The workshop Intro to GIS that took place on November 15th focused on 4 main points:

1- What is GIS and what is spatial data? (https://community.esri.com/t5/arcgis-online-documents/the-language-of-spatial-analytics-poster/ta-p/915498 

2- How does GIS combine spatial data into a map?

3- Where do I find spatial data?

4- What GIS software is right for my project?

One thing that was really interesting for me was the differences between Mercator projection versus Albers is an equal-area. The Mercator projection and the Albers equal-area projection are two distinct map projections that serve different purposes and exhibit contrasting characteristics. The Mercator projection, developed by Gerardus Mercator in the 16th century, is known for preserving angles and maintaining straight lines, particularly useful for navigation. However, it significantly distorts the area which  leads to an exaggerated representation of landmasses. As you can see from the map below that NY to LA is shown as 3124.67 miles. On the other hand, the Albers equal-area projection, designed by Heinrich C. Albers in the 19th century, prioritizes preserving accurate relative sizes of areas at the expense of distorting shapes and angles. This is particularly suitable for thematic mapping and spatial analysis where accurate comparisons of areas are crucial. The projection measures in the same example of the distance between NY to LA is 2455.03 miles. It is only off by 4.03 miles compared to 673.67 miles measured by Mercator projection.

Also, I found the guiding questions for choosing cartographic software very helpful and will be using it moving forward with my pending projects and wanted to share with you in case you might find it useful as well. 

1- Would you be willing to pay for a service?

2- Is the map for internal or external use?

3- Will you be working in a team?

4- Do you want to do data analysis in addition to visualization?

5- What is your time commitment?

6- Do you want your final map to be static (an image) or interactive (a web-based application)?

The world of GIS software can be overwhelming. But it was such a well organized and interesting workshop that I am looking forward to attending more next semester.

I have a really hard time reading on screens and the article mentioned a few things that really resonated as explanations. The idea that it “leeches” more attention than paper in particular. I have ADD and have wondered if that has anything to do with my screen hatred, so maybe that’s the connection.

I was really interested in the links between understanding physical space and the mental representations the brain creates for text. It resonated because when I read online, I have the same feeling as when I get lost which is frequent. I have a lot of moments where I suddenly feel really disoriented even in places I’ve been to many times, and it’s a horrible, unsettling feeling. That’s how I feel trying to read denser articles on a screen! It’s like I’m missing some very important piece of information and I am really distracted trying to figure out what it is.

I’ve also noticed that it feels much easier to read actual scans of books, like the ones you can borrow from the Internet Archive. Thinking about the map thing, I wonder if the scans give more signposts to help organize what I’m reading even though it’s still online. With old paperback books, they yellow and get tattered so all of the individual books I’ve read as scans are physically unique. I approach them the same way I approach a physical book without thinking about it: I look at the cover, the back, the last page, and the table of contents before I start. The progress bars feel more meaningful to, maybe because I go through that process. Like the article said, it also feels comfortable to go back and forth in a color book scan than in an article or book that’s just type on a scrolling screen. One thing that helps me comprehend text on screens is using a stylus to take notes, so I wonder if that works because it creates a better map through the article. It also helps to take notes on paper while I read, but not nearly as much as writing on the document.  

My last thought is that I love the fact about how when you read cursive, your brain writes out the letters to understand it!

When I saw the theme of this week’s readings on our class syllabus, I knew that I would encounter questions and topics previously raised in my professional life. I work for a nonprofit cultural organization, and, having recently submitted grant reports and applications to the National Endowment for the Humanities, think often about the complexity of advocating for public work. When considering my own work, I am often troubled by the issues of reach and sustainability: I’ve seen great projects get funded, executed, and then become almost immediately obsolete. Throughout my reading this week, I was most interested in learning about the relationship between the public and the relevance of a humanities project: I considered the question of whether the creation or the presentation of a project matters more in engaging the public. Through Sheila Brennan’s thorough examination of public DH initiatives, I can more clearly see a connection between the potential longevity of a project and the impact the project has on its audience. 

I appreciated Brennan’s enumeration of the steps to take into account when “doing public digital humanities”. In her description, a successful project will 1) identify its audience, 2) make the project accessible on platforms and in languages familiar to the target audience, 3) make the project’s navigational paths easy to understand and welcoming to users, and 4) have a meaningful name. I compared these necessities to a recent grant application that my coworkers and I submitted to the NEH. Although we did not apply to the Digital Humanities Advancement Grant that Brennan writes about in her NEH blog posts, we did apply to funding for a specifically public humanities project. Our application was rejected, and I think that this was largely due to a failure to place the public first. The project I submitted an application for involved a discussion series, which relied on public engagement in order to gather together a national audience. However, information about the discussion series — both in past iterations of the project and in the application — was difficult to find. By hosting the humanities project within the organization’s larger website, details about upcoming talks were buried. Website users had to know specific search terms in order to locate event details on the site, which drove away potential audience members. Furthermore, the discussion series was not very collaborative with the communities it intended to serve. The project sought audience feedback after each event, but did not include the audience in the process of designing each talk. This imbued the overall public humanities project with a paternalistic feeling: the organization entered communities with pre-prescribed information, and then administered a survey about what could be done differently “next time”, without even guaranteeing that there would be a next time. After reading through Brennan’s suggestions for designing a successful public digital humanities project, I can see clearly that the role of the public is essential in designing a project and then facilitating its preservation. If the public can see their own interests and perspectives represented in a project, then they will be more likely to support efforts to recreate the project, share it widely, and continue adding knowledge into the project’s database. 

After I read Brennan’s piece “Public, First”, I wanted to get a better sense of what might constitute a public digital humanities project. I visited the website for the Roy Rosenzweig Center for History and New Media, and browsed through some of the work led by this institution. I loved the creativity and participation embedded in projects like “Hearing the Americas” and “Amboyna Conspiracy Trial”, and wondered the types of cultural preservation practices used in these projects could be applied to other niches of the humanities as well. History seems to lend itself very well to public digital humanities work: why is this? Can other areas of scholarly inquiry — even outside of the humanities — invite the public in?

I recently attended a workshop hosted by The Pudding — a digital publication that puts out lots of great data-driven pieces with a focus on culture and media — on the subject of using ChatGPT to conduct research. I thought I’d share some of their findings and insights, in case it’s helpful for others considering how best to use AI, if at all, in their research.

The Pudding team members walked us through an internal project where they’d designated ChatGPT as their AI “intern.” The goal was not to let ChatGPT do everything, but rather assist and support at each stage of the research process to make the team’s work more efficient.

They defined their research process in five stages: idea, data, storyboard, design, and development. They focused on using ChatGPT for the first three. The team already had a research topic in mind — an analysis of clutch shooting in basketball — but turned to ChatGPT to help flesh it out.

Even at the idea stage, they quickly encountered that ChatGPT has a nasty habit of equivocation. It did not want to commit to making decisions. It also expressed itself verbosely and with a lot of fluff. The researchers were able to minimize some of these tendencies by explicitly telling the system to avoid them — however, this dragged out the process of actually getting any results.

ChatGPT proved useful for generating tons of ideas for research direction, but these were mostly surface-level. The team had to sift through dozens of bad ideas to find one that might have legs. Still, I feel this can be a useful exercise when brainstorming if you feel you’re running up against a wall.

Next came the data work. The Pudding team already had a dataset handy for clutch basketball shots, but they needed to get it into the right shape for analysis. ChatGPT had more success here: It seems to perform well at taking data in one format and converting it to another, such as appending or removing columns in a tabular dataset, modifying text strings, etc. — steps that might otherwise require significant Excel intervention or programming. That being said, it’s crucial to review ChatGPT’s work each time it cleans or processes data. Sometimes it loses track of the most recent dataset and returns to answers from earlier in the chat log. 

Nevertheless, being able to use natural language to engage with code is a huge plus. What used to be locked up in programming languages that you had to learn, you can now do with simple words.

The promise of accelerating data cleaning is a bright spot, especially, in my eyes, for digital humanities researchers. In their article “Against Cleaning” in Debates in the Digital Humanities 2019, Katie Rawson and Trevor Muñoz suggest that the amorphous work of “cleaning” data takes up to 80% of data workers’ time in research. 80%! Even cutting that down by 10 or 20 percentage points would have a massive impact on DH researchers.

Back to The Pudding’s AI intern project: Another area where ChatGPT can deliver impact is exploratory analysis. When first forming or testing hypotheses, the team found that ChatGPT could rapidly summarize data — including visualizing the data with a range of charts — in ways that would otherwise require a lot of programming. 

These two promising use cases (data cleaning and exploratory analysis) point to a broader theme here: that ChatGPT can be very helpful for automating or accelerating rote data work — and very unhelpful when it comes to the more creative work, such as ideation and storyboarding. 

At the end of the project, the team decided against publishing the research, but they plan on releasing the cleaned dataset — a nice service for other researchers hoping to look at the same topic.

Throughout the process, The Pudding researchers were continually confronted with the same question: At what point is it more productive to just do the work, rather than keep prompting ChatGPT and QAing its results? There isn’t an easy answer here — it depends on the nuances of the task at hand.

P.S. Because the subject was using ChatGPT in research, I tried using ChatGPT to help me outline this blog post using the notes I’d taken during the workshop. But the output wasn’t particularly helpful, and I decided it’d be faster to just write the damn thing myself. However, if this were for a longer research article, and if my notes were more extensive/thorough, I could see this being a potential time-saver for scoping out my writing.

For the Text Analysis Praxis, I decided to use Voyant to look at Collective Wisdom: Co-Creating Media for Equity and Justice by Katerina Cizek, William Uricchio, and 12 other authors. Initially, I was searching MIT Press Open for a text that might be interesting and happened to run across the digital version of Collective Wisdom, which I already had the physical copy of, but have not read yet. My thought was, “I suppose I can do a ‘distant’ read before the ‘close’ read.

First, I filtered out the basic frequent terms, so that the Cirrus view would yield more useful results to inspect. They were what I expected given that the book’s subject matter. The most frequent words in the corpus being media (566); creation (524); new (432); film (378); and project (346). Other terms that I singled out to look more into were community, systems, documentary, journalism, and ai.

I toggled through some of the corpus and visualization tools. Speaking strictly on sheer fascination, the Bubbles tool was  amusing. I decided to leave the sound on too while the system cycled through the frequencies of the terms in the document. The links view was also of interest. Network displays of information are helpful when looking at correlations and relationships.

The context tool was helpful in exploring some of the terms that were also frequently used. Since the book has about 13 authors, I was curious if I would be able to identify the varied usage of certain terms. I saw this clearly when looking at community. At times the term was used in the first person, like belonging through personal heritage and others were more institution-based. There were instances that were observations or criticisms from outside of a community or against a particular community.

Other terms I investigated were:

Ai: I was glad to see it was largely referencing artists who were trying to utilize AI in their practice or attention to its harms where people of color are concerned.

Journalism: The context I pulled out was collaborative or co-creative use of journalism. In another class, I’ve become more familiar with data journalism and was interested to see how it may be used in the text.

Initially, I was concerned that I should have chosen a more meaningful topic for this assignment, but the 6th entry of the Data-Sitters Club, Voyant’s Big Day, reminded me that the text and reason for exploration don’t have to be profound. As a fan of the Baby-Sitters Club, I found this project to be a delightful surprise. Using Voyant to explore the series never crossed my mind. The questions that the post author, Katherine Bowers, mentions are a useful starting point when attempting a text mining project:

“Is close or distant reading the best approach for the questions you have?”

“Is the corpus complete? What are the characteristics of the corpus? What’s missing?”

“How does the data skew? What’s skewing the data?”

If I could create a manageable dataset for Lemony Snicket’s A Series of Unfortunate Events, I would explore it like the Data Sitters Club did. For now, I am curious if my ‘distant’ then ‘close’ read will have any significant impact on my experience with Collective Wisdom.

Yesterday, I attended the GCDI’s workshop on “Tools of Digital Humanities”, taught by Tuka Al-Sahlani. This hourlong workshop offered a survey of five pillars in DH scholarship, with demonstrations of some open-access, minimal computational tools that can be used to support DH practitioners. In order to address a range of tools as they relate to the needs of DH projects, Tuka focused on five components of DH: digital pedagogy, data visualization, mapping, digital archives, and digital publishing. Before we dove into the tools associated with each of these categories, Tuka offered us examples of DH projects that fit into each one. This helped our group consider what types of projects, research, and work are included in the umbrella of DH. This consideration speaks to the persistent challenge of defining DH, and I liked that this workshop used practice as a means of finding a definition. 

As Sarah mentioned in her workshop blog post, one of the tools our group looked at was Voyant, a text analysis tool that featured in this week’s readings. Learning about text can be uploaded into Voyant, I thought about how other tools – like Scrapy – can be essential to furnish the textual data needed to run an analysis. Using tools in conjunction with one another underscores the value of interdisciplinary skillsets for DH scholars: with a basic understanding of Python, someone will be better equipped to extract and study the exact text they need for a project. Following our unit on open access, I felt curious about the potential benefits and downsides to open access DH tools. For example, does uploading a text into Voyant mean that this text is now part of the public domain? Who can view the results of users’ analyses? Do independent projects contribute to a larger knowledge database? I would love to learn more about the privacy measures guaranteed (or not) by open access tools like Voyant. 

The open and public nature of many DH tools was impressive to me. At one point, Tuka mentioned that UMap, a digital mapping tool, was preferred by users for being more ethical than its peer sites (like Google Maps). This comment made me think about how community input can be instrumental in regulating ethical standards in scholarship. So many DH tools rely on collaboration in order to be taught, supplemented, and improved; and it seems that community stakeholders also play a role in ensuring the integrity (and thus, usability) of DH tools. This form of practice and participation reflects debates in the more general field of DH, where the scholars we’ve read in class are constantly questioning who is able to engage with DH and to what ends. Ultimately, Tuka concluded, digitization should aim to bring disparate materials into a cohesive narrative. The tools we explored in the workshop can help us capture data, analyze data, display data, and then make the information derived from these steps meaningful to a wider audience.

Tonight’s GCDI Fellow workshop was very informative, with plenty of sources, tools, and projects shared that would be helpful to any new DH scholars.

Tuka started off by introducing herself, and she made it very approachable by explaining what she does. She also explained throughout the workshop which tools she uses for what she does, and mentioned other GCDI Fellows who would be helpful to contact for specific questions or assistance. Tuka isn’t a very coding-heavy DH scholar, and shared with us that she is primarily a linguist, which made learning from her perfect for a week where we will be discussing text mining/analysis.

In Tuka’s overview of Digital Humanities, it’s clear that even experienced scholars haven’t participated in all forms of DH, because there are just too many. While we stuck to seeing projects that met five forms of scholarship, there are countless more that can fall under the DH umbrella. Some of the examples that Tuka mentioned which I hadn’t been exposed to before were physical computing and app development. I know what these things are, but haven’t had the opportunity to explore them in a pedagogical context. Tuka mentioned her love, and GCDI’s love, for open source tools and data. This makes sense as we are all in a student/instructor role where accessible tools and data are likely to be the ones that we choose to use. Following our discussion in class last week, this was helpful to see how core the concept of open source is to all DH scholars.

Of the tools that we explored in the workshop, I was particularly excited by Scrapy. This open source tool allows you to, using Python, perform simple web scraping. I have only ever performed web scraping using R, so it was very helpful to see another tool and how it may compare to the R process of web scraping. Extracting data from websites can be a very exciting way to explore, and makes you see the content in a whole new way. New approaches to doing this help make the process smoother and more approachable to beginners, such as myself. For any other R users out there, they shared the link for the CUNY Commons RUG (R Users Group). This space is for R users of all levels to discuss, problem solve, and highlight their exciting work.

Tuka also reviewed Voyant, which many of us seem to have used to text mining as a Praxis assignment. We replied on an existing corpus, Mary Shelley’s Frankenstein, which exists in the website to be analyzed. Doing this portion together as a group was helpful- despite having used Voyant myself, there was a lot to learn!

One final aspect of the workshop that stood out to me was the inclusion of the Preserve the Baltimore Uprising Archive Project. Using Omeka, this archive DH project commemorates the 2015 activist movement in Baltimore and is intended to preserve those efforts and document this historical moment in time, which continues today. I grew up in Baltimore and was there until 2016, so this movement had a massive impact on my development not only as an activist, but as a person. Seeing such a wonderful preservation of those efforts to support and stand up for the Black community following George Floyd’s murder, a group of efforts centered around pain and oppression, was incredibly special. The project has diverse perspectives and is dynamic is its inclusion of content. People can submit their own original content to be included, only with approval, and it showcases items as well as larger oral histories. Oral activism was a key part of this movement, and preserving those oral histories seems to be such a beautiful and kind way to recognize Baltimore’s heartbreak at that moment. The project includes a lesson plan for instructing oral histories to encourage active engagement with the project.

This workshop touched on a wide array of branches within DH, both in the usages of the scholarship and the tools to do so. For someone who is new to DH, I found this to make it seem approachable and exciting.