These data include egg mass counts and adult capture-mark-recapture histories for Foothill Yellow-legged frogs at two streams in northern California. Data were collected from the South Fork Eel River and its tributary, Fox Creek, from 1993-2019. Data from Hurdygurdy Creek were collected from 2002-2008.

Garces_restrepo_et_al_2018_Cecropia&Coussapoa_locationsCecropia obtusifolia and Coussapoa villosa locations in an agro-ecosystem located in the Caribbean coastal plain of northeastern Costa Rica (10.328 N, 283.598 W).Garces_restrepo_et_al_2018_Three-toed_sloths_resigthsThree-toed_sloths_resigths in an agro-ecosystem located in the Caribbean coastal plain of northeastern Costa Rica (10.328 N, 283.598 W). March 2010 and ended in March 2014.survival_three-toed_sloths/* Adults survival three-toed sloths, Encounter occasions=49, groups=1, individual covariates=3, individual covariates names = sex (0=male, 1=female), cecropia density (tree/ha), Coussapoa density (tree/ha), number_core_area, proportion_forest_in_the_core_area. Each column corresponds to whether the individual was detected in a month, the history of capture began in March 2010 and ended in March 2014.*/ /* Juveniles survival three-toed sloths, Encounter occasions=51, groups=2 (10=year1, 01=year2), individual covariates=3, f...

A study comparing reintroduction scenarios for the San Francisco gartersnake (Thamnophis sirtalis tetrataenia), an endangered subspecies native to San Mateo County and Santa Cruz County in northern California. Models for snake survival, growth, fecundity, and reproductive status were used to construct a demographic population model. Data are posterior distributions for demographic parameters from Markov Chain Monte Carlo sampling in hierarchical Bayesian models.

The set contains reports on the annual assessment of the physical fitness of the population of Ukraine, prepared by the Ministry of Molosport. In particular, (1) Report on the results of the annual assessment of physical fitness of the population of Ukraine, (2) Summary report by regions; (3) Data archive (ZIP) of reports received from regional state administrations, ministries and other central bodies, respondents. Data are given in the context of each reporting period (calendar year).

These Demographic Data are U.S. Census American Community Survey Data, from the 2014 5-year set. Data Driven Detroit calculated densities (Per Sq Mile) by dividing the population by the ALAND10 field, which is the census land area field, in square meters.

Integral projection models (IPMs) are a powerful and popular approach to modeling population dynamics. Generalized linear models form the statistical backbone of an IPM. These models are typically fit using a frequentist approach. We suggest that hierarchical Bayesian statistical approaches offer important advantages over frequentist methods for building and interpreting IPMs, especially given the hierarchical nature of most demographic studies. Using a stochastic IPM for a desert cactus based on a 10-year study as a worked example, we highlight the application of a Bayesian approach for translating uncertainty in the vital rates (e.g., growth, survival, fertility) to uncertainty in population-level quantities derived from them (e.g., population growth rate). The best-fit demographic model, which would have been difficult to fit under a frequentist framework, allowed for spatial and temporal variation in vital rates and correlated responses to temporal variation across vital rates. The corresponding posterior probability distribution for the stochastic population growth rate (λS) indicated that, if current vital rates continue, the study population will decline with nearly 100% probability. Interestingly, less-supported candidate models that did not include spatial variance and vital rate correlations gave similar estimates of λS. This occurred because the best-fitting model did a much better job of fitting vital rates to which the population growth rate was weakly sensitive. The cactus case study highlights several advantages of Bayesian approaches to IPM modeling, including that they: (1) provide a natural fit to demographic data, which are often collected in a hierarchical fashion (e.g., with random variance corresponding to temporal and spatial heterogeneity); (2) seamlessly combine multiple data sets or experiments; (3) readily incorporate covariance between vital rates; and, (4) easily integrate prior information, which may be particularly important for species of conservation concern where data availability may be limited. However, constructing a Bayesian IPM will often require the custom development of a statistical model tailored to the peculiarities of the sampling design and species considered; there may be circumstances under which simpler methods are adequate. Overall, Bayesian approaches provide a statistically sound way to get more information out of hard-won data, the goal of most demographic research endeavors.

These data comprise Census records relating to the Alaskan people's population demographics for the State of Alaskan Salmon and People (SASAP) Project. Decennial census data were originally extracted from IPUMS National Historic Geographic Information Systems website: https://data2.nhgis.org/main(Citation: Steven Manson, Jonathan Schroeder, David Van Riper, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 12.0 [Database]. Minneapolis: University of Minnesota. 2017. http://doi.org/10.18128/D050.V12.0). A number of relevant tables of basic demographics on age and race, household income and poverty levels, and labor force participation were extracted.

  These particular variables were selected as part of an effort to understand and potentially quantify various dimensions of well-being in Alaskan communities.
  The file "censusdata_master.csv" is a consolidation of all 21 other data files in the package. For detailed information on how the datasets vary over different years, view the file "readme.docx" available in this data package.

  The included .Rmd file is a script which combines the 21 files by year into a single file (censusdata_master.csv). It also cleans up place names (including typographical errors) and uses the
  USGS place names dataset and the SASAP regions dataset to assign latitude and longitude values and region values to each place in the dataset. Note that some places were not assigned a region or
  location because they do not fit well into the regional framework.

  Considerable heterogeneity exists between census surveys each year. While we have attempted to combine these datasets in a way that makes sense, there may be some discrepancies or unexpected values.
  Please send a description of any unusual values to the dataset contact.

1.The research framework of eco‐evolutionary dynamics is increasing in popularity, as revealed by a steady stream of review articles and a recent and influential book, but primary empirical research is lagging behind. Moreover, the few empirical case studies demonstrating eco‐evolutionary dynamics might not be entirely representative.

2.Much current research on eco‐evolutionary dynamics is focused on how ecological interactions lead to natural selection on phenotypic traits (“eco‐evo”), and in turn how the evolutionary change in such traits feed back on ecological dynamics (“evo‐eco”). A key feature of eco‐evolutionary dynamics is thus a feedback loop between ecology (e. g. population dynamics) and evolution (i. e. genetic change).

3.In contrast to previous research on eco‐evolutionary dynamics driven by natural selection, the role of eco‐evolutionary feedbacks in sexual selection and sexual conflict are largely unknown. Here, I review theory and the limited empirical evidence in this area and identify some promising future lines of research.

4.I update a past review on contemporary evolution of secondary sexual traits in natural populations and formulate six explicit and rigorous criteria for contemporary evolution of secondary sexual traits by natural or sexual selection or sexual conflict. I then discuss the other key prediction of eco‐evolutionary dynamics (i. e. evolution by sexual selection or sexual conflict shapes ecological dynamics). My overview reveal that our current knowledge in this area is limited and mainly come from theoretical models and laboratory experiments.

5.A major challenge in eco‐evolutionary dynamics is therefore to link ecological and population dynamics with sexual selection and sexual conflict. This is not an easy task but might be possible with carefully chosen study systems and methods.

BPOP_summaryBlack duck breeding population estimates used in the analysis.Code for Combined SurvivalExecutes the first analysis described in the paper. Compares models with differing annual density dependent parameters.Code for Split SurvivalExecutes the second analysis described in the paper. Compares models with differing seasonal density dependent parameters.

These data were compiled here to fit various versions of Bayesian population models and compare their performance, primarily the time required to make inferences using different softwares and versions of code. The humpback chub data were collected by US Geological Survey and US Fish and Wildlife service in the Colorado and Little Colorado Rivers from April 2009 to October 2017. Adult fish were captured using hoop nets and electro-fishing, measured for total length and given individual marks using passive integrated transponders that were scanned when fish were recaptured. The other three datasets were collected by US Forest Service. Owl data for the N-occupancy model was collected between 1990 and 2015. Owl data for the two-species example was collected between 1990 and 2011. Both owl data sets were collected in a ~1000 km2 area in the Roseburg District of the Bureau of Land Management in western Oregon, USA. Owl vocalizations (vocal lures) were used to detect barred owl or spotted owl pairs in 158 survey polygons spread throughout the study area. The avian community occupancy data were collected from 1991 to 1995 across 92 sites in the Chiricahua Mountains of southeastern Arizona, USA. 149 species were detected through repeated point counts in each year.

Related article: Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39.

In this dataset:

We present temporally dynamic population distribution data from the Helsinki Metropolitan Area, Finland, at the level of 250 m by 250 m statistical grid cells. Three hourly population distribution datasets are provided for regular workdays (Mon – Thu), Saturdays and Sundays. The data are based on aggregated mobile phone data collected by the biggest mobile network operator in Finland. Mobile phone data are assigned to statistical grid cells using an advanced dasymetric interpolation method based on ancillary data about land cover, buildings and a time use survey. The data were validated by comparing population register data from Statistics Finland for night-time hours and a daytime workplace registry. The resulting 24-hour population data can be used to reveal the temporal dynamics of the city and examine population variations relevant to for instance spatial accessibility analyses, crisis management and planning.

Please cite this dataset as:

Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39. https://doi.org/10.1038/s41597-021-01113-4

Organization of data

The dataset is packaged into a single Zipfile Helsinki_dynpop_matrix.zip which contains following files:

HMA_Dynamic_population_24H_workdays.csv represents the dynamic population for average workday in the study area.

HMA_Dynamic_population_24H_sat.csv represents the dynamic population for average saturday in the study area.

HMA_Dynamic_population_24H_sun.csv represents the dynamic population for average sunday in the study area.

target_zones_grid250m_EPSG3067.geojson represents the statistical grid in ETRS89/ETRS-TM35FIN projection that can be used to visualize the data on a map using e.g. QGIS.

Column names

YKR_ID : a unique identifier for each statistical grid cell (n=13,231). The identifier is compatible with the statistical YKR grid cell data by Statistics Finland and Finnish Environment Institute.

H0, H1 ... H23 : Each field represents the proportional distribution of the total population in the study area between grid cells during a one-hour period. In total, 24 fields are formatted as “Hx”, where x stands for the hour of the day (values ranging from 0-23). For example, H0 stands for the first hour of the day: 00:00 - 00:59. The sum of all cell values for each field equals to 100 (i.e. 100% of total population for each one-hour period)

In order to visualize the data on a map, the result tables can be joined with the target_zones_grid250m_EPSG3067.geojson data. The data can be joined by using the field YKR_ID as a common key between the datasets.

License Creative Commons Attribution 4.0 International.

Related datasets

Järv, Olle; Tenkanen, Henrikki & Toivonen, Tuuli. (2017). Multi-temporal function-based dasymetric interpolation tool for mobile phone data. Zenodo. https://doi.org/10.5281/zenodo.252612

Tenkanen, Henrikki, & Toivonen, Tuuli. (2019). Helsinki Region Travel Time Matrix [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3247564

ABOUT

The Global Population Growth Dataset provides a comprehensive record of population trends across various countries over multiple decades. It includes detailed information such as the country name, ISO3 country code, year-wise population data, population growth, and growth rate. This dataset is valuable for researchers, demographers, policymakers, and data analysts interested in studying population dynamics, demographic trends, and economic development.

Key features of the dataset:

✅ Covers multiple countries and regions worldwide
✅ Includes historical and recent population data
✅ Provides year-wise population growth and growth rate (%)
✅ Categorizes data by country and decade for better trend analysis

This dataset serves as a crucial resource for analyzing global population trends, understanding demographic shifts, and supporting socio-economic research and policy-making.

FILE INFORMATION

The dataset consists of structured records related to country-wise population data, compiled from official sources. Each file contains information on yearly population figures, growth trends, and country-specific data. The structured format makes it useful for researchers, economists, and data scientists studying demographic patterns and changes. The file type is CSV.

COLUMNS DESCRIPTION

• Country – The name of the country.
• ISO3 – The three-letter ISO code of the country.
• Year – The year corresponding to the population data, useful for trend analysis.
• Population – The total population of the country for the given year.
• Population Growth – The absolute increase in population compared to the previous year.
• Growth Rate (%) – The percentage change in population compared to the previous year.
• Decade – The decade classification (e.g., 1990s, 2000s) for grouping long-term trends.

In 2022, roughly 13.2 percent of the Belgium population was at risk of poverty, which is defined as 60 percent of the average disposable income in the country. People with an income lower than this are considered poor. Women were especially vulnerable, as the share of women living under the poverty threshold was over 0.5 percentage points higher than the share of men.

 Severe material deprivation in Belgium 

In the last decade, the share of people suffering from severe material deprivation in Belgium has decreased, but as of 2021 still 3.6 percent of the population fit the EU definition, checking four out of these nine indicators: the inability to pay bills, the inability to properly heat the house, inability to deal with unexpected expenses, inability to eat meat, fish or chicken every two days, inability to go on a week-long holiday abroad, inability to afford a car, inability to afford a washer, inability to buy a television and inability to afford a telephone connection. For example, in 2021 4.2 percent of Belgians were unable to pay their bills on time.

Nearly one quarter of Belgians cannot afford a holiday  

 Although the share of Belgians unable to afford a washing machine or telephone was relatively low (1.1 and 0.2 percent respectively), some other indicators proved to be a lot more problematic. In 2021, just under 24 percent of Belgians were not able to pay unexpected expenses, and roughly the same amount of people could not afford to go on a week-long holiday either.

About this dataset

Context

The dataset tabulates the Hernando County population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Hernando County across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Hernando County was 212,807, a 2.84% increase year-by-year from 2022. Previously, in 2022, Hernando County population was 206,935, an increase of 3.11% compared to a population of 200,693 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Hernando County increased by 81,400. In this period, the peak population was 212,807 in the year 2023. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Hernando County is shown in this column.
• Year on Year Change: This column displays the change in Hernando County population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Hernando County Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Grand Forks population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Grand Forks across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Grand Forks was 58,921, a 0.39% increase year-by-year from 2022. Previously, in 2022, Grand Forks population was 58,694, a decline of 0.05% compared to a population of 58,724 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Grand Forks increased by 9,609. In this period, the peak population was 59,142 in the year 2020. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Grand Forks is shown in this column.
• Year on Year Change: This column displays the change in Grand Forks population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Grand Forks Population by Year. You can refer the same here

Platelet counts (±standard error of the mean, s.e.m.) and best-fit LS model parameters from fits to population survival data for each genotype, with 95% C.I.'s from the Monte Carlo technique in brackets.

Indicative parish population estimates derived from ONS mid-2018 small area population estimates, using best-fit aggregation.

Indicative parish population estimates derived from ONS mid-2018 small area population estimates, using best-fit aggregation, with estimates provided in broad age groups.

Please note that these most recent estimates are not released officially at parish level or to the latest parish boundaries by the Office of National Statistics or Cambridgeshire County Council. The data contained within this report has derived from a best-fit aggregation of smaller level geographies to try and give the best possible insight into parish level.

A global database of Census Data that provides an understanding of population distribution at administrative and zip code levels over 55 years, past, present, and future.

Leverage up-to-date census data with population trends for real estate, market research, audience targeting, and sales territory mapping.

Self-hosted commercial demographic dataset curated based on trusted sources such as the United Nations or the European Commission, with a 99% match accuracy. The global Census Data is standardized, unified, and ready to use.

Use cases for the Global Census Database (Consumer Demographic Data)

• Ad targeting

• B2B Market Intelligence

• Customer analytics

• Real Estate Data Estimations

• Marketing campaign analysis

• Demand forecasting

• Sales territory mapping

• Retail site selection

• Reporting

• Audience targeting

Census data export methodology

Our consumer demographic data packages are offered in CSV format. All Demographic data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Product Features

• Historical population data (55 years)

• Changes in population density

• Urbanization Patterns

• Accurate at zip code and administrative level

• Optimized for easy integration

• Easy customization

• Global coverage

• Updated yearly

• Standardized and reliable

• Self-hosted delivery

• Fully aggregated (ready to use)

• Rich attributes

Why do companies choose our demographic databases

• Standardized and unified demographic data structure

• Seamless integration in your system

• Dedicated location data expert

Note: Custom population data packages are available. Please submit a request via the above contact button for more details.

The Population Database of Mexico contains geographically referenced population data for Mexican states, municipalities and localities from the 1990 Mexican population and housing census. The data include population by gender and age group for approximately 83.7% of the Mexican population. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).

Supplementation programs, which release captive-born individuals into the wild, are commonly used to demographically bolster declining populations. In order to evaluate the effectiveness of these programs, the reproductive success of captive-born individuals released into the wild is often compared to the reproductive success of wild-born individuals in the recipient population (relative reproductive success, RRS). However, if there are heritable reductions in fitness associated with captive breeding, gene flow from captive-born individuals into the wild population can reduce the fitness of the wild population. Here, we show that when captive ancestry in the wild population reduces mean population fitness, estimates of RRS are upwardly biased, meaning that the relative fitness of captive-born individuals is over-estimated. Furthermore, the magnitude of this bias increases with the length of time that a supplementation program has been releasing captive-born individuals. This phenomenon has long-term conservation impacts since management decisions regarding the design of a supplementation program and the number of individuals to release can be based, at least in part, on RRS estimates. Therefore, we urge caution in the interpretation of relative fitness measures when the captive ancestry of the wild population cannot be precisely measured.