Mid-scale RI-1 (M1:IP): EduceLab: Infrastructure for Next-Generation Heritage Science

This project will create EduceLab, a data-centric instrument platform for Heritage Science (HS). HS applies the tools, techniques, and rigor of scientific measurement and inquiry to diverse heritage contexts, such as artifacts and curated collections, biological remains, and human-impacted environments. The proposed mid-scale instrument ecosystem is designed around key scientific capabilities that the research community has embraced as crucial and fundamental to addressing the challenging variability of HS contexts.

The ecosystem includes materials characterization; advanced multimodal imaging (tomography, photography, photogrammetry); cyberinfrastructure and methodologies for capturing, structuring, and processing large-scale data sets; and mobile and flexibly-deployed instrumentation for in-situ data acquisition and initial evaluations. Viewed as a holistic ecosystem uniquely capable of addressing previously insoluble research questions in HS, EduceLab will be commissioned as operational clusters based on usage patterns that match the diverse HS communities and constituencies: laboratory equipment in a fixed, controlled environment for precise measurement and analysis (BENCH); mobile equipment that can be deployed on-site for collections and landscapes that cannot travel (MOBILE); and a protean environment for envisioning, building, and testing custom instrument configurations that address contexts impervious to laboratory analysis and on-site capabilities (FLEX).

The clusters will be interconnected via cyberinfrastructure (CYBER) that powers efficient data flow through the entire infrastructure, from data acquisition to structured analysis, supporting the application of new techniques in data science and artificial intelligence. EduceLab will function as a novel Mid-scale Research Infrastructure – a variable scientific lens – for the rigorous, data-driven exploration of compelling heritage science research questions, engendering new discoveries as well as fundamental scientific advancement.

Headquartered within the University of Kentucky’s William S. Webb Museum for Anthropology, EduceLab will build cohesion across a diverse group of national and international stakeholders and constituents: trainees/students; researchers in HS and in areas where data acquisition activities overlap; practitioners at institutions with active heritage collections and connected research efforts; and community members with investments in the ongoing conservation, preservation, study, dissemination, and educational activities.

EduceLab in its organization will allow rigorous scientific exploration in the face of the unique challenges posed by natural and cultural heritage contexts. The EduceLab ecosystem will answer ongoing demands for agility and innovation in measurement, computational, and instrumentation solutions to these problems. Such advances will in turn continuously promote knowledge, growth, and innovation across a range of scientific disciplines involved in HS, including data science, computer science, computer vision, imaging science, systems engineering, chemical and materials engineering, anthropology, and cyberinfrastructure.

The proposed instrumentation strongly aligns with the National Science Foundation’s “Big Ideas” initiatives, including: Growing Convergence Research (EduceLab and HS is a convergence activity in itself, blending a number of scientific disciplines and relying on robust collaborations for success); Harnessing the Data Revolution (EduceLab activities will be data-driven and data-intensive, relying on emerging computational tools at play across disciplines like chemistry, physics, and computer science to capture the essence of objects and their environments); and NSF INCLUDES (the EduceLab ecosystem combines STEM with heritage fields in a culturally informed way to strategically grow the interest and participation of underrepresented groups in STEM fields and increase societal public literacy and engagement with science and technology.).

This project is jointly funded by Mid-Scale Research Infrastructure-Track 1 program, Information and Intelligent Systems, the Established Program to Stimulate Competitive Research (EPSCoR), and the Office of Advanced Cyberinfrastructure.

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.