Resources for Digital Humanities and Social Sciences

Annual Digital Humanities Summer Institute at the University of Victoria offers courses on research computing topics in humanities and social sciences. Some of DHSI courses each year are taught by our analysts and staff.

“3D visualization for the humanities”: ZIP file with slides, scripts, and data files

Table of Contents: “Photogrammetry on HPC clusters” • “Text analysis in 3D” • “Learning Regular Expressions with Smart Tools” • “Exploration of tools and approaches for Humanists” • “Tips & tools for mining Twitter data for research” • “Text parsing & matching with High Performance Computing resources” • “Getting started on the cloud” • “Tools for Handling Big Data & Computing Demands in Humanities & Social Science Research”

“Photogrammetry on HPC clusters”

Webinar (2024-Feb-20) with Alex Razoumov

The term photogrammetry typically refers to the process of constructing a 3D model by analyzing a series of photographs of the same subject captured from various angles. Widely employed in fields such as mapping, surveying, architecture, archaeology, and cultural heritage preservation, photogrammetry is very expensive computationally and is traditionally done with commercial packages, even on large workstations and HPC clusters. Regrettably, in the eyes of many academic researchers, open-source photogrammetry tools have still not caught up with proprietary software in terms of both performance and output quality. Even further, running open-source photogrammetry on HPC clusters is far from trivial. In this webinar, we demonstrate a couple of photogrammetry workflows that we have run successfully on Cedar. We take a look at OpenDroneMap and Meshroom and discuss all steps from taking initial images to building and displaying a final 3D textured model.

PDF slides

“Text analysis in 3D”

Webinar (2023-Nov-28) with Alex Razoumov

3D visualization is surprisingly little used to visualize data and connections in digital humanities, despite the fact that the 3rd dimension can provide many benefits, from decluttering your plot to encoding one of the variables. In this webinar I present a workflow to analyze a text corpus consisting of works from multiple authors, and visualize the differences between their vocabularies in 3D. I complement this visualization with 3D graphs to link similar texts. Throughout this webinar I am using only open-source tools – Python and several 3rd-party Python libraries, ParaView, and VTK, and show 3D visualizations that can easily scale to millions of texts and can work for any input language.