NBIS/ELIXIR-SE Tools for Reproducible Research – ONLINE
April 24 @ 09:00 – April 28 @ 15:30 CEST
NBIS / ELIXIR-SE course is open for PhD students, postdocs, group leaders and core facility staff interested in making their computational analysis more reproducible. International applications are welcome, but we will give approximately half of the participant slots to applicants from Swedish universities at minimum, due to the national role NBIS plays in Sweden.
Responsible teachers: Erik Fasterius, John Sundh
Contact information: firstname.lastname@example.org
Application open: Feb 27, 2023
Application deadline: Mar 31, 2023
Confirmation to accepted participants: Apr 7, 2023
This online training event has no fee. However, if you accept a position at the workshop and do not participate (no-show) you will be invoiced 2,000 SEK.
*Please note that NBIS cannot invoice individuals
One of the key principles of proper scientific procedure is the act of repeating an experiment or analysis and being able to reach similar conclusions. Published research based on computational analysis, e.g. bioinformatics or computational biology, have often suffered from incomplete method descriptions (e.g. list of used software versions); unavailable raw data; and incomplete, undocumented and/or unavailable code. This essentially prevents any possibility of attempting to reproduce the results of such studies. The term “reproducible research” has been used to describe the idea that a scientific publication based on computational analysis should be distributed along with all the raw data and metadata used in the study, all the code and/or computational notebooks needed to produce results from the raw data, and the computational environment or a complete description thereof.
Reproducible research not only leads to proper scientific conduct but also provides other researchers the access to build upon previous work. Most importantly, the person setting up a reproducible research project will quickly realize the immediate personal benefits: an organized and structured way of working. The person that most often has to reproduce your own analysis is your future self!
- Good practices for data analysis
- Version control and collaborative code development
- Package and environment management
- Workflow management
- Documentation and reporting
- Containerized computational environments
By the end of the course the student will be able to:
- Organize and structure computational projects
- Track changes and collaborate on code using Git
- Install packages and manage software environments using Conda
- Structure computational steps into workflows with Snakemake and Nextflow
- Create automated reports and document their analyses with RMarkdown and Jupyter
- Package and distribute computational environments using Docker and Singularity
Required for being able to follow the course and to complete computer exercises:
- Familiarity with using the terminal (e.g. be familiar with commands such as ls, cd, touch, mkdir, pwd, wget, man, etc.)
- A computer with a webcam
- You will be asked to install the video conferencing software zoom (https://zoom.us/) to be able to participate in the course
- Some knowledge in R and/or python is beneficial but not strictly required
The course can accommodate 20 participants. Selection criteria include correct entry requirements, motivation to attend the course as well as gender and geographical balance. Academic affiliated registrants are prioritized prior to participants from the industry.
Please note that NBIS training events do not provide any formal university credits. The training content is estimated to correspond to a certain number of credits, however the estimated credits are just guidelines. If formal credits are crucial, the student needs to confer with the home department before submitting a course application in order to establish whether the course is valid for formal credits or not.