Biomedical and Health Sciences

This dataset consists of carefully curated audio recordings that capture the distinct sounds produced by multiple individuals walking in various environments. Designed to support research in sound recognition, activity analysis, and the study of human behaviour, it provides a rich resource for understanding how group dynamics influence acoustic patterns. Each recording is accompanied by detailed metadata, including the number of participants, environmental context, and surface characteristics.

This dataset contains Wi-Fi sensing data using Channel State Information (CSI) for various sleep disturbance parameters, from respiratory disturbances, to motion-based disturbances from posture shifts, leg restlessness and confusional arousals.The Wi-Fi CSI data was collected using the Wi-Fi module on the ESP32 Microcontroller units using the esp32-csi-tool.The Wi-Fi CSI respiratory disturbance data is accompanied by respiration belt data taken with the Wi-Fi measurements simultaneously using the Neulog NUL-236 respiration belt logger as ground truth.

To download the dataset without purchasing an IEEE Dataport subscription, please visit: https://zenodo.org/records/13738598

Please cite the following paper when using this dataset:

N. Thakur, “Mpox narrative on Instagram: A labeled multilingual dataset of Instagram posts on mpox for sentiment, hate speech, and anxiety analysis,” arXiv [cs.LG], 2024, URL: https://arxiv.org/abs/2409.05292

Abstract

Brain-Computer Interface (BCI) technology facilitates a direct connection between the brain and external devices by interpreting neural signals. It is critical to have datasets that contain patient's native languages while developing BCI-based solutions for neurological disorders. However, present BCI research lacks appropriate language-specific datasets, particularly for languages such as Telugu, which is spoken by more than 90 million people in India.

Numerous studies have demonstrated that microbes play a vital role in human health, making the identification of potential microbe-drug associations critical for drug discovery and clinical treatment. In this manuscript, we proposed a novel prediction model named GTDEKAN by integrating an aware Transformer network with a Dual Cross-Attention (DCA) module (including a Channel Cross-Attention and a Spatial Cross-Attention) and an Enhanced Kolmogorov-Arnold Network (EKAN) to infer potential microbe-drug associations.

Persistent viruses like influenza, HIV, Coronavirus exemplify the challenge of viral escape, significantly hindering the development of long-lasting vaccines and effective treatments.  This study leverages a Long Short-Term Memory (LSTM) based deep learning architecture to analyze an extensive dataset of over 3.1 million unique viral spike protein sequences, with SARS-CoV-2 serving as the primary example. Our model, Escape Elite Network(EEN) outperforms existing methods in detecting escape mutations across diverse datasets.

This dataset comprises radar-acquired signals from 15 subjects walking on a treadmill, aimed at exploring methodologies for non-contact vital sign detection under conditions of significant body movement. Each subject participated in four experimental sessions, where radar data were collected using two Continuous Wave (CW) radars positioned to capture signals from the front and back of the subject. The data includes both raw and demodulated signals synchronized with ground-truth data obtained from a BioPac system.

This dataset contains still thermal frames from thirty patients undergoing awake craniotomy for brain tumor resection. The data were used as part of a study on automated craniotomy masking, where the portion of the craniotomy image which contans the brain is identified. The data contains manually generated gold-standard masks, as well as masks created with the proposed method in "Automated Craniotomy Masking for Intraoperative Thermography".

This dataset comprises high-resolution imaging data of biological porcine, clinically approved porcine and bovine, and chick embryo heart tissues. The dataset includes comprehensive anatomical and structural details, making it valuable for research in cardiovascular biology, tissue engineering, and computational modeling. The porcine and bovine heart samples are clinically approved, ensuring relevance for translational and preclinical studies. The chick embryo heart data provides insights into early cardiac development.

Supplementary video for the article "A Programmable CMOS DEP Chip for Cell Manipulation"

This video demonstrates the use of a CMOS DEP chip for particle manipulation, including particle patterning, concentration control, and single particle manipulation, all performed on the same chip and sample.