A two-day introduction to modern evidence-based teaching practices, built and maintained by the Carpentry community.

Software Carpentry lesson that teaches how to use databases and SQL In the late 1920s and early 1930s, William Dyer, Frank Pabodie, and Valentina Roerich led expeditions to the Pole of Inaccessibility in the South Pacific, and then onward to Antarctica. Two years ago, their expeditions were found in a storage locker at Miskatonic University. We have scanned and OCR the data they contain, and we now want to store that information in a way that will make search and analysis easy. Three common options for storage are text files, spreadsheets, and databases. Text files are easiest to create, and work well with version control, but then we would have to build search and analysis tools ourselves. Spreadsheets are good for doing simple analyses, but they don’t handle large or complex data sets well. Databases, however, include powerful tools for search and analysis, and can handle large, complex data sets. These lessons will show how to use a database to explore the expeditions’ data.

Workshop overview for the Data Carpentry genomics curriculum. Data Carpentry’s aim is to teach researchers basic concepts, skills, and tools for working with data so that they can get more done in less time, and with less pain. This workshop teaches 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. This workshop is designed to be taught over two full days of instruction. Please note that workshop materials for working with Genomics data in R are 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. 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.

Library Carpentry lesson: An introduction to Git. What We Will Try to Do Begin to understand and use Git/GitHub. You will not be an expert by the end of the class. You will probably not even feel very comfortable using Git. This is okay. We want to make a start but, as with any skill, using Git takes practice. Be Excellent to Each Other If you spot someone in the class who is struggling with something and you think you know how to help, please give them a hand. Try not to do the task for them: instead explain the steps they need to take and what these steps will achieve. Be Patient With The Instructor and Yourself This is a big group, with different levels of knowledge, different computer systems. This isn’t your instructor’s full-time job (though if someone wants to pay them to play with computers all day they’d probably accept). They will do their best to make this session useful. This is your session. If you feel we are going too fast, then please put up a pink sticky. We can decide as a group what to cover.

Workshop overview for the Data Carpentry Social Sciences curriculum. Data Carpentry’s aim is to teach researchers basic concepts, skills, and tools for working with data so that they can get more done in less time, and with less pain. This workshop teaches data management and analysis for social science research including best practices for data organization in spreadsheets, reproducible data cleaning with OpenRefine, and data analysis and visualization in R. This curriculum is designed to be taught over two full days of instruction. Materials for teaching data analysis and visualization in Python and extraction of information from relational databases using SQL are in development. 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.

This lesson is part of the Software Carpentry workshops that teach how to use version control with Git. Wolfman and Dracula have been hired by Universal Missions (a space services spinoff from Euphoric State University) to investigate if it is possible to send their next planetary lander to Mars. They want to be able to work on the plans at the same time, but they have run into problems doing this in the past. If they take turns, each one will spend a lot of time waiting for the other to finish, but if they work on their own copies and email changes back and forth things will be lost, overwritten, or duplicated. A colleague suggests using version control to manage their work. Version control is better than mailing files back and forth: Nothing that is committed to version control is ever lost, unless you work really, really hard at it. Since all old versions of files are saved, it’s always possible to go back in time to see exactly who wrote what on a particular day, or what version of a program was used to generate a particular set of results. As we have this record of who made what changes when, we know who to ask if we have questions later on, and, if needed, revert to a previous version, much like the “undo” feature in an editor. When several people collaborate in the same project, it’s possible to accidentally overlook or overwrite someone’s changes. The version control system automatically notifies users whenever there’s a conflict between one person’s work and another’s. Teams are not the only ones to benefit from version control: lone researchers can benefit immensely. Keeping a record of what was changed, when, and why is extremely useful for all researchers if they ever need to come back to the project later on (e.g., a year later, when memory has faded). Version control is the lab notebook of the digital world: it’s what professionals use to keep track of what they’ve done and to collaborate with other people. Every large software development project relies on it, and most programmers use it for their small jobs as well. And it isn’t just for software: books, papers, small data sets, and anything that changes over time or needs to be shared can and should be stored in a version control system.