Topic 2 (ii) Metadata concepts, standards, models and registries Questions from opening session Common terminology – SDMX’s MCV, definitions for terms such as survey Classification / typology of statistical metadata

Topic 2 (ii) Metadata concepts, standards, models and registries Metadata classifications Multidimensional classification for statistical metadata – by purpose (who, why), by content (what), by source and by form (how) Metadata by usage or purpose All processes produce metadata metadata X each process – paradata (peridata) Metadata by content – object attachment Corporate metadata repository Process, data, conceptual object, collection source Finally combines –source X use

Metadata classifications (2) Do we need a metadata classification by form – coded, structured, organization ? SDMX requirements Structural metadata – descriptors of the data (Bo’s Table 2c) Reference metadata – contents and quality of data (Bo’s tables 2a, 2b and 2d) Archive metadata Administrative – Bo’s table 2b Structure – Bo’s table 2c Survey and definitional – Bo’s tables 2a, 2b, 2d

Metadata registries and harmonized content Metadata classifications and standards – How do we move to more harmonization of metadata standards and classifications? ESS – metadata are documented according to a standard metadata SDMX metadata standards – good start – should definitions be adopted as part of METIS CMF? Role of interoperability – should they be part of the CMF? PC AXIS – SDMX SDMX – 11179 other mappings / crosswalks

Data semantics and interoperability (data and semantics) Two papers – US paper on data and datatypes; Statistics Netherlands on metadata models (classifications and datasets) Data semantics – better understanding of the meaning of data (paragraph 2 US and paragraph 9 NLD) Synonymous information and equality Mechanisms needed to identify metadata items that are the same – synonyms For categorical data – equality is defined the same way as for numbers - semantics of values must be the same to allow for comparisons Are these ideas similar?

Statistical classifications Logic of classification systems is essentially Boolean algebra – new view of classifications Related view ontological systems Figures 5 and 6 – Is the rule the classes must be mutual exclusive not respected? Conclusion: formal semantics can play a constructive role in the development of models for statistical metadata How does this view relate to Dan’s – ontology is formal means for organizing data and data descriptions