List of datasets for machine-learning research

These datasets are used in machine learning (ML) research and have been cited in peer-reviewed academic journals. Datasets are an integral part of the field of machine learning. Major advances in this field can result from advances in learning algorithms (such as deep learning), computer hardware, and, less-intuitively, the availability of high-quality training datasets. High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to produce because of the large amount of time needed to label the data. Although they do not need to be labeled, high-quality datasets for unsupervised learning can also be difficult and costly to produce.

Many organizations, including governments, publish and share their datasets. The datasets are classified, based on the licenses, as Open data and Non-Open data.

The datasets from various governmental-bodies are presented in List of open government data sites. The datasets are ported on open data portals. They are made available for searching, depositing and accessing through interfaces like Open API. The datasets are made available as various sorted types and subtypes.

The data portal is classified based on its type of license. The open source license based data portals are known as open data portals which are used by many government organizations and academic institutions.

The data portal sometimes lists a wide variety of subtypes of datasets pertaining to many machine learning applications.

The data portals which are suitable for a specific subtype of machine learning application are listed in the subsequent sections.

These datasets consist primarily of text for tasks such as natural language processing, sentiment analysis, translation, and cluster analysis.

These datasets consist of sounds and sound features used for tasks such as speech recognition and speech synthesis.

Datasets containing electric signal information requiring some sort of signal processing for further analysis.

Datasets from physical systems.

Datasets from biological systems.

This section includes datasets that deals with structured data.

This section includes datasets that contains multi-turn text with at least two actors, a "user" and an "agent". The user makes requests for the agent, which performs the request.

As datasets come in myriad formats and can sometimes be difficult to use, there has been considerable work put into curating and standardizing the format of datasets to make them easier to use for machine learning research.

• OpenML: Web platform with Python, R, Java, and other APIs for downloading hundreds of machine learning datasets, evaluating algorithms on datasets, and benchmarking algorithm performance against dozens of other algorithms.
• PMLB: A large, curated repository of benchmark datasets for evaluating supervised machine learning algorithms. Provides classification and regression datasets in a standardized format that are accessible through a Python API.
• Metatext NLP: https://metatext.io/datasets web repository maintained by community, containing nearly 1000 benchmark datasets, and counting. Provides many tasks from classification to QA, and various languages from English, Portuguese to Arabic.
• Appen: Off The Shelf and Open Source Datasets hosted and maintained by the company. These biological, image, physical, question answering, signal, sound, text, and video resources number over 250 and can be applied to over 25 different use cases.

• Comparison of deep learning software
• List of manual image annotation tools
• List of biological databases