298 machine learning datasets
298 dataset results
The medaka (Oryzias latipes) and the zebrafish (Danio rerio) are used as a model organism for a variety of subjects in biomedical research. The presented work aims to study the potential of automated ventricular dimension estimation through heart segmentation in medaka. For more on this, it's time for a closer look on our paper and the supplementary materials.
SynD is a synthetic energy dataset with a focus on residential buildings. This dataset is the result of a custom simulation process that relies on power traces of household appliances. The output of simulations is the power consumption of 21 household appliances as well as the household-wide consumption (i.e. mains). Therefore, SynD's can be used for Non-Intrusive Load Monitoring, also referred to as Energy Disaggregation.
The Tufts fNIRS to Mental Workload (fNIRS2MW) open-access dataset is a new dataset for building machine learning classifiers that can consume a short window (30 seconds) of multivariate fNIRS recordings and predict the mental workload intensity of the user during that window.
A dataset with $23\,870$ digital trajectories (i.e. time series) of handwritten lower- and uppercase Latin letters and Arabic numbers ($a$-$z$, $A$-$Z$, $0$-$9$), generated by $77$ experts using a Wacom Pen Tablet. An expert is considered a proficient user of the recorded symbols, in this case adult native German speakers.
FHRMA is an open-source project for Fetal Heart Rate Morphological Analysis containing Matlab source code and datasets. As a sub-project, it includes a deep learning method and dataset for automatic identification of the maternal heart rate (MHR) and, more generally, false signals (FSs) on fetal heart rate (FHR) recordings. The challenge concerns particularly the FHR signal recorded with Doppler sensors, on which MHR interference and other FSs are particularly common, but the dataset also includes FHR recorded with scalp-ECG. The training and validation dataset contained 1030 expert-annotated periods (mean duration: 36 min) from 635 recordings. Labels consist of annotating each time sample as either 1: False signal; 0: True signal, or -1: do not know or irrelevant.
X-Wines is a consistent wine dataset containing 100,646 instances and 21 million real evaluations carried out by users. Data were collected on the open Web in 2022 and pre-processed for wider free use. They refer to the scale 1–5 ratings carried out over a period of 10 years (2012–2021) for wines produced in 62 different countries.
The dataset comprises patches of size 512x512 pixels collected from Sentinel-2 L2A satellite mission. All reported forest fires are located in California. For each area of interest, two images are provided: pre-fire acquisition and post-fire acquisition. Each image is composed of 12 different channels, collecting information from the visible spectrum, infrared and ultrablue.
Human activity recognition and clinical biomechanics are challenging problems in physical telerehabilitation medicine. However, most publicly available datasets on human body movements cannot be used to study both problems in an out-of-the-lab movement acquisition setting. The objective of the VIDIMU dataset is to pave the way towards affordable patient tracking solutions for remote daily life activities recognition and kinematic analysis.
PJM Hourly Energy Consumption Data PJM Interconnection LLC (PJM) is a regional transmission organization (RTO) in the United States. It is part of the Eastern Interconnection grid operating an electric transmission system serving all or parts of Delaware, Illinois, Indiana, Kentucky, Maryland, Michigan, New Jersey, North Carolina, Ohio, Pennsylvania, Tennessee, Virginia, West Virginia, and the District of Columbia.
This study’s sample consists of seven corporations (Black Rock, Google, Meta, JP Morgan, Walgreens, Netflix, and Pepsico) analyzed across seven quarters beginning in 2021. The data includes the implied volatility level (annualized) for the day before, the day of, and the day following the earnings report. This information was obtained from the Bloomberg Terminal dataset BVOL. The data we read from the terminal is based on Bloomberg’s algorithm for calculating the implied volatility for different strikes. The value is the same for both calls and puts, which makes comparisons and calculations more straightforward. The dataset contains a mixture of high-growth, high-risk technology corporations that saw strong market tailwinds during the previous year and steady, high-dividend-paying equities. For a more comprehensive conclusion, we analyze the implied volatility levels across three expirations to determine the influence of each expiration. The shortest maturity spans from 1 to 4 days, wh
This dataset collects transparency disclosures about the sexual exploitation of children by social media and their reports about such activity and material to the national clearinghouse, the National Center for Missing and Exploited Children (NCMEC).
Objective This study introduces the BlendedICU dataset, a massive dataset of international intensive care data. This dataset aims to facilitate generalizability studies of machine learning models, as well as statistical studies of clinical practices in the intensive care units.
Mudestreda Multimodal Device State Recognition Dataset obtained from real industrial milling device with Time Series and Image Data for Classification, Regression, Anomaly Detection, Remaining Useful Life (RUL) estimation, Signal Drift measurement, Zero Shot Flank Took Wear, and Feature Engineering purposes.
We introduce an open-source physical-layer dataset of Bluetooth Low Energy (BLE) IoT sensor devices recorded in an anechoic chamber using USRP x310. With a 100Msps sampling rate, it covers the entire BLE spectrum, featuring on-body and off-body scenarios with 13 BLE devices (ESP32s) from the same manufacturer. the goal is to study the physical layer characteristics of both on-body and off-body signals. The dataset is also available through MongoDB with a Python tool for analysis; for more details, please visit our GitHub page. https://github.com/mkashani-phd/BLEWBAN_Dataset
We introduce an annotated dataset of five thousand human labeled pareidolic face images, called ``Faces in Things''. Faces in Things is derived from the LAION-5B dataset and annotated for key face attributes and bounding boxes
This dataset presents a novel, multi-variate time series specifically designed for advancing research in spatio-temporal forecasting. The primary goal of this dataset is to facilitate the accurate prediction of traffic throughput volumes across 5G communication networks.
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