24,175 datasets found

STL-CQA is a dataset for chart question answering, containing multiple chart types and natural language patterns.

FigureQA is a dataset for visual question answering, containing line plots, bar charts, pie plots, and dot line plots.

DVQA is a dataset for visual question answering, containing bar charts and natural language patterns.

PlotQA is a large-scale and diverse dataset for chart question answering, containing multiple chart types and natural language patterns.

Chart question answering (CQA) is a task used for assessing chart comprehension, which is fundamentally different from understanding natural images.

The dataset used in this study is a large electronic health record (EHR) dataset containing information on hospital admissions, catheter episodes, and patient outcomes.

ImageMonkey proposed another method for acquiring visual data.

The dataset used for the experiments in the paper, containing 4,337 compounds with mutagenicity data.

The dataset used for the experiments in the paper, containing 28,147 compounds with a simple rule for classification.

The dataset used for the experiments in the paper, containing 12,000 molecules with 12 biological effects.

The dataset is a network of 5 NDIR (Non-dispersive infrared) CO2 sensors which are co-located in an office environment during a full working week.

The dataset used in the Revealing Choice Bracketing experiment, consisting of choices from five decision rounds, each consisting of one or two subdecisions.

The dataset used in the paper is a multi-modal dataset for time-of-flight (ToF) imaging, which includes indoor sequences with challenging light and scene conditions.

Magellan: Toward building entity matching management systems.

Real data for asset bubbles detection using LSTM network

Simulated data for asset bubbles detection using LSTM network

Machine-learned 3D building vectorization from satellite imagery.

ALOS World 3D (AW3D) global DSM generation.

National urban database and access portal tool.

A novel Level of Detail-1 (LoD-1) building dataset derived from a Deep Neural Network (DNN) called GLOBUS.