This grant supports the continued operation and growth of the Community Health Analytics Open Source Software (CHAOSS) project.  Led by Sean Goggins and Matt Germonprez, CHAOSS is an organization devoted to the creation thriving open source software communities through the development and adoption of research-driven 'community metrics' that assess the responsiveness, engagement, inclusion, and other properties of the distributed community of developers who build and maintain a given project. Well-designed community metrics are critical signals of the resilience and sustainability of an open source project, can inform risk assessment decisions about the use of a particular piece of open source software, and can facilitate efforts to improve the well-being of project contributors and users. Since its founding, CHAOSS has grown from initially defining a handful of metrics to supporting entire systems of practice and research across industry and academia. CHAOSS’ activities include research on open source project health and sustainability, growing and supporting a community which helps define what open source project health and sustainability mean, public dissemination of databases and resources for informed decision-making, and continued maintenance and development of CHAOSS data and tools. Grant funds will support the continuation and expansion of these activities, as well as several new ones, including the launch of a new data science initiative aimed at lowering barriers to adoption; support for regional community leads in under-resourced areas of Africa, Asia, and Latin America, and the execution of plans to move the organization toward independent financial sustainability.

This grant provides ongoing support to Ian Cosden at Princeton University, who is formalizing 'US-RSE', a professional association for research software engineers (RSEs) based in the United States. RSEs are a relatively new role within research organizations and typically combine software engineering expertise with experience in specific research disciplines, a role that has become increasingly important as research teams grow more dependent on software to do their work. Grant funds will allow Cosden and the Steering Committee to scale up US-RSE’s efforts by hiring key staff members, supporting an annual conference, awarding community-driven mini-grants, and upgrading critical organizational infrastructure.

The Institute for Research on Innovation in Science (IRIS) maintains a dataset linking university grant and HR/procurement data with scholarly publications. That data has become essential infrastructure for the growing research community focused on measuring scientific productivity and the return on public and private investments in science. This grant funds efforts by Jinseok Kim and Jason Owen-Smith to expand this database by adding in linkages to research software, creating a new resource that can be used to begin to quantify the role software plays in scientific productivity. Unlike publications and patents, software doesn’t necessarily have well-curated author lists, and citations to software codebases are not necessarily well-structured for data mining. On the other hand, versioning platforms like Github have much more granular data on the specific contributions by individuals to codebases over time, which could enable very detailed analyses on who does what kinds of software work.In order to enable research on individual contributions to software as products of research, the IRIS team will identify relevant software repositories and link contributor usernames to the faculty, students, and staff who are represented in university records. Kim will identify software referenced in a corpus of papers, then develop algorithmic ways to match the names of software projects with active Github repositories. Next, he will use a set of name disambiguation methods to link contributors to those repositories with people already represented in the IRIS data, in the process linking those repositories and contributions to funding and other IRIS entities.

Computational modeling is a key tool for a number of scientific disciplines, but good practices in software engineering are not necessarily adopted by the developers of those models. To take one example, interoperability can be especially important to modeling, as the linking of models can be critical to particular research questions, but many models aren’t released with the metadata necessary to make this possible.Funds from this grant support the two years of activities at the Open Modeling Foundation (OMF), a modeling standards organization, aimed at advancing best practices in scientific modeling. The OMF’s activities are structured through three working groups: a Standards working group that will develop and maintain community-driven recommendations for computational modeling; a Certification working group that will develop methods to affirm when models meet those standards; and an Education and Training working group that will develop and maintain curricula to encourage best modeling practices by the broader scientific community with a particular focus on students and early career researchers. Each working group has an initial chair who will recruit one or more co-chairs within the first year, with a particular eye toward diversifying the OMF leadership.