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Tools for Reproducible Research#

Course overview#

GitHub repository 14 - 16 Juin, 2023

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 reproducing the results of such studies. The term “reproducible research” has been used to describe the idea that a scientific publication 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 enables other researchers to build upon previous work. Most importantly, the person who organizes their work with reproducibility in mind 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!

Course content and learning outcomes#

The following topics and tools are covered in the course:

  • Data management
  • Project organisation
  • Git
  • Conda
  • Snakemake
  • Nextflow
  • R Markdown
  • Jupyter
  • Docker
  • Singularity

At the end of the course, students should be able to:

  • Use good practices for data analysis and management
  • Clearly organise their bioinformatic projects
  • Use the version control system Git to track and collaborate on code
  • Use the package and environment manager Conda
  • Use and develop workflows with Snakemake and Nextflow
  • Use R Markdown and Jupyter Notebooks to document and generate automated reports for their analyses
  • Use Docker and Singularity to distribute containerized computational environments

Application#

This is an SouthGreen course. The course is open for PhD students, postdocs, group leaders and core facility staff related to the SouthGreen Platform (IRD, CIRAD, INRAE and the Alliance Bioversity international-CIAT).

The only entry requirements for this course is a basic knowledge of Unix systems (i.e. being able to work on the command line) as well as at least a basic knowledge of either R or Python.

Due to limited space the course can accommodate maximum of 20 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. We also take in consideration a balance between the different institute and department.

Please note that SouthGreen 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.

By accepting to participate in the course, you agree to follow the Code of Conduct.

Schedule#

You can find the course schedule at this page.

Location#

This course round is given on site. It will take place in the Badiane room at Agropolis.

Course material#

The pre-course setup page lists all the information you need before the course starts. The most important part is the installation and setup of all the tools used in the course, so make sure you've gone through it all for the course start.

Teachers#

  • Jacques Dainat (course responsible) ORCID iD icon
  • Thomas Denecker (teacher) ORCID iD icon
  • Aurore Comte (teacher) ORCID iD icon
  • Gautier Sarah (teacher) ORCID iD icon
  • Julie Orjuela (teacher) ORCID iD icon
  • Nicolas Fernandez (teacher) ORCID iD icon
  • Sébastien Ravel (Helper) ORCID iD icon

Contact#

To contact us, please send a mail using the contact form available here.

Acknowledgement#

This work is based on the NBIS / ELIXIR course Tools for Reproducible Research that can be found here. We extend our gratitude to the creators and providers of the training material used in this course.