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: 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 Digital 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

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  • grantee: Community Initiatives
    amount: $497,338
    city: San Francisco, CA
    year: 2017

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

    • Program Digital 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

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  • grantee: Rensselaer Polytechnic Institute
    amount: $774,770
    city: Troy, NY
    year: 2017

    To support the Research Data Alliance regional U.S. organization

    • Program Digital Technology
    • Sub-program Data & Computational Research
    • Investigator Leslie Borrelli

    The Research Data Alliance is an international grassroots organization that brings technologists, developers, and researchers together to jointly develop and adopt data-sharing infrastructure, tools, and practices. RDA working groups tackle some of the thorniest topics facing data science today, including reproducibility, data preservation, interoperability, data citation, and best practices for data repositories. RDA provides useful services to the data-driven research community, including to many grantees supported through the Foundation’s Digital Information Technology program. Funds from this grant provide core operating support to the U.S. regional chapter of the RDA and support efforts to build out the organization’s U.S. administrative infrastructure and grow its membership base. Funded activities over the next three years include the production of reports detailing RDA data sharing recommendations, member outreach, creation of adoption case studies for RDA products and services, trainings, annual stakeholder meetings, and the development of a long term business plan for independent sustainability.

    To support the Research Data Alliance regional U.S. organization

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  • grantee: Council on Library and Information Resources
    amount: $925,362
    city: Washington, DC
    year: 2017

    To support data and software curation postdoctoral fellowships, in order to develop emerging leaders in the field and build capacity within academic libraries

    • Program Digital Technology
    • Sub-program Data & Computational Research
    • Investigator Charles Henry

    This grant provides three years of support to an ongoing postdoctoral fellowship program administered by the Council on Library and Information Resources (CLIR) that aims both to grow data and software curation capacity within research libraries and to develop the next generation of data and software curators who will bring deep research experience into the organizational context of the university library. Fellows are PhD-level researchers who are selected, in part, for their potential to build collaborative relationships with natural and social scientists across the university. Since the launch of the program in 2012, fellows have been placed at a wide variety of universities, working with scientists and library staff on projects to improve the university’s data and software curation services and responding to requests from researchers to build tools and resources that speak to their needs. Recent participating institutions include UC Berkeley, MIT, Yale, the California Digital Library, Vanderbilt, the Federal Reserve Bank of Kansas City and the U.S. Agency for International Development. Funds from this grant will support the 2018-2020 class of CLIR fellows, which includes a cohort of four software curation fellows as well as four additional data curation fellows in the natural and social sciences. In addition to covering some salary, travel, and professional development support for fellows, grant funds cover operational costs associated with the administration of the program.

    To support data and software curation postdoctoral fellowships, in order to develop emerging leaders in the field and build capacity within academic libraries

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  • grantee: FORCE11
    amount: $20,000
    city: San Diego, CA
    year: 2017

    To partially support the 2017 Future of Research Communication and eScholarship meeting

    • Program Digital Technology
    • Sub-program Data & Computational Research
    • Investigator Cameron Neylon

    To partially support the 2017 Future of Research Communication and eScholarship meeting

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  • grantee: Columbia University
    amount: $15,000
    city: New York, NY
    year: 2017

    To support a meeting on offline data transfer networks

    • Program Digital Technology
    • Sub-program Data & Computational Research
    • Investigator Mark Hansen

    To support a meeting on offline data transfer networks

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  • grantee: University of California, Riverside
    amount: $499,480
    city: Riverside, CA
    year: 2017

    To support continued development of a browser-based interactive platform for exploring -omic datasets

    • Program Digital Technology
    • Sub-program Data & Computational Research
    • Investigator Holly Bik

    Bioinformaticist Holly Bik was particularly interested in broadening the ability of metagenomics researchers to take advantage of data visualization in order to explore and understand population distributions. With Sloan support, Bik developed Phinch, a web-based visualization platform that easily integrates with common tools like QIIME. This grant provides three years of funding to Bik to scale up Phinch and grow its user base into a sustainable community-supported software project. Her plan is to begin with a user workshop to refine already-collected requirements from existing users and metagenomics pipeline maintainers, then move back into active development. The technical goals laid out for the platform include the integration of statistical tools into visualization interfaces, an important step to help researchers move from exploration of data through visualization into more robust analysis.

    To support continued development of a browser-based interactive platform for exploring -omic datasets

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  • grantee: New York University
    amount: $550,000
    city: New York, NY
    year: 2017

    To support the development of and data exchange between Datavyu (a tool for video coding) and Databrary (a platform for archiving and controlled sharing of video data)

    • Program Digital Technology
    • Sub-program Data & Computational Research
    • Investigator Karen Adolph

    In the behavioral sciences, fields like child development and behavioral ecology often rely on video as a primary source of research data. The major research repositories of behavioral video, however, do not have much more sophistication than YouTube, relying on keywords and transcripts for discovery and failing to leverage incredibly sophisticated coding data into analytic tools. Karen Adolph at NYU and Rick Gilmore of Penn State are accomplished psychologists who are responsible for developing a leading video coding tool, the open source Datavyu, and an innovative platform for behavioral video archiving, Databrary. The former is notable for its flexibility and fine-grained resolution, and the latter for its ability to set precise access controls to comply with the myriad restrictions related to the use of human subjects of the projects whose data it hosts. This grant supports an 18-month project by Adolph and Gilmore to use Datavyu and Databrary to model integration between coding tools and data repositories more generally. Since both platforms are open source and have active user communities, they are excellent candidates to prototype how standards-compliant coding data might be transferred into a data repository alongside its raw video, and how that repository might then leverage that coding data into new discovery and analytic interfaces. This work could generalize to a host of other coding tools, not to mention the handful of other social science data archives like ICPSR and Dataverse that are tentatively moving into hosting behavioral video data.

    To support the development of and data exchange between Datavyu (a tool for video coding) and Databrary (a platform for archiving and controlled sharing of video data)

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  • grantee: Julia Computing
    amount: $912,609
    city: Newton, MA
    year: 2017

    To support the continued development of the Julia programming language

    • Program Digital Technology
    • Sub-program Data & Computational Research
    • Investigator Viral Shah

    Developed by a small group of MIT computer science students, Julia was designed to be the “Goldilocks” of computer programming languages, combining the ease of use of high level languages like R or Python with the computing power of workhorse languages like C or Fortran. Julia has steadily grown in popularity since its 2012 release and has found particularly enthusiastic use in economics and finance. Further improving the language however, requires addressing several key pain points for research users. Funds from this grant support a project to update the Julia language and substantially improve usability for researchers by improving documentation and error messaging, building a substantially faster compiler, and developing a package manager to facilitate the discovery and use of third-party extensions. In addition, this grant includes resources for a concerted push to diversify the currently overwhelmingly white and male Julia developer community. Testing the application of models that have been successful in other open source software projects, the team will devote substantial effort to engagement with women and underrepresented minority groups, and offer travel subsidies for participation in Julia events to diversify its community.

    To support the continued development of the Julia programming language

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  • grantee: The University of Chicago
    amount: $750,000
    city: Chicago, IL
    year: 2017

    To study how the choice of computational tools such as programming languages and data-analysis environments impacts their users

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

    In linguistics, the Sapir-Whorf hypothesis holds that the structure of a language affects its speakers’ world view and modes of thought. University of Chicago computational sociologist James Evans and University of Wisconsin cognitive scientist Gary Lupyan hypothesize that a version of this hypothesis applies to programming languages. They propose to explore the “cognitive and social consequences of programming and data analysis environment” choices, specifically how the characteristics of programming languages might influence a developer’s efficiency, creativity, and collaboration. To evaluate this hypothesis, Evans and Lupyan will undertake exploratory studies of observational data on software development broadly then then look more closely at specific cases in scientific software development. They will use large-scale project data from GitHub to determine which specific features of programming languages (e.g., static vs. dynamic variable typing) might be best operationalized as independent variables that influence the ways in which developers think and work. They will then test the hypotheses that surface through that exploratory work using a series of comparative-language experiments to be run in constrained development environments, including the Jupyter Notebook platform. Grant funds provide three years of research support for the project.

    To study how the choice of computational tools such as programming languages and data-analysis environments impacts their users

    More