  Datasets play a central role in the training and evaluation of machine
learning (ML) models. But they are also the root cause of many undesired model
behaviors, such as biased predictions. To overcome this situation, the ML
community is proposing a data-centric cultural shift where data issues are
given the attention they deserve, and more standard practices around the
gathering and processing of datasets start to be discussed and established.
  So far, these proposals are mostly high-level guidelines described in natural
language and, as such, they are difficult to formalize and apply to particular
datasets. In this sense, and inspired by these proposals, we define a new
domain-specific language (DSL) to precisely describe machine learning datasets
in terms of their structure, data provenance, and social concerns. We believe
this DSL will facilitate any ML initiative to leverage and benefit from this
data-centric shift in ML (e.g., selecting the most appropriate dataset for a
new project or better replicating other ML results). The DSL is implemented as
a Visual Studio Code plugin, and it has been published under an open source
license.
