TITLE: Dataset Definition Standard (DDS) 

ABSTRACT: This document gives a set of recommendations to build and manipulate the
datasets used to develop and/or validate machine learning models such as deep
neural networks. This document is one of the 3 documents defined in [1] to
ensure the quality of datasets. This is a work in progress as good practices
evolve along with our understanding of machine learning. The document is
divided into three main parts. Section 2 addresses the data collection
activity. Section 3 gives recommendations about the annotation process.
Finally, Section 4 gives recommendations concerning the breakdown between
train, validation, and test datasets. In each part, we first define the desired
properties at stake, then we explain the objectives targeted to meet the
properties, finally we state the recommendations to reach these objectives.
This document gives a set of recommendations to build and manipulate the
datasets used to develop and/or validate machine learning models such as deep
neural networks. This document is one of the 3 documents defined in [1] to
ensure the quality of datasets. This is a work in progress as good practices
evolve along with our understanding of machine learning. The document is
divided into three main parts. Section 2 addresses the data collection
activity. Section 3 gives recommendations about the annotation process.
Finally, Section 4 gives recommendations concerning the breakdown between
train, validation, and test datasets. In each part, we first define the desired
properties at stake, then we explain the objectives targeted to meet the
properties, finally we state the recommendations to reach these objectives.
Dataset Definition Standard (DDS)
http://arxiv.org/abs/2101.03020v1