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Digital Humanities at Waterloo
Want to learn more about Digital Humanities?
Visit the Library's guide on Digital Humanities for more information!
Library Resources
Statistical Modeling and Analysis for Database Marketing by ISBN: 1439860912Publication Date: 2011-12-19
Online Tutorial Courses
Linkedin Learning
University of Waterloo faculty, staff and students can access web-based training from LinkedIn Learning using their WatIAM credentials.
Over 13,000 online courses to help you learn software, creative, IT, and business skills.
Watch from your computer, tablet, or mobile device. Switch back and forth as you choose.
- Data Analytics Specialist - Learning PathThis Linkedin Learning path offers a curation of courses and materials which provide a foundational understanding of statistics, data mining, predictive analytics, big data and meta-analysis. 14 hours, 19 minutes.
- Data Scientist Learning Path - Linked InThis learning path delivers a curation of courses and videos to build foundational and applicable skills in data science, including data mining and interpretation. 17 hours.
- Foundations of Data Mining - Linkedin LearningThis course provides a foundational overview on the use and implementation of text and data mining tools. 4 hour, 40 minutes.
- R for Data Mining - Linkedin LearningThis Linkedin Learning course delves into using R for data mining. 3 hours and 51 minutes.
Networking
There are many ways to find out about research in humanities computing or digital humanities. You can search on social media with the #digitalhumanities. Below are some links that will help you to learn more about projects, scholars and work being conducted in the digital humanities.
- Association for Computers in the Humanities
- Canadian Association for Research Computing in the Arts
- Canadian Society for Digital Humanities
- CenterNet: An International Network of Digital Humanities Centres
- Dan Choen's Digital Humanities Blog
- DARIAH- Digital Reseach Infrastructure for Arts and Humanities
- dh+lib
- Digital Humanities Now
- European Association for Digital Humanities
- HASTAC (Humanities, Arts, Science, and Technology Alliance and Collaboratory
- Humanities Computing Media Centre
- Theoretia.ca (Geoffrey Rockwell's Blog)
Further Reading
- Digital Humanities Data CurationA resource for data curation in the digital humanities.
- Programming Historian: LessonsTutorials on a range of text analysis tools and techniques including web scraping, cleaning and transforming data, sentiment analysis, stylometry, basic text processing in R, text mining in Python, and topic modeling with Mallet.
- Text Encoding InitiativeThe Text Encoding Initiative (TEI) is a consortium which collectively develops and maintains a standard for the representation of texts in digital form
- Topic Modeling: A Basic IntroductionA succinct introduction to basic concepts and tools for topic modeling, with links to further resources.
- Using AntConc to Analyze TextHelpful guide on using AntConc from Manhattan College.
- Value and Benefit of Text MiningProviding a UK perspective, this Jisc resource outlines the the costs, benefits, barriers and risks of TDM, and its impact on science, art and economy.
- What is Text and Data Mining?OpenEdition Books provides an excellent overview of TDM in this white paper from a French perspective.
Acknowledgements
Guide created and prepared using adaptations, inclusions, or references from the following:
Boston College Libraries: Text and Data Mining
Illinois University Library: Text Mining Tools & Methods
Northeastern University NULab: Text Analysis Resources
Penn State University Libraries: Text Analysis
University of Alberta: Digital Humanities
University of Alberta: Text Mining
University of North Carolina at Chapel Hill: Text and Data Mining
University of Queensland (Australia): Text Mining & Text Analysis
University of Toronto: Text and Data Mining Tools Overview
Wellesley College: Text Analysis & Text Mining
Ahmed, W., Bath, P. and Demartini, G. (2017) Chapter 4 Using Twitter as a Data Source: An Overview of Ethical, Legal, and Methodological Challenges. In: Woodfield, K., (ed.) The Ethics of Online Research. Advances in Research Ethics and Integrity (2). Emerald , pp. 79-107. ISBN 978-1-78714-486-6
Beninger, K., Fry, A., Jago, N. (2014). Research Using Social Media: Users Views. London: NatCen.
Deegan, J. (2020). The ethics of using Twitter data for research. Retrieved from https://jasondeegan.com/twitter-data-ethics/jason/
Dicks, B, Mason, B, Williams, M.L. (2006). Ethnography and data reuse: Issues of context and hypertext. Methodological Innovations 1(2): 33–46.
Shermer, M. (2008). Patternicity: Finding meaningful patterns in meaningless noise. Retrieved from https://www.scientificamerican.com/article/patternicity-finding-meaningful-patterns/
Van Dijck, J. (2013). The Culture of Connectivity. Oxford: Oxford University Press.
Williams, M. L., Burnap, P., & Sloan, L. (2017). Towards an ethical framework for publishing Twitter data in social research: Taking into account users’ views, online context and algorithmic estimation. Sociology, 51(6), 1149-1168.