This section offers practical examples of how different types of researchers in the arts, whether they’re new to digital tools or more technically experienced, might approach their projects using UBC’s digital infrastructure and services. These example profiles are not real individuals but are representative of common research needs. Each profile outlines a fictional researcher’s background, technical skill level, goals, and the recommended tools and support they could use to accomplish their needs.
Background: Ali is an Associate Professor in History with limited technical experience. Despite her lack of technical expertise, she wants to explore digital archives and map historical migrations using interactive timelines. However, Ali doesn’t know where to begin or what tools are available to her.
The Challenge: Introducing Rebecca to the ideal tools for her work and explaining them in a beginner-friendly manner
Ideal Tools: ARC OnDemand, Jupyter Notebooks with guided templates, DRAC for shared cloud workspaces
Support Needs: Intro to data storage and digital mapping tools; a flowchart to guide her based on research goals
Background: Rebecca is a graduate student in Linguistics, analyzing large corpora of speech data as part of her thesis. She requires scalable computing for processing and analyzing language data with Python and Natural Language Processing (NLP) libraries, which she is already a bit familiar with. She’s heard of UBC Sockeye, but she doesn’t know much about it other than that.
The Challenge: Her laptop can’t handle the processing load, and she isn’t sure how to get started with Sockeye
Ideal Tools: ARC Sockeye for computing; ARC Chinook for storing audio files; Python, JupyterHub, and PyTorch for Natural Language Processing.
Support Needs: Consultation with ARC, Python-based tutorials
Background: Bill is a digital humanities researcher archiving visual media from 20th-century newspapers. Being quite educated in the field, all he needs is stable long-term storage and the ability to batch-process Optical Character Recognition (OCR) and metadata extraction.
Challenge: Bill is concerned about sustainability and Canadian data compliance
Ideal Tools: ARC Chinook for storage; OCR pipeline using Python/PyTorch
Support Needs: Consultation with ARC, Information regarding Data Sensitivity at UBC
Background: Lea is an Indigenous studies researcher working with communities to preserve oral histories. She wants to create a culturally respectful, controlled-access digital collection
Challenge: She needs help choosing a CMS that supports access permissions and long-term preservation
Ideal Tools: Murkutu for content and access management; Chinook for secure storage
Support Needs: Ethical hosting practices, Indigenous data governance support