Geographical comparability refers to the degree of comparability between similar survey results measuring the same phenomenon across geographical areas or regions. The surveys are in general conducted by different statistical agencies, referring to populations in different geographical areas, sometimes based on a harmonised methodology.
" ; swvs:term_status "upload" ; skos:altLabel "Geographical comparability" ; skos:broaderExtent to which statistics are comparable between geographical areas.
" ; skos:exactMatchStructural metadata are needed to identify, use, and process data matrixes and data cubes, e.g. names of columns or Dimensions of statistical cubes. Structural metadata must be associated with the statistical data and reference metadata, otherwise it becomes impossible to identify, retrieve and navigate the data or reference metadata.
In SDMX structural metadata are not limited to describing the structure of data and reference metadata. The structural metadata in SDMX include many of the other constructs to be found in the SDMX Information Model including data discovery, data and metadata Constraints (used for both data validation and data discovery), data and structure mapping, data and metadata reporting, statistical processes.
" , "Structural 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,
"@sr ; dcterms:requiresMetadata that identify and describe data and reference metadata.
"@sr , "Metadata that identify and describe data and reference metadata.
" ; skos:inSchemeEach level of the classification is defined in terms of the categories at the next lower level of the classification.
In SDMX this is known as a level based hierarchy. SDMX also has the concept of the value based hierarchy where the hierarchy of categories are not organised into formal levels.
" ; dcterms:requiresClassification structure arranged in levels of detail from the broadest to the most detailed level.
" ; skos:inSchemeMaintainable 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 Codelist are the Codes) are \"final\" and there are restrictions on what type of change is allowed without changing the version.
" ; dcterms:requiresConstruct that contains structures capable of providing a Maintenance Agency to an object.
" ; skos:inSchemeThe population coverage describes the types of population as regards their earnings, the types of education, etc., covered by the statistics whenever applicable.
" ; swvs:term_status "newTerm" ; skos:definition "Definition of the main types of population covered by the statistics.
" ; skos:inSchemeAn 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.
" ; swvs:term_status "upload" ; skos:definition "Combination of actions that result in the production, distribution and consumption of goods or services.
" ; skos:inSchemeThe institutional mandate also includes arrangements or procedures to facilitate data sharing and coordination between data producing agencies.
" ; swvs:term_status "upload" ; skos:definition "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.
" ; skos:exactMatchThe specification of the format information for the Codes, such as whether the Codes are alphabetic, numeric or alphanumeric, and the code length.
" ; dcterms:requiresSpecification of the Representation for the Codes in a Codelist.
" ; skos:inSchemeAccess to the release calendar information. A hyperlink should be provided if available.
" ; dcterms:requiresDescription of how the release calendar can be accessed.
" ; skos:exactMatchThe Representation can be enumerated or non-enumerated. An enumerated Representation can be a Codelist, 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, integer, and the time formats within the Observational Time Period hierarchy such as Standard Time Period and Time Range.
" ; dcterms:requiresAllowable value or format for Component or Concept when reported.
" ; skos:inSchemeSeries are an ordered sequence of qualitative or quantitative data samples or observations used to predict or demonstrate trends through time and space. The series can be classified by the criteria used to arranged them: time (historical or chronological), geolocation (spatial or geographical), occurrence (condition or frequency).
Time series is a basic building block of many datasets. It groups data that share the same dimension values except for the time dimension, allowing users to see changes in data over time, holding all other dimensions constant. Series is the generic concept, of which time series is the most common example. A series can be disambiguated by any single dimension, as long as the values for other dimensions do not change.
" ; dcterms:requiresSet of data observations disambiguated by the values of a single dimension, usually time.
" ; skos:inScheme1) 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 Codelists must not necessarily be implemented in all Data Structure Definitions (DSDs) but they potentially could. For example, Codelist \"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 Codelist for decimals will be absolutely necessary.
2) Codelists 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 Codelists 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 Codelist without taking into account the fact that other domains might use the same classification system.
3) Codelists recommended as CDCL by the SDMX Statistical Working Group (SWG) although they are in principle maintained by third organisations.
Examples: CL_AREA (based on the ISO 3166 alpha-2 codes for countries); CL_CURRENCY (based on the ISO 4217 3-character codes for currencies).
Explanatory note: In these cases, the value added by the SWG is to propose guidelines on specific methodological issues, e.g. how to code a country that has been split into several new entities.
SDMX Codelist meeting at least one of the criteria below:
1) Potential application across all statistical domains;
2) Codelist maintained by the SDMX Statistical Working Group (SWG) on its initiative;
3) Codelist recommended as CDCL by the SDMX SWG although they are in principle maintained by third organisations.
This metadata element refers to descriptions of the types of prices used to value flows and stocks, or other units of measurements used for recording the phenomena being observed; the time of recording of the flows and stocks or the time of recording of other phenomena that are measured, including the reference period employed; and the grossing/netting procedures that are used.
Accounting conventions may refer to whether the data are recorded on a cash/accrual or mixed accounting basis, the time of their recording and the reference period (fiscal or calendar year) employed. The description could also include how consistent the practices used are with internationally accepted standards - such as the Balance of Payments Manual (BPM) or the System of National Accounts (SNA) - or good practices.
" , "Овај елемент метапода се односи на описе врсте цена које се користе за вреднување токова и залиха, или других јединице мерења које се користе за снимање феномена који се посматрају; време снимања токова и залиха или времена снимања других феномена који се мере, укључујући референтни период који се користи; и процедуре надраже/исплате које се користе.
Accounting conventions може се односи на то да ли су подаци записани по касшовим/акруалним или смешеним рачуноводственим принципима, времену њиховог снимања и референтни период (фискални или календарски година) који се користи. Опис такође може укључивати колико су пракси које се користе усклађене са међународно прихваћеним стандардима - као што су Balance of Payments Manual (BPM) или System of National Accounts (SNA) - или добри пракси.
"@sr ; swvs:term_status "upload" ; skos:broaderPractical procedures, standards and other aspects used when compiling data from diverse sources under a common methodological framework.
" , "Практичне процедуре, стандарде и други аспекти који се користе приликом компилирања података из разних извора подједаним методолошким оквиром.
"@sr ; skos:inSchemeThe 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 Codelist or a Data Structure Definition) or via the container in which it is maintained such as a code (maintained artefact is a Codelist) or a Dimension (maintained artefact is a Data Structure Definition).
" ; dcterms:requiresOrganisation or other expert body responsible for the operational maintenance of commonly used metadata artefacts.
" ; skos:inSchemeSDMX-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).
" ; dcterms:requiresXML format for the exchange of SDMX-structured data and metadata.
" ; skos:inSchemeThere are four types of Item Schemes in SDMX: Codelist, Concept Scheme, Category Scheme, Organisation Scheme (and four sub schemes: Agency, Data Provider, Data Consumer, Organisation Unit).
" ; swvs:term_status "upload" ; skos:broader sip-sdmx-concept:CATEGORY_SCH ,Descriptive information for an arrangement or division of objects into groups based on characteristics which the objects have in common.
" ; skos:inSchemeThere are two types of Constraints: Content Constraints and Attachment Constraints.
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.
An Attachment Constraint describes subsets of the content of a Data or Metadata Set in terms of the content regions or in terms of the set of key combinations to which attributes or reference metadata (as defined by structure definitions) may be attached.
" , "Constraint може бити различитих типова. На пример, Content Constraint прецизира \"дозвољени садржај\" (који се користи за ограничавање вредности које су дозвољене када се подаци или метаподаци пријављују или размењују), или \"стварни\" садржај (кључеви серија и/или димензија и атрибут вредности присутне у извору података). У сваком од ових случајева ограничење прецизира подскуп целокупне кубе података који теоретски могу бити присутни према спецификацији Data structure definition или Metadata structure definition.
"@sr ; dcterms:requiresСпецификација подскупа могућег садржаја података или метаподатака који се могу изводити из листа кодова коришћених у структури података или метаподатака.
"@sr , "Specification of a subset of the possible content of data or metadata that can be derived from the Codelists used in a data or metadata structure.
" ; skos:inSchemeThis metadata element is needed in order to differentiate the compiling organisation from the organisation disseminating the data. The dissemination agency could be different from the reporting agency and the compilation agency.
" ; swvs:term_status "newTerm" ; skos:definition "Organisation disseminating the data being reported.
" ; skos:inSchemeArrangements or procedures for data sharing and coordination between data producing agencies.
" ; skos:exactMatchThe 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.
" ; swvs:term_status "upload" ; skos:broaderTimespan or point in time to which the observation actually refers.
" ; skos:exactMatchStandard 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.
" ; swvs:term_status "upload" ; skos:definition "Definition of the price per unit, for goods and services flows and asset stocks.
" ; skos:exactMatchУ SDMX, скупови података се извештавају или објављују у складу са дефиницијом тока података. Дефиниција тока података идентификује data structure definition и може бити повезана са једним или више тематских домена. Ово олакшава претрагу података према организованим category schemes. „Dataflow“, у овом контексту, је апстрактан концепт скупова података, тј. структура без икаквих података. Док data structure definition дефинише димензије, атрибуте, мере и повезану репрезентацију која чини ваљану структуру података и повезаних метаподатака садржаних у скупу података, дефиниција Dataflow повезује data structure definition са једном или више категорија. Ово даје систему могућност да наведе који скупови података треба да буду извештени за дату категорију и који скупови података могу бити извештени коришћењем data structure definition. Дефиниција Dataflow такође може имати придружене додатне метаподатке, који дефинишу квалитативне информације и ограничења у коришћењу data structure definition, у смислу периодичности извештавања или спецификације подскупа кодова који ће се користити у димензији.
"@sr , "In SDMX, Data Sets are reported or disseminated according to a Dataflow Definition. The Dataflow 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.
" ; dcterms:requiresСтруктура која описује, категорише и ограничава допуштени садржај једног