Blog Post#2: Short Response to Weekly Reading – History and the Archive

This week’s readings emphasizes ethical considerations when engaging with histories and archives of marginalized communities and navigating collections rooted in colonialism. Anderson points out in “Towards Slow Archives”, “the history of collection is the history of colonialism”. The colonial lens is evidenced in the history of violent dispossession, exploitation, and erasure of indigenous culture. 

Johnson’s discussion of the legacy of commodifying Black data argues against the spectacularizing of the horrific violence on Black bodies and proposes black digital practice as a way of challenging the problematic narrative. In the video, Cotera poses a similar argument in favor of the Chicano community retaining ownership of the archives primarily composed of their oral histories and archival documents. 

Cotera challenges our notion of archives by positioning the domestic spaces occupied by the Chicano women as archives and the women as knowledge repositories. Cotera and her students worked to slowly build trust with the community of women contributing to the collection and the value of the relationships is evident in the collection design. Access to the resultant collection of Chicano feminist history is restricted through login requirements and scholars/ students/ users engaging with the archives are expected to obtain permission from the rightsholder—the Chicano women. Cotera admits that enforcement might not be practically possible and the system largely relies on ethical, responsible behavior from the users. 

The painstaking approach to building and managing the collection is mirrored in the “slow archive” process (Anderson) that suggests slowing down to refocus archival practices to reflect indigenous temporalities and hold communal relationships at the heart to the process. The objective is to find safe spaces to share within the community and cites the example of Mukurtu CMS, a community digital access platform that utilizes Traditional Knowledge labels to provide cultural context and exercise control over access. Another initiative discussed is Local Contexts that works to reclaim intellectual property power structures and legitimize indigenous governance frameworks for decision making around ownership and access to cultural heritage collections.

Carrying forward the line of inquiry on online interventions, Johnson’s advocacy of social media as a tool for cultural critique and creation of online communities finds a common thread in the BLM movement’s online organization of activism efforts for social justice. “Beyond the Hashtags” report shows how digital tools provided a platform for oppressed voices to reach a wider audience and how BLM succeeded in engaging productively with the policymakers, media and the general public. The culmination of the discussions on knowledge production and ethical approaches to culturally sensitive archives was the initiative ”Possibly Impossible Research Project” that shares the benefits students and the scholarly community could derive from designing research assignments that contribute to a larger objective and allow emerging scholars to engage early in their academic journey with primary sources.

Two Workshops: “How do you DH?” and “Discovering NYC Open Data”

“How do you DH?” 

I attended the “How do you DH?” workshop on 7th October mainly to learn about the methods relevant to the field. The workshop briefly discussed the possible DH dissertation formats that could be either wholly digital or a mix of the traditional and digital approach and more importantly provided an overview of the methods employed. 

The presentation slides used in the session list the DH methods on slide 11 and the following slide lists a simplified version. The subsequent slides provide a couple of examples for each of the methods. Especially useful are links to text analysis tools such as Voyant (slide 17) that allow you to enter text and view the result immediately OR the data visualization catalogue (slide 23) that could be referenced for exploring the right tools for our project. 

The workshop ended with a breakout room session that allowed us to clarify the format and methods as each group reviewed the method and tools used in sample projects. The exercise was adapted from the Miriam Posner video, “How Did They Make That?” that we discussed in our September 11th class.

Discovering NYC Open Data

Last week, I attended the NYC Open Data class run by the Open Data Ambassadors Program. The workshop provided a quick overview of the history of open data, other available resources and the instructor demonstrated site navigation, finding, reading, filtering and visualizing datasets. The session was informative, although if you have explored the site beforehand some of the content around site navigation might be redundant. The session will be good for people new to the site as you are encouraged to follow along on your own devices.

Recordings of earlier sessions are available here along with a list of additional resources, and useful applications available in the NYC Open Data Project Gallery such as the NYC Population FactFinder. There was a useful Q&A session at the end of the workshop and they mentioned that their team is usually very responsive to queries received online as well. If you are interested in attending one of their monthly sessions, check out their class schedule.

Workshop 2: How do you do DH?

Today I attended another GC Digital Initiatives workshop How do you do DH? It was presented by a fellow DH student. In it, we were given a general overview of what DH is and why it might be used. We talked about different methods and tools used within DH. There was some interactivity to check for understanding, and to also gain insight on participant’s individual interests. 

We also participated in a breakout room group activity where we were given a project to analyze to determine if it was a DH project, what methods and tools were used, and if the project was successful in whatever goal it had intended to pursue. Projects ranged from dissertations simply published online, a podcast series as dissertation, a gamified DH experience, and an online digital exhibit. 

Overall, this was a good introduction to the topic, especially if one is completely new to DH. I made sure to save a copy of the slides which included links to helpful websites to determine which tools to use for what methods. The presentation was very approachable, and made me want to seek out other workshops from the GC Digital Initiatives in the future.

Workshop

I took part in an engaging 2-hour virtual workshop on Python, hosted by GC Digital Initiatives. While Python itself can be quite a complex language, the workshop was thoughtfully designed to accommodate specially the beginners. As a Software Developer I already have some basic knowledge about python programming language. Also in the workshop the content started from the very basics and I think which really helped beginners to grasp foundational concepts, like variables, data types, functions, lists, and error handling.

But there is this one thing I particularly enjoyed and that was we worked through an asynchronous tutorial on the DHRIFT site during the live session. I have never used this site before, and I found it quite interesting. It has the built-in Python code editor that allowed us to practice in real-time without the hassle of installing any libraries and it was quite fast. The instructor kept the class interactive with problem-solving discussions and mini quizzes.

The session was professionally delivered and provided solid insights into Python’s structure and logic. I kind of knew most of the stuff but again this helped me in reviewing most of the things again. I am yet not master of python and mastering Python needs significant time and effort; I hope I will get some more expertise on this.

ITP Skills Labs | Workshop #1

A couple of days ago, I attended the “Digital Mapping Platforms as Praxis for Teaching and Learning” ITP skill lab, which explored how digital tools like OpenStreetMap can be integrated into educational settings. As someone currently enrolled in a mapping course where we’re using geographic software like MapInfo, I found OpenStreetMap much simpler and straightforward. It’s a great option for beginners—particularly for K-12 and undergraduate students—or anyone needing a refresher, like me and possibly you! Compared to MapInfo, which involves downloading large datasets and geocoding locations, OpenStreetMap offers a more manual, hands-on approach.

During the workshop, we used the CUNY Digital History Archive to map historical locations. Instead of dealing with large data files like in MapInfo, the process was, actually, much more straightforward. We selected subjects from the CUNY archive that interested us, each with an image and a location, then we pinned the address or coordinates into OpenStreetMap. After pinning them, we added custom features like images and descriptions using simple lines of code provided by the platform.

For those who couldn’t attend, one of the key takeaways for me was how accessible and user-friendly OpenStreetMap is. It’s perfect for quick mapping projects or classroom settings where students can jump in without a steep learning curve. While it’s more manual than MapInfo, that simplicity was actually a major advantage—it allows for a more interactive and customizable experience without getting bogged down by large datasets or complex processes. This makes it an excellent tool for brainstorming how digital mapping can promote collaboration, whether you’re mapping historical events, neighborhoods, buildings, or anything with coordinates, I assume.

Overall, this workshop was a helpful refresher for anyone working with mapping tools and a great introduction to OpenStreetMap’s flexibility. If you’re looking for an easier platform to get started with, or you missed the session, I’d definitely recommend trying out OpenStreetMap—it’s free!

You can sign up here if you’d like to give it a try 🙂

Workshop: Intro to Python

Today I attended the GC Digital Initiatives online workshop Intro to Python. The session was 2-hours long, which is about as long as you would like a workshop to be to not get total brain fatigue. However, as may be evident, Python is a complex tool that requires hours of dedication to understand and then experiment with implementing. 

I have no experience with this programming language, and felt like I was in the right place. The level of the content was right from the beginning, providing baby steps to start to understand the foundation of the logic of this tool. As discussed in a previous class, we worked synchronously together through a new asynchronous tutorial offered via the DHRIFT site. This provided a clear and organized structure for the class, as well as an aesthetically pleasing, but also delightfully minimal visual to follow along with. 

We were introduced to types, variables, running scripts, functions, errors, lists, and more. Cleverly the DHRIFT site had a live Python code editor embedded into the page so we could all practice without needing to download and install any software, or be moving back and forth through several windows. The moderator engaged us in thinking through problems and there were mini-quizzes we did together to check our understanding. 

Overall it was a good experience, and was professionally and kindly done. Just for my own personal sake, I am not wrapping my head around how this tool is actually used and implemented and if it is worth the time vs. output ratio. I think I would really need to put in some serious hours to begin to crack this code. All in all though, it was good. Again, just personally, I think I would rather pursue other digital tools that are more approachable before diving into Python. 

As mentioned already in class, I think the DHRIFT site is a great (and developing) site to find asynchronous and approachable tutorials for a variety of digital tools. I am looking forward to checking out their text mining tutorial next.

Data Visualization Praxis Assignment

For the assignment, I decided to visualize the leading causes of death in New York City. Using data from NYC Open Data, I wanted to study which causes of death affected different racial and ethnic groups. I created two visualizations on Tableau Public. I have never really used the platform before and struggled a little bit. One of the most challenging parts of making the visualizations was the inconsistencies in the dataset. In the Race and Ethnicity columns, there were different ways of labeling the same group. For example, Non-Hispanic Black and Black Non-Hispanic were showing up as other labels. I figured out how to use Tableau’s calculated fields to clean the data and make the data consistent. I also wanted to graph the total death rates but a lot of the data had missing values. At first, I thought about removing these values but reflecting on “Against Cleaning”, I wondered what information would be hidden or lost if I cleaned the data too much.  

The line graph, Leading Causes of Death in NYC, 2007-2022, shows the top 5 causes of death in NYC, including Diseases of the Heart, All other causes, Malignant neoplasms (cancers), Influenza and Pneumonia, and COVID-19. In 2020, you can see the large increase in deaths caused by COVID-19 and the drop that followed as vaccines and public health measures were implemented

The second visualization, “Trends in Leading Causes of Deaths by Race/Ethnicity, NYC (2015-2021), shows how causes of death affected different racial and ethnic groups. The graph uses a gradient where dark larger red squares represent higher death rates.

Praxis Assignment- Data Viz

Link to my data viz: https://public.tableau.com/app/profile/micaiah.davis/viz/IceCreamFlavors_17272250945740/Dashboard1#1

I did my data visualization in Tableau Desktop since that was what I was most familiar with, then saved it to Tableau Public for everyone to access. I haven’t used it in a while, and I figured the refresher would be helpful since I plan to use it for my capstone project. I wanted to have some fun with this assignment, so I looked at ice cream data. Originally, I wanted to look at popular ice cream flavors and how they might have changed over the years, but I couldn’t find a suitable dataset for that. The closest I found was a dataset on Kaggle that looked at the rating count, ingredients, reviews, and descriptions for different ice cream flavors across four brands.

The hardest part of the process was the data cleaning which I was hoping to avoid since the original dataset had symbols in some of the ice cream names and the brands were written as acronyms, but I wanted them spelled out. The description of each flavor also included a lot of symbols, but I left those alone since some were long and I wasn’t using that field in my viz. I cleaned up the names in Excel Power Query before using it in Tableau.

I found “Against Cleaning” particularly interesting because it gave a new perspective on data cleaning. Whenever I thought of data cleaning, it meant ensuring a standard was used across all fields(data). Are the titles of the ice cream flavors all in caps or is only the first letter of each word capitalized? Are ingredients separated by commas or colons? Are brand names abbreviated or spelled out? It never occurred to me that from another perspective, data cleaning could be seen as “wiping away what is different” which depending on the context prevents outliers from being explored, a sentiment shared by Lini in one of her annotations of the reading. If the data doesn’t tell the expected narrative, then it needs to be “cleaned” until it does. This line from the reading also stood out to me “To ask research questions, we needed to create our own dataset…” because it’s something I likely will have to do for my capstone project.

Reading Response #2

September 25th – Data & Visualization, Short Response: 

For my second reading response, I wanted to dive into a few projects that caught my attention this week: Two Plantations, Every Upcoming Solar Eclipse (until 2080), and Gendered Language in Teacher Reviews. Each of these visualizations uses data to tell a story in a way that’s both informative and interactive, but their subject matter and the way they engage viewers are quite different, which is what makes them stand out.

The Two Plantations project struck me immediately because of how it maps out the lives of enslaved people across two plantations—Mount Airy in Virginia and Mesopotamia in Jamaica. The way this project brings historical records to life is extremely powerful. The interactive map, family trees, and details about individuals allow viewers to see the real human impact behind the data. What I found especially compelling is how it doesn’t just give you names or numbers; it restores a sense of identity and connection to people who were often reduced to property. This project is a strong example of how digital humanities can reframe historical narratives and add new layers of understanding. It’s a moving reminder of the importance of telling stories that might otherwise remain buried, humanizing the data in ways traditional history often overlooks.

On the other hand, the Every Upcoming Solar Eclipse (until 2080) visualization takes a more scientific approach but is still very captivating. Created by Denise Lu for The Washington Post, the interactive globe shows all solar eclipses’ paths until 2080. This project brilliantly uses digital tools to visualize something that would be hard to grasp otherwise. What stands out about this visualization is how it lets users interact with the data personally—you can enter your birth year and see how many eclipses will happen during your lifetime. The use of light and dark shades to represent time, combined with hover text tooltips, makes it both engaging and accessible. This project showcases how digital visualizations can make complex scientific information more relatable to a wider audience.

In addition to these projects, I explored Ben Schmidt’s Gendered Language in Teacher Reviews, which uses data from RateMyProfessor to analyze how students describe male and female professors differently. I found it super, super fun to play around with different search terms and see how language changes based on gender. This project is particularly engaging because it lets the user drive the experience—you input specific terms and get immediate visual results. The ability to personalize the data output based on user input adds a unique level of interaction and makes the findings feel more relevant. It got me thinking about how one would create such an interactive and tailored visualization, where the user shapes the narrative rather than passively absorbing information.

One of the most surprising takeaways from this week’s readings and projects is just how many tools and resources are available for creating digital humanities visualizations. I had no idea there were so many options out there, as highlighted in the NYU guide on digital humanities tools. It’s exciting to think about the possibilities for using these tools to create my own visualizations and tell stories in dynamic, user-driven ways.

Overall, these projects show the power of digital tools in making abstract or complex data come to life. Whether it’s through uncovering hidden histories, mapping celestial events, or revealing gendered biases in language, the potential for digital humanities to reshape how we interact with information is both vast and inspiring.

Praxis – Data Viz

Since I work closely with the Administration of Child Services (ACS), I wanted to use NYC Open Data and look at payroll data before and after covid 19. What I found was a data file titled

Citywide Payroll Data (Fiscal Year).I filtered the data to show ACS data for the 5 boroughs from 2019-2022. The 3 visualizations are overtime hours, median and average pay, and median pay by title description. Other filters included Pay Basis is Annual only, excluding hourly pay basis.

I decided to use Tableau because it is a common program used at my place of work. I was pleasantly surprised by how little experience is needed to navigate the program, and it was fairly simple to select and compare different types of visualizations for the data.

After reading “Against Cleaning”, I wanted to use the data as is and see what it can show. Although I didn’t start with the idea to investigate over time hours the visualization “Median Paid Over Time Hours” showed a significant dip of hours across all boroughs during covid 19. The interesting part of this visualization shows Manhattan, with close to 0 over time hours paid across all 4 years.

A challenge with my visualizations were adding labels to the boroughs as side from the key. A viewer would need to hover or select the key to distinguish boroughs on two of the visualizations. I was unable to publish my visualizations to be public, as of now the publication can viewed by other tableau users.

Tableau – Visualizations