The article “Mortality in the North Dublin Union during the Great Famine” by Timothy Guinnane and Cormac O Grada brings up some interesting points. The article is focused on mortality rates in workhouses in Northern Ireland during the Great Famine. One thing that I noticed in the article is that they only used information form a single workhouse. I don’t believe this is an accurate way to represent mortality rates or an accurate way for people to study this part of history. Using only one workhouse is a very small sample size and there could have been variables at that specific workhouse which weren’t taken into account. The researchers also could have been biased, considering that the topic is so important, and trying to push for change. My classmate JN made a similar point when they said “no data is immune to outside conditions.” The public is not generally involved in the processes of these studies, so how can people blindly trust the information that they provide?

I found this piece on psychohistory by Isaac Asimov to be very intriguing, but at the same time somewhat confusing. Asimov defines psychohistory to be a branch of mathematics which “deals with the reactions of human conglomerates to fixed social and economic stimuli”, but also says that this is accomplished using non-mathematical methods and concepts. This left me confused as to how psychohistory can be a branch of mathematics which doesn’t use mathematical concepts, but Asimov does not explain this much further. I also don’t believe that the story-like writing style of this piece is an effective way to help the readers understand psychohistory. With no background knowledge of what the two people are talking about, it’s hard to follow the conversation completely. But, overall the piece is interesting and opens many interesting questions, such as how much should the public know in regards to the use of psychohistory. The student J-OS said that “the social issues we try to solve are never answered through one set of mathematical data, but rather through the relations and trends between multiple data sets recorded by our society.” I think this is a good way to look at psychohistory and to gain a better understanding of it.

This article by The Future of Research Communications and e-Scholarship discusses challenges and guidelines to creating and using fair data which is accessible and usable by different groups of people. The article lays out a general guideline  using the acronym FAIR: Findable, Accessible, Interoperable and Re-usable. These facets are then explained in deeper detail in the article, allowing people who are not very educated on the use of data to better understand what these guidelines mean and how they are connected. In RC’s response to this article, they said “if all data work/collection follows universal standards it would greatly increase ease of use for every part involved.” The student explained the overall objective of this article and these guidelines very well. Although this quote is much easier said than done, this article and thinking like this are a start.

This reading consists of a thread on Twitter regarding safety and use of data through social medias by John Abowd, the chief scientist of the US Census Bureau. Abowd discusses how data that is made public may not be as safe as it used to be or as many people believe it to be. If too much data is released, there is the possibility of database reconstruction, meaning people or a program could use the data to identify individuals whose names are not meant to be public knowledge. This could be harmful to many individuals who believed that their information was safe and also brings up the question of how much control people have over their data. I like that Abowd is very honest and explains things in a way which are understandable to people with little knowledge on the subject. In a response by JH, the student discusses the positives and negatives of social media data being used by researchers. Overall, I agree with their statements and believe that there is a lot which needs to be changed in regards to social media and these changes should be a larger topic of conversation.  

The thing I liked the most about this reading that it investigates a popular notion : “‘10% rule’ to predict the popularity of Hollywood titles in Australia, expecting American films to earn around one-tenth of their domestic box office receipts”. I think this sort of research is very important, because we should have scientific support behind claims that might impact how millions of dollars are distributed. I often ask myself what my fellow classmate did: Is this study even relevant? .  Considering the economic implications, here we can firmly say, yes, it is relevant. I also really like the idea that you take data that’s sort of floating out there and you bring it together to say something new. 

I think this was my favorite reading this semester. I find it super interesting to think about how we can use social media data for research and how it has been used. I think this article does a great job of giving a matter-of-fact run down of the benefits and dangers of using social media data, without glorifying it or demonizing it. Another great thing that it’s an easy to understand reading, and as my classmate PE noted: “data should be findable, accessible, interoperable and reusable to all in order for it to be considered fair data”. 

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.

During an age when data is becoming generate as an increasing rate, standard practices of using this data need to be implemented to ensure the usability of this data. This paper explains the eScience ecosystem and the challenge behind regulating such a vast field with so many players. Creating free and usable data is key behind a robust research community.

User, ‘pippinev’, points out the meaning behind this paper in the following quote. ” It is important to note that this document is a general ‘guide to FAIRness of data’, not a ‘specification'”. This paper simply points out expect practices of creating and sharing data. FAIR stands for the, “Findable, Accessible, Interoperable and Re-usable” methods of data. There remains much debate as to the best practices to share and maintain data, but there are some key underlying beliefs. But, “the methods to access and/or download [data] should be well described and preferably fully automated and using well established protocols.” When we look at dataset of small size, such as ones used in class with 100 rows or less this remains less important. Yet, when we collect and share public dataset of millions of rows that can be used for public benefit, it becomes important to create an MLA style process for sharing this data. We all look forward to discovering the incites of big data, assuming we can analyze it! #FAIR

The core debate behind this paper is whether scholarly work published in print or online holds the same significance in the academic community. During a rapid period of digital change taking place within our world, certain areas of change experience moral debate as they shift. Another issue present in this article is how analysis taking place compares to conventional analysis. It is noted that people try and define a “computer” and thus, all following work either created on that computer or published in digital form compares to paper copy. 

Since no users have commented on this article, I will expand by analysis on the following quote, “the question, rather, is whether the manipulation of features, objects, and states of interest using the language of coding or programming (however abstracted by graphical systems) constitutes theorizing. The main debate here mentions the assistance of tools a computer gives a user, and thus not as important as free-hand writing. Questioning the difference between coding and writing language as the same act, even more important, do they hold the same level of significance in the academic community. Beyond the specifics of this article is to mention the amazing debate that occurs when an industry becomes shocked by innovation.

This paper comments on the structure of the political system, specifically the relationship between minority groups and their need to increase their limited political experience. When a minority candidate looks to run for office, the need the support of a major  political party or various other groups to, “obtain the political resources needed to win a share of urban power”. User ‘pippinev’ finds this above quote important as it further explains the power dynamics within out political system. We would hope that any person who wishes to fun for office has ample resources to demonstrate their ideas on a national scale. This however clearly remains untrue. 

Within the introduction, we find that geography is a key driver behind the relationship minority public service members hold with their communities. We look at the factors, “macro-level processes that influence a city of region”. The expected outcomes of minority candidates is driven mainly by the geographic region they live within. When the number of immigrants is high in that region, the expected change of winning the vote will increase, and therefore decrease the dependance a minority candidate needs from major party leaders. 

Multiple spacial mapping techniques are used to demonstrate these outcomes and offer additional incite about projected political outcomes in the future. This paper takes a very specific method to defining the relationship all candidates hold with their district. The key lesson from this paper is to see the meaningful information we can derive from seemingly soft topics such as politics by using hard computer analysis.