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Author Madin, Joshua ♦ Bowers, Shawn ♦ Schildhauer, Mark ♦ Krivov, Sergeui ♦ Pennington, Deana ♦ Villa, Ferdinando
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Ecological Data ♦ Complex Ecological Data ♦ Semantic Concept ♦ Natural Environment ♦ Contact Information ♦ Darwin Core ♦ Specific Aspect ♦ Standard Protocol ♦ Labor-intensive Task ♦ Synthetic Analysis ♦ Powerful Way ♦ Data Owner ♦ Major Challenge ♦ Well-established Repository ♦ Variable Name ♦ Semantic Subtlety ♦ Data Content ♦ Focused Study ♦ Ecological Observation Data ♦ Relevant Data ♦ Data Type ♦ Important Step ♦ Access Ecological Data ♦ Ecological Data Set ♦ Broad Range ♦ Metadata Standard ♦ Keyword Description
Description Research in ecology increasingly relies on the integration of traditionally small, focused studies, to produce larger datasets that allow for more powerful, synthetic analyses. The results of these synthetic analyses are critical in guiding decisions about how to sustainably manage our natural environment, so it is important for researchers to effectively discover relevant data, and appropriately integrate these within their analysis. This is a major challenge however, as ecological data encompass an extremely broad range of data types, structures, and semantic concepts. Moreover, ecological data is widely distributed, with few well-established repositories or standard protocols for their archiving and retrieval. These factors presently make the discovery and integration of ecological data sets a highly labor-intensive task. Metadata standards such as EML and Darwin Core are important steps for improving our ability to discover and access ecological data, but are limited to describing only a few, relatively specific aspects of data content (e.g., data owner and contact information, variable “names”, keyword descriptions, etc.). A more flexible and powerful way to capture the semantic subtleties of complex ecological data, its structure and contents, and the inter-relationships among data
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2007-01-01
Publisher Institution Proceedings of the 5th International Conference on Ecological Informatics ISEI5