Data Structure Definition V10

Overview

An SDMX Data Structure Definition (DSD) describes the structure and dimensionality of a dataset in terms of its 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 Dataflows references a single DSD which describes the structure of the dataset that the Dataflow represents

Each Dimension and Attribute is described by a 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.

Ordering of Components in a DSD
The Dimensions in a DSD have a defined order and together form the dataset's Series Key.
To illustrate the principle, here's a simple 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. So for this example, the Series Key is:

INDICATOR.REF_AREA.FREQUENCY

DSDs are reusable in that each can be used by multiple different Dataflows. It's important where a number of different datasets need to be collected or disseminated that all share the same dimensionality and coding schemes. But also allows standard DSDs to be published for use

Consider three datasets on the topics of Education, Health and Infrastructure. A simple DSD could be designed suitable for all three datasets as follows:

In this model, indicators would need to be chosen carefully to avoid ambiguity between datasets. Nevertheless, it avoids proliferation of data structure definitions.