The Blue-Cloud technical framework: Data Discovery and Access Service and Virtual Labs to enhance collaborative Open Science in marine research.
Sept 21, 12.30 CEST
Sept 22, 18.00 CEST
Libraries, research administrators, Open Science Infrastructure providers, funders
Collaborative platforms for all research artifacts, Interoperability across domains and services, Local and global collaborations: people and networks, Research analytics and visualizations, Text and data mining for/from research, Thematic Clouds
Data discovery and access, Datasets, Virtual Research Environment, EOSC, Ocean
Blue-Cloud is the thematic EOSC for the marine domain, delivering a collaborative virtual environment to enhance FAIR and Open Science, underpinned by simplified access to an unprecedented wealth of marine data resources and interoperable added-value services and products.
Blue-Cloud federates leading European marine data infrastructures and e-infrastructures, allowing researchers to combine, reuse, and share quality data across disciplines and countries.
The federation takes place at the levels of data resources, computing resources and analytical service resources. A Blue-Cloud Data Discovery and Access Service (DDAS) is developed to facilitate sharing with users of multidisciplinary datasets. A Blue Cloud-Virtual Research Environment (VRE) was established to enable the sharing of computing and analytical services for specific applications.
The DDAS architecture is based upon a combination of the GeoDab metadata broker service of CNR-IIA, and the SeaDataNet CDI service modules as developed by MARIS, IFREMER, and EUDAT. The overall concept is that the DDAS harvests metadata from the data infrastructures federated in Blue-Cloud by means of protocols such as CSW or OAI-PMH, providing discovery and access to users through a user-friendly interface.
The VRE is developed by the Italian National Research Council (CNR), built on the D4Science infrastructure and the gCube open source technology. Services include Data Analytics (Data Miner, Software and Algorithms Importer (SAI), RStudio, JupyterHub), Spatial Data Infrastructure to store, discover, access, and manage vectorial and raster georeferenced datasets, and services and components enabling users to document and then either share with selected colleagues or make available online any generated product (e.g. analytical methods, workflows, processes, notebooks). Being enriched with automatically generated provenance metadata, those products enable reusability, repeatability and reproducibility and promote Open Science.
In this demo, we will explain how to access and use these services, which are open for testing to researchers from all domains of ocean science.