  The NLP pipeline has evolved dramatically in the last few years. The first
step in the pipeline is to find suitable annotated datasets to evaluate the
tasks we are trying to solve. Unfortunately, most of the published datasets
lack metadata annotations that describe their attributes. Not to mention, the
absence of a public catalogue that indexes all the publicly available datasets
related to specific regions or languages. When we consider low-resource
dialectical languages, for example, this issue becomes more prominent. In this
paper we create \textit{Masader}, the largest public catalogue for Arabic NLP
datasets, which consists of 200 datasets annotated with 25 attributes.
Furthermore, We develop a metadata annotation strategy that could be extended
to other languages. We also make remarks and highlight some issues about the
current status of Arabic NLP datasets and suggest recommendations to address
them.
