This grant provides three years of continuing support to the National Action Council for Minorities in Engineering to administer scholarships for graduate students supported through the Foundation’s Minority Ph.D. program and the Sloan Indigenous Graduate Partnership.  Funded activities include the timely execution of scholarship payments, accurate accounting of scholarship disbursements and balances, data collection on supported students, filing regular reports to the Foundation on scholarship disbursements, maintenance of the program’s website and associated forms, and the production of a series of webinars aimed at supported scholars.

In contrast with academics in other fields, economists and other social scientists have been slow to adopt new open source programming languages, instead sticking with expensive proprietary applications like Matlab and STATA when doing modeling or running complex analyses on data.  Because such programs cannot be used without a license, their popularity hamstrings reproducibility, hampers archiving, and hinders reuse of research that employs them. This grant funds efforts by Nobel Prize–winner Tom Sargent of New York University and computational economist John Stachurski of Australian National University to speed the adoption of Python—a compact, powerful open source programming and computation platform—among economists and social scientists.  Funds from this grant will bring Stachurski to NYU for a year to work with Sargent on expanding and promoting the usefulness of Python to economists everywhere.  They will develop free Python modules and teaching materials, publicize the capabilities of the new iPython notebook, give presentations, publish a textbook, and further develop the materials and resources freely available on their website, quant-econ.net.

The Center for Mathematical Talent (CMT) was established in 2010 at New York University's prestigious Courant Institute for Mathematics.  Its mission is to identify, motivate, and nurture those underserved and underrepresented students in New York City schools who could excel in mathematics.  This grant provides three years of continued support for the activities of the CMT, including partnering with other educational organizations to set up satellite programs for students unable or unwilling to travel to Manhattan, training public school teachers and others to run extracurricular programs like “math circles,” and developing educational materials, like math games, designed to present mathematics in ways that are challenging, fun, and engaging.  In addition, CMT plans over the next three years to double the numbers of students and instructors reached; diversify its sources of support; restructure its website to better serve its core audiences, and refine its data collection procedures so as to better measure program impact.

This grant funds a project led by Nicholas Bloom (Stanford University) and Steven Davis (University of Chicago) to examine the relationship between uncertainty in the business community and economic performance.  Partnering with the U.S. Census Bureau, Bloom and Davis will survey the nearly 45,000 U.S. business establishments in the 2016 Annual Survey of Manufacturing, asking respondents to provide forecasts about the coming year, including expected demand for products, prices, cost increases, employment, planned investment, and both industry- and economy-wide performance outcomes.  The resulting will be a powerful new dataset that will provide the first direct measure of uncertainty in the business community.  In a related effort, Bloom and Davis will partner with the Federal Reserve Bank of Atlanta to survey a smaller sample of 1,000 businesses on a monthly basis, providing a complementary dataset that will be able to measure how business uncertainty changes over time and in response to new information.   Bloom and Davis plan to use these datasets to construct new measures of economic uncertainty and address a variety of questions, including the impact of uncertainty on business and aggregate economic performance; whether firm-level uncertainty reduces investment, hiring, and R&D; whether firm forecasts of business conditions and outcomes exhibit cognitive biases, and if so, whether these biases vary by firm age, size, performance, management experience, or external conditions.

Funds from this grant provide three years of continued support for two programs at the City University of New York aimed at supporting faculty and students in STEM disciplines. The first, CUNY's Summer Undergraduate Research Program (C-SURP), provides talented undergraduates with the opportunity to engage in hands-on, in-the-lab research, assisting CUNY science faculty with ongoing research projects during the summer. Grant funds will support 10 students in each of 2015, 2016, and 2017, providing a $4,000 housing allowance and a $4,000 living stipend to each student. The second supported program under this grant is CUNY's Junior Faculty Research Award in Science and Engineering (J-FRASE) program, which supports promising early-career STEM faculty at CUNY with a $50,000 fellowship for use in research. Over the course of the next three years, 12 faculty will receive fellowships through this grant.

In 2013, the Foundation approved a one?year grant to rOpenSci, a collective of data scientists, to build and promote a suite of “packages” for R, a powerful programming language and software environment for statistical computing and graphics.  The packages aimed to greatly simplify the process of gathering data from various archives and services commonly used by researchers.  Such software modules dramatically lower the barriers to R use, freeing researchers from having to write their own idiosyncratic code when parsing data from commonly used repositories like Dryad, the Global Biodiversity Information Facility, or the Biodiversity Heritage Library. This grant provides continued support for this project.  The project team will continue software development, shifting their focus to several generic needs like spatial data analysis and the submission of data to repositories for publication, as well as supporting R interoperability with popular emerging tools for data management like Dat.  To further lower barriers to R use in data-driven research, rOpenSci will also develop openly licensed curricular “modules” that could be incorporated into graduate seminars or informal workshops.  In speed adoption, rOpenSci will cultivate an initial cohort of a dozen “ambassadors” from across the natural and social sciences who will develop domain-specific R packages and lead various outreach and community-building efforts in their home disciplines.

Funds from this grant support efforts by an international team of researchers including Kristin Eschenfelder and Greg Downey of the University of Wisconsin, Madison School of Library and Information Studies and Kalpana Shankar at the University College Dublin School of Information and Library Studies to develop a set of case studies of social science data archives. Beginning with a pilot case study already underway of the University of Michigan’s Interuniversity Consortium for Political and Social Research, the team will select a cohort of up to five data archives for study that meet specific criteria, including longevity, collections that are contributed by others, and funding models that do not rely on direct government support.  The researchers will draw on interviews and archival research to develop detailed histories of these archives with a particular focus on how they evolved their current access policies and business models.  The ensuing case studies will help provide a more robust foundation for discussions about how data archives might be made sustainable over the long term.

Raw data needs preparation to be useful for research. In some cases, what is needed is cleanup and normalization; in others, tagging or categorization of dataset elements. Depending on the domain and kind of data, computers can do much of the necessary work, but some tasks, due to fuzziness or complexity in the data, are currently beyond the bounds of computation. Much data prep requires human eyes, human minds, human judgment, and human labor, a daunting demand when the size of many modern scientific datasets is measured in terabytes. The Zooniverse project, an international effort initially based at Oxford University and now housed primarily at the Adler Planetarium in Chicago, offers a straightforward solution to this problem: divide the work into very granular tasks, gather a large crowd of science enthusiasts, and let them loose on the data.  The result has been remarkably successful.  Over the past few years the project has launched more than 20 citizen science research projects across meteorology, ecology, astrophysics, history, zoology, pathology, and geology and has attracted more than 1.5 million registered and anonymous users, including a core group of 15,000 dedicated volunteers who contribute at least monthly. Funds from this grant support the next phase in Zooniverse’s evolution:  enabling faster growth to meet the explosive demand for Zooniverse projects.  The Zooniverse team plans to decentralize their governance model, expanding their online platform to allow community volunteers to take part in core management functions while still maintaining peer review and oversight by the research community to ensure that the project’s high scientific standards are met.  The result will be a more self-sustaining and scalable model, which the Adler Planetarium is committed to maintain as a national leader in using citizen science to serve its research and educational missions.