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Cleaning Data

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In addition to constructing data, digital humanists regularly work with "large or dense cultural datasets, which call for new processing and interpretation methods" (Kaplan, 2015).  Because 'Big Data' is frequently messy, our first task is to learn how to clean data. 

This week we will discuss some of the promises and pitfalls of working with 'Big Data,' and you will learn how to clean data using OpenRefine.

Annotation #5

1. Guldi, Jo, and David Armitage. “Big Questions, Big Data.” In The History Manifesto, Cambridge: Cambridge University Press, 2014. Hypothesis link.

2. Wueste, Elizabeth. “Big Data, Big Problems.” Eidolon, December 18, 2017. Hypothesis link.

3. Schöch, Christof. “Big? Smart? Clean? Messy? Data in the Humanities.” Journal of Digital Humanities, November 22, 2013. http://journalofdigitalhumanities.org/2-3/big-smart-clean-messy-data-in-the-humanities/. Hypothesis link.

Assignment #5

https://programminghistorian.org/en/lessons/cleaning-data-with-openrefine




 

 

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