# Dataset Definition Standard (DDS)

arXiv: https://arxiv.org/abs/2101.03020 · HTML: https://mlsanity.com/paper/2101.03020/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 7 Jan 2021.

## Builds on (most cited first, 2 of 2)

- [Active Learning for Deep Detection Neural Networks](https://mlsanity.com/paper/1911.09168/) (2019)
- [Ensuring Dataset Quality for Machine Learning Certification](https://mlsanity.com/paper/2011.01799/) (2020)

## Built on by (most cited first, 3 of 3)

- [Quality issues in Machine Learning Software Systems](https://mlsanity.com/paper/2208.08982/) (2022)
- [Quality Issues in Machine Learning Software Systems](https://mlsanity.com/paper/2306.15007/) (2023)
- [Standardness Clouds Meaning: A Position Regarding the Informed Usage of Standard Datasets](https://mlsanity.com/paper/2406.13552/) (2024)

## Abstract (arXiv)

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.