I really liked that your project chose a relevant topic. I think using Story Map tool of ArcGIS was a very effective choice even though you were primarily performing text analysis. I lioked how thorough the project is, taking the viewers through every step, starting form data sources (which many of the groups put at the end). When I was looking at the project (08/05), the timeline from Knightlab wasn’t properly embedded. I think the project would be great if you fix it or (if ArcGIS won’t let you properly embed) give a legend/description before linking the timeline.

p.s. I really liked that on the map showing your colleges’ location you used ‘the home of archives’ rather than just a location

Since the project pitch, I was very excited to see the final project of this group. I really liked the usage of Timeline JS. Personally, I’ve never used the tool, but I loved the way you implemented it. Going through the history of accessibility of each college is a very big goal but you managed to give an overview in an effective and engaging manner. You also did a great job at highlighting the accessibility issues. I think it’d be great if somewhere at the end of the project you’d give a list of suggestions on what colleges can do to improve it.

I like the unique approach of this group. Using word map and word clouds is something I personally couldn’t imagine integrating in our project, but I find it fascinating that this group found a way to integrate these functions. However, I wish there was a more in-depth agenda to the visualizations. In the context of the project, it’ll be easier to understand but I think visualizations is something that should be able to speak on its own and let the viewers draw conclusions. Right now, I think there needs to be more information about what the data is and what these visualizations do.

I really like the wide approach you take initially with this graph and how you narrow it down later. I think this particular graph would be more persuasive and interesting if you can add a timeline to it in a sense of maybe adding a time period of the record you analyzed or something like that. It can also just be elaborated on in the agenda.

I like how specific the research questions are. You have the materials on your hands that can definitely answer them. However, my primary concern is that they might be too narrow. How can this project become more helpful in regards to social justice for the broader audience? Maybe, you can add more about the Native American Tribes in it or something in this direction to make it a little broader.

This sounds like a great project idea. I liked the narrow focus on accessibility specifically at Hamilton and Vassar. I assume all member of the team are form either of these two, which will add a personal perspective and a more insightful look into this issue. I’m wondering whether you all would like to add a section to this project with suggestions to the universities on how to improve accessibility based on your research findings or not? I think it can add practical application to the project.

There’s an ongoing debate on how much text should be added to the maps as they’re usually perceived as concise ways to represent information. It’s big area of struggle for many mapping projects. But I agree that Holocaust Geographies provided the historic context necessary to fully understand the maps presented and the relevance this research has to the society and social justice. The website does a great job at providing all these details to users while still using the maps effectively.

I understand the harm that comes with geospatial data of race, public housing, etc. mentioned in this section of the reading. However, I believe access to it is crucial to the development of disciplines like sociology, anthropology, history, etc. These data give an opportunity for researchers in history, for example, to provide additional context to events as well as learn and educate others on a deeper level. GIS is already impacting this discipline. Recently, I was very surprised to hear that one of my mentors at Washington and lee is currently getting a Master’s in historical geography – a whole degree that’s focused on the intersection of GIS and history.

Nowadays, AI is basically everywhere, so it’s no surprise it found the way to Voyant Tools. When I was using Voyant, I didn’t try the Veliza tool, but based on this description, it definitely has a room for improvement. With the impact AI has on writing, I think with some technological advances, Veliza can be very helpful in the future. Researchers will be able to enter the hypothesis into the text box and get the stats from the uploaded document, which will save time and be more detailed than manual search, especially in large texts.

I disagree that computational techniques shouldn’t be considered an upgrade. Yes, many of the things technologies allow people to do in the humanities were previously available to do “by hand”, but introduction and implementation of different new technologies in the field makes the work more efficient and gives researchers the time to pursue new creative angles on topics rather than being stuck on one or two theories for a longer period of time.

It’s so interesting that you pointed out the appearance aspect of it. For me, uniformity is about a standardized format but more information-wise (same categories, capitalized or not capitalized, date formatting, etc.). I did not think about looking at this dataset from more of a physical perspective like column and information ‘size’.

It’s fascinating that Instagram posts now found their way into archives. Usually, archives associate with something tangible but as the world become more digitalized, we see the tendency of online objects that are a vital part of human history being archived. However, Jesse brings up a great point that there’s a limited context around digital objects, which has to do with the format. I’m intrigued to see how this obstacle is going to change the word of archives and what solution will be created if any. I see many potential issues as people can delete and edit posts that have archival value and with time it’ll be hard to track for archival record.

When it comes to data analysis, I usually think of data as a source of information rather than it being a research object by itself. The term “raw data” has been used in all my classes, starting from accounting and finishing with introduction to digital culture and information. Yes, we’ve talked about biases that come up in different data sets, but usually this conversation is related to so-called “post-production” of data – either us, students, using it, or someone else and we reverse engineered where it came from. So, reading about an approach to data, even ‘raw’ data, as a constructed artifact is very refreshing. It’s extremely important to look at how the raw data was collected and what was left out by collectors initially to have a full image of what’s going on.

The Internet has been at the center of debates between different generations for a long time. One of the benefits I usually bring up in related arguments is the immediate accessibility of information at the top of your fingers. But I think about it from the consumer’s point of view – finding what’s needed and using it. Taking a creator’s approach is a new perspective I gained from this article. The DH projects described here are not only reimagining the power dynamics behind the collections/archives management but also giving this power to users. “Invisible Australians” and other mentioned DH projects use multimedia storytelling tools to flip ideas of the hegemonic domain of the matrix of domination (D'Ignazio and Klein) to their advantage and present the stories of oppression from a new perspective, using the resources that were created based on the circulating oppressive ideas.

You took an interesting approach of seeing a matrix of domination within the story itself. When I was looking into Mapping Memories of Africville for the Lab Assignment, I thought more of the outside approach – what issues with the project itself can be outlined in the Matrix of Power. For example, the interpersonal domain – Danielle Mahon isn’t a woman of color and not objective observations in the interviews, etc. Hegemonic domain – showing that the park built for the community of Africville solved the issue to some extent, which circulates the toxic idea of reparations when it’s okay to destroy communities if you give them something in return, etc.

I think believing in objectivity of data is fundamentally wrong. Before taking a Statistics class, I thought researchers can minimize biases by taking them into consideration when collecting data, but after 2 chapters of Data Feminism, I realize that there are way more requirements to make data even a little bit objective. Even with 4 actions (collect, analyze, imagine, and teach), I don’t think it’s possible in the current state of the world to make data objective. Even if the collection process will be as little biased as humanely possible, the interpretation process of the data will have the researchers’ biases in it (privileged hazard and other biases).

The societal systems are supposed to support humanity, but in the reality, they work against minorized groups. It’s the first time I heard about the Matrix of Power, but hegemonic and interpersonal domains explained why these oppressive systems are still in place – people themselves make them work. This topic is especially relevant with the rise of AI. I agree that AI’s threat is programmed discrimination. But I think it’s important to understand that even though ways of diversifying databases can be harmful, as these examples in the text show, it’s a trade-off to make the system more inclusive. In the future, there will be better ways, but right now is the foundational time of AI. The faster we can respond to the arising issues – the easier they’re to fix. It also means there will be less damage in the long-run technology-wise.

As someone with a minor in Digital Culture and Information, I started reading this chapter with an understanding of what digital humanities is. There were endless debates around the definition in my classes, but in the end we all agreed that it encompasses an effective usage of technologies in humanities subjects. However, after reading the chapter, I started questioning whether digital humanities should imply creation of something new or improvements in already existing methods. The chapter doesn't give a definite answer but it raises important questions on how the DH field should be approached. The debate between practice and theory is applicable to every field, and I believe all areas of scholarship should combine both creation and improvement, especially Digital Humanities.

I think this is the case not only for DH but also for more established areas of scholarship. To this day, experts argue where the lines of, for example, economics and politics, or other disciplines lie. There is, for sure, more gray and unexplored areas when it comes to DH, but I don't think it's a unique characteristic.