Imagine if every scientist wrote their own recipes but didn't list the measurements or the oven temperature; no one could replicate the results. This project creates a universal 'quality checklist' and a toolkit to make sure research code is reliable and easy for others to use. It's like moving from handwritten notes to a professional, standardized manual for scientific software.
Scientific software is often unreliable, poorly documented, and difficult to reuse, leading to wasted research budgets and failed reproducibility. There is also a lack of professional recognition for the engineers who write this critical code.
The RSQKit, a toolkit containing a catalogue of tools and services to assess and improve software quality and FAIRness. It also includes a TechRadar for best practices and a training platform.
If you are a drug discovery firm dealing with unreliable simulation code that fails during peer review — this project developed the RSQKit that provides a catalogue of tools to assess and improve software quality. This ensures your research software is reproducible and meets high standards.
If you are a scientific software vendor dealing with fragmented coding practices across your research teams — this project developed a set of best practices and training resources. This allows you to standardize development and increase the reuse of your internal code.
If you are a climate modeling agency dealing with software that is hard to maintain after the original developer leaves — this project developed a system for software recognition and curation. This helps you maintain sustainable software and credit the engineers who keep it running.
Based on available project data, no pricing or cost structures are mentioned as the project focuses on community-led frameworks and open science principles.
The project aims to standardize practices across 5 EOSC Science Clusters, suggesting a high capacity for scaling across diverse scientific domains.
Based on available project data, the project aligns with FAIR and open science principles, though specific license types are not listed.
The project creates recognition mechanisms for developers by linking credits to established platforms like ORCID.
The project runs from 2024-03-01 to 2027-02-28, with mid-term demonstrators planned for the assessment process.
The consortium is heavily weighted toward the public sector, consisting of 19 partners from 9 countries. It is composed of 10 research organizations and 8 universities, with 0% industry participation. This indicates the project is currently driven by academic and research excellence rather than commercial market pull.
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