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Work Package 4:
Structured data from 

economic and social history
Auke Rijpma
January 2016
Predecessors
• Economic and social historians often work with
structured (tabular) data.
• Includes large data-projects…
• …and many small data sets.
Problems to solve
• Finding data in multiples repositories.
Problems to solve
• Finding data in multiples repositories.
• Harmonisation.
Problems to solve
• Finding data in multiples repositories.
• Harmonisation.
• Linking datasets to answer new questions.
• Analysis of multilevel & big data sets.
• Isolated and unknown datasets.
• Reproducability v. disposable science.
What we propose
• Gather and curate important datasets and place them
on the Clariah Structured Data Hub.
• Use web-based linked data-technology to augment,
harmonise, link, and query datasets.
• Provide tooling and incentives to upload new datasets.
• Uploading and describing your data gives you
augmentation, harmonisation, and links to other micro
and macro datasets.
Empower Individual Researchers
• Augment and link individual datasets according to best
practices of the community or against colleagues
• Share machine-interpretable code books with fellow
researchers
• Align codes and identifiers across datasets
• Publish standards-compliant, reusable datasets
Grow a giant graph of interconnected
datasets
Tools to explore, visualise, query, and
analyse datasets.
Future CSDH
• Upload, describe, and store data.
• Augment, harmonise, and link data.
• Find, explore, query, visualise, and analyse data.
• Share data, queries, and results.
Today’s CSDH
• Prototype up and running.
• Loosely interconnected parts without a “hood”.
• QBer (Rinke Hoekstra): intake, data description, harmonisation, linking.
• Dedicated data pipelines.
• Triplestore, data-API, queries (Kathrin Dentler).
• Grlc (Albert Meronyo): Query-API .
• Come see our demos and visit Github repos: https://
github.com/clariah/!
Utrecht 1829 Utrecht 1839
QBer
Triplestore, data-API,
queries, queries-API

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Struc data Auke Rijpma

  • 1. Work Package 4: Structured data from 
 economic and social history Auke Rijpma January 2016
  • 2. Predecessors • Economic and social historians often work with structured (tabular) data. • Includes large data-projects… • …and many small data sets.
  • 3. Problems to solve • Finding data in multiples repositories.
  • 4.
  • 5. Problems to solve • Finding data in multiples repositories. • Harmonisation.
  • 6.
  • 7.
  • 8. Problems to solve • Finding data in multiples repositories. • Harmonisation. • Linking datasets to answer new questions. • Analysis of multilevel & big data sets. • Isolated and unknown datasets. • Reproducability v. disposable science.
  • 9.
  • 10. What we propose • Gather and curate important datasets and place them on the Clariah Structured Data Hub. • Use web-based linked data-technology to augment, harmonise, link, and query datasets. • Provide tooling and incentives to upload new datasets. • Uploading and describing your data gives you augmentation, harmonisation, and links to other micro and macro datasets.
  • 11. Empower Individual Researchers • Augment and link individual datasets according to best practices of the community or against colleagues • Share machine-interpretable code books with fellow researchers • Align codes and identifiers across datasets • Publish standards-compliant, reusable datasets Grow a giant graph of interconnected datasets
  • 12. Tools to explore, visualise, query, and analyse datasets.
  • 13. Future CSDH • Upload, describe, and store data. • Augment, harmonise, and link data. • Find, explore, query, visualise, and analyse data. • Share data, queries, and results.
  • 14. Today’s CSDH • Prototype up and running. • Loosely interconnected parts without a “hood”. • QBer (Rinke Hoekstra): intake, data description, harmonisation, linking. • Dedicated data pipelines. • Triplestore, data-API, queries (Kathrin Dentler). • Grlc (Albert Meronyo): Query-API . • Come see our demos and visit Github repos: https:// github.com/clariah/!
  • 15. Utrecht 1829 Utrecht 1839 QBer