DARE · Project

A Platform That Lets Data-Heavy Teams Run Massive Experiments Faster and Reproducibly

digitalTestedTRL 5Thin data (2/5)

Imagine you're a scientist working with enormous datasets — terabytes of earthquake readings or climate simulations — and every time you run an experiment, you basically have to reinvent the wheel. DARE built a kind of universal workbench that lets research teams plug in their data, run complex analyses across multiple computing systems, and actually reproduce their results later. Think of it like going from hand-crafting each analysis from scratch to having a well-organized kitchen where all the tools, recipes, and ingredients are standardized and ready to go. The platform connects big-data technologies already proven in business with the specific needs of European research infrastructures.

By the numbers

consortium partners

countries represented

total deliverables produced

EUR 2,957,500

EU funding received

demo software deliverables

The business problem

What needed solving

Organizations working with massive, diverse datasets — especially in earth science, climate, and other data-intensive domains — waste enormous time reinventing data processing workflows that are neither reproducible nor reusable. Standard big-data technologies proven in commercial settings are often not adapted for scientific or specialized analytical workloads. This leads to long release cycles, throw-away products, and inability to scale experiments across different computing infrastructures.

The solution

What was built

DARE delivered a full integrated software platform with 38 deliverables including: a big-data analytics toolkit, data-lineage tracking services, data consolidation and linking tools for harmonizing diverse datasets, a software API, monitoring and management tools, data-driven abstraction specification tools, and a semantic registry — all packaged as openly accessible, configurable, and deployable software.

Audience

Who needs this

Environmental data analytics companies processing large geospatial or sensor datasetsClimate modelling and risk assessment firms handling complex simulationsResearch infrastructure technology providers building platforms for scientific institutionsLarge enterprises with internal R&D teams running data-intensive experimentsPublic research organizations seeking reproducible data processing capabilities

Business applications

Who can put this to work

Environmental Data Services

enterprise

Target: Companies providing earth science data analytics, seismic monitoring, or geospatial intelligence

If you are an environmental data company dealing with massive, heterogeneous datasets from sensors, satellites, and field stations — DARE developed an integrated software stack with big-data analytics and data-lineage tracking that lets you process and link diverse datasets reproducibly. The platform was specifically tested with EPOS earth science infrastructure across a consortium of 10 partners in 6 countries.

Climate Analytics and Modelling

mid-size

Target: Companies offering climate risk assessment, weather modelling, or carbon accounting platforms

If you are a climate analytics firm struggling with long release cycles when building data-driven products from complex climate simulations — DARE built rapid prototyping tools and data consolidation services that harmonize and connect large, varying datasets. The project delivered 38 deliverables including semantic registry and API components designed for climate research infrastructure IS/ENES2.

Research Infrastructure Technology

any

Target: Companies building platforms or tools for research institutions and data centers

If you are a technology provider serving research institutions and you need to offer reproducible, scalable data processing — DARE created a deployable software package with data-driven abstraction specifications, monitoring tools, and big-data analytics toolkits. The open-source stack was built by 5 research organizations and 3 universities, meaning it was designed by the people who actually use these systems daily.

Frequently asked

Quick answers

What would it cost to adopt or integrate the DARE platform?

DARE was funded with EUR 2,957,500 in EU contribution and its deliverables describe openly accessible code and documentation bundled in configurable and deployable software packages. Based on available project data, the platform components appear to be open-source, meaning acquisition cost would be low but integration and customization would require engineering investment.

Can this handle industrial-scale data volumes?

The project was specifically designed for 'data, complexity and computing extremes' and exascale data resources, according to its objectives. It was tested with real research infrastructures (EPOS for earth science, IS/ENES2 for climate) that handle massive datasets. However, the 10% industry ratio in the consortium suggests scaling was validated in research rather than commercial production environments.

What is the IP situation and can I license this technology?

The deliverables explicitly mention 'openly accessible code and documentation' in deployable software packages. Based on available project data, the core components appear to be open-source. Specific licensing terms would need to be confirmed with the coordinator, NCSR Demokritos in Greece.

How mature is the technology — is it ready to deploy?

The project delivered version II iterations of all major components (analytics toolkit, data lineage services, API, integrated stack), indicating at least two development cycles. The final deliverable describes a full integrated software stack with semantic registry. Based on available project data, this is a tested research platform, not a commercial product.

Does it integrate with existing data infrastructure?

Yes — integration was a core design goal. The data consolidation and linking toolkit was built to interact with 'a multitude of external databases and registries.' The platform includes execution mapping services, monitoring tools, and APIs specifically designed to bridge over existing infrastructures and services.

Who built this and do they offer support?

The consortium of 10 partners across 6 countries was led by NCSR Demokritos (Greece), with 5 research organizations, 3 universities, and 1 industry partner. The project ended in December 2020. Based on available project data, ongoing commercial support would need to be arranged directly with the consortium partners.

Consortium

Who built it

The DARE consortium of 10 partners across 6 countries (Germany, Greece, France, Italy, Netherlands, UK) is heavily research-oriented: 5 research organizations and 3 universities, with only 1 industry partner and zero SMEs. The 10% industry ratio signals this was built by researchers for researchers, not for the commercial market. The coordinator, NCSR Demokritos in Greece, is a major national research center. For a business considering adoption, this means the technology is scientifically robust but would likely need a commercial integrator to package it for enterprise use.

NATIONAL CENTER FOR SCIENTIFIC RESEARCH "DEMOKRITOS"Coordinator · EL
NATIONAL INFRASTRUCTURES FOR RESEARCH AND TECHNOLOGYparticipant · EL
ATHINA-EREVNITIKO KENTRO KAINOTOMIAS STIS TECHNOLOGIES TIS PLIROFORIAS, TON EPIKOINONION KAI TIS GNOSISparticipant · EL
CENTRE EUROPEEN DE RECHERCHE ET DEFORMATION AVANCEE EN CALCUL SCIENTIFIQUEparticipant · FR
KARLSRUHER INSTITUT FUER TECHNOLOGIEparticipant · DE
KONINKLIJK NEDERLANDS METEOROLOGISCH INSTITUUT-KNMIparticipant · NL
THE UNIVERSITY OF LIVERPOOLparticipant · UK
ISTITUTO NAZIONALE DI GEOFISICA E VULCANOLOGIAparticipant · IT
THE UNIVERSITY OF EDINBURGHparticipant · UK

How to reach the team

NCSR Demokritos, Greece — contact via SciTransfer for a warm introduction to the development team

Order a report on this consortium See NATIONAL CENTER FOR SCIENTIFIC RESEARCH "DEMOKRITOS" profile →

Next steps

Talk to the team behind this work.

Want to know if DARE's data platform components could accelerate your data processing pipeline? SciTransfer can arrange a technical briefing with the development team and assess fit for your specific use case.

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