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Precision Public Health: Dataset Usability

This guide documents the initative to develop a infrastructure of precision public health @UF

Why is Data Usability Important ?

Spatial data usability can be effected by differences in data structures, geographic scale and language. A lack of guiding principles may cause dataset fragmentation, gaps in availability,incompleteness and duplication of information. To ensure the usability of data there are several vital dimensions and elements that should be included. It is important to incorporate data usability and "tidy" dataset techniques as a tactic to reduce the time needed to clean, verify and test datasets usability prior to manipulate.

Dimensions of Data Usability

There are multiple dimensions to data usability:

  • Relevance: data must address an information need
  • Quality: data must be of acceptable quality for the intended purpose
  • Coverage and Granularity: data must have adequate coverage and be structured at the right level of granularity
  • Accessibility and Documentation: data must be accessible, with sufficient metadata for potential users to understand their derivation and meaning
  • Ease of Analysis: appropriate tools must be available to manipulate the data (e.g., filtering, sorting, aggregating) and viewing the data (e.g., mapping and charting). In some cases, specialized methodologies and tools are needed to perform statistical analysis or predictive modeling


Elements of Data Usability

Source: Directions Magazine (2010) Evaluating the Usability of Aggregated Datasets in the GIS4EU Project


Library of Congress Dataset Usability

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