CD – Data Cultures

Posted on November 29, 2018 by CD

The “R” in FAIR

Warning: Undefined variable $num in /home/shroutdo/public_html/courses/wp-content/plugins/single-categories/single_categories.php on line 126

Warning: Undefined variable $posts_num in /home/shroutdo/public_html/courses/wp-content/plugins/single-categories/single_categories.php on line 127

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

Posted on November 13, 2018 by CD

A response to Native Cartography

The Native cartography video made me think a lot about how coming from different backgrounds we see things very differently. And often times these different backgrounds are not represented in our everyday world, in this case maps.

For this post I wanted to share a series of photographs by Edward Burtynsky which he has called “Anthropocene”. These photographs relate to Enote’s work in some ways as they too bring forward a new narrative, a narrative about the environmental poor. The aerial photographs he takes are mesmerising, because from a first look you’re not sure what you’re looking at. With a small description, you start to see the saws, the logs, and the phosphorous. These are places in our world that exist but not advertised because it hurts big corporation businesses. Sharing these images sparks conversation about pollution, similar to how Enote’s maps spark conversation about the Zuni people. These images recognises these places are vulnerable, and in danger.

I wonder if more images about the mills in Lewiston/Auburn are shared with the community will people spark more conversation . In some ways, the museum is already doing that by telling the history, giving the mills and the people who were there recognition.

Morenci Copper Mine

Logbooms, Canada

Saw Mills

Posted on November 1, 2018 by CD

Social Networks and 9/11

The article, Using Metadata to Find Paul Revere, was a very interesting article and somewhat an echo of the Laura Klein article. Discussing social networks and their ability to identify individuals linked to acts of terrorism with only the use of metadata, and without knowing any context.

This therefore led me to think about the 9/11 terrorist attacks and if any social networks had been done after the attacks, or during to identify those involved. I found that Valdis Krebs, a data scientist and developer of a network analysis software, created a social network of the attack while information of the attack was being made public. When the identity of the 19 hijackers had been made public, he began his social network analysis and visually mapped out their ties. In his research article he discusses the way in which network analysis is being used today is mostly in prosecution and not necessarily in prevention. He explains that uncovering criminal networks is often very difficult because of the nature of their network, in that they are covert network in which associations between those involved are made less apparent by the fact that there are not being activated or are activated through other means that won’t appear as strongly on the map.

So whilst I was persuaded from the Paul Revere reading that social networks analysis would be the easiest way to uncover terrorists plots. From what I gathered from Krebs research, social network analysis is not as simple as it sounds when dealing with illegal organisations.

Link to Krebs article: http://insna.org/PDF/Connections/v24/2001_I-3-7.pdf

Posted on October 30, 2018 by CD

Making silences visible

“A Report Has Come Here”

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, she creates an arc diagram to visualize the people with whom Jefferson corresponded with about Hemings. Klein discovers through this diagram a little bit more about Hemings’ life such as his suicide but concludes that “we realise just how little about the life of James Hemings we will ever truly know.”

In some ways, the work we’ve been doing with the Lewiston workers interviews is rendering the silences of these workers visible. Whilst, these interviews exist in the world, there is a need to go through them and analyze them, to uncover more information. The data currently exists, but nothing is being made of it in trying to understand the secrets of these workers and the factories they worked at. Through our final project I hope to illuminate the past and inform the present; by understanding how the local residents treated and perceived the French immigrants back then, to inform the present in regards to the treatment of immigrant African communities in L/A. Having a better understanding of the past, and more public/visible information about it, will perhaps help avoid the mistakes that were made.

An article that reinforced this idea of the importance of data from the past and how they can inform the present is “Truth and Reconciliation: Archivists as Reparations Activisits”, by Anna Robinson-Sweet. She explores the relationships that requires archivists to take on the role of reparations activists in the campaign for black reparations in the U.S. An article published this year, a lot more momentum is driving this movement of challenging scholars and archivists in the way they collect, understand and analyze knowledge of the past.

Posted on October 23, 2018 by CD

Meaning in Text

I found Jockers and Underwood’s discussion on how to find meaning in digital text very interesting. What particularly stroke me was the following quote that “no amount of counting can produce meaning”. Shedding light on the fact that although digital text can do a lot of things such as rearranging words and grouping them together; ultimately the power of interpretation and meaning still lies in the hands of the individual. Nonetheless, text analysis and visualization help us achieve that meaning, as it allows us to experiment with the representation, and isolate different factors that can highlight connections and relationships that we would have otherwise missed if we weren’t able to visualize the data.

The idea behind this reading strongly reminded me of the “Alien Reading”. PE expresses in their post on this reading that they found happiness in the fact that computers cannot so easily understand the written word, that it “takes more than a software to understand humans and their written thoughts”. I thought this was a beautiful takeaway from the reading, and a thought I strongly agree with. Computers more and more these days allow humans to shortcut a lot of things, except derive meaning; preserving human’s ability to think and be conscious, a characteristic that defines us in the animal world and makes humans.

Posted on October 11, 2018 by CD

Theodossiou: Unemployment and Mental Distress

In exploring the relationship between unemployment and mental distress, the reading highlighted some of the difficulties in collecting data on mental distress. They brought up many other variables that could play a role in explaining why unemployed individuals may be suffering from mental distress, such as their marital status. Because of this, it was hard for the researcher to determine the causal relationships and if mental distress was the effect of unemployment or vice versa. I think that the likelihood for reverse causation, and confounding variables are errors for causal inference that are at a much higher stake in observational and survey psychology experiments.

In relation to JN’s post, who asked “can any data be truly representative of the situation it’s attempting to model?”, I think this study would suggests no. It gives you, somewhat, an idea of the relationship explored in the research question, which I think the researcher explored in detail. Although, I still struggle with the fact that data can be representative of mental distress because it varies from person to person and is something so personal to the individual. I think the psychology is a difficult and tricky field in which outside variables can strongly influence data results.

Posted on October 4, 2018 by CD

Martell: Differences Do Not Matter

The Martell article “Differences Do Not Matter” highlights the struggles of collecting data for under-represented/discriminated groups. Something that was very crucial in collecting accurate representational data of homosexual men was the defining what a homosexual man is. An example they gave was avoiding misclassifying heterosexuals who have had same sex as gay man, and instead only look into data where individuals had self-identified themselves as gay. This reminds me of something that Michael Somkuti had pointed out in his “Psychohistory” post that spoke about modelers having to be conscious of the type of data and parameters they are using in order to produce sound outputs. I think this perfectly fits with this article and being conscious here is even more important than it would otherwise be, as you are dealing with a sexual identity. Data thus has to be consciously collected because the ramifications on the population that identify with this sexual identity are at stake.

Posted on October 2, 2018 by CD

Guest Editors’ Foreword: Perspectives on creating dataset

I thought that this article was interesting because it delved into the process of creating datasets to answer the following research topic: the influence of physical height to analyse the impact of long-term changes in nutrition and health on social and economic behaviour. I found it compelling because it was concerned with some ethical values on how to collect the data. The DAE program wants to collect data that also investigates intergenerationally linked families to account for the cultural factors that may influence the variables. Other values they want to consider is representation and time scale, to account for the different actors in the decision-making process of economic policies. I found that this article was very mindful of the different ways to approach the question, and consider the different views that could influence the outcome. Only reading the introduction, I would be even more interested in reading the obstacles they faced when collecting the data and how feasible it was.