Nationell dataverkstaden bör skapa ett nationellt ekosystem hur data kopplas ihop 5 star

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• skall data som data fungera så måste det finnas ett ekosystem där data kan kopplas ihop, dagens dataset är oftast inte kopplade till varandra --> mervärdet lågt --> skall saker fungera skall det vara 5-star data och FAIRDATA Se DIGG #82 #85 och vad ChatGPT säker det behövs för kompetens

  • Behovet att enkelt koppla ihop data samma som = reconciliation services se #79
  • Det skall kunna gå att att med ett knapp tryck hämta hem vilka som är ansvariga hos landets 290 kommuner för deras respektive anslagstavla jmf #19 jmf hur dåligt det är idag 2022 då vi måste fråga varje myndighet

Exempel hur utegyms data idag fulöversätts hos europeiska dataportalen eftersom inte kunskapsgrafer finns som har samma som mellan dataset och kopplingar till andra kunskapsgrafer för dom som laddar upp data på EDP

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Se samma för DIGG #70

• Ora Lassila, Amazon - https://www.lassila.org/
  • KGC 2022 - Will Knowledge Graphs Save Us From the Mess of Modern Data Practice

Lesson learned: People and processes are the challenge

skrivit boken "Designing and Building Knowledge graphs"

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Hur skall bra data skapas? Se exempel hur iNaturalist gör det

Bra artikel i The New York Times om hur iNaturlist jobbar med att kategorisera arter dvs. ett mönster vi vill se med Linked data

on many of the iNaturalist faithful that maybe they are on to something much bigger — a model for using the web that is governed by cooperation, not combat.

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Fundering på hur data specas idag med hårdkodade fält länk

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Massa månader och massa EU pengar så blev det en PDF med 20 frågor - En modell för att mäta kommuners öppna-data-mognad - Jörgen Dehlin Samhällsbyggnadsavdelningen Länsstyrelsen Skåne

5 minuter med chatGPT Open Data Maturity Assessment:

Swedish municipals are trying to measure the Open Data maturity can you give us 20 questions to ask and how to measure it

Certainly! To assess the Open Data maturity of Swedish municipalities, consider the following 20 questions. You can measure the maturity based on the thoroughness and quality of their responses:

1. Does the municipality have a clear Open Data policy or strategy? Measure: Existence of a well-defined policy or strategy document.

2. Is there a dedicated team or individual responsible for Open Data initiatives? Measure: Presence of designated roles, teams, or task forces.

3. How frequently is the Open Data portal updated? Measure: Update frequency (daily, weekly, monthly, etc.).

4. Are the datasets provided in machine-readable formats (e.g., CSV, JSON, XML)? Measure: Percentage of datasets in machine-readable formats.

5. How many datasets are currently available in the Open Data portal? Measure: Number of datasets.

6. What percentage of datasets include metadata to facilitate understanding and use? Measure: Percentage of datasets with metadata.

7. Are the datasets categorized by topic, sector, or department? Measure: Clear categorization of datasets.

8. Are there clear licensing terms, allowing for the reuse and redistribution of data? Measure: Presence of licensing information for each dataset.

9. Does the municipality provide support and resources to help users understand and use the datasets? Measure: Availability of user guides, tutorials, or FAQs.

10. Are there mechanisms for users to provide feedback on the datasets? Measure: Presence of feedback channels (e.g., email, forms, forums).

11. Does the municipality actively engage with citizens and stakeholders to identify data needs and priorities? Measure: Evidence of outreach initiatives or consultations.

12. Are there regular assessments of data quality and completeness? Measure: Existence of data quality control processes.

13. Does the municipality collaborate with other municipalities, regions, or countries to share best practices and improve Open Data initiatives? Measure: Evidence of collaboration or partnerships.

14. Is there a process in place to handle requests for new datasets or data updates? Measure: Established process for handling data requests.

15. Are the datasets published on the Open Data portal accessible to people with disabilities? Measure: Compliance with accessibility standards (e.g., WCAG).

16. Are datasets downloadable in bulk or available through APIs? Measure: Availability of bulk downloads or APIs.

17. How does the municipality promote the use and awareness of its Open Data portal? Measure: Promotion channels (e.g., social media, newsletters, events).

18. Are there success stories or case studies showcasing the impact of Open Data? Measure: Availability of impact stories or examples.

19. Does the municipality track and report on the usage of its Open Data portal? Measure: Usage statistics, such as unique visitors, downloads, and API calls.

20. Does the municipality have plans for continuous improvement of its Open Data initiatives? Measure: Evidence of future plans or a roadmap for improvements.

Each question can be assigned a score based on the quality and comprehensiveness of the response. Summing up the scores for all questions will give you an overall Open Data maturity score for the municipality.

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continue with questions about linked data maturity. ontologies used, how connected the datasets are i.e. use owl:sameAs

To assess the Linked Data maturity of Swedish municipalities, consider the following additional questions. You can measure the maturity based on the thoroughness and quality of their responses:

21. Does the municipality use any standard ontologies or vocabularies in their datasets (e.g., FOAF, Schema.org, SKOS, Dublin Core)? Measure: Adoption of standard ontologies or vocabularies.

22. Are custom ontologies created and used when no standard ontology fits the municipality's needs? Measure: Existence of custom ontologies and their alignment with standard ontologies.

23. How many datasets include RDF (Resource Description Framework) triples for representing linked data? Measure: Number or percentage of datasets using RDF.

24. Are the municipality's datasets connected to external datasets using owl:sameAs or other linking properties? Measure: Number or percentage of datasets with external connections.

25. Does the municipality publish Linked Data according to the 5-star Open Data model proposed by Tim Berners-Lee? Measure: Compliance with the 5-star model.

26. Are the municipality's Linked Data resources dereferenceable, providing both human-readable and machine-readable representations? Measure: Percentage of dereferenceable Linked Data resources.

27. Does the municipality provide SPARQL endpoints or other query interfaces to enable users to explore and retrieve Linked Data? Measure: Availability of query interfaces (e.g., SPARQL endpoints).

28. Are there guidelines or best practices in place for producing and publishing Linked Data in the municipality? Measure: Existence of guidelines or best practices.

29. How does the municipality ensure data quality and consistency in their Linked Data resources? Measure: Data quality control processes for Linked Data.

30. Are there any tools or applications developed by the municipality or third parties that utilize the Linked Data? Measure: Number of tools or applications using Linked Data.

31. Is there a plan to continuously improve the municipality's Linked Data initiatives and expand the use of Linked Data technologies? Measure: Evidence of future plans or a roadmap for Linked Data improvements.

32. Does the municipality participate in any Linked Data-related projects, networks, or initiatives at regional, national, or international levels? Measure: Involvement in Linked Data projects or networks.

    Each question can be assigned a score based on the quality and comprehensiveness of the response. Summing up the scores for all questions will give you an overall Linked Data maturity score for the municipality. Combining the Open Data and Linked Data maturity scores will provide a more comprehensive view of the municipality's overall data maturity.

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Add some questions to. check the Linked data and Knowledge graph maturity regarding skills in the team that should deliver Open data

To assess the Linked Data and Knowledge Graph maturity regarding skills in the team responsible for delivering Open Data, consider the following questions. You can measure the maturity based on the quality and depth of their responses:

33. Does the team have experience in working with RDF (Resource Description Framework) and other Linked Data technologies? Measure: Evidence of relevant experience or projects.

34. Are there team members with expertise in developing or working with ontologies, vocabularies, or taxonomies? Measure: Number of team members with expertise in ontologies, vocabularies, or taxonomies.

35. Have team members been trained or certified in Linked Data, Semantic Web technologies, or Knowledge Graphs? Measure: Number of team members with relevant training or certifications.

36. Does the team have experience with SPARQL or other query languages for retrieving and manipulating Linked Data? Measure: Evidence of experience with SPARQL or other query languages.

37. Have team members been involved in any projects related to the development, implementation, or maintenance of Knowledge Graphs? Measure: Number of team members with relevant project experience.

38. Does the team have experience working with various Linked Data serialization formats, such as RDF/XML, Turtle, N-Triples, or JSON-LD? Measure: Familiarity with multiple serialization formats.

39. Is there expertise within the team for integrating Linked Data with other data sources, such as databases, APIs, or data streams? Measure: Evidence of data integration experience.

40. Does the team have experience in data modeling, specifically for Linked Data and Knowledge Graphs? Measure: Number of team members with data modeling experience.

41. Are team members familiar with best practices for publishing and consuming Linked Data and Knowledge Graphs? Measure: Familiarity with best practices and guidelines.

42. Has the team collaborated with other teams, organizations, or experts in the Linked Data and Knowledge Graph communities? Measure: Evidence of collaboration or partnerships.

43. Does the team possess any domain-specific knowledge that helps them create and maintain high-quality Linked Data and Knowledge Graphs in their specific domain? Measure: Number of team members with relevant domain-specific knowledge.

Each question can be assigned a score based on the quality and depth of the response. Summing up the scores for all questions will give you an overall Linked Data and Knowledge Graph maturity score for the team's skills. Combining this score with the previous Open Data, Linked Data, and Knowledge Graph maturity scores will provide a more comprehensive view of the municipality's overall data maturity.

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Exempel hur Wikidata använder det svenska verktyget Grafana för att se kvalitet samma borde kunna skapas för ett nationellt ekosystem av Nationell dataverkstaden

Hur Wikidata jobbar

• Wikidata edits

• entityschema
• Wikidata / Wikibase API
• Wikidata datamodel

• Wikidata model Lexemes

• wikidata-datamodel-references
• wikidata-datamodel-statements

• Wikidata Edits

• Wikidata Entity usage

• Wikidata Quality

• wikidata-query-service-ui

• Wikidata Site stats

Annat verktyg - Wikimedia statistics

• sv.wikipedia.org/reading/top-viewed-articles

• stats.wikimedia.org/#/sv.wikipedia.org

• wikidata.org

Annat verktyg - Wikiscan

• wikidata.wikiscan.org/user/Salgo60

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Bra video varför LLM inte är lösningen utan Kunskapsgrafer behövs

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Se POC svenska Grillplatser