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Learning to Work With Emotionally Distressing Data

Writer's picture: Zane WolfZane Wolf

Trigger Warning: This post discusses data related to child fatalities caused by being trapped in hot cars, including a brief description of a particularly distressing incident. Please take care of yourself when reading this blog.


I recently had my first experience working with emotionally distressing data for an article at Scientific American addressing the near constant rate of pediatric vehicular heat deaths in the United States. I volunteered to make the graphics for this article and knew that this would be one of the challenges of this project. However, I wasn't sure how to prepare—not that there was a whole lot of time do so before starting to dig into the data—and I wasn't sure how I would respond.


It was rough, to be honest.


I believe if I had been working with a fixed dataset, I might have had an easier time. I could have compartmentalized a bit more effectively after the initial emotional impact of digging into the data. However, I had to manually add new incidents, new deaths, to the dataset about three times a week for each of the three weeks I worked on this article. Starting my days by having to comb through the incident reports to collect the information and add the children to the dataset was rough. I came to dread opening my inbox in the morning. Each new incidence report meant I had to wrestle with the emotional weight of the data over and over and over again.


There were a few particularly heart-wrenching incidences that hit a bit harder than the others. One of those emails came in during my weekly check-in with Amanda and Jen, and I started tearing up during the meeting just reading about the context in the email. A woman had attempted to kill both herself and her son. She half-succeeded, and I needed to update my dataset.


Amanda and Jen saw my reaction and offered to take over. I remember my instinctive response: Thank you, but my reaction doesn't mean there's anything wrong or that something needs fixing.


And I still stand by that. There is nothing wrong with feeling impacted emotionally by data. To feel is human. And I didn't want to tap out because I knew from the get-go this would a pivotal learning experience as a data designer. I needed to figure out how I could work with this data, because chances are it won't be the last sensitive dataset I visualize.


Once I realized I was having a harder time than I initially expected, I reached out to the data viz community on Bluesky for advice. Several people shared their own experiences and difficulties working with sensitive data. One of the highly suggested resources was the article, "Feminicide Data, Emotional Labor, and Self Care" by Catherine D'Ignazio, co-author of Data Feminism.


This article recommends tactics like taking breaks from the data, channeling the emotions into activities, creating/finding and leaning on community support, setting boundaries for when and how you work with the data, and focusing on other aspects of the project such as learning new skills.


After the first week, which was the hardest partially due to unrelated but cumulative factors dealing with the topic of death (such as that being the week of 9/11), I made sure to take the weekend off completely. I hung out with friends, went to a comedy show, grabbed ice cream, spent hours in Central Park, played board games, and watched some comfort movies. Overall, I became more cognizant about setting healthy boundaries and actively taking care of my emotional and mental health. (I'm also in therapy, but didn't have an appointment at this time.)


Realizing that I was already instinctually following some of the recommended practices helped a good deal, actually. It made me feel more confident in my ability to work with this type of data, even if this first time was harder than I thought it would be.Working with emotionally distressing data is rough, but everybody struggles and supports each other and I learned how to take better care of myself throughout the process. Next time might not be better, but I'll be better prepared.


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