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Home > The FRB in action > The FRB’s programs & projects > Synthesis at CESAB > Expand your skills > Biodiversity data: From data collection to publication
First edition in 2024

Biodiversity data: From data collection to publication

This five-day training course aims to work on the different stages of the data cycle, from acquisition to opening, including management, storage and the drafting of data papers.

Biodiversity data: From data collection to publication

The CESAB (Centre for Biodiversity Synthesis and Analysis), the French Biodiversity Data Hub (PNDB, Pôle National de Données de Biodiversité), and GBIF France (Global Biodiversity Information Facility for France) organize the first edition of the training course “Biodiversity data: From data collection to publication”. This five-day course aims to 1) contextualize the issues surrounding the understanding, sharing and (re)use of biodiversity data and metadata, and 2) enhance the skills of communities involved in one or more stages of the data cycle.


This training is given in French and takes place in the autumn, at CESAB offices in Montpellier.



Find the training course on GitHub



A good mastering of the R software is required.


List of organisers :

  • Camille COUX (FRB-CESAB)
  • Yvan LE BRAS (PNDB)
  • Olivier NORVEZ (PNDB)
  • Anne-Sophie ARCHAMBEAU (GBIF France)
  • Sophie PAMERLON (GBIF France)



This workshop takes place in autumn


• Pre-registration opening:



• Pre-registration closure and confirmation:



5, rue de l’École de médecine

34000 Montpellier





Camille COUX



Programme example

This is an example of the course programme, which may be subject to slight changes from year to year.

The first four days will consist of lectures followed by exercises. Sub-group projects will be carried out on the last day.


Introduction and overview

  • What is data/metadata?
  • Major types of biodiversity data
  • The data ecosystem in France and in academia
  • The data cycle
  • Achieving a high degree of “FAIRification”
  • Current challenges (in academia and beyond)

Data acquisition

  • Best practices for data collection (field, laboratory, etc.)
  • Access to existing data (APIs, web scraping, text mining, etc.)
  • Raw data vs. derived data

Data management

  • Structuring (SQL, CSV files, etc.)
  • Processing, cleaning and standardizing

Legal aspects

  • Sharing, licensing, etc.

Opening data

  • Data management plan
  • (Meta)data: standards
  • Storage and archiving
  • Dissemination and sharing
  • Enhanced value: the data paper
Training course track