In a recent blog post, I introduced the Database of Cross-Linguistic Norms, Ratings, and Relations for Words and Concepts (NoRaRe) and demonstrated how to add new data sets (Tjuka 2021). The database currently includes 65 unique word and concept properties based on 98 different data sets across 40 languages (NoRaRe v0.2, Tjuka et al. 2021a) and can easily be expanded further. But what can we do with the data? The article presenting the NoRaRe database already included three case studies that illustrate the application of the database (Tjuka et al. 2021b). This blog post, therefore, provides a tutorial on how to compare NoRaRe data sets in R by conducting a new case study that correlates ratings on arousal in English and Dutch.
There has been much discussion about the reproducibility of research and how it can be improved (Munafò et al. 2017). One antidote to the “reproducibility crisis” seems obvious: data sharing. However, one additional point that is not mentioned as often is the standardization of shared data to make them comparable. Especially for cross-linguistic studies, this is an important step that needs to take place so that we can conduct reproducible studies in different languages. The NoRaRe database (Tjuka et al. 2021a) is a resource that provides standardized cross-linguistic data on norms, ratings, and relations published in psychology and linguistics. In this blog post, I describe a beginner’s guide to adding data sets to NoRaRe.
In lexicography, there are two commonly used types of semantic data categorization: semantic domains and semantic labels. The difference between the two approaches is simple: semantic domains presume that each lexical meaning belongs to one and only one group (or sub-group of a larger group). Semantic labels do not have such limitations.
The CLICS database in its current format makes direct use of the data assembled by the Concepticon project in order to aggregate lexical data from different sources. At the same time, the CLICS database itself can be seen as an interesting conceptlist, providing information on concept polysemy and semantic similarity.
While the core of the Concepticon project (https://concepticon.clld.org, List et al. 2019) are the numerous conceptlists which are constantly being added by the growing list of contributors, we have already from the beginning of the project, with the first version (List et al. 2016) tried to collect various kinds of concept metadata for all our concept sets.