Imagine trying to find a needle in a haystack, but the haystack is made of millions of different research papers and data files. This project builds a giant, smart digital library that automatically connects the dots between these documents. It helps researchers quickly see which findings are actually reliable and which trends are truly growing.
Companies waste significant time and money manually searching through fragmented, unstructured scientific papers to find reliable data. There is currently no easy way to verify if a research result is reproducible before investing in it.
A two-tier service architecture featuring a scientific data-lake-as-a-service and AI-assisted tools for discovering research impact and reproducibility.
If you are a drug discovery firm dealing with fragmented oncology research — this project developed a Scientific Lake service that extracts knowledge from unstructured data to accelerate the identification of research trends. This helps in finding valuable research objects faster.
If you are an R&D center dealing with inconsistent experimental results — this project developed a Smart Reproducibility Assistance service that highlights easy-to-reproduce research outputs. This reduces wasted spend on failed replications.
If you are a transport planner dealing with diverse scholarly content on urban mobility — this project developed an SKG Interoperability Framework that standardizes how scientific content is exposed. This allows for faster integration of academic insights into city planning.
Based on available project data, no specific pricing or cost model is mentioned; it is described as a service built on the open EOSC and OpenAIRE ecosystems.
The project is designed as a 'data-lake-as-a-service' and is currently being tested in 4 real-world pilots across different domains, suggesting a scalable architecture.
Based on available project data, specific licensing terms are not provided, though it leverages open ecosystem services like OpenAIRE.
It uses an SKG Interoperability Framework to standardize how contents are exposed to developers of added-value services.
The project period runs from 2023-01-01 to 2026-03-31.
The consortium consists of 14 partners across 9 countries, showing a strong European footprint. It is heavily weighted toward research (6) and universities (5), with a 14% industry ratio including 2 SMEs. This suggests the project is primarily driven by academic excellence but has a dedicated path to industrial application through its SME partners.
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