Research project initialization and organization following reproducible research guidelines
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Reproducible Research Project Initialization

Research project initialization and organization following reproducible research guidelines.


|- doc/             # documentation for the study and other explanatory material
|  +- paper/        # manuscript(s), whether generated or not
|- data/            # raw and primary data, are not changed once created
|  |- raw_internal/ # raw data generated in-lab or by collaborators, will not be altered
|  |- raw_external/ # data from third-party sources, databases etc, will not be altered
|  |- intermediate/ # intermediate data on its way from raw to final, are not changed once created
|  +- final/        # final data for figures/visualizations, will not be altered once created
|- code/            # all programmatic code relating to the project
|  |- # tool for generating new Python scripts containing boilerplate code
|  |-       # tool for generating new R scripts containing boilerplate code
|  +-       # tool for generating new R-markdown scripts containing boilerplate code
|- results          # all output from workflows and analyses
|  |- figures/      # graphs, likely designated for manuscript figures
|  +- pictures/     # diagrams, images, and other non-graph graphics
|- .env             # a file to store passwords and usernames needed for the scripts. This will not get synced to GitHub.
|- notebook.txt     # a lab notebook where activities relating to this project should be entered
|- requirements.txt # the requirements file for reproducing the analysis environment,
|                     e.g. generated with `pip freeze > requirements.txt`
|- scratch/         # temporary files that can be safely deleted or lost
|-        # the top level description of content
|- study.Rmd        # executable Rmarkdown for this study, if applicable
|- Makefile         # executable Makefile for this study, if applicable
|- study.Rproj      # RStudio project for this study, if applicable
+- datapackage.json # metadata for the (input and output) data files

How to use

  • Create a new directory for your project.

  • Download the latest version of this repository, and unzip it in the directory you just created.

  • Create a .env file using a basic text editor (keep it empty if you like) and place in the project base directory (where noteboox.txt and requirements.txt are). The code templates will not work without it.

  • Open this document in an editor. Change the first line to reflect the title of your research study, and delete the rest of the content in this file. You can, but are not obligated to keep the Acknowledgements section. The relative python paths will fail if you do not!

  • Make use of the Python template file "" in the code folder each time a new Python script is needed. That way is has all the boilerplate code in it.

  • Go forth and experiment, keeping files in their appropriate places.

  • Don't ever edit your raw data, especially not manually, and especially not in Excel. Don't overwrite your raw data. Don't save multiple versions of the raw data. Treat the data (and its format) as immutable. The code you write should move the raw data through a pipeline to your final analysis. You shouldn't have to run all of the steps every time you want to make a new figure, but anyone should be able to reproduce the final products with only the code in "code" and the data in "data/raw_external" or "data/raw_internal".

  • To the greatest extent possible, download external data programmatically and not through point-and-click in your browser. This creates automatic documentation (your code) of where the external data can be found.

To the extent possible under law, the author(s) of this template have dedicated all copyright and related and neighboring rights to it to the public domain worldwide under the CC0 Public Domain Dedication. The template and all other content in the reproducible-research-init repository is distributed without any warranty.

Key concepts and goals

See Noble 2009 for a full description of and argument for the principle template structure. Some concepts and goals that guided this work:

  • (Good) There is a folder for the raw data, which do not get altered, or intermixed with data that is the result of manual or programmatic manipulation. I.e., derived data is kept separate from raw data, and raw data are not duplicated.
  • (Good) Code is kept separate from data.
  • (Better) Manuscript production output is kept separate from everything else.
  • (Good) There is a scratch directory for experimentation. Everything in the scratch directory can be deleted at any time without negative impact.
  • (Better) There should be a in every directory, describing the purpose of the directory and its contents.
  • (Best) There is a top-level Makefile or Rmarkdown file that documents the computational study in executable form. Those files may call out to other Makefile's or .Rmd files in subdirectories.
  • (Best) There is a formal metadata descriptor at the root of the package that describes all the important input and output data files.
  • (Meta) The template structure should have no special software or skill prerequisites to install. Specifically, deploying the template structure should not require installing git, or using the command line.


The initial file and directory structure of this project was developed by a group of participants in the Reproducible Science Curriculum Workshop, held at NESCent in December 2014. The structure is based on, and heavily follows the one proposed by Noble 2009, with a few but small modifications. The original repository has been modified by Martin Engqvist and the one presented here differs in some ways.