Grants Database

The Foundation awards approximately 200 grants per year (excluding the Sloan Research Fellowships), totaling roughly $80 million dollars in annual commitments in support of research and education in science, technology, engineering, mathematics, and economics. This database contains grants for currently operating programs going back to 2008. For grants from prior years and for now-completed programs, see the annual reports section of this website.

Grants Database

Grantee
Amount
City
Year
  • grantee: Harvard University
    amount: $751,941
    city: Cambridge, MA
    year: 2015

    To facilitate social science research on large-scale datasets by expanding the capabilities of Dataverse repository software

    • Program Digital Technology
    • Sub-program Data & Computational Research
    • Investigator Gary King

    There are currently no academic social science repositories that can routinely handle terabytes of data. This despite the fact that the rise of the Internet and new sensing technologies are creating large new datasets of potential interest to social scientists, like phone usage data or geospatial social media data. This grant supports efforts by Gary King at Harvard University to expand the popular Dataverse platform so that it becomes the first data archiving and management application capable of handling social science data at the terabyte scale. Fully open source, Dataverse is a decentralized web application that allows individual institutions to download and run their own instances. Universities and research labs can manage their data easily while at the same time configuring the system to meet their own needs and comply with their own institutional policies. Funds from this grant will fund the technical development of the Dataverse platform to accommodate the immense logistical and resource challenges posed by “big data” datasets, expanding the power of an increasingly important resource for social scientists everywhere.

    To facilitate social science research on large-scale datasets by expanding the capabilities of Dataverse repository software

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  • grantee: FPF Education and Innovation Foundation
    amount: $75,000
    city: Washington, DC
    year: 2015

    To partially support a meeting on ethical review processes in corporate human subjects research settings

    • Program Digital Technology
    • Sub-program Data & Computational Research
    • Investigator Jules Polonetsky

    To partially support a meeting on ethical review processes in corporate human subjects research settings

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  • grantee: University of Washington
    amount: $19,975
    city: Seattle, WA
    year: 2015

    To support a workshop on the repair and maintenance of technological systems from a historical and sociological perspective

    • Program Digital Technology
    • Sub-program Data & Computational Research
    • Investigator Daniela Rosner

    To support a workshop on the repair and maintenance of technological systems from a historical and sociological perspective

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  • grantee: University of Washington
    amount: $36,500
    city: Seattle, WA
    year: 2015

    To support the incorporation of Optical Character Recognition tools into a citizen science data transcription platform

    • Program Digital Technology
    • Sub-program Data & Computational Research
    • Investigator Kevin Wood

    To support the incorporation of Optical Character Recognition tools into a citizen science data transcription platform

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  • grantee: Boston Symphony Orchestra
    amount: $20,000
    city: Boston, MA
    year: 2015

    To add streaming audio capabilities to the HENRY open-source performing arts research portal

    • Program Digital Technology
    • Sub-program Data & Computational Research
    • Investigator Bridget Carr

    To add streaming audio capabilities to the HENRY open-source performing arts research portal

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  • grantee: The Goodly Institute
    amount: $9,000
    city: Oakland, CA
    year: 2015

    To partially support the development of software

    • Program Digital Technology
    • Sub-program Data & Computational Research
    • Investigator Nicholas Adams

    To partially support the development of software

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  • grantee: University of California, Los Angeles
    amount: $1,424,012
    city: Los Angeles, CA
    year: 2015

    To study how disciplinary configurations, scale, and methods of collection influence the circulation of scientific research data

    • Program Digital Technology
    • Sub-program Data & Computational Research
    • Investigator Christine Borgman

    This grant supports a project by UCLA Professor of Information Studies Christine Borgman to investigate the role of three key variables that influence the circulation of data in a given scientific community: diversity of disciplines, degree of centralization of data collection, and scale of data (i.e., “big” vs. “long-tail”). Through a set of research sites drawn from astronomy, ocean science, and biomedicine, and leveraging over a decade of data collected and coded from additional research sites, Borgman and her team will chart how these three attributes influence data practices. The resulting work will shed light on how the structure of scientific collaborations affects the willingness to share data, and help identify those areas of the scientific enterprise that may be more or less amenable to widespread data sharing. In addition to academic publications, Borgman’s work will produce implementable guidelines that could inform the design of future efforts by private and government funders interested in increasing data sharing in the sciences.

    To study how disciplinary configurations, scale, and methods of collection influence the circulation of scientific research data

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  • grantee: Carnegie Mellon University
    amount: $1,098,493
    city: Pittsburgh, PA
    year: 2015

    To study and develop best practices for community code engagements in the context of scientific software development

    • Program Digital Technology
    • Sub-program Data & Computational Research
    • Investigator James Herbsleb

    Recent work by Jim Herbsleb at Carnegie Mellon University found that volunteer contributions to open source software development projects increased in the aftermath of “community code engagement” (CCE) events like hackathons or summer coding projects. Yet little is known about how exactly CCEs lead to more contributions from volunteers, what makes for a good CCE, and what pitfalls to avoid. This grant funds efforts by Jim Herbsleb to continue his examination of how CCEs spur contributions to scientific software development and to compile a list of best practices for CCE design and implementation. Over the next three years, Herbsleb and his team will study successful and failed CCEs through participant observation, semistructured interviews, and quantitative analysis of software version histories to determine contribution patterns. He will then develop a set of best practices for CCE design and test these guidelines in a series of pilot projects.  Herbsleb and his team will then develop a CCE Toolkit that they will introduce to scientific software developers at a series of workshops attached to disciplinary meetings. The project promises to provide useful new information on how to spur engagement in community software development, an activity that is likely to become increasingly important as science moves further and further into the information age.

    To study and develop best practices for community code engagements in the context of scientific software development

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  • grantee: Columbia University
    amount: $600,007
    city: New York, NY
    year: 2015

    To support the development, maintenance, and dissemination of Stan, a probabilistic programming language that simplifies Bayesian modeling and data analysis

    • Program Digital Technology
    • Sub-program Data & Computational Research
    • Investigator Andrew Gelman

    Bayesian statistical analysis is powerful, yet it is infrequently used in many scientific domains. Calculating Bayesian probability distributions is complicated, and available computer programs designed to do the job are slow and inefficient. As a result, a useful intellectual tool for the scientific analysis of data lies largely untapped. This grant supports development of Stan, a powerful, open source computing platform designed by Columbia University statistician Andrew Gelman that calculates Bayesian probabilities quickly and efficiently. Funds from this grant will support Gelman’s efforts to build out the capabilities of Stan, allowing it to seamlessly interact with other computing platforms like R, Python, and Julia that see wide use in the scientific community. Additional funds support development of Stan’s technical capabilities, allowing it to efficiently handle certain complex statistical models and community development and outreach through the organization of conferences and online users groups.

    To support the development, maintenance, and dissemination of Stan, a probabilistic programming language that simplifies Bayesian modeling and data analysis

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  • grantee: Data & Society Research Institute
    amount: $49,975
    city: New York, NY
    year: 2015

    To map how computer scientists navigate issues of privacy, ethics, and equitable access to data; and to explore how research libraries might support better practices

    • Program Digital Technology
    • Sub-program Data & Computational Research
    • Investigator Danah Boyd

    To map how computer scientists navigate issues of privacy, ethics, and equitable access to data; and to explore how research libraries might support better practices

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