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We studied 11 long-term data infrastructure projects, most of which focused on the Earth Sciences, to understand characteristics that contributed to their project sustainability. Among our sample group, we noted the existence of three different types of project groupings: Database, Framework, and Middleware. Most efforts started as federally funded research projects, and our results show that nearly all became organizations in order to become sustainable. Projects were often funded for short time scales but had the long-term burden of sustaining and supporting open science, interoperability, and community building–activities that are difficult to fund directly. This transition from ‘project’ to ‘organization’ was challenging for most efforts, especially in regard to leadership change and funding issues.Some common approaches to sustainability were identified within each project grouping. Framework and Database projects both relied heavily on the commitment to, and contribution from, a disciplinary community. Framework projects often used bottom-up governance approaches to maintain the active participation and interest of their community. Database projects succeeded when they were able to position themselves as part of the core workflow for disciplinary-specific scientific research. Middleware projects borrowed heavily from sustainability models used by software companies, while maintaining strong scientific partnerships. Cyberinfrastructure for science requires considerable resources to develop and sustain itself, and much of these resources are provided through in-kind support from academics, researchers, and their institutes. It is imperative that more work is done to find appropriate models that help sustain key data infrastructure for Earth Science over the long-term. 

The field of geology is poised to make a fundamental transition in the quality, character, and types of science that are possible for practitioners. Geologists are developing data systems—consistent with their workflow—to digitally collect, store, and share data. Separately, geologists and cognitive scientists have been working together to develop tools that can characterize the level of uncertainty of both data and models. The transformational change comes from the simultaneous combination of these two approaches: digital data systems designed to capture and convey scientific uncertainty. This approach promotes better data collection practice, improves reproducibility, and increases trust in community-based digital data. We applied these methods—attending to uncertainty and its incorporation into digital repositories—to the Sage Hen Flat pluton in eastern California, USA, where two published maps provide different interpretations. Incorporating uncertainty into our workflow, from field data collection to publication, allows us to move beyond binary choices (e.g., is this data/model right or wrong?) to a more nuanced view (e.g., what is my level of uncertainty about the data/model?) that is shareable with the larger community.