The Multilingual Picture Database

Jon Andoni Duñabeitia, Ana Baciero, Kyriakos Antoniou, Mark Antoniou, Esra Ataman, Cristina Baus, Michal Ben-Shachar, Ozan Can Çağlar, Jan Chromý, Montserrat Comesaña, Maroš Filip, Dušica Filipović Đurđević, Margaret Gillon Dowens, Anna Hatzidaki, Jiří Januška, Zuraini Jusoh, Rama Kanj, Say Young Kim, Bilal Kırkıcı, Alina LeminenTerje Lohndal, Ngee Thai Yap, Hanna Renvall, Jason Rothman, Phaedra Royle, Mikel Santesteban, Yamila Sevilla, Natalia Slioussar, Awel Vaughan-Evans, Zofia Wodniecka, Stefanie Wulff, Christos Pliatsikas

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.

Original languageEnglish
Article number431
JournalScientific data
Issue number1
StatePublished - Dec 2022

Bibliographical note

Funding Information:
This research has been partially funded by the following grants: RED2018-102615-T and PID2021-126884NB-I00 from the Spanish Government and H2019/HUM-5705 from the Comunidad de Madrid granted to JAD; an Australian Research Council grant awarded to MA (DP190103067); a Ramon y Cajal research program awarded to CB (RYC2018-026174-I); an Israel Science Foundation grant awarded to MBS (1083/17); Funding by the Alexander von Humboldt Foundation awarded to JC; a Specifický vysokoškolský výzkum grant awarded to JC and JJ (260555); a Specifický vysokoškolský výzkum grant awarded to MF (260481); a National Research Fund awarded to SYK (NRF-2019R1G1A1100192); University of Helsinki funds awarded to AL; a Horizon 2020 grant awarded to NTY (H2020-MSCA-ITN-2017, 765556); an Academy of Finland grant awarded to HR (321460); an AcqVA Aurora Center of Excellence grant awarded to JR; an National Science Centre Poland grant awarded to ZW (2015/18/E/HS6/00428); a Spanish Government research grant awarded to MS (PGC2018-097970-B-I00); University of Montreal funds awarded to PR, and a Saint Petersburg State University grant awarded to NS (75288744, 121050600033-7); an award by the Cyprus Research and Innovation Foundation to KA (CULTURE/AWARD-YR/0421B/0005). The authors would like to thank the following colleagues for their support with various tasks, including translation of the materials, and data collection, screening, and preprocessing: Yolanda Acedo, Andrea Balázs, Ariane Brucher, Lihi Catz, Candela Dindurra, Ewa Haman, Marie Anna Hamanová, Máté Hegedűs, Boyoung Lee, Pantelis Lioumis, Viktória Balla, Magda Łuniewska, Yijin Lin, Gábor Marics, Khadidja Meftah, Ksenija Mišić, Marisol Murujosa, Helena Oliveira, Fanni Patay, Edurne Petrirena, Shen Qinfang, Michał Remiszewski, Rebeca Sanchez, Dana Suri-Barot and Agata Wolna.

Publisher Copyright:
© 2022, The Author(s).


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