# Data Embroidery with Black-and-White Textures

Tingying He, Petra Isenberg, Tobias Isenberg

► To cite this version:

Tingying He, Petra Isenberg, Tobias Isenberg. Data Embroidery with Black-and-White Textures. alt.VIS Workshop at IEEE VIS 2023, Oct 2023, Melbourne, Australia. hal-04197527

HAL Id: hal-04197527

https://hal.science/hal-04197527v1

Submitted on 6 Sep 2023

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
Data Embroidery
with Black-and-White Textures

<figure><img src="image_2.png" /></figure>

<figure><img src="image_3.png" /></figure>
# Data Embroidery with Black-and-White Textures

Tingying He ID, Petra Isenberg ID, Tobias Isenberg ID

<figure><img src="image_4.png" /><figcaption>Fig. 1: (a): An embroidered chart with black-and-white textures displaying the results of a survey within a family. (b): A canvas bag featuring the embroidered chart on the left. (c) and (d): the bag being used within the family.</figcaption></figure>

**Abstract**—We investigated data embroidery with black-and-white textures, identifying challenges in the use of textures for machine embroidery based on our own experience. Data embroidery, as a method of physically representing data, offers a unique way to integrate personal data into one's everyday fabric-based objects. Owing to their monochromatic characteristics, black-and-white textures promise to be easy to employ in machine embroidery. We experimented with different textured visualizations designed by experts and, in this paper, we detail our workflow and evaluate the performance and suitability of different textures. We then conducted a survey on vegetable preferences within a family and created a canvas bag as a case study, featuring the embroidered family data to show how embroidered data can be used in practice.

**Index Terms**—Textures, black-and-white, design, data physicalization, personal data visualization.

## 1 INTRODUCTION

Data embroidery [13] is an innovative technique for data physicalization [7]. Machine embroidery as a computer numerically controlled (CNC) technology makes it possible to produce complex data em-
brolderies (relatively) quickly and integrate them into fabric-based
personal belongings [13]. Data embroidery of personal data has poten-
tial because a less conventional approach to visualization may stimulate
people to explore their own data more intensively [12]. It can also serve
as an ambient visualization within a home setting, thereby initiating di-
alogues with curious visitors [9]. Data embroidery can, like in our case,
be accessible to a broad set of the population through local Fablabs.

A promising yet so far unexplored avenue within data embroidery
involves the use of black-and-white textures. Before the ubiquity of
color printing, these monochromatic textures served as a powerful
visual channel for data visualization (e. g., see the OldVisOnline collec-
tion [15]). Their inherent simplicity facilitates the conversion of images
to embroidery files, overcoming challenges associated with the lower
color resolution of embroidery machines compared to color screens.
Moreover, they eliminate the need for multiple color changes during
the embroidery process, enhancing efficiency.

In our work we explore data embroidery with black-and-white tex-
tures and contribute the following: (1) a detailed and hands-on workflow
for creating data embroidery from an existing black-and-white textured
chart image, (2) preliminary evaluations of which textures can be most
effectively translated into data embroidery, and (3) a showcase of data
embroidery of personal data with black-and-white textures—a canvas
bag with an embroidered chart visualizing a within-family survey.

## 2 RELATED WORK

Many researchers and artists have experimented with embroidery as
a means to create visualization artwork. For instance, Liz Bravo [2]
manually embroidered charts of the distribution of U.S. cotton from
1942 to 1948, a visualization originally created by Mary Eleanor Spear,
a pioneer in data visualization. Olivia Johnson [3] used cross-stitch
techniques to create charts about gender inequality and discrimination
at workplaces [4]. Hand embroidery has also been used to explore
personal data. For example, Jane Zhang [14] logged her anxiety over
21 days and created an embroidered chart to visualize it—akin to other
efforts in personal data visualization [8]. In the visualization research
literature, Smit [11] has expanded the fabric-based data visualization
landscape by exploring hand knitting as a potential medium for data
physicalization, resulting in several data knitualization works. All these
efforts, however, have employed manual methods to represent data on
fabric, while we explore a more automated process.

Machine embroidery has gradually started to draw researchers' at-
tention as well. Wannamaker et al. [13] explored the use of CNC
embroidery machines for expressing personal data and embroidered
a personal data physicalization representing text message data on a
blanket. Schneider [10] provided a tutorial on data visualization with
machine embroidery using Ink/Stitch [1], in which he outlined a gen-
eral workflow of computerized embroidery. This workflow notably
includes an essential step of reducing colors of the drawing to adapt
it to the embroidery constraints, which underscores the suitability of
monochromatic charts for machine embroidery. We used this process
as an inspiration but specifically focused on textured visualizations.

## 3 MACHINE EMBROIDERY FOR VISUALIZATION

We first detail the workflow we followed for creating data embroidery
from a chart image, as well as the choices we made during this process.
We then discuss the common issues we encountered during our data
embroidery process and possible troubleshooting methods.

<footnote>* Tingying He (何汀漾), Petra Isenberg, and Tobias Isenberg are with Université Paris-Saclay, CNRS, Inria, LISN, France. E-mail: {tingying.he | petra.isenberg | tobias.isenberg}@inria.fr.</footnote>
<figure><img src="image_5.png" /><figcaption>Fig. 4: Some embroidery samples during our experimentation.</figcaption></figure>

(e. g., the right-most bars in Fig. 5(a) and (b), respectively) proved problematic for the embroidery process. For example, small dotted textures are prone to cause issues during the embroidery process: their final appearance is not clean (Fig. 5(a), right-most bar) and the process often leads to problems as evident in the complex threads on the back side due to the bobbin thread becoming entangled (Fig. 6, left-most bar), while the textures with fewer problems have leaner back sides (visible for the rest of Fig. 6).

**Simplicity vs. Complexity.** The resolution of the embroidery affects the results of complex patterns. Designs with numerous small details can be difficult to embroider, leading to a loss in clarity and accuracy (e. g., the tomato of the right-most bar in Fig. 5 lacks the needed detail that separates the main body from the stem, which leads to it being shown essentially as a large dot). Therefore, we observed that simpler patterns lend themselves better to the embroidery process. In our experiments, simple icon designs were translated more effectively into embroidered designs compared to their more complex counterparts.

To illustrate which textures perform better and to facilitate comparisons, we selected some typical examples and visualized their performance using bar charts. The previously mentioned examples in Fig. 5 compare the performance of the different designs, one for geometric textures and one for iconic textures. In addition, machine embroidery tends to struggle with text, in particular with the small text that is often used for labels or legends in visualizations. In fact, when we look at the reverse side of the embroidery result (Fig. 6), we see that text exhibits similar problems as the textures with scattered elements we mentioned before. Please note that the data shown in Fig. 5 and 6 are our own subjectively perceived/assessed values of ease of reproduction, they are not precise measurements.

# 5 EMBROIDERING DATA OF A FAMILY SURVEY

To evaluate the embroidery workflow and to investigate its use for visualizing personal data, we embroidered the results of a family survey using visualization with black-and-white textures.

**Data Collection.** The last two authors conducted a survey on their extended family members' preferences for seven vegetables (carrots, celery, corn, eggplant, mushrooms, olives, and tomatoes). We asked each family member to rate each of the seven vegetables on a scale of 1 to 5 (1 = "I don't eat it at all," 2 = "I can eat it if necessary," 3 = "it's ok, I neither like nor dislike it," 4 = "I like it," 5 = "it's among my most favorite vegetables"). 11 family members participated in this survey. We calculated the average rating score for each vegetable, and present the result in Table 1.

**Visual Representation.** We created a bar chart titled "How much does my family like vegetables" to display the results. The x-axis represents the type of vegetable, while the y-axis indicates the degree of liking according to the mentioned scale. We selected the texture design from a collection of visualization designs with black-and-white textures, gathered from visualization design experts. We chose this

<figure><img src="image_6.png" /><figcaption>Fig. 5: Two embroidered charts showing the performance of different textures in machine embroidery: (a) for geometric textures, and (b) for iconic textures.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 6: The reverse side of Fig. 5(a). The messy threads indicate that these textures or text elements are problematic to embroider.</figcaption></figure>

particular design not just because it was composed of lines—making it well-suited for the embroidery process—but also because it appealed to a particular family member. In addition, iconic textures can have semantic association, thereby freeing the chart from the need for labels and thus making it extremely suitable for data embroidery.

**Embroidery.** To convert the chart into an embroidery, we followed the embroidery process as detailed in Sec. 3.1. The entire embroidery process took approximately 2 hours, including troubleshooting. Fig. 1(a) shows the data embroidery result. We sewed the embroidered piece onto a canvas bag (see Fig. 1(b)). By integrating family data into such an everyday item that is also used for grocery shopping, this data embroidery can act as a daily reminder of the family's preferences.

<table><caption>Table 1: The average scores of the family members' preference towards each of the seven vegetables.</caption><thead><tr><th>carrots</th><th>celery</th><th>corn</th><th>eggplant</th><th>mushrooms</th><th>olives</th><th>tomatos</th></tr></thead><tbody><tr><td>4.33</td><td>2.33</td><td>4.11</td><td>2.78</td><td>4.00</td><td>2.56</td><td>3.89</td></tr></tbody></table>
<figure><img src="image_8.png" /><figcaption>Fig. 7: Two 3D printed textured charts, one with geometric textures, and another with iconic textures.</figcaption></figure>

# 6 DISCUSSION AND FUTURE WORK

Given the monochromatic nature of black-and-white textures and, in particular, the semantic association of iconic textures, we envision that they have the potential to become an important visual channel for data embroidery. Our study represents a first step in exploring this technique and we provide our experiences for future embroidery work. Such future work could focus more systematically on evaluating the impact of data embroidery with black-and-white textures in terms of both efficiency and aesthetics [5].

While our work primarily focuses on embroidery, visualizations featuring black-and-white textures can also be easily created physically through a variety of other methods such as 3D printing, laser engraving, vinyl cutting, or embossing. We experimented with 3D printing of textured visualizations and produced two charts—one with geometric textures and another with iconic textures (see Fig. 7). Compared to embroidery, setting up a 3D printer is simpler and less prone to errors. The process follows basic 3D printing steps: converting the image file (PNG or SVG) to STL using stand-alone or online converters, slicing the STL model to GCODE using a slicer, and, ultimately, importing the GCODE to the 3D printer for printing. The printing process was error-free—textured pieces are not different from any other 3D print.

Our exploration of physicalization using monochrome charts can also open up an interesting additional avenue for future investigation: studying their potential used by visually impaired individuals. Given their tactile nature, these charts could potentially serve as valuable tools for data representation for this population.

# ACKNOWLEDGMENTS

We thank all visualization experts who provided us with textured visualization designs, as their invaluable insights and expertise served as a crucial cornerstone of our work. We also thank Romain Di Vozzo and Fablab UPSaclay (fablabdigiscope.universite-paris-saclay.fr) for providing us with equipment, training, and technical support.

# IMAGES/GRAPHS/PLOTS/TABLES/DATA LICENSE/COPYRIGHT

We as authors state that all of our own figures, graphs, plots, and data tables in this article (i. e., those not marked) are and remain under our own personal copyright, with the permission to be used here. We also make them available under the Creative Commons Attribution 4.0 International (©CC BY 4.0) license and share them at osf.io/f5b6d/.

# REFERENCES

[1] Ink/Stitch. Open-source machine embroidery design platform; url: inkstitch.org. Last accessed: July 2023.

[2] L. Bravo. Data and technique: Reflections on visualizing by hand. Nightingale, Journal of the Data Visualization Society; blog post: www.nightingaledvs.com/data-and-technique-reflections-on-visualizing-by-hand/, Oct. 2021. Last accessed: July 2023.

[3] J. Brewer. Women's work uses cross-stitched data visualisations to explore discrimination in the workforce. It's Nice That, Inspiring Creativity; blog post: www.itsnicethat.com/news/olivia-johnson-womens-work-cross-stitch-data-visualisation-illustration-240820, Aug. 2020. Last accessed: July 2023.

[4] F. Cabric, M. Vilborg Bjarnadóttir, M. Ling, G. Linda Rafnsdóttir, and P. Isenberg. Eleven years of gender data visualization: A step towards more inclusive gender representation. IEEE Trans Vis Comput Graph, 30(1), 2024. To appear; preprint doi: 10.48550/arXiv.2308.14415.

[5] T. He, P. Isenberg, R. Dachselt, and T. Isenberg. BeauVis: A validated scale for measuring the aesthetic pleasure of visual representations. IEEE Trans Vis Comput Graph, 29(1):363–373, 2023. doi: 10.1109/TVCG.2022.3209390

[6] T. He, Y. Zhong, P. Isenberg, and T. Isenberg. Design characterization for black-and-white textures in visualization. IEEE Trans Vis Comput Graph, 30(1), 2024. To appear; preprint doi: 10.48550/arXiv.2307.10089.

[7] Y. Jansen, P. Dragicevic, P. Isenberg, J. Alexander, A. Karnik, J. Kildal, S. Subramanian, and K. Hornbæk. Opportunities and challenges for data physicalization. In Proc. CHI, pp. 3227–3236. ACM, New York, 2015. doi: 10.1145/2702123.2702180

[8] S. Posavec and G. Lupi. Dear Data. Princeton Architectural Press and Particular Books, 2016. url: dear-data.com/thebook.

[9] Z. Pousman, J. Stasko, and M. Mateas. Casual information visualization: Depictions of data in everyday life. IEEE Trans Vis Comput Graph, 13(6):1145–1152, 2007. doi: 10.1109/TVCG.2007.70541

[10] D. K. Schneider. Data visualization with machine embroidery. EDUTECH Wiki, url: edutechwiki.unige.ch/en/DataVisualization_with_machine Embrindy, Oct. 2018. Last accessed: July 2023.

[11] N. Smit. Data knitualization: An exploration of knitting as a visualization medium. In Proc. alt.VIS workshop at IEEE VIS, 2021. doi: 10.31219/osf.io/xahj9

[12] S. Wang, Y. Tanahashi, N. Leaf, and K.-L. Ma. Design and effects of personal visualizations. IEEE Comput Graph Appl, 35(4):82–93, 2015. doi: 10.1109/MCG.2015.74

[13] K. Wannamaker, W. J. Willett, L. A. Oehlberg, and S. Carpendale. Data embroidery: Exploring alternative mediums for personal physicalization. In Posters at IEEE VIS, 2019. Extended abstract and poster, handle: 1880/110218.

[14] J. Zhang. Hacking social media to tell meaningful data visualization stories: Exploring anxiety with embroidery. Blog post: janezhgw.mediun.com/hacking-social-media-to-tell-meaningful-data-visualization-stories-exploring-anxiety-with-4ff29a8d79e2, Jan. 2018. Last accessed: July 2023.

[15] Y. Zhang, R. Jiang, L. Xie, Y. Zhao, C. Liu, T. Ding, S. Chen, and X. Yuan. OldVisOnline: Curating a dataset of historical visualizations. IEEE Trans Vis Comput Graph, 30(1), 2024. To appear; preprint doi: 10.48550/arXiv.2308.16053.

[16] Y. Zhong, T. Isenberg, and P. Isenberg. Black-and-white textures for visualization on e-ink displays. In Posters at IEEE VIS, 2020. Extended abstract and poster, url: hal.science/hal-02944212.