Dinophysis phenology in European coastal waters

This dataset contains all the data we analysed for our work on the phenology of the toxic dinoflagellate genus Dinophysis in European coastal waters. To get a good understanding of this dataset, we suggest looking at the R scripts we used in our analysis, available in this public github repository:  https://github.com/vpochic/DinophysisPhenology
The dataset contains data of various natures that come from different sources:
- Geolocalised Dinophysis presence/absence data from long-term monitoring survey of the Continuous Plankton Recorder in North-Atlantic waters 
- Geolocalised phytoplankton count data and hydrological measurements data from the REPHY (French monitoring network for phytoplankton and hydrology in coastal waters)
- Geolocalised data from oceanographic (GAMAR, doi:10.1016/j.jmarsys.2025.104068) and atmospheric (ERA5, doi:10.1002/qj.3803) models
- Reflectance data from pixels in Sentinel-2 MSI (ESA) satellite images
- A compilation of eDNA data (metabarcoding) collected from plankton samples at 2 sampling sites in coastal France in a previous research program (doi:10.12770/53dfe316-a6d4-426f-8983-56f2b37a5e8a)
- Data from lab experiments conducted on phytoplankton cultures between 2016 and 2024: phytoplankton cell counts, fluorescence microscopy images and videos.
Additionnaly, it also contains a shapefile reprensenting european rivers (source: https://www.hydrosheds.org/products/gloric via https://milospopovic.net/map-rivers-with-sf-and-ggplot2-in-r/) used for visualisation purposes, and ouputs from statistical models applied on the time-series data described above (generated with the R scripts in the github repository, see above).
The dataset contains several empty folders (with only a README.md file) whose purpose is to welcome data files generated by the R scripts in the aforementioned github repository (https://github.com/vpochic/DinophysisPhenology). This way, we hope our work is easily reproducible by downloading this dataset and putting the "Data" folder in the same folder as the R scripts/R project, so that the scripts can call data files and store generated ones properly. In the github repository, you will find a map that explains which data file is called by which script, and what is generated by each script as a result.