What dimensions define data quality in research databases?

What dimensions define data quality in research databases?

• A. Color, size, novelty, popularity.
• B. Only timeliness matters.
• C. Accuracy, completeness, consistency, timeliness, validity, accessibility.
• D. Speed of access, license cost, vendor support.

Answer: (C)

Data quality in research databases is assessed across several interconnected dimensions that reflect how trustworthy and usable the data are. The key ones are accuracy (how close data values are to the true values), completeness (whether all required data and records are present), consistency (data align without contradictions across datasets or within fields), timeliness (how up-to-date the data are), validity (data conform to defined rules, formats, and constraints), and accessibility (data can be retrieved and used by authorized users in usable formats). Together, these aspects ensure researchers can rely on the data for accurate analyses and conclusions. The other options miss essential parts of data quality: color, size, novelty, and popularity aren’t standard quality dimensions; focusing only on timeliness ignores accuracy, completeness, and other critical factors; and speed of access or costs relate to performance or procurement rather than the intrinsic quality of the data itself.

Data quality in research databases is assessed across several interconnected dimensions that reflect how trustworthy and usable the data are. The key ones are accuracy (how close data values are to the true values), completeness (whether all required data and records are present), consistency (data align without contradictions across datasets or within fields), timeliness (how up-to-date the data are), validity (data conform to defined rules, formats, and constraints), and accessibility (data can be retrieved and used by authorized users in usable formats). Together, these aspects ensure researchers can rely on the data for accurate analyses and conclusions. The other options miss essential parts of data quality: color, size, novelty, and popularity aren’t standard quality dimensions; focusing only on timeliness ignores accuracy, completeness, and other critical factors; and speed of access or costs relate to performance or procurement rather than the intrinsic quality of the data itself.