“mechanisms [that] are designed to prevent the introduction of errors into a data set, a process known as data contamination” QA/QC from Brunt 2000 • Commission: Incorrect or inaccurate data are entered into a dataset • – Can be easy to find • – Malfunctioning instrumentation • • Sensor drift • • Low batteries • • Damage • • Animal mischief • – Data entry errors • Omission: Data or metadata are not recorded • – Difficult or impossible to find • – Inadequate documentation of data values, sampling methods, anomalies in field, human errors
Verification and Validation • Verification – has data been entered correctly? • Validation – does data make sense ecologically? • Handout 5-1: Example of data verification request • Handout 5-2: Response from person responsible for data entry • Handout 5-3: Example of a data sheet with quality assurance samples • Handout 5-4: Example of a data sheet with a missing value
Verification and Validation • Handout 5-6: Total carbon duplicates - to test precision of measurements • Handout 5-7: Total carbon duplicates – to establish measurement quality objectives • Handout 5-8: % plant cover duplicates – comparison of results from routine field crews with an independent QA crew
Verification Process Types • Visual review at data entry (2nd person check) • Visual review after data entry (print out and review) • Duplicate data entry (enter random data in a testing DB) • Project Leaders are fully responsible for data (both verification and validation) • 100% of records checked by data entry staff • >= 10% random records checked by Project Leader for verification
NPS Director’s Order 11BEnsuring Quality of Information Disseminated by the NPS Defines quality as three key components Objectivity Utility Integrity You Passed!