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.
Color, emotion, and human body parts are often considered universal semantic domains. However, cross-linguistic comparison of the connections within each of the domains reveals interesting differences across languages (e.g., Jackson et al. 2019). Researchers in lexical typology systematically collect the names for colors, emotions, and human body parts across various languages and analyze the principles of how naming patterns evolve. Using the same word for multiple concepts or colexification, for example, Vietnamese xanh denoting the colors green and blue, is often discussed in those studies. To study the colexifications across the three semantic domains color, emotion, and human body part, we need a set of comparable concepts. This blog post introduces a list of 192 concepts across all three domains based on the available concept sets in Concepticon (List et al. 2016, 2021).
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 the summer of 2018, I set out to collect data for my master’s thesis (Tjuka 2019). The goal was to elicit body part terms that can also refer to object and landscape features. This was BC (before COVID-19), so I was able to meet with informants living in Berlin at the time and conduct my urban fieldwork study. The informants who participated in the study spoke one of 13 different languages (e.g., Wolof, Vietnamese, Czech). As a first task, I elicited 28 body part terms to get a sense of the naming patterns in each language. This blog post provides background information and introduces the resulting multilingual body part concept list.
In 2016, the Concepticon was introduced as a reference catalog for linguistic data of various kinds (List et al. 2016). The aim of the Concepticon project is to collect concept lists and link the glosses in the lists to unified concept sets (List et al. 2016; List et al. 2020). The project is a collaborative effort and the group of editors is constantly adding new data to the Concepticon (https://concepticon.clld.org/). In 2019, we implemented a review process that works similar to a submission process at an academic journal and is improving the quality of the resource in many ways. This blog post describes our effort to improve data validity by reviewing every single concept list that is added to the Concepticon. The database is curated openly on GitHub, so you can follow our review process by looking at several examples here: https://github.com/concepticon/concepticon-data
Over the past year, the doctoral students of our department have been organizing a weekly journal club. For me as a linguist, the discussions of various research articles opened up a whole new perspective on science in general and linguistics in particular. I learned about the interests and viewpoints of my fellow doctoral students and other researchers in our department working on historical linguistics, language documentation, and cultural evolution. In addition, organizing a journal club also helped me learn how to spark and lead a discussion. I hope the following personal insights offer some ideas and guidelines on how you can organize a journal club of your own.
I noticed that scientists deal with digital communication very differently: some avoid all sorts of platforms, others are much more present and involved in the discussions that are taking place in the online world. Digital communication as a linguist (or scientist in general) also includes sharing your research output. This does not have to be an article in a high-ranking journal. As a student, you can start by publishing your thesis, conference presentation slides, or a preprint. In this post, I’ll illustrate some of the possibilities that linguists and other researchers have to discuss and share their work.
The body of most human beings consist of similar parts such as a head, arms, legs, and so on. Many body parts also occur in animals. The shapes and functions of body parts are universal across cultures, but speakers of various languages choose to categorize the body differently. For example, Vietnamese has a single word (tay) for the concepts HAND and ARM. The universality of the human body and its categorization into different parts have attracted attention across research areas such as lexical typology and cognitive science. Therefore, I present a comprehensive list of human and animal body part terms based on German which were mapped to the concepts in the Concepticon (List et al. 2020). The list is intended for investigations on cross-linugistic naming patterns of body parts.
Scientific data should be openly accessible. This includes databases which are designed for collaborative work. However, in most cases, these databases are only extended by a team of experts. If a database is truly collaborative, the workflows need to be accessible for everybody. The Concepticon database (List et al., 2019) invites contributors to include their own data sets. This requires a transparent description of the contributing process.