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0-9Page: 0. Organisation structure (Germany)Official statistics in Germany is characterised by a decentralised political system that divides the functions of government between the central government and 16 regional (Länder) governments. As a result, each region (Land) may have a statistical office
Page: 1 Introduction
Introduction 1. GSIM V1.0 is the first internationally endorsed reference framework for statistical information. This over-arching framework will play an important part in modernizing, streamlining and aligning the production and standards associated with
Page: 1. Introduction
{scrollbar} Statistical metadata systems play a fundamental role in statistical organizations. Such systems comprise the people, processes and technology used to manage statistical metadata. This publication provides guidance to senior managers in underst
Page: 1. Introduction
1.1 Background Statistical metadata systems (SMS) should serve statistical organizations as tools for the efficient management and performance of statistical information systems. Globalization has brought several issues in statistical production to g
Page: 1. Introduction (Albania)
1.1 Metadata strategy One of the main priorities in five years strategic plan named Official Statistical Program; (PSZ 2007-2011) was implementation of metadata in statistics. The main purpose was to inform users of statistical data about: which statist
Page: 1. Introduction (Albanian Institute of Statistics)
Page: 1. Introduction (Australia) Next section Preface to most recent update (2011.1) The previous major update to this case study occurred in the first half of 2009. As recorded in the document entitled A Brief History of Metadata (in the ABS) (referenced simply as BHM hereafter), which
Page: 1. Introduction (Australian Bureau of Statistics)
Page: 1. Introduction (Austria) Metadata strategy History At Statistics Austria the development of cross-domain metadata systems already began in the early 1970s, with the statistical output database ISIS (Integrated Statistical Information System) which is still in use (see section 2.
Page: 1. Introduction (Canada)
1.1 Metadata strategy One of the primary objectives of the Integrated Metadatabase (IMDB) is to inform users on concepts, methodologies and data accuracy. The IMDB provides the metadata to support the statistical products released by Statistics Canada's D
Page: 1. Introduction (Central Statistical Bureau of Latvia)
Page: 1. Introduction (Croatia) Next section 1.1 Metadata Strategy There were three principal reasons to implement metadata in the Croatian Central Bureau for Statistics (CBS): to standardize definitions across all statistical activities to move the production of statistics closer to th
Page: 1. Introduction (Czech Republic)
1.1. Metadata strategy A redesign of the Statistical Information System (SIS) was launched in the CZSO in 2004. The project has been centrally managed and monitored by the top management of the CZSO. The first important step was design of a new SIS archit
Page: 1. Introduction (Czech Statistical Office)
Page: 1. Introduction (German Federal Statistical Office) Page: 1. Introduction (Germany) 1.1 Metadata Strategy Metadata management has been an issue in the statistical system in Germany for many years. Maybe typical for a federal system, solutions have been found and implemented in isolated areas but they have not been coordinated through a c
Page: 1. Introduction (Germany)
1.1 Metadata strategy Explanation of the overall strategy for managing metadata across the organization. For example, the mandate/programme providing framework for the project, and basic metadata management principles used. 1.2 Current situation Outline t
Page: 1. Introduction (Latvia)
1.1 Metadata strategy The common strategy of CSB is available at link: http://www.csb.gov.lv/csp/content/?cat=4417 In 1992 the Latvian government launched, with the assistance of the Commission of the European Communities, a programme to innovate the Cent
Page: 1. Introduction (Netherlands)
1.1 Metadata strategy Within the framework of the new business architecture (BA) data and metadata should be stored once and reused as much as possible. The aim is to provide for an office-wide service for the storage and the retrieval of data and metadat
Page: 1. Introduction (New Zealand)
Next Section Like many National Statistical Offices around the world, Statistics New Zealand faces a number of 'external' and 'internal' challenges in the years ahead. 'External' challenges include: the need to minimise respondent burden, improve timeline
Page: 1. Introduction (Norway)
Next section 1.1 Metadata Strategy Statistics Norway has in the course of time developed many different metadata systems. This led to the same information being stored several times in several places making the availability of updated and consistent infor
Page: 1. Introduction (Portugal)
Next section 1.1 Metadata Strategy The National Statistical System The National Statistical System (NSS) consists of: The Statistical Council (SC); The National Statistical Institute - Statistics Portugal (SP). The Statistical Council (SC) is the state bo
Page: 1. Introduction (Slovenia)
1.1 Metadata strategy General strategy: Where standards, identifiers, procedures and tools are developed, their use is mandatory within the office. SORS corporate metadata strategy was not published as a single document. However, the authors would like t
Page: 1. Introduction (South Africa)
High-Level Organisational Structure Number of Staff: ± 2,000 Figure 1: Stats SA Organization Chart The Data Management and Information Delivery (DMID) project (magenta shaded box) is located within the Data Management and Technology Division (DMT) The
Page: 1. Introduction (Statistical Office of the Republic of Slovenia)
Page: 1. Introduction (Statistics Austria) Page: 1. Introduction (Statistics Canada) Page: 1. Introduction (Statistics Croatia) Page: 1. Introduction (Statistics Finland) Next section 1.1 Metadata strategy Statistics Finland has not laid down a specific metadata strategy, but a policy definition on the development of a centralised statistical metadata system is included in the agency’s ICT strategy. Statistics Finland inte
Page: 1. Introduction (Statistics Netherlands)
Page: 1. Introduction (Statistics New Zealand) Page: 1. Introduction (Statistics Norway) Page: 1. Introduction (Statistics Portugal) Page: 1. Introduction (Statistics South Africa) Page: 1. Introduction (Statistics Sweden) Page: 1. Introduction (Sweden) Organisation Metadata strategy Statistics Sweden has been active in the metadata field for a long time and has developed several metadata systems and templates that contain a lot of metadata. A metadata model for covering the whole production process was
Page: 1. Introduction (UNIDO)
Organization Details 1. UNIDO was set up in 1966 and became a specialized agency of the United Nations in 1985. As part of the United Nations common system, UNIDO has responsibility for promoting industrialization throughout the developing world, in coope
Page: 1. Introduction (United Nations Industrial Development Organization (UNIDO))
Page: 10. References {scrollbar} ABS, Strategy for End-to-End Management of ABS Metadata, (2003) AMRADS, EU project, Final Report of the Working Group on Metadata, (2003) Booleman, M, Statistics Netherlands, The Dutch Metadata Model, (2004) Cluster of Systems of Metadata for
Page: 2 A conceptual model
A conceptual model 5. GSIM is a conceptual model. Being a conceptual model, the focus of GSIM is on high-level concepts and the relationships between them, and not on implementation details. 6. By design, GSIM does not refer to any specific IT setting or
Page: 2. The role of a statistical metadata system
{scrollbar} What is a statistical metadata system? Metadata can be defined as "data that define and describe other data" whereas statistical metadata are "data about statistical data, and comprise data and other documentation that describe objects in a fo
Page: 2. Modelling the Information and Processes of a Statistical Organization (Albanian Institute of Statistics)
Page: 2. Modelling the Information and Processes of a Statistical Organization (Australian Bureau of Statistics) Page: 2. Modelling the Information and Processes of a Statistical Organization (Central Statistical Bureau of Latvia) Page: 2. Modelling the Information and Processes of a Statistical Organization (Czech Statistical Office) Page: 2. Modelling the Information and Processes of a Statistical Organization (German Federal Statistical Office) Page: 2. Modelling the Information and Processes of a Statistical Organization (Statistical Office of the Republic of Slovenia) Page: 2. Modelling the Information and Processes of a Statistical Organization (Statistics Austria) Page: 2. Modelling the Information and Processes of a Statistical Organization (Statistics Canada) Page: 2. Modelling the Information and Processes of a Statistical Organization (Statistics Croatia) Page: 2. Modelling the Information and Processes of a Statistical Organization (Statistics Finland) Page: 2. Modelling the Information and Processes of a Statistical Organization (Statistics Netherlands) Page: 2. Modelling the Information and Processes of a Statistical Organization (Statistics New Zealand) Page: 2. Modelling the Information and Processes of a Statistical Organization (Statistics Norway) Page: 2. Modelling the Information and Processes of a Statistical Organization (Statistics Portugal) Page: 2. Modelling the Information and Processes of a Statistical Organization (Statistics South Africa) Page: 2. Modelling the Information and Processes of a Statistical Organization (Statistics Sweden) Page: 2. Modelling the Information and Processes of a Statistical Organization (United Nations Industrial Development Organization (UNIDO)) Page: 2. Statistical metadata systems and the statistical business process (Albania) 2.1 Statistical business process model 2.2 Current system(s) 2.3 Costs and Benefits 2.4 Implementation strategy The project is implemented with a step-wise approach. The first phase of project served as a preparatory phase. A business structure survey was
Page: 2. Statistical metadata systems and the statistical business process (Australia)
Next section 2.1 Statistical business process Since GSBPM (Generic Statistical Business Process Model) reached full maturity with the release of V4.0 agreed in April 2009 it has been regarded as the preferred reference model for the statistical business p
Page: 2. Statistical Metadata Systems and the Statistical Business Process (Canada)
2.1 Statistical business process Figure 1 shows where the metadata in the IMDB supports the statistical business process. While the metadata layer extends across all of the phases of the statistical business process, metadata in the IMDB currently support
Page: 2. Statistical metadata systems and the statistical business process (Croatia)
Next section 2.1 Statistical business process The majority of CBS´s statistical surveys are processed in following steps: Planning all activities Survey design and description Data capture and file transfer Validity checking against preset rules and pro
Page: 2. Statistical metadata systems and the statistical business process (Czech Republic)
2.1 Statistical survey life cycle SMS is an integral part of SIS. The main goal of the SMS is to support the statistical business process (collection, production and dissemination of statistical information). The following scheme demonstrates the structur
Page: 2. Statistical metadata systems and the statistical business process (Germany)
2.1 Statistical business process model There are several business models in use in Destatis and the Verbund. The first is a Destatis business process model prepared by the administration department (fig.1). It highlights supporting and management function
Page: 2. Statistical Metadata Systems and the Statistical Business Process (Latvia)
2.1 Statistical business process model The Statistical Business Process Model of CSB, presented in Scheme 2 (SBPM of CSB) defines the business processes needed to produce official statistics at CSB (for the description of quality assessment of processes a
Page: 2. Statistical metadata systems and the statistical business process (Netherlands)
2.1 Statistical business process model The BA principles: A strict distinction is made between the data that are actually processed and the metadata that describe the definitions, the quality and the process activities No regular production takes pl
Page: 2. Statistical metadata systems and the statistical business process (New Zealand)
Next Section 2.1 Statistical business process Statistics New Zealand's business process model is illustrated here. Extensive consultation was undertaken across the organisation to develop the generic Business Process Model (gBPM), including many business
Page: 2. Statistical metadata systems and the statistical business process (Norway)
Next section 2.1 Statistical survey life cycle Our statistical life cycle model is based on the work done by Statistics New Zealand, by Statistics Sweden and by METIS. The phases we are currently using are as follows: 1. Specify Needs, 2. Design, 3. Build
Page: 2. Statistical metadata systems and the statistical business process (Portugal)
Next section 2.1 Statistical business process The life cycle of primary statistical operations is the subject of the Statistical Production Procedures Handbook (in the approval stage), as shown in Figure 2. The processes are divided into sub-processes and
Page: 2. Statistical Metadata Systems and the Statistical Business Process (Slovenia)
2.1 Statistical business process model SORS is one of the offices that are (was) studying processes carefully. Looking back it now, it seems that idea was on the table already in 2002. Top management started to talk about "pillars" of official statistics;
Page: 2. Statistical metadata systems and the statistical business process (South Africa)
The essence of Stats SA's meta-information system is captured by how the organisation uses the metadata. Metadata is used internal to the organisation to enable statistical production processes. This means that metadata is used during various stages of st
Page: 2. Statistical Metadata Systems and the Statistical Business Process (Statistics Finland)
Next section 2.1 Statistical business process model Statistics Finland has adopted a process model based on the GSBPM. We also aim to develop our model according to the lines adopted in GSBPM development. In our model however, we have come up with a solut
Page: 2. Statistical metadata systems and the statistical business process (Sweden)
2.1 Statistical business process model The Swedish business process model is based on the New Zealand model. It is therefore also very closely related to the METIS model with the exception of archive on the process level. It contains 9 processes. 1. Speci
Page: 2. Statistical metadata systems and the statistical business process (UNIDO)
13. Statistical activity of UNIDO started with establishment of Industrial Statistics database in 1977 to meet the internal needs of the organization for an accurate assessment of structure and growth of industrial sector. In terms of the external data so
Page: 2. Statistical Metadata Systems and the Statistical Business Process Page (Austria)
2.1 Statistical business process model Within the framework of the STAT+ project, a model of the statistical life cycle (called the "4-layer-model" because of the four data systems it defines) was elaborated at Statistics Austria in 2002. The model distin
Page: 2. Statistical metadata systems and the statistical business processes
Australia Canada Croatia New Zealand Norway Portugal South Africa Sweden
Page: 3 Understanding GSIM
Understanding GSIM 10. The documentation of GSIM v1.0 has a number of layers. Each layer provides more detail on the model and is targeted at different audiences. There is: A number of brochures which provide a high level introduction to GSIM A Communicat
Page: 3. Users of the statistical metadata system
{scrollbar} A primary challenge for the SMS is to cope with the requirements of diverse groups of metadata users. The use of evolving information and communication technologies has resulted in more users of statistics and a diversification of needs. Effor
Page: 3. Metadata in each phase of the statistical business process (Australia)
Next section 3.1 Metadata Classification The ABS doesn't have a formal "taxonomy" of metadata. One was proposed early in development of the 2003 metadata strategy but it wasn't included in the final document. It was found that discussions about how to "cl
Page: 3. Metadata in each phase of the statistical business process (Croatia)
Next section 3.1 Metadata Classification Croatian Bureau of Statistics developed its own metadata model called CROMETA based on Reference ModelTM from the MetaNet project of Eurostat. It includes also some specific metadata from CBS and has nine groups or
Page: 3. Metadata in each phase of the statistical business process (Czech Republic)
3.1 Metadata Classification Statistical metadata include content oriented and technological metadata. Both groups are needed for design, implementation and running of STs. Content oriented metadata are presented in the following groups: 1. Metadata on s
Page: 3. Metadata in each phase of the statistical business process (Germany)
3.1 Metadata Classification The RDC-metadata system uses a classification that distinguishes between semantic, technical and administrative metadata. Semantic metadata include definitions of variables and other definitions as well as all kinds of methodol
Page: 3. Metadata in each phase of the statistical business process (Netherlands)
3.1 Metadata Classification Metadata is classified according three criteria. The first criterion is when it is developed. Ex ante metadata is developed during the first three phases of the Generic Statistical Business Process Model (GSBPM): Specify needs,
Page: 3. Metadata in each phase of the statistical business process (New Zealand)
Next Section 3.1 Metadata Classification The MetaNet Reference ModelTM (Version 2) categorises types of metadata in the following way: Conceptual Metadata describes the basic idea (concept) behind the metadata object e.g. conceptual data elements, classif
Page: 3. Metadata in each phase of the statistical business process (Norway)
Next section 3.1 Metadata Classification The only metadata classification we have needed in-house so far is the distinction between conceptual and contextual metadata. This probably reflects the fact that we are only just beginning to try to integrate the
Page: 3. Metadata in each phase of the statistical business process (Portugal)
Next section 3.1 Metadata Classification One way of regarding the role that metadata can play is to identify their function in the different statistical processes and respective tasks: Statistical metadata functions: Contextualising data and supporting th
Page: 3. Metadata in each phase of the statistical business process (South Africa)
Metadata are used and/or produced in each phase of the statistical value chain. This strong link between the between the SVC and metadata informs all the development of the metadata subsystem. Stats SA's Statistical Value Chain Statistics South Africa's
Page: 3. Metadata in each phase of the statistical business process (UNIDO)
25. The metadata is classified according to their usage and their role in the statistical production process. The main types of metadata according to this criteria are as follows: Definitional metadata - The definitional metadata refer to metadata that ac
Page: 3. Metadata in each phase of the Statistical Business Process page (Austria)
3.1 Metadata Classification Statistics Austria has no "official" classification of metadata. But during the conceptual work for BASIS 2000+, STAT+ and the integrated metadata system IMS a multidimensional approach - similar to Bo Sundgren's proposal in wo
Page: 3. Statistical metadata in each phase of the statistical business process (Albania)
3.1 Metadata Classification 3.2 Metadata used/created at each phase 3.3 Metadata relevant to other business processes
Page: 3. Statistical Metadata in each phase of the Statistical Business Process (Australian Bureau of Statistics)
Page: 3. Statistical metadata in each phase of the statistical business process (Canada) 3.1 Metadata classification At the heart of any survey or statistical program, and throughout their lifecycle, are the concepts and variables one wants to measure. To promote coherence, these concepts and variables have to be standardized, as well as the
Page: 3. Statistical Metadata in each phase of the Statistical Business Process (Central Statistical Bureau of Latvia)
Page: 3. Statistical Metadata in each phase of the Statistical Business Process (Czech Statistical Office) Page: 3. Statistical Metadata in each phase of the Statistical Business Process (German Federal Statistical Office) Page: 3. Statistical Metadata in Each Phase of the Statistical Business Process (Latvia) 3.1 Metadata Classification The CSB of Latvia doesn't have a formal classification of metadata. However it could be classified as follows (5 groups): 1. Dissemination metadata - all metadata is foreseen for end users, such as classification, data interpre
Page: 3. Statistical Metadata in each phase of the Statistical Business Process (Slovenia)
3.1 Metadata Classification not elaborated 3.2 Metadata used/created at each phase not elaborated 3.3 Metadata relevant to other business processes MFERAC a uniform computerised accountancy system for the implementation of the state budget. SPIS SPIS (off
Page: 3. Statistical Metadata in each phase of the Statistical Business Process (Statistical Office of the Republic of Slovenia)
Page: 3. Statistical Metadata in each phase of the Statistical Business Process (Statistics Austria) Page: 3. Statistical Metadata in each phase of the Statistical Business Process (Statistics Canada) Page: 3. Statistical Metadata in each phase of the Statistical Business Process (Statistics Croatia) Page: 3. Statistical Metadata in Each Phase of the Statistical Business Process (Statistics Finland) Next section 3.1 Metadata Classification 1) Statistical metadata. Statistical metadata consist of :- descriptions and definitions of statistical data and variables- classifications- variable formulas and unit of measurement 2) Statistical data quality. St
Page: 3. Statistical Metadata in each phase of the Statistical Business Process (Statistics Netherlands)
Page: 3. Statistical Metadata in each phase of the Statistical Business Process (Statistics New Zealand) Page: 3. Statistical Metadata in each phase of the Statistical Business Process (Statistics Norway) Page: 3. Statistical Metadata in each phase of the Statistical Business Process (Statistics Portugal) Page: 3. Statistical Metadata in each phase of the Statistical Business Process (Statistics South Africa) Page: 3. Statistical Metadata in each phase of the Statistical Business Process (Statistics Sweden) Page: 3. Statistical metadata in each phase of the statistical business process (Sweden) 3.1 Metadata Classification Statistics Sweden does not use any metadata classification other than the distinction between reference metadata and formalized metadata. The reference metadata are used for descriptions of the production process and for the pu
Page: 3. Statistical Metadata in each phase of the Statistical Business Process (United Nations Industrial Development Organization (UNIDO))
Page: 4 GSIM as a communication tool GSIM as a communication tool 26. A major barrier to effective collaboration within and between statistical organizations has been the lack of common terminology. 27. A 'survey' in Statistics Canada, for example, is a 'survey instance' according to the UNE
Page: 4. Benefits for users
{scrollbar} An effective SMS can provide the following benefits to all users of statistical metadata: Better quality statistical information; Improved interpretability of statistics; Improved quality of metadata; Better discovery, retrieval and exchange o
Page: 4. Statistical Metadata Systems (Albanian Institute of Statistics)
Page: 4. Statistical Metadata Systems (Australian Bureau of Statistics) Page: 4. Statistical Metadata Systems (Central Statistical Bureau of Latvia) Page: 4. Statistical Metadata Systems (Czech Statistical Office) Page: 4. Statistical Metadata Systems (German Federal Statistical Office) Page: 4. Statistical Metadata Systems (Statistical Office of the Republic of Slovenia) Page: 4. Statistical Metadata Systems (Statistics Austria) Page: 4. Statistical Metadata Systems (Statistics Canada) Page: 4. Statistical Metadata Systems (Statistics Croatia) Page: 4. Statistical Metadata Systems (Statistics Finland) Page: 4. Statistical Metadata Systems (Statistics Netherlands) Page: 4. Statistical Metadata Systems (Statistics New Zealand) Page: 4. Statistical Metadata Systems (Statistics Norway) Page: 4. Statistical Metadata Systems (Statistics Portugal) Page: 4. Statistical Metadata Systems (Statistics Sweden) Page: 4. Statistical Metadata Systems (United Nations Industrial Development Organization (UNIDO)) Page: 4. System and design issues Australia Canada Croatia New Zealand Norway Portugal
Page: 4. System and design issues (Albania)
4.1 IT Architecture The physical database model for the Central MetaData Repository is built on MetaPlus from Statistics Sweden. The MetaPlus model have been adjusted to meet the needs for INSTAT. This will mainly be done by merging the adjusted MetaPlus
Page: 4. System and design issues (Australia)
Next section 4.1 IT Architecture ABS Enterprise Architecture harnesses The Open Group Architecture Framework (TOGAF) which recognises domains of business, data, applications and technology architecture. In describing "IT Architecture" below, reference is
Page: 4. System and design issues (Austria)
4.1 IT Architecture One of the cornerstones of Statistics Austria's IT strategy is the use of the database DB2 on the mainframe computer. Web applications run on Linux servers (which are logical partitions of the mainframe). Server programs are developed
Page: 4. System and design issues (Canada)
4.1 IT architecture The IMDB project was initiated in 1998. The ANSI X3.285 Metamodel for the Management of Shareable Data and U.S. Bureau of Census's Corporate Metadata Repository (CMR)[1] model were chosen as the basis for the data model design. The ANS
Page: 4. System and design issues (Croatia)
Next section 4.1 IT Architecture CROMETA METADATA SERVER CROMETA Metadata Server is a comprehensive solution that covers all aspects of a modern metadata repository. The server is not designed for methodologists, statisticians nor systems developers sol
Page: 4. System and design issues (Czech Republic)
4.1 IT Architecture IT architecture of SMS is an integral part of IT architecture of SIS. The SMS is a necessary precondition for all statistical data warehouse operations. Data warehouse will finally become the only place to store all statistical data
Page: 4. System and design issues (Germany)
4.1 IT Architecture Given that the Verbund - explicitly and implicitly - follows a step by step approach, no single, all-encompassing metadata system exists or will exist in the future. Instead the metadata architecture will consist of different independe
Page: 4. System and Design Issues (Latvia)
4.1 IT Architecture Before development and implementation of the system classic Stove Pipe data processing approach with all appropriate technical incompatibilities existed as a consequence of the wide range of technology solutions that were in use. As th
Page: 4. System and design issues (Netherlands)
4.1 IT Architecture 4.2 Metadata Management Tools Data and metadata is stored with the help of Documentum, except for classifications and code lists: they are stored with the help of the Classification Server (a tailor made tool). Technical facilities gua
Page: 4. System and design issues (New Zealand)
Next Section 4.1 IT Architecture The introduction of Service Oriented Architecture (SOA) into Statistics NZ was the culmination of researching industry trends and evaluating those trends against the new technical challenges that were arising in response t
Page: 4. System and design issues (Norway)
Next section 4.1 IT Architecture Statistics Norway's technical solutions shall be built mainly upon the principles of service-oriented architecture. Guidelines on this are presented in Norway's eGovernment plan. All solutions for external users and most s
Page: 4. System and design issues (Portugal)
Next section 4.1 IT Architecture Each subsystem in the integrated metadata system has a similar architecture: a database, two Web applications (one for consultation and the other for management) and a view that provides metadata to be reused by other syst
Page: 4. System and design issues (Slovenia)
4.1 IT Architecture SORS is a user-oriented organisation which is organised with an emphasis on processes and the quality and protection of data provided by reporting units. Information technology (IT) must follow the mission, vision and values of SORS, a
Page: 4. System and design issues (South Africa)
The design of the system will conform to Stats SA's Enterprise architecture. One of the main components of this enterprise architecture is the IT architecture. The software architecture of all the applications developed in the ESDMF is dictated by the IT
Page: 4. System and Design Issues (Statistics Finland)
Next section 4.1 IT Architecture Statistics Finland's common metadata system is implemented according to the principles of service-based architecture. Services meeting the needs of different user groups and client systems have a key role in the service-ba
Page: 4. System and design issues (Sweden)
4.1 IT Architecture The IT architecture at Statistics Sweden aims at maintaining a comprehensive Swedish statistics system. It should be focusing on: Consistency, structured and well documented data warehouse(s) Use registers when possible Minimize dupl
Page: 4. System and design issues (UNIDO)
44. The overall structure of the Integrated Statistical Development Environment is presented in Figure 2. The system utilizes a 3-tier architecture build on .Net technology. The data and metadata are stored in centralized database, and the user interacts
Page: 5 Implementing GSIM at a business level
Implementing GSIM at a business level 37. GSIM can either be mapped to an existing information model or adopted as is by a statistical organization. The steps to adopting GSIM are outlined in the following two sections. Please see the GSIM Specification f
Page: 5. Vision, strategy and implementation
{scrollbar} The focus of this section is on the preparation of a corporate SMS vision, related planning and on the major characteristics of a metadata management framework and management strategy. SMS vision The vision should clearly state the goals or ai
Page: 5. Organizational and workplace culture issues
Australia Canada Croatia New Zealand Norway Portugal
Page: 5. Organizational and Workplace Culture Issues (Latvia)
5.1 Overview of roles and responsibilities There are several organizational units involved in development and maintenance of metainformation systems. The information with regard to the roles and responsibilities of the staff is available in Annual Report
Page: 5. Organizational and workplace culture issues (Albania)
5.1 Overview of roles and responsibilities Subject matter statisticians responsible for the content and for documenting the statistical activities in SCBDOC template IT developers responsible for system architects, web designing and database management Me
Page: 5. Organizational and workplace culture issues (Australia)
Next section 5.1 Overview of roles and responsibilities Initiation of IMTP in February 2010 led to significant adjustment of roles and responsibilities within the ABS. The 2003 metadata management strategy had stated that, in terms of governance Metadata
Page: 5. Organizational and workplace culture issues (Austria)
5.1 Overview of roles and responsibilities Statistical projects are planned and compiled in four subject matter divisions. The survey manager is responsible to compile a standard-documentation. When electronic questionnaires are used for raw data collecti
Page: 5. Organizational and workplace culture issues (Canada)
5.1 Overview of roles and responsibilities The Integrated Metadatabase (IMDB) is Statistics Canada's corporate metadatabase initially put in place to allow proper interpretability of the data released to public. The development and maintenance of the IMDB
Page: 5. Organizational and workplace culture issues (Croatia)
Next section 5.1 Overview of roles and responsibilities In CBS there are 25 subject-matter departments with statisticians responsible for respective statistical surveys. They manage statistical surveys according the periodicity and they are responsible fo
Page: 5. Organizational and workplace culture issues (Czech Republic)
5.1 Overview of roles and responsibilities The organisation model of SMS project management must be presented in the context of the model of SIS Redesign management. Organisation structure of SMS is composed of the Project Steering Committee (PSC), Task
Page: 5. Organizational and workplace culture issues (Germany)
5.1 Overview of roles and responsibilities A central metadata unit has been established. Currently the central metadata unit works on the coordination of the different parts in the organisation(s) that deal with metadata. Metadata used by Destatis' output
Page: 5. Organizational and workplace culture issues (Netherlands)
5.1 Overview of roles and responsibilities Apart from the general default roles that Documentum provides (such as Author and Owner) DSC distinguishes Metadata Administrator (responsible for, e.g., the maintenance of office-wide metadata standards, such as
Page: 5. Organizational and workplace culture issues (New Zealand)
Next Section 5.1 Overview of metadata audiences and use of metadata To ensure that metadata is relevant and useful a high level analysis of the audiences of the metadata environment has been completed. The audiences have also been identified using severa
Page: 5. Organizational and workplace culture issues (Norway)
Next section 5.1 Overview of roles and responsibilities Subject matter statistician, survey manager, metadata manager, senior advisers in standards, IT developers, system architects, solution architects and web designers are all important roles in metadat
Page: 5. Organizational and workplace culture issues (Portugal)
Next section 5.1 Overview of roles and responsibilities The metadata system user profiles that interact with the life cycle of surveys are as follows: Metadata system manager This job has thus far been done by the metadata system manager, whose duties are
Page: 5. Organizational and workplace culture issues (Slovenia)
5.1 Overview of roles and responsibilities Component Responsible SORS unit Classification server General methodology and standards Registry of surveys, survey instances, working plan General methodology and standards Annual programme of statistical survey
Page: 5. Organizational and workplace culture issues (South Africa)
Roles in metadata/statistical lifecycle management In order to understand the user requirements, we engaged the survey divisions as pilot groups. We involved them in verifying our understanding of the requirements, which was used to design and implement t
Page: 5. Organizational and Workplace Culture Issues (Statistics Finland)
Next section 5.1 Overview of roles and responsibilities A high-level organisation structure map on the Statistics Finland website:http://tilastokeskus.fi/org/tilastokeskus/organisaatio_en.html In connection with Statistics Finland's organisational change
Page: 5. Organizational and workplace culture issues (Sweden)
5.1 Overview of roles and responsibilities Statistics Sweden has decentralized responsibility for registering metadata except for the statistical databases for which registering metadata is centralized. Guidelines and follow-up There is a general directo
Page: 5. Organizational and workplace culture issues (UNIDO)
51. No specialized metadata roles are necessary, since the processing of the metadata is tightly coupled with the processing of that data and the responsibilities are organized by country, i.e. each statistical staff member is responsible for a given numb
Page: 5. System and design issues (Albanian Institute of Statistics)
Page: 5. System and design issues (Australian Bureau of Statistics) Page: 5. System and design issues (Central Statistical Bureau of Latvia) Page: 5. System and design issues (Czech Statistical Office) Page: 5. System and design issues (German Federal Statistical Office) Page: 5. System and design issues (Statistical Office of the Republic of Slovenia) Page: 5. System and design issues (Statistics Austria) Page: 5. System and design issues (Statistics Canada) Page: 5. System and design issues (Statistics Croatia) Page: 5. System and design issues (Statistics Finland) Page: 5. System and design issues (Statistics Netherlands) Page: 5. System and design issues (Statistics New Zealand) Page: 5. System and design issues (Statistics Norway) Page: 5. System and design issues (Statistics Portugal) Page: 5. System and design issues (Statistics South Africa) Page: 5. System and design issues (Statistics Sweden) Page: 6 Implementing GSIM at a technical level Implementing GSIM at a technical level 54. In order to effectively implement GSIM at a technical level, it should be implemented at a business level first. 55. An organization may choose to implement GSIM as the information model that defines their operat
Page: 6. Core principles for metadata management
{scrollbar} This section focuses on the management of statistical metadata in the SMS framework. It presents the principles to be taken into the consideration when preparing the SMS vision, global architecture and when implementing the SMS. The principles
Page: 6. Attachments and links (UNIDO)
1. Erwin-full.pdf: Data Model of INDSTAT database 2. Malaysia.xls - An example of filled-in Questionnaire in English (2007) 3. Madagascar.xls - An example of filled-in Questionnaire in French (2007) 4. Puerto Rico.xls - An example of filled-in Questionnai
Page: 6. Lessons learned (Albania)
Page: 6. Lessons learned (Australia) 6.1 Lessons Learned While technology is a vital enabler, metadata management should be driven, governed and presented as primarily a business issue rather than a technical issue. This requires proponents of metadata management focus first on business outc
Page: 6. Lessons learned (Austria)
6.1 It is not a new discovery that the subject of statistical metadata is an extremely complex one. Even now, almost three and a half decades after Bo Sundgren first used the term, different individuals may still mean quite different things or place empha
Page: 6. Lessons learned (Canada)
The progress made during the variable documentation phase, as well as with the methodology and data accuracy documentation phase, leads us to conclude that it is more efficient to start documenting the metadata right at the outset of any new survey design
Page: 6. Lessons learned (Croatia)
Next section Lessons learned The most important questions are still unanswered since the central metadata management system is not deployed yet. The complete ISIS information system in general and CROMETA system in particular will force big changes upon t
Page: 6. Lessons learned (Czech Republic)
6.1 Some most important experiences and conclusions from our practice: SMS strategy in terms of contents and methodology must be fully in the responsibility of the statistical office, SMS design and implementation should be organized in the multidisciplin
Page: 6. Lessons learned (Germany)
Metadata management is a communication challenge. We found two issues were particularly difficult to communicate: Metadata management is tricky. Statistical data is inherently volatile. For any given data, an endless number of transformations are possible
Page: 6. Lessons Learned (Latvia)
The list mentioned below provides key points of "lessons learned" from planning developing and maintaining metadata management system: Design of the new information system should be based on the results of deep analysis of the statistical processes and
Page: 6. Lessons learned (Netherlands)
6.1 Small projects that deliver in short cycles; Use of external off-the-shelf software is possible without too much adjustments in specs; Keep in control of outsourced development activities; It is a challenge to formulate a convincing business case for
Page: 6. Lessons learned (New Zealand)
Next Section 1. Apart from 'basic' principles, metadata principles are quite difficult. To get a good understanding of and this makes communication of them even harder. As it is extremely important to have organisational buy-in, the communication of the o
Page: 6. Lessons learned (Norway)
Next section Lessons learned Top management support is essential. Make a metadata strategy. It is important that we can refer to formal documents like the metadata- and IT-strategy (which has been approved by the board of directors) in our metadata work.
Page: 6. Lessons learned (Portugal)
Back to table of contents (Portugal) Lessons learned We have certainly learned some lessons from the implementation of the integrated metadata system, which has been more systematic in the last six years, some because we have seen that our options have ha
Page: 6. Lessons learned (Slovenia)
1. Participation in pilot projects enables less experienced employees to gain the experience necessary for independent work. Due to the similarity of the statistical process, it is very important for the IT personnel in SORS to gain experience from other
Page: 6. Lessons learned (South Africa)
6. Attachments & Links Documents to be attached: Survey Metadata Standard Template (Survey Metadata Capturing Tool_v0.10.doc) Flow Chart (Flowchart4(4).vsd) Web page of the Metadata Capture Tool in MHT format (Summary of Survey Metadata Record.mht) SASQAF
Page: 6. Lessons Learned (Statistics Finland)
Next section 6.1 A metadata system complying with the uniform architecture is not just a technological renewal, but its implementation will require change in work procedures, responsibilities and organisation of tasks. The change in work procedures above
Page: 6. Lessons learned (Sweden)
When building metadata systems: Involve different types users at an early stage, we used different groups for: Methodologists Subject matter statisticians System developers Make it a content, not an IT -driven project Make simple prototypes as early as po
Page: 6. Lessons learned (UNIDO)
Page: 6. Organizational and workplace culture issues (Albanian Institute of Statistics) Page: 6. Organizational and workplace culture issues (Australian Bureau of Statistics) Page: 6. Organizational and workplace culture issues (Central Statistical Bureau of Latvia) Page: 6. Organizational and workplace culture issues (Czech Statistical Office) Page: 6. Organizational and workplace culture issues (German Federal Statistical Office) Page: 6. Organizational and workplace culture issues (Statistical Office of the Republic of Slovenia) Page: 6. Organizational and workplace culture issues (Statistics Austria) Page: 6. Organizational and workplace culture issues (Statistics Canada) Page: 6. Organizational and workplace culture issues (Statistics Croatia) Page: 6. Organizational and workplace culture issues (Statistics Finland) Page: 6. Organizational and workplace culture issues (Statistics Netherlands) Page: 6. Organizational and workplace culture issues (Statistics New Zealand) Page: 6. Organizational and workplace culture issues (Statistics Norway) Page: 6. Organizational and workplace culture issues (Statistics Portugal) Page: 6. Organizational and workplace culture issues (Statistics South Africa) Page: 6. Organizational and workplace culture issues (Statistics Sweden) Page: 6. Organizational and workplace culture issues (United Nations Industrial Development Organization (UNIDO)) Page: 7 Examples of Implementing GSIM Examples of Implementing GSIM 57. GSIM v1.0 is the first public release of GSIM. As such, statistical organizations have not yet had the opportunity to implement it. However, a number of organizations have started to plan how they will use GSIM. 58. This
Page: 7. Corporate governance models for metadata management
{scrollbar} General considerations It is not sensible to prescribe an ideal model for corporate governance of metadata. Every statistical organization works under different legislation, organizational arrangements, organization culture, business rules and
Page: 7. Attachments and links (Albania)
Page: 7. Attachments and links (Austria) "IMS (Integrated Metadata System) - An Architecture for an Expandable Metadata Repository to Support the Statistical Life Cycle" (working paper 5 of the 2007 METIS workshop): http://www.unece.org/stats/documents/ece/ces/ge.40/2007/wp.5.e.pdf "e-Quest: A
Page: 7. Attachments and Links (Latvia)
Handbook on Design And Implementation Of Business Surveys. Edited by Ad Willeboordse. October 1997; Guidelines for the modeling of statistical data and metadata. UNITED NATIONS. Conference of European statisticians. Methodological material. Geneva, 1995;
Page: 7. Attachments and links (Netherlands)
7.1 Talling Gelsema, Considerations for the design of the data service centre metadata model, November 2009.
Page: 7. Attachments and links (Slovenia)
Annex 1: Organisation scheme (SORS) Annex 2: Process model (SORS) Annex 3: Functional scheme with sub processes covered in the ISIS (SORS) Annex 4: TQMStrategy 2006 (SORS) Annex 5: Quality of statistics with links (SORS) Annex 6: SORS PRIORITIES 2009 (SOR
Page: 7. Attachments and Links (Statistics Finland)
7.1 Introduction to Statistics Finland’s strategy, finances and operating environment: http://tilastokeskus.fi/org/index_en.html
Page: 7. Attachments and links (Sweden)
MetaPlus online https://www.h2.scb.se/metadata/Default.aspx?amne=AM The classification data base https://www.h2.scb.se/metadata/klassdb.aspx The statistical database http://www.ssd.scb.se/databaser/makro/start.asp?lang=2
Page: 7. Attachments and links page (Canada)
Page: 7. Lessons learned (Australian Bureau of Statistics) Page: 7. Lessons learned (Central Statistical Bureau of Latvia) Page: 7. Lessons learned (Czech Statistical Office) Page: 7. Lessons learned (German Federal Statistical Office) Page: 7. Lessons learned (Statistical Office of the Republic of Slovenia) Page: 7. Lessons learned (Statistics Austria) Page: 7. Lessons learned (Statistics Canada) Page: 7. Lessons learned (Statistics Croatia) Page: 7. Lessons learned (Statistics Finland) Page: 7. Lessons learned (Statistics Netherlands) Page: 7. Lessons learned (Statistics New Zealand) Page: 7. Lessons learned (Statistics Norway) Page: 7. Lessons learned (Statistics Portugal) Page: 7. Lessons learned (Statistics Sweden) Page: 7. Summary of attachments and links (Croatia) 7.1 Attachments 7.2 Links Type your links here.
Page: 7. Summary of attachments and links (New Zealand)
Back to table of contents 7.1 Attachments 7.2 Links Type your links here.
Page: 7. Summary of attachments and links (Norway)
Back to Statistics Norway table of contents 7.1 Attachments 7.2 Links Home page for Statistics Norway www.ssb.no Organisational structure http://www.ssb.no/english/about_ssb/organisationmap/ Strategy documents http://www.ssb.no/english/about_ssb/strategy/
Page: 8. Case studies and experiences
{scrollbar} Case studies about current metadata management systems and processes are being collected from statistical offices for publishing within the Common Metadata Framework Part D. They are published and maintained using wiki software that allows sta
Page: 8. Attachments and links (Central Statistical Bureau of Latvia)
Page: 8. Attachments and links (Statistical Office of the Republic of Slovenia) Page: 8. Attachments and links (Statistics Austria) Page: 8. Attachments and links (Statistics Canada) Page: 8. Attachments and links (Statistics Finland) Page: 8. Attachments and links (Statistics Norway) Page: 8. Attachments and links (Statistics Sweden) Page: 8. Attachments and links (United Nations Industrial Development Organization (UNIDO)) Page: 9. Glossary of terms and abbreviations {scrollbar} Corporate metadata repository A database system that stores metadata records for an organization or group of organizations. Metadata Data that define and describe other data. Statistical metadata are defined as data about statistical data, and
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APage: A Brief History of Metadata (in the ABS)Introduction The following document charts the historical arc of metadata management strategies and developments pursued by the ABS, particularly over the past two decades, culminating in the strategy for transformation of statistical information manageme
Page: A. Introduction
4. There is a widespread interest across statistical organizations in being able to trace how statistical information (for example, data and metadata) "flow" through statistical business processes (into processes and out of processes). Interested parties
Page: A. GSIM Extension Methodology
Namespaces 113.As part of the GSIM v1.0 release, the Enterprise Architect file which contains the UML models will be released. In this file there are five 'namespaces' (or 'packages') – one for each of the GSIM Groups. 114. Any organization extending
Page: A. Introduction
132. GSIM must be implementable: In order to support the implementation of the GSIM reference framework, many known standards and tools have also been examined, to ensure that the reference framework is complete and useful in this respect. This sect
Page: ABS IMTP
The ABS Information Management Transformation Program: A statistical metadata perspective Introduction This document provides a brief introduction to the Information Management Transformation Program (IMTP) which is currently being undertaken by the Austr
Page: ABS MRR PoC Demonstration
The recordings accessible via this page are working materials. ABS intends to make an improved set of recordings of the PoC available (possibly via YouTube) in future. At that time the links on this page will be redirected to those recordings. As an int
Page: ABS Prices processes mapped to GSBPM
At the moment, there is a project within the Australian Bureau of Statistics called the Prices System Improvement Project (PSIP). The objective of PSIP is to undertake an improvement of the Prices business processes and the system that supports the Prices
Page: Add "dissemination services" sub-process
The model has no sub-process for providing dissemination services. GSIM describes objects for such services, but no existing GSBPM sub-process has these objects as inputs and outputs. For more context see our METIS paper.
Page: Agenda for SDMX DDI Dialogue 2011.05.02
Dear colleagues, I am sending this message to those who have already participated in the dialogue, expressed an interest to join, or have been proposed by others. The third session of the informal SDMX / DDI Dialogue is scheduled to take place next Monday
Page: Albanian Institute of Statistics
Page: Albanian Institute of Statistics (Archived) Organization Name Institute of Statistics, Republic of Albania Total number of staff The total number of staff is 169: 97 workers in Headquarter office and 74 workers in regional offices (in county level). Organization structure Contact Person for metadat
Page: Annex A. Extending the model
109. One of the GSIM design principles is that GSIM can easily be adapted and extended to meet users' needs. It is expected that some implementers may wish to extend GSIM, by adding detail and indicating which information objects are used, and exactly
Page: Annex A_ Scenarios
Annex A_ Scenarios 95. The examples offered in this annex are not taken from concrete implementations of GSIM (as these don't currently exist). They have been devised with a teaching goal in mind. They illustrate how some of the main activities of a stati
Page: Annex A Extending the model
Note: To translate this paper into over 50 languages, please see the Automatic translation option at the top of the screen Annex A Extending the model 109. One of the GSIM design principles is that GSIM can easily be adapted and extended to meet user
Page: Annex B. Influence of existing standards
Page: Annex B_ Template for Case Study Annex B Template for Case Study GENERIC STATISTICAL INFORMATION MODEL (GSIM) IMPLEMENTATION CASE STUDY < COUNTRY / ORGANIZATION NAME> This template was created to guide the content and format of case studies on the implementation of the Generic Statistica
Page: Annex B Influence of existing standards
Note: To translate this paper into over 50 languages, please see the Automatic translation option at the top of the screen Annex B Influence of existing standards A. Introduction 132. GSIM must be implementable: In order to support the implementati
Page: Annex C. Glossary
Object Group Definition Explanatory Text Synonyms Acquisition Activity Business The set of executed processes and the actual resources required as inputs and produced as outputs to acquire data about a given Population for a particular reference period. I
Page: Annex C Glossary
Word and PDF versions of document AnnexC Glossary _.docx AnnexC Glossary _.pdf Note: To translate this paper into over 50 languages, please see the Automatic translation option at the top of the screen A Object Group Definition Explanatory Text Synonyms
Page: Annex D UML class diagrams and object descriptions
Annex D. UML class diagrams and object descriptions.docx Word version of this section of document only
Page: Annex D. UML class diagrams and object descriptions
Page: Archive - Old Versions of the Structure Diagram Version 1.0 (14/04/2009) Version 2.0 (21/05/2009) Version 3.0 (22/06/2009) Version 4.0 (30/06/2009) Copy of latest version (v5.0)
Page: Archived Use cases
Archived
Page: Australian Bureau of Statistics
Page: Australian Bureau of Statistics (Archived) Organization Name Australian Bureau of Statistics Website http://www.abs.gov.au Organization Chart ABS Organisation Chart as at June 2011.pdf Total number of staff 3400 (approx) 47% in Central Office (Canberra) 53% across 8 state/territory offices Contac
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BPage: B. Business Group11. The Business group is used to capture the designs and plans of Statistical Programs. This includes the identification of a Statistical Need, the Acquisition, Production and Dissemination Activities that comprise the Statistical Programs and the evalua
Page: B. Generic Statistical Business Process Model (GSBPM)
134. GSBPM provides descriptions of business processes that can occur throughout the statistical production process. It is a framework for categorizing processes. In order to describe a process in a level of actionable detail, more information is needed.
Page: B. Administrative Attributes
127. GSIM does not model the information used by statistical organizations to administer and maintain their metadata - there are too many potential differences. Such administrative attributes are also very dependent on implementation, and GSIM is a co
Page: Background Documents
Overview History of Key Initiatives Statistical Concepts SDMX MCV SDMX MCV ontology Technical Standards Introduction to DDI for NSIs SDMX DDI and SDMX; complementary not competing standards Methodological Guidelines and Recommendations CORE Information Mo
Page: Background to GSIM
A set of draft working notes are available which summarise background information related to this initiative. These working notes cover the background to GSIM the expected relationship between GSIM and standards such as SDMX and DDI-L the context of GSIM
Page: Base Group
Base Artefacts Class Diagram The Identifiable Artefact has three associations to Contextual String – one for each of name, description, and documentation. The value in the Contextual String is given a context by the Context Key which can be Type or Langua
Page: Best Practices in Designing Websites for Dissemination of Statistics
Name and version: Best Practices in Designing Websites for Dissemination of Statistics (United Nations and Economic Commission for Europe, Conference of European Statisticians, Methodological Material) Alternative name: Best Practices in Designing Website
Page: Brochures
Word versionGSIM Brochure.docx Word VersionGSIM Brochure VADIM.docx Standards Based Statistical Production.docx Modernizing Statistical Production and Services.docx
Page: Business case for applying DDI & SDMX
Initial (incomplete) draft An initial draft is now available Subsection 4.4 and Section 5 are incomplete in the initial draft, although the document broadly identifies the planned content in both cases. The draft is too lengthy at the moment. Once partici
Page: Business Group
Information Request Class Diagram An organization will react and change due to a variety of needs. In simple terms, these may be divided into at least two types of Statistical Need: an Information Request or an Environment Change. When an organization rec
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CPage: C. Data Documentation Initiative (DDI)139. The DDI Alliance supports the development of the GSIM information model and finds many parallels between the model and the DDI Lifecycle specification. The DDI Alliance is interested in working closely with the GSIM group to extend the modeling e
Page: C. Production Group
30. The Production group is used to describe each step in the statistical process, with a particular focus on describing the inputs and outputs of these steps. A business process can be specified in terms of: The Process Steps which need to be undertaken
Page: Central Statistical Bureau of Latvia
Page: Central Statistical Bureau of Latvia (Archived) Organization name Central Statistical Bureau of Latvia (CSB) http://www.csb.gov.lv/ Total number of staff The total number of employees in 2010 is 573. (At the beginning of 2009 there were 601 employess. 23% of all employees work in the regional structura
Page: Comments from Sergio concerning links with Dublin Core and RDF
A note about expressing Dublin Core in RDF "The semantics of Dublin Core is expressed in RDF and is maintained by an international, cross-disciplinary group of professionals from librarianship, computer science, text encoding, the museum community, and ot
Page: Common Warehouse Metamodel - ISO IEC 19504
Name and version: Common Warehouse Metamodel Alternative name: CWM Valid: From March 2004 Description: Specification for the metadata in support of exchange of data between tools. Intended use: As a means for recording the metadata to achieve data exchan
Page: Communication Resources
GSBPM Presentation Current version (August 2009) Some ideas on communicating the GSBPM This short paper from CSO Ireland (Joe Treacy) contains some ideas on how over-arching / management processes can contribute to building greater awareness of the GSBPM.
Page: Complete case study (Australia)
1. Introduction 2. Statistical metadata systems and the statistical business process 3. Metadata in each phase of the statistical business process 4. System and design issues 5. Organizational and workplace culture issues 6. Lessons learned
Page: Complete case study (Croatia)
1. Introduction 2. Statistical metadata systems and the statistical business process 3. Metadata in each phase of the statistical business process 4. System and design issues 5. Organizational and workplace culture issues 6. Lessons learned
Page: Complete case study (Germany)
1. Introduction 2. Statistical metadata systems and the statistical business process 3. Metadata in each phase of the statistical business process 4. System and design issues 5. Organizational and workplace culture issues 6. Lessons learned
Page: Complete case study (New Zealand)
1. Introduction 2. Statistical metadata systems and the statistical business process 3. Metadata in each phase of the statistical business process 4. System and design issues 5. Organizational and workplace culture issues 6. Lessons learned
Page: Complete case study (Norway)
Page: Complete case study (Portugal) 1. Introduction 2. Statistical metadata systems and the statistical business process 3. Metadata in each phase of the statistical business process 4. System and design issues 5. Organizational and workplace culture issues 6. Lessons learned
Page: Complete case study (Slovenia)
Organization Name Statistical Office of the Republic of Slovenia (SORS) Ljubljana, Vozarski pot 12 SI- 1000 Ljubljana SLOVENIA Total number of staff Situation at SORS end February 2009: there are 386 employees. 198 of them have graduate or post-graduate
Page: Concepts Group
Concept-Population Inheritance Class Diagram A particular characteristic about a Population is described by a Variable. For example, the Concept person in the Population of adult in Netherlands can in a survey be a unique combination of age, sex, income,
Page: Contents
Common Metadata Framework Part A: Statistical Metadata in a Corporate Context Foreword 1. Introduction 2. The role of a statistical metadata system 3. Users of the statistical metadata system 4. Benefits for users 5. Vision, strategy and implementation 6.
Page: Core Group
Members Franck Cotton, INSEE Dan Gillman, US Bureau of Labor Statistics Arofan Gregory, Metadata Technology Alistair Hamilton, ABS Thérèse Lalor, ABS Juan Muñoz Lopez, INEGI Achim Wackerow, GESIS Adam Wronski, Eurostat Steven Vale, UNECE Notes of Meetings
Page: Corporate Metadata Repository - CMR
Name and version: Corporate Metadata Repository Model Alternative name: CMR Valid: From 1998 Description: This is a statistical metadata model that integrates a develpomental version of edition 2 of ISO/IEC 11179 and a business data model derivable from t
Page: CRISTAL Model
Name and version: Cristal Alternative name: Valid: From January 2009 Description: Generic model for complex structuring and restructuring of hierarchical classification structures and classification code systems. The classification objects such as categor
Page: Czech Statistical Office
Page: Czech Statistical Office (Archived) Organization Name Czech Statistical Office(CZSO) Total number of staff Total staff of the CZSO: 1480 persons Approx. 650 employees are in headquarters in Prague, the other employees are in regional data processing departments outside Prague (6 locations),
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DPage: D. Concepts Group62. The GSIM Concepts Group contains sets of information objects that describe and define the terms used when talking about real-world phenomena that the statistics measure in their practical implementation. 63. The information objects in this group are u
Page: D. Statistical Data and Metadata eXchange (SDMX)
Relationship with GSIM Business Group 158. In general, SDMX does not cover explicitly the constructs in the Business group. However, the SDMX 'Metadata Structure Definition' and related 'Metadata Set' are used to describe and to provide quality, methodolo
Page: Data and Metadata Reporting and Presentation Handbook
Name and version: Data and Metadata Reporting and Presentation Handbook (OECD) Alternative name: none Valid: From February 2007 Description: The Data and Metadata Reporting and Presentation Handbook provides a comprehensive reference set of internationa
Page: Data Documentation Initiative - DDI
Name and version: Data Documentation Initiative (DDI), version 3.1 Alternative name: [none] Valid: From October 2009 (version 3.1) Description: The Data Documentation Initiative is a standard for technical documentation describing social science data. The
Page: Data Quality Assurance Framework - DQAF, and Special Data Dissemination Standard - SDDS
Name and version: Data Quality Assurance Framework Alternative name: DQF Valid: From July 2003 Description: The DQAF is used for comprehensive assessments of national data quality. It covers institutional environments, statistical processes, and character
Page: Destatis Process Model
Page: Distinction between "work" processes and "change" processes There is a grouping of the Level 1 processes GSBPM users will understand, but which does not form part of the model. GSBPM processes 4 through 7 (Collect, Process, Analyze, Disseminate) form what we might call the “work” process of the statistical office.
Page: Documentation for the GSIM Sprint 1
Context Documents GSIM Stakeholder Map GSIM Metasystem Background Documents Sprint Documents Program for Sprint Participants in Sprint 1 Sprint Participant Expectations Indicative Plan for GSIM Sprint 1 Participant Profile Information Update 1 Information
Page: Dublin Core
Name and version: Dublin Core Metadata Element Set, version 1.1 Alternative name: Dublin Core Valid: From January 2008 Description: The Dublin Core Metadata Element Set is a vocabulary of fifteen generic properties useful for describing a wide range of r
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EPage: E. ISO_IEC 11179169. ISO/IEC 11179 is a standard for describing and managing the meaning and representation of data. It specifies the kind and quality of metadata necessary to describe data. The GSIM Concepts Group contains a terminological description of data. This
Page: E. Structures Group
84. The GSIM Structures Group contains sets of information objects that describe and define the terms used in relation to data and their structure. Like the information objects in the Concepts Group, the information objects in this group are used as Proce
Page: Enterprise Architect file
Gsimv1_0 (2).EAPEnterprise Architect file This file is for users who want to fully explore the UML model. You need to have the Enterprise Architect program installed. Extracts from the UML can be found in Annex D of the GSIM Specification Extracting f
Page: ESS Quality and Performance Indicators
Name and version: Alternative name: Valid: Description: Intended use: Maintenance organization: ISO Standard Number: References: Relationships to other standards: Format: Language: Template last updated / validated:
Page: ESS Standard for Quality Reports (ESQR)
Name and version: ESS Standard for Quality Reports (2009 edition) Alternative name: ESQR Valid: From 2009 Description: The general aim of this Standard (ESQR) is to provide recommendations for comprehensive quality reporting for a full range of statistic
Page: Existing resources related to the relationship between SDMX and DDI
The following list is recognised as incomplete. DDI and SDMX: Complementary, Not Competing, Standards DDI/SDMX Workshop Wiesbaden, Germany, June 18th 2008 (in particular from Slide 108) Exploring the relationship between DDI, SDMX and the Generic Statisti
Page: eXtensible Business Reporting Language - XBRL
Name and version: eXtensible Business Reporting Language - XBRL, version 2.1 Alternative name: XBRL Valid: From December 2003 Description: XBRL is the language for e-communication of business and financial data that is revolutionising business reporting
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FPage: F. Base Group103.The GSIM Base Group consists of several information objects that can be seen as the fundamental building blocks that support many of the other information objects and relationships in the model. These information objects form the nucleus for the appli
Page: F. ISO 704
174. Both GSIM and 11179 base their description of data on the principles laid out in ISO 704. However, GSIM does a more careful job of making sure these principles are followed precisely. In GSIM, Populations, Variables, and Categories (called a prop
Page: Foreword
{scrollbar} The United Nations Economic Commission for Europe (UNECE) is pleased to present this publication, Part A of the Common Metadata Framework. The development of a common framework for statistical metadata was initiated by the 2004 Joint UNECE/Eur
Page: Further Reading
GSIM : An innovative collaboration which facilitates international collaboration What is an information model? Information Modelling - from design to implementation (1999)Reference Model What is a Reference Model?Reference Model.pdf Other industrie
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GPage: G. Neuchâtel Terminology for Classifications 176. A statistical classification is often described as a tool that is used to handle and structure objects systematically into categories in the production of statistics http://unstats.un.org/unsd/class/intercop/expertgroup/2011/AC234-6.PDF. Neuchâtel
Page: Generic Statistical Business Process Model
Name and version: Generic Statistical Business Process Model, Version 4.0 Alternative name: GSBPM Valid: From April 2009 Description: The GSBPM provides a framework to describe the statistical production process in terms of standard components (phases an
Page: Generic Statistical Business Process Model Paper
English GSBPM v4.0.doc GSBPM Final.pdf GSBPM.ppt(ISI Conference, August 2009) French* Modèle générique du processus de production statistique - v4.0.doc Modèle générique du processus de production statistique - v4.0.pdf *Translation kindly provided by I
Page: Generic Statistical Information Model (GSIM)
Reader Guides can be found at the bottom of the page Brochures Brochures This page contains a number of brochures aimed at introducing GSIM and the work of the High Level Group for the Modernisation of Statistical Production and Services. GSIM Com
Page: German Federal Statistical Office
Page: German Federal Statistical Office (Archived) Organization Name Federal Statistical Office (Destatis), Germany Total number of staff about 2700 (as of January 2007, 27.8% part time employees. Regional (Länder) offices are entirely independent organisations and have their own figures. There is no offi
Page: GIS ISO 19115
Name and version: ISO 19115:2003 Alternative name: Geographic information - Metadata Valid: From May 2003. Latest amendment 2006 (ISO 19115:2003/Cor 1:2006). Extensions for imagery and gridded data 2009 (ISO 19115-2:2009). Description: ISO 19115:2003 defi
Page: Google Analytics
Page: GSBPM and Process Quality Management Please add comments / ideas / links and resources Survey on process-related metadata ESS survey questionnaire on process-related metadata - part of Eurostat's work for implementing the ESS vision on better harmonisation and integration of statistical busi
Page: GSBPM discussion forum
Comments or questions about the GSBPM? Ideas for revision? Experiences to share? Use these discussion forums to share your thoughts. You need to be logged in to view and participate in the forums. To request a username and password contact support.stat
Page: GSBPM Notes of Meetings
Meeting Minutes : Task Force on the Further Development of the GSBPM Note: The work of this Task Force ended with the METIS Workshop in October 2011. 9th teleconference 2cd September 2011 Present: Alistair,August, Dan, Hakan, Jenny, Klas, Mats, Sergio, S
Page: GSIM and standards
GSIM and other standards DDI Presentation at European DDI conference - Bergen, December 2012 SDMX Presentation at SDMX Technical Working Group - December 2012 11179 and Neuchatel Group GSIM Object 11179 Neuchatel Concepts Classification Cl
Page: GSIM Brainstorming
This page is for ideas and suggestions regarding the nature, contents and purpose of the Generic Statistical Information Model (GSIM). Please feel free to add your thoughts, to help ensure nothing important is overlooked during the next stages of GSIM dev
Page: GSIM Communication Paper
Note: To translate this paper into over 50 languages, please see the Automatic translation option at the top of the screen GSIM Communication 1_0.docx Word version of document Generic Statistical Information Model (GSIM): Communication Paper for a General
Page: GSIM Integration Workshop
Following a busy few months for the GSIM Task Teams, an integration workshop will be held in The Hague, Netherlands on 17-21 September. The aim is to integrate the results of the Task Teams into GSIM version 0.8, which is scheduled for release around the
Page: GSIM Specification
Word and PDF versions of document Specification_.docxSpecification_.pdf Note: To translate this paper into over 50 languages, please see the Automatic translation option at the top of the screen Generic Statistical Information Model (GSIM): Specification
Page: GSIM Sprint - As it Happens
A daily blog by Steve Vale on the progress of the sprint - 20 February to 2 March 2012 Sunday 19 February Arrived in Ljubljana. Air France tried to send my luggage to N'djamena, but fortunately I noticed in time, and do have clean clothes to wear tomorrow
Page: GSIM Sprint 1 - Home Page
GSIM Sprint 1 was hosted by the Statistical Office of the Republic of Slovenia (SORS) in Ljubljana, Slovenia from 20 February to 2 March 2012. GSIM Sprint 1 as it happened Genesis of GSIM Sprint 1 The Generic Statistical Information Model (GSIM) is a corn
Page: GSIM Sprint 2
GSIM Sprint 2 will be hosted by the Statistical Office of the Republic of Korea (KOSTAT) in Daejeon, Korea from 16 April to 27 April 2012.The program will start at 09.00 on Monday 16 April and close at 18:30 on Friday 27 April. GSIM Sprint 2 - As it Happe
Page: GSIM Sprint 2 - As it Happens
A daily blog by Thérèse Lalor on the progress of the Sprint 2 - 16 to 28 April 2012 Saturday 14 April - Sunday 15 April The Sprint Organising Team spent Friday setting up the conference rooms at the Statistical Training Institute. We have been enjoying th
Page: GSIM User Guide
Word and PDF versions of document GSIM User Guide.docxGSIM User Guide.pdf Note: To translate this paper into over 50 languages, please see the Automatic translation option at the top of the screen Generic Statistical Information Model (GSIM): User Guide (
Page: GSIM v0.8
After the successful conclusion of the GSIM Integration Workshop, a new version of GSIM (v0.8) has been released. Please feel free to send a link to this page to anyone that you think might be interested. Key changes between GSIM v0.4 and v0.8 include: -
Page: GSIM v1.0
Please see GSIM homepage
Page: GSIM v1.0 discussion forum
As organisations begin to use GSIM, this discussion forum will be used to explore issues with the model or propose extensions to the model. Instructions for use: To reply to a topic, click on the name of the topic in the table below. Then choose 'add comm
Page: GSIM Version 0.3
Following the successful conclusion of the first GSIM Sprint, the High-Level Group for Strategic Developments in Business Architecture in Statistics authorised the release of version 0.3 of the GSIM document for public consultation. GSIM Version 0.3 Общая
Page: GSIM Version 0.4
Following the successful conclusion of the second GSIM Sprint, the High-Level Group for Strategic Developments in Business Architecture in Statistics authorised the release of version 0.4 of the GSIM documents for public consultation. Please feel free to
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HPage: H. Business Process Model and Notation (BPMN)179. BPMN provides a standard means to document business processes, including representing them graphically. GSIM does not try to duplicate the richness of modelling in BPMN. It simply aims to establish a high level connection. 180. There are two
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IPage: I. Introduction 1. The GSIM Specification is the most detailed level of the Generic Statistical Information Model (GSIM). It provides a set of standardized, consistently described information objects, which are the inputs and outputs in the design and production
Page: I. COmmon Reference Environment (CORE)
183. The CORE model is a communication protocol for the exchange of information between a CORE service (a service designed with the help of CORE information objects) and its environment (an implementation of CORE on any specific platform). The CORE model
Page: Identifying IOCs from GSBPM
This page summarises an exercise initiated by Jenny Linnerud from Statistics Norway to "text mine" documentation of GSBPM in order to identify empirically the "information objects" that the documentation of GSBPM recognises as relevant to (eg inputs to an
Page: II. Information in a statistical organization
Page: Integration Workshop - as it Happens This blog is posted by Steven Vale, to keep the wider GSIM community, and anyone else that is interested, up to date on the progress during the workshop. Please feel free to add comments here, or on the LinkedIn group "Business Architecture in Statistics"
Page: Issue 10:Naming of Representation Object
In both the DDI and SDMX mapping the naming of the GSIM information object "Representation" has been queried. Representation means something quite different in the standards and perhaps this will cause confusion?
Page: Issue 11: Classification: Cardinality Issue
Context - Current GSIM Cardinality: Classification Item: Classification Item 1..* Level (Mandatory, Many) Classification Item is grouped by Level A Classification Item must have at least one Level Classification Scheme: Classification Scheme 1..* Cl
Page: Issue 12: Classifications vs Code Lists
What is a real life example of the difference between a Classification and a Code List?GSIM documents that there is a difference between the two, but when you get right down to it, where do you draw this line? In your opinion, what is the boundary betwe
Page: Issue 13: Survey and frame populations
From GSIM v1.0 Glossary Survey Population A Population for which information can be obtained in a survey. A Population which can realistically be studied (example: people currently residing in the province of Ontario not in an institution nor in a remote
Page: Issue 14 - Relationships
One suggestion at METIS 2013 was to include relationships in the glossary. One could use rules like: always include inheritance and subtypes, consider including components and aggregates, can include other associations. Alternatvely one could have always
Page: Issue 15: Make GSIM a "simple, easy to understand view" of statistical information objects
As outlined in Section IV of our METIS paper, in order to meet GSIM's objectives to enable communicating, educating and collaborating, I, with my IMF colleagues, feel the model needs fewer objects overall, and these objects should be at a higher level. Su
Page: Issue 1: Individual units vs "unit types"
ABS Problem Statement The ABS (Australian Bureau of Statistics) is currently developing a Metadata Registry and Repository (MRR) aligned with GSIM. While aligned with GSIM, the definition of objects for the ABS MRR will requires attributes additional t
Page: Issue 2: Code - CodeItem, Category - CategoryItem
When mapping GSIM to our information model we got a bit confused about the difference between Code - CodeItem and Category - CategoryItem. A CodeItem combines the meaing of the Category with a representation, like in "F-female", where female is the Catego
Page: Issue 3: Design Context
The definition of the Design Context according to the Annex C Glossary is "Methodological metadata that provide the basis for the specification of the information objects required as input to and output from the Process Step Design including Process Metho
Page: Issue 4: Business Service and Dissemination Service
Issue left over from finalisation of GSIM v1.0: Should Dissemination Service be a subtype of Business Service? Currently in the model we have an object in the Production Group called Business Service. Object Group Definition Notes Business Service Produ
Page: Issue 5: States of data
Netherlands and Norway operate with at least 4 different steady states for data e.g. raw data, clean microdata, macrodata/statistical data, published data. How is this modelled in GSIM? There is Unit Data and Dimensional Data, but how do we model the amou
Page: Issue 6: Non Structured data set
Issue raised by GSIM / DDI mapping work: What is a non structured data set? Why do we need it?
Page: Issue 7: Is there a difference between Dimensional Data and Unit Data?
There has been much debate whether GSIM should make a differentiation between unit data and dimensional data. For GSIM v1.0, it was decided that the distinction was retained. There were two reasons for this: The distinction is important enough distinction
Page: Issue 8: Hierarchies in Code Lists and Category Sets
Currently in GSIM it seems you can only build 'flat' Category Sets and Code Lists - the only time a hierarchy is introduced is through the inclusion of Level in a Classification Item for Classification Schemes. Some thoughts for discussion: Should it be p
Page: Issue 9: Removing Data Channel
The DDI mapping group have queried the usefulness of the Data Channel object. What is a data channel really? It is the combination of instrument, mode and data resource Is this a candidate for removal from the model?
Page: Issues with recommendations
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JPage: J. The Open Group Architectural Framework (TOGAF)186. TOGAF is widely recognized and used within statistical organizations as well as many other organizations around the world. Most other architectural frameworks are basically consistent with TOGAF although the terms and precise concepts used withi
Page: Justifying object inclusion in GSIM
When justifying changes to GSIM, I would find it very useful to understand how the proposed objects relate not just to other GSIM objects, but also to GSBPM sub-processes (as per the diagram below, from the GSIM documentation). This will help make it clea
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KPage: Key existing corporate metadata systemsCollection Management System (CMS) This manages high level information about "statistical activities" ("collections") undertaken by the ABS. These "statistical activities" include surveys, censuses, statistical analysis of administrative data sources and
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MPage: Maintaining the Common Metadata FrameworkStrategy Paper approved by the METIS Steering Group - February 2011 Introduction 1. In 2004, the Bureau of the Conference of European Statisticians approved the creation of a Common Metadata Framework (CMF) under the supervision of the Steering
Page: Metadata Case Studies
Case Studies Add a new case study. Would you like to add a case study? First, contact us for a user account, then Click here to add a new case study. The case study template was created to ensure consistency and comparability amongst case studies. Ca
Page: Metadata Flows within the GSBPM
Please add comments / suggestions / links / resources [Al] Draft issues paper for this work program item. [Eurostat] Monitoring of National Metadata Systems 2008_2009 - 2009_2010.pdf [Steve] Related to this point, there have been some discussions recent
Page: Metadata Registries - ISO IEC 11179
Name and version: ISO/IEC 11179 Alternative name: Metadata registries (MDR) Valid: From 2005 (edition 2) Description: This is a standard for describing and managing the meaning and representation of data. The basic semantic unit is a concept. The standa
Page: Metis links check
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Page: METIS Meetings
Work Session on Statistical Metadata 6-8 May 2013 Geneva, Switzerland Workshop on Statistical Metadata: Implementing the GSBPM and Combining Metadata Standards 5-7 October 2011 Geneva, Switzerland Work Session on Statistical Metadata 10-12 March 20
Page: METIS Steering Group
The Conference of European Statisticians Steering Group on Statistical Metadata (usually abbreviated to "METIS Steering Group") is responsible for developing and maintaining the Common Metadata Framework, as well as organising METIS Work Sessions and Work
Home page: METIS-wiki
This is the place for people working in official statistics to share information and ideas about statistical metadata. Main features: The Common Metadata Framework Common Metadata Framework: the four parts of this framework concentrate on differen
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NPage: Neuchâtel Model - Classifications and VariablesName and version: Neuchâtel Terminology Model for classifications (version 2.1) and variables (version 1.0) Alternative name: Neuchâtel Model for classifications and variables Valid: From 2004 Description: In 2004, the Neuchâtel Group issued version 2.1
Page: Nordic Metamodel for PC-Axis
Name and version: Nordic Metamodel, version 2.2 Alternative name(s): Valid: From June 2008 Description: The Nordic Metamodel was developed by Statistics Sweden, and has become increasingly linked with their popular "PC-Axis" suite of dissemination softwar
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OPage: Old versions of GSIMGSIM v0.8 GSIM v0.4 released for public comment GSIM Version 0.3 GSIM Common Reference Model v0.1 Background to GSIM
Page: Old versions of the GSBPM
The links below are to outdated versions of the GSBPM, retained for archive purposes only. To see the latest version click here. Version 3.1 (December 2008) (PDF file - presented at the METIS Workshop, Lisbon, March 2009) Version 2.0 (August 2008) (PDF f
Page: Other references - external focus and dissemination
The following reference material has been provided to give a dissemination perspective Making Data Meaningful, Vol. 1 - Storytelling about statistics Making Data Meaningful, Vol 2 - Visual presentations of statistical information Making Data Meaningful, V
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PPage: Panel Discussion QuestionsSession ii: Implementing DDI in statistical organisations; For the statistical organisations which use DDI in practice - To what extent is DDI really implemented within the statistical production process ? - What are the based-DDI tools used in practice b
Page: Paper for METIS 2010
Word Version PDF Version Powerpoint Presentation
Page: Papers about the GSBPM
National implementations National implementations of the GSBPM - a summary based on METIS case studies Generic Statistical Business Process Model (GSBPM) and its contribution to modelling business processes- experiences from the Australian Bureau of Stati
Page: Part A - Statistical Metadata in a Corporate Context
This part of the Common Metadata Framework highlights the role of statistical metadata systems in a statistical organization. It is focused on managerial issues relevant to the corporate governance of statistical metadata systems. Expected readers are exp
Page: Part B - Metadata Concepts, Standards, Models and Registries
Part B offers an overview of existing resources (standards, concepts, models, best practices and other methodological materials), which are likely to be applicable when designing and implementing statistical metadata systems. It is designed primarily as a
Page: Part B - Notes of Teleconferences
15) 14 September 2010 Present: Alice Born, Dan Gillman, Jana Meliskova, Steve Vale The remaining tasks are to: Finalise the resource mapping diagrams (Sergio and Steve) Produce the final report of the Task Force (Jana). This report will be based on the pa
Page: Part B - Relevant papers
Just a little note to inform that the links to two relevant papers to CMF - Part B, namely Neuchâtel Terminology Model, are broken.
Page: Part B - Work in Progress
Text of Part B Template Archive - Old Versions of the Structure Diagram Archive - Old Versions of the Relationships Diagram Notes of Teleconferences Paper for METIS 2010 Members of Part B Task Force: Sergio Bacelar Max Booleman Alice Born Dan Gillman Hami
Page: Part C - Metadata and the Statistical Business Process
Part C is currently under development. Statistical metadata are relevant to all stages of the statistical business process. Many statistical organizations have initially focused on metadata disseminated with statistics, however, they are now increasingly
Page: Part D - Implementation
Part D focuses on the experiences of statistical organizations that have recently implemented or re-engineered their statistical metainformation systems. These experiences are presented through a growing series of case studies which are updated by contrib
Page: Planning the METIS Workshop
Metis Title: Workshop on Implementing the GSBPM and Integrating Metadata Standards Dates: 5-7 October 2011 Location: Geneva, Palais des Nations Main website: http://www.unece.org/stats/documents/2011.10.metis.htm Panel discussion questions
Page: Presentations and Posters
Presentations Posters Wordcloud poster Middle level technical poster Middle level simple EDDI poster
Page: Previous GSIM workshops
GSIM Sprint 1 GSIM Sprint 2
Page: Production Group
Production -Overall Class Diagram Process Steps can contain "sub-steps", those "sub-steps" can contain "sub-steps" within them and so on indefinitely. Each Process Step in a statistical business process has been included to serve some purpose. The purpose
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RPage: Recommendations on Formats Relevant to the Downloading of Data from the InternetName and version: Recommendations on Formats Relevant to the Downloading of Statistical Data from the Internet (United Nations and Economic Commission for Europe, Conference of European Statisticians, Methodological Material) Alternative name: Recommendat
Page: Recommended changes to the GSBPM
Recommended changes to the GSBPM OECD Expert Group for International Collaboration on Microdata Access April 2013 Background: At the second meeting of the OECD CSTAT Expert Group for International Collaboration on Microdata Access in December 2012, a work
Page: References (UNIDO)
Fröschl, K.A. and Yamada, T. (2000) The UNIDO Industrial Statistics Information Exchange Architecture (An Integrated Statistical Data and Data Documentation Framework). Working Paper No. 19, Work Session on Statistical Metadata (METIS), Conference of Euro
Page: Relationship of "The Caterpillar" to GSBPM
The Caterpillar was developed by the ABS as a reference model to support the Business Statistics Innovation Program (BSIP) launched in 2002. It became, across the ABS, the most widely used reference model for the statistical business model within the ABS
Page: Relationships between Resources
The diagram below illustrates the main relationships between the different metadata resources described in Part B of the Common Metadata Framework. The diagram is also available in PDF format. In the PDF version, you can click on the box representing a re
Page: Relationships to other standards
First proposal of formalization of relationships between standards This is an incomplete first proposal (that I set to Dan) of the relations between resources, according almost literally with the text used by the different authors of the description of al
Page: Review of Progress on the Common Metadata Framework
In line with paragraph 8 of the terms of reference for the Steering Group on Statistical Metadata, the Bureau of the Conference of European Statisticians reviewed progress on the development of the Common Metadata Framework at its October 2009 meeting. Th
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SPage: SDMX - Cross-domain ConceptsName and version: SDMX Content-Oriented Guidelines, Annex 1: Cross-Domain Concepts (2009 version) Alternative name: Cross-domain Concepts Valid: From 2009 Description: Cross-domain Concepts describe concepts relevant to many statistical domains. The use o
Page: SDMX - Metadata Common Vocabulary
Name and version: SDMX Content-Oriented Guidelines, Annex 4: Metadata Common Vocabulary (2009 version) Alternative name: Metadata Common Vocabulary (MCV) Valid: From 2009 Description: The Metadata Common Vocabulary (MCV) contains concepts and related defi
Page: SDMX Cross-domain Code Lists
Name and version: SDMX Content-Oriented Guidelines, Annex 2: Cross-domain Code Lists (2009 version) Alternative name: Cross-domain Code Lists Valid: From 2009 Description: Cross-domain Code Lists are related to some of the Cross-domain Concepts. They are,
Page: SDMX DDI Dialogue - Overview Page
Recent Updates Notes of Core Group teleconference on 19 March 2013 GSIM to DDI and SDMX Mapping Work Paper on the relationship between SDMX and DDI : The Data Documentation Initiative (DDI): An Introduction for National Statistical Institutes Introduction
Page: SDMX DDI Dialogue Session - 2011.05.02
This was a brief lunch time discussion held during the SDMX Global Conference. It is the first session where the SDMX Secretary (August Götzfried) and the DDI Alliance Director (Mary Vardigan) were able to participate in the dialogue directly. An agenda w
Page: SDMX DDI Dialogue Sessions - 2011.03.30
Session Structure As outlined on the main page, the sessions on 30 March continued a dialogue engaging the two standards bodies (e.g. representatives of the SDMX Secretariat and the DDI Alliance) as well as third party stakeholders who have an interest in
Page: SearchNavigation
Page: Some notes and reflections about CMF Part B Dear colleagues, 1. In the diagram v. 2.0 it remains the designation "CMR Central Metadata Repository" and not as Dan said "Corporate Metadata Repository"; 2. Title: why "Metadata related standards and models" and not simply "Metadata standards and
Page: Statistical Classifications
Name and version: Statistical classifications Alternative name(s): Nomenclature, reference classification, standard classification Valid: From January 2009 Description: A set of discrete, exhaustive and mutually exclusive observations, which can be assign
Page: Statistical Data and Metadata eXchange (SDMX)
Name: Statistical Data and Metadata Exchange Alternative name: SDMX Version: 2.0 Valid: From November 2005 Description: The Statistical Data and Metadata Exchange (SDMX) initiative sets technical standards and content-oriented guidelines to facilitate the
Page: Statistical Office of the Republic of Slovenia
Page: Statistical Office of the Republic of Slovenia (Archive) Organization Name Statistical Office of the Republic of Slovenia (SORS) Ljubljana, Vozarski pot 12 SI- 1000 Ljubljana SLOVENIA Total number of staff Situation at SORS end February 2009: there are 386 employees. 198 of them have graduate or post-graduate
Page: Statistical Subject-matter Domains
Name and version: Statistical Subject-matter Domains (2009 version) Alternative name: Classification of Statistical Activities, DISA classification Valid: From January 2009 Description: A statistical subject-matter domain refers to a statistical activity
Page: Statistical Units
Name and version: Statistical unit Alternative name: Unit of observation, object class, units (classified), object Valid: From January 2009 Description: Statistical units are entities, respondents to a survey or things used for purpose of calculation or m
Page: Statistical Variables and Characteristics
Name and version: Statistical variables and characteristics Alternative name(s): Statistical concept, data element, data element concept, property Valid: Not applicable Description: Based on ISO/IEC11179: An abstraction of a property of an object or of a
Page: Statistics Austria
Page: Statistics Austria (Archive) What to do now? Click on edit in the top right corner of the page to begin working on it Change the title of this page to your organization's name (e.g. Statistics Denmark) Enter in the missing text (and delete these instructions) Organization Name Sta
Page: Statistics Canada
Page: Statistics Canada (Archived) Organization Name Statistics Canada Total number of staff Contact Person for metadata Alice Born Job title: Director, Standards Division Email: alice.born@statcan.gc.ca Telephone: +1 613.951.8577 Table of contents
Page: Statistics Croatia
Page: Statistics Croatia (Archived) Organization Name Croatian Bureau of Statistics Total number of staff 422 Contact Person for metadata Name: Maja Ledic Blazevic Job title: Unit leader Email: majalb@dzs.hr Telephone: +385 1 48 06 201 Table of contents
Page: Statistics Finland
Page: Statistics Finland (Archived) Organization Name Statistics Finland Total number of staff Central office in Helsinki: approximately 950 persons Approx. 200 statistical interviewers around the country. Contact Person for metadata Saija Ylönen Job title: Head of Development/ Metadata ser
Page: Statistics Netherlands
Page: Statistics Netherlands (Archived) Organization Name Statistics Netherlands Total number of staff Approximately 2200 employees Contact Person for metadata Max Booleman Job title: Senior advisor/ Division of Methodology and Quality Email: m.booleman@cbs.nl Telephone: +31 70 337 4455 Table o
Page: Statistics New Zealand
Page: Statistics New Zealand (Archived) Organization Name Statistics New Zealand Total number of staff 850 FTE Contact Person for metadata Name: Matjaz Jug or Hamish James Job title: Chief Information Officer, Manager - Information Management Email: matjaz.jug@stats.govt.nz hamish.james@s
Page: Statistics Norway
Page: Statistics Norway (Archived) Organization Name Statistics Norway Total number of staff 1000 employees. Approximately 550 in Oslo and 450 in Kongsvinger. Contact Person for case study Name: Jenny Linnerud Job title: Senior advisor Email: jal@ssb.no Telephone: +47 21 09 45 22 Tab
Page: Statistics Portugal
Page: Statistics Portugal (Archived) Organization Name Statistics Portugal Total number of staff Statistics Portugal is comprised of a group of skilled professionals, employing 710 staff, (about 75% in Lisbon and 25% spread by four delegations across the mainland). Contact Person for metadat
Page: Statistics South Africa
Page: Statistics South Africa (Archived) Organization Name Statistics South Africa Total number of staff ± 2,000 Contact Person for metadata Job title: Email: Telephone: +27-12-310-8911 Table of contents Download the full case study in PDF
Page: Statistics Sweden
Page: Statistics Sweden (Archived) Organization Name Statistics Sweden Total number of staff Approx. 1370 employees (approx. 540 in Stockholm, approx. 680 in Örebro and approx. 160 field interviewers around the country). Contact Person for metadata Klas Blomqvist, Process Department Job
Page: Structures Group
DataSet Class Diagram A Data Structure structures zero or more Data Sets and has one or more Identifier Components, one or more Measure Components. and zero or more Attribute Components. The Identifier Component, Measure Component and Attribute Component
Page: Suggestions for improving the GSBPM
(need to write a little bit of explanatory text here, and maybe start things off with some categories...) Add new topic
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TPage: Task Force MembersThérèse Lalor and Alistair Hamilton (Australia) Ebbo Petrikovits (Czech Republic) Joe Treacy (Ireland) Max Booleman and Jean-Pierre Kent (Netherlands) Jenny Linnerud (Norway) Mónica Isfan and Sérgio Bacelar (Portugal) Klas Blomqvist and Mats Bergdahl (Swe
Page: Task Force on the Further Development of the GSBPM
Notes of meetings Task Force Members Planning the METIS Workshop
Page: Template
Version 5 - Agreed January 2011 Headings mapped to Dublin Core terms (Dublin Core terms in brackets) Name and version (Title): Alternative name (Alternative): Version: Valid (Valid): Description (Description): Intended Use (part of Description): Maint
Page: The Common Metadata Framework
The Common Metadata Framework is published online, and is divided into four parts, each of which concentrates on different practical and theoretical aspects of statistical metadata systems, and provides vital knowledge for anyone working with statistical
Page: The Generic Statistical Business Process Model
Papers about the GSBPMGeneric Statistical Business Process Model Paper Metadata Flows within the GSBPM GSBPM discussion forum Communication Resources GSBPM and Process Quality Management Task Force on the Further Development of the GSBPM Old versions o
Page: Tool kit
Aim is to provide editable versions of all diagrams used in GSIM documentation Communication document diagrams - includes diagrams shown below Specification Section II diagrams - includes diagrams from Section II. Information in a statistical organ
Page: Tourism Process
Postal Survey to collect information on domestic and international overnight travel by Irish residents Your browser does not support iframes
Page: TreeNavigation
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UPage: UNECE Guidelines for Statistical Metadata on the InternetName and version: UNECE Guidelines for Statistical Metadata on the Internet (United Nations Economic Commission for Europe Conference of European Statisticians Statistical Standards and Studies - No.52) Alternative name: Guidelines for Statistical Metadat
Page: UNECE Guidelines for the Modelling of Statistical Data and Metadata
Name and version: UNECE Guidelines for the Modelling of Statistical Data and Metadata (United Nations and Economic Commission for Europe, Conference of European Statisticians, Methodological Material) Alternative name: Guidelines for the Modelling of Stat
Page: United Nations Industrial Development Organization (UNIDO)
Page: United Nations Industrial Development Organization (UNIDO) (Archived) Organization Name Organization Name: United Nations Industrial Development Organization (UNIDO) Research and Statistics Branch Website http://www.unido.org Organization Chart Total number of staff Contact Person for metadata Valentin Todorov Job title
Page: Usage scenarios for SDMX and DDI
Context for usage scenarios The set of draft usage scenarios set out below have been prepared on an informal, working document basis, based on input from a number of NSIs. They form one set of "discussion starter" inputs to the dialogue process on 30 Marc
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VPage: v0.1 GSIM Common Reference ModelThis version was available for comment from 20 June to 1 July 2011. Introduction This page is intended to document progress in regard to the GSIM Common Reference Model. This page will always refer to the most recent material that has been made available
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Welcome to the Statistical Metadata (METIS) wiki. This is a place for sharing information and discussing statistical metadata systems.
It is built using wiki software called Confluence, providing users with the ability to edit their own content and comment on others. The site is currently closed to the public while in pilot phase.
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Central Statistical Office, Ireland |
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