“Formulating Ambiguity in a Database” is one of two posts published on the nodegoat blog to help illuminate the process of data modeling in the humanities. Drawing on insights learned through their workshops, the post aims to cover more fundamental questions that get to the heard of humanist’s concerns regarding structured data:
As soon as we start talk about ‘data’ it is important to keep two things in mind. First, we should be ready to reflect on the fact that data oriented processes can dehumanise data. This process has been described by Scott Weingart in his essay on digitising and storing holocaust survivor stories. Even though we can efficiently organise large collections of data, the implications of this process have to be taken into account.
Second: working with a digital tool, does not mean that you can only work with binary oppositions or uncontested timestamps. On the contrary: by creating a good data model, you are able to include all the nuances, irregularities, contradictions and vagueness in your database. A good data model is capable to make these insights and observations explicit. Instead of smoothing out irregularities in the data by simplifying the data model, the model should be adjusted so it reflects the existing complexities, vagueness, and uncertainties.
The post goes on to include detailed sections dedicated to five areas:
- How to determine the scope of your research?
- How to reference entries in a dataset and how to deal with conflicting sources?
- How to deal with unknown/uncertain primary source material?
- How to deal with unique/specific objects in a table/type?
- How to use/import ‘structured’ data?
nodegoat is a project of the Lab1100 group.
This post was produced through a cooperation between Suse Cairns, Rebecca Dowson, Kelsey Diane George, Alix Keener, Joseph Koivisto, Benedikt Kroll, and Chella Vaidyanathan (Editors-at-large for the week), Roxanne Shirazi (Editor for the week), and Caitlin Christian-Lamb, Caro Pinto and Patrick Williams (dh+lib Review Editors).