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Library Carpentry

The Library Carpentry module 'Tidy data for librarians' is maintained by Jez Cope.

Background

Library Carpentry is a software skills training programme aimed at library and information professions. It builds on the work of Software Carpentry and Data Carpentry.

These materials are based on the Library Carpentry materials initially developed and taught by James Baker, Owen Stephens and Daniel van Strien in 2015. http://librarycarpentry.github.io/outline/. The original Library Carpentry contained four modules:

  • Introduction, including jargon busting, data structures and regular expressions
  • The Unix shell, including use of the command line and commands such asgrep and sed to find data within files
  • Git and version control
  • Using OpenRefine for data clean up.

On 2-3 June 2016 during the Mozilla Science Lab Global Sprint these existing first four repositories were forked (links below) and re-developed within individual repositories, with the addition of SQL and Python modules, topics which many librarians have expressed interest in learning.

The Library Carpentry sprint was co-ordinated by Belinda Weaver and attracted global participation.

Contribution

There are many ways of contributing to Library Carpentry:

Code of Conduct

All participants should agree to abide by the Software Carpentry Code of Conduct.

Authors

Library Carpentry is authored and maintained by the community.

This module in particular is heavily based on the Data Carpentry Spreadsheets for Ecology lesson maintained by Aleksandra Pawlik and Tracy Teal, with contributions from Christie Bahlai, Aleksandra Pawlik, Jennifer Bryan, Alexander Duryee, Jeffrey Hollister, Daisie Huang, Owen Jones, Ben Marwick, and Tracy Teal.

Citation

Please cite as:

Library Carpentry. Tidy data for Librarians. March 2017.

License

All the lessons are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Exceptions: embeds to and from external sources, and direct quotations from speakers.

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Languages

  • Python 62.2%
  • HTML 24.7%
  • CSS 4.0%
  • Makefile 3.7%
  • R 3.7%
  • JavaScript 1.2%
  • Other 0.5%