Table of Terms with Commonly Used Words

Last modified by Artur on 2025/07/14 10:16

Name	Definition	Comment
Adjustment	Set of procedures employed to modify statistical data to enable it to conform to national or international standards or to address data quality differences when compiling specific data sets.	Adjustments may be associated with changes in definitions, exchange rates, prices, seasons and other factors. Adjustments are in particular applied to compile consistent time series, but the concept is also used for describing adjustments related to other types of data. Adjustment can be distinguished from editing and imputation, in that before adjustment, the data are already of sufficient quality to be considered usable.
Agency scheme	Maintained collection of maintenance agencies.	In SDMX the Agency Scheme contains a non-hierarchic list of maintenance agencies. Each maintenance agency can have a single agency scheme, and may have none. The agencies in the agency scheme are deemed to be sub agencies of the maintenance agency of the scheme in which they reside. The top-level agency scheme is the scheme for which SDMX is the maintenance agency (SDMX agency scheme), and every agency in every agency scheme must be related directly or indirectly via intervening agency schemes, to an agency registered in the SDMX agency scheme. In this way each agency can be identified uniquely by the combination of agencies in the path from the SDMX agency scheme to the agency scheme in which it resides, plus its own identity in that scheme.
Artefact	Abstract concept denoting an element in the SDMX model having specific characteristics which are inherited by other elements.	Artefacts provide features which are reusable by derived elements to support general functionality such as identity, versioning etc. Examples of SDMX artefacts are identifiable artefacts and "maintainable artefacts".
Attachment level	Property of an attribute defining the object to which data or metadata are linked.	For each attribute specified in a data structure, there is a definition of whether this attribute takes: a value for each observation in the data set, or; a value for each time series in the data set, or; a value for each group in the data set, or; a single value for the entire data set. Some metadata concepts (e.g. frequency) may not be meaningful at the observation level, but only when applied to a higher level (e.g. to a time series of observations). Time, on the other hand, is meaningful at observation level, because every observation is associated with a specific point or period in time. Data Structure Definitions and Metadata Structure Definitions provide information about the level at which a particular concept descriptor is relevant: at observation level, time series level, group level, dataset level or even agency level. This is known as the attachment level of the concept.
Attribute	Statistical concept providing qualitative information about a specific statistical object.	The specific statistical object in a data set can be a data set, observation, series key or partial key, and in a metadata set can be any object in the SDMX Information Model. Concepts such as units, magnitude, currency of denomination, titles (these are all commonly specified as attributes in a data structure) and methodological comments, quality statements (commonly specified as attributes in a metadata structure ) can be used as attributes in the context of an agreed data exchange. The Attribute Value is the reported value in a data set or a metadata set such as a specific currency or a specific dissemination policy applicable to the object to which the attribute value is attached.
Attribute relationship	Specification of the type of artefact to which a data attribute can be attached in a data set.	A part of the specification of Attribute in a Data Structure Definition denotes to which part of the data the Attribute can relate in a data set. This can be the entire data set, specific grouping of the dimensions, or an observation. This is a version 2.1 construct. In version 2.0 this was known as the "attachment level".
Category	Structural metadata concept that classifies structural metadata objects.	The Category can link to any identifiable object and can help discovery of structural metadata. In a data dissemination or data collection system the Category will probably link to a Dataflow or Metadataflow to support data or metadata discovery or data or metadata collection management. The Category can link to multiple identifiable objects and any identifiable object can link to multiple categories, possibly in different category schemes. The link between a single category and a single identifiable object is contained in a Categorisation.
Code	Language independent set of letters, numbers or symbols that represent a concept whose meaning is described in a natural language.	The Code in SDMX contains the Id (the code), and a name and description either or both of which can be multi-lingual.
Code list	Predefined set of terms from which some statistical coded concepts take their values.	The SDMX technical standards are sufficiently generic to allow institutions to adopt and implement any specific representation. However, the use of common code lists will facilitate users to work even more efficiently as it eases the maintenance of, and reduces the need for, mapping systems and interfaces delivering data and metadata to users. Therefore, a choice over code lists has a great impact on the efficiency of data sharing. From version 2.1 of the standard it is possible to exchange and disseminate a partial code list which is extracted from the full code list and which supports the dimension values valid for a particular Data Structure Definition (DSD). The content of the partial code list is specified on a Constraint and can be specified for any object to which a Constraint may be attached. This makes it possible to use common (and often quite large) Code Lists in multiple DSDs and then to limit their content for use in a specific DSD.
Coherence	Adequacy of statistics to be reliably combined in different ways and for various uses.	When originating from different sources, and in particular from statistical surveys using different methodology, statistics are often not completely identical, but show differences in results due to different collection methodology concepts, classifications and methodological standards. There are several areas where the assessment of coherence is regularly conducted: between provisional and final statistics, between annual and short-term statistics, between statistics from the same socio-economic domain, and between survey statistics and national accounts. The concept of coherence is closely related to the concept of comparability between statistical domains. Both coherence and comparability refer to a data set with respect to another. The difference between the two is that comparability refers to comparisons between statistics based on usually unrelated statistical populations and coherence refers to comparisons between statistics for the same or largely similar populations. In the Data Quality Assessment Framework (DQAF) of the International Monetary Fund, the term "consistency" is used for indicating "logical and numerical coherence". In that framework, "internal consistency" and "intersectoral and cross-domain consistency" can be mapped to "internal coherence" and "cross-domain coherence" respectively.
Coherence - internal	Extent to which statistics are consistent within a given data set.	This metadata element is used to describe the differences in the statistical results calculated for the same statistical domain, based on stable or changing methodology (e.g. between provisional and final statistics or between different reference years showing break in series). Frequently, a group of statistics of a different type (in monetary value, in volume or constant price, price indicators, etc.) measure the same phenomenon using different methodologies. For instance, statistics on employment, depending on whether they result from employers' declarations or household surveys do not lead exactly to the same results. However, there are often differences in the concepts used (de-jure or de-facto population, for instance), in the registration date, in the cif/fob registration for external trade, etc. It is very important to check that these representations do not diverge too much in order to anticipate users' questions and for preparing corrective actions.
Comment	Descriptive text which can be attached to data or metadata.	In data messages, a comment may be defined as an Attribute and can contain a descriptive text which can be attached to any construct specified in the Attribute Relationship. In metadata sets a comment can be attached to any object in the SDMX Information Model that can be identified (known as an Identifiable Artefact in the model). For example Agency, Provision Agreement, Dataflow, Code, Concept. In both of these types of messages the relevant Concept (e.g. COMMENT) must be declared in the structure definition (Data Structure Definition or Metadata Structure Definition) together with the object to which it is allowed to be attached in the data set or metadata set. Note that in a data structure (version 2.1 onwards) it is possible to define the attribute relationship of any Concept used as an Attribute to more than one of data set, group, series, observation. This is not possible using V 2.0. In version 2.0 it is necessary to declare multiple Concepts (e.g. CONCEPT_SERIES, CONCEPT_OBS) to achieve this.
Comparability	Extent to which differences between statistics can be attributed to differences between the true values of the statistical characteristics.	Comparability aims at measuring the impact of differences in applied statistical concepts and definitions on the comparison of statistics between geographical areas, non-geographical dimensions, or over time. Comparability of statistics, i.e. their usefulness in drawing comparisons and contrast among different populations, is a complex concept, difficult to assess in precise or absolute terms. In general terms, it means that statistics for different populations can be legitimately aggregated, compared and interpreted in relation to each other or against some common standard. Metadata must convey such information that will help any interested party in evaluating comparability of the data, which is the result of a multitude of factors. In some quality assurance frameworks, e.g. the European Statistics Code of Practice, comparability is strictly associated with the coherence of statistics.
Compiling agency	Organisation collecting and/or elaborating the data being reported.	The concept is needed as two agencies might be compiling the exact same data but using different sources or concepts (the latter would be partially captured by the dimensions). The provider ID may not be sufficient, as one provider could disseminate the data compiled by different compiling agencies.
Concept	Unit of thought created by a unique combination of characteristics.	At an abstract level, a Concept is defined in the Generic Statistical Information Model (GSIM) as a "unit of thought differentiated by characteristics". Concepts are used in different ways throughout the statistical lifecycle, and each role of a Concept is described using different information objects (which are subtypes of Concept). A Concept can be used in these situations: (a) As a characteristic. The Concept is used by a Variable to describe the particular characteristic that is to be measured about a Population. For example, to measure the Concept of gender in a population of adults in the Netherlands, the Variable combines this Concept with the Unit Type "person". (b) As a Unit Type or a Population. To describe the set of objects that information is to be obtained about in a statistical survey. For example, the Population of adults in Netherlands based on the Unit Type of persons. (c) As a Category to further define details about a Concept. For example, Male and Female for the Concept of Gender. Codes can be linked to a Category via a Node (i.e., a Code Item or Classification Item), for use within a Code List or Statistical Classification. In SDMX the concept can be given a Core Representation such as a reference to a code list for an enumerated representation or other values such as "integer" or "string" for a non-enumerated representation. This representation can be overridden in the data structure when the concept is used as a dimension or attribute. A concept with a core representation could be regarded as a represented variable.
Concept scheme	Set of concepts that are used in a data structure definition or metadata structure definition.	Structural definitions of both data and reference metadata associate specific statistical concepts with their representations, whether textual, coded, etc. In SDMX these concepts are taken from a concept scheme which is maintained by a specific agency. Concept schemes group a set of concepts, provide their definitions and names. It is possible for a single concept scheme to be used both for data structures and metadata structures. A core representation of each concept can be specified (e.g. a code list, or other representations such as "date").
Confidentiality - redistribution authorisation policy	Secondary recipient(s) to whom the sender allows the primary recipient to forward restricted data.	This concept is used in the exchange of restricted data in cases where the sender explicitly allows subsequent forwarding of these data to other organisations.
Confidentiality - status	Information about the confidentiality status of the object to which this attribute is attached.	This concept is related to data and determines the exact status of the value. i.e. if a specific value is confidential or not. This concept is always coded, i.e. it takes its value from the respective code list.
Contact email address	E-mail address of the contact points for the data or metadata.
Contact fax number	Fax number of the contact points for the data or metadata.
Contact mail address	Postal address of the contact points for the data or metadata.
Contact name	Name of the contact points for the data or metadata.
Contact organisation	Organisation of the contact point(s) for the data or metadata.
Contact organisation unit	Addressable subdivision of an organisation.	This contact refers to the contact point for data and metadata.
Contact person function	Area of technical responsibility of the contact, such as "methodology", "database management" or "dissemination".
Contact phone number	Telephone number of the contact points for the data or metadata.
Content-Oriented Guidelines	Practices for creating interoperable elements in the SDMX model using the SDMX Technical Specifications.	The SDMX Content-Oriented Guidelines comprise the: Cross-Domain Concepts; Cross-Domain Code Lists; Statistical Subject-Matter Domains; and the SDMX Glossary. The Guidelines focus on the harmonisation of specific concepts and terminology that are common to a large number of statistical domains. Such harmonisation is useful for the efficient exchange of comparable data and metadata.
Cross-domain concept	Standard concept, covering structural and reference metadata, which should be used in several statistical domains wherever possible to enhance possibilities of the exchange of data and metadata between organisations.	Within SDMX, cross-domain concepts are envisaged to cover various elements describing statistical data and their quality. When exchanging statistics, institutions can select from a standard set of content-oriented concepts. The list of concepts and their definitions reflects recommended practices and can be the basis for mapping between internal systems when data and metadata are exchanged or shared between and among institutions.
Data collection method	Method applied for gathering data for official statistics.	There are a number of data collection methods used for official statistics, including computer-aided personal or telephone interview (CAPI/CATI), mailed questionnaires, electronic or internet questionnaires and direct observation. The data collection may be exclusively for statistical purposes, or primarily for non-statistical purposes. In quality assurance frameworks, descriptions of data collection methods should include the purpose for which the data were collected, the period the data refer to, the classifications and definitions used, and any constraints related to further use of these data.
Data presentation - detailed description	Detailed description of the disseminated data.	Data can be displayed to users as tables, graphs or maps. According to the United Nations' Fundamental Principles of Official Statistics, the choice of appropriate presentation methods should be made in accordance with professional considerations. Data presentation includes the description of the dataset disseminated with the main variables covered, the classifications and breakdowns used, the reference area, a summary information on the time period covered and, if applicable, the base period used.
Data provider scheme	Maintained collection of data providers.	In SDMX a Data Provider Scheme contains a non-hierarchic list of data providers. Each maintenance agency can have a single data provider scheme, and may have none. The identity of the data provider is a combination of the identity of the data provider scheme (which includes the maintenance agency) in which it resides and the identity of the data provider in that scheme. The Data Provider is the owning organisation of data and reference metadata. These data and reference metadata are reported, exchanged, or disseminated as SDMX data sets and SDMX metadata sets. The type of data and metadata that are available are specified in a Dataflow and Metadataflow. The union of one data provider and one Dataflow or Metadataflow is known as a Provision Agreement. In a data collection scenario the data provider is the organisation reporting the data or reference metadata and information can be linked with the provision agreement. Information linked to the provision agreement can specify where the data or reference metadata are located (data registration) and the data collector (as the Agency of the provision agreement) can specify validation Constraints such as allowable dimension values or series keys for which data can be reported. In a data dissemination scenario information linked to the provision agreement can specify the location of the data source and the content of the data source in terms of series keys available (Constraint).
Data set	Organised collection of data defined by a Data Structure Definition (DSD).	Within SDMX, a data set can be understood as a collection of similar data, sharing a structure, which extends over a period of time. The data set can be represented physically in three fundamental forms: Generic Data Set: this format allows the representation of data structured according to any data structure definition; Structure Specific Data Set: this format allows the representation of data structured according to a specific data structure definition; SDMX-EDI Data Set: a specific case of generic using the UN/EDIFACT syntax and which has limitations on what can be represented. It supports time series only. The Structure Specific format is new to SDMX version 2.1 and combines the functionalities of the version 2.0 Compact and Cross Sectional formats.
Data structure definition	Set of structural metadata associated to a data set, which includes information about how concepts are associated with the measures, dimensions, and attributes of a data cube, along with information about the representation of data and related descriptive metadata.	A DSD defines the structure of an organised collection of data (Data Set) by means of concepts with specific roles, and their representation. In order to exchange or disseminate statistical information, an institution needs to specify which statistical concepts are necessary for identifying the series (and for use as dimensions) and which statistical concepts are to be used as attributes and measures. These definitions form the data structure definition. In a data collection scenario the specification of the data structure definition is often a collaborative venture between the collecting institution and its partners. There are three types of construct in the DSD: Dimension, Attribute, and Measure. Each of these combines a Concept with its representation (this can be either a reference to a Code list or a non-coded data type such as "integer", "string", "date/time"). The roles of the three types of construct (Dimension, Attribute, and Measure) are as follows: A Dimension is an identifying component, sometimes referred to as a "classificatory variable". When a value is given to each of the Dimensions in a data set (this is often called a "key" or a "series") the resulting key, when combined with a time value, uniquely identifies an observation. For instance, country, indicator, measurement unit, frequency, and time dimensions together identify the cells in a cross-country time series with multiple indicators (for example, gross domestic product, gross domestic debt) measured in different units (for example, various currencies, percent changes) and at different frequencies (for example, annual, quarterly). The cells in such a multi-dimensional table contain the observation values. The DSD construct that specifies the Concept and expected representation of an observation is called a Measure. The semantics of the measure are derived from the Dimensions or a sub set of them and, if not specified in a Dimension, an Attribute indicating the measurement unit e.g. indicator and measure unit (gross domestic product percentage change). Additional metadata that are useful for understanding or processing the observed value or the context of data set or series are called an Attribute in the DSD. Examples of an attribute are a note on the observation, a confidentiality status, or the unit of measure used, or the Title of a series.
Dataflow	Structure which describes, categorises and constrains the allowable content of a data set that providers will supply for different reference periods.	In SDMX, data sets are reported or disseminated according to a data flow definition. The data flow definition identifies the data structure definition and may be associated with one or more subject-matter domains. This facilitates the search for data according to organised category schemes. A "Dataflow", in this context, is an abstract concept of the data sets, i.e. a structure without any data. While a data structure definition defines dimensions, attributes, measures and associated representation that comprise the valid structure of data and related metadata contained in a data set, the Dataflow definition associates a data structure definition with one or more category. This gives a system the ability to state which data sets are to be reported for a given category and which data sets can be reported using the data structure definition. The Dataflow definition may also have additional metadata attached, defining qualitative information and constraints on the use of the data structure definition, in terms of reporting periodicity or specifying the subset of codes to be used in a dimension.
Dimension	Statistical concept used in combination with other statistical concepts to identify a statistical series or individual observations.	In SDMX, "dimension" is a statistical concept used (most probably together with other statistical concepts) to identify a series, e.g. a statistical concept indicating a particular economic activity or a geographical reference area.
Dissemination format - news release	Regular or ad-hoc press releases linked to the data.	This concept covers press releases or other kind of similar releases linked to data or metadata.
Dissemination format - other formats	References to the most important other data dissemination done.	Examples of other dissemination formats are analytical publications edited by policy users. This concept includes, as a sub-element, "Supplementary data", i.e. any customised tabulation that can be provided to meet specific requests (including information on procedures for obtaining access to these data).
Documentation on methodology	Descriptive text and references to methodological documents available.	"Documentation on methodology" refers to the availability of documentation related to various aspects of the data, such as methodological documents, summary notes or papers covering concepts, scope, classifications and statistical techniques.
Education level	Highest level of an educational programme the person has successfully completed.	The highest level of an educational programme the person has successfully completed is also called "educational attainment of a person". At international level, the ISCED (International Standard Classification of Education, developed and maintained by UNESCO) is the standard classification of educational attainment.
Expenditure according to purpose	Breakdown of spending by institutional sectors between major expenditure functions.	This concept is typically used in the SNA (System of National Accounts) where transactions are first analysed according to their nature, then, for certain sectors or kind of transactions, from the expenditure side, by purpose, answering the question "for what purpose"?. The classifications supporting this concept are the following: Classification of the functions of government (COFOG); Classification of individual consumption by purpose (COICOP); Classification of the purposes of non-profit institutions serving households (COPNI), and; Classification of outlays of producers by purpose (COPP). The main purpose of these classifications is to provide statistics which experience has shown to be of general interest for a wide variety of analytical uses. For example, COICOP shows items such as household expenditure on food, health and education services all of which are important indicators of national welfare; COFOG shows government expenditure on health, education, defence and so on and is also used to distinguish between collective services and individual consumption goods and services provided by government.
Group key structure	Set of metadata concepts that define a partial key derived from the dimension descriptor in a Data Structure Definition.	The group key's structure that comprises the subset of dimensions that specifies the structure of the partial key.
Hierarchical code	Code reference that is part of a hierarchy.	The Hierarchical Code references a Code in a Code List and can have child Hierarchical Codes. It can also reference a Level in a Hierarchical Code List.
Hierarchical code list	Organised collection of codes that may be part of many parent/child relationships with other codes in the scheme, as defined by one or more hierarchies of the scheme.	The Code List in SDMX can be hierarchical but it is capable of being processed as flat list as each Code can have only one parent code. A Hierarchical Code List (HCL) is able to have multiple hierarchies and can have formal Levels. The Codes used in an HCL are derived from one or more Code Lists therefore an HCL can combine Codes from multiple Code Lists and define hierarchies from these Codes. For example, adding geographic codes such as continents or regions.
Hierarchy	Classification structure arranged in levels of detail from the broadest to the most detailed level. Each level of the classification is defined in terms of the categories at the next lower level of the classification.	In SDMX this is known as a level based hierarchy. SDMX also has the concept of the value based hierarchy where the hierarchy of categories are not organised into formal levels.
Institutional mandate - legal acts and other agreements	Legal acts or other formal or informal agreements that assign responsibility as well as the authority to an agency for the collection, processing, and dissemination of statistics.	The concept covers provision in law assigning responsibility to specific organisations for collection, processing, and dissemination of statistics in one or several statistical domains. In addition, non-legal measures such as formal or informal administrative arrangements employed to specific organisations for collection, processing, and dissemination of statistics in one or several statistical domains should also be described.
International string	Construct defining multi-lingual text for the same underlying concept.	This is associated with the Name and Description of a structural metadata artefact. The text has an associated language therefore it is possible to define multi-lingual names and descriptions for any one structural metadata object such as a Code or Concept.
isIncluded	Construct that indicates whether the contained values of a container object is to be included or excluded from the valid list of values.	This is used in validity Constraints to specify if the constraint lists the items that are included in the list of valid contents, or are to be excluded from the list of valid contents.
Level	Identifiable position to which codes in a scheme of codes are related.	In a "level based" hierarchy the level describes a group of Codes which are characterised by homogeneous coding, and where the parent of each Code in the group is at the same higher level of the Hierarchy. In a "value based" hierarchy the level describes information about the Hierarchical Codes at the specified nesting level (Source SDMX (2016)). A Statistical Classification has a structure which is composed of one or several Levels. A Level often is associated with a Concept, which defines it. A linear classification has only one Level (Source: GSIM Glossary).
Maintainable artefact	Construct that contains structures capable of providing a maintenance agency to an object.	Maintainable artefacts inherit the capability of having versioning name, identity and annotations. In addition a maintainable artefact can have an indication that the artefact and its contained items (e.g. the contained items of a Code List are the Codes) are "final" and there are restrictions on what type of change is allowed without changing the version.
Maintenance agency	Organisation or other expert body responsible for the operational maintenance of commonly used metadata artefacts.	The maintenance agency is responsible for all administrative and operational issues relating to an artefact or set of artefacts. It is the point of contact for all stakeholders for all issues related to the artefact(s) under its responsibility. The maintenance agency is not a decision-making body. Decisions are made collaboratively among the owners of the artefact. Each identifiable SDMX artefact must have a single maintenance agency (though the maintenance agency could actually consist of several organisations or bodies), either directly (such as code list or a data structure definition) or via the container in which it is maintained such as a code (maintained artefact is a Code List) or a dimension (maintained artefact is a data structure definition).
Map	Correspondence between two or more objects.	In SDMX there are a variety of such correspondences. Item Scheme Map. Codes, concepts, categories, and organisations (data providers, data consumers, organisation units) are mapped in Code List Map, Concept Scheme Map, Category Scheme Map, Organisation Scheme Map. Each map is a correspondence between the items in one scheme or list and the items in a second scheme or list, where the schemes or list must be of the same type (e.g. code lists to code list). Each scheme or list map contains a map for each item in the scheme or list - Code Map, Concept Map, Category Map, Organisation Map, Structure Map. Data and metadata structures can be mapped at level of the components comprising the structure (Component Map). The map can be specified at the level of the Dataflow or Data Structure, or the Metadataflow or Metadata Structure. The map takes into the constraints that are attached to the structural artefact that is mapped. Each component in a component map can be associated with an appropriate item scheme map that specifies the correspondence between the item schemes in the source and target components.
Measure	Statistical concept for which data are provided in a data set.	In a SDMX data set, the instance of a measure is often called an observation.
Member selection	Set of permissible values for one component of a data or metadata structure.	This is a part of a Constraint.
Metadata key set	Set of metadata keys.	This is a part of a Constraint.
Metadata key value	Value in a metadata set of an identifier component defined in a metadata structure definition.	This is a part of a Constraint
Metadata repository	Place where logically organised statistical metadata are stored that allows for querying, editing and managing of metadata.	In SDMX reference metadata often relate to objects of the SDMX Information Model. These can be structural objects such as Dataflow, Code, Concept or data set objects such as partial keys (e.g. the value of a specific Dimension such as a country in the context of the data set) or even observations. These metadata need to be managed and made accessible not only to systems disseminating the metadata but often also to systems concerned with data discovery, query, and data visualisation. Many dissemination systems unite the reference metadata with the data to which they pertain, even though these metadata are collected by different mechanisms, by different systems, and stored in different databases from the data.
Metadata set	Organised collection of reference metadata.	In SDMX the metadata set must conform to the specification in a Metadata Structure Definition. The metadata set contains one or more reports, each report comprising the metadata content (a set of attributes and corresponding content), and the identification of the precise object to which the metadata are to be attached. The metadata can be attached to any SDMX artefact that can be identified (e.g. structural artefact such as a code, concept, dimension or a part of a data set such as a partial series key or observation). In SDMX the type of report defined in a Metadata Structure Definition is known as reference metadata which are typified by quality metadata but can contain any type of metadata. These metadata are generally not reported with the data (as data attributes in a data set) and are often collected to a different schedule to the data, are derived from separate (from the data) repositories and collected from/reported by systems different from the statistical data warehouse.
Metadata structure definition	Specification of the allowed content of a metadata set in terms of attributes for which content is to be provided and to which type of object the metadata pertain.	An MSD defines the reference metadata to be collected or reported by specifying the concepts required, how these relate to each other, their presentational structure and to which objects they are to be attached.
Metadata update - last certified	Date of the latest certification provided by the domain manager to confirm that the metadata posted are still up-to-date, even if the content has not been amended.	In statistical agencies, the domain manager is often asked to certify that the metadata are checked and updated at regular time intervals. The date of the latest certification is to be retained. The concept is relevant for metadata reporting from countries to international organisations within metadata standards initiatives.
Metadata update - last update	Date of last update of the content of the metadata.	The last update of the content of metadata should be retained. The update can concern one single concept, but also the metadata file as a whole. The concept is also relevant for metadata reporting from countries to international organisations within metadata standards initiatives.
Metadataflow	Collection of metadata concepts, structure and usage when used to collect or disseminate reference metadata.	A reference metadata set also has a set of structural metadata which describes how it is organised. This metadata identifies what reference metadata concepts are being reported, how these concepts relate to each other (typically as hierarchies), what their presentational structure is, how they may be represented (as free text, as coded values, etc.), and with which formal object types they are associated.
Non-response error	Error that occurs when the survey fails to get a response to one, or possibly all, of the questions.	Non-response errors result from a failure to collect complete information on all units in the selected sample. These are known as "unit non-response" and "item non-response". Non-response errors affect survey results in two ways. First, the decrease in sample size or in the amount of information collected in response to a particular question results in larger standard errors. Second, and perhaps more important, a bias is introduced to the extent that non-respondents differ from respondents within a selected sample. Non-response errors are determined by collecting any or all of the following: unit response rate, weighted unit response rate, item response rate, item coverage rate, refusal rate, distribution of reason for non-response, comparison of data across contacts, link to administrative data for non- respondents, estimate of non-response bias (Statistical Policy Working Paper 15: Quality in Establishment Surveys, Office of Management and Budget, Washington D.C., July 1988, page 68).
Number of data table consultations	Number of consultations of data tables within a statistical domain for a given time period.	By "number of consultations" it is meant the number of data tables views, where multiple views in a single session count only once. This indicator contributes to the assessment of users' demand of data (level of interest), for the assessment of the relevance of subject-matter domains. An informative and straightforward way to represent the output of this indicator is by plotting the figures over time in a graph.
Number of metadata consultations	Number of metadata file consultations within a statistical domain for a given time period.	By "number of consultations" it is meant the number of times a metadata file is viewed. The indicator contributes to the assessment of users' demand of metadata (level of interest), for the assessment of the relevance of subject-matter domains. An informative and straightforward way to represent the output of this indicator is by plotting the figures over time in a graph.
Occupation	Job or position held by an individual who performs a set of tasks and duties.	Occupation refers to the type of work done during the reference period by the person employed (or the type of work done previously, if the person is unemployed), irrespective of the industry or the status in employment in which the person should be classified. Occupation is defined in terms of jobs or posts. "Job" is defined by the International Labour Organisation (ILO) as a set of tasks and duties executed, or meant to be executed, by one person. A set of jobs whose main tasks and duties are characterised by a high degree of similarity constitutes an occupation. Persons are classified by occupation through their relationship to a past, present or future job. The international standard for classification of occupations is the International Standard Classification of Occupations (ISCO). Therefore the concept is normally coded.
Proportion of common units	Proportion of units covered by both the survey and the administrative sources in relation to the total number of units in the survey.	This indicator is used when administrative data are combined with survey data in such a way that data on unit level are obtained from both the survey and one or more administrative sources (some variables come from the survey and other variables from the administrative data) or when data for part of the units come from survey data and for another part of the units from one or more administrative sources. The indicator provides an idea of completeness/coverage of the sources - to what extent units exist in both administrative data and survey data.
Reference area	Country or geographic area to which the measured statistical phenomenon relates.	The concept refers to the country, geographical or political group of countries or regions within a country. The concept is subject to a variety of hierarchies, as countries comprise territorial entities that are states (as understood by international law and practice), regions and other territorial entities that are not states but for which statistical data are produced internationally on a separate and independent basis.
Reference metadata	Metadata describing the contents and the quality of the statistical data.	Preferably, reference metadata should include all of the following: "conceptual" metadata, describing the concepts used and their practical implementation, allowing users to understand what the statistics are measuring and, thus, their fitness for use; "methodological" metadata, describing methods used for the generation of the data (e.g. sampling, collection methods, editing processes); "quality" metadata, describing the different quality dimensions of the resulting statistics (e.g. timeliness, accuracy). Note that (a) does not define the actual structure of a data set in terms of concepts used, their representation, and role (dimensions, attributes, measures) in a data structure. These metadata are referred to as Structural Metadata.
Relevance	Degree to which statistical information meets the real or perceived needs of clients.	Relevance is concerned with whether the available information sheds light on the issues that are important to users. Assessing relevance is subjective and depends upon the varying needs of users. The Agency's challenge is to weight and balance the conflicting needs of current and potential users to produce statistics that satisfy the most important needs within given resource constraints. In assessing relevance, one approach is to gauge relevance directly, by polling users about the data. Indirect evidence of relevance may be found by ascertaining where there are processes in place to determine the uses of data and the views of their users or to use the data in-house for research and other analysis. Relevance refers to the processes for monitoring the relevance and practical usefulness of existing statistics in meeting users' needs and how these processes impact the development of statistical programmes.
Representation	Allowable value or format for component or concept when reported.	The representation can be enumerated or non-enumerated. An enumerated representation can be a Code List, Concept Scheme, Category Scheme, Organisation Unit Scheme, Data Provider Scheme, Data Consumer Scheme, Agency Scheme. A non-enumerated representation is a specification of the valid content in terms of data types such as boolean, string, date/time, integer.
SDMX Information Model	Conceptual model for defining and describing the classes, attributes, and relationships of the SDMX standard.	This model is represented in UML (Unified Modelling Language). Section Two of the SDMX technical standard (SDMX Information Model) describes the parts of the model that pertain to structural metadata. Additional structures that relate to subscription (request to be notified of changes) and notification (of the changes) are described in Section Five of the SDMX technical standard (Registry Specification). All implementation artefacts such as SDMX-ML and SDMX-EDI specifications for data and structures are derived from the SDMX Information Model and there is a close correlation between the model and these implementation artefacts. This close correlation results in the ability to build syntax and version independent software that can work at the level of the model but which support the various syntaxes and versions of the SDMX implementation artefacts.
SDMX-EDI	UN/EDIFACT format for exchange of SDMX-structured data and metadata for time series.	SDMX-EDI is a message designed for the exchange of statistical information between organisations in a platform independent manner. The SDMX-EDI format is drawn from the GESMES/TS version 3.0 implementation guide, published as a standard of the SDMX initiative. GESMES (Generic Statistical Message) is a United Nations standard (EDIFACT message) allowing partner institutions to exchange statistical multi-dimensional arrays in a generic but standardised way. GESMES/TS (TS stands for "time series" and the specification is limited to supporting time series data) is an Implementation Guide specifying the use of GESMES for time series data and related metadata, and structural metadata - it can be regarded as a profile of GESMES. In the SDMX standard the GESMES/TS profile is known as SDMX-EDI. It defines the structures of GESMES that are available for use in SDMX-EDI thus allowing partner institutions to design and to build the applications needed to "read" and "write" SDMX-EDI messages.
Series key	Cross product of values of dimensions, where either the cross product or the cross product combined with a time value, identifies uniquely an observation.	Most series keys are combined with a time value in a data set in order to identify uniquely an observation. There may be particular series keys that do not require a time value in order to achieve this, so the "Time Dimension is not obligatory in an SDMX Data Structure Definition. In an SDMX data set there must be a value for all of the Dimensions specified in the Data Structure Definition when reporting data for a series key. The combination of the semantic of the names of the concepts used by the Dimension (excluding time) describes a series key. Unless the Data Structure Definition contains multiple measures this semantic is often the semantic of the observation.
Sex	State of being male or female.	This concept is applied if data need to be categorised by sex. The concept is in general coded, i.e. represented through a code list. It applies not only to human beings but also to animals and other living organisms.
Statistical concepts and definitions	Description of the statistical domain under measure as well as the main variables provided.	This metadata element is used to define and describe the type of variable provided (raw figures, annual growth rates, index, flow or stock data, etc.) referring to internationally accepted statistical standards, guidelines, or good practices on which the concepts and definitions that are used for compiling the statistics are based. Discrepancies should be documented.
Statistical subject-matter domain	Statistical activity that has common characteristics with respect to concepts and methodologies for data collection, manipulation and transformation.	Within SDMX, the list of Statistical Subject-Matter Domains (aligned to the Classification of International Statistical Activities maintained by the Conference of European Statisticians of the United Nations Economic Commission for Europe, UNECE) is a standard reference list against which the categorisation schemes of various participants in exchange arrangements can be mapped to facilitate data and metadata exchange. This allows the identification of subject-matter domain groups involved in the development of guidelines and recommendations relevant to one or more statistical domains. Each of these groups could define domain-specific data structure definitions, concepts, etc.
Statistical unit	Entity for which information is sought and for which statistics are ultimately compiled.	The statistical unit is the object of a statistical survey and the bearer of statistical characteristics. These units can, in turn, be divided into observation units and analytical units. Statistical units for economic statistics are defined on the basis of three criteria: Legal, accounting or organisational criteria; Geographical criteria; Activity criteria. Statistical units comprise the enterprise, enterprise group, kind-of-activity unit (KAU), local unit, establishment, homogeneous unit of production, persons, households, geographical areas, events etc. Statistical units can be categorised into basic statistical units, i.e. those for which data are collected, and derived statistical units, i.e. those which are constructed during the statistical production process. A basic statistical unit is the most detailed level to which the obtained characteristics can be attached.
Structural metadata	Metadata that identify and describe data and reference metadata.	Structural metadata are needed to identify, use, and process data matrixes and data cubes, e.g. names of columns or dimensions of statistical cubes. Structural metadata must be associated with the statistical data and reference metadata, otherwise it becomes impossible to identify, retrieve and navigate the data or reference metadata. In SDMX structural metadata are not limited to describing the structure of data and reference metadata. The structural metadata in SDMX include many of the other constructs to be found in the SDMX Information Model including data discovery, data and metadata constraints (used for both data validation and data discovery), data and structure mapping, data and metadata reporting, statistical processes,
Structural validation	Process to determine the validity of data and reference metadata using structural metadata.	In part the validation can be performed by processes that check the syntax of the data for conformance with the standard, for example a process for validating an XML instance (e.g. an SDMX data set) against the XML schema that defines the allowable structure and content of the instance. In SDMX the structural metadata contain additional metadata that can be used for validation but which cannot be expressed in an XML schema. Examples of these additional metadata include Constraints and Data Providers. The Constraint is used to specify the codes that are contained in a code list and which are valid for the type (sub set) of data that are to be expressed in data set in given context. The Data Provider specifies which type of data is expected or allowed to be reported or disseminated by a specific individual or organisation.
Structure set	Maintainable collection of Structure Maps that link components together in a source/target relationship where there is a semantic equivalence between the source and the target components.	The Structure Set can contain maps between two item schemes of the same type: Code List, Concept Scheme, Organisation Unit Scheme, Data Provider Scheme, Data Consumer Scheme. The Structure Set can also contain a map between two Data Structures i.e. map of the Dimensions and Attributes and corresponding code values where these are also mapped. A typical use of Structure Sets are to provide mappings between an SDMX data structure used in an internal system with an SDMX structure of an external dataset when imported to or exported from the internal system.
Timeliness - source data	Time between the end of a reference period and actual receipt of the data by the compiling agency.	Compared to the parent concept - timeliness - this concept only covers the time period between the end of the reference period and the receipt of the data by the data compiling agency. This time period is determined by factors such as delays reflecting the institutional arrangements for data transmission.
Title	Textual label used as identification of a statistical object.	Title is a short name describing and identifying a statistical object it is attached to. In SDMX, a title can be referred, for example, to a time series as a "time series title", or to an observation as an "observation title". This concept may be used several times in a DSD by suffixing the ID corresponding to the attachment level, e.g. TITLE_TS (series level), or TITLE_OBS (observation level).
Unit of measure	Unit in which the data values are expressed.	The unit of measure is a quantity or increment by which something is counted or described, such as kg, mm, C, F, monetary units such as Euro or US dollar, simple number counts or index numbers. The unit of measure has a type (e.g. currency) and, in connection with the unit multiplier, provides the level of detail for the value of the variable. For data messages, the concept is usually represented by codes. For metadata messages the concept is usually represented by free text.
Valuation	Definition of the price per unit, for goods and services flows and asset stocks.	Standard national accounts valuations include the basic price (what the seller receives) and the purchaser's price (what the purchaser pays). The purchaser's price is the basic price, plus taxes less subsidies on products, plus invoiced transportation and insurance services, plus distribution margin. Other valuation bases may be used in other contexts. International trade in goods considers the free on board (fob) price and cost-insurance-freight price, among others. The concept refers to valuation rules used for recording flows and stocks, including how consistent the practices used are with internationally accepted standards, guidelines, or good practices.
Versionable artefact	Construct that contains structures capable of providing a version to an object.	The version is mandatory and other attributes (such as "to" and "from" validity dates) are optional. Versionable artefacts inherit the capability of having names, identity and annotations.