IREF535324false2026-04-05T01:45:35ZList of concepts for National Accounts DSDsFrequencyAdjustment indicatorReference areaCounterpart areaReporting institutional sectorCounterpart institutional sectorConsolidation StatusAccounting EntryStocks, Transactions, Other FlowsInstrument and Assets ClassificationOriginal and Residual MaturityActivity classificationActivity classification (input-output industry*industry)Product classificationProduct classification (input-output product*product)Expenditure (COFOG, COICOP, COPP or COPNI)Pension Fund TypeUnitCurrency of denominationValuationPricesTransformationCustom breakdown codificationStocks, Transactions, Other Flows TOReporting areaTime periodObservation valueReference year (price)Collection periodPeriod in which data was collectedEmbargo dateEDP working balance basisGFS economic functionGFS tax categoryTable identifierReference period detail [deprecated]Reference year startReference year endDataset commentComments to the observation valueSeries commentConfidentiality statusDecimalsObservation statusTime formatTime period collectionTitleTitle ComplementUnit multiplierLast Update DateLabel for custom breakdownCompiling organisationPre-break valueUnderlying compilationCurrency code used for compilationUsed to specify the underlying currency if in UNIT_MEASURE e.g. XDC is usedDissemination organisationThe organisation disseminating the dataConcept scheme for Prices DSDsFrequency of observationTime interval at which observations occur over a given time period.Time periodTimespan or point in time to which the observation actually refers.Observation valueValue of a particular variableReference areaCountry or geographic area to which the measured statistical phenomenon relatesIndicator typeDifferentiates between index and weight series. For Index, unit is IX, For weight unit can be "per 1", "per 100" or "per 1000"Index typeDifferentiates between different types of indicesBase periodPeriod of time used as the base of an index number, or to which a constant series refersBase period of the weightThe attribute is intended not to be used, but kept as a fallback in case we cannot sufficiently describe the data without it (see tab "BASE_PER Issue").Base period startNon-calendar year base period (start) The attribute is intended not to be used, but kept as a fallback in case we cannot sufficiently describe the data without it. Preference is to add the duration to the notation of the BASE_PER dimension. Example to be provided in the guidelines.Base period endNon-calendar year base period (end). The attribute is intended not to be used, but kept as a fallback in case we cannot sufficiently describe the data without it. Preference is to add the duration to the notation of the BASE_PER dimension. Example to be provided in the guidelines.Item in the classificationContains items used in the price index classification (e.g. COICOP and custom aggregates build from COICOP items).Geographical coverageDefines which type of region is used to compile the indexPopulation coverageSeasonal adjustmentStatistical technique used to remove the effects of seasonal and calendar influences operating on a data series.Time transformationTime-related operation performed on a time series, solely involving observations of that time series.Unit of measureUnit in which the data values are expressed.Custom breakdownneeds custom breakdown labelCustom breakdown labelEmbargo timeExact time at which the data can be made available to the public.Reporting year start dayNon-calendar year reporting (start day)Reporting year end dayNon-calendar year reporting (end day)Comments on dataset levelComments to the observation valueComments on series levelConfidentiality statusInformation about the confidentiality status of the object to which this attribute is attachedDecimalsNumber of digits of an observation to the right of a decimal point.Observation statusInformation on the quality of a value or an unusual or missing valueTime formatTechnical format for the representation of timeTime period collectionDates or periods during which the observations have been collected (such as middle, average or end of period) for the target reference periodTitleTextual label used as identification of a statistical object.Unit multiplierExponent in base 10 specified so that multiplying the observation numeric values by 10^UNIT_MULT gives a value expressed in the unit of measureData Update – Last UpdateObservation pre-break valueObservation, at a time series break period, that was calculated using the old methodologyData compilationOperations performed on data to derive new information according to a given set of rules.Compiling agencyOrganisation collecting and/or elaborating the data being reportedDissemination organisationThe organisation disseminating the dataAdministered prices indicatorStart yearThis field cannot be left emptyEnd yearIn case there is no ending date and price continues to be administered, the field should be left emptyAP approximate shareApproximate share as parts in 1 of administered subclasses in the corresponding classList of concepts for SEEA DSDsDemand or ProductionAccounting entryAccounting entryCounterpart areaCounterpart areaUnits interacting with the environment (e.g. emitting green house gases and air pollutants, suppliers / consumers of energy, water, natural resources)Covers ISIC/NACE, households and TotalBridging itemsItems used to bridge for territorial coverageAir pollutants and greenhouse gassesAir pollutants classificationMaterialsMaterials classification (products, natural inputs, semi-manufactured products, balancing items)Water flowssources of abstracted water, waste water, return flows of water… (+breakdowns)Energy flowsnatural energy inputs (broken down), energy products (broken down), residuals (broken down)Stocks, transactions and other flowsopening/closing stock + flows as defined in SEEALand coverLand coverLand useforestry, agriculture, aquaculture, not in use…Land useforestry, agriculture, aquaculture, not in use…Unit of measureUnit in which the data values are expressed.ProductCPCSectorReporting institutional sectorFrequency of observationTime interval at which observations occur over a given time period.Time periodTimespan or point in time to which the observation actually refers.Reference areaReporting Country in ISO code (The country, or geographical/political group of countries that the measured economic phenomen on relates to)Observation valueThe value of the observationConfidentiality statusInformation about the confidentiality status of the object to which this attribute is attached.Observation status 1Information on the quality of a value or an unusual or missing value.Observation status 2Information on the quality of a value or an unusual or missing value.Observation status 3Information on the quality of a value or an unusual or missing value.Observation status 4Information on the quality of a value or an unusual or missing value.Comments on dataset levelComments on dataset levelComments to the observation valueComments to the observation valueDetailed description of the group of seriesDetailed description of the group of seriesUnit multiplierEmbargo date and timeDecimalsTitleTitle ComplementData Update – Last UpdateCompiling organisationPre-break valueDissemination organisationThe organisation disseminating the dataSDG Concept SchemeConcept Scheme for SDG IndicatorsFrequency of observationTime interval at which observations occur over a given time period.Reference areaCountry or geographic area to which the measured statistical phenomenon relates.SexState of being male or female.AgeLength of time that an entity has lived or existed.Education levelHighest level of an educational programme the person has successfully completed.OccupationJob or position held by an individual who performs a set of tasks and duties.Time periodTimespan or point in time to which the observation actually refers.Observation statusInformation on the quality of a value or an unusual or missing value.Observation valueValue of a particular variable.Unit multiplierExponent in base 10 specified so that multiplying the observation numeric values by 10^UNIT_MULT gives a value expressed in the unit of measure.Unit of measureUnit in which the data values are expressed.Base periodPeriod of time used as the base of an index number, or to which a constant series refers.SDG SeriesSDG Indicator or Series. Uses the CETS convention for encoding.Degree of urbanisationRefers to Total, Urban, or Rural location.Income or wealth quantileIncome or wealth quantileCustom BreakdownUsed to represent user-defined breakdowns.Composite breakdownRepresents an amalgamation of infrequently used breakdowns.Disability statusRepresents a person's disability status.Nature of data pointsInformation on the production and dissemination of the data (e.g.: if the figure has been produced by the country, estimated by an international agency, etc.)Time period detailsWhen TIME_PERIOD refers to a date range, this attribute is used to provide metadata on the actual range the observation refers to (e.g. for period '2001-2003' TIME_PERIOD would be 2002 but the actual dates --2001-2003-- would be expressed here).Observation-level footnotesAdditional information on specific aspects of each observation, such as how the observation was computed/estimated or details that could affect the comparability of this data point with others in a time series.Time period or range (ISO8601)ISO8601 representation of the actual time interval to which the observation refersUpper bound valueUpper bound value for the indicator or series.Lower bound valueLower bound value for the indicator or series.Reporting typeRepresents whether the official SDG series is reported by a national, regional, or international agency.Source detailsInformation on the name and specific details of each observation's data source. Both the sources for the numerator and denominator could be specified here.Economic activityClassification of economic activitiesTime series-level footnotesAdditional information on specific aspects applicable to all observations in the time series.Geoinformation URLLocation of geoinformationGeoinformation typeType of geionformation provided in attribute GEO_INFO_URLLabel for custom breakdownInformation provided in the CUST_BREAKDOWN dimension, when applicableType of productType of productDate of the most recent change of the series.Date of the most recent change of the series.SDG Metadata ConceptsMetadata concepts for Sustainable Development Goals Indicators0. Indicator informationDescription of the Goal, Target, Indicator and data series - if applicable. The date/period when this metadata file is last updated should be provided. The linkages with any other Goals and Targets under the SDG framework, if any, should be described. The name of international organisation(s) responsible for monitoring this indicator should be provided.0.a. GoalSDG Goal number and name.0.b. TargetSDG Target number and name.0.c. IndicatorSDG Indicator number and name.0.d. SeriesDescription of SDG data series.0.e. Metadata updateThe date when this metadata report is last updated.0.f. Related IndicatorsLinkages with any other Goals and Targets.0.g. International organisations(s) responsible for global monitoringGlobal reporting: International organizations (departments/offices) responsible for monitoring this indicator at the global level. rnCountry reporting: This concept has no national counterpart.1. Data reporterInformation of the contact persons and their organization responsible for the reporting of the indicator or time series specified.1.a. OrganisationAgency responsible for reporting of the indicator or time series specified below.1.b. Contact person(s)Name(s) of the contact points for the data or metadata.1.c. Contact Organisation UnitOrganisation unit information of the contact points for the data or metadata.1.d. Contact Person FunctionFunctional title(s) of the contact points for the data or metadata.1.e. Contact PhonePhone number(s) of the contact points for the data or metadata.1.f. Contact MailMailing address(es) of the contact points for the data or metadata.1.g. Contact emailsE-mail address(es) of the contact points for the data or metadata.2. Definition, concepts and classificationsPrecise definition of the indicator including references to standards and classifications, preferably relying on international agreed definitions. The indicator definition should be unambiguous and be expressed in universally applicable terms. It must clearly express the unit of measurement (proportion, dollars, number of people, etc.). rnrnPrecise definition of all different concepts and terms associated with the indicator, also including reference to any associated classifications.2.a. Definition and ConceptsPrecise definition of the indicator preferably relying on internationally agreed definitions. The indicator definition should be unambiguous and be expressed in universally applicable terms. Precise definition of all different concepts and terms associated with the indicator, also including reference to any associated classifications.2.b. Unit of MeasureDescription of the unit of measurement (proportion, dollars, number of people, etc.)2.c. ClassificationsDescribe references to both national and international standards and classification being used. [Information to be provided where applicable.]rn3. Data source type and data collection methodDescription of data sources, data collection methods, and related information such as descriptions of all data collection methods, data collection and release calendar, data providers and compilers, as well as their institutional mandates.3.a. Data sourcesDescription of all actual and recommended sources of data. This description should include, when applicable, any changes of the data source over time, details of denominator (if from a different source) and any other relevant information related to the origin of the source or indicator. Similar details should be given for administrative sources.3.b. Data collection methodDescription of all methods used for data collection. This description should include, when applicable, the sample frame used, the questions used to collect the data, the type of interview, the dates/duration of fieldwork, the sample size and the response rate. Some additional information on questionnaire design and testing, interviewer training, methods used to monitor non-response etc. should be provided here. Questionnaires used should be annexed (if very long: via hyperlink).3.c. Data collection calendarDates when source collection is next planned.3.d. Data release calendarExpected dates of release of new data for this indicator, including the year (or, ideally, the quarter/month when the next data point associated with the indicator will become available).3.e. Data providersIdentification of national and/or international data provider(s), specifying the organization(s) responsible for producing the data.3.f. Data compilersOrganization(s) responsible for compilation of on this indicator either at national or global level.3.g. Institutional MandateDescription of the 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 for this indicator.4. Other methodological considerationsOther methodological considerations. These include rationale behind the indicator, comments and limitations, computation methodology at the country, regional, and global level, treatment of missing values if applicable, and information related to quality assurance.4.a. RationaleDescription of the purpose and rationale behind the indicator, as well as examples and guidance on its correct interpretation and meaning.4.b. Comment and limitationsComments on the feasibility, suitability, relevance and limitations of the indicator. Also includes data comparability issues, presence of wide confidence intervals (such as for maternal mortality ratios); provides further details on additional non-official indicators commonly used together with the indicator.4.c. Method of computationExplanation of how the indicator is calculated, including mathematical formulas and descriptive information of computations made on the source data to produce the indicator (including adjustments and weighting). This explanation should also highlight cases in which mixed sources are used or where the calculation has changed over time (i.e., discontinuities in the series).4.d. ValidationDescription of process of monitoring the results of data compilation and ensuring the quality of the statistical results, including consultation process with countries on the national data submitted to the SDGs Indicators Database. Descriptions and links to all relevant reference materials should be provided.4.e. AdjustmentsGlobal reporting: Description of any adjustments with respect to use of standard classifications and harmonization of breakdowns for age group and other dimensions, or adjustments made for compliance with specific international or national definitions.rnNational reporting: This concept is typically not applicable for national reporting.4.f. Treatment of missing values (i) at country level and (ii) at regional level.Global reporting: (National level) Description of the methodology employed for producing estimates for the indicator when country data are not available, including any mathematical formulas and description of additional variables used as input into the estimation process.rn(Regional level) Description of how missing values for individual countries or areas are imputed or otherwise estimated by international agencies to derive regional or global aggregates of the indicator. rnNational reporting: This concept is not applicable for national reporting.4.g. Regional aggregationsGlobal reporting: Description of the methodology, including any mathematical formulas, used for the calculation of the regional/global aggregates from the country values. Description of the weighting structure used for aggregating country indicator values to regional and global levels. Additional methodological details on how the data from countries or areas is assembled by custodian international agencies to provide regional and global aggregates. This is distinct from the method of computation, which looks at how the indicator is compiled at a national level. rnNational reporting: This concept is not applicable for national reporting.4.h. Methods and guidance available to countries for the compilation of the data at the national levelGlobal reporting: Description of methodology used by countries for the compilation of data at national level and the relevant international recommendations and guidelines available to countries. Descriptions and links to all relevant reference materials should be provided. rnNational reporting: For national reporting a country may refer to the globally available metadata and explain how it is being used.4.i. Quality managementDescription of systems and frameworks in place within an organisation to manage the quality of statistical products and processes.rn4.j. Quality assuranceDescription of practices and guidelines focusing on quality in general and dealing with quality of statistical programmes at your agency, including measures for ensuring the efficient use of resources.rn4.k. Quality assessmentDescription of overall evaluation of fulfilling quality requirements, based on standard quality criteria.5. Data availability and disaggregationGlobal reporting: Indicate for how many countries the data for this indicator are already currently available on a regular basis. Data availability by regional breakdowns and time periods can also be descibed here. Describe the specification of the dimensions and levels used for disaggregation of the indicator (e.g., income, sex, age group, geographic location, disability status, etc.).rnNational reporting: Data availability by sub-national breakdowns and time periods can be descibed here. Describe the specification of the dimensions and levels used for disaggregation of the indicator (e.g., income, sex, age group, geographic location, disability status, etc.).6. Comparability / Deviation from international standardsExplanation on the differences between country produced and internationally estimated data on this indicator, highlighting and summarising the main sources of differences.7. References and DocumentationDescriptions and links to all relevant reference materials related to this indicator.8. TranslationsAdditional information on translation of reference metadata set.Balance of Payments Concept SchemeFrequencyFrequencyAdjustment indicatorAdjustment indicatorReference country or areaReference country or areaCounterpart AreaCounterpart AreaReference sectorInstitutional sector in the Reference country or areaCounterpart sectorCounterpart institutional sector in the reporting country or areaFlows and stocks indicatorFlows and stocks indicatorAccounting entriesTypes of accounting entries (i.e. credit, debit, balance)International accounts itemItem classification for international accounts statisticsFunctional categoryClassification by functional of the financial investmentInstrument and assets classificationBreakdown by financial instruments and assetsType of entityIdentifies structural and locational characteristics of the statistical unit which belong to a multinational group of enterprises as defined by the Framework of Direct Investment Relationship (FDIR)MaturityOriginal maturity of financial instrument, combined with remaining maturityUnit of measureUnit of measure for reporting the time-seriesCurrency of denominationCurrency of issuance for financial items or invoicing for goods and servicesValuationPrice valuationCompiliation methodologyIdentify the compilation methodology of otherwise identical conceptsTime formatObservation statusConfidentiality statusPre-break valueComments to the observation valueTitle complement: Detailed description of the seriesShort titleUnit multiplierDecimalsTime period collectionReference period detailCompiling organisationUnderlying compilationTime PeriodObseravation ValueData dissemination agencyTitle ComplementBalance of Payments Concept SchemeFrequencyFrequencyAdjustment indicatorAdjustment indicatorReference country or areaReference country or areaCounterpart AreaCounterpart AreaReference sectorInstitutional sector in the Reference country or areaCounterpart sectorCounterpart institutional sector in the reporting country or areaFlows and stocks indicatorFlows and stocks indicatorAccounting entriesTypes of accounting entries (i.e. credit, debit, balance)International accounts itemItem classification for international accounts statisticsFunctional categoryClassification by functional of the financial investmentInstrument and assets classificationBreakdown by financial instruments and assetsMaturityOriginal maturity of financial instrument, combined with remaining maturityUnit of measureUnit of measure for reporting the time-seriesCurrency of denominationCurrency of issuance for financial items or invoicing for goods and servicesValuationPrice valuationCompiliation methodologyIdentify the compilation methodology of otherwise identical conceptsType of entityBreakdown by type of enterpriseTime formatObservation statusConfidentiality statusPre-break valueComments to the observation valueTitle complement: Detailed description of the seriesShort titleUnit multiplierDecimalsTime period collectionReference period detailCompiling organisationUnderlying compilationTime PeriodObseravation ValueData dissemination agencyTitle ComplementConcepts for EcoFin DSDData DomainReference Country or areaEconomic IndicatorCounterpart AreaCommentBase PeriodUnit MultiplierObservation StatusConcepts for ALT_FISCAL_DSDFISCAL Economic IndicatorForeign Direct Investment Concept SchemeFrequencyFrequencyMeasurement principleIdentifies the methodology used to compile the foreign direct investment aggregates. There are two principles: "asset liability" principle and "directional principle"Reference country or areaReference country or areaCounterpart AreaCounterpart AreaReference SectorInstitutional sector in the Reference country or areaLevel of counterpartIdentifies the relationship between the direct investor and the direct investment enterprise according to the Framework of Direct Investment Relationship (FDIR)Flows and stocks indicatorFlows and stocks indicatorAccounting entriesTypes of accounting entries (i.e. credit, debit, balance)International accounts itemItem classification for international accounts statisticsFunctional categoryClassification by functional of the financial investmentInstrument and assets classificationBreakdown by financial instruments and assetsType of entityIdentifies structural and locational characteristics of the statistical unit which belong to a multinational group of enterprises as defined by the Framework of Direct Investment Relationship (FDIR)Unit of measureUnit of measure for reporting the time-seriesFDI relationshipIdentifies whether the framework used for reporting FDI statistics is based on the influence relationship (>10%) or control relationship (>50%)ValuationPrice valuationStatistical unitIdentifies the structure of the institutional unit covered by a statistical survey and the bearer of statistical characteristicsIndustrial activity, NACE and ISICIndustrial activity according to ISIC4/NACE Rev.2Activity AllocationIdentifies the basis used to determine the economic activity of the investment, i.e. if it is the activity of the direct investment enterprise or the direct investor or other criteriaTime formatObservation statusConfidentiality statusPre-break valueComments to the observation valueTitle complement: Detailed description of the seriesShort titleUnit multiplierDecimalsReference period detailCompiling organisationTime PeriodObservation ValueDissemination organisationThe organisation disseminating the dataSDMX Cross Domain Concept SchemeThe Cross Domain Concept Scheme (starting from v2.0) is created from the set of concepts in the SDMX Glossary, which is an SDMX guideline containing concepts and related definitions that are useful for building and understanding data and metadata exchange arrangements based on SDMX. The Glossary provides definition of terms found in the SDMX Information Model, Data Structure Definitions (DSDs) and Metadata Structure Definitions (MSDs) at the time of the present release. It is recommended as a single entry point to a common SDMX terminology to be used in order to facilitate communication and understanding of the standard.In short, the overall message of the glossary is the following: if a term is used, then its precise meaning should correspond to the SDMX Glossary definition, and any reference to a particular phenomenon described in the SDMX Glossary should use the appropriate term.Version 1.0 of the SDMX Glossary, which replaces the Metadata Common Vocabulary (MCV) published in 2009, was finalised in February 2016. The Cross Domain Concept Scheme v1.0 was created from the MCV concepts.For any question, comment or correction, feel free to contact the SDMX Statistical Working Group (SWG) at the following address: swg@sdmx.org.CONTEXTThis 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringSOURCESDMX (2016) (https://sdmx.org/)Accounting conventionsPractical procedures, standards and other aspects used when compiling data from diverse sources under a common methodological framework.CONTEXTThe 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSAccuracy - overall; Non-sampling error; Sampling errorSOURCESDMX (2016) (https://sdmx.org/)AccuracyCloseness of computations or estimates to the unknown exact or true values that the statistics were intended to measure.CONTEXTThis 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSAccuracy; Non-sampling error; Sampling errorSOURCESDMX (2016) (https://sdmx.org/)Accuracy - overallAssessment of accuracy, linked to a certain data set or domain, which is summarising the various components into one single measure.CONTEXTThe "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.SOURCESDMX (2016) (https://sdmx.org/)Action typeBehaviour to be undertaken by a system processing the information contained in a SDMX message.CONTEXTAdjustments 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSPrice adjustment; Seasonal adjustmentSOURCESDMX (2016) (https://sdmx.org/)AdjustmentSet 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.CONTEXTAge 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".TYPECross-domain conceptRECOMMENDED_REPRESENTATIONCode listSOURCESDMX (2016) (https://sdmx.org/)OTHER_LINKSCode list CL_AGE (https://sdmx.org/?page_id=3215)CODELIST_IDCL_AGEAgeLength of time that an entity has lived or existed.CONTEXTIn 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.RELATED_TERMSData consumer scheme; Data provider scheme; Item scheme; Maintenance agencySOURCESDMX (2016) (https://sdmx.org/)Agency schemeMaintained collection of maintenance agencies.CONTEXTThe annotation in SDMX is way of extending the functionality of SDMX structural metadata.RELATED_TERMSAnnotation; Artefact; Identifiable artefact; Maintainable artefact; Nameable artefact; Versionable artefactSOURCESDMX (2016) (https://sdmx.org/)Annotable artefactConstruct capable of defining annotationsCONTEXTThe 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.RELATED_TERMSAnnotable artefactSOURCESDMX (2016) (https://sdmx.org/)AnnotationConstruct that contains user or organisation-specific metadata.CONTEXTArtefacts 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".RELATED_TERMSAnnotable artefact; Identifiable artefact; Maintainable artefact; Nameable artefact; Versionable artefactSOURCESDMX (2016) (https://sdmx.org/)ArtefactAbstract concept denoting an element in the SDMX model having specific characteristics which are inherited by other elements.CONTEXTOutbound 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSComparability; Comparability - geographical; Comparability - over timeSOURCEEurostat "ESS Guidelines for the Implementation of the ESS Quality and Performance Indicators (QPI)", Luxembourg, 2014 (http://ec.europa.eu/eurostat/documents/64157/4373903/02-ESS-Quality-and-performance-Indicators-2014.pdf/5c996003-b770-4a7c-9c2f-bf733e6b1f31)Asymmetry for mirror flows statistics - coefficientDifference or absolute difference of inbound and outbound flows between a pair of countries divided by the average of these two values.CONTEXTFor 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.RELATED_TERMSAttribute; Attribute relationshipSOURCESDMX (2016) (https://sdmx.org/)Attachment levelProperty of an attribute defining the object to which data or metadata are linked.CONTEXTThe 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.RELATED_TERMSAttachment level; Constraint; Dataflow; Data structure definition, DSD; Metadata structure definition, MSDSOURCESDMX (2016) (https://sdmx.org/)AttributeStatistical concept providing qualitative information about a specific statistical object.CONTEXTA 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".RELATED_TERMSAttachment levelSOURCESDMX (2016) (https://sdmx.org/)Attribute relationshipSpecification of the type of artefact to which a data attribute can be attached in a data set.CONTEXTThe 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).TYPECross-domain conceptRECOMMENDED_REPRESENTATIONCode list; DateTime; StringRELATED_TERMSBase weight; Reference periodSOURCESDMX (2016) (https://sdmx.org/)CODELIST_IDCL_BASE_PERBase periodPeriod of time used as the base of an index number, or to which a constant series refers.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONCode list; StringRELATED_TERMSBase periodSOURCEThe International Statistical Institute, "The Oxford Dictionary of Statistical Terms", edited by Yadolah Dodge, Oxford University Press, 2003CODELIST_IDCL_BASE_WEIGHTBase weightWeights 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.CONTEXTThe 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.RELATED_TERMSCategory scheme; Dataflow; MetadataflowSOURCESDMX (2016) (https://sdmx.org/)CategoryStructural metadata concept that classifies structural metadata objects.CONTEXTThe Category Scheme comprises a hierarchy of categories which may include any type of useful classification for the organisation of data and metadata.RELATED_TERMSCategory; Item schemeSOURCESDMX (2016) (https://sdmx.org/)Category schemeDescriptive information for a subdivision of categories into groups based on characteristics, which the objects have in common.CONTEXTThe civil status is often referred to as marital status and represented through codes of the respective code list.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONCode listSOURCESDMX (2016) (https://sdmx.org/)OTHER_LINKSCode list CIVIL_STATUS (https://sdmx.org/?page_id=3215)CODELIST_IDCL_CIVIL_STATUSCivil statusLegal, conjugal status of each individual in relation to the marriage laws or customs of the country.CONTEXTThis 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringSOURCESDMX (2016) (https://sdmx.org/)Classification systemArrangement or division of objects into groups based on characteristics which the objects have in common.CONTEXTThe Code in SDMX contains the Id (the code), and a name and description either or both of which can be multi-lingual.RELATED_TERMSCoding format; ConstraintSOURCESDMX (2016) (https://sdmx.org/)CodeLanguage independent set of letters, numbers or symbols that represent a concept whose meaning is described in a natural language.CONTEXTThe 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.RELATED_TERMSCoding format; Constraint; Item schemeSOURCESDMX (2016) (https://sdmx.org/)OTHER_LINKSGuidelines for the Creation and Management of SDMX Code Lists (https://sdmx.org/wp-content/uploads/SDMX_Guidelines_for_CDCL_v1_0.pdf); List of available SDMX cross-domain code lists (https://sdmx.org/?page_id=3215)Code listPredefined set of terms from which some statistical coded concepts take their values.CONTEXTThe specification of the format information for the codes, such as whether the codes are alphabetic, numeric or alphanumeric, and the code length.RELATED_TERMSCode; Code list; LevelSOURCESDMX (2016) (https://sdmx.org/)Coding formatSpecification of the representation for the codes in a code list.CONTEXTWhen 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSCoherence - cross-domain; Coherence - internal; Coherence - National Accounts; Coherence - sub-annual and annual statistics; ComparabilitySOURCESDMX (2016) (https://sdmx.org/)CoherenceAdequacy of statistics to be reliably combined in different ways and for various uses.CONTEXTThis 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).TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSCoherence; Coherence - internal; Coherence - National Accounts; Coherence - sub-annual and annual statisticsSOURCESDMX (2016) (https://sdmx.org/)Coherence - cross domainExtent to which statistics are reconcilable with those obtained through other data sources or statistical domains.CONTEXTThis 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSCoherence; Coherence - cross-domain; Coherence - National Accounts; Coherence - sub-annual and annual statisticsSOURCESDMX (2016) (https://sdmx.org/)Coherence - internalExtent to which statistics are consistent within a given data set.CONTEXTThis 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSCoherence; Coherence - cross-domain; Coherence - internal; Coherence - sub-annual and annual statisticsSOURCESDMX (2016) (https://sdmx.org/)Coherence - National AccountsExtent to which statistics are reconcilable with National Accounts.CONTEXTCoherence 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSCoherence; Coherence - cross-domain; Coherence - internal; Coherence - National AccountsSOURCESDMX (2016) (https://sdmx.org/)Coherence - sub-annual and annual statisticsExtent to which statistics of different frequencies are reconcilable.CONTEXTIn 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringSOURCESDMX (2016) (https://sdmx.org/)CommentDescriptive text which can be attached to data or metadata.CONTEXTComparability 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSAsymmetry for mirror flow statistics; Coherence; Comparability - geographical; Comparability - over timeSOURCESDMX (2016) (https://sdmx.org/)ComparabilityExtent to which differences between statistics can be attributed to differences between the true values of the statistical characteristics.CONTEXTGeographical 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSAsymmetry for mirror flow statistics; Comparability; Comparability - over timeSOURCESDMX (2016) (https://sdmx.org/)Comparability - geographicalExtent to which statistics are comparable between geographical areas.CONTEXTComparability 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSAsymmetry for mirror flow statistics; Comparability; Comparability - geographicalSOURCESDMX (2016) (https://sdmx.org/)Comparability - over timeExtent to which statistics are comparable or reconcilable over time.CONTEXTThe 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONCode listSOURCESDMX (2016) (https://sdmx.org/)CODELIST_IDCL_ORGANISATIONCompiling agencyOrganisation collecting and/or elaborating the data being reported.CONTEXTIn 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.RELATED_TERMSFacet; Metadata Structure Definition, MSD; SDMX Information Model, SDMX-IMSOURCESDMX (2016) (https://sdmx.org/)ComponentStructural artefact used to define the structure of a data or metadata set.CONTEXTAt 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.RELATED_TERMSConcept scheme; Dimension; Metadata structure definition, MSDSOURCEGeneric Statistical Information Model (GSIM) Specification (Version 1.1, December 2013. United Nations Economic Commission for Europe (UNECE), on behalf of the international statistical community (http://www1.unece.org/stat/platform/display/gsim/Generic+Statistical+Information+Model)ConceptUnit of thought created by a unique combination of characteristics.CONTEXTStructural 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").RELATED_TERMSConcept; Item scheme; Reference metadataSOURCESDMX (2016) (https://sdmx.org/)Concept schemeSet of concepts that are used in a data structure definition or metadata structure definition.CONTEXTData 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSConfidentiality - data treatment; Confidentiality - policy; Confidentiality - redistribution authorisation policy; Confidentiality - statusSOURCESDMX (2016) (https://sdmx.org/)ConfidentialityProperty of data indicating whether they are subject to dissemination restrictions.CONTEXTThis 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.).TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSConfidentiality; Confidentiality - policy; Confidentiality - redistribution authorisation policy; Confidentiality - statusSOURCESDMX (2016) (https://sdmx.org/)Confidentiality - data treatmentRules applied for treating the data set to ensure that private information from individual units cannot be accessed and to prevent unauthorised disclosure.CONTEXTThis metadata element is used to provide textual descriptions and references to legislation or other rules related to statistical confidentiality.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSConfidentiality; Confidentiality - data treatment; Confidentiality - redistribution authorisation policy; Confidentiality - statusSOURCESDMX (2016) (https://sdmx.org/)Confidentiality - policyLegislative measures or other formal procedures which prevent unauthorised disclosure of data that identify a person or economic entity either directly or indirectly.CONTEXTThis concept is used in the exchange of restricted data in cases where the sender explicitly allows subsequent forwarding of these data to other organisations.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSConfidentiality; Confidentiality - data treatment; Confidentiality - policy; Confidentiality - statusSOURCESDMX (2016) (https://sdmx.org/)Confidentiality - redistribution authorisation policySecondary recipient(s) to whom the sender allows the primary recipient to forward restricted data.CONTEXTThis 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONCode listRELATED_TERMSConfidentiality; Confidentiality - data treatment; Confidentiality - policy; Confidentiality - redistribution authorisation policySOURCESDMX (2016) (https://sdmx.org/)OTHER_LINKSCode list CL_CONF_STATUS (https://sdmx.org/?page_id=3215)CODELIST_IDCL_CONF_STATUSConfidentiality - statusInformation about the confidentiality status of the object to which this attribute is attached.CONTEXTA 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.RELATED_TERMSAttribute; Code; Code list; Member selection; Member value; Metadata key set; Metadata key valueSOURCESDMX (2016) (https://sdmx.org/)ConstraintSpecification 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.CONTEXT"Contact" describes contact points for the data or metadata, including how to reach the contact points.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSContact email address; Contact fax number; Contact mail; Contact name; Contact organisation; Contact organisation unit; Contact person function; Contact phone numberSOURCESDMX (2016) (https://sdmx.org/)ContactIndividual or organisational contact points for the data or metadata, including information on how to reach the contact points.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSContact; Contact fax number; Contact mail; Contact name; Contact organisation; Contact organisation unit; Contact person function; Contact phone numberSOURCESDMX (2016) (https://sdmx.org/)Contact email addressE-mail address of the contact points for the data or metadata.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSContact; Contact email address; Contact mail address; Contact name; Contact organisation; Contact organisation unit; Contact person function; Contact phone numberSOURCESDMX (2016) (https://sdmx.org/)Contact fax numberFax number of the contact points for the data or metadata.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSContact; Contact email address; Contact fax number; Contact name; Contact organisation; Contact organisation unit; Contact person function; Contact phone numberSOURCESDMX (2016) (https://sdmx.org/)Contact mail addressPostal address of the contact points for the data or metadata.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSContact; Contact email address; Contact fax number; Contact mail address; Contact organisation; Contact organisation unit; Contact person function; Contact phone numberSOURCESDMX (2016) (https://sdmx.org/)Contact nameName of the contact points for the data or metadata.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONString; Code listRELATED_TERMSContact; Contact email address; Contact fax number; Contact mail address; Contact name; Contact organisation unit; Contact person function; Contact phone numberSOURCESDMX (2016) (https://sdmx.org/)Contact organisationOrganisation of the contact point(s) for the data or metadata.CONTEXTThis contact refers to the contact point for data and metadata.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSContact; Contact email address; Contact fax number; Contact mail address; Contact name; Contact organisation; Contact person function; Contact phone numberSOURCESDMX (2016) (https://sdmx.org/)Contact organisation unitAddressable subdivision of an organisation.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSContact; Contact email address; Contact fax number; Contact mail address; Contact name; Contact organisation; Contact organisation unit; Contact phone numberSOURCESDMX (2016) (https://sdmx.org/)Contact person functionArea of technical responsibility of the contact, such as "methodology", "database management" or "dissemination".TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSContact; Contact email address; Contact fax number; Contact mail address; Contact name; Contact organisation; Contact organisation unit; Contact person functionSOURCESDMX (2016) (https://sdmx.org/)Contact phone numberTelephone number of the contact points for the data or metadata.CONTEXTThe 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.RELATED_TERMSCross-Domain Code List, CDCL; Cross-Domain Concept, CDC; Statistical subject-matter domainSOURCESDMX (2016) (https://sdmx.org/)OTHER_LINKSContent-Oriented Guidelines (https://sdmx.org/?page_id=4345)Content-Oriented Guidelines, COGPractices for creating interoperable elements in the SDMX model using the SDMX Technical Specifications.CONTEXTThe 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: a) the number of respondents; b) the average time required to provide the information, including time spent after receipt of the questionnaire ("recontact time"); and c) the hourly cost of a respondent's time.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSCost and burden - efficiency management; Cost and burden - resourcesSOURCESDMX (2016) (https://sdmx.org/)Cost and burdenCost associated with the collection and production of a statistical product, as well as the burden imposed on respondents.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSCost and burden; Cost and burden - resourcesSOURCESDMX (2016) (https://sdmx.org/)Cost and burden - efficiency managementCost-benefit analysis, effectiveness of execution of medium term statistical programmes, and ensuring efficient use of resources.CONTEXTIt may include the contribution of respondent time in supplying information (burden) as a distinct subject under this heading.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSCost and burden; Cost and burden - efficiency managementSOURCESDMX (2016) (https://sdmx.org/)Cost and burden - resourcesStaff, facilities, computing resources, and financing to undertake statistical production.CONTEXTThe "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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONCode listRELATED_TERMSReference areaSOURCESDMX (2016) (https://sdmx.org/)OTHER_LINKSCode list CL_AREA (https://sdmx.org/?page_id=3215)CODELIST_IDCL_AREACounterpart reference areaSecondary area, as opposed to reference area, to which the measured data are in relation.CONTEXTCoverage errors include over-coverage, under-coverage and misclassification. Incomplete sampling frames often result in coverage errors.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSMeasurement error; Model assumption error; Non-response error; Non-sampling error; Over-coverage rate; Processing error; Proportion of common unitsSOURCEStatistical Office of the United Nations, "Handbook of Household Surveys, Revised Edition", (para. 8.4), Studies in Methods, Series F, No. 31, United Nations, New York, 1984 (http://ec.europa.eu/eurostat/ramon/statmanuals/files/household_surveys_1984_EN.pdf)Coverage errorError 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.CONTEXT1) 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.RELATED_TERMSContent-Oriented Guidelines, COGSOURCESDMX (2016) (https://sdmx.org/)Cross-domain code list, CDCLSDMX 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.CONTEXTWithin 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.RELATED_TERMSContent-Oriented Guidelines, COG; Reference metadata; Structural metadataSOURCESDMX (2016) (https://sdmx.org/)Cross-domain concept, CDCStandard 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONCode listSOURCESDMX (2016) (https://sdmx.org/)OTHER_LINKSCode list CL_CURRENCY (https://sdmx.org/?page_id=3215)CODELIST_IDCL_CURRENCYCurrencyMonetary denomination of the object being measured.CONTEXTThere 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringSOURCESDMX (2016) (https://sdmx.org/)Data collection methodMethod applied for gathering data for official statistics.CONTEXTIn 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSData validationSOURCESDMX (2016) (https://sdmx.org/)Data compilationOperations performed on data to derive new information according to a given set of rules.CONTEXTAn 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.RELATED_TERMSItem schemeSOURCESDMX (2016) (https://sdmx.org/)Data consumerEntity that uses data.CONTEXTIn 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.RECOMMENDED_REPRESENTATIONStringRELATED_TERMSAgency scheme; Data provider scheme; Maintenance agencySOURCESDMX (2016) (https://sdmx.org/)Data consumer schemeMaintained collection of data consumers.CONTEXTThis information is in the Header of a data set, typically for processing by the receiving system in its administration of the data set.SOURCESDMX (2016) (https://sdmx.org/)Data extraction dateDate and time that the data are gathered from a data source.CONTEXTIn 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.RELATED_TERMSAttribute; Category; Data set; MetadataflowSOURCESDMX (2016) (https://sdmx.org/)DataflowStructure which describes, categorises and constrains the allowable content of a data set that providers will supply for different reference periods.CONTEXTData 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSData presentation - summary descriptionSOURCESDMX (2016) (https://sdmx.org/)Data presentation - detailed descriptionDetailed description of the disseminated data.CONTEXTThis 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).TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSData presentation - detailed descriptionSOURCESDMX (2016) (https://sdmx.org/)Data presentation - summary descriptionMain characteristics of the data set described in an easily understandable manner, referring to the data and indicators disseminated.CONTEXTData 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONString; Code listRELATED_TERMSData provider scheme; Item schemeSOURCESDMX (2016) (https://sdmx.org/)CODELIST_IDCL_ORGANISATIONData providerOrganisation or individual that reports or disseminates data or reference metadata.CONTEXTIn 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).RELATED_TERMSAgency scheme; Data consumer scheme; Data providerSOURCESDMX (2016) (https://sdmx.org/)Data provider schemeMaintained collection of data providers.CONTEXTPreliminary 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSData revision - policy; Data revision - practice; Data revision - studiesSOURCESDMX (2016) (https://sdmx.org/)Data revisionChange in a value of a statistic released to the public.CONTEXTThis metadata element is used to describe the general guidelines for handling data revisions applied by a data providing agency.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSData revision; Data revision - practice; Data revision - studiesSOURCESDMX (2016) (https://sdmx.org/)Data revision - policyPolicy aimed at ensuring the transparency of disseminated data, whereby preliminary data are compiled that are later revised.CONTEXTThis 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSData revision; Data revision - policy; Data revision - studiesSOURCESDMX (2016) (https://sdmx.org/)Data revision - practiceInformation on the data revision practice.CONTEXTDescription 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSData revision; Data revision - policy; Data revision - practiceSOURCESDMX (2016) (https://sdmx.org/)Data revision - studiesInformation about data revision studies and analyses.CONTEXTWithin 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.RELATED_TERMSDataflow; Data structure definition, DSDSOURCESDMX (2016) (https://sdmx.org/)Data setOrganised collection of data defined by a Data Structure Definition (DSD).CONTEXTThe 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.SOURCESDMX (2016) (https://sdmx.org/)Data sourceLocation or service from where data or metadata can be obtained.CONTEXTA 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.RELATED_TERMSAttribute; Dimension; Data set; MeasureSOURCESDMX (2016) (https://sdmx.org/)Data structure definition, DSDSet 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.CONTEXTData 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSData compilationSOURCESDMX (2016) (https://sdmx.org/)Data validationProcess of monitoring the results of data compilation and ensuring the quality of the statistical results.CONTEXTA 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONCode listSOURCESDMX (2016) (https://sdmx.org/)OTHER_LINKSCode list CL_DECIMALS (https://sdmx.org/?page_id=3215)CODELIST_IDCL_DECIMALSDecimalsNumber of digits of an observation to the right of a decimal point.CONTEXTIn 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.RELATED_TERMSConcept; Data Structure Definition, DSD; Series keySOURCESDMX (2016) (https://sdmx.org/)DimensionStatistical concept used in combination with other statistical concepts to identify a statistical series or individual observations.CONTEXTThis 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).TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSDissemination format - microdata access; Dissemination format - news release; Dissemination format - online database; Dissemination format - publications; Dissemination format - other formatsSOURCESDMX (2016) (https://sdmx.org/)Dissemination formatMedia by which statistical data and metadata are disseminated.CONTEXTThis metadata element indicates whether micro-data are also disseminated, e.g. to researchers. Access conditions should be described in short.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSDissemination format; Dissemination format - news release; Dissemination format - online database; Dissemination format - publications; Dissemination format - other formatsSOURCESDMX (2016) (https://sdmx.org/)Dissemination format - microdata accessInformation on whether micro-data are also disseminated.CONTEXTThis concept covers press releases or other kind of similar releases linked to data or metadata.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSDissemination format; Dissemination format - microdata access; Dissemination format - online database; Dissemination format - publications; Dissemination format - other formatsSOURCESDMX (2016) (https://sdmx.org/)Dissemination format - news releaseRegular or ad-hoc press releases linked to the data.CONTEXTThis 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSDissemination format; Dissemination format - microdata access; Dissemination format - news release; Dissemination format - publications; Dissemination format - other formatsSOURCESDMX (2016) (https://sdmx.org/)Dissemination format - online databaseInformation about on-line databases in which the disseminated data can be accessed.CONTEXTThis 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSDissemination format; Dissemination format - microdata access; Dissemination format - news release; Dissemination format - online database; Dissemination format - other formatsSOURCESDMX (2016) (https://sdmx.org/)Dissemination format - publicationsRegular or ad-hoc publications in which the data are made available to the public.CONTEXTExamples 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).TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSDissemination format; Dissemination format - microdata access; Dissemination format - news release; Dissemination format - online database; Dissemination format - publicationsSOURCESDMX (2016) (https://sdmx.org/)Dissemination format - other formatsReferences to the most important other data dissemination done.CONTEXT"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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSDocumentation on methodology - advance noticeSOURCESDMX (2016) (https://sdmx.org/)Documentation on methodologyDescriptive text and references to methodological documents available.CONTEXTThis metadata element informs users in advance about major changes in methodology, source data, and statistical techniques.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSDocumentation on methodologySOURCESDMX (2016) (https://sdmx.org/)Documentation on methodology - advance noticePolicy 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.CONTEXTA DSD for global use is meeting one of the two criteria below: 1) 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; 2) 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.RELATED_TERMSLocal DSDSOURCESDMX (2016) (https://sdmx.org/)DSD for global useDSD agreed by a number of international organisations for use within their respective constituenciesCONTEXTAn 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONCode listSOURCESDMX (2016) (https://sdmx.org/)OTHER_LINKSCode list CL_ACTIVITY (https://sdmx.org/?page_id=3215)CODELIST_IDCL_ACTIVITYEconomic activityCombination of actions that result in the production, distribution and consumption of goods or services.CONTEXTThe 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONCode listSOURCESDMX (2016) (https://sdmx.org/)OTHER_LINKSInternational Standard Classification of Education (ISCED) (http://uis.unesco.org/sites/default/files/documents/international-standard-classification-of-education-isced-2011-en.pdf)CODELIST_IDCL_EDUCATION_LEVELEducation levelHighest level of an educational programme the person has successfully completed.CONTEXTUsually, 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".TYPECross-domain conceptRECOMMENDED_REPRESENTATIONDateTimeSOURCESDMX (2016) (https://sdmx.org/)Embargo timeExact time at which the data can be made available to the public.CONTEXTThis 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONCode listSOURCESDMX (2016) (https://sdmx.org/)OTHER_LINKSCode lists CL, COFOG, CL_COICOP, CL_COPNI, CL_COPP (http://sdmx.org/?page_id=3215)CODELIST_IDCL_COFOG; CL_COICOP; CL_COPNI; CL_COPPExpenditure according to purposeBreakdown of spending by institutional sectors between major expenditure functions.CONTEXTThis specifies the valid format for a non-enumerated domain for a component.RELATED_TERMSComponentSOURCESDMX (2016) (https://sdmx.org/)FacetFormat specification of a component's content when reported in a data or metadata set.CONTEXTA 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.RELATED_TERMSOwnership groupSOURCESDMX (2016) (https://sdmx.org/)Fast-track changeProcedure followed to update at short notice an SDMX artefact, e.g. a code list.CONTEXTThe 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONCode listRELATED_TERMSFrequency of dissemination; Frequency of observationSOURCESDMX (2016) (https://sdmx.org/)OTHER_LINKSCode list CL_FREQ (https://sdmx.org/?page_id=3215)CODELIST_IDCL_FREQFrequency of data collectionTime interval at which the source data are collected.CONTEXTThe frequencies with which data are released, which could be different from the frequency of data collection.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONCode listRELATED_TERMSFrequency of data collection; Frequency of observationSOURCESDMX (2016) (https://sdmx.org/)OTHER_LINKSCode list CL_FREQ (https://sdmx.org/?page_id=3215)CODELIST_IDCL_FREQFrequency of disseminationTime interval at which the statistics are disseminated over a given time period.CONTEXTIf 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".TYPECross-domain conceptRECOMMENDED_REPRESENTATIONCode listRELATED_TERMSFrequency of data collection; Frequency of disseminationSOURCESDMX (2016) (https://sdmx.org/)OTHER_LINKSCode list CL_FREQ (https://sdmx.org/?page_id=3215)CODELIST_IDCL_FREQFrequency of observationTime interval at which observations occur over a given time period.CONTEXTThe 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.SOURCESDMX (2016) (https://sdmx.org/)OTHER_LINKSSDMX Global Registry (https://registry.sdmx.org/); SDMX Global Registry Content Policy (https://sdmx.org/wp-content/uploads/SDMX_Global_Registry_Content_Policy_5-3-2015.docx)Global registryCentral and discoverable repository for SDMX structural metadata.CONTEXTA group key is derived from the dimensionality of the series key for the purpose of attaching data attributesSOURCESDMX (2016) (https://sdmx.org/)Group keySet of key values that comprise a partial key.CONTEXTThe group key's structure that comprises the subset of dimensions that specifies the structure of the partial key.SOURCESDMX (2016) (https://sdmx.org/)Group key structureSet of metadata concepts that define a partial key derived from the dimension descriptor in a Data Structure Definition.CONTEXTThe 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.SOURCESDMX (2016) (https://sdmx.org/)Hierarchical codeCode reference that is part of a hierarchy.CONTEXTThe 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.SOURCESDMX (2016) (https://sdmx.org/)Hierarchical code listOrganised 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.CONTEXTIn 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.RELATED_TERMSLevelSOURCEUnited Nations Glossary of Classification Terms; prepared by the Expert Group on International Economic and Social Classifications, unpublished on paper (http://unstats.un.org/unsd/class/family/glossary_short.asp)HierarchyClassification 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.CONTEXTThe 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: 1. A user identifies a dataset through the graphical user interface (GUI) of the hub using the structural metadata, and requests it; 2. the hub translates the user request in one or more queries and sends them to the related data providers' systems; 3. data providers' systems process the query and send the result to the hub in standard format (e.g. SDMX-ML 2.1); 4. 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.RELATED_TERMSPull (reporting method)SOURCESDMX (2016) (https://sdmx.org/)Hub (dissemination architecture)Method of registering, querying, and disseminating data or reference metadata by means of a central, service-based platform (the hub).CONTEXTIn SDMX the identity comprises a mandatory Id and some optional attributes. Identifiable artefacts inherit the capability of having annotations.RELATED_TERMSAnnotable artefact; Artefact; Maintainable artefact; Nameable artefact; Versionable artefactSOURCESDMX (2016) (https://sdmx.org/)Identifiable artefactConstruct that contains structures capable of providing identity to an object.CONTEXTImputation 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.TYPECross-domain conceptRELATED_TERMSImputation rateSOURCEEconomic Commission for Europe of the United Nations (UNECE), "Glossary of Terms on Statistical Data Editing", Conference of European Statisticians Methodological material, Geneva, 2000 (http://www.unece.org/fileadmin/DAM/stats/publications/editingglossary.pdf)OTHER_LINKSStatistics Canada, "Statistics Canada Quality Guidelines", various online editions (http://www5.statcan.gc.ca/bsolc/olc-cel/olc-cel?catno=12-539-X&CHROPG=1&lang=eng)ImputationProcedure for entering a value for a specific data item where the response is missing or unusable.CONTEXTThe 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSImputationSOURCEEurostat, "ESS Guidelines for the Implementation of the ESS Quality and Performance Indicators (QPI)", Luxembourg, 2014 (http://ec.europa.eu/eurostat/documents/64157/4373903/02-ESS-Quality-and-performance-Indicators-2014.pdf/5c996003-b770-4a7c-9c2f-bf733e6b1f31)Imputation rateRatio of the number of replaced values to the total number of values for a given variable.CONTEXTSuch 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.SOURCESDMX (2016) (https://sdmx.org/)Incremental updateData or metadata message that is used for changing a part of the content of a data/metadata set.CONTEXTIt also includes arrangements or procedures to facilitate data sharing and coordination between data producing agencies.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSInstitutional mandate - data sharing; Institutional mandate - legal acts and other agreementsSOURCESDMX (2016) (https://sdmx.org/)Institutional mandateSet 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSInstitutional mandate; Institutional mandate - legal acts and other agreementsSOURCESDMX (2016) (https://sdmx.org/)Institutional mandate - data sharingArrangements or procedures for data sharing and coordination between data producing agencies.CONTEXTThe 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSInstitutional mandate; Institutional mandate - data sharingSOURCESDMX (2016) (https://sdmx.org/)Institutional mandate - legal acts and other agreementsLegal 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.CONTEXTThis 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.SOURCESDMX (2016) (https://sdmx.org/)International stringConstruct defining multi-lingual text for the same underlying concept.CONTEXTThis 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.SOURCESDMX (2016) (https://sdmx.org/)isExternalReferenceConstruct that indicates whether an object is available in the metadata source that contains its identifier or whether the object itself is available elsewhere.CONTEXTThis 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.SOURCESDMX (2016) (https://sdmx.org/)isIncludedConstruct that indicates whether the contained values of a container object is to be included or excluded from the valid list of values.CONTEXTA 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSUnit non-response rateSOURCEEurostat, "ESS Guidelines for the Implementation of the ESS Quality and Performance Indicators (QPI)", Luxembourg, 2014 (http://ec.europa.eu/eurostat/documents/64157/4373903/02-ESS-Quality-and-performance-Indicators-2014.pdf/5c996003-b770-4a7c-9c2f-bf733e6b1f31)Item non-response rateRatio 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.CONTEXTThere 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).RELATED_TERMSAgency Scheme; Category Scheme; Code List; Concept Scheme; Data Consumer Scheme; Data Provider SchemeSOURCESDMX (2016) (https://sdmx.org/)Item schemeDescriptive information for an arrangement or division of objects into groups based on characteristics which the objects have in common.CONTEXTIn 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).RELATED_TERMSCoding format; HierarchySOURCESDMX (2016) (https://sdmx.org/)OTHER_LINKSUNECE Generic Statistical Information Model (GSIM), GSIM Glossary, last consulted 15 February 2015 (http://www1.unece.org/stat/platform/display/gsim/Glossary)LevelIdentifiable position to which codes in a scheme of codes are related.CONTEXTAn example is a structure for use in internal production processes.RELATED_TERMSDSD for global useSOURCESDMX (2016) (https://sdmx.org/)Local DSDDSD developed for the specific needs of one organisation only.CONTEXTMaintainable 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.RELATED_TERMSAnnotable artefact; Artefact; Identifiable artefact; Nameable artefact; Versionable artefactSOURCESDMX (2016) (https://sdmx.org/)Maintainable artefactConstruct that contains structures capable of providing a maintenance agency to an object.CONTEXTThe 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).TYPECross-domain conceptRECOMMENDED_REPRESENTATIONCode listRELATED_TERMSAgency scheme; Data consumer scheme; Ownership groupSOURCESDMX (2016) (https://sdmx.org/)CODELIST_IDCL_AGENCYMaintenance agencyOrganisation or other expert body responsible for the operational maintenance of commonly used metadata artefacts.CONTEXTIn 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.SOURCESDMX (2016) (https://sdmx.org/)MapCorrespondence between two or more objects.CONTEXTIn a SDMX data set, the instance of a measure is often called an observation.RELATED_TERMSData Structure Definition, DSDSOURCESDMX (2016) (https://sdmx.org/)MeasureStatistical concept for which data are provided in a data set.CONTEXTMeasurement 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSCoverage error; Model assumption error; Non-response error; Non-sampling error; Over-coverage rate; Processing errorSOURCEThe Cambridge Dictionary of Statistics, B.S. Everitt, Cambridge University Press, 1998OTHER_LINKSStatistics Canada, "Statistics Canada Quality Guidelines", 4th edition, October 2003 (http://www.statcan.gc.ca/pub/12-539-x/12-539-x2003001-eng.pdf)Measurement errorError in reading, calculating or recording a numerical value.CONTEXTThis is a part of a Constraint.RELATED_TERMSMember value; ConstraintSOURCESDMX (2016) (https://sdmx.org/)Member selectionSet of permissible values for one component of a data or metadata structure.CONTEXTThis is a part of a Constraint.RELATED_TERMSMember selection; ConstraintSOURCESDMX (2016) (https://sdmx.org/)Member valueSingle value of the set of values for a member selection.CONTEXTThis indicator shows to what extent metadata of a specific type are available compared to what should be available.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringSOURCEEurostat, "ESS Guidelines for the Implementation of the ESS Quality and Performance Indicators (QPI)", Luxembourg, 2014 (http://ec.europa.eu/eurostat/documents/64157/4373903/02-ESS-Quality-and-performance-Indicators-2014.pdf/5c996003-b770-4a7c-9c2f-bf733e6b1f31)Metadata completenessRatio of the number of metadata elements provided to the total number of metadata elements applicable.CONTEXTA 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.RELATED_TERMSCategory; DataflowSOURCESDMX (2016) (https://sdmx.org/)MetadataflowCollection of metadata concepts, structure and usage when used to collect or disseminate reference metadata.CONTEXTThis is a part of a Constraint.RELATED_TERMSConstraintSOURCESDMX (2016) (https://sdmx.org/)Metadata key setSet of metadata keys.CONTEXTThis is a part of a ConstraintRELATED_TERMSConstraintSOURCESDMX (2016) (https://sdmx.org/)Metadata key valueValue in a metadata set of an identifier component defined in a metadata structure definition.CONTEXTIn 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.SOURCESDMX (2016) (https://sdmx.org/)Metadata repositoryPlace where logically organised statistical metadata are stored that allows for querying, editing and managing of metadata.CONTEXTIn 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.RELATED_TERMSMetadata structure definition, MSD; Reference metadataSOURCESDMX (2016) (https://sdmx.org/)Metadata setOrganised collection of reference metadata.CONTEXTAn 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.RELATED_TERMSAttribute; Component; Concept; Metadata set; Reference metadataSOURCESDMX (2016) (https://sdmx.org/)Metadata structure definition, MSDSpecification 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.CONTEXTThe 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONDateTimeRELATED_TERMSMetadata update - last certified; Metadata update - last posted; Metadata update - last updateSOURCESDMX (2016) (https://sdmx.org/)Metadata updateDate on which the metadata element was created or modified.CONTEXTIn 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONDateTimeRELATED_TERMSMetadata update; Metadata update - last posted; Metadata update - last updateSOURCESDMX (2016) (https://sdmx.org/)Metadata update - last certifiedDate 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.CONTEXTThe date of the last posting (dissemination) of the metadata on the web site should be retained.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONDateTimeRELATED_TERMSMetadata update; Metadata update - last certified; Metadata update - last updateSOURCESDMX (2016) (https://sdmx.org/)Metadata update - last postedDate of the latest dissemination of the metadata.CONTEXTThe 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONDateTimeRELATED_TERMSMetadata update; Metadata update - last certified; Metadata update - last postedSOURCESDMX (2016) (https://sdmx.org/)Metadata update - last updateDate of last update of the content of the metadata.CONTEXTError due to domain specific models needed to define the target of estimation.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSCoverage error; Measurement error; Non-response error; Non-sampling error; Over-coverage rate; Processing errorSOURCEEurostat, "Assessment of Quality in Statistics: Glossary", Working Group, Luxembourg, October 2003 (http://ec.europa.eu/eurostat/ramon/coded_files/QGLOSSARY 2003.pdf)Model assumption errorError 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.CONTEXTThe name is mandatory and the description is optional. Each can have multilingual variants. Nameable artefacts inherit the capability of having identity and annotations.RELATED_TERMSAnnotable artefact; Artefact; Identifiable artefact; Maintainable artefact; Versionable artefactSOURCESDMX (2016) (https://sdmx.org/)Nameable artefactConstruct that contains structures capable of providing a name and a description to an object.CONTEXTNon-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).TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSCoverage error; Measurement error; Model assumption error; Non-sampling error; Over-coverage rate; Processing errorSOURCESDMX (2016) (https://sdmx.org/)OTHER_LINKSStatistical Policy Working Paper 15: "Quality in Establishment Surveys", Office of Management and Budget, Washington D.C., July 1988, page 68 (https://fcsm.sites.usa.gov/files/2014/04/spwp15.pdf)Non-response errorError that occurs when the survey fails to get a response to one, or possibly all, of the questions.CONTEXTNon-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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSAccuracy; Accuracy - overall; Coverage error; Measurement error; Over-coverage rate; Sampling errorSOURCESDMX (2016) (https://sdmx.org/)Non-sampling errorError in sample estimates which cannot be attributed to sampling fluctuations.CONTEXTThe 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.RELATED_TERMSSDMX registry; SubscriptionSOURCESDMX (2016) (https://sdmx.org/)NotificationInformation sent to a person or application as a result of an event in an SDMX registry.CONTEXTBy "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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSNumber of metadata consultationsSOURCEEurostat, "ESS Guidelines for the Implementation of the ESS Quality and Performance Indicators (QPI)", Luxembourg, 2014 (http://ec.europa.eu/eurostat/documents/64157/4373903/02-ESS-Quality-and-performance-Indicators-2014.pdf/5c996003-b770-4a7c-9c2f-bf733e6b1f31)Number of data table consultationsNumber of consultations of data tables within a statistical domain for a given time period.CONTEXTBy "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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSNumber of data table consultationsSOURCEEurostat, "ESS Guidelines for the Implementation of the ESS Quality and Performance Indicators (QPI)", Luxembourg, 2014 (http://ec.europa.eu/eurostat/documents/64157/4373903/02-ESS-Quality-and-performance-Indicators-2014.pdf/5c996003-b770-4a7c-9c2f-bf733e6b1f31)Number of metadata consultationsNumber of metadata file consultations within a statistical domain for a given time period.CONTEXTAt 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONAlphaNumericSOURCESDMX (2016) (https://sdmx.org/)Observation pre-break valueObservation, at a time series break period, that was calculated using the old methodology.CONTEXTThis 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).TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringSOURCESDMX (2016) (https://sdmx.org/)OTHER_LINKSCode list CL_OBS_STATUS (https://sdmx.org/?page_id=3215); Possible Ways of Implementing CL_OBS_STATUS Code List (https://sdmx.org/wp-content/uploads/CL_OBS_STATUS_implementation_20-10-2014.doc)CODELIST_IDCL_OBS_STATUSObservation statusInformation on the quality of a value or an unusual or missing value.CONTEXT"Observation value" is the field which holds the data.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONAlphaNumericSOURCESDMX (2016) (https://sdmx.org/)Observation valueValue of a particular variable.CONTEXTOccupation 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONCode listSOURCESDMX (2016) (https://sdmx.org/)OTHER_LINKSCode list CL_OCCUPATION (https://sdmx.org/?page_id=3215)CODELIST_IDCL_OCCUPATIONOccupationJob or position held by an individual who performs a set of tasks and duties.CONTEXTIn 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.SOURCESDMX (2016) (https://sdmx.org/)Organisation unit schemeCollection of organisation units.CONTEXTOver-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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSCoverage error; Measurement error; Model assumption error; Non-response error; Non-sampling error; Processing error; Proportion of common unitsSOURCEEurostat, "ESS Guidelines for the Implementation of the ESS Quality and Performance Indicators (QPI)", Luxembourg, 2014 (http://ec.europa.eu/eurostat/documents/64157/4373903/02-ESS-Quality-and-performance-Indicators-2014.pdf/5c996003-b770-4a7c-9c2f-bf733e6b1f31)OTHER_LINKSEurostat, "Assessment of Quality in Statistics: Glossary", Working Group, Luxembourg, October 2003 (http://unstats.un.org/unsd/dnss/docs-nqaf/Eurostat-GLOSSARY_1.pdf)Over-coverage rateProportion of units accessible via the frame that do not belong to the target population (are out-of-scope).CONTEXTThe 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.RELATED_TERMSFast-track change; Maintenance agencySOURCESDMX (2016) (https://sdmx.org/)Ownership groupSet of organisations which collegially endorse the responsibility for the governance of an SDMX Data Structure Definition and its related artefacts.CONTEXTVarious 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONCode listRELATED_TERMSAdjustment; Seasonal adjustmentSOURCESDMX (2016) (https://sdmx.org/)CODELIST_IDCL_PRICE_ADJUSTPrice adjustmentStatistical technique used to remove the effects of price influences operating on a data series.CONTEXTSources 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSCoverage error; Measurement error; Model assumption error; Non-response error; Non-sampling error; Over-coverage rate; Proportion of common unitsSOURCEUnited States Federal Committee on Statistical Methodology, "Statistical Policy Working Paper 15: Quality in Establishment Surveys", Washington D.C., July 1988, page 79 (https://fcsm.sites.usa.gov/files/2014/04/spwp15.pdf)Processing errorError in final survey results arising from the faulty implementation of correctly planned implementation methods.CONTEXTTo 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSProfessionalism - code of conduct; Professionalism - impartiality; Professionalism - methodology; Professionalism - statistical commentarySOURCESDMX (2016) (https://sdmx.org/)OTHER_LINKSUnited Nations Fundamental Principles of Official Statistics (http://unstats.un.org/unsd/dnss/gp/FP-Rev2013-E.pdf)ProfessionalismStandard, skill and ability suitable for producing statistics of good quality.CONTEXTThis 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSProfessionalism; Professionalism - impartiality; Professionalism - methodology; Professionalism - statistical commentarySOURCESDMX (2016) (https://sdmx.org/)Professionalism - code of conductProvisions for assuring the qualifications of staff and allowing staff to perform their functions without intervention motivated by non-statistical objectives.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSProfessionalism; Professionalism - code of conduct; Professionalism - methodology; Professionalism - statistical commentarySOURCESDMX (2016) (https://sdmx.org/)Professionalism - impartialityElements providing assurances that statistics are produced on an impartial basis.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSProfessionalism; Professionalism - code of conduct; Professionalism - impartiality; Professionalism - statistical commentarySOURCESDMX (2016) (https://sdmx.org/)Professionalism - methodologyElements providing assurances that the choices of sources and statistical techniques as well as decisions about dissemination are informed solely by statistical considerations.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSProfessionalism; Professionalism - code of conduct; Professionalism - impartiality; Professionalism - methodologySOURCESDMX (2016) (https://sdmx.org/)Professionalism - statistical commentaryElements providing assurances that the statistical entity is entitled to comment on erroneous interpretation and misuse of statistics.CONTEXTThis 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSCoverage error; Over-coverage rate; Processing errorSOURCEEurostat "ESS Guidelines for the Implementation of the ESS Quality and Performance Indicators (QPI)", Luxembourg, 2014 (http://ec.europa.eu/eurostat/documents/64157/4373903/02-ESS-Quality-and-performance-Indicators-2014.pdf/5c996003-b770-4a7c-9c2f-bf733e6b1f31)Proportion of common unitsProportion of units covered by both the survey and the administrative sources in relation to the total number of units in the survey.CONTEXTLinks 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.SOURCESDMX (2016) (https://sdmx.org/)Provision agreementArrangement within which the information provider supplies data or metadata.CONTEXTIn 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.RELATED_TERMSHub (dissemination architecture); Push (reporting method)SOURCESDMX (2016) (https://sdmx.org/)Pull (reporting method)Data or reference metadata reporting method that requires the provider to make the information available at an accessible web location.CONTEXTPunctuality 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringSOURCESDMX (2016) (https://sdmx.org/)PunctualityTime lag between the actual delivery of the data and the target date when it should have been delivered.CONTEXTDifferent data collecting organisations have varying methods of implementing a push reporting method. Most of these use web technology or email.RELATED_TERMSPull (reporting method)SOURCESDMX (2016) (https://sdmx.org/)Push (reporting method)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.CONTEXTThis 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSQuality management - quality assessment; Quality management - quality assurance; Quality management - quality documentationSOURCESDMX (2016) (https://sdmx.org/)Quality managementSystems and frameworks in place within an organisation to manage the quality of statistical products and processes.CONTEXTThe overall assessment of data quality may include the result of a scoring or grading process for quality. Scoring may be quantitative or qualitative.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSQuality management; Quality management - quality assurance; Quality management - quality documentationSOURCESDMX (2016) (https://sdmx.org/)Quality management - quality assessmentOverall evaluation of data quality, based on standard quality criteria.CONTEXTThis 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSQuality management; Quality management - quality assessment; Quality management - quality documentationSOURCESDMX (2016) (https://sdmx.org/)Quality management - quality assuranceGuidelines focusing on quality in general and dealing with quality of statistical programmes, including measures for ensuring the efficient use of resources.CONTEXTThis 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSQuality management; Quality management - quality assessment; Quality management - quality assuranceSOURCESDMX (2016) (https://sdmx.org/)Quality management - quality documentationDocumentation on procedures applied for quality management and quality assessment.CONTEXTThe 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONCode listRELATED_TERMSCounterpart reference areaSOURCESDMX (2016) (https://sdmx.org/)OTHER_LINKSCode list CL_AREA (https://sdmx.org/?page_id=3215, See under "Geographical area")CODELIST_IDCL_AREAReference areaCountry or geographic area to which the measured statistical phenomenon relates.CONTEXTPreferably, reference metadata should include all of the following: a) "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; b) "methodological" metadata, describing methods used for the generation of the data (e.g. sampling, collection methods, editing processes); c) "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.RELATED_TERMSConcept scheme; Cross-domain concept, CDC; Metadata set; Metadata structure definition, MSD; Structural metadata; Structural validationSOURCESDMX (2016) (https://sdmx.org/)Reference metadataMetadata describing the contents and the quality of the statistical data.CONTEXTIn 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONDateTime; StringRELATED_TERMSBase period; Time periodSOURCESDMX (2016) (https://sdmx.org/)Reference periodTimespan or point in time to which the measured observation is intended to refer.CONTEXTThis 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSRelease policy - release calendar; Release policy - release calendar access; Release policy - transparency; Release policy - user accessSOURCESDMX (2016) (https://sdmx.org/)Release policyRules for disseminating statistical data to interested parties.CONTEXTAn 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSRelease policy; Release policy - release calendar access; Release policy - transparency; Release policy - user accessSOURCESDMX (2016) (https://sdmx.org/)Release policy - release calendarSchedule of statistical release dates.CONTEXTAccess to the release calendar information. A hyperlink should be provided if available.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSRelease policy; Release policy - release calendar; Release policy - transparency; Release policy - user accessSOURCESDMX (2016) (https://sdmx.org/)Release policy - release calendar accessDescription of how the release calendar can be accessed.CONTEXTThis statement does not describe the release policy itself.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSRelease policy; Release policy - release calendar; Release policy - release calendar access; Release policy - user accessSOURCESDMX (2016) (https://sdmx.org/)Release policy - transparencyStatement describing whether and how the release policy is disseminated to the public.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSRelease policy; Release policy - release calendar; Release policy - release calendar access; Release policy - transparencySOURCESDMX (2016) (https://sdmx.org/)Release policy - user accessPolicy 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.CONTEXTRelevance 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSRelevance - completeness; Relevance - data completeness rate; Relevance - user needs; Relevance - user satisfactionSOURCESDMX (2016) (https://sdmx.org/)RelevanceDegree to which statistical information meets the real or perceived needs of clients.CONTEXTThe measurement of the availability of statistics normally refers to data sets and compares the required data set to the available one.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSRelevance; Relevance - data completeness rate; Relevance - user needs; Relevance - user satisfactionSOURCESDMX (2016) (https://sdmx.org/)Relevance - completenessExtent to which all statistics that are needed are available.CONTEXTFor 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.RECOMMENDED_REPRESENTATIONStringRELATED_TERMSRelevance; Relevance - completeness; Relevance - user needs; Relevance - user satisfactionSOURCESDMX (2016) (https://sdmx.org/)Relevance - data completeness rateRatio 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.CONTEXTFor 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.RECOMMENDED_REPRESENTATIONStringRELATED_TERMSRelevance; Relevance - completeness; Relevance - user needs; Relevance - user satisfactionSOURCESDMX (2016) (https://sdmx.org/)Relevance - data completeness rate (for data producers)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.CONTEXTFor 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.RECOMMENDED_REPRESENTATIONStringRELATED_TERMSRelevance; Relevance - completeness; Relevance - user needs; Relevance - user satisfactionSOURCESDMX (2016) (https://sdmx.org/)Relevance - data completeness rate (for data users)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.CONTEXTWith 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSRelevance; Relevance - completeness; Relevance - data completeness rate; Relevance - user satisfactionSOURCESDMX (2016) (https://sdmx.org/)Relevance - user needsDescription of requirements with respect to the statistical output.CONTEXTIn 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSRelevance; Relevance - completeness; Relevance - data completeness rate; Relevance - user needsSOURCESDMX (2016) (https://sdmx.org/)Relevance - user satisfactionDescription of how well the disseminated statistics meet the expressed user needs.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONCode listSOURCESDMX (2016) (https://sdmx.org/)CODELIST_IDCL_ORGANISATIONReporting agencyOrganisation that supplies the data for a given instance of the statistics.CONTEXTThis is used to group Dataflows and Metadataflows to support data publication.RELATED_TERMSReporting taxonomy; RepresentationSOURCESDMX (2016) (https://sdmx.org/)Reporting categoryComponent of a reporting taxonomy that gives structure to a report and links to data and metadata.CONTEXTThis is used to group the Reporting Categories that link to Dataflows and Metadataflows to support data publication.RELATED_TERMSReporting categorySOURCESDMX (2016) (https://sdmx.org/)Reporting taxonomyScheme which defines the composition structure of a data report where each component can be described by an independent Dataflow Definition or Metadataflow Definition.CONTEXTThe 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.RELATED_TERMSReporting categorySOURCESDMX (2016) (https://sdmx.org/)RepresentationAllowable value or format for component or concept when reported.CONTEXTSampling 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSAccuracy; Accuracy - overall; Non-sampling errorSOURCESDMX (2016) (https://sdmx.org/)Sampling errorPart 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.CONTEXTSDMX-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.RELATED_TERMSSDMX information model, SDMX-IMSOURCESDMX (2016) (https://sdmx.org/)SDMX-EDIUN/EDIFACT format for exchange of SDMX-structured data and metadata for time series.CONTEXTThis 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.RELATED_TERMSComponent; SDMX-EDI; SDMX-JSON; SDMX-ML; SDMX technical specificationSOURCESDMX (2016) (https://sdmx.org/)SDMX Information Model, SDMX-IMConceptual model for defining and describing the classes, attributes, and relationships of the SDMX standard.CONTEXTSDMX-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.RELATED_TERMSSDMX Information model, SDMX-IMSOURCESDMX (2016) (https://sdmx.org/)SDMX-JSONJSON format for the dissemination of SDMX-structured data and metadata on the web.CONTEXTSDMX-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).RELATED_TERMSSDMX information model, SDMX-IM; SDMX registry interfaceSOURCESDMX (2016) (https://sdmx.org/)SDMX-MLXML format for the exchange of SDMX-structured data and metadata.CONTEXTThe 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.RELATED_TERMSNotification; SDMX registry interfaceSOURCESDMX (2016) (https://sdmx.org/)SDMX RegistryRepository for structural metadata and registered data sources whose interfaces and behaviour comply with the SDMX technical standards.CONTEXTThe 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.RELATED_TERMSSDMX-ML; SDMX registrySOURCESDMX (2016) (https://sdmx.org/)SDMX registry interface (in the context of registry)SDMX-ML specification of the allowable constructs that an SDMX registry must consume or output in its response.CONTEXTThe 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.RELATED_TERMSSDMX information model, SDMX-IMSOURCESDMX (2016) (https://sdmx.org/)SDMX Technical SpecificationSet of standards enabling interoperable implementations within and between systems concerned with the exchange, reporting and dissemination of statistical data and related metadata.CONTEXTSeasonal 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONCode listRELATED_TERMSAdjustment; Price adjustmentSOURCEAustralian Bureau of Statistics, "An Analytical Framework for Price Indexes in Australia: Glossary and References", Canberra, 1997 (http://www.abs.gov.au/ausstats/abs@.nsf/bb8db737e2af84b8ca2571780015701e/ff4de83064a2e425ca25697e0018fd44!OpenDocument)OTHER_LINKSU.S. Bureau of Labor Statistics, Online glossary, last consulted February 2014 (http://www.bls.gov/bls/glossary.htm); Code list CL_SEASONAL_ADJUST (https://sdmx.org/?page_id=3215)CODELIST_IDCL_SEASONAL_ADJUSTSeasonal adjustmentStatistical technique used to remove the effects of seasonal and calendar influences operating on a data series.CONTEXTThe 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).TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSTime coverageSOURCESDMX (2016) (https://sdmx.org/)Sector coverageMain economic or other sectors covered by the statistics.CONTEXTMost 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.RELATED_TERMSDimension; Sibling groupSOURCESDMX (2016) (https://sdmx.org/)Series keyCross product of values of dimensions, where either the cross product or the cross product combined with a time value, identifies uniquely an observation.CONTEXTThis 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONCode listSOURCESDMX (2016) (https://sdmx.org/)OTHER_LINKSCode list CL_SEX (https://sdmx.org/?page_id=3215)CODELIST_IDCL_SEXSexState of being male or female.CONTEXTOriginally from SDMX-EDI, a sibling group is uniquely identified by a data set identifier combined with the sibling group key.RELATED_TERMSSeries KeySOURCEEuropean Central Bank (ECB), Bank for International Settlements (BIS), Eurostat, International Monetary Fund (IMF), Organisation for Economic Co-operation and Development (OECD), "GESMES/TS User Guide", Release 3.00, February, 2003; unpublished on paper (https://circabc.europa.eu/sd/d/7fec1f91-2e09-4cc3-9ae7-f9d43281fc50/GesmesTS_rel3.pdf)Sibling groupSet of time series whose keys differ only in the value taken by the frequency dimension.CONTEXTThis 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.).TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringSOURCESDMX (2016) (https://sdmx.org/)Source data typeCharacteristics and components of the raw statistical data used for compiling statistical aggregates.CONTEXTThis 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringSOURCESDMX (2016) (https://sdmx.org/)Statistical concepts and definitionsDescription of the statistical domain under measure as well as the main variables provided.CONTEXTSDMX 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.SOURCESDMX (2016) (https://sdmx.org/)Statistical data and metadata exchange, SDMXTechnical standard and content-oriented guidelines for the exchange and sharing of statistical information between organisations.CONTEXTA 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.SOURCESDMX (2016) (https://sdmx.org/)Statistical indicatorData 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.CONTEXTThere 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).TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringSOURCESDMX (2016) (https://sdmx.org/)Statistical populationTotal membership or population or "universe" of a defined class of people, objects or events.CONTEXTWithin 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.RELATED_TERMSContent-Oriented Guidelines, COGSOURCESDMX (2016) (https://sdmx.org/)OTHER_LINKSList of subject-matter domains (https://sdmx.org/wp-content/uploads/03_sdmx_cog_annex_3_smd_2009.pdf)Statistical subject-matter domainStatistical activity that has common characteristics with respect to concepts and methodologies for data collection, manipulation and transformation.CONTEXTThe 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: 1) Legal, accounting or organisational criteria; 2) Geographical criteria; 3) 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringSOURCESDMX (2016) (https://sdmx.org/)Statistical unitEntity for which information is sought and for which statistics are ultimately compiled.CONTEXTThe 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).SOURCESDMX (2016) (https://sdmx.org/)Statistical variableCharacteristic 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.).CONTEXTStructural 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,RELATED_TERMSCross-domain concept, CDC; Reference metadata; Structural validationSOURCESDMX (2016) (https://sdmx.org/)Structural metadataMetadata that identify and describe data and reference metadata.CONTEXTIn 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.RELATED_TERMSReference metadata; Structural metadataSOURCESDMX (2016) (https://sdmx.org/)Structural validationProcess to determine the validity of data and reference metadata using structural metadata.CONTEXTThe 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.SOURCESDMX (2016) (https://sdmx.org/)Structure setMaintainable 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.CONTEXTThe 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.RELATED_TERMSNotificationSOURCESDMX (2016) (https://sdmx.org/)SubscriptionIndication that a person or application is to be notified when a predefined event occurs in an SDMX registry.CONTEXTThe 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSSector coverageSOURCESDMX (2016) (https://sdmx.org/)Time coverageReference metadata element specifying the period of time for which data are provided.CONTEXTThe technical time format and its related code list are part of the technical standards for SDMX-EDI and SDMX-XML.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONCode listSOURCESDMX (2016) (https://sdmx.org/)OTHER_LINKSCode list CL_TIME_FORMAT (https://sdmx.org/?page_id=3215)CODELIST_IDCL_TIME_FORMATTime formatTechnical format for the representation of time.CONTEXTThis indicator quantifies the gap between the release date of the final results and the end of the reference period.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSTime lag - first results; TimelinessSOURCEEurostat, "ESS Guidelines for the Implementation of the ESS Quality and Performance Indicators (QPI)", Luxembourg, 2014 (http://ec.europa.eu/eurostat/documents/64157/4373903/02-ESS-Quality-and-performance-Indicators-2014.pdf/5c996003-b770-4a7c-9c2f-bf733e6b1f31)Time lag - final resultsNumber of days (or weeks or months) from the last day of the reference period to the day of publication of complete and final results.CONTEXTThis indicator quantifies the gap between the release date of first results and the date of reference for the data.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSTime lag - final results; TimelinessSOURCEEurostat, "ESS Guidelines for the Implementation of the ESS Quality and Performance Indicators (QPI)", Luxembourg, 2014 (http://ec.europa.eu/eurostat/documents/64157/4373903/02-ESS-Quality-and-performance-Indicators-2014.pdf/5c996003-b770-4a7c-9c2f-bf733e6b1f31)Time lag - first resultsNumber of days (or weeks or months) from the last day of the reference period to the day of publication of first results.CONTEXTTimeliness 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSTime lag - final results; Time lag - first results; Timeliness - source dataSOURCESDMX (2016) (https://sdmx.org/)TimelinessLength of time between data availability and the event or phenomenon they describeCONTEXTCompared 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSTimelinessSOURCESDMX (2016) (https://sdmx.org/)Timeliness - source dataTime between the end of a reference period and actual receipt of the data by the compiling agency.CONTEXTThe 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONObservationalTimePeriodRELATED_TERMSReference period; Time period - collectionSOURCESDMX (2016) (https://sdmx.org/)Time periodTimespan or point in time to which the observation actually refers.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONCode listRELATED_TERMSTime periodSOURCESDMX (2016) (https://sdmx.org/)OTHER_LINKSCode list CL_TIME_PER_COLLECT (https://sdmx.org/?page_id=3215)CODELIST_IDCL_TIME_PER_COLLECTTime period - collectionDates or periods during which the observations have been collected (such as middle, average or end of period) for the target reference period.CONTEXTExamples 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONCode listSOURCESDMX (2016) (https://sdmx.org/)CODELIST_IDCL_TRANSFORMATIONTime transformationTime-related operation performed on a time series, solely involving observations of that time series.CONTEXT"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).TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringSOURCESDMX (2016) (https://sdmx.org/)TitleTextual label used as identification of a statistical object.CONTEXTIn some databases, it is referred to as SCALE, MAGNITUDE or POWER, e.g. UNIT_MULT 6 means that observations are in millions.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONCode listSOURCESDMX (2016) (https://sdmx.org/)OTHER_LINKSCode list CL_UNIT_MULT (https://sdmx.org/?page_id=3215)CODELIST_IDCL_UNIT_MULTUnit multiplierExponent in base 10 specified so that multiplying the observation numeric values by 10^UNIT_MULT gives a value expressed in the unit of measure.CONTEXTThe 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONStringRELATED_TERMSItem non-response rateSOURCEEurostat, "ESS Guidelines for the Implementation of the ESS Quality and Performance Indicators (QPI)", Luxembourg, 2014 (http://ec.europa.eu/eurostat/documents/64157/4373903/02-ESS-Quality-and-performance-Indicators-2014.pdf/5c996003-b770-4a7c-9c2f-bf733e6b1f31)Unit non-response rateRatio of the number of units with no information or not usable information to the total number of in-scope (eligible) units.CONTEXTThe 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONCode listSOURCESDMX (2016) (https://sdmx.org/)CODELIST_IDCL_UNIT_MEASUREUnit of measureUnit in which the data values are expressed.CONTEXTAllowed values are mandatory or conditional. Note that in an incremental update a set of data or metadata may omit mandatory attributes.SOURCESDMX (2016) (https://sdmx.org/)Usage statusIndication of the dependency of the presence of a data or metadata attribute when reported in a Data or Metadata Set.CONTEXTThe 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.SOURCESDMX (2016) (https://sdmx.org/)Validation and transformation language, VTLLanguage used by statisticians to express logical validation rules and transformations on data, whether described as a dimensional table or as unit-record data.CONTEXTStandard 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.TYPECross-domain conceptRECOMMENDED_REPRESENTATIONCode list; StringSOURCESDMX (2016) (https://sdmx.org/)CODELIST_IDCL_VALUATIONValuationDefinition of the price per unit, for goods and services flows and asset stocks.CONTEXTIn SDMX this construct is a part of the unique identification of the object.SOURCESDMX (2016) (https://sdmx.org/)VersionConstruct that enables a system to distinguish between one state of an object and another where the contents of the object have changed.CONTEXTThe 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.RELATED_TERMSAnnotable artefact; Artefact; Identifiable artefact; Maintainable artefact; Nameable artefactSOURCESDMX (2016) (https://sdmx.org/)Versionable artefactConstruct that contains structures capable of providing a version to an object.Global Metadata Concept SchemeThe Global Reference Metadata Concept Scheme (“Global MCS”) enumerates a standard list of reference metadata concepts. The Global MCS is constructed from a subset of SDMX cross-domain concepts that are related to reference metadata in the SDMX Glossary Version 2.0. When the SDMX Glossary was compiled, all relevant reference metadata concepts were included that can be mapped to metadata frameworks used by several international organisations.Data sourceLocation or service from where data or metadata can be obtained.Compiling agencyOrganisation collecting and/or elaborating the data being reported. Example usage: if an organisation receives data from a member agency and publishes it, the "Compiling agency" concept would be the member agency.Dissemination agencyOrganisation disseminating the data being reported.ContactIndividual or organisational contact points for the data or metadata. Recommended usage. Use either: the summary "Contact" concept but not the granular "Contact xxx" concepts, or; the granular "Contact xxx" concepts but not the "Contact" concept.Contact email addressE-mail address of the contact points for the data or metadata.Contact fax numberFax number of the contact points for the data or metadata.Contact mail addressPostal address of the contact points for the data or metadata.Contact nameName of the contact points for the data or metadata.Contact organisationOrganisation of the contact point(s) for the data or metadata. Example usage: if an organisation receives data from a member agency and publishes it, the "Contact organisation" concept would usually be the publishing organisation.Contact organisation unitAddressable subdivision of an organisation.Contact person functionArea of technical responsibility of the contact, such as "methodology", "database management" or "dissemination".Contact phone numberTelephone number of the contact points for the data or metadata.Institutional mandateSet 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. Recommended usage. Use either: the summary "Institutional mandate" concept but not the granular "Institutional mandate xxx" concepts, or; the granular "Institutional mandate xxx" concepts but not the "Institutional mandate" concept.Institutional mandate – data sharingArrangements or procedures for data sharing and coordination between data producing agencies.Institutional mandate - legal acts and other agreementsLegal 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.ConfidentialityProperty of data indicating whether they are subject to dissemination restrictions. Recommended usage. Use either: the summary "Confidentiality" concept but not the granular "Confidentiality xxx" concepts, or; the granular "Confidentiality xxx" concepts but not the "Confidentiality" concept.Confidentiality - data treatmentRules applied for treating the Data Set to ensure that private information from individual units cannot be accessed and to prevent unauthorised disclosure.Confidentiality - policyLegislative measures or other formal procedures which prevent unauthorised disclosure of data that identify a person or economic entity either directly or indirectly.ProfessionalismStandard, skill and ability suitable for producing statistics of good quality. Recommended usage. Use either: the summary "Professionalism" concept but not the granular "Professionalism xxx" concepts, or; the granular "Professionalism xxx" concepts but not the "Professionalism" concept.Professionalism - code of conductProvisions for assuring the qualifications of staff and allowing staff to perform their functions without intervention motivated by non-statistical objectives.Professionalism - impartialityElements providing assurances that statistics are developed, produced and disseminated in a neutral (impartial) manner, and that all users are given equal treatment.Professionalism - methodologyElements providing assurances that the choices of sources and statistical techniques as well as decisions about dissemination are informed solely by statistical considerations.Professionalism - statistical commentaryElements providing assurances that the statistical entity is entitled to comment on erroneous interpretation and misuse of statistics.Cost and burdenCost associated with the collection and production of a statistical product, as well as the burden imposed on respondents. Recommended usage. Use either: the summary "Cost and burden" concept but not the granular "Cost and burden xxx" concepts, or; the granular "Cost and burden xxx" concepts but not the "Cost and burden" concept.Cost and burden - efficiency managementCost-benefit analysis, effectiveness of execution of medium term statistical programmes, and ensuring efficient use of resources.Cost and burden – resourcesMetadata element providing assurances that staff, facilities, computing resources, and financing to undertake statistical production are commensurate with statistical programs.Statistical populationTotal membership or population or "universe" of a defined class of people, objects or events.Reference areaCountry or geographic area to which the measured statistical phenomenon relates.CoverageDefinition of the scope of the data compiled. Recommended usage. Use either: the summary "Coverage" concept but not the granular "xxx coverage" concepts and/or "Economic sector" concept; or the granular "xxx coverage" concepts and/or "Economic sector" but not the "Coverage" concept.Geographical coverageCharacterisation of the statistical units according to geographical criteria. Example usage: May exclude small parts of the national territory amounting to no more than 2% of the national population and the national territories.Population coverageDefinition of the main types of population covered by the statistics.Sector coverageSector(s) covered by the statistics.Economic sectorHigh-level grouping of economic activities based on the types of goods and services produced.Time coveragePeriod of time for which data are provided.Data descriptionMetadata element describing the main characteristics of the Data Set in an easily understandable manner, referring to the main data and indicators disseminated.Source data typeCharacteristics and components of the raw statistical data used for compiling statistical aggregates.Data collection methodMethod applied for gathering data for official statistics.Reference periodTimespan or point in time to which the measured observation is intended to refer.Base periodPeriod of time used as the base of an index number, or to which a constant series refers.Base weightWeights 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.Frequency of observationTime interval at which observations occur over a given time period.Frequency of data collectionTime interval at which the source data are collected.Classification systemMetadata element used to a) list the classification(s) being used for a given Data Set or set of Data Sets, and b) describe how these conform to internationally agreed standards, guidelines, or good practices.Statistical concepts and definitionsDefinitions and descriptions of the main variables provided.Statistical unitEntity for which information is sought and for which statistics are ultimately compiled.Unit of measureUnit in which the data values are expressed.Accounting conventionsPractical procedures, standards and other aspects used when compiling data from diverse sources under a common methodological framework.ValuationDefinition of the price per unit, for goods and services flows and asset stocks.Data compilationOperations performed on data to derive new information according to a given set of rules.AdjustmentSet 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. Recommended usage. Use either: the summary "Adjustment" concept but not the granular "xxx adjustment" concepts, or; the granular "xxx adjustment" concepts but not the "Adjustment" concept.Price adjustmentStatistical technique used to remove the effects of price influences operating on a data series.Seasonal adjustmentStatistical technique used to remove the effects of seasonal and calendar influences operating on a data series.Data validationProcess of monitoring the results of data compilation and ensuring the quality of the statistical results.Data revisionChange in a value of a statistic released to the public. Recommended usage. Use either: the summary "Data revision" concept but not the granular "Data revision xxx" concepts; or, the granular "Data revision xxx" concepts but not the "Data revision" concept.Data revision - policyPolicy aimed at ensuring the transparency of disseminated data, whereby preliminary data are compiled that are later revised.Data revision - practiceInformation on the data revision practice.Data revision - studiesInformation about data revision studies and analyses.ImputationProcedure for entering a value for a specific data item where the response is missing or unusable.Imputation rateRatio of the number of replaced values to the total number of values for a given variable.Quality managementSystems and frameworks in place within an organisation to manage the quality of statistical products and processes. Recommended usage. Use either: the summary "Quality management" concept but not the granular "Quality management xxx" concepts, or; the granular "Quality management xxx" concepts but not the "Quality management" concept.Quality management - quality assessmentOverall evaluation of data quality, based on standard quality criteria.Quality management - quality assuranceGuidelines focusing on quality in general and dealing with quality of statistical programmes, including measures for ensuring the efficient use of resources.Quality management - quality documentationDocumentation on procedures applied for quality management and quality assessment.AccuracyCloseness of computations or estimates to the unknown exact or true values that the statistics were intended to measure. Recommended usage. Use either: the summary "Accuracy" concept but not the granular "Accuracy - overall", "Non-sampling error" or "Sampling error" concepts, or; the granular "Accuracy - overall", "Non-sampling error" or "Sampling error" concepts but not the "Accuracy" concept.Accuracy - overallAssessment of accuracy, linked to a certain Data Set or domain, which is summarising the various components into one single measure.Sampling errorPart 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.Non-sampling errorError in sample estimates which cannot be attributed to sampling fluctuations.Coverage errorError 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.Measurement errorError in reading, calculating or recording a numerical value.Non-response errorError that occurs when the survey fails to get a response to one, or possibly all, of the questions.Processing errorError in final survey results arising from the faulty implementation of correctly planned implementation methods.Model assumption errorError 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.CoherenceAdequacy of statistics to be reliably combined in different ways and for various uses. Recommended usage. Use either: the summary "Coherence" concept but not the granular "Coherence xxx" concepts, or; the granular "Coherence xxx" concepts but not the "Coherence" concept.Coherence - cross domainExtent to which statistics are reconcilable with those obtained through other Data Sources or statistical domains.Coherence - internalExtent to which statistics are consistent within a given Data Set.ComparabilityExtent to which differences between statistics can be attributed to differences between the true values of the statistical characteristics. Recommended usage. Use either: the summary "Comparability" concept but not the granular "Comparability xxx" concepts, or; the granular "Comparability xxx" concepts but not the "Comparability" concept.Comparability - geographicalExtent to which statistics are comparable between geographical areas.Comparability - over timeExtent to which statistics are comparable or reconcilable over time.PunctualityTime lag between the actual delivery of the data and the target date when it should have been delivered.RelevanceDegree to which statistical information meets the real or perceived needs of clients. Recommended usage. Use either: the summary "Relevance" concept but not the granular "Relevance xxx" concepts, or; the granular "Relevance xxx" concepts but not the "Relevance" concept.Relevance - completenessExtent to which all statistics that are needed are available.Relevance - user needsDescription of requirements with respect to the statistical output.Relevance - user satisfactionDescription of how well the disseminated statistics meet the expressed user needs.TimelinessLength of time between data availability and the event or phenomenon they describe Recommended usage. Use either: the summary "Timeliness" concept but not the granular "Timeliness xxx" concepts, or; the granular "Timeliness xxx" concepts but not the "Timeliness" concept.Timeliness - source dataTime between the end of a reference period and actual receipt of the data by the compiling agency.Release policyRules for disseminating statistical data to interested parties. Recommended usage. Use either: the summary "Release policy" concept but not the granular "Release policy xxx" concepts, or; the granular "Release policy xxx" concepts but not the "Release policy" concept.Release policy - release calendarSchedule of statistical release dates.Release policy - release calendar accessDescription of how the release calendar can be accessed.Release policy - transparencyStatement describing whether and how the release policy is disseminated to the public.Release policy - user accessPolicy 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.Frequency of disseminationTime interval at which the statistics are disseminated over a given time period.Dissemination formatMedia by which statistical data and metadata are disseminated. Recommended usage. Use either: the summary "Dissemination format" concept but not the granular "Dissemination format xxx" concepts, or; the granular "Dissemination format xxx" concepts but not the "Dissemination format" concept.Dissemination format - microdata accessInformation on whether microdata are also disseminated.Dissemination format - news releaseRegular or ad-hoc press releases linked to the data.Dissemination format - online databaseInformation about on-line databases in which the disseminated data can be accessed.Dissemination format - publicationsRegular or ad-hoc publications in which the data are made available to the public.Dissemination format - other formatsReferences to the most important other data dissemination done.Documentation on methodologyDescriptive text and references to methodological documents available.Documentation on methodology - advance noticePolicy 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.Recommended uses and limitationsMetadata element guiding users with limited knowledge of the statistics presented and helping them determine whether the product meets their requirements.Metadata updateDate on which the metadata element was created or modified. Recommended usage. Use either: the summary "Metadata update" concept but not the granular "Metadata update xxx" concepts, or; the granular "Metadata update xxx" concepts but not the "Metadata update" concept.Metadata update - last certifiedDate 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.Metadata update - last postedDate of the latest dissemination of the metadata.Metadata update - last updateDate of last update of the content of the metadata.CommentDescriptive text which can be attached to data or metadata.Technical informationTechnical details related to the data, for example the storage system used, or technical systems related to the exchange.SDMX Concept Scheme for Concept RolesA concept role gives a particular context to a concept for easy and systematic interpretation by machine processing and visualization tools. For example, the concepts REPORTING_AREA and COUNTERPART_AREA are different concepts but they are both geographical characteristics, therefore they can be associated with the same concept role ID: “GEO”. This allows visualization systems to interpret these concepts as geographical data in order to generate maps. For any question, comment or correction, feel free to contact the SDMX Statistical Working Group (SWG) at the following address: swg@sdmx.org.CONTEXTUsed for concepts that contain descriptive information about the data. Examples include footnotes, notes, reference metadata within the data message.CommentDescriptive text which can be attached to data or metadata.CONTEXTIdentifies the subject to whom the data refers (e.g. the reporting agent for primary reporting, the country for secondary reporting). Note: This concept role has been included for compatibility with SDMX 2.0.EntityDescribes the subject of the data set (e.g. a country).CONTEXTAllows to display flags close to the observation value or partial key in statistical tables. Examples of qualitative information that can be exchanged via a flag are "estimated value", "break in series", “confidential”.FlagCoded attribute in a data set that represents qualitative information for the cell or partial key (e.g. series) value.CONTEXTIdentifies the observation's time interval for processing and/or presenting on a graph's time axis. For example, 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”. Note: This concept role has been included for compatibility with SDMX 2.0.FrequencyTime interval at which the source data are collected.CONTEXTUsed for concepts which refer to geographical or political groups of countries or regions within a country. It can allow for the representation of geographical information on maps or graphs.GeographicalGeographic area to which the measured statistical phenomenon relates.CONTEXTAllows systems to distinguish operation concepts from other concepts (entities, variables) which may be prioritised differently in navigation, presentation and processing. An example is to indicate that this concept codes information such as seasonal adjustment or time transformations.Statistical operationSignifies statistical operations have been done on the observations.CONTEXTUsed to distinguish concepts that represent statistical variables (e.g. sex, age) from concepts that are strictly related to the operation performed over data (e.g. Adjustment). This identification of this concept is crucial for statisticians as it is very important in survey data to identify variables. For example, in a DSD representing the GDP of a country we will have the reference area, the unit of measure of the GDP and the value of the GDP. The unit of measure is a concept used to understand or interpret data but it is not a statistical variable, which are reference area and the GDP observed value. The concept of variable is emphasized strongly in GSIM but not in SDMX, and the inclusion of this information as a Concept role, incorporates this information into SDMX.VariableCharacteristic 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.EDUC_UOE Concept SchemeEducation typeDetermines whether education programmes cover formal initial and/or formal adult educationEducation levelAn ordered set which groups and classifies education programmes according to the knowledge, skills, competencies and qualifications which they are designed to impartFields of educationFields of education based on ISCED 2013GradeIndicates the specific stage, class or year of instruction within the ISCED level in initial educationIntensityIntensity of participationCountry of originCountry of origin of statistical unitsMobilityMeasures transfers between education systems (such transfers include the physical crossing of a border)Statistical unitEntity for which information is sought and for which statistics are ultimately compiledTIME_PERIODTimespan or point in time to which the observation actually refersObservation valueValue of a particular variableFrequency of observationTime interval at which observations occur over a given time periodReference areaCountry or geographic area to which the measured statistical phenomenon relatesReference institutional sectorReference institutional sectorCounterpart institutional sectorCounterpart institutional sectorExpenditure typeSpecifies the type of expenditure data whether direct, transfers, etcSexState of being male or femaleAgeLength of time that an entity has lived or existedUnit of measureUnit in which the data values are expressedTable identifierList of current data collection tablesReference date for agesReference date for agesOrigin criterionCriterion used for country of originReference year startStart of the reference period. For example, start of school year or financial yearReference year endEnd of reference period. For example, end of school year or financial yearSchool year start for FIN-STUDSchool year start for FIN-STUD (YYYY-MM-DD)School year end for FIN-STUDSchool year end for FIN-STUD (YYYY-MM-DD)Type of expenditure reportedSpecifies whether the expenditure reported is actual or budget expenditureObservation statusInformation on the quality of a value or an unusual or missing valueConfidentiality statusInformation about the confidentiality status of the object to which this attribute is attachedObservation commentComments to the observation valueDecimalsNumber of digits of an observation to the right of a decimal pointTime period collectionDates or periods during which the observations have been collected for the target reference periodCompiling agencyOrganisation collecting and/or elaborating the data being reportedUnit multiplierExponent in base 10 used for calculating the actual value in the unit of measureBreakdown groupGroup of dimensions by which the data are decomposedInternational Merchandise Trade StatisticsFrequencyTime PeriodReference periodTrade flowCommodity codeConfidentialized commodity codeCommodity codeConfidentialized commodity codeCustom commodity breakdownPrimary partner areaConfidentialized primary partner areaAdditional partner areaConfidentialized additional partner areaMode of transport at the borderConfidentialized Mode of transport at the borderCustoms or statistical procedureEconomic activityTransformationObservation typeObservation ValueReference Period Start DateUnit multiplierUnit measureComments to the observation valueTrade SystemCustom commodity breakdown codeCustom commodity breakdown descriptionType of primary partner areaType of additional areaAnnotation of primary partner areaAnnotation of additional partner areaObservation statusOBS_CONFMeasures concept schemeSupplementary quantityFOB-type valueCIF-type valueNet WeightGross WeightPrice indexUnit value indexVolume indexFreight and InsuranceInsurance CostFreight Cost