Merging datasets with LingPy and the CLDF curation framework

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 “Merging datasets with LingPy and the CLDF curation framework”

From Fieldwork to Trees 2: Cognate coding

In a previous post (Kaiping 2018), I described how to convert matrix-shape word lists given in Excel into the long format LingPy and other software can work with. My motivation for this was to provide my colleague Yunus Sulistyono with a good way to compare the lexicon of his Alorese [alor1247] dialects, and to understand the relationship between them. In this post, the data is automatically cognate coded and converted into CLDF. Continue reading “From Fieldwork to Trees 2: Cognate coding”

Representing Structural Data in CLDF

The Cross-Linguistic Data Formats initiative (CLDF, https://cldf.clld.org, Forkel et al. 2018) has helped a lot in preparing the CLICS² database of cross-linguistic colexifications (https://clics.lingpy.org, List et al. 2018), since linking our data to Concepticon (https://concepticon.clld.org, List et al. 2016) and Glottolog (https://glottolog.org, Hammarström et al. 2018) has provided incredible help in merging the different datasets into a big comparative dataset.

CLDF, however, is not restricted to lexical data, but can also be successfully used to store structural data, although — due to the nature of structural data — it is much more difficult to compare different datasets.

Continue reading “Representing Structural Data in CLDF”