The continued and dramatic rise in the size of data sets has meant that new methods are required to model and analyze them. This timely account introduces topological data analysis (TDA), a method for modeling data by geometric objects, namely graphs and their higher-dimensional versions: simplicial complexes. The authors outline the necessary background material on topology and data philosophy for newcomers, while more complex concepts are highlighted for advanced learners. The book covers all the main TDA techniques, including persistent homology, cohomology, and Mapper. The final section focuses on the diverse applications of TDA, examining a number of case studies drawn from monitoring the progression of infectious diseases to the study of motion capture data. Mathematicians moving into data science, as well as data scientists or computer scientists seeking to understand this new area, will appreciate this self-contained resource which explains the underlying technology and how it can be used.

Demands for excellence and efficiency have created an ableist culture in academia. What impact do these expectations have on disabled, chronically ill and neurodivergent colleagues? This important and eye-opening collection explores ableism in academia from the viewpoint of academics' personal and professional experiences and scholarship. Through the theoretical lenses of autobiography, autoethnography, embodiment, body work and emotional labour, contributors from the UK, Canada and the US present insightful, critical, analytical and rigorous explorations of being 'othered' in academia. Deeply embedded in personal experiences, this perceptive book provides examples for universities to develop inclusive practices, accessible working and learning conditions and a less ableist environment.