  In multi-user environments in which data science and analysis is
collaborative, multiple versions of the same datasets are generated. While
managing and storing data versions has received some attention in the research
literature, the semantic nature of such changes has remained under-explored. In
this work, we introduce \texttt{Explain-Da-V}, a framework aiming to explain
changes between two given dataset versions. \texttt{Explain-Da-V} generates
\emph{explanations} that use \emph{data transformations} to explain changes. We
further introduce a set of measures that evaluate the validity,
generalizability, and explainability of these explanations. We empirically
show, using an adapted existing benchmark and a newly created benchmark, that
\texttt{Explain-Da-V} generates better explanations than existing data
transformation synthesis methods.
