We illustrate how combining retrospective and prospectiveprovenance can yield scientifically meaningful hybrid provenancerepresentations of the computational histories of data produced during a script run. We use scripts from multiple disciplines (astrophysics, climate science, biodiversity data curation, and social network analysis), implemented in Python, R, and MATLAB, to highlight the usefulness of diverse forms of retrospectiveprovenance when coupled with prospectiveprovenance. Users provide prospective provenance, i.e., the conceptual workflows latent in scripts, via simple YesWorkflow annotations, embedded as script comments. Runtime observables can be linked to prospective provenance via relational views and queries. These observables could be found hidden in filenames or folder structures, be recorded in log files, or they can be automatically captured using tools such as noWorkflow or the DataONE RunManagers. The YesWorkflow toolkit, example scripts, and demonstration code are available via an open source repository.

DataONE is a federated data network focusing on earth and environmental science data. We present the provenance and search features of DataONE by means of an example involving three earth scientists who interact through a DataONE Member Node. DataONE provenance systems enable reproducible research and facilitate proper attribution of scientific results transitively across generations of derived data products.

The DataONE federated data network has adopted and extended the PROV model to support the collection, storage, indexing, and user browsing of the provenance of data packages stored in its member nodes. The PROV extension, ProvONE, adds provenance elements (entity and relationships types) for describing process structure alongside the data dependencies that originate from process execution. The ProvONE model was defined in 2015 and its specification is available online [1], while provenance support based on the model is now (2016) in its pre-production phase.