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: 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

    More
  • 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

    More
  • 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)

    More
  • 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

    More
  • 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
  • grantee: Aspiration
    amount: $20,000
    city: San Francisco, CA
    year: 2017

    To support participation in a summit on the sustainability of open source software projects

    • Program Digital Technology
    • Sub-program Data & Computational Research
    • Investigator Allen Gunn

    To support participation in a summit on the sustainability of open source software projects

    More
  • grantee: NumFOCUS
    amount: $20,000
    city: Austin, TX
    year: 2017

    To support travel by students and junior faculty to a workshop focused on the development of scientific software using the R statistical computing language

    • Program Digital Technology
    • Sub-program Data & Computational Research
    • Investigator Karthik Ram

    To support travel by students and junior faculty to a workshop focused on the development of scientific software using the R statistical computing language

    More
  • grantee: University of California, Office of the President
    amount: $20,000
    city: Oakland, CA
    year: 2017

    To develop connections between open source scientific software developers, through a one-day meeting

    • Program Digital Technology
    • Sub-program Data & Computational Research
    • Investigator Guenter Waibel

    To develop connections between open source scientific software developers, through a one-day meeting

    More
  • grantee: Columbia University
    amount: $313,241
    city: New York, NY
    year: 2017

    To enable greater use of machine learning techniques in scientific research through technical and user experience improvements to scikit-learn

    • Program Digital Technology
    • Sub-program Data & Computational Research
    • Investigator Andreas Mueller

    Written in Python, scikit-learn is an open source machine learning software package used widely across the natural and social sciences (the “software paper” that introduced scikit-learn in 2011 has been cited over 4,700 times). Its maintainers have identified a set of improvements that would make it substantially more efficient for scientific users and enable more reproducible research, but which would require more focused time than any contributor can currently offer. This grant provides funds to Columbia University’s Andreas Mьller, one of the current core maintainers of scikit-learn, to design and implement the identified improvements. These include more flexible data types, better integration with Jupyter notebooks for model exploration, and some technical fixes that will substantially improve platform stability and performance.

    To enable greater use of machine learning techniques in scientific research through technical and user experience improvements to scikit-learn

    More
  • grantee: Code for Science and Society
    amount: $394,000
    city: Portland, OR
    year: 2017

    To develop software for open, reproducible, version-controlled, and testable spreadsheets

    • Program Digital Technology
    • Sub-program Data & Computational Research
    • Investigator Nokome Bentley

    A whole lot of science takes place in spreadsheets. Many researchers still bring their data into Excel as a convenient environment for exploration and analysis. Unfortunately, Excel has none of the attributes of a modern platform for reproducible computational research: it is not easily extensible to interoperate with data repositories; does not easily allow for version control; and cannot take advantage of substantial investments in open source scientific software packages. Nokome Bentley, a New Zealand-based fisheries scientist and software developer, has been developing a project called Stencila Sheets, an authoring tool that offers users familiar Google Docs–style interfaces, but is something quite different under the hood. His vision is a spreadsheet where each cell can hold data or code written in R, Python, Julia, or several other computing languages, with the output of a given cell addressable by any other cell in the sheet. The proximate goal is not to develop a direct competitor to Excel, but rather to offer spreadsheet users an easy bridge into the open-source ecosystem of reproducible computational science. Funds from this grant will allow further development of the Stencila platform over the next year, including increased integration with the Jupyter computing ecosystem, the development of a standalone desktop client, and the addition of features like real-time collaboration and import/export from other platforms.

    To develop software for open, reproducible, version-controlled, and testable spreadsheets

    More