19,997 machine learning datasets
19,997 dataset results
What is BabbleCor?
Dataset release for the BMVC 2021 Paper "Few-Shot Domain Adaptation for Low Light RAW Image Enhancement"
The goal of this project is to present two new datasets that seek to expand the capability of the Learning to See in the Dark Low-light enhancement CNN for the Canon 6D DSLR, and explore how the network performs when modified in various ways, both pruning it and making it deeper.
The code that created this dataset can be seen in https://github.com/nitzanfarhi/SecurityPatchDetection and can be reproduced by running: console python data_collection\create_dataset.py --all -o data_collection\data Notice that this dataset doesn't include the commits' generated data as it is very big. This can be generated by running only : console python data_collection\create_dataset.py --commits -data_collection\data
Overview: This dataset encompasses a compilation of 6,700 executed scoops (excavations), mapped across a vast spectrum of materials, terrain topography, and compositions.
Dataset of 30 4K HDR environment maps and trained models.
Data was acquired to investigate the effects of morphine withdrawal in rats, both in the brain neurochemistry and ultrasonic vocalisation.
The DAS-2 traces were kindly provided by the Advanced School for Computing and Imaging (ASCI), the owner of the DAS-2 system. To use these traces, you must include an acknowledgement to the source of the data in any published material that refers to the data. Please also consider refering to the Grid Workloads Archive in the acknowledgements.
This dataset artifact contains the intermediate datasets from pipeline executions necessary to reproduce the results of the paper. We share this artifact in hopes of providing a starting point for other researchers to extend the analysis on notebooks, discover more about their accessibility, and offer solutions to make data science more accessible. The scripts needed to generate these datasets and analyse them are shared in the Github Repository for this work.
Dungeons & Dragons (D&D) is a tabletop roleplaying game with complex natural language interactions between players and hidden state information. Recent work has shown that large language models (LLMs) that have access to state information can generate higher quality game turns than LLMs that use dialog history alone. However, previous work used game state information that was heuristically created and was not a true gold standard game state. We present FIREBALL, a large dataset containing nearly 25,000 unique sessions from real D&D gameplay on Discord with true game state info. We recorded game play sessions of players who used the Avrae bot, which was developed to aid people in playing D&D online, capturing language, game commands and underlying game state information. We demonstrate that FIREBALL can improve natural language generation (NLG) by using Avrae state information, improving both automated metrics and human judgments of quality. Additionally, we show that LLMs can generate
This dataset contains information on application install interactions of users in the Myket android application market. The dataset was created for the purpose of evaluating interaction prediction models, requiring user and item identifiers along with timestamps of the interactions. Hence, the dataset can be used for interaction prediction and building a recommendation system. Furthermore, the data forms a dynamic network of interactions, and we can also perform network representation learning on the nodes in the network, which are users and applications.
SDoH Human Annotated Demoographic Robustness (SHADR) Dataset Overview The Social determinants of health (SDoH) play a pivotal role in determining patient outcomes. However, their documentation in electronic health records (EHR) remains incomplete. This dataset was created from a study examining the capability of large language models in extracting SDoH from the free text sections of EHRs. Furthermore, the study delved into the potential of synthetic clinical text to bolster the extraction process of these scarcely documented, yet crucial, clinical data.
The Automated Surface Observing Systems (ASOS) program is a joint effort of the National Weather Service (NWS), the Federal Aviation Administration (FAA), and the Department of Defense (DOD). These automated systems collect observations on a continual basis, 24 hours a day.
The dataset is generated from the study of computational reproducibility of Jupyter notebooks from biomedical publications. Our focus lies in evaluating the extent of reproducibility of Jupyter notebooks derived from GitHub repositories linked to publications present in the biomedical literature repository, PubMed Central. We analyzed the reproducibility of Jupyter notebooks from GitHub repositories associated with publications indexed in the biomedical literature repository PubMed Central. The dataset includes the metadata information of the journals, publications, the Github repositories mentioned in the publications and the notebooks present in the Github repositories.
This repository contains the dataset for the study of the computational reproducibility of Jupyter notebooks from biomedical publications. We analyzed the reproducibility of Jupyter notebooks from GitHub repositories associated with publications indexed in the biomedical literature repository PubMed Central. The dataset includes the metadata information of the journals, publications, the Github repositories mentioned in the publications and the notebooks present in the Github repositories.
MEIS comprises a total of 2,639 images in the size of 1024 × 768 toward two recording views (Aortic Valve (AV) and Left Ventricle (LV)) with 1,521 (747 in AV + 774 in LV) images for training and 1,118 (559 in AV + 559 in LV) for testing, respectively. Each view must be detected with two objects to calculate the measurement indicators. That is in total with four object classes (two objects in each view): aortic root (AoR) and left atrium (LA) in AV; interventricular septum (IVS) and left ventricular posterior wall (LVPW) in LV. The medical meaning and purpose of each indicator are listed in the following: • AV: LA-Dimension and AoR-Dimension can be measured for calculating different indicators, such as AoR/LA ratio, to examine the state of the aortic valve. • LV: 6 measurements include IVSs, IVSd, LVIDs, LVIDd, LVPWs, and LVPWd. These concerned thicknesses and dimensions in LV recording are used to estimate other cardiac functions through specific medical formulas, including LV mass, LV
We build a new evaluation set by adding spotting words to the images of ImageNet 2012 evaluation sets. There are 1,000 categories in ImageNet. For each category c, we find its most confusing category c*and spot the category name to every evaluation image.
ISEKAI dataset’s images are generated by Midjourney’s text-to-image model using well-crafted instructions. Images were manually selected to ensure core concept consistency. The dataset currently comprises 20 groups, and 40 categories in total (continues to grow). Each group pairs a new concept with a related real-world concept, like "octopus vacuum" and "octopus." These can serve as challenging negative samples for each other. Each concept has no less than 32 images, supporting multi-shot examples.
The dataset contains generated random signals for autoencoding purposes. It was used as a benchmark for autoencoder performance comparison. All dataset files are "pickled" and placed in the folder datasets in https://github.com/rsusik/raesc
This repository contains the code and data to reproduce all results in "Climate uncertainty impacts on social cost of carbon and optimal mitigation pathways", Smith et al. (2023), Environmental Research Letters.