Artist in the Archive: MARC Madness

I got a wee bit lost on this assignment and jumped around from the many possible ways to satisfy its requirements. I initially started by modifying the ‘marc_template’ from Jer’s github to traverse through other sets of keywords. I played around with examining flowers and electrical appliances in the visual materials. One thing I immediately realized was that I was not familiar with MARC records well enough to traverse it as freely as I would have liked. I found this link in Jer’s code comments: https://folgerpedia.folger.edu/Interpreting_MARC_records#2xx, but nonetheless found it was necessary to do quite a bit of digging to pull fields that Jer hadn’t already pre-populated into his parser.

I then tried to play around with Jer’s network graph based on the name authority records. Again, ran into similar issues with MARC documentation – I eventually found a side that documented how to pull gender information from MARC records, but then promptly misplaced the site amongst my many tabs.

I decided to change tacks slightly by pursuing modifications on Jer’s network graph Glitch, but found Sigma.js’s documentation to be somewhat lacking. I decided my contribution for this week would be to clean up the json that Jer had used in his occupation graph. For this, I used a bit of python that I ran right from my Terminal. Code as follows:

import json

data = { "nodes": [ ... ] } # I copy/pasted Jer's JSON into this variable
data_list = data["nodes"]

filtered_data = [node for node in data_list if node['label']]
with open('cleaned_occupations.json', 'w') as outfile:\
	json.dump(filtered_data, outfile

After a few minor edits to that output, (I added back in the { "nodes": and trailing }), I uploaded that to a gist on github, and adjusted Glitch network graph (forked from Jer, with a few styling adjustments).

It does not really look very different from what we had last week, but the data is cleaner. If you insist on seeing it: https://glitch.com/edit/#!/tender-pony?path=views/index.html:32:17

I tried to through this into Mike Bostock’s force-directed, but realized my data was structured differently than his (no explicit links in my set). A mess nonetheless: https://observablehq.com/d/1403340886c072e5