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250 results for “Linguistics”
Supplementary data for the article Linguistic system and sociolinguistic environment as competing factors in linguistic variation: A typological approach
<p>This material contains the dataset and the R-scripts from the <a href="https://version.helsinki.fi/gramadapt/linguistic-system-and-sociolinguistic-environment">gitlab repository</a> of the following article. Please cite the article when using the data.</p> <p>Sinnemäki, Kaius 2020. Linguistic system and sociolinguistic environment as competing factors in linguistic variation: A typological approach. <em>Journal of Historical Sociolinguistics</em> 6(2): 20190101. <a href="https://doi.org/10.1515/jhsl-2019-1010">https://doi.org/10.1515/jhsl-2019-1010</a></p>
An agent-based model of the origins of modern linguistic complexity – supplementary information
<p>A central question in the evolution of human language is whether it emerged as a result of one specific event or from a mosaic-like constellation of different phenomena and their interactions. Three potential processes have been identified by recent research as the potential <em>primum mobile</em> for the origins of modern linguistic complexity: Self-domestication, characterized by a reduction in reactive aggression and often associated with a gracilization of the face; changes in early brain development manifested by globularization of the skull; and demographic expansion of H. sapiens during the Middle Pleistocene. We developed an agent-based model to investigate how these three factors influence transmission of information within a population. Our model shows that there is an optimal degree of both hostility and mental capacity at which the amount of transmitted information is the largest. It also shows that linguistic communi- ties formed within the population are strongest under circumstances where individuals have high levels of cognitive capacity available for information processing and there is at least a certain degree of hos- tility present. In contrast, we find no significant effects related to population size.</p>
Dataset for the paper "Typology of partitives", Linguistics
<p>This is the raw dataset for the paper "Typology of partitives", accepted for publication in Linguistics</p>
Linguistic praxeological organization of French as the schooling language
<p>Elements of the linguistic praxeological organization of French as the schooling language used to produce the framework (https://zenodo.org/deposit/4462850) used for the PEAPL project (https://blog.hepfr.ch/create/peapl/) and the framework used for the COMPER project (https://comper.fr/accueil).</p> <p>The following article will give explanations about the elaboration of the linguistic praxeological organizations: https://www.researchgate.net/publication/333244876_Francais_langue_de_scolarisation_Reflexions_sur_les_referentiels_de_competences_et_l'adaptive_learning</p>
Supplementary Material for "Sequence Comparison in Historical Linguistics"
<p>This is the official version of the supplementary material which was used as the basis for the study on "Sequence Comparison in Historical Linguistics" (List, Düsseldorf, Düsseldorf University Press).</p>
Linguistic Atlas Projects - Atlanta Survey Project - Version 1
<p>Director:<br> William A. Kretzschmar, Jr.</p> <p>Status of work:<br> Field work complete in 2003. Direct CD recordings; some full transcriptions have been completed.</p> <p>Areas covered:<br> Fulton and DeKalb counties in the Atlanta, GA, metropolitan area.</p> <p>Subjects:<br> 18 speakers selected by random phone sampling, with later in-person interviews. African American and nonAfrican American subjects, stratified by gender and occupational status.</p> <p>Nature of recordings:<br> Guided conversational interview expected to last one hour, adapted from Western States interview. Fixed format elicitation in which speakers read words from cards. </p> <p>Archives:<br> Original materials are archived in the Special Collections repository of the Library, University of Georgia, Athens, Georgia 30602. For access to original materials, contact the Linguistic Atlas Project office.</p>
Phlorest phylogeny derived from Hruschka et al. 2015 'Detecting regular sound changes in linguistics as events of concerted evolution'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Hruschka, D. J., Branford, S., Smith, E. D., Wilkins, J., Meade, A., Pagel, M., & Bhattacharya, T. (2015). Detecting regular sound changes in linguistics as events of concerted evolution. Current Biology, 25(1), 1-9.</p> </blockquote>
Phlorest phylogeny derived from De Filippo et al. 2012 'Bringing together linguistic and genetic evidence to test the Bantu expansion'
<p>Cite the source of the dataset as:</p> <blockquote> <p>De Filippo, C., Bostoen, K., Stoneking, M., & Pakendorf, B. (2012). Bringing together linguistic and genetic evidence to test the Bantu expansion. Proceedings of the Royal Society B: Biological Sciences, 279(1741), 3256–3263. doi:10.1098/rspb.2012.0318</p> </blockquote>
Database of Cross-Linguistic Norms, Ratings, and Relations for Words and Concepts as CLDF dataset
<p>Cite the source of the dataset as:</p> <blockquote> <p>Tjuka, Annika, Robert Forkel, and Johann-Mattis List. 2022. Linking Norms, Ratings, and Relations of Words and Concepts Across Multiple Language Varieties. Behavior Research Methods 54. 864–884. DOI: 10.3758/s13428-021-01650-1</p> </blockquote>
Linguistic-symbolic classification of occupations
<p>As a result, it has been considered the following occupational categories, based on the degree of symbolic analysis and language intensity: (A) high symbolic analysts, (B) low symbolic analysts, (C) high intensity oral interaction with public (high public service), (D) low intensity oral interaction with public (low public service), and (E) manual labor occupations with limited symbolic and oral demands. Further, and within (B) category -low symbolic analysts-, it is distinguished between (B1) those whose work is generally inside the organization (such as file clerks) and (B2) those whose work includes interacting with the public (such as receptionists). In a similar way, (C) category is also divided into (C1) category of nurses and (C2-C5) which group the remainder of the high public service occupations.</p> <p>The result of the categorization of occupations by language use is summarized in the table below:</p> <table> <thead> <tr> <th>Major occupational classification</th> <th>Linguistic characteristics of occupation</th> <th>Sub classification</th> <th>Example of occupation</th> </tr> </thead> <tbody> <tr> <td>A: High symbolic analysts</td> <td>Produce/consume long or complex written communications, with variable but often important oral communication</td> <td>A1. Upper management</td> <td>Chief executive, human resource executive</td> </tr> <tr> <td> </td> <td> </td> <td>A2. Professionals</td> <td>Lawyer, doctor</td> </tr> <tr> <td> </td> <td> </td> <td>A3. Lower management</td> <td>First line manager/supervisor</td> </tr> <tr> <td> </td> <td> </td> <td>A4. High symbolic analysts, not managers</td> <td>Public relations specialists, computer systems specialists</td> </tr> <tr> <td>B: Low symbolic analysts</td> <td>Produce/consume short or simple written communications, with variable but often important oral communication</td> <td>B1. Low symbolic analysts with low likelihood of public interaction</td> <td>File clerks</td> </tr> <tr> <td> </td> <td> </td> <td>B2. Low symbolic analysts with high likelihood of public interaction</td> <td>Receptionists, billing/appointment clerks</td> </tr> <tr> <td>C: In-person service workers with high communicative demands</td> <td>Important oral communication, limited but present written skills, and high public interaction</td> <td>C1. Nurses</td> <td>Nurses</td> </tr> <tr> <td> </td> <td> </td> <td>C2. Assistants and technicians in public service settings</td> <td>Medical technicians</td> </tr> <tr> <td> </td> <td> </td> <td>C3. Police, etc.</td> <td>Police, detectives, investigators</td> </tr> <tr> <td> </td> <td> </td> <td>C4. Firefighters, emergency medical technicians</td> <td>Firefighters, emergency medical technicians</td> </tr> <tr> <td> </td> <td> </td> <td>C5. Miscellaneous</td> <td>Counselors, dispatchers</td> </tr> <tr> <td>D. In-person service workers with low communicative demands</td> <td>Simple oral communication and public interaction, very limited or no writing</td> <td>(no subcategories in our study)</td> <td>home health care aides, security guards</td> </tr> <tr> <td>E. Manual work</td> <td>Limited oral and written consumption and production</td> <td>E1. Skilled manual work</td> <td>Plumber</td> </tr> <tr> <td> </td> <td> </td> <td>E2. Unskilled manual work</td> <td>Janitor</td> </tr> </tbody> </table> <p> </p>
LLODIA (Linguistic Linked Open Data for Diachronic Analysis)
<p>LLODIA (Linguistic Linked Open Data for Diachronic Analysis) model developed within the Nexus Linguarum WG4 UC4.2.1 use case in humanities.</p>
Global linguistic data
This repository contains the source linguistic data files for an analysis of global language characteristics as they relate to global environmental variables
The relationship between linguistic expression and symptoms of depression, anxiety, and suicidal thoughts: A longitudinal study of blog content
<p>To investigate the associations between linguistic features and symptoms of depression, generalised anxiety, and suicidal ideation, we extracted linguistic features from individuals’ blog content and correlated it with validated mental health data in a longitudinal study (n=38). Depressive symptoms were assessed using the self-report Patient Health Questionnaire (PHQ-9), anxiety symptoms using the self-report Generalised Anxiety Disorder Scale (GAD-7), and social media data was analysed using the Linguistic Inquiry and Word Count (LIWC) tool for linguistic features. Bivariate and multivariate analyses were performed to investigate the correlations between the linguistic features and mental health scores between subjects. We then used the multivariate regression model to predict longitudinal changes in mood within subjects.</p>
A cross-linguistic computational approach on chance resemblances
<p>Material for the "A cross-linguistic computational approach on chance resemblances" presentation, with raw wordlists, lexeme analyses, and distribution statistics.</p>
CLDF dataset derived from Galucio et al.'s "Lexical Distances within the Tupian Linguistic family" from 2015
<p>Cite the source of the dataset as:</p> <blockquote> <p>Galucio, Ana Vilacy and Meira, Sérgio and Birchall, Joshua and Moore, Denny and Gabas Júnior, Nilson and Drude, Sebastian and Storto, Luciana and Picanço, Gessiane and Rodrigues, Carmen Reis. (2015). Genealogical relations and lexical distances within the Tupian linguistic family. Boletim do Museu Paraense Emílio Goeldi. Ciências Humanas, 10(2), 229-274. https://dx.doi.org/10.1590/1981-81222015000200004</p> </blockquote>
EvoBib: A Bibliographic Database and Quote Collection for Historical Linguistics
<p>This databases offers 4564 references dealing with computer-assisted language comparison in a broad sense. In addition, the database offers 8364 distinct quotes collected from 5063 references. The majority of the references in the quote database overlaps with those in the bibliographic database. The quotes are organized by keywords and can browsed with a full text and a keyword search. </p> <p>The data (references and quotes) underlying each new release are provided here, the data can be browsed at <a href="https://evobib.digling.org/">https://evobib.digling.org/</a>.</p> <p>If you use the database, I would appreciate if you could this in your research:</p> <blockquote> <p>List, Johann-Mattis (2024): EvoBib: A bibliographic database and quote collection [Database, Version 1.8.0]. Passau: Chair for Multilingual Computational Linguistics. URL: <a href="https://evobib.digling.org/" rel="nofollow">https://evobib.digling.org/</a></p> </blockquote> <p> </p> <p> </p> <p> </p>
CLDF dataset derived from Carling's "Diachronic Atlas of Comparative Linguistics" from 2017
<p>Cite the source of the dataset as:</p> <blockquote> <p>Carling, Gerd (ed.) 2017. Diachronic Atlas of Comparative Linguistics Online. Lund: Lund University. (DOI/URL: https://diacl.ht.lu.se/). Accessed on: 2019-02-07.</p> </blockquote>
CLDF dataset derived from List et al.'s "Database of Cross-Linguistic Colexifications (Version 1.0)" from 2014
<p>Cite the source of the dataset as:</p> <blockquote> <p>List, Johann-Mattis, Thomas Mayer, Anselm Terhalle, and Matthias Urban (2014). CLICS: Database of Cross-Linguistic Colexifications. Marburg: Forschungszentrum Deutscher Sprachatlas (Version 1.0).</p> </blockquote>
LOGOS Data from the Database of Cross-Linguistic Colexifications (Version 1.0)
<p>Cite the source of the dataset as:</p> <blockquote> <p>List, Johann-Mattis, Thomas Mayer, Anselm Terhalle, and Matthias Urban (2014). CLICS: Database of Cross-Linguistic Colexifications. Marburg: Forschungszentrum Deutscher Sprachatlas (Version 1.0).</p> </blockquote>
Euclide, the crow, the wolf and the pedestrian: distance metrics for linguistic typology - Dataset
<p>This repository contains the distance matrices and code for the paper "Euclide, the crow, the wolf and the pedestrian: distance metrics for linguistic typology"</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.