|Title||Principals of Data Quality|
|Publication Type||Web Article|
|Year of Publication||2005|
There are many data quality principles that apply when dealing with species data and especially with the spatial aspects of those data. These principles are involved at all stages of the data management process. A loss of data quality at any one of these stages reduces the applicability and uses to which the data can be adequately put. These include: Data capture and recording at the time of gathering, Data manipulation prior to digitisation (label preparation, copying of data to a ledger, etc.), Identification of the collection (specimen, observation) and its recording, Digitisation of the data, Documentation of the data (capturing and recording the metadata), Data storage and archiving, Data presentation and dissemination (paper and electronic publications, web-enabled databases, etc.), Using the data (analysis and manipulation). All these have an input into the final quality or “fitness for use” of the data and all apply to all aspects of the data – the taxonomic or nomenclatural portion of the data – the “what”, the spatial portion – the “where” and other data such as the “who” and the “when” (Berendsohn 1997). Before a detailed discussion on data quality and its application to species-occurrence data can take place, there are a number of concepts that need to be defined and described. These include the term data quality itself, the terms accuracy and precision that are often misapplied, and what we mean by primary species data and species-occurrence data.