We have been discussing different aspects and stages of digital scholarship projects. In this session we will discuss how you can find and assess tools.
Read the following post.
- We will use this a a frame for our discussion
- eternal september of the digital humanities by Bethany Nowviskie
- How does the author discuss communities of practice in DH? How have they changed for her? What is your experience with them? What do you think about the ‘eternal September’ metaphor? What does it mean to practice as digital humanist?
- Digital Humanities and data
- Acquiring, cleaning, and creating data
- Identifying the differences between unstructured, semi-structured, and structured data.
- Distinguishing between different file types, their definitions, and applications.
- Applying intellectual property rights to the downloading and sharing of data.
- Practicing different ways of downloading or creating data.
- The Command Line (Python)
- Working with Tools (openrefine, Voyant)
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Where are you in the process?
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Your goals and your constraints (limitations of time, money, expertise, etc.) will dictate your choices (topics, training, partners, tools, programming, etc.)
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In this example from Programming Historian, you can see how lessons fall into broad phases or categories.
What you will find in this Repository
Choose a Tool: Geospatial data
Session Leaders: Rafia Mirza & Jonathan McMichael
- Written by Rafia Mirza. Edited by Joanna Russell Bliss
Our curriculum is based on the Digital Research Institute (DHRI) Curriculum by Graduate Center Digital Initiatives.
This repository contains information for using and contributing to the Digital Humanities Research Institute curriculum
Digital Research Institute (DRI) Curriculum by Graduate Center Digital Initiatives is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Based on a work at https://github.com/DHRI-Curriculum. When sharing this material or derivative works, preserve this paragraph, changing only the title of the derivative work, or provide comparable attribution.