This grant supports a project by UCLA Professor of Information Studies Christine Borgman to investigate the role of three key variables that influence the circulation of data in a given scientific community: diversity of disciplines, degree of centralization of data collection, and scale of data (i.e., “big” vs. “long-tail”). Through a set of research sites drawn from astronomy, ocean science, and biomedicine, and leveraging over a decade of data collected and coded from additional research sites, Borgman and her team will chart how these three attributes influence data practices. The resulting work will shed light on how the structure of scientific collaborations affects the willingness to share data, and help identify those areas of the scientific enterprise that may be more or less amenable to widespread data sharing. In addition to academic publications, Borgman’s work will produce implementable guidelines that could inform the design of future efforts by private and government funders interested in increasing data sharing in the sciences.

Recent work by Jim Herbsleb at Carnegie Mellon University found that volunteer contributions to open source software development projects increased in the aftermath of “community code engagement” (CCE) events like hackathons or summer coding projects. Yet little is known about how exactly CCEs lead to more contributions from volunteers, what makes for a good CCE, and what pitfalls to avoid. This grant funds efforts by Jim Herbsleb to continue his examination of how CCEs spur contributions to scientific software development and to compile a list of best practices for CCE design and implementation. Over the next three years, Herbsleb and his team will study successful and failed CCEs through participant observation, semistructured interviews, and quantitative analysis of software version histories to determine contribution patterns. He will then develop a set of best practices for CCE design and test these guidelines in a series of pilot projects.  Herbsleb and his team will then develop a CCE Toolkit that they will introduce to scientific software developers at a series of workshops attached to disciplinary meetings. The project promises to provide useful new information on how to spur engagement in community software development, an activity that is likely to become increasingly important as science moves further and further into the information age.

Bayesian statistical analysis is powerful, yet it is infrequently used in many scientific domains. Calculating Bayesian probability distributions is complicated, and available computer programs designed to do the job are slow and inefficient. As a result, a useful intellectual tool for the scientific analysis of data lies largely untapped. This grant supports development of Stan, a powerful, open source computing platform designed by Columbia University statistician Andrew Gelman that calculates Bayesian probabilities quickly and efficiently. Funds from this grant will support Gelman’s efforts to build out the capabilities of Stan, allowing it to seamlessly interact with other computing platforms like R, Python, and Julia that see wide use in the scientific community. Additional funds support development of Stan’s technical capabilities, allowing it to efficiently handle certain complex statistical models and community development and outreach through the organization of conferences and online users groups.

This grant continues support for the development of Dat, a software platform for the versioning and management of tabular datasets. Inspired by Git, the popular system for version control among distributed software developers, Dat supports the tracking of dataset versions not just at the file level, but at the individual cell level, cataloging cell-by-cell changes to the data. A 2014 grant from the Sloan Foundation has enabled lead developer Max Ogden to move the system from a sketch to a substantial prototype, to ensure that the platform was developed with scientific data in mind, and to launch pilot applications in the sciences using genomic and astronomical data. Funds from this grant will allow Ogden, partnering with Waldo Jaquith of the U.S. Open Data Institute, to move from the current working prototype to a full version 1.0 release. Additional funds support outreach and partnership-building with labs and academic research institutions.

This grant supports the continued development of the Jupyter Notebook, an open source platform for interactive computing that aims to bring the traditional research notebook into the digital age, enabling researchers to capture, log, and version their work from data collection through stages of cleaning, linking, and preparation all the way to analysis and publication. Grant funds will allow the project, led by physicists-turned-data-scientists Fernando Perez and Brian Granger, to hire a project manager and user interface designer, enhance coordination with the growing community of Juypter volunteer developers, and add new features to the platform, including simultaneous multi-user editing, interactive computing capabilities, and better integration with scholarly publishing systems.

Funds from this grant will help Greg Caporaso develop an interactive educational text, An Introduction to Applied Bioinformatics (IAB), that will introduce readers to the core concepts and algorithms of bioinformatics. Focusing on applications to real-world problems, the project will produce a set of interactive notebooks that will allow students to learn about the complex computational methods used in modern bioinformatics in an engaging, hands-on fashion using live code that can be altered, tweaked, executed, and adapted to their own research or data. The project represents an innovative experiment in how advances in information technology are opening new frontiers for high-quality education on computational methods.