Presentation Open Access

Building a Distributed, Credible and Scalable Research and Data Infrastructure for Open Science

Anton Polishko; Artur Kiulian; Maksym Komar; Vyacheslav Tykhonov

At CoronaWhy we are building a Common Research and Data Infrastructure for Open Science that can be used by researchers coming from various scientific communities involved in COVID-19 research. This distributed and scaled infrastructure follows Reproducible Science and FAIR principles and should be suitable for other important scientific challenges such as cancer
and AIDS research. The vision of the community is to build this Artificial Intelligence infrastructure completely from Open Source components and with publicly available ML models like scispaCy developed by AI2 and other organizations. All data should be published and curated in Dataverse where the provenance information is also available for every dataset. To make the pipeline reliable and verified by human experts, we are running two different annotations services, Hypothesis for the evaluation of the statements extracted from COVID-19 related papers and Doccano for Natural Language Processing annotations.

 

Building a Distributed, Credible and Scalable Research and Data Infrastructure for Open Science

https://www.youtube.com/watch?v=aeS-vzbMm7I

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