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Scientific workflow management systems (WfMS) provide a systematic way to streamline necessary processes in scientific research. The demand for FAIR (Findable, Accessible, Interoperable, and Reusable) workflows is increasing in the scientific community, particularly in GIScience, where data is not just an output but an integral part of iterative advanced processes. Traditional WfMS often lack the capability to ensure geospatial data and process transparency, leading to challenges in reproducibility and replicability of research findings. This paper proposes the conceptualization and development of FAIR-oriented GIScience WfMS, aiming to incorporate the FAIR principles into the entire lifecycle of geospatial data processing and analysis. To enhance the findability and accessibility of workflows, the WfMS utilizes Harvard Dataverse to share all workflow-related digital resources, organized into workflow datasets, nodes, and case studies. Each resource is assigned a unique DOI (Digital Object Identifier), ensuring easy access and discovery. More importantly, the WfMS complies with the Common Workflow Language (CWL) standard to guarantee interoperability and reproducibility of workflows. It also enables the integration of diverse tools and software, supporting complex analyses that require multiple processing steps. This paper demonstrates the prototype of the GIScience WfMS and illustrates two geospatial science case studies, reflecting its flexibility in selecting appropriate techniques for various datasets and research goals. The user-friendly workflow designer makes it accessible to users with different levels of technical expertise, promoting reusable, reproducible, and replicable GIScience studies. 

The frequency of infectious disease outbreaks and pandemics is rising, demanding an understanding of their drivers. Common wisdom suggests that increases in outbreak frequency are driven by socioeconomic factors such as globalization and urbanization, yet, the majority of disease outbreaks are caused by zoonotic pathogens that can be transmitted from animals to humans, suggesting the important role of ecological and environmental drivers. Previous studies of outbreak drivers have also failed to quantify the differences between major classes of pathogens, such as bacterial and viral pathogens. Here, we reconsider the observed drivers of a global sample of 300 zoonotic outbreaks, including the 100 largest outbreaks that occurred between 1977 and 2017. We show that socioeconomic factors more often trigger outbreaks of bacterial pathogens, whereas ecological and environmental factors trigger viral outbreaks. However, socioeconomic factors also act as amplifiers of viral outbreaks, with higher case numbers in viral outbreaks driven by a larger proportion of socioeconomic factors. Our results demonstrate that it is useful to consider the drivers of global disease patterns in aggregate due to commonalities that cross disease systems. However, our work also identifies important differences between the driver profiles of bacterial and viral diseases in aggregate.