This dataset contains counts of deaths for California as a whole based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in California regardless of the place of residence (by occurrence) and deaths to California residents (by residence), whereas the provisional data table only includes deaths that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve. The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj. The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 . The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 . The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed. COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken down by race and ethnicity. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the COVID-19 update. The following data show the number of COVID-19 cases and associated deaths per 100,000 population by race and ethnicity. Crude rates represent the total cases or deaths per 100,000 people. Age-adjusted rates consider the age of the person at diagnosis or death when estimating the rate and use a standardized population to provide a fair comparison between population groups with different age distributions. Age-adjustment is important in Connecticut as the median age of among the non-Hispanic white population is 47 years, whereas it is 34 years among non-Hispanic blacks, and 29 years among Hispanics. Because most non-Hispanic white residents who died were over 75 years of age, the age-adjusted rates are lower than the unadjusted rates. In contrast, Hispanic residents who died tend to be younger than 75 years of age which results in higher age-adjusted rates. The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used. Rates are standardized to the 2000 US Millions Standard population (data available here: https://seer.cancer.gov/stdpopulations/). Standardization was done using 19 age groups (0, 1-4, 5-9, 10-14, ..., 80-84, 85 years and older). More information about direct standardization for age adjustment is available here: https://www.cdc.gov/nchs/data/statnt/statnt06rv.pdf Categories are mutually exclusive. The category “multiracial” includes people who answered ‘yes’ to more than one race category. Counts may not add up to total case counts as data on race and ethnicity may be missing. Age adjusted rates calculated only for groups with more than 20 deaths. Abbreviation: NH=Non-Hispanic. Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical

This dataset contains counts of deaths for California residents by ZIP Code based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths of California residents. The data tables include deaths of residents of California by ZIP Code of residence (by residence). The data are reported as totals, as well as stratified by age and gender. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve.

The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj.

The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 .

The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 .

The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed.

COVID-19 tests, cases, and associated deaths that have been reported among Connecticut residents. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Hospitalization data were collected by the Connecticut Hospital Association and reflect the number of patients currently hospitalized with laboratory-confirmed COVID-19. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the daily COVID-19 update.

Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical examiner) using their best clinical judgment. Additionally, all COVID-19 deaths, including suspected or related, are required to be reported to OCME. On April 4, 2020, CT DPH and OCME released a joint memo to providers and facilities within Connecticut providing guidelines for certifying deaths due to COVID-19 that were consistent with the CDC’s guidelines and a reminder of the required reporting to OCME.25,26 As of July 1, 2021, OCME had reviewed every case reported and performed additional investigation on about one-third of reported deaths to better ascertain if COVID-19 did or did not cause or contribute to the death. Some of these investigations resulted in the OCME performing postmortem swabs for PCR testing on individuals whose deaths were suspected to be due to COVID-19, but antemortem diagnosis was unable to be made.31 The OCME issued or re-issued about 10% of COVID-19 death certificates and, when appropriate, removed COVID-19 from the death certificate. For standardization and tabulation of mortality statistics, written cause of death statements made by the certifiers on death certificates are sent to the National Center for Health Statistics (NCHS) at the CDC which assigns cause of death codes according to the International Causes of Disease 10th Revision (ICD-10) classification system.25,26 COVID-19 deaths in this report are defined as those for which the death certificate has an ICD-10 code of U07.1 as either a primary (underlying) or a contributing cause of death. More information on COVID-19 mortality can be found at the following link: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Mortality/Mortality-Statistics

Data are reported daily, with timestamps indicated in the daily briefings posted at: portal.ct.gov/coronavirus. Data are subject to future revision as reporting changes.

Starting in July 2020, this dataset will be updated every weekday.

Additional notes: As of 11/5/2020, CT DPH has added antigen testing for SARS-CoV-2 to reported test counts in this dataset. The tests included in this dataset include both molecular and antigen datasets. Molecular tests reported include polymerase chain reaction (PCR) and nucleic acid amplicfication (NAAT) tests.

A delay in the data pull schedule occurred on 06/23/2020. Data from 06/22/2020 was processed on 06/23/2020 at 3:30 PM. The normal data cycle resumed with the data for 06/23/2020.

A network outage on 05/19/2020 resulted in a change in the data pull schedule. Data from 5/19/2020 was processed on 05/20/2020 at 12:00 PM. Data from 5/20/2020 was processed on 5/20/2020 8:30 PM. The normal data cycle resumed on 05/20/2020 with the 8:30 PM data pull. As a result of the network outage, the timestamp on the datasets on the Open Data Portal differ from the timestamp in DPH's daily PDF reports.

Starting 5/10/2021, the date field will represent the date this data was updated on data.ct.gov. Previously the date the data was pulled by DPH was listed, which typically coincided with the date before the data was published on data.ct.gov. This change was made to standardize the COVID-19 data sets on data.ct.gov.

Starting April 4, 2022, negative rapid antigen and rapid PCR test results for SARS-CoV-2 are no longer required to be reported to the Connecticut Department of Public Health as of April 4. Negative test results from laboratory based molecular (PCR/NAAT) results are still required to be reported as are all positive test results from both molecular (PCR/NAAT) and antigen tests.

On 5/16/2022, 8,622 historical cases were included in the data. The date range for these cases were from August 2021 – April 2022.”

Data for deaths by leading cause of death categories are now available in the death profiles dataset for each geographic granularity.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

Cause of death categories for years 1999 and later are based on tenth revision of International Classification of Diseases (ICD-10) codes. Comparable categories are provided for years 1979 through 1998 based on ninth revision (ICD-9) codes. For more information on the comparability of cause of death classification between ICD revisions see Comparability of Cause-of-death Between ICD Revisions.

This file contains COVID-19 death counts, death rates, and percent of total deaths by jurisdiction of residence. The data is grouped by different time periods including 3-month period, weekly, and total (cumulative since January 1, 2020). United States death counts and rates include the 50 states, plus the District of Columbia and New York City. New York state estimates exclude New York City. Puerto Rico is included in HHS Region 2 estimates. Deaths with confirmed or presumed COVID-19, coded to ICD–10 code U07.1. Number of deaths reported in this file are the total number of COVID-19 deaths received and coded as of the date of analysis and may not represent all deaths that occurred in that period. Counts of deaths occurring before or after the reporting period are not included in the file. Data during recent periods are incomplete because of the lag in time between when the death occurred and when the death certificate is completed, submitted to NCHS and processed for reporting purposes. This delay can range from 1 week to 8 weeks or more, depending on the jurisdiction and cause of death. Death counts should not be compared across states. Data timeliness varies by state. Some states report deaths on a daily basis, while other states report deaths weekly or monthly. The ten (10) United States Department of Health and Human Services (HHS) regions include the following jurisdictions. Region 1: Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont; Region 2: New Jersey, New York, New York City, Puerto Rico; Region 3: Delaware, District of Columbia, Maryland, Pennsylvania, Virginia, West Virginia; Region 4: Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee; Region 5: Illinois, Indiana, Michigan, Minnesota, Ohio, Wisconsin; Region 6: Arkansas, Louisiana, New Mexico, Oklahoma, Texas; Region 7: Iowa, Kansas, Missouri, Nebraska; Region 8: Colorado, Montana, North Dakota, South Dakota, Utah, Wyoming; Region 9: Arizona, California, Hawaii, Nevada; Region 10: Alaska, Idaho, Oregon, Washington. Rates were calculated using the population estimates for 2021, which are estimated as of July 1, 2021 based on the Blended Base produced by the US Census Bureau in lieu of the April 1, 2020 decennial population count. The Blended Base consists of the blend of Vintage 2020 postcensal population estimates, 2020 Demographic Analysis Estimates, and 2020 Census PL 94-171 Redistricting File (see https://www2.census.gov/programs-surveys/popest/technical-documentation/methodology/2020-2021/methods-statement-v2021.pdf). Rates are based on deaths occurring in the specified week/month and are age-adjusted to the 2000 standard population using the direct method (see https://www.cdc.gov/nchs/data/nvsr/nvsr70/nvsr70-08-508.pdf). These rates differ from annual age-adjusted rates, typically presented in NCHS publications based on a full year of data and annualized weekly/monthly age-adjusted rates which have been adjusted to allow comparison with annual rates. Annualization rates presents deaths per year per 100,000 population that would be expected in a year if the observed period specific (weekly/monthly) rate prevailed for a full year. Sub-national death counts between 1-9 are suppressed in accordance with NCHS data confidentiality standards. Rates based on death counts less than 20 are suppressed in accordance with NCHS standards of reliability as specified in NCHS Data Presentation Standards for Proportions (available from: https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf.).

The data presented here consists of three parts: Dataset 1: In this set, we extract 327,322 faces from our entire collection of 3389 issues, and automatically classified each face as male or female. We present this data as a single table with columns identifying the date, issue, page number, the coordinates identifying the position of the face on the page, and classification (male or female). The coordinates identifying the position of the face on the page are based on the size and resolution of the pages found in the “Time Vault”. Dataset 2: Dataset 2 consists of 8,789 classified faces from 100 selected issues. Human labor was used to identify and extract 3,299 face images from 39 issues, which were later classified by another set of workers. This selection of 39 issues contains one issue per decade spanned by the archive plus one issue per year between 1961 and 1991, and the extracted face images were used to train the face extraction algorithm. The remaining 5,490 faces from 61 issues were extracted via machine learning before being classified by human coders. These 61 issues were chosen to complement the first selection of 39 issues: one issue per year for all years in the archive excluding those between 1961 and 1991. Thus, Dataset 2 contains fully-labelled faces from at least one issue per year. Dataset 3: In the interest of transparency, Dataset 3 consists of the raw data collected to create Dataset 2, and consists of 2 tables. Before explaining these tables we first briefly describe our data collection and verification procedures, which have been fully described elsewhere. A custom AMT interface was used to enable human labors to classify faces according the categories in Table 4. Each worker was given a randomly-selected batch of 25 pages, each with a clearly highlighted face to be categorized, of which three pages were verification pages with known features, which were used for quality control. Each face was labeled by two distinct human coders, determined at random so that the paring of coders varied with the image. A proficiency rating was calculated for each coder by considering all images they annotated and computing the average number of labels that matched those identified by the image’s other coder. The tables in Dataset 2 were created by resolving inconsistencies between the two image coders by selecting the labels from the coder with the highest proficiency rating. Prior to calculating the proficiency score, all faces that were tagged as having ‘Poor’ or ‘Error’ image quality by either of the two coders were eliminated. Due to technical bugs when the AMT interface was first implemented, a small number of images were only labeled once; these were also eliminated from Datasets 2 and 3. In Dataset 3, we present the raw annotations for each coder that tagged each face, along with demographic data for each coder. Dataset 3 consists of two tables: the raw data from each of the two sets of coders, and the demographic information for each of the coders.

Overview: This is a large-scale dataset with impedance and signal loss data recorded on volunteer test subjects using low-voltage alternate current sine-shaped signals. The signal frequencies are from 50 kHz to 20 MHz.

Applications: The intention of this dataset is to allow to investigate the human body as a signal propagation medium, and capture information related to how the properties of the human body (age, sex, composition etc.), the measurement locations, and the signal frequencies impact the signal loss over the human body.

Overview statistics:

• Number of subjects: 30
• Number of transmitter locations: 6
• Number of receiver locations: 6
• Number of measurement frequencies: 19
• Input voltage: 1 V
• Load resistance: 50 ohm and 1 megaohm

Measurement group statistics:

• Height: 174.10 (7.15)
• Weight: 72.85 (16.26)
• BMI: 23.94 (4.70)
• Body fat %: 21.53 (7.55)
• Age group: 29.00 (11.25)
• Male/female ratio: 50%

Included files:

• experiment_protocol_description.docx - protocol used in the experiments
• electrode_placement_schematic.png - schematic of placement locations
• electrode_placement_photo.jpg - visualization on the experiment, on a volunteer subject
• RawData - the full measurement results and experiment info sheets
• all_measurements.csv - the most important results extracted to .csv
• all_measurements_filtered.csv - same, but after z-score filtering
• all_measurements_by_freq.csv - the most important results extracted to .csv, single frequency per row
• all_measurements_by_freq_filtered.csv - same, but after z-score filtering
• summary_of_subjects.csv - key statistics on the subjects from the experiment info sheets
• process_json_files.py - script that creates .csv from the raw data
• filter_results.py - outlier removal based on z-score
• plot_sample_curves.py - visualization of a randomly selected measurement result subset
• plot_measurement_group.py - visualization of the measurement group

CSV file columns:

• subject_id - participant's random unique ID
• experiment_id - measurement session's number for the participant
• height - participant's height, cm
• weight - participant's weight, kg
• BMI - body mass index, computed from the valued above
• body_fat_% - body fat composition, as measured by bioimpedance scales
• age_group - age rounded to 10 years, e.g. 20, 30, 40 etc.
• male - 1 if male, 0 if female
• tx_point - transmitter point number
• rx_point - receiver point number
• distance - distance, in relative units, between the tx and rx points. Not scaled in terms of participant's height and limb lengths!
• tx_point_fat_level - transmitter point location's average fat content metric. Not scaled for each participant individually.
• rx_point_fat_level - receiver point location's average fat content metric. Not scaled for each participant individually.
• total_fat_level - sum of rx and tx fat levels
• bias - constant term to simplify data analytics, always equal to 1.0

CSV file columns, frequency-specific:

• tx_abs_Z_... - transmitter-side impedance, as computed by the `process_json_files.py` script from the voltage drop
• rx_gain_50_f_... - experimentally measured gain on the receiver, in dB, using 50 ohm load impedance
• rx_gain_1M_f_... - experimentally measured gain on the receiver, in dB, using 1 megaohm load impedance

Acknowledgments: The dataset collection was funded by the Latvian Council of Science, project “Body-Coupled Communication for Body Area Networks”, project No. lzp-2020/1-0358.

References: For a more detailed information, see this article: J. Ormanis, V. Medvedevs, A. Sevcenko, V. Aristovs, V. Abolins, and A. Elsts. Dataset on the Human Body as a Signal Propagation Medium for Body Coupled Communication. Submitted to Elsevier Data in Brief, 2023.

Contact information: info@edi.lv

Child Fatalities by Submission Type

  Description

Counts and rates of child fatalities by file submission type for the most recent federal fiscal year for which data are available. To view more National Child Abuse and Neglect Data System (NCANDS) findings, click link to summary page below: https://healthdata.gov/stories/s/kaeg-w7jc

  Dataset Details

Publisher: U.S. Department of Health & Human Services Last Modified: 2024-06-28 Contact: HealthData.gov Team… See the full description on the dataset page: https://huggingface.co/datasets/HHS-Official/child-fatalities-by-submission-type.

MyAutoData

Actual dataset Location on Kaggle Contains data scrapped by MyAutoScrapper (Written in go)

Purpose

Since this kaggle dataset real car deals, placed by real humans with pictures, It can be used for real world Machine Learning(ML) or Machine Vision. Price predictions, image processing, machine vision etc.

Data structure

This dataset contains data.csv file, which has 100 000 car deal detail. Each row representing each deal. data.csv has 18 columns: - ID: Represents unique identifier for each entry, also for each id, there is a sub-folder in images respectively, which contains images for the given deal. ID is an integer starting from 0. - Manufacturer: A string identifying car manufacturer. - Model: A string identifying car model. - Year: An Integer for the car production year. - Category: A type of the vechile (Sedan, Cabriolet, etc.). - Mileage: An integer representing car mileage in kilometers. - FuelType: A Fuel type the car uses. - EngineVolume: A Floating point number, representing engine volume in litres. - DriveWheels: A String representing car drive wheels (i.e. Front, Rear, 4x4, etc.). - GearBox: A string to identify gear box of the transmission (Manual, Automatic, etc.) - Doors: A string representing car doors (4, 4/5, etc.) - Wheel: Steering wheel position (Left Wheel, Right Wheel) - Color: Color of the car body. - InteriorColor: Interior color. - VIN: VIN number of the vechile, represented as a string. - LeatherInterior: A boolean value, true if car has a leather interior. - Price: Price of the car in USD. If ommited, meants price was set as negotiable. - Clearance: A boolean value identifying, whether customs has been cleared of not.

Important Note! (Disclaimer)

None of the fields (Except ID) are guaranteed to be filled, or filled with correct information. Since, people sometimes don't enter correct information, or hide some information for reasons. But for most of the entries, most of the fields are supposed to be filled with correct information.

A geometric shape regularity effect in the human brain: fMRI dataset

Authors:

• Mathias Sablé-Meyer*
• Lucas Benjamin
• Cassandra Potier Watkins
• Chenxi He
• Maxence Pajot
• Théo Morfoisse
• Fosca Al Roumi
• Stanislas Dehaene

*Corresponding author: mathias.sable-meyer@ucl.ac.uk

Abstract

The perception and production of regular geometric shapes is a characteristic trait of human cultures since prehistory, whose neural mechanisms are unknown. Behavioral studies suggest that humans are attuned to discrete regularities such as symmetries and parallelism, and rely on their combinations to encode regular geometric shapes in a compressed form. To identify the relevant brain systems and their dynamics, we collected functional MRI and magnetoencephalography data in both adults and six-year-olds during the perception of simple shapes such as hexagons, triangles and quadrilaterals. The results revealed that geometric shapes, relative to other visual categories, induce a hypoactivation of ventral visual areas and an overactivation of the intraparietal and inferior temporal regions also involved in mathematical processing, whose activation is modulated by geometric regularity. While convolutional neural networks captured the early visual activity evoked by geometric shapes, they failed to account for subsequent dorsal parietal and prefrontal signals, which could only be captured by discrete geometric features or by more advanced transformer models of vision. We propose that the perception of abstract geometric regularities engages an additional symbolic mode of visual perception.

Notes about this dataset

We separately share the MEG dataset at https://openneuro.org/datasets/ds006012. Below are some notes about the fMRI dataset of N=20 adult participants (sub-2xx, numbers between 204 and 223), and N=22 children (sub-3xx, numbers between 301 and 325).

• The code for the analyses is provided at https://github.com/mathias-sm/AGeometricShapeRegularityEffectHumanBrain
  However, the analyses work from already preprocessed data. Since there is no custom code per se for the preprocessing, I have not included it in the repository. To preprocess the data as was done in the published article, here is the command and software information:
  • fMRIPrep version: 20.0.5
  • fMRIPrep command: /usr/local/miniconda/bin/fmriprep /data /out participant --participant-label <label> --output-spaces MNI152NLin6Asym:res-2 MNI152NLin2009cAsym:res-2
• Defacing has been performed with bidsonym running the pydeface masking, and nobrainer brain registraction pipeline.
  The published analyses have been performed on the non-defaced data. I have checked for data quality on all participants after defacing. In specific cases, I may be able to request the permission to share the original, non-defaced dataset.
• sub-325 was acquired by a different experimenter and defaced before being shared with the rest of the research team, hence why the slightly different defacing mask. That participant was also preprocessed separately, and using a more recent fMRIPrep version: 20.2.6.
• The data associated with the children has a few missing files. Notably:
  1. sub-313 and sub-316 are missing one run of the localizer each
  2. sub-316 has no data at all for the geometry
  3. sub-308 has eno useable data for the intruder task Since all of these still have some data to contribute to either task, all available files were kept on this dataset. The analysis code reflects these inconsistencies where required with specific exceptions.

Cervical Cancer Risk Factors for Biopsy: This Dataset is Obtained from UCI Repository and kindly acknowledged! This file contains a List of Risk Factors for Cervical Cancer leading to a Biopsy Examination! About 11,000 new cases of invasive cervical cancer are diagnosed each year in the U.S. However, the number of new cervical cancer cases has been declining steadily over the past decades. Although it is the most preventable type of cancer, each year cervical cancer kills about 4,000 women in the U.S. and about 300,000 women worldwide. In the United States, cervical cancer mortality rates plunged by 74% from 1955 - 1992 thanks to increased screening and early detection with the Pap test. AGE Fifty percent of cervical cancer diagnoses occur in women ages 35 - 54, and about 20% occur in women over 65 years of age. The median age of diagnosis is 48 years. About 15% of women develop cervical cancer between the ages of 20 - 30. Cervical cancer is extremely rare in women younger than age 20. However, many young women become infected with multiple types of human papilloma virus, which then can increase their risk of getting cervical cancer in the future. Young women with early abnormal changes who do not have regular examinations are at high risk for localized cancer by the time they are age 40, and for invasive cancer by age 50. SOCIOECONOMIC AND ETHNIC FACTORS Although the rate of cervical cancer has declined among both Caucasian and African-American women over the past decades, it remains much more prevalent in African-Americans -- whose death rates are twice as high as Caucasian women. Hispanic American women have more than twice the risk of invasive cervical cancer as Caucasian women, also due to a lower rate of screening. These differences, however, are almost certainly due to social and economic differences. Numerous studies report that high poverty levels are linked with low screening rates. In addition, lack of health insurance, limited transportation, and language difficulties hinder a poor woman’s access to screening services. HIGH SEXUAL ACTIVITY Human papilloma virus (HPV) is the main risk factor for cervical cancer. In adults, the most important risk factor for HPV is sexual activity with an infected person. Women most at risk for cervical cancer are those with a history of multiple sexual partners, sexual intercourse at age 17 years or younger, or both. A woman who has never been sexually active has a very low risk for developing cervical cancer. Sexual activity with multiple partners increases the likelihood of many other sexually transmitted infections (chlamydia, gonorrhea, syphilis).Studies have found an association between chlamydia and cervical cancer risk, including the possibility that chlamydia may prolong HPV infection. FAMILY HISTORY Women have a higher risk of cervical cancer if they have a first-degree relative (mother, sister) who has had cervical cancer. USE OF ORAL CONTRACEPTIVES Studies have reported a strong association between cervical cancer and long-term use of oral contraception (OC). Women who take birth control pills for more than 5 - 10 years appear to have a much higher risk HPV infection (up to four times higher) than those who do not use OCs. (Women taking OCs for fewer than 5 years do not have a significantly higher risk.) The reasons for this risk from OC use are not entirely clear. Women who use OCs may be less likely to use a diaphragm, condoms, or other methods that offer some protection against sexual transmitted diseases, including HPV. Some research also suggests that the hormones in OCs might help the virus enter the genetic material of cervical cells. HAVING MANY CHILDREN Studies indicate that having many children increases the risk for developing cervical cancer, particularly in women infected with HPV. SMOKING Smoking is associated with a higher risk for precancerous changes (dysplasia) in the cervix and for progression to invasive cervical cancer, especially for women infected with HPV. IMMUNOSUPPRESSION Women with weak immune systems, (such as those with HIV / AIDS), are more susceptible to acquiring HPV. Immunocompromised patients are also at higher risk for having cervical precancer develop rapidly into invasive cancer. DIETHYLSTILBESTROL (DES) From 1938 - 1971, diethylstilbestrol (DES), an estrogen-related drug, was widely prescribed to pregnant women to help prevent miscarriages. The daughters of these women face a higher risk for cervical cancer. DES is no longer prsecribed.

Note: This COVID-19 data set is no longer being updated as of December 1, 2023. Access current COVID-19 data on the CDPH respiratory virus dashboard (https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/Respiratory-Viruses/RespiratoryDashboard.aspx) or in open data format (https://data.chhs.ca.gov/dataset/respiratory-virus-dashboard-metrics).

As of August 17, 2023, data is being updated each Friday.

For death data after December 31, 2022, California uses Provisional Deaths from the Center for Disease Control and Prevention’s National Center for Health Statistics (NCHS) National Vital Statistics System (NVSS). Prior to January 1, 2023, death data was sourced from the COVID-19 registry. The change in data source occurred in July 2023 and was applied retroactively to all 2023 data to provide a consistent source of death data for the year of 2023.

As of May 11, 2023, data on cases, deaths, and testing is being updated each Thursday. Metrics by report date have been removed, but previous versions of files with report date metrics are archived below.

All metrics include people in state and federal prisons, US Immigration and Customs Enforcement facilities, US Marshal detention facilities, and Department of State Hospitals facilities. Members of California's tribal communities are also included.

The "Total Tests" and "Positive Tests" columns show totals based on the collection date. There is a lag between when a specimen is collected and when it is reported in this dataset. As a result, the most recent dates on the table will temporarily show NONE in the "Total Tests" and "Positive Tests" columns. This should not be interpreted as no tests being conducted on these dates. Instead, these values will be updated with the number of tests conducted as data is received.

The Department of Human Services Annual Report (Annual Report) sets out the department’s activities for the financial year with a focus on the department's performance and financial details, amongst other information.

The Annual Report is prepared in accordance with the Requirements for Annual Reports, issued by the Department of the Prime Minister and Cabinet. Under the Public Service Act 1999 (Cth.) the Secretary of the Department of Human Services is required, after the end of each financial year, to provide a report on the department's activities to the Minister, for presentation to the Parliament. Annual Reports are tabled in parliament usually in late October and are available to the public online shortly after tabling.

Reports prior to 2012-2013 can be found on the Department of Human Services website.

If you require statistics at a more detailed level, please contact statistics@humanservices.gov.au. The department charges on a cost recovery basis for providing more detailed statistics and their provision is subject to privacy considerations.

Disclaimer: This data is provided by the Department of Human Services (Human Services) for general information purposes only. While Human Services has taken care to ensure the information is as correct and accurate as possible, we do not guarantee, or accept legal liability whatsoever arising from, or connected to its use.

We recommend that users exercise their own skill and care with respect to the use of this data and that users carefully evaluate the accuracy, currency, completeness and relevance of the data for their needs.

Universal Thermal Climate Index (UTCI) is a physiological temperature that is widely used in biometeorological studies to assess the heat stress felt by humans. UTCI considers the shortwave and longwave radiation incident on humans from the six cubical directions as well as air temperature, humidity, wind speed and clothing. As a part of NOAA National Integrated Heat Health Information System (NIHHIS) and NASA Interdisciplinary Research in Earth Science (IDS) project, we have generated the UTCI data for Austin, Texas and surrounding peri-urban area at 2-meters spatial resolution for the year 2017. Details on data generation and methodology can be found in Kamath et al., (2023) but are summarized here.

1. Datasets and model used

The solar and longwave environmental irradiance geometry (SOLWEIG) model was used to simulate shadows, mean radiant temperature (T_MRT) and the UTCI (Lindberg et al., 2008). T_MRT is the equivalent temperature due to exposure to absorbed shortwave and longwave radiation from all directions in a standing position. SOLWEIG was forced using near-surface ERA-5 data available at a spatial resolution of 0.25°x 0.25°. Building, vegetation heights, and digital terrain model were again derived from 3DEP LiDAR point cloud data. SOLWEIG was run using the urban multi-scale environment predictor (UMEP) (Lindberg et al., 2018) plug-in with QGIS.

2. Data availability

Diurnal UTCI data were calculated for typical meteorological clear sky days corresponding to Summer and Fall. The typical clear sky day was selected using the 10-year Typical meteorological Year (TMY) for Austin, Texas (30.2672° N, 97.7431° W) provided by National Solar Radiation Database (NSRDB). More details on TMY files can be found at: https://nsrdb.nrel.gov/data-sets/tmy

Additionally, data is developed for heat hazard for daytime Human Heat Health Index (H3I) calculation as defined by Kamath et al., (2023). Briefly, this heat hazard is defined as the fraction of the day when the UTCI exceeds certain threshold. The threshold used to calculate heat hazard for Summer and Fall were 35° C and 32°C, respectively that imply strong heat stress (Jendritzky et al., 2012). Note that UTCI is on a different scale compared to air temperature, and could yield different heat stress levels.

3. Data format

The georeferenced UTCI and heat hazard data are available in the geoTIFF file format. The files can be readily visualized using GIS software such as QGIS and ArcGIS, as well as programing languages such as Python.

4. Companion dataset

Based on the calculated UTCI here, the potential locations for tree planting were calculated to increase the shade to reduce heat vulnerability for Austin, Texas. [https://doi.org/10.5281/zenodo.6363494]

References

1. Kamath, H. G., Martilli, A., Singh, M., Brooks, T., Lanza, K., Bixler, R. P., ... & Niyogi, D. (2023). Human heat health index (H3I) for holistic assessment of heat hazard and mitigation strategies beyond urban heat islands. Urban Climate, 52, 101675.
2. Lindberg, F., Holmer, B., & Thorsson, S. (2008). SOLWEIG 1.0–Modelling spatial variations of 3D radiant fluxes and mean radiant temperature in complex urban settings. International journal of biometeorology, 52, 697-713.
3. Lindberg, F., Grimmond, C. S. B., Gabey, A., Huang, B., Kent, C. W., Sun, T., ... & Zhang, Z. (2018). Urban Multi-scale Environmental Predictor (UMEP): An integrated tool for city-based climate services. Environmental modelling & software, 99, 70-87.
4. Jendritzky, G., de Dear, R., & Havenith, G. (2012). UTCI—why another thermal index?. International journal of biometeorology, 56, 421-428.
5. Bixler, R. P., Coudert, M., Richter, S. M., Jones, J. M., Llanes Pulido, C., Akhavan, N., ... & Niyogi, D. (2022). Reflexive co-production for urban resilience: Guiding framework and experiences from Austin, Texas. Frontiers in Sustainable Cities, 4, 1015630.
6. Lanza, K., Jones, J., Acuña, F., Coudert, M., Bixler, R. P., Kamath, H., & Niyogi, D. (2023). Heat vulnerability of Latino and Black residents in a low-income community and their recommended adaptation strategies: A qualitative study. Urban Climate, 51, 101656.

Introduction

Welcome to the Native American Multi-Year Facial Image Dataset, thoughtfully curated to support the development of advanced facial recognition systems, biometric identification models, KYC verification tools, and other computer vision applications. This dataset is ideal for training AI models to recognize individuals over time, track facial changes, and enhance age progression capabilities.

Facial Image Data

This dataset includes over 5,000+ high-quality facial images, organized into individual participant sets, each containing:

Diversity & Representation

Image Quality & Capture Conditions

To ensure model generalization and practical usability, images in this dataset reflect real-world diversity:

Metadata

Each participant’s dataset is accompanied by rich metadata to support advanced model training and analysis, including:

Use Cases & Applications

This dataset is highly valuable for a wide range of AI and computer vision applications:

Secure & Ethical Collection

Dataset Updates & Customization

To keep pace with evolving AI needs, this dataset is regularly updated and customizable. Custom data collection options include:

Global Human Footprint Index represents the relative human influence in each terrestrial biome expressed as a percentage. The purpose is to provide an updated map of anthropogenic impacts on the environment in geographic projection which can be used in wildlife conservation planning, natural resource management, and research on human-environment interactions. Dataset SummaryThe Global Human Footprint Index Dataset of the Last of the Wild Project, Version 2, 2005 (LWP-2) is the Human Influence Index (HII) normalized by biome and realm. The HII is a global dataset of 1-kilometer grid cells, created from nine global data layers of human population pressure (population density), human land use and infrastructure (built-up areas, nighttime lights, land use/land cover), and human access (coastlines, roads, railroads, navigable rivers). A value of zero represents the least influenced–the “most wild” part of the biome with value of 100 representing the most influenced (least wild) part of the biome. The dataset is produced by the Wildlife Conservation Society (WCS) and the Columbia University Center for International Earth Science Information Network (CIESIN).Recommended CitationWildlife Conservation Society - WCS, and Center for International Earth Science Information Network - CIESIN - Columbia University. 2005. Last of the Wild Project, Version 2, 2005 (LWP-2): Global Human Footprint Dataset (Geographic). Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H4M61H5F. Accessed DAY MONTH YEAR.

After May 3, 2024, this dataset and webpage will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, and hospital capacity and occupancy data, to HHS through CDC’s National Healthcare Safety Network. Data voluntarily reported to NHSN after May 1, 2024, will be available starting May 10, 2024, at COVID Data Tracker Hospitalizations.

The following dataset provides facility-level data for hospital utilization aggregated on a weekly basis (Sunday to Saturday). These are derived from reports with facility-level granularity across two main sources: (1) HHS TeleTracking, and (2) reporting provided directly to HHS Protect by state/territorial health departments on behalf of their healthcare facilities.

The hospital population includes all hospitals registered with Centers for Medicare & Medicaid Services (CMS) as of June 1, 2020. It includes non-CMS hospitals that have reported since July 15, 2020. It does not include psychiatric, rehabilitation, Indian Health Service (IHS) facilities, U.S. Department of Veterans Affairs (VA) facilities, Defense Health Agency (DHA) facilities, and religious non-medical facilities.

For a given entry, the term “collection_week” signifies the start of the period that is aggregated. For example, a “collection_week” of 2020-11-15 means the average/sum/coverage of the elements captured from that given facility starting and including Sunday, November 15, 2020, and ending and including reports for Saturday, November 21, 2020.

Reported elements include an append of either “_coverage”, “_sum”, or “_avg”.

• A “_coverage” append denotes how many times the facility reported that element during that collection week.
• A “_sum” append denotes the sum of the reports provided for that facility for that element during that collection week.
• A “_avg” append is the average of the reports provided for that facility for that element during that collection week.

The file will be updated weekly. No statistical analysis is applied to impute non-response. For averages, calculations are based on the number of values collected for a given hospital in that collection week. Suppression is applied to the file for sums and averages less than four (4). In these cases, the field will be replaced with “-999,999”.

A story page was created to display both corrected and raw datasets and can be accessed at this link: https://healthdata.gov/stories/s/nhgk-5gpv

This data is preliminary and subject to change as more data become available. Data is available starting on July 31, 2020.

Sometimes, reports for a given facility will be provided to both HHS TeleTracking and HHS Protect. When this occurs, to ensure that there are not duplicate reports, deduplication is applied according to prioritization rules within HHS Protect.

For influenza fields listed in the file, the current HHS guidance marks these fields as optional. As a result, coverage of these elements are varied.

For recent updates to the dataset, scroll to the bottom of the dataset description.

On May 3, 2021, the following fields have been added to this data set.

• hhs_ids
• previous_day_admission_adult_covid_confirmed_7_day_coverage
• previous_day_admission_pediatric_covid_confirmed_7_day_coverage
• previous_day_admission_adult_covid_suspected_7_day_coverage
• previous_day_admission_pediatric_covid_suspected_7_day_coverage
• previous_week_personnel_covid_vaccinated_doses_administered_7_day_sum
• total_personnel_covid_vaccinated_doses_none_7_day_sum
• total_personnel_covid_vaccinated_doses_one_7_day_sum
• total_personnel_covid_vaccinated_doses_all_7_day_sum
• previous_week_patients_covid_vaccinated_doses_one_7_day_sum
• previous_week_patients_covid_vaccinated_doses_all_7_day_sum

On May 8, 2021, this data set has been converted to a corrected data set. The corrections applied to this data set are to smooth out data anomalies caused by keyed in data errors. To help determine which records have had corrections made to it. An additional Boolean field called is_corrected has been added.

On May 13, 2021 Changed vaccination fields from sum to max or min fields. This reflects the maximum or minimum number reported for that metric in a given week.

On June 7, 2021 Changed vaccination fields from max or min fields to Wednesday reported only. This reflects that the number reported for that metric is only reported on Wednesdays in a given week.

On September 20, 2021, the following has been updated: The use of analytic dataset as a source.

On January 19, 2022, the following fields have been added to this dataset:

• inpatient_beds_used_covid_7_day_avg
• inpatient_beds_used_covid_7_day_sum
• inpatient_beds_used_covid_7_day_coverage

On April 28, 2022, the following pediatric fields have been added to this dataset:

• all_pediatric_inpatient_bed_occupied_7_day_avg
• all_pediatric_inpatient_bed_occupied_7_day_coverage
• all_pediatric_inpatient_bed_occupied_7_day_sum
• all_pediatric_inpatient_beds_7_day_avg
• all_pediatric_inpatient_beds_7_day_coverage
• all_pediatric_inpatient_beds_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_0_4_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_12_17_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_5_11_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_unknown_7_day_sum
• staffed_icu_pediatric_patients_confirmed_covid_7_day_avg
• staffed_icu_pediatric_patients_confirmed_covid_7_day_coverage
• staffed_icu_pediatric_patients_confirmed_covid_7_day_sum
• staffed_pediatric_icu_bed_occupancy_7_day_avg
• staffed_pediatric_icu_bed_occupancy_7_day_coverage
• staffed_pediatric_icu_bed_occupancy_7_day_sum
• total_staffed_pediatric_icu_beds_7_day_avg
• total_staffed_pediatric_icu_beds_7_day_coverage
• total_staffed_pediatric_icu_beds_7_day_sum

On October 24, 2022, the data includes more analytical calculations in efforts to provide a cleaner dataset. For a raw version of this dataset, please follow this link: https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/uqq2-txqb

Due to changes in reporting requirements, after June 19, 2023, a collection week is defined as starting on a Sunday and ending on the next Saturday.

ABSTRACT: Multiple myeloma, a hematopoietic malignancy of terminally differentiated B cells, is the second most common hematological malignancy after leukemia. While patients have benefited from numerous advances in treatment in recent years resulting in significant increases to average survival time following diagnosis, myeloma remains incurable and relapse is common. To help identify novel therapeutic agents with efficacy against the disease and to search for biomarkers associated with differential response to treatment, a large-scale pharmacological screen was performed with 1,912 small molecule compounds tested at 11 doses for 47 human myeloma cell lines (HMCL). Raw and processed versions of the drug screen dataset are provided, as well as supportive information including drug and cell line metadata and high-level characterization of the most salient features of each. The dataset is publicly available at Zenodo and the workflow code used for data processing and generation of supporting figures and tables can be found at https://github.com/khughitt/hmcl-drug-screen-pipeline.

BackgroundPlague is a zoonotic disease that, despite affecting humans for more than 5000 years, has historically been the subject of limited drug development activity. Drugs that are currently recommended in treatment guidelines have been approved based on animal studies alone–no pivotal clinical trials in humans have yet been completed. As a result of the sparse clinical research attention received, there are a number of methodological challenges that need to be addressed in order to facilitate the collection of clinical trial data that can meaningfully inform clinicians and policy-makers. One such challenge is the identification of clinically-relevant endpoints, which are informed by understanding the clinical characterisation of the disease–how it presents and evolves over time, and important patient outcomes, and how these can be modified by treatment.Methodology/Principal findingsThis systematic review aims to summarise the clinical profile of 1343 patients with bubonic plague described in 87 publications, identified by searching bibliographic databases for studies that meet pre-defined eligibility criteria. The majority of studies were individual case reports. A diverse group of signs and symptoms were reported at baseline and post-baseline timepoints–the most common of which was presence of a bubo, for which limited descriptive and longitudinal information was available. Death occurred in 15% of patients; although this varied from an average 10% in high-income countries to an average 17% in low- and middle-income countries. The median time to death was 1 day, ranging from 0 to 16 days.Conclusions/SignificanceThis systematic review elucidates the restrictions that limited disease characterisation places on clinical trials for infectious diseases such as plague, which not only impacts the definition of trial endpoints but has the knock-on effect of challenging the interpretation of a trial’s results. For this reason and despite interventional trials for plague having taken place, questions around optimal treatment for plague persist.