The Dogon and Bangime Linguistics project (https://dogonlanguages.org) offers a large comparative spreadsheet in which translational equivalents for a huge number of concepts are translated into various Dogon languages. Due to its enormous size, no attempts have been made so far to integrate the spreadsheet with the lexical resources that were compiled as part of the CLDF initiative in order to populate the Lexibank repository. Here, we report a first attempt to circumvent the problems resulting from the size of the spreadsheet by convert not all but parts of the spreadsheet to CLDF Wordlist standards, which allows us to integrate parts of the data with other resources in Lexibank.
Newari is one of the few ancient Sino-Tibetan languages attested in written texts. Since previous studies on the phylogeny of Sino-Tibetan did not take Newari data into account, we felt it is important to close this gap by providing an up-to-date comparative wordlist of Newari varieties. This wordlist has now been finalized in a first version that has additionally been standardized following the recommendations of the Cross-Linguistic Data Formats initiative.
With the introduction of CLDF as a major format for data exchange, there is an increased need in handy solutions for the conversion of CLDF to the formats required by computer-assisted tools like LingPy and EDICTOR, which allow to preprocess data automatically or to curate data by adding detailed annotations. With the publication PyEDICTOR, there is now a very lightweight software package that is supposed to provide first solutions for a successful integration of CLDF with these existing tools for computer-assisted language comparison.
A few days ago, Sidwell and Alwes submitted a very nice dataset on Vietic languages to Zenodo (10.5281/zenodo.5263194). When inspecting the data, I realized that this dataset could be easily converted to our CLDF formats in our new Lexibank standards. Since both authors explicitly invited for discussions of the data and the testing of the results, I thought it would even be better to quickly illustrate the CLDF conversion in a blog post, as this may enable colleagues to do the same with their datasets in the future.