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 reliably reproduced by others. Central concerns include tracking data provenance—the full chain of inputs, transformations, and software versions that led to a given output—and building workflow management systems that automate complex, multi-step analyses in domains like genomics and climate modeling. The field intersects with cyberinfrastructure development and semantic web technologies, which aim to make distributed data sources and services interoperable across institutions. Open challenges include establishing provenance standards that work across heterogeneous computing environments and ensuring that workflows remain executable years after their original publication, as software dependencies and data repositories inevitably change.

Works

471,148

Total citations

577,543

Keywords

Scientific WorkflowsReproducibilityData ProvenanceWorkflow ManagementBioinformaticsSemantic Web Services