This dataset provides the ECG signals recorded in ambulatory (moving) conditions of subjects. The ambulatory ECG (A-ECG) data acquired with two different recorders viz. Biopac MP36 Acquisition system and a self-developed wearable ECG recorder are made available. Total 10 subjects' (with avg. age of 27 years, 1 female and 9 males) ECG signals with four body movements- Left & Right arm up/down, Sitting down & standing up and Waist twist are uploaded.
An EEG signals dataset is also provided here.
Please contact me at: firstname.lastname@example.org for how to use the dataset and further discussion.
This dataset contains light-field microscopy images and converted sub-aperture images.
The folder with the name "Light-fieldMicroscopeData" contains raw light-field data. The file LFM_Calibrated_frame0-9.tif contains 9 frames of raw light-field microscopy images which has been calibrated. Each frame corresponds to a specific depth. The 9 frames cover a depth range from 0 um to 32 um with step size 4 um. Files with name LFM_Calibrated_frame?.png are the png version for each frame.
The migration of cancer cells is highly regulated by the biomechanical properties of their local microenvironment. Using 3D scaffolds of simple composition, several aspects of cancer cell mechanosensing (signal transduction, EMC remodeling, traction forces) have been separately analyzed in the context of cell migration. However, a combined study of these factors in 3D scaffolds that more closely resemble the complex microenvironment of the cancer ECM is still missing.
The datasets is made of a number of zip files. The name of the file identifies the figure (and figure panel) that the data refers to.
Human Proteome and Peptides with up to 2 missed cleavages.
We crawled large amounts of biomedical articles from PubMed for the keyphrase extraction system evaluation.
The articles, that consist of title, abstract and keyphrases provided by the authors, are used for the experiments.
In our paper, cancer-related biomedical articles are selected.
Each document in the dataset consists of title, abstract and keyphrases provided by the authors.
To develop a non-invasive assessment tool using machine learning in supporting a timely, accurate diagnosis in the elderly, we created an annotated dataset of 668 tongue images collected from hospitalized geriatric patients in a tertiary hospital in Shanghai, China. Images were captured via a light-field camera using CIELAB color space (to simulate human visual perception) and then were manually labeled by a panel of subject matter experts after chart reviewing patients’ clinical information documented in the hospital’s information system.
Specific subject area
Diagnosis – Image and text data analysis
Hospitalized geriatric patients are a highly heterogeneous group often with variable diseases and conditions. Physicians, and geriatricians especially, are devoted to seeking non-invasive testing tools to support a timely, accurate diagnosis. Chinese tongue diagnosis, mainly based on the color and texture of the tongue, offers a unique solution.
Type of data
Each patient has a folder with 1 face image, 1 tongue image, and 2 narrative documents. An additional summary formed by table is provided.
How data were acquired
We used a patented light-field camera (CN201520303463.5) called the intelligent mirror using CIE L*a*b* color space. Our data acquisition was handled in a standardized way (i.e., ensuring consistent sitting height and placement of the intelligent mirror) as much as possible.
The face and tongue images belong to raw data and were taken at 600 pixels per inch (about 42.3 µm per pixel) and saved as a *.jpg with minimum compression (10% compression max). One narrative document is annotated and contains the parameters generated by the intelligent mirror when creating the face and tongue images, and the other contains the annotation results from the expert panel (e.g., vital signs, clinical imaging examination, and laboratory indicators).
Parameters for data collection
The study was conducted at a Chinese tertiary, comprehensive hospital. We recruited hospitalized subjects (excluding minority groups or other sensitive or disempowered populations) in the Geriatrics Department beginning in January 1, 2019. Images were captured via a light-field camera using CIELAB color space (to simulate the human visual perception) and then were manually labeled by a panel of subject matter experts after chart reviewing patients’ clinical information documented in the hospital’s information system.
Description of data collection
Data acquisition and image annotation was conducted by subject matter experts including four fully credentialed senior-level physicians (i.e., associate chief physician and above), one resident, and two medical students. One medical student was in charge of data acquisition. The resident consolidated patients’ previous chronic medical history, clinical imaging examination, and laboratory indicators. One physician diagnosed patients’ constitutional types. Another physician gave a final admission diagnosis by considering the patient’s constitution based on both traditional Chinese medicine and Western medicine. Constitutional types are based on TCM analysis and differentiation of pathological conditions in accordance with the eight principal syndromes, namely 八纲辨证, including yin and yang (阴阳), exterior and interior (表里), cold and heat (寒热), and hypofunction and hyperfunction (虚实). All the information from the free-text data labeling was documented digitally by one medical student in Chinese and translated into English. The treatment plan corresponding to the admission diagnosis was reviewed and annotated by the remaining two physicians.
A total of 12 items must be merged into an annotated document, including various indices related to tongue diagnosis, physical or mental factors, clinicians’ observations, and more. To mitigate this, we used a previously designed algorithm to generate templates automatically. Under the K-means paradigm, our previously designed algorithm (1) embedded each annotated document into a vector representation for the first 200 patients, (2) partitioned those vectors into several (e.g., K=10) clusters, and (3) designated each cluster representative as a prototype template, or a vector of real annotated document closest to the centroid. For the remaining 468 patients, we used the specified prototype template to assist with the annotation.
Data source location
Cambridge, MA, USA
Schematic explanation of representations to brain networks during WM tasks. Left upper panel is the location illustration of four fitted sources. A-E present components relative to WM in terms of some specific neurocognitive processes. A. During this duration, selective attention is activated by capitals’ trigger, which induced the attention mechanism in PPC cortex. B.
This dataset was used in the article "Dias-Audibert FL, Navarro LC, de Oliveira DN, Delafiori J, Melo CFOR, Guerreiro TM, Rosa FT, Petenuci DL, Watanabe MAE, Velloso LA, Rocha AR and Catharino RR (2020) Combining Machine Learning and Metabolomics to Identify Weight Gain Biomarkers. Front. Bioeng. Biotechnol. 8:6. doi: 10.3389/fbioe.2020.00006", open access available at: https://doi.org/10.3389/fbioe.2020.00006.
WGMSML-Data folder contains the mass spectra input data for the Matlab scripts which are in WGMSML-MATLAB-SourceCode folder. WGMSML-ExecutionLogsAndPlots contains logs and plots generated by the execution of the Matlab code over the input data. Main scripts are enumerated in the order of execution.
This dataset contains in-silico results of insulin treatment using a fully automated artificial pancreas algorithm based on reinforcement learning for FDA-approved virtual patients (C. D. Man et al., 2014) with type 1 diabetes (10 adults and 10 adolescents).