D4Science is a Data Infrastructure offering services by community-driven virtual research environments.[1]
In particular, it supports communities of practice willing to implement open science practices.[2]
The infrastructure follows the system of systems approach, where the constituent systems (Service providers) offer “resources” (namely services and by them data, computing, storage) assembled together to implement the overall set of D4Science services.[3] In particular, D4Science aggregates “domain agnostic” service providers as well as community-specific ones to build a unifying space where the aggregated resources can be exploited via Virtual research Environments and their services.
Virtual Research Environment as a Service providing any community of practice with a dedicated working environment supporting any knowledge production process in a collaborative way, in fact every VRE enables computer-supported cooperative work by design. D4Science-based VREs are web-based, community-oriented, collaborative, user-friendly, open-science-enabler working environments for scientists and practitioners willing to work together to perform a set of (research) task. From the end-user perspective, each VRE manifests in a unifying web application (and a set of application programming interfaces (APIs)): (a) comprising several applications organised in specific menu items and (b) running in a plain web browser. Every application is providing VRE users with facilities implemented by relying on one or more services provisioned by diverse providers. Among the basic services every VRE is equipped with there are
a Social Networking area enabling collaborative and open discussions on any topic and disseminating information of interest for the community, for example, the availability of a research outcome;
a Workspace for storing, organizing and sharing any version of a research artifact, including dataset and model implementation;
a User Management dashboard for managing membership and roles;
a Catalogue Service recording the assets worth being published thus to make it possible for others to be informed and make use of these assets.
Data Analytics at scale for data analytics including:
a proprietary data analytics platform (DataMiner)[5][6] to execute analytics tasks either by relying on methods provided by the user or by others. It is endowed with importing and sharing facilities for analytics methods implemented in heterogeneous forms including R, Java, Python, and KNIME. The platform enacts tasks execution by a distributed and hybrid computing infrastructure. Moreover, one of the worth highlighting feature of this platform is its open science-friendliness. All the analytics methods integrated in it are exposed by a standard protocol (the OGC WPS protocol) clients can use to get informed on available methods as well as to start processes, monitor their execution and access results. Every analytics task performed by the platform automatically produces a provenance record catering for the reproducibility of the task;
an RStudio-based development environment for R enabling to perform statistical computing tasks in the cloud. This RStudio environment is (i) preconfigured with libraries and packages to ease the execution of common data analytics tasks, and (ii) provides seamless access to the VRE Workspace enabling sharing of resources with other members of the same working environment.
a Jupyter-based notebook environment for developing and executing interactive computing by JupyterLab instances. Each JupyterLab is (i) preconfigured with libraries and packages to ease the execution of common data analytics tasks, and (ii) provides access to the VRE Workspace enabling sharing of resources with other members of the same working environment.
Community
The D4Science Infrastructure serves more than 24,000 registered users (August 2024) through 177 active VREs offered via 20 Science gateways. This extensive infrastructure not only supports a diverse range of scientific communities but also fosters significant engagement and collaboration among researchers worldwide.
Engagement within the D4Science community is robust, with users benefiting from user-friendly application environments tailored to their specific needs. The platform allows users to securely preserve, access, and share their data from anywhere, fostering a collaborative and inclusive research environment. Additionally, groups of users can create their own virtual environments and customise them with the applications they need, further enhancing the platform's flexibility and usability.
Supported communities and cases range from Agri-food[7]
to Social Data Science[8]
,
Earth Science[9]
and Marine Science.[10]
These diverse applications demonstrate the versatility and broad applicability of the D4Science Infrastructure, making it an invaluable resource for researchers across various scientific domains.
History
The D4Science development has been supported by several European-funded projects.
In the context of the H2020 research and innovation programme the maturity level of the D4Science infrastructure was high enough to allow a large and very diverse set of communities of practice to benefit from it and its services and further contribute to its development.
Moreover, the services offered by the infrastructure have been developed to support open science practices.[2]
The operation and improvement of the D4Science infrastructure facilities are still ongoing while its exploitation is progressively growing.
See also
European Open Science Cloud the European initiative for creating an environment for hosting and processing research data and promote open science.
European Grid Infrastructure the e-Infrastructure set up to provide advanced computing and data analytics services for research and innovation.
OpenAIRE the European initiative to shift scholarly communication towards openness and transparency and to facilitate innovative ways to communicate and monitor research.
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