Enhancing Multiscale Geologic Data Collection, Sharing, and Reusability Through the Community-Driven StraboSpot Ecosystem

StraboSpot is an open-source, US NSF-funded, FAIR-aligned, and community-driven data system that enables researchers to collect, store, and share spatially referenced geologic data across scales. StraboSpot's development has been a collaborative initiative, shaped through a series of community workshops initially focused on providing tools that enhance research in structural geology. Over time, other communities have expanded these efforts to include diverse geological subdisciplines. Community workshops have played a crucial role in establishing shared metadata standards, a unified vocabulary, and workflows tailored to meet the unique needs of each user community. This inclusive approach has been instrumental in creating an infrastructure that integrates diverse data types and promotes interdisciplinary collaboration while minimizing barriers to data entry.

The "Spot" approach provides a flexible framework for characterizing spatial areas across all scales, facilitating seamless integration of data ranging from micro-scale laboratory measurements to regional-scale field data. By allowing Spots to nest indefinitely within one another, users can preserve critical information about spatial context, scale, orientation, and inter-relationships. The StraboSpot ecosystem comprises three core applications: StraboMicro, a desktop tool designed for managing and contextualizing laboratory-derived micro-scale data; StraboField, a mobile application tailored for mapping and field-based research; and StraboExperimental, designed for data derived from rock deformation experiments. All data are tied to a shared database that preserves provenance and context. Additionally, researchers can link data to external repositories, such as EarthChem, further enhancing data interoperability.

In response to clear community priorities, StraboSpot is actively enhancing its functionality to support enhanced collaboration and improve data reusability. Current developments include the implementation of Group Workflows, which enable collaboration in both field and laboratory settings while maintaining data provenance and robust version control. Additionally, a Quality Assurance/Quality Control (QA/QC) framework is being developed to improve confidence in shared observational data. This QA/QC system will provide a transparent and systematic mechanism for evaluating data quality and completeness, facilitating the use of shared data sets within and between disciplines.