DEIP is launching a pilot version of the Decentralized Assessment System (DAS) of FAIR datasets for EOSC

The pilot use-cases of the DAS will provide a potential step towards implementing a peer review infrastructure of FAIR datasets. The unexpected benefit of this pilot is aligned with the EOSC Interest Groups on Resercher Engagment and Use Cases. End Quote

(1888PressRelease) October 07, 2020 - The demo is based on DEIP’s own developed deep-tech innovation – Decentralized Assessment System (DAS). DAS is a peer review system that uses an incentive model with reputational rewards and produces a quantifiable metric about the quality and reliability of any dataset(s). DAS is designed specifically for asset assessment in expertise-intensive areas, such as scientific research. Thus, DAS introduces a comprehensive and robust assessment model:

- it sources the consensus about the quality of datasets among the domain experts through
continuous two-level peer-review;

- it ensures fair rewards for contributions and curation efforts;

- it formalizes the result of assessments into explicit metrics/indicators useful for non-experts.

The incentives system is built upon the following assumptions:

1) The scientific community will eventually reach a consensus about the quality and reliability of any datasets;

2) Pioneer supporters should get more rewards and recognition. With these assumptions, the system is designed in such a way that it incentivizes researchers to provide a comprehensive, high-quality, and unbiased review that shows the actual strengths and weaknesses of knowledge and technology under review.

The first step is to survey research communities to discover what criteria are important when assessing datasets reliability, and what the assessment processes within these communities are.

The next step is to implement customization and deploy the DAS platform. This includes deployment and configuration of the DAS, development of a separate portal for data assessment, and running agent-based simulations to determine optimal parameters of the DAS model depending on the population of selected discipline(s) and the number of participating projects.

The following step is to onboard researchers to participate in the pilot data creation and assessment processes. This includes reaching scientific communities, education, and training on how to use the DAS (via webinars).

Finally, the DEIP is going to perform a number of activities to make adjustments to the system to help its adoption and dissemination. Such activities include efficiency analysis, scalability of DAS that is applicable to FAIR data assessment, and creation of activity metrics visualization within the system during the pilot. The DEIP team will also analyze the results of the assessment, discuss it with scientific communities, and determine the ways to enrich metadata in accordance with the domain-specific community standards.

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