Data Structure Definition V10

Overview

An SDMX Data Structure Definition (DSD) provides a template which describes the dimensionality of related datasets in terms of their dimensions, attributes and measures.

Structure Properties

Structure Type	Standard SDMX Structural Metadata Artefact
Maintainable	Yes
Identifiable	Yes
Item Scheme	No
SDMX Information Model Versions	1.0, 2.0, 2.1
Concept ID	DSD

Context within the SDMX 2.1 Information Model

The schematic illustrates the Data Structure Definition artefact within the SDMX 2.1 Information Model

Usage

Data Structure Definitions (DSDs) are used to describe the structure of datasets by specifying their constituent Components:

• Dimensions
• Attributes
• Measures

and optionally the Representation for each Component.

Each Dataflow references a single DSD which describes the structure of the dataset that the Dataflow represents.

Conventions

DSD IDs
DSD IDs are conventionally written uppercase using underscores '_' as separators if required. Examples:

Agency	DSD ID	Description	SDMX-ML
Eurostat	NA_MAIN	European National Accounts Main Aggregated Statistical Indicators	SDMX-ML
IMF	BPO	Balance of Payments and International Investment Position	SDMX-ML
IMF	ALT_FISCAL_DSD	Alternate Fiscal Data Structure Definition	SDMX-ML
World Bank	WDI	World Development Indicators	SDMX-ML

TIME_PERIOD
For Time Series DSDs, the Time Dimension Component is conventionally given the ID 'TIME_PERIOD'.

Data Structure Components

The Role of Concepts In Defining a DSD's Components
Every Dimension, Attribute and Measure is described by a predefined Concept. Concepts have their own default Representation which can be overridden by defining a Local Representation for the Component in the DSD. That's particularly helpful when using some standard Concepts like the SDMX Cross Domain Concepts where the default Representation is 'String', but the Component needs to be Enumerated or have some use case specific restriction on what values are allowable.

Dimensions
A DSDs Dimensions are the minimal set of statistical concepts capable of uniquely identifying a specific series. For Time Series, the Dimensions in combination with the Time Dimension, uniquely identify an Observation.
In this sense, the Dimensions of a dataset together form its primary key.

Ordering of Dimensions in a DSD
The Dimensions in a DSD have a defined order and together form the dataset's Series Key.
Below is a simple example DSD:

Position	Component Type	Component ID	Description
1	Dimension	INDICATOR	Indicator
2	Dimension	REF_AREA	Reference Area
3	Dimension	FREQUENCY	Data Frequency
n/a	Time Dimension	TIME_PERIOD	Observation Time
n/a	Attribute	UNIT_MULT	Unit Multiplier e.g. tens, thousands, millions
n/a	Attribute	Observation Status	Observation Status e.g. Estimated, Final
n/a	Primary Measure	Observation Value	The observation value

The Series Key is the concatenation of the Dimensions in the order specified in the DSD. In this example, the Series Key is:

INDICATOR.REF_AREA.FREQUENCY

Attributes do not form part of the Series Key so have no explicit or implied ordering.

Attributes
Attributes allow extra concepts to be added to the dataset to provide additional information about the variable being measured such as the unit multipler or observation status.
Attributes are unique in that they must be attached to specific levels in the dataset at DSD design time.

Primary Measure
All DSDs must have a Primary Measure Component, which is used for the observation value of the main variable being measured. Like all components, the Primary Measure must reference a Concept. For many series, the measure is numeric, but does not need to be so.

Time Dimension
A Time Dimension is required for DSDs representing Time Series datasets. Again, the Time Dimension must reference a Concept which should have an appropriate time representation - typically Observational Time Period.

Attribute Attachment Levels

In designing a DSD, attributes must be attached to specific levels in the dataset.

Attachment Level	Description
Dataset	A single value for the attribute is set for the complete dataset
Series	A different value for the attribute can be set for each series
Observation	A different value for the attribute can be set for each individual observation in a time series
Group	A different value for the attribute can be set for a Group of series

Example 'Demography' DSD with Attributes attached at the Series and Observation level:

Time Series

DSDs for Time Series are characterised by having an explicit Time Dimension.
In combination with the DSD's other Dimensions, the Time Dimension uniquely identifies an individual Observation within a Dataset.

Non Time Series

DSDs can be designed for non Time Series datasets by excluding the Time Dimension. This supports use cases like census statistics which, although the observations are from a fixed point in time, there's no sequence of observations over a period of time.