The ATIS (Airline Travel Information Systems) is a dataset consisting of audio recordings and corresponding manual transcripts about humans asking for flight information on automated airline travel inquiry systems. The data consists of 17 unique intent categories. The original split contains 4478, 500 and 893 intent-labeled reference utterances in train, development and test set respectively. Source: Spoken Language Intent Detection using Confusion2Vec

Text corpus with almost one billion words of training data for statistical language modeling benchmarking. The scale of approximately one billion words attempts to strike a balance between the relevance of the benchmark in a world of abundant data against the ease with which researchers can evaluate their modeling approaches. Monolingual english data was obtained from the WMT11 website and prepared using a variety of best-practices for machine learning dataset preparations.

Dataset is constructed from single intent dataset ATIS. This is a publically available multi intent dataset, which can be downloaded from https://github.com/LooperXX/AGIF/data

This is a dataset for intent detection and slot filling for the Vietnamese language. The dataset consists of 5,871 gold annotated utterances with 28 intent labels and 82 slot types.

The PATIS is a Persian language dataset for intent detection and slot filling.

This dataset is part of the publication "A bi-atrial statistical shape model for large-scale in silico studies of human atria: Model development and application to ECG simulations" by Nagel et al. (https://doi.org/10.1016/j.media.2021.102210). It includes a bi-atrial statistical shape model built based on 47 MR and CT images (Left atrium segmentation challenge (Tobon-Gomez, 2015), Left atrium fibrosis and scar segmentation challenge (Karim, 2013), Left atrial wall thickness challenge (Karim, 2018)). ScalismoLab (https://scalismo.org) was used for parts of the model generation. Further Details are explained in the paper. The SSM is available as an h5 file including information about the mean shape's vertex locations and their triangulation as well as the eigenvectors and -values. 100 random instances derived from the model are available. Each zip file contains the volumetric bi-atrial geometry as vtk file, which was augmented in a post-processing step with a homogeneous wall thickness, fiber orientation, intra-atrial bridges and material tags so that they are ready to use for electrophysiological simulations of atrial signals. Furthermore, the scalar field resulting from computing the gradient of the Laplace equation with the boundary conditions described by Piersanti et al. (Modeling cardiac muscle fibers in ventricular and atrial electrophysiology simulations, Computer Methods in Applied Mechanics and Engineering, 2020, https://doi.org/10.1016/j.cma.2020.113468) are available on the left and the right atrial instances. Furthermore, 95 geometries with uniformly distributed left atrial volumes are available in LAE_geometries.zip.

Neural network model files and Madgraph event generator outputs used as inputs to the results presented in the paper "Learning to discover: expressive Gaussian mixture models for multi-dimensional simulation and parameter inference in the physical sciences" arXiv:2108.11481; 2022 Mach. Learn.: Sci. Technol. 3 015021 Code and model files can be found at: https://github.com/darrendavidprice/science-discovery/tree/master/expressive_gaussian_mixture_models

Dataset Description: NBA Team Statistics, Historical Performance & Betting Odds (2015-2019) ## Overview This dataset contains team-level box score statistics, historical win percentages, and closing betting odds for NBA games from 2015 to 2019. It supports research in sports analytics, predictive modeling, and betting market efficiency. ## Contents Box Score Statistics (2015-2019): Points, rebounds, assists, turnovers, shooting percentages, etc. Historical Team Performance (2014-2018): Each team’s winning percentage over five seasons. Betting Odds (2018-2019): Closing moneyline odds and more ## Use Cases Predictive Modeling: Forecasting NBA game outcomes using historical data. Betting Market Analysis: Evaluating odds efficiency in capturing true probabilities. Sports Analytics: Analyzing trends in team performance and statistical patterns.

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