Administered by the Council for Economic Education (CEE), the Sloan Teaching Champion Awards recognize excellent high school economics teachers from the New York metropolitan area. The candidates are selected annually based on their effectiveness, creativity, and ability to motivate underserved students. Three winning teachers receive a cash award of $5,000, and their schools each receive $2,500 to support economics education. Honorees are recognized at the CEE’s Visionary Awards dinner, which is attended by academic and practicing economists as well as business and civic leaders. Funds from this grant support administration of the Sloan Teaching Champion Awards for two years. Additional funds support a series of activities by CEE aimed at strengthening economic education in the New York metropolitan area, including six professional development workshops for economics teachers, a three-day teacher boot camp, a pilot program to test innovative economics curricula, and outreach efforts to increase participation.

Most of the advances in microbiology over the past 15 years have focused on bacteria and, to a lesser extent, on archaea and viruses. Protists (microbial eukaryotes), on the other hand, are relatively unstudied, in part because their genomes are large, complex, and poorly represented in the reference genome collections. Funds from this grant support work by Professor Jane Carlton, a leading protist metagenomic expert, to conduct a pilot project to discover protists in pets and pests in all five boroughs of New York City. Carlton will team up with researchers at Fordham University, Barnard College, Hunter College, and the Department of Environmental Protection to collect samples from 20 cats, 20 dogs, 20 rats, 20 mice, 20 cockroaches, and 20 pigeons from each of the five boroughs of New York City, for a total of 600 samples. The team will then use wet-lab methods and computational pipelines to characterize protists found in sewage collected from 14 NYC treatment plants, which service the five NYC boroughs. These data will then be used to amplify and characterize the 18S rRNA marker gene from the pet and pest samples to characterize community diversity and look for associations between the protists found in sewage and the pets and pests that harbor them. The overarching goal is to develop and demonstrate the viability of methods to reliably discover protists in host organisms.

As computers and computational analysis becomes an increasingly central part of scientific practice, more and more scientists are becoming better and better at writing and amending software and code. What scientists often don’t know how to do, however, is to transition a piece of software from something built in their own lab to a sustainable open source, community-driven project. Open source software development, however, has proven to be one of the singularly most influential paths to widespread adoption, dissemination, and innovation in software development. In order for open source to be a viable sustainability strategy for some scientific software, there needs to be better support and training for scientists to “do open source.” This grant funds an initiative at the Mozilla Foundation to help train scientists in the launch and management of open source software development projects. Funded activities include the development of an expanded open science curriculum that details best practices for open source software development, project management, community organizing and facilitation, engaging noncoders, and data management. Additional grant funds support a series of workshops, online chats, and conference calls on these and related topics and and a community-based mentorship program.

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. Funds from this grant support a team at Abt Associates to document and evaluate the individual and joint progress of the three Moore-Sloan Data Science Environments. Combining qualitative and quantitative data collection and analysis, the Abt team will document DSE goals and activities, provide annual reports to each DSE on its progress, and produce three major reports: a landscape survey of data science efforts in top U.S. research universities broadly (to contextualize the DSE activities); an implementation study of the actual execution of the DSE activities at the three universities; and an impact study that aims to understand the consequences of the unique DSE interventions on individual career paths and research outcomes as well as on institutional structures.

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.  

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.