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.

Curriculum Advisors are part of a team that provides the oversight, vision, and leadership for a particular set of lessons. More information about the role of the Curriculum Advisory Committee can be found in the Carpentries Handbook.

Curriculum materials

Curriculum materials under development

Semester materials

Community-Contributed materials

The Carpentries also shares The Carpentries Community Developed Lessons. This includes The Carpentries Incubator (lessons under development and seeking peer review), and The CarpentriesLab (lessons that have been vetted by The Carpentries but are not part of our standard offerings).


Astronomy Curriculum

The Foundations of Astronomical Data Science curriculum covers a range of core concepts necessary to efficiently study the ever-growing datasets developed in modern astronomy. This curriculum teaches learners to perform database operations (SQL queries, joins, filtering) and to create publication-quality data visualisations. This curriculum assumes some prior knowledge of Python and exposure to the Bash shell, equivalent to that taught in a Software Carpentry workshop.

Lessons

Lesson Site Repository Reference Instructor Notes Maintainer(s)
Foundations of Astronomical Data Science Azalee Bostroem, David DeMuth, Ralf Kotulla, Rodolfo Montez Jr.

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     Fabrice Rwasimitana, Juan Ugalde, Ethan White
Data Organization in Spreadsheets for Ecologists Monah Abou Alezz, Peter R. Hoyt
Data Cleaning with OpenRefine for Ecologists Luis J Villanueva
Data Management with SQL for Ecologists Katy Felkner, Sathya Pandalai
Data Analysis and Visualization in R for Ecologists Tobias Busch, François Michonneau, Brian Seok
Data Analysis and Visualization in Python for Ecologists Alex Pakalniskis, Sarah Pohl

Lecciones en español

Lección Sitio web Repositorio Referencia Guía del instructor Mantenedor(es)
Análisis y visualización de datos usando Python (Beta) Irene Ramos Pérez, Agustina Pesce, Vini Salazar, Heladia Salgado

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-Organised 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 Karega Pauline, Sangram Keshari Sahu
Project Organization and Management for Genomics Peter Hoyt, Karega Pauline, Jake Szamosi
Introduction to the Command Line for Genomics Saba Ferdous, Akshay Paropkari
Data Wrangling and Processing for Genomics Valerie Gartner, Josh Herr, Asela Wijeratne, Rhondene Wint
Introduction to Cloud Computing for Genomics Amanda Charbonneau, Anuj Guruacharya, Wendy Wong

Lessons in Development

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

Geospatial Data Curriculum

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-Organised Data Carpentry Geospatial workshops.

Lessons

Lesson Site Repository Reference Instructor Notes Maintainer(s)
Geospatial Workshop Overview   Jemma Stachelek
Introduction to Geospatial Concepts   Rohit Goswami, Girmaye Misgna, Aditya Ranganath, Tyson Swetnam
Introduction to R for Geospatial Data Luca Di Stasio, Juan Fung, Mike Mahoney
Introduction to Geospatial Raster and Vector Data with R Ivo Arrey, Drake Asberry, Jemma Stachelek

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-Organised 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     Jesse Sadler
Data Organization in Spreadsheets for Social Scientists Trevor Burrows
Data Cleaning with OpenRefine for Social Scientists Ben Companjen
Data Analysis and Visualization with R for Social Scientists Juan Fung, Jesse Sadler, Eirini Zormpa

Lessons in Development

Lesson Site Repository Reference Instructor Notes Maintainer(s)
Data Analysis and Visualization with Python for Social Scientists *alpha* Annajiat Alim Rasel
Data Management with SQL for Social Scientists *alpha*

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. If you are interested in contributing to the development of Data Carpentry lessons in general, visit the Help Wanted page on the Carpentries website to find a list of issues in need of attention.

Image Processing 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 processing in Python. This lesson is currently being piloted at different institutions. 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. Development for these lessons has been supported by a grant from the Sloan Foundation/

Lessons

Lesson Site Repository Reference Instructor Notes Maintainer(s)
Image Processing with Python Kimberly Meechan, Mark Meysenberg, David Palmquist, Ulf Schiller, Robert Turner

Economics Curriculum

A Data Carpentry curriculum for Economics is being developed by Dr. Miklos Koren at Central European University. These materials are being piloted locally. Development for these lessons has been supported by a grant from the Sloan Foundation/

Lessons

Lesson Site Repository Reference Instructor Notes Maintainer(s)
Introduction to Stata for Economics Miklós Koren, Arieda Muço, Andras Vereckei
Introduction to the Command Line for Economics Miklós Koren, Arieda Muço, Andras Vereckei

Other curricula

If you are interested in developing other lessons, please visit The Carpentries Incubator.

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 in The Carpentries Lab has been peer-reviewed and published in JOSE, and is ready to be taught by any certified Carpentries instructor (some experience with the netCDF file format and xarray Python library is useful). It is aimed at learners with some prior experience of Python and the Unix Shell, who want to learn how to work with with raster or “gridded” data in Python. As a community-developed lesson, it is currently only available for self-organised workshops. If you have questions about the lesson, please contact the Maintainers listed on the lesson README.