Pantelis Kanellopoulos

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. To that end, the RAISE project will provide the infrastructure for a distributed crowdsourced data processing system, moving from open data to open access data for processing. It will render the mechanism for sending the algorithm to the dataset instead of sending the data to the algorithm. RAISE introduces the Research Analysis Identifier (RAI) and services that will support trustworthy research and data analysis services based on crowdsourced distributed resources along with local resources. Particularly, RAISE will produce the following Outputs: RAI Certified nodes: A trustworthy crowdsourced network offering data storing and processing resources (community resources). The network will also provide the mechanisms for ensuring originality and authentication of data assets and research results., RAI Cloud: Orchestrates the data sharing, processing and finding., The Research Analysis Identifier (RAI): a unique identifier of any result along with the dataset information and the processing script (RAI Registration Service), without disclosing any source code or raw data., Dataset plagiarism identification and dataset proof-of-origin services, to maximize the level of trust of the RAISE system., RAI Synthetic Data Generator and the RAI SDK, to help researchers develop processing scripts by getting access to sample, small scale datasets which mock real data. Within the broad domain of Open Data Science, RAISE will test and assess the effectiveness of RAISE system in real life use cases in 3 scientific domains that are producing large amounts of data and facing major challenges on data sharing, and sensitive data handling. Health Domain, Enviroment Domain, Mobility Domain, and, Interdisciplinary.