In “Lynching, Visualization, and Visibility”, Mullen visualizes the amount and location of Lynchings in United States history. His work is focused on bringing previously invisible data to light as Mullen contrasts the visibility of the act of lynching and its invisibility in government records. Mullen’s highlights the difficulty when dealing with absent data but despite absent data being difficult to fill in, the creation of these data sets importantly shed light on hidden problems. I think that while Mullen is undertaking important and valid work, they created some flawed visuals.

In their post, DG questioned how effective the visuals are and gave strong and valid critiques on all of their visuals. While some of the visuals are clearly flawed, like the first visual showing the number of lynchings per week from 1877-1950. This visual utilized shades of reds that were too close together to easily see differences and the use of by week data seemed to be an odd choice as well. But the authors probably wanted to stay in a red scheme for symbolistic purposes and the choice is understandable. I think Mullen could have spent more time in making effective visuals to further bolster his findings but regardless of the output, the work done in this article provides a template for exploring “missing” history and bringing light to underexposed topics.

This post by The Future of Research Communications and e-Scholarship gives an outline for creating data and research that is readily accessible. They outline the four “FAIR” facets, which state that data should be Findable, Accessible, Interoperable and Re-usable. Anyone who has worked with data recognizes the usefullness and practicality of these guidlines as the process of finding data and performing analysis can be quite difficult. If all data work/collection follows universal standards it would greatly increase ease of use for every part involved.

RF made some great points when talking about data equity. By creating guidelines for data accessibility, it may help open up the flow of information. Historically, data is often tightly held and those who create data can be biased in their creation and analysis so opening up data flows will create more equitable data.

In Developing Things by Ramsey and Rockwell, the authors discuss the topic of scholarship around digital humanities. The article looks at dispelling a lot of the biases against digital work and online publishing.  While I never doubted the usefulness or validity of technological approaches and digital work, I still viewed written material as more scholarly and defensible. To show that digital work holds the same validity, they looked to expand our understanding of theory. If we believe digital work as being based in theory or offering new theory then it becomes easy to accept it as scholarly work. The authors explain that theory doesn’t necessarily have to predict but rather allow for a deeper understanding of something. Their framework for defining digital work seems more convenient than convincing but their article still offers new ideas and pushes for more recognition for digital work. While new ideas in digital humanities and software should definitely be qualified as scholarly work, I agree with the sentiments expressed by MG. MG expressed in their blog post that we still should be careful when examining digital work as there is a plethora of information and sources on the internet with very few being checked for validity. This requires more effort for the reader/examiner and one needs to be careful when trusting that online work is indeed scholarly.

The video by Loften and Vaughan-Lee follows the story of Jim Enote, a Zuni farmer who is creating unique maps that prioritizes story-telling and common ancestry. Enote is pushing our standard definition of what constitutes a map by incorporating personal identities into the map. Rather than viewing the area from an aerial 2D perspective, his maps focus on invoking the sense of place through depictions of landscape and areas noteworthy to the Zuni people. I agree with KS’s skepticism in their post about whether Enote’s work truly classified as a map. While Enote’s work depicts important landscape and features of the Zuni area, it has more value in its ability to envoke feelings and histories for the Zuni people rather than being an accurate representation of the area. While not practical, Enote’s maps allow him to tell a different story and make a statement that you can’t with classically structured maps.

In “‘A Report Has Come Here’: Social Network Analysis in the Papers of Thomas Jefferson“, Klein tells the story of Thomas Jefferson’s relationship with one of his slaves, James Hemmings. The focus on this relationship illustrates how much of the history written about American Slavery is missing perspectives and stories about the relationships between slaves and slaveowners. As CM-A mentioned in their post, most of the information we have from this time is written by white upper-class men so digging deeper into the absent stories can help piece together many more voices. Klein develops an interesting framework about how to deal with these absences in stories by using a social network analysis. It seems to be very difficult to piece together information about relationships during a time where correspondences weren’t digitized, but one can understand the importance (frequency of correspondence) of relationships using social network analysis. I think Klein’s work is a strong step in understanding more about these absent histories but there are still strong limitations on what can be done.

In Sentiment Analysis and Subjectivity, Bing Liu discusses novel techniques in text analysis that aim to find the sentiment embedded in text. Bing importantly notes that this sort of text analysis can only be done now with the rise of the internet. With the plethora of information available in online forums, groups and blogs text analysis has never been easier. The article does well in describing all of the different methods and applications of text analysis but I wish they had gone into more detail about the potential of this tool. For example, measuring news sentiment is increasingly becoming prevalent for political and economic analysis and the ability to separate “fact” from “opinion” or sentiment is huge advancement in text analysis.

There are some caveats to text analysis that I think CM-A explained well. In their post they talked about how these analysis tools still are reliant on how humans interpret what is important to look at. This is particularly important for sentiment analysis since whoever creates the code has to determine what kinds of words or phrases contribute to a particular sentiment, which probably is not a very scientific process.

In Theodossiou’s article, he examines the relationships between unemployment and low paying jobs with a psychological well-being score. In one part of his analysis, Theodossiou looks at different employment characteristics to see how they affect well-being. I thought the inclusion and the results of people out of the labor force were interesting. Since this study was conducted in a relatively strong economic period, I would assume most people out of the labor market would be due to choice and their psychological well-being would not be affected. I was surprised to see that being out of the labor force had significant effects on well-being for certain subgroups.

I thought JN had an interesting stance on this article and although I don’t agree with all of their opinions, I thought that they brought up a good point regarding the subjectiveness of the well-being ranking. I think there is a possibility that a confounding variable could exist that factors into how people subjectively rank their well-being and their ability to find employment. But in general, this article provides a very interesting way of looking at how different factors related to employment status affect the odds of being in a low or high level of psychological well-being.

I thought this reading was very interesting and had several important takeaways. This article emphasizes how occurrences of the Simpson’s paradox are higher than previously thought and how important it is to consider whether a Simpson’s paradox is present in your data. This article discusses how statistical data analysis can result in biased conclusions if you fail to consider how the relationship of data may be different for the population and subgroups, which is particularly interesting given our discussions of how data is not inherently neutral. This article hammers home the point of how important it is to consider different subgroup relationships. After reading through all of the examples of how the direction/magnitude of relationships can change, I cannot help but wonder if I’ve made causal inferences about data that would not hold up if I looked at subgroup relationships.

Welcome to WordPress. This is your first post. Edit or delete it, then start blogging!