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.