  Writing a scientific article is a challenging task as it is a highly codified
and specific genre, consequently proficiency in written communication is
essential for effectively conveying research findings and ideas. In this
article, we propose an original textual resource on the revision step of the
writing process of scientific articles. This new dataset, called CASIMIR,
contains the multiple revised versions of 15,646 scientific articles from
OpenReview, along with their peer reviews. Pairs of consecutive versions of an
article are aligned at sentence-level while keeping paragraph location
information as metadata for supporting future revision studies at the discourse
level. Each pair of revised sentences is enriched with automatically extracted
edits and associated revision intention. To assess the initial quality on the
dataset, we conducted a qualitative study of several state-of-the-art text
revision approaches and compared various evaluation metrics. Our experiments
led us to question the relevance of the current evaluation methods for the text
revision task.
