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== Linked Data == |
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-The World Wide Web Consortium ([[W3C>>https://www.w3.org/||rel="noopener noreferrer" target="_blank"]]) recommends Linked Data as the most effective way to publish data on the Internet. Linked Data is developed according to the principles of the Semantic Web — a global semantic infrastructure and a set of fundamental rules for representing data on the Internet in a way that allows information systems to interpret its meaning correctly. |
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+The World Wide Web Consortium ([[W3C>>https://www.w3.org/||rel="noopener noreferrer" target="_blank"]]) recommends Linked Data as the most effective way to publish data on the Internet. Linked Data is developed according to the principles of the [[Semantic Web>>https://www.w3.org/standards/||rel="noopener noreferrer" target="_blank"]] — a global semantic infrastructure and a set of fundamental rules for representing data on the Internet in a way that allows information systems to interpret its meaning correctly. |
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-Linked Open Statistical Data (LOSD) refers to statistical datasets published as Linked Data under an open license such as CC BY 4.0, promoting free reuse and wide dissemination. Interoperability is achieved by creating, exchanging, and using LOSD in ways that preserve the meaning and context of the data, regardless of the systems involved. |
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+Linked Open Statistical Data (LOSD) refers to statistical datasets published as Linked Data under an open license such as [[CC BY 4.0>>https://creativecommons.org/licenses/by/4.0/||rel="noopener noreferrer" target="_blank"]], promoting free reuse and wide dissemination. Interoperability is achieved by creating, exchanging, and using LOSD in ways that preserve the meaning and context of the data, regardless of the systems involved. |
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Human understanding and machine interpreting of statistical data is often difficult due to the lack of formalised domain knowledge and the absence of machine-readable, semantically enriched data. Poor semantic structure means that even published linked data can be hard to discover and accurately relate to domain concepts. |
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-The High-Level Group for the Modernisation of Official Statistics (HLG-MOS), under the United Nations Economic Commission for Europe (UNECE), addresses the challenges of data interoperability within national statistical systems. It develops and promotes methods, models (including semantic models such as ontologies), and standards through coordinated initiatives. One of these initiatives is the Data Governance Framework for Statistical Interoperability (DAFI), published in 2023. This framework provides a reference model for implementing governance programs that support the creation, sharing, and use of data in ways that preserve semantic meaning across systems. |
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+The High-Level Group for the Modernisation of Official Statistics ([[HLG-MOS>>https://unece.org/statistics/networks-of-experts/high-level-group-modernisation-statistical-production-and-services||rel="noopener noreferrer" target="_blank"]]), under the United Nations Economic Commission for Europe ([[UNECE>>https://unece.org/ru]]), addresses the challenges of data interoperability within national statistical systems. It develops and promotes methods, models (including semantic models such as ontologies), and standards through coordinated initiatives. One of these initiatives is the Data Governance Framework for Statistical Interoperability (DAFI), published in 2023. This framework provides a reference model for implementing governance programs that support the creation, sharing, and use of data in ways that preserve semantic meaning across systems. |
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Another priority of HLG-MOS is the development of rich (“smart”) metadata — metadata that is standardised (understandable and reusable across contexts), active (capable of driving statistical processes), and aligned with the FAIR principles: Findable, Accessible, Interoperable, and Reusable. |
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