I used the visualization we created in class. This allowed me to follow along so that the visualization turns out as well as possible. The data allowed us to see the authors and the amount of citations the author and co authors had. I choose to present the data in a network graph because it is easy to clusters of citations and easily grasp the data on a first glance.
https://app.flourish.studio/visualisation/11444083/edit
I’ve been very interested in the age of local churches in Tuscaloosa. So I went about looking into the age of churches in town. I originally thought that the information would be readily accessible. This was not the case. I actually ran into an Omeka project that was trying/ had tried to do what I am doing! Their information seems to be coming out of old Tuscaloosa newspapers. My information was being pulled out of their stuff and a database of historical markers, which had the inscription on them. I think this communicates the oldest churches in town, and in my very cursory research i discovered some interesting threads that could lend themselves to more data, perhaps an affiliation chart. I used an assignment that displayed time because I thought that most relevant to what I was looking at.
Mukurtu seems to be a very useful site, similar to Omeka. Mukurtu does have some unique feature such as:
Mukurtu is a site aiming to empower communities to manage, share, narrate, and exchange their digital heritage in culturally relevant and ethically-minded ways. This is different from Omeka in that Omeka is open to communities such as religious studies. Mukurtu seems to be very good at the specific topics they aim to share about. Omeka might be a better platform if you are considering doing something not related to culture and ethnicity. However, if you are planning on presenting data about what Mukurtu is aimed at, then it will be a better site to use.
The fields that the two platforms share in function are these. If they call it something else I’ve noted that.
Title, Description (called Summary), Creator, Source, Publisher, Date (original is added), Contributor (called Communities and protocols), Rights (called Item sharing settings), Relation (called category), Format, Language, Type, identifier, Coverage.
The Omeka model is very broad while the Mukurtu model is tailored specifically to cultural objects. The exclusive labels were reflective of this.
Cultural narrative, Traditional knowledge, Traditional Knowledge labels, People, Transcription, Geocode Address, Latitude, Longitude, Location Description, Collections
Mukurtu has a ton of additional metadata that Omeka does not. For example Mukurtu includes metadata fields like: Media Assets, Cultural Narrative, Traditional Knowledge, and Communities and Protocols. This appears to me as though Mukurtu is a much more specialized system than Omeka, or rather that it was designed specifically for this purpose.
It seems to me that Omeka is supposed to be a relatively barebones system designed for projects focused on large scale projects where the details of community aren’t as strongly expressed. The Omeka model for gathering data appears also to be focused mainly on the collection of various texts rather than media as a whole as is demonstrated in the Mukurtu model. Moreover, Mukurtu demonstrates a bias towards a specific community and as such their metadata is set up to expose the intricacies of that community.
Looking forward I am particularly interested in biblical analysis, into my career and in this class. I am also interested in Christian history, especially in the south. It might be cool to take a look at records of when and where churches in Tuscaloosa were founded.
Early schema might include founding year, guiding bible verses, and congregation size.
Given what we’ve been going over in class so far, I think that I’d like to look into a project having to do with text analysis. Moreover, I’d like to look at antisemitic texts and newspapers published during the time of the Holocaust and then in the same way apple does, create a map that allows the viewer to see where in the world the texts originate from.
To work through this project I’ll need to gather data from what I can only assume are archival collections. I think a good place to start would be with the Holocaust commissions around the United States.
For the final project, I am interested in creating a distribution map that shows the major religions throughout the country. I would make a point distribution to see where religion is clustered throughout the country and see obscurities like religion is low population areas. I am interested to see this because it could tell you a lot about culture of a certain area. You could also tell a lot about the types of people that live in certain areas. I would have to research places of worship in large cities through America. For example I would research the amount of Jewish temples in California place a dot at each one. If I do this throughout the country, it could tell an interesting story about religion in the United States.
For lab 2, I choose to take a look at “The Delek Achives”. This project is a research initiative by the Delek Education Foundation that intends to identity and shine light on religion-based discrimination in India’s schools. This project is different from most by the way it uses real first hand accounts of discrimination rather than data from surveys. Most stories from the project are written by people who are directly associated with the education system in India. The posts are usually concerned with deeply traumatic experiences that come from teachers, students, administration, and even parents. The hope of the Delek Archives is to reimagine the act of archiving as an open collaboration. The project aims to demonstrate that discrimination is just as much a personal narrative as it is a systemic pattern.
I like the approach this project takes. It uses a story to shine light on discrimination rather than relying on data to tell how horrible something like discrimination is. I think people respond much stronger to a personal anecdote than to data. To me, this project is successful and more projects dealing with emotional matter should consider a personal narrative approach.
I choose to highlight this exercise because it demonstrates how important time when dealing with data. When time is not important, the spiral is very loose and with a lot of space in between. When time is the most important component, the closeness becomes very precise and crucial.
For the schema, I would look at how much time was spent actively driving vs how much time the car was parked. This would be fairly straight forward as I would go down the list of times and note when the car was a rest stop, getting food, getting gas, etc. I would then total that time and the time spent driving which is indicated by the long stents in between stops. For the data diaries, I will be doing “How many variations to a line”, “Draw your breathe”, and “Color palette testing”.