According to the European Research Data Landscape – Final report, a survey involving almost 9,898 responders, highlighted some of the main barriers to management and sharing of research data: time, effort, storage, skills required, and the lack of recognition and data protection. RAISE Suite will develop a system specifically designed to remove barriers to data sharing, replacing technological achievements that do not influence researchers’ attitude towards sharing data. To do so, RAISE Suite will develop the solutions required to automate the process from data collection to dataset generation, guided by a FAIR-by-design principle to remove barriers such as perceived effort, time, as well as skills required for data sharing. At the same time, EOSC-RAISE will be integrated into RAISE Suite, for a platform which supports simple dataset sharing and exploitation, mitigating the sense of lack of recognition and data protection among researchers. Furthermore, RAISE Suite will implement a DMP-guided data collection and management policy. In particular, RAISE Suite will not only adopt a Machine Actionable Data Management Plan (ma-DMP), but further extend it to support designated actions, τurning the persistent identifier DMP-ID into the main reference point for the whole data lifecycle, following research activities, making the connections with underlying algorithms and data, and updating the DMP accordingly from collection, depositing and storing, to discovery, management, processing, reusing and exploitation. RAISE Suite capitalises on the results of a previously funded EC initiative. To this end, RAISE Suite will leverage work done by the EOSC-RAISE project, incorporating its technical platform that moves from open data to data open for processing, introducing the technology required to cover the data lifecycle from the data collection to the dataset generation.

The mission of RAISE is to provide the infrastructure for a distributed crowdsourced data processing system, moving from open data to open access data for processing. RAISE will provide the mechanism for sending the algorithm to the dataset instead of sending the data to the algorithm. The real value of open data for the research community is not to access them but to process them as conveniently as possible in order to reduce time-to-result and increase productivity. RAISE aims at promoting a transparent way of sharing and processing data, enabling the research community to publish their work with evidence-based authenticity of the data-analysis performed, ensuring at the same time the accreditation of their work. RAISE will be grounded on the fundamental principle defined in the FAIR Guiding Principles for scientific data management and stewardship (Findability, Accessibility, Interoperability and Reusability). To do so, RAISE brings the processing algorithm (small size) to the dataset (large size) instead of downloading the dataset to the computer where the processing algorithm is. To increase the processing capacity of the dataset repositories, RAISE borrows the crowdsourcing concept where researchers can easily integrate in the existing workflows computers serving both their datasets and the processing capacity. RAISE will produce the following Outputs: 1. A trustworthy crowdsourced network of RAI Certified nodes offering data storing and processing resources 2. The RAI Cloud platform to orchestrate the data sharing, processing and finding. 3. The Research Analysis Identifier ? RAI , a unique identifier of any result along with the dataset information and the processing script, without disclosing any source code or raw data. 4. Dataset plagiarism identification and dataset proof-of-origin services, to maximise the level of trust of the RAISE system. 5. The RAI Synthetic Data Generator