# Title of Dataset: EcoCultural Dataset (Revised) --- Scholars interested in cultural diversity have long suggested that similarities and differences across human populations might be understood, at least in part, as stemming from differences in the social and physical ecologies individuals inhabit. Here, we describe the EcoCultural Dataset (ECD), the most comprehensive compilation to date of country-level ecological and cultural variables around the globe. ECD covers 220 countries, 9 ecological variables operationalized by 11 statistical metrics (including measures of variability and predictability), and 72 cultural variables (including values, personality traits, fundamental social motives, subjective well-being, tightness-looseness, indices of corruption, social capital, and gender inequality). This rich dataset can be used to identify novel relationships between ecological and cultural variables, to assess the overall relationship between ecology and culture, to explore the consequences of interactions between different ecological variables, and to construct new indices of cultural distance. Note: The full dataset with 72 cultural variables is available on OSF (https://osf.io/45am7/). This is the abbreviated version (66 cultural variables) used for analyzes in "How much cultural variation around the globe is explained by ecology?". Here, we provide an answer using nine ecological variables and 66 cultural variables (including personality traits, values, and norms) drawn from the EcoCultural Dataset. We generate a range of estimates by using different statistical metrics (e.g., current levels, average levels across time, unpredictability across time) of each of the ecological variables. Our results suggest that, on average, ecology explains a substantial amount of human cultural variation above and beyond spatial and cultural autocorrelation. The amount of variation explained depended on the metrics used, with current levels and average levels of ecological conditions explaining the greatest amounts of variance in human culture on average (16% and 20% respectively). ## Description of the Data and file structure The data file is available at: https://osf.io/45am7/ (ECD Data v4 RSPB) Rows: Countries by Year Columns: Ecological data from that country in that year (Rows D:L), ecological operationalizations based on data until that year (M:DG), cultural data from that country (DH:FU), geographical descriptors (FV:FX), and cultural distance from America (FY). Cultural data does not vary by year but is presented in every year to aid users in calculating relationships from historical ecological data with more recent cultural data (ex. Does rainfall in 1908 correlate with Openness?). Column Name: Description of Variable country: two-digit country abbreviation year: year of data collection Country: country name rain: Average rainfall per year (mm) temp: Average temperature per year (°C) gdp: GDP per capita (current US$) mortality: External causes of morbidity and mortality lifeexpectancy: Life expectancy at birth, total (years) disease: Percentage of deaths in the population due to HIV/AIDS, respiratory infection, enteric infections, and other communicable infections pop_sqr_km: Persons per km² SWIID: Income inequality unemployment: Total % of the labor force who is unemployed Rainfall.MAPE: Mean Average Percentage Error of Rainfall Temp.MAPE: Mean Average Percentage Error of Temperature GDP.MAPE: Mean Average Percentage Error of GDP Mort.MAPE: Mean Average Percentage Error of Mortality Expect.MAPE: Mean Average Percentage Error of Life Expectancy Disease.MAPE: Mean Average Percentage Error of Disease Threat Pop.MAPE: Mean Average Percentage Error of Population Density SWIID.MAPE: Mean Average Percentage Error of Income Inequality Unemp.MAPE: Mean Average Percentage Error of Unemployment Rainfall.MASE: Mean Average Standard Error of Rainfall Temp.MASE: Mean Average Standard Error of Temperature GDP.MASE: Mean Average Standard Error of GDP Mort.MASE: Mean Average Standard Error of Mortality Expect.MASE: Mean Average Standard Error of Life Expectancy Disease.MASE: Mean Average Standard Error of Disease Threat Pop.MASE: Mean Average Standard Error of Population Density SWIID.MASE: Mean Average Standard Error of Income Inequality Unemp.MASE: Mean Average Standard Error of Unemployment Rainfall.acf: First Order Autocorrelation of Rainfall Temp.acf: First Order Autocorrelation of Temperature GDP.acf: First Order Autocorrelation of GDP Mort.acf: First Order Autocorrelation of Mortality Expect.acf: First Order Autocorrelation of Life Expectancy Disease.acf: First Order Autocorrelation of Disease Threat Pop.acf: First Order Autocorrelation of Population Density SWIID.acf: First Order Autocorrelation of Income Inequality Unemp.acf: First Order Autocorrelation of Unemployment Rainfall.current: Last Available Data Point of Rainfall Temp.current: Last Available Data Point of Temperature GDP.current: Last Available Data Point of GDP Mort.current: Last Available Data Point of Mortality Expect.current: Last Available Data Point of Life Expectancy Disease.current: Last Available Data Point of Disease Threat Pop.current: Last Available Data Point of Population Density SWIID.current: Last Available Data Point of Income Inequality Unemp.current: Last Available Data Point of Unemployment Rainfall.mean: Average of Rainfall Temp.mean: Average of Temperature GDP.mean: Average of GDP Mort.mean: Average of Mortality Expect.mean: Average of Life Expectancy Disease.mean: Average of Disease Threat Pop.mean: Average of Population Density SWIID.mean: Average of Income Inequality Unemp.mean: Average of Unemployment Rainfall.range: Range (maximum - minimum) of Rainfall Temp.range: Range (maximum - minimum) of Temperature GDP.range: Range (maximum - minimum) of GDP Mort.range: Range (maximum - minimum) of Mortality Expect.range: Range (maximum - minimum) of Life Expectancy Disease.range: Range (maximum - minimum) of Disease Threat Pop.range: Range (maximum - minimum) of Population Density SWIID.range: Range (maximum - minimum) of Income Inequality Unemp.range: Range (maximum - minimum) of Unemployment Rainfall.sd: Standard Deviation of Rainfall Temp.sd: Standard Deviation of Temperature GDP.sd: Standard Deviation of GDP Mort.sd: Standard Deviation of Mortality Expect.sd: Standard Deviation of Life Expectancy Disease.sd: Standard Deviation of Disease Threat Pop.sd: Standard Deviation of Population Density SWIID.sd: Standard Deviation of Income Inequality Unemp.sd: Standard Deviation of Unemployment Rainfall.max: Maximum of Rainfall Temp.max: Maximum of Temperature GDP.max: Maximum of GDP Mort.max: Maximum of Mortality Expect.max: Maximum of Life Expectancy Disease.max: Maximum of Disease Threat Pop.max: Maximum of Population Density SWIID.max: Maximum of Income Inequality Unemp.max: Maximum of Unemployment Rainfall.min: Minimum of Rainfall Temp.min: Minimum of Temperature GDP.min: Minimum of GDP Mort.min: Minimum of Mortality Expect.min: Minimum of Life Expectancy Disease.min: Minimum of Disease Threat Pop.min: Minimum of Population Density SWIID.min: Minimum of Income Inequality Unemp.min: Minimum of Unemployment Rainfall.outlier: Percentage of datapoints +/- 2.5 SD from the mean of Rainfall Temp.outlier: Percentage of datapoints +/- 2.5 SD from the mean of Temperature GDP.outlier: Percentage of datapoints +/- 2.5 SD from the mean of GDP Mort.outlier: Percentage of datapoints +/- 2.5 SD from the mean of Mortality Expect.outlier: Percentage of datapoints +/- 2.5 SD from the mean of Life Expectancy Disease.outlier: Percentage of datapoints +/- 2.5 SD from the mean of Disease Threat Pop.outlier: Percentage of datapoints +/- 2.5 SD from the mean of Population Density SWIID.outlier: Percentage of datapoints +/- 2.5 SD from the mean of Income Inequality Unemp.outlier: Percentage of datapoints +/- 2.5 SD from the mean of Unemployment Rainfall.line: Standardized Linear Regression Coefficient of Rainfall Temp.line: Standardized Linear Regression Coefficient of Temperature GDP.line: Standardized Linear Regression Coefficient of GDP Mort.line: Standardized Linear Regression Coefficient of Mortality Expect.line: Standardized Linear Regression Coefficient of Life Expectancy Disease.line: Standardized Linear Regression Coefficient of Disease Threat Pop.line: Standardized Linear Regression Coefficient of Population Density SWIID.line: Standardized Linear Regression Coefficient of Income Inequality Unemp.line: Standardized Linear Regression Coefficient of Unemployment harmony: Harmony embedded: Embedded hierarchy: Hierarchy mastery: Mastery aff.auton: Affective Autonomy intel.auton: Intellectual Autonomy egalitar: Egalitarianism extraversion: Extraversion agreeableness: Agreeableness conscientiousness: Conscientiousness neuroticism: Neuroticism openness: Openness authority: Authority fairness: Fairness harm: Harm ingroup: Ingroup/Loyalty purity: Purity SPO: Self-Protection DIS: Disease Avoidance AFG: Affiliation Group AFI: Affiliation Individual AFX: Affiliation Exclusion STA: Status MAT: Mate Acquisition MRB: Mate Retention Breakup MRT: Mate Retention KCF: Kin Care Family KCC: Kin Care Children PowerDistance: Power Distance Individualism: Individualism Masculinity: Masculinity UncertaintyAvoidance: Uncertainty Avoidance LongTermOrientation: Long Term Orientation Indulgence: Indulgence Self.Efficacy: Self-Efficacy Fear.of.Failure: Fear of Failure Tightness: Tightness RM: Relational Mobility Political.Rights: Political Rights civil.liberties: Civil Liberty National.Pride: National Pride Gender.Egalitarianism: Gender Egalitarianism ruleoflaw: Rule of Law narcissism: Narcissism machiavellanism: Machiavellianism psychopathy: Psychopathy importance.of.work: Importance of Work SCI: Social Capital Index RiskTaking: Risk Taking workethic: Work Ethic corruption: Corruption Index PaceIndex: Pace Index hope: Hope Quotient optimism: Economic Optimism Expressivity: Emotional Expressivity prejudice: Prejudice bad.journalism: World Press Freedom Index GII: Global Innovation Index HDI: Well-Being democracy: Democracy pluralism: Pluralism govt.functioning: Government Functioning participation: Political Participation political.culture: Political Culture CTL_C: Cultural Tightness/Looseness soi: Sociosexual Orientation Index region_short: WHO Six World Regions Latitude: Latitude Longitude: Longitude cultural.distance: Muthukrishna et al.'s Cultural Distance from the US Code for calculating those operationalizations is available on OSF (https://osf.io/45am7/) and descriptions of the operationalizations are available in all related publications. Missing data is coded is "NA". ## Sharing/access Information Links to other publicly accessible locations of the data: The full dataset with 72 cultural variables is available on OSF (https://osf.io/45am7/). This is the abbreviated version (66 cultural variables). Data was sourced from existing publications. For all citations, please see the Supplemental materials of this Dryad dataset or the supplement of the accompanying publications. Citations for data sources are also listed in the "ECD Cultural Variables Codebook RSPB.csv" file. ## Code for the analyses for "How much cultural variation around the globe is explained by ecology?" This file "Ecology and Culture May 15 2023" is a .rmd file (accesible in R). Code is structured in the same order results are presented in the manuscript, followed by the code for creating Figures (which requires "Figure 1 Data.csv"), and the code for the supplemental analyses. ## Attribution Data should be cited as: Wormley AS, Kwon JY, Barlev M, Varnum MEW. 2022 The Ecology-Culture Dataset: A new resource for investigating cultural variation. Sci Data 9, 615. (doi:10.1038/s41597-022-01738-z) OR Wormley AS, Kwon JY, Barlev M, Varnum MEW. 2023 How much cultural variation around the globe is explained by ecology? Proceedings of the Royal Society B: Biological Sciences ## Questions? Additions? Corrections? If you have any feedback on this dataset, please contact Alexandra Wormley at awormley@asu.edu or on Twitter @AlexandraWorm. This is meant to be a living dataset that can be added to as more cultural and ecological time series data becomes available.