National and subnational mid-year population estimates for the UK and its constituent countries by administrative area, age and sex (including components of population change, median age and population density).

Estimated number of persons on July 1, by 5-year age groups and gender, and median age, for Canada, provinces and territories.

Abstract (en): The China Multi-Generational Panel Dataset - Liaoning (CMGPD-LN) is drawn from the population registers compiled by the Imperial Household Agency (neiwufu) in Shengjing, currently the northeast Chinese province of Liaoning, between 1749 and 1909. It provides 1.5 million triennial observations of more than 260,000 residents from 698 communities. The population mainly consists of immigrants from North China who settled in rural Liaoning during the early eighteenth century, and their descendants. The data provide socioeconomic, demographic, and other characteristics for individuals, households, and communities, and record demographic outcomes such as marriage, fertility, and mortality. The data also record specific disabilities for a subset of adult males. Additionally, the collection includes monthly and annual grain price data, custom records for the city of Yingkou, as well as information regarding natural disasters, such as floods, droughts, and earthquakes. This dataset is unique among publicly available population databases because of its time span, volume, detail, and completeness of recording, and because it provides longitudinal data not just on individuals, but on their households, descent groups, and communities. Possible applications of the dataset include the study of relationships between demographic behavior, family organization, and socioeconomic status across the life course and across generations, the influence of region and community on demographic outcomes, and development and assessment of quantitative methods for the analysis of complex longitudinal datasets. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.; Standardized missing values.; Created online analysis version with question text.; Checked for undocumented or out-of-range codes.. Smallest Geographic Unit: Chinese banners (8) The data are from 725 surviving triennial registers from 29 distinct populations. Each of the 29 register series corresponded to a specific rural population concentrated in a small number of neighboring villages. These populations were affiliated with the Eight Banner civil and military administration that the Qing state used to govern northeast China as well as some other parts of the country. 16 of the 29 populations are regular bannermen. In these populations adult males had generous allocations of land from the state, and in return paid an annual fixed tax to the Imperial Household Agency, and provided to the Imperial Household Agency such home products as homespun fabric and preserved meat, and/or such forest products as mushrooms. In addition, as regular bannermen they were liable for military service as artisans and soldiers which, while in theory an obligation, was actually an important source of personal revenue and therefore a political privilege. 8 of the 29 populations are special duty banner populations. As in the regular banner population, the adult males in the special duty banner populations also enjoyed state allocated land free of rent. These adult males were also assigned to provide special services, including collecting honey, raising bees, fishing, picking cotton, and tanning and dyeing. The remaining populations were a diverse mixture of estate banner and servile populations. The populations covered by the registers, like much of the population of rural Liaoning in the eighteenth and nineteenth centuries, were mostly descendants of Han Chinese settlers who came from Shandong and other nearby provinces in the late seventeenth and early eighteenth centuries in response to an effort by the Chinese state to repopulate the region. 2016-09-06 2016-09-06 The Training Guide has been updated to version 3.60. Additionally, the Principal Investigator affiliation has been corrected, and cover sheets for all PDF documents have been revised.2014-07-10 Releasing new study level documentation that contains the tables found in the appendix of the Analytic dataset codebook.2014-06-10 The data and documentation have been updated following re-evaluation.2014-01-29 Fixing variable format issues. Some variables that were supposed to be s...

Estimated number of persons by quarter of a year and by year, Canada, provinces and territories.

AbstractWe investigated the population structure of Grosmannia clavigera (Gc), a fungal symbiont of the mountain pine beetle (MPB) that plays a crucial role in the establishment and reproductive success of this pathogen. This insect–fungal complex has destroyed over 16 million ha of lodgepole pine forests in Canada, the largest MPB epidemic in recorded history. During this current epidemic, MPB has expanded its range beyond historically recorded boundaries, both northward and eastward, and has now reached the jack pine of Alberta, potentially threatening the Canadian boreal forest. To better understand the dynamics between the beetle and its fungal symbiont, we sampled 19 populations in western North America and genotyped individuals from these populations with eight microsatellite markers. The fungus displayed high haplotype diversity, with over 250 unique haplotypes observed in 335 single spore isolates. Linkage equilibria in 13 of the 19 populations suggested that the fungus reproduces sexually. Bayesian clustering and distance analyses identified four genetic clusters that corresponded to four major geographical regions, which suggested that the epidemic arose from multiple geographical sources. A genetic cluster north of the Rocky Mountains, where the MPB has recently become established, experienced a population bottleneck, probably as a result of the recent range expansion. The two genetic clusters located north and west of the Rocky Mountains contained many fungal isolates admixed from all populations, possibly due to the massive movement of MPB during the epidemic. The general agreement in north–south differentiation of MPB and G. clavigera populations points to the fungal pathogen’s dependence on the movement of its insect vector. In addition, the patterns of diversity and the individual assignment tests of the fungal associate suggest that migration across the Rocky Mountains occurred via a northeastern corridor, in accordance with meteorological patterns and observation of MPB movement data. Our results highlight the potential of this pathogen for both expansion and sexual reproduction, and also identify some possible barriers to gene flow. Understanding the ecological and evolutionary dynamics of this fungus–beetle association is important for the modelling and prediction of MPB epidemics. Usage notesGC293-dataThis is a microsatellite data set of 8 loci for the fungus Grosmannia clavigera. Cokumn A contains the strain code, column B represents the sampling location, while columns C-R have the raw data of fragment size.

Analysis of ‘Swiggy Restaurants Dataset of Metro Cities’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/aniruddhapa/swiggy-restaurants-dataset-of-metro-cities on 28 January 2022.

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

Context

This dataset contains swiggy registered restaurants details of major metropoliton cities of India. I have considered only metropoliton cities with population 4.5 million. As per the Census of India 2011 definition of more than 4 million population, some of the major Metropolitan Cities in India are:

Mumbai (more than 18 Million) Delhi (more than 16 Million) Kolkata (more than 14 Million) Chennai (more than 8.6 million) Bangalore (around 8.5 million) Hyderabad (around 7.6 million) Ahmedabad (around 6.3 million) Pune (around 5.05 million) Surat (around 4.5 million)

Content

I have scrapped the data using python. It may not have all the restaurants of a particular city because if during webscrapping any restaurant has not enabled swiggy as their delivery partner, that restaurant's details will not be scrapped. Though I have scrapped same cities multiple times, to include maximum restaurant details. The data is collected on 12th Jan 2022.

Acknowledgements

Thank you swiggy for the dataset.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

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

This dataset contains information about the demographics of all US cities and census-designated places with a population greater or equal to 65,000. This data comes from the US Census Bureau's 2015 American Community Survey. This product uses the Census Bureau Data API but is not endorsed or certified by the Census Bureau.

This report was released in September 2010. However, recent demographic data is available on the datastore - you may find other datasets on the Datastore useful such as: GLA Population Projections, National Insurance Number Registrations of Overseas Nationals, Births by Birthplace of Mother, Births and Fertility Rates, Office for National Statistics (ONS) Population Estimates

FOCUSON**LONDON**2010:**POPULATION**AND**MIGRATION**

London is the United Kingdom’s only city region. Its population of 7.75 million is 12.5 per cent of the UK population living on just 0.6 per cent of the land area. London’s average population density is over 4,900 persons per square kilometre, this is ten times that of the second most densely populated region.

Between 2001 and 2009 London’s population grew by over 430 thousand, more than any other region, accounting for over 16 per cent of the UK increase.

This report discusses in detail the population of London including Population Age Structure, Fertility and Mortality, Internal Migration, International Migration, Population Turnover and Churn, and Demographic Projections.

Population and Migration report is the first release of the Focus on London 2010-12 series. Reports on themes such as Income, Poverty, Labour Market, Skills, Health, and Housing are also available.

REPORT:

Read the full report in PDF format.

https://londondatastore-upload.s3.amazonaws.com/fol/FocusOnLondonCoverweb.jpg" alt="">

PRESENTATION:

To access an interactive presentation about population changes in London click the link to see it on Prezi.com

DATA:

To access a spreadsheet with all the data from the Population and Migration report click on the image below.

Report data

MAP:

To enter an interactive map showing a number of indicators discussed in the Population and Migration report click on the image below.

Interactive Maps

FACTS:

● Top five boroughs for babies born per 10,000 population in 2008-09:

1. Newham – 244.4
2. Barking and Dagenham – 209.3
3. Hackney – 205.7
4. Waltham Forest – 202.7
5. Greenwich – 196.2

-32. Havering – 116.8

-33. City of London – 47.0

● In 2009, Barnet overtook Croydon as the most populous London borough. Prior to this Croydon had been the largest since 1966

● Population per hectare of land used for Domestic building and gardens is highest in Tower Hamlets

● In 2008-09, natural change (births minus deaths) led to 78,000 more Londoners compared with only 8,000 due to migration. read more about this or click play on the chart below to reveal how regional components of populations change have altered over time.

Dataset Source: https://machinehack.com/hackathon/predict_accident_risk_score_for_unique_postcode/data

Data set is for private consumption for the competition.

According to IBEF “Domestic automobiles production increased at 2.36% CAGR between FY16-20 with 26.36 million vehicles being manufactured in the country in FY20.Overall, domestic automobiles sales increased at 1.29% CAGR between FY16-FY20 with 21.55 million vehicles being sold in FY20”.The rise in vehicles on the road will also lead to multiple challenges and the road will be more vulnerable to accidents.Increased accident rates also leads to more insurance claims and payouts rise for insurance companies.

In order to pre-emptively plan for the losses, the insurance firms leverage accident data to understand the risk across the geographical units e.g. Postal code/district etc.

In this challenge, we are providing you the dataset to predict the “Accident_Risk_Index” against the postcodes.Accident_Risk_Index (mean casualties at a postcode) = sum(Number_of_casualities)/count(Accident_ID)

Working example:

Train Data (given)
Accident_ID Postcode Number_of_casualities 1 AL1 1JJ 2 2 AL1 1JP 3 3 AL1 3PS 2 4 AL1 3PS 1 5 AL1 3PS 1 Modelling Train Data (Rolled up at Postcode level)
Postcode Derived_feature1 Derived_feature2 Accident_risk_Index AL1 1JJ _ _ 2 AL1 1JP _ _ 3 AL1 3PS _ _ 1.33 The participants are required to predict the 'Accident_risk_index' for the test.csv and against the postcode on the test data.

Then submit your 'my_submission_file.csv' on the submission tab of the hackathon page.

Pro-tip: The participants are required to perform feature engineering to first roll-up the train data at postcode level and create a column as “accident_risk_index” and optimize the model against postcode level.

Few Hypothesis to help you think: "More accidents happen in the later part of the day as those are office hours causing congestion"

"Postal codes with more single carriage roads have more accidents"

(***In the above hypothesis features such as office_hours_flag and #single _carriage roads can be formed)

Additionally, we are providing you with road network data (contains info on the nearest road to a postcode and it's characteristics) and population data (contains info about population at area level). This info are for augmentation of features, but not mandatory to use.

The provided dataset contains the following files:

Train: 4,84,042 rows x 27 columns

Test: 1,15,958 rows x 27 columns

train.csv & test.csv:

'Accident_ID', 'Police_Force', 'Number_of_Vehicles', 'Number_of_Casualties', 'Date', 'Day_of_Week', 'Time', ‘Local_Authority_(District)', 'Local_Authority_(Highway)', '1st_Road_Class', '1st_Road_Number', 'Road_Type', 'Speed_limit', '2nd_Road_Class', '2nd_Road_Number', 'Pedestrian_Crossing-Human_Control', 'Pedestrian_Crossing-Physical_Facilities', 'Light_Conditions', ‘'Weather_Conditions', 'Road_Surface_Conditions', 'Special_Conditions_at_Site', 'Carriageway_Hazards', 'Urban_or_Rural_Area', 'Did_Police_Officer_Attend_Scene_of_Accident', 'state', 'postcode', 'country'

Population: 8,035 rows x 10 columns

population.csv:

​​'postcode', 'Rural Urban', 'Variable: All usual residents; measures: Value', 'Variable: Males; measures: Value', 'Variable: Females; measures: Value', ‘Variable: Lives in a household; measures: Value', ‘Variable: Lives in a communal establishment; measures: Value', 'Variable: Schoolchild or full-time student aged 4 and over at their non term-time address; measures: Value', 'Variable: Area (Hectares); measures: Value', 'Variable: Density (number of persons per hectare); measures: Value'

Road Network: 91,566 rows x 8 columns

roads_network.csv:

'WKT', 'roadClassi', ‘roadFuncti', 'formOfWay', 'length', 'primaryRou', 'distance to the nearest point on rd', 'postcode’

Overview Swiss Re is one of the largest reinsurers in the world headquartered in Zurich with offices in over 25 countries. Swiss Re’s core expertise is in underwriting in life, health, as well as the property and casualty insurance space whereas its tech strategy focuses on developing smarter and innovative solutions for clients’ value chains by leveraging data and technology.

The company’s vision is to make the world more resilient. Swiss Re believes in applying fresh perspectives, knowledge and capital to anticipate and manage risk to create smarter solutions and help the world rebuild, renew and move forward.About 1300 professionals that work in the Swiss Re Global Business Solutions Center (BSC), Bangalore combine experience, expertise and out-of-the-box thinking to bring Swiss Re's core business to life by creating new business opportunities.

Data format and access

The database consists of full-text patient reviews, reflecting their dissatisfaction with healthcare quality. Materials in Russian have been posted in the «Review list» of the site infodoctor.ru. Publication period: July 2012 to August 2023. The database consists of 18,492 reviews covering 16 Russian cities with population of over one million. Data format: .xlsx.

Data access: 10.5281/zenodo.15257447

Data collection methodology

Based on the fact that negative reviews may be more reliable than positive ones, the authors carried out negative reviews from 16 Russian cities with a population of over one million, for which it was possible to collect representative samples (at least 1000 reviews for each city). We have extracted reviews from the one-star section of this site's guestbook, as they are reliably identified as negative. Duplicates were removed from the database. Personal data in comment texts have been replaced with "##########". The author's gender was determined manually based on his/her name or gender endings in the texts of reviews. Otherwise, we indicated "0" - gender cannot be determined.

For Moscow reviews, classification was carried out using manual markup methods - based on the majority of votes for the review class from 3 annotators (if at least one annotator indicated that it was impossible to determine, the review was classified as #N/A - impossible to clearly determine). For reviews from other cities, classification was made into 3 classes using machine learning methods based on logistic regression. The classification accuracy was 88%.

The medical specialties were distributed into large groups for the convenience of further analysis. The correspondence of medical specialties to large groups is presented in detail in Appendix 1.

Sample structure and description of variables

· CITY – the name of a city with a population of over a million (on a separate sheet – Moscow), the other 15 are Volgograd, Voronezh, Yekaterinburg, Kazan, Krasnodar, Krasnoyarsk, Nizhny Novgorod, Novosibirsk, Omsk, Perm, Rostov-on-Don, Samara, St. Petersburg, Ufa, Chelyabinsk

· TEXT – review text

· GENDER – gender of the review author (2 – female, 1 – male, 0 – cannot be determined)

· CLASS_1 – group of reasons for dissatisfaction with medical care (M – issues of medical content, O – issues of organizational support and economic aspect, C – mixed (combined) class, #N/A – cannot be clearly determined)[1]

· CLASS_2 – group of reasons for dissatisfaction with medical care (0 – issues of medical content, 1 – issues of organizational support and economic aspect, 2 – mixed (combined) class, #N/A – cannot be clearly determined)

· DAY – day of the month the review was posted

· MONTH – month the review was posted

· YEAR – year the review was posted

· DOCTOR_OR_CLINIC – what or who is the review dedicated to – the doctor or the clinic

· SPEC – physician specialty (for observations where the review is dedicated to the physician)

· GROUP_SPEC – a large group of a physician’s specialty

· ID – observation identifier

Database application

The data are suitable for analyzing patient dissatisfaction trends with medical services in Russia over the period from July 2012 to August 2023. This dataset could be particularly useful for healthcare providers, policymakers, and researchers interested in understanding patient experiences and identifying areas for quality improvement in Russian healthcare. Some potential applications include:

• Analyzing geographic patterns of patient complaints across different cities in Russia
• Examining trends in patient dissatisfaction over time
• Identifying common reasons for dissatisfaction with medical care
• Comparing dissatisfaction levels between different medical specialties
• Assessing gender differences in patient complaints

The database provides rich qualitative data through full-text review texts, allowing for in-depth analysis of patient experiences. The structured variables like city, date, doctor/clinic information, etc. enable quantitative analysis as well. This combination of qualitative and quantitative data makes it possible to gain a comprehensive understanding of patient dissatisfaction patterns in Russia's healthcare system over more than a decade.

For researchers specifically interested in healthcare quality issues, this dataset could serve as an important resource for studying patient experiences and outcomes in Russia's medical system. The longitudinal nature of the data (2012-2023) also allows for analysis of changes over time in patient satisfaction.

Overall, this database provides valuable insights into patient perceptions of healthcare quality that could inform policy decisions, quality improvement

Key Table Information.Table Title.Population, Enrollment, and Personal Income: U.S. and State: 2012 - 2023.Table ID.GOVSTIMESERIES.GS00SS16.Survey/Program.Public Sector.Year.2024.Dataset.PUB Public Sector Annual Surveys and Census of Governments.Source.U.S. Census Bureau, Public Sector.Release Date.2025-05-01.Release Schedule.The Annual Survey of School System Finances occurs every year. Data are typically released in early May. There are approximately two years between the reference period and data release..Dataset Universe.Census of Governments - Organization (CG):The universe of this file is all federal, state, and local government units in the United States. In addition to the federal government and the 50 state governments, the Census Bureau recognizes five basic types of local governments. The government types are: County, Municipal, Township, Special District, and School District. Of these five types, three are categorized as General Purpose governments: County, municipal, and township governments are readily recognized and generally present no serious problem of classification. However, legislative provisions for school district and special district governments are diverse. These two types are categorized as Special Purpose governments. Numerous single-function and multiple-function districts, authorities, commissions, boards, and other entities, which have varying degrees of autonomy, exist in the United States. The basic pattern of these entities varies widely from state to state. Moreover, various classes of local governments within a particular state also differ in their characteristics. Refer to the Individual State Descriptions report for an overview of all government entities authorized by state.The Public Use File provides a listing of all independent government units, and dependent school districts active as of fiscal year ending June 30, 2024. The Annual Surveys of Public Employment & Payroll (EP) and State and Local Government Finances (LF):The target population consists of all 50 state governments, the District of Columbia, and a sample of local governmental units (counties, cities, townships, special districts, school districts). In years ending in '2' and '7' the entire universe is canvassed. In intervening years, a sample of the target population is surveyed. Additional details on sampling are available in the survey methodology descriptions for those years.The Annual Survey of Public Pensions (PP):The target population consists of state- and locally-administered defined benefit funds and systems of all 50 state governments, the District of Columbia, and a sample of local governmental units (counties, cities, townships, special districts, school districts). In years ending in '2' and '7' the entire universe is canvassed. In intervening years, a sample of the target population is surveyed. Additional details on sampling are available in the survey methodology descriptions for those years.The Annual Surveys of State Government Finance (SG) and State Government Tax Collections (TC):The target population consists of all 50 state governments. No local governments are included. For the purpose of Census Bureau statistics, the term "state government" refers not only to the executive, legislative, and judicial branches of a given state, but it also includes agencies, institutions, commissions, and public authorities that operate separately or somewhat autonomously from the central state government but where the state government maintains administrative or fiscal control over their activities as defined by the Census Bureau. Additional details are available in the survey methodology description.The Annual Survey of School System Finances (SS):The Annual Survey of School System Finances targets all public school systems providing elementary and/or secondary education in all 50 states and the District of Columbia..Methodology.Data Items and Other Identifying Records.State population (in thousands)Fall enrollmentPersonal income (prior calendar year, in million dollars)Definitions can be found by clicking on the column header in the table or by accessing the Glossary.For detailed information, see Government Finance and Employment Classification Manual..Unit(s) of Observation.The basic reporting unit is the governmental unit, defined as an organized entity which in addition to having governmental character, has sufficient discretion in the management of its own affairs to distinguish it as separate from the administrative structure of any other governmental unit.The reporting units for the Annual Survey of School System Finances are public school systems that provide elementary and/or secondary education. The term "public school systems" includes two types of government entities with responsibility for providing education services: (1) school districts that are administratively and fiscally independent of any other government and are counted as separate governments; and (2) public school system...

Data for CDC’s COVID Data Tracker site on Rates of COVID-19 Cases and Deaths by Vaccination Status. Click 'More' for important dataset description and footnotes

Dataset and data visualization details: These data were posted on October 21, 2022, archived on November 18, 2022, and revised on February 22, 2023. These data reflect cases among persons with a positive specimen collection date through September 24, 2022, and deaths among persons with a positive specimen collection date through September 3, 2022.

Vaccination status: A person vaccinated with a primary series had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after verifiably completing the primary series of an FDA-authorized or approved COVID-19 vaccine. An unvaccinated person had SARS-CoV-2 RNA or antigen detected on a respiratory specimen and has not been verified to have received COVID-19 vaccine. Excluded were partially vaccinated people who received at least one FDA-authorized vaccine dose but did not complete a primary series ≥14 days before collection of a specimen where SARS-CoV-2 RNA or antigen was detected. Additional or booster dose: A person vaccinated with a primary series and an additional or booster dose had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after receipt of an additional or booster dose of any COVID-19 vaccine on or after August 13, 2021. For people ages 18 years and older, data are graphed starting the week including September 24, 2021, when a COVID-19 booster dose was first recommended by CDC for adults 65+ years old and people in certain populations and high risk occupational and institutional settings. For people ages 12-17 years, data are graphed starting the week of December 26, 2021, 2 weeks after the first recommendation for a booster dose for adolescents ages 16-17 years. For people ages 5-11 years, data are included starting the week of June 5, 2022, 2 weeks after the first recommendation for a booster dose for children aged 5-11 years. For people ages 50 years and older, data on second booster doses are graphed starting the week including March 29, 2022, when the recommendation was made for second boosters. Vertical lines represent dates when changes occurred in U.S. policy for COVID-19 vaccination (details provided above). Reporting is by primary series vaccine type rather than additional or booster dose vaccine type. The booster dose vaccine type may be different than the primary series vaccine type. ** Because data on the immune status of cases and associated deaths are unavailable, an additional dose in an immunocompromised person cannot be distinguished from a booster dose. This is a relevant consideration because vaccines can be less effective in this group. Deaths: A COVID-19–associated death occurred in a person with a documented COVID-19 diagnosis who died; health department staff reviewed to make a determination using vital records, public health investigation, or other data sources. Rates of COVID-19 deaths by vaccination status are reported based on when the patient was tested for COVID-19, not the date they died. Deaths usually occur up to 30 days after COVID-19 diagnosis. Participating jurisdictions: Currently, these 31 health departments that regularly link their case surveillance to immunization information system data are included in these incidence rate estimates: Alabama, Arizona, Arkansas, California, Colorado, Connecticut, District of Columbia, Florida, Georgia, Idaho, Indiana, Kansas, Kentucky, Louisiana, Massachusetts, Michigan, Minnesota, Nebraska, New Jersey, New Mexico, New York, New York City (New York), North Carolina, Philadelphia (Pennsylvania), Rhode Island, South Dakota, Tennessee, Texas, Utah, Washington, and West Virginia; 30 jurisdictions also report deaths among vaccinated and unvaccinated people. These jurisdictions represent 72% of the total U.S. population and all ten of the Health and Human Services Regions. Data on cases among people who received additional or booster doses were reported from 31 jurisdictions; 30 jurisdictions also reported data on deaths among people who received one or more additional or booster dose; 28 jurisdictions reported cases among people who received two or more additional or booster doses; and 26 jurisdictions reported deaths among people who received two or more additional or booster doses. This list will be updated as more jurisdictions participate. Incidence rate estimates: Weekly age-specific incidence rates by vaccination status were calculated as the number of cases or deaths divided by the number of people vaccinated with a primary series, overall or with/without a booster dose (cumulative) or unvaccinated (obtained by subtracting the cumulative number of people vaccinated with a primary series and partially vaccinated people from the 2019 U.S. intercensal population estimates) and multiplied by 100,000. Overall incidence rates were age-standardized using the 2000 U.S. Census standard population. To estimate population counts for ages 6 months through 1 year, half of the single-year population counts for ages 0 through 1 year were used. All rates are plotted by positive specimen collection date to reflect when incident infections occurred. For the primary series analysis, age-standardized rates include ages 12 years and older from April 4, 2021 through December 4, 2021, ages 5 years and older from December 5, 2021 through July 30, 2022 and ages 6 months and older from July 31, 2022 onwards. For the booster dose analysis, age-standardized rates include ages 18 years and older from September 19, 2021 through December 25, 2021, ages 12 years and older from December 26, 2021, and ages 5 years and older from June 5, 2022 onwards. Small numbers could contribute to less precision when calculating death rates among some groups. Continuity correction: A continuity correction has been applied to the denominators by capping the percent population coverage at 95%. To do this, we assumed that at least 5% of each age group would always be unvaccinated in each jurisdiction. Adding this correction ensures that there is always a reasonable denominator for the unvaccinated population that would prevent incidence and death rates from growing unrealistically large due to potential overestimates of vaccination coverage. Incidence rate ratios (IRRs): IRRs for the past one month were calculated by dividing the average weekly incidence rates among unvaccinated people by that among people vaccinated with a primary series either overall or with a booster dose. Publications: Scobie HM, Johnson AG, Suthar AB, et al. Monitoring Incidence of COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Status — 13 U.S. Jurisdictions, April 4–July 17, 2021. MMWR Morb Mortal Wkly Rep 2021;70:1284–1290. Johnson AG, Amin AB, Ali AR, et al. COVID-19 Incidence and Death Rates Among Unvaccinated and Fully Vaccinated Adults with and Without Booster Doses During Periods of Delta and Omicron Variant Emergence — 25 U.S. Jurisdictions, April 4–December 25, 2021. MMWR Morb Mortal Wkly Rep 2022;71:132–138. Johnson AG, Linde L, Ali AR, et al. COVID-19 Incidence and Mortality Among Unvaccinated and Vaccinated Persons Aged ≥12 Years by Receipt of Bivalent Booster Doses and Time Since Vaccination — 24 U.S. Jurisdictions, October 3, 2021–December 24, 2022. MMWR Morb Mortal Wkly Rep 2023;72:145–152. Johnson AG, Linde L, Payne AB, et al. Notes from the Field: Comparison of COVID-19 Mortality Rates Among Adults Aged ≥65 Years Who Were Unvaccinated and Those Who Received a Bivalent Booster Dose Within the Preceding 6 Months — 20 U.S. Jurisdictions, September 18, 2022–April 1, 2023. MMWR Morb Mortal Wkly Rep 2023;72:667–669.

Table from the American Community Survey (ACS) 5-year series on income and earning related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes B19025 Aggregate Household Income, B19013 Median Household Income, B19001 Household Income, B19113 Median Family Household Income, B19101 Family Household Income, B19202 Median Nonfamily Household Income, B19201 Nonfamily Household Income, B19301 Per Capita Income/B19313 Aggregate Income/B01001 Sex by Age, C24010 Sex by Occupation of the Civilian Employed Population 16 years and Over, B20017 Median Earnings by Sex by Work Experience for the Population 16 years and over with Earnings, B20001 Sex by Earnings for the Population 16 years and over with Earnings. Data is pulled from block group tables for the most recent ACS vintage and summarized to the neighborhoods based on block group assignment.Table created for and used in the Neighborhood Profiles application.Vintages: 2023ACS Table(s): B19013, B19001, B19113, B19101, B19202, B19201, B19301, B19313, B01001, C24010, B20017, B20001, B19025Data downloaded from: Census Bureau's Explore Census Data The United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This restriction site associated DNA sequencing (RAD-seq) dataset for Antarctic krill (Euphausia superba) includes raw sequence data and summaries for 148 krill from 5 Southern Ocean sites. A detailed README.pdf file is provided to describe components of the dataset. DNA library preparation was carried out in two separate batches by Floragenex (Eugene, Oregon, USA). RAD fragment libraries (SbfI) were sequenced on an Illumina HiSeq 2000 using single-end 100 bp chemistry. As there is no reference genome for Antarctic krill, a set of unique 90 bp sequences (RAD tags) was assembled from 17.3 million single-end reads from an individual krill. We obtained over a billion raw reads from the 148 krill in our study (a mean of 6.8 million reads per sample). The reference assembly contained 239,441 distinct RAD tags. The core genotype dataset exported for downstream data filtering included just those SNPs with genotype calls in at least 80% of the krill samples and contained 12,114 SNPs on 816 RAD tags.

Sample collection table (comma separated):

Southern Ocean Location, Sample Size, Austral Summer, Latitude, Longitude, ID

East Antarctica (Casey), 21, 2010/2011, 64S, 100E, Cas East Antarctica (Mawson), 22, 2011/2012. 66S, 70E, Maw Lazarev Sea, 38, 2004/2005 and 2007/2008, 66S, 0E, Laz Western Antarctic Peninsula, 16, 2010/2011, 69S, 76W, WAP Ross Sea, 23, 2012/2013, 68S, 178E, Ross

Families of tax filers; Census families with children by age of children and children by age groups (final T1 Family File; T1FF).

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Pennsylvania. The dataset can be utilized to gain insights into gender-based income distribution within the Pennsylvania population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Pennsylvania, among individuals aged 15 years and older with income, there were 4.82 million men and 4.86 million women in the workforce. Among them, 2.64 million men were engaged in full-time, year-round employment, while 2.02 million women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 6.63% fell within the income range of under $24,999, while 9.70% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 26.48% of men in full-time roles earned incomes exceeding $100,000, while 16% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

https://i.neilsberg.com/ch/pennsylvania-income-distribution-by-gender-and-employment-type.jpeg" alt="Pennsylvania gender and employment-based income distribution analysis (Ages 15+)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2022 1-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Pennsylvania median household income by gender. You can refer the same here

Canada, with 3.3 people per square kilometre, has one of the lowest population densities in the world. In 2001, most of Canada's population of 30 million lived within 200 kilometres of the United States. In fact, the inhabitants of our three biggest cities — Toronto, Montréal and Vancouver — can drive to the border in less than two hours. Thousands of kilometres to the north, our polar region — the Yukon Territory, the Northwest Territories and Nunavut — is relatively empty, embracing 41% of our land mass but only 0.3% of our population. Human habitation in the solitary north clings largely to scattered settlements: villages among vast expanses of virgin ice, snow, tundra and taiga.

Genetic diversity is theorized to decrease in populations closer to a species' range edge, where habitat may be suboptimal. However, generalist species capable of long-range dispersal may maintain sufficient gene flow to counteract this, though the presence of significant barriers to dispersal (e.g., large water bodies, human-dominated landscapes) may still lead to, and exacerbate, the edge effect. We used microsatellite data for 2,426 gray wolves (Canis lupus) from 24 sub-populations (groups) to model how allelic richness and expected heterozygosity varied with two measures of range edge (mainland-island position, latitude, and distance from range center) across >7.3 million km² of northern North America. We found that allelic richness and expected heterozygosity of island groups was measurably less than that of mainland groups and that these differences increased with the island's distance to the species' range center in the study area. Our results demonstrate how multiple axes of geographic isolation (distance from range center and island habitation) can act synergistically to erode the genetic diversity of wide-ranging terrestrial vertebrate populations despite the counteracting influence of long-range dispersal ability. These findings emphasize how geographic isolation is a potential threat to the genetic diversity and viability of terrestrial vertebrate populations even among species capable of long-range dispersal.

This dataset is comprised of four separate sub-datasets, sourced from Carmichael et al. (2007), Musiani et al. (2007), McNay (2006), and a manuscript in progress (referred to as "MacNulty" in the data files, to be first published in Frevol et al. 2023). The raw data are comprised of sample IDs, latitude and longitude points indicating where the sample was collected or recorded, and microsatellite genetic information. To aid in re-use, the raw genetic data has also been formatted and presented for use with common population genetics software (Genepop, MICROCHECKER, Genetix, FSTAT). The dataset also includes spatial data files of the sub-populations described in the study, allelic richness and expected heterozygosity data derived from the raw and spatial data, and the R script used to create the models.

• Carmichael LE, Krizan J, Nagy JA, et al (2007) Historical and ecological determinants of genetic structure in arctic canids. Molecular Ecology 16:3466–3483. https://doi.org/10.1111/j.1365-294X.2007.03381.x
• McNay ME (2006) Preliminary results of parentage analysis using microsatellite markers from an exploited wolf population in central Alaska. Alaska Department of Fish and Game, Division of Wildlife Conservation, Juneau, Alaska
• Musiani M, Leonard JA, Cluff HD, et al (2007) Differentiation of tundra/taiga and boreal coniferous forest wolves: genetics, coat colour and association with migratory caribou. Molecular Ecology 16:4149–4170. https://doi.org/10.1111/j.1365-294X.2007.03458.x

A dataset listing New York cities by population for 2024.

The National Oceanic and Atmospheric Administration (NOAA) Coral Reef Conservation Program (Coral Program) invests approximately $5 million of its annual operating budget to support the National Coral Reef Monitoring Program (NCRMP) for biological, climate, and socioeconomic monitoring throughout the U.S. Pacific, Atlantic, Caribbean, and Gulf of Mexico coral reef areas. The monitoring program is unique for its national scale across a vast geographic area as well as its progressive inclusion of social science integrated with biophysical science. The effort provides a consistent flow of information about the status and trends of environmental conditions, natural resources, and the people and processes that interact with coral reef ecosystems. The overarching goal is to collect the scientific data needed to evaluate changing conditions of U.S. coral reef ecosystems, which are among the most biologically diverse and economically valuable ecosystems on earth, providing billions of dollars in food, jobs, recreational opportunities, coastal protection, and other important ecosystem services. The program focuses on four monitoring themes: benthic community structure, fish community structure, climate impacts, and socioeconomic condition. Within the benthic theme, the core indicators include: coral species abundance and size structure, coral diversity, coral condition, benthic percent cover, key coral and mobile invertebrate species, and reef rugosity. Data provided here include species abundance. The coral demographics protocol provides more detailed and species-specific insight (‘signal magnitude’) for coral populations. Individual data collections: Gulf of Mexico: https://doi.org/10.7289/v5vd6wts Florida: https://doi.org/10.7289/v5xw4h4z Puerto Rico: https://doi.org/10.7289/v5pg1q23 US Virgin Islands: https://doi.org/10.7289/v5ww7fqk