In 2013, the Foundation partnered with the Gordon and Betty Moore Foundation to launch a five-year, $37.8 million initiative that aspired to advance data-intensive scientific discovery, empowering researchers to be vastly more effective by utilizing new methods, new tools, new partnerships, and new career paths. The initiative led to the funding of three university partnerships, one with New York University, one with the University of California, Berkeley, and one with the University of Washington, to create Data Science Environments (DSEs) that would innovate new models for advancing data science at American universities. The centers would focus on three core goals: crafting meaningful interactions between data scientists and disciplinary scientists, experimenting with long-term, sustainable career paths for data scientists in the university system, and developing new analytical tools and research practices that will empower scholars to work effectively with data. Initial funding in 2013 was for three years. This grant provides the anticipated final two years of funding.  

In 2013, the Foundation partnered with the Gordon and Betty Moore Foundation to launch a five-year, $37.8 million initiative that aspired to advance data-intensive scientific discovery, empowering researchers to be vastly more effective by utilizing new methods, new tools, new partnerships, and new career paths. The initiative led to the funding of three university partnerships, one with New York University, one with the University of California, Berkeley, and one with the University of Washington, to create Data Science Environments (DSEs) that would innovate new models for advancing data science at American universities. The centers would focus on three core goals: crafting meaningful interactions between data scientists and disciplinary scientists, experimenting with long-term, sustainable career paths for data scientists in the university system, and developing new analytical tools and research practices that will empower scholars to work effectively with data. Initial funding in 2013 was for three years. This grant provides the anticipated final two years of funding.  

A 2015 Sloan Foundation grant to nonprofit Phoenix Bioinformatics supported the development and initial deployment of a paywall service for scientific databases. Sloan support enabled the organization to generalize its technical infrastructure to offer database providers fine-grained metering of access (and the ability to flexibly set the boundary between free and paid access), and develop customer-facing tools to allow institutional and national subscribers to manage and report on subscription use. Based on an assessment of its operating costs and likely growth opportunities, the organization has developed a realistic, fee-based funding model that promises to deliver long-term, independent sustainability within the next two years. Funds from this grant provide operational bridge funding to the organization while it implements this plan.

Created by Paul Ginsparg, arXiv is a popular preprint platform that has become an essential scholarly communication tool in much of physics, mathematics, and computer science. It is also running on 25-year-old software written in a language (Perl) for which developers are becoming hard to find, and thus maintenance is increasingly expensive. arXiv’s Cornell-based leadership team is embarking on a campaign to support a soup-to-nuts rebuild of arXiv’s database, submission and review workflows, and public interface. In 2016, the team conducted a user survey to identify features most in demand and hosted a technical workshop to identify the challenges of a redesign. The next step is to move from general principles to initial design and prototyping, testing various infrastructure options for the full rebuild. Funds from this grant will support this 18-month planning effort.  

The writing of scientific software is an increasingly important part of modern scientific practice. Properly rewarding such activity requires the wide adoption of new citation practices where authors formally recognize the software they use in their work. Yet a change in citation practices would leave untouched the scientific literature produced to date, which is filled with explicit or implicit mentions of software in the body, footnotes, figures, or acknowledgments sections of articles. Funds from this grant support a project by James Howison of the University of Texas, Austin, School of Information, to develop means to identify software citations from the current corpus of scientific papers. Howison will assemble a team that includes technologists Heather Pirowar and Jason Priem, compile a gold-standard dataset of software references in the scientific literature, and then develop a machine learning system trained on that dataset to recognize software references in scientific articles. The team will then deploy, test, and refine this system in three different prototypes.

Annual Reviews is a nonpro?t publisher of a prestigious series of multi?author reviews in 46 discipline?speci?c ?elds in natural and social science. From analytic chemistry to economics to virology, these reviews are considered authoritative syntheses of scienti?c developments in each ?eld as determined by 600 leading scientists and academics. Funds from this grant provide partial support for the launch of a digital magazine that would utilize its treasure trove of research to inform the public discourse. This new, web?based magazine will use essays, interviews, videos, podcasts, infographics, and animations to engage a broad audience and will feature the latest scienti?c research on a wide range of subjects, highlighting the real?world signi?cance of scienti?c research and demonstrating how it can illuminate subjects that might otherwise appear opaque, confusing, or controversial. Beyond the research community, the magazine is aimed at non–research professionals, the media, educators and students, policy specialists, patients and patient advocates, and the general public. The magazine would produce five to ten substantive text and multimedia items per week, plus one long video per month and weekly short videos. All items will be supported by two to three AR reviews freely available for a speci?ed period, allowing readers a deep dive into popular social issues. In addition, all magazine content will be free to read and with appropriate attribution to republish online and in print, signi?cantly increasing its value for research, education, and innovation and multiplying the readership, especially on social media.

This grant funds the work of theoretical macroeconomist Xavier Gabaix, who is endeavoring to explain puzzling macroeconomic phenomena by importing into macroeconomic models insights gleaned from behavioral psychology. Contrary to the prevailing wisdom among macroeconomists, Gabaix’s work assumes human beings have limited attentional resources and must make choices about what to pay attention to and what to ignore. When attention is scarce, Gabaix argues, the pressing concerns of today crowd out consideration of distant tomorrows. This much microeconomists have known for some time. Gabaix’s contribution has been to show how this scarcity of attention and the consequent focus on the now can, in the aggregate, have predictable macroeconomic effects. Indeed, in early work Gabaix has used these assumptions to predict certain stubborn macroeconomic facts—like the absence of inflation in the U.S. despite years of low interest rates—that have vexed more traditional economic models. Funds from this grant provide three years of support to Gabaix to expand and continue this work. Supported activities include the testing and calibrating of Gabaix’s models against real-world data and the writing of a textbook that uses his framework to explain standard, well-understood macroeconomic phenomena.

This grant funds a project led by Alanna McCargo and Laurie Goodman at the Urban Institute’s Housing Finance Policy Center, to create a relational research database that links mortgage data made available through the Home Mortgage Disclosure Act with geographic and other data from the Census Bureau’s American Community Survey (ACS). The resulting dataset promises to provide economists and other researchers with a powerful new resource for investigating questions related to loan markets, geographic variations in housing prices, and consumer demand for credit. The Urban Institute team will design and implement a distributed, cloud-based architecture to house the database, and provide online computational access to the data through the Institute’s Spark Social Science computational platform. The team will also create and disseminate public guidelines and best practices for solving common problems with distributed, cloud-based data storage and the analysis of massive datasets.     In addition to the value of the dataset itself to researchers, the project will bolster the Urban Institute’s institutional expertise in addressing legal, security, privacy, and data acquisition and management issues related to large administrative datasets.

The National Academy of Sciences (NAS) was chartered by Abraham Lincoln “to give advice to the nation.” And, man, does it ever. Commissioned studies released during the first few days of September 2016 alone, for example, cover everything from clean electric power options to molybdenum-99 production, from eye health to eldercare. Funders and clients alike know the Academy’s work to be prestigious, authoritative, and impartial, but slow, inefficient, and expensive. Internal studies of NAS operations conducted sporadically over the years have resulted in only modest modifications. Now the incoming president, Marcia McNutt, wants to do more than that. A former editor of Science magazine and the first woman ever elected to lead the Academies, she is committed to comprehensive reform of how the NAS functions. Her first step is commissioning an outside management study by a distinguished but independent panel. The National Academy of Public Administration has agreed to carry out the project. Funds from this grant provide partial support for this independent management study.

Empirical economists tell tales of woe about the difficulties of accessing, processing, linking, and analyzing administrative data. Many have tried to address such impediments independently in the course of this or that research project. A piecemeal approach, however, is less effective than what might be accomplished by working together. This grant supports a project by a team of empirical economists and technologists at Stanford University to build and staff a new Stanford Data Core that will reduce social scientists’ struggles, and enhance their triumphs, with administrative data. Led by principal investigator Mark Cullen, the team has already gained access to over 230 administrative datasets, more than 100 server racks, and petabytes of data storage. The team will begin by harmonizing, documenting, cleaning, and adding to these datasets and then moving computations on them to the cloud in collaboration with Google. After the data have been pulled together, the team will test this new computational environment through the launch of four pilot research projects covering topics in economics from economic opportunity to contract labor markets. Though interesting in themselves, the projects will primarily serve as useful test cases to measure the functioning of the new computational environment. Finally, the project team is particularly keen on finding, sharing, and standardizing solutions to the legal challenges that encumber research on administrative data. Working with university lawyers at Stanford, the team will model what routine nondisclosure and data use agreements can and should look like. They will then promote this resource and these contracts to the wider scientific community.