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.