Difference between revisions of "Data Understandability"
From Earth Science Information Partners (ESIP)
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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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== Subsections == | == Subsections == | ||
Revision as of 09:21, May 28, 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.
Subsections
- Data Quality Recommendations
- Data Quality/Lineage Recommendations
- External References Recommendations
- EOS Core System - Recommendations
- Provenance Recommendations
- User Feedback Recommendations
