Distinguishing Provenance Equivalence of Earth Science Data

Reproducibility of scientific research relies on accurate and precise citation of data and the provenance of that data. Earth science data are often the result of applying complex data transformation and analysis workflows to vast quantities of data. Provenance information of data processing is used for a variety of purposes, including understanding the process and auditing as well as reproducibility. Certain provenance information is essential for producing scientifically equivalent data. Capturing and representing that provenance information and assigning identifiers suitable for precisely distinguishing data granules and datasets is needed for accurate comparisons. This paper discusses scientific equivalence and essential provenance for scientific reproducibility. We use the example of an operational earth science data processing system to illustrate the application of the technique of cascading digital signatures or hash chains to precisely identify sets of granules and as provenance equivalence identifiers to distinguish data made in an an equivalent manner

Modeling domain metadata beyond metadata standards

The Laser Interferometer Gravitational-wave Observatory (LIGO) project to detect gravitational waves represents a complex, distributed scientific endeavor posing specific challenges for reproducibility and data management. The integration of provenance and other metadata information into the workflow stands as one means of addressing such challenges. The goal of a metadata model for the LIGO workflow is the provision of metadata describing all the data products at each significant milestone in the data analysis pipeline. Given both the highly specific domain and the need to support current analysis tools, the development of such a model demands a more complex, comprehensive approach. For this reason, we pursued a multipronged approach to metadata modeling, gathering users’ conceptions, system information, research artifacts, and other organizational documents, and worked to combine the findings into one final model. This approach provided a thorough understanding of the overall research lifecycle and insight into scientific workflow metadata modeling

2015 GREAT Day Program

SUNY Geneseo’s Ninth Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1009/thumbnail.jp