Data in Historical Linguistics 2026 Seminars
Janvier 2026, Londres
Page de l’événement : lien
Lieu : King’s College (Londres)
Date limite de soumission : 5 décembre 2025
Modalités de soumission : Abstracts (maximum 400 words) should describe the dataset (language, size, object, methodology) and specify whether the analysis was conducted manually or computationally. Abstracts and personal information (name, surname, affiliation, email address) should be sent to either of the two convenors, Andrea Farina (andrea.farina@kcl.ac.uk) or Mathilde Bru (mathilde.bru@gmail.com)
Résumé :
We are pleased to announce that the call for abstracts for the third series of the Data in Historical Linguistics seminar series is now open. These seminars will be run by King’s College London and are aimed at PhD students and Early Career Researchers (ECRs). The purpose of this seminar series is to bring together researchers working on historical linguistics with a quantitative approach, and to discuss current avenues of research in this topic. Following on from the success of the first two series, we hope that this third seminar series will continue to nurture international collaboration and establish academic ties among researchers working on similar topics in this field.
Seminars will start in January 2026 and will last one hour.
This year, we welcome abstracts for two possible formats:
Research presentation: a 30-minute talk, followed by 30 minutes of discussion.
Interactive session: a 45-minute hands-on workshop in which the speaker presents and demonstrates digital/computational tools, with opportunities for participants to experiment with the tool themselves, followed by 15 minutes of discussion.
All seminars will be held remotely via Microsoft Teams on Mondays at 5pm GMT. They will provisionally be held fortnightly, depending on the number of speakers.
We invite proposals from PhD students and ECRs working on Historical Linguistics, with an emphasis on quantitative approaches. Abstracts (maximum 400 words) should describe the dataset (language, size, object, methodology) and specify whether the analysis was conducted manually or computationally.