Building a sustainable future for anthropology's archives: Researching primary source data lifecycles, infrastructures, and reuse

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Anthropologists create data that are valuable, unique, and irreplaceable. They often document complex human lifeways, languages, and cultural communities in far greater detail than ever becomes available in a published form. Field notebooks, letters, photographs, sound and film recordings, and other documentation contain the data researchers draw on to publish new ideas, advance our understanding of human life, past and present, and contribute to cultural revitalization and reflection. Despite their importance within and beyond anthropology, recent NSF-funded research has shown these unpublished archival materials are often neither digitized nor findable via web searching. This research project builds on these findings to develop and test best practices for archiving original data across a range of formats. The project has immediate applied implications, builds partnerships between researchers and archival institutions, improves interdisciplinary and collaborative approaches to graduate training, and enhances anthropology’s research infrastructure. For the broad public, this project makes anthropological archives available as primary sources to serve K-12 teachers and students, genealogists, artists, and more. It trains graduate and undergraduate students. The proposed research investigates the following questions: 1) What barriers prevent primary source data from being reusable over time? 2) How can anthropology and related fields sustainably adapt emergent linked data infrastructures in support of broad access to cultural research data? The investigators conduct focus group discussions to understand current movements and future best practices for anthropology’s archival information infrastructure, evaluate test collections in new open access platforms, develop two training modules for anthropologists and data curators, and host a virtual symposium. The project contributes to important debates surrounding the methods by which and the benefits and limitations associated with the sharing of raw data across a variety of formats.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.