What does this tool do? Finds images in video files. You provide image(s) and video(s) and this tool will create an Excel file visually showing the potential matches, the confidence of the match, and the timestamp in the video. How
Fundamentals of Data Research (FDR) Fellows 2021 Come join us on September 10 for this year’s Data Research Fellow presentations! Managed by the Library’s Data & Digital Scholarship program, fifteen fellows (ten faculty, five graduate students) learned how to integrate
“Mapping the Geographic Attention of Waco-Area Residents from 1916-1918” Presentation as part of the Texas Map Society Spring 2021 Meeting To understand which parts of the world attracted the attention of Waco-area residents leading up to the U.S. involvement in
Materials to accompany the ER&L 2021 workshop ‘Learn to Text Data Mine Using Jupyter Notebooks on Google Colab’ Presenter: Joshua Been, Baylor University Libraries Materials: View Workshop Materials on GitHub About: This asynchronous and self-paced workshop is organized into 4
Data Visualization with the HTRC’s Bookworm Tool by Eileen Bentsen, Librarian – English, History, Honors College, & Medical Humanities On October 26, 1921, President Harding gave a speech in Alabama condemning lynching. While the speech, by today’s standards, would be
DVotW (Data Viz of the Week) #6 Published: Monday, September 27, 2020 Title: Text Data Mining Exploration of Works About Karl Barth Contributors: Bill Hair, Joshua Been Data: Gale Digital Scholar Lab (Baylor Only)
DVotW (Data Viz of the Week) #3 Published: Monday, September 7, 2020 Title: Women’s Suffrage in the United Kingdom Contributors: Eileen Bentsen, Joshua Been Data: Gale Digital Scholar Lab (Baylor Only)
https://josh-been.github.io/Freedom-Narratives-Deuteronomy/ Dr. Steve Reid (Truett Seminary) and Josh Been just presented a paper outlining the results of approximately two years of text data mining research into understanding the African American response to the Book of Deuteronomy in the late 19th
Description: All Presidential State of the Union Addresses from 1790-2006 organized by similarity of terms within the text and colors showing 4 communities, or classes, of Addresses. This is an example of applying social network analysis to text similarity. It
Holding a hands-on workshop at SMU in a couple of weeks. Have a collection of digitized books, music lyrics, letters, or articles, and want to visualize a map of all locations mentioned in the texts? A combination of Python libraries,
