This week’s focus on how data functions within humanities research has been eye-opening for me. I’ve never felt particularly drawn to data or quantitative methods, but I’ve had some prior experience with creating datasets for a bibliometric project and with reporting annual statistics to the IMLS in my previous public library role. These experiences gave me a general understanding of data, but our discussions and readings have helped me see its significance more clearly.
Our introduction to Zotero and OpenRefine was especially useful. Zotero, as a research data management tool, allows users to store webpages, PDFs, archival sources, and more in one centralized space. I genuinely wish I had known about it earlier because organizing sources for my MLIS thesis would have been so much easier. Going forward, I can already see how it will streamline future research projects. OpenRefine, on the other hand, is designed for cleaning and restructuring messy datasets. Although I’ve only had a brief overview of its features, I can imagine how helpful it would have been when I was working with inconsistent library statistic data. It seems to be especially valuable for anyone handling large, messy data collections.
One of the most impactful takeaways from this week is the reminder that data is not objective. Rather, data reflects the assumptions, power structures, and biases of the people and institutions that produce it. This is further complicated by the lack of humanities and ethical training in many STEM fields, as well as the underrepresentation of marginalized groups in technology and research spaces. As a result, the data we rely on often mirrors these inequalities. Understanding who is represented in our data and what is missing reveals our societal values.
Catherine D’Ignazio and Lauren Klein’s “The Power Chapter” particularly resonated with me. Their discussion of the disproportionately high maternal mortality rates among Black women and the lack of data collection on pregnancy complications was both shocking and deeply troubling. It underscores how the lack of adequate data can have life-or-death consequences in the health industry. Representation in data collection matters, and the absence of data can reveal systemic issues in our society. This reading reinforced the importance of questioning the power dynamics within data and reshaped how I interpret data.