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The Multilingual Picture Database

Journal
Scientific Data
Date Issued
July 21, 2022
Author(s)
Duñabeitia, Jon Andoni  
Baciero, Ana  
Antoniou, Kyriakos  
Antoniou, Mark  
Ataman, Esra  
Baus, Cristina  
Ben-Shachar, Michal  
Çağlar, Ozan Can  
Chromý, Jan  
Comesaña, Montserrat  
Filip, Maroš  
Đurđević, Dušica Filipović  
Dowens, Margaret Gillon  
Hatzidaki, Anna  
Januška, Jiří  
Jusoh, Zuraini  
Kanj, Rama  
Kim, Say Young  
Kırkıcı, Bilal  
Leminen, Alina  
Lohndal, Terje  
Yap, Ngee Thai  
Renvall, Hanna  
Rothman, Jason  
Royle, Phaedra  
Santesteban, Mikel  
Sevilla, Yamila  
Slioussar, Natalia  
Vaughan-Evans, Awel  
Wodniecka, Zofia  
Wulff, Stefanie  
Pliatsikas, Christos  
DOI
10.1038/s41597-022-01552-7
Abstract
The growing interdisciplinary research field of psycholinguistics is in constant need of new and up-to-date tools which will allow researchers to answer complex questions, but also expand on languages other than English, which dominates the field. One type of such tools are picture datasets which provide naming norms for everyday objects. However, existing databases tend to be small in terms of the number of items they include, and have also been normed in a limited number of languages, despite the recent boom in multilingualism research. In this paper we present the Multilingual Picture (Multipic) database, containing naming norms and familiarity scores for 500 coloured pictures, in thirty-two languages or language varieties from around the world. The data was validated with standard methods that have been used for existing picture datasets. This is the first dataset to provide naming norms, and translation equivalents, for such a variety of languages; as such, it will be of particular value to psycholinguists and other interested researchers. The dataset has been made freely available.
Subjects

Databases

Factual

Language

Multilingualism

Psycholinguistics

Recognition

Psychology

File(s)
Thumbnail Image
Name

The Multilingual Picture.pdf

Size

1.08 MB

Format

Adobe PDF

Checksum (MD5)

dc6484db2674f1900e1784c0d551d78f

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