Big Data Innovation Hubs Mark

A Shared Mission

Data science is happening everywhere—so let’s work together. The Big Data Innovation Hubs bring together a wide-ranging and inclusive community to collaborate with data on shared societal and scientific priorities.

National initiatives like the COVID Information Commons, the Keeping Data Science Broad program, and the Data Sharing & Cyberinfrastructure Working Group are how we convene this grassroots community and amplify impact.

COVID Information Commons

The COVID Information Commons (CIC) is an open website to facilitate knowledge-sharing and collaboration across various COVID research efforts, funded by the NSF Convergence Accelerator and the  NSF Technology, Innovation and Partnerships Directorate. The CIC serves as an open resource for researchers, students, and decision-makers from academia, government, not-for-profits and industry to identify collaboration opportunities, to leverage each other’s research findings, and to accelerate the most promising research to mitigate the broad societal impacts of the COVID-19 pandemic.

Data Science Education and Workforce Working Group

Data literacy is essential to navigate the current deluge of data in almost every scientific discipline and business sector. The United States suffers from a lack of a trained workforce to meet both current and emerging demands, making data science a critical element of educating students for the 21st century. The Data Science Education and Workforce working group has zeroed in on a plan to collect and provide practical resources and models of engagement for broadening and deepening the available pool of data literate talent.

Data Sharing & Cyberinfrastructure Working Group

The Data Sharing and Cyberinfrastructure Working Group is a collaboration across all four of the Big Data Innovation Hubs.  The working group brings together data scientists and cyberinfrastructure professionals to learn about topics of common interest.  Areas covered include data management and analytics tools and frameworks, and use of data science to solve real world problems in areas such as transportation, water quality, public health, disaster response and cybersecurity.

Data Science Resource Repository (DSRR)

The Data Science Resource Repository (DSRR) is a curated set of resources for learners, educators, researchers, career explorers, and professionals that promotes data science literacy.

The DSRR leverages open-source resources and best practices to broadly increase data science capacity, with a specific focus on underserved communities.

Keeping Data Science Broad Webinar Series

Having the skills to understand and make sense of data can provide a sense of power – or conversely, a sense of powerlessness to communities without these skills. The economic and social consequences of the Data Divide severely limit the opportunities of those who are unable to take advantage of the data revolution. The goal of this series is to garner community input into pathways for keeping data science as a discipline broadly inclusive, with input from data science programs in any region across the nation, either traditional or alternative, and from a range of institution types including minority-serving institutions, community colleges, liberal arts colleges, tribal colleges, universities, and industry partners.

Harnessing the Data Revolution (HDR) PI Meeting and Coordination Activity

This is the inaugural PI meeting for the National Science Foundation’s Harnessing the Data Revolution (HDR) Big Idea. HDR is a visionary, national-scale activity to enable new modes of data-driven discovery, allowing fundamentally new questions to be asked and answered in science and engineering frontiers, generating new knowledge and understanding, and accelerating discovery and innovation.

National Student Data Corps (NSDC)

The National Student Data Corps (NSDC) is a community-developed initiative that teaches data science fundamentals to students across the United States and around the world, with a special focus on underserved institutions and students.

The NSDC provides a number of educational resources and professional events related to data science for students, teachers, researchers, and STEM enthusiasts. 


In collaboration with five other U.S. government agencies, the U.S. National Science Foundation (NSF) has invested $26.7 million in 18 projects through its Building the Prototype Open Knowledge Network (Proto-OKN) program. The NEBDHub and colleagues at Wright State University will develop educational materials and tools for people or organizations interested in engaging with the Proto-OKN with support from NSF award #2333532

Seed Fund Awards

Each Big Data Hub provides support for innovative research programs in the form of Seed Funding. Learn more about the various projects supported, including outcomes and impacts, on each of the Hub websites.

Trustworthy Data Working Group

Open science relies on data integrity, collaboration, high performance computing, and scalable tools to achieve results, but currently lacks effective cybersecurity programs that address the trustworthiness of scientific data. The Trustworthy Data Working Group (TDWG) is a collaboration between the four Big Data Hubs and Trusted CI, the NSF Cybersecurity Center of Excellence, along with other partners. The goal of the working group is to understand scientific data security concerns and provide guidance on ensuring data trustworthiness.


Launched by the National Science Foundation (NSF) in 2015, we engage communities, share resources, and build partnerships that harness the data revolution to address societal and scientific challenges.

Metro/Urban Data Science

Precision Medicine

Natural Resources & Hazards 

Big Data Technology


Advanced Materials and Manufacturing

Digital Agriculture

Smart, Connected, and Resilient Communities

Water Quality

Big Data in Health

Health and Disparities

Smart Cities and Communities

Advanced Materials and Manufacturing

Environment and Coastal Hazards

Social Cybersecurity


Education + Data Literacy

Urban to Rural Communities

Responsible Data Science: Security + Privacy Ethics

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