Data Carpentry is a lesson program within The Carpentries. Data Carpentry aims to teach fundamental concepts, skills and tools for working more effectively with data.
From astronomy to molecular biology, our increasing capacity to collect data is changing science. It allows us to ask questions that previously could not have been answered, and it changes the impact that science has on society. Although petabytes of data are now available, most scientific disciplines are failing to translate this sea of data into scientific advances. The missing step between data collection and research progress is a lack of training for researchers in crucial skills for effectively managing and analyzing large amounts of data.
Data Carpentry addresses this gap by teaching researchers the fundamental data skills they need to conduct their work. Our goal is to provide researchers high-quality, domain-specific training covering the full lifecycle of data-driven research. We teach hands-on workshops in data organization, management, and analysis to increase data literacy and improve research efficiency. Our domain-specific approach allows us to tailor the data, content, and tools to reflect the specific data and analysis needs of different areas. Domain specificity also allows us to build new skills on knowledge frameworks familiar to learners, and to motivate workshops using real scientific questions and data relevant to the learners’ field of study. This approach allows learners to see immediate value in the skills they are learning and to put new techniques immediately into practice.
A workshop can’t teach a researcher everything they need to know about data management and analysis, however it drastically reduces the barrier to entry and imparts the skills for continued learning and engagement. Our ultimate goal is to enable data-driven research in diverse disciplines by creating strong communities of data scientists and empowering them to conduct more innovative and effective research.
Funding and Support
For information about our current and past supporters, see Our Supporters.
For a list of papers, and other materials about Data Carpentry, see this page.