Omkar2118/datasets huggingface dataset:
MSQA

Multi-modal situated reasoning in 3D scenes

Introducing paper: Multi-modal Situated Reasoning in 3D Scenes
PEARL30K

The PEARL dataset comprises with 30K pedestrian images, each annotated with 25 attribute categories, spanning over 146 sub-attributes. We have collected images from outdoor surveillance that reflect practical applications and challenges. We comprehensively cover nearly all critical attributes relevant to security surveillance applications, comprising aspects such as body posture, accessories, bag types, clothing styles, colors, and activities. To diversify, we have extracted images from twelve countries that covers seven distinct public locations including streets, parks, airports, stations, college campuses, beaches, and marketplaces. Additionally, we have incorporated four distinct weather conditions: sunny, night-time, rainy, and snow.
Seaquest - OpenAI Gym
Full name: Seaquest - OpenAI Gym

Dataset: The experiments are conducted using the Seaquest environment from the OpenAI Gym framework, which simulates the Atari 2600 game Seaquest. The dataset consists of RGB frames (210x160x3) generated dynamically during training. These frames are preprocessed by converting to grayscale, resizing to 84x84 pixels, and stacking four consecutive frames to form a 4x84x84 tensor, capturing temporal dynamics of the game state. No external or pre-collected dataset is used; the data is produced through real-time interaction with the Gym environment.

Introducing paper: The Arcade Learning Environment: An Evaluation Platform for General Agents
VitalDB

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.

The dataset is provided free of charge to help researchers who want to study and develop new medical AI algorithms using monitoring signals from surgical patients. We expect that the distribution of this world's largest biosignal dataset will greatly contribute to the advancement of medical AI research.

Introducing paper: Domain Knowledge Integrated CNN-xLSTM-xAtt Network with Multi Stream Feature Fusion for Cuffless Blood Pressure Estimation from Photoplethysmography Signals
Dataset - Theory of Mind in LLMs and Humans
Full name: Dataset - Theory of Mind in LLMs and Humans

Testing results for the paper "Testing Theory of Mind in Large Language Models and Humans"
https://zenodo.org/records/15349731
Full name: LoRaWAN Path Loss Measurements in an Indoor Office Setting including Environmental Factors/Conditions

This dataset was collected during a LoRaWAN measurement campaign in a multi-room indoor office environment in 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. 

A detailed methodology and initial analysis are presented in our accompanying  IEEE Access paper:

Nahshon Mokua Obiri and Kristof Van Laerhoven, "A Comprehensive Data Description for LoRaWAN Path Loss Measurements in an Indoor Office Setting: Effects of Environmental Factors," in IEEE Access, vol. 13, pp. 83148-83170, 2025, doi: 10.1109/ACCESS.2025.3569164.
LoRaWAN Path Loss Measurements in an Indoor Office Setting including Environmental Factors/Conditions

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. 



🔗 Additional Notes

The original dataset pulled directly from InfluxDB is provided as 1.unsorted_combined_measurements_data.csv.
The preprocessed version: filtered, sorted, and with variable renaming, aligned to the structure described in our journal paper, is included as 2.aggregated_measurements_data.csv.
The fully cleaned dataset, where anomalies have been removed and SF11/SF12 data isolated, is available as 3.cleaned_dataset_per_device.csv. 
Intermediate processing steps and transformations are fully reproducible and documented in the accompanying GitHub repository, where each stage of the pipeline is implemented and version-controlled. Users can generate intermediate versions as needed by following the processing scripts or reviewing the commit history.

Introducing paper: A Comprehensive Data Description for LoRaWAN Path Loss Measurements in an Indoor Office Setting: Effects of Environmental Factors
FCoT
Full name: Foreground Chain-of-Thought

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:

- A bounding box locating the target object,

- A sequence of foreground/background point prompts for refining the mask,

- Natural language explanations (chain‑of‑thought) generated by Gemini‑2.5‑Pro summarizing the annotation process.

Introducing paper: Seg-R1: Segmentation Can Be Surprisingly Simple with Reinforcement Learning
ok win invite code

The OK Win invite code is 8522713375003. Using this code during registration can unlock welcome bonuses, such as extra cash or game credits. 
Here's how to use it:
Download and Install: Get the OK Win app from the official website or app store. 
Register: Create a new account. 
Enter the Code: During registration, find the field labeled "invite code" or "referral code" and input 8522713375003. 
Enjoy your bonus: After completing registration, you should receive your welcome bonus.
DLCN
Full name: Dynamic-lighting Conditions at Night

DLCN (Dynamic-lighting Conditions at Night) is a dataset collected specifically for rPPG signal evaluation under complex lighting environments.

Introducing paper: Exploring Remote Physiological Signal Measurement under Dynamic Lighting Conditions at Night: Dataset, Experiment, and Analysis