National Ocean Council Vocabulary

Project Title: Taxonomy-based interface for ocean community data within

Background: The National Ocean Council Data and Information Working Group (NOC-DIWG) has the goal of working within the framework of to create an information system that supports the objectives of the National Ocean Policy, especially to support the needs of the marine planning community for data and decision-support tools. Objective 1 of the NOC-DIWG Work Plan is "Improve the discoverability, curation, and presentation of data within's metadata catalog." A task within this objective is:

Task 1.2. Improve discoverability and curation of metadata within the catalog.

  • Building from previous work and in coordination with other relevant Federal data management working groups, develop and promulgate a consensus ocean data taxonomy that maximizes discoverability and enables effective curation of the catalog.
  • Evaluate optimal methodologies for employing such a taxonomy (e.g. through keywords or tags in the metadata records, or through tags within the catalog software external to the metadata). Determine whether existing metadata records need to be amended/updated to comply with the taxonomy, or if records can be tagged without changing the metadata.
  • Ensure that the thematic categories used as search features in the data catalog interface correspond with the taxonomy used in the data provider's metadata.

Results: The current project will develop a taxonomy-based interface that connects with the back-end repository CKAN and can be used as part of the community website to search the complete catalog for information relevant for marine planning, in effect creating a virtual catalog of ocean, coastal, and Great Lakes information that is automatically updated by the core governance processes of

Objective: To demonstrate technical capabilities that are available and can be deployed to implement solutions to key needs identified in the National Ocean Policy in regard to data and the decision-support requirements that arise from data-oriented questions.