Schedule

EIT course proposal

Robin Lovelace
r.lovelace@leeds.ac.uk

University of Leeds, Active Travel England

Rosa Félix

University of Lisbon

October 15, 2024

Introduction

About me and my work

  • Professor of Transport Data Science
  • Work with government
  • Focus on impact
  • R package developer and data scientist
  • New methods for more reproducible, data-driven and participatory transport planning

Learning objectives

  • Be able to share reproducible code for more scientific and transparent transport research
  • To be confident reproducing your own work and that of others
  • To become skilled at using Git and GitHub to manage versions of your code and collaborate with others
  • To be able to write reproducible content that can be exported to a variety of formats with the Quarto system for scientific publishing
  • To understand how Quarto extensions can be used as a basis for creating publication-ready papers
  • To be aware of ‘continuous integration’ and ‘GitHub Actions’ and how they can be used to ensure reproducibility, share your work, and save time
  • Understanding of best practices around code sharing and collaboration for reproducible research in transport planning

Prerequisites

This course assumes working knowledge with R or Python for research. We assume that you are already comfortable with an integrated development environment (IDE), such as RStudio or VS Code. You must have a GitHub account and it will be beneficial to be familiar with the concepts of version control, although we will cover these in the course.

Familiarity with referencing software such as Zotero (recommended) and bibliography file formats such as BibTeX will be beneficial, but not essential.

See the prerequisites page for details and to test your setup.

Questions for students (pre-course)

  • What language would you prefer a course on reproducible research to be taught in?
    • R
    • Python
    • Both
  • Which interactive development environment would you prefer to be used as the main editor used and taught during the course?
    • RStudio
    • VSCode
    • Positron (new data science-focussed IDE by Posit)

Draft agenda and contents

See the schedule

Day 1

Time Session
09:30 - 10:00 Introduction
10:00 - 11:00 Development environments, system commands, and version control
11:00 - 11:15 Break
11:15 - 12:30 Getting set-up with git and github
12:30 - 13:30 Lunch
13:30 - 15:00 Sharing code and data
15:00 - 15:15 Break
15:15 - 16:30 Reproducible papers and documentation with Quarto + Cross-references and citations with Quarto

Day 2

Time Session
09:30 - 10:30 Drafting a reproducible paper
10:30 - 10:45 Break
10:45 - 12:30 Generating reproducible publication-quality visualisations
12:30 - 12:35 Team photo!
12:35 - 13:30 Lunch
13:30 - 14:30 Editing other people’s work
14:30 - 14:45 Break
14:45 - 16:00 Working on papers
16:00 - 16:50 Presentations and wrap-up
16:50 - 17:00 Prize presentation, feedback and close

Practicalities

  • Course website and open, reproducible code: tdscience.github.io/course/
  • In person or online?
  • Teaching assistants
  • Number of participants
  • Incorportating feedback
  • Costs