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Beyond users and speakers, language data also needs to be planned in ways that are good for the dataset authors. Thus, we introduced the DeAR principles:


Data is decentralised with no single team or institution operating a central database. The standard serves as a format to share data and as a means for researchers to create interoperable data of high-quality. We wish to make the standard as easy to use as possible, and to useful tools to its users.

Automated verification

Data is tested automatically against the descriptions in the metadata in order to guarantee data quality. Moreover, data quality can be checked by writing custom tests (as is done in software development), which are run after each change of the data.

Revisable pipelines

Dataset authors must be able to continuously update data presentation, in particular websites, reflecting the evolving nature of data. This is achieved by generating those publications automatically and directly from the standardized dataset. We will create automated tools which can generate user-friendly views of the data (for example static websites, publication ready pdfs, etc.). These can be run again at any point, so that it is easy to re-generate those from the data edited by the researchers.

Both principes A and R fit particularly well with the use of versioning systems such as git, where validation, testing and publishing can be done through continuous development pipelines.