Unemployment Rate in the United States decreased to 4.10 percent in June from 4.20 percent in May of 2025. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Consumer Price Index CPI in the United States increased to 321.47 points in May from 320.80 points in April of 2025. This dataset provides the latest reported value for - United States Consumer Price Index (CPI) - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Inflation Rate in the United States increased to 2.40 percent in May from 2.30 percent in April of 2025. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just * percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.

This dataset contains the Town's Year-to-Date Budget and Actuals for Fiscal Years 2016 through 2019. Fiscal years run from July 1 to June 30.The data comes from the Town's Enterprise Resource Planning (ERP) software and is subject to change until the year's final audit is complete, which typically occurs by October of the following fiscal year. For example, revenues received may be posted back a previous month or expenditures may be reclassified from one expense category to another throughout the year. This data is maintained in a flexible way to produce a variety of financial reports as required by law, including the Town's annually Adopted Budget and Comprehensive Annual Financial Report (CAFR).These reports can be found on the Town's website through the following links:Town of Chapel Hill Adopted Budget Town of Chapel Hill CAFR

Context

The World Happiness Report may be a point of interest survey of the state of worldwide bliss. The primary report was distributed in 2012, the second in 2013, the third in 2015, and the fourth within the 2016 Upgrade. The World Joy 2017, which positions 155 nations by their bliss levels, was discharged at the Joined together Countries at an occasion celebrating Universal Day of Joy on Walk 20th. The report proceeds to pick up worldwide acknowledgment as governments, organizations and respectful society progressively utilize joy pointers to educate their policy-making choices. Driving specialists over areas – financial matters, brain research, overview investigation, national insights, wellbeing, open approach and more – depict how estimations of well-being can be used effectively to evaluate the advance of countries. The reports survey the state of bliss within the world nowadays and appear how the modern science of bliss clarifies individual and national varieties in bliss.

Content

The joy scores and rankings utilize information from the Gallup World Survey. The scores are based on answers to the most life evaluation address inquired within the survey. This address, known as the Cantril step, asks respondents to think of a step with the most excellent conceivable life for them being a 10 and the most exceedingly bad conceivable life being a and to rate their claim current lives on that scale. The scores are from broadly agent tests for the a long time 2013-2016 and utilize the Gallup weights to create the gauges agent. The columns taking after the bliss score assess the degree to which each of six variables – financial generation, social back, life anticipation, flexibility, nonattendance of debasement, and liberality – contribute to making life assessments higher in each nation than they are in Dystopia, a theoretical nation that has values rise to to the world’s least national midpoints for each of the six variables. They have no affect on the full score detailed for each nation, but they do exp

This file contains the Happiness Score for 153 countries along with the factors used to explain the score.

The Happiness Score is a national average of the responses to the main life evaluation question asked in the Gallup World Poll (GWP), which uses the Cantril Ladder.

The Happiness Score is explained by the following factors:

GDP per capita Healthy Life Expectancy Social support Freedom to make life choices Generosity Corruption Perception Residual error The data is described in much more detail here: link

Acknowledgements

I did not create this data, only sourced it. The credit goes to the original Authors:

Editors: John Helliwell, Richard Layard, Jeffrey D. Sachs, and Jan Emmanuel De Neve, Co-Editors; Lara Aknin, Haifang Huang and Shun Wang, Associate Editors; and Sharon Paculor, Production Editor

Citation: Helliwell, John F., Richard Layard, Jeffrey Sachs, and Jan-Emmanuel De Neve, eds. 2020. World Happiness Report 2020. New York: Sustainable Development Solutions Network

The dataset building service market is projected to grow significantly in the coming years, driven by the increasing demand for data-driven insights and the growth of artificial intelligence (AI) and machine learning (ML) technologies. The global dataset building service market size was valued at USD XXX million in 2025 and is expected to expand at a compound annual growth rate (CAGR) of XX% from 2025 to 2033. This growth can be attributed to the increasing adoption of AI and ML technologies, which require large and diverse datasets for training and testing. Additionally, the rising demand for data-driven insights for decision-making is driving the growth of the dataset building services market. Key market trends include the growing popularity of cloud-based dataset building services, the increasing adoption of data annotation and labeling services, and the emergence of new data sources such as social media and IoT devices. The major players in the dataset building service market include Appen, Scale AI, Lionbridge, Samasource, CloudFactory, Deepen AI, and Clarifai. These companies offer a wide range of dataset building services, including data collection, annotation, and labeling. The market is expected to witness further consolidation in the coming years, as larger players acquire smaller companies to expand their service offerings and geographic reach.

GOES-17 (G17) is the second satellite in the US NOAA's GOES-R series. It was launched on 1 Mar 2018 in an interim position at 89.5-deg W for initial Cal/Val, moved to its nominal position at 137.2-deg W in Nov 2018, and declared NOAA operational GOES-West satellite on 12 Feb 2019. Advanced Baseline Imager (ABI) is a 16 channel sensor, of which five (3.9, 8.4, 10.3, 11.2, 12.3 um) are suitable for SST. From altitude 35,800km, G17/ABI maps SST in a Full Disk (FD) area from 163E-77W and 60S-60N, with spatial resolution 2km/nadir to 15km/VZA 67-deg, and 10-min temporal sampling. The ABI L2P SST is derived at the native sensor resolution using NOAA ACSPO system. ACSPO processes every 10-min FD, identifies good-quality ocean pixels (Petrenko et al., 2010) and derives SST using Non-Linear SST (NLSST) algorithm (Petrenko et al., 2014). Unfortunately, the G17 ABI loop heat pipe (LHP) that should maintain the ABI at its intended temperature, is not operating at its designed capacity, which required mitigations to the ACSPO algorithms and releasing an updated ACSPO version 2.71 (Pennybacker et al, 2019). In particular, band 11.2um, most subject to calibration problems, is not used leading to a 3-band (8.4, 10.3, and 12.3um) NLSST, and increased calibration problems prevent SST retrievals at night. As a result, the G17 SST is only reported for 13 out of 24hrs/day, from 20UTC to 08UTC. The 10-min FD data are subsequently collated in time, to produce 1-hr product, with improved coverage and reduced cloud leakages and image noise. The collation algorithm also reduces G17 excessive sensor noise and striping to levels similar to G16. The collated SSTs are only reported over clear-sky water pixels. All pixels with valid SSTs are recommended for use. The L2P is reported in NetCDF4 GDS2 format, 13 granules per day, with a total data volume 0.3GB/day. ACSPO files also report sun-sensor geometry, wind speed and l2p_flags (day/night, land, ice, twilight, glint flags). Per GDS2 specifications, two Sensor-Specific Error Statistics (bias and standard deviation) are reported in each pixel (Petrenko et al., 2016). Pixel earth locations are not reported in the granules, as they remain unchanged from granule to granule. Those can be obtained using a flat lat/lon file or a Python script (see Documentation page). The ACSPO G17 ABI SSTs are continuously validated in SQUAM (Dash et al, 2010). A reduced size (0.1GB/day), 0.02-deg equal-angle gridded L3C product is available at https://podaac.jpl.nasa.gov/dataset/ABI_G17-STAR-L3C-v2.71.

Techsalerator's News Events Data for Greece: A Comprehensive Overview

Techsalerator's News Events Data for Greece is an essential resource for businesses, researchers, and media organizations. This dataset aggregates information on significant news events across Greece, drawing from diverse media sources such as news outlets, online publications, and social platforms. It offers valuable insights for those looking to track trends, analyze public sentiment, or monitor industry-specific developments.

Key Data Fields - Event Date: Captures the exact date of the news event, crucial for analysts monitoring trends over time or businesses responding to market shifts. - Event Title: A brief headline describing the event, allowing users to quickly categorize and assess news content based on relevance. - Source: Identifies the news outlet or platform where the event was reported, helping users track credible sources and assess the reach and influence of the event. - Location: Provides geographic information on where the event took place within Greece, valuable for regional analysis or localized marketing efforts. - Event Description: A detailed summary of the event, outlining key developments, participants, and potential impact. Researchers and businesses use this to understand the context and implications of the event.

Top 5 News Categories in Greece - Politics: Major news coverage on government decisions, political movements, elections, and policy changes affecting the national landscape. - Economy: Focuses on Greece’s economic indicators, inflation rates, international trade, and corporate activities influencing business and finance sectors. - Social Issues: News events covering protests, public health, education, and other societal concerns driving public discourse. - Sports: Highlights events in football, basketball, and other popular sports, often drawing widespread attention and engagement across the country. - Technology and Innovation: Reports on tech developments, startups, and innovations in Greece’s growing tech ecosystem, featuring companies like Vodafone Greece and up-and-coming startups.

Top 5 News Sources in Greece - Kathimerini: A major newspaper providing in-depth coverage of politics, economy, and social issues. - Ta Nea: A prominent source for news related to politics, business, and cultural events across Greece. - Mega Channel: A leading TV network offering real-time updates on current affairs, sports, and entertainment. - Protagon: A well-regarded online news platform known for its investigative journalism and analysis of political and social issues. - Ethnos: A key newspaper providing extensive coverage of national politics, economy, and public affairs.

Accessing Techsalerator’s News Events Data for Greece To access Techsalerator’s News Events Data for Greece, please contact info@techsalerator.com with your specific needs. We will provide a customized quote based on the data fields and records you require, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields - Event Date - Event Title - Source - Location - Event Description - Event Category (Politics, Economy, Sports, etc.) - Participants (if applicable) - Event Impact (Social, Economic, etc.)

Techsalerator’s dataset is an invaluable tool for tracking significant events in Greece. It aids in making informed decisions, whether for business strategy, market analysis, or academic research, offering a clear picture of the country’s news landscape.

Note: Reporting of new COVID-19 Case Surveillance data will be discontinued July 1, 2024, to align with the process of removing SARS-CoV-2 infections (COVID-19 cases) from the list of nationally notifiable diseases. Although these data will continue to be publicly available, the dataset will no longer be updated.

Authorizations to collect certain public health data expired at the end of the U.S. public health emergency declaration on May 11, 2023. The following jurisdictions discontinued COVID-19 case notifications to CDC: Iowa (11/8/21), Kansas (5/12/23), Kentucky (1/1/24), Louisiana (10/31/23), New Hampshire (5/23/23), and Oklahoma (5/2/23). Please note that these jurisdictions will not routinely send new case data after the dates indicated. As of 7/13/23, case notifications from Oregon will only include pediatric cases resulting in death.

This case surveillance public use dataset has 19 elements for all COVID-19 cases shared with CDC and includes demographics, geography (county and state of residence), any exposure history, disease severity indicators and outcomes, and presence of any underlying medical conditions and risk behaviors.

Currently, CDC provides the public with three versions of COVID-19 case surveillance line-listed data: this 19 data element dataset with geography, a 12 data element public use dataset, and a 33 data element restricted access dataset.

The following apply to the public use datasets and the restricted access dataset:

• Data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.
• Data are considered provisional by CDC and are subject to change until the data are reconciled and verified with the state and territorial data providers.
• Some data are suppressed to protect individual privacy.
• Datasets will include all cases with the earliest date available in each record (date received by CDC or date related to illness/specimen collection) at least 14 days prior to the creation of the current datasets. This 14-day lag allows case reporting to be stabilized and ensure that time-dependent outcome data are accurately captured.
• Datasets are updated monthly.
• Datasets are created using CDC’s Policy on Public Health Research and Nonresearch Data Management and Access and include protections designed to protect individual privacy.
• For more information about data collection and reporting, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/about-us-cases-deaths.html.
• For more information about the COVID-19 case surveillance data, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/faq-surveillance.html
  

Overview

The COVID-19 case surveillance database includes individual-level data reported to U.S. states and autonomous reporting entities, including New York City and the District of Columbia (D.C.), as well as U.S. territories and affiliates. On April 5, 2020, COVID-19 was added to the Nationally Notifiable Condition List and classified as “immediately notifiable, urgent (within 24 hours)” by a Council of State and Territorial Epidemiologists (CSTE) Interim Position Statement (Interim-20-ID-01). CSTE updated the position statement on August 5, 2020, to clarify the interpretation of antigen detection tests and serologic test results within the case classification (Interim-20-ID-02). The statement also recommended that all states and territories enact laws to make COVID-19 reportable in their jurisdiction, and that jurisdictions conducting surveillance should submit case notifications to CDC. COVID-19 case surveillance data are collected by jurisdictions and reported voluntarily to CDC.

For more information: NNDSS Supports the COVID-19 Response | CDC.

COVID-19 Case Reports COVID-19 case reports are routinely submitted to CDC by public health jurisdictions using nationally standardized case reporting forms. On April 5, 2020, CSTE released an Interim Position Statement with national surveillance case definitions for COVID-19. Current versions of these case definitions are available at: https://ndc.services.cdc.gov/case-definitions/coronavirus-disease-2019-2021/. All cases reported on or after were requested to be shared by public health departments to CDC using the standardized case definitions for lab-confirmed or probable cases. On May 5, 2020, the standardized case reporting form was revised. States and territories continue to use this form.

Data are Considered Provisional

• The COVID-19 case surveillance data are dynamic; case reports can be modified at any time by the jurisdictions sharing COVID-19 data with CDC. CDC may update prior cases shared with CDC based on any updated information from jurisdictions. For instance, as new information is gathered about previously reported cases, health departments provide updated data to CDC. As more information and data become available, analyses might find changes in surveillance data and trends during a previously reported time window. Data may also be shared late with CDC due to the volume of COVID-19 cases.
• Annual finalized data: To create the final NNDSS data used in the annual tables, CDC works carefully with the reporting jurisdictions to reconcile the data received during the year until each state or territorial epidemiologist confirms that the data from their area are correct.

Access Addressing Gaps in Public Health Reporting of Race and Ethnicity for COVID-19, a report from the Council of State and Territorial Epidemiologists, to better understand the challenges in completing race and ethnicity data for COVID-19 and recommendations for improvement.

Data Limitations

To learn more about the limitations in using case surveillance data, visit FAQ: COVID-19 Data and Surveillance.

Data Quality Assurance Procedures

CDC’s Case Surveillance Section routinely performs data quality assurance procedures (i.e., ongoing corrections and logic checks to address data errors). To date, the following data cleaning steps have been implemented:

• Questions that have been left unanswered (blank) on the case report form are reclassified to a Missing value, if applicable to the question. For example, in the question "Was the individual hospitalized?" where the possible answer choices include "Yes," "No," or "Unknown," the blank value is recoded to "Missing" because the case report form did not include a response to the question.
• Logic checks are performed for date data. If an illogical date has been provided, CDC reviews the data with the reporting jurisdiction. For example, if a symptom onset date in the future is reported to CDC, this value is set to null until the reporting jurisdiction updates the date appropriately.
• Additional data quality processing to recode free text data is ongoing. Data on symptoms, race, ethnicity, and healthcare worker status have been prioritized.

Data Suppression

To prevent release of data that could be used to identify people, data cells are suppressed for low frequency (<11 COVID-19 case records with a given values). Suppression includes low frequency combinations of case month, geographic characteristics (county and state of residence), and demographic characteristics (sex, age group, race, and ethnicity). Suppressed values are re-coded to the NA answer option; records with data suppression are never removed.

Additional COVID-19 Data

COVID-19 data are available to the public as summary or aggregate count files, including total counts of cases and deaths by state and by county. These and other COVID-19 data are available from multiple public locations: COVID Data Tracker; United States COVID-19 Cases and Deaths by State; COVID-19 Vaccination Reporting Data Systems; and COVID-19 Death Data and Resources.

Notes:

March 1, 2022: The "COVID-19 Case Surveillance Public Use Data with Geography" will be updated on a monthly basis.

April 7, 2022: An adjustment was made to CDC’s cleaning algorithm for COVID-19 line level case notification data. An assumption in CDC's algorithm led to misclassifying deaths that were not COVID-19 related. The algorithm has since been revised, and this dataset update reflects corrected individual level information about death status for all cases collected to date.

June 25, 2024: An adjustment

The data comes from The Humane League's US Egg Production dataset by Samara Mendez. Dataset and code is available for this project on OSF at US Egg Production Data Set.

This dataset tracks the supply of cage-free eggs in the United States from December 2007 to February 2021. For TidyTuesday we've used data through February 2021, but the full dataset, with data through the present, is available in the OSF project.

Data Dictionary

egg-production.csv

cage-free-percentages.csv

On an annual basis (based on individual Long-Term Care (LTC) facility fiscal year end), California licensed LTC facilities report detailed financial data on facility information, ownership information, patient days & discharges, Balance Sheet, Equity Statement, Cash Flows, Income Statement, Revenue by type and payer, Expense Detail, and Labor Detail. Based on the selected data set, the pivot tables display summarized data on a Profile page and also provides charts on various data items such as Patient Days, Revenue & Expense, and Revenue.

Core consumer prices in the United States increased 2.80 percent in May of 2025 over the same month in the previous year. This dataset provides - United States Core Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Report of VA Medical Training Programs Database is used to track medical center health services trainees and VA physicians serving as faculty. The database also tracks the number of U.S. and international medical residents on-duty at a Veterans Affairs Medical Center (VAMC). Information in the database comes from all VAMCs that have residency programs. The Office of Academic Affiliations distributes worksheets and memos to participating VAMCs annually. VAMC personnel enter the information electronically into the database located at the Academic Information Management Center (AIMC) in St. Louis, Missouri. The main user of this database is the Office of Academic Affiliations which uses the reports from the system to assist in its decision making.

Sudan SD: Net Official Development Assistance and Official Aid Received: Current Price data was reported at 810.400 USD mn in 2016. This records a decrease from the previous number of 899.780 USD mn for 2015. Sudan SD: Net Official Development Assistance and Official Aid Received: Current Price data is updated yearly, averaging 447.900 USD mn from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 2.566 USD bn in 2008 and a record low of 7.790 USD mn in 1970. Sudan SD: Net Official Development Assistance and Official Aid Received: Current Price data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Sudan – Table SD.World Bank: Defense and Official Development Assistance. Net official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). Net official aid refers to aid flows (net of repayments) from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. Data are in current U.S. dollars.; ; Development Assistance Committee of the Organisation for Economic Co-operation and Development, Geographical Distribution of Financial Flows to Developing Countries, Development Co-operation Report, and International Development Statistics database. Data are available online at: www.oecd.org/dac/stats/idsonline.; Sum;

Abstract

The do-file marital_spouselinks.do combines all data on people's marital statuses and reported spouses to create the following datasets: 1. all_marital_reports - a listing of all the times an individual has reported their current marital status with the id numbers of the reported spouse(s); this listing is as reported so may include discrepancies (i.e. a 'Never married' status following a 'Married' one) 2. all_spouse_pairs_full - a listing of each time each spouse pair has been reported plus summary information on co-residency for each pair 3. all_spouse_pairs_clean_summarised - this summarises the data from all_spouse_pairs_full to give start and end dates of unions 4. marital_status_episodes - this combines data from all the sources to create episodes of marital status, each has a start and end date and a marital status, and if currently married, the spouse ids of the current spouse(s) if reported. There are several variables to indicate where each piece of information is coming from.

The first 2 datasets are made available in case people need the 'raw' data for any reason (i.e. if they only want data from one study) or if they wish to summarise the data in a different way to what is done for the last 2 datasets.

The do-file is quite complicated with many sources of data going through multiple processes to create variables in the datasets so it is not always straightforward to explain where each variable come from on the documentation. The 4 datasets build on each other and the do-file is documented throughout so anyone wanting to understand in great detail may be better off examining that. However, below is a brief description of how the datasets are created:

Marital status data are stored in the tables of the study they were collected in: AHS Adult Health Study [ahs_ahs1] CEN Census (initial CRS census) [cen_individ] CENM In-migration (CRS migration form) [crs_cenm] GP General form (filled for various reasons) [gp_gpform] SEI Socio-economic individual (annual survey from 2007 onwards) [css_sei] TBH TB household (study of household contacts of TB patients) [tb_tbh] TBO TB controls (matched controls for TB patients) [tb_tbo & tb_tboto2007] TBX TB cases (TB patients) [tb_tbx & tb_tbxto2007] In many of the above surveys as well as their current marital status, people were asked to report their current and past spouses along with (sometimes) some information about the marriage (start/end year etc.). These data are stored all together on the table gen_spouse, with variables indicating which study the data came from. Further evidence of spousal relationships is taken from gen_identity (if a couple appear as co-parents to a CRS member) and from crs_residency_episodes_clean_poly, a combined dataset (if they are living in the same household at the same time). Note that co-parent couples who are not reported in gen_spouse are only retained in the datasets if they have co-resident episodes.

The marital status data are appended together and the spouse id data merged in. Minimal data editing/cleaning is carried out. As the spouse data are in long format, this dataset is reshaped wide to have one line per marital status report (polygamy in the area allows for men to have multiple spouses at one time): this dataset is saved as all_marital_reports.

The list of reported spouses on gen_spouse is appended to a list of co-parents (from gen_identity) and this list is cleaned to try to identify and remove obvious id errors (incestuous links, same sex [these are not reported in this culture] and large age difference). Data reported by men and women are compared and variables created to show whether one or both of the couple report the union. Many records have information on start and end year of marriage, and all have the date the union was reported. This listing is compared to data from residency episodes to add dates that couples were living together (not all have start/end dates so this is to try to supplement this), in addition the dates that each member of the couple was last known to be alive or first known to be dead are added (from the residency data as well). This dataset with all the records available for each spouse pair is saved as all_spouse_pairs_full.

The date data from all_spouse_pairs_full are then summarised to get one line per couple with earliest and latest known married date for all, and, if available, marriage and separation date. For each date there are also variables created to indicate the source of the data.
As culture only allows for women having one spouse at a time, records for women with 'overlapping' husbands are cleaned. This dataset is then saved as all_spouse_pairs_clean_summarised.

Both the cleaned spouse pairs and the cleaned marital status datasets are converted into episodes: the spouse listing using the marriage or first known married date as the beginning and the last known married plus a year or separation date as the end, the marital status data records collapsed into periods of the same status being reported (following some cleaning to remove impossible reports) and the start date being the first of these reports, the end date being the last of the reports plus a year. These episodes are appended together and a series of processes run several times to remove overalapping episodes. To be able to assign specific spouse ids to each married episode, some episodes need to be 'split' into more than one (i.e. if a man is married to one woman from 2005 to 2017 and then marries another woman in 2008 and remains married to her till 2017 his intial married episode would be from 2005 to 2017, but this would need to be split into one from 2005 to 2008 which would just have 1 idspouse attached and another from 2008 to 2017, which would have 2 idspouse attached). After this splitting process the spouse ids are merged in.
The final episode dataset is saved as marital_status_episodes.

Analysis unit

Individual

Mode of data collection

Face-to-face [f2f]

The complete data set of annual utilization data reported by hospitals contains basic licensing information including bed classifications; patient demographics including occupancy rates, the number of discharges and patient days by bed classification, and the number of live births; as well as information on the type of services provided including the number of surgical operating rooms, number of surgeries performed (both inpatient and outpatient), the number of cardiovascular procedures performed, and licensed emergency medical services provided.

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

Cloud-based Database Market Outlook

In 2023, the global cloud-based database market size was estimated to be approximately USD 15.5 billion, with projections indicating robust growth to around USD 39.1 billion by 2032, reflecting a compound annual growth rate (CAGR) of 11.0%. This impressive growth trajectory can be attributed to several critical growth factors. The increasing adoption of cloud technologies across various industries, the growing need for scalable and flexible data storage solutions, and the rising awareness of the benefits associated with cloud-based databases are fueling this expansion. Furthermore, businesses are increasingly migrating their on-premises databases to the cloud to enhance operational efficiency, reduce costs, and gain competitive advantages, thus driving the demand for cloud-based databases.

The rapid digital transformation across multiple sectors serves as a significant catalyst for the expansion of the cloud-based database market. Enterprises are increasingly relying on data-driven strategies to enhance their decision-making processes and improve customer experiences. With the proliferation of digital data, organizations are in dire need of efficient data management solutions that can handle large volumes of data with ease. Cloud-based databases offer the perfect solution, providing scalability, flexibility, and real-time access to data, which are crucial in today's fast-paced business environment. Additionally, the emergence of Internet of Things (IoT) devices, artificial intelligence (AI), and big data analytics further propels the demand for cloud databases, as these technologies require robust and flexible data management platforms.

Another vital growth factor is the increasing adoption of hybrid and multi-cloud strategies by organizations worldwide. Companies are no longer reliant on a single cloud provider; instead, they are leveraging multiple platforms to optimize performance, reduce latency, and ensure data backup and recovery. This trend is particularly prominent among large enterprises seeking to enhance their global reach and improve service delivery. The flexibility offered by cloud-based databases supports these strategies by enabling seamless data integration and management across various cloud environments. Moreover, the growing emphasis on cloud-native application development further aligns with the adoption of cloud-based databases, as they provide the necessary infrastructure and tools to support modern application architectures.

Security and compliance concerns have always been a significant consideration for enterprises moving to the cloud. However, advancements in cloud security and the introduction of stringent data protection regulations like GDPR and CCPA have alleviated some of these apprehensions. Cloud service providers are continuously investing in enhancing their security offerings, providing robust encryption, access controls, and compliance certifications to their clients. This, in turn, boosts the confidence of organizations in adopting cloud-based databases, knowing that their data is secure and compliant with industry standards. As businesses increasingly recognize the security advantages offered by cloud platforms, this further accelerates the market's growth.

Regionally, North America is expected to be a dominant player in the cloud-based database market, driven by early adoption of cloud technologies and the presence of major cloud service providers. Europe is also witnessing significant growth, with enterprises in countries like the UK, Germany, and France increasingly shifting towards cloud solutions. The Asia Pacific region is anticipated to experience the highest growth rate, fueled by rapid digitalization and increasing IT investments in countries such as China, India, and Japan. Latin America and the Middle East & Africa are gradually catching up, with businesses recognizing the potential of cloud-based databases in improving operational efficiencies and driving innovation.

Database Type Analysis

When it comes to database types, the market is primarily segmented into SQL and NoSQL databases. SQL databases have been the traditional choice for structured data storage and management, and they continue to hold a significant share of the market. Organizations opt for SQL databases due to their robust support for complex queries, ACID compliance, and established presence in the enterprise IT landscape. The consistent demand for SQL databases can be attributed to their ability to handle transactional data and their widespread use in various applications, including enterprise resource planning (ERP) and customer relation

A. SUMMARY This dataset contains data from financial statements of campaign committees that file with the San Francisco Ethics Commission and (1) contribute to or (2) receive funds from a San Francisco committee which was Primarily Formed for a local election, or (3) filed a Late Reporting Period statement with the SFEC. Financial statements are included for a committee if they meet any of the three criteria for each election included in the search parameters and are not primarily formed for the election. The search period for financial statements begins two years before an election and runs through the next semi-annual filing deadline. The dataset currently filters by the elections of 2024-03-05 and 2024-11-05. B. HOW THE DATASET IS CREATED During an election period an automated script runs nightly to examine filings by Primarily Formed San Francisco committees. If a primarily formed committee reports accepting money from or giving money to a second committee, that second committee's ID number is added to a filter list. If a committee electronically files a late reporting period form with the San Francisco Ethics Commission, the committee's ID number is also included in the filter list. The filter list is used in a second step that looks for filings by committees that file with the San Francisco Ethics Commission or the California Secretary of State. This dataset shows the output of the second step for committees that file with the San Francisco Ethics Commission. The data comes from a nightly search of the Ethics Commission campaign database. A second dataset includes committees that file with the Secretary of State. C. UPDATE PROCESS This dataset is rewritten nightly and is based on data derived from campaign filings. The update script runs automatically on a timer during the 90 days before an election. Refer to the "Data Last Updated" date in the section "About This Dataset" on the landing page to see when the script last ran successfully. D. HOW TO USE THIS DATASET Transactions from all FPPC Form 460 schedules are presented together, refer to the Form Type to differentiate. Transactions from FPPC Form 461 and Form 465 filings are presented together, refer to the Form Type to differentiate. Transactions with a Form Type of D, E, F, G, H, F461P5, F465P3, F496, or F497P2 represent expenditures, or money spent by the committee. Transactions with Form Type A, B1, C, I, F496P3, and F497P1 represent receipts, or money taken in by the committee. Refer to the instructions for Forms 460, 496, and 497 for more details. Transactions on Form 460 Schedules D, F, G, and H are also reported on Schedule E. When doing summary statistics use care not to double count expenditures. Transactions from FPPC Form 496 and Form 497 filings are presented in this dataset. Transactions that were reported on these forms are also reported on the Form 460 at the next filing deadline. If a 460 filing deadline has passed and the committee has filed a campaign statement, transactions on 496/497 filings from the late reporting period should be disregarded. This dataset only shows transactions from the most recent filing version. Committee amendments overwrite filings which come before in sequence. Campaign Committees are required to file statements according to a schedule set out by the C