I recently converted the Gothic dictionary written by Wilhelm Streitberg to a CLDF wordlist. Since I was using Windows, I had some difficulties during the conversion progress, which Unix system users may not have to deal with. I thought it would be useful to share my experience here and point out that users of Windows operating systems should be aware of certain aspects when converting data to CLDF.
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.
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.
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.
After about three years of work on the matter, I have managed (with help of many colleagues who helped in testing) to develop a first approach for the automatic inference of sound correspondence patterns, which will soon be published with Computational Linguistics (List 2019). The key task which this algorithm solves is to take aligned data as input and to compute explicit sound correspondence patterns from the alignments.