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LifeWatch ERIC Service Centre

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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). 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.

  • The LifeWatch ERIC Training Platform offers courses and tutorials for the whole LifeWatch ERIC community seeking to make better use of the infrastructure resources, tools and services. It is based on Moodle and allows to easily access training resources by means of categories.

  • To address the today’s ecological challenges, it is necessary to use data coming from different disciplines and providers. Discovery and integration of data, especially from the ecological domain, is highly labour-intensive and often ambiguous in semantic terms. To improve the location, interpretation and integration of data based on its inherent meaning vocabularies can help in harmonizing and enriching descriptions of data, providing a formal mechanism for the definition of terms and their relationships. To satisfy these emerging needs LifeWatch ERIC developed a Semantic Resources Catalogue (EcoPortal) focused on the Biodiversity and Ecosystem Research. This effort will help to support the community in the management and integration/alignment of their semantics and subsequently also of their data. The main goal of the EcoPortal initiative is to provide a unique platform for vocabularies in the ecological and domain for different kinds of stakeholders.

  • TITAN comprises a well-grounded stack of Big Data technologies including Apache Kafka for inter-component communication, Apache Avro for data serialisation and Apache Spark for data analytics. Furthermore, DRAMA framework is the underlying workflow orchestrator engine used by TITAN.

  • The LifeWatch Italy national node has realised the Phytoplankton Virtual Research Environment (Phyto VRE) for supporting researchers to address basic and applied studies on phytoplankton ecology at a level of resolution going from individual cells to whole assemblages. The Phyto VRE enables researchers to: - produce harmonised data on taxonomy and morphological traits by using the Atlas of Phytoplankton, Atlas of Shapes and Phytoplankton Traits Thesaurus; - access, download, and select LifeWatch Italy datasets (published through the LifeWatch Italy Data Portal and distributed by the LifeWatch ERIC Metadata Catalogue) or upload their own datasets structured according to the Phyto template based on the LifeWatch Italy Data Schema in order to execute the services included in the VRE; - faciliatate the computation of morphological and demographic traits (such as hidden dimension, biovolume, surface area, surface-volume ratio, cell carbon content, etc.) and investigate their distribution patterns at different levels of data aggregation (i.e. spatial, temporal, taxonomic) by means of services, which automate a set of operations written in the R language.

  • This service provides a user-friendly Graphical User Interface (GUI) that allow researchers to run a workflow wrapped into R code for: - the reshaping of the input dataset in order to obtain alien species and native specie richness for each family at the habitat and site level. If more that 1 EUNIS habitat is present in a site, the richness will be calculate for the two (or more that 2) habitats in the site; - the selection of the best fitting model, by calling a set of R functions from the packages lme4 and MuMIn. Initially, a full GLMM model is calculated including both richness and level-1 EUNIS habitat as fixed factor. Subsequently, reduced models are calculated and compared with the full model using the Akaike Information Criteria (AIC). The model showing the best AIC is used to create the output (tables and graph); - the plot of the rarefaction curves on the reshaped dataset.

  • The Traits Computation web service provides a user-friendly Graphical User Interface (GUI) that allow researchers to run a workflow wrapped into R code for the computation of morphological and demographic traits, such as biovolume, surface area, surface-volume ratio, density, cell carbon content, density, carbon content and total biovolume. The service works on datasets structured according to the Phytoplankton Data Template that can be selected by the GUI or uploaded by the researchers. The input file is in CSV format with some mandatory fields according to the calculation type. Before selecting or uploading the input file, users have to specify some parameters (e.g., the calculation type, and the traits to be computed). The web service provides as output a file in .csv format, including all input data and the new calculated traits.

  • The size class distribution web service allows to visualize the distribution of phytoplankton in different size classes selected on the basis of logarithmic values of biovolume or carbon content. The service works on datasets structured according to the Phytoplankton Data Template that can be selected by the GUI or uploaded by the researchers. The input file is in CSV format with some mandatory fields (density, biovolume or cell carbon content). Before selecting or uploading the input file, users have to specify some parameters (e.g., the trait to be used for the ranked distribution, the logarithm base, the spatial and temporal levels for the clusterization of data). The web service provides as output a zip file containing a summary table in csv format and one or more bar plots according to the selected clusters.

  • The size density relationships web service calculates and describes the relationships between size (total biovolume and total carbon content) and density for any given combination of spatial, temporal and taxonomic level of observations. The service works on datasets structured according to the Phytoplankton Data Template that can be selected by the GUI or uploaded by the researchers. The input file is in CSV format with some mandatory fields (density, biovolume or cell carbon content). Before selecting or uploading the input file, users have to specify some parameters (e.g., the trait to be used for the size distribution, the taxonomic level for the size distribution and the spatial and temporal levels for the clusterization of data). The web service provides as output a zip file containing two summary tables in csv format and a scatter plot.

  • 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.