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Creating text-based datasets

Assuming we don't start with an existing primary source or a dataset we would like to explore there are many ways in which we can create digital text resources and text-based datasets.

  • scanning / OCR
  • API
  • web scraping
  • text mining

APIs: Application Programming Interfaces

A computer interface that defines that defines the types of data retrieval calls or requests that user can make and the conventions that authenticated users need to follow.

Example: NYPL API

http://api.repo.nypl.org/

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Web scraping

a series of tasks, usually as part of workflow, to create an original dataset by copying, with the help of software tools and automated processes certain elements and blocks of information from websites and web-based resources, into structured file formats that are ready for computational analysis.

Text mining

The automatic extraction of information from different textual resources with the purpose of revealing patterns, dimensions and relations through computational analysis.

Example: Overview

Overview is a document-mining platform originally developed with investigative journalism in mind.

Data sources and data discovery tools

Google's Ngram Viwers

https://books.google.com/ngrams

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Hathi Trust Digital Library Bookworm

https://bookworm.htrc.illinois.edu/develop/

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Jstor's Text Analyzer

https://www.jstor.org/analyze/

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Internet Archive's Ebooks and Texts

https://archive.org/details/texts

Project Gutenberg

https://www.gutenberg.org/

Specialized data sources and online corpora

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![[DALL·E 2023-03-14 11.22.51 - A university course poster in the style of synthwave.png]] <font style="color:#2454FF"> With and against a note on approaches to A.I.</font> ![[Screenshot 2023-04-05 152941.png]] shifts (scale, speed, access) hard to ignore critical AI established research field and ethical issues with the use of corporate generative AI teachers and pedagogy still at the core

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Jul 10, 2022
Acknowledgements

The NEH CARES DH workshop sessions and accompanying materials have been made possible in part by a major grant from the National Endowment for the Humanities: NEH CARES. Any views, findings, conclusions, or recommendations expressed in this workshop do not necessarily represent those of the National Endowment for the Humanities. Certain materials and resources in the workshop and in this companion have been inspired by the work (definitions of terms, code, tutorials, projects) of several scholars and developers working in the field of text analysis and publishing in digital humanities. A full list can be found in "References" and in each section of this document. Here are some key projects that turned out to be a great source of information and inspiration: Multilingual NLP Resources by Quinn Dombrowski: https://github.com/multilingual-dh/nlp-resources The GO:DH Minimal Computing group

Dec 9, 2020
Text (as) data: digital tools and methods for text analysis

This document is a companion to the NEH CARES DH Workshops "Text (as) Data: Digital Tools and Methods for Text Analysis". It is meant as a live, collaborative, notes document that will provide context, present examples, and key concepts in digital tools and methods for creating, compiling, and analyzing texts. Instructor: Dimitris C. Papadopoulos, NEH CARES DH Instructional Technologist, Division of Humanities and the Arts, the City College of New York *The NEH CARES DH workshop sessions and accompanying materials have been made possible in part by a major grant from the National Endowment for the Humanities: NEH CARES Any views, findings, conclusions, or recommendations expressed in this workshop do not necessarily represent those of the National Endowment for the Humanities.

Nov 10, 2020
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