This dataset reports the daily reported number of the 7-day moving average rates of Deaths involving COVID-19 by vaccination status and by age group. Learn how the Government of Ontario is helping to keep Ontarians safe during the 2019 Novel Coronavirus outbreak. Effective November 14, 2024 this page will no longer be updated. Information about COVID-19 and other respiratory viruses is available on Public Health Ontario’s interactive respiratory virus tool: https://www.publichealthontario.ca/en/Data-and-Analysis/Infectious-Disease/Respiratory-Virus-Tool Data includes: * Date on which the death occurred * Age group * 7-day moving average of the last seven days of the death rate per 100,000 for those not fully vaccinated * 7-day moving average of the last seven days of the death rate per 100,000 for those fully vaccinated * 7-day moving average of the last seven days of the death rate per 100,000 for those vaccinated with at least one booster ##Additional notes As of June 16, all COVID-19 datasets will be updated weekly on Thursdays by 2pm. As of January 12, 2024, data from the date of January 1, 2024 onwards reflect updated population estimates. This update specifically impacts data for the 'not fully vaccinated' category. On November 30, 2023 the count of COVID-19 deaths was updated to include missing historical deaths from January 15, 2020 to March 31, 2023. CCM is a dynamic disease reporting system which allows ongoing update to data previously entered. As a result, data extracted from CCM represents a snapshot at the time of extraction and may differ from previous or subsequent results. Public Health Units continually clean up COVID-19 data, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes and current totals being different from previously reported cases and deaths. Observed trends over time should be interpreted with caution for the most recent period due to reporting and/or data entry lags. The data does not include vaccination data for people who did not provide consent for vaccination records to be entered into the provincial COVaxON system. This includes individual records as well as records from some Indigenous communities where those communities have not consented to including vaccination information in COVaxON. “Not fully vaccinated” category includes people with no vaccine and one dose of double-dose vaccine. “People with one dose of double-dose vaccine” category has a small and constantly changing number. The combination will stabilize the results. Spikes, negative numbers and other data anomalies: Due to ongoing data entry and data quality assurance activities in Case and Contact Management system (CCM) file, Public Health Units continually clean up COVID-19, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes, negative numbers and current totals being different from previously reported case and death counts. Public Health Units report cause of death in the CCM based on information available to them at the time of reporting and in accordance with definitions provided by Public Health Ontario. The medical certificate of death is the official record and the cause of death could be different. Deaths are defined per the outcome field in CCM marked as “Fatal”. Deaths in COVID-19 cases identified as unrelated to COVID-19 are not included in the Deaths involving COVID-19 reported. Rates for the most recent days are subject to reporting lags All data reflects totals from 8 p.m. the previous day. This dataset is subject to change.

Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.

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.

  • CDC Weekly case and death counts (national and state level)
  • CDC County level cases and deaths
  • HHS New hospital admissions
  • CDC NowCast COVID variant proportions (national and regional level)

• 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

• Filter cases by state here

• Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac

• Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true

• Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.

• Pull the 100 counties with the highest per-capita confirmed cases here

• Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.

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

Context

From World Health Organization - On 31 December 2019, WHO was alerted to several cases of pneumonia in Wuhan City, Hubei Province of China. The virus did not match any other known virus. This raised concern because when a virus is new, we do not know how it affects people.

So daily level information on the affected people can give some interesting insights when it is made available to the broader data science community.

The European CDC publishes daily statistics on the COVID-19 pandemic. Not just for Europe, but for the entire world. We rely on the ECDC as they collect and harmonize data from around the world which allows us to compare what is happening in different countries.

Content

This dataset has daily level information on the number of affected cases, deaths and recovery etc. from coronavirus. It also contains various other parameters like average life expectancy, population density, smocking population etc. which users can find useful in further prediction that they need to make.

The data is available from 31 Dec,2019.

Inspiration

Give people weekly data so that they can use it to make accurate predictions.

Contain informative data related to COVID-19 pandemic. Specially, figure out about the First Case and First Death information for every single country. First Case information consist of Date of First Case(s), Number of confirm Case(s) at First Day, Age of the patient(s) of First Case, Last Visited Country and the First Death information consist of Date of First Death and Age of the Patient who died first for every Country mentioning corresponding Continent. The datasets also contain the Binary Matrix of spread chain among different country and region.

Analysis of ‘The Lost Journalists: Dataset of journalist deaths’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/journalist-deathse on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Credit for the original dataset goes to CPJ

About this dataset

In-the-News:

• NRT: AT LEAST 122 MEDIA PROFESSIONALS KILLED GLOBALLY IN 2016
• All Africa: Africa: Journalist Killings Ease From Record Highs As Murders Down, Combat Deaths Up
• BBC: The lost journalists of 2016

https://data.world/api/journalism/dataset/journalist-deaths/file/raw/journalist_deaths_by_year.png" alt="journalist_deaths_by_year.png">

Methodology

CPJ began compiling detailed records on journalist deaths in 1992. We apply strict journalistic standards when investigating a death. One important aspect of our research is determining whether a death was work-related. As a result, we classify deaths as "motive confirmed" or "motive unconfirmed."

We consider a case "confirmed" only if we are reasonably certain that a journalist was murdered in direct reprisal for his or her work; was killed in crossfire during combat situations; or was killed while carrying out a dangerous assignment such as coverage of a street protest. We do not include journalists who are killed in accidents such as car or plane crashes.

We include only confirmed cases in the statistical analyses in this database.

When the motive is unclear, but it is possible that a journalist was killed because of his or her work, CPJ classifies the case as "unconfirmed" and continues to investigate. We regularly reclassify cases based on our ongoing research.

Our archives include narrative capsules of all journalists killed, including the cases in which the motive is unconfirmed. In cases where the place of death is incidental to the journalist's killing, we have listed the country where the fatal attack occurred to be the place of the journalist's death (for example, in a case where a journalist is hit by shrapnel in one country and evacuated to another, where he or she dies, CPJ lists the country in which he or she was hit as the place of death).

CPJ defines journalists as people who cover news or comment on public affairs through any media -- including in print, in photographs, on radio, on television, and online. We take up cases involving staff journalists, freelancers, stringers, bloggers, and citizen journalists. The combination of daily reporting and statistical data forms the basis of our case-driven and long-term advocacy.

In 2003, CPJ began documenting the deaths of media support workers. We did so in recognition of the vital role these individuals play in newsgathering. These workers include translators, drivers, fixers, and administrative workers.

Our archives include narrative capsules for media workers killed on duty. These cases are not included our statistical analyses.

About CPJ

The Committee to Protect Journalists is an independent, nonprofit organization that promotes press freedom worldwide. We defend the right of journalists to report the news without fear of reprisal.

Additional Reading
Investigative journalism in Africa – “Walking through a minefield at midnight”
Iraq: The deadliest war for journalists
Being a journalist in Mexico is getting even more dangerous

Source: Committee to Protect Journalists

This dataset was created by Journalism, News, and Media and contains around 2000 samples along with Date, Unnamed: 18, technical information and other features such as: - Local/ Foreign - Unnamed: 20 - and more.

How to use this dataset

• Analyze Coverage in relation to Taken Captive
• Study the influence of Organization on Unnamed: 21
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Journalism, News, and Media

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Age-standardised mortality rates for deaths involving coronavirus (COVID-19), non-COVID-19 deaths and all deaths by vaccination status, broken down by age group.

How much time do people spend on social media? As of 2024, the average daily social media usage of internet users worldwide amounted to 143 minutes per day, down from 151 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of three hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in the U.S. was just two hours and 16 minutes. Global social media usageCurrently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively. People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events friends. Global impact of social mediaSocial media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general. During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.

[Edit 12/09/2020] You will now find in the files below the last 30 days, too many people do not respect the request not to recover too often the dataset (no interest in recovering every minute while the file changes 4 or 5 times a day) If you want access to the entire history, contact me [Edit 31/03/2020] Since yesterday, I made sure to have the data of the day since the ESSC, so the data of the same day are now available and updated several times a day (about every hour) as the new figures fall all over the world. The data of the previous day is always consolidated around 2am (it is no longer 1h since the time change). If you only want to have the complete data, just don't take into account the last day (today’s date) Here I share the data that I compile with the famous coronavirus infection world map created and maintained by The Johns Hopkins University and which serve me to display ** CoronaVirus statistics worldwide and by country** They share the day’s data each night on a GitHub deposit. My tools compile this new data as soon as they are available and I share the result here. This data is used to display tables and graphs on the CoronaVirus website (Covid19) of Politologue.com https://coronavirus.politologue.com/ This data will allow you to make your own graphs and analyses if you look at the subject. I do not oblige you to do it, but if my compilation allows you to do something about it and saved you time, a link to https://coronavirus.politologue.com/ will be appreciable. Information in files (csv and json) — Number of cases — Number of deaths — Number of healing — Death rate (percentage) — Healing rate (percentage) — Infection rate (persons still infected, not deceased or cured) (percentage) — And for data by country, you will find a field “country” If you integrate the client-side json or csv on a site or application, please keep a cache on your servers without risking an unexpected load on my servers.

The COVID Tracking Project was a volunteer organization launched from The Atlantic and dedicated to collecting and publishing the data required to understand the COVID-19 outbreak in the United States. Our dataset was in use by national and local news organizations across the United States and by research projects and agencies worldwide.

Every day, we collected data on COVID-19 testing and patient outcomes from all 50 states, 5 territories, and the District of Columbia by visiting official public health websites for those jurisdictions and entering reported values in a spreadsheet. The files in this dataset represent the entirety of our COVID-19 testing and outcomes data collection from March 7, 2020 to March 7, 2021. This dataset includes official values reported by each state on each day of antigen, antibody, and PCR test result totals; the total number of probable and confirmed cases of COVID-19; the number of people currently hospitalized, in intensive care, and on a ventilator; the total number of confirmed and probable COVID-19 deaths; and more.

The global number of internet users in was forecast to continuously increase between 2024 and 2029 by in total 1.3 billion users (+23.66 percent). After the fifteenth consecutive increasing year, the number of users is estimated to reach 7 billion users and therefore a new peak in 2029. Notably, the number of internet users of was continuously increasing over the past years.Depicted is the estimated number of individuals in the country or region at hand, that use the internet. As the datasource clarifies, connection quality and usage frequency are distinct aspects, not taken into account here.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of internet users in countries like the Americas and Asia.

This dataset provides values for CORONAVIRUS DEATHS reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

PIONEER: The impact of ethnicity and multi-morbidity on COVID-related outcomes; a primary care supplemented hospitalised dataset Dataset number 3.0

Coronavirus disease 2019 (COVID-19) was identified in January 2020. Currently, there have been more than 65million cases and more than 1.5 million deaths worldwide. Some individuals experience severe manifestations of infection, including viral pneumonia, adult respiratory distress syndrome (ARDS) and death. Evidence suggests that older patients, those from some ethnic minority groups and those with multiple long-term health conditions have worse outcomes. This secondary care COVID dataset contains granular demographic and morbidity data, supplemented from primary care records, to add to the understanding of patient factors on disease outcomes.

PIONEER geography The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. There is a higher than average percentage of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK. Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS. The West Midlands was one of the hardest hit regions for COVID admissions in both wave 1 and 2.

EHR. University Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & 100 ITU beds. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. UHB has cared for >5000 COVID admissions to date.

Scope: All COVID swab confirmed hospitalised patients to UHB from January – May 2020. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes but also primary care records and clinic letters. Serial, structured data pertaining to care process (timings, staff grades, specialty review, wards), presenting complaint, acuity, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed & administered treatments (fluids, antibiotics, inotropes, vasopressors, organ support), all outcomes. Linked images available (radiographs, CT, MRI, ultrasound).

Available supplementary data: Health data preceding and following admission event. Matched “non-COVID” controls; ambulance, 111, 999 data, synthetic data.

Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

SummaryTotal ever COVID-19 cases and deaths at Maryland congregate living facilities.DescriptionDeprecated as of November 17, 2021.The Outbreak-Associated Cases in Congregate Living data dashboard on coronavirus.maryland.gov was redesigned on 11/17/21 to align with other outbreak reporting. Visit MD COVID-19 Congregate Outbreaks to view Outbreak-Associated Cases in Congregate Living data as reported after 11/17/21.The MD COVID-19 Congregate Cases and Deaths total Summary data layer is the cumulative total of COVID-19 cases and deaths that have occured in nursing homes, assisted living facilities, group homes of 10 or more and state and local facilities. Data are reported to MDH by local health departments, the Department of Public Safety and Correctional Services and the Department of Juvenile Services and are updated once weekly.COVID-19 is a disease caused by a respiratory virus first identified in Wuhan, Hubei Province, China in December 2019. COVID-19 is a new virus that hasn't caused illness in humans before. Worldwide, COVID-19 has resulted in thousands of infections, causing illness and in some cases death. Cases have spread to countries throughout the world, with more cases reported daily. The Maryland Department of Health reports daily on COVID-19 cases by county.

SummaryThe cumulative number of confirmed COVID-19 deaths among Maryland residents, by date of death.DescriptionThe MD COVID-19 - Total Confirmed Deaths Date of Death data layer is a collection of the statewide confirmed COVID-19 related deaths that have been reported each day by the Vital Statistics Administration by date of death. A death is classified as confirmed if the person had a laboratory-confirmed positive COVID-19 test result. Some data on deaths may be unavailable due to the time lag between the death, typically reported by a hospital or other facility, and the submission of the complete death certificate. Probable deaths are available from the MD COVID-19 - Total Probable Deaths by Date of Death data layer.COVID-19 is a disease caused by a respiratory virus first identified in Wuhan, Hubei Province, China in December 2019. COVID-19 is a new virus that hasn't caused illness in humans before. Worldwide, COVID-19 has resulted in thousands of infections, causing illness and in some cases death. Cases have spread to countries throughout the world, with more cases reported daily. The Maryland Department of Health reports daily on COVID-19 cases by county.

Context

• The Western African Ebola virus epidemic (2013–2016) was the most widespread outbreak of Ebola virus disease (EVD) in history
• Causing major loss of life and socioeconomic disruption in the region, mainly in Guinea, Liberia, and Sierra Leone.
• The ** first cases** were recorded in Guinea in December 2013;
• Later, the disease spread to neighboring Liberia and Sierra Leone, with minor outbreaks occurring elsewhere.
• It caused significant mortality, with the case fatality rate reported which was initially considered, while the rate among hospitalized patients was 57–59%
• The final numbers 28,616 people, including 11,310 deaths, for a case-fatality rate of 40%.

Content

Each row contains a report from each region/location for each day Each column represents the number of cases reported from each country/region

Inspiration

To see how the epidemic spread worldwide in such a short time

Acknowledgements / Data Source

https://www.who.int/csr/don/archive/disease/ebola/en/ https://data.humdata.org/dataset/ebola-cases-2014

Collection methodology

https://github.com/imdevskp/ebola_outbreak_dataset

Cover Photo

Photo from CDC website https://www.cdc.gov/vhf/ebola/index.html

Similar Datasets

• COVID-19 - https://www.kaggle.com/imdevskp/corona-virus-report
• MERS - https://www.kaggle.com/imdevskp/mers-outbreak-dataset-20122019
• Ebola Western Africa 2014 Outbreak - https://www.kaggle.com/imdevskp/ebola-outbreak-20142016-complete-dataset
• H1N1 | Swine Flu 2009 Pandemic Dataset - https://www.kaggle.com/imdevskp/h1n1-swine-flu-2009-pandemic-dataset
• SARS 2003 Pandemic - https://www.kaggle.com/imdevskp/sars-outbreak-2003-complete-dataset
• HIV AIDS - https://www.kaggle.com/imdevskp/hiv-aids-dataset

The Worldwide Soundscapes project is a global, open inventory of spatio-temporally replicated passive acoustic monitoring meta-datasets (i.e. meta-data collections). This Zenodo entry comprises the data tables that constitute its (meta-)database, as well as their description.

The overview of all sampling sites can be found on the corresponding project on ecoSound-web, as well as a demonstration collection containing selected recordings.

The audio recording criteria justifying inclusion into the meta-database are:

• Stationary (no transects, towed sensors or microphones mounted on cars)
• Passive (unattended, no human disturbance by the recordist)
• Ambient (no directional microphone or triggered recordings)
• Spatially and/or temporally replicated (i.e. multiple sites sampled at the same time and/or multiple days - covering the same daytime - sampled at the same site)

The individual columns of the provided data tables are described in the following. Data tables are linked through primary keys; joining them will result in a database. The data shared here only includes validated collections.

Changes from version 1.0.0

audio data moved to deployments table. datasets are called collections. Lookup fields and some collection table fields removed.

collections

• collection_id: unique integer, primary key
• name: name of the dataset. if it is repeated, incremental integers should be used in the "subset" column to differentiate them.
• ecoSound-web_link: link of validated collections that were uploaded to ecoSound-web
• primary_contributors: full names of people deemed corresponding contributors who are responsible for the dataset
• secondary_contributors: full names of people who are not primary contributors but who have significantly contributed to the dataset, and who could be contacted for in-depth analyses
• date_added: when the datased was added (YYYY-MM-DD)
• URL_open_recordings: internet link of openly-available recordings from this collection
• URL_project: internet link for further information about the corresponding project
• DOI_publication: DOIs of corresponding publications
• core_realm_IUCN: The main, core realm of the dataset
• medium: the physical medium the microphone is situated in
• protected_area: whether the sampling sites were situated in protected areas or not, or only some. boolean
• locality: optional free text about the locality
• spatial_selection: spatial selection criteria that were used to determine in which locations to record sound (ecotone, elevated spot, etc.) - any deviations from randomness
• temporal_exclusion: environmental exclusion criteria that were used to determine which recording days or times to discard
• freshwater_recordist_position: position of the recordist relative to the microphone during sampling (only for freshwater)
• contributor_comments: free-text field for comments by the primary contributors

collections-sites

• dataset_ID: primary key of collections table
• site_ID: primary key of sites table

sites

• site_ID: unique integer, primary key
• site_name: name or code of sampling site as used in respective projects
• latitude_numeric: site's numeric degrees of latitude
• longitude_numeric: site's numeric degrees of longitude
• blurred_coordinates: whether latitude and longitude coordinates are inaccurate, boolean. Coordinates may be blurred with random offsets, rounding, snapping, etc. Indicate the blurring method inside the comments field
• topography_m: vertical position of the microphone relative to the sea level. for sites on land: elevation. For marine sites: depth (negative). in meters. Only indicate if the values were measured by the collaborator.
• freshwater_depth_m: microphone depth, only used for sites inside freshwater bodies that also have an elevation value above the sea level
• realm: Ecosystem type according to IUCN GET https://global-ecosystems.org/
• biome: Ecosystem type according to IUCN GET https://global-ecosystems.org/
• functional_group: Ecosystem type according to IUCN GET https://global-ecosystems.org/
• contributor_comments: free text field for contributor comments

deployments

• dataset_ID: primary key of datasets table
• dataset_name: lookup field
• deployment: use identical subscript letters to denote rows that belong to the same deployment. For instance, you may use different operation times and schedules for different target taxa within one deployment.
• start_date_min: earliest date of deployment start, double-click cell to get date-picker
• start_date_max: latest date of deployment start, if applicable (only used when recorders were deployed over several days), double-click cell to get date-picker
• start_time_mixed: deployment start local time, either in HH:MM format or a choice of solar daytimes (sunrise, sunset, noon, midnight). Corresponds to the recording start time for continuous recording deployments. If multiple start times were used, you should mention the latest start time (corresponds to the earliest daytime from which all recorders are active). If applicable, positive or negative offsets from solar times can be mentioned (For example: if data are collected one hour before sunrise, this will be "sunrise-60")
• permanent: is the deployment permanent (in which case it would be ongoing and the end date or duration would be unknown)?
• variable_duration_days: is the duration of the deployment variable? in days
• duration_days: deployment duration per recorder (use the minimum if variable)
• end_date_min: earliest date of deployment end, only needed if duration is variable, double-click cell to get date-picker
• end_date_max: latest date of deployment end, only needed if duration is variable, double-click cell to get date-picker
• end_time_mixed: deployment end local time, either in HH:MM format or a choice of solar daytimes (sunrise, sunset, noon, midnight). Corresponds to the recording end time for continuous recording deployments.
• recording_time: does the recording last from the deployment start time to the end time (continuous) or at scheduled daily intervals (scheduled)? Note: we consider recordings with duty cycles to be continuous.
• operation_start_time_mixed: scheduled recording start local time, either in HH:MM format or a choice of solar daytimes (sunrise, sunset, noon, midnight). If applicable, positive or negative offsets from solar times can be mentioned (For example: if data are collected one hour before sunrise, this will be "sunrise-60")
• operation_duration_minutes: duration of operation in minutes, if constant
• operation_end_time_mixed: scheduled recording end local time, either in HH:MM format or a choice of solar daytimes (sunrise, sunset, noon, midnight). If applicable, positive or negative offsets from solar times can be mentioned (For example: if data are collected one hour before sunrise, this will be "sunrise-60")
• duty_cycle_minutes: duty cycle of the recording (i.e. the fraction of minutes when it is recording), written as "recording(minutes)/period(minutes)". For example: "1/6" if the recorder is active for 1 minute and standing by for 5 minutes.
• sampling_frequency_kHz: only indicate the sampling frequency if it is variable within a particular dataset so that we need to code different frequencies for different deployments
• recorder
• subset_sites: If the deployment was not done in all the sites of the corresponding datasest, site IDs can be indicated here, separated by commas
• comments

The world population surpassed eight billion people in 2022, having doubled from its figure less than 50 years previously. Looking forward, it is projected that the world population will reach nine billion in 2038, and 10 billion in 2060, but it will peak around 10.3 billion in the 2080s before it then goes into decline. Regional variations The global population has seen rapid growth since the early 1800s, due to advances in areas such as food production, healthcare, water safety, education, and infrastructure, however, these changes did not occur at a uniform time or pace across the world. Broadly speaking, the first regions to undergo their demographic transitions were Europe, North America, and Oceania, followed by Latin America and Asia (although Asia's development saw the greatest variation due to its size), while Africa was the last continent to undergo this transformation. Because of these differences, many so-called "advanced" countries are now experiencing population decline, particularly in Europe and East Asia, while the fastest population growth rates are found in Sub-Saharan Africa. In fact, the roughly two billion difference in population between now and the 2080s' peak will be found in Sub-Saharan Africa, which will rise from 1.2 billion to 3.2 billion in this time (although populations in other continents will also fluctuate). Changing projections The United Nations releases their World Population Prospects report every 1-2 years, and this is widely considered the foremost demographic dataset in the world. However, recent years have seen a notable decline in projections when the global population will peak, and at what number. Previous reports in the 2010s had suggested a peak of over 11 billion people, and that population growth would continue into the 2100s, however a sooner and shorter peak is now projected. Reasons for this include a more rapid population decline in East Asia and Europe, particularly China, as well as a prolongued development arc in Sub-Saharan Africa.

SummaryThe cumulative number of positive COVID-19 cases among Maryland residents.DescriptionThe MD COVID-19 - Total Cases Statewide data layer is a collection of the statewide positive COVID-19 test results that have been reported each day by each local health department via the ESSENCE system.COVID-19 is a disease caused by a respiratory virus first identified in Wuhan, Hubei Province, China in December 2019. COVID-19 is a new virus that hasn't caused illness in humans before. Worldwide, COVID-19 has resulted in thousands of infections, causing illness and in some cases death. Cases have spread to countries throughout the world, with more cases reported daily. The Maryland Department of Health reports daily on COVID-19 cases by county.