COVID-19 Dataset for Correlation Between Early Government Interventions in the Northeastern United States and Peak COVID-19 Disease Burden by Joel Mintz. File Type: Excel Contents: Tab 1 ("Raw")=Raw Data as Downloaded directly from COVID Tracking Project, sorted by date Tab 2-14 ("State Name') = Data Sorted by State Tab 2-14 Headers: Column 1: Population per state, as recorded by latest American Community Survey, maximum (peak) COVID-19 outcome, with date on which outcome occurred. Column 2: Date on which numbers were recorded* Column 3: State Name* Column 4: Number of reported positive COVID-19 tests* Column 5: Number of reported negative COVID-19 tests* Column 6: Pending COVID-19 tests* Column 7: Currently Hospitalized* Column 8: Cumulatively Hospitalized* Column 9: Currently in ICU* Column 10: Cumulatively in ICU* Column 11: Currently on Ventilator Support* Column 12: Cumulatively on Ventilator Support* Column 13: Total Recovered* Column 14: Cumulative Mortality* *Provided in Original Raw Data Column 15: Total Tests Administered (Column 4+Column 5) Column 16: Placeholder Column 17: % of total population tested Column 18: New Cases Per day Column 19: Change in new cases per day Column 20: Positive cases per day per capita in number per/ hundreds of thousands: (Column 18/total population*100000) Column 21: Change in Positive cases per day per capita in number per/ hundreds of thousands: (Column 19/total population*100000) Column 22: Hospitalizations per day per capita in number per/ hundreds of thousands Column 23: Change in Hospitalizations per day per capita in number per/ hundreds of thousands Column 24: Deaths per day per capita in number per/ hundreds of thousands Column 25: Change in Deaths per day per capita in number per/ hundreds of thousands Column 26-31: Columns 20-25 with an applied 5 day moving average filter Column 32: Adjusted hospitalization: (Subtract number of hospitalizations from the initial number of hospitalzations where reporting bean) Column 33: Adjusted hospitalizations per day per capita Column 34: Adjusted hospitalizations per day per capita, with applied 5 day moving average filter

Data forming the Covid-19 Second Generation Surveillance Systems data set relate to demographic and diagnostic information from Pillar 1 swab testing in PHE labs and NHS hospitals for those with a clinical need, and health and care workers and Pillar 2 Swab testing in the community at drive through test centres, walk in centres, home kits returned by posts, care homes, prisons etc).

Timescales for dissemination can be found under 'Our Service Levels' at the following link: https://digital.nhs.uk/services/data-access-request-service-dars/data-access-request-service-dars-process

User Agreement, Public Domain Dedication, and Disclaimer of Liability. By accessing or downloading the data or work provided here, you, the User, agree that you have read this agreement in full and agree to its terms. The person who owns, created, or contributed a work to the data or work provided here dedicated the work to the public domain and has waived his or her rights to the work worldwide under copyright law. You can copy, modify, distribute, and perform the work, for any lawful purpose, without asking permission. In no way are the patent or trademark rights of any person affected by this agreement, nor are the rights that any other person may have in the work or in how the work is used, such as publicity or privacy rights. Pacific Science & Engineering Group, Inc., its agents and assigns, make no warranties about the work and disclaim all liability for all uses of the work, to the fullest extent permitted by law. When you use or cite the work, you shall not imply endorsement by Pacific Science & Engineering Group, Inc., its agents or assigns, or by another author or affirmer of the work. This Agreement may be amended, and the use of the data or work shall be governed by the terms of the Agreement at the time that you access or download the data or work from this Website. Description This dataverse contains the data referenced in Rieth et al. (2017). Issues and Advances in Anomaly Detection Evaluation for Joint Human-Automated Systems. To be presented at Applied Human Factors and Ergonomics 2017. Each .RData file is an external representation of an R dataframe that can be read into an R environment with the 'load' function. The variables loaded are named ‘fault_free_training’, ‘fault_free_testing’, ‘faulty_testing’, and ‘faulty_training’, corresponding to the RData files. Each dataframe contains 55 columns: Column 1 ('faultNumber') ranges from 1 to 20 in the “Faulty” datasets and represents the fault type in the TEP. The “FaultFree” datasets only contain fault 0 (i.e. normal operating conditions). Column 2 ('simulationRun') ranges from 1 to 500 and represents a different random number generator state from which a full TEP dataset was generated (Note: the actual seeds used to generate training and testing datasets were non-overlapping). Column 3 ('sample') ranges either from 1 to 500 (“Training” datasets) or 1 to 960 (“Testing” datasets). The TEP variables (columns 4 to 55) were sampled every 3 minutes for a total duration of 25 hours and 48 hours respectively. Note that the faults were introduced 1 and 8 hours into the Faulty Training and Faulty Testing datasets, respectively. Columns 4 to 55 contain the process variables; the column names retain the original variable names. Acknowledgments. This work was sponsored by the Office of Naval Research, Human & Bioengineered Systems (ONR 341), program officer Dr. Jeffrey G. Morrison under contract N00014-15-C-5003. The views expressed are those of the authors and do not reflect the official policy or position of the Office of Naval Research, Department of Defense, or US Government.

This dataset contains files used to train and test the Multi-Configuration 23 (MC23) functional and to compare the results to other methods. It includes files to carry out electronic structure calculations. These include molecular geometries in xyz format, OpenMolcas input files for CASSCF calculations, converged CASSCF natural orbitals, OpenMolcas basis set files, and Gaussian 16 formatted checkpoint files for KS-DFT calculations. It also includes data used for data processing such as stoichiometries, absolute energies, and reference energies.

Each file in this dataset is a .tar.xz archive. One can extract them by the following command:

tar -xJf name_of_archive.tar.xz

Below is a description of the content of each archive.

gaussian_16_fchk.tar.xz contains Gaussian 16 formatted checkpoint files for all KS-DFT calculations used in this work. The files in the archive are named as functional/database/system.fchk

openmolcas_basis_set.tar.xz contains OpenMolcas basis set files used for multireference calculations. To reproduce the results in this work, the basis set files should be placed in the “basis_library” directory in the OpenMolcas installation location.

openmolcas_wave_function.tar.xz contains files needed by OpenMolcas to reproduce the CASSCF wave function used in this work. The files in the archive are named database/system.*.

• The file system.xyz contains the Cartesian coordinates. Note that for Data Set 2, the coordinates are in the input files system.inp.
• The file system.inp contains the OpenMolcas input file to perform CASSCF calculations.
• The files system.RasOrb, system.rasscf.h5, and system.rasscf.molden contain the converged CASSCF natural orbitals.

gaussian_16_stoichiometry_energy.tar.xz and openmolcas_stoichiometry_energy.tar.xz contain files used for data processing.

• Files with names like database.ref contain information used to calculate the final energies and errors. They are tab-delimited files. Each row represents an energy difference (e.g. atomization energy, barrier height, etc.). The first column contains the name of the energy difference (note: spaces may be present in this column). This is followed by the file names of each electronic structure calculation and the stoichiometries used to calculate the energy difference from the absolute energies. Each name or stoichiometry occupies one column. The second from the last column contains the reference value in kcal/mol. The reference values contain spin–orbit coupling. The last column contains the factor by which the final energy should be divided. This factor usually equals 1, but it can be greater than 1 for databases calculating atomization energies per bond or per atom.
• Files with names like method.elist contain the absolute energies of each electronic structure calculation. They are tab-delimited files. Each row represents an electronic structure calculation, and each row always contains two columns. The first column is the file name of the calculation in the format database/system. The second column is the absolute energy in atomic units extracted from the output file of electronic structure programs.
• The file named SOC.dat contains the spin–orbit coupling term in kcal/mol to be added to each electronic structure calculation prior to calculating energy differences. It has the same format as files with names like method.elist.

The database names in the directory names use a slightly different convention than the ones in the article describing MC23. A prefix DS2_ or DS3_ is used to indicate the data set to which a database belongs, and the number of data points is removed from the database name. For example, the MR-MGN-BE8 database from Data Set 2 has a file name DS2_MR-MGN-BE.

A. SUMMARY This dataset contains COVID-19 positive confirmed cases aggregated by several different geographic areas and by day. COVID-19 cases are mapped to the residence of the individual and shown on the date the positive test was collected. In addition, 2016-2020 American Community Survey (ACS) population estimates are included to calculate the cumulative rate per 10,000 residents.

Dataset covers cases going back to 3/2/2020 when testing began. This data may not be immediately available for recently reported cases and data will change to reflect as information becomes available. Data updated daily.

Geographic areas summarized are: 1. Analysis Neighborhoods 2. Census Tracts 3. Census Zip Code Tabulation Areas

B. HOW THE DATASET IS CREATED Addresses from the COVID-19 case data are geocoded by the San Francisco Department of Public Health (SFDPH). Those addresses are spatially joined to the geographic areas. Counts are generated based on the number of address points that match each geographic area for a given date.

The 2016-2020 American Community Survey (ACS) population estimates provided by the Census are used to create a cumulative rate which is equal to ([cumulative count up to that date] / [acs_population]) * 10000) representing the number of total cases per 10,000 residents (as of the specified date).

COVID-19 case data undergo quality assurance and other data verification processes and are continually updated to maximize completeness and accuracy of information. This means data may change for previous days as information is updated.

C. UPDATE PROCESS Geographic analysis is scripted by SFDPH staff and synced to this dataset daily at 05:00 Pacific Time.

D. HOW TO USE THIS DATASET San Francisco population estimates for geographic regions can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

This dataset can be used to track the spread of COVID-19 throughout the city, in a variety of geographic areas. Note that the new cases column in the data represents the number of new cases confirmed in a certain area on the specified day, while the cumulative cases column is the cumulative total of cases in a certain area as of the specified date.

Privacy rules in effect To protect privacy, certain rules are in effect: 1. Any area with a cumulative case count less than 10 are dropped for all days the cumulative count was less than 10. These will be null values. 2. Once an area has a cumulative case count of 10 or greater, that area will have a new row of case data every day following. 3. Cases are dropped altogether for areas where acs_population < 1000 4. Deaths data are not included in this dataset for privacy reasons. The low COVID-19 death rate in San Francisco, along with other publicly available information on deaths, means that deaths data by geography and day is too granular and potentially risky. Read more in our privacy guidelines

Rate suppression in effect where counts lower than 20 Rates are not calculated unless the cumulative case count is greater than or equal to 20. Rates are generally unstable at small numbers, so we avoid calculating them directly. We advise you to apply the same approach as this is best practice in epidemiology.

A note on Census ZIP Code Tabulation Areas (ZCTAs) ZIP Code Tabulation Areas are special boundaries created by the U.S. Census based on ZIP Codes developed by the USPS. They are not, however, the same thing. ZCTAs are areal representations of routes. Read how the Census develops ZCTAs on their website.

Rows included for Citywide case counts Rows are included for the Citywide case counts and incidence rate every day. These Citywide rows can be used for comparisons. Citywide will capture all cases regardless of address quality. While some cases cannot be mapped to sub-areas like Census Tracts, ongoing data quality efforts result in improved mapping on a rolling bases.

Related dataset See the dataset of the most recent cumulative counts for all geographic areas here: https://data.sfgov.org/COVID-19/COVID-19-Cases-and-Deaths-Summarized-by-Geography/tpyr-dvnc

E. CHANGE LOG

• 9/11/2023 - data on COVID-19 cases by geography over time are no longer being updated. This data is currently through 9/6/2023 and will not include any new data after this date.
• 4/6/2023 - the State implemented system updates to improve the integrity of historical data.
• 2/21/2023 - system updates to improve reliability and accuracy of cases data were implemented.
• 1/31/2023 - updated “acs_population” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
• 1/31/2023 - implemented system updates to streamline and improve our geo-coded data, resulting in small shifts in our case data by geography.
• 1/31/2023 - renamed column “last_updated_at” to “data_as_of”.
• 1/31/2023 - removed the “multipolygon” column. To access the multipolygon geometry column for each geography unit, refer to COVID-19 Cases and Deaths Summarized by Geography.
• 1/22/2022 - system updates to improve timeliness and accuracy of cases and deaths data were implemented.
• 4/16/2021 - dataset updated to refresh with a five-day data lag.

Additional file 1: Table S1. The table provides a listing of all the resources used to collect missing historical testing data points. It consists of 7 columns: Country/State, First Data Point, Last Data Point, Language, Data Type, Test Type reported and the Source reference. The “First Data Point” and “Last Data Point” columns indicate the date of the first and last manually collected data points, respectively. The “Language” column indicates the original language of the resource. The “Data Type” column indicates the format of the data (API: application programming interface, Infographic: uploaded data that gets overridden daily, Daily reports, News reports, Graphs and Machine readable datasets). The “Test Type Reported” column indicates the method used to test for SARS-CoV-2: PCR, serological or unspecified. The “Source” provides the URL to the resource used.

=====================================================================

NII Face Mask Dataset v1.0

=====================================================================

Authors: Trung-Nghia Le (1), Khanh-Duy Nguyen (2), Huy H. Nguyen (1), Junichi Yamagishi (1), Isao Echizen (1)

Affiliations: (1)National Institute of Informatics, Japan (2)University of Information Technology-VNUHCM, Vietnam

National Institute of Informatics Copyright (c) 2021

Emails: {ltnghia, nhhuy, jyamagis, iechizen}@nii.ac.jp, {khanhd}@uit.edu.vn

Arxiv: https://arxiv.org/abs/2111.12888 NII Face Mask Dataset v1.0: https://zenodo.org/record/5761725

=============================== INTRODUCTION ===============================

The NII Face Mask Dataset is the first large-scale dataset targeting mask-wearing ratio estimation in street cameras. This dataset contains 581,108 face annotations extracted from 18,088 video frames (1920x1080 pixels) in 17 street-view videos obtained from the Rambalac's YouTube channel.

• https://www.youtube.com/c/Rambalac

The videos were taken in multiple places, at various times, before and during the COVID-19 pandemic. The total length of the videos is approximately 56 hours.

=============================== REFERENCES ===============================

If your publish using any of the data in this dataset please cite the following papers:

Pre-print version

@article{Nguyen202112888, title={Effectiveness of Detection-based and Regression-based Approaches for Estimating Mask-Wearing Ratio}, author={Nguyen, Khanh-Duy and Nguyen, Huy H and Le, Trung-Nghia and Yamagishi, Junichi and Echizen, Isao}, archivePrefix={arXiv}, arxivId={2111.12888}, url={https://arxiv.org/abs/2111.12888}, year={2021} }

Final version

@INPROCEEDINGS{Nguyen2021EstMaskWearing, author={Nguyen, Khanh-Duv and Nguyen, Huv H. and Le, Trung-Nghia and Yamagishi, Junichi and Echizen, Isao}, booktitle={2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)}, title={Effectiveness of Detection-based and Regression-based Approaches for Estimating Mask-Wearing Ratio}, year={2021}, pages={1-8}, url={https://ieeexplore.ieee.org/document/9667046}, doi={10.1109/FG52635.2021.9667046}}

======================== DATA STRUCTURE ==================================

1. Directory Structure

./NFM ├── dataset │ ├── train.csv: annotations for the train set. │ ├── test.csv: annotations for the test set. └── README_v1.0.md

2. Description for each files in detail.

We use the same structure for two CSV files (train.csv and test.csv). Both CSV files have the same columns: <1st column>: video_id (a source video can be found by following the link: https://www.youtube.com/watch?v=) <2nd column>: frame_id (the index of a frame extracted from the source video) <3rd column>: timestamp in milisecond (the timestamp of a frame extracted from the source video) <4th column>: label (for each annotated face, one of three labels was attached with a bounding box: 'Mask'/'No-Mask'/'Unknown') <5th column>: left <6th column>: top <7th column>: right <8th column>: bottom Four coordinates (left, top, right, bottom) were used to denote a face's bounding box.

============================== COPYING ================================

This repository is made available under Creative Commons Attribution License (CC-BY).

Regarding Creative Commons License: Attribution 4.0 International (CC BY 4.0), please see https://creativecommons.org/licenses/by/4.0/

THIS DATABASE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS DATABASE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE

====================== ACKNOWLEDGEMENTS ================================

This research was partly supported by JSPS KAKENHI Grants (JP16H06302, JP18H04120, JP21H04907, JP20K23355, JP21K18023), and JST CREST Grants (JPMJCR20D3, JPMJCR18A6), Japan.

This dataset is based on the Rambalac's YouTube channel: https://www.youtube.com/c/Rambalac

The data available for future research are 14 kind of files, files are organized in different category based on the usage that can be carried out with the data. Below the information for each kind of data is reported: To develop and test calibration methods for hand-eye calibration with image-based sensors Acquired images - This data set contains either images of a calibration pattern like a checkerboard, or in case of the GridSense sensor, point clouds that can be matched without knowledge of the sensor positions. Hence they can be used to compute the sensor motion in the point cloud frame. Pose information per image – For each of the acquired image, a .txt file containing the referenced pose is provided. This file contains the position (roto-translation 4x4 matrix) of the robot TCP in the robot base frame. This data can be used for recovering the position of the sensor when applying the desired hand-eye transformation. Calibration result – optional – When provided, this data contains the optimized hand-eye transformation, i.e. computed with the calibration procedure developed in the SPIRIT project. Initial guess of calibration (by design) – This data contains the roto-translation 4x4 matrix that can be used as initial guess for the actual hand-eye transform computation. This is retrieved by design from CAD drawings. To perform trajectory planning for robotic inspection tasks 3D model of object – This data contains the file CAD of the object. It is available on request. 3D model of workcell (at least robot + part); stp file - This data contains the file CAD of the workcell kinematic model of robot – This file contains the kinematic model of the robot Part calibration, hand-eye calibration – This file contains a 4x4 transformation matrix that defines the position and the orientation of the part within the workcell. Resulting trajectory, position of camera relative to part – This data contains the trajectory of the robot stored in binary table in the HDF5 format. In each file there are four tables: Timestamp: 1 column x N rows table, where N is the number of position in the trajectory and each row contains the time expressed as seconds. Component 0: contains 6 columns x N rows table, the 6 columns contains the robot joint position (rad) at the corresponding timestamp Component 1: this table exists only if there are two moving components. For test case 1, it contains the position of the conveyor belt. For test case 2 it contains the joint position of the second robot. ID: 1 column x N rows table , used internally by the SPIRIT SW to turn on or off the laser or to know the viewpoints associated to the joint position Video of full scanning process – This file is a video of the scanning process. To develop image mapping/stitching algorithms for 3D objects 3D model of object – This data contains the file CAD of the object. It is available on request. acquired image data - Images/ point clouds that were acquired at a scan of the test parts pose information per image – A Cartesian sensor position for each image/ point cloud. calibration data - Information about the intrinsic sensor parameters. Defect data & detection results Defect documentation – This file contains a report about the defect monitored during the scanning process Performance data – This file contains an evaluation of the results obtained referring to the KPI of the project.

This publication was archived on 12 October 2023. Please see the Viral Respiratory Diseases (Including Influenza and COVID-19) in Scotland publication for the latest data. This dataset provides information on number of new daily confirmed cases, negative cases, deaths, testing by NHS Labs (Pillar 1) and UK Government (Pillar 2), new hospital admissions, new ICU admissions, hospital and ICU bed occupancy from novel coronavirus (COVID-19) in Scotland, including cumulative totals and population rates at Scotland, NHS Board and Council Area levels (where possible). Seven day positive cases and population rates are also presented by Neighbourhood Area (Intermediate Zone 2011). Information on how PHS publish small are COVID figures is available on the PHS website. Information on demographic characteristics (age, sex, deprivation) of confirmed novel coronavirus (COVID-19) cases, as well as trend data regarding the wider impact of the virus on the healthcare system is provided in this publication. Data includes information on primary care out of hours consultations, respiratory calls made to NHS24, contact with COVID-19 Hubs and Assessment Centres, incidents received by Scottish Ambulance Services (SAS), as well as COVID-19 related hospital admissions and admissions to ICU (Intensive Care Unit). Further data on the wider impact of the COVID-19 response, focusing on hospital admissions, unscheduled care and volume of calls to NHS24, is available on the COVID-19 Wider Impact Dashboard. Novel coronavirus (COVID-19) is a new strain of coronavirus first identified in Wuhan, China. Clinical presentation may range from mild-to-moderate illness to pneumonia or severe acute respiratory infection. COVID-19 was declared a pandemic by the World Health Organisation on 12 March 2020. We now have spread of COVID-19 within communities in the UK. Public Health Scotland no longer reports the number of COVID-19 deaths within 28 days of a first positive test from 2nd June 2022. Please refer to NRS death certificate data as the single source for COVID-19 deaths data in Scotland. In the process of updating the hospital admissions reporting to include reinfections, we have had to review existing methodology. In order to provide the best possible linkage of COVID-19 cases to hospital admissions, each admission record is required to have a discharge date, to allow us to better match the most appropriate COVID positive episode details to an admission. This means that in cases where the discharge date is missing (either due to the patient still being treated, delays in discharge information being submitted or data quality issues), it has to be estimated. Estimating a discharge date for historic records means that the average stay for those with missing dates is reduced, and fewer stays overlap with records of positive tests. The result of these changes has meant that approximately 1,200 historic COVID admissions have been removed due to improvements in methodology to handle missing discharge dates, while approximately 820 have been added to the cumulative total with the inclusion of reinfections. COVID-19 hospital admissions are now identified as the following: A patient's first positive PCR or LFD test of the episode of infection (including reinfections at 90 days or more) for COVID-19 up to 14 days prior to admission to hospital, on the day of their admission or during their stay in hospital. If a patient's first positive PCR or LFD test of the episode of infection is after their date of discharge from hospital, they are not included in the analysis. Information on COVID-19, including stay at home advice for people who are self-isolating and their households, can be found on NHS Inform. Data visualisation of Scottish COVID-19 cases is available on the Public Health Scotland - Covid 19 Scotland dashboard. Further information on coronavirus in Scotland is available on the Scottish Government - Coronavirus in Scotland page, where further breakdown of past coronavirus data has also been published.

ContextProbing tasks are popular among NLP researchers to assess the richness of the encoded representations of linguistic information. Each probing task is a classification problem, and the model’s performance shall vary depending on the richness of the linguistic properties crammed into the representation.

This dataset contains five new probing datasets consist of noisy texts (Tweets) which can serve as a benchmark dataset for researchers to study the linguistic characteristics of unstructured and noisy texts.File StructureFormat: A tab-separated text file

Column 1: train/test/validation split (tr-train, te-test, va-validation)

     Column 2: class label (refer to the content

section for the class labels of each task file)

     Column 3: Tweet message (text)

     Column

4: a unique ID Contentsent_len.tsvIn this classification task, the goal is to predict the sentence length in 8 possible bins (0-7) based on their lengths; 0: (5-8), 1: (9-12), 2: (13-16), 3: (17-20), 4: (21-25), 5: (26-29), 6: (30-33), 7: (34-70). This task is called “SentLen” in the paper.word_content.tsvWe consider a 10-way classifications task with 10 words as targets considering the available manually annotated instances. The task is predicting which of the target words appears on the given sentence. We have considered only the words that appear in the BERT vocabulary as target words. We constructed the data by picking the first 10 lower-cased words occurring in the corpus vocabulary ordered by frequency and having a length of at least 4 characters (to remove noise). Each sentence contains a single target word, and the word occurs precisely once in the sentence. The task is referred to as “WC” in the paper. bigram_shift.tsvThe purpose of the Bigram Shift task is to test whether an encoder is sensitive to legal word orders. Two adjacent words in a Tweet are inverted, and the classification model performs a binary classification to identify inverted (I) and non-inverted/original (O) Tweets. The task is referred to as “BShift” in the paper. tree_depth.tsvThe Tree Depth task evaluates the encoded sentence's ability to understand the hierarchical structure by allowing the classification model to predict the depth of the longest path from the root to any leaf in the Tweet's parser tree. The task is referred to as “TreeDepth” in the paper. odd_man_out.tsv

The Tweets are modified by replacing a random noun or a verb o with another noun or verb r. The task of the classifier is to identify whether the sentence gets modified due to this change. Class label O refers to the unmodified sentences while C refers to modified sentences. The task is called “SOMO” in the paper.

Aging dataset of a commercial 22Ah LCO-graphite pouch Li-Po battery.

The cycling procedure involves aging steps consisting of 22 aging cycles at 1C CC discharge and C/2 CC-CV charge, with no pauses in between. Periodic RPTs are carried out after each aging step. In particular, two series of RPTs are alternated, referred to as RPT-A and RPT-B, with this pattern: 22 aging cycles -> RPT-A -> 22 aging cycles -> RPT-A + RPT-B -> repeat.

The RPT-A consists of three high rate cycles (1C CC discharge and C/2 CC-CV charge) with 1 hour rest.The RPT-B consists of three high rate cycles (1C CC discharge and C/2 CC-CV charge) with 1 hour rest, one low rate cycle (C/20) and the HPPC test. In this way, high rate test cycles are carried out periodically every 25 cycles (22 aging + 3 test), whereas low rate test cycles and HPPC are carried out every 50 cycles. The exact number at which each reference performance test was carried out is reported in the sixth column of the data structure.

In total, 1125 cycles were achieved untill SOH 70%.

The cycling reference performance tests (high rate cycling 1C-C/2, and low rate cycling C/20-C/20) are reported in the MATLAB structure called Aging_Dataset_Cycling. On the other, the data of the HPPC tests are reported in the MATLAB structure called Aging_Dataset_HPPC.

The data structure of cycling reference performance tests is a MATLAB cell organized so that in the first row there are data of RPT-A (high rate cycles), and in the second row the data of RPT-B (low rate cycles). In the first column there are discharge data, in the second column the charge data, in the third column the data recorded in the one hour rest after discharge and in the fourth column the data recorded in the one hour rest after charge. In each element of this 2x4 matrix there is a cell containing the structures referring to each reference performance tests. The different reference performance tests are organized so that in the row there are the reference performance tests carried out at different aging cycles (detailed in the vector in the sixth column of the main data structure) and in the column there are the tests repeated at the same aging cycles for statistical studies. Generally RPT-A tests are repeated three times and RPT-B tests are repeated one times. Then, each cell, e.g. D{1,1}{1,1} contains a structure with the data of that test coded as explained in the bullet list below.

The data recorded during the reference performance test, reported in the data structure, were:

Time test [s]. Variable name: Time.

Battery temperature [°C]. Variable name: T_batt.

Ambient temperature [°C]. Variable name: T_amb.

Battery voltage [V]. Variable name: V_batt.

Charging current [A]. Variable name: I_PS

Discharging current [A]. Variable name: I_EL

Laser sensor 1 reading [V]. Variable name: Las1

Laser sensor 2 reading [V]. Variable name: Las2

Battery deformation [mm], meant as the thickness change of the battery. Variable name: Dthk

Deformation measurements were carried out measuring the out-of-plane displacement of the two largest surfaces of the battery with a couple of laser sensors, as explained in these Figures. The two sensor readings are expressed in Volt, ranging from 0V (start measuring distance) to 10V (end measuring distance), and are proportional to the distance between the laser (fixed) and the battery surface (moving because of the thickness change). The reversible deformation within a single cycle is already computed in the variable Battery deformation and it is expressed in millimeter. The reversible deformation is computed as the sum of the two laser readings (1V = 1mm), net of the sum of the two initial laser readings. The single laser readings are useful to compute the irreversible deformation, namely how the thickness of the battery changes during aging. This is possible because the laser remained fixed during the whole aging test, and the reference was not lost. Therefore, to calculate the deformation of the battery at any given moment during the aging test, it is necessary to sum the two laser readings at the given moment and subtract the sum of the two initial laser readings.

Example of the data structure: D{1,1} contains all the discharge data of all the RPT-A tests. In total, there are 47 lines and 4 columns, because RPT-A tests were conducted at 47 different aging levels (the respective number of cycles is reported in the vector stored in the sixth column first row of the main data structure), and the tests are repeated up to 4 times at the same aging level, even if most of the time were repeated just three times. Then, D{1,1}{1,1} contains the discharge data of the first reference performance(RPT-A) test carried out at the first aging level (10 cycles), D{1,1}{1,2} contains the discharge data of the second reference performance(RPT-A) test carried out at the first aging level, D{1,1}{2,1} contains the discharge data of the first reference performance (RPT-A) test carried out at the second aging level (20 cycles), and so on. D{1,2} contains all the charge data of all the RPT-A tests and D{2,1} and D{2,2} contain all the discharge and charge data of the RPT-B (low rate-C/20) test. The substructures work similarly as described for D{1,1}.

The data structure of the HPPC reference performance tests is a MATLAB cell organized so that in the rows there are the data referring to different aging cycles, and the first ten columns correspond to the SOC at which the HPPC test is carried out, going from 100% to 10%. The 11th contains the number of aging cycles at which the test in that column was carried out. Each structure in this matrix refers to a single HPPC test and contains the following data:

Time test [s]. Variable name: Time.

Battery voltage [V]. Variable name: V_batt.

Charging current [A]. Variable name: I_PS

Discharging current [A]. Variable name: I_EL

Ambient temperature was controlled with a climatic chamber and it was kept constant at 20°C during all the tests.

Overview

• Introduction
• Object Classes
  • Hammer
  • Hanger Bar
  • Hook
  • Pedestal
  • Pillar
  • Pillar With Bracket
  • Safety Joint

Introduction

This dataset is designed to solve the task of object detection for various construction-related items. The objective is to accurately annotate objects within images using bounding boxes. The classes included are:

• Hammer: Typically used for driving nails into surfaces.
• Hanger Bar: A long bar or rod for hanging items.
• Hook: A curved or angular device used for catching or holding objects.
• Pedestal: A base or support for larger structures.
• Pillar: A vertical, elongated support structure.
• Pillar With Bracket: A pillar with an attached bracket for support.
• Safety Joint: A component designed to ensure secure connections in assemblies.

Object Classes

Hammer

Description

Tools with a handle and a heavy end, often wrapped or covered.

Instructions

• Identify the metal head and the handle.
• The hammer appears wrapped in packaging; include this in the bounding box.

Hanger Bar

Description

Long, thin, rectangular metallic or plastic rods.

Instructions

• Annotate groups of these bars with one box
• Extend the bounding box along the entire visible length of the bar.
• If bars overlap, annotate individually where distinct sections are visible.
• Exclude bars if more than 80% obscured.

Hook

Description

Curved or angled, small metallic pieces, often grouped.

Instructions

• Enclose the entire hook, ensuring all curves are within the boundary.
• Do not separate hooks if grouped closely; annotate collectively.
• Ignore isolated parts with unidentified purposes.

Pedestal

Description

Rectangular or cylindrical bases, typically longer in form.

Instructions

• Align the bounding box tightly along the edges.
• Ensure differentiation from similar long objects by checking the base support features.
• Exclude any significantly blurred sections.

Pillar

Description

Long, hollow structures with cut-out rectangular patterns.

Instructions

• Cover the full visible length, including pattern cut-outs.
• Annotate distinctly from similar structures without patterns.
• Avoid grouping; maximum one box per pillar.

Pillar With Bracket

Description

Pillars similar to standard ones but with bracket attachments.

Instructions

• Highlight both the pillar and the attached bracket distinctly.
• Ensure the bounding box encloses both components without external objects.
• Clearly separate from regular pillars by identifying bracket presence.

Safety Joint

Description

Angular, zig-zag structures, often paired or grouped tightly.

Instructions

• Contain all visible zig-zag components within one box if adjacent.
• Clearly separate overlapping joints into distinct annotations.
• Avoid annotating unrelated angular objects nearby.

Mismatching and partial shading identification in photovoltaic arrays by an artificial neural network ensemble

Michel Piliougine (mpiliouginerocha@unisa.it) and Giovanni Spagnuolo (gspagnuolo@unisa.it)

UNIVERSITA DEGLI STUDI DI SALERNO DIEM – Via Giovanni Paolo II 132. 84084 Fisciano (SA), Italy

Date: May 22nd, 2021 Version: 1.0

Filename: dataset.mat Description:

This file contains the dataset related to the article titled Mismatching and partial shading identification in photovoltaic arrays by an artificial neural network ensemble

This file has been stored using the native Matlab format for its workspace (*.mat)

Now, the variables stored in the file are described:

Variables: (*) full_curvesOk_simulated Cell array with 5000 full IV curves simulated using the SDM, assuming uniform conditions Each IV curve is a 300 x 2 matrix (double), with column 1= voltage (V), column 2 = current (A) (all the points of the simulated IV curve, not only those around MPP)

  From this variable, the following splitting has been made:
  TRAINING => from index 1 to 3500
  VALIDATION => from index 3501 to 4000
  TEST => from index 4001 to 5000 (referred as TEST#1 in the paper)


(*) full_curvesSh_simulated
  Cell array with 5000 full IV curves simulated using the SDM, assuming FAULTY conditions (steeped curve)
  Each IV curve is a 300 x 2 matrix (double), with column 1= voltage (V), column 2 = current (A)
     (all the points of the simulated IV curve, not only those around MPP) 

  From this variable, the following splitting has been made:
  TRAINING => from index 1 to 3500
  VALIDATION => from index 3501 to 4000
  TEST => from index 4001 to 5000 (referred as TEST#1 in the paper)


(*) full_curvesOk_experimental
  Cell array with 1000 full IV curves measured of a real PV module, assigned to be measured under
  normal operating conditions, and assumed to be classified as HEALTHY

  Each IV curve is a n x 2 matrix (double), with column 1= voltage (V), column 2 = current (A)
     (all the points of the simulated IV curve, not only those around MPP)
  where n is the number of experimental points of each IV curve, although it is around 100, but it could
  be different for each measured IV curve.

  All these curves has been used for testing
  TEST => from index 1 to 1000 (referred as TEST#2 in the paper)


(*) full_curvesSh_experimental
  Cell array with 1000 full IV curves measured of a real PV module, assigned to be measured under
  mismatched operating conditions, and assumed to be classified as FAULTY

  Each IV curve is a n x 2 matrix (double), with column 1= voltage (V), column 2 = current (A)
     (all the points of the simulated IV curve, not only those around MPP)
  where n is the number of experimental points of each IV curve, although it is around 100, but it could
  be different for each measured IV curve.

  All these curves has been used for testing
  TEST => from index 1 to 1000 (referred as TEST#2 in the paper)

Supplementary dataset 1

This file contains datasets for all 230 overlapped DEGs (Fig. 6A and B) among RTM, MTM, and control group compared with the PCOS group in F0 model mice. It has two sheets: "F0 up-regulated DEGs" contained all DEGs that have significantly higher (|log2| > 1) expression levels in RTM, MTM, and control group than PCOS group; "F0 down-regulated DEGs" contained all DEGs that have significantly lower (|log2| > 1) expression level in RTM, MTM, and control group than PCOS group. For each gene, both FPKM of each repeat and the average FPKM of three repeats are shown.

Supplementary dataset 2

This file, corresponding to Fig. 7, contains 4 sheets of datasets for all DEGs that overlapped between human PCOS studies and corrent study. From left to right, in sheet 1, the datasets from column A-AB are for the DEGs in current study; the datasets from column AD-BL are for the DEGs in Lan et al (36). In sheet 2, the datasets from column A-AA are for the DEGs in our study; the datasets from column AC-AI are for the DEGs in Wissing et al (37). In sheet 3, the datasets from column A-AB are for the DEGs in our study; the datasets from column AD-AL are for the DEGs in Lu et al (38). In sheet 4, the datasets from column A-AB are for the DEGs in our study; the datasets from column AD-AG are for the DEGs in Kaur et al (39). For a clear distinguishment between our data and the reference data, column AC in sheet 1, column AB in sheet 2, column AC in sheet 3, and column AC in sheet 4 are left blank and highlighted in green; Two columns with title "symbol" and red typed in each sheet list all overlapping DEGs between our data and reference data.

Supplementary dataset 3

This file contains 4 sheets. Each sheet includes KEGG information for the DEGs that overlapped between human PCOS studies and current study.

Supplementary dataset 4

This dataset contains all 126 overlapped DEGs (Supplementary Fig. 6A and B) among RTM, MTM, and control group compared with the PCOS group in F1 model mice. It has two sheets: "F1 up-regulated DEGs" contained all DEGs that have significantly higher (|log2| > 1) expression level in RTM, MTM, and control group than in PCOS group; "F1 down-regulated DEGs" contained all DEGs that have significantly lower (|log2| > 1) expression level in RTM, MTM, and control group than in PCOS group. For each gene, both FPKM of each repeat and the average FPKM of three repeats are shown.

According to the standard of land use code by fundamental geographic data set: FGDS), Thailand [5] land use classification requires an analysis and transformation of satellite images data together with field survey data. In this article, researchers studied only land use in water bodies. The water bodies in this research can be divided into 2 levels: natural body of water (W1) artificial body of (W2) water.

The aerial image data used in this research was 1:50 meters. Every aerial image had 650x650 pixels. Those images included water bodies type W1 and W2 as shown in Figure 3a. Ground truth of all aerial images was set for before sending it to be analyzed and interpreted by remote sensing experts. This assured that the water bodies groupings were correct. An example of ground truth, which has been checked by experts. Ground truth has been used in learning the algorithm in deep learning mode and also used in further evaluation.

The aerial images used in this experiment consists of water body: types W1 (see, Figure 3, Column 1, 2, and 3) and W2 (see, Figure 3 Column 4). Aerial image water resources dataset, AIWR has 800 images. Data were chosen at random and divided into 3 sections: training, validation, and test set with ratio 8:1:1. Therefore, 640 aerial images were used for learning and creating the model, 80 images were used for validation, and the remaining 80 images were used for test.

Data to support the findings reported in the journal paper Lomurno, E., Dui, L. G., Gatto, M., Bollettino, M., Matteucci, M., & Ferrante, S. (2023). Deep Learning and Procrustes Analysis for Early Dysgraphia Risk Detection with a Tablet Application. Life, 13(3), 598. Caratterizzazione_y3b_pulito.xls: Subject ID Age at the beginning of the study Handedness Sex Score_BVSCO_pulito.xls: Subject ID columns B-D: graphemes in the three tests of the BVSCO-2 columns E-G: z-score computed on normative data for the three tests of the BVSCO-2 median z-score: computed on columns E-G at risk: if median z-score is under the -2 threshold (0=not at risk, 1=at risk) executions.mat: a matrix for each time point (y1, y2a, y2b, y3a, y3b) and game (copa_q = copy square, copia_seq = copy sequence, tunnel_q = tunnel square, tunnel_ele = tunnel word). first column of all the cell arrays: subject ID second column for the copy games: three sub-matrices with an execution for each level (spontaneous, big, small) columns 2:N for the tunnel games: an average execution for each index of difficulty for each execution, the columns of the sub-matrices are normalized time, normalized x, normalized y and pressure Copy games A matrix for each game and each time point with a number of rows equal to the number of children and a number of columns equal to 2: First column: array with school and child code Second column: executions For the two copy games, the data has a dimension of 3 x 1, where 3 are the modalities (spontaneous, big, small) and 1 is the data matrix. By accessing the data matrix we have four variables, i.e., normalized time, normalized x, normalized y and pressure. Tunnel games A matrix for each game and each time point with a number of rows equal to the number of children and a number of columns equal to index of difficulty number +1: First column: array with school and child code From the second column onwards: executions, each column corresponds to the "typical" execution (the average) of an ID (5 for square tunnel and 8 for ele tunnel). Sub-matrices contain the data matrix with its four variables. i.e., normalized time, normalized x, normalized y and pressure.

mLab App Paradata DataThis data is part of the publications associated with the analysis of paradata and user engagement with the mLab App, a digital health application designed to support at-home HIV testing. The dataset includes detailed user interactions, testing results, and metadata collected during a multi-site clinical trial. The data is divided into several files, each with its own schema and purpose, as described below.**Notes:**- The data has been de-identified and cleaned.- The datasets originated from a REDCap project. Due to the different needs that the custom reports used to generate the dataset served, there may be redunancies across the different files.- The user_id assigned to each user is consistent across each of the files (i.e., test windows in test_windows.csv for user_id == 10 correspond to user_id == 10 in para.csv, info.csv, etc.).- Paradata collection was designed to operate asynchronously, ensuring that no user interactions were disrupted during data collection. As mLab was a browser-based technology, when users use browser navigation rapidly, there can be things that appear out of order (as we noted in our manuscripts).- Dataframe column datatypes have been converted to satisfy specific analyses. Please check datatypes and convert as needed for your particular needs.- Due to the sensitive nature of the survey data, the CSV/parquet files for survey are not included in this data repository. They will be made available upon reasonable request.- For detailed descriptions of the study design and methodology, please refer to the associated publication.## File Descriptions### facts.csv / facts.parquetThis file records the educational facts shown to users._Column Name_: Descriptiondisplay_timestamp: Unix timestamp when an educational fact was displayed to the user.session_id: Unique identifier for the user’s session when the fact was shown.user_id: Unique identifier for the user the fact was shown to.fact_category: Category of the educational fact displayed to the user.fact_index: Index number of the fact shown to the user.fact_text: Text of the educational fact displayed.### info.csv / info.parquetThis file contains user-specific metadata, and repeated data about each user (alerts and pinned facts)._Column Name_: Descriptionuser_id: Unique identifier for the user.redcap_repeat_instrument: REDCap field indicating the repeat instrument used. For general information about the user (userlocation and numberoflogins), redcap_repeat_instrument is blank. For repeated data (alerts, pinned facts, scheduled tests), redcap_repeat_instrument will identify the instrument.redcap_repeat_instance: Instance number of the repeat instrument (if applicable).user_location: Location of the user (if available). (1: New York City cohort; 2: Chicago cohort)alert_date: A unix timestamp of when an alert was sent to the user.number_of_logins: Total number of logins by the user.alert_subject: Subject or type of the alert sent.alert_read: Indicates whether the alert was read by the user (1: True; 0: False).end_date: Unix timestamp of the end date of scheduled tests.start_date: Unix timestamp of the start date of scheduled tests.fact_category: Category of the educational fact pinned by the user.fact_index: Index number of the fact pinned by the user.fact_text: Text of the educational fact pinned by the user.fact_link: Link to additional information associated with the fact pinned by the user (if available).### para.csv / para.parquetThis file includes paradata (detailed in-app user interactions) collected during the study._Column Name_: Descriptiontimestamp: A timezone-naive timestamp of the user action or event.session_id: Unique identifier for the user’s session.user_id: Unique identifier for the user.user_action: Specific user action (e.g., button press, page navigation). "[]clicked" indicates a pressable element (i.e., button, collapsible/expandable menu) is pressed.current_page: Current page of the app being interacted with.browser: Browser used to access the app.platform: Platform used to access the app (e.g., Windows, iOS).platform_description: Detailed description of the platform.platform_maker: Manufacturer of the platform.device_name: Name of the device used.device_maker: Manufacturer of the device used.device_brand_name: Brand name of the device used.device_type: Type of device used (Mobile, Computer, etc.).user_location: Location of the user (1: New York City cohort; 2: Chicago cohort).### survey.csv / survey.parquetThis file contains survey responses collected from users.*NOTE: Due to the sensitive nature of this data, CSV/parquet files are not included in this data repository. They will be made available upon reasonable request.*_Column Name_: Descriptionuser_id: Unique identifier for the user.timepoint: Timepoint of the survey (baseline/0 months, 6 months, 12 months).race: Race of the user.education: Education level of the user.health_literacy: Health literacy score of the user.health_efficacy: Health efficacy score of the user.itues_mean: Information Technology Usability Evaluation Scale (ITUES) mean score.age: Age of the user.### tests.csv / tests.parquetThis file contains data related to the HIV self-tests performed by users in the mLab App._Column Name_: Descriptionuser_id: Unique identifier for the user that took the test.visual_analysis_date: A unix timestamp of the visual analysis of the test by the user.visual_result: Result of the visual analysis (positive, negative).mlab_analysis_date: A unix timestamp of the analysis conducted by the mLab system.mlab_result: Result from the mLab analysis (positive, negative).signal_ratio: Ratio of the intensity of test signal to the control signal.control_signal: mLab calculated intensity of the control signal.test_signal: mLab calculated intensity of the test signal.browser: Browser used to access the app (from the User Agent string).platform: Platform used to access the app (e.g., Windows, iOS) (from the User Agent string).platform_description: Detailed description of the platform (from the User Agent string).platform_maker: Manufacturer of the platform (from the useragUser Agentent string).device_name: Name of the device used (from the User Agent string).device_maker Manufacturer of the device used (from the User Agent string).device_brand_name: Brand name of the device used (from the User Agent string).device_type: Type of device used (Mobile, Computer, etc.) (from the User Agent string).### test_windows.csv / test_windows.parquetThis file contains information on testing windows assigned to users._Column Name_: Descriptionuser_id: Unique identifier for the user.redcap_repeat_instance: Instance of the repeat instrument.start_date: Start date of the (hard) testing window.end_date: End date of the (hard) testing window.## CitationIf you use this dataset, please cite the associated mLab and mLab paradata publications.

Intro

This dataverse contains the data referenced in Rieth et al. (2017). Issues and Advances in Anomaly Detection Evaluation for Joint Human-Automated Systems. To be presented at Applied Human Factors and Ergonomics 2017.

Content

Each .RData file is an external representation of an R dataframe that can be read into an R environment with the 'load' function. The variables loaded are named ‘fault_free_training’, ‘fault_free_testing’, ‘faulty_testing’, and ‘faulty_training’, corresponding to the RData files.

Each dataframe contains 55 columns:

Column 1 ('faultNumber') ranges from 1 to 20 in the “Faulty” datasets and represents the fault type in the TEP. The “FaultFree” datasets only contain fault 0 (i.e. normal operating conditions).

Column 2 ('simulationRun') ranges from 1 to 500 and represents a different random number generator state from which a full TEP dataset was generated (Note: the actual seeds used to generate training and testing datasets were non-overlapping).

Column 3 ('sample') ranges either from 1 to 500 (“Training” datasets) or 1 to 960 (“Testing” datasets). The TEP variables (columns 4 to 55) were sampled every 3 minutes for a total duration of 25 hours and 48 hours respectively. Note that the faults were introduced 1 and 8 hours into the Faulty Training and Faulty Testing datasets, respectively.

Columns 4 to 55 contain the process variables; the column names retain the original variable names.

Acknowledgements

This work was sponsored by the Office of Naval Research, Human & Bioengineered Systems (ONR 341), program officer Dr. Jeffrey G. Morrison under contract N00014-15-C-5003. The views expressed are those of the authors and do not reflect the official policy or position of the Office of Naval Research, Department of Defense, or US Government.

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This dataset comprises 153 subfolders within a primary directory named data, derived from 85 participants. Each participant typically contributes 2–3 subfolders, contingent on the completeness and quality of their M-mode echocardiography (UCG) recordings. Subfolder names follow the format: hdata + SubjectID + EJ/XJ/ZJ to denote the specific cardiac region captured in the ultrasound data:EJ denotes M-mode imaging of the mitral valve, XJ denotes M-mode imaging of the left ventricle, and ZJ denotes M-mode imaging of the aortic valve.For instance, a participant with identifier “001” may have subfolders named hdata1EJ, hdata1XJ, and/or hdata1ZJ, corresponding to each available M-mode echocardiographic segment. Each subfolder contains five distinct files, described in detail below.1 BCG J-peak file(1) File name: hdata+subjectID+EJ/XJ/ZJ_BCG.csv(2) Content: J-peak positions in the BCG signal, presented in two columns:(3) The first column provides the raw data point index.(4) The second column specifies the corresponding time (in seconds) for each J-peak.2 ECG R-peak file(1) File name: hdata+subjectID+EJ/XJ/ZJ_ECG.csv(2) Content: R-peak positions in the ECG signal, also in two columns:(3) The first column provides the raw data point index.(4) The second column specifies the corresponding time (in seconds) for each R-peak.3 Ultrasound video(1) File name: hdata+subjectID+EJ/XJ/ZJ_UCG.AVI(2) Content: An AVI-format video of the simultaneously acquired M-mode echocardiogram. The suffix EJ, XJ, or ZJ indicates whether the imaging targeted the mitral valve, left ventricle, or aortic valve, respectively.4 Signal data(1) File name: signal.csv(2) Content: Three columns of time-series data sampled at 100 Hz. Raw BCG signal (Column 1).ECG data (Lead V2 or another designated lead) (Column 2). Denoised BCG signal (Column 3), derived using the Enhanced Singular Value Thresholding (ESVT) algorithm.5 Signal visualization(1) File name: signal.pdf(2) Content: A graphical representation of the signals from signal.csv. This file facilitates quick inspection of waveform alignment and overall signal quality.In addition to the data directory, an Additional_info folder provides participant demographic and clinical details. Each row in subject_info.csv corresponds to an individual participant, listing their ID, sex, weight, height, age, heart rate, ejection fraction(EF) (%). These parameters establish an informative link between each participant’s anthropometric profile, cardiac function metrics, and the corresponding BCG, ECG, and ultrasound data.