IREF419431false2026-05-13T03:12:21ZSDMX Cross Domain Concept SchemeThe Cross Domain Concept Scheme (starting from v2.0) is created from the set of concepts in the SDMX Glossary, which is an SDMX guideline containing concepts and related definitions that are useful for building and understanding data and metadata exchange arrangements based on SDMX. The Glossary provides definition of terms found in the SDMX Information Model, Data Structure Definitions (DSDs) and Metadata Structure Definitions (MSDs) at the time of the present release. It is recommended as a single entry point to a common SDMX terminology to be used in order to facilitate communication and understanding of the standard.In short, the overall message of the glossary is the following: if a term is used, then its precise meaning should correspond to the SDMX Glossary definition, and any reference to a particular phenomenon described in the SDMX Glossary should use the appropriate term.Version 1.0 of the SDMX Glossary, which replaces the Metadata Common Vocabulary (MCV) published in 2009, was finalised in February 2016. The Cross Domain Concept Scheme v1.0 was created from the MCV concepts.For any question, comment or correction, feel free to contact the SDMX Statistical Working Group (SWG) at the following address: swg@sdmx.org.CONTEXTWhen originating from different sources, and in particular from statistical surveys using different methodology, statistics are often not completely identical, but show differences in results due to different collection methodology concepts, classifications and methodological standards. There are several areas where the assessment of coherence is regularly conducted: between provisional and final statistics, between annual and short-term statistics, between statistics from the same socio-economic domain, and between survey statistics and national accounts. The concept of coherence is closely related to the concept of comparability between statistical domains. Both coherence and comparability refer to a data set with respect to another. The difference between the two is that comparability refers to comparisons between statistics based on usually unrelated statistical populations and coherence refers to comparisons between statistics for the same or largely similar populations. In the Data Quality Assessment Framework (DQAF) of the International Monetary Fund, the term "consistency" is used for indicating "logical and numerical coherence". In that framework, "internal consistency" and "intersectoral and cross-domain consistency" can be mapped to "internal coherence" and "cross-domain coherence" respectively.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSCoherence - cross-domain; Coherence - internal; Coherence - National Accounts; Coherence - sub-annual and annual statistics; ComparabilitySOURCESDMX (2016) (https://sdmx.org/)CoherenceAdequacy of statistics to be reliably combined in different ways and for various uses.