National course open for PhD students (prioritized), postdocs, researchers and other employees within all Swedish universities with interest in making their computational analysis more reproducible.
The course is organized by NBIS
Responsible teachers: Leif Wigge, John Sundh
Application open: February 21
Application deadline: April 14
Confirmation to accepted participants: April 22
1300 SEK (includes course dinner, lunches, coffee and snacks)
*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!
In this course you will learn how to make your data analyses reproducible.
In particular, you will learn:
Required for being able to follow the course and to complete computer exercises:
The course can accommodate 20 participants. Selection criteria include correct entry requirements, motivation to attend the course as well as gender and geographical balance.
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