This dataset shows daily confirmed and probable cases of COVID-19 in New York City by date of specimen collection. Total cases has been calculated as the sum of daily confirmed and probable cases. Seven-day averages of confirmed, probable, and total cases are also included in the dataset. A person is classified as a confirmed COVID-19 case if they test positive with a nucleic acid amplification test (NAAT, also known as a molecular test; e.g. a PCR test). A probable case is a person who meets the following criteria with no positive molecular test on record: a) test positive with an antigen test, b) have symptoms and an exposure to a confirmed COVID-19 case, or c) died and their cause of death is listed as COVID-19 or similar. As of June 9, 2021, people who meet the definition of a confirmed or probable COVID-19 case >90 days after a previous positive test (date of first positive test) or probable COVID-19 onset date will be counted as a new case. Prior to June 9, 2021, new cases were counted ≥365 days after the first date of specimen collection or clinical diagnosis. Any person with a residence outside of NYC is not included in counts. Data is sourced from electronic laboratory reporting from the New York State Electronic Clinical Laboratory Reporting System to the NYC Health Department. All identifying health information is excluded from the dataset. These data are used to evaluate the overall number of confirmed and probable cases by day (seven day average) to track the trajectory of the pandemic. Cases are classified by the date that the case occurred. NYC COVID-19 data include people who live in NYC. Any person with a residence outside of NYC is not included.

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

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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

Contact tracing close contacts includes an interview with the initial case to collect basic information, identify contacts, and provide resources and instructions for isolation. Contact tracing is not conducted for deceased individuals, or residents of jails and long term care facilities. These cases are excluded from this calculation, and are handled in separate and specialized health investigations. Close contacts without valid contact information are not included in the metric. If contact information is identified at a later date, the contact is included in the metric at that time, even though it may have passed the ideal contact window. An individual can be a close contact of multiple positive cases. Three contact attempts are made before a contact is marked loss-to-follow up. The moving average of the percentage of close contacts with a contact attempt within two days will be calculated using a 7-day window, inclusive of the end date. The result will be a 7-day average weighted by the number of cases on that day. Currently, since there are too few days to calculate a 7-day average, each day will build, from a 3-day average on June 14, a 4-day average on June 15, etc., through a 7-day average starting on June 18. We begin reporting on June 12 as data were transitioned to the new contact tracing system between Jun 3rd - Jun 11th, which prevented our ability to accurately estimate the number of call attempts during the transition period. We will build up to a 7-day average on the June 18 notification date. Data are subject to change on a daily basis and reported at a 4-day lag for proper analysis. This data is used to calculate the Reopening DC metric with the percentage of close contacts of positive cases with at least one contact attempt is made within two days of case notification to DC Health.

Florida COVID-19 Cases by Zip Code exported from the Florida Department of Health GIS Layer on date seen in file name. Archived by the University of South Florida Libraries, Digital Heritage and Humanities Collections. Contact: LibraryGIS@usf.edu.Please Cite Our GIS HUB. If you are a researcher or other utilizing our Florida COVID-19 HUB as a tool or accessing and utilizing the data provided herein, please provide an acknowledgement of such in any publication or re-publication. The following citation is suggested: University of South Florida Libraries, Digital Heritage and Humanities Collections. 2020. Florida COVID-19 Hub. Available at https://covid19-usflibrary.hub.arcgis.com/.https://doi.org/10.5038/USF-COVID-19-GISLive FDOH Data Source: https://services1.arcgis.com/CY1LXxl9zlJeBuRZ/arcgis/rest/services/Florida_Cases_Zips_COVID19/FeatureServerFor data 5/10/2020 or after: Archived data was exported directly from the live FDOH layer into the archive. For data prior to 5/10/2020: Data was exported by the University of South Florida - Digital Heritage and Humanities Collection using ArcGIS Pro Software. Data was then converted to shapefile and csv and uploaded into ArcGIS Online archive. For data definitions please visit the following box folder: https://usf.box.com/s/vfjwbczkj73ucj19yvwz53at6v6w614hData definition files names include the relative date they were published. The below information was taken from ancillary documents associated with the original layer from FDOH.Q. How is the zip code assigned to a person or case? Cases are counted in a zip code based on residential or mailing address, or by healthcare provider or lab address if other addresses are missing.Q. Why is the city data and the zip code data different? The zip code data is supplied to a healthcare worker, case manager, or lab technician by each individual during intake when a test is first recorded. When entering a zip code, the system we use automatically produces a list of cities within that zip code for the individual to further specify where they live. Sometimes the individual uses the postal city, which may be Miami, when in reality that person lives outside the City of Miami boundaries in the jurisdiction of Coral Gables. Many zip codes contain multiple city/town jurisdictions, and about 20% of zip codes overlap more than one county. Q: How is the Zip Code data calculated and/or shown? If a COUNTY has five or more cases (total): • In zip codes with fewer than 5 cases, the total number of cases is shown as “<5”. • Zip codes with 0 cases in these counties are “0" or "No cases.” • All values of 5 or greater are shown by the actual number of cases in that zip code. If a COUNTY has fewer than five total cases across all of its zip codes, then ALL of the zip codes within that county show the total number of cases as "Suppressed." Q: My zip code says "SUPPRESSED" under cases. What does that mean? IF Suppressed: This county currently has fewer than five cases across all zip codes in the county. In an effort to protect the privacy of our COVID-19-Positive residents, zip code data is only available in counties where five or more cases have been reported. Q: What about PO Box zip codes, or zip codes with letters, like 334MH? PO Box zip codes are not shown in the map. “Filler” zip codes with letters, like 334MH, are typically areas where no or very few people live – like the Florida Everglades, and are shown on the map like any other zip code. Key Data about Cases by Zip Code: ZIP = The zip code COUNTYNAME = The county for the zip code (multi-part counties have been split) ZIPX = The unique county-zip identifier used to pair the data during updates POName = The postal address name assigned to the zip code place_labels = A list of the municipalities intersecting the zip code boundary c_places = The list of cities cases self-reported as being residents of Cases_1 = The number of cases in each zip code, with conditions*LabelY = A calculated field for map display only. All questions regarding this dataset should be directed to the Florida Department of Health.

NOTE: This dataset has been retired and marked as historical-only.

This dataset is a companion to the COVID-19 Daily Cases and Deaths dataset (https://data.cityofchicago.org/d/naz8-j4nc). The major difference in this dataset is that the case, death, and hospitalization corresponding rates per 100,000 population are not those for the single date indicated. They are rolling averages for the seven-day period ending on that date. This rolling average is used to account for fluctuations that may occur in the data, such as fewer cases being reported on weekends, and small numbers. The intent is to give a more representative view of the ongoing COVID-19 experience, less affected by what is essentially noise in the data.

All rates are per 100,000 population in the indicated group, or Chicago, as a whole, for “Total” columns.

Only Chicago residents are included based on the home address as provided by the medical provider.

Cases with a positive molecular (PCR) or antigen test are included in this dataset. Cases are counted based on the date the test specimen was collected. Deaths among cases are aggregated by day of death. Hospitalizations are reported by date of first hospital admission. Demographic data are based on what is reported by medical providers or collected by CDPH during follow-up investigation.

Denominators are from the U.S. Census Bureau American Community Survey 1-year estimate for 2018 and can be seen in the Citywide, 2018 row of the Chicago Population Counts dataset (https://data.cityofchicago.org/d/85cm-7uqa).

All data are provisional and subject to change. Information is updated as additional details are received and it is, in fact, very common for recent dates to be incomplete and to be updated as time goes on. At any given time, this dataset reflects cases and deaths currently known to CDPH.

Numbers in this dataset may differ from other public sources due to definitions of COVID-19-related cases and deaths, sources used, how cases and deaths are associated to a specific date, and similar factors.

Data Source: Illinois National Electronic Disease Surveillance System, Cook County Medical Examiner’s Office, U.S. Census Bureau American Community Survey

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.

The data in this dataset are monthly listings of the number of Temporary Assistance for Needy Families (TANF), Safety Net Assistance-Maintenance of Effort (SNA-MOE), and Safety Net Assistance Non-Maintenance of Effort (SNA Non-MOE) cash assistance cases with earned monthly income, and the average gross earned monthly income and average net earned monthly income (after applying earned income disregards) for these cases. Data is presented by case type for each local social services district. The dataset is from the NYS Office of Temporary and Disability Assistance (OTDA) and is updated monthly.

The data in this dataset are monthly listings of the number of Temporary Assistance for Needy Families (TANF), Safety Net Assistance-Maintenance of Effort (SNA-MOE), and Safety Net Assistance Non-Maintenance of Effort (SNA Non-MOE) cash assistance cases with earned monthly income, and the average gross earned monthly income and average net earned monthly income (after applying earned income disregards) for these cases. Data is presented by case type for each local social services district. The dataset is from the NYS Office of Temporary and Disability Assistance (OTDA) and is updated monthly.

This is the place to look for important information about how to use this dataset, so please expand this box and read on!

This is the source data for some of the metrics available at https://www.chicago.gov/city/en/sites/covid-19/home/latest-data.html.

For all datasets related to COVID-19, see https://data.cityofchicago.org/browse?limitTo=datasets&sortBy=alpha&tags=covid-19.

This dataset is a companion to the COVID-19 Daily Cases and Deaths dataset (https://data.cityofchicago.org/d/naz8-j4nc). The major difference in this dataset is that the case, death, and hospitalization corresponding rates per 100,000 population are not those for the single date indicated. They are rolling averages for the seven-day period ending on that date. This rolling average is used to account for fluctuations that may occur in the data, such as fewer cases being reported on weekends, and small numbers. The intent is to give a more representative view of the ongoing COVID-19 experience, less affected by what is essentially noise in the data.

All rates are per 100,000 population in the indicated group, or Chicago, as a whole, for “Total” columns.

Only Chicago residents are included based on the home address as provided by the medical provider.

Cases with a positive molecular (PCR) or antigen test are included in this dataset. Cases are counted based on the date the test specimen was collected. Deaths among cases are aggregated by day of death. Hospitalizations are reported by date of first hospital admission. Demographic data are based on what is reported by medical providers or collected by CDPH during follow-up investigation.

Denominators are from the U.S. Census Bureau American Community Survey 1-year estimate for 2018 and can be seen in the Citywide, 2018 row of the Chicago Population Counts dataset (https://data.cityofchicago.org/d/85cm-7uqa).

All data are provisional and subject to change. Information is updated as additional details are received and it is, in fact, very common for recent dates to be incomplete and to be updated as time goes on. At any given time, this dataset reflects cases and deaths currently known to CDPH.

Numbers in this dataset may differ from other public sources due to definitions of COVID-19-related cases and deaths, sources used, how cases and deaths are associated to a specific date, and similar factors.

Data Source: Illinois National Electronic Disease Surveillance System, Cook County Medical Examiner’s Office, U.S. Census Bureau American Community Survey

The Kudos dataset (extracted from Kudos in February 2016) is analysed in the research article with the title "Analysing researchers' outreach efforts and the association with publication metrics: A case study of Kudos". This research paper is a result of a joint research collaboration between Kudos and CHESS, Nanyang Technological University, Singapore. Kudos made funds available to CHESS to perform the study and also provided the dataset used for the analysis.In recent years, social media and scholarly collaboration networks have become increasingly accepted as effective tools for discovering and sharing research. Altmetrics are also becoming more common, as they reflect impact fast, are openly accessible and represent both academic and lay audiences, unlike traditional metrics such as citation counts. As a researcher, it still remains challenging to know whether the efforts to increase the visibility and outreach of your research on social media are associated with improved publication metrics.In this paper, we analyse the effectiveness of common online channels used for sharing publications using Kudos (https://www.growkudos.com, launched in May 2014), a web-based service that aims to help researchers increase the outreach of their publications, as a case study. We extracted a dataset from Kudos of 20,775 unique publications that had been claimed by authors, and for which actions had been taken to explain or share via Kudos. For 4,867 of these, full text download data from publishers was available. Our findings show that researchers are most likely to share their work on Facebook, but links shared on Twitter are most likely to be clicked on. A Mann-Whitney U test revealed that a treatment group (publications having actions in Kudos) had a significantly higher median average of 149 full text downloads (23.1% more) per publication as compared to a control group (having no actions in Kudos) with a median average of 121 full text downloads per publication. These findings suggest that performing actions on publications, such as sharing, explaining, or enriching, could help to increase the number of full text downloads of a publication.The DOIs of the publications in the dataset have been anonymised to protect the privacy of the users in Kudos. A readme text file is provided describing the data fields of the four datasets.All fields in the CSV file should be imported (e.g., into Excel) as text values.

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days. In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 hourly data on single levels from 1940 to present".

BR: Survey Mean Consumption or Income per Capita: Total Population: 2011 PPP per day data was reported at 20.390 Intl $/Day in 2019. This records an increase from the previous number of 20.250 Intl $/Day for 2014. BR: Survey Mean Consumption or Income per Capita: Total Population: 2011 PPP per day data is updated yearly, averaging 20.320 Intl $/Day from Dec 2014 (Median) to 2019, with 2 observations. The data reached an all-time high of 20.390 Intl $/Day in 2019 and a record low of 20.250 Intl $/Day in 2014. BR: Survey Mean Consumption or Income per Capita: Total Population: 2011 PPP per day data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Brazil – Table BR.World Bank.WDI: Social: Poverty and Inequality. Mean consumption or income per capita (2011 PPP $ per day) used in calculating the growth rate in the welfare aggregate of total population.; ; World Bank, Global Database of Shared Prosperity (GDSP) (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).; ; The choice of consumption or income for a country is made according to which welfare aggregate is used to estimate extreme poverty in the Poverty and Inequality Platform (PIP). The practice adopted by the World Bank for estimating global and regional poverty is, in principle, to use per capita consumption expenditure as the welfare measure wherever available; and to use income as the welfare measure for countries for which consumption is unavailable. However, in some cases data on consumption may be available but are outdated or not shared with the World Bank for recent survey years. In these cases, if data on income are available, income is used. Whether data are for consumption or income per capita is noted in the footnotes. Because household surveys are infrequent in most countries and are not aligned across countries, comparisons across countries or over time should be made with a high degree of caution.

It is fairly well-known that proper time series analysis requires that estimated equations be balanced. Numerous scholars mistake this to mean that one cannot mix orders of integration. Previous work has clarified the distinction between equation balance and having different orders of integration, and shown that mixing orders of integration does not increase the risk of Type I error when using the GECM/ADL, that is, so long as equations are balanced (and other modeling assumptions are met). This paper builds on that research to assess the consequences for Type II error when employing those models. Specifically, we consider cases where a true relationship exists, the left- and right-hand sides of the equation mix orders of integration, and the equation still is balanced. Using the asymptotic case, we find that the different orders of integration do not preclude identification of the true relationship using the GECM/ADL. We then highlight that estimation is trickier in practice, over finite time, as data sometimes do not reveal the underlying process. But, simulations show that even in these cases, researchers will typically draw accurate inferences as long as they select their models based on the observed characteristics of the data and test to be sure that standard model assumptions are met. We conclude by considering the implications for researchers analyzing or conducting simulations with time series data.

On 6/28/2023, data on cases by vaccination status will be archived and will no longer update.

A. SUMMARY This dataset represents San Francisco COVID-19 positive confirmed cases by vaccination status over time, starting January 1, 2021. Cases are included on the date the positive test was collected (the specimen collection date). Cases are counted in three categories: (1) all cases; (2) unvaccinated cases; and (3) completed primary series cases.

1. All cases: Includes cases among all San Francisco residents regardless of vaccination status.

2. Unvaccinated cases: Cases are considered unvaccinated if their positive COVID-19 test was before receiving any vaccine. Cases that are not matched to a COVID-19 vaccination record are considered unvaccinated.

3. Completed primary series cases: Cases are considered completed primary series if their positive COVID-19 test was 14 days or more after they received their 2nd dose in a 2-dose COVID-19 series or the single dose of a 1-dose vaccine. These are also called “breakthrough cases.”

On September 12, 2021, a new case definition of COVID-19 was introduced that includes criteria for enumerating new infections after previous probable or confirmed infections (also known as reinfections). A reinfection is defined as a confirmed positive PCR lab test more than 90 days after a positive PCR or antigen test. The first reinfection case was identified on December 7, 2021.

Data is lagged by eight days, meaning the most recent specimen collection date included is eight days prior to today. All data updates daily as more information becomes available.

B. HOW THE DATASET IS CREATED Case information is based on confirmed positive laboratory tests reported to the City. The City then completes quality assurance and other data verification processes. Vaccination data comes from the California Immunization Registry (CAIR2). The California Department of Public Health runs CAIR2. Individual-level case and vaccination data are matched to identify cases by vaccination status in this dataset. Case records are matched to vaccine records using first name, last name, date of birth, phone number, and email address.

We include vaccination records from all nine Bay Area counties in order to improve matching rates. This allows us to identify breakthrough cases among people who moved to the City from other Bay Area counties after completing their vaccine series. Only cases among San Francisco residents are included.

C. UPDATE PROCESS Updates automatically at 08:00 AM Pacific Time each day.

D. HOW TO USE THIS DATASET Total San Francisco population estimates 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). To identify total San Francisco population estimates, filter the view on “demographic_category_label” = “all ages”.

Population estimates by vaccination status are derived from our publicly reported vaccination counts, which can be found at COVID-19 Vaccinations Given to SF Residents Over Time.

The dataset includes new cases, 7-day average new cases, new case rates, 7-day average new case rates, percent of total cases, and 7-day average percent of total cases for each vaccination category.

New cases are the count of cases where the positive tests were collected on that specific specimen collection date. The 7-day rolling average shows the trend in new cases. The rolling average is calculated by averaging the new cases for a particular day with the prior 6 days.

New case rates are the count of new cases per 100,000 residents in each vaccination status group. The 7-day rolling average shows the trend in case rates. The rolling average is calculated by averaging the case rate for a part

Appendix A includes an analysis of conclusion and amendment dates of tax treaties. It is part of the thesis "A Multilateral Agreement for International Taxation: Designing an instrument to modernise international tax law.'The point of Appendix A is to calculate the average time tax treaties are updated. Data from the IBFD’s tax treaty database on tax treaties, in force on 1 January 2013, was used to calculate the average time (i.e., the ‘estimated mean’) in which an OECD-member country’s tax treaty was updated. In the database, the date of conclusion of each selected tax treaty was coded, as well as the date of: (1) any new bilateral tax treaty concluded within the same tax treaty relationship; (2) a protocol to a treaty or (3) an exchange of notes or any other mutual agreement, provided that this agreement changed the wording of the tax treaty in question.Only the treaties of the founding states of the OECD were taken into account. These are: the Republic of Austria, the Kingdom of Belgium, the Dominion of Canada, the Kingdom of Denmark, the French Republic, the Federal Republic of Germany, the Kingdom of Greece, the Republic of Iceland, the Republic of Ireland, the Italian Republic, the Grand Duchy of Luxembourg, the Kingdom of the Netherlands, the Kingdom of Norway, the Portuguese Republic, the Kingdom of Spain, the Kingdom of Sweden, the Swiss Confederation, the Turkish Republic, the United Kingdom of Great Britain and Northern Ireland, and the United States of America. The count includes tax treaties of these countries with non-OECD member countries. In counting the amendments to tax treaties in force, terminated treaties which have not been followed up by a new treaty, abandoned treaties as well as treaties that were not ratified before 1-1-2013, were excluded. Moreover, all pre-war (1940) treaties were not considered.----------------------------------------------------------------------------------------------------------------------------------------------------------------Appendix B includes the tax treaty case law analysis to the thesis "A Multilateral Agreement for International Taxation, Designing an instrument to modernise international tax law". Appendix B consists of a code book (description of coding per case) as well as a summary Excel file.Appendix B comprises an analysis of relevant case law decided by courts in OECD member countries. The point of the analysis is to provide some (factual) insight in the way the OECD Commentary is used to ‘modernise’ the terms of a tax treaty through interpretive rule-stretching. To circumvent discussions about the ‘status’ of the OECD Commentary under international law and in the process of tax treaty interpretation – this issue is still under debate in tax law doctrine –the reasoning of the courts as to the interpretive relevance of the OECD Commentary was not considered in the analysis. Instead, it focuses on the Commentary’s effects under five different groups of circumstances. The selection of cases as well as the relevant circumstances can be described as follows:Case selectionFirst, a group of relevant cases, all settled within the jurisdiction of OECD member countries, was selected by using a specific search query within the database of the International Tax Law Reports. The search function was applied on 1 January 2013 and brought up about 150 judgements in which the word ‘interpretation’ was found within the same paragraph as the words ‘tax treaty’, ‘tax agreement’ or ‘tax convention’.If a case included more than one interpretative issue, each issue was assessed as if it were an individual case. This was for instance the case when a judge clearly interpreted two distinct treaty terms, or when a judgement dealt with the application of more than one tax treaty.From these ‘cases’, an additional selection excluded those which clearly fell outside the ambit of this research. Cases deselected were those in which the Commentary could clearly not have played a role, i.e.,(1) those that dealt with the interpretation of domestic tax law rather than the term of a tax treaty; (2) those not related to a tax treaty on income and capital, but e.g., to inheritance tax treaties; (3) those that dealt with tax treaty provisions that were clearly not in conformity with the OECD MTC and (4) those that dealt with the interpretation of treaty terms such as ‘profits’, ‘income’ or ‘gains’, which require domestic law rules to be calculated or determined, rather than the interpretative rules of the OECD Commentary.The use of the search function, in combination with the additional selection, resulted in a sample of ‘neutral’ cases (i.e., cases on tax treaty interpretation in which the Commentary could have been, but also in which it could not have been, of relevance). This allowed for the generalisation of the Commentary’s relevance under a set of varying circumstances.Coding and grouping of circumstancesIn accordance with the facts of a case, each case first coded on and then grouped within five categories of circumstances. The circumstances, here formulated in the form of questions, are:1. Was a reservation on the provisions of the OECD MTC or an observation on the Commentary submitted?2. Was the relevant treaty term not defined in the treaty?3. Was one of the treaty parties not a member of the OECD?4. Did the Commentary exist before the conclusion of the relevant bilateral tax treaty?5. If question 4 was answered with no (i.e. the commentary was adopted after the conclusion of the relevant bilateral tax treaty), was that Commentary:a. similar to;b. expounding on;c. gap-filling in relation to;d. or contradictory to Commentary existing at the time of conclusion of a treaty?For each case, questions 1 to 4 were answered with either ‘yes’ or ‘no’, question 5 with ‘a’; ‘b’; ‘c’ and ‘d’. The answers were coded per case in a codebook and its related Excel database, which can be found in Appendix B. If a question could be answered with ‘yes’, that case was placed in that group. If a case could be placed in more than one group, it was.AnalysisSubsequently, the influence of the Commentaries in all of the cases selected was determined, distinguishing between three possible entries: either the Commentary was used by the court, and therefore of relevance, or it was not used or disregarded by the court, and therefore not of relevance. In some cases, the Commentary’s influence could not be determined or established. These cases were coded ‘N.A (‘not applicable’). For each circumstance, this then resulted in a list of cases in which the Commentary was of relevance, a list of cases in which the Commentary was not of relevance, and a list of cases in which the relevance of the Commentary could not be determined.The gradual normative influence of the Commentary on each decision (e.g., decisive, supplementary, etc.) was not considered: coding the relative influence of the Commentary proved too problematic (one of the problems was that in most decisions, courts do not give explicit reasons for their use of Commentary).The research setup is also included in a separate PDF file. The .docx format of the files ‘Appendix B code book of tax treaty case law analysis on the status of the OECD Commentary 2016.docx’ and ‘Research setup of Appendixes A and B.docx’ were provided by the depositor. DANS added the .pdf format of these files for preservation purposes.

1.Summary

Datasets used for testing the performance of discharge estimation algorithms built in support of the Surface Water and Ocean Topography satellite mission. The benchmarking manuscript entitled “Exploring the factors controlling the performance of the Surface Water and Ocean Topography mission discharge algorithms” is currently under review at Water Resources Research. Once the manuscript is accepted, its DOI will be included here.

2.File description

The dataset is divided into four groups: 1-Ideal data, 2-Varying Temporal Sampling, 3-Measurement Uncertainty, and 4-SWOT Sampling and Uncertainty. Ideal data contains daily measurements with no observational uncertainty. Varying Temporal Sampling downsamples the ideal measurements considering different temporal frequencies with complete sets assuming: 1 measurement every 2 days, 3 days, 4 days, 5 days, 7 days, 10 days, and 21 days. The measurement uncertainty set adds errors to cross-sectional heights and widths, which are used to compute reach average height, width, and slope considering error corruption. The final set SWOT Sampling and Uncertainty accounts for SWOT temporal sampling and measurement uncertainty. Sets containing uncertainty have extra height, width, and slope attributes with the word true appended to the attribute name. Such attributes represent the uncorrupted measurements at the cross-section and reach scales. Height, width, and slopes for the SWOT sampling and Uncertainty dataset containing the value of negative 9999 denote points that are not observed at a particular location and time step.

Data will be contained in one NetCDF file per river. The file contains the following groups and variables:

/River_Info/

Name: River name, data type: char

QWBM: Mean annual discharge from the water balance model WBMsed (Cohen et al., 2014)

rch_bnd: Reach boundaries measured in meters from the upstream end of the model

gdrch: Reaches used in the study. Used to exclude small reaches defined around low-head dams and other obstacles where Manning’s equation should not be applied.

/XS_Timeseries/

t: Time measured in days since the first day or “0-January-0000” for cases when specific dates were available. Dimension: 1,time step.

Z: Bed elevation in meters. Dimension: Cross-section, time step.

xs_rch: Reach number for each cross-section. Dimension: Cross-section,1.

X: Flow distance measured from the most upstream end of the model to the cross-section (meters). Dimension: Cross-section, 1.

longitude: Cross-section longitude in decimal degrees. Dimension: Cross-section,1.

latitude: Cross-section latitude in decimal degrees. Dimension: Cross-section,1.

W: River width in meters. Dimension: Cross-section, time step.

Wtrue: River width in meters. Dimension: Cross-section, time step. Only present in datasets containing measurement uncertainty, in which case, this variable holds the water surface elevation value with no uncertainty.

Q: Discharge (m³/s). Dimension: Cross-section, time step.

H: Water surface elevation in meters. Dimension: Cross-section, time step.

Htrue: Water surface elevation in meters. Dimension: Cross-section, time step. Only present in datasets containing measurement uncertainty, in which case, this variable holds the water surface elevation value with no uncertainty.

A: Cross-sectional area of flow in m². Dimension: Cross-section, time step.

P: Wetted perimeter in meters. Dimension: Cross-section, time step.

n: Manning’s roughness. Dimension: Cross-section, time step.

/Reach_Timeseries/

t: Time measured in days since the first day or “0-January-0000” for cases when specific dates were available. Dimension: 1,time step.

W: Reach averaged river width in meters. Dimension: Reach, time step.

Wtrue: Reach averaged river width in meters. Dimension: Reach, time step. Only present in datasets containing measurement uncertainty, in which case, this variable holds the width value with no uncertainty.

Q: Reach averaged discharge (m³/s). Dimension: Reach, time step.

H: Reach averaged water surface elevation in meters. Dimension: Reach, time step.

Htrue: Reach averaged water surface elevation in meters. Dimension: Reach, time step. Only present in datasets containing measurement uncertainty, in which case, this variable holds the water surface elevation value with no uncertainty.

S: Reach averaged water surface slope in meters per meter. Reach, time step.

Strue: Reach averaged water surface slope in meters per meter. Dimension: Reach, time step. Only present in datasets containing measurement uncertainty, in which case, this variable holds the slope value with no uncertainty.

A: Reach averaged area of flow in m². Dimension: Reach, time step.

References

Cohen, S., A. J. Kettner, and J. P. M. Syvitski (2014), Global suspended sediment and water discharge dynamics between 1960 and 2010: Continental trends and intra-basin sensitivity, Glob. Planet. Change, 115, 44-58, doi: https://doi.org/10.1016/j.gloplacha.2014.01.011.

Key figures on fertility, live and stillborn children and multiple births among inhabitants of The Netherlands.

Available selections: - Live born children by sex; - Live born children by age of the mother (31 December), in groups; - Live born children by birth order from the mother; - Live born children by marital status of the mother; - Live born children by country of birth of the mother and origin country of the mother; - Stillborn children by duration of pregnancy; - Births: single and multiple; - Average number of children per female; - Average number of children per male; - Average age of the mother at childbirth by birth order from the mother; - Average age of the father at childbirth by birth order from the mother; - Net replacement factor.

CBS is in transition towards a new classification of the population by origin. Greater emphasis is now placed on where a person was born, aside from where that person’s parents were born. The term ‘migration background’ is no longer used in this regard. The main categories western/non-western are being replaced by categories based on continents and a few countries that share a specific migration history with the Netherlands. The new classification is being implemented gradually in tables and publications on population by origin.

Data available from: 1950 Most of the data is available as of 1950 with the exception of the live born children by country of birth of the mother and origin country of the mother (from 2021, previous periods will be added at a later time), stillborn children by duration of pregnancy (24+) (from 1991), average number of children per male (from 1996) and the average age of the father at childbirth (from 1996).

Status of the figures: The 2023 figures on stillbirths and (multiple) births are provisional, the other figures in the table are final.

Changes per 17 December 2024: Figures of 2023 have been added. The provisional figures on the number of live births and stillbirths for 2023 do not include children who were born at a gestational age that is unknown. These cases were included in the final figures for previous years. However, the provisional figures show a relatively larger number of children born at an unknown gestational age. Based on an internal analysis for 2022, it appears that in the majority of these cases, the child was born at less than 24 weeks. To ensure that the provisional 2023 figures do not overestimate the number of stillborn children born at a gestational age of over 24 weeks, children born at an unknown gestational age have now been excluded.

When will new figures be published? Final 2023 figures on the number of stillbirths and the number of births are expected to be added to the table in de third quarter of 2025. In the third quarter of 2025 final figures of 2024 will be published in this publication.

Jamaica Survey Mean Consumption or Income per Capita: Bottom 40% of Population: 2017 PPP per day data was reported at 8.130 Intl $/Day in 2021. This records a decrease from the previous number of 8.990 Intl $/Day for 2018. Jamaica Survey Mean Consumption or Income per Capita: Bottom 40% of Population: 2017 PPP per day data is updated yearly, averaging 8.560 Intl $/Day from Dec 2018 (Median) to 2021, with 2 observations. The data reached an all-time high of 8.990 Intl $/Day in 2018 and a record low of 8.130 Intl $/Day in 2021. Jamaica Survey Mean Consumption or Income per Capita: Bottom 40% of Population: 2017 PPP per day data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Jamaica – Table JM.World Bank.WDI: Social: Poverty and Inequality. Mean consumption or income per capita (2017 PPP $ per day) of the bottom 40%, used in calculating the growth rate in the welfare aggregate of the bottom 40% of the population in the income distribution in a country.;World Bank, Global Database of Shared Prosperity (GDSP) (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).;;The choice of consumption or income for a country is made according to which welfare aggregate is used to estimate extreme poverty in the Poverty and Inequality Platform (PIP). The practice adopted by the World Bank for estimating global and regional poverty is, in principle, to use per capita consumption expenditure as the welfare measure wherever available; and to use income as the welfare measure for countries for which consumption is unavailable. However, in some cases data on consumption may be available but are outdated or not shared with the World Bank for recent survey years. In these cases, if data on income are available, income is used. Whether data are for consumption or income per capita is noted in the footnotes. Because household surveys are infrequent in most countries and are not aligned across countries, comparisons across countries or over time should be made with a high degree of caution.

Florida COVID-19 Cases by Zip Code exported from the Florida Department of Health GIS Layer on date seen in file name. Archived by the University of South Florida Libraries, Digital Heritage and Humanities Collections. Contact: LibraryGIS@usf.edu.Please Cite Our GIS HUB. If you are a researcher or other utilizing our Florida COVID-19 HUB as a tool or accessing and utilizing the data provided herein, please provide an acknowledgement of such in any publication or re-publication. The following citation is suggested: University of South Florida Libraries, Digital Heritage and Humanities Collections. 2020. Florida COVID-19 Hub. Available at https://covid19-usflibrary.hub.arcgis.com/.https://doi.org/10.5038/USF-COVID-19-GISLive FDOH Data Source: https://services1.arcgis.com/CY1LXxl9zlJeBuRZ/arcgis/rest/services/Florida_Cases_Zips_COVID19/FeatureServerFor data 5/10/2020 or after: Archived data was exported directly from the live FDOH layer into the archive. For data prior to 5/10/2020: Data was exported by the University of South Florida - Digital Heritage and Humanities Collection using ArcGIS Pro Software. Data was then converted to shapefile and csv and uploaded into ArcGIS Online archive. For data definitions please visit the following box folder: https://usf.box.com/s/vfjwbczkj73ucj19yvwz53at6v6w614hData definition files names include the relative date they were published. The below information was taken from ancillary documents associated with the original layer from FDOH.Q. How is the zip code assigned to a person or case? Cases are counted in a zip code based on residential or mailing address, or by healthcare provider or lab address if other addresses are missing.Q. Why is the city data and the zip code data different? The zip code data is supplied to a healthcare worker, case manager, or lab technician by each individual during intake when a test is first recorded. When entering a zip code, the system we use automatically produces a list of cities within that zip code for the individual to further specify where they live. Sometimes the individual uses the postal city, which may be Miami, when in reality that person lives outside the City of Miami boundaries in the jurisdiction of Coral Gables. Many zip codes contain multiple city/town jurisdictions, and about 20% of zip codes overlap more than one county. Q: How is the Zip Code data calculated and/or shown? If a COUNTY has five or more cases (total): • In zip codes with fewer than 5 cases, the total number of cases is shown as “<5”. • Zip codes with 0 cases in these counties are “0" or "No cases.” • All values of 5 or greater are shown by the actual number of cases in that zip code. If a COUNTY has fewer than five total cases across all of its zip codes, then ALL of the zip codes within that county show the total number of cases as "Suppressed." Q: My zip code says "SUPPRESSED" under cases. What does that mean? IF Suppressed: This county currently has fewer than five cases across all zip codes in the county. In an effort to protect the privacy of our COVID-19-Positive residents, zip code data is only available in counties where five or more cases have been reported. Q: What about PO Box zip codes, or zip codes with letters, like 334MH? PO Box zip codes are not shown in the map. “Filler” zip codes with letters, like 334MH, are typically areas where no or very few people live – like the Florida Everglades, and are shown on the map like any other zip code. Key Data about Cases by Zip Code: ZIP = The zip code COUNTYNAME = The county for the zip code (multi-part counties have been split) ZIPX = The unique county-zip identifier used to pair the data during updates POName = The postal address name assigned to the zip code place_labels = A list of the municipalities intersecting the zip code boundary c_places = The list of cities cases self-reported as being residents of Cases_1 = The number of cases in each zip code, with conditions*LabelY = A calculated field for map display only. All questions regarding this dataset should be directed to the Florida Department of Health.

Florida COVID-19 Cases by Zip Code exported from the Florida Department of Health GIS Layer on date seen in file name. Archived by the University of South Florida Libraries, Digital Heritage and Humanities Collections. Contact: LibraryGIS@usf.edu.Please Cite Our GIS HUB. If you are a researcher or other utilizing our Florida COVID-19 HUB as a tool or accessing and utilizing the data provided herein, please provide an acknowledgement of such in any publication or re-publication. The following citation is suggested: University of South Florida Libraries, Digital Heritage and Humanities Collections. 2020-2021. Florida COVID-19 Hub. Available at https://covid19-usflibrary.hub.arcgis.com/.https://doi.org/10.5038/USF-COVID-19-GISLive FDOH Data Source: https://services1.arcgis.com/CY1LXxl9zlJeBuRZ/arcgis/rest/services/Florida_Cases_Zips_COVID19/FeatureServerFor data 5/10/2020 or after: Archived data was exported directly from the live FDOH layer into the archive. For data prior to 5/10/2020: Data was exported by the University of South Florida - Digital Heritage and Humanities Collection using ArcGIS Pro Software. Data was then converted to shapefile and csv and uploaded into ArcGIS Online archive. For data definitions please visit the following box folder: https://usf.box.com/s/vfjwbczkj73ucj19yvwz53at6v6w614hData definition files names include the relative date they were published. The below information was taken from ancillary documents associated with the original layer from FDOH.Q. How is the zip code assigned to a person or case? Cases are counted in a zip code based on residential or mailing address, or by healthcare provider or lab address if other addresses are missing.Q. Why is the city data and the zip code data different? The zip code data is supplied to a healthcare worker, case manager, or lab technician by each individual during intake when a test is first recorded. When entering a zip code, the system we use automatically produces a list of cities within that zip code for the individual to further specify where they live. Sometimes the individual uses the postal city, which may be Miami, when in reality that person lives outside the City of Miami boundaries in the jurisdiction of Coral Gables. Many zip codes contain multiple city/town jurisdictions, and about 20% of zip codes overlap more than one county. Q: How is the Zip Code data calculated and/or shown? If a COUNTY has five or more cases (total): • In zip codes with fewer than 5 cases, the total number of cases is shown as “<5”. • Zip codes with 0 cases in these counties are “0" or "No cases.” • All values of 5 or greater are shown by the actual number of cases in that zip code. If a COUNTY has fewer than five total cases across all of its zip codes, then ALL of the zip codes within that county show the total number of cases as "Suppressed." Q: My zip code says "SUPPRESSED" under cases. What does that mean? IF Suppressed: This county currently has fewer than five cases across all zip codes in the county. In an effort to protect the privacy of our COVID-19-Positive residents, zip code data is only available in counties where five or more cases have been reported. Q: What about PO Box zip codes, or zip codes with letters, like 334MH? PO Box zip codes are not shown in the map. “Filler” zip codes with letters, like 334MH, are typically areas where no or very few people live – like the Florida Everglades, and are shown on the map like any other zip code. Key Data about Cases by Zip Code: ZIP = The zip code COUNTYNAME = The county for the zip code (multi-part counties have been split) ZIPX = The unique county-zip identifier used to pair the data during updates POName = The postal address name assigned to the zip code place_labels = A list of the municipalities intersecting the zip code boundary c_places = The list of cities cases self-reported as being residents of Cases_1 = The number of cases in each zip code, with conditions*LabelY = A calculated field for map display only. All questions regarding this dataset should be directed to the Florida Department of Health.