Periodic Reporting for period 1 - RAISE (Research Analysis Identifier SystEm)
Période du rapport: 2022-10-01 au 2023-09-30
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.
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.
Based on the project’s objectives, the consortium has made significant progress towards delivering scientific impact where its key accomplishments are listed below:
· An agile iterative methodology has been adopted to connect with the RAISE community and allow them to test different releases of the RAISE system (see roadmap at https://raise-science.eu/raise-community/) and provide feedback for changes.
· A workshop was organized for introducing researchers to the initial mock-up of RAISE’s approach. The aim was to show the interaction with basic features to help the participants understand better the system and, also, receive valuable insights for the improvement of user experience.
· The initial RAISE solution prototype was deployed in M12, including the registration of research analysis results, producing RAI ids. The current RAI id implementation will require updates to incorporate EOSC PID policy recommendations.
· The incipient state of RAI id PID, initial opening of the RAISE solution, and required publication revision and publication timing has not allowed to provide any publication with a RAI id for the moment, but the work towards technology enablers of this KPI is progressing.
· DANS FAIRdata and ARDC FAIR assessment tools were studied to include FAIR best practices in RAISE's data management solution's design and to incrementally evolve the solution towards the KPIs achievement.
· The RAISE Certified Node, a crowdsourced processing system architecture of distributed Nodes, is under development.
· The RAISE Blockchain Server, which will ensure the source of research analysis results and make them resistant to modification, is under development.
· The RAISE initial prototype has been deployed.
· Communication and awareness activities are underway to promote the RAISE platform and its potential benefits to the EOSC community and beyond.
· The RAISE RAI id PID solution is in its initial version and still needs to be matured.
· RAI ids need to be included in publications through established review and publication processes before having RAISE RAI id PID solutions adopted as part of publishing organizations' processes.
· An agile iterative methodology has been adopted to connect with the RAISE community and allow them to test different releases of the RAISE system (see roadmap at https://raise-science.eu/raise-community/) and provide feedback for changes.
· A workshop was organized for introducing researchers to the initial mock-up of RAISE’s approach. The aim was to show the interaction with basic features to help the participants understand better the system and, also, receive valuable insights for the improvement of user experience.
· The initial RAISE solution prototype was deployed in M12, including the registration of research analysis results, producing RAI ids. The current RAI id implementation will require updates to incorporate EOSC PID policy recommendations.
· The incipient state of RAI id PID, initial opening of the RAISE solution, and required publication revision and publication timing has not allowed to provide any publication with a RAI id for the moment, but the work towards technology enablers of this KPI is progressing.
· DANS FAIRdata and ARDC FAIR assessment tools were studied to include FAIR best practices in RAISE's data management solution's design and to incrementally evolve the solution towards the KPIs achievement.
· The RAISE Certified Node, a crowdsourced processing system architecture of distributed Nodes, is under development.
· The RAISE Blockchain Server, which will ensure the source of research analysis results and make them resistant to modification, is under development.
· The RAISE initial prototype has been deployed.
· Communication and awareness activities are underway to promote the RAISE platform and its potential benefits to the EOSC community and beyond.
· The RAISE RAI id PID solution is in its initial version and still needs to be matured.
· RAI ids need to be included in publications through established review and publication processes before having RAISE RAI id PID solutions adopted as part of publishing organizations' processes.
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.
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.