Additional Resources for Reproducible Science

The following lists of resources below were compiled by Francisco Rodriguez-Sanches¹

General Resources

• Reproducible research in computational science
• Nature special: Challenges in irreproducible research
• Ten simple rules for reproducible computational research
• Best practices for scientific computing
• Good enough practices for scientific computing
• Initial steps toward reproducible research
• Tools for reproducible research
• The tao of open science for ecology
• Towards standard practices for sharing computer code and programs in neuroscience
• Reproducible research is still a challenge
• rOpenSci reproducibility guide
• Reproducible research course
• Report writing for data science in R
• Implementing reproducible research
• Reproducible Research with R and Rstudio
• Digital History Methods in R
• Doing reproducible science: from your hard-won data to a publishable manuscript without going mad

Data Handling

• A guide to data management in ecology and evolution (British Ecological Society)
• Ecoinformatics: supporting ecology as a data-intensive science
• Ten simple rules for the care and feeding of scientific data
• Ten simple rules for digital data storage
• Nine simple ways to make it easier to (re)use your data
• Ten simple rules for creating a good data management plan
• Data Management Planning Tool
• Ejemplo de data management plan
• Bad data guide
• Data Carpentry Spreadsheets for Ecology
• DataONE Best Practices
• Tidy data
• Spreadsheet help
• Data organization
• Repositorios de datos
• Open Science Framework
• Paquetes de rOpenSci para publicación de datos
• Paquete de R para interaccionar con Open Science Framework
• Ecological Metadata Language
• The what, why, and how of born-open data

Data Analysis and Dynamic Documents

• knitr
• rmarkdown
• IPython
• Jupyter
• Interactive notebooks: sharing the code
• Dynamic documents with R and knitr
• Report writing for data science in R
• Implementing reproducible research
• Reproducible Research with R and Rstudio

Version Control (Git & GitHub)

• Why you need version control
• A Quick Introduction to Version Control with Git and GitHub
• Git can facilitate greater reproducibility and increased transparency in science
• Git and GitHub (Hadley Wickham)
• R development using GitHub
• Happy Git and GitHub for the useR
• Working with RStudio, Git, GitHub (STAT 545)
• Version control with git (R. Fitzjohn)
• Version control with Git (Software Carpentry)
• A basic tutorial to version control using git (Jon Lefcheck)
• Push, Pull, Fork - GitHub for academics
• Git for beginners - the definitive practical guide (Stackoverflow)
• Git - the simple guide
• Code School Git intro
• GitHub tutorial (Karl Broman)
• Getting git right (Atlassian)
• Hello World (GitHub first steps)
• GitHub guides

Project Organization and Package Creation

• A quick guide to organizing computational biology projects
• Reproducible Research Project Initialization
• Designing projects
• Rstudio projects
• Using R packages as research compendiums
• R packages (H. Wickham)
• R packages (K. Broman)
• Choose a license
• A minimal tutorial on make (K. Broman)
• remake

Dependency Management

• rctrack: An R package that automatically collects and archives details for reproducible computing
• checkpoint package
• packrat: reproducible package management for R
• Enhancing reproducibility and collaboration via management of R package cohorts
• An introduction to Docker for reproducible research
• R Docker tutorial
• drat: R repositories made easy

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