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The following page contains video training, seminars, lectures, events, and materials designed to help you to get the most out of your data.

A collection of resources provided for the N8 CIR Digital Health community from workshops and meetings.

Find out how Melodee Wood uses Python to prepare and analyse images and text using the Python programming language.

This page contains links to resources used as part of the recent HPC for Healthcare facilitated by Will Furnass and Mark Dunning of the University of Sheffield.

In July 2020 Emma Rand of the University of York ran a course introducing R and RStudio to help researchers to produce reproducible analyses of their data. This resource page includes slides and video from that workshop to help you get started in R.

Slides, links and video from Daniel van Strien's computer vision workshop.

In this two part workshop Olivia Vane of the British Library takes you through a beautifully illustrated history of data visualisation before outlining some of the things you need to consider when preparing your own data visualisations.

Across four dates in June 2021 Michael Falk, then based at the University of Kent, ran four separate workshops exploring Machine Learning in the Humanities.

Links to videos and resources from a series of workshops organised by N8 CIR exploring ethics in computational research.

Join Melodee Beals as she introduces and explores how to use Python in the humanities.

During spring 2021 IBM are delivering a series of workshops and training sessions on N8 CIR's new high-performance computing platform, Bede. This page contains videos and copies of the slides from each of these sessions.

A collection of resources provided for the N8 CIR Digital Humanities community from workshops and meetings.

A collection of resources provided for the N8 CIR Machine Learning community from workshops and meetings.

This five-week workshop series was held at the University of York in April and May, 2025. It introduces participants to PyTorch and its applications in deep learning. Designed for researchers and students interested in machine learning, the sessions provide hands-on experience with neural networks, from basic tensor operations to training custom models on real-world data.