Difference between revisions of "Data Understandability"

From Federation of Earth Science Information Partners
 
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Understandability is the ability to understand the meaning or construe the significance of data by analyzing contextual information. The who, what, when, where, and under what conditions the data was collected are just a sampling of the contextual information that must be addressed in the metadata in order for data to be interpreted correctly.
 
Understandability is the ability to understand the meaning or construe the significance of data by analyzing contextual information. The who, what, when, where, and under what conditions the data was collected are just a sampling of the contextual information that must be addressed in the metadata in order for data to be interpreted correctly.
 
Thus, understandability of a dataset depends on the completeness of the metadata. This sub-section aims to clearly depict understandability documentation needs and concepts and map to them in the different dialects — thereby facilitating completeness and increasing the understandability of the data.
 
Thus, understandability of a dataset depends on the completeness of the metadata. This sub-section aims to clearly depict understandability documentation needs and concepts and map to them in the different dialects — thereby facilitating completeness and increasing the understandability of the data.
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*[[Data Understanding - Quality (ISO-19157)]]
 
*[[Data Understanding - Quality (ISO-19157)]]

Latest revision as of 10:58, July 29, 2015

Background

Understandability is the ability to understand the meaning or construe the significance of data by analyzing contextual information. The who, what, when, where, and under what conditions the data was collected are just a sampling of the contextual information that must be addressed in the metadata in order for data to be interpreted correctly. Thus, understandability of a dataset depends on the completeness of the metadata. This sub-section aims to clearly depict understandability documentation needs and concepts and map to them in the different dialects — thereby facilitating completeness and increasing the understandability of the data.



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