This Digital Humanities Quarterly article discusses probabilistic text mining as a way to learn about social interactions from Britain in the early-modern period—around the 16 th and 17 th century. Researchers developed a model (or matrix) that creates a map of relationships between individuals that are
referred to in scholarly texts. This is interesting because it lies at the intersection of computer
science and the humanities. In the fifth section, “Humanities Significance,” it is noted that
while humanists care about documentary evidence of connections between people, they would need to do such an unbelievable amount of research to develop a map like the one this model produces. Thus, while this is not perfect, it can definitely be used to get a sense of social networks. And moving forward, the opportunities are profound. Models like this can be used to study social networks in other places—all that is needed “is machine-readable text in which the concurrence of names is a reasonable indicator
of connections between persons.” It is truly amazing how groups can identify source texts that can be used as evidence of historical relationships to then serve as the material for network analysis. We see in the reading how marriage certificates, archival letters—any type of document that historians could use to link individuals together are used. For our work in DCS 104, the relationships among mill workers, their families, and their community can be used by looking at address data.

Despite the modern world becoming more and more digital, text is still paramount in communicating in every facet of life. Visualizations are increasingly taking the place of text because they are a more efficient and appealing way of displaying information. We can see how groups like Wordle and the New York Times use word clouds because they easily visually show the difference in words used when talking about one variable versus another, for example boys and girls. Interactive visualizations are also becoming increasingly popular, where viewers can not only see the visual but they can manipulate it to gain more specific information on a particular topic, which provides the viewer with an opportunity to gain more knowledge than before. Digital texts contain specific units of information that can be altered and shifted as desired through designed algorithms. There are also many different kinds of text files, some more complex than others. The continually expanding software and ideas in the realm of text analysis is always opening new doors for different applications, and we can see how the possibilities never seem limited.

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.

I thought the article from Force 11 for today was very interesting and it seems like the group working on this idea did an in-depth, impressive job coming up with these standards for data on the internet. The FAIR acronym they came up with was very clear and well-done, giving specifics for each part and made me realize the importance of having legitimate data on the internet. I also was intrigued by the “candidate FAIRport” definition that they came up with: 1. Contains FAIR Data Objects (to be judged by the endorsing authority), 2. Provides these Data Objects under well defined accessibility for Re-use. 3. Has a full and open description of all technologies, controlled vocabularies and formats used. All of the aspects of the data being “FAIR” as they describe come with a lot of complication and important side notes that were interesting and at times confusing to read about. My classmate “TB” said that they thought metadata subjectivity could have used more discussion, and I agree with this. I hadn’t thought this before reading this post, but TB said “For things that are objective such as author and character count, other metadata could be less objective”, which makes me agree that there could have been more discussion and explanation for that part of the article.  

Today’s article on the FAIR principles of data use gave four broad methods of effectively producing fair data and further explained these ways. It tells us that data should aim to be   Find able, Accessible, Interoperable and Reusable.  It is, however, slightly difficult to this achievement as there is no universal storage of data that ensure that all data recorded is done with unique, non repeated identifiers and are done in language language and format that is accessible to all. Also, technology advances at different velocities in different parts of the world and advanced techniques that make data FAIR in some areas could also be making it harder to understand or translate for those in other areas.

Data can be one of the most useful and critical attributes to our society yet can only be helpful and supportive if it is understood  and applied correctly. Prior to reading this article I did not really think about the importance of once Data is collected and obtain what is the “next” step in order of this data to be valid or even valuable. The Acronym “Fair” revolving about data is very useful because it simply states the characteristics Data should reflect. If data is not findable, accessible, interoperable, and Reusable then it should be noted that this data should not be used because it most likely won’t help support what point one is trying to validate. My colleague who wrote The “R” in FAIR made a remark that I also agree with the concept of “Reusable” data. What makes data so necessary is that it can be used in various situations and support different types of viewpoints. This blog also points out that data can used over and over again and never goes away which is important to remember when dealing with data. 

Looking back at the posts from last week, I really enjoyed reading the post “The Nature of Scholarship.”  The author brought up many interesting points.  Specifically,  the author states that semantics in this topic.  Sometimes, when we say scholarship, we might mean different things than other person who also says scholarship.  And, that the article was general at some points.  With that said, the author acknowledges the article brings up some interesting topics, but lacks  execution.

For this week’s reading, I think it did an OK job of condensing what we have been talking about surrounding the ethics of data in class.  At times, I found it a little hard to follow and the layout of the web page was not very helpful.  However, it did do a good job of giving scenarios as to why data should be findable, accessible, interoperable, and reusable.

KS in their post on the FAIR data article published an image of a recycling sign with the words “data” in it, accompanied with the caption “data should be more like recycling”. I thought that this comparison was powerful, because in fact in the acronym FAIR, the R stands for reusable which is also part of the Rs of recycling (Reduce, reuse, recycle). I therefore thought that I would compare the other facets of FAIR to those of recycling.

For “Findable”, I think is the most difficult because it is quite specific to data and individuals don’t go looking for waste. Nonetheless, individuals do look for ways to recycle. Which brings us to “Accessible”, having the ability to access data easily for everyone is important, just as it is important for recycling to be available for individuals because it has to be a combined effort if one wants to see significant change. As for interoperable, the way this would fit into recycling is that recycling efforts should be operating alongside other efforts to reduce one’s carbon footprint to be the most effective it can be.

In looking into the FAIR principles, I found a summary video that uses good examples, and highlights interesting point. One in particular and that we’ve discussed many times in class and that is what the future of data will look like. There has been increasingly amounts of “fragmented patches of information that cannot talk to each other”, which has become a problem because the growth in information is therefore not effectively translating into increased knowledge. Setting the FAIR principle has therefore become more imperative than ever. Just as it has become imperative to coordinate our efforts towards reducing human waste and excess pollution. A standard of stewardship needs to be applied to control the growth of data and the direction in which it is going that will benefit everyone, as it is necessary to have stewardship over climate change issues that will help reduce carbon emissions.

Link to video: https://www.youtube.com/watch?v=K40utIzUzOk

(this is one of my “other format” responses)

Data should be more like recycling.  Recycling is FAIR!