Rosa Donna van Koningsbruggen

Once I encountered the data set of Robin Weis [7], I knew I wanted to create a physical data representation of crying data. Specifically, my idea was to create a data physicalisation: a physical artefact that represent data through it material and geometric properties [5]. Data physicalisations have been created for centuries (see dataphys.org/list), however, no known example represents crying data.

Initially, I wanted to use Weis’ data set, as this would allow me to use a ‘large’ data set on crying, rather than having to create one from scratch and only have the time to create a small data set. However, whilst developing concepts, I realised that I missed the personal connection to the data and that certain aspects –the more qualitative aspects, such as the emotion behind the cry– were missing. Therefore, I tracked my own crying data, based on the categories created by Weis. To this I added the emotion behind the tears, and left out the time and duration of the cry, as I wanted to focus on the experience—not tracking the data. This resulted in the following set of 8 cries:

When	Location	Emotion	Intensity
18.10.2022	Home	Angry	3
20.10.2022	Home	Angry	4
16.11.2022	Bike	Sad	2
16.11.2022	Home	Happy	1
25.11.2022	Home	Sad	2
01.12.2022	Home	Sad	3
23.12.2022	Parents’ home	Angry	3
07.02.2023	Home	Sad	2

Whilst creating this data set, I started to think of what a physicalisation of cries would look like. During my presentation, I already stated that: “I don’t want to design a fountain”. Although I must admit that this would be fun, it felt too ‘easy’ and I wanted to challenge myself by imposing the limitation of not representing tears through water. Instead, I decided to focus on the idea of tears leaving our body and brining those tears back, which inspired me to create a data physicalisation which you can eat [2]: where the cries are communicated through taste and texture. Thus the question became, what do tears taste like?

A small background on the taste of tears:

Our tears are composed of water, oils, proteins, and electrolytes, such as sodium and potassium [3]. Due to the electrolytes, tears have a salty taste. There are 3 types of tears: basal/continuous, reflex, and emotional tear [1]. The emotional tears ‘flush out’ stress hormones, whilst at the same time releasing hormones which make you feel better, such as endorphins. Because of this composition, emotional tears are the least salty [3].

Based on the background research, I decided to create chocolate bonbons (chocolate contains endorphins after all), to represent my 8 crying episodes. The bonbon would consist of a ganache and a galette. Each galette is made by boiling glucose syrup and chocolate to 145 degrees Celsius, pouring the liquid on a piece of baking paper to let it cool down, after which it is blended till a powder, which can be evenly distributed using a sieve. The shape and size of the galette would represent the intensity of a cry. The taste of the ganache and galette represents the emotion (Figure 1).

*Figure 1. The shape and size of the tears. Each tear would represent the intensity of a cry. Left is the lowest intensity (1) and right the highest (5).*

However, an experiment with the galette showed that I could not control the shape: the galette melted and expanded in the oven; even after freezing it first (Figure 2). Therefore, I decided to focus on the texture of the galette to represent the intensity, where I based the texture on conceptual metaphors (e.g., rough is bad, in this case angry) [4]. Besides texture, I would further use the intensity of the taste to represent the intensity of the cry. Thus, both the texture and intensity of the taste represent the intensity of the cry.

Figure 2. Picture of initial experiments with the tear shapes. These either expanded (thus losing their shape) or burned because of the size differences within the shape. Therefore, a new mapping was created, using the texture of the galette. *Bottom right shows the used stencil.*

The taste of the bonbons represents the emotion of the cry, for which I was inspired by a blog post, which states that the chemical composition of tears changes their taste: angry tears would consist of a lower concentration of moisture and have a higher sodium concentration (thus being saltier), sad tears taste more acidic, and happy tears are supposedly sweeter [6]. I could not find scientific papers to back up these statements. Therefore, this only served as inspiration and is not something I consider to be proven.

Inspired by this, I created the following mappings:

Emotion	Taste	Texture
Angry	Very salty. As anger is often associated with heat, these bonbons contain chilli to make them spicy. The base of the bonbons is dark, bitter chocolate (80%).	Rough
Sad	Salty and acidic, through a higher concentration of lemon juice in the home made glucose syrup. The bonbons are based on milk chocolate, so they are less bitter than the angry tears.	Less rough, by baking it at a lower temperature. However, the texture is still perceivable when eating the galette.
Happy	Sweet and salty, the bonbons are based on white chocolate for intense sweetness.	Glossy and smooth

This mapping was applied to the bonbons, which consist of: (1) a ganache base, made of the respective chocolate (white=happy, milk=sad, pure=angry), salt, and in case of angry, chili, and (2) a chocolate galette made of sugar, the respective chocolate, and home made glucose syrup. The glucose syrup was made by boiling water, sugar, and lemon juice to a 118 degrees Celsius, which allowed me to control the acidity levels. Overall, I created the following bonbons per emotion:

Angry

Sad

Figure 4. 4 Bonbons representing ‘sad’ cries. The texture of the galettes is less rough than the angry cries and the galettes have been coated with cocoa powder to create a thin veil over the galette, making them easier to differentiate from the angry cries. Due to the milk chocolate and lower concentration of salt, these bonbons are not as bitter and salty as the angry cries. However, the ratio of lemon juice in the glucose syrup has been increased for an increased acidity.

*Figure 4.1. A close up of a sad cry bonbon.*

Happy

Data representation

Together these bonbons resulted in the following overall data physicalisation:

Figure 6. By tasting each bonbon, the intensity and emotion of the cry can be determined. The higher the intensity, the stronger the flavourings (salt, chilli, or acidity). Reflecting on this creation, it is probably difficult to determine the exact intensity of the emotion (and perhaps even emotion) by only eating 1 bonbon. Therefore, either a legend needs to be given to clarify the concept. This legend should be edible as well, so we can determine what the minimum (lowest intensity) and maximum are (highest intensity).

References

1. Nicholas M. Farandos, Ali K. Yetisen, Michael J. Monteiro, Christopher R. Lowe, and Seok Hyun Yun. 2015. Contact Lens Sensors in Ocular Diagnostics. Advanced Healthcare Materials 4, 6: 792–810. https://doi.org/10.1002/adhm.201400504

2. Florian ’ Floyd’ Mueller, Sarah Goodwin, Han Phan, Rohit Khot, Kim Marriott, Jionghao Lin, Yan Wang, Tim Dwyer, Jialin Deng, Kun-Ting Chen, and Kim Mar-Riott. Data as Delight: Eating data; Data as Delight: Eating data. https://doi.org/10.1145/3411764.3445218

3. William H. Frey, Denise Desota-Johnson, Carrie Hoffman, and John T. McCall. 1981. Effect of Stimulus on the Chemical Composition of Human Tears. American Journal of Ophthalmology 92, 4: 559–567. https://doi.org/10.1016/0002-9394(81)90651-6

4. Jörn Hurtienne, Christian Stößel, and Katharina Weber. 2009. Sad is heavy and happy is light. In Proceedings of the 3rd International Conference on Tangible and Embedded Interaction – TEI ’09 (TEI ’09), 61. https://doi.org/10.1145/1517664.1517686

5. Yvonne Jansen, Pierre Dragicevic, Petra Isenberg, Jason Alexander, Abhijit Karnik, Johan Kildal, Sriram Subramanian, and Kasper Hornbæk. 2015. Opportunities and Challenges for Data Physicalization. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, 3227–3236. https://doi.org/10.1145/2702123.2702180

6. Eunah Lee Kwon. 2008. Different Tears Different Taste. University Newspaper. Retrieved from http://smtimes.sookmyung.ac.kr/news/articleView.html?idxno=286

7. Robin Weis. 2016. Crying. Retrieved from http://robinwe.is/explorations/cry.html

Hi everyone, first of all: Happy New Year (within the Gregorian calendar). I hope you all had a nice restorative time during this small lecture break.

Here follows the blogpost describing the existing crying material I have selected:

589 Days of Crying by Robin Weis

For my PhD-research, I am exploring personal data, what they are, and what they mean to people. Part of my research is exploring how to move beyond our current understanding of (personal) data, which is often limited to easy to quantify activities and phenomena (e.g., step count and heart rate data), and explore how to incorporate qualitative experiences, as these are quintessential as to how we understand our worlds. With this lens and interest, I discovered the work of Robin Weis.

Figure 1. Robin Weis’ classification scale for the type / intensity of cry. Own visualisation.

For personal reasons, Robin Weis started to track her cries, collect a database, and visualise this. This process required her to define what counts as a cry (“I defined a “cry” to begin once I shed a tear and end when I regained composure.”), distinguish between different types / intensities of crying (see Figure 1), and determine exclusion criteria (cries triggered by “sensational stimuli” such as allergies or laughter were not included). Within this frame, the date, time, intensity, location, and context of 589 days of crying were tracked and visualised, resulting in the following visualisation:

Figure 2. Crying data represented in a parallel set plot by Robin Weis. Taken from https://www.vox.com/2016/5/25/11744768/crying-spreadsheet

Personally, I like the decision to go with the parallel set plot. Although this is a traditional visualisation technique (of which could be argued that they do not offer the same depth and connection as other types of data representations, e.g. [1,2]), the flow of the chart reminds me of the flow of her tears—probably an intentional decision.

Interestingly, this datafication experience had both positive (e.g., “I came to terms with my dissonance about being strong and sensitive at the same time.”) and negative consequences (e.g., “It became a compulsion to check the time every time I started (and stopped) crying”). Robin Weis’ reflection on the process already gives insights in what it is like to data-fy such a personal (and sensitive) experience. However, for me, it also triggers questions. For example, what other aspects could have been tracked? And what about the vanity of crying (do you gave the feeling you need to stay composed or could you fully let go)?

Sources:

The original work and article can be found here: http://robinwe.is/explorations/cry.html . Together with the more elaborate article for vox.com (content warning, the article mentions a suicide attempt): https://www.vox.com/2016/5/25/11744768/crying-spreadsheet

All quotes are taken from the vox.com article, as it contained more elaborate descriptions.

Referenced papers:

[1] Dietmar Ofenhuber. 2020. What we talk about when we talk about data physicality. IEEE Computer Graphics and Applications 40, 6 (June 2020), 25–37. https://doi.org/10.1109/MCG.2020.3024146

[2] Rosa van Koningsbruggen and Eva Hornecker. 2021. “It’s Just a Graph” – The Effect of Post-Hoc Rationalisation on InfoVis Evaluation. In Creativity and Cognition (C&C ’21). Association for Computing Machinery, New York, NY, USA, Article 45, 1–10. https://doi.org/10.1145/3450741.3465257