A visual exploration of authorship across The X-Files, Millennium, Harsh Realm, and The Lone Gunmen.
This interactive visualization maps every writer who contributed to Chris Carter’s television universe. By connecting episodes to their authors across four major series, the project reveals patterns of collaboration, frequency of contribution, and cross-series involvement.
To highlight the creative network of Carter’s shows contributors
Concept, data curation and research, design, and development
Wikipedia, IMDb
The original poster turned out to be quite data-dense, and I wanted to have a version that would allow me to explore the details. It also seemed like a great and full of joy opportunity to further explore Svelte framework and practice integrating it with D3.js to create interactive data visualizations.
Data visualization is designed as a circular chart with screenwriter modules arranged around the perimeter.
Each module contains
In the center is a non-ribbon chord diagram, connecting all the writers who collaborated on the episodes: the more episodes were written together, the thicker the line.
So the first thing I did was a basic interaction option for each author that highlights all the collaborators and the corresponding episodes, recalculated appropriately, throughout the circular layout.
This, along with basic sorting options, allowed for a clearer look at the writers' collaborations and highlighted the episodes that the writers worked on together, which was not possible to detect using the static version only.
Nice, but I'd like to have more tools for deeper exploration.
What I wanted most was to see when a particular author worked: whether he started from the beginning of the project or joined later; what was the frequency of participation in the work during the series' release.
I was not interested in the specific dates or seasons when the episodes were released, but rather the sequence itself. Also, despite the fact that the plots of the different series don't really intertwine with each other, I wanted to maintain the relative sequence of episode releases for all shows overall.
So, I plotted episodes as strips on an ordinal scale (not a time scale) common to all series, and sorted by release date.
Episodes color coded according to the TV shows also serve as a color legend for the main circular vis.
Additionally indicated the distribution of episodes by season. And for The X-Files the episodes are divided into groups: Mythology, dedicated to the main plot of the government conspiracy and the threat of alien colonization, and Monster of the Week.
Working closely with the main visualization, the timeline allows to see the density of scriptwriters’ participation in projects, what contributions they made, and at which stages they were most active.
And the interactive features of the timeline itself allow to filter authors by participation in the series, work on individual seasons, and Myth/MotW episodes.
Overall, at this stage I have satisfied my curiosity.
However, with the intention of making the work public, I faced the necessarity to add some explanations so that it would be clear how to use this tool.
For the circular chart I added a separate layer on top of the data visualization with text tips, each of which directly points to the elements and provides explanations.
Separately, I added a dynamic block with a colored legend for the rating, which also indicates active episodes and calculates the average rating for the selected group.
Well, it is not a mobile first design.
Actually, it is not even a desktop first design, but a print layout adapted to a digital format.
Anyway, I decided to try to keep the functionality and analytical potential for the mobile version.
I got rid of the circular chart because it would be useless on a small screen. Therefore, the main format for viewing the list of screenwriters has become a table, with the timeline playing a secondary role.
After all, you can view it on your mobile device, but this is an option for the most die-hard fans 👽
This project merges storytelling and data analysis, offering fans and researchers a way to explore authorship, illuminate creative networks and temporal patterns in serialized media.
It brings analytical value and helps