Difference between revisions of "Glossary"
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Statistical concept providing qualitative information about a specific statistical object. | Statistical concept providing qualitative information about a specific statistical object. | ||
| − | In practice, Attributes are [[Component|Components]] used in [[Data Structure Definition|Data Structure Definitions]] to add additional information which is not required to uniquely identify series or observations. Attributes can be attached at different levels in the dataset: | + | In practice, Attributes are [[/Glossary#Component|Components]] used in [[Data Structure Definition|Data Structure Definitions]] to add additional information which is not required to uniquely identify series or observations. Attributes can be attached at different levels in the dataset: |
* Dataset | * Dataset | ||
* Group | * Group | ||
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Structural artefact used to define the structure of a Data or Metadata Set. | Structural artefact used to define the structure of a Data or Metadata Set. | ||
| − | In a [[Data Structure Definition]], the [[Dimension|Dimensions]] and [[Attribute|Attributes]] are its components. | + | In a [[Data Structure Definition]], the [[Glossary#Dimension|Dimensions]] and [[[Glossary#Attribute|Attributes]] are its components. |
=Dimension= | =Dimension= | ||
Revision as of 04:52, 3 November 2025
Contents
Attribute
Statistical concept providing qualitative information about a specific statistical object.
In practice, Attributes are Components used in Data Structure Definitions to add additional information which is not required to uniquely identify series or observations. Attributes can be attached at different levels in the dataset:
- Dataset
- Group
- Series
- Observation
Component
Structural artefact used to define the structure of a Data or Metadata Set.
In a Data Structure Definition, the Dimensions and [[[Glossary#Attribute|Attributes]] are its components.
Dimension
Statistical concept used in combination with other statistical concepts to identify a statistical series or individual observations.
In an SDMX Data Structure Definition (DSD), Dimensions differ from Attributes in that they are all required to uniquely indentify a series. Another way to think about it is that the collection of Dimensions in a DSD form the primary key.
Maintainable artefact
An SDMX maintainable artefact is one that can be independently created and maintained by a maintenance agency.
Maintainable artefacts have:
- ID
- Version
- Name
- Description (optional)
- Annotations (optional)
Items such as Codes and Concepts, are not maintainable artefacts because they can only exist within their containers, in this case Codelists and Concept Schemes respectively.
URN
Maintainable artefacts can be uniquely referenced by a URN:
urn:sdmx:org.sdmx.infomodel.datastructure.Dataflow=WB:LABOR_FORCE(1.0)
The above example is the URN from a World Bank Labor Force Dataflow where:
| Agency | WB (World Bank) |
| Dataflow ID | LABOR_FORCE |
| Version | 1.0 |
Examples The following SDMX structures are maintainable artefacts:
- Agency Schemes
- Attachment Constraints
- Category Schemes
- Codelists
- Concept Schemes
- Content Constraints
- Data Consumers
- Data Providers
- Data Structure Definitions
- Dataflows
- Hierarchical Codelists
- Metadata Structure Definitions
- Metadataflows
- Organisation Unit Schemes
- Processes
- Provision Agreements
- Reporting Taxonomies
- Structure Sets
- Metadata Reports
The following are additional maintainable artefacts supported by Fusion Registry that are not part of the published SDMX standard:
- Validation Schemes
Representation
The representation of a Concept or Component defines the set of legal values.
There are two different representation types:
| Enumerated | A discrete list of values defined by a Codelist |
| Non-Enumerated | Fundamental data types including. |
There are several different representations in SDMX-ML, taken from XML Schemas and common programming languages. These are listed below:
SDMX-ML Data Type
- String
- Big Integer
- Integer
- Long
- Short
- Decimal
- Float
- Double
- Boolean
- URI
- DateTime
- Time
- GregorianYear
- GregorianMonth
- GregorianDay
- Day, MonthDay, Month
- Duration
There are also a number of SDMX-ML data types which do not have correspondences with common programming languages:
SDMX-ML Data Type
- Alpha (common:AlphaType, string which only allows A-z)
- AlphaNumeric (common:AlphaNumericType, string which only allows A-z and 0-9)
- BasicTimePeriod (common: BasicTimePeriodType, a union of GregorianTimePeriod and DateTime)
- Count (xs:integer, a sequence with an interval of "1")
- ExclusiveValueRange (xs:decimal with the minValue and maxValue facets supplying the bounds)
- GregorianTimePeriod (common:GregorianTimePeriodType, a union of GregorianYear, GregorianMonth, and GregorianDay)
- IdentifiableReference (types for each IdentifiableObject)
- InclusiveValueRange (xs:decimal with the minValue and maxValue facets supplying the bounds)
- Incremental (xs:decimal with a specified interval; the interval is typically enforced outside of the XML validation)
- KeyValues (common:DataKeyType)
- Numeric (common:NumericType, string which only allows 0-9, but is not numeric so that is can having leading zeros)
- ObservationalTimePeriod (common: ObservationalTimePeriodType, a union of StandardTimePeriod and TimeRange).
- ReportingDay (common:ReportingDayType)
- ReportingMonth (common:ReportingMonthType)
- ReportingQuarter (common:ReportingQuarterType)
- ReportingSemester (common:ReportingSemesterType)
- ReportingTimePeriod (common:ReportingTimePeriodType, a union of ReportingYear, ReportingSemester, ReportingTrimester, ReportingQuarter, ReportingMonth, ReportingWeek, and ReportingDay).
- ReportingTrimester (common:ReportingTrimesterType)
- ReportingWeek (common:ReportingWeekType)
- ReportingYear (common:ReportingYearType)
- StandardTimePeriod (common: StandardTimePeriodType, a union of BasicTimePeriod and TimeRange).
- TimeRange (common:TimeRangeType, startDateTime + Duration)
- XHTML (common:StructuredText, allows for multi-lingual text content that has XHTML markup)
SDMX Metadata Registry
The central repository and maintenance tool for all of the SQL structural metadata including Codelists, Concepts and Data Structure Definitions.
Time Dimension
The Component in a Time Series Data Structure Definition that holds the observation time.
For Time Series, an observation is uniquely identified by the combination of the series dimensions and the time dimension.
Time Series
Series of observations (data points) in time order.
In many SDMX use cases, time series have a defined frequency which is either explicitly defined using a 'FREQ' or similar dimension, or implicitly derived from the observation timestamps. For instance, observations with timestamps of '2015, 2016 and 2017' imply the frequency is annual. Whereas observations with timestamps '2015-Q1, 2015-Q2 and 2015-Q3' imply quarterly.