Jeremy Boggs jeremyboggs

Composting DH Links

In the process of doing this review, I personally began to see benefits in possible shifts for how we come to terms with—and reconsider terms for—brokenness, rot, and decay in Digital Humanities scholarship specifically but also in human creations more broadly.

Method.

Download the Simple CSV data available at the Index of Digital Humanites Conferences.
Create a set of Python scripts to complete a few tasks:
1. Parse the conference works for any potential URLs mentioned.
First pass over the data relies on the URLExtract library to find addresses.

Wikis As/For Archives

Blog Posts

"Archival Wikis: In Search of a Digital Community for Archive Users"
"Archives-wiki Part One: A Proposal" and "Archives-wiki Part Two: How It Will Work" detail some thoughts and processes on the (now-defunct) Archives Wiki devleoped by the American Historical Association. Other related content in their Perspectives on History archive
Community Archiving Workshop: About provides resources for working with communities to archive materials.
[RoPA: Roadmap for Participatory Archiving](https:/

Text Analysis + Machine Learning Resources

Recommended Getting Started

Brandon Walsh and Sarah Horowitz, Introduction to Text Analysis: A Coursebook
NLTK Book

Next Steps, More In-Depth Learning

Programming Historian: Distant Reading Lessons Index includes lessons on specific methods, including stylometrics, sentiment analysis, and topic modeling.

Game design platforms

Game Suggestions

title	author	layout
TEI Resources	jeremy-boggs	page

TEI is a XML-based approach for encoding texts in machine-readable ways for humanities and social sciences applications.

Learning Resources

DH Portfolio Workshop

Books and Such

Chapter Two: Meet the Elements in Elements of User Experience by Jesse James Garrett.
The Shape of Design by Frank Chimero
Visualizing Information for Advocacy

	/* latin */
	@font-face {
	font-family: 'Nanum Pen Script';
	font-style: normal;
	font-weight: 400;
	font-display: swap;
	src: url(https://fonts.gstatic.com/s/nanumpenscript/v25/daaDSSYiLGqEal3MvdA_FOL_3FkN6zn0aeNBYTQ.woff2) format('woff2');
	unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
	}

	import json
	import csv

	# Identify the JSONL fields we wish to retrieve and write
	# to the CSV file.
	fields = ['id','title','text','url']

	# Open a new CSV file and open the existing JSONL file.
	with open('pages.csv', 'w') as csv_file, open("pages.jsonl", "r") as json_file:
	# Create a CSV writer object.

	import requests
	from bs4 import BeautifulSoup

	headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:121.0) Gecko/20100101 Firefox/121.0'}
	page_url = 'https://example.com'
	page_text = requests.get(page_url,headers=headers).text
	soup = BeautifulSoup(feed_text);
	print(soup);