| Popis: |
Since DDI Lifecycle 3.2, the variable cascade has allowed data producers to define the concordance among variables in datasets. Since at least 2014, several longitudinal studies and national statistics organizations have used this metadata structure to document and publish information on variables within single studies or organizations. Separating data definitions into conceptual variables, represented variables, and instance variables allows precise documentation of the data and how it changes over time. The same basic metadata structure can be used to document concordance across studies. Since separate organizations often create conceptual variables with similar meanings as conceptual variables from other organizations, the challenge becomes declaring the comparability of conceptual variables. For the intellectual content work of deciding on comparability, this presents an easier task than individual concording a large number of instance variables. For the technical task of defining the concordance in a structured manner, DDI Lifecycle offers a standardized solution. This presentation will provide an analysis of the DDI Lifecycle metadata structure used to perform the cross study concordance; discuss the workflow used to harmonize hundreds of variables from several large, longitudinal studies; and demonstrate the software tools used to create, publish, and visualize the data concordance. Recorded version of the whole session: https://youtu.be/mBJhJPGM60g |
