FAIR Principles and Open Data

OpenEPI endorses the FAIR Data Principles as a framework to promote the broadest possible reuse of collected data.

In addition to the direct requirements described in this document, all entities aspiring to be OpenEPI compliant are encouraged to follow the FAIR data principles. They are guidelines to improve the Findability, Accessibility, Interoperability and Reuse of digital assets.

The FAIR data principles emphasize machine-actionability as we increasingly rely on computational support to distribute, handle and manage data, due to rapid increases in generated volumes and complexity.

The FAIR data principles:

• Support knowledge discovery and innovation
• Support data and knowledge integration
• Promote sharing and reuse of data
• Are discipline-independent and allow for differences in discipline
• Help data and metadata to be ‘machine readable’, supporting new discoveries through the harvest and analysis of multiple datasets.

The principles stress that data must be retrievable without specialized or proprietary tools or communication methods, and that data should be released with a clear and accessible usage license. Individuals and organizations that put FAIR data principles into practice may do so under a variety of data usage licenses. In other words, FAIR does not necessarily imply Open; data can be FAIR and shared under restrictions. OpenEPI requires data to be dedicated to the public domain using the Creative Commons Zero (CC0) Public Domain Dedication, or licensed under the Creative Commons Attribution 4.0 International license (CC BY 4.0), or an equivalent license, and for all partners to follow the FAIR principles.

Findable

The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of datasets and services, so this is an essential component of the FAIRification process .

Using such a repository and identifier ensures that your dataset continues to be available to both humans and machines in a usable form in the future. To aid discoverability, data should also be followed by metadata appropriate for both finding (for instance by search engines) and in more detail describing the data.

The content and format of metadata is often guided by the specific discipline and/or repository, through the use of a standardized metadata scheme.

When depositing data in a repository, it is important that you fill in as many fields as possible as this information usually contributes to the metadata record(s).

In some cases, especially when using a discipline-or domain-specific repository, the submission of specific metadata files alongside the data may be required.

Actions

• F1. (Meta)data are assigned a globally unique and persistent identifier
• F2. Data are described with rich metadata (defined by R1 below)
• F3. Metadata clearly and explicitly include the identifier of the data they describe
• F4. (Meta)data are registered or indexed in a searchable resource

Accessible

Once the user finds the required data, she/he/they need to know how they can be accessed, possibly including authentication and authorisation. Data supporting partner research aspiring to be OpenEPI compliant should be openly published under the CC0 public domain dedication or the CC BY 4.0 license - both of which facilitate legal data reuse.

In these cases, OpenEPI has policies in place to allow the publication of articles associated with such data, while still maintaining the appropriate level of security. For guidance, please see the Availability statements section above.

Actions

• A1. (Meta)data are retrievable by their identifier using a standardized communications protocol
  • A1.1 The protocol is open, free, and universally implementable
  • A1.2 The protocol allows for an authentication and authorisation procedure, where necessary
• A2. Metadata are accessible, even when the data are no longer available

Interoperable

The data usually need to be integrated with other data. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing. Interoperable data can be compared and combined with data from different sources by both humans and machines – promoting integrative analyses.

To bolster interoperability, data should be stored in a non-proprietary open file format and described using a standard vocabulary (where available). In some cases, the preferred file formats and vocabularies will be dictated by the repository you choose to host your data.

Actions

• I1. (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
• I2. (Meta)data use vocabularies that follow the FAIR principles
• I3. (Meta)data include qualified references to other (meta)data

What resources are available to help make data FAIR?

This list of resources can provide best practices and guidance to support providers aiming to make data FAIR:

• GO FAIR: FAIR Principles
• F1000 Getting Started Guide – Simple steps and best practices to follow to make data FAIR and Open when publishing a research article.
• How to Make Your Research Data FAIR – Explanation of FAIR principles and translated into practical information for researchers.
• Output Management Plan Template – Guidelines on FAIR Data Management and OMP template example
• Metadata Standards Directory – Online catalog that can be searched for discipline-specific standards and associated tools.
• FAIRSharing.org – A curated and searchable portal of data standards, databases, and policies across many scientific disciplines.
• Fairsfair.eu - European Open Science Cloud support on FAIR.