FAIR-Aware Tool FAQ

This topic has been created to collate and display Frequently asked questions regarding the FAIR-Aware tool.

If you have a question or query regarding the FAIR-Aware tool, please reply to this topic to share your question. This topic is monitored by the FAIRsFAIR team and we will endeavour to answer your question or provide feedback as soon as possible. The list of FAQ’s below is also periodically updated to reflect any new questions or discussions taking place within this Topic and the wider FAIR-Aware category.

For more information about the FAIR-Aware tool, please see the About the FAIR-Aware tool topic or visit the FAIR Aware website: https://fairaware.dans.knaw.nl/

Frequently Asked Questions (FAQ)

  • Q: Who created FAIR-Aware?
    A: FAIR-Aware was created by a team of partner organisations within the FAIRsFAIR project. DANS, DCC, and UniHB worked together to initially develop the tool. After the end of the FAIRsFAIR project (February 2022), DANS will continue to maintain FAIR-Aware.

  • Q: Why was FAIR-Aware created?
    A: Task 4.5 of the FAIRsFAIR project was dedicated to creating tools to assess the level of FAIRness of data(sets). However, the user feedback had shown a high demand for an educational tool on the FAIR principles. That’s why it was decided to focus one of the tools on raising awareness and educating users about the FAIR principles in practice.

  • Q: How does FAIR-Aware work?
    A: FAIR-Aware is a self-assessment. The user answers 10 simple questions about different FAIR practices to test their awareness and willingness to comply with those practices. Each question is enriched with extensive guidance texts, which can be consulted to learn more about the FAIR practice and the practical ways in which the user can apply this to their daily practice.

  • Q: Who can use FAIR-Aware?
    A: FAIR-Aware can be used by many different stakeholders. Anyone can use the tool to assess their knowledge and learn more about FAIR (skills). Researchers and research supporters can use the tool to apply the FAIR practices to their data(set). Policy makers, funders, and repository managers can also use FAIR-Aware to see what recommendations are made and what their responsibilities in the FAIRification process can be. With the addition of the new trainer functionality, FAIR-Aware is also excellently suited for trainers, or researchers investigating topics related to FAIR (awareness).

  • Q: What can I use FAIR-Aware for?
    A: FAIR-Aware can be used to assess your own knowledge on the FAIR Data Principles. You can do this at any time during your research, but we recommend using the tool either before you deposit your data(set) in a data repository, or when you are planning your research (e.g., when you are drafting your Data Management Plan). The tool will help you become aware of certain FAIR practices and will give you practical guidance on how to implement them.
    You can also use this tool as a conversation starter, by encouraging others (colleagues, students, data support staff, etc.) to do the assessment as well. Discussing your results could help identify shared difficulties and possible solutions.

  • Q: For which scientific domains is FAIR-Aware relevant?
    A: FAIR-Aware is discipline-agnostic, which means it is meant to be of use for anyone, regardless of their scientific field or domain. All questions and guidance texts are crafted to be relevant for any user.

  • Q: Can I save my results from the FAIR-Aware assessment?
    A: Yes. After you’ve submitted your answers, you will be brought to the results page. You can print this page to save your scores and answers.

  • Q: Does FAIR-Aware process my personal data?
    A: No. FAIR-Aware does not process any personal data. We recommend you do not provide any personal data in the feedback forms either.

  • Q: Are there any other versions or translations of the FAIR Aware tool available?
    A: Yes, the FAIR Aware tool is also available in French: Outil FAIR-Aware – DoRANum

  • Q: Where can I find more information about FAIR-Aware?
    A: FAIRsFAIR deliverable D4.5 “Report on FAIR Data Assessment Toolset and Badging Scheme” provides more in depth information about FAIR-Aware, the FAIR metrics it is based on, and the other FAIR assessment tool created in the FAIRsFAIR project: F-UJI. This report contains information about the technical implementation of the tool and how it has been developed throughout the project.

  • Q:What is the list of domains in the ‘About You’ section based on
    A: This list is based on the Re3data classification used in their metadata schema v3.0, which is based in turn on the German DFG classification

Why is there not just one metadata format for all kinds of research / data?

Thank you for your question Tracy!

Many domains and disciplines are interested in specific metadata elements that are not as relevant to other domains and disciplines. This is why certain communities have developed their own metadata standards. However, some general purpose standards that can be used regardless of domain or discipline also exist, such as the Dublin Core Metadata Initiative (DCMI).

Metadata modeling and formatting are separate concerns. It is reasonable that different scientific domains and studies within domains may have widely varying modeling concerns. Controlled vocabulary terms, validity constraints, and other metadata elements will surely vary and evolve over time.

What’s not as obvious is why different scientific domains and studies within domains would have different formatting concerns. Different software applications and tools may have their preferred metadata formats for operational convenience. Thus, as some software gains prominence in a specific domain, its preferred format may be adopted by other tools in the ecosystem for ease of exchange and integration.

For there to be a single metadata format that is universally adopted for metadata exchange — that is, a format that a given software tool may convert to a preferred internal format for convenience of use by the tool — that format would need to be able to communicate the model being used as well. Thus, the format would need to host a language for defining models.

There have been some efforts at this. One effort that has gained some recognition in the FAIR data community is that of the Semantic Web set of standards. Specifically, the Resource Description Framework (RDF) base model, exchanged using a handful of standardized plain-text formats such as JSON-LD, and using RDF-expressed modeling languages such as RDFS (RDF Schema), OWL (Web Ontology Language), and SHACL (Shapes Constraint Language), is one effort towards a universal “meta-model” for defining and exchanging metadata models along with the metadata itself, in plain-text formats that both humans and machines can interpret unambiguously, if only to convert metadata to preferred internal modeling languages and formats.