NOFE

post@nofe.no

This pre-conference workshop aimed for early-stage researchers and young epidemiologists is arranged in conjunction with the NOFE conference October 29-30 2025, in cooperation with The Department of Global Public Health and Primary Care (IGS). 

Title: Reproducible Research Workflows in the Era of Big Data and Machine Learning 

Date: Tuesday 28.10.2025 

Time: 10.00 – 16.00 

Location: Midgard, Alrek helseklynge. Årstadveien 17 (Naviger til bygning) 

Attendance/Registration

Attendance is free for everyone interested. We reserve the right to give priority to attendees of the NOFE-conference and NOFE-members.

Lunch and coffee/tea will be served. Please note any dietary requirements on the registration form.

Registration is now closed.

What is the workshop about?

Apart from an introduction to machine learning methods for epidemiologists, this workshop will introduce tools and techniques that support reproducible research practices, from data analysis to automated reporting, using Quarto and R. You’ll learn how to streamline your workflow and improve scientific transparency, regardless of your preferred programming language.

Workshop program

  • Session 1: Primer of Machine-Learning Methods for the Epidemiologist. Christian Page
  • Session 2: Why reproducibility? Julia Romanowska
  • Lunch
  • Session 3: When to think about reproducibility? Walk-through of a typical scientific project: how to make it reproducible at each step. Julia Romanowska
  • Session 4: How to do reproducible reports? Hands-on session using Quarto: create beautiful, reproducible, programming-language agnostic reports! Julia Romanowska

Requirements to get the most out of the workshop:

  • have tried analysis of data
  • have tried writing about the results, creating a presentation or a poster
  • have been frustrated with the need to re-run analyses and keeping track of all the versions
  • …and are willing to improve their workflow and make their science more reproducible.

Technical requirements:

Do you have questions? jenny.lindroos@uib.no 

Speakers:

Julia Romanowska is a bioinformatician and data analyst at the University of Bergen. She earned her PhD in Biophysics from the University of Warsaw. Dr. Romanowska has held postdoctoral positions at the Heidelberg Institute for Theoretical Studies and the Institute for Global Public Health and Primary Care (IGS) at the University of Bergen. Dr. Romanowska is also a researcher at the Norwegian Institute of Public Health (NIPH), where she contributes to the START project (Study of Assisted Reproductive Technology). Her research spans computational biology, genetic epidemiology, and bioinformatics, with significant contributions to understanding gene-environment interactions and DNA methylation. She is currently co-PI of the DRONE (Drug Repurposing fOr NEurological diseases) project. She is involved in the development of the R-package Haplin. She also teaches the introduction to R-programming and data analysis for PhD students in medicine and she is a co-founder of R-Ladies Bergen. Since March 2024, she is part of the editorial team in Journal of Open Source Software. 

Christian Magnus Page is a researcher at the department of physical health and aging at the Norwegian Institute of Public Health. He holds a PhD in Genetic Epidemiology from the University of Oslo, an MSc in Statistics, and a BSc in Mathematical Biology from NTNU. He has worked extensively with genetic and epigenetic epidemiology in multiple large Norwegian cohorts, including the Norwegian Mother and Child cohort (MoBa), the Health Survey in Mid-Norway (HUNT), the Norwegian Woman and Cancer Cohort (NOWAC) and the Tromsø study, and have been key member of the Pregnancy and Childhood Epigenetics (PACE) consortium since 2015. Dr. Page has held multiple positions in Biostatistics, Epidemiology, and Genetic Epidemiology throughout his career, mostly focusing on drawing inference from large complex data sources, such as national medical registry linkages or large, often nation-wide, cohorts. Page’s research spans the Developmental Origin of Health and Disease (DOHaD), integer-generational transmission of health and disease, medication and disease management during pregnancy, and aging research. His current research is on computational epidemiology and complex statistical modelling in registry data, with application on causality, spatial and temporal modelling, and developing novel epidemiological designs.