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Led by Susanna-Assunta Sansone and Terri Atwood 

Data Carpentry is inspired by the Software Carpentry model (Software Carpentry training is also featured in ELIXIR-UK as a model for computational training for researchers. Data Carpentry is also developed in Parallel by a group of Software Carpentry instructors and researchers related to NSF BIO-funded synthesis & eScience centers (US).

Data Carpentry is an off-shoot of Software Carpentry designed to teach basic concepts, skills and tools for working more effectively with data.

In many domains of research the rapid generation of large amounts of data is fundamentally changing how research is done. The deluge of data presents great opportunities, but also many challenges in managing, analyzing and sharing data. Data Carpentry aims to teach the skills that will enable researchers to be more effective and productive. The workshop is designed for learners with little to no prior knowledge of programming, shell scripting, or command line tools.

Learning objectives for the workshops: Researchers should be able to retrieve, view, manipulate, analyze and store their's and other's data in an open and reproducible way. Researchers learn best when material is taught in their domain, so workshops will be domain-specific.

The following teaching subjects consist the core of Data Carpentry:

  • How to use spreadsheet programs (such as Excel) more effectively, and the limitations of such programs.
  • Getting data out of Excel and into more powerful tools --- using R or Python.
  • Using databases, including managing and querying data in SQL.
  • Workflows and automating repetitive tasks, in particular using the command line shell and shell scripts.

Data Carpentry Directors Board:

  • Karen Cranston (NESCent)
  • Hilmar Lapp (NESCent)
  • Karthik Ram (rOpenSci)
  • Aleksandra Pawlik (Elixir UK, Software Sustainability Institute, UK)
  • Tracy Teal (Michigan State University)
  • Ethan White (Utah State University)
  • Greg Wilson (Software Carpentry)

 Data Carpentry boocatmps so far:

Other resources:

https://openhatch.org/wiki/Community_Data_Science_Workshops

http://networkcollectiv.es/2014/03/09/community-data-science-workshops/

 

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