2. Predecessors
• Economic and social historians often work with
structured (tabular) data.
• Includes large data-projects…
• …and many small data sets.
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
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/!