The talk covers the issue of storing a comparable learner corpus on a data repository. Comparable corpora are made up of many files which need to be accessed in an orderly manner so as to extract coherent datasets. Subsequent analyses can then be conducted in order to make comparisons between speakers of different L1s or L2s. We illustrate the issue with the Corpus InterLangue (CIL) which is a learner corpus of L2 French and English. He showed that the corpus storage architecture now supports online extractions and comparisons in both languages. Based on the Huma-Num Nakala infrastructure and with the use of R scripts, it is possible to extract corpus items, annotate texts automatically and create datasets supporting comparisons. Comparability is ensured in several stages. Firstly, the French and English subsets of the corpus were collected on the same basis, i.e. identical tasks, similar proficiency, same file types and same metadata types. Secondly, the corpus data was formatted following the same transcription protocol and data formats (WAV for audio recordings, XML and txt for transcriptions, and CSV for metadata). Finally, the queries can be conducted with scripts that apply consistent extraction on the basis of identical metadata information. The scripts also include automated linguistic annotation with UDPipe, providing French and English texts with Universal Dependency and Part-of-Speech annotation. The scripts can be modified as they are distributed under the Creative Commons licence.