Contextualized, Trustworthy, and Collective Scientific Decision Workflows

Abstract

Science gateways often capture workflow execution but not the rationale behind human decisions such as dataset selection, algorithm choice, parameterization, and result interpretation, which limits reproducibility. We propose a framework that captures and shares scientific decisions as verifiable and reusable knowledge assets. We formalize decision workflows as a Markov Decision Process (MDP), in which experimental contexts are defined as states, user choices as actions, and multi-criteria constraints as reward functions. To ensure tamper-evident provenance and secure attribution, decision records use JSON-LD linked to Decentralized Identifiers as Verifiable Credentials (VCs). The architecture consists of three layers: a User layer for interaction and control, a Service layer for ranking and credential management, and a Data layer for semantic storage and blockchain-based verification. We implemented a prototype on the Ethereum Sepolia testnet to demonstrate end-to-end decision capture, credential issuance, selective disclosure, and blockchain-based verification. In a user study, all finalized decisions were captured as structured records, and 45 of 51 were issued as user-controlled VCs. The MDP-based ranking achieved a Top-1 agreement of 92.2% with user selections, substantially outperforming an approximate random baseline. Interaction patterns suggest two modes: guided exploration for non-expert users and selective deviation from ranked recommendations by expert users. These results indicate that decision provenance can complement traditional workflow provenance by preserving the reasoning behind scientific choices. By combining decentralized identity, semantic interoperability, and probabilistic decision modeling, the framework supports transparent, user-controlled, and collaborative experimentation and provides a basis for collective reuse of decision knowledge.