Geoweaver for Boosting Scientific Research Productivity and FAIRness

Scientific productivity is a long-time existent issue across all the cyberinfrastructure-backed research areas. It is a
known issue that many research groups suffer low productivity due to the lack of FAIR principles in practice for their workflows and data products. Non-sharable and non-reusable code and datasets drive students and researchers to repeat the same data retrieval and cleaning procedures repeatedly, wasting numerous hours on the same steps by new onboarded members or even the original contributor researcher who forgot and cannot reproduce their results
after a while. Geoweaver, a GUI-based scientific workflow management system, is developed to address this low-FAIRness-caused productivity issue while reducing the learning costs for researchers with a less technical background. This paper will use a machine learning workflow in Geoweaver as a demonstration example. The example use case utilizes PyTorch to create and deploy a crop classification operational model to classify cropland in Kenya during the growing season (i.e., partially-observed time series) and produces a 10-meter resolution probability cropland map
for the 2021-2022 growing season