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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. 

Systematic reviews and meta-analyses are important techniques because they synthesize results from multiple primary studies on a similar topic. To influence policy, practice, and research, however, synthesis researchers must translate the results for various audiences. Ideally, the translation drives future research agendas, informs policymaking, or assists in practical decision-making. An Evidence Gap Map (EGM), a graphical or tabular visualization of systematic review and meta-analysis results, is one ideal translation technique because it provides a structured framework to assess contexts for which primary evidence is available or to determine whether the effectiveness of an intervention or a program differs across populations, conditions, and settings. To bolster the field and promote the use of EGMs, we provide an overview of what constitutes an informative EGM, detail multiple examples of EGMs using extant meta-analytic results, and present a free R Shiny application we created to easily generate EGMs from typical meta-analytic datasets. We conclude by reviewing education-based systematic reviews that included an EGM to describe the current state of the field.