In SDMX an Organisation Unit Scheme comprises a flat or hierarchical list of organisation units. Each maintenance agency can have multiple organisation unit schemes, and may have none. The identity of the organisation unit is a combination of the identity of the organisation unit scheme (which includes the maintenance agency) in which it resides and the identity of the organisation unit in that scheme. The Organisation Unit plays no direct role in support of the functionality of SDMX systems as documented in the technical standards (whereas Agency, Data Provider, and Data Consumer do play a distinct role). Therefore, this type of organisation can play any role and have any behaviour that is internal to the systems that use it.
Collection of organisation units.
In SDMX this construct is a part of the unique identification of the object.
Construct that enables a system to distinguish between one state of an object and another where the contents of the object have changed.
Geographical comparability refers to the degree of comparability between similar survey results measuring the same phenomenon across geographical areas or regions. The surveys are in general conducted by different statistical agencies, referring to populations in different geographical areas, sometimes based on a harmonised methodology.
Extent to which statistics are comparable between geographical areas.
This metadata element refers to all the planned and systematic activities implemented that can be demonstrated to provide confidence that the data production processes will fulfil the requirements for the statistical output. This includes the design of programmes for quality management, the description of planning process, scheduling of work, frequency of plan updates, and other organisational arrangements to support and maintain planning function.
Guidelines focusing on quality in general and dealing with quality of statistical programmes, including measures for ensuring the efficient use of resources.
The information model at the core of this International Standard has been developed to support statistics as collected and used by governmental and supra-national statistical organisations, and this model is also applicable to other organisational contexts involving statistical data and related metadata. This set of standards comprises a number of specifications covering the Information Model, various syntax implementations of the model, metadata registry for storage, query, and retrieval, and web services for both data and structural metadata.
Set of standards enabling interoperable implementations within and between systems concerned with the exchange, reporting and dissemination of statistical data and related metadata.
Data Providers are maintained in a Data Provider Scheme. The Data Provider can be linked to the type of data (Dataflow) or reference metadata (Metadata Flow) that it reports or disseminates. This link provides the data collection system or data dissemination system.
Organisation or individual that reports or disseminates data or reference metadata.
Recommended representation: String; Code list Codelist ID: CL_ORGANISATION
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 activity that has common characteristics with respect to concepts and methodologies for data collection, manipulation and transformation.
Usually, there is a time delay between the finalisation of the production process of statistical data and the moment when the data produced are released and made available to the users. This point in time where data are made publicly available is called "embargo time".
Exact time at which the data can be made available to the public.
Imputation is the process used to determine and assign replacement values for missing, invalid or inconsistent data. This can be done by changing some of the responses or assigning values when they are missing on the record being edited to ensure that estimates are of high quality and that a plausible, internally consistent record is created.
Procedure for entering a value for a specific data item where the response is missing or unusable.
This is a part of a Constraint.
Single value of the set of values for a member selection.
Fax number of the contact points for the data or metadata.
This metadata element is used to provide documentation regarding the source data used and the way they are adjusted, in order to give compilers the possibility of incorporating new and more accurate information into estimates, thus improving their accuracy without introducing breaks in the time series. It also describes the revision status of available data. Data may also be subject to regular or ad hoc revisions as a result of the introduction of new classifications, compilation frameworks and methodologies which result in the compilation of historical data that replace previously released data. Whether or not such changes constitute an actual "revision" or the compilation of a "new" series is a matter of judgment to be done by the statistical agency.
Information on the data revision practice.
Dates or periods during which the observations have been collected (such as middle, average or end of period) for the target reference period.
Recommended representation: Code list Codelist ID: CL_TIME_PER_COLLECT
Policy for release of the data to users, scope of dissemination (e.g. to the public, to selected users), how users are informed that the data are being released, and whether the policy determines the dissemination of statistical data to all users.
A constraint can be of a variety of types. For example, a Content Constraint specifies either the "allowable content" (used to restrict the values allowed when data or metadata are reported or exchanged), or the "actual" content (series keys and/or dimension and attribute values present in a data source). In each of these cases the constraint specifies a sub set of the full cube of data that could theoretically be present according to the specification of the Data Structure Definition or Metadata Structure Definition.
Specification of a subset of the possible content of data or metadata that can be derived from the code lists used in a data or metadata structure.
This concept covers press releases or other kind of similar releases linked to data or metadata.
Regular or ad-hoc press releases linked to the data.
Coverage errors include over-coverage, under-coverage and misclassification. Incomplete sampling frames often result in coverage errors.
Error caused by a failure to cover adequately all components of the population being studied, which results in differences between the target population and the sampling frame.
This metadata element is used to describe the main sources of random and systematic error in the statistical outputs and provide a summary assessment of all errors with special focus on the impact on key estimates. The bias assessment can be in quantitative or qualitative terms, or both. It should reflect the producer's best current understanding (sign and order of magnitude) including actions taken to reduce bias. Revision aspects should also be included here if considered relevant.
Assessment of accuracy, linked to a certain data set or domain, which is summarising the various components into one single measure.
Monetary denomination of the object being measured.
Recommended representation: Code list Codelist ID: CL_CURRENCY
The measurement of the availability of statistics normally refers to data sets and compares the required data set to the available one.
Extent to which all statistics that are needed are available.
It may include the contribution of respondent time in supplying information (burden) as a distinct subject under this heading.
Staff, facilities, computing resources, and financing to undertake statistical production.
At a time series break period, two observations may be recorded: the pre-break value produced on the basis of the old methodology and the post-break value, as measured by the new methodology. SDMX allows for a pre-break value in the case of a series break, where one would use the observation value to show the post-break value.
Observation, at a time series break period, that was calculated using the old methodology.
Recommended representation: AlphaNumeric
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.
Description of the statistical domain under measure as well as the main variables provided.
This is used in structural metadata where the object is not contained in the structural metadata made available (e.g. in a structure message or in an SDMX Registry), but has a URI reference from where it can be obtained. Note that this is only available for maintainable objects such as a Code List, and not for individual Codes.
Construct that indicates whether an object is available in the metadata source that contains its identifier or whether the object itself is available elsewhere.
This summary description should provide an immediate understanding of the data to users (also to those who do not have a broader technical knowledge of the data set in question).
Summary description of data representation
Main characteristics of the data set described in an easily understandable manner, referring to the data and indicators disseminated.
This metadata element is used to describe the rules applied when treating the data with regard to statistical confidentiality (e.g. aggregation rules when disseminating data, provision of unit records, etc.).
Rules applied for treating the data set to ensure that private information from individual units cannot be accessed and to prevent unauthorised disclosure.
In the SDMX Information Model it is an abstract super class whose sub classes are the content of a Data Structure Definition or Metadata Structure Definition such as a Dimension or Attribute. A "Component List" is an abstract super class whose sub classes are the lists of Dimensions, Attributes, and Measures defined in a content of a Data Structure Definition key family or Metadata Structure Definition. The component specification includes its representation which can be enumerated or non-enumerated. An enumerated representation of a component links to a code list and a non-enumerated representation is specified in terms of Facets which define characteristics such as "string", "integer", "date/time" etc.
Structural artefact used to define the structure of a data or metadata set.
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.
Construct that contains structures capable of providing a version to an object.
This metadata element is used to describe the policies promoting the recruitment and promotion of staff based on relevant aptitude; providing guidelines for staff behaviour and procedures to make these guidelines known to staff; and prescribing other practices that provide assurances of the independence, integrity, and accountability of the statistical agency.
Provisions for assuring the qualifications of staff and allowing staff to perform their functions without intervention motivated by non-statistical objectives.
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).
Organisation or other expert body responsible for the operational maintenance of commonly used metadata artefacts.
Recommended representation: Code list Codelist ID: CL_AGENCY
With respect to the statistical data to be provided, the main users (e.g. official authorities, the public or others) and user needs should be stated, e.g. official authorities with the needs for policy indicators, national users, etc.
Description of requirements with respect to the statistical output.
In a SDMX registry environment the Data Provider will fulfil its data reporting requirements when the registry has accepted the registration. The URL should be checked by the registry as being valid and the registry may check that the data service or data set are valid.
Data or reference metadata reporting method that requires the provider to make the information available at an accessible web location.
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.
Code reference that is part of a hierarchy.
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).
Textual label used as identification of a statistical object.
In quality assurance frameworks, Data compilation refers to the description of statistical procedures used for producing intermediate data and final statistical outputs. Data compilation covers, among other things, the use of weighting schemes, methods for imputing missing values or source data, statistical adjustment, balancing/cross-checking techniques and relevant characteristics of the specific methods applied.
Operations performed on data to derive new information according to a given set of rules.
The specification of the format information for the codes, such as whether the codes are alphabetic, numeric or alphanumeric, and the code length.
Specification of the representation for the codes in a code list.
It also includes arrangements or procedures to facilitate data sharing and coordination between data producing agencies.
Set of rules or other formal set of instructions assigning responsibility as well as the authority to an organisation for the collection, processing, and dissemination of statistics.
The annotation in SDMX is way of extending the functionality of SDMX structural metadata.
Construct capable of defining annotations
The SDMX global registry is the central reference point and authoritative source for SDMX global Data Structure Definitions and related objects. The contents of the Global Registry are subject to the SDMX Global Registry contents policy which defines the criteria that the SDMX artefacts must meet before the artefacts can be included in the Global Registry.
Central and discoverable repository for SDMX structural metadata.
This metadata element provides a link to the on-line database where the data are available, with a summary identification of domain names as released on the website, as well as the related access conditions.
Dissemination format for online database
Information about on-line databases in which the disseminated data can be accessed.
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.
Structure which describes, categorises and constrains the allowable content of a data set that providers will supply for different reference periods.
There are four types of Item Scheme in SDMX: Code List, Concept Scheme, Category Scheme, Organisation Scheme (and four sub schemes: Agency, Data Provider, Data Consumer, Organisation Unit).
Descriptive information for an arrangement or division of objects into groups based on characteristics which the objects have in common.
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.
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.
Non-sampling errors may arise from many different sources such as defects in the sampling frame, faulty demarcation of sample units, defects in the selection of sample units, mistakes in the collection of data due to personal variations, misunderstanding, bias, negligence or dishonesty on the part of the investigator or of the interviewer, mistakes at the stage of the processing of the data, etc. Non-sampling errors may be categorised as: Coverage errors (or frame errors) due to divergences between the target population and the frame population; Measurement errors occurring during data collection; Nonresponse errors caused by no data collected for a population unit or for some survey variables; Processing errors due to errors introduced during data entry, data editing, sometimes coding and imputation; Model assumption errors.
Error in sample estimates which cannot be attributed to sampling fluctuations.
An example is a structure for use in internal production processes.
DSD developed for the specific needs of one organisation only.
Originally from SDMX-EDI, a sibling group is uniquely identified by a data set identifier combined with the sibling group key.
Set of time series whose keys differ only in the value taken by the frequency dimension.
The Code in SDMX contains the Id (the code), and a name and description either or both of which can be multi-lingual.
Language independent set of letters, numbers or symbols that represent a concept whose meaning is described in a natural language.
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.
Highest level of an educational programme the person has successfully completed.
Recommended representation: Code list Codelist ID: CL_EDUCATION_LEVEL
"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.
Descriptive text and references to methodological documents available.
In a SDMX data set, the instance of a measure is often called an observation.
Statistical concept for which data are provided in a data set.
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.
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.
Arrangements or procedures for data sharing and coordination between data producing agencies.
An activity can be said to take place when resources such as equipment, labour, manufacturing techniques or products are combined, leading to specific goods or services. Thus, an activity is characterised by an input of resources, a production process and an output of products.
Combination of actions that result in the production, distribution and consumption of goods or services.
Recommended representation: Code list Codelist ID: CL_ACTIVITY
The civil status is often referred to as marital status and represented through codes of the respective code list.
Legal, conjugal status of each individual in relation to the marriage laws or customs of the country.
Recommended representation: Code list Codelist ID: CL_CIVIL_STATUS
A high item non-response rate indicates difficulties in providing information, e.g. a sensitive question or unclear wording for social statistics or information not available in the accounting system for business statistics.
Ratio between the in-scope (eligible) units which have not responded to a particular item and the in-scope units that are required to respond to that particular item.
In quality assurance frameworks this element indicates how the views and opinions of the users are collected. If user satisfaction surveys are conducted, the way users' views and opinions are collected should be described and the main results shown (in the form of a user satisfaction index if available); the date of the most recent user satisfaction survey should also be mentioned. Otherwise, any other indication or measure to determine user satisfaction might be used.
Description of how well the disseminated statistics meet the expressed user needs.
A simple aggregation such as the number of accidents, total income or women Members of Parliament, is not in itself an indicator, as it is not comparable between populations. However, if these values are standardised, e.g. number of accidents per thousand of population, average income, or women Members of Parliament as a percentage of the total, the result meets the criteria for an indicator.
Data element that represents statistical data for a specified time, place, and other characteristics, and is corrected for at least one dimension (usually size) to allow for meaningful comparisons.
This metadata element is used to document the methods and standards for assessing data quality, based on standard quality criteria such as relevance, accuracy and reliability, timeliness and punctuality, accessibility and clarity, comparability, and coherence.
Documentation on procedures applied for quality management and quality assessment.
Quality management - quality documentation
"Observation value" is the field which holds the data.
Various economic aggregates (e.g. GDP, investment, household consumption) are calculated so that changes in value terms can be divided up into a factor that reflects the underlying price changes and a factor which reflects the volume changes. As a result of this sub-division, one can get an idea of how these aggregates develop after adjustment for price changes. For example, in order to measure the volume growth of GDP and its components, it is therefore necessary to remove the effect of price changes from the changes in value, by keeping prices "constant" as it were.
Statistical technique used to remove the effects of price influences operating on a data series.
Recommended representation: Code list Codelist ID: CL_PRICE_ADJUST
SDMX-ML (SDMX markup language) is an XML implementation of the SDMX Information Model. In addition to supporting the collection and dissemination of statistical multi-dimensional arrays in a generic but standardised way, the SDMX-ML supports constructs that aid data validation, data discovery, mapping (of data sets) reference metadata, and process. The markup language uses the XML syntax and the allowable markup is specified and documented in Section 3 of the SDMX technical standards (Schema and Documentation).
XML format for the exchange of SDMX-structured data and metadata.
This indicator shows to what extent metadata of a specific type are available compared to what should be available.
Ratio of the number of metadata elements provided to the total number of metadata elements applicable.
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.
Entity for which information is sought and for which statistics are ultimately compiled.
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.
Degree to which statistical information meets the real or perceived needs of clients.
Data are protected by confidentiality in cases where unauthorised disclosure could be prejudicial or harmful to the interest of the source or other relevant parties. For instance, data allowing the identification of a physical or legal person, either directly or indirectly, may be characterised as confidential according to the relevant national or international legislation. Unauthorised disclosure of data that are restricted or confidential is not permitted and even legislative measures or other formal provisions may be used to prevent disclosure. Often, there are procedures in place to prevent disclosure of restricted or confidential data, including rules applying to staff, aggregation rules when disseminating data, provision of unit records, etc.
Property of data indicating whether they are subject to dissemination restrictions.
The Category Scheme comprises a hierarchy of categories which may include any type of useful classification for the organisation of data and metadata.
Descriptive information for a subdivision of categories into groups based on characteristics, which the objects have in common.
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".
Abstract concept denoting an element in the SDMX model having specific characteristics which are inherited by other elements.
The SDMX Registry must comply with the Registry Interface API and web services specification for query. An SDMX Registry is not obliged to implement all of the APIs.
SDMX-ML specification of the allowable constructs that an SDMX registry must consume or output in its response.
SDMX registry interface (in the context of registry)
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").
Set of concepts that are used in a data structure definition or metadata structure definition.
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.
Time between the end of a reference period and actual receipt of the data by the compiling agency.
This metadata element provides references to the most important data dissemination done through paper or on-line publications, including a summary identification and information on availability of the publication means.
Regular or ad-hoc publications in which the data are made available to the public.
Postal address of the contact points for the data or metadata.
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.
Detailed description of data representation
Detailed description of the disseminated data.
Data presentation - detailed description
This metadata element is used to describe the general guidelines for handling data revisions applied by a data providing agency.
Policy aimed at ensuring the transparency of disseminated data, whereby preliminary data are compiled that are later revised.
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.
Organisation collecting and/or elaborating the data being reported.
Recommended representation: Code list Codelist ID: CL_ORGANISATION
The frequencies with which data are released, which could be different from the frequency of data collection.
Time interval at which the statistics are disseminated over a given time period.
Recommended representation: Code list Codelist ID: CL_FREQ
For a specific key variable this indicator can be calculated in two forms: a) for data producers, and b) for data users. The indicator shows to what extent statistics are available compared to what should be available. For producers, It can be used to evaluate the degree of compliance by a given Member State for a given dataset and period. For users, it can be used to: identify whether important variables are missing for some individual Member State or alternatively; give users an overall measurement (aggregate across countries and/or key variables) of the availability of statistics. The indicator should be accompanied by information about which variables are missing and the reasons for incompleteness as well as, where relevant, the impact of the missing data on aggregates, and plans for improving completeness in the future.
Ratio of the number of data cells provided to the number of data cells required or relevant. The ratio is computed for a chosen dataset and a given period.
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.
Extent to which statistics are consistent within a given data set.
Such data sets contain only the data that need to be updated. For any one series the data may contain only attributes (i.e. no observations); or just data (i.e. no attributes); or a mixture of observations and attributes. Note that in an incremental update a set of data or metadata may omit mandatory attributes.
Data or metadata message that is used for changing a part of the content of a data/metadata set.
Elements providing assurances that the statistical entity is entitled to comment on erroneous interpretation and misuse of statistics.
Professionalism - statistical commentary
A DSD for global use is meeting one of the two criteria below: It is designed as a standard data structure for global use (i.e. having a very wide geographical coverage or cross-domain nature), with more than one SDMX sponsor organisation represented in the ownership group and one of the members of the ownership group acting as maintenance agency on behalf of the ownership group; DSDs labelled as "global" by the SDMX sponsors considering the recognised expertise in the domain concerned of one of the organisations represented in the ownership group and the potential usefulness of the artefact for the whole SDMX community; in this case the DSD will have to meet strict criteria of versioning, governance, maintenance, adoption and endorsement.
DSD agreed by a number of international organisations for use within their respective constituencies
The group key's structure that comprises the subset of dimensions that specifies the structure of the partial key.
Set of metadata concepts that define a partial key derived from the dimension descriptor in a Data Structure Definition.
The SDMX Global Registry has the ability to send a Notification message either by means of an e-mail or by an SDMX message to a URL of a service that will process the notification. The sending of a Notification is triggered by an event in the registry that affects a structural metadata object in the registry, such as a change to a Code List, a deletion of a Code List, or the addition of a new Code List. The Notification is only created if there is one or more Subscriptions held for the object in question and it is sent only to the email addresses and URLs specified in the Subscriptions.
Information sent to a person or application as a result of an event in an SDMX registry.
This metadata element informs users in advance about major changes in methodology, source data, and statistical techniques.
Documentation on methodology advance notice
Policy on notifying the public of changes in methodology, indicating whether the public is notified before a methodological change affects disseminated data and, if so, how long before.
Documentation on methodology - advance notice
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.
Collection of metadata concepts, structure and usage when used to collect or disseminate reference metadata.
The sector coverage delimits the statistical results with regard to the main sectors covered. These sectors can be institutional sectors, economic or other sectors (e.g. local government sector, agriculture, forestry, or business services).
Main economic or other sectors covered by the statistics.
Access to the release calendar information. A hyperlink should be provided if available.
Description of how the release calendar can be accessed.
Release policy - release calendar access
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.
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.
In some databases, it is referred to as SCALE, MAGNITUDE or POWER, e.g. UNIT_MULT 6 means that observations are in millions.
Exponent in base 10 specified so that multiplying the observation numeric values by 10^UNIT_MULT gives a value expressed in the unit of measure.
Recommended representation: Code list Codelist ID: CL_UNIT_MULT
Organisation of the contact point(s) for the data or metadata.
Recommended representation: String; Code list
An organisation can play a number of organisation roles. In the SDMX information model, three roles are identified at present: Data Provider; Data Consumer; Maintenance Agency. The Data Consumer is relevant for data and reference metadata dissemination. Such systems may require access control. The data consumer can be linked to the Dataflows and Metadataflows via a Provision Agreement thus enabling a dissemination system to validate which consumers have access to which data and reference metadata.
Cost-benefit analysis, effectiveness of execution of medium term statistical programmes, and ensuring efficient use of resources.
The SDMX Global Registry has a facility that enables a user to subscribe to events in the registry such as a change to a Code List, a deletion of a Code List, or the addition of a new Code List. When such an event takes place the registry will send an SDMX Notification message to the email or URL address in the Subscription.
Indication that a person or application is to be notified when a predefined event occurs in an SDMX registry.
The date of the metadata update may refer to the update of a whole metadata set or to the update of any single metadata item. The update can refer to the file update (with or without change in the content) or to the date on which the metadata have been posted on the web.
Date on which the metadata element was created or modified.
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.
Unit in which the data values are expressed.
Recommended representation: Code list Codelist ID: CL_UNIT_MEASURE
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.
Country or geographic area to which the measured statistical phenomenon relates.
Recommended representation: Code list Codelist ID: CL_AREA
In many cases, the reference period and time period will be identical, but there are also cases where they are different. This can happen if data are not available for the target reference period, but are available for a time period which is judged to be sufficiently close. For example, the reference period may be a calendar year, whereas data may only be available for a fiscal year. In such cases, reference period should refer to the target reference period rather than the actual time period of the data. The difference between target and actual reference period can be highlighted in a free text note.
Timespan or point in time to which the measured observation is intended to refer.
Recommended representation: DateTime; String
A group key is derived from the dimensionality of the series key for the purpose of attaching data attributes
Set of key values that comprise a partial key.
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.
Date of last update of the content of the metadata.
In SDMX a Data Consumer Scheme comprises a non-hierarchic list of data consumers. Each maintenance agency can have a single data consumer scheme, and may have none. The identity of the data consumer is a combination of the identity of the data consumer scheme (which includes the maintenance agency) in which it resides and the identity of the data consumer in that scheme.
Maintained collection of data consumers.
The overall assessment of data quality may include the result of a scoring or grading process for quality. Scoring may be quantitative or qualitative.
Overall evaluation of data quality, based on standard quality criteria.
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.
Property of an attribute defining the object to which data or metadata are linked.
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.
Job or position held by an individual who performs a set of tasks and duties.
Recommended representation: Code list Codelist ID: CL_OCCUPATION
The "counterpart area" (also known as vis-a-vis area") is related to statistics on foreign trade, migration or other domains. It determines, from the point of view of the reporting country, the corresponding area to which the economic or other flows are related to (for instance, in statistics on imports, the counterpart reference area is the area of origin of the goods). A categorisation of IDs per attachment level (COUNTERPART_AREA_DSET for dataset, COUNTERPART_AREA_GRP for group) is recommended.
Secondary area, as opposed to reference area, to which the measured data are in relation.
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.
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.
Relevance - data completeness rate (for data users)
Telephone number of the contact points for the data or metadata.
This metadata element is used to describe the differences in the statistical results calculated on the basis of different statistical domains, or surveys based on different methodologies (e.g. between annual and short-term statistics or between social statistics and national accounts).
Extent to which statistics are reconcilable with those obtained through other data sources or statistical domains.
This statement does not describe the release policy itself.
Statement describing whether and how the release policy is disseminated to the public.
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.
Descriptive text which can be attached to data or metadata.
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.
Adequacy of statistics to be reliably combined in different ways and for various uses.
Age can be expressed as a number, e.g. 25 years old, or as a range, e.g. "between 25 and 29 years" or "6 to 11 months".
Length of time that an entity has lived or existed.
Recommended representation: Code list Codelist ID: CL_AGE
This is used to group the Reporting Categories that link to Dataflows and Metadataflows to support data publication.
Scheme which defines the composition structure of a data report where each component can be described by an independent Dataflow Definition or Metadataflow Definition.
This is a part of a Constraint
Value in a metadata set of an identifier component defined in a metadata structure definition.
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.
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 frequencies with which the source data are collected and produced could be different: a time series could be collected from the respondents at quarterly frequency but the data production may have a monthly frequency. The frequency of data collection should therefore be described.
Time interval at which the source data are collected.
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.
Method applied for gathering data for official statistics.
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).
References to the most important other data dissemination done.
SDMX is an ISO standard designed to describe statistical data and metadata, normalise their exchange, and improve their efficient sharing across organisations. The SDMX initiative is sponsored by seven international organisations (Bank of International Settlements, European Central Bank, Eurostat, International Monetary Fund, Organisation for Economic Co-operation and Development, United Nations Statistical Division and World Bank) to facilitate the exchange of statistical data and metadata using information technologies. This standard provides an integrated approach to facilitating statistical data and metadata exchange, enabling interoperable implementations within and between systems concerned with the exchange, reporting and dissemination of statistical data and their related meta-information. It is not just a format for data exchange: it includes a set of technical standards and content-oriented guidelines, and is supported by an IT architecture and tools to be used for the efficient exchange and sharing of statistical data and metadata. Taken together, those elements may be used to support improved business processes for any statistical organisation.
Technical standard and content-oriented guidelines for the exchange and sharing of statistical information between organisations.
Elements providing assurances that statistics are produced on an impartial basis.
The time period covered can be indicated as a time interval, e.g. "1985 to 2006" for annual time series data, or as several intervals or values of time.
Reference metadata element specifying the period of time for which data are provided.
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.
Maintained collection of maintenance agencies.
The base period refers to the period when the published index is 100, or to which weights or base data refer to. It can be one single year (e.g. 1995=100) but it may be as short as one day or as long as a specified number of years. Base period may include an indication of the value of the series in the base period (usually 1 or 100).
Period of time used as the base of an index number, or to which a constant series refers.
Recommended representation: Code list; DateTime; String Codelist ID: CL_BASE_PER
The technical time format and its related code list are part of the technical standards for SDMX-EDI and SDMX-XML.
Technical format for the representation of time.
Recommended representation: Code list Codelist ID: CL_TIME_FORMAT
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.
Predefined set of terms from which some statistical coded concepts take their values.
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.
Breakdown of spending by institutional sectors between major expenditure functions.
Recommended representation: Code list Codelist ID: CL_COFOG; CL_COICOP; CL_COPNI; CL_COPP
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.
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.
This indicator quantifies the gap between the release date of first results and the date of reference for the data.
Number of days (or weeks or months) from the last day of the reference period to the day of publication of first results.
In SDMX the identity comprises a mandatory Id and some optional attributes. Identifiable artefacts inherit the capability of having annotations.
Construct that contains structures capable of providing identity to an object.
This metadata element is used to indicate whether the data set is based on a survey, on administrative data sources, on a mix of multiple data sources or on data from other statistical activities. If sample surveys are used, some sample characteristics should also be given (e.g. population size, gross and net sample size, type of sampling design, reporting domain etc.). If administrative registers are used, the description of registers should be given (source, primary purpose, etc.).
Characteristics and components of the raw statistical data used for compiling statistical aggregates.
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.
Proportion of units covered by both the survey and the administrative sources in relation to the total number of units in the survey.
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.
Unit of thought created by a unique combination of characteristics.
This item is normally coded and uses codes providing information about the status of a value, with respect to events such as "break", "estimated value", "forecast", "missing value", or "provisional value". In some cases, there is more than one event that may have influenced the value (e.g. a break in methodology may be accompanied with the fact that an observation is an estimate).
Information on the quality of a value or an unusual or missing value.
Recommended representation: String Codelist ID: CL_OBS_STATUS
Name of the contact points for the data or metadata.
Seasonal adjustment removes the effects of events that follow a more or less regular pattern each year. These adjustments make it easier to observe the cyclical and other non-seasonal movements in a data series.
Statistical technique used to remove the effects of seasonal and calendar influences operating on a data series.
Recommended representation: Code list Codelist ID: CL_SEASONAL_ADJUST
To retain trust in official statistics, the statistical agencies need to decide according to strictly professional considerations, including scientific principles and professional ethics, on the methods and procedures for the collection, processing, storage and presentation of statistical data (United Nations Fundamental Principles of Official Statistics, principle 2). This metadata element describes the elements providing assurances that: statistics are produced on an impartial basis; elements providing assurances that the choices of sources and statistical techniques as well as decisions about dissemination are informed solely by statistical considerations; elements providing assurances that the recruitment and promotion of staff are based on relevant aptitude; elements providing assurances that the statistical entity is entitled to comment on erroneous interpretation and misuse of statistics, guidelines for staff behaviour and procedures used to make these guidelines known to staff; other practices that provide assurances of the independence, integrity, and accountability of the statistical agency.
Standard, skill and ability suitable for producing statistics of good quality.
There are two types of population: target population and survey population. A "target population" is the population outlined in the survey objects about which information is to be sought and a "survey population" is the population from which information is obtained in a survey. The target population is also known as the scope of the survey and the survey population as the coverage of the survey. For administrative data sources, the corresponding populations are the "target population", as defined by the relevant legislation and regulations, and the actual "client population" ("United Nations Glossary of Classification Terms" prepared by the Expert Group on International Economic and Social Classifications).
Total membership or population or "universe" of a defined class of people, objects or events.
This is used to group Dataflows and Metadataflows to support data publication.
Component of a reporting taxonomy that gives structure to a report and links to data and metadata.
Timeliness refers to the speed of data availability, whether for dissemination or for further processing, and it is measured with respect to the time lag between the end of the reference period and the release of data. Timeliness is a crucial element of data quality: adequate timeliness corresponds to a situation where policy-makers can take informed decisions in time for achieving the targeted results. In quality assessment, timeliness is often associated with punctuality, which refers to the time lag between the release date of data and the target date announced in some official release calendar.
Length of time between data availability and the event or phenomenon they describe
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".
Specification of the type of artefact to which a data attribute can be attached in a data set.
Data validation describes methods and processes for assessing statistical data, and how the results of the assessments are monitored and made available to improve statistical processes. All the controls made in terms of quality of the data to be published or already published are included in the validation process. Validation also takes into account the results of studies and analysis of revisions and how they are used to improve statistical processes. In this process, two dimensions can be distinguished: i) validation before publication of the figures and ii) validation after publication.
Process of monitoring the results of data compilation and ensuring the quality of the statistical results.
SDMX-JSON is a data exchange format for data discovery and data visualization on the web. It conforms to JSON (JavaScript Object Notation) standard specification, and it supports the SDMX 2.1 Information Model. SDMX-JSON is compatible with the SDMX RESTful Web Services API, and it supports all features of the SDMX RESTful API for data queries. The SDMX-JSON data exchange format is documented in the SDMX-JSON Data Message specification.
JSON format for the dissemination of SDMX-structured data and metadata on the web.
Organisation that supplies the data for a given instance of the statistics.
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.
Correspondence between two or more objects.
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.
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.
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.
Allowable value or format for component or concept when reported.
The un-weighted rate shows, for a particular variable, the proportion of units for which a value has been imputed due to the original value being a missing, implausible, or inconsistent value in comparison with the number of units with a value for this variable. The weighted rate shows, for a particular variable, the relative contribution of imputed values to the estimate of this item/variable.
Ratio of the number of replaced values to the total number of values for a given variable.
Sampling errors are distinct from errors due to imperfect selection, bias in response or estimation, errors of observation and recording, etc. For probability sampling, the random variation due to sampling can be calculated. For non-probability sampling, random errors cannot be calculated without reference to some kind of model. The totality of sampling errors in all possible samples of the same size generates the sampling distribution of the statistic which is being used to estimate the parent value.
Part of the difference between a population value and an estimate thereof, derived from a random sample, which is due to the fact that only a subset of the population is enumerated.
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.
Extent to which differences between statistics can be attributed to differences between the true values of the statistical characteristics.
The name is mandatory and the description is optional. Each can have multilingual variants. Nameable artefacts inherit the capability of having identity and annotations.
Construct that contains structures capable of providing a name and a description to an object.
The glossary is implemented as a concept set, enriched with lexical notes, cross-references, comments, examples, and semantic relationships between terms. While not intended to cover the full range of statistical terminology, the glossary focuses on terms central to metadata design and the implementation of SDMX-based exchange frameworks. As part of the Interoperability Basis initiative, we carried out substantial semantic enrichment of the glossary. This included: Introducing formal semantic links between terms using SKOS and DCTERMS Aligning glossary entries with the concept scheme from the SDMX Content-Oriented Guidelines (2009), ensuring skos:exactMatch where applicable Enhancing the glossary's internal coherence and machine-readability to support integration into linked data environments This enrichment strengthens the glossary’s role as a semantic backbone for SDMX metadata systems and improves its interoperability across platforms and statistical domains. This glossary is based on version 2.0 of the SDMX glossary as published in the official global registry. While version 2.1 exists in HTML format, it is not available in a machine-readable form and has not been integrated into the registry. For this reason, version 2.0 was used as the reference. Please also note that in this version of the glossary the property "source" has not been displayed due to the lack of information in the original SDMX glossary XML published in the official global registry. We look forward to a revised and comprehensive glossary as part of SDMX version 3, and will carry out a review of sources once such an update becomes available.
The SDMX Glossary is an official guideline that defines key concepts used in SDMX-based data and metadata exchange. It provides definitions for terms found in the SDMX Information Model, Data Structure Definitions (DSDs), and Metadata Structure Definitions (MSDs), and is designed to serve as a single point of reference for shared terminology.
Description of periodic studies related to data revisions. These studies can contain quantitative measures of the effects of revisions, such as mean revision and revision variance in estimates.
Information about data revision studies and analyses.
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.
Conceptual model for defining and describing the classes, attributes, and relationships of the SDMX standard.
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).
Maintained collection of data providers.
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.
Structural metadata concept that classifies structural metadata objects.
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.
Cross product of values of dimensions, where either the cross product or the cross product combined with a time value, identifies uniquely an observation.
The term "variable" is meant here in the mathematical sense, i.e. a quantity which may take any one of specified set of values. It is convenient to apply the same word to denote non-measurable characteristics, e.g., sex is a variable in this sense since any human individual may take one of two "values", male or female. It is useful, but far from being the general practice, to distinguish between a variable as so defined and a random variable (The International Statistical Institute, "The Oxford Dictionary of Statistical Terms", edited by Yadolah Dodge, Oxford University Press, 2003).
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" etc.).
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.
Statistical concept used in combination with other statistical concepts to identify a statistical series or individual observations.
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.
Metadata describing the contents and the quality of the statistical data.
The location is a resolvable URL. There are three types of data source: simple: where the URL will return a file; REST: where a REST query will return a file; queryable: where the URL refers to a service which can be queried.
Location or service from where data or metadata can be obtained.
Relevance - data completeness rate (for data producers)
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 consultations of data tables within a statistical domain for a given time period.
1) Potential application across all statistical domains. Examples: CL_OBS_STATUS, CL_CONF_STATUS, CL_DECIMALS, CL_UNIT_MULT, CL_AREA. Explanatory note: Key term for this criterion is "potential". These code lists must not necessarily be implemented in all Data Structure Definitions (DSDs) but they potentially could. For example, code list Unit multiplier could possibly be used in all implementations dealing with statistical figures but some implementations might not see the need for such a dimension because the statistical values do not require it, e.g. average number of children per household. Inversely, in this example a code list for decimals will be absolutely necessary. 2) Code lists maintained by the SWG on its initiative because 1) they are intended for broad use within the SDMX community and 2) there is a strong need for harmonisation across domains which are not necessarily closely connected with each other. Examples for case 1: CL_AGE, CL_CIVIL_STATUS, CL_FREQ, CL_TIME_FORMAT, CL_SEX, CL_ADJUSTMENT. Explanatory note: By proposing such code lists it is hoped to promote harmonisation across domains and provide ready-to-use artefacts to implementers. Example for case 2: CL_ACTIVITY. Explanatory note: International activity classifications are typically used in different statistical domains (e.g. economic versus social statistics). Without an established CDCL made available in centralised registries, the risk is that one domain develops a code list without taking into account the fact that other domains might use the same classification system. 3) Code lists recommended as CDCL by the SDMX Statistical Working Group (SWG) although they are in principle maintained by third organisations. Examples: CL_AREA (based on the ISO 3166 alpha-2 codes for countries); CL_CURRENCY (based on the ISO 4217 3-character codes for currencies). Explanatory note: In these cases, the value added by the SWG is to propose guidelines on specific methodological issues, e.g. how to code a country that has been split into several new entities.
SDMX code list meeting at least one of the criteria below:1) Potential application across all statistical domains; 2) Code list maintained by the SDMX Statistical Working Group (SWG) on its initiative; 3) Code list recommended as CDCL by the SDMX SWG although they are in principle maintained by third organisations.
Over-coverage arises from the presence in the frame of units not belonging to the target population (e.g. deceased persons still listed in a population register or no longer operating enterprises still in the business register) and of units belonging to the target population that appear in the frame more than once.
Proportion of units accessible via the frame that do not belong to the target population (are out-of-scope).
The ratio can be weighted or un-weighted. The un-weighted unit non-response rate shows the result of the data collection in the sample (the units included), rather than an indirect measure of the potential bias associated with non-response. The design-weighted unit non-response rate shows how well the data collection worked considering the population of interest. The size-weighted unit non-response rate would represent an indirect indicator of potential bias caused by non-response prior to any calibration adjustments.
Ratio of the number of units with no information or not usable information to the total number of in-scope (eligible) units.
The assumption is that this logical formalisation of validation and transformation rules will be transformed into specific programming languages for execution (SAS, R, Java, SQL, etc.), but will provide a "neutral" expression of the processing taking place.
Language used by statisticians to express logical validation rules and transformations on data, whether described as a dimensional table or as unit-record data.
This indicator quantifies the gap between the release date of the final results and the end of the reference period.
Number of days (or weeks or months) from the last day of the reference period to the day of publication of complete and final results.
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.
UN/EDIFACT format for exchange of SDMX-structured data and metadata for time series.
The annotation construct in SDMX is available to most of the SDMX structural metadata artefacts. This facility is essentially a flexible extension mechanism allowing metadata to be added to SDMX structural metadata or to a data set. Note that whilst the SDMX annotation has a specific structure (Title, Type, URL, Text) individual organisations are free to use these in any way and any combination they wish. An Annotation can only be processed in a meaningful way (i.e. other than viewing it) by systems that understand the semantic of the Annotation.
Construct that contains user or organisation-specific 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.
Organised collection of reference metadata.
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.
Definition of the price per unit, for goods and services flows and asset stocks.
Recommended representation: Code list; String Codelist ID: CL_VALUATION
Sources of processing errors include all post-collection operations, as well as the printing of questionnaires. Most processing errors occur in data for individual units, although errors can also be introduced in the implementation of systems and estimates. In survey data, for example, processing errors may include transcription errors, coding errors, data entry errors and errors of arithmetic in tabulation.
Error in final survey results arising from the faulty implementation of correctly planned implementation methods.
This information is in the Header of a data set, typically for processing by the receiving system in its administration of the data set.
Date and time that the data are gathered from a data source.
Contact describes contact points for the data or metadata, including how to reach the contact points.
Individual or organisational contact points for the data or metadata, including information on how to reach the contact points.
This metadata element is used to report, where relevant, the results of comparisons with the National Account framework and feedback from National Accounts with respect to coherence and accuracy problems and should be a trigger for further investigation.
Extent to which statistics are reconcilable with National Accounts.
A decimal is a fraction that has a denominator of a power of ten, the power depending on or deciding the decimal place. It is indicated by a decimal point to the left of the numerator, the denominator being omitted. Zeros are inserted between the point and the numerator, if necessary, to obtain the correct decimal place. Examples of decimals are 0.04 = 4/100 or 0.126 = 126/1000.
Number of digits of an observation to the right of a decimal point.
Recommended representation: Code list Codelist ID: CL_DECIMALS
Punctuality may be calculated, for instance, with reference to target dates announced in an official release calendar, laid down by regulations or previously agreed among partners.
Time lag between the actual delivery of the data and the target date when it should have been delivered.
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.
Recommended representation: Code list Codelist ID: CL_SEX
An advance release calendar is the schedule for release of data, which are publicly disseminated so as to provide prior notice of the precise release dates on which a national statistical agency, other national agency, or international organisation undertakes to release specified statistical information to the public. Such information may be provided for statistical releases in the coming week, month, quarter or year.
Schedule of statistical release dates.
The hub architecture supports the "pull" method only i.e., a group of partners agree on providing access to their data directly from their database according to standard processes, formats and technologies (e.g. web service). From the data management point of view, the hub is also based on a pre-specified datasets, which are not kept locally at the central hub system. The query process operates as follows: A user identifies a dataset through the graphical user interface (GUI) of the hub using the structural metadata, and requests it; the hub translates the user request in one or more queries and sends them to the related data providers' systems; data providers' systems process the query and send the result to the hub in standard format (e.g. SDMX-ML 2.1); the hub puts together all the results originated in all implicated data providers' systems and presents them in the requested format. This could be a human-readable, non-SDMX format such as a table.
Method of registering, querying, and disseminating data or reference metadata by means of a central, service-based platform (the hub).
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.
Institutional mandate for 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.
Institutional mandate - legal acts and other agreements
This metadata element refers to the various means of dissemination used for making the data available to the public. It includes a description of the various formats available, including where and how to get the information (for instance paper, electronic publications, on-line databases).
Media by which statistical data and metadata are disseminated.
This metadata element is used to a) list the classification(s) being used for a given data set or set of data sets, b) and describe how these conform to internationally agreed standards, guidelines, or good practices. It should also be used to document the deviations of the classification system(s) compared to statistical standards, guidelines, or good practices, when relevant.
Arrangement or division of objects into groups based on characteristics which the objects have in common.
Links the Data Provider to the relevant Structure Usage (e.g. Dataflow Definition or Metadataflow Definition) for which the provider supplies data or metadata. The agreement may constrain the scope of the data or metadata that can be provided.
Arrangement within which the information provider supplies data or metadata.
The date of the last posting (dissemination) of the metadata on the web site should be retained.
Date of the latest dissemination of the metadata.
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.
Organised collection of data defined by a Data Structure Definition (DSD).
This contact refers to the contact point for data and metadata.
Addressable subdivision of an organisation.
Coherence between sub-annual and annual statistical outputs is a natural expectation but the statistical processes producing them are often quite different. This metadata element is used to compare sub-annual and annual estimates and, eventually, describe reasons for lack of coherence between sub-annual and annual outputs.
Coherence between sub-annual and annual statistical outputs
Coherence between sub-annual and annual outputs
Extent to which statistics of different frequencies are reconcilable.
Coherence - sub-annual and annual statistics
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,
Metadata that identify and describe data and reference metadata.
If a data series has a constant time interval between its observations, this interval determines the frequency of the series (e.g. monthly, quarterly, yearly). "Frequency" - also called "periodicity" - may refer to several stages in the production process, e.g. in data collection or in data dissemination. (e.g., a time series could be available at annual frequency but the underlying data are compiled monthly). Therefore, "Frequency" can be broken down into "Frequency - data collection" and "Frequency - data dissemination".
Time interval at which observations occur over a given time period.
Examples of such time transformations are growth rates, cumulative sums over N periods and moving averages. Operations on time series not entailing a "time" component (e.g. ratios) are not to be considered as time transformations.
Time-related operation performed on a time series, solely involving observations of that time series.
Recommended representation: Code list Codelist ID: CL_TRANSFORMATION
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.
Statistical concept providing qualitative information about a specific statistical object.
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.
Number of metadata file consultations within a statistical domain for a given time period.
The daily maintenance of the artefacts is delegated to one of the members of the ownership group, called the "maintenance agency". Proposals for changes are proposed by the maintenance agency but the decision-making body is the ownership group. There can be several distinct maintenance agencies within a given global SDMX implementation.
Set of organisations which collegially endorse the responsibility for the governance of an SDMX Data Structure Definition and its related artefacts.
This specifies the valid format for a non-enumerated domain for a component.
Format specification of a component's content when reported in a data or metadata set.
Area of technical responsibility of the contact, such as "methodology", "database management" or "dissemination".
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.
Process to determine the validity of data and reference metadata using structural metadata.
Comparability over time refers to the degree of comparability between the results of two or several surveys related to the same domain, carried out by the same statistical agency.
Extent to which statistics are comparable or reconcilable over time.
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.
Information about the confidentiality status of the object to which this attribute is attached.
Recommended representation: Code list Codelist ID: CL_CONF_STATUS
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.
Construct that contains structures capable of providing a maintenance agency to an object.
The accuracy of statistical information is the degree to which the information correctly describes the phenomena. It is usually characterised in terms of error in statistical estimates and is often decomposed into bias (systematic error) and variance (random error) components. Accuracy can be expressed as either measures of accuracy (numerical results of the methods for assessing the accuracy of data) or qualitative assessment indicators. It may also be described in terms of the major sources of error that potentially cause inaccuracy (e.g., coverage, sampling, non-response, response error). Accuracy is associated with the "reliability" of the data, which is defined as the closeness of the initial estimated value to the subsequent estimated value.
Closeness of computations or estimates to the unknown exact or true values that the statistics were intended to measure.
This metadata element refers to descriptions of the types of prices used to value flows and stocks, or other units of measurements used for recording the phenomena being observed; the time of recording of the flows and stocks or the time of recording of other phenomena that are measured, including the reference period employed; and the grossing/netting procedures that are used. Accounting conventions may refer to whether the data are recorded on a cash/accrual or mixed accounting basis, the time of their recording and the reference period (fiscal or calendar year) employed. The description could also include how consistent the practices used are with internationally accepted standards - such as the Balance of Payments Manual or SNA (System of National Accounts) - or good practices.
Practical procedures, standards and other aspects used when compiling data from diverse sources under a common methodological framework.
This metadata element is used to provide textual descriptions and references to legislation or other rules related to statistical confidentiality.
Legislative measures or other formal procedures which prevent unauthorised disclosure of data that identify a person or economic entity either directly or indirectly.
The cost is associated with a statistical product and can be financial, human or time-related. It may consist of staff costs, data collection costs and other costs related to reporting obligations. The burden is often measured by costs for the respondents (businesses, institutions, households, individuals) imposed by a statistical obligation. The overall burden of delivering the information depends on: the number of respondents; the average time required to provide the information, including time spent after receipt of the questionnaire ("recontact time"); the hourly cost of a respondent's time.
Cost associated with the collection and production of a statistical product, as well as the burden imposed on respondents.
Outbound and inbound flows should be considered to be any kind of flows specific to each subject matter domain (amounts of products traded, number of people visiting a country for tourism purposes, etc.). In domains where mirror statistics are available it is possible to assess geographical comparability measuring the discrepancies between inbound and outbound flows for pairs of countries. Mirror data can help checking the consistency of data reporting, of data, of the reporting process and the definitions used. Finally, they can help to estimate missing data. For the users the asymmetries indicators provide some indication of overall data credibility.
Difference or absolute difference of inbound and outbound flows between a pair of countries divided by the average of these two values.
Asymmetry for mirror flows statistics - coefficient
The "Action type" specifies, for a data or a structure message, the action to be performed, e.g. append new data, replace or delete the data, as specified in the technical specifications.
Behaviour to be undertaken by a system processing the information contained in a SDMX message.
The functionality and behaviour of a repository for structural metadata is specified as part of the SDMX standard. In order for this repository to be compliant with the SDMX specification it must support the ability to accept for submission SDMX structural and data source metadata and the ability to accept an SDMX-compliant query for the metadata. An SDMX Registry is provided as a web service and the technical mechanisms used for the submission and query are specified in the SDMX Registry Specification and the SDMX Web Services Guidelines. It is not obligatory for an SDMX-compliant registry to support all of the SDMX structural metadata nor all of the varieties of methods of query and response specified in the SDMX Registry Specification and SDMX Web Services Guidelines. However, in order to be SDMX-compliant an SDMX Registry must comply with the SDMX Registry Specification and the SDMX Web Services Guidelines.
Repository for structural metadata and registered data sources whose interfaces and behaviour comply with the SDMX technical standards.
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 redistribution authorisation policy
Secondary recipient(s) to whom the sender allows the primary recipient to forward restricted data.
Confidentiality - redistribution authorisation policy
This metadata element refers to the application of a formalised system that documents the structure, responsibilities and procedures put in place for satisfying users, while continuing to improve the data production and dissemination process. It also includes how well the resources meet the requirement.
Systems and frameworks in place within an organisation to manage the quality of statistical products and processes.
This metadata element indicates whether micro-data are also disseminated, e.g. to researchers. Access conditions should be described in short.
Information on whether micro-data are also disseminated.
This metadata element is used to describe the policy for release of the data to the public, how the public is informed that the data are being released, and whether the data are disseminated to all interested parties at the same time.
Rules for disseminating statistical data to interested parties.
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).
Error that occurs when the survey fails to get a response to one, or possibly all, of the questions.
Elements providing assurances that the choices of sources and statistical techniques as well as decisions about dissemination are informed solely by statistical considerations.
Error due to domain specific models needed to define the target of estimation.
Error that occurs due the use of methods, such as calibration, generalised regression estimator, calculation based on full scope or constant scope, benchmarking, seasonal adjustment and other models not included in other accuracy components, in order to calculate statistics or indexes.
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.
Place where logically organised statistical metadata are stored that allows for querying, editing and managing of metadata.
Weights of a weighting system for an index number computed according to the information relating to the base period instead, for example, of the current period.
Recommended representation: Code list; String Codelist ID: CL_BASE_WEIGHT
Different data collecting organisations have varying methods of implementing a push reporting method. Most of these use web technology or email.
Data or reference metadata reporting method that requires the provider to make the information available by means of transfer such as email or other electronic method.
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.
Construct that indicates whether the contained values of a container object is to be included or excluded from the valid list of values.
E-mail address of the contact points for the data or metadata.
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.
Construct defining multi-lingual text for the same underlying concept.
Allowed values are mandatory or conditional. Note that in an incremental update a set of data or metadata may omit mandatory attributes.
Indication of the dependency of the presence of a data or metadata attribute when reported in a Data or Metadata Set.
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).
Identifiable position to which codes in a scheme of codes are related.
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.
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.
The observation corresponds to a specific point in time (e.g. a single day) or a period (e.g. a month, a fiscal year, or a calendar year). This is used as a time stamp and is of particular importance for time series data. In cases where the actual time period of the data differs from the target reference period, time period refers to the actual period.
Timespan or point in time to which the observation actually refers.
Recommended representation: ObservationalTimePeriod
Measurement errors occur when the response provided differs from the real value. Such errors may be attributable to the respondent, the interviewer, the questionnaire, the collection method or the respondent's record-keeping system. Errors may be random or they may result in a systematic bias if they are not random. Measurement error in a survey response may result from respondents' confusion, ignorance, carelessness or dishonesty; error attributable to the interviewer, may be a consequence of poor or inadequate training, prior expectations regarding respondents' responses, or deliberate errors; and error attributable to the wording of the questions in the questionnaire, the order or context in which the questions are presented, and the method used to obtain the responses.
Error in reading, calculating or recording a numerical value.
Preliminary data are revised when more and better source data become available, or due to a change in methodology. Data revision describes the policy and practice for identifying the revision status of the data, as well as the availability of revision studies and analyses.
Change in a value of a statistic released to the public.
A fast-track change request can be triggered by any of the organisations in the ownership group. Only changes not breaking backwards compatibility can be issued as fast-track. Fast-track changes follow the same change management process as normal changes but are applied with immediate effect if approved and do not need to wait until the next annual maintenance cycle.
Procedure followed to update at short notice an SDMX artefact, e.g. a code list.
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.
Practices for creating interoperable elements in the SDMX model using the SDMX Technical Specifications.
Set of permissible values for one component of a data or metadata structure.