11 Geoscience Education in the Western U.S and México. Practices, Pathways, and Partnerships.

Promotes the exchange of educational practices, and collaborations between U.S.A. and México to strengthen geoscience education, fostering academic networks, pedagogical innovation, and collaborative training for students and faculty.

Wednesday, 22 April 2026
1:30 PM - 5:30 PM
LMH, Deli

Increasing the Value of Observational Datasets in Digital Data Systems through Critical Outcrops

Paper number: 11-5
Program: Technical Sessions
Time: 2:55 PM – 3:15 PM

Authors

Abstract

Digital geologic data collection provides the opportunity to transition science practice to a workflow in which data is collected and stored by groups and/or reused by others: This approach affords inclusivity and provides the ability to work at the interfaces between sub-disciplines.  The challenge of this approach is to record public data in a way that provides information about context, including its quality and importance, that would allow insights by someone who had not collected the data.  

We introduce the concept of a critical outcrop, which is an outcrop of particular importance in a field area, as a possible key to effectively sharing large digital field datasets for public use and collaboration. There are four different types of critical outcrops: Exemplar, Confuser, Disambiguator, and Anchor.  Each type of critical outcrop is salient (important) to the resultant models, but in different ways.  Critical outcrops were often chosen because they closely reflected an outcrop or relationship that had been seen somewhere else in the world.  Thus, the choice of critical outcrops indicates that analogical thinking is actively being used by practitioners.  Reporting on critical outcrops – and the analogy that is being made to other field areas – will greatly facilitate science communication and more robust scientific interpretations.  Moreover, providing a higher level of supplied information increases trust among participating scientists, and thus increases trust in entire datasets.  

Geological Society of America Abstracts with Programs. Vol. 58, No. 3

doi: 10.1130/abs/2026CD-410937

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