WikiData Visualization Major Assignment #1 – Holden Habermacher

WikiData Query Link: https://w.wiki/7kCH My visualization shows comic characters sorted by sex or gender. I altered an SDSU Comics Project query about ethnic groups of all comic characters.

This shows what I entered into the Query Builder. The displayed items on the Query Helper were “instance of any subclass of comics character” and “sex or gender”.

Sample image of data excluding male, male organism, female, and female organism categories as they are too large to display. This search filters the data pulled into two categories: “ethicgroupLabel” and “characters”. The labels “male organism”, “male”, “female organism”, and “female” created the largest categories. Other labels include intersex, trans woman, agender, genderfluid, hermaphroditism, neutral sex, gender queer, and nonbinary. This query pulled the most data of any query I have tried. I believe this is because the most common label attached to a comic character is their gender. The table visualization type was the best way to display this data. The table showed the volume of characters in each category which enables the user to quantify data within each gender. Other types of visualizations placed data points on a graph which hurts the users’ ability to quantify and analyze the data. The table showed that “male” characters were the most common with “female” characters coming in close second. Gender is an interesting topic for comics characters. Especially for anime characters and nonhuman beings, placing them into heteronormative gender categories can be a difficult task. Gender placements in Wikidata are sometimes in conflict with other databases. For example, the character Ryou Asuka is labeled under the hermaphrodite category through my Wikidata query, but Google labels Asuka as a man. This irregularity is a great example of errors because of human interpretation. Data entered into databases are structured and explicit, but the humans who entered that data can have differing opinions. The human who entered the Google data had a different interpretation of Ryou Asuka than the human who entered the data into Wikidata. To lessen human errors, peer checking and research standards should be enforced so that all data is as correct as (humanly) possible.