Exporting Data Cleaning Steps

Last updated on 2023-10-27 | Edit this page

Estimated time: 15 minutes

Overview

Questions

  • How can we document the data-cleaning steps we’ve applied to our data?
  • How can we apply these steps to additional data sets?

Objectives

  • Describe how OpenRefine generates JSON code.
  • Demonstrate ability to export JSON code from OpenRefine.
  • Save JSON code from an analysis.
  • Apply saved JSON code to an analysis.

Export the steps to clean up and enhance the dataset

As you conduct your data cleaning and preliminary analysis, OpenRefine saves every change you make to the dataset. These changes are saved in a format known as JSON (JavaScript Object Notation). You can export this JSON script and apply it to other data files. If you had 20 files to clean, and they all had the same type of errors (e.g. species name misspellings, leading white spaces), and all files had the same column names, you could save the JSON script, open a new file to clean in OpenRefine, paste in the script and run it. This gives you a quick way to clean all of your related data.

  1. In the Undo / Redo section, click Extract..., and select the steps that you want to apply to other datasets by clicking the check boxes.
  2. Copy the code from the right hand panel and paste it into a text editor (like NotePad on Windows or TextEdit on Mac). Make sure it saves as a plain text file. In TextEdit, do this by selecting Format > Make plain text and save the file as a txt file.

Let’s practice running these steps on a new dataset. We’ll test this on an uncleaned version of the dataset we’ve been working with.

Exercise

  1. Download an uncleaned version of the dataset from the Setup page or use the version of the raw dataset you saved to your computer.
  2. Start a new project in OpenRefine with this file and name it something different from your existing project.
  3. Click the Undo / Redo tab > Apply and paste in the contents of txt file with the JSON code.
  4. Click Perform operations. The dataset should now be the same as your other cleaned dataset.

For convenience, we used the same dataset. In reality you could use this process to clean related datasets. For example, data that you had collected over different fieldwork periods or data that was collected by different researchers (provided everyone uses the same column headings).

Reproducible science

Now, that you know how scripts work, you may wonder how to use them in your own scientific research. For inspiration, you can read more about the succesful application of the reproducible science principles in archaeology or marine ecology:

  1. Marwick et al. (2017) Computational Reproducibility in Archaeological Research: Basic Principles and a Case Study of Their Implementation
  2. Stewart Lowndes et al. (2017) Our path to better science in less time using open data science tools

Key Points

  • All changes are being tracked in OpenRefine, and this information can be used for scripts for future analyses or reproducing an analysis.
  • Scripts can (and should) be published together with the dataset as part of the digital appendix of the research output.