https://github.com/vasishth/reproducibleworkflows

Materials for MPI Leipzig workshop: https://www.cbs.mpg.de/events/23219/1413783
https://github.com/vasishth/reproducibleworkflows

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Materials for MPI Leipzig workshop: https://www.cbs.mpg.de/events/23219/1413783

• Host: GitHub
• URL: https://github.com/vasishth/reproducibleworkflows
• Owner: vasishth
• Created: 2020-02-09T14:42:17.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2020-02-10T16:20:35.000Z (over 6 years ago)
• Last Synced: 2025-10-17T07:39:56.115Z (8 months ago)
• Language: TeX
• Size: 17.7 MB
• Stars: 2
• Watchers: 2
• Forks: 0
• Open Issues: 0
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  • Readme: README.md

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README

          
# Workshop: Project Planning - From Experiment Design to Manuscript and Data Release

Event Open Science Initiative

    - Date: Feb 12, 2020

    - Time: 09:00 - 13:00

    - Speaker: Shravan Vasishth

    - Location: MPI for Human Cognitive and Brain Sciences

    - Room: Charlotte Buehler Room (C402)

    - Host: CBS Open Science

A recent analysis of publicly released data accompanying published papers in Cognition showed that not all published numbers could be reproduced, even though the data and code were available (https://royalsocietypublishing.org/doi/full/10.1098/rsos.180448). The authors state that: "...suboptimal data curation, unclear analysis specification and reporting errors can impede analytic reproducibility, undermining the utility of data sharing and the credibility of scientific findings." In this workshop, I will suggest one way to minimize the chances of producing irreproducible results, focusing on repeated measures 2x2 factorial designs as a case study.

The steps I will discuss are:

- Experiment design, and planning sample size using simulated data

- Defining the analysis plan using simulated data

- Checking that your experiment software actually collects the data you need

- Once data are collected, visualizing and summarizing the data

- Creating an R package to document and release your data and analyses

- Code refactoring

- Integrating the data analysis into the manuscript

- Releasing data and code: a suggested checklist 

# Lecture Materials

You can download all materials from [here](https://github.com/vasishth/ReproducibleWorkflows/archive/master.zip).

If you use github, the archive can be cloned by typing the following on the command line:

   git clone https://github.com/vasishth/ReproducibleWorkflows.git