certification/ evaluation | data service providers | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 25.4: Metrics for the assessment of research contributions, organisations and projects should take the past FAIRness of data sets and other related outputs into account. This can include citation metrics, but appropriate alternatives should also be found for the research, researchers and research outputs being assessed. | Certification/ evaluation | institutions | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 25.5: The results of monitoring processes should be used to inform and iterate data policy. | Policy, Certification/ evaluation | policy makers | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 26.1: The development of next generation metrics must take into account the full range of valuable research outputs and FAIR Digital Objects, including data, code and models. A variety of ways of assessing influence and impact should be incorporated. | Certification/ evaluation | coordination fora | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 26.2: Citation of data and other research outputs needs to be encouraged and supported - for example, by including sections in publishing templates that prompt researchers to reference materials, and providing citation guidelines when data, code or other outputs are accessed. | Interlinking/ interoperability | data service providers, publishers, data stewards | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 26.3: The Joint Data Citation Principles should be actively endorsed, adopted and implemented in the scholarly literature for attribution and in research assessment frameworks for recognition and career advancement. | Interlinking/ interoperability | data service providers, publishers, data stewards | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 26.4: A broader range of metrics must be developed to recognise contributions beyond publications and citation. These should recognise and reward Open and FAIR data practices. | Capacity building/ incentivisation | institutions, coordination fora | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 27.1: The Rules of Participation for EOSC must be consulted on widely, drawing in views from a broad range of stakeholder groups beyond the core European research infrastructures and e-infrastructures to include research communities, institutions, publishers, commercial service providers and international perspectives. | Policy | research community, data service providers, data stewards, standards bodies, coordination fora, policy makers, research funders, institutions, publishers | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 27.2: The resulting Rules must be fit-for-purpose to enable existing data services and capacities developed by different communities to be exploited for best return on investment. The Rules should be reviewed regularly to ensure they remain viable. | Policy | coordination fora | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 27.3: The EOSC governance board should ensure the FAIR criteria are addressed in the Rules of Participation so the services provided in EOSC form part of the global FAIR data ecosystem. | Policy | coordination fora | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 3.1: Registries need to be developed and implemented for all of the FAIR components and in such a way that they know of each other's existence and can interact. Work should begin by enhancing existing registries for policies, standards and repositories to make these comprehensive, and to initiate registries for Data Management Plans (DMPs) and identifiers. | Access/ integration, Discovery/ indexing/ search, Interlinking/ interoperability | data service providers | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 3.2: By default, the FAIR ecosystem as a whole and each of its individual components should work for humans and for machines. Policies and DMPs should be machine-readable and actionable. | Machine-actionability | data service providers, policy makers | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 3.3: The infrastructure components that are essential in specific contexts and fields, or for particular parts of research activity, should be clearly defined. | Policy | data service providers, research community, coordination fora | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 3.4: Testbeds need to be used to continually evaluate, evolve, and innovate the ecosystem. | Certification/ evaluation | data service providers, coordination fora | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 4.1: Enabling mechanisms must be funded and implemented to support research communities to develop and maintain their disciplinary interoperability frameworks. This work needs to be recognised and incentivised to reward stakeholders for enabling FAIR sharing. | Capacity building/ incentivisation, Sustainability/ funding/ business-model | research funders, data service providers, research community, institutions | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 4.2: Examples of FAIR use cases and success stories should be developed to convince reluctant research communities of the benefits in defining their disciplinary interoperability framework. | Capacity building/ incentivisation | coordination fora | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 4.3: Disciplines and interdisciplinary research programmes should be encouraged to engage with international collaboration mechanisms to develop interoperability frameworks. Common standards, intelligent crosswalks, brokering mechanisms and semantic technologies should all be explored to break down silos between communities and support interdisciplinary research. | Interlinking/ interoperability, Capacity building/ incentivisation | coordination fora | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 4.4: Mechanisms should be facilitated to promote the exchange of good practices and lessons learned in relation to the implementation of FAIR practices both within and across disciplines. Case studies for cross-disciplinary data sharing and reuse should also be collected, shared and used as a basis for the development of good practice. | Capacity building/ incentivisation | policy makers, coordination fora | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 4.5: The components of the FAIR ecosystem should adhere to common standards to support disciplinary frameworks and to promote interoperability and reuse of data across disciplines. | Certification/ evaluation | data service providers | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 5.1: Research communities must be required, supported and incentivised to consider data management and appropriate data sharing as a core part of all research activities. They should establish a Data Management Plan at project outset to consider the approach for creating, managing and sharing all research outputs (data, code, models, samples etc.) | Policy, Capacity building/ incentivisation | research funders, research community, institutions, policy makers, data service providers | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 5.2: Data Management Plans should be living documents that are implemented throughout the project. A lightweight data management and curation statement should be assessed at project proposal stage, including information on costs and the track record in FAIR. A sufficiently detailed DMP should be developed at project inception. Project end reports should include reporting against the DMP. | Policy, Quality control/ curation | research community, data stewards, policy makers | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 5.3: Data Management Plans should be tailored to disciplinary needs to ensure that they become a useful tool for projects. Research communities should be inspired and empowered to provide input to the disciplinary aspects of DMPs and thereby to agree model approaches, exemplars and rubrics that help to embed FAIR data practices in different settings. | Quality control/ curation | research community, data stewards | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 5.4: The harmonisation of DMP requirements across research funders, universities and other research organisations, as has been initiated by Science Europe and some RDA groups, should be further stimulated. | Quality control/ curation | research funders, institutions, coordination fora | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 6.1: Policy guidelines should recognise the diversity of research contributions (including publications, data sets, code, models, online resources, teaching materials) made during a researcher's career and explicitly include these in templates and schema for curricula vitarum, for researchers' applications and activity reports. | Policy, Capacity building/ incentivisation | policy makers, institutions | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 6.2: Credit should be given for all roles supporting FAIR data, including data analysis, annotation, management and curation, as well as for participation in the definition of interoperability frameworks, whether contributing to existing resources or developing new. | Policy, Capacity building/ incentivisation | policy makers, institutions, data stewards, research community | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 6.3: Evidence of past practice in support of FAIR data should be included in assessments of research contribution. Such evidence should be required in grant proposals (for both research and infrastructure investments), among hiring criteria, for career advancement and other areas where evaluation of research contribution has a legitimate role to play. This should include assessment of graduate students. | Policy | research funders, institutions, research community, policy makers | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 6.4: Contributions to the development and operation of certified and trusted infrastructures that support FAIR data should be recognised, rewarded and appropriately incentivised in a sustainable way. | Policy, Capacity building/ incentivisation | research funders, institutions | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 7:3: Field-specific approaches to expressing semantic relationships should be more closely aligned with web-scale technologies and standards. | Interlinking/ interoperability | coordination fora, data service providers, standards bodies | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 7.1: Programs need to be funded to make semantic interoperability more practical, including the further development of metadata specifications and standards, vocabularies and ontologies, along with appropriate validation infrastructure. | Sustainability/ funding/ business-model, Metadata richness/ ingest/ submission, Interlinking/ interoperability | research funders, standards bodies, coordination fora, data service providers | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 7.2: To achieve interoperability between repositories and registries, common protocols should be developed that are independent of the data organisation and structure of various services. | Interlinking/ interoperability | coordination fora, data service providers, standards bodies | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 8.1: Automated workflows between the various components of the FAIR data ecosystem should be developed by means of coordinated activities and testbeds. | Interlinking/ interoperability | data service providers, coordination fora | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 8.2: Metadata standards should be adopted and used consistently in order to enable machines to discover, assess and utilise data at scale. | Interlinking/ interoperability, Metadata richness/ ingest/ submission, Discovery/ indexing/ search, Access/ integration | data service providers, data stewards, coordination fora | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 8.3: Structured discoverability and profile matching mechanisms need to be developed and tested to broker requests and mediate metadata, rights, usage licences and costs. | Interlinking/ interoperability, Discovery/ indexing/ search | data service providers | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 9.1: A programme of activity is required to incentivise and assist existing domain repositories, institutional services and other valued community resources to achieve certification, in particular through CTS. | Certification/ evaluation, Sustainability/ funding/ business-model | research funders, coordination fora, standards bodies | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 9.2: A transition period is needed to allow existing repositories without certifications to go through the steps needed to achieve trustworthy digital repository status. Concerted support is necessary to assist existing repositories in achieving certification. Repositories may need to adapt their services to enable and facilitate machine processing and to expose their holdings via standardised protocols. | Certification/ evaluation | data service providers, coordination fora | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 9.3: As certification frameworks emerge for components of the FAIR data ecosystem other than repositories, similar support programmes should be put in place to incentivise accreditation and ensure data service providers can meet the required service standards. | Certification/ evaluation | coordination fora, standards bodies | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Action 9.4: Mechanisms need to be developed to ensure that the FAIR data ecosystem as a whole is fit for purpose, not just assessed on a per service basis. | Certification/ evaluation | coordination fora, data service providers, institutions | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Rec. 1: Define FAIR for implementation | Policy, Capacity building/ incentivisation | research funders, coordination fora, policy makers, institutions, standards bodies | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Rec. 10: Professionalise data science and data stewardship roles and train researchers | Capacity building/ incentivisation, Sustainability/ funding/ business-model | data stewards, institutions, research funders, coordination fora | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Rec. 11: Implement curriculum frameworks and training | Capacity building/ incentivisation | coordination fora, institutions, policy makers | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Rec. 12: Develop metrics for FAIR Digital Objects | Certification/ evaluation | coordination fora, research community | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Rec. 13: Develop metrics to certify FAIR services | Certification/ evaluation, Discovery/ indexing/ search | coordination fora, research community, data service providers | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Rec. 14: Provide strategic and coordinated funding | Sustainability/ funding/ business-model | data service providers, institutions, research funders, coordination fora, policy makers | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Rec. 15: Provide sustainable funding | Certification/ evaluation, Sustainability/ funding/ business-model | data service providers, research community | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Rec. 16: Apply FAIR broadly | Policy, Capacity building/ incentivisation | coordination fora, institutions, data stewards, policy makers | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Rec. 17: Align and harmonise FAIR and Open data policy | Policy, Capacity building/ incentivisation | coordination fora, research funders, policy makers, data service providers, institutions, publishers, data stewards | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Rec. 18: Cost data management | Sustainability/ funding/ business-model | research funders, institutions, data stewards, research community, data stewards | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Rec. 19: Select and prioritise FAIR Digital Objects | Capacity building/ incentivisation, Policy | research community, data stewards, policy makers, institutions, research funders | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Rec. 2: Implement a model for FAIR Digital Objects | Interlinking/ interoperability, Discovery/ indexing/ search, Access/ integration, Capacity building/ incentivisation, Metadata richness/ ingest/ submission, Quality control/ curation | data service providers, data stewards, research community, institutions, coordination fora | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Rec. 20: Deposit in Trusted Digital Repositories | Policy, Capacity building/ incentivisation, Discovery/ indexing/ search | publishers, research community, coordination fora, policy makers, institutions, data service providers | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Rec. 21: Encourage and incentivise reuse of FAIR outputs | Sustainability/ funding/ business-model, Capacity building/ incentivisation | research funders, institutions | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Rec. 22: Use information held in Data Management Plans | Metadata richness/ ingest/ submission, Interlinking/ interoperability, Machine-actionability, Discovery/ indexing/ search, Access/ integration, Capacity building/ incentivisation | data service providers, institutions, standards bodies, coordination fora, research community, data stewards | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Rec. 23: Develop FAIR components to meet research needs | Capacity building/ incentivisation, Certification/ evaluation | data service providers, research community, coordination fora, research funders | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Rec. 24: Incentivise research infrastructures and other services to support FAIR data | Sustainability/ funding/ business-model, Certification/ evaluation | coordination fora, policy makers, standards bodies, data service providers, research funders, institutions | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Rec. 25: Implement FAIR metrics to monitor uptake | Sustainability/ funding/ business-model, Certification/ evaluation, Policy | coordination fora, research funders, data service providers, institutions, policy makers | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Rec. 26: Support data citation and next generation metrics | Certification/ evaluation, Interlinking/ interoperability, Capacity building/ incentivisation | data service providers, publishers, data stewards, institutions, coordination fora | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Rec. 27: Open EOSC to all providers but ensure services are FAIR | Policy | research community, data service providers, data stewards, standards bodies, coordination fora, policy makers, research funders, institutions, publishers | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Rec. 3: Develop components of a FAIR ecosystem | Access/ integration, Discovery/ indexing/ search, Interlinking/ interoperability, Machine-actionability, Policy, Certification/ evaluation | data service providers, policy makers, research community, coordination fora | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Rec. 4: Develop interoperability frameworks for FAIR sharing within disciplines and for interdisciplinary research | Capacity building/ incentivisation, Sustainability/ funding/ business-model, Interlinking/ interoperability | research funders, data service providers, research community, institutions, policy makers, coordination fora | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Rec. 5: Ensure Data Management via DMPs | Policy, Capacity building/ incentivisation, Quality control/ curation | research funders, research community, institutions, policy makers, data service providers, data stewards, coordination fora | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Rec. 6: Recognise and reward FAIR data and data stewardship | Policy, Capacity building/ incentivisation | policy makers, institutions, data stewards, research community, research funders | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Rec. 7: Support semantic technologies | Sustainability/ funding/ business-model, Metadata richness/ ingest/ submission, Interlinking/ interoperability | research funders, standards bodies, coordination fora, data service providers | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Rec. 8: Facilitate automated processing | Interlinking/ interoperability, Metadata richness/ ingest/ submission, Discovery/ indexing/ search, Access/ integration | data service providers, data stewards, coordination fora | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Rec. 9: Develop assessment frameworks to certify FAIR services | Certification/ evaluation, Sustainability/ funding/ business-model | research funders, coordination fora, standards bodies, data service providers | Turning FAIR into reality: Final report and action plan from the European Commission expert group on FAIR data |
Implement best practice data publishing workflows and standards and make researchers aware of that | Capacity building/ incentivisation | data stewards | Workflows for Research Data Publishing: Models and Key Components |
