...
This section could describe the expertise required. Perhaps the Build Your Team step could then be an aggregation of all the “Expertise requirements for this step” steps that someone needs to fulfil his/her FAIRification goals.
[Hannah: I would say expertise depends a bit on which tool you use; most checklists and questionnaires are pretty low effort and self-explenatory. But some of the more automated tools require some (programming) skills]
How to
There are many assessment tools to do a pre-FAIR assessment of your (meta)data. Based on the 2022 publication FAIR assessment tools FAIRassist holds a (manually created) collection with various different tools. These include manual questionnaires or checklists and automated tests that help users understand how to achieve a state of "FAIRness", and how this can be measured and improved. Furthermore, a 2022 publication (FAIR assessment tools: evaluating use and performance and x, y, z the following tools could be considered:
Online self-assessment surveys
These tools provide allow you to fill in an online form and then give e.g. a score to indicate the FAIRness of your (meta)data.
...
Tool
...
) compared a number of tools. Of these and the tools listed on FAIRassist, we suggest that the following can be considered for your pre-FAIR assessment:
[Hannah: Fieke pointed out something important; there are basically two kinds of tools for the FAIR assessment. One group assesses (often in a semi-automated way) the FAIRness of (meta)data which already has a persistent identifyer (such as a DOI). The other group assesses FAIRness (often in the form of a survey, questionnaire or checklist) of (meta)data without persistent identifyer.]
Online self-assessment surveys
These tools allow you to fill in an online form. The result of the survey can be e.g. a score to indicate the FAIRness of your (meta)data. Some tools additionally provide advice on how to improve FAIRness at different levels.
Tool | Description - Nakijken of de paper iets moois heeft staan |
|---|---|
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. | |
Provided by DANS, this online survey gives a FAIRness score. Furthermore, it provides advice on how to improve the FAIRness of your (meta)data.SATIFYD [Hannah; according to the review paper, this tool ‘assesses the user's understanding of the FAIR principles rather than the FAIRness of his/her dataset. FAIR-aware is not further considered in this paper’. Maybe throw it out as well?] | |
Provided by DANS, this online survey gives a FAIRness score. Furthermore, it provides advice on how to improve the FAIRness of your (meta)data. | |
Allows you to automatically assess digital objects as well as add a new project to their repository (??) |
...
Also has a Chrome browser plugin to automatically check FAIR assessments for available projects [Hannah; I don’t know how useful this is in the context of our metroline; also the paper states it as quite a time investment and ] |
Online (Semi-) automated
These tools do an automatic assessment by reading the metadata available at a certain URI.
Offline self-assessment
RDA- [Hannah: this links to another page on the confluence] and this one [Hannah; these are jupyter notebooks to use for data from specific databases; can be extended/adjusted with your own dataset; it seems a bit of a larger effort to use for a ‘quick’ FAIR assessment of your (meta)data]
FAIRchecker; this tool automatically provides a score for all aspects of FAIR from a URI
Offline self-assessment
GARDIAN (link from paper is dead, could be somewhere around here, can’t find it though)it though)
[Hannah: the 2022 paper does not recommend using offline tools, and I kind of agree. So maybe we don’t include this category at all? Especially because one of the links is dead anyways..]
we evaluated FAIR assessment tools in terms of 1) the prerequisite knowledge needed to run the tools, 2) the ease and effort needed to use them and 3) the output of the tool, with respect to the information it contains and the consistency between tools. This should help users, e.g., in the nanosafety domain, to improve their methods on storing, publishing and providing research data. To do this we provide
guidance for researchers to pick a tool for their needs and be aware of its strong points and weaknesses.
The selected tools were split up into four different sections, namely online self-assessment/survey, (semi-)automated, offline self-assessment and other types of tools. The tool selection was based on online searches in June 2020.
They compare:
we evaluated FAIR assessment tools in terms of 1) the prerequisite knowledge needed to run the tools, 2) the ease and effort needed to use them and 3) the output of the tool, with respect to the information it contains and the consistency between tools. This should help users, e.g., in the nanosafety domain, to improve their methods on storing, publishing and providing research data. To do this we provide assessment/survey, (semi-)automated, offline self-assessment and other types of tools. The tool selection was based on online searches in June 2020.
They compare:
The FAIR Data Maturity Model
...
reviews ten FAIR assessment tools that have been evaluated and characterized using two datasets from the nanomaterials and microplastics risk assessment domain.
we evaluated FAIR assessment tools in terms of 1) the prerequisite knowledge needed to run the tools, 2) the ease and effort needed to use them and 3) the output of the tool, with respect to the information it contains and the consistency between tools. This should help users, e.g., in the nanosafety domain, to improve their methods on storing, publishing and providing research data. To do this we provide guidance for researchers to pick a tool for their needs and be aware of its strong points and weaknesses.
The selected tools were split up into four different sections, namely online self-assessment/survey, (semi-)automated, offline self-assessment and other types of tools. The tool selection was based on online searches in June 2020.
They compare:
These resources include manual questionnaires, checklists and automated tests that help users understand how to achieve a state of "FAIRness", and how this can be measured and improved.Online self-assessment survey
Online (Semi-) automated
Offline self-assessment
GARDIAN (link from paper is dead, may be somewhere around here, can’t find it though)
Other
There is also FAIRassist with lots of tools and questionnaires:
, may be somewhere around here, can’t find it though)
Other
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]
[Sander]
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.
...