A growing number of publishers and funding agencies require scientists to make their data available upon publication. Four foundational principles – Findability, Accessibility, Interoperability, and Reusability (FAIR) – support data producers and users by increasing added-value gained by contemporary, formal scholarly digital publishing. Data literacy and management are becoming basic skills for scientists.
In this workshop, students will collaborate in teams of 3 on their own projects, while acquiring the non-digital and digital knowledge necessary to fulfil novel standards in data management and analysis reports. The workshop is particularly suited for groups aiming at fostering collaboration between their members; participants will explain their workflow and their data to their colleagues as part of the work.
Data management in a Reproducible Research Workflow (RRW)
- From experimental design to publication
- The art of the spreadsheet: csv
xlsx, tidy data, interoperability, machine and human readability
- Metadata: experiment and sample wide, content, timing
- Data inventory, folder organisation, file names, backup
- Open& FAIR data: repositories, licences, FAIR principles
Reproducibility and data analysis
- Version control & helper tools (git, Rstudio, github)
- How to combine data from different sources
- Data modification, analysis documentation with Excel and R and Rstudio
- Make your analysis human readable – code commenting: conventions and examples, dplyr package
Our courses are geared towards adult learning and use participatory approaches. The trainer encourages participants to add their experience and knowledge to the course content. Topics covered are backed by real examples and relate to the participants’ field of research.
Before the course, participants can submit specific questions and their own presentation examples by email. The course content will be adjusted to the specific needs and requirements of the participants.
Participants are handed out reading material to be discussed during the course as well as a course summary with their achievements.
Course duration: 2 consecutive days (9am – 5pm)
Number of participants: 8-12
Trainer: Julien Colomb
Contact us for a custom proposal
The course can be aligned to your requirements regarding duration, form and content.
Wilkinson, MD. et al., (Dec 2016), The FAIR Guiding Principles for scientific data management and stewardship. nature.com/scientificdata