This week’s readings have helped me think more about the importance and implications behind correctly recording metadata. Metadata is not just simply just data about data, it is also a powerful tool that “gives meaning and structure to a collection of items.”1Its effects span beyond just digital humanists and researchers in the library. Well-organized metadata can aid in creating an accessible and inclusive space for its users, in addition to accurately and respectfully describe the history of the community to which it belongs. I will certainly keep this in mind as we continue to work in ACDC and proceed with our project.
In the data visualization workshop, my partner and I choose to look at Amherst’s Report to Secondary Schools from 2013-2022, using the “Snapshot” overview section. We were particularly interested in studying how the demographics of Amherst’s enrolled classes have changed over time. By recording this data and then using Tableau as our data visualization tool, we were able to discover some interesting relationships between some of these categories. I was very interested in studying underrepresented groups at Amherst such as students of color and first generation/low-income students. However, the reports only disclosed the percentage of first-generation students from 2003 to 2011 and 2018 to 2022, and instead reported percentage of low-income students during that gap from 2012 to 2018. The inconsistency in the reporting of data limited the types of analysis we could conduct with this set; but even so, I was still pleased to be able to find some interesting relationships. For example, there has been a noticeable increase over time in percentage of students receiving grants/aid and in percentage of students of color. However, at first glance the same relationship did not exist for percentage of first-generation or low-income students. This makes me wonder if that increase is from an active effort to increase the low-income population or if it is primarily from having a higher proportion of middle-class students who require significantly less aid. In addition, I also wonder what the reasoning was behind reporting low-income instead of first-generation percentage in those six years? The answers (as well as missing data) will likely come to me if I continue my research. This is definitely something I would like to look into further if I have time.
For visual learners, data visualization is certainly a helpful tool. It helped me see the relationships between different factors more clearly and dig deeper into the meaning behind these data points. I look forward to find a way to incorporate something similar into our final project. With only three weeks left of this fellowship, I am definitely ready to fully immerse myself into our project.
1McCulloch, Alissa. “We need to talk about cataloguing: the #NLS9 transcript.” Cataloguing the Universe: A work in progress, WordPress, 11 July, 2019. lissertations.net/post/1177