A Blockchain-Based Architecture for Trust in Collaborative Scientific Experimentation

Abstract

In scientific collaboration, data sharing, the exchange of ideas and results are essential to knowledge construction and the development of science. Hence, we must guarantee interoperability, privacy, traceability (reinforcing transparency), and trust. Provenance has been widely recognized for providing a history of the steps taken in scientific experiments. Consequently, we must support traceability, assisting in scientific results’ reproducibility. One of the technologies that can enhance trust in collaborative scientific experimentation is blockchain. This work proposes an architecture, named BlockFlow, based on blockchain, provenance, and cloud infrastructure to bring trust and traceability in the execution of collaborative scientific experiments. The proposed architecture is implemented on Hyperledger, and a scenario about the genomic sequencing of the SARS-CoV-2 coronavirus is used to evaluate the architecture, discussing the benefits of providing traceability and trust in collaborative scientific experimentation. Furthermore, the architecture addresses the heterogeneity of shared data, facilitating interpretation by geographically distributed researchers and analysis of such data. Through a blockchain-based architecture that provides support on provenance and blockchain, we can enhance data sharing, traceability, and trust in collaborative scientific experiments.

Advancing Research Reproducibility in Machine Learning through Blockchain Technology

“…Bathen and Jadav (2022) introduces a framework that combines AutoML techniques with blockchain to fully decentralize the design and training process, with the aim of introducing trust in AutoML pipelines without trusting the nodes performing various design, training, and testing steps. Coelho et al (2022) proposes a blockchain platform architecture for collaborative research and reproducibility of experiment results, evaluating it using a scenario about genomic sequencing of the SARS-CoV-2 coronavirus. Khoi Tran et al (2022) designs a decentralized platform based on blockchain and smart contracts for disseminating, storing, and updating ML provenance.…”