In mid-October 2024, Proochista Ariana announced the Oxford iHealth Champions programme - a re-brand of earlier efforts to encourage and support community-building among our Oxford IHTM alumni towards continued learning, collaboration, and impactful action along the lines of open and reproducible science. Nine months on, we are happy to welcome our latest batch of iHealth Champions from Oxford IHTM Class of 2025.

By the end of July 2025, we are expecting delivery of a compute server/workstation capable of running large language models (LLMs) efficiently. This machine will allow for local LLM deployment to support development and use of artificial intelligence workflows in-house. This is part of Oxford IHTM’s efforts to support its current students and its alumni in learning and applying modern tools for global health projects.

After the pilot run of the Open and Reproducible Science in R module for IHTM Class 2021-2022, it was apparent based on student feedback that learning to code in R was something most of our students valued. As with other elements of the course, it is clear that more impactful learning happens when students use whatever they have learned on an actual analytic task.

October is here again and it is time to welcome our new cohort - Class 2025 - of the MSc in International Health and Tropical Medicine. We met all 19 students of the current cohort last week on the 1st and 2nd of October as they went through the now customary laptop setup for the Open and Reproducible Science in R module.

It’s been a few years in the making. With ongoing collaborations with IHTM alumni on projects in Liberia and Seychelles and having initiated the Open and Reproducible Science in R module 4 years ago, we’ve finally been able to formalise our vision of global health partnerships and collaboration.

Boxplots (also called box-and-whisker plots) are a graphical tool used to summarise and display the distribution of a continuous variable. They are useful for several reasons: Identifying Outliers : Boxplots clearly highlight outliers (values that fall significantly outside the range of most of the data). Outliers are shown as individual points beyond the “whiskers” of the plot.

Boxplots (also called box-and-whisker plots) are a graphical tool used to summarise and display the distribution of a continuous variable. They are useful for several reasons: Identifying Outliers : Boxplots clearly highlight outliers (values that fall significantly outside the range of most of the data). Outliers are shown as individual points beyond the “whiskers” of the plot.

A histogram is a graphical representation of the distribution of numerical data.

A histogram is a graphical representation of the distribution of numerical data.