Wikidata Visualization: Manga Time!
Wikidata is a massive hub of knowledge. Its structured nature, based on the principles of the web, makes it a powerful resource for researchers, developers, and enthusiasts across the very diverse world. By querying Wikidata, you can tap into a wealth of information, extract insights, and discover connections that were previously hidden within unstructured data. Utilizing wikidata, you can create a data visualization, a tool that can be considered as indispensable for turning raw data into actionable knowledge. As such, I created a data visualization utilizing wikidata to talk about a particular form of comic that is very popular which is that of manga.
The reason for me landing on manga was definitely due to the complexity of wikidata. I previously tried different entries, some more specific than others when building the query, however getting results was still difficult. I previously tried creating a wikidata query surrounding a famous comic creator I am studying, specifically that of Aaron McGruder. When creating the Query I was stuck with not as many results as wanted, as it only produced four results and I really could not do much with it. Similar to that of the next attempt which was surrounding the famous DC comics batman, that produced results that were also not usable. This was surprising considering how many projects this character has been involved in, but nonetheless it was not that useful.
The link to the query can be found here: https://w.wiki/7qRs
The query that was built was used to find manga, which produced a massive amount of results. The query utilized was able to find the wikidata code for each particular manga, as well as said manga’s publication date, said manga’s publisher wikidata code, said manga’s author, and the title of the manga. When working on this query I ran into trouble due to the vast results that came about. I previously did not input code that generated the publisher’s wikidata for the given manga, which as previously mentioned, generated a massive amount of results. Even with the added code, the results still come out to about 204 which is really vast.
The link to the table: https://w.wiki/7qRu
When speaking of the results, the list included some popular anime that are known for years. This included the likes of manga from Beast Children, Double Taisei, Hell Warden Higuma, Jujustu Kaisen, Beastars, Black Clover, Seraph of the End, One Piece, and even Naruto. These names of famous manga have several entries on the table which is why it probably generated such a vast amount of results. Another thing to note is that the manga are in order according to the publication date as well.
With the results that were generated from the table, I thought it would be best to input them in the graph. This in turn created a vast web of information that was really insane to look. Many of the entries were interlocked with one another specifically in areas surrounding that of the publisher. For instance, the bubble highlighting Seraph of the End holds several different connecting bubbles such as that of “Manga series,” “shonen”, “Japan,” or even, “Japanese.” Other of the entries on the graph share some of these bubbles as well.
In conclusion, the Wikidata queries are interesting tools to processing data and can produce awesome forms of data visualization. This manga query really was quite interesting considering the vast amount of results and the interconnectedness the graph shows. I think wikidata is such an amazing tool and data visualization is just as beneficial. We can utilize these tools to formulate and understand data better in ways that are not always presented to us.