- Best Practices for Scientific Computing by Greg Wilson, et. al.
- Tidy Data by Hadley Wickham
A Quick Guide to Organizing Computational Biology Projects by William Stafford Noble
- Ten Simple Rules for Reproducible Computational Research by Geir Kjetil Sandve, et. al.
- Reproducibility in Computational and Experimental Mathematics Lecture Videos from ICERM
- Tools for Reproducible Research - a collection of resources from Karl Broman
- Scientific workflows for computational reproducibility in the life sciences: Status, challenges and opportunities by Sarah Cohen-Boulakia, et. al.
edX Course: Principles, Statistical and Computational Tools for Reproducible Science: This free course covers fundamentals of reproducible science, case studies, data provenance, statistical methods for reproducible science, computational tools for reproducible science, and reproducible reporting science. These concepts are intended to translate to fields throughout the data sciences: physical and life sciences, applied mathematics and statistics, and computing.
- Code Ocean - A research collaboration platform. With direct access to cloud resources and reproducibility best practices built in, no extra software or hardware is needed.
- Columbia is currently conducting a pilot with Code Ocean. Visit https://codeocean.com/portal/columbia for more information and to sign up.
- Code Ocean is available to consult with Columbia researchers on using Code Ocean's platform. Email [email protected] to schedule your consultation.
- The Digital Science Center provides a wide range of software to support research and coursework in several science and engineering disciplines. All of the software below is available for use on the computers located in theScience & Engineering Library.
- List of software and tools either available on the machines in the Research Data Service, or tools available online with the level of support available for each