19,997 machine learning datasets
19,997 dataset results
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This is a comprehensive dataset of 6,388 surgical patients composed of intraoperative biosignals and clinical information. The biosignal data included in the dataset is high quality data such as 500 Hz waveform signals and numeric values at intervals of 1-7 seconds. More than 60 surgery related clinical information is also provided to help interpret the signals.
Testing results for the paper "Testing Theory of Mind in Large Language Models and Humans"
Click to add a brief description of the dataset (Markdown and LaTeX enabled).
This dataset was collected during a LoRaWAN measurement campaign in a multi-room indoor office environment at the University of Siegen, Germany. It contains over 1.7 million time-stamped records from 6 LoRaWAN nodes transmitting once per minute to a single gateway. Each record includes environmental parameters: temperature, relative humidity, barometric pressure, particulate matter (PM2.5), and carbon dioxide (CO₂); as well as device metadata such as RSSI, SNR, spreading factor (SF), etc. The dataset also includes the effective signal power (ESP) and the noise (NP) for LoRaWAN propagation analysis purposes. The dataset is designed to support research on indoor wireless propagation, distance estimation, environment-aware modeling, among other IoT use cases and applications in line with the 6G flagship demands.
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FCoT (Chain‑of‑Thought Segmentation) is replicate the step-by-step reasoning process a human annotator follows when using SAM2 to generate masks. Each example pairs an image with:
DLCN (Dynamic-lighting Conditions at Night) is a dataset collected specifically for rPPG signal evaluation under complex lighting environments.
TeleSim is a network-aware hardware-in-the-loop dataset designed to evaluate the performance of telerobotic systems under varying network conditions. It includes 300 fine-manipulation trials using a 6-DoF robotic arm and simulated networks in OMNeT++. Each trial captures:
The dataset is described in the README file in the official repository of the paper
Dataset for our paper Disambiguation-Centric Finetuning Makes Enterprise Tool-Calling LLMs More Realistic and Less Risky which includes 5000 enterprise tools and the corresponding dialogues generated using DiaFORGE UTC data engine.
This paper constructs 7-digit product Supply-Use Tables (SUTs) and symmetric Input-Output Tables (IOTs) for the Indian economy using microdata from the Annual Survey of Industries (ASI) for the period 2016-2021. We outline the methodology for generating input flows and reconciling registered and unregistered sector data via NPCMS-NIC concordance. The transition from SUTs to IOTs is explained using the Industry Technology Assumption. We apply this framework to analyse the economic impact—specifically Domestic Value Added (DVA) and employment influenced by production and exports. A case study of India's mobile phone sector reveals significant output growth, import substitution, an increase in exports, a shift in DVA/FVA shares, notable employment growth, with a leaning towards contractual labour, and increased female participation. These tables are valuable for analysing sectoral interdependencies and industrial policy effectiveness in India.
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