Standard features

Each VRE is equipped with a simple graphical user interface that offers access to:

  • Social networking features with an integrated mailing system, that trainers and course participants can use to communicate and exchange opinions and results on their research.
  • Data storage and file sharing facilities (e.g., presentations, documentation, URLs, etc).
  • Member management: The VRE can be open, restricted or private (it’s up to the instructor). If restricted or private, access to the VRE is limited to those possessing the credentials.
  • Computational resources required to run model simulations.
  • Integrated surveys: The VRE lets you create surveys that instructors can use to assess the course or to set up questionnaires that participants are required to complete.

Customized features

According to the needs of the trainer, each VRE can be populated with specific data and algorithms, with facilities for managing data, parameterizing models and providing standard and a variety of computational intensive models.

Here are some examples of the most-used features:

  • Data Analytics at Scale: a facility enabling users to benefit from the offerings of the DataMiner service and interactively execute a large array of data analytics tasks on datasets. These algorithms range from those implemented to produce a species distribution map using either an expert system or a machine learning model to those used to analyse climatic change and its effect on species distribution, to those for estimating similarities among habitats, and approaches for stock assessment.
  • R Studio as-a-Service: a facility enabling users to access a fully-fledged RStudio® working environment directly from the VRE. This environment is integrated with the rest of the VRE facilities enabling the usage of files from the workspace and the storage of new files in the workspace. Some R scripts executed on a local machine could take hours / days to complete, whereas if you exploit the VRE the time can be dramatically reduced to seconds / minutes.
  • Algorithm Importer: a facility enabling users to transform R-based algorithms and methods into DataMiner algorithms, i.e. methods that can be executed by the data analytics platform. This transformation assists in annotating the code, thus making it possible for Data Miner to properly execute it.
  • Resource Catalogue: a facility enabling users to access the BlueBRIDGE resources (incl. services, datasets, products).
  • Geospatial Data Viewer: a facility enabling users to discover and visualize GIS layers, e.g. species distribution maps that have been generated and published. This facility relies on the GeoExplorer portlet, and allows effective exploitation of the maps generated and comparison and analysis of the diverse distributions through map overlay, transect production and value inspection.
  • BiOnym: an environment to compare a set of scientific names against taxonomic reference lists including recognised ones like Catalogue of Life.
  • Species Data Discovery: a facility enabling users to discover and manage species data products (occurrence data and taxonomic data) from a number of heterogeneous providers (including GBIF and speciesLink for occurrences data, as well as ASFIS, BrazilianFlora, CatalogueOfLife, IRMNG, IT IS, NCBI, WoRDSS, WoRMS for taxonomic data) in a seamless way. Once discovered, objects can be stored in the workspace for future uses.
  • Species Viewer: a facility enabling users to discover and browse species products (namely species distribution maps). This facility supports discovery mechanisms ranging from a simple search based on species names to very specific search criterion, and also offers a comprehensive set of products visualisation approaches.

Additional facilities are already available or can be implemented by BlueBRIDGE.