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Tool for converting and linking statistical datasets
to a cloud of interconnected historical datasets.
QB’er - Demonstrati...
GOAL OF THIS
PRESENTATION
From CSV files and structured statistical data to (harmonized)
Interlinked data on the Web
Data ...
• Gather and enter own data
• Find data on multiple repositories
• Download
• Clean and reshape
• Merge
• Clean and reshap...
PROBLEM
Disconnected data and efforts
We keep repeating ourselves and do this repeatedly for the same
datasets
Comparabili...
https://blog.gaijinpot.com/knowledge-sharing-economy/
LOSS OFF..
Provenance
Cleaning efforts (sometimes up to 60% of the work)
Valuable mappings (discarding time consuming prio...
SOLUTION: INTEGRATE DISSIMILAR
DATA IN FLEXIBLE AND
ACCOUNTABLE WAYS
HARMONIZATION AND RDF
What we want is harmonization by way of;
Standardization and Classification
 Flexible approach whil...
QB’ER
Empower individual researchers to:
Code and harmonize individual datasets according to best practices of the
communi...
INPUT
INPUT
INPUT
INPUT
DEMO EXAMPLE
Nieuwkomers in de Utrechtse volkstelling van 1829 en 1839
http://hdl.handle.net/10622/KMAJLE
Utrecht 1829
Utrecht 1839
Variables
Values
DEMO
Qb’er Demonstration Video
TO CONCLUDE…
• Generic, domain-independent tool
• Uploading of a dataset and extraction of variables and value
Frequencies...
QUESTIONS ?
QB’er - Demonstration
Ashkan Ashkpour – CLARIAH WP4
07-10-2016
QB'er demonstration
QB'er demonstration
QB'er demonstration
QB'er demonstration
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QB'er demonstration

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QB'er demonstration

  1. 1. Tool for converting and linking statistical datasets to a cloud of interconnected historical datasets. QB’er - Demonstration Ashkan Ashkpour, IISH – CLARIAH WP4 07-10-2016
  2. 2. GOAL OF THIS PRESENTATION From CSV files and structured statistical data to (harmonized) Interlinked data on the Web Data Tooling Interlinked Datasets on the web
  3. 3. • Gather and enter own data • Find data on multiple repositories • Download • Clean and reshape • Merge • Clean and reshape… • Analyse PROBLEM - Today’s Workflow
  4. 4. PROBLEM Disconnected data and efforts We keep repeating ourselves and do this repeatedly for the same datasets Comparability across time and datasets
  5. 5. https://blog.gaijinpot.com/knowledge-sharing-economy/
  6. 6. LOSS OFF.. Provenance Cleaning efforts (sometimes up to 60% of the work) Valuable mappings (discarding time consuming prior work) Expert decisions Discoverability
  7. 7. SOLUTION: INTEGRATE DISSIMILAR DATA IN FLEXIBLE AND ACCOUNTABLE WAYS
  8. 8. HARMONIZATION AND RDF What we want is harmonization by way of; Standardization and Classification  Flexible approach while providing accountability
  9. 9. QB’ER Empower individual researchers to: Code and harmonize individual datasets according to best practices of the community (e.g. HISCO, SDMX, Worldbank, etc.) or against their colleagues Share their own code lists with fellow researchers Align code lists across datasets Publish their standards-compliant datasets on a Structured Data Hub Collaborative growing of a graph of interconnected datasets
  10. 10. INPUT
  11. 11. INPUT
  12. 12. INPUT
  13. 13. INPUT
  14. 14. DEMO EXAMPLE Nieuwkomers in de Utrechtse volkstelling van 1829 en 1839 http://hdl.handle.net/10622/KMAJLE
  15. 15. Utrecht 1829
  16. 16. Utrecht 1839 Variables Values
  17. 17. DEMO Qb’er Demonstration Video
  18. 18. TO CONCLUDE… • Generic, domain-independent tool • Uploading of a dataset and extraction of variables and value Frequencies • Mapping of variable values to codes (while preserving the originals!) • Publishing of dataset structure as Linked Data • Align codes and identifiers across datasets • Provenance of all assertions to the SDH traceable to time and person • Crowd-based production of code lists and mappings • Sharing / Reuse other people’s work (or stand on the shoulders of giants) • No disposable research
  19. 19. QUESTIONS ? QB’er - Demonstration Ashkan Ashkpour – CLARIAH WP4 07-10-2016

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