Methodological approaches to foster best practices in Software Engineering and Open Science in Research Software projects

• Grant number: 24/22432-4
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: July 01, 2025
• End date: June 30, 2026
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Paulo Roberto Miranda Meirelles
• Grantee: Julia Calixto Rosa
• Host Institution: Instituto de Matemática e Estatística (IME). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Associated research grant: 23/18026-8 - Center for Data Science in Public Statistics, AP.CCD

Abstract

Open Science seeks to make research more accessible, transparent, and reproducible, including the software used in scientific production. Research Software must be discoverable, well-documented, openly licensed, and reproducible to achieve this. The use of free/libre software, by promoting transparency, audibility, and collaboration, naturally aligns with these principles, strengthening the dissemination and sustainability of scientific tools. This project aims to identify and catalog methodologies that guide the adoption of best practices in Software Engineering and Open Science within consolidated Research Software projects -- those that already have an active user base and a mature functional structure. We aim to map patterns, guidelines, and strategies that support the transition of such projects into sustainable free software initiatives aligned with Open Science best practices. As a case study, we will analyze the Cortext platform, a tool for computational document analysis, with over one thousand active users annually across approximately 50 countries. Although it relies on free/libre software libraries, Cortext is not yet fully distributed under a free software license, which limits its alignment with Open Science principles. Furthermore, its codebase requires improvements in maintainability and modularity to facilitate external contributions and expand its developer community. By applying and evaluating the identified methodologies in the context of Cortext, we aim to broaden its adoption, particularly in Brazil, and strengthen its scientific transparency and reproducibility. The platform implementation at the Center for Data Science for Public Statistics (CCDEP/SEADE/FAPESP) will demonstrate its potential in strategic projects for São Paulo and Brazil. (AU)