Topic 2 (ii) Metadata concepts, standards, models and registries

1
Topic 2 (ii) Metadata concepts,
standards, models and registries

• Questions from opening session
• Common terminology SDMXs MCV, definitions for
  terms such as survey
• Classification / typology of statistical metadata

2
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

3
Metadata classifications (2)

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

4
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

5
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?

6
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 Dans ontology is
  formal means for organizing data and data
  descriptions