Summary and Setup

The “FAIR for Busy Biologists” course caters to researchers in biological and biomedical fields (PhD students, postdocs, technicians and even PIs) interested in Open Science and data management.

This course provides a concise introduction to the FAIR (Findable, Accessible, Interoperable, Reusable) data principles. Participants will learn how to implement FAIR practices throughout their research projects, fostering data reuse and collaboration.

Derived from the comprehensive “FAIR in Bio Practice” program, it focuses on core topics, featuring concise content and targeted exercises to deliver essential knowledge in a shorter timeframe.

Introduction

In today’s scientific landscape, the demand for impactful research extends beyond traditional academic papers. Narrative CVs now require evidence of open practices, community building, and public datasets, underline the importance of good data management for the career progression. Join us for a two-session workshop designed specifically for busy biologists looking to enhance their productivity through effective data management practices.

  • Open Science and FAIR Principles: Understand the importance of Open Science and the FAIR (Findable, Accessible, Interoperable, Reusable) principles in modern research.
  • Metadata: Learn how to create and utilize metadata to enhance the discoverability and interpretability of your research data.
  • Tidy Data Tables: Explore techniques for organizing and structuring data tables to facilitate analysis and collaboration.
  • File Naming and Organization: Develop strategies for naming and organizing files to streamline workflows.
  • Jupyter Notebook: Discover the power of Jupyter Notebooks for reproducible research and effective data documentation.
  • Data Repositories: Explore options for storing and sharing research data in reputable data repositories.

This hands-on 2-sessions (3.15 each) workshop covers the basics of Open Science, FAIR and data management practices.

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 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 )

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

Prerequisite

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

Discussion

There is another course aimed at PIs running their research groups FAIR in (Biological) Practice for PIs.

No setup is needed.

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