The course is aimed at non-PI researchers in biomedical sciences (PhD students, postdocs, technicians, etc…) who are interested in Open Science, FAIR principles and data management. This training is aimed at those who want to familiarise themselves with these concepts and apply them throughout their projects’ life cycle.
Introduction
Open Science is disruptive. It will change how we do research and how society benefits from it. Making data re-usable is the key to this, and FAIR principles are a way to achieve this.
- But what does this mean in practice?
- How can a biologist incorporate these principles in their daily workflow?
- We will learn that becoming FAIR (Findable, Accessible, Interoperable, Reusable) and following Open Science practices is a process.
We will teach you how through planning and using the correct 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 who actively generate or analyse biological data, mostly wet labb 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 T. Wain (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 )
Prerequisites
You don’t need to have prior knowledge of data managemet or programming skills but you 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
There may be additional pre-requisites for individual episodes/lessons in the extras section.
Senior PIs running their groups rather than performing experiments should come to the FAIR in (Biological) Practice for PIs course, which will be held in 2022.
Bioinformaticians and those who mostly develop scientific software may prefer 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 managment strategy
- prepare their data for re-use
- apply aproaches and tools into a FAIR-ready research data lifecycle
- find suitable resources for delivering Open Science and FAIR data