This week was all about loosening what had become a tight focus on getting required tasks done. It started with the in-class critique of our project presentation which nudged me to return to the more expansive “What if?” thinking with which we launched the project. Micki suggested really playing with the visualizations in counterfactual ways. What if we don’t represent whiteness in the graphics?* What if we push back against the census categories?

My initial play with the first question was deflating: in both protagonist and author graphs, an early award winner of color makes the removal of the “nearly solid bar” of data representing whiteness not considerably more interesting than with it. So, I’ll need to play around this week with conditional statements that sort awardees by decade or that translate our race/ethnicity data into more equitable categories than the Census divisions.

Also widening my scope was my videoconference with GC Digital Fellow Rafael Portela to review the python code I’d created to scrape Caldecott awardees. While the Newbery scrape yielded data just as I’d hoped, the two Caldecott scrapes resulted in messier .csv files that required loads of cleaning. I’d turned to Rafael (Rafa) in hopes of figuring out whether that mess was a result of the architecture of the sites’ HTML or my code.

Our meeting underscored what DH is all about: a manner of thinking more than an amalgamation of skills. Having asked me to send him the python files in advance, Rafa began by saying, “I haven’t done any data scraping with python myself.” But, he had already played with the files I’d sent, and he had looked at my code less as a data scraper than as a text parser. He asked just the right questions to get me thinking about ways my code might have been more efficient and effective.

He did indeed confirm that the sites’ HTML was what caused my Caldecott scrapes to be messy. But, he also helped me identify two ways that I might have counteracted that. First, he noted that using the element inspector in Chrome reveals a little more about the architecture of the site than merely viewing the source code. (It turns out that there was a hidden attribute in the anchor tags of the Madison Public Library site that would have helped me grab both medal winners and honorees in one go, though I still would have had a lot of cleaning to do.)

He also pointed out that using python not just for scraping but also for cleaning would have been wise. Using regular expressions to parse the data may have resulted in clean columns of year of award, title, author, and illustrator (if different from the author). He also pointed me to Beautiful Soup’s documentation to consider ways to handle multiple attributes of scraped tags in the future.

If our group has time or if we continue the project beyond the scope of the semester, I’m hoping to try using regular expressions to parse scraped data in code rather than relying on Excel’s text-to-columns feature and a lot of post-scraping work. Meanwhile, I’ll continue to do research to fix our current data set (this week, I got rid of ? and null values in the protagonist data), and I’ll start to push our visualizations further as Georgette and Emily finish up the Caldecott research.

*Actually, Georgette had suggested this in last Sunday’s team meeting, so it was a double nudge from Micki.

As our group continues to do its research, a data story is emerging: children’s book awards can be beneficial, providing exposure and longer shelf life to quality books, but with that power, they can neglect important voices and provide longer shelf life to societal inequality. This week, I focused on how we might start to tell that story of our data through visualizations.

Two weeks ago, I created a set of four waffle charts about the Newbery Awards that statically celebrate* one extreme (the number of female authors at nearly 70%!) while exposing the shocking other extreme (that less than 10% of authors were people of color). So, this week’s task was to think about what our audience would want to see next. Perhaps they might want to dive into why female authorship is so high. Has authoring children’s books been considered largely a motherly, and therefore gendered, office? Have societal roles provided women more experience selecting and reading children’s books, thereby giving them an edge and a path into the market? Were selection committees more female than male? If female authorship is so high, why are protagonists only 30% female? All of the questions seemed interesting, and I did some research to see if there may be a compelling reason to find and present data to answer them.

The research was pretty fascinating. Perhaps my favorite find was that the selection committees seem to have been exclusively female until 1964, when Spencer G. Shaw, an African-American male librarian, entered the scene and continued to sit on the committee for four years. He seems to have opened up the committee to male participation, though that body of deciders remained largely female, even up to 2020.

Another highlight of my research was a 2011 study from Florida State University focused on a far broader selection of children’s books published throughout the 20^th century. Its findings confirmed that the Newbery awardees, at least in terms of the gender of the protagonists, mirrored larger children’s book publishing trends as well as trends among other media, such as cartoons. Among the study’s many interesting finds, a news summary about the study shares that “males are central characters in 57 percent of children’s books published per year, while only 31 percent have female central characters”—nearly the same ratio in Newbery Awards. The study itself also affirmed the purpose of our investigation, noting: “Adults also play important roles as they select books for their own children and make purchasing decisions for schools and libraries…Therefore combating the patterns we found with ‘feminist stories’ requires parents’ conscious efforts. While some parents do this, most do not” (219).

But, since women already comprised the majority of authors and female protagonists were at 30%, I thought the racial disparity might be the more important next step. More than questions about gender, I thought readers might be eager to understand more deeply the startling statistic about authors of color.

I assumed that the first question on our users’ minds might be whether authorship has gotten more diverse over time, so I created a visualization to answer that question. I included a filter so that our users could determine if there was more author diversity in the Honors, as opposed to the Medals themselves, and I added information in the tool tip to help users identify quickly the specific books and authors represented by each data point—hopefully to promote the books by authors of color. Those tool tips also offer additional race/ethnicity data beyond what the US census categories allow, hopefully shedding light on both the oddity of the census categories and a tiny bit of diversity within the white authors themselves (as some white authors are Jewish or come from recently immigrated families from European countries). What I think is most effective about the chart is how clearly it illustrates that the Civil Rights era changed the scene. Only two authors of color appear prior to 1969—Indian-American author Dhan Gopal Mukerji and African American author Arna Bontemps. From 1969 on, the picture is rosier for black authors (moreso than any other group of color), but the awards remain clearly and heavily skewed toward white writers. As Georgette put it during our feedback session, there’s practically a solid line of white authorship throughout the history of the awards.

For contrast, I created a graph of protagonists’ races over time—a very different constellation of data points, and one that begs the question of who has been given license to write about whom over time. Here’s how it looks right now:

(Or, explore it on Tableau Public.)

Tonight, my group provided helpful feedback, and, after I make some changes, we’ll start to get outside critiques this week. I’m eager to make sure that the choices we make are those that benefit our users—choices that help them decide how to let these awards inform their own selections as they pick books for their collections, students, and kids.

*While such a majority might not feel like cause to celebrate—after all, wouldn’t it be great if a fuller spectrum of gender was represented and if author gender mimicked, proportionally, society?—the Newberys were created only two years after women received the right to vote. So, we celebrate that the awards seemed to be a consistent and viable place of recognition for women in a century that was far less equitable to them. (Interestingly, the FSU study does note that the percent of female protagonists is slightly higher during years of greater awareness of women’s issues on the national political stage.)

I want to reflect about what we have done in the research area since the beginning of the semester. I haven’t seen it yet in the webpage because we have our group meeting tomorrow, but I know that Ashley and Chris uploaded the first two VRs on our website, which is exciting. One of the discussions we had at the beginning of the semester aimed to define what criteria we would consider for searching and mostly for selecting what VRs will be included in our database. We knew that what is available is not a reason per se, and even if we wanted to include what is available, we needed to find a narrative that explains why this is a reason for including a VR in our database.

Our aim was to create a Virtual Reconstruction database of archeological sites or objects. Since this was still too broad, we decided to focus on archeological sites in peril due to environmental damages, armed conflict or war, lack of investment in its preservation, earthquakes and other natural disasters, pollution, and poaching. This delimitation of the scope of the sites that we include in our database is important because it gives our project a strong conceptual lens and a clear standpoint to engage our VR digital project with the VR projects already being developed by different individual and institutions: scholars in universities, people in the game industry, artists, start-ups, among others. The second discussion we had is whether we would focus on a country and region of the world, and whether we would focus on a particular historical period. We decided to leave these options open and it turn out to be a good decision, which will be explained below. The third discussion we had is whether we would include natural sites in danger, e.g., parks, besides archeological sites or objects in peril. We decided to include only VRs of archeological sites, that is, sites that required human intervention and creation, for example, architecture, art, and/or religious buildings. Once we defined these criteria, the purpose of our VR digital project became much clear for all of us, not only in a practical sense, but rather it gave us a better understanding of the project we wanted to create. We needed also a new name, which is: Heritage Reconstructed: Virtualizations of Sites in Peril.

We began by exploring UNESCO Word Heritage in danger list. The list includes 53 sites in danger and includes cultural (archeological) sites and natural sites. We only focused on archeological sites. We made a search of VRs available online country by country, using different key words to make the search. Having completed the search following the UNESCO’s list, we identified two patterns: one, there are not many VRs for the sites the UNESCO considers in danger. Second, the VRs available are about countries and archeological sites that come from the same geographical region and mostly are in danger, or were destroyed, by war and terrorist groups, e.g., Irak, Libya, Syria, and Afghanistan.

We also included VRs sites that randomly appeared in our search thanks to the work of a wise algorithm. We found VRs that are publicly available, but also VRs that were created by the game industry; by start-ups, such as ICONEM, which dedicates to the digitization of endangered cultural heritage sites; Rekrei, a crowdsourced project, which also creates 3D representations of sites in danger; and CyArc-ICOMOS-Google created five VRs of sites taken from UNESCO list, which are mostly in danger due to climate change. We still have to figure out whether we will be able to upload the VRs these organizations have in their sites in our digital project or we will have to include just the link.

As noted previously, Ashley and Chris uploaded two VRs in our website. The following task for us is to review the VRs we have collected and decide which ones we will include, and contact the organizations I mentioned before, which have many VRs on archeological sites in peril, and ask them whether they allow us to upload their VRs in our website.

This week was all about bandwidth and RAM, both literal and metaphorical. I had trouble connecting to our class’s Tuesday Zoom session—certainly a matter of too many devices and too many programs demanding too much of a limited Internet connection in my getaway in Kentucky. I also had trouble working on the visualizations this week—certainly a matter of too many worries and too many plans to make demanding too much of a brain that would ordinarily be on spring break, my annual reboot.

So, eager not to hold my team back with my distractedness, I turned to low-level but time-consuming tasks: scraping and cleaning data. Our team is interested in comparing our current data set of Newbery authors and protagonists to data about other awards. So, I scraped the basics on the next most recognizable children’s book honor: the Caldecotts, which recognize excellence in picture books.

The scraping experience reminded me that most online python tutorials work with best-case scenarios. The videos that taught me how to scrape earlier this semester drew from huge, well-established websites (the New York Times and monster.com) to demonstrate the power of the code. The few sites offering a full list of Caldecott winners were less established, and the HTML was erratic at best. The site from which I scraped the Caldecott honorees fortunately organized those winners in lists, so I could find all <li>, but within those lists, they only sometimes embedded the title in anchor tags, and they often included manual spacing and tabs for no apparent reason. About half of the time, the books were illustrated by someone other than the author, so splitting the results by the word “by,” as I was able to do for the Newberys, got tricky. So, my code just grabbed the titles and attempted to grab authors, resulting in a two-column .csv file in desperate need of real cleaning—a far cry from the comparatively tidy results of the Newbery scrape. But that kind of cleaning—split screening the data and the original site and checking manually for errors—was exactly the kind of mindless labor I needed. Now, we’ve got the years, titles, authors, and illustrators, and already, without further research, we can see that the Caldecotts are much more diverse than the Newberys. But of course, further research is what we now need as we identify author and protagonist gender and race/ethnicity more precisely.

Another great boon this week: Meg reminded us that what we’re doing matters. She crafted an initial blog post for our website intended to remind our users that as children consume books in isolation—away from school and peers and the outside world—that parents need to make sure that those pages reflect themselves and others. If children only read from a small slice of the literature available, they will be isolated indeed. We hope our project can help parents make well-informed decisions this spring, when reading might be, in some ways, the only contact kids have with the outside world.

(On a side note, I’ve remembered this week what we learned last term in the Intro to DH class: that the data infrastructure in the US allows us access to our jobs and each other in this time of crisis in a way that few other countries’ infrastructure can. I’m wondering how we might use that access to support those without it. Thoughts?)