Preferred Label : Data Quality;
NCIt definition : The condition of information that describes its credibility, authenticity, and appropriateness
for its function. Important aspects include source recognition, accuracy, innovativeness,
and information on changes to the data.;
Alternative definition : CDISC-GLOSS: A dimension of data contributing its trustworthiness and pertaining to
accuracy, sensitivity, validity, and suitability to purpose. NOTE: Key elements of
data quality include attribution, legibility (decipherable, unambiguous), contemporaneousness,
originality (i.e., not duplicated), accuracy (ALCOA), precision, completeness, consistency
(logical, not out of range), and those who have modified the data. Scientists may
reasonably trust data that are accurate (high quality) that have also been reviewed
by investigators and protected from unauthorized alteration (high integrity). [After
ICH E6; After MHRA GXP Data Integrity Guidance and Defintions, Revision 1, March 2018;
After 21 CFR Part 11] See also ALCOA, ALCOA , ALCOA , traceability (data), data integrity,
electronic data transfer.;
Origin ID : C142491;
UMLS CUI : C0242483;
Automatic exact mappings (from CISMeF team)
- Currated CISMeF NLP mapping
- See also inter- (CISMeF)
- Semantic type(s)
- concept_is_in_subset
