struc data auke rijpma
TRANSCRIPT
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Work Package 4: Structured data from
economic and social historyAuke Rijpma January 2016
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Predecessors
• Economic and social historians often work with structured (tabular) data.
• Includes large data-projects…
• …and many small data sets.
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Problems to solve
• Finding data in multiples repositories.
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Problems to solve
• Finding data in multiples repositories.
• Harmonisation.
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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.
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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.
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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
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Tools to explore, visualise, query, and analyse datasets.
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Future CSDH
• Upload, describe, and store data.
• Augment, harmonise, and link data.
• Find, explore, query, visualise, and analyse data.
• Share data, queries, and results.
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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/!
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Utrecht 1829 Utrecht 1839
QBer
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Triplestore, data-API, queries, queries-API