19,997 machine learning datasets
19,997 dataset results
We present three defect rediscovery datasets mined from Bugzilla. The datasets capture data for three groups of open source software projects: Apache, Eclipse, and KDE. The datasets contain information about approximately 914 thousands of defect reports over a period of 18 years (1999-2017) to capture the inter-relationships among duplicate defects.
This dataset was collected with FLOBOT - an advanced autonomous floor scrubber - includes data from four different sensors for environment perception, as well as the robot pose in the world reference frame. Specifically, FLOBOT relies on a 3D lidar and a RGB-D camera for human detection and tracking, and a second RGB-D and a stereo camera for dirt and object detection. Data collection was performed in four public places (three of them are released in this dataset), two in Italy and two in France, in FOLBOT working mode with the corresponding testing procedures for final project validation
The Collision Avoidance Challenge dataset is the official dataset used during the ESA's Kelvins competition for "Collision Avoidance Challenge". The dataset is a collection of Conjunction Data Messages (CDMs) received by ESA from 2015 to 2019. The CDMs have been anonymised for distribution. The initial raw data, as well as the labels that were kept private during the competition, are also released. ESA thanks the US Space Surveillance Network for the provision of surveillance data supporting safe operations of ESA’s spacecraft. In addition, we are grateful to the agreement which allows to publicly release the current dataset.
This is a benchmark of data loss bugs for android apps. It is a public benchmark of 110 data loss faults in Android apps that we systematically collected to facilitate research and experimentation with these problems. The benchmark is available on GitLab and includes the faulty apps, the fixed apps (when available), the test cases to automatically reproduce the problems, and additional information that may help researchers in their tasks.
PFN-VT is a dataset for the estimation of tactile properties from vision, such as slipperiness or roughness. The dataset is collected with a webcam and uSkin tactile sensor mounted on the end-effector of a Sawyer robot, which strokes the surfaces of 25 different materials.
This is a dataset of Internet malicious activity (mal-activity in short). It contains more than 51 million mal-activity reports involving 662K unique IP addresses covering the period form January 2007 to June 2017. Leveraging the Wayback Machine, antivirus (AV) tool reports and several additional public datasets (e.g., BGP Route Views and Internet registries) the data is enriched with historical meta-information including geo-locations (countries), autonomous system (AS) numbers and types of mal-activity.
This is a collection of datasets related to Covid-19. It consists of large scale and real-time pandemic related data from multiple sources, including disease related data from official public health organizations and digital media, demographic data, mobility data, and user generated data from social media (i.e., Reddit).
VAST Absorption is a dataset of spatial binaural features annotated with acoustic properties such as the 3D source position and the walls’ absorption coefficients.
ColosseumRL is a framework for research in reinforcement learning in n-player games.
This is a dataset for anomalies detection in 3D printing.
The image collection of the IAPR TC-12 Benchmark consists of 20,000 still natural images taken from locations around the world and comprising an assorted cross-section of still natural images. This includes pictures of different sports and actions, photographs of people, animals, cities, landscapes, and many other aspects of contemporary life. Each image is associated with a text caption in up to three different languages (English, German and Spanish).
Experiments on a metal milling machine for different speeds, feeds, and depth of cut. Records the wear of the milling insert, VB. The data set was provided by the BEST lab at UC Berkeley.
Wiki-Reliability is the first dataset of English Wikipedia articles annotated with a wide set of content reliability issues. Templates are tags used by expert Wikipedia editors to indicate content issues, such as the presence of "non-neutral point of view" or "contradictory articles", and serve as a strong signal for detecting reliability issues in a revision. We select the 10 most popular reliability-related templates on Wikipedia, and propose an effective method to label almost 1M samples of Wikipedia article revisions as positive or negative with respect to each template. Each positive/negative example in the dataset comes with the full article text and 20 features from the revision's metadata. We provide an overview of the possible downstream tasks enabled by such data, and show that Wiki-Reliability can be used to train large-scale models for content reliability prediction.
gComm is a step towards developing a robust platform to foster research in grounded language acquisition in a more challenging and realistic setting. It comprises a 2-D grid environment with a set of agents (a stationary speaker and a mobile listener connected via a communication channel) exposed to a continuous array of tasks in a partially observable setting. The key to solving these tasks lies in agents developing linguistic abilities and utilizing them for efficiently exploring the environment. The speaker and listener have access to information provided in different modalities, i.e. the speaker's input is a natural language instruction that contains the target and task specifications and the listener's input is its grid-view. Each must rely on the other to complete the assigned task, however, the only way they can achieve the same, is to develop and use some form of communication. gComm provides several tools for studying different forms of communication and assessing their genera
PINN
This resource contains 10.5 million paragraphs with associated statement labels, realized as one paragraph per file, one sentence per line. Each file is placed in a subdirectory named after its annotated class. The statements were extracted from author-annotated environments, where we only selected the first paragraph,immediately following the heading. Headings include both structural sections (e.g. Introduction), as well as scholarly statement annotations, (e.g. Definition, Proof, Remark).
This is a second public release of the arXMLiv dataset generated by the KWARC research group. It contains 1,232,186 HTML5 scientific documents from the arXiv.org preprint archive, converted from their respective TeX sources. A 13% increase in available articles over the 08.2017 release.
214 videos under various extreme sight conditions for audiovisual repetition counting
The 5th Ninapro database includes 10 intact subjects recorded with two Thalmic Myo (https://www.myo.com/) armbands. The database can be used to test the Myo armbands separately as well. The 5th Ninapro database is thoroughly described in the paper: "Stefano Pizzolato, Luca Tagliapietra, Matteo Cognolato, Monica Reggiani, Henning Müller, Manfredo Atzori, Comparison of six electromyography acquisition setups on hand movement classification tasks, PLOS One, 2017"