PyGeoweaver: Tangible workflow tool for enhancing scientific research productivity and FAIRness

Scientific research faces workflow inefficiencies and reproducibility issues. PyGeoweaver, a Python library, helps manage data pipelines to address these problems. It stands out for its accessibility and ease of use, making it ideal for individual researchers and small teams. Unlike traditional systems that demand extensive technical expertise, it is designed to be beginner-friendly, allowing anyone to work with it without encountering significant technical hurdles. It can help researchers intuitively design workflows, retrieve data, and analyse it efficiently. It also offers compelling benefits for AI use cases, enabling researchers to develop and deploy AI models with ease. Serving as a decentralised hub for workflow management, it ensures discoverability, accessibility, and reusability of scientific workflows, fostering collaboration and research reproducibility. The paper will introduce its technical framework, present successful applications, and share insights from user feedback. Based on our research experiences in the past two years, PyGeoweaver has proven to be a valuable tool, enabling researchers to attain significant research progress with minimal technical complexities.