FIRST ATTEMPT: Network Analysis
*note: after working with this CSV data, I determined it was too messy to clean with even the powerful tools of OpenRefine and would require too much more manual labor than it was worth. SO my actual midterm project is below, but I left my work from this network attempt *
After perusing the data options, I decided to look into the X-Men CSV files. I am a fan of comics and the superhero genre. Although I am not an X-Men fan, I was excited to analyze some of the inter-character relationships. The dataset I picked focused on these interactions, identifying some weirdly particular interactions such as which other characters someone might have kissed, hugged, or even slept in the same bed as…
This particular dataset took a lot of cleaning. The goal of the cleaning was to format the data properly so that I could use Gephi’s network analysis software properly on it. As I soon realized, this data is very poorly made. Trying to clean it enough to use in a meaningful way was a major undertaking.
The main format for importing csv’s into Gephi depends of whether the data is for nodes or edges.
- For nodes, the only required column is an id column. This id column indicates what the node is and how it will connect to the edge data. An additional column that is useful is the label column, which as you can guess provides the visual labels for the nodes.
- For edges, the required columns are a source and a target column. The source is the connection to the nodes and should have the same values as the id column from the nodes data. The target is the which other nodes this source node connects to.
- Note: other columns providing additional information are allowed and provide a deeper analysis.
To make the nodes dataset, I took the original data and cut off all edge-related variables (this person does this with other people). I formatted the data so that there was only 1 character per row (so 23 characters/rows) for the nodes data, and then a row for each character relationship in the edge data. This became tricky with the edge data as the source and target names were not written the same and so new nodes were created for each of those relationships.
After all this data cleaning, I tried to use the data for network analysis in Gephi. However, I realized that the difference in names between the source and target was a bigger issue than I had realized. It was very widespread too and not a task that I could fix automatically. The names were similar but different enough that faceting/clustering would not combine them. It would take manual labor that was not worth my time to try and format these names correctly. I, therefore, decided I would rather spend my time implementing a 3D render of the buddha preaching object from the British Museum
ATTEMPT 2: Buddha Preaching 3D Model
Introduction
For my midterm mini-project, I ended up creating a 3D model of a Buddha preaching object. My goal was to model the Buddha preaching object using photogrammetry to visualize the object online. The process can be done in Recap Photo, and the result was a success 3D rasterization of the object that can be easily orbited to provide a realistic online depiction of the object.
Sources
The object is from the British Museum and photos/models were taken by Daniel Pett (dpett@britishmuseum.org) for use in the 3D recreation of a British Museum object.
Processes
I used Autodesk recap photo in combination with the Autodesk desktop connector to process the provided photos. Once the model was created, I used the slicing feature on Recap photo to cleave off the artwork attached to the back, which was a part of a different piece. I then also shaved the sides of the Buddha preaching object and exported it as OBJ to embed on a blog post.
Presentation
The process for constructing and displaying this model is thoroughly documented in this page and displayed in the midterm blog post. Having saved the file to this page, I can embed it into my blog post.
Significance
By choosing to display the Buddha preaching object this way, readers can get an understanding of the object from anywhere, anytime. This type of digital approach allows the object to live on forever and be more appreciated in the modern world.
