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Data quality refers to the level of completeness of the pro forma data fields, and the accuracy with which information in the original source file (the medical record) is extracted and subsequently recorded on the pro forma.
Generally speaking, data quality refers to its fitness for serving its purpose in a given context [5].
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In this paper, data quality mainly refers in a data storage sense (e.g. accuracy, completeness and integrity) and not in a toxicological sense (e.g. the quality of experimental results).
In a toxicological sense, data quality may refer to not only the recorded chemical information, but also the quality of experimental results.
Collecting additional data regarding data quality (sometimes referred to as 'meta-data') can be helpful to the statistician at the analysis stage.
Quality refers to the information being correct and not misleading.
Quality refers to design quality, which may deteriorate over time.
Functional quality refers to how the service is being delivered.
aQuality-of-Data is a novel concept, akin to SLA, different from data quality, that traditionally refers to other issues such as internal data correctness, semantic coherence, data adherence to real-life sources, or data appropriateness for managerial and business decisions.
This procedure simply involves replacing the original value retrieved from the database by a poor data value, retrieved from our list of data quality problems (please refer to Table 1).
By using single value mutations, we might actually be able to emulate some data quality problems that refer to multiple values (e.g., violation of referential integrity, violation of functional dependency).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com