As a source of animal and plant population data, the Global Population Dynamics Database (GPDD) is unrivalled. Nearly five thousand separate time series are available here. In addition to all the population counts, there are taxonomic details of over 1400 species. The type of data contained in the GPDD varies enormously, from annual counts of mammals or birds at individual sampling sites, to weekly counts of zooplankton and other marine fauna. The project commenced in October 1994, following discussions on ways in which the collaborating partners could make a practical and enduring contribution to research into population dynamics. A small team was assembled and, with assistance and advice from numerous interested parties we decided to construct the database using the popular Microsoft Access platform. After an initial design phase, the major task has been that of locating, extracting, entering and validating the data in all the various tables. Now, nearly 5000 individual datasets have been entered onto the GPDD. The Global Population Dynamics Database comprises six Tables of data and information. The tables are linked to each other as shown in the diagram shown in figure 3 of the GPDD User Guide (GPDD-User-Guide.pdf). Referential integrity is maintained through record ID numbers which are held, along with other information in the Main Table. It's structure obeys all the rules of a standard relational database.

Table from the American Community Survey (ACS) 5-year series on household types and population related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes B11003 Family Type by Presence and Age of Own Children under 18 Years, B11005 Households by Presence of People Under 18 Years by Household Type, B11007 Households by Presence of People 65 Years and Over by Household Type, B11001 Household Type (Including Living Alone), B11002 Household Type by Relatives and Nonrelatives for Population in Households, B25003 Tenure, B25008 Total Population in Occupied Housing Units by Tenure, B09019 Household Type (Including Living Alone) by Relationship. Data is pulled from block group tables for the most recent ACS vintage and summarized to the neighborhoods based on block group assignment.

Working with partners across NIH, led by NIMHD and the NLM OBSSR-Behavioral Ontology Working Group, MeSH on November 29, 2022 added Federally recognized American Indian and Alaskan Native (AI/AN) tribal names and ethnic/ethnolinguistic minority terms as newly created type 5 SCR designated as “Population Groups”. These minority names (1,700+ terms) were mapped and reviewed by subject matter experts and scientists within NIH and from outside including Network of the National Library of Medicine members.

Structure: All type 5 SCRs have common fields 1. CC=5 Population Group 2. ST=T098 Population Groups 3. HM= At least one HM is to an MH under Population Groups [M01.686] 4. TH= NIMHD(2023)

Hungary - Distribution of population by household types: Three or more adults was 11.30% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Hungary - Distribution of population by household types: Three or more adults - last updated from the EUROSTAT on April of 2025. Historically, Hungary - Distribution of population by household types: Three or more adults reached a record high of 14.00% in December of 2009 and a record low of 10.70% in December of 2022.

Estonia - Distribution of population by household types: Single person was 22.30% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Estonia - Distribution of population by household types: Single person - last updated from the EUROSTAT on June of 2025. Historically, Estonia - Distribution of population by household types: Single person reached a record high of 22.30% in December of 2024 and a record low of 15.00% in December of 2009.

Ecological theories often encompass multiple levels of biological organization, such as genes, individuals, populations, and communities. Despite substantial progress toward ecological theory spanning multiple levels, ecological data rarely are connected in this way. This is unfortunate because different types of ecological data often emerge from the same underlying processes and, therefore, are naturally connected among levels. Here, we describe an approach to integrate data collected at multiple levels (e.g., individuals, populations) in a single statistical analysis. The resulting integrated models make full use of existing data and might strengthen links between statistical ecology and ecological models and theories that span multiple levels of organization. Integrated models are increasingly feasible due to recent advances in computational statistics, which allow fast calculations of multiple likelihoods that depend on complex mechanistic models. We discuss recently developed integrated models and outline a simple application using data on freshwater fishes in south-eastern Australia. Available data on freshwater fishes include population survey data, mark-recapture data, and individual growth trajectories. We use these data to estimate age-specific survival and reproduction from size-structured data, accounting for imperfect detection of individuals. Given that such parameter estimates would be infeasible without an integrated model, we argue that integrated models will strengthen ecological theory by connecting theoretical and mathematical models directly to empirical data. Although integrated models remain conceptually and computationally challenging, integrating ecological data among levels is likely to be an important step toward unifying ecology among levels.

Abstract

The 2019 Nauru mini census was carried out to update statistics on the population and the socio-economic situation of all persons living in private households in Nauru. Furthermore, the data collected in this census will be used as a sampling frame for future surveys that will be conducted in the country.

Geographic coverage

National coverage.

Analysis unit

Household and Individual.

Kind of data

Census/enumeration data [cen]

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was developped in English using the World Bank software called Survey Solutions.

The questionnaire is dividied into 4 main sections which are: - Household ID and Building Type: identification of the household; -Person Roster: questions related to household members (=individual characteristics, education, economic activities, disability); -Agriculture, Fisheries, Livestock and Aquaculture: questions related to these activities by household members; -Household: questions related to dwelling characteristics (=materials used for the dwelling, water storage).

There are also 3 categories that are for the interviewers' use: -Geographic Information + Photo; -Appendices: interviewer instructions and EA categories; -Legend: legend and structure of information in the questionnaire.

Demographic data are important to wildlife managers to gauge population health, to allow populations to be utilised sustainably, and to inform conservation efforts. We analysed published demographic data on the world’s wildfowl to examine taxonomic and geographic biases in study, and to identify gaps in knowledge. Wildfowl (order: Anseriformes) are a comparatively well studied bird group which includes 169 species of duck, goose and swan. In all, 1,586 wildfowl research papers published between 1911 and 2010 were found using Web of Knowledge (WoK) and Google Scholar. Over half of the research output involved just 15 species from seven genera. Research output was strongly biased towards ‘high income’ countries, common wildfowl species, and measures of productivity, rather than survival and movement patterns. There were significantly fewer demographic data for the world’s 31 threatened wildfowl species than for non-threatened species. Since 1994, the volume of demographic work on threatened species has increased more than for non-threatened species, but still makes up only 2.7% of total research output. As an aid to research prioritisation, a metric was created to reflect demographic knowledge gaps for each species related to research output for the species, its threat status, and availability of potentially useful surrogate data from congeneric species. According to the metric, the 25 highest priority species include thirteen threatened taxa and nine species each from Asia and South America, and six from Africa.

Finland - Distribution of population by household types: Single person was 25.80% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Finland - Distribution of population by household types: Single person - last updated from the EUROSTAT on April of 2025. Historically, Finland - Distribution of population by household types: Single person reached a record high of 25.80% in December of 2024 and a record low of 19.00% in December of 2010.

About this dataset

Context

The dataset tabulates the population of Hope by race. It includes the population of Hope across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Hope across relevant racial categories.

Key observations

The percent distribution of Hope population by race (across all racial categories recognized by the U.S. Census Bureau): 94.72% are white, 0.50% are Asian and 4.78% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Hope
• Population: The population of the racial category (excluding ethnicity) in the Hope is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Hope total population. 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 Hope Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Decatur County by race. It includes the population of Decatur County across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Decatur County across relevant racial categories.

Key observations

The percent distribution of Decatur County population by race (across all racial categories recognized by the U.S. Census Bureau): 92.93% are white, 1.64% are Black or African American, 0.29% are American Indian and Alaska Native, 0.52% are Asian, 0.01% are Native Hawaiian and other Pacific Islander, 1.04% are some other race and 3.56% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Decatur County
• Population: The population of the racial category (excluding ethnicity) in the Decatur County is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Decatur County total population. 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 Decatur County Population by Race & Ethnicity. You can refer the same here

Population Projections: Population residing in Spain on January 1, by sex, age and year. Annual. National.

About this dataset

Context

The dataset tabulates the population of El Segundo by race. It includes the population of El Segundo across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of El Segundo across relevant racial categories.

Key observations

The percent distribution of El Segundo population by race (across all racial categories recognized by the U.S. Census Bureau): 62.79% are white, 2.80% are Black or African American, 0.17% are American Indian and Alaska Native, 11.55% are Asian, 3.08% are some other race and 19.61% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the El Segundo
• Population: The population of the racial category (excluding ethnicity) in the El Segundo is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of El Segundo total population. 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 El Segundo Population by Race & Ethnicity. You can refer the same here

The layers on this map contain population, employed labour force counts, private dwelling counts, and employment counts at Census Subdivision and Census Tract geographies from the 2006, 2011, and 2016 Census. Definitions include:Population counts: the total population aggregated from different ages in each census tract.Employment counts: the number of labour force aged 15 years and over having an usual work place or working at home at places of work in each census tract, excluding workers with a non-fixed place-of-work.Employed labour force counts: the number of employed labour force aged 15 years and over having a usual work place or working at home at places of residence in each census tract including workers with a non-fixed place-of-work.Private dwellings count: the number of households aggregated from different types of dwellings in each census tract.Note: Population counts are from long census survey forms, covering 25% of the population. The other three variables are from short census survey forms, covering 100% population.Note about the Legend: the Employment and Population values are normalized by Quantiles. Each colour has the same number of features and will not necessarily represent the same values in different layers.CSDUID census subdivision idCSDNAME, census subdivision namePopulation, population in 2006LaborForce, labour force in 2006Household, household in 2006Job, employment in 2006Les couches de cette carte comprennent la population, la population active occupée, les logements privés et le nombre d’emplois dans les secteurs et subdivisions de recensement de 2006, 2011 et 2016. Quelques définitions :• Chiffres de population : population totale, agrégée par âge dans chacun des secteurs de recensement.• Chiffres de l’emploi : population active occupée âgée de 15 ans et plus ayant un lieu habituel de travail ou travaillant à domicile dans chacun des secteurs de recensement, excluant les travailleurs dont le lieu de travail est variable.• Chiffres de la population active occupée : population active occupée âgée de 15 ans et plus ayant un lieu habituel de travail ou travaillant au lieu de résidence dans chacun des secteurs de recensement, incluant les travailleurs dont le lieu de travail est variable.• Chiffres des logements privés : nombre de ménages agrégés selon différents types de logements dans chacun des secteurs de recensement.Nota : Les chiffres de population active occupée sont issus du questionnaire détaillé du recensement, qui couvre le quart de la population. Les trois autres variables sont issues du questionnaire abrégé, qui couvre la totalité de la population.Remarque concernant la légende : Les chiffres de population et les chiffres de l’emploi sont normalisés par quantile. Chaque couleur présente la même portion des cas, mais ne représente pas nécessairement les mêmes valeurs pour chaque couche.CSDUID identifiant de la subdivision de recensementCSDNAME, nom de la subdivision de recensementPopulation, population en 2006LaborForce, population active en 2006Household, ménages en 2006Job, emplois en 2006

This Web Map shows the Hong Kong Population Distribution by Housing Type by Large Tertiary Planning Unit Group in 2021. It is a subset of the 2021 Population Census made available by the Census and Statistics Department under the Government of Hong Kong Special Administrative Region (the "Government") at https://portal.csdi.gov.hk ("CSDI Portal"). The source data is in CSV format and has been processed and converted into Esri File Geodatabase format and then uploaded to Esri’s ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.For details about the data, source format and terms of conditions of usage, please refer to the website of CSDI Portal at https://portal.csdi.gov.hk.

In Sweden, the most common type of household in 2022 was cohabiting people with children aged zero to 24 years. Over 1.9 million women and two million men lived in this type of household. Moreover, nearly 1.2 million men and the same number of women were cohabiting or married without children. Of the singles with children between zero and 24 years, there were significantly more women than men.

observations_matrixmatrixes of visits and repeated visits within the same occasion. Correspond to two 229*11 matrixes (nsite x nyear), first eleven rows correspond to visits and next eleven rows correspond to repeated visits within the same occasion.propagule_pressureCovariates year and site-specific (nsite x (n-1)year). Corresponds to the propagule pressure index.site_specific_covariatesCovariates site-specific (nsite x n site-specific covariates). S = site size; V = vegetation cover; C = site connectivity; Stab = site stabilitysite_state_matrixMatrix (nsite x nyear) of site state. 0 = non visited site or missing data; 1 = site in state W (Wet); 2 = site in state D (Dry).state_matrixState matrix (nsite x nyear) of observations. 0 = non visited site or missing data; 1 = species not detected (i.e., either dry or wet site); 2 = species detected (i.e., necessarily a wet site).year_specific_covariatesCovariates year-specific ((n-1)year x n year-specific covariates). LRS = little rainy seas...

About this dataset

Context

The dataset tabulates the population of South El Monte by race. It includes the population of South El Monte across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of South El Monte across relevant racial categories.

Key observations

The percent distribution of South El Monte population by race (across all racial categories recognized by the U.S. Census Bureau): 13.98% are white, 0.97% are Black or African American, 1.20% are American Indian and Alaska Native, 20.15% are Asian, 44.94% are some other race and 18.78% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the South El Monte
• Population: The population of the racial category (excluding ethnicity) in the South El Monte is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of South El Monte total population. 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 South El Monte Population by Race & Ethnicity. You can refer the same here

Germany - Distribution of population by household types: Three or more adults was 6.80% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Germany - Distribution of population by household types: Three or more adults - last updated from the EUROSTAT on May of 2025. Historically, Germany - Distribution of population by household types: Three or more adults reached a record high of 7.40% in December of 2020 and a record low of 5.80% in December of 2019.

Determining population demographic rates is fundamental to understanding differences in species life-history strategies and their capacity to coexist. Calculating demographic rates, however, is challenging and requires long-term, large-scale censuses. Body size may serve as a simple predictor of demographic rate; can it act as a proxy for demographic rate when those data are unavailable? We tested the hypothesis that maximum body size predicts species' demographic rate using repeated censuses of the 77 most common liana species on the Barro Colorado Island, Panama (BCI) 50-ha plot. We found that maximum stem diameter does predict species' population turnover and demography. We also found that lianas on BCI can grow to the enormous diameter of 635 mm, indicating that they can store large amounts of carbon and compete intensely with tropical canopy trees. This study is the first to show that maximum stem diameter can predict plant species' demographic rates and that lianas can attain extremely large diameters. Understanding liana demography is particularly timely because lianas are increasing rapidly in many tropical forests, yet their species-level population dynamics remain chronically understudied. Determining per-species maximum liana diameters in additional forests will enable systematic comparative analyses of liana demography and potential influence across forest types.