This paper by D’Ignazio and Klein emphasizes the importance of omission. Previously, we have discussed the dangers of omission in relation to Simpsons paradox and amounted variable bias (described by my classmate as occurring when “a variable that is correlated with both the dependent and one or more included independent variables is omitted from a regression equation.” In this case, unacknowledged confounding variables account for misleading conclusions. In a more recent article, omission is discussed in the context of digital humanities. As another classmate of mine said, “Lauren Klein brings up a very pertinent question on how scholars account for absences in the archival record. Interested in revealing the absence of information regarding Jefferson’s head Chef, James Hemings,” Klein developed a social network (as the author of the Paul Revere work also did to uncover hidden data) and uncovered a wealth of information concerning Hemings that had not otherwise surfaced.

Hemings was a slave, yet was one of the few who was literate. However, despite their friendship, even Jefferson himself refused to write Hemings directly. Thus, we must turn to the mentions of Hemings in Jefferson’s letters to others. This tactic, while a valuable source of information that would not otherwise have been readily available, is still plagued by the issue of omission and bias. Hemings was a slave, a member of a marginalized population of which few were literate. Women too, were often illiterate, and even those in such groups who could write were often not taken seriously and certainly not published (unless under an alias).

This is the issue that is addressed by D’Ignazio and Klein. Their feminist theory (which advocates not only for women but all marginalized groups), seeks “to challenge the idea that science and/or technology is objective and neutral by demonstrating how scientific thought is situated in particular cultural, historical, economic, and social systems. Feminist STS, both implicitly and explicitly, looks to the perspectives of those marginalized by current power configurations (including and especially those marginalized because of gender, sexuality, race, and/or ethnicity) as a way of exposing how their perspectives are not included in what is considered “objective”truth.”

A long quote, but one that accurately summarizes their mission, and introduces yet another reason for caution when analyzing data of the digital humanities. It is so easy to look at a data set or a work and to pick out what’s wrong, yet its harder and perhaps more important to think further and consider what’s missing. Similar to considering confounding variables that may result in omission bias and ruin the validity of the study, one must not accept the records of history as objective fact.

Looking back at last week, I enjoyed reading “Response: Using metadata to find Paul Revere.”  The author highlighting something i had not considered when conducting network analysis.   Without any prior knowledge of the topic, bias could arise from the analysis.  Meaning, the results from network analysis only are meaningful in the context of prior knowledge. This is a really good point, and something worth considering when conducting the network analysis for the interviews.

For this week’s reading, I think it brings up many good points.  It seems the problem the authors are attempting to create is the reductive nature of data visualizations.  There tends to be a lack of context,  overall lack of methods, and acknowledgment.  Admittedly, I never considered employing feminist theory to correct for this issue, but it seems like a viable option.

Feminist digital humanities is not only extremely crucial to giving a voice to women in this field but it also allows people the opportunity to question. Under the umbrella of feminist theory and its relationship with digital humanities delves into the importance of nature and culture as well as other binaries. Reason and emotion are two binaries that would never have been included in the discipline of Digital Humanities without Feminist theory. Visualization in DH is vital for people to question or develop different perspectives about the way in which society treats people based on the way society groups people and influence others. The blog “Feminist Data Visualization” emphasizes my points about rethinking binaries and how critical it is to our world.

This video for me was very Ironic.  Data visualization at its most basic form is the process by which we shine light onto our data so that people can easily understand its conclusions.  Data visualization, while often convoluted, is designed such that the data can actually speak for itself. Just as the way that a picture is worth a 1000 words, I believe that a great graph is the most effective way to understand data.  With this in mind, I was shocked at how helpful Hans Rosling’s presentation of human life expectancy vs income graph. Hans Rosling proved that the context in which we read and visualize data can sometimes be just as powerful as the insights themselves.  In many cases, the context in which our data persists is inextricable to the conclusions formed. In this case, Hans Rosling’s step by step explanation of population rates in light of events such as world wars and flu outbreaks was extremely helpful. This goes to show the power of data context as well as supplemental information about the data itself.  

In watching this video, I agree with KS in that Rosling’s presence behind the graph made the data science itself much more accessible.  So often I feel as though data is meant to be presented in a complicated way as a means for achieving complicated or sophisticated insights.  By standing behind his graph and walking through his conclusions in plain english, Rosling did a great job of inviting people into his research.

It is great to read about intersectional feminism being applied to technology and data, because in a male dominated field it is important to know which information is biased towards a patriarchal society.  D’Ignazio acknowledged that collecting and presenting data can be biased by who is creating it, who it is created for, and the societal influences around it. The article concluded that to further embrace intersectional feminism, the field needs to; rethink binaries, embrace pluralism, examine power, consider context, legitimize embodiment and affect, and make labor visible. Critical thinking about all of these categories will allow the audience and the author to remove some of the societal inequalities that all STEM fields currently have.

MS-B mentioned in their post that by data should have the flexibility to be collected in a fluid manner, and the technology should adapt to be able to handle that sort of analysis. If data was collected in a fluid manner since it’s creation, we wouldn’t have the check”male or female” box as often as we do.

For this class, I read “Feminist Data Visualization” by Catherine D’Ignazio and Lauren F. Klein, which was relevant to mean a girl studying in this field. Reading about the feminist approach that they took was interesting because of all the different aspects of feminism that can be analyzed through data, many of which have changed throughout time; power, context, embodiment. I enjoyed the connection between humanities and visualization that was discussed because from what I’ve come across so far, the importance of humanities in data isn’t emphasized enough. Contributions from feminist thought can make a big impact. The “Design Process Questions” at the end of each section were good for critical thinking for me as I read and made me realize how important generating effective questions are when analyzing data.

I really enjoyed the “200 countries, 200 years, 4 minutes” video by Hans Rosling.  It showed an interesting form of data analysis (though it showed its age).  I’m not even sure what to call it, maybe AR visuals?  I see this type of thing all the time in NFL broadcasts now. I would love to see a similar video made today with the advancements in graphics.  Most importantly, I think this video was a good reminder that data can be interesting and fun.  I was actually shocked at how many views/how well received the video was.  To be honest, I didn’t think him standing behind the graph added a lot of value to the video for me, but it seems people liked it.  This is a helpful reminder that accessibility is crucial for any field of research to make progress educating in the public – that human skills are also needed, and that is what the digital humanities hopes to offer.

Elvis Perez commented on youtube, “I’m only watching this video because my teacher assigned me homework on this.”  As am I bud, but I think Conservative Developer made a great point in saying “lool same, but it is interesting.”  This video definitely got me thinking of how to try out and implement different forms of data visualization myself!

After reading Graphical Approaches to the Digital Humanities by Johanna Drucker, I learned of the complex formula needed to display information digitally. What really sparked my interest was the spreadsheet.  Drucker mentions that spreadsheets are as old as civilization itself. Spreadsheets are a convenient way to relay information in a clear an organized fashion. I believe the progression of the spreadsheet can be a symbol of human digital progress. The spreadsheet has always dominated business and accounting and continues to this day. However, people no longer use paper spreadsheets and instead use programs like Microsoft Excel.  The invention of digital spreadsheets changed business and even made personal computers a necessity. College students now still need to learn excel for many full time positions.  This shows how important it is to learn tools that allow you to visualize data in the modern age.

I drew some parallels to Making Meaning Count, by Stefan Sinclair and Geoffrey Rockwell. They mention a new way of visualizes data called the word cloud. The wordcloud and spreadsheet are similar in a way because it is just words. It only shows the data itself, but with formatting.  In the post Text Analysis in Professional Sports by NB, the author examines the bias associated with athletics and I would be curious of other visual ways to show this bias.

For this week, we read the article, Graphical Approaches to the Digital Humanities by Johnna Drucker. The first main point to outline are the different types of graphical representations that are used. The following is a short list of potential mediums to display data, many being used across the same data which might reveal different incites. A few forms to mention are: pie charts, scatter plots, bar charts, network diagrams, tree diagrams, etc. We also not the evolution of these certain graphical representations through the years. We can assume that over time, different methods were created to display the same data in new and unique ways. For example, bar charts were a rather new addition to the forms of graphical representation. Bar charts were invented and first used in the fields of accounting and statistics.

Further commenting on  ‘sjaloway’ work, we note the following block of text from the article, “From a critical point of view, however, the message is more skeptical and suggests a radical rethinking of the epistemological assumptions that the statisticians have bequeathed us. The fault is not with the source, since it is the borrowing for humanistic projects that is problematic, not the statistical graphics themselves. They work just fine for statistical matters (Borner, 2010). There is an important like between the humanities and the underlying statistics of graphical representation. In many cases, the underlying statistics can account for the accuracy in the data and the following interpretations may be wrong. This alludes to the importance to choose the proper medium to display data and be sure the your following conclusions on that data align with the unbiased incite.

Catherine D’Ignazio and Lauren Klein’s paper Feminist Data Visualization provides insight on how those in the computational studies can better represent their data with equity.  This paper brought together concepts, resulting in a guide for their term Feminist Data Visualization [D’Ignazio Pg 1].  One idea that stood out to me was their point on rethinking binaries. In a space where booleans are in common use (and for good reasons), it is often easy to look at the inputs / outputs of an algorithm and determine that they are either correct or incorrect. D’Ignazio and Klien state that a powerful way to make your data more feminist is by doing data collection and classification while accounting for fluid categories [Pg 2]. This allows one to cover a wider breadth of data and have accurate representation, one specific example being gender.  Some might say that this process of collection may lead to the creation of messy data. But it is important to note that computational tools are there to further sort and provide insight into that mess, allowing one to find new insights on the problem being investigated.