Scientific Computing and Data Management

Scientific computing and data management examines how researchers design, execute, and preserve the computational processes that produce scientific results, with particular attention to whether those results can be independently verified and retraced. At its core, the work addresses data provenance—the detailed record of where data came from and how it was transformed—alongside the systems that orchestrate complex, multi-step analytical pipelines common in fields like bioinformatics. As computational research scales across distributed cyberinfrastructure and increasingly relies on third-party services and automated workflows, ensuring reproducibility becomes both more consequential and more technically demanding. Active directions include developing standards for capturing workflow metadata in interoperable ways, and understanding how semantic technologies can make distributed computational services discoverable and composable without sacrificing the auditability that rigorous science requires.

Works

479,638

Total citations

584,695

Keywords

Scientific WorkflowsReproducibilityData ProvenanceWorkflow ManagementBioinformaticsSemantic Web Services