Our Core Curriculum consists of the lessons in the table below. These have been taught many times, and have been further refined after instructor and learner feedback. For more information regarding core lessons and workshops, see Our Workshops and the Workshop Overview.
The lessons introduce terms, phrases, and concepts in software development and data science, how to best work with data structures, and use regular expressions in finding and matching data. We introduce the Unix-style command line interface, and teach basic shell navigation, as well as the use of loops and pipes for linking shell commands. We also introduce grep for searching and subsetting data across files. Exercises cover the counting and mining of data. In addition, we cover working with OpenRefine to transform and clean data, and the benefits of working collaboratively via Git/GitHub and using version control to track your work.
|Workshop Overview||Stable||Shari Laster*, Carmi Cronje, Paul R. Pival, Anton Angelo (Past Maintainer: James Baker, Chris Erdmann)|
|Introduction to Working with Data (Regular Expressions)||Stable||Shari Laster*, Carmi Cronje, Paul R. Pival, Anton Angelo (Past Maintainers: James Baker, Chris Erdmann)|
|The UNIX Shell||Stable||Danielle Kane*, Nilani Ganeshwaran, John Wright, Anna Oates, Tim Dennis (Past Maintainer: Belinda Weaver)|
|OpenRefine||Stable||Erin Carrillo*, Owen Stephens, Paul R. Pival, Kristin Lee (Past Maintainers: Carmi Cronje, Chris Erdmann, Juliane Schneider)|
|Introduction to Git||Beta||Silvia di Giorgio, Thea Atwood, Eric Lopatin, Drew Heles, Eva Seidlmayer (Past Maintainers: Katrin Leinweber, Belinda Weaver, Jez Cope, Chris Erdmann)|
The following Library Carpentry lessons can also be taught in addition to our core curriculum. Some of the lessons have been taught infrequently and still need further work. We would value any feedback on these lessons.
|SQL||Stable||Jordan Pedersen* , Kristin Lee, Chris Erdmann, Lise Doucette (Past Maintainers: Elaine Wong, Janice Chan)|
|Tidy Data||Beta||Sherry Lake*, Tim Dennis, Thea Atwood, Erika Mias (Past Maintainer: Jez Cope)|
|Webscraping||Alpha||Joshua Dull*, Thomas Guignard (Past Maintainer: Belinda Weaver)|
|Introduction to Python||Alpha||Elizabeth Wickes*, Laura Wrubel, Konrad Foerstner, Drew Heles (Past Maintainers: Carlos Martinez, Richard Vankoningsveld)|
|Introduction to Data for Archivists||Alpha||Katherine Koziar*, Jeanine Finn, and Scott Peterson (Past Maintainers: Jenny Bunn, Noah Geraci, and James Baker)|
The Top 10 FAIR Data & Software Things are brief guides (stand alone, self paced training materials), called "Things", that can be used by the research community to understand FAIR in different contexts but also as starting points for conversations around FAIR.
|Top 10 FAIR Data & Software Things||Beta||Liz Stokes, Chris Erdmann, Juande Santander-Vela (looking for Maintainers)|
The following lessons are conceptual (pre-alpha) and are currently being discussed and/or under development. Issues/pull requests are one way to see current activity but you can also reach out to the Maintainers to see where the lesson discussions/development are at.
|MarcEdit||Conceptual||Owen Stephens*, Jennifer Eustis, Abigail Sparling (looking for Maintainers)|
|Wikidata||Conceptual||Till Sauerwein, Muhammad Elhossary, Konrad Förstner*, Rabea Müller (looking for Maintainers)|
|FAIR Data & Software||Conceptual||Chris Erdmann*, Liz Stokes, Kristina Hettne, Carmi Cronje (looking for Maintainers)|
|Introduction to R||Conceptual||Clarke Iakovakis*, John Little, Stéphane Guillou, Tim Dennis (looking for Maintainers)|
|XML||Conceptual||Catherine Smith, Jesse Johnston, Phil Reed, Katrina Simone Fenlon, Nilani Ganeshwaran (looking for Maintainers)|
In addition, lessons on Digital Preservation and Text and Data Mining are being discussed.
Our lessons are in various stages of development - stable, beta, alpha, and conceptual.
These lessons are mature and ready to be taught. Most have been taught multiple times. The content is well-established, but minor changes and improvements (e.g. better explanations, spelling/grammar corrections, improved exercises) are always welcome.
These lessons are largely complete and should be ready to teach, but would benefit from improvements based on feedback from instructors who have taught them. New sections and rewrites/reorganisations of existing sections will be considered.
These lessons are under active development and may not be ready to teach without additional preparation and background knowledge. Further development work is strongly encouraged - please get in touch or check out outstanding issues on GitHub to find out what is needed.
These lessons are still in the conceptual phase where community members have just started to discuss general ideas , learner profiles, goals, summative and fomative assessments, concept maps, software and data to be used, how long the lesson should be, and connecting the dots before moving to the alpha phase.
All contributions are welcome. The level of work may vary depending on the status of the lesson. We recommend that you @mention the Maintainers of the lesson if you are picking up the tasks described in one of the open lesson issues or pull requests.
Our recommended process for developing a new lesson is as follows:
In order to maintain consistent quality and style in the Library Carpentry lessons, we have a community-driven set of expectations for what a good lesson should look like. These should guide the review process at steps 5 and 8 above. Lesson developers and reviewers should also review The Carpentries Handbook, especially the section on Lesson Development and consult with the Curriculum Advisory Committee.
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).