Introduction to Data Management Practices
April 5 @ 08:00 – April 7 @ 17:00 CEST
National course open for PhD students, postdocs, researchers and other employees within all Swedish universities. This course will introduce important aspects of Research Data Management through a series of lectures and hands-on computer exercises. The course is intended for researchers that want to take the first steps towards a more systematic and reproducible approach to analysing and managing research data.
Note: We follow the recommendations and guidelines from Swedish authorities and Folkhälsomyndigheten. The course is designed to be an interactive face-to-face event. However, we follow the situation carefully and will deliver the course online if needed.
Contact: edu.intro-dm@nbis.se
Important dates
Application is open now
Application closes: 2022-03-15
Confirmation to accepted students: 2022-03-21
Course fee
1500 SEK paid by invoice to NBIS. This includes lunches, coffee and snacks. Please note that NBIS cannot invoice individuals.
Due to limited space the course can accommodate a maximum of 25 participants. If we receive more applications, participants will be selected based on several criteria. Selection criteria include correct entry requirements, motivation to attend the course as well as gender and geographical balance.
Entry requirements
No previous programming experience is required but you are required to bring your own laptop with the required software pre-installed.
Installation instructions will be provided before the course starts.
Course content
Topics covered will include:
• Open Science and FAIR in practice
• Organising data, files and folders in research projects
• Versioning data, documents and scripts with Git
• Describing data with metadata
• Cleaning tabular data and metadata with OpenRefine
• Submitting data to public data repositories
• Writing basic recipes for data analysis and visualisation with R
(Links to an external site.)
Learning objectives:
• To get acquainted with, and reflect upon, the principles of Open
Science and FAIR
• To understand the importance of metadata, and how it affects
“FAIRness”
• To learn how to organise files to make project work more efficient
• To learn to clean up messy tabular data and metadata
• To learn how to find, and submit to, relevant public repositories for
data publication
• To learn to apply simple version control practices on files
• To learn to start using R to analyse data
Google Calendar 
