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This discussion focuses on improving transparency in data access, selection, and processing workflows used in scientific research. It covers the importance of replicability in producing reproducible science, methods to document and capture data workflows, and the significance of citable products and algorithms. We will explore the potential of shared platforms to facilitate data annotation, reference datasets, and digital library services, as well as the need for effective management of derived data, including time series and algorithm provenance to ensure scientific integrity.
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First Discussions • Transparent data access • Data selection on raw, QC, and derived products • Shell we use the astronomers model? • Information Centre? • Definition of a processing chain (Language)? • Replicability?
Reproducible Science(Sharing of derived data) • Capture the workflow • Capture data and algorithm provenance • Workbench • Including endorsed waveform (time series) processing primitives • Workflow language • Controllable sharing of results • Citable products and algorithms • Derived data (catalogue, time series, selections,…) • Annotation service and tools (visibility) • Reference data set and models (bench marking) • Digital library service
