Provenance for actionable data products and indicators in marine ecosystem assessments

Printer-friendly version

Presented at the AGU Fall Meeting 2013


Ecosystem-based management of Large Marine Ecosystems (LMEs) involves the sharing of data and information products among a diverse set of stakeholders – from environmental and fisheries scientists to policy makers, commercial entities, nonprofits, and the public. Often the data products that are shared have resulted from a number of processing steps and may also have involved the combination of a number of data sources. The traceability from an actionable data product or indicator back to its original data source(s) is important not just for trust and understanding of each final data product, but also to compare with similar data products produced by the different stakeholder groups. For a data product to be traceable, its provenance, i.e., lineage or history, must be recorded and preferably machine-readable. We are collaborating on a use case to develop a software framework for the bi-annual Ecosystem Status Report (ESR) for the U.S. Northeast Shelf LME. The ESR presents indicators of ecosystem status including climate forcing, primary and secondary production, anthropogenic factors, and integrated ecosystem measures. Our software framework retrieves data, conducts standard analyses, provides iterative and interactive visualization, and generates final graphics for the ESR. The specific process for each data and information product is updated in a metadata template, including data source, code versioning, attribution, and related contextual information suitable for traceability, repeatability, explanation, verification, and validation. Here we present the use of standard metadata for provenance for data products in the ESR, in particular the W3C provenance (PROV) family of specifications, including the PROV-O ontology which maps the PROV data model to RDF. We are also exploring extensions to PROV-O in development (e.g., PROV-ES for Earth Science Data Systems, D-PROV for workflow structure). To associate data products in the ESR to domain-specific ontologies we are also exploring the Global Change Information System ontology, BCO-DMO Ocean Data Ontology, and other relevant published ontologies (e.g., Integrated Ocean Observing System ontology). We are also using the mapping of ISO 19115-2 Lineage to PROV-O and comparing both strategies for traceability of marine ecosystem indicators. The use of standard metadata for provenance for data products in the ESR will enable the transparency, and ultimately reproducibility, endorsed in the recent NOAA Information Quality Guidelines. Semantically enabling not only the provenance but also the data products will yield a better understanding of the connected web of relationships between marine ecosystem and ocean health assessments conducted by different stakeholder groups.

Project Website


DateCreated ByLink
December 14, 2013
Patrick WestDownload

Related Projects:

ECOOP LogoEmploying Cyber Infrastructure Data Technologies to Facilitate IEA for Climate Impacts in NE & CA LME's (ECO-OP)
Principal Investigator: Peter Fox
Co Investigator: Andrew Maffei
Description: The purpose of this INTEROP proposal is to facilitate the deployment of an Integrated Ecosystem Approach (IEA) to management in the Northeast and California Current Large Marine Ecosystems (LMEs). The direct result of the proposed activity will be application-level data and information enhanced communication for developing the consensus networks to define the specific components of interest to support the implementation of NOAA’s Driver-Pressure-State-Impact Response framework (DPSIR) decision framework and the cyberinfrastructure technologies to ensure data interoperability and reuse.

Related Research Areas:

Knowledge Provenance
Lead Professor: Deborah L. McGuinness
Description: Knowledge Provenance
Concepts: Provenance,
Semantic eScience
Lead Professor: Peter Fox
Science has fully entered a new mode of operation. E-science, defined as a combination of science, informatics, computer science, cyberinfrastructure and information technology is changing the way all of these disciplines do both their individual and collaborative work.
As semantic technologies have been gaining momentum in various e-Science areas (for example, W3C's new interest group for semantic web health care and life science), it is important to offer semantic-based methodologies, tools, middleware to facilitate scientific knowledge modeling, logical-based hypothesis checking, semantic data integration and application composition, integrated knowledge discovery and data analyzing for different e-Science applications.
Partially influenced by the Artificial Intelligence community, the Semantic Web researchers have largely focused on formal aspects of semantic representation languages or general-purpose semantic application development, with inadequate consideration of requirements from specific science areas. On the other hand, general science researchers are growing ever more dependent on the web, but they have no coherent agenda for exploring the emerging trends on the semantic web technologies. It urgently requires the development of a multi-disciplinary field to foster the growth and development of e-Science applications based on the semantic technologies and related knowledge-based approaches.

Concepts: eScience
Lead Professor: Peter Fox
Description: In the last 2-3 years, Informatics has attained greater visibility across a broad range of disciplines, especially in light of great successes in bio- and biomedical-informatics and significant challenges in the explosion of data and information resources. Xinformatics is intended to provide both the common informatics knowledge as well as how it is implemented in specific disciplines, e.g. X=astro, geo, chem, etc. Informatics' theoretical basis arises from information science, cognitive science, social science, library science as well as computer science. As such, it aggregates these studies and adds both the practice of information processing, and the engineering of information systems.
Concepts: , eScience