AgiBot World 2026
AgiBot World 2026 (styled AGIBOT WORLD 2026 by its publisher) is a real-world robot manipulation dataset released in stages during 2026 by the Chinese robotics company AgiBot.
Data & Datasets
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Amazon Mechanical Turk
Amazon Mechanical Turk (MTurk) is a crowdsourcing marketplace operated by Amazon in which businesses and researchers ("requesters") post small paid tasks that a distributed pool of workers completes over the…
BookCorpus
BookCorpus (also written BooksCorpus, and sometimes called the Toronto Book Corpus) is a text dataset built from free, self-published English-language ebooks scraped from the distribution platform Smashwords.
Bucketing
Bucketing, also called binning or discretization, is a feature engineering technique in machine learning that converts a continuous feature into a small number of discrete categories by dividing its range of…
C4 (Colossal Clean Crawled Corpus)
C4 (Colossal Clean Crawled Corpus) is a roughly 750 GB, cleaned, English-language web text dataset of about 365 million documents and 156 billion tokens that Google created from the April 2019 Common Crawl…
CIFAR-10
CIFAR-10 is a labeled dataset of 60,000 small color images sorted into 10 mutually exclusive object categories, with 6,000 images per class, used as a standard benchmark for image classification.
COCO dataset
COCO (Common Objects in Context) is a large-scale dataset for object detection, image segmentation, keypoint detection, and image captioning.
Categorical Data
Categorical data, also called qualitative data, is data whose values are discrete labels or groups (such as colors, country names, or blood types) rather than measurable quantities, so they cannot be…
CharXiv
CharXiv is a benchmark for evaluating chart understanding in multimodal large language models (MLLMs), built by researchers at Princeton Language and Intelligence with collaborators at the University of…
Class-Imbalanced Dataset
A class-imbalanced dataset is a dataset in which the distribution of examples across the target classes is significantly unequal, so that one class (the majority class) contains far more samples than one or…
Common Corpus
Common Corpus is the largest fully open, multilingual dataset for pretraining large language models, assembled and released by the French AI research lab Pleias.
Common Crawl
Common Crawl is a nonprofit 501(c)(3) organization that maintains a free, open repository of web crawl data, and it is the single largest publicly available source of text used to train large language models.
Common Pile
Common Pile v0.1 is an 8 terabyte corpus of openly licensed and public domain text, released on June 5, 2025, by EleutherAI and a consortium of more than two dozen academic and industry collaborators.
Continuous Feature
A continuous feature is a numeric input variable in machine learning and statistics that can take any value within a range, including decimals and fractions, rather than a fixed set of categories or counts.
Convenience Sampling
Convenience sampling (also called grab sampling, accidental sampling, or opportunity sampling) is a non-probability sampling method in which data points or participants are selected because they are easy to…
Cosmopedia
Cosmopedia is an open synthetic pretraining dataset released by Hugging Face in February 2024, made up of textbooks, blog posts, stories, and WikiHow-style articles written entirely by a large language model.
Coverage Bias
Coverage bias is a type of selection bias that occurs when the method used to collect data systematically excludes part of the target population
DCLM (DataComp for Language Models)
DCLM, short for DataComp for Language Models (also styled DataComp-LM), is an open benchmark, dataset, and software framework, released in June 2024
Data Augmentation
Data augmentation is a set of techniques that artificially expand the size and diversity of a training dataset by applying label-preserving transformations to existing examples, rather than collecting new…
Data Provenance Initiative
The Data Provenance Initiative (DPI) is a volunteer-led, multi-institution research collective that audits and documents the licenses, sources, creators, and consent status of the datasets most widely used to…
Data Set or Dataset
A dataset (also written as "data set") is a structured collection of data points used to train, validate, and evaluate machine learning models.
Data contamination
Data contamination is the presence of benchmark evaluation data in the corpus used to train a model
Data preprocessing
Data preprocessing is the set of operations applied to raw data to clean and transform it into a form a machine learning model can use, covering deduplication, type fixing, missing-value imputation, outlier…
Data-centric AI (DCAI)
Data-centric AI (DCAI) is the discipline of systematically engineering and improving the data used to train a machine learning model, rather than holding the data fixed and iterating mainly on model…
DatologyAI
DatologyAI is a Redwood City, California artificial-intelligence startup that builds automated tools for curating, deduplicating, and composing the training datasets used by foundation models.
Dense Feature
A dense feature is a feature in machine learning whose vector representation consists mostly or entirely of non-zero values, typically stored as a dense numeric array (a vector or tensor) of floating-point…
Derived label
A derived label is a label that has been generated programmatically or inferred from other observable signals, rather than collected from direct human annotation of the variable a model is trying to predict.
Dimension Reduction
Dimensionality reduction, also known as dimension reduction, is the process of transforming data from a high-dimensional space into a lower-dimensional space while retaining as much of the meaningful structure…
Discrete Feature
A discrete feature is a feature (a variable in a dataset) that takes one of a finite or countably infinite set of distinct values, such as a category or an integer count
Distribution shift
Distribution shift is the condition in which the probability distribution that produced a model's training data differs from the distribution that produces the data the model actually encounters at test or…
Dolma
Dolma is an open three-trillion-token English pretraining corpus released by the Allen Institute for AI (AI2) to power its fully open OLMo language models and to let researchers study how training data shapes…
Downsampling
Downsampling is the process of reducing the number of samples, the spatial resolution, or the number of data instances in a signal, image, or dataset in order to lower computational cost and memory use while…
Ego-Exo4D
Ego-Exo4D is a large-scale, multimodal, multiview video dataset and benchmark suite for computer vision research on skilled human activity, a central resource in egocentric vision.
Ego4D
Ego4D is a large-scale egocentric (first-person) video dataset and benchmark suite for computer vision, assembled by Meta AI (then Facebook AI Research) together with a consortium of 13 universities and labs…
Feature
In machine learning and statistics, a feature is an individual measurable property or characteristic of a phenomenon being observed, used as an input variable from which a model learns patterns and makes…
Feature Cross
A feature cross (also called a crossed feature or feature interaction) is a synthetic feature created by combining two or more existing features so that a model can learn their joint effect on a prediction.
Feature Engineering
Feature engineering is the process of using domain knowledge to create, transform, and select features from raw data so that machine learning models can learn more effectively.
Feature Extraction
Feature extraction is the process of transforming raw data into a smaller set of derived, informative numerical variables called features that capture the essential structure of the input while discarding…
Feature Selection
Feature selection is the process of choosing a subset of the most relevant input variables (features) from a larger candidate pool for use in a machine learning model, with the goal of finding the smallest set…
Feature Set
A feature set is the complete collection of input variables (features, attributes, or predictors) that a machine learning model uses to learn patterns and make predictions.
Feature Vector
A feature vector is an n-dimensional, ordered list of numerical values that represents the measurable properties of an object, data point, or observation in a format suitable for processing by machine learning…
FineWeb
FineWeb is a large-scale, open pretraining dataset for large language models (LLMs) created by Hugging Face.
FineWeb-2
FineWeb-2 (also written FineWeb2) is a massively multilingual web pretraining dataset released by Hugging Face in December 2024.
FineWeb-Edu
FineWeb-Edu is an open, English-language pretraining dataset of roughly 1.3 trillion tokens
Ground Truth
Ground truth is verified, correct information that serves as the authoritative reference for training and evaluating machine learning models.
HotpotQA
HotpotQA is a large-scale, multi-hop question answering dataset of about 112,779 crowd-authored question-and-answer pairs over English Wikipedia, whose answers cannot be found in any single paragraph and…
How to Prevent OpenAI and Google From Training Their LLMs on Your Website's Data
You can stop OpenAI, Google, Anthropic, and most other major AI companies from using your website to train large language models (LLMs) by adding a small set of directives to your site's robots.txt file.
Imbalanced Dataset
An imbalanced dataset is a dataset used in machine learning where the classification categories are not approximately equally represented, so that one class (the majority class) contains far more samples than…
Index (Figure AI)
Index is a proprietary data-collection program and mobile application operated by Figure AI.
Instance
In machine learning and statistics, an instance is a single data point in a dataset: the values of one row of features and, optionally, a label .
Inter-rater agreement
Inter-rater agreement is the degree of consensus among two or more independent raters when they label or score the same set of items.
Iris dataset
The Iris dataset, sometimes referred to as Fisher's Iris dataset or the Iris flower dataset, is a multivariate dataset introduced by the British statistician and biologist Ronald Fisher in his 1936 paper "The…
LAION
LAION (Large-scale Artificial Intelligence Open Network) is a German non-profit organization
LAION-5B
LAION-5B is an open dataset of approximately 5.85 billion CLIP-filtered image and text pairs scraped from the public internet, released by LAION (Large-scale Artificial Intelligence Open Network) on March 31
LVIS (Large Vocabulary Instance Segmentation)
LVIS (Large Vocabulary Instance Segmentation, pronounced "el-vis") is a large-scale instance segmentation benchmark for computer vision that targets the long-tailed regime of object detection.
Label
In machine learning, a label is the target output value associated with a single training example: the correct answer that a supervised learning model is trained to predict.
Low-Resource Languages
Low-resource languages, in natural language processing (NLP), are languages with little of the raw material that modern language technology depends on: digitized text, parallel translation corpora, annotated…
MMMLU
MMMLU (Multilingual Massive Multitask Language Understanding) is a multilingual evaluation dataset published by OpenAI on September 23, 2024.
MNIST
The Modified National Institute of Standards and Technology (MNIST) database is a collection of 70,000 grayscale images of handwritten digits (0 through 9) that has served as one of the most widely used…
Mecka
Mecka (Mecka AI) is a robotics data company that builds large-scale human-motion datasets used to train robots.