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.

Recent technological advances have made it possible to assign Digital Object Identifiers (DOIs) to software projects, allowing authors to cite them in just the same way they have traditionally cited a journal article or study. Yet we have not seen much movement toward the actual citation of software by authors—a problem, since citation remains the primary way to acknowledge valuable work among scientists. The problem appears to be cultural, not technical, and it thus makes sense to focus on change at a disciplinary level. Astronomy presents an ideal opportunity to model a best-practice approach to software citation in the sciences. This grant funds an effort by the American Astronomical Society (AAS) to develop and implement a new "software broker" system that would automate the creation and management of metadata about software versions, licensing, and authorship. The move would prompt software developers to fully document their code in structured ways that could easily be imported into discovery tools like the Astronomical Data Service (ADS), which tracks citations across formal and preprint articles and serves as a search interface across the astronomy literature. Though developed within astronomy, most of the systems and workflows to be developed are generic and applicable much more broadly.