  Data is a critical element in any discovery process. In the last decades, we
observed exponential growth in the volume of available data and the technology
to manipulate it. However, data is only practical when one can structure it for
a well-defined task. For instance, we need a corpus of text broken into
sentences to train a natural language machine-learning model. In this work, we
will use the token \textit{dataset} to designate a structured set of data built
to perform a well-defined task. Moreover, the dataset will be used in most
cases as a blueprint of an entity that at any moment can be stored as a table.
Specifically, in science, each area has unique forms to organize, gather and
handle its datasets. We believe that datasets must be a first-class entity in
any knowledge-intensive process, and all workflows should have exceptional
attention to datasets' lifecycle, from their gathering to uses and evolution.
We advocate that science and engineering discovery processes are extreme
instances of the need for such organization on datasets, claiming for new
approaches and tooling. Furthermore, these requirements are more evident when
the discovery workflow uses artificial intelligence methods to empower the
subject-matter expert. In this work, we discuss an approach to bringing
datasets as a critical entity in the discovery process in science. We
illustrate some concepts using material discovery as a use case. We chose this
domain because it leverages many significant problems that can be generalized
to other science fields.
