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Big Data and Research Opportunities Using HRAF Databases

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Book cover Big Data in Computational Social Science and Humanities

Part of the book series: Computational Social Sciences ((CSS))

Abstract

The HRAF databases, eHRAF World Cultures and eHRAF Archaeology, each containing large corpora of curated text subject-indexed at the paragraph-level by anthropologists, were designed to facilitate rapid retrieval of information. The texts describe social and cultural life in past and present societies around the world. As of the spring of 2018, eHRAF contains almost three million indexed “paragraph” units from over 8000 documents describing over 400 societies and archaeological traditions. This chapter first discusses concrete problems of scale resulting from large numbers of complex elements retrieved by any given search. Second, we discuss potential and partial solutions that resolve these problems to advance research, whether based on specific hypotheses, classification or identifying and evaluating embedded patterns of relationships. Third, we discuss new kinds of research possibilities that can be further advanced, have not yet been successfully attempted, or have not even been considered using anthropological data because of scale and complexity of achieving a result.

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Author information

Authors and Affiliations

  1. University of Kent, Canterbury, UK

    Michael D. Fischer

  2. Human Relations Area Files at Yale University, New Haven, CT, USA

    Michael D. Fischer & Carol R. Ember

Authors
  1. Michael D. Fischer
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  2. Carol R. Ember
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Corresponding author

Correspondence to Carol R. Ember .

Editor information

Editors and Affiliations

  1. AI-ECON Research Center, Department of Economics, National Chengchi University, Taipei, Taiwan

    Shu-Heng Chen

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Fischer, M.D., Ember, C.R. (2018). Big Data and Research Opportunities Using HRAF Databases. In: Chen, SH. (eds) Big Data in Computational Social Science and Humanities. Computational Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-95465-3_17

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  • DOI: https://doi.org/10.1007/978-3-319-95465-3_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95464-6

  • Online ISBN: 978-3-319-95465-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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