List-2018-Colexifications & Rzymski-2020-Colexifications

datasets.tsv

@@ -115,6 +115,8 @@ Zalizniak-2020-SemanticShifts	Anna Zalizniak and Anna Smirnitskaya and Maksim Ru
 Mochizuki-2026-Sensorimotor	Mochizuki, Masaya and Ota, Naoto	2026	ratings	Japanese	Japanese	https://doi.org/10.3758/s13428-025-02939-1	Mochizuki2026	This list includes ratings of body-object interaction (BOI), i.e., the extent to which the word refers to an object or thing a human body can easily interact with, for 5,637 Japanese words. Items were rated on a 7-point scale (1 = low body–object interaction, 7 = high body–object interaction).		
 Dulay-2026a-AoA	Dulay, Katrina May and Mirković, Jelena and Fua, Margaret Mary Rosary Carmel and Prabhu, Deeksha and Nag, Sonali	2026	ratings	Filipino	Filipino	https://doi.org/10.3758/s13428-025-02876-z	Dulay2026	This list contains age of acquisition (AoA) ratings for 885 Filipino words, collected from parents, teachers, and experts. In addition to the ratings, the original dataset includes the age band of first occurrence of a given word in a children’s book corpus, as well as expert ratings of the age suitability of the source books. While the original study was conducted with Filipino words, the present mapping is based on the English translations given for these items. The original study further includes AoA ratings for Kannada words, which can be found in a separate list, see Dulay-2026b-AoA.
 Dulay-2026b-AoA	Dulay, Katrina May and Mirković, Jelena and Fua, Margaret Mary Rosary Carmel and Prabhu, Deeksha and Nag, Sonali	2026	ratings	Kannada	Kannada	https://doi.org/10.3758/s13428-025-02876-z	Dulay2026	This list contains age of acquisition (AoA) ratings for 885 Kannada words, collected from parents, teachers, and experts. In addition to the ratings, the original dataset includes the age band of first occurrence of a given word in a children’s book corpus, as well as expert ratings of the age suitability of the source books. While the original study was conducted with Kannada words, the present mapping is based on the English translations given for these items. The original study further includes AoA ratings for Filipino words, which can be found in a separate list, see Dulay-2026a-AoA.
+List-2018-Colexifications	List, Johann-Mattis and Greenhill, Simon J. and Anderson, Cormac and Mayer, Thomas and Tresoldi, Tiago and Forkel, Robert	2018	relations	English	Global	http://clics.clld.org	List2018	This is the concept list underlying the second release of the [Database of Cross-Linguistic Colexifications (CLICS2)](http://clics.clld.org) (for the first version see, [List 2014](:bib:List2014). Based on the CLDF framework, 15 data sets were included in the second version of CLICS. The data comprises 1220 language varieties and 2486 distinct Concepticon concept sets. Instead of providing full graphs for the networks, we provide links to the major clusters for each concept in CLICS. The column ``CENTRAL_CONCEPT`` lists the most central node in each of the community clusters in CLICS2. The community identifier is listed in column ``COMMUNITY``. The columns ``FAMILY_FREQUENCY``, ``LANGUAGE_FREQUENCY`` and ``WORD_FREQUENCY`` list the number of times a translation for a given concept occurs in the CLICS2 data. The columns ``DEGREE``, ``WEIGHTED_FAMILY_DEGREE``  and ``WEIGHTED_LANGUAGE_DEGREE`` contain the unweighted and weighted degree for each node.		List-2018-1105
+Rzymski-2020-Colexifications	Christoph Rzymski and Tiago Tresoldi and Simon J. Greenhill and Mei-Shin Wu and Nathanael E. Schweikhard and Maria Koptjevskaja-Tamm and Volker Gast and Timotheus A. Bodt and Abbie Hantgan and Gereon A. Kaiping and Sophie Chang and Yunfan Lai and Natalia Morozova and Heini Arjava and Nataliia Hübler and Ezequiel Koile and Steve Pepper and Mariann Proos and Briana Van Epps and Ingrid Blanco and Carolin Hundt and Sergei Monakhov and Kristina Pianykh and Sallona Ramesh and Russell D. Gray and Robert Forkel and Johann-Mattis List	2020	relations	English	Global	http://clics.clld.org	Rzymski2020	This is the concept list underlying the third release of the [Database of Cross-Linguistic Colexifications (CLICS3)](http://clics.clld.org) (for the first version see, [List 2014](:bib:List2014). The data comprises 3156 language varieties and 2906 distinct Concepticon concept sets. Instead of providing full graphs for the networks, we provide links to the major clusters for each concept in CLICS. The column ``CENTRAL_CONCEPT`` lists the most central node in each of the community clusters in CLICS3. The community identifier is listed in column ``COMMUNITY``. The columns ``FAMILY_FREQUENCY``, ``LANGUAGE_FREQUENCY`` and ``WORD_FREQUENCY`` list the number of times a translation for a given concept occurs in the CLICS3 data. The columns ``DEGREE``, ``WEIGHTED_FAMILY_DEGREE``  and ``WEIGHTED_LANGUAGE_DEGREE`` contain the unweighted and weighted degree for each node.		Rzymski-2020-1624
 Wang-2026-Utility	Wang, Andrew and Brysbaert, Marc and Günther, Fritz	2026	ratings	English	English	https://doi.org/10.3758/s13428-026-03051-8	Wang2026	This list contains ratings of utility of 80,000+ English words and multiword expressions (MWE) given on a best-worst scale (BWS). Participants were asked to select the most useful and the least useful word or multiword expression out of a 6-tuple. Utility scores were computed using the Value Learning algorithm [(Hollis 2018](:bib:Hollis2018). For each item, the number of times it was selected as “most useful” was summed and the number of times it was selected as “least useful” was subtracted. This best–worst difference score was then scaled across all items to a 0–1 range. The list also includes familiarity scores by an LLM (GPT-4). Originally, these scores were given on a 7-point scale (1 = completely unfamiliar, 7 = completely familiar), however only items with a familiarity score >3.5 were included in the analysis with the exception of a small number of catch trials that fall below this threshold. Further, some items exceed the threshold of 7 slightly. Multilex frequencies are provided in the original dataset but not included here.	
 Jiang-2026-Frequency	Jiang, Zehua R. and Siew, Cynthia S.Q.	2026	norms	Singapore English	Singapore English	https://doi.org/10.3758/s13428-026-03012-1	Jiang2026	This list contains frequency and contextual diversity measures for words in the Singapore English National Speech Corpus (NSC) [(Koh et al. 2019)](:bib:Koh2019).	
-Song-2026-Emotions	Song, Dangui	2026	ratings	Chinese	Chinese	https://doi.org/10.3758/s13428-026-03010-3	Song2026	 This list contains ratings for Chinese words on ten discrete emotion categories: happiness, anger, sadness, fear, disgust, anxiety, surprise, contentment, amusement and serenity. Participants were native speakers of Chinese and rated words on a 5-point scale (1 = not at all, 5 = extremely).
+Song-2026-Emotions	Song, Dangui	2026	ratings	Chinese	Chinese	https://doi.org/10.3758/s13428-026-03010-3	Song2026	 This list contains ratings for Chinese words on ten discrete emotion categories: happiness, anger, sadness, fear, disgust, anxiety, surprise, contentment, amusement and serenity. Participants were native speakers of Chinese and rated words on a 5-point scale (1 = not at all, 5 = extremely).