Data-Management

A tutorial on data-management basics using OpenRefine.

As scholars seeking to use digital methods in our research, we need to understand how we can make both quantitative and qualitative data machine-readable and machine-usable. But what is data anyway? What is a dataset? Where can we find raw data? How do we know whether our data is "clean" or "dirty"? How can we clean "dirty" data? These are a few of the questions which this workshop sets out to address.

This tutorial was adapted from a 2017 Data Managment workshop designed by Rachael Starry and the tri-cods tidy-data module.

Learning Goals

• Understand the structure and appearance of datasets
• Discover where and how to find data online
• Apply learned concepts in order to clean raw datasets in OpenRefine

Learning Path

1. Thinking About Data

2. Finding Data

3. Messy & Tidy Data

4. Introduction to OpenRefine

5. Data Cleaning

Additional Resources

• OpenRefine documentation
• Cleaning Data with OpenRefine lesson from Programming Historian
• "Tidy Data" by Hadley Wickham
• The European Spreadsheet Risks Interest Group for a curious and comic collection of messy data horror stories

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