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Updated: Jun 20, 2025

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FAIRsoft-a practical implementation of FAIR principles for research software.

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We developed quantitative indicators to assess Life Sciences research software quality, inspired by FAIR data principles. This work provides objective measures to improve software sustainability and reproducibility in research.

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Area of Science:

  • Life Sciences
  • Computational Biology
  • Bioinformatics

Background:

  • Research software in Life Sciences faces challenges in reproducibility and verification.
  • Issues include poor documentation, opacity, errors, and unavailability, impacting scientific quality.
  • The FAIR (findable, accessible, interoperable, reusable) data principles offer a model for software quality.

Purpose of the Study:

  • To propose quantitative indicators for Life Sciences research software quality.
  • To implement these indicators using the FAIR Principles on the OpenEBench platform.
  • To analyze current software quality practices and identify areas for improvement.

Main Methods:

  • Developed quantitative indicators based on a pragmatic interpretation of FAIR Principles.
  • Implemented indicators on OpenEBench, ELIXIR's platform for scientific benchmarking and software quality observation.
  • Collected and analyzed software metadata from 11 diverse sources.

Main Results:

  • Generated a comprehensive series of quantitative indicators for research software.
  • Provided objective insights into current practices for software quality features.
  • Identified opportunities for enhancing the quality and sustainability of Life Sciences research software.

Conclusions:

  • The developed indicators offer a pragmatic approach to assessing research software quality.
  • Implementation on OpenEBench facilitates benchmarking and monitoring of software features.
  • This work supports the advancement of reproducible and sustainable research in Life Sciences.