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: North Carolina State University
    amount: $48,002
    city: Raleigh, NC
    year: 2018

    To co-fund a workshop on data science within Land Grant Universities

    • Program Technology
    • Sub-program Data & Computational Research
    • Investigator Marc Hoit

    To co-fund a workshop on data science within Land Grant Universities

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  • grantee: Regents of the University of Idaho
    amount: $20,000
    city: Moscow, ID
    year: 2018

    To support the first U.S. Semantic Technologies Symposium

    • Program Technology
    • Sub-program Data & Computational Research
    • Investigator Xiaogang Ma

    To support the first U.S. Semantic Technologies Symposium

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  • grantee: Columbia University
    amount: $20,000
    city: New York, NY
    year: 2018

    To partially support a summit of data science leadership across US universities

    • Program Technology
    • Sub-program Data & Computational Research
    • Investigator Jeannette Wing

    To partially support a summit of data science leadership across US universities

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  • grantee: University of California, Berkeley
    amount: $18,500
    city: Berkeley, CA
    year: 2017

    To study private sector research and data sharing practices

    • Program Technology
    • Sub-program Data & Computational Research
    • Investigator Deirdre Mulligan

    To study private sector research and data sharing practices

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  • grantee: Open Source Hardware Association
    amount: $58,920
    city: Boulder, CO
    year: 2017

    To support the development of a dynamic, web-based platform to facilitate the adoption, licensing, and improvement of open source hardware

    • Program Technology
    • Sub-program Data & Computational Research
    • Investigator Alicia Gibb

    To support the development of a dynamic, web-based platform to facilitate the adoption, licensing, and improvement of open source hardware

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  • grantee: University of California, Davis
    amount: $50,000
    city: Davis, CA
    year: 2017

    To further develop tools for the distributed transcription and classification of data from historic sources

    • Program Technology
    • Sub-program Data & Computational Research
    • Investigator Peter Brantley

    To further develop tools for the distributed transcription and classification of data from historic sources

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  • grantee: Georgetown University
    amount: $7,000
    city: Washington, DC
    year: 2017

    To partially support a meeting on the capacity of organizations at the local, state, national and international levels to utilize data to advance science and solve social problems

    • Program Technology
    • Sub-program Data & Computational Research
    • Investigator Michael Bailey

    To partially support a meeting on the capacity of organizations at the local, state, national and international levels to utilize data to advance science and solve social problems

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  • grantee: University of California, Berkeley
    amount: $659,359
    city: Berkeley, CA
    year: 2017

    To support improvements to NumPy, an essential numerical computing utility for the Python programming language

    • Program Technology
    • Sub-program Data & Computational Research
    • Investigator Jonathan Dugan

    If you are working with data using the Python programming language, you probably rely on an open source software library called NumPy which provides tools to store large multidimensional arrays and matrices, algorithms for their analysis and manipulation, and means to move them from one software package to another. Without NumPy, scientific computing in Python would be slower, more cumbersome, and more error-prone. Initially released in 2005, NumPy’s core code has built up a substantial “technical debt,” which not only constrains the future development of the platform but also creates a high barrier to entry into its open source developer community. This grant supports an ambitious project led by NumPy core developer Nathaniel Smith to discharge this technical debt and set in place standards and architecture to encourage more sustainable development going forward. Using this funding, Smith and a team of developers will develop new modular systems for creating data types and arrays of data within NumPy; conduct a wholesale clean-up of the NumPy codebase; and launch a new community engagement process that includes face-to-face meetings, the onboarding of new contributors, and processes for proposing and evaluating larger architectural changes to the platform.

    To support improvements to NumPy, an essential numerical computing utility for the Python programming language

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  • grantee: Hopewell Fund
    amount: $211,091
    city: Washington, DC
    year: 2017

    To develop centralized coordination capacity within the Data Science Environment partnership for alumni networking, evaluation, and internal and external communications

    • Program Technology
    • Sub-program Data & Computational Research
    • Investigator Ali Ferguson

    The Moore-Sloan Data Science Environments (DSEs) are a major collaboration between Sloan and the Gordon and Betty Moore Foundation to support three university-based data science centers devoted to empowering data-driven research through the creation of new tools, resources, infrastructure, and career paths that help university researchers make the most of the possibilities that data science opens for the 21st century scientist. Supported centers have been launched at the University of California, Berkeley; NYU; and the University of Washington. This grant provides funds for the hire and support of a DSE coordinator who will take responsibility for internal communication between the DSEs, serve as a visible point of contact for inquiries and outward messaging for best practices coming out of the DSEs, and develop a network to connect and support “alumni” who have in one way or another left the DSE universities and are now building data science capacity at other universities. This new coordinator position will be initially housed within the Hopewell Fund, an arm of the New Venture Fund.

    To develop centralized coordination capacity within the Data Science Environment partnership for alumni networking, evaluation, and internal and external communications

    More
  • grantee: NumFOCUS
    amount: $497,338
    city: Austin, TX
    year: 2017

    To support the development of data and computational skills training curricula in image analysis, economics, and chemistry

    • Program Technology
    • Sub-program Data & Computational Research
    • Investigator Tracy Teal

    Data Carpentry is community-driven organization that develops and teaches workshops on the fundamental data skills needed to conduct research. A sister effort to Software Carpentry, which provides researchers with hands-on training in the basic software engineering skills that are increasingly needed for the conduct of 21st century science but are unlikely to be taught in standard scientific PhD curricula, Data Carpentry workshops target researchers who think of themselves not as software developers, but who may write custom code for the management, preparation, and analysis of their research data. Because the size, shape, and format of data differ substantially across disciplines, the “Data Carpentry” curriculum is necessarily domain-specific in a way that Software Carpentry is not. After initial successes in ecology, genomics, geospatial data, and biology, the Data Carpentry leaders will use the funds from this grant to grow into new disciplines (image analysis, economics, and chemistry), in the process standardizing their curriculum development processes in order to make it easier to form new disciplinary communities. Over the next two years, Data Carpentry plans to assemble Advisory Committees for each area of focus, run curriculum-building hackathons, and then pilot each bootcamp several times before releasing to the broader community of Software/Data Carpentry members.

    To support the development of data and computational skills training curricula in image analysis, economics, and chemistry

    More
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