This VLab was conceived and used to support the ICES training course on Social Science Methods for Natural Scientists (Date: 29-28 May 2016, Location: Brest, France). The objective of the course was to provide participants with a solid foundation for working effectively with stakeholders in (cooperative) research projects, as well as having a better appreciation of the strengths and application of the social sciences in fisheries research. In addition to the basic functionalities, as a workspace for sharing objects of interest, a social networking area for supporting the discussions among members and a user management facility for managing membership, this VRE is specifically equipped with the following capability: Tabular Data Management: a facility enabling users to import, curate and manage tabular data. This feature can support data managers during the whole life cycle of data management from data capture to publication and visualisation. It enables data managers to import and transform datasets (CSV, SDMX, JSON) into tabular resources (i.e. tabular data having proper types associated with columns eventually referring to code lists) and reference datasets (code lists) representing recognized value instances of the elements the dataset is about (e.g., species, zones, countries). This functionality guarantees that the tabular resources are compliant with the defined types and code lists. Besides the curation, the facility supports the analysis of the data by enabling a user to perform operations like grouping and filtering, producing charts and GIS maps (if the data have geographic features) and analysing the data via an R environment as well as via the data analytics facilities. Finally, the environment supports the publishing of tabular resources in the infrastructure by equipping them with rich metadata so that such resources can be used in other application contexts Data Analytics at Scale: a facility enabling users to benefit from the offering of the DataMiner service and interactively execute a large array of data analytics tasks on datasets. These algorithms range from data clustering and anomalies detection methods (e.g. DBScan and KMeans) to algorithms for manipulating datasets from the geospatial perspective (e.g. transform FAO Area Code in latitude and longitude) Species Data Discovery: 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, and 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 Geospatial Data View: facility enabling users to discover and visualize GIS layers, e.g. species distribution maps, Sea Surface Temperature, that have been generated and/or published. This facility relies on the GeoExplorer portlet and makes it possible to effectively exploit the generated maps and perform comparisons and analysis of the diverse distributions by enabling maps overlay, transects production and values inspection