We facilitate and develop lessons for Data Carpentry workshops. These lessons are distributed under the CC-BY license and are free for re-use or adaptation, with attribution. We’ve had people use the lessons in courses, to build new lessons, or use them for self-guided learning.

Data Carpentry workshops are domain-specific, so that we are teaching researchers the skills most relevant to their domain and using examples from their type of work. Therefore we have several types of workshops and curriculum is organized by domain.

Workshop materials

Workshop materials under development or consideration

Semester materials

Community-Contributed materials

These are Carpentries style lessons that have been contributed by community members and are not part of Data Carpentry’s official offerings.


Ecology Curriculum

This workshop uses a tabular ecology dataset from the Portal Project Teaching Database and teaches data cleaning, management, analysis, and visualization. There are no pre-requisites, and the materials assume no prior knowledge about the tools. We use a single dataset throughout the workshop to model the data management and analysis workflow that a researcher would use.

The Ecology workshop can be taught using R or Python as the base language.

Lessons in English

Lesson Site Repository Reference Instructor Notes Maintainer(s)
Ecology Workshop Overview     Karen Cranston, Aleksandra Pawlik, Tracy Teal, Ethan White
Data Organization in Spreadsheets for Ecologists Christie Bahlai, Tracy Teal, Peter R. Hoyt
Data Cleaning with OpenRefine for Ecologists Deborah Paul, Cam Macdonell
Data Management with SQL for Ecologists Timothée Poisot, Rémi Rampin, Donal Heidenblad
Data Analysis and Visualization in R for Ecologists François Michonneau, Auriel Fournier, Ana Costa Conrado, Brian Seok
Data Analysis and Visualization in Python for Ecologists April Wright, Maxim Belkin, Tania Allard

Lecciones en español

Lección Sitio web Repositorio Referencia Guía del instructor Mantenedor(es)
Análisis y visualización de datos usando Python (Beta) Paula Andrea Martinez, Heladia Salgado, Rayna Harris

La plantilla de taller también está disponible en español. Si está interesado en participar con nuestras lecciones, contáctenos en team@carpentries.org.


Genomics Curriculum

The focus of this workshop is on working with genomics data, and data management and analysis for genomics research, including best practices for organization of bioinformatics projects and data, use of command line utilities, use of command line tools to analyze sequence quality and perform variant calling, and connecting to and using cloud computing.

More information about hosting and teaching a Genomics workshop can be found on our FAQ page.

Interested in teaching these materials? We have an onboarding video and accompanying slides available to prepare Instructors to teach these lessons. After watching this video, please contact team@carpentries.org so that we can record your status as an onboarded Instructor. Instructors who have completed onboarding will be given priority status for teaching at centrally-organized Data Carpentry Genomics workshops.

Please note that workshop materials for working with Genomics data in R in “alpha” development. These lessons are available for review and for informal teaching experiences, but are not yet part of The Carpentries’ official lesson offerings.

Lessons

Lesson Site Repository Reference Instructor Notes Maintainer(s)
Genomics Workshop Overview Erin Becker
Project Organization and Management for Genomics Roselyn Lemus, Yujuan Gui, Mateusz Kuzak, Rayna Harris, Peter Hoyt
Introduction to the Command Line for Genomics Shichen Wang, Anita Schürch, Bastian Greshak
Data Wrangling and Processing for Genomics Josh Herr, Fotis Psomopoulos
Introduction to Cloud Computing for Genomics Bob Freeman, Darya Vanichkina, Kevin Buckley, Amanda Charbonneau

Lessons in Development

Lesson Site Repository Reference Instructor Notes Maintainer(s)
Data Analysis and Visualization in R *alpha* Naupaka Zimmerman, Ahmed Moustafa, Krzysztof Poterlowicz, Jason Williams

Social Science Curriculum

This workshop uses a tabular interview dataset from the SAFI Teaching Database and teaches data cleaning, management, analysis and visualization. There are no pre-requisites, and the materials assume no prior knowledge about the tools. We use a single dataset throughout the workshop to model the data management and analysis workflow that a researcher would use.

The Social Sciences workshop can be taught using R as the base language. Interested in teaching these materials? We have an onboarding video and accompanying slides available to prepare Instructors to teach these lessons. After watching this video, please contact team@carpentries.org so that we can record your status as an onboarded Instructor. Instructors who have completed onboarding will be given priority status for teaching at centrally-organized Data Carpentry Social Sciences workshops.

Please note that workshop materials for working with Social Science data in Python and SQL are under development.

Lessons

Lesson Site Repository Reference Instructor Notes Maintainer(s)
Social Science Workshop Overview     TBA
Data Organization in Spreadsheets for Social Scientists Chris Prener
Data Cleaning with OpenRefine for Social Scientists Geoff LaFlair
Data Analysis and Visualization with R for Social Scientists Juan Fung, Angela Li

Lessons in Development

Lesson Site Repository Reference Instructor Notes Maintainer(s)
Data Analysis and Visualization with Python for Social Scientists *alpha* Stephen Childs, Geoffrey Boushey
Data Management with SQL for Social Scientists *alpha* Peter Smyth

Geospatial Data Workshop

This workshop is co-developed with the National Ecological Observatory Network (NEON). It focuses on working with geospatial data - managing and understanding spatial data formats, understanding coordinate reference systems, and working with raster and vector data in R for analysis and visualization.

Join the geospatial curriculum email list to get updates and be involved in conversations about this curriculum.

Interested in teaching these materials? We have an onboarding video and accompanying slides available to prepare Instructors to teach these lessons. After watching this video, please contact team@carpentries.org so that we can record your status as an onboarded Instructor. Instructors who have completed onboarding will be given priority status for teaching at centrally-organized Data Carpentry Geospatial workshops.

Lessons

Lesson Site Repository Reference Instructor Notes Maintainer(s)
Geospatial Workshop Overview   Anne Fouilloux, Arthur Endsley, Chris Prener, Jeff Hollister, Joseph Stachelek, Leah Wasser, Michael Sumner, Michele Tobias, Stace Maples
Introduction to Geospatial Concepts   Tyson Swetnam, Chris Prener
Introduction to R for Geospatial Data Lachlan Deer, Juan Fung
Introduction to Geospatial Raster and Vector Data with R Joseph Stachelek, Lauren O'Brien, Jane Wyngaard

Materials in Early Development

These materials are in early stages of development, and have not yet been incorporated into the official Data Carpentry lesson offerings. If you are interested in being involved in developing one of these lessons, see the information under each lesson description. If you are interested in developing a different curriculum, using The Carpentries lesson templates and pedagogical model, see our Curriculum Development Handbook for information about how to get started.

Digital Humanities Curriculum

Many groups are piloting different versions of this curriculum. There is not yet one set of lessons under active development.

If you are interested in following or being involved in development of this curriculum, please sign up for the dh-curriculum email list

Image analysis Curriculum

With support from an NSF iUSE grant, Dr. Tessa Durham Brooks and Dr. Mark Meysenburg at Doane College, Nebraska, USA have developed a curriculum for teaching image analysis in Python. The materials are available, and are being piloted locally. This pilot phase will be followed by a clean-up phase to incorporate suggestions and feedback from the pilots into the lessons and to make the lessons teachable by the broader community.

If you are interested in contributing to this curriculum, please visit its GitHub repository. For broader discussion about image analysis curriculum development, sign up for the image-analysis-curriculum email list.

Economics Curriculum

A pre-alpha version of a potential Data Carpentry curriculum for Economics is being developed by Dr. Miklos Koren at Central European University. These materials will be piloted in June 2019.

If you are interested in following or being involved in development of this curriculum, please visit the associated GitHub repositories for the Stata and bash shell lessons. For broader discussion about economics curriculum development, sign up for the economics-curriculum email list.

Astronomy Curriculum

Development of a Data Carpentry lesson immediately aimed at astronomy, but which can easily be extended to other physics based disciplines. American Institute of Physics/Member Society Venture Partnership funding is supporting the development and testing of the lesson. Lesson development will begin the AAS hack day and will continue throughout the next two years. If you are interested in contributing in any way, please join the astronomy-curriculum email list. We would especially like to encourage anyone who is part of an AIP member society (Acoustical Society of America, American Association of Physicists in Medicine, American Association of Physics Teachers, American Astronomical Society, American Crystallographic Association, American Meteorological Society, American Physics Society, AVS: Science & Technology of Materials, Interfaces, and Processing, The Optical Society, and the Society of Rheology) to join as we are eager to develop lessons that can be easily extended into these sub-fields.

Other curriculum

If you are interested in developing other lessons or getting updates on other topics, see the lessons ideas github repository for topics under consideration or discussion, or to propose new ideas.

Semester materials

Biology Semester-long Course

The Biology Semester-long Course was developed and piloted at the University of Florida in Fall 2015. Course materials include readings, lectures, exercises, and assignments that expand on the material presented at workshops focusing on SQL and R. The course is accessible to:

Community-contributed materials

Python for Atmosphere and Ocean Scientists

This lesson is currently in the alpha stage. To move it to the beta stage, we are looking for feedback from people interested to teach this material. If you’re a qualified Carpentries instructor and would be interested in teaching the lessons (some experience with the netCDF file format and xarray Python library is useful), please get in touch with either Damien Irving or Francois Michonneau. You can also request a workshop at your institution by contacting us and we’ll reach out to instructors. There is no fee for a pilot workshop, but you would need to cover travel expenses for instructors.