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    DATA QUALITY ASSESSMENT IN CCAMLR: REQUIREMENTS FOR MINIMUM INTEGRITY TESTING TO ENSURE THAT DATA ARE FIT FOR PURPOSE

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    Document Number:
    WG-SAM-09/05
    Author(s):
    Secretariat
    Abstract

    The level of data quality assessment and ‘certification’ as ‘fit for purpose’ depends upon the different requirements for the data, together with a recognition that, however thorough a validation exercise the data are exposed to, there will always be some data that require some additional confirmation of their validity. In the context of CCAMLR, data may undergo one set of quality testing in order to establish whether they are appropriate for entry into databases, and further testing, as required, to determine data integrity for a specific analysis. Identifying these levels of data validation is important for prioritizing the work of the Secretariat in data quality assessment, and also in ensuring that users of CCAMLR data are fully aware of the integrity procedures that have been applied to the data. CCAMLR data are validated at various levels, from data collection and submission by owners/originators, through the Secretariat’s data processing, to analyses conducted by scientists and Working Groups. Data errors and discrepancies are resolved in consultation with data owners/originators and, where possible, corrections and annotations are made to individual records. WG-SAM-08/13 illustrated inconsistencies and errors in fishery data which originated at the vessel-level, and indicated that some errors were not detected during the Secretariat’s data validation. Further, some data had been inadvertently replicated by the Secretariat following repeated data submissions. The methods reported in that paper are now being modified for implementation as part of the Secretariat’s continued work on improving the quality of CCAMLR data. Further work to improve the assessment of data quality and the use of CCAMLR data includes, inter alia, developing further validation procedures and data quality metrics, and integrating these procedures across related CCAMLR datasets. Consideration may also be given to establishing a formal data review/reconciliation procedure with Members to ensure that CCAMLR data are consistent and current with those of data owners/originators.

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