> For the complete documentation index, see [llms.txt](https://fairsharing.gitbook.io/fairsharing/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://fairsharing.gitbook.io/fairsharing/fair-assistance/database-level-fairness.md).

# Database-level FAIRness

This section covers the way in which curated FAIRsharing content can be used to assess the FAIRness of databases within our registry.

The pages within this section describe how each FAIR principle is implemented in the context of evaluation of database-level FAIRness. It helps databases managers understand how to better describe their resource to showcase that FAIRness, and helps the research community gain a greater understanding of those databases.

## Introduction

FAIRsharing stores a wide range of human- and machine-accessible metadata describing the key characteristics of standards, databases and policies. For databases, these metadata describe aspects such as identifier practices, governance, stewardship, preservation policies, standards implementation, and interoperability features.

To help the research community understand and evaluate these characteristics, FAIRsharing provides two FAIR benchmarks: **FAIR ARK**, a general benchmark applicable to any database registered in FAIRsharing, and **FAIR LS-ARK**, a specialised benchmark tailored to life science resources. These ARK-based benchmarks evaluate the characteristics of databases represented within FAIRsharing. Individual benchmarks may specialise these definitions for particular domains or communities while maintaining a common conceptual framework.

These benchmarks function at the level of the database itself rather than at the level of individual datasets, records, or other digital objects contained within it. They operationalise FAIR assessment using curated FAIRsharing metadata describing repository characteristics and linked governance artefacts such as standards, identifier schemas, organisations, publications, policies and certifications. Together, these provide structured evidence that can be used to support transparent and reproducible FAIR assessment.

Database-level FAIRness assessment is intended to provide guidance for database managers, helping them to provide more complete descriptions of how they align with FAIR. Certification frameworks are broader than FAIR and provide a more formal methodology. However, in developing these benchmarks, we have created mappings to community best practices, trust frameworks, and certification schemes (see the Certification Crosswalk below). These mappings support transparency in our benchmarks and comparison with existing repository governance and trust frameworks.

## Glossary

This glossary helps you understand the **Database** **FAIR-enabling characteristics** table for each page in this section.

<table><thead><tr><th width="105.40234375"></th><th></th></tr></thead><tbody><tr><td>Symbol</td><td>Meaning</td></tr><tr><td>🏛</td><td><strong>Database governance</strong> (Information about the database)– Characteristics that describe the database itself, including its stewardship, governance, sustainability, funding, responsibility, policies, provenance, and trustworthiness.</td></tr><tr><td>🧩</td><td><strong>Database FAIR-support capability</strong> (Information about what the database enables its users to do) – Characteristics that describe how the database enables FAIR practices for the records it contains. These capabilities may include support for standards, identifiers, provenance, documentation, training, discoverability, interoperability, or other FAIR-enabling features.</td></tr></tbody></table>

## Certification Crosswalk

Each metric may align with one or more external repository certification, trust, or FAIR-enablement frameworks, including:  &#x20;

* FAIRest: Mathieu d'Aquin, Fabian Kirstein, Daniela Oliveira, Sonja Schimmler, Sebastian Urbanek; FAIREST: A Framework for Assessing Research Repositories. Data Intelligence 2023; 5 (1): 202–241. doi: <https://doi.org/10.1162/dint_a_00159>&#x20;
* Core Trust Seal (CTS): <https://doi.org/10.5281/zenodo.17660462>&#x20;
* Nestor: <https://d-nb.info/135630141X/34>&#x20;
* Lin, D., Crabtree, J., Dillo, I. *et al.* The TRUST Principles for digital repositories. *Sci Data* 7, 144 (2020). <https://doi.org/10.1038/s41597-020-0486-7>. \
  *Note that more information on a TRUST benchmark for FAIRsharing records is available* [*here*](/fairsharing/fair-assistance/trust-evaluation.md)*.*&#x20;
* TTRAM: L'Hours, H., Kleemola, M., Parkes, O., Recker, J., Duvaud, S., van Horik, R., Alaterä, T. J., Liberante, F., Conzett, P., Kaartinen, H., Bäckman, S., & Esteves, E. (2025). FIDELIS TTRAMatrix v01.00 Template (v01.00). Zenodo. <https://doi.org/10.5281/zenodo.17144259>&#x20;
* USRN: USRN Desirable Characteristics of Digital Publication Repositories: <https://sparcopen.org/wp-content/uploads/2022/10/Desirable-Characteristics-of-Digital-Publication-Repositories-APPROVED-20230331.pdf>&#x20;

These alignments support interoperability and comparability across repository governance frameworks.&#x20;

Alignment symbols: - = no alignment with any metrics for the principle; ✔ = alignment with at least one metric for the principle; ✔✔ = alignment with over half of the metrics for the principle.

<table><thead><tr><th width="106.37109375">FAIR Principle</th><th>FAIRest</th><th width="128">Core Trust Seal</th><th>Nestor</th><th>TRUST</th><th>TTRAM</th><th>USRN</th></tr></thead><tbody><tr><td>F1</td><td>✔✔</td><td>✔✔</td><td>✔✔</td><td>-</td><td>✔ </td><td>✔✔</td></tr><tr><td>F2</td><td>-</td><td>✔✔</td><td>✔✔</td><td>✔✔</td><td>✔✔</td><td>-</td></tr><tr><td>F3</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>F4</td><td>✔✔</td><td>✔✔</td><td>✔</td><td>✔</td><td>-</td><td>✔✔</td></tr><tr><td>A1</td><td>✔✔</td><td>-</td><td>✔</td><td>-</td><td>✔</td><td>✔</td></tr><tr><td>A2</td><td>✔</td><td>✔✔</td><td>✔✔</td><td>✔✔</td><td>✔✔</td><td>✔✔</td></tr><tr><td>I1</td><td>✔</td><td>-</td><td>✔✔</td><td>-</td><td>-</td><td>✔✔</td></tr><tr><td>I2</td><td>-</td><td>-</td><td>-</td><td>-</td><td>✔✔</td><td>-</td></tr><tr><td>I3</td><td>✔✔</td><td>✔</td><td>✔✔</td><td>✔</td><td>✔✔</td><td>✔</td></tr><tr><td>R1.1</td><td>✔✔</td><td>✔✔</td><td>✔✔</td><td>✔✔</td><td>✔✔</td><td>✔✔</td></tr><tr><td>R1.2</td><td>- </td><td>✔ </td><td>✔✔</td><td>✔</td><td>✔✔</td><td>- </td></tr><tr><td>R1.3</td><td>✔</td><td>✔</td><td>✔</td><td>✔</td><td>✔✔</td><td>-</td></tr></tbody></table>

FAIR Assistance for Repositories and Knowledgebases Benchmark\
(FAIR ARK)
----------

This benchmark collects all of the metrics that are used to produce an evaluation of the FAIR-enabling qualities of databases registered in FAIRsharing. Find out more at <https://fairsharing.org/7162>.

## FAIR Assessment of Life Science Repositories and Knowledgebases Benchmark (FAIR LS-ARK)

This benchmark collects all of the metrics that are used to produce an evaluation of the FAIR-enabling qualities of life science databases registered in FAIRsharing. Find out more at <https://fairsharing.org/7456>.