Biomedical and Health Sciences
Non-invasive monitoring and surveillance methods of blood glucose measurement can provide ease of use and simplicity for different individuals while reducing the risks and damages of invasive methods. The non-invasive method based on photoplethysmography (PPG) signal is one of the innovative methods on this topic which numerous studies have been conducted by research centers and various companies. However, due to various reasons, the reviewed dataset was not available and no standard dataset has been published on this topic.
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Tourism is increasing worldwide and has many benefits for countries and cities, such as creating jobs, increasing company revenue, and improving government tax collection. As such, tourism is an unstoppable trend followed by countries and municipalities that try to stimulate this activity. However, unexpected impacts of this, in principle, wealthy activity must be observed.
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Two publicly available datasets, the PASS and EmpaticaE4Stress databases, were utilised in this study. They were chosen because they both used the same Empatica E4 device, which allowed the acquisition of a variety of signals, including PPG and EDA. The dataset consists of in 1587 30-second PPG segments. Each segment has been filtered and normalized using a 0.9–5 Hz band-pass and min-max normalization scheme.
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The ultrasound robot dataset comprises the original real-time ultrasound video streams captured by the ultrasound device during each experiment, along with the ultrasound video streams processed by the image segmentation model. It also includes real-time positional data of the robot, the area of the gallbladder extracted from the image segmentation, and the target positional data output by the algorithm. Additionally, the dataset contains a series of recorded videos documenting the scanning process of the autonomous ultrasound system.
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This dataset integrates three Publicly available sources of drug-target interaction data: the Human dataset, the Biosnap dataset, and the DrugBank dataset, combining them into a comprehensive resource for drug discovery and bioinformatics research. It includes a diverse set of human proteins identified as potential drug targets, along with a variety of corresponding drug molecules. Each drug-target pair is accompanied by interaction labels, indicating whether the drug interacts with the protein target.
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This dataset integrates three valuable sources of drug-target interaction data: the Human dataset, the Biosnap dataset, and the DrugBank dataset, combining them into a comprehensive resource for drug discovery and bioinformatics research. It includes a diverse set of human proteins identified as potential drug targets, along with a variety of corresponding drug molecules. Each drug-target pair is accompanied by interaction labels, indicating whether the drug interacts with the protein target.
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The Human dataset provides a comprehensive collection of drug-target interactions specific to human proteins, aimed at facilitating research in drug discovery and bioinformatics. This dataset includes a diverse range of human proteins as drug targets, along with associated drug molecules and their respective interaction labels. The data consists of molecular descriptors of drugs, protein sequences, and experimentally validated interactions sourced from various biological databases.
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The Human dataset provides a comprehensive collection of drug-target interactions specific to human proteins, aimed at facilitating research in drug discovery and bioinformatics. This dataset includes a diverse range of human proteins as drug targets, along with associated drug molecules and their respective interaction labels. The data consists of molecular descriptors of drugs, protein sequences, and experimentally validated interactions sourced from various biological databases.
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Create Graph：we collected asymmetric drug-drug interaction (DDI) entries from version 5.1.12 of DrugBank, released on March 14, 2024. After a thorough double-check, we removed drugs with incorrect SMILES strings or those that could not be represented by Morgan fingerprints . This filtering resulted in a dataset containing 1,752 drugs and 508,512 asymmetric interactions. Subsequently, we organized the DDI entries into a directed interaction network, where directed edges represent the asymmetric interactions between drugs.
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The 'AirScript' dataset consists of surface electromyography (sEMG) signals obtained while writing the uppercase English alphabets (A–Z) in free space. The Delsys Trigno device was used to record forearm muscle activity from 16 subjects. Every subject performs two trials for each letter, thus resulting in 52 samples per subject. sEMG signals obtained from all subjects were stored at a 2000 Hz sampling rate for high temporal resolution. The dataset consists of raw sEMG signals that are stored in subject-specific folders and saved as `.npy` files.
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