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Isolation of Human Islets from Partially Pancreatectomized Patients

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Published on: July 30, 2011

Lessons learned from implementing FAIRification workflows in diabetes research in Germany.

Esther Thea Inau¹, Angela Dedié², Ivona Anastasova²

¹Medical Informatics Laboratory, University Medicine Greifswald, Greifswald, Germany.

PLOS Digital Health

|January 13, 2026

Summary

This summary is machine-generated.

Comparing two FAIR data workflows for diabetes research revealed that domain-specific adaptations enhance efficiency but not necessarily FAIRness scores for core datasets. Early planning of FAIR data strategies is crucial.

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Area of Science:

Health Informatics
Data Science
Research Data Management

Background:

The FAIR principles (Findable, Accessible, Interoperable, Reusable) are essential for maximizing scientific data value.
Research communities develop domain-specific workflows to achieve FAIR data.
Implementing FAIR data workflows requires careful consideration of standards and practices.

Purpose of the Study:

To compare the implementation of a generic versus a domain-specific FAIRification workflow.
To assess the impact of these workflows on FAIRness scores using diabetes research data from Germany.
To identify requirements for effective FAIR data core dataset implementation.

Main Methods:

Applied two externally developed structured FAIRification workflows: one generic, one domain-specific.
Utilized metadata from diabetes research in Germany.
Employed the FAIR data maturity model from the Research Data Alliance (RDA).

Main Results:

Both generic and domain-specific workflows required similar resources for implementation.
Both workflows resulted in equivalent FAIRness ratings for the core datasets.
Adaptations for health research data improved workflow efficiency but not core dataset FAIRness scores.

Conclusions:

Domain-specific workflow adaptations for health data enhance efficiency but do not inherently increase FAIRness scores for core datasets.
A list of universal requirements for FAIRification of core datasets was identified.
Early strategic planning and infrastructure development are vital for successful FAIR data implementation.