Nongeospatial metadata for the ecological sciences

Publication Type:

Journal Article

Source:

Ecological Applications, Ecological Society of America, Washington, DC, Volume 7, Issue 1, p.330-342 (1997)

Call Number:

A97MIC01IDUS

Abstract:

Issues related to data preservation and sharing are receiving increased attention from scientific societies, funding agencies, and the broad scientific community. Ecologists, for example, are increasingly using data collected by other scientists to address questions at broader spatial, temporal, and thematic scales (e.g., global change, biodiversity, sustainability). No data set is perfect and self-explanatory. Ecologists must, therefore, rely upon a set of instructions or documentation to acquire a specific data set, determine its suitability for meeting specific research objectives, and accurately interpret results from subsequent processing, analysis, and modeling. “Metadata” represent the set of instructions or documentation that describe the content, context, quality, structure, and accessibility of a data set. Although geospatial metadata standards have been developed and widely endorsed by the geographical science community, such standards do not yet exist for the ecological sciences. In this paper, we examine potential benefits and costs associated with developing and implementing metadata for nongeospatial ecological data. We present a set of generic metadata descriptors that could serve as the basis for a “metadata standard” for nongeospatial ecological data. Alternative strategies for metadata implementation that meet differing organizational or investigator-specific objectives are presented. Finally, we conclude with several recommendations related to future development and implementation of ecological metadata.

Notes:

Reference Code: A97MIC01IDUS

Full Citation: Michener W.K., J.W. Brunt, J.J. Helly, T.B. Kirchner, and S.G. Stafford .1997. Nongeospatial metadata for the ecological sciences. Ecological Applications 7(1):330-342.

Location: COMMUNITY CLASSIFICATION - SAMPLING AND ANALYSIS

Keywords: data archive, data lineage, data management, information science, metadata, quality assurance