Overview

The ease with which large amounts scientific data can be distributed and shared today facilitates interdisciplinary, integrative science. Yet, formal training to evaluate data quality is discipline-specific and interdisciplinary scientists who use data from a broad range of disciplines often have no formal training in evaluating data quality from another branch of science. The following educational modules focus upon measures and metrics to evaluate data quality in biological and environmental science.

Definitions

Data - as used here refers to numeric or categorical values obtained by direct measurement (e.g., a length, temperature, age). 

Data quality - Four categories of data quality have been defined: intrinsic, contextual, representational, and accessibility (Wang et al. 2002) . The topics in this module deal primarily with intrinsic data quality. 

Data quality assessment - the process of evaluating whether or not a data set is suitable for its intended purpose.

Correspondence to:

Karen.Watanabe@asu.edu