Data sharing and standardization in Linguistics

Abstract Linguistic typology stands to gain significantly from advances in the use of extremely large, high-quality and reusable datasets. However, our ability to secure these gains depends on the availability of precise and comparable machine-readable data. Here we identify the challenges and opportunities ahead. We introduce the DeAR principles (Decentralized, Automatically verified, Revisable), designed to guide and assist researchers to create diverse, high-resolution and robust datasets. We demonstrate the DeAR principles in action through the example of Paralex, a data standard (i.e., set of scientific conventions) developed collaboratively for lexicons of morphologically inflected forms. Our proposals aim to foster a more resilient and equitable infrastructure for the future of linguistic research.

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Publication Details

Journal
Linguistic Typology
Published
2026-11-06
DOI
https://doi.org/10.1515/lingty-2025-0083
Primary Topic
Research Data Management Practices
Type
article
Field-Weighted Citation Impact
0.00

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article

Data sharing and standardization in Linguistics

Helen Sims‐Williams, Erich R. Round, Robert Malouf, Matías Guzmán Naranjo et al.
Linguistic Typology
Research Data Management Practices
article

Data sharing and standardization in Linguistics

Helen Sims‐Williams, Erich R. Round, Robert Malouf, Matías Guzmán Naranjo, Farrell Ackerman, Jeff Parker, Borja Herce, Greville G. Corbett, Theodorus Fransen, Sacha Beniamine, Cormac Anderson, Olivier Bonami, Matteo Pellegrini, Jules Bouton, Mae Carroll, Andrea Sims, Dunstan Patrick Brown
article en

Abstract

Abstract Linguistic typology stands to gain significantly from advances in the use of extremely large, high-quality and reusable datasets. However, our ability to secure these gains depends on the availability of precise and comparable machine-readable data. Here we identify the challenges and opportunities ahead. We introduce the DeAR principles (Decentralized, Automatically verified, Revisable), designed to guide and assist researchers to create diverse, high-resolution and robust datasets. We demonstrate the DeAR principles in action through the example of Paralex, a data standard (i.e., set of scientific conventions) developed collaboratively for lexicons of morphologically inflected forms. Our proposals aim to foster a more resilient and equitable infrastructure for the future of linguistic research.

Linguistic Typology
Brigham Young University (US), Università Cattolica del Sacro Cuore (IT), Centre National de la Recherche Scientifique (FR), The University of Queensland (AU), The University of Melbourne (AU), University of Zurich (CH), Université Paris Cité (FR), San Diego State University (US), University of Surrey (GB), University of California San Diego (US), Laboratoire de Linguistique Formelle (FR), University of Bergen (NO), University of York (GB), The Ohio State University (US)
UK Research and Innovation, Leverhulme Trust, European Commission, H2020 Marie Skłodowska-Curie Actions
Openalex Percentile: Top 73%
Research Data Management Practices
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