DPH note about change from 7-day to 14-day metrics: As of 10/15/2020, this dataset is no longer being updated. Starting on 10/15/2020, these metrics will be calculated using a 14-day average rather than a 7-day average. The new dataset using 14-day averages can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/hree-nys2 As you know, we are learning more about COVID-19 all the time, including the best ways to measure COVID-19 activity in our communities. CT DPH has decided to shift to 14-day rates because these are more stable, particularly at the town level, as compared to 7-day rates. In addition, since the school indicators were initially published by DPH last summer, CDC has recommended 14-day rates and other states (e.g., Massachusetts) have started to implement 14-day metrics for monitoring COVID transmission as well. With respect to geography, we also have learned that many people are looking at the town-level data to inform decision making, despite emphasis on the county-level metrics in the published addenda. This is understandable as there has been variation within counties in COVID-19 activity (for example, rates that are higher in one town than in most other towns in the county). This dataset includes a weekly count and weekly rate per 100,000 population for COVID-19 cases, a weekly count of COVID-19 PCR diagnostic tests, and a weekly percent positivity rate for tests among people living in community settings. Dates are based on date of specimen collection (cases and positivity). A person is considered a new case only upon their first COVID-19 testing result because a case is defined as an instance or bout of illness. If they are tested again subsequently and are still positive, it still counts toward the test positivity metric but they are not considered another case. These case and test counts do not include cases or tests among people residing in congregate settings, such as nursing homes, assisted living facilities, or correctional facilities. These data are updated weekly; the previous week period for each dataset is the previous Sunday-Saturday, known as an MMWR week (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf). The date listed is the date the dataset was last updated and corresponds to a reporting period of the previous MMWR week. For instance, the data for 8/20/2020 corresponds to a reporting period of 8/9/2020-8/15/2020. Notes: 9/25/2020: Data for Mansfield and Middletown for the week of Sept 13-19 were unavailable at the time of reporting due to delays in lab reporting.

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

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

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

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.

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

Abstract

This dataset contains statistics on deaths in South Africa in 2009. The registration of deaths in South Africa is regulated by the Births and Deaths Registration Act, 51 of 1992. The South African Department of Home Affairs (DHA) is responsible for the registration of deaths in South Africa. The data is collected with two instruments: The death register and the medical certificate in respect of death. The staff of the DHA Registrar of Deaths section fills in the former while the medical practitioner attending to the death completes the latter. Causes of death are coded by the Department of Home Affairs according to the tenth revision of the International Classification of Diseases (ICD-10) ICD-10, as required by the World Health Organization for their member countries. The data is used by the Department of Home Affairs to update the Population Register. The forms are sent to Statistics South Africa (Stats SA) for their use for statistical purposes. From the two forms sent to Stats SA, the following data items of the deceased are extracted: place of residence, place of death, date of death, month and year of registration, sex, marital status, occupation, underlying cause of death, whether or not the death was certified by a medical practitioner, and whether or not the deceased died in a health institution or nursing home. From 1991 death notifications do not require data on population group, and therefore this dataset includes death data for all population groups. This dataset excludes 2010 deaths that were not registered, and late registrations which would not have been available to Stats SA in time for the production of the dataset.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

The data covers all deaths that occurred in 2009 and registered at the Department of Home Affairs.

Kind of data

Administrative records data [adm]

Mode of data collection

Other [oth]

Research instrument

The data is collected with notification / death register / still birth instrument.

Overview

The publication of the data and story is EMBARGOED until 3:01 a.m. ET on Monday, April 4, 2022. It is intended for print publication on or after April 4. The data may be used for reporting immediately.

This dataset includes a combined set of congressional earmarks tied to the $1.5 trillion federal spending bill passed in March 2022.

The source documents were released by the relevant appropriations committees as PDF files. The AP has extracted the information and compiled a spreadsheet with all 4,975 listed projects. Each row lists the federal agency and program that administers the money. There’s also a description of the project — sometimes frustratingly vague — and its location, the amount approved and the House and Senate members who requested them.

The data can be searched and sorted to see who got what. Many projects were requested by multiple lawmakers, but there’s no double-counting — each project is listed only once. Some projects may have lawmakers from only one chamber doing the requesting, while others can have members from both the House and Senate. If a lawmaker’s name does not appear at all in the dataset, that means they didn’t receive projects.

The data accompanies a story published on April 4, 2022 that detailed the spending earmarked by members of Congress in the latest federal funding bill passed and signed by the President. The story found:

• "Home-district projects for members of Congress are back, sprinkled across the government-wide $1.5 trillion bill President Joe Biden signed recently. The official tally shows amounts modest by past standards yet spread widely around the country — and that understate what lawmakers are claiming credit for.
• The bipartisan measure, financing federal agencies this year, contains 4,975 such projects worth $9.7 billion, according to an Associated Press examination of items attributed to specific lawmakers in documents accompanying the bill. The listed projects, long called earmarks, ranged from $4,000 for evidence detection equipment for Huntington, West Virginia, to $350 million to help restore Florida’s vast but imperiled Everglades.

• The projects' reemergence after an 11-year hiatus, with transparency requirements and other curbs, marks a revival of expenditures that let lawmakers tout achievements to voters and help party leaders build support for legislation. While still vilified by some, especially conservatives, as emblems of influence peddling and wasteful spending, they've been embraced by lawmakers from both parties, who cite Congress’ constitutional power of the purse and say they know their local needs."

  Findings

• There were 4,975 earmarked projects worth a total of $9.7 billion included.

• Retiring Sen. Richard Shelby attained $126 million for two campuses of the University of Alabama, his alma mater, including for an endowment for its flagship Tuscaloosa campus to hire science and engineering faculty. There was also hundreds of millions to improve the city of Mobile's seaport and airport, part of a total $648 million he amassed for his state.

• Senate Majority Leader Chuck Schumer, D-N.Y., had 203 projects for New York, ranging from $27 million to upgrade Fort Drum's water systems to $44,000 for neighborhood improvements in the city of Geneva, the AP found. Facing what should be easy reelection this fall, Schumer totaled $314 million, including at least $23 million for hospitals, violence prevention and other programs in his home borough of Brooklyn.

• Of five senators facing tough reelection races this fall, three Democrats received at least $81 million each in projects: Sens. Mark Kelly of Arizona, Catherine Cortez Masto of Nevada and Raphael Warnock of Georgia. Two others, Sens. Maggie Hassan, D-N.H., and Ron Johnson, R-Wis., requested and received none.

• While House Minority Leader Kevin McCarthy, R-Calif., wasn't listed as getting any projects, his top two lieutenants were. No. 2 leader Steve Scalise, R-La., got $31 million, including $5 million for Louisiana State University aerospace research. No. 3 GOP leader Elise Stefanik, R-N.Y., won $35 million, including sharing credit with Schumer and Gillibrand for improving Fort Drum's $27 million water project.

  About This Data

  The original source of the data was 10 PDF files which can be found here: https://www.appropriations.senate.gov/imo/media/doc/AG CPF CDS FINAL FOR STATEMENT.pdf https://www.appropriations.senate.gov/imo/media/doc/CJS_CDS_V6.pdf https://www.appropriations.senate.gov/imo/media/doc/Defense_CDS.pdf https://www.appropriations.senate.gov/imo/media/doc/EW_CDSV5.pdf https://www.appropriations.senate.gov/imo/media/doc/FSGG Printed CDS Table.pdf https://www.appropriations.senate.gov/imo/media/doc/HOMELAND_CDS.pdf https://www.appropriations.senate.gov/imo/media/doc/INT_CDS_V3.PDF https://www.appropriations.senate.gov/imo/media/doc/LHHS_CDS_V3 (GPO Turn 3-5).pdf https://www.appropriations.senate.gov/imo/media/doc/MilCon_CDS.pdf https://www.appropriations.senate.gov/imo/media/doc/THUD_CDS_V5.pdf

Each file contained earmarks tied to a certain appropriations subject area or funding steam, such as agriculture, defense or transportation.

The AP extracted the information from these documents and then combined them into a single dataset. Several of the documents included additional columns that were not present in the majority of the appropriations tables, and yet others included columns with different names and/or that were filled in with slightly different information.

The AP reconciled and consolidated these disparate columns together to create a dataset including the most relevant pieces of information in a standardized way.

Please note the source documents identified requesting members of congress by their name alone, and that multiple House or Senate requestors were listed together in a single column. The AP did not change that method for this data release. This means you can filter by a member's name to see all of his or her earmarks, however you won't be able to create "top 10 members" or "top 10 states" rankings using the file alone as it is provided. (For the initial story accompanying this data release, the AP also relied on separate work by the nonprofit group Taxpayers for Common Sense who had conducted work to standardize and match up the member names to states and legislative votes. Reporters may contact them as well to obtain the TCS dataset if you should wish.)

​

Included Data

earmarks_combined - An Excel spreadsheet containing the earmarked projects

Additional Data Queries

Filter by project location

Filter by requestor in either chamber

Filter by Senate requestor

Filter by House requestor

Total dollars earmarked by appropriations category

Using This Data

Notes and suggestions for reporters on using this data for their own stories

Caveats

• Please note the source documents identified requesting members of congress by their name alone, and that multiple House or Senate requestors were listed together in a single column. The AP did not change that method for this data release. This means you can filter by a member's name to see all of his or her earmarks, however you won't be able to create "top 10 members" or "top 10 states" rankings using the file alone as it is provided. (For the initial story accompanying this data release, the AP also relied on separate work by the nonprofit group Taxpayers for Common Sense who had conducted work to standardize and match up the member names to states and legislative votes. Reporters may contact them as well to obtain the TCS dataset if you should wish.)
• Some earmarks may have resulted from a request by the Biden White House itself, though only a small number of the appropriations documents provided information on those instances. The AP’s standardized dataset includes only the ultimate total requested for the project, even if in some instances part or all of that earmark originated with the Biden administration. While this affects only a small number of records, some notable instances include the $350 million Everglades restoration project, requested by Rep. Brian Mast, R-Fla., and at least $99 million that Sen. Shelby procured was also proposed by the president.
• Location information can be a bit fluid in how it was documented by the appropriations committees. Sometimes a city,

Number and percentage of live births, by month of birth, 1991 to most recent year.

The Gross Domestic Product (GDP) in China was worth 17794.78 billion US dollars in 2023, according to official data from the World Bank. The GDP value of China represents 16.88 percent of the world economy. This dataset provides - China GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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