1. Petr Elias Czech Statistical Office
WHAT IS METADATA?
Petr Elias
Czech Statistical Office

2. The main goal of statistics
???
...to produce statistical data,
interpret them and
make them available to users.

3. Example of data to be published
Average household size
Netherlands
Published: January 2012
2,7
Survey: Census 2011
Preliminary results

4. Average household size
Statistical metadata
Data about statistical data (OECD definition)
Information about
data
processes (of producing and using data)
tools involved
(UNECE definition)
2,7
Published: January 2012
Average household size
Netherlands
Survey: Census 2011
Preliminary results

5. Users of metadata Data providers and interviewers Statisticians
questionnaires
Statisticians
data processing and analyses
End-users
search for and understanding of data

6. Metadata coverage (GSBPM*)
* GSBPM = Generic Statistical Business Process Model
Source:

7. Categories of metadata
Structural metadata
Reference metadata =
Identification and description of data
Description of the content and the quality of data

8. = Names of columns / dimensions
Structural metadata
Must be associated with data
= Names of columns / dimensions
Necessary for:
identification, retrieval and navigation through the data
understanding the data from matrixes and data cubes

9. Structural metadata – example
Nights spent by non-EU residents inside EU – per population
2005 
2006 
2007 
2008 
2009 
2010 
EU - 27
427 
459 
458 
441 
419 
473 
Austria
1109 
1175 
1162 
1186 
1121 
1223 
Estonia
287 
359 
356 
374 
483 
Finland
319 
379 
417 
446 
401 
422 
Germany
220 
249 
246 
251 
234 
268 
Hungary
266 
270 
253 
248 
213 
243 
Italy
726 
777 
772 
734 
693 
755 
Luxembourg
472 
497 
533 
519 
447  : 
Netherlands
310 
327 
329 
289 
316 
Slovakia
134 
152 
151 
135 
103 
117 
Slovenia
481 
529 
585 
653 
579 
632 

10. Reference metadata „Documentation“ covering: Concepts Methodology
e.g. definitions, practical implementation of concepts
Methodology
e.g. sampling, collection methods, editing processes
Quality
e.g. timeliness, accuracy

11. Reference metadata – example
ESMS reference metadata structure used by Eurostat (for Census)
Contact
organisation
organisation unit
name
person function
mail adress
address
phone number
fax number
Metadata update
last certified
last posted
last update
Statistical presentation
data description
classification system
coverage – sector
statistical concepts and definitions
statistical unit
statistical population
reference area
coverage – time
base period
Unit of measure
Reference period
Institutional mandate
legal acts and other agreements
data sharing
Confidentiality
policy
data treatment
Release policy
release calendar
release calendar access
release policy – user access
Frequency of dissemination
Dissemination format
news release
publications
online database
microdata access
other
Accessibility of documentation
documentation on methodology
documentation on quality management
Quality management
quality assurance
assessment
Relevance
user needs
user satisfaction
completeness
Accuracy
overall accuracy
sampling error
non-sampling error
Timeliness and punctuality
timeliness
punctuality
Comparability
geographical
over time
Coherence
cross domain
internal
Cost and burden
Data revision
practice
Statistical processing
source data
frequency of data collection
data collection
data validation
data compilation
adjustment
Comment

12. Standardisation of metadata
Exercise 1
Team A
Why standardisation?
Team B
Who develops and implements standards?
Team C
What can be standardised?

13. Standardisation of metadata – WHY?
Common vocabulary
Comparability
Data exchange – compatibility
Reduction of costs

14. Standardisation of metadata – WHO?
Major players
International organisations
World: UNECE, OECD, IMF, WCO, WHO, World Bank, BIS...
EU: European Commission (Eurostat), ECB...
National statistical institutes
participation in international standardisation projects

15. Standardisation of metadata – WHAT?
Content
code lists & classifications (Neuchâtel model, SDMX)
variables
Technology
file structure & format (XML)
editting applications (Metadata handler)
transmission standards (SDMX)

16. Statistical unit / person
Common vocabulary
Population
Survey sample
Statistical unit / person
...
Statistical measure
Total
Index
Median
...
Variables
Economic activity
Marital status
...
Measurement unit
Piece %
Euro
...
Classifications
NACE Rev.2
...
Code lists
Marital status value

17. Common vocabulary Terminology (1/2)
Population: Population is the total membership or population or "universe" of a defined class of people, objects or events.
Target population (= scope of the survey): A target population is the population outlined in the survey - objects about which information is to be sought.
Survey population (= coverage of the survey): A survey population is the population from which information can be obtained in the survey.
Survey sample: A sample is a subset of a population where elements are selected based on a randomised process with a known probability of selection.
Statistical unit: An object of statistical survey and the bearer of statistical characteristics. The statistical unit is the basic unit of statistical observation within a statistical survey.
Observation unit: Observation units are those entities on which information is received and statistics are compiled. (e.g. establishment, person)
Reporting unit: Reporting units are units that supplie the data for a given survey instance. (e.g. enterprise, person)
Analytical unit: Analytical units represent real or artificially constructed units, for which statistics are compiled. (e.g. corporation, person)

18. Common vocabulary Terminology (2/2)
Variable: A variable is a characteristic of a unit being observed that may assume more than one of a set of values to which a numerical measure or a category from a classification can be assigned (e.g. income, age, weight, etc. and "occupation", "industry", "disease”).
Measurement unit: A measurement unit has a type (e.g. currency: Euro, …) and provides the level of detail (e.g. Euro, 1000 Euro) for the value of the variable.
Classification: A classification is a set of discrete, exhaustive and mutually exclusive observations, which can be assigned to one or more variables to be measured in the collation and/or presentation of data.
Code list: A code list is a predefined list from which some statistical concepts (coded concepts) take their values.
Statistical measure: A summary (means, mode, total, index etc.) of the individual quantitative variable values for the statistical units in a specific group (study domains).

19. Common vocabulary – Neuchâtel model
Developed by
Neuchâtel Group
RuN Software Werkstatt

20. Common vocabulary – Neuchâtel model
Purpose
to define common language & common perception of the structure of classifications and links among them

21. Common vocabulary – Neuchâtel model
Classification Family
Classification Item
Classification Level
Classification Version
Classification
Classification Variant
Correspondence Table
Correspondence Item
Classification Index
Classif. Index Entry
Case Law

22. Common vocabulary – Neuchâtel model
Terminology
Classification family: A classification family comprises a number of classifications, which are related from a certain point of view.
Classification: Classification describes the ensemble of one or several consecutive classification versions. It is a "name" which serves as an umbrella for the classification version(s).
Classification version: A classification version is a list of mutually exclusive categories representing the version-specific values of the classification variable. A classification version has a certain normative status and is valid for a given period of time.
Classification level: A classification structure (classification version or classification variant) is composed of one or several levels. In a hierarchical classification the items of each level but the highest are aggregated to the nearest higher level. A linear classification has only one level.
Classification variant: A classification variant is based on a classification version. In a variant, the categories of the classification version are split, aggregated or regrouped to provide additions or alternatives to the standard order and structure of the base version.
Correspondence table: A correspondence table expresses the relationship between different versions or variants of the same classification or between versions or variants of different classifications.
Classification index: A classification index is an ordered list (alphabetical, in code order etc.) of classification index entries. A classification index relates to one particular classification version or variant.
Case law: Case law is an agreed assignment of a classification items to a phenomena where it is not easy for users to classify. The aim is to have the standardised explanation of classifications.

23. Common vocabulary – SDMX

24. Common vocabulary – SDMX
Standardisation of
Metadata common vocabulary
Cross-domain concepts
Cross-domain code lists
Data structure definitions (structural metadata)
Metadata structure definitions (reference metadata)
File format (XML)
Tools
More information –

25. Any questions?