FAIR data: Findable, Accessible, Interoperable, Reusable

The ADS and the FAIR Data Principles

The ADS is an advocate for FAIR and the FAIR principles for data stewardship. As such the ADS recognise that while preservation and dissemination of data remain of core importance, stewardship should also include demonstratable quantitative and qualitative evidence for data reuse. The ADS is actively investigating how the datasets it curates can be fully compliant with the FAIR principles and is working within SSHOC, ARIADNEplus and E-RIHS to promote this.

As a result when you deposit your datasets with the ADS, you can be confident that your data becomes FAIR data.

What is FAIR Data?

FAIR data principles: Findable, Accessible, Interoperable, Reusable

(after Bezjak et al. 2018.)

How is ADS data FAIR data?

Each of the FAIR Principles and sub-principles is described below, along with the specific ways in which the ADS ensures compliance with all aspects of FAIR.

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Findable

F1. (Meta)data are assigned a globally unique and persistent identifier. More info

For a fuller discussion of the ADS metadata and the use of persistent identifiers see our Metadata Overview page.

F2. Data are described with rich metadata (defined by R1 below).More info

F3. Metadata clearly and explicitly include the identifier of the data they describe.More info

  • All persistent identifiers for ADS collections are clearly displayed, alongside data, within each archive interface.
  • The ADS supports the use of additional or supplemental identifiers relating to the dataset that link to external repositories, agencies or resources. This includes identifiers for physical, as well as digital, collections.

F4. (Meta)data are registered or indexed in a searchable resource.More info

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Accessible

A1. (Meta)data are retrievable by their identifier using a standardised communications protocol. More info

  • All ADS datasets utilise the HTTPS protocol to ensure free and open access to resources and to facilitate data retrieval.
  • In rare instances, where discrete data objects are too large to support easy exchange using HTTPS, the ADS makes data available 'on request' using free and open exchange services (e.g. University of York DropOff Service, etc.).

A1.1 The protocol is open, free, and universally implementable. More info

  • The ADS uses the HTTPS protocol for the sharing of resources and transfer of datasets. This is widely supported, open, and freely available.
  • The repository utilises open and free file-sharing services where files or datasets are too large for easy exchange using HTTPS. Typically the ADS utilises the open and free University of York DropOff Service to share data when this is necessary.

A1.2 The protocol allows for an authentication and authorisation procedure, where necessary. More info

  • The use of HTTPS provides authentication of the ADS website, and ensures the protection of the privacy and integrity of disseminated data. The repository ensures that all server-side digital certificates are current and up to date.

A2. Metadata are accessible, even when the data are no longer available. More info

  • Yes. As an accredited digital repository the ADS supports long-term preservation and access of its holdings, consequently all datasets and metadata are maintained in perpetuity.
  • The ADS maintains a clear Appraisal and Deaccession Policy which outlines current practice for datasets removed from the archives holdings. In such instances the ADS is committed to suporting identifiers (DOIs), maintaining resource discovery metadata, and updating current information on resources.
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Interoperable

I1. (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation. More info

I2. (Meta)data use vocabularies that follow FAIR principles. More info

For a wider discussion on the vocabularies used in ADS metadata see our Strategy and Standards page.

I3. (Meta)data include qualified references to other (meta)data. More info

  • The ADS supports the qualified referencing with and between publications, datasets and resources. Where available the repository uses sustainable referencing, e.g. DOIs.
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Reusable

R1. Meta(data) are richly described with a plurality of accurate and relevant attributes. More info

R1.1. (Meta)data are released with a clear and accessible data usage license. More info

R1.2. (Meta)data are associated with detailed provenance. More info

  • The ADS provides detailed provenance metadata for all data. At a collection level this is clearly expressed in the archive interface and discovery metadata, but also at a file level within the technical metadata disseminated alongside the data.

R1.3. (Meta)data meet domain-relevant community standards. More info

  • Yes, the ADS utilises a qualified Dublin Core metadata standard for all collection level metadata (noted above). The repository also uses standardised templates to ensure metadata consistency. All data must be accompanied by appropriate, file specific 'technical' metadata, this is derived from recognised community standards (Guides to Good Practice) to ensure consistency. All (meta)data is accepted, preserved and disseminated in sustainable, open formats. These are expressed in the 'Guidelines for Depositors' and the ADS' Data Procedures. The repository employs appropriate vocabularies to qualitatively describe datasets (noted above) and document preservation actions.

What is FAIR Data?

The FAIR Principles provide an important framework to evaluate and publish data in order to facilitate discovery, provide sustaible access to resources, and encourage and enable better sharing and reuse of data. To achieve these goals the core principles emphasise:

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Findablility:



improving the discoverablility of data through the use of appropriate documentation and metadata, and supporting the use of sustainable referencing of resources.

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Accessiblility:



ensuring the sustainable availablility of digital assets.

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Interoperablility:



providing both syntactically parseable and semantically understandable datasets and metadata, and facilitating data exchange and reuse between researchers, organisations, institutions across national and international boundaries.

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Reusablility:



sufficiently documenting and sharing data using the least restrictive licences possible, thereby facilitating data reuse and supporting the integration of other data sources.

In an environment where humans increasingly rely on computational systems and processes to find, access, interoperate, and reuse data these principles emphasise machine-actionability with limited, or minimal, human intervention.

Find out more about the FAIR data principles via the Force11 community, GoFair, or OpenAire.

Resources

Collins, S., Genova, F., Harrower, N., Hodson, S., Jones, S., Laaksonen, L. et al. (2018). Turning FAIR into reality. Final Report and Action Plan from the European Commission Expert Group on FAIR Data.

FAIRsFAIR Fostering Fair Data Practices in Europe - aims to supply practical solutions for the use of the FAIR data principles throughout the research data life cycle. Emphasis is on fostering FAIR data culture and the uptake of good practices in making data FAIR.

Force11 aims to improve research practices by supporting innovations in the ways knowledge is created and shared across research disciplines, communities, sectors and timeframes. This includes a group working on the FAIR principles.

GO FAIR Initiative is a bottom-up, stakeholder-driven and self-governed initiative that aims to implement the FAIR data principles. It offers an open and inclusive ecosystem for individuals, institutions and organisations working together

OpenAIRE aims to shift scholarly communication towards openness and transparency and facilitate innovative ways to communicate and monitor research. An OpenAIRE Task Force on Research Data Management is active in creating materials for supporting FAIR.

References

Baca, M. (ed.) (2016). Introduction to Metadata. Getty Research Institute: Los Angeles. https://www.getty.edu/publications/intrometadata/, acccessed 05/11/2020.

Bezjak, S., Clyburne-Sherin, A., Conzett, P., Fernandes, P., Görögh, E., Helbig, K et al. (2018). Open Science Training Handbook (Version 1.0). Zenodo. http://doi.org/10.5281/zenodo.1212496, accessed 05/11/2020.