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
Multi-SimLex (https://multisimlex.com) is a multilingual resource which provides user ratings for word pairs translated into different languages. The data is important for the evaluation of methods that derive word embeddings from large corpora. While it is on the one hand desirable to link such a large dataset to Concepticon, it is difficult to do so in concrete, given that the datasets represents word similarity ratings withouth any clear reference to concepts. In this post, I will show how the data can nevertheless be linked to Concepticon, and how the original Multi-SimLex data can be represented without losing any information in the form of a Concepticon Concept List.
Semantic data are notoriously difficult to handle. In contrast to the form-part of the linguistic sign, meanings are not organized sequentially, but rather network-like (List 2014: 34f). As a result, we often encounter problems when trying to model complex relations between different meanings, specifically in those cases, where we have only tables as our base material. This blog post tries to summarize how major types of semantic data are handled in the Concepticon project and how they can be accessed in code.
For a long time, I have been wondering about the origin of the German wordlist in the Intercontinental Dictionary Series (Key and Comrie 2016. Not only are many of the words given as translations for the large concept list of 1310 items very archaic variants, which are no longer in use, we also find many annoying problems, such as unusual spellings (consequently avoiding the letter “ß”, which is still in use, even if some people think differently), wrong translations, and, of course, no phonetic transcriptions. Already during my doctoral studies, I therefore started to work on a refined list, but I soon had so many other things on my plate, that I never really managed to finish this work. Recently, however, I realized that my previous work which I had done years ago was far more complete than I had thought, and I had even added information on potential borrowings, extracted from Kluge’s (2002) etymological dictionary. Given that this list can come in handy in various ways, I decided to finish the work and publish the list officially in a very first version.
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.
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.
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.
One of the crucial steps in creating a database of lexical cognates is the selection of concepts one wants to use for a given study. While many scholars use the classical Swadesh list of 200 items) (Swadesh 1952) for this purpose, or the combined list of 207 items, in which the former has been merged with Swadesh’s updated list of 100 items (Swadesh 1955), and which is often mistakenly attributed to Swadesh himself, although the first official reference seems to be Comrie (1977), it is useful to give the selection of concepts some more thought initially.
Mapping a given concept list to Concepticon can be done in a straight-forward way, even if automatic mappings need manual refinement. But what can we do when having to deal with larger datasets, say, a dictionary, from which we want to extract specific concepts, such as, for example, the ones in the classical Swadesh list of 100 items (Swadesh 1955)?
Imagine you have two different datasets, both containing approximately the same concepts, but slightly different numbers of columns and — more importantly — potentially identical identifiers in the first column. A bad idea for merging these datasets would be to paste them in Excel or some other kind of spreadsheet software, and then trying to manually adjust all problems that might occur during this process.
A better idea is to just use LingPy and our CLDF curation framework Continue reading
Robert Forkel just published a very nice cookbook example for our CLICS database (List et al. 2018f, http://clics.clld.org), where you can find out how to manipulate the data further, apart from just installing it and running it to replicate our analyses.
This cookbook tells you how the underlying SQLITE database is structured and how you can, after installing CLICS and the respective packages, access the data to conduct studies of your own.
As a little example of what you can do with the new CLICS API, let me illustrate in this post, how we can use the old CLICS data (underlying the version 1.0 by List et al. 2014, http://clics.lingpy.org), available from here, in the new application, specifically the standalone that we provide.
When dealing with linguistic datasets, we may often want to export only a small part of our data, for example, only vocabulary in a certain range, such as the Swadesh list of 200 items or the list of 35 items by Yakhontov (originally published in Starostin 1991. Thanks to the
pyconcepticon API and LingPy’s built-in export functions for wordlists, this task can be done just in a few lines of code, as we will see below. If you prefer to see the raw code instead of the step-by-step explanation below, you can find a GitHub Gist here.