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Data sharing and standardization in Linguistics

Year: 2026
Type: article
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.
DOI: 10.1515/lingty-2025-0083
Authors: Sacha Beniamine, Jules Bouton, Mae Carroll, Matteo Pellegrini, Cormac Anderson, Erich Round, Olivier Bonami, Dunstan Brown, Matías Guzmán Naranjo, Borja Herce, Andrea D. Sims, Helen Sims-Williams, Farrell Ackerman, Greville Corbett, Robert Malouf, Jeff Parker, Theodorus Fransen
Institutions: University of Surrey, Centre National de la Recherche Scientifique, Université Paris Cité, Laboratoire de Linguistique Formelle, The University of Melbourne, Università Cattolica del Sacro Cuore, The University of Queensland, University of York, University of Bergen, University of Zurich, The Ohio State University, University of California San Diego, San Diego State University, Brigham Young University
Language: English
Open Access status: diamond
OpenAlex ID: W7157310250
FWCI:
Cites: 61
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Related to: 10