From 1 - 6 / 6
  • Categories  

    Soil moisture data of the eLTER site Rollesbroich provided in the scope of the H2020 project "ENVRI-FAIR", for the purpose of the Soil Water Use Case.

  • Data Services provide the users with tools in order to: a) publish their datasets and make them available to the community by providing information that allows a user to locate and access the resource and its curator/creator, b) import their datasets to the Lifewatch Greece Infrastructure and to GBIF or MedOBIS, c) perform biodiversity data and information quality improvement, and d) search about datasets of interest by providing an efficient way of querying semantic networks. The schema of the data that is provided by the users is mapped to the semantic model of the LWI and the data is transformed to LWI format before it is stored to the Infrastructure. The semantic model is based on CIDOC CRM (http://www.cidoc-crm.org/), CRM dig, CRM geo, CRM sci and MarineTLO (http://www.ics.forth.gr/isl/MarineTLO/). Login is required to access the service.

  • 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 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.

  • The LifeWatch Italy national node has realised the Alien and Invasive Species Virtual Research Environment (Alien Species VRE) for supporting researchers to address basic and applied studies on ecosystem vulnerability to alien species arrival. The Alien Species VRE allows to: - access and download harmonised data on the national distribution of species of fauna and flora belonging to different habitats (marine, fresh and transitional waters, and terrestrial) published through the LifeWatch Italy Data Portal and distributed by the LifeWatch ERIC Metadata Catalogue; - upload their own datasets structured according to the LifeWatch Italy Data Schema in order to execute the service included in the VRE.

  • PEMA is a HPC-centered, containerized assembly of key metabarcoding analysis tools. It supports the downstream analysis of four marker genes (16S/18S rRNA, ITS and COI) but also, by allowing the user to train the classifiers with custom reference databases, it can be used for further marker genes. By combining state-of-the art technologies and algorithms with an easy to get-set-use framework, PEMA allows researchers to tune thoroughly each study thanks to roll-back checkpoints and on-demand partial pipeline execution features.