The proliferation of open-source scientific software for science and research presents opportunities and challenges. In this paper, we introduce the SciCat dataset -- a comprehensive collection of Free-Libre Open Source Software (FLOSS) projects, designed to address the need for a curated repository of scientific and research software. This collection is crucial for understanding the creation of scientific software and aiding in its development. To ensure extensive coverage, our approach involves selecting projects from a pool of 131 million deforked repositories from the World of Code data source. Subsequently, we analyze README.md files using OpenAI's advanced language models. Our classification focuses on software designed for scientific purposes, research-related projects, and research support software. The SciCat dataset aims to become an invaluable tool for researching science-related software, shedding light on emerging trends, prevalent practices, and challenges in the field of scientific software development. Furthermore, it includes data that can be linked to the World of Code, GitHub, and other platforms, providing a solid foundation for conducting comparative studies between scientific and non-scientific software.

翻译：开源科学软件在科学研究中的普及既带来了机遇也带来了挑战。本文介绍了SciCat数据集——一个涵盖自由及开源软件（FLOSS）项目的综合性集合，旨在满足对科学及研究软件精选存储库的需求。该数据集对于理解科学软件的创建过程并辅助其开发至关重要。为确保广泛覆盖，我们的方法从World of Code数据源的1.31亿个去派生仓库中筛选项目，随后利用OpenAI的先进语言模型分析README.md文件。分类标准聚焦于三类软件：以科学为目的设计的软件、与研究相关的项目以及研究支持类软件。SciCat数据集旨在成为研究科学相关软件的重要工具，揭示科学软件开发领域的新趋势、主流实践与挑战。此外，该数据集还包含可链接至World of Code、GitHub等平台的数据，为开展科学与非科学软件的对比研究提供了坚实支撑。