5 datasets found
  1. Z

    Webis-Web-Errors-19

    • data.niaid.nih.gov
    • webis.de
    • +1more
    Updated Jul 24, 2024
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Stein, Benno (2024). Webis-Web-Errors-19 [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_2549837
    Explore at:
    Dataset updated
    Jul 24, 2024
    Dataset provided by
    Potthast, Martin
    Kiesel, Johannes
    Stein, Benno
    Hubricht, Fabienne
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The Webis-Web-Errors-19 comprises various annotations for the 10,000 web page archives of the Webis-Web-Archive-17. The annotations are whether the page is (1) mostly advertisement, (2) cut off, (3) still loading, (4) pornographic; and whether it shows (not/a bit/ very) (5) pop-ups, (6) CAPTCHAs, or (7) error messages. If you use this dataset in your research, please cite it using this paper.

  2. C

    Allegheny County COVID-19 Tests, Cases and Deaths (Archive)

    • data.wprdc.org
    csv, html
    Updated Jun 13, 2024
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Allegheny County (2024). Allegheny County COVID-19 Tests, Cases and Deaths (Archive) [Dataset]. https://data.wprdc.org/dataset/allegheny-county-covid-19-tests-cases-and-deaths
    Explore at:
    html, csv(34046863), csv(339166949), csv, csv(277234), csv(16109), csv(14904), csv(840)Available download formats
    Dataset updated
    Jun 13, 2024
    Dataset provided by
    Allegheny County
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Area covered
    Allegheny County
    Description

    COVID-19 Cases information is reported through the Pennsylvania State Department’s National Electronic Disease Surveillance System (PA-NEDSS). As new cases are passed to the Allegheny County Health Department they are investigated by case investigators. During investigation some cases which are initially determined by the State to be in the Allegheny County jurisdiction may change, which can account for differences between publication of the files on the number of cases, deaths and tests. Additionally, information is not always reported to the State in a timely manner, delays can range from days to weeks, which can also account for discrepancies between previous and current files. Test and Case information will be updated daily. This resource contains individuals who received a COVID-19 test and individuals whom are probable cases. Every day, these records are overwritten with updates. Each row in the data reflects a person that is tested, not tests that are conducted. People that are tested more than once will have their testing and case data updated using the following rules:

    1. Positive tests overwrite negative tests.
    2. Polymerase chain reaction (PCR) tests overwrite antibody or antigen (AG) tests.
    3. The first positive PCR test is never overwritten. Data collected from additional tests do not replace the first positive PCR test.

    Note: On April 4th 2022 the Pennsylvania Department of Health no longer required labs to report negative AG tests. Therefore aggregated counts that included AG tests have been removed from the Municipality/Neighborhood files going forward. Versions of this data up to this cut-off have been retained as archived files.

    Individual Test information is also updated daily. This resource contains the details and results of individual tests along with demographic information of the individual tested. Only PCR and AG tests are included. Every day, these records are overwritten with updates. This resource should be used to determine positivity rates.

    The remaining datasets provide statistics on death demographics. Demographic, municipality and neighborhood information for deaths are reported on a weekly schedule and are not included with individual cases or tests. This has been done to protect the privacy and security of individuals and their families in accordance with the Health Insurance Portability and Accountability Act (HIPAA). Municipality or City of Pittsburgh Neighborhood is based off the geocoded home address of the individual tested.

    Individuals whose home address is incomplete may not be in Allegheny County but whose temporary residency, work or other mitigating circumstance are determined to be in Allegheny County by the Pennsylvania Department of Health are counted as "Undefined".

    Since the start of the pandemic, the ACHD has mapped every day’s COVID tests, cases, and deaths to their Allegheny County municipality and neighborhood. Tests were mapped to patient address, and if this was not available, to the provider location. This has recently resulted in apparent testing rates that exceeded the populations of various municipalities -- mostly those with healthcare providers. As this was brought to our attention, the health department and our data partners began researching and comparing methods to most accurately display the data. This has led us to leave those with missing home addresses off the map. Although these data will still appear in test, case and death counts, there will be over 20,000 fewer tests and almost 1000 fewer cases on the map. In addition to these map changes, we have identified specific health systems and laboratories that had data uploading errors that resulted in missing locations, and are working with them to correct these errors.

    Due to minor discrepancies in the Municipal boundary and the City of Pittsburgh Neighborhood files individuals whose City Neighborhood cannot be identified are be counted as “Undefined (Pittsburgh)”.

    On May 19, 2023, with the rescinding of the COVID-19 public health emergency, changes in data and reporting mechanisms prompted a change to an annual data sharing schedule for tests, cases, hospitalizations, and deaths. Dates for annual release are TBD. The weekly municipal counts and individual data produced before this changed are maintained as archive files.

    Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

  3. High-Frequency Monitoring of COVID-19 Impacts on Households 2021-2022,...

    • microdata.worldbank.org
    • catalog.ihsn.org
    Updated Jul 11, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    World Bank (2023). High-Frequency Monitoring of COVID-19 Impacts on Households 2021-2022, Rounds 1-3 - Malaysia [Dataset]. https://microdata.worldbank.org/index.php/catalog/4449
    Explore at:
    Dataset updated
    Jul 11, 2023
    Dataset authored and provided by
    World Bankhttp://worldbank.org/
    Time period covered
    2021 - 2022
    Area covered
    Malaysia
    Description

    Abstract

    The World Bank has launched a fast-deploying high-frequency phone-based survey of households to generate near real time insights into the socio-economic impact of COVID-19 on households which hence to be used to support evidence-based policy responses to the crisis. At a time when conventional modes of data collection are not feasible, this phone-based rapid data collection method offers a way to gather granular information on the transmission mechanisms of the crisis on the populations, to identify gaps in policy responses, and to generate insights to inform scaling up or redirection of resources as the crisis unfolds.

    Geographic coverage

    National

    Analysis unit

    Individual, Household-level

    Sampling procedure

    A mobile frame was generated via random digit dialing (RDD), based on the National Numbering Plans from the Malaysian Communications and Multimedia Commission (MCMC). All possible subscriber combinations were generated in DRUID (D Force Sampling's Reactive User Interface Database), an SQL database interface which houses the complete sampling frame. From this database, complete random telephone numbers were sampled. For Round 1, a sample of 33,894 phone numbers were drawn (without replacement within the survey wave) from a total of 102,780,000 possible mobile numbers from more than 18 mobile providers in the sampling frame, which were not stratified. Once the sample was drawn in the form of replicates (subsamples) of n = 10.000, the numbers were filtered by D-Force Sampling using an auto-dialer to determine each numbers' working status. All numbers that yield a working call disposition for at least one of the two filtering attempts were then passed to the CATI center human interviewing team. Mobile devices were assumed to be personal, and therefore the person who answered the call was the selected respondent. Screening questions were used to ensure that the respondent was at least 18 years old and within the capacity of either contributing, making or with knowledge of household finances. Respondents who had participated in Round 1 were sampled for Round 2. Fresh respondents were introduced in Round 3 in addition to panel respondents from Round 2; fresh respondents in Round 3 were selected using the same procedure for sampling respondents in Round 1.

    Mode of data collection

    Computer Assisted Telephone Interview [cati]

    Research instrument

    The questionnaire is available in three languages, including English, Bahasa Melayu, and Mandarin Chinese. It can be downloaded from the Downloads section.

    Response rate

    In Round 1, the survey successfully interviewed 2,210 individuals out of 33,894 sampled phone numbers. In Round 2, the survey successfully re-interviewed 1,047 individuals, recording a 47% response rate. In Round 3, the survey successfully re-interviewed 667 respondents who had been previously interviewed in Round 2, recording a 64% response rate. The panel respondents in Round 3 were added with 446 fresh respondents.

    Sampling error estimates

    In Round 1, assuming a simple random sample, with p=0.5 and n=2,210 at the 95% CI level, yields a margin of sampling error (MOE) of 2.09 percentage points. Incorporating the design effect into this estimate yields a margin of sampling error of 2.65% percentage points.

    In Round 2, the complete weight was for the entire sample adjusted to the 2021 population estimates from DOSM’s annual intercensal population projections. Assuming a simple random sample with p=0.5 and n=1,047 at the 95% CI level, yields a margin of sampling error (MOE) of 3.803 percentage points. Incorporating the design effect into this estimate yields a margin of sampling error of 3.54 percentage points.

    Among both fresh and panel samples in Round 3, assuming a simple random sample, with p=0.5 and n=1,113 at the 95% CI level yields a margin of sampling error (MOE) of 2.94 percentage points. Incorporating the design effect into this estimate yields a margin of sampling error of 3.34 percentage points.

    Among panel samples in Round 3, with p=0.5 and n=667 at the 95% CI level yields a margin of sampling error (MOE) of 3.80 percentage points. Incorporating the design effect into this estimate yields a margin of sampling error of 4.16 percentage points.

  4. Johns Hopkins COVID-19 Case Tracker

    • data.world
    csv, zip
    Updated Jun 8, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    The Associated Press (2025). Johns Hopkins COVID-19 Case Tracker [Dataset]. https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker
    Explore at:
    zip, csvAvailable download formats
    Dataset updated
    Jun 8, 2025
    Dataset provided by
    data.world, Inc.
    Authors
    The Associated Press
    Time period covered
    Jan 22, 2020 - Mar 9, 2023
    Area covered
    Description

    Updates

    • Notice of data discontinuation: Since the start of the pandemic, AP has reported case and death counts from data provided by Johns Hopkins University. Johns Hopkins University has announced that they will stop their daily data collection efforts after March 10. As Johns Hopkins stops providing data, the AP will also stop collecting daily numbers for COVID cases and deaths. The HHS and CDC now collect and visualize key metrics for the pandemic. AP advises using those resources when reporting on the pandemic going forward.

    • April 9, 2020

      • The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County.
    • April 20, 2020

      • Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well.
    • April 29, 2020

      • The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.
    • September 1st, 2020

      • Johns Hopkins is now providing counts for the five New York City counties individually.
    • February 12, 2021

      • The Ohio Department of Health recently announced that as many as 4,000 COVID-19 deaths may have been underreported through the state’s reporting system, and that the "daily reported death counts will be high for a two to three-day period."
      • Because deaths data will be anomalous for consecutive days, we have chosen to freeze Ohio's rolling average for daily deaths at the last valid measure until Johns Hopkins is able to back-distribute the data. The raw daily death counts, as reported by Johns Hopkins and including the backlogged death data, will still be present in the new_deaths column.
    • February 16, 2021

      - Johns Hopkins has reconciled Ohio's historical deaths data with the state.

      Overview

    The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

    The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

    This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

    The AP is updating this dataset hourly at 45 minutes past the hour.

    To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

    Queries

    Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

    Interactive

    The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.

    @(https://datawrapper.dwcdn.net/nRyaf/15/)

    Interactive Embed Code

    <iframe title="USA counties (2018) choropleth map Mapping COVID-19 cases by county" aria-describedby="" id="datawrapper-chart-nRyaf" src="https://datawrapper.dwcdn.net/nRyaf/10/" scrolling="no" frameborder="0" style="width: 0; min-width: 100% !important;" height="400"></iframe><script type="text/javascript">(function() {'use strict';window.addEventListener('message', function(event) {if (typeof event.data['datawrapper-height'] !== 'undefined') {for (var chartId in event.data['datawrapper-height']) {var iframe = document.getElementById('datawrapper-chart-' + chartId) || document.querySelector("iframe[src*='" + chartId + "']");if (!iframe) {continue;}iframe.style.height = event.data['datawrapper-height'][chartId] + 'px';}}});})();</script>
    

    Caveats

    • This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website.
    • In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules.
    • In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county"
    • This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members.
    • Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.
    • Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey.
    • The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories -- from Large Central Metro to Non-Core -- according to population and other characteristics. More details about the classifications can be found here.

    Johns Hopkins timeseries data - Johns Hopkins pulls data regularly to update their dashboard. Once a day, around 8pm EDT, Johns Hopkins adds the counts for all areas they cover to the timeseries file. These counts are snapshots of the latest cumulative counts provided by the source on that day. This can lead to inconsistencies if a source updates their historical data for accuracy, either increasing or decreasing the latest cumulative count. - Johns Hopkins periodically edits their historical timeseries data for accuracy. They provide a file documenting all errors in their timeseries files that they have identified and fixed here

    Attribution

    This data should be credited to Johns Hopkins University COVID-19 tracking project

  5. CDC COVID-19 Community Levels by County

    • opendata.ramseycounty.us
    application/rdfxml +5
    Updated Jun 9, 2025
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Center for Disease Control and Prevention (2025). CDC COVID-19 Community Levels by County [Dataset]. https://opendata.ramseycounty.us/Public-Health/CDC-COVID-19-Community-Levels-by-County/uazb-iwdp
    Explore at:
    application/rdfxml, json, xml, csv, tsv, application/rssxmlAvailable download formats
    Dataset updated
    Jun 9, 2025
    Dataset provided by
    Centers for Disease Control and Preventionhttp://www.cdc.gov/
    Authors
    Center for Disease Control and Prevention
    License

    https://www.usa.gov/government-workshttps://www.usa.gov/government-works

    Description

    This public use dataset has 11 data elements reflecting United States COVID-19 community levels for all available counties. This dataset contains the same values used to display information available on the COVID Data Tracker at: https://covid.cdc.gov/covid-data-tracker/#county-view?list_select_state=all_states&list_select_county=all_counties&data-type=CommunityLevels The data are updated weekly.

    CDC looks at the combination of three metrics — new COVID-19 admissions per 100,000 population in the past 7 days, the percent of staffed inpatient beds occupied by COVID-19 patients, and total new COVID-19 cases per 100,000 population in the past 7 days — to determine the COVID-19 community level. The COVID-19 community level is determined by the higher of the new admissions and inpatient beds metrics, based on the current level of new cases per 100,000 population in the past 7 days. New COVID-19 admissions and the percent of staffed inpatient beds occupied represent the current potential for strain on the health system. Data on new cases acts as an early warning indicator of potential increases in health system strain in the event of a COVID-19 surge. Using these data, the COVID-19 community level is classified as low, medium, or high. COVID-19 Community Levels can help communities and individuals make decisions based on their local context and their unique needs. Community vaccination coverage and other local information, like early alerts from surveillance, such as through wastewater or the number of emergency department visits for COVID-19, when available, can also inform decision making for health officials and individuals.

    See https://www.cdc.gov/coronavirus/2019-ncov/science/community-levels.html for more information.

    For the most accurate and up-to-date data for any county or state, visit the relevant health department website. COVID Data Tracker may display data that differ from state and local websites. This can be due to differences in how data were collected, how metrics were calculated, or the timing of web updates.

    For more details on the Minnesota Department of Health COVID-19 thresholds, see COVID-19 Public Health Risk Measures: Data Notes (Updated 4/13/22). https://mn.gov/covid19/assets/phri_tcm1148-434773.pdf

    Note: This dataset was renamed from "United States COVID-19 Community Levels by County as Originally Posted" to "United States COVID-19 Community Levels by County" on March 31, 2022. March 31, 2022: Column name for county population was changed to “county_population”. No change was made to the data points previous released. March 31, 2022: New column, “health_service_area_population”, was added to the dataset to denote the total population in the designated Health Service Area based on 2019 Census estimate. March 31, 2022: FIPS codes for territories American Samoa, Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands were re-formatted to 5-digit numeric for records released on 3/3/2022 to be consistent with other records in the dataset. March 31, 2022: Changes were made to the text fields in variables “county”, “state”, and “health_service_area” so the formats are consistent across releases. March 31, 2022: The “%” sign was removed from the text field in column “covid_inpatient_bed_utilization”. No change was made to the data. As indicated in the column description, values in this column represent the percentage of staffed inpatient beds occupied by COVID-19 patients (7-day average). March 31, 2022: Data values for columns, “county_population”, “health_service_area_number”, and “health_service_area” were backfilled for records released on 2/24/2022. These columns were added since the week of 3/3/2022, thus the values were previously missing for records released the week prior. April 7, 2022: Updates made to data released on 3/24/2022 for Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands to correct a data mapping error.

  6. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Stein, Benno (2024). Webis-Web-Errors-19 [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_2549837

Webis-Web-Errors-19

Explore at:
Dataset updated
Jul 24, 2024
Dataset provided by
Potthast, Martin
Kiesel, Johannes
Stein, Benno
Hubricht, Fabienne
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

The Webis-Web-Errors-19 comprises various annotations for the 10,000 web page archives of the Webis-Web-Archive-17. The annotations are whether the page is (1) mostly advertisement, (2) cut off, (3) still loading, (4) pornographic; and whether it shows (not/a bit/ very) (5) pop-ups, (6) CAPTCHAs, or (7) error messages. If you use this dataset in your research, please cite it using this paper.

Search
Clear search
Close search
Google apps
Main menu