One key to open reproducible science is to provide rigorous organization of all workflow code. Not just for when you send your project to someone else. A future version of yourself will also benefit when you return to an organized workflow after some time away.

This repository provides a Python script and Jupyter Notebook both containing an example workflow structure for open reproducible science projects. The example workflow contains the following stages:

• Environment Setup
• User-Defined Variables
• Data Acquisition
• Data Preprocessing
• Data Processing
• Data Postprocessing
• Data Visualization
• Data Export

In addition to the example files that contain all workflow stages, this repository includes a Makefile to create placeholder scripts organized by workflow task, as follows:

01-code-scripts/
├── 01-acquire-data.py
├── 02-preprocess-data.py
├── 03-process-data.py
├── 04-postprocess-data.py
└── 05-visualize-data.py

Complementary resources:

• Medium Article
• Video Tutorial

To run this project locally, you will need:

• Conda (Miniconda or Anaconda)

To run this project in a web browser, click the icon below to launch the project with Binder:

Within Binder, click "New" and select "Terminal" to open a terminal in the root project folder.

From the terminal, you can create and activate the project Conda environment.

Create environment:

$ conda env create -f environment.yml

Activate environment:

$ conda activate reproducible-science

From the terminal (locally or within Binder), you can run the example workflow, create the placeholder scripts, and delete the placeholder scripts.

Run example workflow:

$ make run

Create placeholder scripts:

$ make scripts

Delete placeholder scripts:

$ make clean

Contains all example code.

Contains information required to create the Conda environment.

Contains instructions to execute the code.