RDM Jumpstart – Data Management for Reproducible Research

RDM Jumpstart – Data Management for Reproducible Research

Join us May 12–16, 2025 for the pilot edition of our research data management week-long workshop, RDM Jumpstart! 

This workshop will introduce attendees to best practices in research data management (RDM) using common tools to support research transparency and reproducibility. Robust implementation of RDM principles enables researchers to address bias and reproducibility, effectively share their research, and ensure long term access to their research inputs and outputs. From research question development to findings dissemination, RDM underpins a fruitful and successful academic career. 

 

What will we cover? 

Sessions will address the importance and underlying principles of RDM; we’ll explore issues related to RDM and the growing landscape of RDM-related requirements stemming from funders and publishers. Using the R programming language, the Open Science Framework (OSF), and Borealis (Dataverse), we’ll explore solutions to address these issues and enable compliance with funder and publisher requirements. 

All attendees will work with a common dataset to explore how to ask questions of data using common computational tools. Throughout, attendees will be introduced to: the documentation and metadata requirements to ensure accessibility; considerations to address different aspects of reproducibility; practices to maintain their data’s integrity; and ways to ensure their final data deposit is adherent to FAIR (Findable, Accessible, Interoperable, Reusable) principles. 

 

Registration 

Please visit the event website for registration and more details. 

Please note, this program is offered in English. 

 

Draft program 

Subject to change 

Day 1 

  • What is Research Data Management 
  • Principles of RDM and FAIR (Findable, Accessible, Interoperable, Reusable) 
  • Integrating RDM in the research data lifecycle 
  • DMPs (Data Management Plans) 
  • Setting up a project in OSF 

Day 2 

  • Licensing concerns as they relate to data 
  • Introduction to R and RStudio 

Day 3 

  • File organization, naming, and version control 
  • Data types in R 
  • Proprietary and non-proprietary data storage formats 
  • Preparing data for analysis: recoding, creating new variables, and filters 

Day 4 

  • Best practices for code management 
  • Data visualization using R 
  • Structured practice time and Q&A 

Day 5 

  • Preparing data, code, documentation, and other materials for deposit 
  • Depositing data to Borealis 
  • Guest speakers and wrap-up