Tools and practices for collaborative, reproducible data science

An introduction to “good enough” practices to create a shared virtual environment allowing you to develop reproducible workflows for your analysis and manage your data.

Julien Brun , Carrie Kappel , Julie Stewart Lowndes
05-08-2020

Curriculum at a glance

This module is an introduction to the data science support NCEAS is providing to LTER and SNAPP working groups followed by a discussion on best practices about data management in a distributed team setup. Participants will have the opportunity to brainstorm on their data and computing needs. In the second part of the workshop, an introduction to the use of NCEAS analytical server and the concept of collaborative coding as a distributed team will be demonstrated to empower participants to develop their analytical workflows in a remote setup.


Workshop material: https://docs.google.com/document/d/1cTIYyfc0J564PxpF0-xE_bbH6TIK2tq1F5WkDOx0lrw/edit?usp=sharing

Blog post: https://www.nceas.ucsb.edu/news/developing-reproducible-workflows-collaboratively

Corrections

If you see mistakes or want to suggest changes, please create an issue on the source repository.

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Text and figures are licensed under Creative Commons Attribution CC BY 4.0. Source code is available at https://github.com/brunj7/brunj7.github.io, unless otherwise noted. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".