“More often than not, the categories that our historical sources used to divide up our data are not the same ones we’re interested in analyzing.” (Posner, “Humanities Data: A Necessary Contradiction“)
“So, how do we produce more work like this—work that understands data as already “cooked” and then uses that data to expose structural bias?” (D’Ignazio and Klein, Data Feminism, at https://data-feminism.mitpress.mit.edu/pub/czq9dfs5/release/3#nxlmy2b3sf5)
Take a look at the browsable google sheets export of the WCMA collection database. This spreadsheet includes all the works at the Williams College Museum of Art. The content of the catalog was compiled from the museum’s historical catalog, with records as far back as the 19th c., and includes recent acquisitions up to this year.
- In what way is this data? In what way is it metadata? (Refer to the reading by Christoph Schoch if you need a reminder of these terms)
- Imagine you are writing a datasheet to contextualize this data set. What are three topics or questions that you would want to address in the datasheet? For each of the three things, briefly explain why it is relevant for this data set. (You might want to return to the assigned chapter in Data Feminism for some ideas)
- What fields don’t exist that you might want to have about this collection? Where could someone get the information to fill in that data?
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