Visualization link: https://w.wiki/7kCQ
This is a table depicting illustrators who have won the Dwayne McDuffie Award for Diversity in Comics, according to Wikidata. I used the Wikidata Query Service and built my own SPARQL code. A table is typically one of the most common visualizations and one of the simplest. There is not much data in the table, as only four items popped up. Further, nothing is being compared, so a table is the best way to represent the data. There is much missing, however. The award began in 2015 and there hasn’t been a new awardee since 2020, so there are not many winners. This award is important because it celebrates comic artists who make an effort to diversify their works. They only show four of the winners, and within those winners only one author when a majority of the books had multiple authors. For Nilah Magruder, who won the award for her book M.F.K., the book does not even pop up, only her name. There are so many gaps in Wikidata concerning this award.
This visualization is a continuation of my weekly Wikidata assignment. There were not many specific queries that resulted in Steenz’s name popping up. I think this is because there is little on their Wikidata profile and that made finding queries that even included their name hard. Steenz has won this award for their book Archival Quality. This was one of the only searches where their name popped up. Even when I tried to go broader, like simply looking for African-American illustrators, their names did not show up in the list. I think this experience showed me the reality of data collection and how important it is to have data on file for people. This award I looked up specifically is for diversity, meaning most of the people who are nominated and won it are people of color. However, there is not much on their Wikidata profile, which could hide them from website searches, burying their works and contributions. Our reading, Data Feminism, highlighted the problem of not having diverse data, as it is reflected in algorithms and AI. Algorithms and AI routinely disregard this type of information because the data is not there to be fed to them.
The problem does not only lie with comic artists of color, however. I wanted to focus on the comic strip Steenz writes for, Heart of the City, for this assignment. However, it was still difficult to find queries that contained the comic strip. Overall, unless it is insanely popular like Charlie Brown or Garfield, it seems like comics are severely underrepresented and understudied. Much of the readings in this class point to this conclusion as well. Because they are understudied, it makes sense there is little data on them overall. This only highlights the importance of classes like these, which focus on underrepresented fields of study: digital humanities and comics.