Humanities Data

In this week’s Module 7 we focused on humanities data and how to create, refine/cleanse, and analyze said data for our research purposes. In Miriam Posner’s article Humanities Data: A Necessary Contradiction, she emphasizes how humanist tend to not use data sheets, or even consider their source material “data”, even though they also tend to store their source materials in spreadsheets or air tables which would be considered data. Creating data from humanities sources makes it easier to identifying key information without having to reread or research all the material again to find the information needed. When source material is cataloged in a spreadsheet it’s easy and convenient to access. Data answers many questions for us based on how we set up our data sheets, for instance in the creating data assignment, the spreadsheet was broken into 12 columns including age, where they were enslaved, and where the interview is taking place. All of these are questions that other methods could technically answer but in a spreadsheet this information is easily accessible. I found myself rereading the source material multiple times in order to create the data sheet, but once it was created and I had to compare my data with Dr. Robertson’s I no longer had to go back and forth and spend so much time just rereading for information. Another aspect of using humanities data is data cleansing and transforming. In the article Data Scopes for Digital History Research by Rick Hoekstra and Marin Koolen, they discuss the multiple data transforming activities including selection, modeling, normalization, linking, and classification. I think the acts of normalization and modeling ned to be transparent since modeling is like a frame of reference for source material and data to work off of and can manipulate, and normalization is “the process of bringing surface forms expressed in data back to an underlying standard form” that can be used to create data clusters and form relationship connections between the data that otherwise would not be as clear or present at all. It is important that researchers keep track of their data and how it is transformed so readers and others a like are aware of how data has been manipulated, changed, interpreted, and presented to them and may not be exactly like the original data.