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  • 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 intersects the map(s) (geographical spatial polygons) from GBIF occurrence(s) with the locations of a list of taxa detected from eDNA metabarcoding and which were not included in the NIS checklist to verify if such eDNA detection(s) is/are likely to be new NIS detected in that location from eDNA sequences. It represents the Step 5 of the Metabarcoding Workflow within the Internal Joint Initiative.

  • Following the FAIR principles and best practices, the LifeWatch Italy Training Catalogue hosts the metadata of relevant learning resources so that these can be shared, searched, discovered, accessed and reused. The LifeWatch Italy 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 checking which taxa detected and identified from eDNA metabarcoding sequences are listed as NIS and which taxa are not (i.e. native or unrecorded NIS). This service uses available GBIF (Global Biodiversity Information Facility) records of NIS for each Country provided by the Invasive Species Specialist Group (ISSG). it checks if a species is present in the checklist and if this is the case, the species is flagged as being invasive for that country by adding 1 (yes) or 0 (no) to the column isInChecklist of the data frame, and the checklist key or a note to the ref_checklistKey column, for the corresponding cases. It represents the Step 3 of the Metabarcoding 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 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.

  • A service that aims at calculating the trophic position of the consumer (i.e. invader) by using a Bayesian model and stable isotope data of one prey species used as a baseline. The service replaces the NA values of the consumer's trophic position within the column "trophicLevel” of the "SIA.csv” with the numeric value of the estimated trophic position within the output file "troph_SIA.csv". This service uses the R package tRrophicPosition v. 0.8 that is based on a powerful approach of Markov Chain Monte Carlo simulations provided by JAGS and it represents Step 1 of the Crustaceans Workflow within the Internal Joint Initiative.

  • This step of the ARMS IJI workflow does the following: - Input: the output from the previous step (running a taxon check on the input species names using the WoRMS taxon checker); - Input: geographical locations for the sample(s) that those species were found at; - Check: using the WRIMS taxon match webservice to check the known distribution of the species; - Output: information about whether the species are alien or native to the location they were found at. It represents the Step 8 of the ARMS Workflow within the Internal Joint Initiative.

  • This service aims at building two datacubes based on the occurrences. The datacube aggregates (=counts) occurrences on a regular grid. This count has a stochastic component that takes the uncertainty field into account. All this process has been developed in R in the frame of the TrIAS project. It represents the Step 4 of the Biotope vulnerability Workflow within the Internal Joint Initiative.