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Best practices for spatial language data harmonization, sharing and map creation—A case study of Uralic
Despite remarkable progress in digital linguistics, extensive databases of geographical language distributions are missing. This hampers both studies on language spatiality and public outreach of language diversity. We present best practices for creating and sharing digital spatial language data by...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9176854/ https://www.ncbi.nlm.nih.gov/pubmed/35675367 http://dx.doi.org/10.1371/journal.pone.0269648 |
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author | Rantanen, Timo Tolvanen, Harri Roose, Meeli Ylikoski, Jussi Vesakoski, Outi |
author_facet | Rantanen, Timo Tolvanen, Harri Roose, Meeli Ylikoski, Jussi Vesakoski, Outi |
author_sort | Rantanen, Timo |
collection | PubMed |
description | Despite remarkable progress in digital linguistics, extensive databases of geographical language distributions are missing. This hampers both studies on language spatiality and public outreach of language diversity. We present best practices for creating and sharing digital spatial language data by collecting and harmonizing Uralic language distributions as case study. Language distribution studies have utilized various methodologies, and the results are often available as printed maps or written descriptions. In order to analyze language spatiality, the information must be digitized into geospatial data, which contains location, time and other parameters. When compiled and harmonized, this data can be used to study changes in languages’ distribution, and combined with, for example, population and environmental data. We also utilized the knowledge of language experts to adjust previous and new information of language distributions into state-of-the-art maps. The extensive database, including the distribution datasets and detailed map visualizations of the Uralic languages are introduced alongside this article, and they are freely available. |
format | Online Article Text |
id | pubmed-9176854 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-91768542022-06-09 Best practices for spatial language data harmonization, sharing and map creation—A case study of Uralic Rantanen, Timo Tolvanen, Harri Roose, Meeli Ylikoski, Jussi Vesakoski, Outi PLoS One Research Article Despite remarkable progress in digital linguistics, extensive databases of geographical language distributions are missing. This hampers both studies on language spatiality and public outreach of language diversity. We present best practices for creating and sharing digital spatial language data by collecting and harmonizing Uralic language distributions as case study. Language distribution studies have utilized various methodologies, and the results are often available as printed maps or written descriptions. In order to analyze language spatiality, the information must be digitized into geospatial data, which contains location, time and other parameters. When compiled and harmonized, this data can be used to study changes in languages’ distribution, and combined with, for example, population and environmental data. We also utilized the knowledge of language experts to adjust previous and new information of language distributions into state-of-the-art maps. The extensive database, including the distribution datasets and detailed map visualizations of the Uralic languages are introduced alongside this article, and they are freely available. Public Library of Science 2022-06-08 /pmc/articles/PMC9176854/ /pubmed/35675367 http://dx.doi.org/10.1371/journal.pone.0269648 Text en © 2022 Rantanen et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Rantanen, Timo Tolvanen, Harri Roose, Meeli Ylikoski, Jussi Vesakoski, Outi Best practices for spatial language data harmonization, sharing and map creation—A case study of Uralic |
title | Best practices for spatial language data harmonization, sharing and map creation—A case study of Uralic |
title_full | Best practices for spatial language data harmonization, sharing and map creation—A case study of Uralic |
title_fullStr | Best practices for spatial language data harmonization, sharing and map creation—A case study of Uralic |
title_full_unstemmed | Best practices for spatial language data harmonization, sharing and map creation—A case study of Uralic |
title_short | Best practices for spatial language data harmonization, sharing and map creation—A case study of Uralic |
title_sort | best practices for spatial language data harmonization, sharing and map creation—a case study of uralic |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9176854/ https://www.ncbi.nlm.nih.gov/pubmed/35675367 http://dx.doi.org/10.1371/journal.pone.0269648 |
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