Tool Review: Hypothesis

In my role as Digital Humanities Librarian, I manage a monthly DH newsletter for faculty on campus to share readings, events, and other DH items. In the newsletter I highlight one DH tool each month, creating a sample project and sharing my thoughts. To keep the emails short, my full thoughts will be posted here under the Tool Reviews category.


Hypothesis is an open source web-annotation tool that allows users to annotate webpages, PDFs, and other online documents either privately, in groups, or publicly. It is currently operated by anno and in August 2022 ITHAKA invested in the tool. As of September 2022 Hypothesis had been used to generate over 40 million annotations

Rather than a tool to be used for the creation of digital projects, Hypothesis is primarily a digital pedagogy tool. It allows for social reading of texts. For example you could have your students read and annotate one of their assigned readings in a private Hypothesis group for your class, which would allow them to reply to each other’s annotations, and learn from each other and be in conversation with one another outside of class. 


These annotations function as digital marginalia and users can highlight or comment on sections of the text, as well as reply to other comments. This means users can also link to related content creating a richer reading experience, especially if readers post questions or reply to questions with more contextual or background information. 

As mentioned, these annotations need not be visible to the public. They could be private annotations if readers want a better way to mark up their readings digitally, or it can be done in private groups, so only other members of the group can see them. 

I imagine that most people will use Hypothesis through the browser extension, which allows users to “turn on” annotations for any webpage they are on. Once the extension is activated it is easy to select which group you want to annotate within. Then you can highlight, comment, and reply to your heart’s content. You simply select the text you are interested in and select whether you are making an annotation or a highlight and then keep on reading. By clicking on portions of the text that are already highlighted you can see other people’s annotations in the sidebar, and in that same area you can reply to annotations.  

It is also possible to integrate Hypothesis into your institution’s LMS, although that is a larger project that would require your institution’s IT/education technology/etc. department to be involved. I stand corrected! They have integration for all of the major LMSs, which will also automatically make a group for your class.


Honestly, I think this tool would be useful for any class that has assigned readings, which is to say all classes. It creates more asynchronous opportunities for the class to learn together outside of the classroom, and a more intuitive way of having conversations with their peers about readings than forum posts. While I think this is appropriate for all student levels, this Liquid Margins episode (embedded below) discusses how particularly useful it is in first year seminar courses. 

This post from the Hypothesis blog on 10 Ways to Annotate With Students provides some great inspiration for teachers looking to integrate social annotating to their curricula. In particular I think the first point about seeding a document with a few starter annotations is important to keep in mind, especially the first few times you are introducing this type of assignment. Speaking from experience, I used Hypothesis for a course in grad school, it is intimidating to make the first few annotations. Even more so when you aren’t sure what sort of annotations you should be making. It could also be worth sharing the Annotation Tips for Students resource Hypothesis has made to help give students ideas of what makes a useful annotation. 

One specific way I think it can be useful is for having students annotate/comment on the class syllabus. There have been many conversations, especially on Twitter, on what is gained by having students annotate their syllabuses and although those examples are using Google Docs for the annotations, I think Hypothesis would be another good tool for that sort of interaction.  


I think the main limitation for this tool is that it requires some time to get it set up at the start. If you want your class to be commenting in a class-specific group, and not publicly for example, you need to create the group and make sure all of your students make accounts and get added to the group. Then you need to make sure they know how to make sure they are posting to the correct group so that their classmates can see their contributions. Apart from that, as long as you are annotating publicly available text-based content I don’t think there are many limitations. 

Again, see my note above about the LMS integration. If you use a major LMS it will be easy to get your class up and running with Hypothesis.

And one of the strongest affordances is how it allows students to thread conversations in the text they are discussing, and not asking them to read something and then have a conversation on a class forum hosted elsewhere. By keeping the conversation in the document itself it can help tie what is being discussed directly to the text.


Now, as the Twitter conversations I linked to earlier mention, Google Docs’s commenting feature can function in almost the exact same ways as Hypothesis. The advantage of Google Docs is that the feature is native to the platform and doesn’t require users to download extensions or create new accounts, but it only works for Google Docs whereas Hypothesis could be used for any publicly available web page. But if you want students to annotate your class syllabus or assignment pages and they already are Google Docs, then it may make more sense to just use the commenting feature in the tool. 

There is another social annotation tool out there, Perusall. And while it is also free, it seems that the main additional feature is that it allows students to purchase textbooks within their platform which can then be socially annotated, while also allowing students to other types of web content like articles and webpages.  


Kalir, Jeremiah H. “Designing a Social Learning Analytics Tool for Open Annotation and Collaborative Learning.” In Learning Analytics in Open and Distributed Learning: Potential and Challenges, edited by Paul Prinsloo, Sharon Slade, and Mohammad Khalil, 77–89. SpringerBriefs in Education. Singapore: Springer Nature, 2022.

———. “Open Web Annotation as Collaborative Learning.” First Monday, June 1, 2019.

———. “Social Annotation Enabling Collaboration for Open Learning.” Distance Education 41, no. 2 (April 2, 2020): 245–60.

Kalir, Jeremiah Holden, Esteban Morales, Alice Fleerackers, and Juan Pablo Alperin. “‘When I Saw My Peers Annotating’: Student Perceptions of Social Annotation for Learning in Multiple Courses.” Information and Learning Sciences 121, no. 3/4 (January 1, 2020): 207–30.

Wranovix, Matthew, and Mary Isbell. “The Digital Common Read: Creating a Space for Authentic Engagement with Social Annotation.” Journal of the European Honors Council 4, no. 1 (June 30, 2020): 1–10.

Zucker, Lauren, Jeremiah Kalir, Michelle Sprouse, and Jeremy Dean. “Foregrounding the Margins: A Dialogue about Literacy, Learning, and Social Annotation.” Teaching/Writing: The Journal of Writing Teacher Education 10, no. 1 (March 24, 2021).

Tool Review: Prusa i3 MK3S+

In my role as Digital Humanities Librarian, I manage a monthly DH newsletter for faculty on campus to share readings, events, and other DH items. In the newsletter I highlight one DH tool each month, creating a sample project and sharing my thoughts. To keep the emails short, my full thoughts will be posted here under the Tool Reviews category.


In some ways this review is less about one specific tool and more about a collection of tools or even more about a method, 3D printing. This is in part because of how many tools are required to make a 3D print and because while my library has a Prusa i3 MK3S+ my review probably could apply to a variety of 3D printers. The main collection of tools I will be covering are:

In order to test the tool I did two test prints. For these prints I used files provided by museums that have created 3D scans of their collections. My first print was the queen from the Lewis Chess Set from the British Museum and the second print was the Fonseca Bust, a portrait bust of a Flavian woman from Staten Museum for Kunst via Scan the World (both can be seen below).


Luckily, this project was a collaboration between me and another member of my library, Tim Kail, who manages the 3D printer. Tim was already working on building a LibGuide on how to use the 3D printer (link to come once it is published) and I was the beta-tester for it. This also meant I did not have to teach myself how to use it and the workflow was already established. 

The Flavian bust file loaded into Prusa Slicer

To get started users need to first decide what it is they are printing, and either find an object/file they would like to print or to design an object using a tool like Tinkercad. Tinkercad is a free web-based 3D modeling program, which makes it useful because no one needs to download software to get started. Once the object is ready the file needs to be saved as an .stl and uploaded to Prusa Slicer. In our library we have a dedicated computer for the 3D printer, which has this installed on it. Essentially Prusa Slicer converts the file into slices which can be printed by the printer. At this stage they can see how long the print will take and then once they are ready export the g-code. This new file then needs to be put on an SD card, which is then inserted into the printer. Then comes the printing!

Progress about halfway through the print.

The printing itself will probably be the longest part of the process, and it is something that should be taken into account when planning projects. One 5-inch tall object took ~7 hours to print, but will vary based on size, complexity, and probably by printer. Once the object is printed it could be considered completely, but there is also the option to smooth the object out via sanding or the application of epoxies as well as painting the final print. 


I think this could be a useful tool in any course that studies material culture, like history, art history or archaeology. This would be especially suited for objects that would’ve been held, like coins or tools, as it would allow students to get a feel for the object. (Although the 3D printed object would most certainly be lighter than the original due to the material and how it prints objects with a lattice inside rather than as solid objects.) It also would augment studies of 3D objects where otherwise students would only have access to photographs of the object from set views. With the 3D object they would be able to study the object from more angles, but if students had access to the 3D scan or file of the object they wouldn’t need to necessarily print the object to be able to do that. They could just interact with the digital file. 

Those uses mostly focus on cases where students would use a provided file, rather than constructing a 3D object themselves. That itself could be useful to help students do close readings of visual resources in order to recreate them in a 3D environment, but I imagine unless they are in a 3D design class it may not be as feasible for students to have the sort of time, resources, or skills to be able to do that.


In this process I encountered a few complications. Some of the museum files I was considering were only provided as .obj, whereas we needed them to be an .stl. I tried to use Tinkercad to convert them to .stls, but the files were too large for Tinkercad to open, which is a trade off for using a web-based application. So I needed to find an alternative software to use, and I liked Blender best. Blender is also free but a desktop application, so students would have to download it to be able to use it. We are considering adding it to the computer stationed at the 3D printer in case students need to convert their files. 

A 30 minute print (left) versus a 7 hour print (right).

The amount of time you have available to print is something that cannot be ignored. We found there is a huge difference in the fidelity and detail possible between a short print (30 min) and a long one (7 hours) which you can see above. We imagine most students would be doing prints somewhere between these two, but to make an object with a high enough level of detail for meaningful study will take a lot of time. We don’t want the 3D printer to run unsupervised, so we will not be allowing students to run overnight prints or to print when the printer cannot be staffed by one of us, and this will limit the size of prints we are able to approve. One can “cut” the files into smaller pieces in Prusa Slicer, so if students really wanted to print large items we could run them in multiple segments, which could be glued together.


There are many other models of 3D printers out there, so it is not worth listing them all here. So if this Prusa printer won’t work for you, there very well could be one that is a better fit for your needs.

As already mentioned, Blender is a great alternative to Tinkercad if you need a more powerful 3D modeler. 


There are a few resources that can help you think about 3D printing as a scholarly activity. First, as part of my Digital Humanities LibGuide I compiled a list of museums and cultural heritage organizations that have published 3D files/scans of their collections. There are also many 3D scanning apps that could be used to create 3D files to print. Perhaps a future review will look at one or two of these as an additional way to design 3D printed objects. I also found the following readings and sample assignments useful in my thinking of how to integrate 3D printing into the classroom:

Embodied Data: A Reading List

Because of my research into embodied data, I have been hoarding readings on physical data visualizations. In the interests of making it more shareable I will also maintain it here! I am always looking for more materials, so if you know of something I am missing (or if you wrote something I should be aware of) please reach out. I would love to read it.

While I am grouping this under the term ’embodied data’ it also covers:

  • Physical data visualiztions
  • Crafted data visualizations
  • Data materialization


I hope to continue to add to this and I will note here when the most recent update was made.

9/30 – Updated a few broken/changed links!
10/13 – Added Borsi, Varga, and Korompai 2022
11/18 – Added Meech 2022


Berger, Claudia. “‘Embodied Data Visualizations’ at DH Unbound 2022.” Claudia Berger (blog), May 31, 2022.

bettinanissen. “Crypto-Knitting Circles.” Data Things (blog), June 4, 2020.

Borsi, Júlia, Tamás Varga, and István Korompai. “What’s a ‘Handmade Data Object’ Anyway?” Nightingale, October 13, 2022.

Bravo, Liz. “Data and Technique: Reflections on Visualizing by Hand, Nightingale.” Nightingale (blog), October 20, 2021.

Briney, Kristin. “Crafting a COVID Visualization: How I Processed Pandemic Anxiety and Grief with Yarn, Nightingale.” Nightingale, January 11, 2022.

Cleghorn, Ripley. “Why You Should Close the Computer for Your Next Data Visualization.” Nightingale (blog), October 2, 2019.

Cunliffe, Jordan. Record, Map and Capture in Textile Art: Data Visualization in Cloth and Stitch. 1st edition. Batsford, 2022.

giorgialupi. “Data Humanism.” Accessed February 23, 2022.

Dix, Alan, and Layda Gongora. “Externalisation and Design.” In Procedings of the Second Conference on Creativity and Innovation in Design – DESIRE ’11, 31. Eindhoven, Netherlands: ACM Press, 2011.

Ferrara, Silvia. “How The Inca Used Knots To Tell Stories.” Literary  Hub (blog), March 8, 2022.

Gibney, Danièle. “Crossing into Datavis.” Nightingale, January 5, 2022.

Gollihue, Krystin, and Mai Nou Xiong-Gum. “Dataweaving: Textiles as Data Materialization.” 25.1, August 15, 2020.

Haas, Angela M. “Wampum as Hypertext: An American Indian Intellectual Tradition of Multimedia Theory and Practice.” Studies in American Indian Literatures 19, no. 4 (2008): 77–100.

Houser, Heather. “Infowhelm: When Eco-Data Becomes Eco-Art – ASLE.” Accessed May 5, 2022.

Johnson, India. “The Soul of Data: Data Physicalizations on Fabric, Nightingale.” Nightingale (blog), March 8, 2022.

Kardos, Ann. Unseen Labor. University of Massachusetts Amherst Libraries, 2022.

Knight, Kim Brillante. “Danger, Jane Roe! Material Data Visualization as Feminist Praxis.” In Bodies of Information: Intersectional Feminism and Digital Humanities. Debates in the Digital Humanities. Minneapolis, MN: U of Minnesota Press, 2018.

———. “Fashioning Circuits | Labs & Studios.” ATEC at UT Dallas (blog). Accessed January 11, 2022.

List of Physical Visualizations and Related Artifacts. Accessed September 26, 2022. .

Lupi, Giorgia. “Drawing and Data Visualizations: A Tool To Allow Connections To Be Made.” Tableau Public, November 26, 2014.

Lupi, Giorgia, Stefanie Posavec, and Maria Popova. Dear Data. Illustrated edition. New York: Princeton Architectural Press, 2016.

Meech, Sam. “Fabrications: Using Knitted Artworks to Challenge Developers’ Narratives of Regeneration and Recognise Manchester’s South Asian Working Class Textiles Businesses.” TEXTILE, October 11, 2022, 1–22.

Monteiro, Stephen. The Fabric of Interface: Mobile Media, Design, and Gender. Cambridge, MA, USA: MIT Press, 2017.

Nithya Subramanian. “I’ve Been Working on an Embroidered Data Visualisation of the Plastic Waste That Washes up on Beaches around the World. Those Gray Kantha Stitches? Each One Is a Cigarette Butt Found along 10 Km of Beach in Germany. #WIP #dataviz Https://T.Co/Zayo7n3UCl.” Tweet. @nithya_sub (blog), June 16, 2021.

Rajko, Jessica J., Jacqueline Wernimont, and Stjepan Rajko. “The Living Net: A Haptic Experience of Personal Data.” In Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems, 449–52. Denver Colorado USA: ACM, 2017.

Roizman, Violeta. “Sweeping Untufted Charts Under the Rug.” Data Science by Design, June 8, 2022.

Smith, Nancy. “Data Quilts.” some quiet future, 2022.

Stark, Luke. “Come on Feel the Data (and Smell It).” The Atlantic, May 19, 2014.

“The Future of Data Science Includes Slow Data Science – Data Science by Design.” Accessed September 20, 2022.

Vande Moere, Andrew, and Stephanie Patel. “The Physical Visualization of Information: Designing Data Sculptures in an Educational Context.” In Visual Information Communication, edited by Mao Lin Huang, Quang Vinh Nguyen, and Kang Zhang, 1–23. Boston, MA: Springer US, 2009.

Webber-Bey, Deimosa. “Runaway Quilt Project: Digital Humanities Exploration of Quilting During the Era of Slavery.” The Journal of Interactive Technology and Pedagogy, no. 6 (November 30, 2014).

Wernimont, Jacqueline, and Elizabeth M Losh. “Wear and Care Feminisms at a Long Maker Table,” n.d., 12.

Williams, Dee. “How, and Why, We Sketch When Visualizing Data.” Nightingale (blog), June 5, 2021.

Tool Review: Storiiies

In my role as Digital Humanities Librarian, I manage a monthly DH newsletter for faculty on campus to share readings, events, and other DH items. In the newsletter I highlight one DH tool each month, creating a sample project and sharing my thoughts. To keep the emails short, my full thoughts will be posted here under the Tool Reviews category.


Storiiies is a tool developed by Cogapp, a UK-based digital agency, that allows user to annotate images to create interactive stories. It is free, web-based, and extremely easy to use. As the name suggests, it is based on the IIIF standards, however users do not need to be familiar with the standard to use the tool. Although those who do use IIIF can use the data that is created for other uses.

To test the tool I created a sample story using the painting The Money Family In Their Garden at Argenteuil by Edouard Manet. As a note, I did not write any of the material in this story, I took the text from the catalog entry written by Jane R Becker for the Met Museum. You can view my project by clicking the image below or visiting it here: . Unfortunately my WordPress plan won’t allow me to embed the project, but I am pursuing a work around for future reviews.

My sample Story


To get started users can add images either by adding an IIIF manifest or info.json URL (if they know it) or they can simply upload the image from a file. If they upload the image Storiiies will create an IIIF manifest for the file. But again, users need not be aware of IIIF to create a story. It is simply there for advanced users if they wish.

The first step of creating a story

As a note, users do not have to make an account. Storiiies needs an email so that they can send you the links for both the finished version of the project and the page where it can continue to be edited.

The way the annotations work is as users add the text of the annotation they can zoom in, or out, of the work to set the view that is visible. It doesn’t require any coding, you use their zoom tools and whatever is visible on the screen is what is shown in the final project. It is also simple to rearrange and edit the annotations as you work.

How to add annotations to the image

For more information I recommend this video Cogapp published about the tool. It is just under 30 minutes long and is extremely informative.


I believe Storiiies is a useful tool for any project or assignment where a student is close reading an image. It doesn’t just have to be for art history, although it is extremely well suited to explore art in depth, One of their sample projects annotates a photograph of a wasp to explain the physiology of the insect. One could also upload a map to explain either details of the map itself, or explore the map in the context of a particular event. This project by Jo-Ann Wong, “Hong Kong and its History” is a great example of how maps can be utilized in the tool. Arts classes could also use the tool to allow students to explain their artwork by pointing out details, or to have students use the annotations to critique each other’s work.


Storiiies is so easy to use because it is such a straight forward tool. That does have a few drawbacks, annotations (at least as far as I can tell) are plain text only. It would be wonderful to be able to include links in the text at some point, as well as do some minor text formatting. The final projects are also embeddable (although some platforms, *coughs WordPress*, won’t allow iFrames on their platform.) which makes them easy to share on a course page or in blog posts. And for those advanced users who are familiar with IIIF, they can take the data that is created and add it to other IIIF viewers to interact with. This is not my area of expertise, and I do recommend watching the video above for more information if this is something you are interested in. The fact that is creates data that is reusable and interoperable is very exciting though.


Now Storiiies isn’t the only tool to close read images. There are a few others out there and if Storiiies doesn’t quite meet your needs one of these may be a better fit.

  • StoryMapJS “Gigapixel”
    While StoryMapJS is primarily a mapping tool, there is a way to use their gigapixel tool to explore images instead. It does require that you host the image file somewhere else, but you can create richer annotations that include other media.
  • Juncture
    If these are not powerful enough for you Juncture may be a better option. It is a much more advanced tool to create visual essays that explore images, but would probably not be suitable for beginners. The essays are coded in GitHub and they have very clear documentation if you want to give it a try.

I may do reviews of these tools in the future, so keep an eye out if you are curious about the distinctions between them.

preservation x visualization

While I had the design of the front of the quilt more or less sorted since the start of the project, the back remained a question for a bit longer. Initially I considered just making a larger tree map showing the percentage each color was used in the poem, however I was really curious if there were ways the back of the quilt could *do* more. I was inspired by Deimosa Webber-Bey’s quilt “Maker Unknown” and how the quilt also functions as a preservation of the digital aspects of the project. She printed the code that runs her website, I believe, on fabric and used it to create the border around the quilt. This way as the digital version inevitably crumbles, the physical quilt provides a key to rebuild it. 

While I didn’t want to print code or something similar on fabric, this got me thinking about what more could I be doing to have the physical quilt be part of my preservation plan. I have been maintaining an OSF repository for this project; it has all my data, PDFs of these updates, and all of the other media associated with this project. But could I make the quilt a repository as well?  

the back of a quilt showing the distribution of colors through rectangular color-coded rectangles
The back of the quilt

I decided to make the back of the quilt function as both a visualization of my dataset and a more granular breakdown of my data, which could assist a future researcher rebuild my dataset if my repository failed. I displayed the colors sequentially in the order they appeared in the text broken up by book. While the quilt alone cannot be used to recreate my data, it does not show the line numbers or the actual words, you can see total counts easily and the breakdown of the distribution of the words. This way if you wanted to compare my data with a similar dataset you could easily see on the quilt back where those discrepancies are. 

As a visualization it functions to show how certain colors are consistently present in each book, or are more rare accents in the text. I really like how it demonstrated how the sixth book is so different from the rest of the text as the colors really do reflect changes in the narrative. 

One lesson I really learned from this portion of the project is the need to establish and define my units at the start of the project. Initially when I started this project I was using ½ inch seam allowances, and when I designed and calculated the fabric needs for the back of the quilt I used ½ inch seam allowances. However, as you may have seen, I took a little break in this project and by the time I restarted it and was sewing the back I had forgotten that and used ¼ seam allowances. This altered the final dimensions of the quilt back visualization, but luckily it still fit on the quilt top. But for my next quilted data project I need to really decide, and document, my parameters so that I don’t make these sort of mistakes again.

Updates and Next Steps:

I actually have finished the quilt! I am planning one more post about the project with some final reflections about what I learned about the text and the process of quilting data. I think it may include another video so that I can really show all of the details of the quilt.

a modern patchwork quilt hanging from a library shelf
The final quilt!

One reason for my mini-hiatus was I was in the midst of some job search hullabaloo, but I can now say I am the Digital Humanities Librarian at Sarah Lawrence College. I have been here a few weeks, and now that I am settled I am hoping I can start thinking about my next research projects. I am supporting some work on digital environmental humanities projects, so I am hoping I can tie my next quilt into some of those ideas.

New readings:

Guthrie, Laurin C. 2022. “Natural-Born Subversive: Dede Styles on Living and Dyeing in Swannanoa, North Carolina.” Southern Cultures 28 (1): 66–81.

Roizman, Violeta. 2022. “Sweeping Untufted Charts Under the Rug.” Data Science by Design. June 8, 2022.

“Embodied Data Visualizations” at DH Unbound 2022

On May 17 I gave a presentation “Embodied Data Visualizations: Integrating Crafting into Digital Humanities” at DH Unbound. This post is adapted from the talk – however since I used notes and not a script it may not exactly match. If you’ve been reading all of my updates here some of this might be repetitive, but there are some new ideas. Thank you!

Thank you for having me here today. My talk is “Embodied Data Visualizations: Integrating Crafting into Digital Humanities” and is about a work in progress project I am in the midst of.

This is the link for my slides. I know that if you are watching on a laptop, shared screens can be small and hard to follow. This was you can follow along zoomed in, and you have access to the full references for each slide in the notes, links, and alt text for the images.

Lately I’ve been thinking about what I’ve been calling “embodied data visualizations.” But other have been talking about this idea with other names, like:

  • Data physicalization (Vande Moere and Patel 2009; Johnson 2022)
  • Data materialization (Gollihue and Xiong-Gum 2020; Knight 2018)
  • And data visceralization (Bench and Elswit 2022; Stark 2014)

While I don’t think these concepts are all interchangeable, there is a lot of overlap between them. Essentially, for me, embodied data visualizations are the process of translating data into physical objects. Today I am focusing on sewing/quilting/crafting but there are countless other physical containers, like this TikTok where Humphrey Yang visualizes Jeff Bezos’s net worth via rice where one grain equals $100,000.

While these do not result in digital projects, I think they are still digital humanities.

There is, or should be, space in DH for non-digital projects. Especially if we take a liberal definition of DH. Right now, I am thinking of DH as using technologies to help us answer and think about humanities questions. Technology doesn’t have to be digital. In fact, textiles are one of the oldest technologies out there and could have a lot of potential in a DH context. Textiles allow scholars to materialize data decoupled from screens and interrogate was Gollihue and Xiong Gum call the “arbitrary division between old and new media privileging visualization over embodiment” (2020). This calls for different types of relationships between scholars and viewers and the final data product.

This leads to questions about gender and technology and why traditionally masculine technology, and I apologize here for the gender binary, like coding and software development is celebrated in the academy over crafts, which are traditionally feminine. By opening our focus in DH, we make space for so many others.

Many crafters outside academia are already doing data work, both through making and following patterns but also in the topics of their work, like the crocheters making blankets that visualize a year of temperatures, this councilor who knit a color-coded scarf that visualize how much more men speak then women in meetings (Ryder 2019), and quilters who use their quilts to visualize and track personal data from habit and fitness trackers. We need to learn from them and credit this type of work.

I’ve also found once you start talking about it, you start to see craftwork all over the academy. This is just a snapshot of the projects are articles I’ve been gathering but demonstrates both the rich variety of topics covered and media used . In particular Nightingale, the data visualization journal, has been publishing a number of pieces on this type of data work, I only included a couple here (Johnson 2022; Briney 2022; Gibney 2022) and the #DHMakes (and #DHSewing) tags on Twitter have been very generative for this type of thinking.

I have also been very lucky to find a community of data quilters in my own community at Pratt Institute, where I recently graduated from. First, Deimosa Webber-Bay, a fellow alum of Pratt’s School of Information, who through her Runaway Quilt Project made two quilts, Maker Unknown (2013) and Maker Known (2014), which took data about quilts and embodied them back in quilt form (Webber-Bey 2014). Second, Nancy Smith, a faculty member of the School of Information, who is making quilts that visualize glacier melt and climate change (Smith 2022).

Recently, the three of us were able to speak and we identified a few strengths and affordances of these sort of projects.

The first thing that stood out was the preservation and longevity of these types of projects. While digital DH projects seem to have a shelf-life of about 5 years (Meneses and Furuta 2019)  quilts are artefacts that are traditionally passed down for generations. By making our projects in the form of quilts, we are giving them a longer life than if there was just a digital portal. Webber-Bay even printed code along the edge of one of her quilts since it would last longer than her website.

The act of making abstract ideas physical forces makers to develop a relationship with their datasets, as there is a greater need to critically assess the types of stories it can tell since you can’t just play the same way you can if you just put it in Tableau to see what is there and call it a day. Also as these physical visualizations take more time, the projects become more personal too.

We also anecdotally found that these types of visualization were more charismatic to viewers. People tend to be more curious about the quilts than traditional visualizations, and they spend more time looking at them and asking us questions about them. It creates a deeper relationship between the subject of the quilt and its viewer. Vande Moere and Patel have found this as well with their study of data sculptures and that the “novelty and aesthetic of its sculptural form and its physical affordances often drive the interest of people to attempt to decipher the content” (2009).

This brings me to my project, the Bellum Civile quilt. It feels like there should be a cute portmanteau here, but I can’t make it work in a way that sounds good. I would like the stress that this is a work in progress, so I don’t yet have conclusions or final reflections on this work.

First, a little background. The Bellum Civile (BC) is an epic Latin poem about the Roman civil war that was incomplete at the death of its author, Lucan, in 65 AD. I studied this poem in my Classics MA and we discussed the frequent use of black and red (Tucker 1970) throughout the text in the context of its contemporary literary styles.

Later in an Information Visualization course I took at Pratt I compiled all the references to color in the poem to compare the poem both to contemporary authors and the earlier epic poems that it references.

As I decided to pursue an embodied data visualization project, I thought this would translate well, as it was not a stretch to visualize the use of color through fabric, especially since I already had the dataset. I felt like it was a fitting way to give the data a second, or really third, life. But I didn’t want to simply make a bar chart in fabric. I wanted to translate the visualization into quilt form, taking advantage of all the elements of quilt design.

The elements of a quilt that I identified to work start with a quilt top that is made up of individual quilt blocks. In traditional quilts these blocks are usually uniform in size, but I took each of these blocks to visualize different categories of color use. I went through my data and grouped the objects being described by color into seven categories, like people, physical landscape, and man-made objects. Each block then corresponds to one category with each square of color representing one instance of the color. Each block then varied in size and design. What I like is this turned the quilt top into a physical data dashboard, and I have since labeled each block to help it translate a little more easily as a dashboard.

Then there is the quilt back, which is usually simpler as it is the side not on display. I am planning on making one large visualizing the sequence of color in the text to understand how they colors group together.

Then there is the binding, which goes around the edges of the quilt. I am thinking of using this to show the percentages of the colors in the entire text.

And finally, there is the actual quilting, or the sewing that attaches the three layers (quilt top, stuffing, and quilt back) together. I want to use this to show the number of words for each color in the text. For example, there are eight words for red, so I would have 8 rows of red stitching as opposed to only two black rows for the two words for black in the text.

This is a video I filmed of the process of sewing one of these blocks together, I won’t have time to play the whole video, but it is linked in my slides, so you are welcome to watch it later. What really struck me with this project was how the process of sewing this quilt was so similar to the process of data visualization: 90% of sewing isn’t actually the act of sewing. Instead, it is measuring, cutting, trimming, and ironing fabric, rethreading the machine when it gets stuck, ripping out seams when you’ve made a mistake. Just like so little of the process of making a data visualization is the act of visualizing it. So much of the work is the labor of gathering, tidying, and debugging data. And from this I really think there is potential for a hands-on crafting workshop as an opportunity for teaching about data through making some of the abstract labor physical. It would obviously have to be something smaller than a quilt, but I think the parallels are compelling.

Because this is an experiment, I’ve been documenting the process in more or less monthly updates. This has been so useful because I have been able to gather feedback earlier on in the process when it can actually help guide the project. It also has been great as it gives me some public accountability to finish it. So far I’ve written an introduction to the project, a piece about getting my data ready, and a piece about designing the quilt itself.  And I will be turning this presentation into a post (hi!) that I will hope to have posted in the next few weeks.

So what are the next steps. First my biggest priority right now is finishing the quilt itself, which I am hoping to do this summer and maybe into the fall if need be. While I am working on it I’d like to continue to document the progress and then write some sort of reflection at the end of the project.

But thinking further out and how I want to expand these ideas beyond just this specific quilt, I would like to do something bigger, both in terms of the size of quilt and the dataset. My dataset is about a hundred rows and this quilt is designed to be a wall hanging. Next I would like to make a bigger quilt, probably bed sized, of a larger dataset, maybe from NYC Open Data. There are a number of datasets related to some of my environmental humanities interest, and I’d like to enrich a dataset and turn it into quilt while also thinking about ways to connect to, and visualize, ideas of place. Also, I would really like to design a hands-on workshop, either geared towards students or those who teach about data, using sewing as a way to explain the process of data work.

All of my references are here, but like I mentioned earlier you can see the specific references for each slide in its notes sections.

Thank you, I look forward to hearing your comments and questions about all of this.


I also wanted to take a moment to share some of my reflections after presenting this talk. Someone, I think it was Nikki Stevens, asked me how I was using elements other than color to represent my data. Up to this point I was only relying on color, but since I’ve been thinking about it, I want to experiment with some other types of elements. Right now, I want to try using the direction of the quilting stitches to represent parts of speech, for example all adjectives would be horizontal rows while verbs would be vertical columns.

One thing I loved about some of the sessions I attended was other scholars using craftwork for their research. I really would love to find a community to work with more, such as virtual sewing circles. I would love to establish some more of a community outside of annual conferences.


Bench, Harmony, and Kate Elswit. 2022. “Visceral Data for Dance Histories: Katherine Dunham’s People, Places, and Pieces.” TDR 66 (1): 37–61.

Briney, Kristin. 2022. “Crafting a COVID Visualization: How I Processed Pandemic Anxiety and Grief with Yarn, Nightingale.” Nightingale, January.

Gibney, Danièle. 2022. “Crossing into Datavis.” Nightingale, January.

Gollihue, Krystin, and Mai Nou Xiong-Gum. 2020. “Dataweaving: Textiles as Data Materialization.” 25.1, August.

Johnson, India. 2022. “The Soul of Data: Data Physicalizations on Fabric, Nightingale.” Nightingale (blog). March 8, 2022.

Knight, Kim Brillante. 2018. “Danger, Jane Roe! Material Data Visualization as Feminist Praxis.” In Bodies of Information: Intersectional Feminism and Digital Humanities. Debates in the Digital Humanities. Minneapolis, MN: U of Minnesota Press.

Meneses, Luis, and Richard Furuta. 2019. “Shelf Life: Identifying the Abandonment of Online Digital Humanities Projects.” Digital Scholarship in the Humanities 34 (Supplement_1): i129–34.

Ryder, Sherie. 2019. “Councillor’s Colour-Coded Knitting Shows ‘Men Talk Too Much.’” BBC News (blog). May 16, 2019.

Smith, Nancy. 2022. “Data Quilts: Visualizing Climate Change.” Some Quiet Future. 2022.

Stark, Luke. 2014. “Come on Feel the Data (and Smell It).” The Atlantic. May 19, 2014.

Tucker, Robert. 1970. “The Colors of Lucan.” The Classical Bulletin 46 (4): 56–58.

Vande Moere, Andrew, and Stephanie Patel. 2009. “The Physical Visualization of Information: Designing Data Sculptures in an Educational Context.” In Visual Information Communication, edited by Mao Lin Huang, Quang Vinh Nguyen, and Kang Zhang, 1–23. Boston, MA: Springer US.

Webber-Bey, Deimosa. 2014. “Runaway Quilt Project: Digital Humanities Exploration of Quilting During the Era of Slavery.” The Journal of Interactive Technology and Pedagogy, no. 6 (November).

Translating Methods: Designing A Physical Data Dashboard

New month, new update. Today I am writing about the process of designing my visualizations/quilt blocks. If you are new here you can read my previous posts: an introduction to the project and an update about my data.

Designing an Embodied Visualization

When I first conceived of this project I had a pretty simple design in mind. I was planning on taking a tree map of my data, generated in Tableau, and copying those dimensions in fabric. I was then thinking about ways of layering more data onto this simpler visualization, perhaps embroidering more common objects in the dataset and using hand quilted lines to show the number of words used for each color. But ultimately I moved away from this plan, I wanted to think more about how I could translate my data into a quilt, rather than just copying a computer generated visualization. How could I use the traditional elements of a quilt, typically equally sized square quilt blocks, to represent my data? 

a tree map of colors showing that black and red are the most common colors in the dataset
A tree map showing the frequency of each color in the text

This was happening at the same time I was working with my data, and as I described in my data post, I decided it was more interesting to think about what the colors in my data were describing rather than just how many colors there were in total. By categorizing those objects, I found a more compelling visual narrative. So rather than making one large visualization, I made a quilt block for each category, thus translating my data into a more traditional quilt form AND turning my quilt into a data dashboard. 

While quilt blocks are traditional uniform in size and similar in design, due to the differing number of instances of each category that was not going to be possible since I didn’t want to make it based on percentages. Instead the size of the block shows how common, or uncommon, that category is in the text. This also meant each block could not have the same pattern, so instead I designed each block individually. 

Before I designed the blocks I needed to determine what my units were. I decided my base unit would be one instance equaled one 2 inch square. However if I only used squares the patterns weren’t going to be that interesting, so I allowed myself to also use half square triangles (HST) where 2 HST equaled one instance as well. HST are a classic element of quilt designs and this gave me more artistic freedom. 

The only remaining issue was that each category wasn’t necessarily a square number, so I allowed myself another element. I added a “null” color, in this case light gray, that was also going to be used for the borders between the blocks. This allowed me to make rectilinear blocks. I did add a few restraints, I tried to limit how many null squares I could add and I had to keep the null squares on the edges of the block. This way once the borders were added they would hopefully fade into the background and look less like it is part of the visualization.

I finished designing the quilt top while I was working with my data, but I look forward to continuing to think about the design, because that process is not over. I want to use every available element of the quilt to represent data. When thinking about a quilt there are a few key elements: the quilt top, the quilt backing, the stitches that attach the two sides (and battling) together, the binding on the edge of the quilt. I have a few ideas, but I am trying to not rush this process and take time to make the final decision. I wound up using more of the light gray fabric than I anticipated, so I’ve ordered more, and I am taking a break from this part of the project as I wait for it to arrive. 

Sewing As Data Work    

Nicole Cote, in her incredible repository of data visualization resources VisDepot, makes the case that data sketching should be an integral part of the visualization process. Before you generate a visualization it is a great practice to brainstorm on paper what you want your visualizations to be. Otherwise I think it is too easy to get stuck generating the same types of visualizations over and over again, because they are the ones you know. The sketching stage of this project was so invaluable for the final product. I was able to do so much of my thinking and analysis by spending dedicated time sketching and experimenting with designs. This isn’t a step I always have taken in the past, but it is one I am realizing the importance of more and more. This project just highlighted that lesson for me.

I recorded a video of the process of creating one of the quilt blocks to show during my DH Unbound presentation. I thought it would be a more interesting way to show people the process than showing photos or describing the work. One of the outcomes of even just editing the video was I realized that at least 80% of sewing isn’t actually sewing. The actual act of sewing might only be like 5% of sewing. So much of the process is measuring, cutting, trimming, and ironing the fabric before you can even start sewing. And once you start sewing you still have to be ironing and other maintenance tasks regularly. I feel like this is a strong parallel for what it’s like to visualize data. Most of the work is gathering, tidying, and fixing data. The act of visualizing the data is only a small part of the process. In this way a data sewing project, or other data craft, could be a great tool to teach the labor aspects of data work. I believe embodied data has a lot of potential for data pedagogy, and this labor parallel is just one way it can help us teach about data. 

Updates and Next Steps

  • I am going to update my data and documentation in my repository to reflect new terminology. At some point I decided “people” was a more accurate descriptor than “bodies” as bodies felt like it was implying corpses, when in fact most of the people were still alive and described things like hair. I made this change after I uploaded my materials, so I need to update that. 
  • As I’ve mentioned previously, I am presenting this project in May at DH Unbound. As I am taking a pause from the sewing/designing of the quilt I want to use the next few weeks to wrap my head around the academic theories and projects that are inspiring my process. I think my next post will actually be the script of my talk rather than a normal update, and I want to use the time to really think about the narrative of my work. 


Cote, Nicole. (2021). VisDepot: An Introductory Resource for Data Visualization, v1.0.1.

Data Deep Dive: Getting My Ducks in Order

It has been just over a month, so it is time for my next update! For those of you who want to read my introduction to this project you can read it here – “Data Craft: Exploring Projects in Embodied Data.” I spent February reacquainting myself with my dataset, as I hadn’t really interacted with it since I initially gathered it in fall 2019, and finalizing any changes I needed to make for this project.


One of the biggest changes to my dataset came from comparing my data to the only other study of color in the Bellum Civile (BC) I have been able to find (Tucker, 1970). While this article doesn’t include a full dataset, it does have a pretty detailed chart I was able to use as a point of comparison. 

a table showing where red, black, white, yellow, blue, purple, green, and gold appears in the text.
The distribution of color in the BC from Tucker 1970 (p 56).

Through using this chart I was able to verify each word in my dataset. The times Tucker had more instances of a color than I did I was able to use the article to determine what word I was missing, and from there decide whether or not it needed to be included in my dataset. The biggest addition was the noun “livor” and its verb “liveo.” Both can be translated as relating to bruises, “bruise” and “to bruise,” but I found they also have strong color connotations in their translation, “a bluish mark” and “to turn black and blue.” Additionally through working through Tucker’s data I decided a few of the words I had included aren’t actually color words. There are nouns I included that had color, for example “smaragdus” or “emerald,” but I decided that while it had color it was not functioning as a color word. I also decided that some of the words Tucker included weren’t strictly about color and decided to not add them. For example Tucker includes “igneus” (fiery,) which while can mean red to me is more about describing the brightness when comparing something to fire rather than the color of fire, similarly “aureus” (golden) to me isn’t describing something as yellow, but more comparing it to the metal. Sure that can include the color but is also the brightness and other qualities of the metal. 

Once I was fairly confident in the words in my dataset I decided to expand what I was logging about the words. While pure instances of color are interesting, I am also curious in how the colors are being used, so I went through the text to determine what object was being described by the color. Some of these were easier than others, but once I had that information I realized it was a little too granular to be useful to me. So I looked at those objects and started to categorize them into larger buckets. This certainly evolved as I worked on them, but ultimately ended up with seven categories: people, dye, gore, man-made objects, natural objects, natural phenomenon, and physical landscape.

I am not sure a dataset is ever complete, but at some point you have to decide it is good enough and it is time to move on to the next stage of your research. I could probably spend months gently tweaking, expanding, and refining my data, but if I did I would never move forward with my analysis. And in the interest of working publicly and transparently, I made an OSF repository for this project. I uploaded my dataset, as well as documentation for it, so that this data can be reused by other scholars. I am also uploading PDFs of these posts to the repository, and once I hit that stage I will add images of the design and final quilt. I’ve already changed the domain of this site once since this project started (welcome to, I’ve officially retired my old comics/illustration web portfolio so I was able to make the switch) and I know it is not realistic for me to commit to keeping this site going forever. Through the repository some of this work can live on longer. Maybe once the work is done I can find a different home for it, but for now OSF it is.

I recently rediscovered Giorgia Lupi’s data humanism manifesto and as I have been working in my dataset I’ve been thinking about how this project has some of the qualities Lupi described. It is certainly a small dataset, the final visualization will not be as “exact” as a computer generated chart, I think this project certainly fits into data possibilities and data drawing, and spending more time with my data was one of the initial aspects of this project I was drawn to. Lupi describes this movement, “We are ready to question the impersonality of a merely technical approach to data and to begin designing ways to connect numbers to what they really stand for: knowledge, behaviors, people” (Lupi, n.d.). I love this. This is the work I want to do. I want to encourage deeper relationships with data and make data stories personal, even if in this case my data isn’t literally about people. Jacqueline Wenimont is also talking about this sort of datawork through her weaving project, which she described as being a “data visceralization.” I think this is the strength of embodied data. The process of making data physical, or remaking it physical since data can be describing things that were originally physical, can foster new, and deeper, relationships to the narratives the data contains.


I will be presenting on this project at DH Unbound. I will be talking about my ideas around embodied data and how this experiment is going. If you will be attending come say hi! 

I’ve also started working on the design, and my fabrics arrived. It is so exciting to have something tangible, even if it is just a few doodles and a pile of linen. But it makes this whole project feel realer. 

Next Steps

  • Finalize the design of the quilt top and write about the process for “translating” data into quilt blocks
  • Start sewing (the fun part!)
  • Think more about the affordances of a quilt for a visualization and a pedagogical tool, maybe write something up for the next update. 

I know I said I would write something on gender and technology, but that is a much larger topic and theme throughout this project so I would like to hold off on that until a little later.  Possibly as part of my DH Unbound talk.


Lupi, Giorgia. (n.d.) Data Humanism. Giorgialupi. Retrieved February 23, 2022, from

Tucker, R. (1970). The Colors of Lucan. The Classical Bulletin, 46(4), 56.

Data Craft: Exploring Projects in Embodied Data

I’m starting a new project. It is a little weird, I realize some might not think of it as digital humanities (DH) but I do (more on that in a future update). Over the next few months, maybe longer maybe shorter, I am going to be uses quilts to represent the use of color in Lucan’s Bellum Civile (BC), and the affordances of physical data visualizations more generally. For those not familiar the BC is a Latin epic war poem, also known as the Pharsalia, written between 61 and 65 AD. But this project is less specifically about the BC than it is an exploration of new ways of interacting with data. This is a case study, and since this is a text I studied in grad school it is one I feel comfortable playing with. (I’ve also previously visualized the colors of this text in comparison with its contemporary works as well as earlier examples of epic war poems – but that is a conversation for another day.)  

I am doing this because I love data. And I love to think of new ways we can interact with data and foster deeper relationships to our data. As a librarian, I also think there could be a lot of potential in these types of projects to encourage more critical work with data and for teaching data literacy. But I don’t know this for sure, hence this exploration.  

Another big factor for this project is that I don’t currently within the academy. While I really want to pursue personal research projects, that is not something that there is space for within my job. I spent some time thinking about what types of projects I want to do, would be motivated to keep working on without any external deadlines, and that wouldn’t drain me if I could only work on them over weekends and in the evenings. This combination of my love of crafts, especially fiber/textile-based crafts, and research seems to fit the bill.  

I want to document this work publicly and regularly because this is a new space for me, and the few conversations I’ve had about it with my peers have surfaced so many amazing resources, articles, and other projects that I did not know about but will be immensely helpful for my thinking around these questions. By posting my work in progress I hope to get this feedback as I am designing and working on my project so that I have time to integrate into the work itself. As this is an experiment, if this is ever to be published in a more traditional sense, having my challenges and decision-making already documented will help me demonstrate the process and not just the finished product. 

Now for some more “academic” thoughts on this project.  

I have been thinking of this category of work as “embodied data” – that is data that has been made physical in a variety of ways, like embroidery, weaving, sculpture, and so many countless other ways. This has also been described as “data materialization” (Gollihue and Xiong-Gum 2020; Knight 2018). By making data physical I think these types of projects are literally making abstract concepts tangible, and thus allows views (or users or whatever other term you gravitate towards) to connect to the ideas in a deeper way. One project that successfully takes advantages of the affordances of making data physical are Humprey Yang’s TikToks that use piles rice to visualize amounts of money and show just how large various billionaire’s net worths are. 

Another project I find very inspiring: using embroidery to visualize plastic waste found on a beach

And that is some of the beauty of embodied data visualizations, they are happening inside AND outside academia, sometimes by people who might not consider themselves as working with data or producing visualizations. For example, the trend of crocheting temperature blankets, or knitting shawls representing the time men spoke in city council meetings.

A project visualization the gender of who talks in meetings through knitting

These projects turn the concept of who gets to do data work on its head.  But I don’t want to ignore the work that is happening within the academy/or academic adjacent spaces. There are many projects I want to spend some more time thinking about and through. I am sure this list is just a beginning, and I always welcome recommendations of where I should be looking! 

  • Dataweaving: Textiles as Data Materialization (Gollihue and Xiong-Gum 2020) 
  • Material Data Visualization as Feminist Praxis (Knight 2018) 
  • A Haptic Experience of Personal Data (Rajko, Wernimont, and Rajko 2017) 
  • The Physical Visualization of Information (Vande Moere and Patel 2009) 
  • Wampum as Hypertext (Haas 2008) 
  • Crafting a COVID Visualization (Briney 2022) 
  • Crossing into Datavis (Gibney 2022) 

This is just the beginning. I would like to spend the next month working on the following: 

Finalize the work on my actual dataset. There is a little more work I need to do on my actual dataset before I can start thinking of translating it into a quilt. I have gathered all the references to color in the poem but would like to record what thing is actually being described in each instance.  

Once I am happy with my dataset, because it probably will never be “done,” I would like to start thinking about the main design elements of the quilt. I already have a few ideas, but I’ll wait to share what that all looks like until my next update.  

While all this is happening, I am going to be spending some time thinking about DH and what technologies “count” and how/when DH isn’t digital. Very related to that line of thinking is thinking about gender, labor, and technology skills and why we tend to value a very specific skill set in academic work.  

And because I love a list of action items and deliverables: 

  • Get my dataset to a point where I feel comfortable analyzing it 
  • Start work on preliminary designs of the quilt and identify ways I can encode different facets of data in the visualization 
  • Write a short piece in my next report on DH, gender, and technology.  

Thank you all for listening and joining this journey with me. Until next time! 


Briney, Kristin. 2022. “Crafting a COVID Visualization: How I Processed Pandemic Anxiety and Grief with Yarn, Nightingale.” Nightingale, January. 

Gibney, Danièle. 2022. “Crossing into Datavis.” Nightingale, January. 

Gollihue, Krystin, and Mai Nou Xiong-Gum. 2020. “Dataweaving: Textiles as Data Materialization.” 25.1, August. 

Haas, Angela M. 2008. “Wampum as Hypertext: An American Indian Intellectual Tradition of Multimedia Theory and Practice.” Studies in American Indian Literatures 19 (4): 77–100. 

Knight, Kim Brillante. 2018. “Danger, Jane Roe! Material Data Visualization as Feminist Praxis.” In Bodies of Information: Intersectional Feminism and Digital Humanities. Debates in the Digital Humanities. Minneapolis, MN: U of Minnesota Press. 

Rajko, Jessica J., Jacqueline Wernimont, and Stjepan Rajko. 2017. “The Living Net: A Haptic Experience of Personal Data.” In Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems, 449–52. Denver Colorado USA: ACM. 

Vande Moere, Andrew, and Stephanie Patel. 2009. “The Physical Visualization of Information: Designing Data Sculptures in an Educational Context.” In Visual Information Communication, edited by Mao Lin Huang, Quang Vinh Nguyen, and Kang Zhang, 1–23. Boston, MA: Springer US. 

Native Land: Connecting Virtual Attendees to Physical Spaces

“I am calling in from Lenapehoking, the occupied ancestral lands of the Lenape upon which I am a settler. I invite you to learn more about the lands you are on using the link I am posting in the chat…”

I have heard, and recited, variations of this message countless times over the past 18 months, followed by a link to (Native Land). It has been ever present in Zoom conferences, webinars, and other remote events. I’m even guilty of using a screenshot of the map as a slide background (Fig. 1). Native Land feels to be of this moment, even though it was not created for this moment.1

Figure 1: A slide of acknowledgements with a screenshot of Native Land as its background.

Native Land is a map that depicts the traditional and ancestral lands of Indigenous peoples across the world, though it is based out of what is now known as Canada. It was first created in 2015 by Victor Temprano, a settler, but since 2018 it has been managed by Native Land Digital, an Indigenous-led organization. One of the goals of the map is to create “spaces where non-Indigenous people can be invited and challenged to learn more about the lands they inhabit, the history of those lands, and how to actively be part of a better future going forward together” (“ – Why It Matters” n.d.). This map helps engage those listening to land acknowledgments in virtual environments to think about their own homes and educate themselves, an important first step in decolonization only if it is followed up with further action.2  

Even though it was created years ago with different goals in mind, Native Land’s adoption in this remote environment addresses the conflicting desires of speaking to a local place-based reality while we are sharing a digital space. Land acknowledgements over Zoom cannot speak to the experience of every attendee the way a land acknowledgement at an in-person conference can. We are not sharing the same physical space. We may not even be on the same continent. By sharing a link to Native Land, it has become a tool to help connect attendees in that moment, giving space for us all to think about our individual relationships to the land we are occupying. 

Figure 2: Native Lands default settings zoomed in to display Turtle Island/North America.

So far I have been talking about the map without discussing the actual map. When the map is first opened the user is greeted with an almost stained glass version of the world. Rather than viewing land divided by modern political boundaries, which even when the boundaries are contested are presented as static permanent lines, the land is covered with colorful overlapping shapes (Fig. 2). These overlaps are made visible, and highlighted, because the shapes are semi transparent and create new colors as they intersect. This contests western ideas of ownership, where land belongs to one party or another. Instead we can see the multilevel, and shifting, relationships a variety of Indigenous nations have to the land. 

Users can navigate the map by searching using an address or by zooming in and out on portions of the map. While the user can add a layer of the map showing the modern political boundaries and names recognized by governments (by toggling “labels”), these are off by default. This can make it surprisingly difficult to find a specific location without using the search function, as our familiar landmarks and ways of understanding land have been removed. Just as the iconic photo “Earthrise” introduced many people to a new way of looking at our planet, this map helps encourage users to question their relationship to a physical space. Where am I from? A country? State? County? 

Additionally, users can overlay layers that show language ranges and treaty agreements. The language feature is one in particular I would like to focus on, as I am more interested in centering the Indigenous histories of the land than those of the dominant governments although the two are inextricably intertwined. The map of languages presents another level of relationships between the Indigenous nations. While the map of territories shows lots of overlapping and densely populated entities, the map of languages is simpler. The overlapping regions are smaller, and there appear to be fewer, but larger, entities (Fig. 3). This seems to suggest how multiple nations used related languages and perhaps the relationships between local nations. It also could have to do with a lack of data as native languages are disappearing, and much of that knowledge has already been lost. To me though this serves as a reminder that these nations were not just in relationship with the physical land, but one another as well. 

Figure 3: A comparison of the same region with just the territories displayed (left) and the languages displayed (right).

My own research interests have been understanding how digital humanities methods can strengthen environmental humanities topics. As I see it, environmental humanities studies relationships with Land, not just the physical land but all of its human and non-human residents (for more on these relationships see: Liboiron 2021, 6-7). Native Land helps reinforce that in order to be in good relationship with the Land you also have to be in good relationship with its original caretakers. I am drawn to critical settler cartography (Fujikane 2021) and how I can use maps to develop those relationships, in particular between human and the non-human inhabitants that are often overlooked, like plants (Parsley 2020). While Native Land does not explicitly address the non-human inhabitants of the places it maps, it lays the foundation to think about how we are all in relation with one another, the Land, and everything else we share space with. 


1:  As a settler, I am writing this from the perspective of non-native users of this map. I have a unique relationship to the map and the topics it raises which is informed by my relation to the land. I can only speak from the relationship as settler-scholar, and I acknowledge that those who have a different relationship to the land will have a different experience using this map.

2: As land acknowledgements become more common, they provide what could be the start of “real decolonizing opportunities” but they are also worryingly moving towards performative allyship without any real action or commitment behind them (Stewart Ambo and Yang 2021). When including land acknowledgements it is also important to think about what actions can be taken by organizations and individuals (“Beyond Land Acknowledgment: A Guide” 2021).


“Beyond Land Acknowledgment: A Guide.” 2021. Native Governance Center (blog). September 8, 2021.

Fujikane, Candace. 2021. Mapping Abundance for a Planetary Future: Kanaka Maoli and Critical Settler Cartographies in Hawai’i. Duke University Press Books.

Liboiron, Max. 2021. Pollution Is Colonialism. Durham: Duke University Press.

“ – Why It Matters.” n.d. – Our Home on Native Land. Accessed August 27, 2021.

Parsley, Kathryn M. 2020. “Plant Awareness Disparity: A Case for Renaming Plant Blindness.” PLANTS, PEOPLE, PLANET 2 (6): 598–601.

Stewart-Ambo, Theresa, and K. Yang. 2021. “Beyond Land Acknowledgment in Settler Institutions.” Social Text 39 (March): 21–46.