Lesson plan 1: FAIR in a nutshell

FAIR elements:

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.

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, they need to know how can they be accessed, possibly including authentication and authorisation.

A1. (Meta)data are retrievable by their identifier using a standardised 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.

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

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

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

Reusable

The ultimate goal of FAIR is to optimise the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings.

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

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

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

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

Primary audience(s): Bachelor's, master's, PhD degree students

Learning outcomes:

  • Can paraphrase the FAIR principles

  • Can explain why the FAIR principles were developed

  • Can recognise the relationship between FAIR, RDM and Open

  • Can plan for FAIR research outputs

  • Can write and develop a research data management plan

  • Can apply the principles to their own work

  • Can evaluate the FAIRness of their own work or the work of others

Summary of tasks/actions:

  1. Introduction to the FAIR principles

    1. What is FORCE11 and where did the need to define the FAIR principles come from?

    2. What do the FAIR principles stand for [Wilkinson et al. 2016]?

      1. Findable

      2. Accessible

      3. Interoperable

      4. Reusable

  2. Explain the difference and overlap between FAIR, Open Data and research data management

    1. Define Open Data

    2. Define research data management

    3. Show the relationship between FAIR, Open Data and RDM [Higman et al. 2019]

      1. Intersections between the terms

      2. Distinctions between the terms

  3. How to make data FAIR? [The Top 10 FAIR data and software things; Knight 2015; PARTHENOS 2019]

    1. F is for making data findable

      • Look for existing data in repositories (see [lesson plan 10] (10LessonPlan.md))

      • Upload to and share your data via a repository (see lesson plan 11)

      • Describe your data with as much detail as possible (see lesson plan 6)

      • Apply a persistent identifier(see lesson plan 8)

    2. A is for making data accessible

      • Consider what can and will be shared under which conditions (see lesson plan 13).

      • Obtain participant consent and perform risk management(see lesson plan 12)

    3. I is for making data interoperable

      • Use open, standardised and common formats(see lesson plan 5

      • Consistent vocabulary

      • Apply common metadata standards(see lesson plan 6

      • Linked data

    4. R is for making data reusable

      • Consider permitted use

      • Apply appropriate license(see lesson plan 9)

      • Add sufficient documentation and provenance information (see lesson plan 3)

      • When using data of others, give credit by data citation (see lesson plan 10)

Materials/Equipment

  • Computer/laptop

  • Internet/browser

References

DeiC. Myths about FAIR. (Part of FAIR for Beginners). https://www.deic.dk/en/data-management/instructions-and-guides/FAIR-for-Beginners

Higman, Rosie, et al. "Three Camps, One Destination: The Intersections of Research Data Management, FAIR and Open." Insights the UKSG Journal, vol. 32, May 2019, p. 18, doi: https://doi.org/10/gf4jhr

Jones, Sarah & Grootveld, Marjan. (2017, November). How FAIR are your data? Zenodo. http://doi.org/10.5281/zenodo.3405141

Knight, Gareth. Preparing Data for Sharing: The FAIR Principles. Presentation, 1 December 2015. Available at: https://www.slideshare.net/lshtm/preparing-data-for-sharing-the-fair-principles

Library Carpentry. The Top 10 FAIR Data and Software things. https://librarycarpentry.org/Top-10-FAIR/, also: https://doi.org/10/gkbnxv)

PARTHENOS. PARTHENOS Guidelines to FAIRify Data Management and make data reusable. 2019. https://doi.org/10.5281/zenodo.3368858

Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., . . . Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, 9. Doi: https://doi.org/10.1038/sdata.2016.18

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