Biology subjects

Bonino da Silva Santos, L. O.

Publications and source records attributed to Bonino da Silva Santos, L. O..

3 recordsLinked to original sources

bioRxiv2024

The FAIR Data Point Populator: collaborative FAIRification and population of FAIR Data Points

BackgroundUse of the FAIR principles (Findable, Accessible, Interoperable and Reusable) allows the rapidly growing number of biomedical datasets to be optimally (re)used. An important aspect of the FAIR principles is metadata. The FAIR Data Point specifications and reference implementation have been designed as an example on how to publish metadata according to the FAIR principles. Various tools to create metadata have been created, but many of these have limitations, such as interfaces that are not intuitive, metadata that does not adhere to a common metadata schema, limited scalability, and inefficient collaboration. We aim to address these limitations in the FAIR Data Point Populator. ResultsThe FAIR Data Point Populator consists of a GitHub workflow together with Excel templates that have tooltips, validation and documentation. The Excel templates are targeted towards non-technical users, and can be used collaboratively in online spreadsheet software. A more technical user then uses the GitHub workflow to read multiple entries in the Excel sheets, and transform it into machine readable metadata. This metadata is then automatically uploaded to a connected FAIR Data Point. We applied the FAIR Data Point Populator on the metadata of two datasets, and a patient registry. We were then able to run a query on the FAIR Data Point Index, in order to retrieve one of the datasets. ConclusionThe FAIR Data Point Populator addresses several limitations of other tools. It makes creating metadata easier, ensures adherence to a common metadata schema, allows bulk creation of metadata entries and increases collaboration. As a result of this, the barrier of entry for FAIRification is lower, which enables the creation of FAIR data by more people.

bioinformatics↗

bioRxiv2018

Evaluating FAIR-Compliance Through an Objective, Automated, Community-Governed Framework

With the increased adoption of the FAIR Principles, a wide range of stakeholders, from scientists to publishers, funding agencies and policy makers, are seeking ways to transparently evaluate resource FAIRness. We describe the FAIR Evaluator, a software infrastructure to register and execute tests of compliance with the recently published FAIR Metrics. The Evaluator enables digital resources to be assessed objectively and transparently. We illustrate its application to three widely used generalist repositories - Dataverse, Dryad, and Zenodo - and report their feedback. Evaluations allow communities to select relevant Metric subsets to deliver FAIRness measurements in diverse and specialized applications. Evaluations are executed in a semi-automated manner through Web Forms filled-in by a user, or through a JSON-based API. A comparison of manual vs automated evaluation reveals that automated evaluations are generally stricter, resulting in lower, though more accurate, FAIRness scores. Finally, we highlight the need for enhanced infrastructure such as standards registries, like FAIRsharing, as well as additional community involvement in domain-specific data infrastructure creation.

scientific communication and education↗

bioRxiv2017

A design framework and exemplar metrics for FAIRness

\"FAIRness\" - the degree to which a digital resource is Findable, Accessible, Interoperable, and Reusable - is aspirational, yet the means of reaching it may be defined by increased adherence to measurable indicators. We report on the production of a core set of semi-quantitative metrics having universal applicability for the evaluation of FAIRness, and a rubric within which additional metrics can be generated by the community. This effort is the output from a stakeholder-representative group, founded by a core of FAIR principles co-authors and drivers. We now seek input from the community to more broadly discuss their merit.

scientific communication and education↗