Datasets archived here consist of all data analyzed in Duan et al. 2015 from Journal of Applied Ecology. Specifically, these data were collected from annual sampling of emerald ash borer (Agrilus planipennis) immature stages and associated parasitoids on infested ash trees (Fraxinus) in Southern Michigan, where three introduced biological control agents had been released between 2007 - 2010. Detailed data collection procedures can be found in Duan et al. 2012, 2013, and 2015. Resources in this dataset:Resource Title: Duan J Data on EAB larval density-bird predation and unknown factor from Journal of Applied Ecology. File Name: Duan J Data on EAB larval density-bird predation and unknown factor from Journal of Applied Ecology.xlsxResource Description: This data set is used to calculate mean EAB density (per m2 of ash phloem area), bird predation rate and mortality rate caused by unknown factors and analyzed with JMP (10.2) scripts for mixed effect linear models in Duan et al. 2015 (Journal of Applied Ecology).Resource Title: DUAN J Data on Parasitism L1-L2 Excluded from Journal of Applied Ecology. File Name: DUAN J Data on Parasitism L1-L2 Excluded from Journal of Applied Ecology.xlsxResource Description: This data set is used to construct life tables and calculation of net population growth rate of emerald ash borer for each site. The net population growth rates were then analyzed with JMP (10.2) scripts for mixed effect linear models in Duan et al. 2015 (Journal of Applied Ecology).Resource Title: DUAN J Data on EAB Life Tables Calculation from Journal of Applied Ecology. File Name: DUAN J Data on EAB Life Tables Calculation from Journal of Applied Ecology.xlsxResource Description: This data set is used to calculate parasitism rate of EAB larvae for each tree and then analyzed with JMP (10.2) scripts for mixed effect linear models on in Duan et al. 2015 (Journal of Applied Ecology).Resource Title: READ ME for Emerald Ash Borer Biocontrol Study from Journal of Applied Ecology. File Name: READ_ME_for_Emerald_Ash_Borer_Biocontrol_Study_from_Journal_of_Applied_Ecology.docxResource Description: Additional information and definitions for the variables/content in the three Emerald Ash Borer Biocontrol Study tables: Data on EAB Life Tables Calculation Data on EAB larval density-bird predation and unknown factor Data on Parasitism L1-L2 Excluded from Journal of Applied Ecology Resource Title: Data Dictionary for Emerald Ash Borer Biocontrol Study from Journal of Applied Ecology. File Name: AshBorerAnd Parasitoids_DataDictionary.csvResource Description: CSV data dictionary for the variables/content in the three Emerald Ash Borer Biocontrol Study tables: Data on EAB Life Tables Calculation Data on EAB larval density-bird predation and unknown factor Data on Parasitism L1-L2 Excluded from Journal of Applied Ecology Fore more information see the related READ ME file.

Effective conservation and management of animal populations requires knowledge of abundance and trends. For many species, these quantities are estimated using systematic visual surveys. Additional individual-level data are available for some species. Integrated population modelling (IPM) offers a mechanism for leveraging these datasets into a single estimation framework. IPMs that incorporate both population- and individual-level data have previously been developed for birds, but have rarely been applied to cetaceans. Here, we explore how IPMs can be used to improve the assessment of cetacean populations. We combined three types of data that are typically available for cetaceans of conservation concern: population-level visual survey data, individual-level capture-recapture data, and data on anthropogenic mortality. We used this IPM to estimate the population dynamics of the Cook Inlet population of beluga whales (CIBW; Delphinapterus leucas) as a case study. Our state-space IPM included a population process model and three observational submodels: 1) a group detection model to describe group size estimates from aerial survey data; 2) a capture-recapture model to describe individual photographic capture-recapture data; and 3) a Poisson regression model to describe historical hunting data. The IPM produces biologically plausible estimates of population trajectories consistent with all three datasets. The estimated population growth rate since 2000 is less than expected for a recovering population. The estimated juvenile/adult survival rate is also low compared to other cetacean populations, indicating that low survival may be impeding recovery. This work demonstrates the value of integrating various data sources to assess cetacean populations and serves as an example of how multiple, imperfect datasets can be combined to improve our understanding of a population of interest. The model framework is applicable to other cetacean populations and to other taxa for which similar data types are available.

Methods /Data/CIBW_RSideCapHist_McGuire&Stephens.csv contains a matrix of right side capture histories (1 = captured, 0 = not captured) for each individual (rows) and year (columns). Photographic capture-recapture data were collected by Tamara McGuire. These data are made available here, without restriction, but anyone wishing to use these data is requested to contact tamaracookinletbeluga@gmail.com, who can provide further information on how raw data were processed to provide capture histories.

/Data/CIBW_HuntData_Mahoney&Shelden2000.xlsx contains the minimum documented number of animals killed (MinKilled) for years between 1950 and 1998 as published in Mahoney and Shelden 2000. Entries which are NA indicate that no data were available for that year.

/Data/CIBW_Abundance_HobbsEtAl2015.xlsx contains the total group size estimates from Hobbs et al. 2015.

/Data/CIBW_Abundance_BoydEtAl2019.txt contains an array with dimensions [1:1000, 1:8, 1:11] containing 1000 posterior samples of total group size for up to 8 survey days over 11 years, as described in Boyd et al. 2019.

A central challenge in applied ecology is understanding the effect of anthropogenic fatalities on wildlife populations and predicting which populations may be particularly vulnerable and in greatest need of management attention. We used 3 approaches to investigate potential effects of fatalities from collisions with wind turbines on 14 raptor species for both current (106 GW) and anticipated future (241 GW) levels of installed wind energy capacity in the United States. Our goals were to identify species at relatively high vs low risk of experiencing population declines from turbine collisions and to also compare results generated from these approaches. Two of the approaches used a calculated turbine-caused mortality rate to decrement population growth, where population trends were derived either from the North American Breeding Bird Survey or a matrix model parameterized from literature-derived demographic values. The third approach was potential biological removal, which estimates the number of fatalities that allow a population to reach and maintain its optimal sustainable population set by management objectives

Dataset contains counts and measures for individuals from the 2013, 2018, and 2023 Censuses. Data is available by statistical area 1.

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The variables included in this dataset are for the census usually resident population count (unless otherwise stated). All data is for level 1 of the classification.

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The variables for part 2 of the dataset are:

• Individual home ownership for the census usually resident population count aged 15 years and over
• Usual residence 1 year ago indicator
• Usual residence 5 years ago indicator
• Years at usual residence
• Average years at usual residence
• Years since arrival in New Zealand for the overseas-born census usually resident population count
• Average years since arrival in New Zealand for the overseas-born census usually resident population count
• Study participation
• Main means of travel to education, by usual residence address for the census usually resident population who are studying
• Main means of travel to education, by education address for the census usually resident population who are studying
• Highest qualification for the census usually resident population count aged 15 years and over
• Post-school qualification in New Zealand indicator for the census usually resident population count aged 15 years and over
• Highest secondary school qualification for the census usually resident population count aged 15 years and over
• Post-school qualification level of attainment for the census usually resident population count aged 15 years and over
• Sources of personal income (total responses) for the census usually resident population count aged 15 years and over
• Total personal income for the census usually resident population count aged 15 years and over
• Median ($) total personal income for the census usually resident population count aged 15 years and over
• Work and labour force status for the census usually resident population count aged 15 years and over
• Job search methods (total responses) for the unemployed census usually resident population count aged 15 years and over
• Status in employment for the employed census usually resident population count aged 15 years and over
• Unpaid activities (total responses) for the census usually resident population count aged 15 years and over
• Hours worked in employment per week for the employed census usually resident population count aged 15 years and over
• Average hours worked in employment per week for the employed census usually resident population count aged 15 years and over
• Industry, by usual residence address for the employed census usually resident population count aged 15 years and over
• Industry, by workplace address for the employed census usually resident population count aged 15 years and over
• Occupation, by usual residence address for the employed census usually resident population count aged 15 years and over
• Occupation, by workplace address for the employed census usually resident population count aged 15 years and over
• Main means of travel to work, by usual residence address for the employed census usually resident population count aged 15 years and over
• Main means of travel to work, by workplace address for the employed census usually resident population count aged 15 years and over
• Sector of ownership for the employed census usually resident population count aged 15 years and over
• Individual unit data source.

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Download lookup file for part 2 from Stats NZ ArcGIS Online or embedded attachment in Stats NZ geographic data service. Download data table (excluding the geometry column for CSV files) using the instructions in the Koordinates help guide.

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Footnotes

Te Whata

Under the Mana Ōrite Relationship Agreement, Te Kāhui Raraunga (TKR) will be publishing Māori descent and iwi affiliation data from the 2023 Census in partnership with Stats NZ. This will be available on Te Whata, a TKR platform.

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Geographical boundaries

Statistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023. Address data from 2013 and 2018 Censuses was updated to be consistent with the 2023 areas. Due to the changes in area boundaries and coding methodologies, 2013 and 2018 counts published in 2023 may be slightly different to those published in 2013 or 2018.

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Subnational census usually resident population

The census usually resident population count of an area (subnational count) is a count of all people who usually live in that area and were present in New Zealand on census night. It excludes visitors from overseas, visitors from elsewhere in New Zealand, and residents temporarily overseas on census night. For example, a person who usually lives in Christchurch city and is visiting Wellington city on census night will be included in the census usually resident population count of Christchurch city.

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Population counts

Stats NZ publishes a number of different population counts, each using a different definition and methodology. Population statistics – user guide has more information about different counts.

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Caution using time series

Time series data should be interpreted with care due to changes in census methodology and differences in response rates between censuses. The 2023 and 2018 Censuses used a combined census methodology (using census responses and administrative data), while the 2013 Census used a full-field enumeration methodology (with no use of administrative data).

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Study participation time series

In the 2013 Census study participation was only collected for the census usually resident population count aged 15 years and over.

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About the 2023 Census dataset

For information on the 2023 dataset see Using a combined census model for the 2023 Census. We combined data from the census forms with administrative data to create the 2023 Census dataset, which meets Stats NZ's quality criteria for population structure information. We added real data about real people to the dataset where we were confident the people who hadn’t completed a census form (which is known as admin enumeration) will be counted. We also used data from the 2018 and 2013 Censuses, administrative data sources, and statistical imputation methods to fill in some missing characteristics of people and dwellings.

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Data quality

The quality of data in the 2023 Census is assessed using the quality rating scale and the quality assurance framework to determine whether data is fit for purpose and suitable for release. Data quality assurance in the 2023 Census has more information.

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Concept descriptions and quality ratings

Data quality ratings for 2023 Census variables has additional details about variables found within totals by topic, for example, definitions and data quality.

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Disability indicator

This data should not be used as an official measure of disability prevalence. Disability prevalence estimates are only available from the 2023 Household Disability Survey. Household Disability Survey 2023: Final content has more information about the survey.

Activity limitations are measured using the Washington Group Short Set (WGSS). The WGSS asks about six basic activities that a person might have difficulty with: seeing, hearing, walking or climbing stairs, remembering or concentrating, washing all over or dressing, and communicating. A person was classified as disabled in the 2023 Census if there was at least one of these activities that they had a lot of difficulty with or could not do at all.

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Using data for good

Stats NZ expects that, when working with census data, it is done so with a positive purpose, as outlined in the Māori Data Governance Model (Data Iwi Leaders Group, 2023). This model states that "data should support transformative outcomes and should uplift and strengthen our relationships with each other and with our environments. The avoidance of harm is the minimum expectation for data use. Māori data should also contribute to iwi and hapū tino rangatiratanga”.

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Confidentiality

The 2023 Census confidentiality rules have been applied to 2013, 2018, and 2023 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2023 Census data. Counts are calculated using fixed random rounding to base 3 (FRR3) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.

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Measures

Measures like averages, medians, and other quantiles are calculated from unrounded counts, with input noise added to or subtracted from each contributing value

This dataset provides detailed statistics from the 2017 census of Pakistan, covering demographic and geographic data for various administrative units across the country. The data includes population counts, sex ratios, population density, urban proportions, and household sizes, among other metrics. It allows for a comprehensive analysis of both rural and urban populations across different regions, including provinces like Punjab, Sindh, Khyber Pakhtunkhwa, Balochistan, and the federal territories.

Key Features:

Administrative Unit: The geographical or administrative division, such as Pakistan, rural, urban, and specific regions like provinces or territories. Area (Sq. Km.): The total area of the region in square kilometers. Population (2017): The population count based on the 2017 census. Population (1998): The population count based on the 1998 census, allowing for growth rate analysis. Sex Ratio: The ratio of males to females in the population. Population Density: Number of individuals per square kilometer. Urban Proportion: Percentage of the population living in urban areas. Average Household Size: The average number of individuals living in a household. Potential Use Cases:

Population growth analysis over the years. Urban vs. rural population studies. Regional demographic trends and comparisons. Analysis of population density and its relation to area size. Understanding household size variations across different regions.

Abstract

Censuses are principal means of collecting basic population and housing statistics required for social and economic development, policy interventions, their implementation and evaluation.The census plays an essential role in public administration. The results are used to ensure: • equity in distribution of government services • distributing and allocating government funds among various regions and districts for education and health services • delineating electoral districts at national and local levels, and • measuring the impact of industrial development, to name a few The census also provides the benchmark for all surveys conducted by the national statistical office. Without the sampling frame derived from the census, the national statistical system would face difficulties in providing reliable official statistics for use by government and the public. Census also provides information on small areas and population groups with minimum sampling errors. This is important, for example, in planning the location of a school or clinic. Census information is also invaluable for use in the private sector for activities such as business planning and market analyses. The information is used as a benchmark in research and analysis.

Census 2011 was the third democratic census to be conducted in South Africa. Census 2011 specific objectives included: - To provide statistics on population, demographic, social, economic and housing characteristics; - To provide a base for the selection of a new sampling frame; - To provide data at lowest geographical level; and - To provide a primary base for the mid-year projections.

Geographic coverage

National

Analysis unit

Households, Individuals

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

About the Questionnaire : Much emphasis has been placed on the need for a population census to help government direct its development programmes, but less has been written about how the census questionnaire is compiled. The main focus of a population and housing census is to take stock and produce a total count of the population without omission or duplication. Another major focus is to be able to provide accurate demographic and socio-economic characteristics pertaining to each individual enumerated. Apart from individuals, the focus is on collecting accurate data on housing characteristics and services.A population and housing census provides data needed to facilitate informed decision-making as far as policy formulation and implementation are concerned, as well as to monitor and evaluate their programmes at the smallest area level possible. It is therefore important that Statistics South Africa collects statistical data that comply with the United Nations recommendations and other relevant stakeholder needs.

The United Nations underscores the following factors in determining the selection of topics to be investigated in population censuses: a) The needs of a broad range of data users in the country; b) Achievement of the maximum degree of international comparability, both within regions and on a worldwide basis; c) The probable willingness and ability of the public to give adequate information on the topics; and d) The total national resources available for conducting a census.

In addition, the UN stipulates that census-takers should avoid collecting information that is no longer required simply because it was traditionally collected in the past, but rather focus on key demographic, social and socio-economic variables.It becomes necessary, therefore, in consultation with a broad range of users of census data, to review periodically the topics traditionally investigated and to re-evaluate the need for the series to which they contribute, particularly in the light of new data needs and alternative data sources that may have become available for investigating topics formerly covered in the population census. It was against this background that Statistics South Africa conducted user consultations in 2008 after the release of some of the Community Survey products. However, some groundwork in relation to core questions recommended by all countries in Africa has been done. In line with users' meetings, the crucial demands of the Millennium Development Goals (MDGs) should also be met. It is also imperative that Stats SA meet the demands of the users that require small area data.

Accuracy of data depends on a well-designed questionnaire that is short and to the point. The interview to complete the questionnaire should not take longer than 18 minutes per household. Accuracy also depends on the diligence of the enumerator and honesty of the respondent.On the other hand, disadvantaged populations, owing to their small numbers, are best covered in the census and not in household sample surveys.Variables such as employment/unemployment, religion, income, and language are more accurately covered in household surveys than in censuses.Users'/stakeholders' input in terms of providing information in the planning phase of the census is crucial in making it a success. However, the information provided should be within the scope of the census.

1. The Household Questionnaire is divided into the following sections:
2. Household identification particulars

3. Individual particulars Section A: Demographics Section B: Migration Section C: General Health and Functioning Section D: Parental Survival and Income Section E: Education Section F: Employment Section G: Fertility (Women 12-50 Years Listed) Section H: Housing, Household Goods and Services and Agricultural Activities Section I: Mortality in the Last 12 Months The Household Questionnaire is available in Afrikaans; English; isiZulu; IsiNdebele; Sepedi; SeSotho; SiSwati;Tshivenda;Xitsonga

4. The Transient and Tourist Hotel Questionnaire (English) is divided into the following sections:

5. Name, Age, Gender, Date of Birth, Marital Status, Population Group, Country of birth, Citizenship, Province.

6. The Questionnaire for Institutions (English) is divided into the following sections:

7. Particulars of the institution

8. Availability of piped water for the institution

9. Main source of water for domestic use

10. Main type of toilet facility

11. Type of energy/fuel used for cooking, heating and lighting at the institution

12. Disposal of refuse or rubbish

13. Asset ownership (TV, Radio, Landline telephone, Refrigerator, Internet facilities)

14. List of persons in the institution on census night (name, date of birth, sex, population group, marital status, barcode number)

15. The Post Enumeration Survey Questionnaire (English)

These questionnaires are provided as external resources.

Cleaning operations

Data editing and validation system The execution of each phase of Census operations introduces some form of errors in Census data. Despite quality assurance methodologies embedded in all the phases; data collection, data capturing (both manual and automated), coding, and editing, a number of errors creep in and distort the collected information. To promote consistency and improve on data quality, editing is a paramount phase in identifying and minimising errors such as invalid values, inconsistent entries or unknown/missing values. The editing process for Census 2011 was based on defined rules (specifications).

The editing of Census 2011 data involved a number of sequential processes: selection of members of the editing team, review of Census 2001 and 2007 Community Survey editing specifications, development of editing specifications for the Census 2011 pre-tests (2009 pilot and 2010 Dress Rehearsal), development of firewall editing specifications and finalisation of specifications for the main Census.

Editing team The Census 2011 editing team was drawn from various divisions of the organisation based on skills and experience in data editing. The team thus composed of subject matter specialists (demographers and programmers), managers as well as data processors. Census 2011 editing team was drawn from various divisions of the organization based on skills and experience in data editing. The team thus composed of subject matter specialists (demographers and programmers), managers as well as data processors.

The Census 2011 questionnaire was very complex, characterised by many sections, interlinked questions and skipping instructions. Editing of such complex, interlinked data items required application of a combination of editing techniques. Errors relating to structure were resolved using structural query language (SQL) in Oracle dataset. CSPro software was used to resolve content related errors. The strategy used for Census 2011 data editing was implementation of automated error detection and correction with minimal changes. Combinations of logical and dynamic imputation/editing were used. Logical imputations were preferred, and in many cases substantial effort was undertaken to deduce a consistent value based on the rest of the household’s information. To profile the extent of changes in the dataset and assess the effects of imputation, a set of imputation flags are included in the edited dataset. Imputation flags values include the following: 0 no imputation was performed; raw data were preserved 1 Logical editing was performed, raw data were blank 2 logical editing was performed, raw data were not blank 3 hot-deck imputation was performed, raw data were blank 4 hot-deck imputation was performed, raw data were not blank

Data appraisal

Independent monitoring and evaluation of Census field activities Independent monitoring of the Census 2011 field activities was carried out by a team of 31 professionals and 381 Monitoring

Our study on saplings was conducted in six forested sites in three southern Michigan counties: Ingham Co. (three sites), Gratiot Co. (two sites), and Shiawassee Co. (one site), with 10 to 60 km between sites.Data set one - on the fate and density of emerald ash borer larvae and associated parasitoids on ash saplings from both biocontrol-release and non-release control plots in southern Michigan during the three-year study (2013–2015). Data set one was used for calculations and associated analyses for of the parameters presented in Figure 1, 2, 3, and 4.Data set two - on ash tree abundance (per 100 m2) and healthy conditions (or crown classes) at the six study sites in southern Michigan observed in summer 2015. Data set two was used for estimation of tree density (Figure 5) and healthy condition (or crown classes).Resources in this dataset:Resource Title: Emerald ash borer biocontrol in ash saplings: the potential for early stage recovery of North American ash trees. File Name: Sapling Data 2013-2015 FINAL.xlsx Resource Description: Data set one - on fate and density of emerald ash borer larvae and/or pupae and associated mortality factors (parasitoids, predators, and undetermined diseases/plant resistance /competition)Resource Title: Emerald ash borer biocontrol in ash saplings: the potential for early stage recovery of North American ash trees. File Name: MI Ash Transect 2015 - All trees.xlsx Resource Description: Data on ash abundance and healthy conditions from transect surveyResource Title: Data Dictionary - EAB biocontrol in ash saplings. File Name: EAB_data_dictionary.csvResource Title: 2013-2014 data sorted. File Name: 2013-2014_data_sorted_EAB.csv Resource Description: Data set one - on fate and density of emerald ash borer larvae and/or pupae and associated mortality factors (parasitoids, predators, and undetermined diseases/plant resistance /competition)Resource Title: 2014-2015 data sorted. File Name: 2014-2015_data_sorted_EAB.csv Resource Description: Data set one - on fate and density of emerald ash borer larvae and/or pupae and associated mortality factors (parasitoids, predators, and undetermined diseases/plant resistance /competition)Resource Title: 2015-2016 data sorted. File Name: 2015-2016_data_sorted_EAB.csv Resource Description: Data set one - on fate and density of emerald ash borer larvae and/or pupae and associated mortality factors (parasitoids, predators, and undetermined diseases/plant resistance /competition)Resource Title: Combined: Emerald ash borer biocontrol in ash saplings: the potential for early stage recovery of North American ash trees. File Name: Emerald ash borer biocontrol in ash saplings the potential for early stage recovery of North American ash trees.csv Resource Description: Data set one - on fate and density of emerald ash borer larvae and/or pupae and associated mortality factors (parasitoids, predators, and undetermined diseases/plant resistance /competition) All 3 sets (2013-2016) combined into a CSV for visualization purposesResource Title: Emerald ash borer biocontrol in ash saplings: the potential for early stage recovery of North American ash trees. File Name: MI Ash Transect 2015 - All trees.csv Resource Description: Data on ash abundance and healthy conditions from transect survey (CSV version for data visualization)Resource Title: Estimates of the net population growth rate of emerald ash borer on saplings from life tables constructed from Dataset One. File Name: DUAN J Data on EAB Life Tables Calculation for Saplings 2013-2015.xlsx Resource Description: This life table of emerald ash borer on saplings was constructed from Dataset One and used to estimate the next population growth rate according to method described in Duan et al. (2014, 2017)Resource Title: Estimates of the net population growth rate of emerald ash borer on saplings from life tables constructed from Dataset One. File Name: EAB_Life_Tables_Calculation_for_Saplings_2013-2015.csv Resource Description: CSV version of the data - This life table of emerald ash borer on saplings was constructed from Dataset One and used to estimate the next population growth rate according to method described in Duan et al. (2014, 2017)

Approximately 25% of mammals are currently threatened with extinction, a risk that is amplified under climate change. Species persistence under climate change is determined by the combined effects of climatic factors on multiple demographic rates (survival, development, reproduction), and hence, population dynamics. Thus, to quantify which species and regions on Earth are most vulnerable to climate-driven extinction, a global understanding of how different demographic rates respond to climate is urgently needed. Here, we perform a systematic review of literature on demographic responses to climate, focusing on terrestrial mammals, for which extensive demographic data are available. To assess the full spectrum of responses, we synthesize information from studies that quantitatively link climate to multiple demographic rates. We find only 106 such studies, corresponding to 87 mammal species. These 87 species constitute < 1% of all terrestrial mammals. Our synthesis reveals a strong mismatch between the locations of demographic studies and the regions and taxa currently recognized as most vulnerable to climate change. Surprisingly, for most mammals and regions sensitive to climate change, holistic demographic responses to climate remain unknown. At the same time, we reveal that filling this knowledge gap is critical as the effects of climate change will operate via complex demographic mechanisms: a vast majority of mammal populations display projected increases in some demographic rates but declines in others, often depending on the specific environmental context, complicating simple projections of population fates. Assessments of population viability under climate change are in critical need to gather data that account for multiple demographic responses, and coordinated actions to assess demography holistically should be prioritized for mammals and other taxa.

Methods For each mammal species i with available life-history information, we searched SCOPUS for studies (published before 2018) where the title, abstract, or keywords contained the following search terms:

Scientific species namei AND (demograph* OR population OR life-history OR "life history" OR model) AND (climat* OR precipitation OR rain* OR temperature OR weather) AND (surv* OR reprod* OR recruit* OR brood OR breed* OR mass OR weight OR size OR grow* OR offspring OR litter OR lambda OR birth OR mortality OR body OR hatch* OR fledg* OR productiv* OR age OR inherit* OR sex OR nest* OR fecund* OR progression OR pregnan* OR newborn OR longevity).

We used the R package taxize (Chamberlain and Szöcs 2013) to resolve discrepancies in scientific names or taxonomic identifiers and, where applicable, searched SCOPUS using all scientific names associated with a species in the Integrated Taxonomic Information System (ITIS; http://www.itis.gov).

We did not extract information on demographic-rate-climate relationships if:

A study reported on single age or stage-specific demographic rates (e.g., Albon et al. 2002; Rézoiki et al. 2016)
A study used an experimental design to link demographic rates to climate variation (e.g., Cain et al. 2008)
A study considered the effects of climate only indirectly or qualitatively. In most cases, this occurred when demographic rates differed between seasons (e.g., dry vs. wet season) but were not linked explicitly to climatic factors (e.g., varying precipitation amount between seasons) driving these differences (e.g., de Silva et al. 2013; Gaillard et al. 2013).

We included several studies of the same population as different studies assessed different climatic variables or demographic rates or spanned different years (e.g., for Rangifer tarandus platyrhynchus, Albon et al. 2017; Douhard et al. 2016).

We note that we can miss a potentially relevant study if our search terms were not mentioned in the title, abstract, or keywords. To our knowledge, this occurred only once, for Mastomys natalensis (we included the relevant study [Leirs et al. 1997] into our review after we were made aware that it assesses climate-demography relationships in the main text).

Lastly, we checked for potential database bias by running the search terms for a subset of nine species in Web of Science. The subset included three species with > three climate-demography studies published and available in SCOPUS (Rangifer tarandus, Cervus elaphus, Myocastor coypus); three species with only one climate-demography study obtained from SCOPUS (Oryx gazella, Macropus rufus, Rhabdomys pumilio); and another three species where SCOPUS did not return any published study (Calcochloris obtusirostris, Cynomops greenhalli, Suncus remyi). Species in the three subcategories were randomly chosen. Web of Science did not return additional studies for the three species where SCOPUS also failed to return a potentially suitable study. For the remaining six species, the total number of studies returned by Web of Science differed, but the same studies used for this review were returned, and we could not find any additional studies that adhered to our extraction criteria.

Description of key collected data

From all studies quantitatively assessing climate-demography relationships, we extracted the following information:

Geographic location - The center of the study area was always used. If coordinates were not provided in a study, we assigned coordinates based on the study descriptions of field sites and data collection.
Terrestrial biome - The study population was assigned to one of 14 terrestrial biomes (Olson et al. 2001) corresponding to the center of the study area. As this review is focused on general climatic patterns affecting demographic rates, specific microhabitat conditions described for any study population were not considered.
Climatic driver - Drivers linked to demographic rates were grouped as either local/regional precipitation & temperature values or derived indices (e.g., ENSO, NAO). The temporal extent (e.g., monthly, seasonal, annual, etc.) and aggregation type (e.g., minimum, maximum, mean, etc.) of drivers was also noted.
Demographic rate modeled - To facilitate comparisons, we grouped the demographic rates into either survival, reproductive success (i.e., whether or not reproduction occurre, reproductive output (i.e., number or rate of offspring production), growth (including stage transitions), or condition that determines development (i.e., mass or size). 
Stage or sex modeled - We retrieved information on responses of demographic rates to climate for each age class, stage, or sex modeled in a given study.
Driver effect - We grouped effects of drivers as positive (i.e., increased demographic rates), negative (i.e., reduced demographic rate), no effect, or context-dependent (e.g., positive effects at low population densities and now effect at high densities). We initially also considered nonlinear effects (e.g., positive effects at intermediate values and negative at extremes of a driver), but only 4 studies explicitly tested for nonlinear effects, by modelling squared or cubic climatic drivers in combination with driver interactions. We therefore considered nonlinear demographic effects as context dependent.  
Driver interactions - We noted any density dependence modeled and any non-climatic covariates included (as additive or interactive effects) in the demographic-rate models assessing climatic effects.
Future projections of climatic driver - In studies that indicated projections of drivers under climate change, we noted whether drivers were projected to increase, decrease, or show context-dependent trends. For studies that provided no information on climatic projections, we quantified projections as described in Detailed description of climate-change projections below (see also climate_change_analyses_mammal_review.R).

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).

1. Increased anthropogenic influence on the environment has accentuated the need to assess how climate and other environmental factors drive vital rates and population dynamics of different types of organisms. However, to allow distinction between effects of multiple correlated variables, and to capture the effects of rare and extreme climatic conditions, studies extending over decades are often necessary.

2. In this study we used an individual-based dataset collected in three populations of Pulsatilla vulgaris subsp. gotlandica during 34 years, to explore the effects of variation in precipitation and temperature on vital rates and population dynamics.

3. Most of the observed conspicuous variation in flowering among years was associated with differences in precipitation and temperature in the previous summer and autumn with a higher incidence of flowering following summers with high precipitation and low temperatures. In contrast, climatic variables had no significant effects on individual growth or survival.

4. Although the weather-driven variation in flowering had only moderate absolute effects on the population growth rate, simulated persistent changes in average precipitation and temperature resulted in considerable reductions in population sizes compared with current conditions. Analyses carried out with with subsets of data consisting of 5 and 10 years yielded results that strongly deviated from those based on the full data set.

5. Synthesis: The results of this study illustrate the importance of long-term demographic monitoring to identify key climatic variables affecting vital rates and driving population dynamics in long-lived organisms.

Dataset Overview

This dataset provides a comprehensive overview of internet usage across countries as of 2024. It includes data on the percentage of the population using the internet, sourced from multiple organizations such as the World Bank (WB), International Telecommunication Union (ITU), and the CIA. The dataset covers all United Nations member states, excluding North Korea, and provides insights into internet penetration rates, user counts, and trends over recent years. The data is derived from household surveys and internet subscription statistics, offering a reliable snapshot of global digital connectivity.

Data Science Applications

This dataset can be used in various data science applications, including: - Digital Divide Analysis: Evaluate disparities in internet access between developed and developing nations. - Trend Analysis: Study the growth of internet penetration over time across different regions. - Policy Recommendations: Assist policymakers in identifying underserved areas and strategizing for improved connectivity. - Market Research: Help businesses identify potential markets for digital products or services. - Correlation Studies: Analyze relationships between internet penetration and socioeconomic indicators like GDP, education levels, or urbanization.

Column Descriptors

The dataset contains the following columns: 1. Location: Country or region name. 2. Rate (WB): Percentage of the population using the internet (World Bank data). 3. Year (WB): Year corresponding to the World Bank data. 4. Rate (ITU): Percentage of the population using the internet (ITU data). 5. Year (ITU): Year corresponding to the ITU data. 6. Users (CIA): Estimated number of internet users in absolute terms (CIA data). 7. Year (CIA): Year corresponding to the CIA data. 8. Notes: Additional notes or observations about specific entries.

Ethically Mined Data

The data has been sourced from publicly available and reputable organizations such as the World Bank, ITU, and CIA. These sources ensure transparency and ethical collection methods through household surveys and official statistics. The dataset excludes North Korea due to limited reliable information on its internet usage.

Acknowledgements

This dataset is based on information compiled from: - World Bank - International Telecommunication Union - CIA World Factbook - Wikipedia's "List of countries by number of Internet users" page

Special thanks to these organizations for providing open access to this valuable information, enabling deeper insights into global digital connectivity trends.

Citations: [1] https://en.wikipedia.org/wiki/List_of_countries_by_number_of_Internet_users [2] https://en.wikipedia.org/wiki/List_of_countries_by_number_of_Internet_users

This dataset was developed for our study on plant allometry and extinction vulnerabilities. In this study we gathered plant demographic data from various sources and related this to maximum plant height. We derived allometric relationships with maximum plant height for the intrinsic population growth rate, variance in population growth rate due to environmental stochasticity and the maximum plant density. These relationships were used to relate maximum plant height to the Mean time to extinction and the Probability of extinction. The results of this research will soon be published as ‘Relating plant height to demographic rates and extinction vulnerability’ (forthcoming).In this repository we provide only the processed data from this study because the original data was gathered from existing open access and semi-open access databases. Original data for this project was gathered from the following databases: (1) TRY Plant Trait Database, (2) COMPADRE Plant Matrix Database Version 4.0.1, (3) Biomass allocation and growth data of seeded plantsThe processed data is stored in two files:- 2017_deJonge_MaximumPlantDensity.csv: This file contains 230 datapoints with maximum plant density (per square meter) and average individual plant mass (grams).A description of the methodology used to obtain the processed data is given in a PDF file called ‘2017_deJonge_Methodology.pdf’. Additionally, a description file called 'description.pdf' is added, which includes the information mentioned above.

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

This dataset contains data from an analysis of differences in growth rate among three different barnacle populations breeding at different latitudes, described in the paper: Boom, Michiel P., van der Jeugd, H.P., Steffani, B., Nolet, B.A., Larsson, K., & Eichhorn, G. (2021), Postnatal growth rate varies with latitude in range-expanding geese – the role of plasticity and day length. Journal of Animal Ecology.

The postnatal growth period is a crucial life stage, with potential lifelong effects on an animal’s fitness. How fast animals grow depends on their life history strategy and rearing environment, and interspecific comparisons generally show higher growth rates at higher latitudes. However, to elucidate the mechanisms behind this gradient in growth rate, intraspecific comparisons are needed.

Recently, barnacle geese expanded their Arctic breeding range from the Russian Barents Sea coast southwards, and now also breed along the Baltic and North Sea coasts. Baltic breeders shortened their migration, while barnacle geese breeding along the North Sea stopped migrating entirely.

We collected cross-sectional data on gosling tarsus length, head length and body mass, and constructed population-specific growth curves to compare growth rates among three populations (Barents Sea, Baltic Sea and North Sea) spanning 17° in latitude.

Growth rate was faster at higher latitudes, and the gradient resembled the latitudinal gradient previously observed in an interspecific comparison of precocial species. Differences in day length among the three breeding regions could largely explain the observed differences in growth rate. In the Baltic, and especially in the Arctic population, growth rate was slower later in the season, most likely because of the stronger seasonal decline in food quality.

Our results suggest that differences in postnatal growth rate between the Arctic and temperate populations are mainly a plastic response to local environmental conditions. This plasticity can increase the individuals’ ability to cope with annual variation in local conditions, but can also increase the potential to re-distribute and adapt to new breeding environments.

Methods We collected biometric data on growing goslings during long-term studies in colonies from three study-populations: 1) A long-distance migratory population breeding in the Arctic in Kolokolkova Bay along the Barents Sea coast (68°35’N, 52°20’E), data collected in 6 years between 2003 and 2015; 2) A short-distance migratory population breeding on Gotland in the Baltic Sea (57°25’N, 18°53’E) data collected in 15 years between 1986 and 2000; 3) A sedentary population breeding in the Netherlands along the North Sea (51°40’N, 4°14’E) data collected in 5 years between 2004 and 2018 (Larsson et al., 1988; Van der Jeugd et al., 2003, 2009; Eichhorn et al., 2010).

Our analysis is based on all measured goslings with known age (Sample sizes: Barents Sea = 392; Baltic Sea = 933; North Sea = 116). Sex was determined based on cloacal inspection. Goslings were weighed in a bag using a Pesola spring scale with an accuracy of ± 5 g (if <600 g) or a digital hand scale or Pesola spring scale with an accuracy of ± 10 g (if >600 g). A calliper (± 0.1 mm) was used to measure the outer length of the bent tarsus. Head length was measured using a ruler (± 1 mm).

The number of daylight hours that had accumulated between hatching and capture was calculated for each gosling. Daylight was determined as the period between dawn and dusk, and was calculated based on the coordinates of the three breeding colonies using the R package “Suncalc” (see associated manuscript referenced above).

We calculated relative hatch dates by centralizing hatch dates within each cohort, because years can differ in onset of spring and consequently in timing of breeding and hatching. For the calculation of the relative hatch date for each gosling, we used the mean hatch date of the colonies (not only of the recaptured goslings), as established from nest monitoring (see associated manuscript referenced above for details).

Climate often drives ungulate population dynamics, and as climates change, some areas may become unsuitable for species persistence. Unraveling the relationships between climate and population dynamics, and projecting them across time, advances ecological understanding that informs and steers sustainable conservation for species. Using pronghorn (Antilocapra americana) as an ecological model, we used a Bayesian approach to analyze long-term population, precipitation, and temperature data from 18 subpopulations in the southwestern United States. We determined which long-term (12 and 24 months) or short-term (gestation trimester and lactation period) climatic conditions best predicted annual rate of population growth (λ). We used these predictions to project population trends through 2090. Projections incorporated downscaled climatic data matched to pronghorn range for each population, given a high and a lower atmospheric CO2 concentration scenario. Since the 1990s, 15 of the pronghorn subpopulations declined in abundance. Sixteen subpopulations demonstrated a significant relationship between precipitation and λ, and in 13 of these, temperature was also significant. Precipitation predictors of λ were highly seasonal, with lactation being the most important period, followed by early and late gestation. The influence of temperature on λ was less seasonal than precipitation, and lacked a clear temporal pattern. The climatic projections indicated that all of these pronghorn subpopulations would experience increased temperatures, while the direction and magnitude of precipitation had high subpopulation-specific variation. Models predicted that nine subpopulations would be extirpated or approaching extirpation by 2090. Results were consistent across both atmospheric CO2 concentration scenarios, indicating robustness of trends irrespective of climatic severity. In the southwestern United States, the climate underpinning pronghorn subpopulations is shifting, making conditions increasingly inhospitable to pronghorn persistence. This realization informs and steers conservation and management decisions for pronghorn in North America, while exemplifying how similar research can aid ungulates inhabiting arid regions and confronting similar circumstances elsewhere. Long-term data from annual aerial surveys of pronghorn subpopulations in Utah, Arizona, New Mexico, and western Texas were used to calculate annual rates of population growth (λ). When subpopulation-specific harvest and translocation data were available, population estimates for calculating λ were adjusted according to the following equation: λt = Nt/(Nt-1 - h - r + a), where λt is population change from time t-1 to t, Nt and Nt-1 are population estimates from current and previous surveys, respectively, h is number of pronghorn harvested, and r and a are number of individuals removed from and released into the population, respectively, through translocations. Only population estimates from surveys conducted in consecutive years were used to calculate λ. If λ = 2, the associated surveys were removed from analyses because λ would be considered to be derived from unreliable or unstandardized population estimates, resulting in biologically unrealistic population growth rates. Monthly climate data (precipitation [mm/day] and mean temperature [degrees C]) were from 14 x 14 km cells from pronghorn range in each subpopulation in Utah, Arizona, New Mexico, and western Texas. Means across grids were calculated to obtain monthly values of precipitation and temperature. Two realistic future global climate scenarios were compared; a lower (Representative Concentrations Pathways 4.5) and a high (Representative Concentrations Pathways 8.5) atmospheric CO2 concentration scenario. Standardized precipitation index for 3-, 6-, 12-, and 24-month periods were calculated from all available monthly precipitation data using program SPI SL 6 (National Drought Mitigation Center 2014). Monthly mean temperature, total precipitation, and mean SPI (3-, 6-, and 12-month periods) were summarized by important periods in an adult female pronghorn's annual reproductive cycle relative to peak fawning (i.e., early, mid-, and late gestation [3 months each] and lactation [4 months]). Mean temperature and total precipitation were also calculated for 12 and 24 months preceding each population survey. Historic pronghorn population trends in relation to temperature and precipitation were assessed using integrated Bayesian population models. All models included a covariate for density effect (i.e., population in the previous year). Precipitation and temperature model comparison sets were run separately, and each model set included a null model (i.e., only density covariate, no climate covariates). These top individual precipitation and temperature covariates were then combined in models (i.e., one precipitation and temperature covariate per model), and these combined models were run including a term for the interaction between precipitation and temperature using the following equation: ln(λt) = Alpha + Beta1XN[t-1] + Beta2Xprec + Beta3Xtemp + Beta4Xprec*temp. Projected climate data for each pronghorn subpopulation was used to predict λt for each year to 2090. An integrated modeling approach was used, whereby the best performing model climatic predictors from historic population trends for each pronghorn subpopulation was embedded in that subpopulation pronghorn population projection model.

Abstract

In 1985 the population and health observatory was established at Mlomp, in the region of Ziguinchor, in southern Senegal (see map). The objective was to complement the two rural population observatories then existing in the country, Bandafassi, in the south-east, and Niakhar, in the centre-west, with a third observatory in a region - the south-west of the country (Casamance) - whose history, ethnic composition and economic situation were quite different from those of the regions where the first two observatories were located. It was expected that measuring the demographic levels and trends on those three sites would provide better coverage of the demographic and epidemiological diversity of the country.

Following a population census in 1984-1985, demographic events and causes of death have been monitored yearly. During the initial census, all women were interviewed concerning the birth and survival of their children. Since 1985, yearly censuses, usually conducted in January-February, have been recording demographic data, including all births, deaths, and migrations. The completeness and accuracy of dates of birth and death are cross-checked against those of registers of the local maternity ward (_95% of all births) and dispensary (all deaths are recorded, including those occurring outside the area), respectively. The study area comprises 11 villages with approximately 8000 inhabitants, mostly Diola. Mlomp is located in the Department of Oussouye, Region of Ziguinchor (Casamance), 500 km south of Dakar.

On 1 January 2000 the Mlomp area included a population of 7,591 residents living in 11 villages. The population density was 108 people per square kilometre. The population belongs to the Diola ethnic group, and the religion is predominantly animist, with a large minority of Christians and a few Muslims. Though low, the educational level - in 2000, 55% of women aged 15-49 had been to school (for at least one year) - is definitely higher than at Bandafassi. The population also benefits from much better health infrastructure and programmes. Since 1961, the area under study has been equipped with a private health centre run by French Catholic nurses and, since 1968, a village maternity centre where most women give birth. The vast majority of the children are totally immunized and involved in a growth-monitoring programme (Pison et al.,1993; Pison et al., 2001).

Geographic coverage

The Mlomp DSS site, about 500 km from the capital, Dakar, in Senegal, lies between latitudes 12°36' and 12°32'N and longitudes 16°33' and 16°37'E, at an altitude ranging from 0 to 20 m above sea level. It is in the region of Ziguinchor, Département of Oussouye (Casamance), in southwest Senegal. It is locates 50 km west of the city of Ziguinchor and 25 kms north of the border with Guinea Bissau. It covers about half the Arrondissement of Loudia-Ouolof. The Mlomp DSS site is about 11 km × 7 km and has an area of 70 km2. Villages are households grouped in a circle with a 3-km diameter and surrounded by lands that are flooded during the rainy season and cultivated for rice. There is still no electricity.

Analysis unit

Individual

Universe

At the census, a person was considered a member of the compound if the head of the compound declared it to be so. This definition was broad and resulted in a de jure population under study. Thereafter, a criterion was used to decide whether and when a person was to be excluded or included in the population.

A person was considered to exit from the study population through either death or emigration. Part of the population of Mlomp engages in seasonal migration, with seasonal migrants sometimes remaining 1 or 2 years outside the area before returning. A person who is absent for two successive yearly rounds, without returning in between, is regarded as having emigrated and no longer resident in the study population at the date of the second round. This definition results in the inclusion of some vital events that occur outside the study area. Some births, for example, occur to women classified in the study population but physically absent at the time of delivery, and these births are registered and included in the calculation of rates, although information on them is less accurate. Special exit criteria apply to babies born outside the study area: they are considered emigrants on the same date as their mother.

A new person enters the study population either through birth to a woman of the study population or through immigration. Information on immigrants is collected when the list of compounds of a village is checked ("Are there new compounds or new families who settled since the last visit?") or when the list of members of a compound is checked ("Are there new persons in the compound since the last visit?"). Some immigrants are villagers who left the area several years before and were excluded from the study population. Information is collected to determine in which compound they were previously registered, to match the new and old information.

Information is routinely collected on movements from one compound to another within the study area. Some categories of the population, such as older widows or orphans, frequently move for short periods of time and live in between several compounds, and they may be considered members of these compounds or of none. As a consequence, their movements are not always declared.

Kind of data

Event history data

Frequency of data collection

One round of data collection took place annually, except in 1987 and 2008.

Sampling procedure

No samplaing is done

Sampling deviation

None

Mode of data collection

Proxy Respondent [proxy]

Research instrument

List of questionnaires: - Household book (used to register informations needed to define outmigrations) - Delivery questionnaire (used to register information of dispensaire ol mlomp) - New household questionnaire - New member questionnaire - Marriage and divorce questionnaire - Birth and marital histories questionnaire (for a new member) - Death questionnaire (used to register the date of death)

Cleaning operations

On data entry data consistency and plausibility were checked by 455 data validation rules at database level. If data validaton failure was due to a data collection error, the questionnaire was referred back to the field for revisit and correction. If the error was due to data inconsistencies that could not be directly traced to a data collection error, the record was referred to the data quality team under the supervision of the senior database scientist. This could request further field level investigation by a team of trackers or could correct the inconsistency directly at database level.

No imputations were done on the resulting micro data set, except for:

a. If an out-migration (OMG) event is followed by a homestead entry event (ENT) and the gap between OMG event and ENT event is greater than 180 days, the ENT event was changed to an in-migration event (IMG). b. If an out-migration (OMG) event is followed by a homestead entry event (ENT) and the gap between OMG event and ENT event is less than 180 days, the OMG event was changed to an homestead exit event (EXT) and the ENT event date changed to the day following the original OMG event. c. If a homestead exit event (EXT) is followed by an in-migration event (IMG) and the gap between the EXT event and the IMG event is greater than 180 days, the EXT event was changed to an out-migration event (OMG). d. If a homestead exit event (EXT) is followed by an in-migration event (IMG) and the gap between the EXT event and the IMG event is less than 180 days, the IMG event was changed to an homestead entry event (ENT) with a date equal to the day following the EXT event. e. If the last recorded event for an individual is homestead exit (EXT) and this event is more than 180 days prior to the end of the surveillance period, then the EXT event is changed to an out-migration event (OMG)

In the case of the village that was added (enumerated) in 2006, some individuals may have outmigrated from the original surveillance area and setlled in the the new village prior to the first enumeration. Where the records of such individuals have been linked, and indivdiual can legitmately have and outmigration event (OMG) forllowed by and enumeration event (ENU). In a few cases a homestead exit event (EXT) was followed by an enumeration event in these cases. In these instances the EXT events were changed to an out-migration event (OMG).

Response rate

On an average the response rate is about 99% over the years for each round.

Sampling error estimates

Not applicable

Data appraisal

CenterId Metric Table QMetric Illegal Legal Total Metric Rundate
SN012 MicroDataCleaned Starts 18756 2017-05-19 00:00
SN012 MicroDataCleaned Transitions 0 45136 45136 0 2017-05-19 00:00
SN012 MicroDataCleaned Ends 18756 2017-05-19 00:00
SN012 MicroDataCleaned SexValues 38 45098 45136 0 2017-05-19 00:00
SN012 MicroDataCleaned DoBValues 204 44932 45136 0 2017-05-19 00:00

Context

This is a dataset of the most highly populated city (if applicable) in a form easy to join with the COVID19 Global Forecasting (Week 1) dataset. You can see how to use it in this kernel

Content

There are four columns. The first two correspond to the columns from the original COVID19 Global Forecasting (Week 1) dataset. The other two is the highest population density, at city level, for the given country/state. Note that some countries are very small and in those cases the population density reflects the entire country. Since the original dataset has a few cruise ships as well, I've added them there.

Acknowledgements

Thanks a lot to Kaggle for this competition that gave me the opportunity to look closely at some data and understand this problem better.

Inspiration

Summary: I believe that the square root of the population density should relate to the logistic growth factor of the SIR model. I think the SEIR model isn't applicable due to any intervention being too late for a fast-spreading virus like this, especially in places with dense populations.

After playing with the data provided in COVID19 Global Forecasting (Week 1) (and everything else online or media) a bit, one thing becomes clear. They have nothing to do with epidemiology. They reflect sociopolitical characteristics of a country/state and, more specifically, the reactivity and attitude towards testing.

The testing method used (PCR tests) means that what we measure could potentially be a proxy for the number of people infected during the last 3 weeks, i.e the growth (with lag). It's not how many people have been infected and recovered. Antibody or serology tests would measure that, and by using them, we could go back to normality faster... but those will arrive too late. Way earlier, China will have experimentally shown that it's safe to go back to normal as soon as your number of newly infected per day is close to zero.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F197482%2F429e0fdd7f1ce86eba882857ac7a735e%2Fcovid-summary.png?generation=1585072438685236&alt=media" alt="">

My view, as a person living in NYC, about this virus, is that by the time governments react to media pressure, to lockdown or even test, it's too late. In dense areas, everyone susceptible has already amble opportunities to be infected. Especially for a virus with 5-14 days lag between infections and symptoms, a period during which hosts spread it all over on subway, the conditions are hopeless. Active populations have already been exposed, mostly asymptomatic and recovered. Sensitive/older populations are more self-isolated/careful in affluent societies (maybe this isn't the case in North Italy). As the virus finishes exploring the active population, it starts penetrating the more isolated ones. At this point in time, the first fatalities happen. Then testing starts. Then the media and the lockdown. Lockdown seems overly effective because it coincides with the tail of the disease spread. It helps slow down the virus exploring the long-tail of sensitive population, and we should all contribute by doing it, but it doesn't cause the end of the disease. If it did, then as soon as people were back in the streets (see China), there would be repeated outbreaks.

Smart politicians will test a lot because it will make their condition look worse. It helps them demand more resources. At the same time, they will have a low rate of fatalities due to large denominator. They can take credit for managing well a disproportionally major crisis - in contrast to people who didn't test.

We were lucky this time. We, Westerners, have woken up to the potential of a pandemic. I'm sure we will give further resources for prevention. Additionally, we will be more open-minded, helping politicians to have more direct responses. We will also require them to be more responsible in their messages and reactions.

These are the two demographic datasets used in the manuscript "Exploring population responses to environmental change when there is never enough data; a factor analytic approach".Abstract1. Temporal variability in the environment drives variation in vital rates, with consequences for population dynamics and life history evolution. Integral projection models (IPMs) are data-driven structured population models widely used to study population dynamics and life history evolution in temporally variable environments. However, many data sets have insufficient temporal replication for the environmental drivers of vital rates to be identified with confidence, limiting their use for evaluating population level responses to environmental change.2. Parameter selection, where the kernel is constructed at each time step by randomly selecting the time-varying parameters from their joint probability distribution, is one approach to including stochasticity in IPMs. We consider a factor analytic (FA) approach for modelling the covariance matrix of time-varying parameters, whereby latent variable(s) describe the covariance among vital rate parameters. This decreases the number of parameters to estimate and, where the covariance is positive, the latent variable can be interpreted as a measure of environmental quality. We demonstrate this using simulation studies and two case studies.3. The simulation studies suggest the FA approach provides similarly accurate estimates of stochastic population growth rate to estimating an unstructured covariance matrix. We demonstrate how the latent parameter can be perturbed to show how selection on reproductive delays in the monocarp Carduus nutans changes under different environmental conditions. We develop a demographic model of the fire dependent herb Eryngium cuneifolium to show how a putative driver of the variation in environmental quality can be incorporated with the addition of a single parameter. Using perturbation analyses we determine optimal management strategies for this species.4. This approach estimates fewer parameters than previous approaches and allows novel eco-evolutionary insights. Predictions on population dynamics and life history evolution under different environmental conditions can be made without necessarily identifying causal factors. Putative environmental drivers can be incorporated with relatively few parameters, allowing for predictions on how populations will respond to changes in the environment.The Carduus nutans data are owned by CSIRO. The Eryngium cuneifolium were provided by the Archbold Biological Station. Example code for parameterising an IPM using the FA approach are available on github with DOI: http://doi.org/10.5281/zenodo.1312855.

The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to seek the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems. The microphysical parameters in the data set were derived from 2D probe data collected by the NCAR aircraft during FIRE II. The 2D-C data are converted to size spectra according to the guidelines given in Heymsfield and Baumgardner (1985, Bull. Amer. Meteoro. Soc.), where one element is added to the size of a particle along the the flight direction to account for the probe's intrinsic start-up time. Size is determined as the maximum dimension ($D_{max}$) along the flight direction or optical array axis. The nominal size resolution for the Sabreliner 2D probe is 50 microns/per shadowed optical array element, for the King Air is 25 microns/bin. Sample area (SA) is derived using the depth of field estimates reported by Knollenberg (1970). Particles are binned into 32 size categories, nonuniformly spaced with higher resolution in the smaller classes. Particles within each size bin are subdivided into 10 ``area ratio (AR)'' bins, where AR represents the ratio of particle area to the area of discs of diameter $D_{max} The microphysical parameters in the data set were derived from 2D probe data collected by the NCAR Sabreliner during FIRE II. The derivation of the microphysical parameters is outlined in the later reference to Heymsfield (1977). The vertical velocity is the steady-state velocity in cm s-1 to keep the relative humidity at it's currently measured value. Differential growth rate represents the growth rate of the particle population of different sizes at the current relative humidity. The Total differential growth rate is thesum of the growth rate in all channels. The assumptions used for the IWC calculations are reported in Heymsfield; also, generic size to mass equations are used. Precipitation rate is calculated from particle size and terminal velocity data, integrated over the size spectrum. Concentration data are as derived above. Number of crystal-crystal collisions are derived from the data reported by Hindman and the crystal terminal velocities. Water vapor density andsupersaturation information in this data set should not be used--it is unreliable. Curve fits to the data using least squares methods are provided. VARIABLE DESCRIPTION UNITS ------------------------------------------------------------------------------- IT1,IT2 MEASUREMENT TIME INTERVAL HH/MM/SS PS STATIC PRESSURE mb TEMP AMBIENT TEMPERATURE degreesC ALT PRESSURE ALTITUDE m USTAR VERTICAL VELOCITY NEEDED TO KEEP THE cm/s RELATIVE HUMIDITY CONSTANT DBARM MEDIAN PARTICLE MASS WEIGHTED DIAMETER cm DMAX MAXIMUM PARTICLE DIAMETER cm W1 DIFFUSIONAL GROWTH RATE IN CHANNEL 1 g/sec W2 DIFFUSIONAL GROWTH RATE IN CHANNEL 2 g/sec W3 DIFFUSIONAL GROWTH RATE IN CHANNEL 3 g/sec W4 DIFFUSIONAL GROWTH RATE IN CHANNEL 4 g/sec WTOT TOTAL DIFFUSTIONAL GROWTH RATE g/sec DT8 DEPLETION TIME (8 micron droplets) sec DT12 DEPLETION TIME (12 micron droplets) sec TMASS1 IWC IN CHANNEL 1 g/m^3 TMASS2 IWC IN CHANNEL 2 g/m^3 DPTC DEW POINT TEMPERATURE (EG&G) degreesC RH RELATIVE HUMIDITY (EG&G) % RIWC ICE WATER CONTENT g/m^3 XM1 ICE WATER CONTENT BASED ON SNOW HABIT g/m^3 XM2 ICE WATER CONTENT BASED ON SMALL SNOW g/m^3 HABIT XM3 ICE WATER CONTENT BASED ON LARGE SNOW g/m^3 HABIT R PRECIPITATION RATE mm/hr DBZ RADAR REFLECTIVITY FACTOR decibels VBAR MEAN REFLECTIVITY WEIGHTED WITH THE cm/s TERMINAL VELOCITY TTCONC TOTAL PARTICLE CONCENTRATION #/L CBIN1 PARTICLE CONCENTRATION WITHIN THE RANGE 200#/L LE CBIN2 PARTICLE CONCENTRATION WITHIN THE RANGE #/L 200-500 CBIN3 PARTICLE CONCENTRATION WITHIN THE RANGE 500-800 #/L CBIN4 PARTICLE CONCENTRATION WITHIN THE RANGE 800 #/L GT CE8 COLLECTION EFFICIENCY (8 micron none droplets) CE12 COLLECTION EFFICIENCY (12 micron none droplets) TMASS3 IWC IN CHANNEL 3 g/m^3 TMASS4 IWC IN CHANNEL 4 g/m^3 TIMP # OF CRYSTAL-CRYSTAL COLUMNS sec^(1-) RHORH WATER VAPOR DENSITY g/cm^3 SI SUPERSATURATION WITH RESPECT TO ICE % SW SUPERSATURATION WITH RESPECT TO WATER % LAMBDA COEFFICIENTS USED TO FIT THE EQUATION #/cm^3 NZERO N=N0EXP(-LAMBDAD) #/L/mm RSQ COEFFICIENT OF THE FIT none ICP PROBE TYPE (C OR P) none

Top Covid19 Countries and Health Demographic Trend

Context

This is a time-series trend data collection with a series of json files primarily focused on countries most impacted by Covid-19. The tree formatted time series data should be able to enable various different kinds of analysis to answer questions about what may make a country's health system vulnerable to Covid-19 and what health demographics may help reducing the impact.

Demographic metrics

Healthcare GDP Expenditure 
Healthcare Employment
Hospital Bed Capacity
Air Pollution and Death Rate
Chronic illnesses and DALYs(Disability-Adjusted Life Years)
Body Weight 
Elderly(Aged 65+) Population
CT Scanner Density
Tobacco Consumption(Smoker population %)

More metrics can be added upon request.

Data Normalization

The raw CSV includes many different types of measurements such as number, percentage and per 1 million population. This data normalizes the time_series data by selecting data that is more about density, and number per capita data rather than absolute numbers. This could help doing comparison among nations since they may vary significantly on population.

Content

Most of the JSON files contain time_series data. For people who want to use the data as country metadata, the most-recent data attribute is collected in top_countries_latest_fact_summary.json

The JSON data focuses on the above mentioned demographic areas in a simple tree schema { Country_name: { metric_name:[ List of {year, value, unit} ] } }

Data source & License

The data is sourced from OECD(https://stats.oecd.org/) and GDHX(http://ghdx.healthdata.org/). The json files with prefix "gbd_" are from GDHX

Following citation is needed for using GDHX data:

GBD Results tool: Use the following to cite data included in this download: Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2017 (GBD 2017) Results. Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2018. Available from http://ghdx.healthdata.org/gbd-results-tool.

Inspiration

• Where does US rank in term of Healthcare/Preventive spending in GDP, hospital bed/ICU bed/physician density and long-term illness? In which areas can US do more to prevent future Cov-19 crisis?

• Is there correlation in a nation's medical preparedness and the rate of growth in confirmation, death rate and recovery rate? From GBD data graphs, it seems that Dalys(DALYs (Disability-Adjusted Life Years), rate per 100k) can divided nations into different camps.

• How does death rate from Cov-19 correlate with Death rate related to Cardiovascular diseases and Chronic respiratory diseases?

• What trends can we discover in various nation's health demographics over time? Are some areas getting better while others getting worse?

• With time span from 2010 to 2018, this dataset can also correlate with data related to recent outbreaks such as seasonal flus, Avian influenza, etc.

Example Notebook

With some quick analysis, it shows that the US actually ranks higher than China for DALYs(Disability-adjusted life years) caused by Chronic Respiratory conditions, which could be due to seasonal allergies. It seems counter-intuitive that this may suggest that countries with cleaner air may have higher burden of people with Chronic Respiratory conditions that may have made them more vulnerable in the Covid-19 crisis.

Example Kernel: https://www.kaggle.com/timxia/bar-chart-comparison-of-countries https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4802460%2F2fce05195108856422b437316f34e837%2FTobacco.png?generation=1585936274243838&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4802460%2Fe8db14764a47a8bce48fa79bdfdfb0f1%2FChronicDisease.png?generation=1585936274372639&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4802460%2Fc534d40af042b9a503325f41c49b83cb%2FAirPollution.png?generation=1585936274337626&alt=media" alt="">