Topic: reproducible research

Learn to conduct reproducible research data analysis in R and RStudio without prior R experience. Lectures, live coding and practical exercises introduce vectors, matrices, data frames, lists, packages and functions. Participants import and inspect data, handle missing values, subset and join datasets, calculate summaries and export results. Further sessions develop ggplot2 visualisations, R Markdown reports, project organisation, reusable functions and strategies for finding help and troubleshooting errors. Exercises can use supplied datasets or participants’ own research data. 2–6 November

Reproducible research can turn scientific observations into reliable, reusable knowledge. This talk presents ReproNim and related community infrastructure for sharing data and computational workflows. It examines versioned, re-executable analysis at scale using OpenNeuro, together with harmonization and querying across dozens of research sites. ReproLakes and ReproPonds combine public and private metadata stores with FAIR and reproducible practices to improve discovery and reuse. The discussion addresses both technical obstacles and research-culture challenges, offering practical approaches fo

Recognises sustained scientific contributions to healthcare sterilization, sterility assurance and contamination control, including reproducible research, innovative methods and technologies adopted in healthcare, regulation or industry. Nominations close 15 January 2027.