This lesson is still being designed and assembled (Pre-Alpha version)

FAIR in (biological) practice

The course is aimed at non-PI researchers in biomedicine science (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 be familiar with these concepts and apply them throughout their project’s life cycle.

Introduction

FIXME Open Science is disruptive. It will change how we do reasearch and how society benefits from it. Making data re-usable is key to this, and FAIR principles are a way: to achieve it.

We will teach you how planning and using the correct set of tools you can make your outputs ready for public sharing and reuse.

This hands-on 4-sessions workshop covers the basics of Open Science and FAIR practices, and looks at how to use these ideas in your own projects. The workshop is a mix of lectures and hands-on lessons where you will use the approaches learned and implement some of the discussed practices.

Target Audience

PhD students, postdocs, technicians who actively generate or analyse biological data, in majority bench biologists. This course can also be interesting to modellers, 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 as 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 (XL)
  • 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 prerequisites for individual episodes/lessons in the extras section.

Senior PIs running their groups rather than performing experiments should come to FIXME COURSE. Bioinformaticians and those who mostly develop scientific software may prefer software specific courses like FIXME FIXME.

Learning Objectives

After following this lesson, learners will be able to:

  • explain the elements Open Science movement.
  • explain the FAIR principles and understand their importance.
  • plan their own data managment strategy.
  • prepare their data for re-use.
  • aply aproaches and tools into FAIR-ready research data cycle.
  • find suitable resources for delivering Open Science and FAIR data

Ed_DaSH

Schedule

Setup Download files required for the lesson
00:00 1. Welcome Who are we and what are we going to learn?
00:00 2. Introduction to Open Science What is Open Science?
How can I benefit from Open Science?
Why has Open Science become a hot topic?
00:00 3. Being FAIR How to get more value from your own data?
What are the FAIR guidelines?
Why being FAIR matters?
00:00 4. Introduction to metadata What is metadata?
What do we use metadata for?
Is metadata important for data Reuse (the R in FAIR)?
00:30 5. The research data life cycle What is the research data life cycle?
Can planning help me manage my data and make it FAIR?
What is a Data Management Plan (DMP)?
01:00 6. Record keeping How and why to keep good quality records for experiments?
How to streamline and simplify record keeping and its maintenance with electronic lab notebooks and online protocols?
How to keep records FAIR?
01:00 7. Working with files How should a project folder be organized?
What are naming conventions?
How should I name my files?
How does project organisation help to make data FAIR?
01:35 8. (Meta)data in Excel

01:35 9. Templates for consistency

01:35 10. Jupyter notebooks for data analysis

01:35 11. Version control

01:35 12. Public repositories

01:35 13. ?Putting it all together?

01:35 14. Where to next

01:35 15. Template

01:35 Finish

The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.