This dataset contains counts of deaths for California counties 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 each California county regardless of the place of residence (by occurrence) and deaths to residents of each California county (by residence), whereas the provisional data table only includes deaths that occurred in each county 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.

All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name

Context

Country-Level Information On COVID-19 Cases

Content

Dataset : COVID-19 Dataset.csv

It Contains: Continent Name Country Name Last Updated Date Total Cases Total Deaths Total Cases Per Million Total Deaths Per Million Total Tests Positivity Rate Total Vaccinations People Vaccinated People Fully Vaccinated Population Density of the Country Median Age Aged 65 Older Aged 70 Older GDP Per Capita of the Country

Acknowledgements

• ourworldindata.org : ourworldindata.org/coronavirus-source-data
• World Health Organization (WHO): https://www.who.int/

The World Ocean Database (WOD) is the world's largest publicly available uniform format quality controlled ocean profile dataset. Ocean profile data are sets of measurements of an ocean variable vs. depth at a single geographic location within a short (minutes to hours) temporal period in some portion of the water column from the surface to the bottom. To be considered a profile for the WOD, there must be more than a single depth/variable pair. Multiple profiles at the same location from the same set of instruments is an oceanographic cast. Ocean variables in the WOD include temperature, salinity, oxygen, nutrients, tracers, and biological variables such as plankton and chlorophyll. Quality control procedures are documented and performed on each cast and the results are included as flags on each measurement. The WOD contains the data on the originally measured depth levels (observed) and also interpolated to standard depth levels to present a more uniform set of iso-surfaces for oceanographic and climate work. The source of the WOD is more than 20,000 separate archived datasets contributed by institutions, project, government agencies, and individual investigators from the United States and around the world. Each dataset is available in its original form in the National Centers for Environmental Information data archives. All datasets are converted to the same standard format, checked for duplication within the WOD, and assigned quality flags based on objective tests. Additional subjective flags are set upon calculation of ocean climatological mean fields which make up the World Ocean Atlas (WOA) series. The WOD consists of periodic major releases and quarterly updates to those releases. Each major release is associated with a concurrent release of a WOA release, and contains final quality control flags used in the WOA, which includes manual as well as automated steps. Each quarterly update release includes additional historical and recent data and preliminary quality control. The latest major release was WOD 2018 (WOD18), which includes nearly 16 million oceanographic casts, from the second voyage of Captain Cook (1772) to the modern Argo floats (end of 2017). The WOD presents data in netCDF ragged array format following the Climate and Forecast (CF) conventions for ease of use mindful of space limitations.

A. SUMMARY This archived dataset includes data for population characteristics that are no longer being reported publicly. The date on which each population characteristic type was archived can be found in the field “data_loaded_at”.

B. HOW THE DATASET IS CREATED Data on the population characteristics of COVID-19 cases are from:  * Case interviews  * Laboratories  * Medical providers    These multiple streams of data are merged, deduplicated, and undergo data verification processes.  

Race/ethnicity * We include all race/ethnicity categories that are collected for COVID-19 cases. * The population estimates for the "Other" or “Multi-racial” groups should be considered with caution. The Census definition is likely not exactly aligned with how the City collects this data. For that reason, we do not recommend calculating population rates for these groups.

Gender * The City collects information on gender identity using these guidelines.

Skilled Nursing Facility (SNF) occupancy * A Skilled Nursing Facility (SNF) is a type of long-term care facility that provides care to individuals, generally in their 60s and older, who need functional assistance in their daily lives.  * This dataset includes data for COVID-19 cases reported in Skilled Nursing Facilities (SNFs) through 12/31/2022, archived on 1/5/2023. These data were identified where “Characteristic_Type” = ‘Skilled Nursing Facility Occupancy’.

Sexual orientation * The City began asking adults 18 years old or older for their sexual orientation identification during case interviews as of April 28, 2020. Sexual orientation data prior to this date is unavailable. * The City doesn’t collect or report information about sexual orientation for persons under 12 years of age. * Case investigation interviews transitioned to the California Department of Public Health, Virtual Assistant information gathering beginning December 2021. The Virtual Assistant is only sent to adults who are 18+ years old. https://www.sfdph.org/dph/files/PoliciesProcedures/COM9_SexualOrientationGuidelines.pdf">Learn more about our data collection guidelines pertaining to sexual orientation.

Comorbidities * Underlying conditions are reported when a person has one or more underlying health conditions at the time of diagnosis or death.

Homelessness Persons are identified as homeless based on several data sources: * self-reported living situation * the location at the time of testing * Department of Public Health homelessness and health databases * Residents in Single-Room Occupancy hotels are not included in these figures. These methods serve as an estimate of persons experiencing homelessness. They may not meet other homelessness definitions.

Single Room Occupancy (SRO) tenancy * SRO buildings are defined by the San Francisco Housing Code as having six or more "residential guest rooms" which may be attached to shared bathrooms, kitchens, and living spaces. * The details of a person's living arrangements are verified during case interviews.

Transmission Type * Information on transmission of COVID-19 is based on case interviews with individuals who have a confirmed positive test. Individuals are asked if they have been in close contact with a known COVID-19 case. If they answer yes, transmission category is recorded as contact with a known case. If they report no contact with a known case, transmission category is recorded as community transmission. If the case is not interviewed or was not asked the question, they are counted as unknown.

C. UPDATE PROCESS This dataset has been archived and will no longer update as of 9/11/2023.

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

This dataset includes many different types of characteristics. Filter the “Characteristic Type” column to explore a topic area. Then, the “Characteristic Group” column shows each group or category within that topic area and the number of cases on each date.

New cases are the count of cases within that characteristic group where the positive tests were collected on that specific specimen collection date. Cumulative cases are the running total of all San Francisco cases in that characteristic group up to the specimen collection date listed.

This data may not be immediately available for recently reported cases. Data updates as more information becomes available.

To explore data on the total number of cases, use the ARCHIVED: COVID-19 Cases Over Time dataset.

E. CHANGE LOG

• 9/11/2023 - data on COVID-19 cases by population characteristics over time are no longer being updated. The date on which each population characteristic type was archived can be found in the field “data_loaded_at”.
• 6/6/2023 - data on cases by transmission type have been removed. See section ARCHIVED DATA for more detail.
• 5/16/2023 - data on cases by sexual orientation, comorbidities, homelessness, and single room occupancy have been removed. See section ARCHIVED DATA for more detail.
• 4/6/2023 - the State implemented system updates to improve the integrity of historical data.
• 2/21/2023 - system updates to improve reliability and accuracy of cases data were implemented.
• 1/31/2023 - updated “population_estimate” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
• 1/5/2023 - data on SNF cases removed. See section ARCHIVED DATA for more detail.
• 3/23/2022 - ‘Native American’ changed to ‘American Indian or Alaska Native’ to align with the census.
• 1/22/2022 - system updates to improve timeliness and accuracy of cases and deaths data were implemented.
• 7/15/2022 - reinfections added to cases dataset. See section SUMMARY for more information on how reinfections are identified.

Context

The United States Census Bureau’s international dataset provides estimates of country populations since 1950 and projections through 2050. Specifically, the dataset includes midyear population figures broken down by age and gender assignment at birth. Additionally, time-series data is provided for attributes including fertility rates, birth rates, death rates, and migration rates.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.census_bureau_international.

Sample Query 1

What countries have the longest life expectancy? In this query, 2016 census information is retrieved by joining the mortality_life_expectancy and country_names_area tables for countries larger than 25,000 km2. Without the size constraint, Monaco is the top result with an average life expectancy of over 89 years!

standardSQL

SELECT age.country_name, age.life_expectancy, size.country_area FROM ( SELECT country_name, life_expectancy FROM bigquery-public-data.census_bureau_international.mortality_life_expectancy WHERE year = 2016) age INNER JOIN ( SELECT country_name, country_area FROM bigquery-public-data.census_bureau_international.country_names_area where country_area > 25000) size ON age.country_name = size.country_name ORDER BY 2 DESC /* Limit removed for Data Studio Visualization */ LIMIT 10

Sample Query 2

Which countries have the largest proportion of their population under 25? Over 40% of the world’s population is under 25 and greater than 50% of the world’s population is under 30! This query retrieves the countries with the largest proportion of young people by joining the age-specific population table with the midyear (total) population table.

standardSQL

SELECT age.country_name, SUM(age.population) AS under_25, pop.midyear_population AS total, ROUND((SUM(age.population) / pop.midyear_population) * 100,2) AS pct_under_25 FROM ( SELECT country_name, population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population_agespecific WHERE year =2017 AND age < 25) age INNER JOIN ( SELECT midyear_population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population WHERE year = 2017) pop ON age.country_code = pop.country_code GROUP BY 1, 3 ORDER BY 4 DESC /* Remove limit for visualization*/ LIMIT 10

Sample Query 3

The International Census dataset contains growth information in the form of birth rates, death rates, and migration rates. Net migration is the net number of migrants per 1,000 population, an important component of total population and one that often drives the work of the United Nations Refugee Agency. This query joins the growth rate table with the area table to retrieve 2017 data for countries greater than 500 km2.

SELECT growth.country_name, growth.net_migration, CAST(area.country_area AS INT64) AS country_area FROM ( SELECT country_name, net_migration, country_code FROM bigquery-public-data.census_bureau_international.birth_death_growth_rates WHERE year = 2017) growth INNER JOIN ( SELECT country_area, country_code FROM bigquery-public-data.census_bureau_international.country_names_area

Update frequency

Historic (none)

Dataset source

United States Census Bureau

Terms of use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

See the GCP Marketplace listing for more details and sample queries: https://console.cloud.google.com/marketplace/details/united-states-census-bureau/international-census-data

Project Summary This dataset contains all qualitative and quantitative data collected in the first phase of the Pandemic Journaling Project (PJP). PJP is a combined journaling platform and interdisciplinary, mixed-methods research study developed by two anthropologists, with support from a team of colleagues and students across the social sciences, humanities, and health fields. PJP launched in Spring 2020 as the COVID-19 pandemic was emerging in the United States. PJP was created in order to “pre-design an archive” of COVID-19 narratives and experiences open to anyone around the world. The project is rooted in a commitment to democratizing knowledge production, in the spirit of “archival activism” and using methods of “grassroots collaborative ethnography” (Willen et al. 2022; Wurtz et al. 2022; Zhang et al 2020; see also Carney 2021). The motto on the PJP website encapsulates these commitments: “Usually, history is written only by the powerful. When the history of COVID-19 is written, let’s make sure that doesn’t happen.” (A version of this Project Summary with links to the PJP website and other relevant sites is included in the public documentation of the project at QDR.) In PJP’s first phase (PJP-1), the project provided a digital space where participants could create weekly journals of their COVID-19 experiences using a smartphone or computer. The platform was designed to be accessible to as wide a range of potential participants as possible. Anyone aged 15 or older, living anywhere in the world, could create journal entries using their choice of text, images, and/or audio recordings. The interface was accessible in English and Spanish, but participants could submit text and audio in any language. PJP-1 ran on a weekly basis from May 2020 to May 2022. Data Overview This Qualitative Data Repository (QDR) project contains all journal entries and closed-ended survey responses submitted during PJP-1, along with accompanying descriptive and explanatory materials. The dataset includes individual journal entries and accompanying quantitative survey responses from more than 1,800 participants in 55 countries. Of nearly 27,000 journal entries in total, over 2,700 included images and over 300 are audio files. All data were collected via the Qualtrics survey platform. PJP-1 was approved as a research study by the Institutional Review Board (IRB) at the University of Connecticut. Participants were introduced to the project in a variety of ways, including through the PJP website as well as professional networks, PJP’s social media accounts (on Facebook, Instagram, and Twitter) , and media coverage of the project. Participants provided a single piece of contact information — an email address or mobile phone number — which was used to distribute weekly invitations to participate. This contact information has been stripped from the dataset and will not be accessible to researchers. PJP uses a mixed-methods research approach and a dynamic cohort design. After enrolling in PJP-1 via the project’s website, participants received weekly invitations to contribute to their journals via their choice of email or SMS (text message). Each weekly invitation included a link to that week’s journaling prompts and accompanying survey questions. Participants could join at any point, and they could stop participating at any point as well. They also could stop participating and later restart. Retention was encouraged with a monthly raffle of three $100 gift cards. All individuals who had contributed that month were eligible. Regardless of when they joined, all participants received the project’s narrative prompts and accompanying survey questions in the same order. In Week 1, before contributing their first journal entries, participants were presented with a baseline survey that collected demographic information, including political leanings, as well as self-reported data about COVID-19 exposure and physical and mental health status. Some of these survey questions were repeated at periodic intervals in subsequent weeks, providing quantitative measures of change over time that can be analyzed in conjunction with participants' qualitative entries. Surveys employed validated questions where possible. The core of PJP-1 involved two weekly opportunities to create journal entries in the format of their choice (text, image, and/or audio). Each week, journalers received a link with an invitation to create one entry in response to a recurring narrative prompt (“How has the COVID-19 pandemic affected your life in the past week?”) and a second journal entry in response to their choice of two more tightly focused prompts. Typically the pair of prompts included one focusing on subjective experience (e.g., the impact of the pandemic on relationships, sense of social connectedness, or mental health) and another with an external focus (e.g., key sources of scientific information, trust in government, or COVID-19’s economic impact). Each week,...

Social media companies are starting to offer users the option to subscribe to their platforms in exchange for monthly fees. Until recently, social media has been predominantly free to use, with tech companies relying on advertising as their main revenue generator. However, advertising revenues have been dropping following the COVID-induced boom. As of July 2023, Meta Verified is the most costly of the subscription services, setting users back almost 15 U.S. dollars per month on iOS or Android. Twitter Blue costs between eight and 11 U.S. dollars per month and ensures users will receive the blue check mark, and have the ability to edit tweets and have NFT profile pictures. Snapchat+, drawing in four million users as of the second quarter of 2023, boasts a Story re-watch function, custom app icons, and a Snapchat+ badge.

Between Oct. 14, 2014, and May 21, 2015, Pew Research Center, with generous funding from The Pew Charitable Trusts and the Neubauer Family Foundation, completed 5,601 face-to-face interviews with non-institutionalized adults ages 18 and older living in Israel.

The survey sampling plan was based on six districts defined in the 2008 Israeli census. In addition, Jewish residents of West Bank (Judea and Samaria) were included.

The sample includes interviews with 3,789 respondents defined as Jews, 871 Muslims, 468 Christians and 439 Druze. An additional 34 respondents belong to other religions or are religiously unaffiliated. Five groups were oversampled as part of the survey design: Jews living in the West Bank, Haredim, Christian Arabs, Arabs living in East Jerusalem and Druze.

Interviews were conducted under the direction of Public Opinion and Marketing Research of Israel (PORI). Surveys were administered through face-to-face, paper and pencil interviews conducted at the respondent's place of residence. Sampling was conducted through a multi-stage stratified area probability sampling design based on national population data available through the Israel's Central Bureau of Statistics' 2008 census.

The questionnaire was designed by Pew Research Center staff in consultation with subject matter experts and advisers to the project. The questionnaire was translated into Hebrew, Russian and Arabic, independently verified by professional linguists conversant in regional dialects and pretested prior to fieldwork.

The questionnaire was divided into four sections. All respondents who took the survey in Russian or Hebrew were branched into the Jewish questionnaire (Questionnaire A). Arabic-speaking respondents were branched into the Muslim (Questionnaire B), Christian (Questionnaire C) or Druze questionnaire (D) based on their response to the religious identification question. For the full question wording and exact order of questions, please see the questionnaire.

Note that not all respondents who took the questionnaire in Hebrew or Russian are classified as Jews in this study. For further details on how respondents were classified as Jews, Muslims, Christians and Druze in the study, please see sidebar in the report titled "http://www.pewforum.org/2016/03/08/israels-religiously-divided-society/" Target="_blank">"How Religious are Defined".

Following fieldwork, survey performance was assessed by comparing the results for key demographic variables with population statistics available through the census. Data were weighted to account for different probabilities of selection among respondents. Where appropriate, data also were weighted through an iterative procedure to more closely align the samples with official population figures for gender, age and education. The reported margins of sampling error and the statistical tests of significance used in the analysis take into account the design effects due to weighting and sample design.

In addition to sampling error and other practical difficulties, one should bear in mind that question wording also can have an impact on the findings of opinion polls.

This dataset contains information on antibody testing for COVID-19: the number of people who received a test, the number of people with positive results, the percentage of people tested who tested positive, and the rate of testing per 100,000 people, stratified by ZIP Code Tabulation Area (ZCTA) neighborhood poverty group. These data can also be accessed here: https://res1githubd-o-tcom.vcapture.xyz/nychealth/coronavirus-data/blob/master/totals/antibody-by-poverty.csv Exposure to COVID-19 can be detected by measuring antibodies to the disease in a person’s blood, which can indicate that a person may have had an immune response to the virus. Antibodies are proteins produced by the body’s immune system that can be found in the blood. People can test positive for antibodies after they have been exposed, sometimes when they no longer test positive for the virus itself. It is important to note that the science around COVID-19 antibody tests is evolving rapidly and there is still much uncertainty about what individual antibody test results mean for a single person and what population-level antibody test results mean for understanding the epidemiology of COVID-19 at a population level. These data only provide information on people tested. People receiving an antibody test do not reflect all people in New York City; therefore, these data may not reflect antibody prevalence among all New Yorkers. Increasing instances of screening programs further impact the generalizability of these data, as screening programs influence who and how many people are tested over time. Examples of screening programs in NYC include: employers screening their workers (e.g., hospitals), and long-term care facilities screening their residents. In addition, there may be potential biases toward people receiving an antibody test who have a positive result because people who were previously ill are preferentially seeking testing, in addition to the testing of persons with higher exposure (e.g., health care workers, first responders.) Neighborhood-level poverty groups were classified in a manner consistent with Health Department practices to describe and monitor disparities in health in NYC. Neighborhood poverty measures are defined as the percentage of people earning below the Federal Poverty Threshold (FPT) within a ZCTA. The standard cut-points for defining categories of neighborhood-level poverty in NYC are: • Low: <10% of residents in ZCTA living below the FPT • Medium: 10% to <20% • High: 20% to <30% • Very high: ≥30% residents living below the FPT The ZCTAs used for classification reflect the first non-missing address within NYC for each person reported with an antibody test result. Rates were calculated using interpolated intercensal population estimates updated in 2019. These rates differ from previously reported rates based on the 2000 Census or previous versions of population estimates. The Health Department produced these population estimates based on estimates from the U.S. Census Bureau and NYC Department of City Planning. Rates for poverty were calculated using direct standardization for age at diagnosis and weighting by the US 2000 standard population. Antibody tests are categorized based on the date of specimen collection and are aggregated by full weeks starting each Sunday and ending on Saturday. For example, a person whose blood was collected for antibody testing on Wednesday, May 6 would be categorized as tested during the week ending May 9. A person tested twice in one week would only be counted once in that week. This dataset includes testing data beginning April 5, 2020. Data are updated daily, and the dataset preserves historical records and source data changes, so each extract date reflects the current copy of the data as of that date. For example, an extract date of 11/04/2020 and extract date of 11/03/2020 will both contain all records as they were as of that extract date. Without filtering or grouping by extract date, an analysis will almost certain

A randomised controlled trial of an intervention designed to teach reading and language skills to children with Down syndrome. The study was conducted by researchers at Down Syndrome Education International and at The Centre for Reading and Language at the University of York. This archive includes data from a randomised controlled trial of an intervention designed to teach reading and language skills to children with Down syndrome. The study was conducted by researchers at Down Syndrome Education International and at The Centre for Reading and Language at the University of York. Our research explores how children with Down syndrome develop and learn, and identifies more effective teaching approaches and therapies. We have a unique focus and a successful 30-year track record in delivering research that today is improving educational outcomes for many thousands of young people with Down syndrome around the world. We focus on practical scientific studies of young people with Down syndrome that identify their precise learning difficulties and that evaluate teaching approaches and therapies designed to meet their particular needs. We take a 'real world' view of development, studying learning for young people with Down syndrome in everyday family life and in school. Many things influence development, including our genes, our families and communities, and the opportunities that we are offered. Our research examines all of these factors in the context of the complex and cumulative processes of human development. Trained TAs delivered a reading and language intervention to children on an individual basis in daily 40-min sessions in the children’s schools. The performance of children who received the intervention for 40 weeks (intervention group) was compared to a waiting control group who continued with their regular education during the first 20 weeks (which included one-to-one support from a TA) before receiving the intervention in just the second 20-week period. This information is included in our paper; for further information and description of the study and link to handbook and resources for teachers, please see our RLI website (Related Resources).

THIS DATASET WAS LAST UPDATED AT 8:11 PM EASTERN ON SEPT. 10

OVERVIEW

2019 had the most mass killings since at least the 1970s, according to the Associated Press/USA TODAY/Northeastern University Mass Killings Database.

In all, there were 45 mass killings, defined as when four or more people are killed excluding the perpetrator. Of those, 33 were mass shootings . This summer was especially violent, with three high-profile public mass shootings occurring in the span of just four weeks, leaving 38 killed and 66 injured.

A total of 229 people died in mass killings in 2019.

The AP's analysis found that more than 50% of the incidents were family annihilations, which is similar to prior years. Although they are far less common, the 9 public mass shootings during the year were the most deadly type of mass murder, resulting in 73 people's deaths, not including the assailants.

One-third of the offenders died at the scene of the killing or soon after, half from suicides.

About this Dataset

The Associated Press/USA TODAY/Northeastern University Mass Killings database tracks all U.S. homicides since 2006 involving four or more people killed (not including the offender) over a short period of time (24 hours) regardless of weapon, location, victim-offender relationship or motive. The database includes information on these and other characteristics concerning the incidents, offenders, and victims.

The AP/USA TODAY/Northeastern database represents the most complete tracking of mass murders by the above definition currently available. Other efforts, such as the Gun Violence Archive or Everytown for Gun Safety may include events that do not meet our criteria, but a review of these sites and others indicates that this database contains every event that matches the definition, including some not tracked by other organizations.

This data will be updated periodically and can be used as an ongoing resource to help cover these events.

Using this Dataset

To get basic counts of incidents of mass killings and mass shootings by year nationwide, use these queries:

Mass killings by year

Mass shootings by year

To get these counts just for your state:

Filter killings by state

Definition of "mass murder"

Mass murder is defined as the intentional killing of four or more victims by any means within a 24-hour period, excluding the deaths of unborn children and the offender(s). The standard of four or more dead was initially set by the FBI.

This definition does not exclude cases based on method (e.g., shootings only), type or motivation (e.g., public only), victim-offender relationship (e.g., strangers only), or number of locations (e.g., one). The time frame of 24 hours was chosen to eliminate conflation with spree killers, who kill multiple victims in quick succession in different locations or incidents, and to satisfy the traditional requirement of occurring in a “single incident.”

Offenders who commit mass murder during a spree (before or after committing additional homicides) are included in the database, and all victims within seven days of the mass murder are included in the victim count. Negligent homicides related to driving under the influence or accidental fires are excluded due to the lack of offender intent. Only incidents occurring within the 50 states and Washington D.C. are considered.

Methodology

Project researchers first identified potential incidents using the Federal Bureau of Investigation’s Supplementary Homicide Reports (SHR). Homicide incidents in the SHR were flagged as potential mass murder cases if four or more victims were reported on the same record, and the type of death was murder or non-negligent manslaughter.

Cases were subsequently verified utilizing media accounts, court documents, academic journal articles, books, and local law enforcement records obtained through Freedom of Information Act (FOIA) requests. Each data point was corroborated by multiple sources, which were compiled into a single document to assess the quality of information.

In case(s) of contradiction among sources, official law enforcement or court records were used, when available, followed by the most recent media or academic source.

Case information was subsequently compared with every other known mass murder database to ensure reliability and validity. Incidents listed in the SHR that could not be independently verified were excluded from the database.

Project researchers also conducted extensive searches for incidents not reported in the SHR during the time period, utilizing internet search engines, Lexis-Nexis, and Newspapers.com. Search terms include: [number] dead, [number] killed, [number] slain, [number] murdered, [number] homicide, mass murder, mass shooting, massacre, rampage, family killing, familicide, and arson murder. Offender, victim, and location names were also directly searched when available.

This project started at USA TODAY in 2012.

Contacts

Contact AP Data Editor Justin Myers with questions, suggestions or comments about this dataset at jmyers@ap.org. The Northeastern University researcher working with AP and USA TODAY is Professor James Alan Fox, who can be reached at j.fox@northeastern.edu or 617-416-4400.

Please cite our paper if you publish material based on those datasets

G. Khodabandelou, V. Gauthier, M. El-Yacoubi, M. Fiore, "Estimation of Static and Dynamic Urban Populations with Mobile Network Metadata", in IEEE Trans. on Mobile Computing, 2018 (in Press). 10.1109/TMC.2018.2871156

Abstract

Communication-enabled devices that are physically carried by individuals are today pervasive,
which opens unprecedented opportunities for collecting digital metadata about the mobility of large populations. In this paper, we propose a novel methodology for the estimation of people density at metropolitan scales, using subscriber presence metadata collected by a mobile operator. We show that our approach suits the estimation of static population densities, i.e., of the distribution of dwelling units per urban area contained in traditional censuses. Specifically, it achieves higher accuracy than that granted by previous equivalent solutions. In addition, our approach enables the estimation of dynamic population densities, i.e., the time-varying distributions of people in a conurbation. Our results build on significant real-world mobile network metadata and relevant ground-truth information in multiple urban scenarios.

Dataset Columns

This dataset cover one month of data taken during the month of April 2015 for three Italian cities: Rome, Milan, Turin. The raw data has been provided during the Telecom Italia Big Data Challenge (http://www.telecomitalia.com/tit/en/innovazione/archivio/big-data-challenge-2015.html)

1. grid_id: the coordinate of the grid can be retrieved with the shapefile of a given city
2. date: format Y-M-D H:M:S
4. landuse_label: the land use label has been computed by through method described in [2]
5. population: Census population of a given grid block as defined by the Istituto nazionale di statistica (ISTAT https://www.istat.it/en/censuses) in 2011
6. estimation: Dynamics density population estimation (in person) as the result of the method described in [1]
7. area: surface of the "grid id" considered in km^2
8. geometry: the shape of the area considered with the EPSG:3003 coordinate system (only with quilt)

Note

Due to legal constraints, we cannot share directly the original data from the Telecom Italia Big Data Challenge we used to build this dataset.

Easy access to this dataset with quilt

Install the dataset repository:

$ quilt install vgauthier/DynamicPopEstimate

Use the dataset with a Panda Dataframe

>>> from quilt.data.vgauthier import DynamicPopEstimate
>>> import pandas as pd
>>> df = pd.DataFrame(DynamicPopEstimate.rome())

Use the dataset with a GeoPanda Dataframe

>>> from quilt.data.vgauthier import DynamicPopEstimate
>>> import geopandas as gpd
>>> df = gpd.DataFrame(DynamicPopEstimate.rome())

References

[1] G. Khodabandelou, V. Gauthier, M. El-Yacoubi, M. Fiore, "Population estimation from mobile network traffic metadata", in proc of the 17th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), pp. 1 - 9, 2016.

[2] A. Furno, M. Fiore, R. Stanica, C. Ziemlicki, and Z. Smoreda, "A tale of ten cities: Characterizing signatures of mobile traffic in urban areas," IEEE Transactions on Mobile Computing, Volume: 16, Issue: 10, 2017.

This dataset contains responses from an online survey of 2187 participants primarily located in the UK. All participants stated that they had used the UK National Health Service (NHS) at some time in their lives. The data were collected between December 2018 and August 2019. Participants' views on data sharing - this dataset contains information about people's willingness to share mental and physical health data for research purposes. It also includes information on willingness to share other types of data, such as financial information. The dataset includes participants' responses to questions relating to mental health data sharing, including the trustworthiness of organisations which use such data, how much the presence of different governance measures (such as deidentification, opt-out, etc.) would alter their views, and whether they would be less likely to access NHS mental health services if they knew their data might be shared with researchers. Participants' satisfaction and interaction with UK mental and physical health services - the dataset includes information regarding participants' views on and interaction with NHS services. This includes ratings of satisfaction at first contact and in the previous 12 months, frequency of use, and type of treatment received. Information about participants - the dataset includes information about participants' mental and physical health, including whether or not they have experience with specific mental health conditions, and how they would rate their mental and physical health at the time of the survey. There is also basic demographic information about the participants (e.g. age, gender, location etc.). ## This item has been replaced by the one which can be found at https://hdl.handle.net/10283/4467 ##

John Ioannidis and co-authors [1] created a publicly available database of top-cited scientists in the world. This database, intended to address the misuse of citation metrics, has generated a lot of interest among the scientific community, institutions, and media. Many institutions used this as a yardstick to assess the quality of researchers. At the same time, some people look at this list with skepticism citing problems with the methodology used. Two separate databases are created based on career-long and, single recent year impact. This database is created using Scopus data from Elsevier[1-3]. The Scientists included in this database are classified into 22 scientific fields and 174 sub-fields. The parameters considered for this analysis are total citations from 1996 to 2022 (nc9622), h index in 2022 (h22), c-score, and world rank based on c-score (Rank ns). Citations without self-cites are considered in all cases (indicated as ns). In the case of a single-year case, citations during 2022 (nc2222) instead of Nc9622 are considered.

To evaluate the robustness of c-score-based ranking, I have done a detailed analysis of the matrix parameters of the last 25 years (1998-2022) of Nobel laureates of Physics, chemistry, and medicine, and compared them with the top 100 rank holders in the list. The latest career-long and single-year-based databases (2022) were used for this analysis. The details of the analysis are presented below: Though the article says the selection is based on the top 100,000 scientists by c-score (with and without self-citations) or a percentile rank of 2% or above in the sub-field, the actual career-based ranking list has 204644 names[1]. The single-year database contains 210199 names. So, the list published contains ~ the top 4% of scientists. In the career-based rank list, for the person with the lowest rank of 4809825, the nc9622, h22, and c-score were 41, 3, and 1.3632, respectively. Whereas for the person with the No.1 rank in the list, the nc9622, h22, and c-score were 345061, 264, and 5.5927, respectively. Three people on the list had less than 100 citations during 96-2022, 1155 people had an h22 less than 10, and 6 people had a C-score less than 2.
In the single year-based rank list, for the person with the lowest rank (6547764), the nc2222, h22, and c-score were 1, 1, and 0. 6, respectively. Whereas for the person with the No.1 rank, the nc9622, h22, and c-score were 34582, 68, and 5.3368, respectively. 4463 people on the list had less than 100 citations in 2022, 71512 people had an h22 less than 10, and 313 people had a C-score less than 2. The entry of many authors having single digit H index and a very meager total number of citations indicates serious shortcomings of the c-score-based ranking methodology. These results indicate shortcomings in the ranking methodology.

Abstract copyright UK Data Service and data collection copyright owner.The Skills Survey is a series of nationally representative sample surveys of individuals in employment aged 20-60 years old (since 2006, the surveys have additionally sampled those aged 61-65). The surveys aim to investigate the employed workforce in Great Britain. Although they were not originally planned as part of a series and had different funding sources and objectives, continuity in questionnaire design has meant the surveys now provide a unique, national representative picture of change in British workplaces as reported by individual job holders. This allows analysts to examine how various aspects of job quality and skill levels have changed over 30 years.The first surveys in the series were carried out in 1986 and 1992. These surveys also form part of this integrated data series, and are known as the Social Change and Economic Life Initiative (SCELI) and Employment in Britain (EIB) studies respectively.The 1997 survey was the first to collect primarily data on skills using the job requirements approach. This focused on collecting data on objective indicators of job skill as reported by respondents. The 2001 survey assessed how much had changed between the two surveys and a third survey in 2006 enhanced the time series data, while providing a resource for analysing skill and job requirements in the British economy at that time. The 2012 survey aimed to again add to the time series data and, coinciding as it did with a period of economic recession, to provide insight into whether workers in Britain felt under additional pressure/demand from employers as a result of redundancies and cut backs. In addition, a series dataset, covering 1986, 1992, 1997, 2001, 2006 and 2012 is also available . A follow-up to the 2012 survey was conducted in 2014, revisiting respondents who had agreed to be interviewed again. The 2017 survey was the seventh in the series, designed to examine to what extent pressures had continued as a result of austerity and economic uncertainties triggered, for example, by Brexit as well as examining additional issues such as productivity, fairness at work and the retirement intentions of older workers.Each survey comprises a large number of respondents: 4,047 in the 1986 survey; 3,855 in 1992; 2,467 in 1997; 4,470 in 2001; 7,787 in 2006; 3,200 in 2012; and 3,306 in 2017. The Skills Survey, 1997 was carried out in 1997 as part of the ESRC’s ‘Learning Society’ programme of research, was designed to extend the evidence about trends over time in ‘broad skills’ such as the qualifications required for job entry, the length of time it takes to train and the period taken to learn to do a job well. The Skills Survey, 1997 had the following objectives:to develop further the concept of and methodology for measuring different types of skills using an individual-based surveyto investigate the impact of various antecedents on skills, including personal characteristics and, especially, forms of education and trainingto investigate the impact of the possession of various skills on payto investigate which skills are changing during the 1990s, and to what extentto investigate how skills are distributed among the employed population and how far the pattern of skill and skill change corresponds to a learning society, and consider appropriate policy conclusions Main Topics: The dataset contains information on people in work and their jobs. The topic areas covered include:job detailsjob analysisattitudes and management skillscompetencetransferability of skills, pay and qualificationsjob held five years agodemographics Multi-stage stratified random sample Face-to-face interview

Context

On January 30, 2020, the International Health Regulations Emergency Committee of the World Health Organization declared the outbreak a public health emergency of international concern (PHEIC). On January 31, 2020, Health and Human Services Secretary Alex M. Azar II declared a public health emergency (PHE) for the United States to aid the nation’s healthcare community in responding to COVID-19. On March 11, 2020 WHO publicly characterized COVID-19 as a pandemic.

Content

The data files present the total confirmed cases, total deaths and daily new cases and deaths by country. This data is sourced from the World Health Organization (WHO) Situation Reports (which you find here). The WHO Situation Reports are published daily [reporting data as of 10am (CET; Geneva time)]. The main section of the Situations Reports are long tables of the latest number of confirmed cases and confirmed deaths by country.

This dataset has five files : - total_cases.csv : Total confirmed cases - total_deaths.csv : Total deaths - new_cases.csv : New confirmed cases - new_deathes.csv : New deaths - full_data.csv : put it all files together

Acknowledgements

This dataset is sourced from WHO and confirmed by OurworldInData Special Thank to Hannah Ritchie that did a great reports explaining those datasets.

Inspiration

Insights on - Confirmed cases is what we do know - Confirmed COVID-19 cases by country - How we can make preventive measures - Growth of cases: How long did it take for the number of confirmed cases to double? - Understanding exponential growth - Try to predict the spread of COVID-19 ahead of time .

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

Cities can be tremendously efficient. It is easier to provide water and sanitation to people living closer together, while access to health, education, and other social and cultural services is also much more readily available. However, as cities grow, the cost of meeting basic needs increases, as does the strain on the environment and natural resources. Data on urbanization, traffic and congestion, and air pollution are from the United Nations Population Division, World Health Organization, International Road Federation, World Resources Institute, and other sources.

This dataset contains crash information from the last five years to the current date. The data is based on the National Incident Based Reporting System (NIBRS). The data is dynamic, allowing for additions, deletions and modifications at any time, resulting in more accurate information in the database. Due to ongoing and continuous data entry, the numbers of records in subsequent extractions are subject to change.About Crash DataThe Cary Police Department strives to make crash data as accurate as possible, but there is no avoiding the introduction of errors into this process, which relies on data furnished by many people and that cannot always be verified. As the data is updated on this site there will be instances of adding new incidents and updating existing data with information gathered through the investigative process.Not surprisingly, crash data becomes more accurate over time, as new crashes are reported and more information comes to light during investigations.This dynamic nature of crash data means that content provided here today will probably differ from content provided a week from now. Likewise, content provided on this site will probably differ somewhat from crime statistics published elsewhere by the Town of Cary, even though they draw from the same database.About Crash LocationsCrash locations reflect the approximate locations of the crash. Certain crashes may not appear on maps if there is insufficient detail to establish a specific, mappable location.