The Carpentries comprises Software Carpentry and Data Carpentry, communities of Instructors, Trainers, Maintainers, helpers, and supporters who share a mission to teach foundational coding and data science skills to researchers. In January, 2018, The Carpentries was formed by the merger of Software Carpentry and Data Carpentry.
While individual lessons and workshops continue to be run under each lesson project, The Carpentries provide overall staffing and governance, as well as support for assessment, instructor training and mentoring. Memberships are joint, and the Carpentries project maintains a shared Code of Conduct. The Carpentries is a fiscally sponsored project of Community Initiatives, a registered 501(c)3 non-profit based in California, USA.
Since 1998, Software Carpentry has been teaching researchers across all disciplines the foundational coding skills they need to get more done in less time and with less pain. Its volunteer instructors have run hundreds of events for thousands of learners around the world. Now that all research involves some degree of computational work, whether with big data, cloud computing, or simple task automation, these skills are needed more than ever.
Data Carpentry develops and teaches workshops on the fundamental data skills needed to conduct research. Its target audience is researchers who have little to no prior computational experience, and its lessons are domain specific, building on learners' existing knowledge to enable them to quickly apply skills learned to their own research. Data Carpentry workshops take researchers through the entire data life cycle.
Library Carpentry is in discussions with The Carpentries to be a Lesson Project, like Software Carpentry* and Data Carpentry. Library Carpentry develops lessons and teaches workshops for and with people working in library- and information-related roles. Its goal is to create an on-ramp to empower this community to use software and data in their own work, as well as be advocates for and train others in efficient, effective and reproducible data and software practices.