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The Intermediate Data Structure (IDS) for Longitudinal Historical Microdata, version 4.

George Alter1, Kees Mandemakers2

  • 1Inter-university Consortium for Political and Social Research & University of Michigan.

Historical Life Course Studies
|December 4, 2018
PubMed
Summary
This summary is machine-generated.

Version 4 of the Intermediate Data Structure (IDS) is now available, offering an updated standard for historical population data. This release includes significant user-driven enhancements for improved data management and analysis in longitudinal studies.

Keywords:
Comparative ResearchData ModelDemographyEntity Attribute Value ModelHistorical DemographyHistoryIDSIntermediate Data StructureLife CoursesSocial History

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Area of Science:

  • Historical demography
  • Social science data management
  • Computational history

Background:

  • The Intermediate Data Structure (IDS) is a foundational data format for historical population databases.
  • Previous versions were published in 2009 and subsequently updated.
  • Ongoing user engagement has driven the evolution of the IDS standard.

Purpose of the Study:

  • To present version 4 of the Intermediate Data Structure (IDS), the latest official standard.
  • To detail the significant updates and enhancements incorporated into this new version.
  • To provide guidance for the effective use of the IDS in historical research.

Main Methods:

  • Incorporation of a new table for hierarchical 'context' relationships.
  • Development of decision schemes for recording complex relationships.
  • Addition of fields to the metadata table for richer data description.
  • Establishment of rules for handling stillbirth data.
  • Introduction of a reciprocal model for representing relationships.
  • Provision of guidance for linking IDS data with geospatial information.
  • Development of an extended IDS for computed variables.

Main Results:

  • Version 4 of the IDS offers a more robust and flexible framework for historical population data.
  • Enhanced relationship modeling and metadata capabilities improve data integration and analysis.
  • Specific guidelines address previously complex data types like stillbirths.
  • The extended IDS facilitates the inclusion of derived and computed variables.

Conclusions:

  • Version 4 represents a significant advancement in the standardization of historical population data.
  • The updated IDS supports more sophisticated and comprehensive analyses of longitudinal datasets.
  • This standard will benefit researchers working with large-scale historical population data.