NSF HDR Community

The National Science Foundation (NSF) Harnessing the Data Revolution (HDR) Institutes are an integrated fabric of interrelated institutes that aim to accelerate discovery and innovation in multiple areas of data-intensive science and engineering.

Accelerated AI Algorithms for Data-Driven Discovery

Multi-disciplinary and geographically distributed entity with the primary mission to lead a paradigm shift in applying real-time artificial intelligence (AI) at scale to advance scientific knowledge and accelerate discovery.

Webpage: a3d3.ai, award: OAC-2117997 

Institute for Geospatial Understanding through an Integrative Discovery Environment

Vision: Digital discovery and innovation through harnessing the geospatial data revolution. Mission: Transform convergence and geospatial sciences for holistic sustainability solutions.

Webpage: iguide.illinois.edu, award: OAC-2118329

Institute for Data Driven Dynamical Design

Transform how scientists & engineers harness data when designing materials and structures. From chemistry to civil engineering, create platforms that accelerate the discovery of new mechanisms and dynamics through the union of human & machine intelligence. Train the next generation and engage with the broader data-driven community.

Webpage: mines.edu/id4, award: OAC-2118201

Institute for Harnessing Data and Model Revolution in the Polar Regions

Advances our understanding of the response of polar regions to climate change and its global impacts by deeply integrating data science and polar science to spur physics-informed, data-driven discoveries.

Webpage: iharp.umbc.edu, award: OAC-2118285

Imageomics Institute

Mission: Create a collaborative research, training, and community-facing environment for extracting existing and new biological traits from images of organisms, with infrastructure for cyber, information, and model development. Vision: Establish a new scientific field that harnesses data science, computing, and rapidly expanding collections of biological image data to accelerate biological understanding of phenotypic traits extracted from images of organisms.

Webpage: imageomics.org, award: OAC-2118240