Challenges and Opportunities in Scientific Data Discovery and Reuse in the Data Revolution: Harnessing the Power of AI

Carnegie Mellon University, Pittsburgh PA

Investigators

Abstract

The volume and heterogeneity of scientific data goes beyond a researcher?s ability to find relevant data, make different formats interoperable, and deal with difficult ontological and language issues. Progress requires bringing together experts in data curation with experts in Artificial Intelligence (AI) to begin a dialog and collaboration on AI tools to span the gap between data and its reuse. The PIs will organize the conference "Challenges and Opportunities in Scientific Data Discovery and Reuse in the Data Revolution: Harnessing the Power of AI"; the objective is to bring together diverse stakeholders including research librarians and data managers, the AI community, and users and consumers of scientific data to dialog around critical places in the data lifecycle where AI could be groundbreaking. This project is supported by the National Science Foundation?s Public Access Initiative which is managed by the NSF Office of Advanced Cyberinfrastructure on behalf of the Foundation. 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.