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Introduction to Reproducible Data Science

Ch. 1 Preface

1.1 Intentions of this Book and Web Course

  • To train the next-generation of scientists to work with data - regardless of the type, format, or volume.
  • Make available a set of open-source materials to learn how to engage in reproducible science, leveraging code-based techniques.
  • This repository is intended to house various sample workflows, and code snippets, to support research + data science activities.

1.2 Background about the Problem

A reproducibility crisis (Ioannidis, 2005; Open Science Collaboration, 2015) has emerged as a threat to the scientific enterprise.

  • Ioannidis, John P A. 2005. “Why Most Published Research Findings Are False.” PLoS Medicine 2 (8): e124. doi:10.1371/journal.pmed.0020124.
  • Open Science Collaboration. 2015. “Estimating the Reproducibility of Psychological Science.” Science 349 (6251): aac4716–aac4716. doi:10.1126/science.aac4716.

1.3 Background about the Author

Over the last decade I’ve engaged in learning opportunities to become proficient across topics including data wrangling and modeling of text, image, video, and eye-tracking data, as well as more recently sensor data, and look forward to training the next generation of scientists on code-based methods to apply in their research.

  • Nelson Roque, PhD
  • Assistant Professor, Department of Psychology
  • University of Central Florida
  • Lab website

1.4 Before you get started…

This is an informational box. You will see these boxes throughout the course when there is an important topic that requires your attention.