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  • This service allows to choose which column of the MasterARMS file contains the specific data to process. Moreover, it permits to provide additional files or arguments as parameters. It represents the Step 4 of the ARMS Workflow within the Internal Joint Initiative.

  • 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 represents the Step 2 of the Metabarcoding Workflow within the Internal Joint Initiative. It aims at converting the CSV into rdata. It takes as input the Species_occ.csv file (output of the Step 1 Metabarcoding Runner), verifies the checklists available for each country and retrieves the first one. It produces two rdata files, that will be the inputs for the Step 3 GBIF NIS Verifier of the Metabarcoding workflow.

  • Following the FAIR principles and best practices, the LifeWatch ERIC Training Catalogue hosts the metadata of relevant learning resources so that these can be shared, searched, discovered, accessed and reused. The LifeWatch ERIC training catalogue’s accurate and descriptive metadata allow all users to find the most appropriate and well-suited educational resources for their needs. Metadata are based on a subset of the IEEE Standard for Learning Object Metadata (IEEE 2002) that has been customised in order to be compliant with the EOSC Training Resource Profile - Data Model. The detail page of each single metadata record includes all the descriptive information and, on the right side of the page, a button “Start the course” that allows to access the resource and hence to start the training.

  • 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 to uploading a local file in the ARMS validation case. It represents the Step 2 of the ARMS 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.

  • It is a Support Vector Machine, pixel-based, classifier trained for a multi-class problem. It represents the Step 4 of the Ailanthus 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 modeling trophic positions as a function of environmental drivers (e.g., through GAMs, etc.) by using the "Bioclim_predic.csv" file. It represents the final step (Step 5) of the Crustaceans Workflow within the Internal Joint Initiative.