What is SSC-NG?

The Statistical Software Components (SSC) archive is a vital, open-access repository of software packages for the statistical programming language Stata. Stata is widely used across the social sciences, particularly in economics. As of February 2025, there are 3,460 packages on SSC contributed by 1,430 authors, with 1,324,321 package downloads in January 2025 alone. The most heavily used packages have close to 70,000 downloads.

While Stata is a commercial product, the SSC archive is entirely community-run, relying on software created and contributed by volunteers from multiple disciplines. SSC has successfully fostered community contributions for decades, but its current infrastructure has personnel and organizational fragility, limits the findability and accessibility of its resources by modern standards, and is maintained through primarily manual processes.

This project — SSC Next Generation (SSC-NG) — will develop the tools, organizational structure, and infrastructure needed to address these issues and prepare the SSC archive for the next decade, while preserving strong backward compatibility with the present system. We do not intend to break what works well, but to make the parts that need to be more robust, and more efficient.

Intellectual Merit

The project advances knowledge by transforming how researchers discover and access statistical software components in one of social science’s most widely-used quantitative environments. By developing tools to convert Stata-specific documentation (SMCL) into standard web formats and implementing modern search and navigation capabilities, this work creates new technical approaches for making legacy statistical software more discoverable and accessible. The project will also generate new understanding of software usage patterns in quantitative social science research, going beyond traditional citation metrics to capture actual utilization through download statistics and dependencies.

Broader Impacts

This project benefits the broad community of social science researchers who rely on Stata for quantitative analysis. By improving the findability, accessibility, preservation, and reproducibility of statistical software packages, the project accelerates research productivity and promotes code reuse across disciplines. By increasing the transparency of research compendia broadly, it increases the accessibility and credibility of that research. By enhancing the SSC archive’s infrastructure while maintaining its community-driven nature, the project strengthens the culture of open-source software sharing in the quantitative social sciences. The improved documentation system will also facilitate better teaching and learning of statistical methods across the social sciences. The project also directly trains four students at various stages of their careers and in various disciplines.

For the detailed roadmap of how this is being built, see the Development Plan. For who is steering the project, see Governance.