I like this method of organizing the data that your group collected. While some other categorizations such as frequency of protests or size of the protests are more reliant on having an exhaustive data set that is evenly distributed between the schools, the distribution of types of protests relies less on the data set being standardized. In the case of your project, can provide interesting insight regardless of the dataset being fully complete. For example, it can tell us what types of protests were more frequent in different time periods. Good work on this project, I like how your group provides so many different ways of viewing and organizing the same dataset!

Wow! This is a very timely and well executed project, I like how the timeline is color coded based on each college's information, and it is very effective in showing the "clustering" of activism mentioned in the analysis portion. Furthermore, the juxtaposition of events that both positively and negatively contribute to the civil rights movement is interesting and shows how progress is often accompanied by resistance from those with opposing opinions. I would be interested to see how these three colleges differ in their current racial diversity profiles and how their respective administrations work to cultivate acceptance of minority students.

I like how your group used this visualization as an opportunity to isolate mentions of "men" and "women" It would be easy to neglect the fact that these words are used in conjunction with one another. Not only does this graph highlight that important distinction, but I think that it does a good job of bringing awareness to this idea that, even within the context of the suffrage movement women were mentioned mostly with respect to their male counterparts. The aspect of this graph that surprises me the most is that mentions of only "men" are still higher than those of "women" for the majority of the document. I would be interested to read a qualitative analysis of this graph to determine why this occurs, and, furthermore, why mentions of "women" finally exceed those of "men" at the end of the document.

This is a really informative visualization, I like how your group was able to create separate and distinct categorizations for the types of protests that provide helpful insight into the school environments and student bodies of these schools. I would be interested in reading the qualitative analysis of this graph to dinf out more about the nature of the protests themselves or if the definition of "small" or "large" protest varies between Haverford and Vassar. I would also be interested to see a version of this graph where the vertical axis is a measure of percentage of protests rather than number of protests. This type of graph could control for the size differences between the two schools.

I like this question because it relates to the idea of whether or not the movement itself was present/ to what extent, but also, more importantly relates to the idea of how the movement was documented in the press and in more private news sources such as notebooks or scrapbooks. The variations in documentation extent and types of documentation could show the willingness of the school to publicly address its alliance (or lack thereof) with the suffrage movement.

Analyzing trends in yearbooks is a really cool idea, especially because yearbook format is semi-standardized and many yearbooks will contain different iterations of the same information. I like how this project setup allows for some control of the content variability and will isolate for more specific variations within it. I wonder if there is a way to use a visual analysis program to analyze class race distribution? I also wonder if the yearbooks will be in PDF or photograph form. If they are photographed, it could be trickier to preform textual analysis, but perhaps some text conversion took could be used to convert the photo to text. This idea of using a standardized dataset is appealing to me with respect to how it could apply to my own group project and finding sources that could be similar between different institutions and throughout time

This sounds like a really effective mapping strategy. I didn't look at this map specifically, but I noticed that most of the maps I looked at had a similar two-fold presentation method, which could not be accomplished if not for their digital presentation method. For example the maps would not only show a data set for slavery, but the used could see how this data changed from ear to year. Expanding upon this multifaceted data presentation to provide individual context into data points seems like a very effective strategy to add onto these other digital mapping features. It could be a good way to hold the data presented accountable by providing source information for each point, and furthermore, it could be a way to provide context and add significance to the data as you mentioned here.

This is an interesting example of how data mapping can be effective in two very different ways. Either the data can be clearly displayed to convey a specific set of information to the viewer, or the overall trend or presence of the data itself can be used to prove a point, as shown in this example. This raises the question of how data can be adequately cleaned and when it makes sense to clean data. Data cleaning is a process that involves altering the data to make it more accessible, and it could have been used in the case of this evictions map to make the data more readable, however it seems that in this example, the pros of showing the overwhelming volume of evictions outweighed the cons of altering and possibly skewing or casting subjectivity onto the data.

This is a cool observation! I also chose to analyze The Great Gatsby and I had some similar observations about the word frequencies, but I neglected looking at the word associations tool. I could see how this tool could be very helpful in making the voyant text analysis more multifaceted and comprehensive. Simply using word frequencies to asses a text is difficult because it entirely decontextualizes the words. This tool that you used helps to partially solve this problem by providing some context trends and helping the analyst understand a bit more about the tones and trends of the document being analyzed.

This is an interesting point about the complementary relationship between quantitative and qualitative analysis of texts that occurs in the digital humanities. On one hand, by restructuring humanities data we are 'solving' for a specific question or aspect information contained within the data itself, but by doing this we are also eliminating answers that can be found by structuring the data in other ways or by analyzing the text from a qualitative standpoint.

This is a really cool project idea! It is amazing that so much data related to COVID and mental health can be compiled, however i can see how it would be hard to contextualize and go through the sources of such a large data set. The importance of contextualizing data sets was talked about quite a bit in the articles for this week; while I understand why it is important and, theoretically, how to provide context, it seems as if it may be much easier said than done. This makes me wonder, in the case of a project like yours, would it even be possible to provide context to all of the sources in the way that many of this weeks articles talked about? it seems to me as if this would undermine the efficiency of working with datasets rather than taking a humanist approach to data analysis.

This point about where to draw the line between data and artifacts is interesting considering that digital humanities is built upon the very concept of turning artifacts into data. This connects to the concepts in the article by Trevor Owens about the various properties of data. If we view data as an artifact which can also serve as a source of evidence, then we can preserve the integrity and multifaceted nature of the dataset while still using it so serve the purpose of providing a specific source of numerical evidence. It seems to me that this idea is very important to the digital humanities considering the susceptibility to losing the integrity and humanist nature of original data sources when viewing them as sources for discrete data sets.

I like this idea of analyzing viewership over time! Taking advantage of the digitization of documents through organizing the documents using various categories as well as keyword searches has been a focus of many of our readings. This idea that we could further take advantage of the ability of technology to track viewership and user interest is something I hadn't yet thought about. Furthermore, collecting data for how long viewers spend looking at any given article could have implications for user interest and whether or not users find the contend of the articles engaging or appealing to them.

This idea reminded me of some of the digital humanities projects I looked at last week. For both of the projects I analyzed, there was a diverse array of ways in which the material could be organized. The debate mentioned in this article of whether or not respect des fonds is a logical or effective approach to data organization is arguably unnecessary in the context of the digital humanities. A platform for data presentation can include tools that organize the data in ways that comply and fail to comply with this principle. However, as this article mentions, the user interface of the website is a crucial part of digital data presentation, and if the platform is not user friendly, the user will be unable to reap the unique benefits of the digital platform.

This is a really interesting point. When I think about aging, I tend to think that the physical effects of getting older and the way relationships change as a person gets older are relatively uniform between all people. However, i realize that this is a result of my own bias and perhaps because I have not been exposed to a vast or diverse array of aging people. This idea made me think about how access to healthcare, the ability to actually retire, and a persons ability to maintain relationships will vary significantly based on their socioeconomic status. This project would be a very powerful tool in bringing awareness to the problems faced by older people who are inherently disadvantaged as a result of their physical decline