Reproducible Quantitative Methods: Data analysis workflow using R

Key Info
Description - a brief synopsis, abstract or summary of what the learning resource is about: 

Reproducibility and open scientific practices are increasingly being requested or required of scientists and researchers, but training on these practices has not kept pace. This course, offered by the Danish Diabetes Academy, intends to help bridge that gap. This course is aimed mainly at early career researchers (e.g. PhD and postdocs) and covers the fundamentals and workflow of data analysis in R.

This repository contains the lesson, lecture, and assignment material for the course, including the website source files and other associated course administration files. 

 By the end of the course, students will have:

  1. An understanding of why an open and reproducible data workflow is important.
  2. Practical experience in setting up and carrying out an open and reproducible data analysis workflow.
  3. Know how to continue learning methods and applications in this field.

Students will develop proficiency in using the R statistical computing language, as well as improving their data and code literacy. Throughout this course we will focus on a general quantitative analytical workflow, using the R statistical software and other modern tools. The course will place particular emphasis on research in diabetes and metabolism; it will be taught by instructors working in this field and it will use relevant examples where possible. This course will notteach statistical techniques, as these topics are already covered in university curriculums.

For more detail on the course, check out the syllabus at:

Authoring Person(s) Name: 
Luke W. Johnston
Daniel Witte
João Santiago
Anna Schritz
Authoring Organization(s) Name: 
Danish Diabetes Academy
License - link to legal statement specifying the copyright status of the learning resource: 
Creative Commons Attribution 4.0 International - CC BY 4.0
Access Cost: 
No fee
Citation - format of the preferred citation for the learning resource: 
Luke W Johnston, Daniel R Witte, João Santiago, & Anna Schritz. (2019, March). Reproducible Quantitative Methods: Data analysis workflow using R (Version v1.0.0). Zenodo.
Primary language(s) in which the learning resource was originally published or made available: 
More info about
Keywords - short phrases describing what the learning resource is about: 
Data analysis
Open science
R software
Scientific reproducibility
Workflows - Core Trustworthy Data Repositories Requirements
Subject Discipline - subject domain(s) toward which the learning resource is targeted: 
Social and Behavioral Sciences
Published / Broadcast: 
Monday, April 22, 2019
ID - identifier that provides the means to locate the learning resource or its citation: 
Type - namespace prefix for the citable locator, if any: 
Publisher - organization credited with publishing or broadcasting the learning resource: 
Danish Diabetes Academy
Media Type - designation of the form in which the content of the learning resource is represented, e.g., moving image: 
Collection - a group or set of items that comprise a single learning resource, e.g., a PDF version of a slide presentation, an audio file of the presentation and a textual representation of the oral transcription of the presentation.
Contact Person(s): 
Luke W. Johnston
Daniel Witte
João Santiago
Anna Schritz
Contact Organization(s): 
Danish Diabetes Academy
Educational Info
Purpose - primary educational reason for which the learning resource was created: 
Instruction - detailed information about aspects or processes related to data management or data skills.
Learning Resource Type - category of the learning resource from the point of view of a professional educator: 
Course - series of units and lessons used to teach the skills and knowledge required by its curriculum.
Target Audience - intended audience for which the learning resource was created: 
Data manager
Early-career research scientist
Mid-career research scientist
Research faculty
Research scientist
Intended time to complete - approximate amount of time the average student will take to complete the learning resource: 
More than 1 hour (but less than 1 day)