Tools for Integrating Data by Complex, Dynamic Categories

ABSTRACT A key challenge in conducting comparative analyses across social units, such as religions, ethnicities, or cultures, is that data on these units is often encoded in distinct and incompatible formats across diverse datasets. This can involve simple differences in the variables and values used to encode these units (e.g., Roman Catholic is V130 = 1 vs. Q98A = 2 in two different datasets) or differences in the resolutions at which units are encoded (Maya vs. Kaqchikel Maya). These disparate encodings can create substantial challenges for the efficiency and transparency of data syntheses across diverse datasets. We introduce a user‐friendly set of tools to help users translate four kinds of categories (religion, ethnicity, language, and subdistrict) across multiple, external datasets. We outline the platform's key functions and current progress, as well as long‐range goals for the platform.

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Publication Details

Journal
Proceedings of the Association for Information Science and Technology
Published
2024-10-01
DOI
https://doi.org/10.1002/pra2.1145
Primary Topic
Complex Systems and Time Series Analysis
Type
article
Field-Weighted Citation Impact
0.00
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article

Tools for Integrating Data by Complex, Dynamic Categories

Yi‐Yun Cheng, Cindy Huang, Matthew A. Peeples, Daniel J. Hruschka et al.
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Proceedings of the Association for Information Science and Technology
Complex Systems and Time Series Analysis
article

Tools for Integrating Data by Complex, Dynamic Categories

Yi‐Yun Cheng, Cindy Huang, Matthew A. Peeples, Daniel J. Hruschka, I‐Han Hsiao, Robert Bischoff, Harsha Kasi
article en

Abstract

ABSTRACT A key challenge in conducting comparative analyses across social units, such as religions, ethnicities, or cultures, is that data on these units is often encoded in distinct and incompatible formats across diverse datasets. This can involve simple differences in the variables and values used to encode these units (e.g., Roman Catholic is V130 = 1 vs. Q98A = 2 in two different datasets) or differences in the resolutions at which units are encoded (Maya vs. Kaqchikel Maya). These disparate encodings can create substantial challenges for the efficiency and transparency of data syntheses across diverse datasets. We introduce a user‐friendly set of tools to help users translate four kinds of categories (religion, ethnicity, language, and subdistrict) across multiple, external datasets. We outline the platform's key functions and current progress, as well as long‐range goals for the platform.

Proceedings of the Association for Information Science and TechnologyVol. 61(1)
Rutgers, The State University of New Jersey (US), Santa Clara University (US), Arizona State University (US)
Openalex Percentile: Top 78%
Complex Systems and Time Series Analysis
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Tools for Integrating Data by Complex, Dynamic Categories — Yi‐Yun Cheng, Cindy Huang, et al. · Proceedings of the Association for Information Science and Technology (2024) | TGRS Research Map | TGRS