The course is aimed at researchers in biological and biomedical sciences (PhD students, postdocs, technicians, etc…) who are interested in Open Science and data management. The course explains the FAIR (Findable, Accessible, Interoperable, Reusable) principles for data re-use, and how to practically apply FAIR principles throughout projects’ life cycles.
Introduction
Open Science is disruptive. It is changing how we do research and how society benefits from it.
- But what does this mean in practice?
- How can a biologist incorporate these principles in their daily workflow?
- We will learn that becoming FAIR and following Open Science practices is a process.
We will teach you how through planning and using a powerful set of tools, you can make your outputs ready for public sharing and reuse.
This hands-on 4-session workshop covers the basics of Open Science and FAIR practices, and looks at how to use these core principles in your own projects. The workshop is a mix of lectures and hands-on lessons where you will use the approaches learned and will implement some of the discussed practices.
Target Audience
PhD students, postdocs, technicians, all who actively generate or analyse biological data, mostly experimental biologists. This course can also be of interest to mathematical/biological/computational modelers, data analysts, project managers and advocates of FAIR/Open Data.
Example of a learner profile:
Shania Wang (she/her) - junior postdoc Shania is starting her first postdoctoral stay. She has recently obtained a PhD in Plant Biology and is very skilled in running plant molecular and physiology experiments. She recently published her first publication, but found the process of preparing all the figures and required information for the publisher super tedious and time consuming. As a postdoc, she must find an effective way to organise her own research project and the PhD students she is going to supervise…(read more )
For instructors
Course instructors should check the instructors folder which contains:
- notebooks with exercises to by copied to etherpad before the lessons
- PowerPoint slides to be used during the delivery rather than the plain website text
Prerequisites
You don’t need to have prior knowledge of data management or programming skills. You do need to be willing to learn how to apply FAIR principles in your daily research life.
We expect you to:
- have a bio/medical background
- know basics of scientific communication, publications, citations and their importance for careers
- familiarity with spreadsheets (Excel)
- familiarity with online bioscience data resources (in general, not any particular resource); for example: searching publications
- know your ways around files and directories on your own machine
Another course aimed at PIs running their research groups FAIR in (Biological) Practice for PIs is under development in 2022.
Bioinformaticians and those who mostly develop scientific software may also benefit from software specific courses such as Open Science with R or other courses developed by the Carpentries Incubator.
Learning Objectives
After following this lesson, learners will be able to:
- explain elements of the Open Science movement
- explain FAIR principles and understand their importance
- plan their own data management strategy
- prepare their data for re-use
- apply approaches and tools into a FAIR-ready research data lifecycle
- find suitable resources for delivering Open Science and FAIR data
