Library

Do you want to learn how to use copyrighted material in your research, and how to protect your own rights as an author? If so, this workshop is for you!

Embarking on the journey of writing your thesis or dissertation? This online workshop offers valuable tools and techniques to support your thesis writing process.

This session focuses on centroid-based approaches such as k-means and k-medoids. We examine initialization strategies, distance metrics, convergence behaviour, and model diagnostics.

In this workshop, attendees will explore the diversity of mushrooms and learn the basic tools needed for proper mushroom identification.

This session will address the visualization of standard deviation (s.d.), standard error of the mean (s.e.m.), and confidence interval (CI) error bars to build understanding of uncertainty in data analysis.

In this workshop, participants are invited to explore how to practically and creatively incorporate disability justice principles in participatory and community-engaged research.

Participants will gain practical experience with data collection, processing, and AI-assisted defect assessment, and learn how sensory measurements can be applied to support engineering decision-making.

This session will introduce participants to the concept of P values and their role in hypothesis testing, highlighting that P values reflect the probability of observing the data under the null hypothesis, and not necessarily the biological significance of the findings.

This workshop introduces fuzzy clustering methods, with emphasis on the Fuzzy C-Means algorithm. We discuss how partial membership values are assigned, how the objective function governs cluster formation, and when fuzzy approaches are preferable to hard partitions.

In this workshop, participants are invited to explore how to practically and creatively incorporate disability justice principles in participatory and community-engaged research.

We’ll explore strategies and provide sample abstracts and lay summaries from diverse fields and purposes, including theses, conferences, and funding applications.

This workshop introduces distribution-based clustering techniques, with emphasis on Gaussian mixture models. We discuss likelihood-based estimation, model selection, and cluster uncertainty.