Research Analysis Identifier SystEm

Project Information

RAISE

Grant agreement ID: 101058479

DOI

10.3030/101058479

Project closed

EC signature date 7 June 2022

Start date 1 October 2022

End date 31 January 2026

Funded under

• Research infrastructures

Total cost

€ 4 836 125,00

EU contribution

€ 4 836 125,00

Coordinated by

ARISTOTELIO PANEPISTIMIO THESSALONIKIS
Greece

Periodic Reporting for period 3 - RAISE (Research Analysis Identifier SystEm)

Reporting period: 2024-10-01 to 2026-01-31

Summary of the context and overall objectives of the project

RAISE's mission is to establish the infrastructure for a decentralized crowdsourced data processing system, transitioning from open data to open-access data for processing. The key innovation lies in RAISE's capability to dispatch algorithms to datasets, as opposed to transferring the entire dataset to the algorithm. For the research community, the true value of open data lies not just in accessibility but in streamlined processing to enhance efficiency and productivity. RAISE is dedicated to fostering a transparent approach to sharing and processing data, empowering researchers to publish work with evidence-based authentication of data analysis while ensuring accreditation.

Adhering to the FAIR Guiding Principles (Findability, Accessibility, Interoperability, and Reusability) for scientific data management and stewardship, RAISE fundamentally shifts the traditional approach. Rather than downloading large datasets to a computer housing the processing algorithm, RAISE takes a novel approach by bringing the processing algorithm (small in size) to the dataset (large in size). To bolster dataset repository processing capacities, RAISE adopts the crowdsourcing concept, allowing researchers to seamlessly integrate computers into existing workflows to serve both datasets and processing needs.

RAISE is poised to deliver several impactful outcomes, including:

1. Establishing a reliable crowdsourced network of RAI Certified nodes offering data storage and processing resources.
2. Introducing the RAI Cloud platform to manage data sharing, processing, and discovery.
3. Introducing the Research Analysis Identifier (RAI), a unique identifier for any result, accompanied by dataset information and processing scripts, all without revealing source code or raw data.
4. Providing services for dataset plagiarism identification and proof-of-origin, maximizing trust in the RAISE system.
5. Developing the RAI Synthetic Data Generator to further enhance the system's capabilities.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

During the final reporting period, the consortium reached full technical maturity of the RAISE platform, delivering substantial scientific impact through its deployment, validation, and application in real-world research environments, as summarised below:
1) Robust Development and Evaluation Framework: Established a controlled testing/release pipeline with DSOP, OSAQ, SUS/QUESI metrics, confirming enhanced research workflows and Open Science adoption.
2) RAI PID Operationalization: Integrated DOI-compliant identifiers via DataCite for traceable, citable, reproducible outputs.
3) FAIR Data Infrastructure: Implemented OAI-PMH, metadata validation, and plagiarism detection for interoperable, integrity-assured data management.
4) Scaled Distributed Infrastructure: Deployed 24 RAI Certified Nodes for secure federated analysis, preserving institutional data sovereignty.
5) Enhanced Secure Architecture: Upgraded blockchain backend for superior traceability, efficiency, and security.
6) Production Deployment: Delivered end-to-end workflows via RAI Central Hub and Portal, supporting data governance, distributed execution, and provenance tracking.
7) Seamless Researcher Integration: Provided SDKs, IDE plugins, and collaborative workspaces for frictionless adoption in existing environments.
8) Real-World Validation: Pilots across biomedical, time-series, and environmental domains demonstrated gains in reproducibility, collaboration, and governance.
9) Reproducible Workflows and Outputs: Executed auditable processes on heterogeneous datasets, yielding 16 publications with RAI identifiers (including external contributions).
10) Agile Refinement: Incorporated continuous user feedback and UX testing to optimize usability and alignment with research needs.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

RAISE aims for cutting-edge advancements beyond the current state of the art, focusing on two key areas: technical innovations supporting open data processing and understanding the existing regulatory frameworks for FAIR data access. Recent technical progress includes:
1. Developing the RAISE blockchain server, integrating immutable identifiers for research components, and ongoing refinement of the RAI PID solution.
2. Advancing Metadata Standardization and Interoperability to capture project-specific data effectively.
3. Achieving strides in Synthetic Data Generation for enhanced privacy and machine learning performance.
4. Beginning development of a state-of-the-art Plagiarism Checker to safeguard researchers' work.
Additionally, RAISE has made headway in comprehending open science conditions:
1. Implementing Agile Methodology via the RAISE community for active researcher involvement.
2. Gaining insights into Researcher Needs for Open Science through interviews and pilots.
3. Initiating a Common Requirements Space, collaborating with EOSC projects and ensuring user-aligned solutions.