This service aims to compute a list of taxa IDs detected from metabarcoding sequences of environmental DNA (eDNA) samples. This service contains eight substeps (1.1 to 1.8) implemented as a unique step. The eight substeps perform (1) sequencing error correction (using BayesHammer-SPAdes); (2) pairwise alignment, (3) pre-filtering, (4) dereplication, (5) attribute filtering, (6) clustering and OTU tab-producer (using OBITools); (7) taxonomic assignment (using blastn); (8) OTUs table generator. Several types of eDNA samples can be processed (i.e. water, feces, soil). It represents the Step 1 of the Metabarcoding Workflow within the Internal Joint Initiative.

This service aims at creating a stack of coregistered multi-spectral raster images. It groups two seasons images (summer and autumn) and stack them to obtain a unique image at native resolution (2 meters) by using the Worldview-2 sensor. It represents the Step 5 of the Ailanthus Workflow within the Internal Joint Initiative.

This service aims at creating a vector from the coordinates in the “Pop_troph_SIA.csv” file and convert it into a shapefile. It represents the Step 3.1 of the Crustaceans Workflow within the Internal Joint Initiative.

This service aims to reformat and produce final output in variuos formats for human and machine2machine reading. It represents the Step 9 of the ARMS Workflow within the Internal Joint Initiative.

This service aims at getting the ARMS collection file from the IMIS database. Since the URL of the ARMS file changes each time the file is updated, and this update is transmitted to the IMIS metadata record, it is necessary to always access the URL of the file from the field in the json format of the IMIS metadata record rather than via a fixed URL. This service represents the Step 1 of the ARMS Workflow within the Internal Joint Initiative.

It is a Support Vector Machine classifier trained for a 2-classes problem. It represents the Step 9 of the Ailanthus Workflow within the Internal Joint Initiative.

This service aims at producing the zonal statistics. The Python and C++ codes provides a generic tool to extract the statistics from a geographic layer with quantitative values for each patch of a categorical map. For example, it is used in this workflow to compute the average vulnerability inside different administrative polygons. It represents the Step 7 (final step) of the Biotope vulnerability Workflow within the Internal Joint Initiative.

This service aims at running various statistical analyses in RvLab on the data produced in ARMS workflow. It represents the Step 10 of the ARMS Workflow within the Internal Joint Initiative.

This service aims at extracting the Deciduous Vegetation Layer from multiclass land cover map at 30 meters, resampling it at 2 meters and masking all the pixels, of the 2 meters stack, not overlaid by the deciduous vegetation layer. It represents the Step 8 of the Ailanthus Workflow within the Internal Joint Initiative.

This service aims at creating a stack of coregistered multi-spectral raster images. It groups four multi-seasons images and stack them to obtain a unique image at native resolution (30 meters) by using the Landsat 5 sensor. It represents the Step 3 of the Ailanthus Workflow within the Internal Joint Initiative.