Page Comparison

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• Data stewards: can help filling out the surveys and questionnaires

• Research Software Engineer: can help running some of the specialised software

• ELSI experts: can help filling out the ELSI related questions surveys and questionnaires

How to 

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There are many tools which can help you to gain insight into the FAIRness of your (meta)data before you commence the FAIRification process. Broadly, they fall into two categories: online self-assessment surveys and (semi) automated tests. The first category presents the user with an online form, which the users fills in manually, whereas the second category performs (semi) automated tests on a dataset by providing the tool with, for example, a link to the an already published dataset. In both cases, the result gives an indication about the FAIRness of the (meta)data. Additionally, tools may give advice how to improve FAIRness. It is important to bear in mind, that outcomes of tools may vary due to differences in tests performed and subjectivity of the self-assessments surveys.

Based on FAIRassist, a website with a manually created collection of various tools, and the publication FAIR assessment tools: evaluating use and performance, several of the more popular tools can be found below.

Online self-assessment surveys

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surveys

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Tool	DescriptionQuick user guide	Work on your side
ARDC FAIR self assessment	Provided by Australian Research Data Commons, this 12-question online survey provides a visual indication on the FAIRness level of your (meta)data and provides resources on how to improve it.	Fill in the 12 questions in the survey, potentially with help of a FAIR expert/data steward.
SATIFYD	Provided by DANS, this online survey gives a FAIRness score. Furthermore, it provides advice on how to improve the FAIRness of your (meta)data.	Fill in the 12 questions in the survey, potentially with assistance of a FAIR expert/data steward.
The FAIR Data Maturity ModelThe FAIR Data	Maturity Model aims to harmonise outcomes of FAIR assessment tools to make these comparable. Based on the FAIR principles and sub-principles, they have the Research Data Alliance created a list of universal 'maturity indicators'. Their work resulted in a checklist (with extensive description of all maturity indicators), which can be used to assess the FAIRness of your (meta)dataThese indicators are designed for re-use in evaluation approaches and are accompanied by guidelines for their use. The guidelines are intended to assist evaluators to implement the indicators in the evaluation approach or tool they manage. Their work resulted in a checklist (with extensive description of all maturity indicators), which can be used to harmonise outcomes as well as directly assess the FAIRness of your (meta)data. (??????????????)	Download the excel file from Zenodo and in the ‘FAIR Indicators_v0.05’ tab, give a score to the 41 different ‘maturity indicators’, by selecting the level from the drop-down menu in the ‘METRIC’- column, that fits the status if your (meta)data best. Potentially perform this with assistance of a FAIR expert/data steward. View the results in the ‘LEVELS' tab. Detailed definitions and examples for all 'maturity indicators’ can be found in the documentation on zenodo.	FAIRplus Dataset Maturity Model	The FAIRplus dataset maturity indicators were created based on previous work by the Research Data Alliance (RDA, see FAIR Data Maturity Model above) and the FAIRsFAIR projects. This model evaluates FAIRness of data in three categories (Content related, Representation and format, Hosting environment capabilities) and five levels of maturity per category (ranging from Single Use Data to Managed Data Assets). For each category, indicators have been defined to describe the requirements to reach a certain level of maturity in that category.
The spreadsheet used to assess the maturity of your dataset can be found on GitHub. In the 'FAIR-DSM Assessment Sheet v1.2' tab, a pre- and post-FAIR assessment can be performed, potentially with assistance from a FAIR expert/data steward. [Hannah: unfortunately there is not really a user guide, so one has to guess how to fill this in]FIP Mini Questionnaire & FIP Datastewardship Wizard	A FAIR Implementation Profile (FIP) is a collection of FAIR implementation choices made for all FAIR Principles by a community (for example a research project or an institute). It was developed by the GO FAIR Foundation.   Once published, a FIP can be reused by others, thus acting as a recipe for making data FAIR by a community based on agreements and standards within that community. Therefore, a FIP aids in achieving FAIR principle R1.3, which states that “(Meta)data meet domain-relevant community standards."  Q: Where can we find the community FIPs?? Q: How can we get a useful score / outcome that will help us? Advice?	Fill in the 10 questions in the Mini Questionnaire or create an account on the Datastewardship Wizard for a more user-friendly expierence.

Online (Semi-) automated tests

For even more surveys and (semi-) automated tools, see FAIRassist.

The FAIR Data Maturity Model

FAIR assessment tools vary greatly in their outcomes. The FAIR Data Maturity Model (created by the Research Data Alliance, or RDA) aims to harmonise outcomes of FAIR assessment tools to make these comparable. Based on the FAIR principles and sub-principles, they have created a list of universal 'maturity indicators'. Their work resulted in a checklist (with extensive description of al maturity indicators), which can be used to assess the FAIRness of your (meta)data.

The FAIRplus dataset maturity indicators were created based on previous work around the FAIR indicators, done by the Research Data Alliance (RDA) and the FAIRsFAIR projects:

• FAIR Data Maturity Model Working Group. (2020). FAIR Data Maturity Model. Specification and Guidelines (1.0). https://doi.org/10.15497/rda00050

• Devaraju, Anusuriya, Huber, Robert, Mokrane, Mustapha, Herterich, Patricia, Cepinskas, Linas, de Vries, Jerry, L’Hours, Herve, Davidson, Joy, & Angus White. (2020). FAIRsFAIR Data Object Assessment Metrics (0.4). Zenodo. https://doi.org/10.5281/zenodo.4081213

In the definitions of the FAIRplus-DSM indicators, you will find a link to the corresponding RDA or FAIRsFAIR indicator when they are related.

Hannah mentions the Data Maturity Model. This is also here on FAIRplus. There is also this Github from FAIRplus and the sheet for the actual assessment is here. Could be worrying: last update was last year.

[Hannah: I cannot really find a clear description on how to use it, only a huge excel file (for which you have to dig quite deeply into the GitHub, maybe we can link to it here if we include it https://github.com/FAIRplus/Data-Maturity/tree/master/docs/assessment ?]

Related: in the FAIRtoolkit they describe Data Capability Maturity Model:

• Most recently, CMM has been adapted by the FAIRplus IMI consortium [7] to improve an organisation’s life science data management process, which is the basis for the method described here.

• The FAIR data CMM method identifies 1) important organisational aspects of FAIR data transformation and management, 2) a sequence of levels that form a desired path from an initial state to maturity and 3) a set of maturity indicators for measuring the maturation levels.

  • e.g. Findability Maturity Indicators. Also describes some team requirements.

[Hannah; I think this is also more about assessing FAIR in an organization?]

[Sander: so far I have a feeling:

• The FAIRplus-DSM indicators are not used anywhere except by the FAIRcookbook (which I think is FAIRplus)

• The FAIR Data Maturity Model is (was?) at least used somewhat. Google e.g. "RDA-R1.2-01M"

• Wilkinson’s gen2 is from 2019, which is from before the Maturity Model stuff. But apparently he believes it’s fine the way it is

]

FAIR maturity evaluation system

FAIR Implementation Profiles (FIPs)

Potentially: compare the community FIP with your own fingerprint. This gives an indication on whether you meet R1.3?

‘The FAIR Principle R1.3 states that “(Meta)data meet domain-relevant Community standards”. This is the only explicit reference in the FAIR Principles to the role played by domain-specific communities in FAIR. It is interesting to note that an advanced, online, automated, FAIR maturity evaluation system [22] did not attempt to implement a maturity indicator for FAIR Principle R1.3. It was not obvious during the development of the evaluator system how to test for “domain-relevant Community standards” as there exists, in general, no venue where communities publicly and in machine-readable formats declare data and metadata standards, and other FAIR practices. We propose the existence of a valid, machine-actionable FIP be adopted as a maturity indicator for FAIR Principle R1.3.’

[Hannah: This might not be applicable to all FAIRification processes, but I think it might be of added value in some cases to assess the current FAIRness also in relation to community standards, so I think it might be nice to include this as well. However, I find it hard to find any information about community standards..]

FIP Mini Questionnaire from GO-FAIR: https://www.go-fair.org/how-to-go-fair/fair-implementation-profile/fip-mini-questionnaire/

[Mijke: RDMkit has a page on this → https://rdmkit.elixir-europe.org/compliance_monitoring#how-can-you-measure-and-document-data-management-capabilities ] 

More Checklists and tools:

• A Checklist produced for use at the EUDAT summer school to discuss how FAIR the participant's research data were and what measures could be taken to improve FAIRness:

  • https://zenodo.org/records/1065991#.Xs_XpC2cbOQ%C2%A0 [Hannah; this is also an offline checklist; not sure if we should recommend to consider. I also think it is rather limited compared to the rest of the tools/checklists]

FAIR Guidance [https://www.ejprarediseases.org/fair_guidance/] 

FAIR Assessment Tools 

There is growing interest in the degree to which digital resources adhere to the goals of FAIR – that is, to be Findable, Accessible, Interoperable, and Retrievable by both humans and, more importantly, by machines acting on behalf of their human operator. Unfortunately, the path to FAIRness was left undefined by the original FAIR Principles paper, which chose to remain agnostic about which technologies or approaches were appropriate. As such, until recently, it has been impossible to make objectively valid statements about the degree to which a data object exhibits “FAIRness”.   

With the encouragement of journal editors and other stakeholders who have a need to evaluate author/researcher claims regarding the FAIRness of their outputs, a group consisting of FAIR experts, journal editors, data repository hosts, internet researchers, and software developers assembled to jointly define a set of formal metrics that could be applied to test the FAIRness of a resource.  The first edition of these metrics was aimed at self-assessment, in the form of a questionnaire; however, upon review of the validity of several completed self-assessments by data owners, we determined that the questions were often answered inconsistently, or incorrectly (knowingly or unknowingly), and often the data provider did not know enough about the data publishing environment to answer the questions at all.  As such, a smaller group of FAIR experts created a second generation of FAIR Metrics that aimed to be fully automatable.  The result was a set of 22 Metrics spanning most FAIR principles and sub-principles, which explicitly describe what is being tested, which FAIR Principle it applies to, why it is important to test this (meta)data feature, exactly how the test will be conducted, and what will be considered a successful result.  

Generic 

If driving user question(s) were defined in Step 1 it should be “answered” in this step. The results of these question(s) are gathered by processing the FAIR machine-readable data. If RDF is the machine-readable format used, then RDF data stores (triple stores) are used to store the machine-readable data, and SPARQL queries are used to retrieve the data required to answer the driving user question(s). 

FAIRCOOKBOOK recipe: [https://faircookbook.elixir-europe.org/content/recipes/introduction/fairification-process.html] 

Phase 3: assess, design, implement, repeat 

Following the selection of the “action” team, an iterative cycle of assessment, design, and implementation in put in place. 

Assessment : Prior to starting the work, the assessment of goals is done to ensure that individuals in the action team are updated and clear with the FAIRification goals formulated by the data owners. This assessment is carried out by review team which could be an independent team or certain individuals from the technical team who are not involved in the action team. The assessment involves a binary decision of “GO” or “NO GO” based on the FAIRification goals and the catalog provided. At this stage, the reviews can also provide suggestion based on their experiences on the resources, tool, or goals. 

Design : Once the team receives a “GO” decision from the review team, the action team now starts by enlisting the steps that need to be done performed to achieve the goal. For each task, the resources, an estimate time duration, as well as the responsible person is selected. 

Implementation : Once the tasks have been selected and assigned, the actual work begins. To ensure that the action team is working smoothly, weekly or bi-weekly meetings is recommended so that the team is aware of the progress. 

Once the implementation of task listed in the design phase are done, the action team assess the work done and checks the aligned with the FAIRification goal. In case more tasks are needed to achieve the goal, a second round of the assess-review-implement cycle takes place as described above with the starting point as the FAIRification goals, the completed tasks and the proposed task 

This phase is usually run in short sprints of 3-month. 

Practical Examples from the Community 

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