B2. gCube / D4Science DataMiner
The attributes marked with a * are confidential and should not be disclosed outside the service provider.
|Service name||gCube / D4Science DataMiner|
|Service area||Data processing and analytics|
|Service description||This service offers a web-based workbench for data analytics compliant with Open Science practices. From the end user perspective, it offers a collaborative-oriented working environment where users:
The data analytics framework is integrated with a shared workspace where the research objects resulting from the analytics tasks are automatically stored together with rich metadata. Objects in the workspace can be shared with coworkers as well as published by a catalogue with a license governing their uses. Moreover, the framework is conceived to operate in the context of one or more Virtual Research Environments, i.e. it is actually made available by a dedicated working environment offering (besides the framework and the workspace) additional services including those for managing users, creating communities, and supporting communication and collaboration among VRE members.
The data analytics framework is conceived to give access to two typologies of resource:
More details on this framework is available at https://wiki.gcube-system.org/gcube/DataMiner_Manager
|Customer group||Any Research Performing Organization willing to provide its scientists with an Open Science compliant data analytics platform.|
|User group||The service is not tailored to serve the needs of a specific community. Rather, it is community agnostic and highly and easily customizable thus to serve the needs of a given community. Customization is achieved by configuring the instance serving a certain Virtual Research Environment with (a) the set of methods to be made available as-a-Service and (b) the resources forming the distributed computing infrastructure dedicated to execute the analytics tasks.
Up to now it has been and is successfully used by a quite rich array of diverse communities, namely those associated with the supported projects,, e.g., i-Marine (fisheries and marine biodiversity scientists), BlueBRIDGE (fisheries and aquaculture scientists, educators & SMEs), SoBigData.eu (social mining scientists), ENVRI+ (environmental scientists), AGINFRA+ (agriculture scientists), EGIP (geothermal scientists).
|Value||The analytics platform is conceived to serve the needs of scientists (in particular, those belonging to the so called long-tail of science) by providing them with an easy to use working environment (nothing need to be installed on users’ machine). It is conceived to hide the technicalities related with the execution of tasks by relying on distributed computing infrastructures. Moreover, it is conceived to be exploitable by third-party software/applications, e.g. R-Studio, Q-GIS, or any workflow management system or application capable to interface with a RESTful service.
Worth highlighting that the platform is Open Science “compliant” (e.g., every method is “published” and citable, every task leads to a research object) and Virtual Research Environment friendly, i.e. it is conceived to be customizable with respect to the methods to offer in a given application context as well as it is conceived to benefit from a collaborative environment for sharing artefacts and comments.
Its characteristics make it particularly suitable to serve typical scientific contexts of the long tail of science.
|Tagline||Open, user friendly and extensible data analytics platform ready for Open Science and VREs.|
|Access policies||Policy-based and Wide-use. D4Science operates a number of instances of this|
|Service management information|
|Service owner *||D4Science.org|
|Contact (internal) *||email@example.com|
|Request workflow *|
|Service request list||Ask to join one of the existing instances hosted by VRE, e.g. the ENVRIplus VRE https://services.d4science.org/group/envriplus/
For having dedicated instances, please contact firstname.lastname@example.org
|Finances & resources|
|Revenue stream(s) *|
 Technology Readiness Levels (TRL) are a method of estimating technology maturity of components during the acquisition process. For non-technical components, you can specify “n/a”. For technical components, you can select them based on the following definition from the EC:
- TRL 1 – basic principles observed
- TRL 2 – technology concept formulated
- TRL 3 – experimental proof of concept
- TRL 4 – technology validated in lab
- TRL 5 – technology validated in relevant environment (industrially relevant environment in the case of key enabling technologies)
- TRL 6 – technology demonstrated in relevant environment (industrially relevant environment in the case of key enabling technologies)
- TRL 7 – system prototype demonstration in operational environment
- TRL 8 – system complete and qualified
- TRL 9 – actual system proven in operational environment (competitive manufacturing in the case of key enabling technologies)