Quality Assurance: Difference between revisions
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= Quality Checks = | = Quality Checks = | ||
The ADA expects a data deposit to be of a certain quality when it is submitted (see [[ | The ADA expects a data deposit to be of a certain quality when it is submitted (see [[Quick Deposit Guide]] for instructions). Nevertheless, all data deposits are assessed for quality by an ADA archivist. This assessment includes both the content and the form. On the content side, the ADA archivist will scrutinise the data for direct and indirect identifiers. On the form side, the ADA archivist will check that all variables have un-ambiguous clear labels for all variables and runs spell checks, as well as basic consistency and completeness checks. | ||
= Data Curation = | = Data Curation = | ||
Revision as of 05:14, 4 September 2024
Quality Checks
The ADA expects a data deposit to be of a certain quality when it is submitted (see Quick Deposit Guide for instructions). Nevertheless, all data deposits are assessed for quality by an ADA archivist. This assessment includes both the content and the form. On the content side, the ADA archivist will scrutinise the data for direct and indirect identifiers. On the form side, the ADA archivist will check that all variables have un-ambiguous clear labels for all variables and runs spell checks, as well as basic consistency and completeness checks.
Data Curation
The ADA archivist will propose changes to the depositor (see Workflows for details on data curation). If the depositor does not agree to changes the ADA archivist deems absolutely necessary, a deposit can be rejected. The ADA archivist will implement the agreed upon changes and furthermore generate versions of the data for SPSS, SAS, STATA and CSV for maximal usability. All submitted data is subject to data-level curation (see Background Part 5).
Documentation & Metadata
Accompanying documents are uploaded to ensure comprehension of the study and the data sets. Moreover, the archivist will liaise with the depositor to ensure all necessary value labels and codes are defined, that metadata fields can be completed according to Data Documentation Initiative (DDI) standards and that the data is understandable to other researchers. It is not a requirement that a study meets all fields of the DDI but it is imperative for it to meet a minimum requirement to enable reuse.
The ADA encourages depositors to include references to related publications and other relevant digital resources in their project metadata with appropriate metadata fields provided on Dataverse for this purpose.
Vocabulary & Classification
The ADA uses APAIS vocabulary for keywords in the Dataverse catalogue, as well as ANZSRC FoR codes for topic classification.