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  • The Atlas of Shapes includes an illustrative scheme of the shapes subdivided in “Simple Shapes” and “Complex Shapes”. Clicking on a specific shape, users are able to see: the code and the name of the shape, the shape view (e.g., lateral, frontal, etc.) with the corresponding linear dimensions, the biovolume and surface area computational models with all the formulae associated. Clicking again on a specific shape, users will be redirected to all taxa present in the Atlas of Phytoplankton that are characterized by the selected shape. Atlas of shapes and Atlas of phytoplankton are integrated and can be easily joint switching from taxonomic identification to morphological characterization of phytoplankton.

  • The Atlas of phytoplankton is a guide for the identification of marine and freshwater species. It includes pictures, synonyms, morphological, morphometric and ecological characteristics and geographical distribution of the taxa. It also provides formulas to calculate the biovolume and surface area based on linear dimensions according to the organism view (e.g., lateral, frontal, etc.).

  • 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.