Changes for page HRAF Advanced Research Centers
Last modified by hrafARC Editors on 2023/09/27 17:10
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- AR-DA-ST-V5-NW-PP.csv
- AR-DA-ST-V5-NW-PP.sav
- AR-DA-ST-V5-PP.csv
- AR-DA-ST-V5-PP.sav
- AR-DA-V5-NW-PP.csv
- AR-DA-V5-NW-PP.sav
- AR-DA-V5-PP.csv
- AR-DA-V5-PP.sav
- ARO Codebook_5.pdf
- CDD_CWD_Combined.xlsx
- CDS_Combined_ann.xlsx
- CSDI_Combined.xlsx
- CSP-AR-Codebook_and_Notes_V5-2.docx
- Climate.mpk
- Climate_Raw_Data.sav
- Climate_Temperature.sav
- Codebook to Accompany Skoggard et al.pdf
- Codebook.pdf
- CulturalEvolution_ACEdata.csv
- CulturalEvolution_ACEdata.sav
- CulturalEvolution_PNASdata.csv
- CulturalEvolution_PNASdata.sav
- DT-GodsAndResourceStress.csv
- DT-GodsAndResourceStress_Long.csv
- DT-MoralHighGods_EP(2).csv
- DT-MoralHighGods_Long_EP(1).csv
- EmberEtAl-2019-LandTenure-Supplement.docx
- EmberEtAl-2019-LandTenureDataSet.csv
- EmberEtAl-2019-LandTenureDataSet.sav
- Ember_JACPR_Codebook_22Dec2017.pdf
- Ember_JACPR_Codebook_Nov7.pdf
- Ember_et_al_EAFrica_PolPart_Codes_JACPR2017.csv
- Ember_et_al_EAFrica_PolPart_Codes_JACPR2017.sav
- Ember_et_al_Worldwide_PolPart_Codes_JACPR2017_SHort.csv
- Ember_et_al_Worldwide_PolPart_Codes_JACPR2017_SHort.sav
- Factors_revised_2020.xlsx
- Factors_revised_Oct20.xlsx
- IBSS_indices_final.sps
- OurBetterNature_ReadmeForDataFile.pdf
- Ranalysis_StressReligionSharing2020-1.R
- SDIIS_Combined_ann.xlsx
- SPSS_PCM.xlsx
- SPSS_indices.xlsx
- Skoggard et al._Data.csv
- Skoggard et al._Data.xlsx
- Social Resilience ARO-DA.sav
- Social Resilience.csv
- Social resilience.csv
- Supplement_Data_April2018.csv
- Supplement_Data_April2018.sav
- Supplementary data ReadMeRevised2.pdf
- ThrIndS_Combined_ann.xlsx
- ThrIndS_tmin_combined.xlsx
- Variables in subdiv_data_rev2020.xlsx
- WCS_ARO.spv
- WCS_ARO.spv [Document1].pdf
- WCS_readme.txt
- allfactors.xlsx
- climaterawdata.csv
- climatetemperature.csv
- coef_var.xlsx
- csdi_combined_ann.xlsx
- gap_fill_sens.zip
- gap_filled_sens.zip
- methods_for_dataset.docx
- readme_final_excel.docx
- readme_final_excel_sens.docx
- readme_final_excel_sens.txt
- rx1s_Combined_ann.xlsx
- rx5s_Combined_ann.xlsx
- subdiv_data_rev2020.csv
- tmax_combined.xlsx
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... ... @@ -1,1 +1,1 @@ 1 -HRAF Advanced Research Cent ers1 +HRAF Advanced Research Centres [EU] - Content
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... ... @@ -4,26 +4,23 @@ 4 4 5 5 = About hrafARC = 6 6 7 - TheHRAF Advanced Research Centers (hrafARC) aim to promote basic and applied research in anthropology and to encourage and support comparative and cross-cultural research. More specifically, hrafARC aims to further the development of anthropology through comparative knowledge based on testable theory, sound research design and systematic methods for the collection and analysis of data. We seek to fulfill the historic mission of anthropology to describe and explain the range of variation in human biology, society, and culture across time and space.7 +HRAF Advanced Research Centers (hrafARC) is a research division of the Human Relations Area Files at Yale University. hrafARC aims to promote basic and applied research in anthropology and to encourage and support comparative and cross-cultural research. More specifically, hrafARC aims to further the development of anthropology through comparative knowledge based on testable theory, sound research design and systematic methods for the collection and analysis of data. We seek to fulfill the historic mission of anthropology to describe and explain the range of variation in human biology, society, and culture across time and space. 8 8 9 9 The main headquarters is at the Human Relations Area Files (HRAF) building in New Haven, CT (755 Prospect Street). [[HRAF>>url:http://hraf.yale.edu||rel="__blank"]] is a nonprofit membership consortium affiliated with Yale University. [[Carol R. Ember>>url:http://hraf.yale.edu/about/staff/carol-r-ember/||rel="__blank"]] directs hrafARC in New Haven and Michael Fischer directs hrafARC in the UK~-~-see [[http:~~/~~/hrafarc.eu/>>url:http://hrafarc.eu/]]. 10 10 11 - ==hrafARC Projects ==11 +{{html}} 12 12 13 -==== Natural Hazards and Cultural Transformations. (2015-2019) ==== 13 +<button value="Donate to hrafARC" style="font-size:14px;color:darkred;weight:bold" title="Donate to hrafARC" type="button" onclick="js:window.location='/Payments/Donate';">Donate to hrafARC </button> 14 +{{/html}} 14 14 15 - Researchersfrom cultural anthropology,archaeology, psychology, geography and climatology conducted three types ofcomparisons~-~-a worldwide cross-cultural comparison usingethnographic data,an diachronic archaeological comparison of 32 traditions before and after major severe climate events, and a comparison of countries. We are looking at a broad variety of possible cultural transformations in response to hazards. These range from diet and subsistence diversity, property systems, mutual aid, political economy, general cultural “tightness" and beliefs about gods involvement with weather. All of these domains have been newly coded for this project.[[Read more>>url:http://hrafarc.org/bin/hrafARC+Research+-+Chacult]]...16 +== hrafARC Current Research == 16 16 17 - ====Social Resilience toNuclear Winter.(2018-2020)====18 +We are currently working on a 5-year NSF-funded interdisciplinary project “Natural Hazards and Cultural Transformations.” Researchers from cultural anthropology, archaeology, psychology, geography and climatology, are conducting three types of comparisons~-~-a worldwide cross-cultural comparison using ethnographic data, an diachronic archaeological comparison of twelve traditions before and after major severe climate events, and a comparison of countries. We are looking at a broad variety of possible cultural transformations in response to hazards. These range from diet and subsistence diversity, property systems, mutual aid, political economy, and general cultural “tightness." All of these domains have been newly coded for this project[[. Read more>>url:http://hrafarc.org/bin/hrafARC+Research+-+Chacult]] ... 18 18 19 -This project employs archaeological and historical information to examine societal resilience to a catastrophic atmospheric event that block the sun and cooled the Northern Hemisphere by roughly 1 degree centigrade, creating widespread social disruption. Peregrine uses this event as a proxy for the expected atmospheric impact of a limited nuclear war in Europe and seeks to identify strategies of resilience by examining those societies that survived, and failed to survive, the A.D. 536 event. [[Read more...>>doc:Peregrine ARO grant.Peregrine ARO grant]] 20 - 21 21 == hrafARC Data Repository == 22 22 23 - ====**2017**====22 +Peregrine, Peter N. 2017. “Political Participation and Long-Term Resilience in Pre-Columbian Societies.” //Disaster Prevention and Management// 26(3). DOI: 10.1108/DPM-01-2017-0013 24 24 25 -Peregrine, Peter N. 2017. “Political Participation and Long-Term Resilience in Pre-Columbian Societies.” //Disaster Prevention and Management// 26(3). [[https:~~/~~/doi.org/10.1108/DPM-01-2017-0013>>url:https://doi.org/10.1108/DPM-01-2017-0013]] 26 - 27 27 * [[Codebook>>attach:CSP-AR-Codebook_and_Notes_V5-2.docx||rel="__blank"]] 28 28 * [[Social Transformation Data (SPSS)>>attach:AR-DA-ST-V5-NW-PP.sav||rel="__blank"]] 29 29 * [[Raw Data (SPSS)>>attach:AR-DA-V5-NW-PP.sav||rel="__blank"]] and [[Index Commands (SPSS)>>attach:IBSS_indices_final.sps||rel="__blank"]] ... ... @@ -30,18 +30,8 @@ 30 30 * [[Social Transformation Data (csv)>>attach:AR-DA-ST-V5-NW-PP.csv||rel="__blank"]] 31 31 * [[Raw Data (csv)>>attach:AR-DA-V5-NW-PP.csv]] 32 32 33 - ====**2018**====30 +Peregrine, Peter N. n.d. "Social Resilience to Climate-Related Disasters in Ancient Societies: A Test of Two Hypotheses" (under review) 34 34 35 -Ember, Carol R., Ian Skoggard, Erik J. Ringen, and Megan Farrer. 2018. "Our Better Nature: Does Resource Stress Predict Beyond-Household Sharing?" //Evolution & Human Behavior// 39 (4): 380-391.[[ https:~~/~~/www.sciencedirect.com/science/article/pii/S1090513817302489>>url:https://www.sciencedirect.com/science/article/pii/S1090513817302489]] 36 - 37 -* [[Codebook>>url:http://hrafarc.org/download/Main/WebHome/Supplementary%20data%20ReadMeRevised2.pdf]] 38 -* [[Data >>url:http://hrafarc.org/download/Main/WebHome/Supplement_Data_April2018.sav]](SPSS) 39 -* [[Data >>url:http://hrafarc.org/download/Main/WebHome/Supplement_Data_April2018.csv]](csv) 40 - 41 -Peregrine, Peter N. 2018 "Social Resilience to Climate-Related Disasters in Ancient Societies: A Test of Two Hypotheses." //Weather, Climate and Society// 10(1): 145-161. [[https:~~/~~/doi.org/10.1175/WCAS-D-17-0052.1>>url:https://doi.org/10.1175/WCAS-D-17-0052.1]] 42 - 43 -Peregrine, Peter N. 2019. "Reducing Post-Disaster Conflict: A Cross-Cultural Analysis Using Archaeological Data." //Environmental Hazards//, 18:2, 93-110, DOI: [[10.1080/17477891.2018.1476317>>url:https://doi.org/10.1080/17477891.2018.1476317]] 44 - 45 45 * [[Codebook>>attach:CSP-AR-Codebook_and_Notes_V5-2.docx||rel="__blank"]] 46 46 * [[Social Transformation Data (SPSS)>>attach:AR-DA-ST-V5-PP.sav||rel="__blank"]] 47 47 * [[Raw Data (SPSS)>>attach:AR-DA-V5-PP.sav||rel="__blank"]] and [[Index Commands (SPSS)>>attach:IBSS_indices_final.sps||rel="__blank"]] ... ... @@ -48,123 +48,8 @@ 48 48 * [[Social Transformation Data (csv)>>attach:AR-DA-ST-V5-PP.csv]] 49 49 * [[Raw Data (csv)>>attach:AR-DA-V5-PP.csv]] 50 50 51 - ==== **2019** ====38 +---- 52 52 53 -Ember, Carol R., Eric C. Jones, Ian Skoggard and Teferi Abate Adem. (% style="font-size: 14px; background-color: rgb(245, 245, 245);" %)2019. "Warfare, Atrocities, and Political Participation: Eastern Africa."(%%) 54 -//Journal of Aggression, Conflict and Peace Research// [[11(1):11-23, >>url:https://doi.org/10.1108/JACPR-05-2017-0290]][[https:~~/~~/doi.org/10.1108/JACPR-05-2017-0290>>url:https://doi.org/10.1108/JACPR-05-2017-0290]] 40 +== HRAF Services Platform == 55 55 56 -* [[Codebook>>attach:Ember_JACPR_Codebook_22Dec2017.pdf]] 57 -* Data for [[Eastern African Sample (SPSS>>attach:Ember_et_al_EAFrica_PolPart_Codes_JACPR2017.sav]]) and [[Worldwide Sample (SPSS)>>attach:Ember_et_al_Worldwide_PolPart_Codes_JACPR2017_SHort.sav]] 58 -* Data for [[Eastern African Sample (csv)>>attach:Ember_et_al_EAFrica_PolPart_Codes_JACPR2017.csv]] and [[Worldwide Sample (csv)>>attach:Ember_et_al_Worldwide_PolPart_Codes_JACPR2017_SHort.csv]] 59 - 60 -Felzer, B.S., Ember, C.R., Chiang, R., and Jiang, M. 2019.** **The relationships of extreme precipitation and temperature events with ethnographic reports of droughts and floods in nonindustrial societies. Weather, Climate, and Society. Early release at: //[[https:~~/~~/doi.org/10.1175/WCAS-D-19-0045.1>>url:https://doi.org/10.1175/WCAS-D-19-0045.1]]// 61 - 62 -* [[readme_final_excel.docx>>url:http://hrafarc.org/download/Main/WebHome/readme_final_excel.docx]] 63 -* [[ThrIndS_Combined_ann.xlsx>>url:http://hrafarc.org/download/Main/WebHome/ThrIndS_Combined_ann.xlsx]] 64 -* [[rx5s_Combined_ann.xlsx>>url:http://hrafarc.org/download/Main/WebHome/rx5s_Combined_ann.xlsx]] 65 -* [[rx1s_Combined_ann.xlsx>>url:http://hrafarc.org/download/Main/WebHome/rx1s_Combined_ann.xlsx]] 66 -* [[SDIIS_Combined_ann.xlsx>>url:http://hrafarc.org/download/Main/WebHome/SDIIS_Combined_ann.xlsx]] 67 -* [[ThrIndS_tmin_combined.xlsx>>url:http://hrafarc.org/download/Main/WebHome/ThrIndS_tmin_combined.xlsx]] 68 -* [[csdi_combined_ann.xlsx>>url:http://hrafarc.org/download/Main/WebHome/csdi_combined_ann.xlsx]] 69 -* [[CDS_Combined_ann.xlsx>>url:http://hrafarc.org/download/Main/WebHome/CDS_Combined_ann.xlsx]] 70 -* [[CDD_CWD_Combined.xlsx>>url:http://hrafarc.org/download/Main/WebHome/CDD_CWD_Combined.xlsx]] 71 -* [[CSDI_Combined.xlsx>>url:http://hrafarc.org/download/Main/WebHome/CSDI_Combined.xlsx]] 72 -* [[tmax_combined.xlsx>>url:http://hrafarc.org/download/Main/WebHome/tmax_combined.xlsx]] 73 -* [[SPSS_indices.xlsx>>url:http://hrafarc.org/download/Main/WebHome/SPSS_indices.xlsx]] 74 -* [[SPSS_PCM.xlsx>>url:http://hrafarc.org/download/Main/WebHome/SPSS_PCM.xlsx]] 75 -* [[coef_var.xlsx>>url:http://hrafarc.org/download/Main/WebHome/coef_var.xlsx]] 76 -* [[allfactors.xlsx>>url:http://hrafarc.org/download/Main/WebHome/allfactors.xlsx]] 77 - 78 -==== **2020** ==== 79 - 80 -Ember, Carol R., Teferi Abate Adem, Tahlisa Brougham, and Emily Pitek. 2020. “Predictors of land privatization: Cross-cultural tests of defendability and resource stress theory” //American Anthropologist//. [[Forthcoming>>url:https://www.researchgate.net/publication/336749932_Ember-AbateAdem-Brougham-Pitek-Land-Defendability]]. 81 - 82 -* [[EmberEtAl-2019-LandTenure-Supplement.docx>>attach:EmberEtAl-2019-LandTenure-Supplement.docx]] 83 -* [[Data (SPSS)>>attach:EmberEtAl-2019-LandTenureDataSet.sav]] 84 -* [[Data (csv)>>attach:EmberEtAl-2019-LandTenureDataSet.csv]] 85 - 86 -((( 87 -Ember, Carol R., Erik Ringen, Jack Dunnington, and Emily Pitek, 2020. "(% style="font-size: 14px; background-color: rgb(245, 245, 245);" %)Resource Stress and Subsistence Diversification Across Societies" //Nature Sustainability//: 1-9. [[DOI>>url:https://doi.org/10.1038/s41893-020-0542-5]] 88 - 89 -* [[Codebook>>attach:Variables in subdiv_data_rev2020.xlsx]] 90 -* [[Data (csv)>>attach:subdiv_data_rev2020.csv]] 91 - 92 -Peregrine, Peter N. 2020. "Climate and social change at the start of the Late Antique Little Ice Age" //The Holocene//. 93 -DOI: [[10.1177/0959683620941079>>url:https://doi.org/10.1177/0959683620941079]] 94 - 95 -* [[Codebook>>attach:ARO Codebook_5.pdf]] 96 -* [[Social Change Data (csv)>>attach:climaterawdata.csv]] 97 -* [[Social Change Data (SPSS)>>attach:Climate_Raw_Data.sav]] 98 -* [[Cultural Evolution Data, Atlas of Cultural Evolution (csv)>>attach:CulturalEvolution_ACEdata.csv]] 99 -* [[Cultural Evolution Data, Atlas of Cultural Evolution (SPSS)>>attach:CulturalEvolution_ACEdata.sav]] 100 -* [[Cultural Evolution Data, Turchin et al. (csv)>>attach:CulturalEvolution_PNASdata.csv]] 101 -* [[Cultural Evolution Data, Turchin et al. (SPSS)>>attach:CulturalEvolution_PNASdata.sav]] 102 -* [[Temperature Data (csv)>>attach:climatetemperature.csv]] 103 -* [[Temperature Data (SPSS)>>attach:Climate_Temperature.sav]] 104 -* [[GIS package (ArcMap)>>attach:Climate.mpk]] 105 - 106 -((( 107 -Peregrine, Peter N. "Social Resilience to climate change during the Late Antique Little Ice Age: A replication study" //Weather, Climate, and Society//. DOI: [[10.1175/WCAS-D-20-0023.1>>url:https://doi.org/10.1175/WCAS-D-20-0023.1]] 108 - 109 -* [[readme>>url:http://hrafarc.org/download/Main/WebHome/WCS_readme.txt]] 110 -* [[Codebook>>url:http://hrafarc.org/download/Main/WebHome/ARO%20Codebook_5.pdf]] 111 -* [[Social Change Data (csv)>>url:http://hrafarc.org/download/Main/WebHome/Social%20resilience.csv]] 112 -* [[Social Change Data (SPSS)>>url:http://hrafarc.org/download/Main/WebHome/Social%20Resilience%20ARO%2DDA.sav]] 113 -* [[SPSS Output (pdf)>>url:http://hrafarc.org/download/Main/WebHome/WCS_ARO.spv%20%5BDocument1%5D.pdf]] 114 -* [[SPSS Output (SPSS)>>url:http://hrafarc.org/download/Main/WebHome/WCS_ARO.spv]] 115 -* Raw coding data (Excel) 116 -* Raw coding data bibliography (Excel) 117 -* Raw coding data (RDF file) 118 -* Raw coding data (RDF ontology) 119 - 120 -Skoggard, Ian. Carol R. Ember, Emily Pitek, Joshua Conrad Jackson, Christina Carolus. 2020. "Resource Stress Predicts Changes in Religious Beliefs and Increases in Sharing Behavior." //Human Nature, //pp. 1-23//. //[[DOI>>url:https://doi.org/10.1007/s12110-020-09371-8]]// 121 -// 122 - 123 -* [[Codebook>>attach:Codebook.pdf]] 124 -* [[Data-ResourceStressGods-short (csv)>>attach:DT-GodsAndResourceStress.csv]] 125 -* [[Data-ResourceStressGods-long (csv)>>attach:DT-GodsAndResourceStress_Long.csv]] 126 -* [[Data-MoralHighGods-short (csv)>>attach:DT-MoralHighGods_EP(2).csv]] 127 -* [[Data-MoralHighGods-long (csv)>>attach:DT-MoralHighGods_Long_EP(1).csv]] 128 -* [[R code>>attach:Ranalysis_StressReligionSharing2020-1.R]] 129 - 130 -==== 2021 ==== 131 - 132 -Ember, C., Skoggard, I., Felzer, B., Pitek, E., and Jiang, M. "Climate variability, drought, and the belief that high gods are associated with weather in nonindustrial societies." //Weather, Climate, and Society vol. 13, no. 2. //[[DOI>>url:http://10.1175/WCAS-D-20-0080.1]] 133 - 134 - 135 -[[readme_final_excel_sens.txt>>attach:readme_final_excel_sens.txt]] 136 - 137 -[[gap_fill_sens.zip>>attach:gap_fill_sens.zip]] 138 - 139 -[[Factors_revised_2020.xlsx>>attach:Factors_revised_2020.xlsx]] 140 - 141 - 142 -Peregrine, Peter N. 2021. "Social Resilience to Nuclear Winter: Lessons from the Late Antique Little Ice Age." //Global Security// 6(1): 57-67. https:~/~/doi.org/10.1080/23779497.2021.1963808 143 - 144 -* [[Codebook>>url:http://hrafarc.org/download/Main/WebHome/ARO%20Codebook_5.pdf]] 145 -* [[Social Change Data (csv)>>url:http://hrafarc.org/download/Main/WebHome/Social%20resilience.csv]] 146 -* [[Social Change Data (SPSS)>>url:http://hrafarc.org/download/Main/WebHome/Social%20Resilience%20ARO%2DDA.sav]] 147 -* SPSS Output (pdf) 148 -* SPSS Output (SPSS) 149 -* Raw coding data (Excel) 150 -* Raw coding data bibliography (Excel) 151 -* Raw coding data (RDF file) 152 -* Raw coding data (RDF ontology) 153 - 154 -((( 155 - 156 -))) 157 - 158 -((( 159 - 160 -))) 161 -))) 162 -))) 163 - 164 -((( 165 -== hrafArc Development == 166 - 167 -[[Development Team Notes>>doc:Design.vDoc||rel="__blank"]] (Internal only) 168 - 169 -[[Development Summary >>doc:Design.vAppSummary]](Internal Only) 170 -))) 42 +HRAF is working on a services platform to support the next generation of eHRAF applications to support teaching and research [[Read more …>> eServices.demos]]
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Peninsula,-.91801802575238,-1.04702404759072,1300,1,1,1,0,0,1,0,0,1,0,4,1,0,5,1,0,3,1,0,2,1,0,3,1,0,0,1,0,0,1,0,1,1,0,1,1,0,0,1,0,0,1,0,1,1,0,0,1,0,1,3,0,1,1,0,0,1,0,0,1,0,0,1,0,0,3,0,1,1,0,4,1,0,4,1,0,1,1,0,3,1,0,4,3,0,3,1,0,99,99,0,2,1,0, ,99,0, ,99,0,200,3,0,1,1,0,0,1,0,2,1,0,2,1,0,0,1,0,2,1,0 3 -GLP-B,Princess Point,-.91801802575238,-.900468338687846,1000,1,1,1,0,3,1,0,0,1,0,4,1,0,5,1,0,3,1,0,2,1,0,3,1,0,0,1,0,0,1,0,1,1,0,1,1,0,0,1,0,0,1,0,1,1,0,0,1,0,1,3,0,99,99,0,0,1,0,0,1,0,0,1,0,0,3,0,1,1,0,4,1,1,4,1,0,1,1,0,3,1,0,99,99,0,99,99,0,99,99,0,2,1,0,Grand Banks,1,0,10,1,0,200,1,0,2,2,0,0,1,0,2,1,0,99,99,0,99,99,0,99,99,0 4 -GLP-C,Early Ontario Iroquois,-.91801802575238,-.106812472907335,750,1,4,2,1,4,1,0,0,1,0,4,1,0,5,1,0,1,1,0,2,1,0,3,1,0,0,1,0,0,1,0,1,1,0,1,1,0,0,1,0,0,1,0,1,1,0,0,1,0,1,3,1,1,3,1,0,1,0,0,1,0,0,1,0,0,3,0,1,1,0,4,1,1,4,1,1,1,1,1,2,1,0,4,3,0,99,99,0,99,99,0,2,1,0,Kraus,1,0,2,1,0,500,1,0,2,1,0,0,1,0,2,1,0,3,1,0,3,1,0,99,99,0 5 -GLP-D,Middle Ontario Iroquois,-.35856308446424,-.343753522089249,550,1,4,1,0,4,1,0,0,1,0,4,1,0,5,1,0,1,1,0,2,1,0,1,1,0,1,1,0,1,1,0,1,1,0,1,1,0,1,1,0,1,1,0,1,1,0,1,1,0,1,3,0,99,99,0,1,1,0,1,1,0,1,1,0,1,1,0,1,1,0,6,1,0,4,1,0,1,1,0,2,1,0,4,3,0,3,1,0,99,99,0,3,3,1,Laurenssen,1,0,2,1,1,720,1,0,2,1,0,0,1,0,2,1,0,3,1,0,3,1,0,99,99,0 6 -ABP-A,Havana Hopewell,-1.10394247609329,-1.2671836139135,1700,2,1,1,0,2,1,0,0,1,0,4,1,0,5,1,0,2,1,0,2,1,0,1,1,0,0,1,0,0,1,0,1,1,0,1,1,0,0,1,0,0,1,0,1,1,0,0,1,0,0,1,0,1,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,5,1,0,3,2,0,4,1,0,3,1,0,99,99,0,3,1,0,99,99,0,2,1,0, ,99,0,2,2,0,200,2,0,1,1,0,0,1,0,2,1,0,1,2,0,0,1,0,2,1,0 7 -ABP-B,Rosewood,-1.10394247609329,-.854983656653947,1550,2,1,1,0,2,1,0,0,1,0,4,1,0,5,1,0,2,1,0,2,1,0,1,1,0,0,1,0,0,1,0,1,1,0,1,1,0,0,1,0,0,1,0,1,1,0,0,1,0,0,1,0,99,99,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,5,1,0,2,1,0,1,1,0,3,1,0,99,99,0,99,99,0,99,99,0,2,1,0, ,99,0,2,2,0,200,2,0,2,1,0,0,1,0,2,1,0,2,1,0,0,1,0,2,1,0 8 -ABP-C,Mund,-1.10394247609329,-.854983656653947,1400,2,1,1,0,2,1,0,0,1,0,4,1,0,5,1,0,2,1,0,2,1,0,1,1,0,0,1,0,0,1,0,1,1,0,1,1,0,0,1,0,0,1,0,1,1,0,0,1,0,0,1,0,99,99,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,5,1,0,2,1,0,1,1,0,3,1,0,99,99,0,99,99,0,99,99,0,2,1,0, ,99,0,2,2,0,200,2,0,2,1,0,0,1,0,2,1,0,2,1,0,0,1,0,2,1,0 9 -ABP-D,Patrick,-1.10394247609329,-.463226915673791,1250,2,1,1,0,3,1,0,0,1,0,4,1,0,5,1,0,2,1,0,2,1,0,1,1,0,0,1,0,0,1,0,1,1,0,1,1,0,0,1,0,0,1,0,1,1,0,0,1,0,0,1,0,99,99,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,5,1,0,4,1,0,3,3,0,3,1,0,99,99,0,99,99,0,99,99,0,2,1,0, ,99,0,2,2,0,200,2,0,2,1,0,0,1,0,2,1,0,2,1,0,2,1,0,2,1,0 10 -ABP-E,Lohmann-Stirling,.939187279129317,-.056726992927785,800,2,77,1,0,3,1,0,0,1,0,4,1,0,5,1,0,2,1,0,4,3,0,1,1,0,4,1,0,4,1,0,2,3,0,2,1,0,99,99,0,4,3,0,5,3,0,1,3,0,99,99,0,1,3,0,4,1,0,3,1,0,2,1,0,2,3,0,3,3,0,6,1,0,4,1,0,7,1,0,3,1,0,8,3,0,6,3,0,8,1,0,5,1,0,Cahokia,1,0,1400,1,0,15000,1,0,3,1,0,0,1,0,2,1,0,2,1,0,2,1,0,2,1,0 11 -ABP-F,Moorehead,.96287589255106,.0496017095031256,725,2,77,1,0,3,1,0,0,1,0,4,1,0,5,1,0,2,1,0,4,1,0,1,1,0,4,1,0,4,1,0,2,3,0,2,1,0,99,99,0,4,3,0,5,3,0,99,99,0,99,99,0,99,99,0,4,1,0,3,1,0,2,1,0,99,99,0,3,3,0,6,1,0,4,3,0,7,1,0,3,1,0,8,3,0,6,3,0,8,3,0,2,1,0,Cahokia,1,0,1400,1,0,3000,1,0,3,1,0,0,1,0,2,1,0,2,1,0,2,1,0,99,99,0 12 -SWP-A,Pioneer/Formative,-.91801802575238,-1.46378648869071,1250,3,4,2,1,3,1,0,0,1,0,4,1,0,5,1,0,2,1,0,2,1,0,1,1,0,0,1,0,0,1,0,1,1,0,1,1,0,0,1,0,0,1,0,1,1,0,0,1,0,0,1,0,1,1,0,0,1,0,0,1,0,0,1,0,0,1,0,1,1,0,4,1,0,4,1,0,7,3,0,3,1,0,4,1,0,3,1,0,99,99,0,2,1,0, ,99,0, ,99,0, ,99,0,2,1,0,0,1,0,2,1,0,2,1,0,0,1,0,0,1,0 13 -SWP-B,Colonial/Preclassic,.213072806524376,.180521212165556,900,3,4,2,1,3,1,0,0,1,0,4,1,0,5,1,0,2,1,0,2,1,0,1,1,0,2,3,0,1,1,0,2,1,0,2,1,0,99,99,0,99,99,0,3,3,0,2,1,0,99,99,0,99,99,0,2,1,0,99,99,0,99,99,0,99,99,0,2,3,1,6,1,0,4,1,0,4,1,0,3,1,0,9,3,0,3,1,0,3,1,0,3,1,0,Snaketown,1,0,100,1,0,2000,1,0,2,1,0,2,1,1,3,1,0,2,1,0,3,1,0,3,1,0 14 -SWP-C,Classic,.687905367513675,.0576422099991029,550,3,4,2,1,3,1,0,0,1,0,4,1,0,5,1,0,2,1,0,6,2,1,1,1,0,88,2,1,1,1,0,2,1,0,2,1,0,99,99,0,2,1,0,4,3,0,4,3,0,99,99,0,99,99,0,4,1,0,3,1,0,3,3,0,99,99,0,1,3,0,6,1,0,3,1,1,7,1,0,2,1,0,8,1,0,99,99,0,99,99,0,5,1,0,Marana,1,0,600,1,0,1000,1,0,2,1,0,2,1,1,2,1,0,1,1,1,3,1,0,3,1,0 15 -MYP-A,Early Classic Maya,.788643787703707,.384291230700842,1400,4,4,1,0,4,1,0,1,1,0,5,1,1,5,1,0,2,1,0,2,1,0,1,3,0,3,1,0,3,1,0,3,1,0,4,1,0,2,1,0,4,1,0,4,1,0,99,99,0,3,1,0,99,99,0,3,1,0,3,1,0,3,1,0,2,3,0,2,3,1,6,1,0,3,1,0,7,1,0,3,1,1,8,3,0,5,1,0,8,1,0,4,2,0,Tikal,2,0,12000,1,0,45000,2,0,2,1,0,2,2,0,3,2,0,2,1,0,3,2,0,3,2,1 16 -MYP-B,Middle Classic May,1.00710209119518,.582502825393933,1100,4,4,1,0,4,1,0,2,1,0,4,1,0,5,2,0,3,2,0,2,1,0,1,3,0,4,1,1,3,1,0,3,1,0,4,1,0,2,3,0,4,1,0,4,1,1,99,99,0,99,99,1,3,1,0,3,1,0,3,1,0,3,1,0,99,99,0,3,3,0,6,1,0,3,1,0,8,1,0,88,3,1,9,1,0,5,1,1,8,1,0,5,2,0,Tikal,1,0,12000,1,0,62000,1,0,2,1,0,3,2,1,99,99,0,2,1,0,3,3,0,3,1,0 17 -MYP-C,Late Classic Maya,.774696528269047,.582502825393933,1000,4,4,2,1,4,2,0,1,2,0,4,3,0,5,3,0,1,3,0,3,2,1,1,3,0,3,2,0,3,1,0,3,3,0,4,2,0,2,3,0,4,1,1,4,2,0,3,3,1,4,2,0,99,99,1,3,1,0,3,1,0,3,1,0,99,99,0,2,3,0,6,1,0,3,1,0,8,1,0,3,2,0,9,2,0,5,3,0,8,2,0,4,2,0,Tikal,2,0,99,99,0,10000,2,0,2,3,0,3,1,0,3,3,0,2,1,0,3,1,1,3,2,0 18 -MYP-D,Postclassic Maya,.728538315594679,.24901059432736,750,4,4,1,0,4,1,0,1,2,0,4,2,0,5,1,0,2,3,1,3,2,1,1,1,0,2,2,0,2,2,0,3,2,0,5,2,1,3,3,0,99,99,0,2,1,0,99,99,1,99,99,0,3,3,0,3,1,0,99,99,0,3,3,1,99,99,0,2,3,0,6,1,0,3,1,0,8,1,0,99,99,0,99,99,0,5,2,0,8,2,0,4,2,0,Coba,2,0,7000,3,0,21000,3,0,3,1,1,2,3,0,99,99,0,2,1,0,2,1,0,3,1,1 19 -MOP-A,Early Moche,.905135309440315,.853247112749737,1700,5,2,1,0,4,1,0,3,1,0,5,1,0,5,1,0,3,1,0,7,1,0,1,1,0,4,1,0,4,1,0,3,1,0,2,3,0,2,3,1,4,1,0,3,2,1,3,2,0,2,2,1,2,2,0,4,1,0,3,3,0,3,3,0,2,2,1,2,2,0,6,1,0,88,88,0,8,1,0,3,2,0,9,1,0,5,1,0,8,1,0,4,1,0, ,99,0,99,99,0,10000,3,0,3,1,0,3,1,0,2,1,0,3,2,0,3,1,0,3,1,0 20 -MOP-B,Middle Moche,.817261490380301,.853247112749737,1450,5,4,3,0,4,1,0,3,1,0,5,1,0,5,1,0,2,1,0,7,2,0,1,1,0,4,1,0,3,1,0,3,2,0,4,2,0,3,3,1,4,1,0,4,1,0,2,2,0,2,2,0,2,2,0,3,2,0,3,2,0,2,1,0,99,99,0,3,1,0,6,1,0,88,88,0,8,1,0,3,1,0,9,1,0,5,1,0,8,1,0,4,1,0,Moche,1,0,300,1,0,10000,1,0,3,1,0,3,1,0,2,1,0,3,1,0,3,2,1,3,1,0 21 -MOP-C,Late Moche,.636771307051804,.993629473490705,1250,5,4,3,0,5,1,0,3,1,0,5,1,0,5,1,0,2,1,0,7,1,0,1,1,0,4,1,0,3,2,0,3,2,0,4,2,0,3,2,0,3,2,1,3,2,0,2,2,0,3,3,1,2,2,0,3,2,0,3,2,0,2,2,0,2,2,0,2,3,0,6,1,0,88,88,0,8,1,0,3,1,0,9,1,0,6,1,0,8,1,0,5,1,0,Galindo,1,0,600,1,0,99,99,0,3,1,0,3,1,0,2,1,0,3,1,0,3,1,0,3,1,0 22 -MOP-D,Chimu,1.25444220062779,.853247112749737,480,5,4,1,0,5,1,0,2,1,0,5,1,0,1,3,0,1,2,0,7,1,0,4,1,0,4,1,0,4,1,0,4,1,0,2,1,0,4,1,0,3,1,0,5,1,0,3,2,0,2,1,0,3,3,0,4,1,0,3,1,0,2,1,0,4,3,0,3,3,0,6,1,0,88,88,0,8,1,0,3,1,1,9,1,0,5,2,1,8,1,0,4,1,0,Chan-Chan,1,0,2000,1,0,30000,1,0,2,1,0,3,1,1,3,1,0,3,1,0,3,1,0,3,1,1 23 - , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , 24 -
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... ... @@ -1,34 +1,0 @@ 1 -Preceding_Period,Name,Start,End,Sequence,SV_1,SV_1_Q,SV_1_R,SV_3,SV_3_Q,SV_3_R,SV_5,SV_5_Q,SV_5_R,SV_7,SV_7_Q,SV_7_R,SV_9,SV_9_Q,SV_9_R,SV_11,SV_11_Q,SV_11_R,SV_12,SV_12_Q,SV_12_R,SV_13,SV_13_Q,SV_13_R,PE_1,PE_1_Q,PE_1_R,PE_3,PE_3_Q,PE_3_R,PE_4,PE_4_Q,PE_4_R,PE_6,PE_6_Q,PE_6_R,PE_9,PE_9_Q,PE_9_R,PE_10,PE_10_Q,PE_10_R,PE_11,PE_11_Q,PE_11_R,PE_12,PE_12_Q,PE_12_R,PE_13,PE_13_Q,PE_13_R,PE_14,PE_14_Q,PE_14_R,PE_15,PE_15_Q,PE_15_R,PE_16,PE_16_Q,PE_16_R,PE_17,PE_17_Q,PE_17_R,PE_18,PE_18_Q,PE_18_R,PE_19,PE_19_Q,PE_19_R,TL_1,TL_1_Q,TL_1_R,TL_2,TL_2_Q,TL_2_R,TL_3,TL_3_Q,TL_3_R,TL_4,TL_4_Q,TL_4_R,TL_5,TL_5_Q,TL_5_R,TL_6,TL_6_Q,TL_6_R,TL_7,TL_7_Q,TL_7_R,TL_8,TL_8_Q,TL_8_R,TL_9,TL_9_Q,TL_9_R,TL_10,TL_10_Q,TL_10_R,TL_11,TL_11_Q,TL_11_R,TL_13,TL_13_Q,TL_13_R,TL_15,TL_15_Q,TL_15_R,TL_16,TL_16_Q,TL_16_R,TL_19,TL_19_Q,TL_19_R,TL_20,TL_20_Q,TL_20_R,TL_21,TL_21_Q,TL_21_R 2 -GLP-A,Point Peninsula,2300,1300,1,1,1,0,0,1,0,0,1,0,4,1,0,5,1,0,3,1,0,2,1,0,3,1,0,0,1,0,0,1,0,1,1,0,1,1,0,0,1,0,0,1,0,1,1,0,0,1,0,1,3,0,1,1,0,0,1,0,0,1,0,0,1,0,0,3,0,1,1,0,4,1,0,4,1,0,1,1,0,3,1,0,4,3,0,3,1,0,99,99,0,2,1,0, ,99,0, ,99,0,200,3,0,1,1,0,0,1,0,2,1,0,2,1,0,0,1,0,2,1,0 3 -GLP-B,Princess Point,1300,1000,1,1,1,0,3,1,0,0,1,0,4,1,0,5,1,0,3,1,0,2,1,0,3,1,0,0,1,0,0,1,0,1,1,0,1,1,0,0,1,0,0,1,0,1,1,0,0,1,0,1,3,0,99,99,0,0,1,0,0,1,0,0,1,0,0,3,0,1,1,0,4,1,1,4,1,0,1,1,0,3,1,0,99,99,0,99,99,0,99,99,0,2,1,0,Grand Banks,1,0,10,1,0,200,1,0,2,2,0,0,1,0,2,1,0,99,99,0,99,99,0,99,99,0 4 -GLP-C,Early Ontario Iroquois,1000,750,1,4,2,1,4,1,0,0,1,0,4,1,0,5,1,0,1,1,0,2,1,0,3,1,0,0,1,0,0,1,0,1,1,0,1,1,0,0,1,0,0,1,0,1,1,0,0,1,0,1,3,1,1,3,1,0,1,0,0,1,0,0,1,0,0,3,0,1,1,0,4,1,1,4,1,1,1,1,1,2,1,0,4,3,0,99,99,0,99,99,0,2,1,0,Kraus,1,0,2,1,0,500,1,0,2,1,0,0,1,0,2,1,0,3,1,0,3,1,0,99,99,0 5 -GLP-D,Middle Ontario Iroquois,750,550,1,4,1,0,4,1,0,0,1,0,4,1,0,5,1,0,1,1,0,2,1,0,1,1,0,1,1,0,1,1,0,1,1,0,1,1,0,1,1,0,1,1,0,1,1,0,1,1,0,1,3,0,99,99,0,1,1,0,1,1,0,1,1,0,1,1,0,1,1,0,6,1,0,4,1,0,1,1,0,2,1,0,4,3,0,3,1,0,99,99,0,3,3,1,Laurenssen,1,0,2,1,1,720,1,0,2,1,0,0,1,0,2,1,0,3,1,0,3,1,0,99,99,0 6 -ABP-A,Havana Hopewell,2150,1700,2,1,1,0,2,1,0,0,1,0,4,1,0,5,1,0,2,1,0,2,1,0,1,1,0,0,1,0,0,1,0,1,1,0,1,1,0,0,1,0,0,1,0,1,1,0,0,1,0,0,1,0,1,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,5,1,0,3,2,0,4,1,0,3,1,0,99,99,0,3,1,0,99,99,0,2,1,0, ,99,0,2,2,0,200,2,0,1,1,0,0,1,0,2,1,0,1,2,0,0,1,0,2,1,0 7 -ABP-B,Rosewood,1700,1550,2,1,1,0,2,1,0,0,1,0,4,1,0,5,1,0,2,1,0,2,1,0,1,1,0,0,1,0,0,1,0,1,1,0,1,1,0,0,1,0,0,1,0,1,1,0,0,1,0,0,1,0,99,99,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,5,1,0,2,1,0,1,1,0,3,1,0,99,99,0,99,99,0,99,99,0,2,1,0, ,99,0,2,2,0,200,2,0,2,1,0,0,1,0,2,1,0,2,1,0,0,1,0,2,1,0 8 -ABP-C,Mund,1550,1400,2,1,1,0,2,1,0,0,1,0,4,1,0,5,1,0,2,1,0,2,1,0,1,1,0,0,1,0,0,1,0,1,1,0,1,1,0,0,1,0,0,1,0,1,1,0,0,1,0,0,1,0,99,99,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,5,1,0,2,1,0,1,1,0,3,1,0,99,99,0,99,99,0,99,99,0,2,1,0, ,99,0,2,2,0,200,2,0,2,1,0,0,1,0,2,1,0,2,1,0,0,1,0,2,1,0 9 -ABP-D,Patrick,1400,1250,2,1,1,0,3,1,0,0,1,0,4,1,0,5,1,0,2,1,0,2,1,0,1,1,0,0,1,0,0,1,0,1,1,0,1,1,0,0,1,0,0,1,0,1,1,0,0,1,0,0,1,0,99,99,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,5,1,0,4,1,0,3,3,0,3,1,0,99,99,0,99,99,0,99,99,0,2,1,0, ,99,0,2,2,0,200,2,0,2,1,0,0,1,0,2,1,0,2,1,0,2,1,0,2,1,0 10 -ABP-E,Lohmann-Stirling,950,800,2,77,1,0,3,1,0,0,1,0,4,1,0,5,1,0,2,1,0,4,3,0,1,1,0,4,1,0,4,1,0,2,3,0,2,1,0,99,99,0,4,3,0,5,3,0,1,3,0,99,99,0,1,3,0,4,1,0,3,1,0,2,1,0,2,3,0,3,3,0,6,1,0,4,1,0,7,1,0,3,1,0,8,3,0,6,3,0,8,1,0,5,1,0,Cahokia,1,0,1400,1,0,15000,1,0,3,1,0,0,1,0,2,1,0,2,1,0,2,1,0,2,1,0 11 -ABP-F,Moorehead,800,725,2,77,1,0,3,1,0,0,1,0,4,1,0,5,1,0,2,1,0,4,1,0,1,1,0,4,1,0,4,1,0,2,3,0,2,1,0,99,99,0,4,3,0,5,3,0,99,99,0,99,99,0,99,99,0,4,1,0,3,1,0,2,1,0,99,99,0,3,3,0,6,1,0,4,3,0,7,1,0,3,1,0,8,3,0,6,3,0,8,3,0,2,1,0,Cahokia,1,0,1400,1,0,3000,1,0,3,1,0,0,1,0,2,1,0,2,1,0,2,1,0,99,99,0 12 -SWP-A,Pioneer/Formative,2000,1250,3,4,2,1,3,1,0,0,1,0,4,1,0,5,1,0,2,1,0,2,1,0,1,1,0,0,1,0,0,1,0,1,1,0,1,1,0,0,1,0,0,1,0,1,1,0,0,1,0,0,1,0,1,1,0,0,1,0,0,1,0,0,1,0,0,1,0,1,1,0,4,1,0,4,1,0,7,3,0,3,1,0,4,1,0,3,1,0,99,99,0,2,1,0, ,99,0, ,99,0, ,99,0,2,1,0,0,1,0,2,1,0,2,1,0,0,1,0,0,1,0 13 -SWP-B,Colonial/Preclassic,1250,900,3,4,2,1,3,1,0,0,1,0,4,1,0,5,1,0,2,1,0,2,1,0,1,1,0,2,3,0,1,1,0,2,1,0,2,1,0,99,99,0,99,99,0,3,3,0,2,1,0,99,99,0,99,99,0,2,1,0,99,99,0,99,99,0,99,99,0,2,3,1,6,1,0,4,1,0,4,1,0,3,1,0,9,3,0,3,1,0,3,1,0,3,1,0,Snaketown,1,0,100,1,0,2000,1,0,2,1,0,2,1,1,3,1,0,2,1,0,3,1,0,3,1,0 14 -SWP-C,Classic,900,550,3,4,2,1,3,1,0,0,1,0,4,1,0,5,1,0,2,1,0,6,2,1,1,1,0,88,2,1,1,1,0,2,1,0,2,1,0,99,99,0,2,1,0,4,3,0,4,3,0,99,99,0,99,99,0,4,1,0,3,1,0,3,3,0,99,99,0,1,3,0,6,1,0,3,1,1,7,1,0,2,1,0,8,1,0,99,99,0,99,99,0,5,1,0,Marana,1,0,600,1,0,1000,1,0,2,1,0,2,1,1,2,1,0,1,1,1,3,1,0,3,1,0 15 -MYP-A,Early Classic Maya,1750,1400,4,4,1,0,4,1,0,1,1,0,5,1,1,5,1,0,2,1,0,2,1,0,1,3,0,3,1,0,3,1,0,3,1,0,4,1,0,2,1,0,4,1,0,4,1,0,99,99,0,3,1,0,99,99,0,3,1,0,3,1,0,3,1,0,2,3,0,2,3,1,6,1,0,3,1,0,7,1,0,3,1,1,8,3,0,5,1,0,8,1,0,4,2,0,Tikal,2,0,12000,1,0,45000,2,0,2,1,0,2,2,0,3,2,0,2,1,0,3,2,0,3,2,1 16 -MYP-B,Middle Classic May,1400,1100,4,4,1,0,4,1,0,2,1,0,4,1,0,5,2,0,3,2,0,2,1,0,1,3,0,4,1,1,3,1,0,3,1,0,4,1,0,2,3,0,4,1,0,4,1,1,99,99,0,99,99,1,3,1,0,3,1,0,3,1,0,3,1,0,99,99,0,3,3,0,6,1,0,3,1,0,8,1,0,88,3,1,9,1,0,5,1,1,8,1,0,5,2,0,Tikal,1,0,12000,1,0,62000,1,0,2,1,0,3,2,1,99,99,0,2,1,0,3,3,0,3,1,0 17 -MYP-C,Late Classic Maya,1100,1000,4,4,2,1,4,2,0,1,2,0,4,3,0,5,3,0,1,3,0,3,2,1,1,3,0,3,2,0,3,1,0,3,3,0,4,2,0,2,3,0,4,1,1,4,2,0,3,3,1,4,2,0,99,99,1,3,1,0,3,1,0,3,1,0,99,99,0,2,3,0,6,1,0,3,1,0,8,1,0,3,2,0,9,2,0,5,3,0,8,2,0,4,2,0,Tikal,2,0,99,99,0,10000,2,0,2,3,0,3,1,0,3,3,0,2,1,0,3,1,1,3,2,0 18 -MYP-D,Postclassic Maya,1000,750,4,4,1,0,4,1,0,1,2,0,4,2,0,5,1,0,2,3,1,3,2,1,1,1,0,2,2,0,2,2,0,3,2,0,5,2,1,3,3,0,99,99,0,2,1,0,99,99,1,99,99,0,3,3,0,3,1,0,99,99,0,3,3,1,99,99,0,2,3,0,6,1,0,3,1,0,8,1,0,99,99,0,99,99,0,5,2,0,8,2,0,4,2,0,Coba,2,0,7000,3,0,21000,3,0,3,1,1,2,3,0,99,99,0,2,1,0,2,1,0,3,1,1 19 -MOP-A,Early Moche,1900,1700,5,2,1,0,4,1,0,3,1,0,5,1,0,5,1,0,3,1,0,7,1,0,1,1,0,4,1,0,4,1,0,3,1,0,2,3,0,2,3,1,4,1,0,3,2,1,3,2,0,2,2,1,2,2,0,4,1,0,3,3,0,3,3,0,2,2,1,2,2,0,6,1,0,88,88,0,8,1,0,3,2,0,9,1,0,5,1,0,8,1,0,4,1,0, ,99,0,99,99,0,10000,3,0,3,1,0,3,1,0,2,1,0,3,2,0,3,1,0,3,1,0 20 -MOP-B,Middle Moche,1700,1450,5,4,3,0,4,1,0,3,1,0,5,1,0,5,1,0,2,1,0,7,2,0,1,1,0,4,1,0,3,1,0,3,2,0,4,2,0,3,3,1,4,1,0,4,1,0,2,2,0,2,2,0,2,2,0,3,2,0,3,2,0,2,1,0,99,99,0,3,1,0,6,1,0,88,88,0,8,1,0,3,1,0,9,1,0,5,1,0,8,1,0,4,1,0,Moche,1,0,300,1,0,10000,1,0,3,1,0,3,1,0,2,1,0,3,1,0,3,2,1,3,1,0 21 -MOP-C,Late Moche,1450,1250,5,4,3,0,5,1,0,3,1,0,5,1,0,5,1,0,2,1,0,7,1,0,1,1,0,4,1,0,3,2,0,3,2,0,4,2,0,3,2,0,3,2,1,3,2,0,2,2,0,3,3,1,2,2,0,3,2,0,3,2,0,2,2,0,2,2,0,2,3,0,6,1,0,88,88,0,8,1,0,3,1,0,9,1,0,6,1,0,8,1,0,5,1,0,Galindo,1,0,600,1,0,99,99,0,3,1,0,3,1,0,2,1,0,3,1,0,3,1,0,3,1,0 22 -MOP-D,Chimu,1250,480,5,4,1,0,5,1,0,2,1,0,5,1,0,1,3,0,1,2,0,7,1,0,4,1,0,4,1,0,4,1,0,4,1,0,2,1,0,4,1,0,3,1,0,5,1,0,3,2,0,2,1,0,3,3,0,4,1,0,3,1,0,2,1,0,4,3,0,3,3,0,6,1,0,88,88,0,8,1,0,3,1,1,9,1,0,5,2,1,8,1,0,4,1,0,Chan-Chan,1,0,2000,1,0,30000,1,0,2,1,0,3,1,1,3,1,0,3,1,0,3,1,0,3,1,1 23 -EGP-1,Lower Egypt Predynastic,7000,5100,6,88,2,0,2,1,0,1,1,0,5,1,0,4,1,0,3,1,0,2,1,0,3,1,0,0,2,0,0,2,0,1,2,0,1,2,0,0,2,0,0,2,0,5,2,0,0,2,0,0,1,0,99,99,0,0,2,1,0,2,0,0,1,0,0,2,0,0,1,0,6,1,0,3,2,1,1,1,0,3,1,0,4,1,0,3,1,0,99,99,0,2,1,0, ,99,0,99,99,0,99,99,0,2,1,0,2,1,0,3,3,0,2,1,0,0,1,0,0,1,0 24 -EGP-2,Early Dynastic Egypt,5100,4200,6,88,2,0,3,1,0,2,1,0,4,1,0,5,1,0,2,2,0,6,1,0,1,1,0,4,1,0,4,1,0,3,1,0,3,1,0,2,1,0,4,1,0,4,1,0,3,1,1,2,1,1,2,1,0,3,1,0,3,1,0,3,1,0,3,1,0,3,1,0,6,1,0,4,1,1,8,1,0,3,3,0,9,1,0,5,1,0,8,1,0,4,3,0,Memphis,1,0,99,99,0,99,99,0,2,1,0,2,1,0,3,3,0,2,1,0,3,1,0,2,1,0 25 -EGP-3,First Intermediate Period,4200,4000,6,4,1,0,3,1,0,3,1,0,4,1,0,5,1,0,3,1,0,2,1,0,1,1,0,4,1,0,4,1,0,4,1,0,3,1,0,2,1,0,3,2,0,4,1,0,2,1,1,2,1,1,3,1,0,4,1,0,3,1,0,3,1,0,4,1,0,3,1,0,6,1,0,4,1,1,8,1,0,4,1,0,9,2,0,5,1,0,8,3,0,5,1,0,Memphis,1,0,99,99,0,99,99,0,2,1,0,3,1,0,3,3,0,3,1,0,3,2,0,3,1,0 26 -NMP-1,Uruk,6000,5200,7,4,1,0,3,1,0,1,1,0,2,2,0,2,1,0,1,2,0,3,2,0,1,1,0,3,2,0,2,1,0,2,1,0,2,1,0,2,1,0,3,1,0,3,1,0,3,2,0,4,2,0,3,1,0,3,2,0,99,99,0,4,1,0,2,1,0,99,99,0,6,1,0,4,1,0,8,1,0,3,1,0,9,1,0,5,2,0,4,1,0,4,1,0,Uruk,1,0,200,1,0,20000,1,0,2,1,0,2,1,0,2,1,0,1,1,0,1,1,0,2,1,0 27 -NMP-2,Sumerian,5200,4300,7,4,1,0,3,1,0,2,1,0,4,1,0,5,1,0,2,2,0,6,1,0,1,1,0,4,1,0,4,1,0,4,1,0,3,1,0,4,1,0,2,2,0,4,1,0,2,2,0,3,1,0,3,1,0,3,1,0,3,2,0,3,1,0,2,2,0,3,2,0,6,1,0,4,1,0,8,1,0,3,1,0,9,1,0,5,1,0,7,1,0,4,1,0,Umma,1,0,400,1,0,26000,1,0,3,1,0,3,1,0,3,3,0,3,1,0,2,1,0,2,1,0 28 -NMP-3,Akkadian,4300,4200,7,99,99,0,2,1,0,3,1,0,2,1,0,88,88,0,1,2,0,6,1,0,1,1,0,99,99,0,99,99,0,3,2,0,4,2,0,2,2,0,3,1,0,4,1,1,2,2,0,3,1,0,3,1,0,3,1,0,4,1,0,3,2,0,3,2,0,4,2,0,6,1,0,4,1,0,8,1,0,3,1,0,8,1,0,5,1,0,8,1,0,5,1,0,Tell Brak,1,0,100,1,0,99,99,0,2,1,0,2,1,0,2,3,0,2,2,0,2,3,0,2,2,0 29 -NEP-1,Early Nordic Bronze Age,3700,3100,8,4,3,0,1,1,0,2,1,0,5,1,0,5,1,0,3,1,0,2,3,0,3,1,0,0,1,0,0,1,0,1,3,0,1,3,0,0,1,0,0,1,0,1,3,0,0,2,0,4,3,0,99,99,0,0,2,0,0,2,0,0,2,0,0,2,0,0,1,0,6,2,0,2,2,0,1,3,0,4,2,0,9,1,0,99,99,0,4,3,1,2,2,0, ,99,0,99,99,0,99,99,0,1,1,1,3,1,0,2,1,0,1,2,1,0,2,0,0,1,0 30 -NEP-2,Late Nordic Bronze Age,3100,2550,8,4,3,0,1,1,0,3,2,0,5,1,0,5,1,0,3,1,0,6,3,0,3,3,0,2,3,0,3,3,0,99,99,0,99,99,0,2,3,0,99,99,0,1,2,0,2,3,0,88,3,0,4,3,0,99,99,0,99,99,0,99,99,1,99,99,0,2,3,0,5,2,0,3,1,0,1,3,1,4,1,0,8,2,0,5,1,0,4,2,0,2,2,0, ,99,0,99,99,0,99,99,0,2,1,0,3,2,0,2,2,0,2,2,0,1,3,0,1,3,0 31 -NEP-3,Pre-Roman Iron Age,2550,1500,8,5,1,1,3,1,0,4,1,0,4,1,0,5,1,0,1,1,0,6,1,0,3,3,0,4,1,0,3,1,0,99,99,0,99,99,1,99,99,0,99,99,0,1,1,0,99,99,0,2,3,0,99,99,0,3,1,1,3,1,0,3,3,0,3,3,0,4,1,0,6,1,0,1,1,0,7,3,0,4,1,0,8,1,0,5,1,0,7,1,0,88,2,0, ,99,0,99,99,0,99,99,0,3,1,0,3,1,0,3,1,0,3,3,0,99,99,0,99,99,0 32 -IRP-1,Middle Harappa,4450,4200,9,5,1,0,2,1,0,2,1,0,4,1,0,4,1,0,3,1,0,2,1,0,1,1,0,2,1,0,1,2,0,3,3,0,3,3,0,99,99,1,2,1,0,3,1,0,3,1,0,3,1,0,3,3,0,2,3,0,2,1,0,1,1,0,1,1,0,3,1,0,6,1,0,4,1,0,8,1,0,3,1,0,9,1,0,3,1,0,7,1,0,3,3,1,Mohenjo Daro,1,0,200,1,0,40000,1,0,2,1,0,2,1,0,2,1,0,2,1,0,3,3,0,3,1,0 33 -IRP-2,Final Harappa,4200,4000,9,5,1,0,2,1,0,2,1,0,4,1,0,4,1,0,2,1,0,2,1,0,1,1,0,2,1,0,1,2,0,3,3,0,3,3,0,99,99,1,2,1,0,3,1,0,3,1,0,3,1,0,3,3,0,2,3,0,2,1,0,1,1,0,1,1,0,3,1,0,6,1,0,4,1,0,8,1,0,3,1,0,9,1,0,3,1,0,7,1,0,3,3,1,Mohenjo Daro,1,0,200,1,0,50000,1,0,3,1,0,3,1,0,2,1,0,2,1,0,3,3,0,3,1,0 34 -
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- DT-GodsAndResourceStress_Long.csv
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-46,Afro-Asiatic,1,1,23,2,1.165442359 184 -46,Afro-Asiatic,1,1,23,3,NA 185 -46,Afro-Asiatic,1,1,23,4,-2.214468868 186 -47,Turkic,1,1,48,1,0.888385296 187 -47,Turkic,1,1,48,2,1.165442359 188 -47,Turkic,1,1,48,3,1.353222151 189 -47,Turkic,1,1,48,4,-0.411152163 190 -48,Indo-European,1,1,36,1,NA 191 -48,Indo-European,1,1,36,2,NA 192 -48,Indo-European,1,1,36,3,NA 193 -48,Indo-European,1,1,36,4,0.222075523 194 -49,Indo-European,NA,0,49,1,NA 195 -49,Indo-European,NA,0,49,2,NA 196 -49,Indo-European,NA,0,49,3,NA 197 -49,Indo-European,NA,0,49,4,-0.187607934 198 -50,Basque,1,1,47,1,NA 199 -50,Basque,1,1,47,2,NA 200 -50,Basque,1,1,47,3,NA 201 -50,Basque,1,1,47,4,0.014805390 202 -51,Indo-European,1,1,47,1,NA 203 -51,Indo-European,1,1,47,2,-1.400410576 204 -51,Indo-European,1,1,47,3,0.270644430 205 -51,Indo-European,1,1,47,4,-0.030757432 206 -52,Uralic,1,1,18,1,-1.117646018 207 -52,Uralic,1,1,18,2,-1.400410576 208 -52,Uralic,1,1,18,3,NA 209 -52,Uralic,1,1,18,4,-0.766077663 210 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-59,Indo-European,1,1,45,2,0.310158047 236 -59,Indo-European,1,1,45,3,0.270644430 237 -59,Indo-European,1,1,45,4,-1.213571865 238 -60,Dravidian,0,0,27,1,-1.117646018 239 -60,Dravidian,0,0,27,2,-1.400410576 240 -60,Dravidian,0,0,27,3,-0.811933291 241 -60,Dravidian,0,0,27,4,-0.213627236 242 -61,Dravidian,NA,0,19,1,NA 243 -61,Dravidian,NA,0,19,2,NA 244 -61,Dravidian,NA,0,19,3,NA 245 -61,Dravidian,NA,0,19,4,0.539954324 246 -62,Austroasiatic,0,0,31,1,NA 247 -62,Austroasiatic,0,0,31,2,-0.545126265 248 -62,Austroasiatic,0,0,31,3,1.353222151 249 -62,Austroasiatic,0,0,31,4,-0.441295394 250 -63,Indo-European,0,0,50,1,NA 251 -63,Indo-European,0,0,50,2,-0.545126265 252 -63,Indo-European,0,0,50,3,NA 253 -63,Indo-European,0,0,50,4,-0.804549287 254 -64,NA,1,1,33,1,NA 255 -64,NA,1,1,33,2,-1.400410576 256 -64,NA,1,1,33,3,0.270644430 257 -64,NA,1,1,33,4,-0.639041621 258 -65,Turkic,1,1,30,1,0.888385296 259 -65,Turkic,1,1,30,2,0.310158047 260 -65,Turkic,1,1,30,3,-0.270644430 261 -65,Turkic,1,1,30,4,-0.296912891 262 -66,Mongolic,NA,NA,32,1,-0.114630361 263 -66,Mongolic,NA,NA,32,2,1.165442359 264 -66,Mongolic,NA,NA,32,3,NA 265 -66,Mongolic,NA,NA,32,4,-0.205654550 266 -67,Sino-Tibetan,NA,0,35,1,NA 267 -67,Sino-Tibetan,NA,0,35,2,NA 268 -67,Sino-Tibetan,NA,0,35,3,NA 269 -67,Sino-Tibetan,NA,0,35,4,0.996315979 270 -68,Sino-Tibetan,0,0,32,1,-1.117646018 271 -68,Sino-Tibetan,0,0,32,2,-1.400410576 272 -68,Sino-Tibetan,0,0,32,3,-0.811933291 273 -68,Sino-Tibetan,0,0,32,4,1.213261184 274 -69,Sino-Tibetan,0,0,31,1,NA 275 -69,Sino-Tibetan,0,0,31,2,NA 276 -69,Sino-Tibetan,0,0,31,3,NA 277 -69,Sino-Tibetan,0,0,31,4,0.456514054 278 -70,Sino-Tibetan,1,1,31,1,0.888385296 279 -70,Sino-Tibetan,1,1,31,2,0.310158047 280 -70,Sino-Tibetan,1,1,31,3,NA 281 -70,Sino-Tibetan,1,1,31,4,0.497973312 282 -71,Sino-Tibetan,NA,0,48,1,NA 283 -71,Sino-Tibetan,NA,0,48,2,NA 284 -71,Sino-Tibetan,NA,0,48,3,-0.811933291 285 -71,Sino-Tibetan,NA,0,48,4,0.025559785 286 -72,Austroasiatic,NA,0,19,1,0.888385296 287 -72,Austroasiatic,NA,0,19,2,-0.545126265 288 -72,Austroasiatic,NA,0,19,3,0.270644430 289 -72,Austroasiatic,NA,0,19,4,1.227529072 290 -73,Austroasiatic,NA,0,47,1,-1.117646018 291 -73,Austroasiatic,NA,0,47,2,1.165442359 292 -73,Austroasiatic,NA,0,47,3,NA 293 -73,Austroasiatic,NA,0,47,4,0.391418033 294 -74,Austronesian,NA,0,29,1,NA 295 -74,Austronesian,NA,0,29,2,NA 296 -74,Austronesian,NA,0,29,3,NA 297 -74,Austronesian,NA,0,29,4,1.085165353 298 -75,Austroasiatic,0,0,46,1,NA 299 -75,Austroasiatic,0,0,46,2,NA 300 -75,Austroasiatic,0,0,46,3,NA 301 -75,Austroasiatic,0,0,46,4,0.581909222 302 -76,Tai-Kadai,0,0,50,1,-1.117646018 303 -76,Tai-Kadai,0,0,50,2,0.310158047 304 -76,Tai-Kadai,0,0,50,3,-0.811933291 305 -76,Tai-Kadai,0,0,50,4,0.041075496 306 -77,Austroasiatic,0,0,12,1,NA 307 -77,Austroasiatic,0,0,12,2,NA 308 -77,Austroasiatic,0,0,12,3,NA 309 -77,Austroasiatic,0,0,12,4,1.299302125 310 -78,Austroasiatic,NA,NA,27,1,NA 311 -78,Austroasiatic,NA,NA,27,2,NA 312 -78,Austroasiatic,NA,NA,27,3,NA 313 -78,Austroasiatic,NA,NA,27,4,-1.164108422 314 -79,Great Andamanese,NA,0,14,1,NA 315 -79,Great Andamanese,NA,0,14,2,NA 316 -79,Great Andamanese,NA,0,14,3,NA 317 -79,Great Andamanese,NA,0,14,4,NA 318 -80,Indo-European,NA,0,16,1,NA 319 -80,Indo-European,NA,0,16,2,NA 320 -80,Indo-European,NA,0,16,3,NA 321 -80,Indo-European,NA,0,16,4,-0.261352564 322 -81,Austronesian,0,0,27,1,NA 323 -81,Austronesian,0,0,27,2,NA 324 -81,Austronesian,0,0,27,3,NA 325 -81,Austronesian,0,0,27,4,-0.618424945 326 -82,Austronesian,1,1,45,1,NA 327 -82,Austronesian,1,1,45,2,NA 328 -82,Austronesian,1,1,45,3,NA 329 -82,Austronesian,1,1,45,4,0.961160376 330 -83,Austronesian,1,1,49,1,-1.117646018 331 -83,Austronesian,1,1,49,2,0.310158047 332 -83,Austronesian,1,1,49,3,NA 333 -83,Austronesian,1,1,49,4,0.252725410 334 -84,Austronesian,NA,0,48,1,NA 335 -84,Austronesian,NA,0,48,2,-0.545126265 336 -84,Austronesian,NA,0,48,3,-0.811933291 337 -84,Austronesian,NA,0,48,4,-0.430263798 338 -85,Austronesian,NA,0,23,1,NA 339 -85,Austronesian,NA,0,23,2,-0.545126265 340 -85,Austronesian,NA,0,23,3,NA 341 -85,Austronesian,NA,0,23,4,0.931190849 342 -86,Austronesian,NA,NA,15,1,NA 343 -86,Austronesian,NA,NA,15,2,-1.400410576 344 -86,Austronesian,NA,NA,15,3,-0.811933291 345 -86,Austronesian,NA,NA,15,4,NA 346 -87,Austronesian,NA,NA,28,1,NA 347 -87,Austronesian,NA,NA,28,2,NA 348 -87,Austronesian,NA,NA,28,3,NA 349 -87,Austronesian,NA,NA,28,4,0.292051940 350 -88,North Halmahera,NA,NA,31,1,NA 351 -88,North Halmahera,NA,NA,31,2,NA 352 -88,North Halmahera,NA,NA,31,3,NA 353 -88,North Halmahera,NA,NA,31,4,-0.316134565 354 -89,Timor-Alor-Pantar,0,0,30,1,NA 355 -89,Timor-Alor-Pantar,0,0,30,2,NA 356 -89,Timor-Alor-Pantar,0,0,30,3,NA 357 -89,Timor-Alor-Pantar,0,0,30,4,-1.486727605 358 -90,NA,0,0,12,1,-1.117646018 359 -90,NA,0,0,12,2,-1.400410576 360 -90,NA,0,0,12,3,-0.811933291 361 -90,NA,0,0,12,4,NA 362 -91,Pama-Nyungan,NA,0,13,1,-0.114630361 363 -91,Pama-Nyungan,NA,0,13,2,-0.545126265 364 -91,Pama-Nyungan,NA,0,13,3,NA 365 -91,Pama-Nyungan,NA,0,13,4,-1.380395610 366 -92,Nuclear Trans New Guinea,NA,0,20,1,-1.117646018 367 -92,Nuclear Trans New Guinea,NA,0,20,2,NA 368 -92,Nuclear Trans New Guinea,NA,0,20,3,0.270644430 369 -92,Nuclear Trans New Guinea,NA,0,20,4,0.695596149 370 -93,Kolopom,NA,NA,22,1,0.888385296 371 -93,Kolopom,NA,NA,22,2,1.165442359 372 -93,Kolopom,NA,NA,22,3,NA 373 -93,Kolopom,NA,NA,22,4,-0.762011530 374 -94,Nuclear Trans New Guinea,0,0,29,1,-1.117646018 375 -94,Nuclear Trans New Guinea,0,0,29,2,NA 376 -94,Nuclear Trans New Guinea,0,0,29,3,-0.811933291 377 -94,Nuclear Trans New Guinea,0,0,29,4,0.342954258 378 -95,Sepik,NA,0,28,1,NA 379 -95,Sepik,NA,0,28,2,NA 380 -95,Sepik,NA,0,28,3,NA 381 -95,Sepik,NA,0,28,4,0.711200922 382 -96,Austronesian,NA,0,26,1,-1.117646018 383 -96,Austronesian,NA,0,26,2,-1.400410576 384 -96,Austronesian,NA,0,26,3,-0.811933291 385 -96,Austronesian,NA,0,26,4,-1.266562874 386 -97,Austronesian,NA,0,26,1,NA 387 -97,Austronesian,NA,0,26,2,NA 388 -97,Austronesian,NA,0,26,3,NA 389 -97,Austronesian,NA,0,26,4,NA 390 -98,Austronesian,NA,0,26,1,1.891400953 391 -98,Austronesian,NA,0,26,2,1.165442359 392 -98,Austronesian,NA,0,26,3,0.270644430 393 -98,Austronesian,NA,0,26,4,-1.737923154 394 -NA,South Bougainville,NA,0,27,1,0.888385296 395 -NA,South Bougainville,NA,0,27,2,NA 396 -NA,South Bougainville,NA,0,27,3,-0.811933291 397 -NA,South Bougainville,NA,0,27,4,-0.483457234 398 -100,Austronesian,NA,0,28,1,0.888385296 399 -100,Austronesian,NA,0,28,2,1.165442359 400 -100,Austronesian,NA,0,28,3,0.270644430 401 -100,Austronesian,NA,0,28,4,NA 402 -101,Austronesian,NA,0,23,1,NA 403 -101,Austronesian,NA,0,23,2,NA 404 -101,Austronesian,NA,0,23,3,NA 405 -101,Austronesian,NA,0,23,4,NA 406 -102,Austronesian,NA,0,32,1,NA 407 -102,Austronesian,NA,0,32,2,-1.400410576 408 -102,Austronesian,NA,0,32,3,-0.811933291 409 -102,Austronesian,NA,0,32,4,NA 410 -103,Austronesian,NA,NA,26,1,NA 411 -103,Austronesian,NA,NA,26,2,NA 412 -103,Austronesian,NA,NA,26,3,NA 413 -103,Austronesian,NA,NA,26,4,NA 414 -104,Austronesian,NA,0,24,1,0.888385296 415 -104,Austronesian,NA,0,24,2,1.165442359 416 -104,Austronesian,NA,0,24,3,1.353222151 417 -104,Austronesian,NA,0,24,4,NA 418 -105,Austronesian,NA,0,27,1,NA 419 -105,Austronesian,NA,0,27,2,NA 420 -105,Austronesian,NA,0,27,3,NA 421 -105,Austronesian,NA,0,27,4,NA 422 -106,Austronesian,NA,0,28,1,-0.114630361 423 -106,Austronesian,NA,0,28,2,1.165442359 424 -106,Austronesian,NA,0,28,3,-0.811933291 425 -106,Austronesian,NA,0,28,4,NA 426 -107,Austronesian,0,0,29,1,NA 427 -107,Austronesian,0,0,29,2,NA 428 -107,Austronesian,0,0,29,3,NA 429 -107,Austronesian,0,0,29,4,NA 430 -108,Austronesian,NA,0,29,1,0.888385296 431 -108,Austronesian,NA,0,29,2,0.310158047 432 -108,Austronesian,NA,0,29,3,0.270644430 433 -108,Austronesian,NA,0,29,4,NA 434 -109,Austronesian,NA,0,28,1,NA 435 -109,Austronesian,NA,0,28,2,-0.545126265 436 -109,Austronesian,NA,0,28,3,NA 437 -109,Austronesian,NA,0,28,4,NA 438 -110,Austronesian,NA,NA,32,1,NA 439 -110,Austronesian,NA,NA,32,2,1.165442359 440 -110,Austronesian,NA,NA,32,3,NA 441 -110,Austronesian,NA,NA,32,4,NA 442 -111,Austronesian,NA,0,29,1,NA 443 -111,Austronesian,NA,0,29,2,NA 444 -111,Austronesian,NA,0,29,3,NA 445 -111,Austronesian,NA,0,29,4,NA 446 -112,Austronesian,NA,0,31,1,-1.117646018 447 -112,Austronesian,NA,0,31,2,0.310158047 448 -112,Austronesian,NA,0,31,3,-0.811933291 449 -112,Austronesian,NA,0,31,4,-0.047927208 450 -113,Austronesian,NA,0,27,1,NA 451 -113,Austronesian,NA,0,27,2,NA 452 -113,Austronesian,NA,0,27,3,NA 453 -113,Austronesian,NA,0,27,4,-0.092607264 454 -114,Sino-Tibetan,NA,0,50,1,NA 455 -114,Sino-Tibetan,NA,0,50,2,NA 456 -114,Sino-Tibetan,NA,0,50,3,NA 457 -114,Sino-Tibetan,NA,0,50,4,0.123275928 458 -115,Tungusic,NA,NA,36,1,NA 459 -115,Tungusic,NA,NA,36,2,NA 460 -115,Tungusic,NA,NA,36,3,NA 461 -115,Tungusic,NA,NA,36,4,0.066682378 462 -116,Koreanic,NA,0,48,1,-0.114630361 463 -116,Koreanic,NA,0,48,2,-0.545126265 464 -116,Koreanic,NA,0,48,3,1.353222151 465 -116,Koreanic,NA,0,48,4,-0.010504153 466 -117,Japonic,0,0,50,1,-1.117646018 467 -117,Japonic,0,0,50,2,-0.545126265 468 -117,Japonic,0,0,50,3,NA 469 -117,Japonic,0,0,50,4,-0.028266043 470 -118,Ainu,NA,0,17,1,-0.114630361 471 -118,Ainu,NA,0,17,2,-1.400410576 472 -118,Ainu,NA,0,17,3,0.270644430 473 -118,Ainu,NA,0,17,4,-0.103598293 474 -119,NA,0,0,19,1,1.891400953 475 -119,NA,0,0,19,2,0.310158047 476 -119,NA,0,0,19,3,0.270644430 477 -119,NA,0,0,19,4,0.032461396 478 -120,Yukaghir,NA,NA,17,1,NA 479 -120,Yukaghir,NA,NA,17,2,NA 480 -120,Yukaghir,NA,NA,17,3,NA 481 -120,Yukaghir,NA,NA,17,4,-0.960436533 482 -121,Chukotko-Kamchatkan,0,0,18,1,-0.114630361 483 -121,Chukotko-Kamchatkan,0,0,18,2,0.310158047 484 -121,Chukotko-Kamchatkan,0,0,18,3,-0.811933291 485 -121,Chukotko-Kamchatkan,0,0,18,4,NA 486 -122,Athabaskan-Eyak-Tlingit,0,0,19,1,NA 487 -122,Athabaskan-Eyak-Tlingit,0,0,19,2,NA 488 -122,Athabaskan-Eyak-Tlingit,0,0,19,3,NA 489 -122,Athabaskan-Eyak-Tlingit,0,0,19,4,-0.675887327 490 -123,Eskimo-Aleut,0,0,21,1,NA 491 -123,Eskimo-Aleut,0,0,21,2,NA 492 -123,Eskimo-Aleut,0,0,21,3,NA 493 -123,Eskimo-Aleut,0,0,21,4,-0.523523495 494 -124,Eskimo-Aleut,NA,0,16,1,1.891400953 495 -124,Eskimo-Aleut,NA,0,16,2,1.165442359 496 -124,Eskimo-Aleut,NA,0,16,3,0.270644430 497 -124,Eskimo-Aleut,NA,0,16,4,-1.330276340 498 -125,Algic,NA,0,15,1,NA 499 -125,Algic,NA,0,15,2,NA 500 -125,Algic,NA,0,15,3,NA 501 -125,Algic,NA,0,15,4,0.049442145 502 -126,Algic,0,0,17,1,NA 503 -126,Algic,0,0,17,2,NA 504 -126,Algic,0,0,17,3,NA 505 -126,Algic,0,0,17,4,0.263084761 506 -127,Algic,NA,0,20,1,NA 507 -127,Algic,NA,0,20,2,NA 508 -127,Algic,NA,0,20,3,NA 509 -127,Algic,NA,0,20,4,-0.020865687 510 -128,Athabaskan-Eyak-Tlingit,0,0,18,1,NA 511 -128,Athabaskan-Eyak-Tlingit,0,0,18,2,NA 512 -128,Athabaskan-Eyak-Tlingit,0,0,18,3,NA 513 -128,Athabaskan-Eyak-Tlingit,0,0,18,4,-0.507859059 514 -129,Athabaskan-Eyak-Tlingit,NA,0,14,1,NA 515 -129,Athabaskan-Eyak-Tlingit,NA,0,14,2,NA 516 -129,Athabaskan-Eyak-Tlingit,NA,0,14,3,NA 517 -129,Athabaskan-Eyak-Tlingit,NA,0,14,4,-0.371900546 518 -130,Athabaskan-Eyak-Tlingit,NA,0,16,1,-1.117646018 519 -130,Athabaskan-Eyak-Tlingit,NA,0,16,2,-1.400410576 520 -130,Athabaskan-Eyak-Tlingit,NA,0,16,3,NA 521 -130,Athabaskan-Eyak-Tlingit,NA,0,16,4,-0.284366587 522 -131,Haida,NA,0,23,1,NA 523 -131,Haida,NA,0,23,2,-1.400410576 524 -131,Haida,NA,0,23,3,-0.811933291 525 -131,Haida,NA,0,23,4,-0.943756356 526 -132,Salishan,0,0,22,1,-1.117646018 527 -132,Salishan,0,0,22,2,-1.400410576 528 -132,Salishan,0,0,22,3,-0.811933291 529 -132,Salishan,0,0,22,4,0.162389228 530 -133,Salishan,NA,0,22,1,NA 531 -133,Salishan,NA,0,22,2,NA 532 -133,Salishan,NA,0,22,3,NA 533 -133,Salishan,NA,0,22,4,0.311683442 534 -134,Algic,NA,0,18,1,NA 535 -134,Algic,NA,0,18,2,NA 536 -134,Algic,NA,0,18,3,NA 537 -134,Algic,NA,0,18,4,NA 538 -135,Pomoan,0,0,22,1,NA 539 -135,Pomoan,0,0,22,2,NA 540 -135,Pomoan,0,0,22,3,NA 541 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... ... @@ -1,189 +1,0 @@ 1 -SCCS_ID,socname,OurSample88,CombinedSample,Com567,Com567_PryorRobust,RobustPriv_Pryor,L2_R,L2_1_234,L2_Pryor_CONAN,L3_R,L4_R,L5_R,L6_R,L7_R,HerdLgAnimORSumHG_5ormore,Irrigation,v150,v150SH14_5,oth_famsh5,PC1_1267_1269cen,v205_Fishing,HuntGath,DepHerdMoveLargeAnimLONG,DepHerdMoveLargeAnimTRUN,DepHerdMoveLargeAnimTRUN_pig0,language_family,Afro_Asiatic,Algic,Arawakan,Athabaskan_Eyak_Tlingit,Atlantic_Congo,Austroasiatic,Austronesian,Chibchan,Dravidian,Eskimo_Aleut,Indo_European,Mande,Nilotic,NuclearTransNewGuinea,Nuclear_Macro_Je,Salishan,Sino_Tibetan,Siouan,Tupian,Turkic,Uralic,Uto_Aztecan,CCR_All3,z_irr_z_CCR,PE_th,PE_pm,PDSI,Interannual_coef_var,agricul1,govt2,govt4,, 2 -1,Nama Hottentot,1,1,3.5,0,NA,2,1,1,3.5,2.5,1.0,1.5,1.0,1,1,1,0,2,NA,1,0,5,1,1,Khoe-Kwadi,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.07,-0.074830107,0.68,0.58,0.1,0.57,0,0,0,, 3 -2,Kung 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-8,Nyakyusa,1,1,7,2,NA,4,1,1,2.0,3.0,3.0,2.5,1.5,0,1,4,0,2,-0.40073,1,0,2,0,0,Atlantic-Congo,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.05,0.058045784,0.52,0.35,0.17,0.55,1,1,0,, 10 -9,Hadza,0,0,NA,NA,0,0,NA,NA,0.0,0.0,0.0,0.0,0.0,1,1,1,0,3,NA,0,1,NA,NA,0,NA,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.1,-0.274143943,0.81,0.92,0.11,0.21,NA,NA,NA,, 11 -10,Luguru,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,5,1,NA,NA,1,0,1,0,0,Atlantic-Congo,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,, 12 -11,Kikuyu,0,1,NA,3,3,NA,NA,1,NA,NA,NA,NA,NA,0,1,5,1,NA,0.69427,0,0,3,0,0,Atlantic-Congo,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,1,1,1,, 13 -12,Ganda,1,1,7,2,NA,4,1,1,5.0,2.5,2.5,2.5,2.0,0,1,5,1,2,NA,1,0,1,0,0,Atlantic-Congo,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,0.77,0.79,NA,0.18,1,1,1,, 14 -13,Mbuti,1,1,3.5,0,0,4,1,1,3.0,3.0,1.0,1.5,1.0,1,1,1,0,1,-1.80288,0,1,NA,NA,0,Atlantic-Congo,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,-0.141268052,0.4,0.2,0.23,0.24,1,1,1,, 15 -14,Nkundo Mongo,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,5,1,NA,NA,2,0,0,0,0,Atlantic-Congo,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,1,1,0,, 16 -15,Banen,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,5,1,NA,NA,0,0,1,0,0,Atlantic-Congo,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,, 17 -16,Tiv,0,1,NA,NA,NA,NA,NA,0,NA,NA,NA,NA,NA,0,1,5,1,NA,0.69427,1,0,1,0,0,Atlantic-Congo,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,1,1,1,, 18 -17,Ibo,0,0,NA,2,2,NA,NA,NA,NA,NA,NA,NA,NA,0,1,5,1,NA,-0.84523,0,0,1,0,0,Atlantic-Congo,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,1,1,0,, 19 -18,Fon,0,1,NA,NA,NA,NA,NA,1,NA,NA,NA,NA,NA,0,1,5,1,NA,NA,1,0,2,0,0,Atlantic-Congo,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,, 20 -19,Ashanti,0,1,NA,NA,NA,NA,NA,1,NA,NA,NA,NA,NA,0,1,5,1,NA,NA,2,0,0,0,0,Atlantic-Congo,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,0,1,1,, 21 -20,Mende,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,5,1,NA,NA,1,0,1,0,0,Mande,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,0,1,0,, 22 -21,Wolof,1,1,6.5,2,2.5,4,1,1,2.0,3.0,2.5,2.0,2.0,0,1,5,1,4,NA,1,0,3,0,0,Atlantic-Congo,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,-0.141268052,0.29,0.75,0.16,0.39,1,1,1,, 23 -22,Bambara,1,1,7,2,NA,4,1,1,2.0,1.5,2.0,2.5,2.5,0,1,5,1,NA,NA,1,0,2,0,0,Mande,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.25,-1.270713122,0.76,0.91,0.27,0.14,1,1,1,, 24 -23,Tallensi,1,0,7,2,2,2,1,NA,2.5,2.0,2.0,2.5,2.5,0,1,5,1,4,0.69427,0,0,2,0,0,Atlantic-Congo,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.05,0.058045784,0.35,0.32,0.13,0.16,0,1,1,, 25 -24,Songhai,1,1,9,3,NA,2,1,1,2.0,2.5,3.0,3.0,3.0,0,1,5,1,2,NA,2,0,3,0,0,Songhay,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.04,0.124483729,0.44,0.41,0.12,0.19,NA,NA,NA,, 26 -25,Pastoral Fulani,1,1,5,NA,0,2,1,1,3.0,3.0,3.0,1.0,1.0,1,1,1,0,5,0.69427,0,0,9,1,1,Atlantic-Congo,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.39023551,0.82,0.82,0,0.46,NA,NA,NA,, 27 -26,Hausa,1,0,9.5,3,3,1,0,NA,1.0,3.0,3.5,3.0,3.0,0,2,5,1,5,0.37505,0,0,3,0,0,Afro-Asiatic,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,0.764796006,0.26,0.3,0.17,0.18,1,1,1,, 28 -27,Massa (Masa),0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,2,5,1,NA,0.69427,3,0,1,0,0,Afro-Asiatic,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.03,-1.033610444,0.24,0.3,0.04,0.28,NA,NA,NA,, 29 -28,Azande,0,1,NA,NA,NA,NA,NA,0,NA,NA,NA,NA,NA,0,1,5,1,NA,NA,1,0,NA,NA,0,Atlantic-Congo,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,0,1,0,, 30 -29,Fur (Darfur),0,0,NA,2,2,NA,NA,NA,NA,NA,NA,NA,NA,0,1,5,1,NA,NA,1,0,3,0,0,Furan,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.1,-0.274143943,0.26,0.33,0.19,0.83,NA,NA,NA,, 31 -30,Otoro Nuba,1,1,9,3,NA,2,1,1,3.0,3.0,3.0,3.0,3.0,0,1,5,1,5,0.37505,0,0,2,0,0,Heibanic,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.39023551,0.04,0.16,0.04,0.2,NA,NA,NA,, 32 -31,Shilluk,0,1,NA,NA,NA,NA,NA,1,NA,NA,NA,NA,NA,0,1,5,1,NA,0.69427,1,0,2,0,0,Nilotic,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,1,1,0,, 33 -32,Mao,1,1,7.5,2,NA,2,1,1,3.0,3.0,3.0,3.0,1.5,0,1,5,1,2,NA,1,0,1,0,0,Blue Nile Mao,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.06,-0.008392162,0.62,0.67,0.07,0.21,NA,NA,NA,, 34 -33,Kaffa (Kafa),0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,5,1,NA,NA,0,0,2,0,0,Ta-Ne-Omotic,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,, 35 -34,Masai,1,1,3,0,0,2,1,1,3.5,3.0,1.0,1.0,1.0,1,1,1,0,5,NA,0,0,9,1,1,Nilotic,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0.1,-0.274143943,0.75,0.94,0.11,0.27,1,0,0,, 36 -35,Konso,1,1,12,3,3,1,0,0,1.0,4.0,4.0,4.0,4.0,0,1,5,1,NA,0.69427,0,0,2,0,0,Afro-Asiatic,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.07,-0.074830107,0.48,0.46,0.34,0.21,NA,NA,NA,, 37 -36,Somali,1,1,4,0,0,2,1,1,4.0,2.5,1.5,1.5,1.0,1,1,1,0,2,0.37505,0,0,9,1,1,Afro-Asiatic,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.11,-0.340581888,0.82,0.6,0.27,0.74,0,1,1,, 38 -37,Amhara,1,0,8,NA,2,1,0,NA,1.0,3.0,3.0,3.0,2.0,0,1,5,1,1,1.32064,1,0,3,0,0,Afro-Asiatic,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.07,-0.074830107,0.57,0.6,0.14,0.25,1,1,0,, 39 -38,Bogo,0,1,NA,3,3,NA,NA,0,NA,NA,NA,NA,NA,1,1,3,0,NA,0.37505,0,0,9,1,1,Afro-Asiatic,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,, 40 -39,Kenuzi Nubians,0,1,NA,3,2.5,NA,NA,0,NA,NA,NA,NA,NA,0,2,5,1,NA,NA,1,0,3,0,0,Nubian,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-2.112654314,0.88,0.58,0,1.73,NA,NA,NA,, 41 -40,Teda,1,1,6,2,NA,2,1,1,3.0,3.0,2.5,2.0,1.5,0,2,1,0,5,0.69427,0,0,3,0,0,Saharan,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-2.112654314,0.81,0.51,0,0.85,1,1,1,, 42 -41,Tuareg,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1,2,1,0,NA,0.69427,0,0,5,1,1,Afro-Asiatic,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,0,0,0,, 43 -42,Riffians,1,1,11,3,NA,1,0,0,1.0,4.0,3.5,4.0,3.5,0,1,5,1,2,NA,1,0,3,0,0,Afro-Asiatic,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.01,0.323797565,0.04,0.04,0.07,0.32,0,1,1,, 44 -43,Egyptians,1,1,10,3,3,1,0,0,1.0,4.0,3.5,4.0,2.5,0,2,5,1,NA,NA,1,0,3,0,0,Afro-Asiatic,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.05,-0.314247864,0.77,0.8,0.04,0.83,1,1,1,, 45 -44,Hebrews,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,5,1,NA,NA,1,0,3,0,0,Afro-Asiatic,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.39023551,0.07,0.14,0,0.45,NA,NA,NA,, 46 -45,Babylonians,1,1,11,3,NA,2,1,1,1.0,4.0,4.0,4.0,3.0,0,2,5,1,2,NA,2,0,2,0,0,Afro-Asiatic,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-2.112654314,0.08,0.07,0.07,0.51,NA,NA,NA,, 47 -46,Rwala Bedouin,1,1,3.5,0,NA,1,0,0,1.0,3.0,1.0,1.5,1.0,1,1,1,0,5,0.37505,0,0,8,1,1,Afro-Asiatic,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.15,-0.606333669,0.61,0.75,0.2,0.34,NA,NA,NA,, 48 -47,Turks,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,5,1,NA,1.32064,0,0,4,0,0,Turkic,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.39023551,0.04,0.06,0,0.32,0,1,0,, 49 -48,Gheg Albanians,0,0,NA,3,2.5,NA,NA,NA,NA,NA,NA,NA,NA,0,1,3,0,NA,NA,0,0,4,0,0,Indo-European,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,1,0,0,, 50 -49,Romans,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,5,1,NA,1.32064,2,0,2,0,0,Indo-European,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,0,1,1,, 51 -50,Basques,0,1,NA,2,2,NA,NA,0,NA,NA,NA,NA,NA,0,1,5,1,NA,NA,2,0,4,0,0,Basque,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,1,1,1,, 52 -51,Irish,1,1,12,3,NA,1,0,0,1.0,4.0,4.0,4.0,4.0,0,1,5,1,NA,-0.84523,0,0,4,0,0,Indo-European,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.39023551,0.78,0.94,0,0.3,0,0,0,, 53 -52,Lapps,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1,1,1,0,NA,NA,2,0,6,1,1,Uralic,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0.15,-0.606333669,0.49,0.48,0.24,0.21,1,0,0,, 54 -53,Yurak Samoyed,1,1,7,NA,1,2,1,1,3.0,2.0,2.0,3.0,2.0,0,1,1,0,5,NA,3,0,4,0,0,Uralic,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0.06,-0.008392162,0.55,0.53,0.11,0.3,0,1,0,, 55 -54,Russians,1,1,4,0,NA,4,1,1,2.0,1.0,1.5,1.5,1.0,0,2,5,1,NA,1.32064,0,0,3,0,0,Indo-European,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0.06,0.045433426,0.85,0.96,0.06,0.2,0,1,0,, 56 -55,Abkhaz,1,1,6.5,2,2,2,1,1,2.5,2.5,2.5,2.5,1.5,1,1,5,1,NA,NA,0,0,6,1,1,Abkhaz-Adyge,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.39023551,0.25,0.42,0,0.16,0,0,0,, 57 -56,Armenians,0,0,NA,3,3,NA,NA,NA,NA,NA,NA,NA,NA,0,1,5,1,NA,NA,0,0,3,0,0,Indo-European,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,, 58 -57,Kurd,1,1,6,NA,3,1,0,0,1.0,1.5,2.0,2.5,1.5,0,2,5,1,1,NA,1,0,4,0,0,Indo-European,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,-2.112654314,0,0.01,0.14,0.57,1,1,0,, 59 -58,Basseri,1,1,4,0,0,2,1,1,3.0,3.0,1.0,1.5,1.5,1,1,1,0,2,NA,0,0,8,1,1,Indo-European,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0.13,-0.473457779,0.66,0.96,0.17,0.66,NA,NA,NA,, 60 -59,Punjabi (West),1,1,12,3,3,1,0,0,2.0,4.0,4.0,4.0,4.0,0,1,5,1,5,NA,0,0,2,0,0,Indo-European,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0.01,0.323797565,0.11,0.06,0.17,0.3,NA,NA,NA,, 61 -60,Gond,1,1,4.5,1,NA,2,1,1,2.0,2.0,1.5,2.0,1.0,0,1,4,0,1,-1.80288,0,0,2,0,0,Dravidian,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.06,-0.008392162,0.5,0.51,0.22,0.24,1,1,0,, 62 -61,Toda,0,1,NA,0,0,NA,NA,1,NA,NA,NA,NA,NA,1,1,3,0,NA,NA,0,0,9,1,1,Dravidian,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,0,1,1,, 63 -62,Santal,1,1,6.5,2,NA,1,0,0,1.0,2.0,2.0,2.5,2.0,0,2,5,1,2,0.23771,1,0,1,0,0,Austroasiatic,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-2.112654314,0,0,0.13,0.19,1,1,0,, 64 -63,Uttar Pradesh,1,1,6.5,2,2,1,0,0,1.0,2.0,2.0,2.5,2.0,0,2,5,1,2,NA,0,0,2,0,0,Indo-European,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0.01,-1.752973024,0.13,0.06,0.14,0.24,0,0,0,, 65 -64,Burusho,1,1,9.5,NA,2,1,0,0,1.0,2.0,3.0,3.0,3.5,0,2,5,1,1,NA,0,0,4,0,0,NA,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.14,2.922883746,0.25,0.52,0.26,0.34,1,0,1,, 66 -65,Kazak,1,1,6,NA,0,2,1,1,3.0,3.0,2.5,2.0,1.5,1,1,2,0,5,0.86408,0,0,8,1,1,Turkic,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0.25,-1.270713122,0.89,0.98,0.28,0.54,0,1,0,, 67 -66,Khalka Mongols,1,1,5,NA,0,4,1,1,3.5,2.5,2.0,1.5,1.5,1,1,2,0,4,NA,0,0,8,1,1,Mongolic,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.09,-0.207705997,0.83,0.94,0.1,0.34,1,1,1,, 68 -67,Lolo,0,1,NA,3,3,NA,NA,0,NA,NA,NA,NA,NA,0,1,5,1,NA,NA,0,0,3,0,0,Sino-Tibetan,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,NA,NA,NA,NA,NA,NA,0,0,0,, 69 -68,Lepcha,1,1,6,2,2,1,0,0,1.0,2.0,2.0,2.0,2.0,0,1,5,1,1,0.37505,0,0,3,0,0,Sino-Tibetan,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.18,-0.805647505,0.83,0.94,0.1,0.22,1,0,1,, 70 -69,Garo,0,1,NA,2,2,NA,NA,1,NA,NA,NA,NA,NA,0,1,5,1,NA,-1.48366,1,0,1,0,0,Sino-Tibetan,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,NA,NA,NA,NA,NA,NA,1,1,0,, 71 -70,Lakher,1,1,8,3,NA,4,1,1,2.0,2.0,3.0,2.0,3.0,0,1,5,1,5,0.86408,1,0,1,0,0,Sino-Tibetan,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.04,0.124483729,0.49,0.52,0.07,0.15,NA,NA,NA,, 72 -71,Burmese,1,1,12,3,2.5,1,0,0,1.0,3.5,4.0,4.0,4.0,0,2,5,1,NA,-1.80288,0,0,2,0,0,Sino-Tibetan,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.02,-1.393291734,0.83,0.91,0.02,0.3,1,1,1,, 73 -72,Lamet,1,1,8.5,3,NA,4,1,1,2.5,3.5,3.0,3.0,2.5,0,1,3,0,2,NA,0,0,2,0,0,Austroasiatic,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.13,-0.473457779,0.84,0.89,0.13,0.14,NA,NA,NA,, 74 -73,Vietnamese,1,1,10,3,NA,1,0,0,1.0,3.0,4.0,3.0,3.0,0,2,5,1,3,-0.08151,2,0,2,0,0,Austroasiatic,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.03,-1.033610444,0.52,0.3,0.1,0.18,1,1,1,, 75 -74,Rhade,0,1,NA,3,3,NA,NA,1,NA,NA,NA,NA,NA,0,1,4,0,NA,0.37505,0,0,1,0,0,Austronesian,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,, 76 -75,Khmer,0,1,NA,NA,NA,NA,NA,0,NA,NA,NA,NA,NA,0,1,5,1,NA,NA,2,0,3,0,0,Austroasiatic,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,1,1,1,, 77 -76,Siamese,1,1,10,3,3,1,0,0,1.0,3.0,3.5,3.5,3.0,0,2,5,1,3,-1.80288,1,0,1,0,0,Tai-Kadai,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.13,2.563202456,0.82,0.92,0.16,0.28,0,0,0,, 78 -77,Semang,0,1,NA,1,0.5,NA,NA,1,NA,NA,NA,NA,NA,1,1,1,0,NA,NA,3,1,NA,NA,0,Austroasiatic,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,1,0,0,, 79 -78,Nicobarese,0,1,NA,NA,NA,NA,NA,0,NA,NA,NA,NA,NA,0,1,5,1,NA,-1.80288,2,0,NA,NA,0,Austroasiatic,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,, 80 -79,Andamanese,0,1,NA,0,0,NA,NA,1,NA,NA,NA,NA,NA,1,1,2,0,NA,-1.80288,4,1,NA,NA,0,Great Andamanese,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,1,1,1,, 81 -80,Vedda,0,1,NA,1,1,NA,NA,1,NA,NA,NA,NA,NA,1,1,2,0,NA,NA,3,1,NA,NA,0,Indo-European,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,1,0,0,, 82 -81,Tanala,0,1,NA,NA,NA,NA,NA,1,NA,NA,NA,NA,NA,0,2,3,0,NA,NA,2,0,2,0,0,Austronesian,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,0,1,0,, 83 -82,Negri Sembilan,0,1,NA,2,2,NA,NA,0,NA,NA,NA,NA,NA,0,2,5,1,NA,NA,1,0,1,0,0,Austronesian,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,1,1,1,, 84 -83,Javanese,1,1,10,3,2.5,1,0,0,1.0,2.0,3.5,3.5,3.0,0,2,5,1,3,NA,0,0,2,0,0,Austronesian,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.06,0.045433426,0.53,0.34,0.13,0.32,1,1,0,, 85 -84,Balinese,1,0,6.5,2,2,4,1,NA,2.0,2.0,2.0,2.5,2.0,0,2,5,1,2,NA,0,0,3,0,0,Austronesian,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-2.112654314,0.44,0,0,0.39,0,1,1,, 86 -85,Iban,1,0,6,2,NA,3,1,NA,2.5,2.5,2.5,2.0,1.5,0,1,4,0,2,NA,2,0,NA,NA,0,Austronesian,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.06,-0.008392162,0.32,0.23,0.17,0.42,1,1,1,, 87 -86,Badjau,0,1,NA,0,0,NA,NA,1,NA,NA,NA,NA,NA,0,1,1,0,NA,NA,8,0,0,0,0,Austronesian,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.39023551,0.42,0,0,0.42,NA,NA,NA,, 88 -87,Toradja,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,5,1,NA,NA,0,0,2,0,0,Austronesian,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,1,1,1,, 89 -88,Tobelorese,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,5,1,NA,NA,2,0,NA,NA,0,North Halmahera,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,, 90 -89,Alorese,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,5,1,NA,NA,0,0,NA,NA,0,Timor-Alor-Pantar,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,0,1,1,, 91 -90,Tiwi,1,1,3.5,0,0,4,1,1,3.0,2.5,1.0,1.5,1.0,1,1,1,0,1,NA,2,1,NA,NA,0,NA,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.39023551,0.94,0,0,0.42,0,1,0,, 92 -91,Aranda,1,1,3,0,0,2,1,1,3.0,3.0,1.0,1.0,1.0,1,1,1,0,2,NA,0,1,NA,NA,0,Pama-Nyungan,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.22,-1.071399286,0.79,0.62,0.29,0.67,0,1,0,, 93 -92,Orokaiva,1,1,6,2,NA,2,1,1,2.5,3.0,2.5,2.5,1.0,0,1,5,1,NA,NA,2,0,NA,NA,0,Nuclear Trans New Guinea,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0.13,-0.473457779,0.46,0.34,0.12,0.27,1,0,0,, 94 -93,Kimam,1,1,4.5,1,NA,4,1,1,3.0,3.0,2.0,1.5,1.0,0,1,5,1,5,-0.08151,1,0,NA,NA,0,Kolopom,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.06,-0.008392162,0.83,0.82,0.05,0.34,NA,NA,NA,, 95 -94,Kapauku,1,0,9,3,NA,2,1,NA,2.0,3.0,3.5,3.0,2.5,0,1,5,1,NA,NA,1,0,NA,NA,0,Nuclear Trans New Guinea,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0.09,-0.207705997,0.39,0.32,0.11,0.48,1,0,0,, 96 -95,Kwoma,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1,1,5,1,NA,NA,0,1,NA,NA,0,Sepik,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,0,0,0,, 97 -96,Manus,1,1,5,1,1,4,1,1,2.0,3.0,1.5,2.0,1.5,0,1,5,1,1,0.69427,9,0,NA,NA,0,Austronesian,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.39023551,0.42,0,0,0.18,0,1,1,, 98 -97,New Ireland,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,5,1,NA,0.23771,4,0,NA,NA,0,Austronesian,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,0,0,1,, 99 -98,Trobrianders,1,1,6.5,2,NA,2,1,1,2.0,3.0,2.0,2.5,2.0,0,1,5,1,5,0.69427,3,0,NA,NA,0,Austronesian,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.39023551,0.45,0,0,0.18,1,1,1,, 100 -99,Siuai,1,1,7,2,NA,4,1,1,2.0,4.0,3.0,2.5,1.5,0,1,5,1,NA,-0.08151,0,0,NA,NA,0,South Bougainville,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.39023551,0.5,0.34,0,0.18,NA,NA,NA,, 101 -100,Tikopia,1,1,6.5,2,NA,2,1,1,2.0,3.0,2.5,2.5,1.5,0,1,5,1,5,0.23771,5,0,NA,NA,0,Austronesian,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.39023551,0,0,0,0.49,1,1,0,, 102 -101,Pentecost,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,5,1,NA,NA,1,0,NA,NA,0,Austronesian,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,, 103 -102,Mbau Fijians,1,1,9,NA,1,2,1,1,2.0,3.0,3.0,3.0,3.0,0,1,5,1,1,-1.80288,5,0,NA,NA,0,Austronesian,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.39023551,0.45,0,0,0.49,NA,NA,NA,, 104 -103,Ajie,0,1,NA,NA,NA,NA,NA,1,NA,NA,NA,NA,NA,0,2,5,1,NA,NA,3,0,NA,NA,0,Austronesian,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,, 105 -104,Maori,1,1,5,1,1,3,1,1,2.5,3.0,2.0,1.5,1.5,0,1,5,1,5,NA,2,0,NA,NA,0,Austronesian,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.07,-0.074830107,0.8,0.97,0.07,0.18,1,1,0,, 106 -105,Marquesans,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,5,1,NA,0.69427,4,0,NA,NA,0,Austronesian,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,0,0,0,, 107 -106,Western Samoans,1,1,8,3,NA,2,1,1,2.0,2.5,3.0,3.0,2.0,0,1,5,1,4,NA,3,0,NA,NA,0,Austronesian,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.39023551,0.45,0,0,0.15,0,1,0,, 108 -107,Gilbertese,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,5,1,NA,NA,4,0,NA,NA,0,Austronesian,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,, 109 -108,Marshallese,1,0,7,NA,3,2,1,NA,2.0,2.0,2.5,2.5,2.0,0,1,5,1,5,1.32064,5,0,NA,NA,0,Austronesian,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.39023551,0,0,0,0.23,1,1,0,, 110 -109,Trukese,1,1,7.5,2,NA,2,1,1,2.0,3.0,3.0,2.5,2.0,0,1,5,1,2,0.23771,5,0,NA,NA,0,Austronesian,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.39023551,0.45,0,0,0.14,1,1,0,, 111 -110,Yapese,1,1,6.5,2,NA,4,1,1,2.0,3.0,2.5,2.0,2.0,0,1,5,1,NA,NA,4,0,NA,NA,0,Austronesian,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.39023551,0.49,0,0,0.21,0,1,0,, 112 -111,Palauans,0,1,NA,NA,NA,NA,NA,1,NA,NA,NA,NA,NA,0,1,5,1,NA,NA,4,0,NA,NA,0,Austronesian,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,,NA,NA,NA,, 113 -112,Ifugao,1,1,9.5,3,NA,1,0,0,1.0,3.0,3.5,3.5,2.5,0,2,5,1,3,-0.40073,1,0,NA,NA,0,Austronesian,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.18,4.361608907,0.5,0.51,0.25,0.23,1,1,1,, 114 -113,Atayal,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,4,0,NA,NA,1,0,NA,NA,0,Austronesian,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,1,1,1,, 115 -114,Chinese,0,1,NA,3,3,NA,NA,0,NA,NA,NA,NA,NA,0,2,5,1,NA,NA,1,0,1,0,0,Sino-Tibetan,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,NA,NA,NA,NA,NA,NA,1,1,0,, 116 -115,Manchu,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,5,1,NA,NA,1,0,2,0,0,Tungusic,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,1,1,0,, 117 -116,Koreans,1,1,8.5,3,NA,1,0,0,1.0,2.5,3.0,3.0,2.5,0,2,5,1,2,0.86408,2,0,2,0,0,Koreanic,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,0.59,0.65,NA,0.27,0,1,0,, 118 -117,Japanese,1,1,10,3,NA,1,0,0,1.0,2.5,2.5,4.0,3.5,0,2,5,1,2,NA,0,0,1,0,0,Japonic,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.06,0.045433426,0.82,0.95,0.06,0.22,0,1,1,, 119 -118,Ainu,1,1,5,1,1,3,1,1,3.0,3.0,1.5,2.5,1.0,1,1,3,0,1,-0.38866,4,1,NA,NA,0,Ainu,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.39023551,0.8,0.91,0,0.2,0,1,0,, 120 -119,Gilyak,1,1,5.5,1,1,2,1,1,3.0,2.5,1.5,2.0,2.0,1,1,2,0,5,NA,5,1,NA,NA,0,NA,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.39023551,0.91,0,0,0.24,1,1,1,, 121 -120,Yukaghir,0,0,NA,2,1.5,NA,NA,NA,NA,NA,NA,NA,NA,1,1,1,0,NA,0.37505,4,1,NA,NA,0,Yukaghir,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,, 122 -121,Chukchee,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1,1,2,0,NA,0.69427,3,0,5,1,1,Chukotko-Kamchatkan,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,1,0.99,1,0.22,0,1,0,, 123 -122,Ingalik,0,0,NA,2,1.5,NA,NA,NA,NA,NA,NA,NA,NA,1,1,3,0,NA,0.69427,5,1,NA,NA,0,Athabaskan-Eyak-Tlingit,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,, 124 -123,Aleut,0,0,NA,0,0,NA,NA,NA,NA,NA,NA,NA,NA,0,1,3,0,NA,0.69427,6,0,NA,NA,0,Eskimo-Aleut,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,1,1,1,, 125 -124,Copper Eskimo,1,1,3.5,0,0,4,1,1,3.0,3.0,1.5,1.0,1.0,0,1,2,0,5,0.69427,6,0,NA,NA,0,Eskimo-Aleut,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.06,-0.008392162,0.44,0.51,0.13,0.34,0,1,0,, 126 -125,Montagnais,0,0,NA,2,2,NA,NA,NA,NA,NA,NA,NA,NA,1,1,2,0,NA,1.32064,2,1,NA,NA,0,Algic,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,0,1,0,, 127 -126,Micmac,0,1,NA,2,2,NA,NA,1,NA,NA,NA,NA,NA,1,1,2,0,NA,-0.08151,4,1,NA,NA,0,Algic,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,1,0,0,, 128 -127,Saulteaux,0,1,NA,2,1.5,NA,NA,1,NA,NA,NA,NA,NA,1,1,2,0,NA,1.32064,4,1,NA,NA,0,Algic,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,0,1,1,, 129 -128,Slave,0,1,NA,2,2,NA,NA,1,NA,NA,NA,NA,NA,1,1,3,0,NA,0.23771,4,1,NA,NA,0,Athabaskan-Eyak-Tlingit,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,, 130 -129,Kaska,0,1,NA,1,1,NA,NA,1,NA,NA,NA,NA,NA,1,1,2,0,NA,0.69427,5,1,NA,NA,0,Athabaskan-Eyak-Tlingit,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,0,1,0,, 131 -130,Eyak,1,1,3,0,0,4,1,1,4.0,3.0,1.0,1.0,1.0,1,1,3,0,1,NA,5,1,NA,NA,0,Athabaskan-Eyak-Tlingit,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.39023551,0.49,0.54,0,0.2,NA,NA,NA,, 132 -131,Haida,1,1,7,NA,3,4,1,1,3.0,3.0,1.0,3.0,3.0,0,1,3,0,1,0.05583,6,0,NA,NA,0,Haida,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.39023551,0.78,0,0,0.15,NA,NA,NA,, 133 -132,Bellacoola,1,1,6,2,2,2,1,1,2.5,2.5,3.0,2.0,1.0,0,1,5,1,1,-0.08151,6,0,NA,NA,0,Salishan,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0.06,-0.008392162,0.84,0.94,0.06,0.18,1,1,1,, 134 -133,Twana,0,1,NA,0,0,NA,NA,1,NA,NA,NA,NA,NA,0,1,2,0,NA,NA,6,0,NA,NA,0,Salishan,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,, 135 -134,Yurok,0,1,NA,2,1.5,NA,NA,1,NA,NA,NA,NA,NA,1,1,5,1,NA,NA,5,1,NA,NA,0,Algic,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,0,1,1,, 136 -135,Pomo (Eastern),0,1,NA,1,0.5,NA,NA,1,NA,NA,NA,NA,NA,1,1,3,0,NA,0.23771,3,1,NA,NA,0,Pomoan,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,0,1,0,, 137 -136,Yokuts (Lake),0,1,NA,1,1,NA,NA,1,NA,NA,NA,NA,NA,1,1,3,0,NA,0.23771,3,1,NA,NA,0,Yokutsan,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,1,0,1,, 138 -137,Paiute (North.),0,1,NA,0,0,NA,NA,1,NA,NA,NA,NA,NA,1,1,2,0,NA,-0.08151,2,1,NA,NA,0,Uto-Aztecan,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,NA,NA,NA,NA,NA,NA,0,1,0,, 139 -138,Klamath,0,1,NA,0,0,NA,NA,1,NA,NA,NA,NA,NA,1,1,2,0,NA,-0.08151,5,1,NA,NA,0,NA,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.39023551,0.01,0.01,0.11,0.24,0,1,0,, 140 -139,Kutenai,0,1,NA,0,0,NA,NA,1,NA,NA,NA,NA,NA,1,1,2,0,NA,NA,4,1,0,0,0,NA,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,, 141 -140,Gros Ventre,0,1,NA,0,0,NA,NA,1,NA,NA,NA,NA,NA,1,1,1,0,NA,NA,0,1,0,0,0,Algic,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,0,0,0,, 142 -141,Hidatsa,1,1,8.5,3,NA,2,1,1,3.0,3.0,3.5,2.5,2.5,0,1,3,0,2,0.37505,1,0,0,0,0,Siouan,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0.06,-0.008392162,1,0.92,0.05,0.4,NA,NA,NA,, 143 -142,Pawnee,1,1,6,2,NA,4,1,1,2.5,3.0,2.5,2.5,1.0,1,1,3,0,4,NA,0,1,0,0,0,Caddoan,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.08,-0.141268052,0.85,0.94,0.08,0.27,0,0,1,, 144 -143,Omaha,0,1,NA,1,1,NA,NA,1,NA,NA,NA,NA,NA,1,1,3,0,NA,NA,1,1,0,0,0,Siouan,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,NA,NA,NA,NA,NA,NA,0,0,NA,, 145 -144,Huron,1,1,8,3,NA,1,0,0,1.0,4.0,3.0,3.0,2.0,0,1,3,0,5,0.69427,3,0,NA,NA,0,Iroquoian,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.39023551,0.51,0.73,0,0.18,NA,NA,NA,, 146 -145,Creek,0,1,NA,1,0.5,NA,NA,1,NA,NA,NA,NA,NA,0,1,3,0,NA,NA,2,0,NA,NA,0,Muskogean,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,0,1,0,, 147 -146,Natchez,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,5,1,NA,NA,2,0,NA,NA,0,NA,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,, 148 -147,Comanche,0,1,NA,0,0,NA,NA,1,NA,NA,NA,NA,NA,1,1,1,0,NA,0.37505,0,1,0,0,0,Uto-Aztecan,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,NA,NA,NA,NA,NA,NA,0,1,0,, 149 -148,Chiricahua,0,1,NA,0,0,NA,NA,1,NA,NA,NA,NA,NA,1,1,1,0,NA,NA,0,1,0,0,0,Athabaskan-Eyak-Tlingit,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,1,0,0,, 150 -149,Zuni,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,2,5,1,NA,NA,0,0,0,0,0,NA,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,1,1,0,, 151 -150,Havasupai,1,1,8,3,NA,1,0,0,1.0,3.5,3.5,2.5,2.0,1,2,2,0,NA,NA,0,1,NA,NA,0,Cochimi-Yuman,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-2.112654314,0,0,0.29,0.47,1,0,0,, 152 -151,Papago,1,1,7,2,NA,4,1,1,2.0,2.5,2.5,2.5,2.0,1,2,3,0,5,0.37505,0,1,NA,NA,0,Uto-Aztecan,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0.1,1.484158586,0.61,0.46,0.25,0.37,1,1,0,, 153 -152,Huichol,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,5,1,NA,NA,1,0,1,0,0,Uto-Aztecan,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,, 154 -153,Aztec,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,2,5,1,NA,0.69427,1,0,NA,NA,0,Uto-Aztecan,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,NA,NA,NA,NA,NA,NA,0,0,0,, 155 -154,Popoluca,1,1,9.5,3,NA,1,0,0,3.0,3.5,3.0,4.0,2.5,0,1,5,1,NA,-1.80288,1,0,NA,NA,0,Mixe-Zoque,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.39023551,0.03,0.09,0.05,0.17,NA,NA,NA,, 156 -155,Quiche,0,1,NA,3,3,NA,NA,0,NA,NA,NA,NA,NA,0,1,5,1,NA,NA,0,0,1,0,0,Mayan,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,, 157 -156,Miskito,1,0,8,NA,1,1,0,NA,1.0,3.0,3.0,2.5,2.5,1,1,5,1,1,NA,2,1,1,0,0,Misumalpan,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.1,-0.274143943,0.55,0.55,0.18,0.19,1,1,0,, 158 -157,Bribri,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,5,1,NA,NA,2,0,NA,NA,0,Chibchan,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,,1,0,0,, 159 -158,Cuna (Tule),0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,5,1,NA,NA,3,0,NA,NA,0,Chibchan,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,1,0,1,, 160 -159,Goajiro,1,1,6.5,2,NA,2,1,1,3.5,3.0,2.5,2.5,1.5,1,1,1,0,4,0.69427,1,0,7,1,1,Arawakan,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.07,-0.074830107,0.67,0.64,0.09,0.2,0,1,0,, 161 -160,Haitians,1,1,9.5,3,2.5,1,0,0,1.0,3.5,3.0,3.0,3.5,0,1,5,1,4,NA,0,0,2,0,0,Indo-European,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0.09,-0.207705997,0.28,0.24,0.34,0.3,1,1,0,, 162 -161,Callinago,0,1,NA,1,1,NA,NA,1,NA,NA,NA,NA,NA,0,1,4,0,NA,NA,5,0,NA,NA,0,Arawakan,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,1,0,0,, 163 -162,Warrau,1,1,4.5,NA,0,4,1,1,3.0,3.0,1.5,2.0,1.0,1,1,2,0,NA,-1.80288,2,1,NA,NA,0,NA,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.03,0.190921674,0.47,0.51,0.13,0.25,1,0,0,, 164 -163,Yanomamo,0,0,NA,3,3,NA,NA,NA,NA,NA,NA,NA,NA,0,1,4,0,NA,-0.40073,0,0,NA,NA,0,Yanomamic,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,1,0,0,, 165 -164,Carib (Barama),1,0,5.5,1,1,1,0,NA,1.5,3.0,2.5,2.0,1.0,0,1,4,0,4,0.69427,2,0,NA,NA,0,Cariban,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.13,-0.473457779,0.47,0.3,0.18,0.3,0,0,1,, 166 -165,Saramacca,1,1,4.5,1,NA,2,1,1,3.5,3.0,1.5,2.0,1.0,0,1,5,1,2,-0.08151,2,0,NA,NA,0,Indo-European,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.39023551,0,0,0.09,0.19,1,0,0,, 167 -166,Mundurucu,0,1,NA,1,1,NA,NA,1,NA,NA,NA,NA,NA,0,1,5,1,NA,NA,2,0,NA,NA,0,Tupian,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,NA,NA,NA,NA,NA,NA,1,0,0,, 168 -167,Cubeo (Tucano),0,1,NA,NA,NA,NA,NA,1,NA,NA,NA,NA,NA,0,1,5,1,NA,NA,3,0,NA,NA,0,Tucanoan,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,0,1,0,, 169 -168,Cayapa,1,1,8.5,3,NA,1,0,0,1.0,3.0,3.5,2.5,2.5,0,1,5,1,1,-1.80288,2,0,NA,NA,0,Barbacoan,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.39023551,0.75,0,0,0.38,1,1,1,, 170 -169,Jivaro,0,1,NA,NA,NA,NA,NA,1,NA,NA,NA,NA,NA,0,1,4,0,NA,-1.80288,1,0,NA,NA,0,Chicham,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,1,1,0,, 171 -170,Amahuaca,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,4,0,NA,-1.80288,1,0,NA,NA,0,Pano-Tacanan,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,, 172 -171,Inca,0,1,NA,0,0,NA,NA,1,NA,NA,NA,NA,NA,0,2,5,1,NA,0.23771,0,0,2,0,0,Quechuan,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,1,1,1,, 173 -172,Aymara,1,1,9.5,3,3,1,0,0,1.0,2.5,3.0,3.0,3.5,0,1,5,1,NA,0.69427,1,0,3,0,0,Aymaran,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.01,0.323797565,0.07,0.1,0.17,1.06,1,1,1,, 174 -173,Siriono,1,1,4,0,0,4,1,1,3.0,3.0,1.0,2.0,1.0,1,1,2,0,1,-1.80288,1,1,NA,NA,0,Tupian,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.11,-0.340581888,0.58,0.68,0.14,0.4,1,0,0,, 175 -174,Nambicuara,0,1,NA,0,0,NA,NA,1,NA,NA,NA,NA,NA,1,1,2,0,NA,NA,1,1,NA,NA,0,Nambiquaran,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,1,1,0,, 176 -175,Trumai,0,1,NA,1,1,NA,NA,1,NA,NA,NA,NA,NA,0,1,5,1,NA,-1.80288,3,0,NA,NA,0,NA,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,1,0,0,, 177 -176,Timbira,0,1,NA,1,1,NA,NA,1,NA,NA,NA,NA,NA,1,1,4,0,NA,NA,1,1,NA,NA,0,Nuclear-Macro-Je,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,1,0,0,, 178 -177,Tupinamba,0,1,NA,1,1,NA,NA,1,NA,NA,NA,NA,NA,0,1,4,0,NA,NA,2,0,NA,NA,0,Tupian,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,NA,NA,NA,NA,NA,NA,0,1,0,, 179 -178,Botocudo,0,1,NA,0,0,NA,NA,1,NA,NA,NA,NA,NA,1,1,1,0,NA,NA,1,1,NA,NA,0,Nuclear-Macro-Je,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,, 180 -179,Shavante,1,1,5,NA,0,4,1,1,3.0,3.0,1.5,2.5,1.0,1,1,2,0,1,-1.80288,1,1,NA,NA,0,Nuclear-Macro-Je,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0.02,0.25735962,0.46,0.6,0.06,0.33,NA,NA,NA,, 181 -180,Aweikoma,0,1,NA,0,0,NA,NA,1,NA,NA,NA,NA,NA,1,1,1,0,NA,NA,0,1,NA,NA,0,Nuclear-Macro-Je,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,1,0,0,, 182 -181,Cayua,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,2,0,NA,NA,1,0,NA,NA,0,Tupian,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,NA,NA,NA,NA,NA,NA,1,0,0,, 183 -182,Lengua,1,1,3,0,0,3,1,1,3.0,3.0,1.0,1.0,1.0,1,1,1,0,NA,NA,2,1,NA,NA,0,Lengua-Mascoy,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.18,-0.805647505,0.77,0.81,0.2,0.54,NA,NA,NA,, 184 -183,Abipon,0,0,NA,0,0,NA,NA,NA,NA,NA,NA,NA,NA,1,1,1,0,NA,NA,1,1,1,0,0,Guaicuruan,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,NA,NA,NA,NA,NA,NA,0,1,0,, 185 -184,Mapuche,1,1,7.5,2,NA,4,1,1,2.0,2.0,2.5,3.0,2.0,0,1,5,1,3,0.69427,1,0,2,0,0,Araucanian,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.39023551,0,0.01,0.12,0.44,0,1,1,, 186 -185,Tehuelche,1,1,3,0,0,3,1,1,3.0,3.0,1.0,1.0,1.0,1,1,1,0,NA,NA,1,1,0,0,0,Chonan,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,-0.938523396,0.79,0.87,0.19,0.44,0,0,0,, 187 -186,Yahgan,1,1,3,NA,2,4,1,1,3.0,3.0,1.0,1.0,1.0,0,1,1,0,3,-1.80288,7,0,NA,NA,0,NA,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.09,-0.207705997,0.46,0.48,0.13,1.16,0,0,0,, 188 -,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 189 -
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... ... @@ -1,47 +1,0 @@ 1 -ID_Culture,Culture_Name,OWC,EA_ID,Place,Time,Country,Independence,R_Pacification,IntFreqSum,ReliabIntFreq,R_IntBNC1,R_IntBNC2,R_IntBNCC3,R_IntBNC4,R_IntBNC5,R_IntBNC1_Pop,R_IntBNC2_Pop,R_IntBNCC3_Pop,R_IntBNC4_Pop,R_IntBNC5_Pop,IntBNC1xPop,IntBNC2xPop,IntBNcC3xPop,IntBNc4xPop,IntBNc5xPop,BNC1to5xPopSumDivByViable,Resolved_Local_Var6,Resolved_Local_Var7,Resolved_Local_Var8,Resolved_Local_Var10,Resolved_Local_Var11,Reversed_Local_Var11,Resolved_Local_Var30,LittleFormalizedLeadership,Factor_LocalPart_Vars67811,Resolved_Multi10M_Range_of_Decision_Making_2coders,rvmult,StateShort,PopulationLog10,PopulationSoc,R_Disasters,DisastersShort 2 -29,Mongo,FO32,Ae24,"Nkundo Mongo, Ilanga group",1930,DRC,1960,3,2,1,8,8,3,8,8,8,8,4,8,8, , ,12, , , , , , , , , , ,0, ,8,0,0, , ,1,1 3 -17,Gwembe Tonga,FQ12,Ac30,Ronga subtribe,1950,Zimbabwe,1980,4,2,1,7,7,7,7,7,7,7,7,7,7, , , , , , , , , , , , , ,0, , , , , , ,4,4 4 -22,Lakeshore Tonga,FR07,Ac13,General,1950,Malawi,1964,4,2,1,7,7,7,7,7,7,7,7,7,7, , , , , , , , , , , , , ,0, , , , , , ,1,1 5 -25,Luo,FL11,Aj6, ,1940,Kenya,1963,4,2.5,2,1,8,1,1,1,1,8,1,1,1,1, ,1,1,1,1, , , , , , , ,0, , , , , , ,3,3 6 -46,Yao,FT07,Ac7,entire society,1920,Mozambique,1975,4,2,1,7,7,7,7,7,7,7,7,7,7, , , , , , , , , , , , , ,0, , , , , , , , 7 -11,Dorobo,FL06,Aa2,North Tindiret forest area,1920,Kenya,1963,9,2,1,7,7,7,7,7,7,7,7,7,7, , , , , , , , , , , , , , , , , , , , , , 8 -12,Ganda,FK07,Ad7,"Kyaddondo district, around Kampala",1875,Uganda,1962,1,3,3,7,7,7,7,7,7,7,7,7,7, , , , , , ,1,2,1,1,4,1,9,0,-1.64337993591686,9,1,1,5.8750612633917,750000,1,1 9 -20,Kaffa,MP14,Ca30,Entire kingdom,1905,Ethiopia,n/a,1,2,1,3,4,4,4,4,3,3,3,3,3,9,12,12,12,12,11.4,1,1,1,1.25,4,1,1,0,-1.96691076322795,1,1,1,5.69897000433602,500000,1,1 10 -40,Shilluk,FY23,Ai6,Entire Kingdom,1910,Sudan,1956,1,10,1,2,1,8,2,8,4,4,8,4,8,8,4, ,8, ,6.66666666666667,1,2,1,3,3,2,9,0,-1.32121318270477,9,1,0,4.77815125038364,60000,3,3 11 -13,Gisu,FK13,Ad9,General,1900,Uganda,1962,2,6,1,2,8,1,3,8,1,8,1,2,8,2, ,1,6, ,3,1,9,1,1.25,9, ,2,0, ,8,0, ,5.38693037036905,243742,3,3 12 -52,Hehe,FN09,Ad8,Iringa group,1890,Tanzania,1964,1,7,3,8,8,8,8,8,8,8,8,8,8, , , , , , ,1.25,1.75,1,1.75,4,1,3,0,-1.64298020116005,2.25,1,0,4.54406804435028,35000,2,2 13 -37,Nyoro,FK11,Ad2,General,1897,Uganda,1962,1,5,3,4,2.5,3,3.5,4,3,2,4,4,3,12,5,12,14,12,11,1.75,2,1.5,2.5,3,2,1.25,0,-.893552285280621,1.75,1,1,5.04139268515823,110000,3,3 14 -38,Rwandans,FO42,Ae10,General,1986,Rwanda,1962,2,8,4,4,4,4,4,4,4,4,4,4,4,16,16,16,16,16,16,1.75,1.75,1.5,2,3.25,1.75,1,0,-1.05497668041142,2,1,1,6.81224469680037,6490000,3,3 15 -2,Azande,FO07,Ai3,Yambio Chiefdom,1905,DRC,1960,1,4,1,4,4,8,4,4,3,3,8,4,3,12,12, ,16,12,13,2,3,3,1,4,1,2,0,-.259465051585853,9,1,0,4.8750612633917,75000,1,1 16 -5,Bemba,FQ05,Ac3,Zambia branch,1897,Zambia,1964,1,4.5,2,8,8,8,8,8,8,8,8,8,8, , , , , , ,2,3,2.5,2.5,3,2,1.5,0,-.121111871044158,2.25,1,0,5.14612803567824,140000,2,2 17 -23,Lozi,FQ09,Ab3,Ruling Luyana,1900,Zambia,1964,1,5,3,9,4,3,8,8,8,3,3,8,8, ,12,9, , ,10.5,2,2,1,3,3,2,1,0,-.996083416366429,9,4,1,4.82607480270083,67000,4,4 18 -54,Pare,FN04,Ad36,South Pare,1890,Tanzania,1964,1,10,1,8,8,8,8,8,8,8,8,8,8, , , , , , ,2,3,1,2,2,3,1,0,-.350385835843252,2,5,0,4.30102999566398,20000,3,3 19 -1,Amhara,MP05,Ca7,Gondar Province,1953,Ethiopia,n/a,2,7,4,2,8,2,3,2,3,8,3,3,2,6, ,6,9,4,6.25,2,2,1,1,4,1,9,0,-1.31825016957852,9,1,1,6.30102999566398,2000000,1,1 20 -3,Barundians,FQ42,Ae8,Burundi (Kingdom/Republic),1986,Burundi,1962+,2,5,3,4,4,4,4,4, , , , , , , , , , , ,2,1.5,2,2.75,3,2,1.5,0,-.790221684681184,3,1,1,6.30102999566398,2000000,2.5,2 21 -6,Bena,FN31,Ad11,"Njonge, Ulanga & Kilombero districts",1897,Tanzania,1964,2,8,4,7,7,7,7,7,7,7,7,7,7, , , , , , ,2,2,1.5,3,3.25,1.75,9,0,-.892811531999057,2,1,0,4.20411998265593,16000,2.5,2 22 -53,Lugbara,FK14,Ai32,entire society,1920,Uganda,1962,2,8,1,8,8,8,8,8,8,8,8,8,8, , , , , , ,2,2.5,2,2.75,3,2,1.75,0,-.466690857370097,8,0,1,5.38381536598043, ,4,4 23 -33,Ngoni,FR05,Ac9,General,1898,Malawi,1964,1,2.5,2,4,2,8,4,8,2,2,8,2,8,8,4, ,8, ,6.66666666666667,2.25,2.5,2,2.25,2,3,2,0,-.0632416625734208,2,4,1,4.92941892571429,85000,3,3 24 -55,Shambala,FN24,Ad10,entire society,1890,Tanzania,1964,1,9.5,2,8,8,8,8,8,8,8,8,8,8, , , , , , ,2.25,2.25,2,2,2,3,2.5,0,-.144124369401193,2.5,4,0,4.90308998699194,80000,4,4 25 -27,Mbundu,FP13,Ab5,Bailundu subtribe,1880,Angola,1975,1,6,4,8,8,8,2,8,8,8,8,3,8, , , ,6, , ,2.5,9,1.75,2,2.5,2.5,2,0, ,9,4,0,5.65321251377534,450000,3,3 26 -31,Ngonde,FN17,Ad16,General,1895,Tanzania,1964,1,9,3,1,1,8,8,8,4,4,8,8,8,4,4, , , , ,2.5,2,3,2,2.25,2.75,1.25,0,.143360822393388,3,4,0,4.60205999132796,40000,3,3 27 -10,Darfur,MQ08,Cb17,Jebel Merra,1999,Sudan,1956,2, ,5,4,4,4,4,4,4,4,4,4,4,16,16,16,16,16,16,2.75,2.5,2.25,2.25,2.5,2.5,2,0,.0301466303249044,2.25,1,1,5.69897000433602,500000,4,4 28 -8,Chagga,FN04,Ad3,"General, Chagga society",1875,Tanzania,1964,1,10,1,1,1,2,4,1,1,1,1,3,1,1,1,2,12,1,3.4,3,1.5,2,2,2.5,2.5,1.5,0,-.304008541736794,1.75,4,0,4.97772360528885,95000,1,1 29 -15,Gikuyu,FL10,Ad4,Motume district (Ft Hall),1885,Kenya,1963,1,4,1,2,2,8,2,8,1,1,8,2,8,2,2, ,4, ,2.66666666666667,3,3.25,2.25,2,2,3,2,0,.515160568998851,2,4,0,5.69897000433602,500000,3.5,3 30 -19,Ila,FQ06,Ac1,General,1880,Zambia,1964,1,3,3,4,2,4,8,4,2,2,2,8,2,8,4,8, ,8,7,3,3,2,2.25,2,3,2,0,.342371075835882,8,0,0,4.21582255515437,16437,2.5,2 31 -24,Luguru,FN27,Ad14,Morogoro district,1895,Tanzania,1964,1,5,3,8,8,8,8,8,8,8,8,8,8, , , , , , ,3,2.25,2,2.25,2,3,2,0,.0997229553525664,8,0,0,5.19033169817029,155000,8, 32 -26,Maasai,FL12,Aj2,"Kisonko or SE Maasai, Tanzania",1900,General,1963,1,9,3,2,2,1,8,8,1,1,8,8,8,2,2, , , ,2,3,3,3,2,2,3,2,0,.709998221176671,8,0,0,5,100000,3.5,3 33 -34,Nuba,FJ21,Ai10,General,1938,Sudan,1956,1,5,3,2,8,8,8,8,1,8,8,8,8,2, , , , , ,3,2.5,2,1.5,2,3,2.5,0,.180605662180338,8,0,0,4.94448267215017,88000,8, 34 -42,Suku,FO46,Ac17,Feshi territory lineage center,1920,DRC,1960,1,6.5,2,1,1,1,4,4,1,1,1,1,1,1,1,1,4,4,2.2,3,3,2,3,3,2,2,0,.0202043226237926,3,1,1,4.90308998699194,80000,1,1 35 -43,Teda,MS22,Cc2,Nomads of Tibesti,1920,Chad,1960,1,6,4,1,1,1,8,8,1,1,8,8,8,1,1, , , ,1,3,3,3,3,9, ,1,0, ,8,0,0,5.29003461136252,195000,4,4 36 -50,Giriama,FL14,Ad32,entire society,1900,Kenya,1963,1,2,1,7,7,7,7,7,7,7,7,7,7, , , , , , ,3,2.5,3,3,2,3,1.75,0,.548232807521127,8,0,1,4.78532983501077,61000,4,4 37 -56,Acoli,FK04,Aj10,entire society,1920,Uganda,1962,2,4.5,2,2,8,3,8,8,1,8,2,8,8,2, ,6, , ,4,3,3,2,2.5,1.75,3.25,1.75,0,.422912764138904,3,4,0,5.33375953179445,2500,2,2 38 -21,Konso,MP17,Ca1,Town of Buso,1935,Ethiopia,n/a,2,6.5,2,1,2,3,2,1,8,1,2,3,8, ,2,6,6, ,4.66666666666667,3.25,2.25,2.25,1.25,2,3,2,0,.27291218327235,4,3,0,4.75966784468963,1100,4,4 39 -47,Jie,FK08,Aj21,entire society,1950,Uganda,1962,2,2,1,8,8,8,8,8,8,8,8,8,8, , , , , , ,3.25,4,3,3,2,3,2,0,1.11481149007234,2.75,4,0,4.26007138798508,18200,3,3 40 -48,Oromo,FL09,Ca39,Oromo of Arsi & Jima,1890,Kenya,1963,1,5,4,2,8,3,2,2,2,8,2,2,2,4, ,6,4,4,4.5,3.75,2,3,3,2,3,1.25,0,.630314718619342,1.5,1,0,6.90308998699194,8000000,1,1 41 -16,Gusii,FL08,Ad12,Kisii District,1915,Kenya,1963,1,8,1,1,2,2,8,2,1,1,1,8,1,1,2,2, ,2,1.75,4,4,3,2,1,4,2,1,1.68082556803819,8,0,0,5.37657695705651,238000,1,1 42 -35,Nuer,FJ22,Aj3,General,1930,Sudan,1956,1,10,1,2,1,2,2,8,1,1,1,2,8,2,1,2,4, ,2.25,4,4,3,2.75,2,3,1.75,1,1.3586588148261,8,0,0,5.63346845557959,430000,4,4 43 -41,Somali,MO04,Ca2,Dolbahanta subtribe,1900,Somali,1960,1,10,1,8,8,8,8,8,8,8,8,8,8, , , , , , ,4,4,3,1,2,3,2,1,1.3586588148261,8,0,0,4.69897000433602,50000,4,4 44 -44,Turkana,FL18,Aj5,entire society,1918,Kenya,1963,1,9,3,2,2,2,2,2,3,2,2,3,2,6,4,4,6,4,4.8,4,3.75,3,3,1.5,3.5,1,1,1.43885948460438,8,0,0,4.47712125471966,30000,4,4 45 -51,Hadza,FN11,Aa9,entire society,1930,Tanzania,1964,1,2,1,7,7,7,7,7,7,7,7,7,7, , , , , , ,4,4,3,4,1,4,1,1,1.68082556803819,8,0,0,2.74036268949424,550,1,1 46 -28,Mbuti,FO04,Aa5,"Epulu net hunters, Ituri Forest",1950,DRC,1960,4,2,1,7,7,7,7,7,7,7,7,7,7, , , , , , ,4,4,3,3,1,4,1,1,1.68082556803819,8,0, , , ,1,1 47 -
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... ... @@ -1,261 +1,0 @@ 1 -id,name$,date$,paccode,intfreq,reliable_intfreq$,revvar6,revvar7,revvar8,revvar10,revvar11,revvar11Reversed,revvar30,LittleFormalCommLeader,rvmult,stateshort,isl,pop,popdate,poplog10 2 -187,Nyakyusa,+1890,1,5,3,3,1,2,2.5,1.75,3.25,1,0,4,1,0,49248,1921,4.69238859815138 3 -188,Bambara,<1900,1,4,2,3,9,2,1.5,3,2,1,9,1,0,0,538450,1900,5.73114538129738 4 -189,Tallensi,+1890,1,3.75,3,3.25,9,3,2.5,1.75,3.25,1.5,9,0,0,0,35000,1931,4.54406804435028 5 -190,Iban,+1820,1,4.75,2,2.5,9,1,9,4,1,1,9,0,0,0,190326,1947,5.27949812031661 6 -192,Manus,+1910,1,3.25,3,4,3.5,3,4,1.25,3.75,1.5,0,0,0,0,2000,1928,3.30102999566398 7 -193,Trobriands,+1890,1,4.25,3,3,1,3,1.5,1.75,3.25,3,0,1,0,1,9000,1920,3.95424250943932 8 -194,Marshallese,+1875,1,4.75,3,1,1,1,1,4,1,3,0,1,0,1,10250,1874,4.01072386539177 9 -195,Truk,+1900,1,5,3,2.75,1,2,1.75,2.75,2.25,2.75,0,0,0,1,11000,1900,4.04139268515823 10 -196,Havasupai,+1880,2,1.25,3,3.25,9,3,2.25,1.75,3.25,9,9,0,0,0,225,1881,2.35218251811136 11 -197,Mapuche,+1873,1,5,3,4,2.25,3,3.5,2,3,2,0,0,0,0,110000,1870,5.04139268515823 12 -201,Camaracoto,+1930,1,1,3,3,3,2.75,4,1,4,2,0,0,0,0,250,1939,2.39794000867204 13 -202,Ontong-Java,+1920,1,2.5,2,3,2.25,1.5,2.5,2.75,2.25,1,0,0,0,1,700,1928,2.84509804001426 14 -203,Macha,+1950,1,4,3,3,9,2.25,1.5,1.5,3.5,2.25,9,0,9,0,2350000,1930,6.37106786227174 15 -204,Mangaians,+1820,2,5,3,3,9,2,9,2.25,2.75,9,9,4,0,1,2500,1823,3.39794000867204 16 -205,Slave,+1860,2,5,3,3.5,4,2.5,3,1.5,3.5,9,0,0,0,0,397,1858,2.59879050676312 17 -206,Uveans,+1830,1,1.5,3,1.5,9,1.25,2,2,3,9,9, ,0,1,3000,1837,3.47712125471966 18 -207,Gisu,+1900,2,4.5,3,1,9,1,1.25,9, ,2,9,0,0,0,2000,1920,3.30102999566398 19 -208,Sarsi,+1880,2,1,3,3,4,2.75,2,9, ,1,0,4,0,0,500,1885,2.69897000433602 20 -209,Topotha,+1930,1.5,8,2,2,9,1.25,2,3,2,2,9,0,0,0,34000,1948,4.53147891704226 21 -210,Mataco,+1860,2,5,3,4,4,3,3.75,3,2,9,1,0,0,0,20000,1886,4.30102999566398 22 -211,Caraja,+1950,2,3,3,3,9,2.75,3.5,2,3,1,9,0,0,0,1510,1939,3.17897694729317 23 -212,Wichita,+1860,1,1,3,2.75,9,2,2,1.5,3.5,9,9, ,0,0,500,1950,2.69897000433602 24 -213,Eastern Mono,+1860,2,1.25,3,2.75,9,2,2.25,1.5,3.5,9,9,0,0,0,1000,1885,3 25 -214,Choiseulese,+1900,1,5,3,3.25,3,3,2.25,2,3,1,0,0,0,0,4051,1931,3.60756224318359 26 -215,Seniang,+1930,2,3.25,3,3.75,4,3,2,2,3,9,1,0,0,0,166,1934,2.22010808804006 27 -216,Stalo,+1880,2,1.25,3,4,4,3,3,2,3,1,1,0,0,0,1896,1879,3.27783833300205 28 -217,Katikitegon,+1800,1,1,2,2.5,2,2.25,3,1.25,3.75,1.5,0,0,0,0,40000,1834,4.60205999132796 29 -218,Kasena,+1910,1.5,2.5,3,3.25,1,2,2,2.25,2.75,2.75,0,0,0,0,43930,1921,4.64276120326532 30 -219,Chinook,+1850,2,2.5,3,1,1,1.25,1.5,3.25,1.75,9,0,0,0,0,509,1849,2.70671778233676 31 -220,Kpelle,+1920,2,4.5,3,3.75,2,3,2.5,2,3,3,0, ,0,0,175000,1950,5.24303804868629 32 -221,Keweyipaya,+1870,2,8,2,4,4,9,4,2,3,1,1,0,0,0, , , 33 -222,Alur,+1890,1,5,3,3,2.5,2.5,9,2.5,2.5,1,0,0,0,0,200000,1890,5.30102999566398 34 -223,Enga,+1950,8,5,3,3.25,4,2.75,2.5,1.75,3.25,2.25,0,0,0,0,100000,1950,5 35 -224,Dilling,+1930,9,1,3, , , , , , , , , ,0,0,8000,1930,3.90308998699194 36 -225,Gosiute,+1860,1,1,2, , , , , , , , , ,0,0,460,1867,2.66275783168157 37 -226,Kamuku,+1920,9, ,2, , , , , , , , , ,0,0,22000,1949,4.34242268082221 38 -227,Ket,+1900,4,1,3, , , , , , , , , ,0,0,1300,1900,3.11394335230684 39 -228,Luapula,+1940,4,1,2, , , , , , , , , ,0,0,100000,1940,5 40 -229,Mesakin,+1940,3,8,3,0,0,0,0,0, ,0,9, ,0,0,6000,1947,3.77815125038364 41 -230,Ngombe,+1900,1,2.75,3,2.5,9,1.75,2.25,2.5,2.5,2.5,9,0,0,0,150000,1940,5.17609125905568 42 -231,Plateau Tong,+1940,4,1,3, , , , , , , , , ,0,0,90000,1948,4.95424250943933 43 -232,Sukuma,+1950,4,1,3, , , , , , , , , ,0,0,1000000,1948,6 44 -233,Tagbanua,+1950,4,1,3, , , , , , , , , ,0,0,7000,1948,3.84509804001426 45 -237,Tarasco,+1500, , , , , , , , , , , , ,0, , , , 46 -240,Wintu,+1860,2,3.75,3,1.75,1,1.75,2,2.5,2.5,9,0,0,0,0, , , 47 -241,Vugusu,+1930,3.5,8,3, , , , , , , , , ,0,0,39500,1939,4.59659709562646 48 -242,Chagga,+1890,2,4.75,3,2.5,1.5,1.75,2.25,3,2,1,0,1,0,0,60000,1890,4.77815125038364 49 -243,Tarasco,+1500,1,8,2,1,1,1,1.5,3.75,1.25,9,0,1,0,0,40000,1525,4.60205999132796 50 -244,Chichimec,+1570,1,5,3,0,0,0,0,0, ,0,9,0,0,0,12000,1540,4.07918124604763 51 -245,Dobuans,+1890,4,1,3, , , , , , , , , ,0,0, , , 52 -246,Lhota Nagas,+1860,1,1,3,2.25,9,2.25,3.5,2,3,3,9,0,0,0,20000,1920,4.30102999566398 53 -247,Lotuko,+1920,1, ,2,2.5,9,3,2.25,1.75,3.25,9,9,1,0,0,3105,1932,3.4920616045126 54 -248,Alkatcho Car,+1865,2,1.25,3,3.5,3.25,2.75,3,2,3,2,0,0,0,0,135,1800,2.13033376849501 55 -249,Lifu Islande,+1910,2,3.25,2,1,1,2,1,4,1,9,0,9,0,1, , , 56 -250,Miami,+1720,2,4,3,0,0,0,0,0, ,0,9, ,0,0,6000,1718,3.77815125038364 57 -253,Sivokakmiet,+1920,9,8,2, , , , , , , , , ,0,0,550,1917,2.74036268949424 58 -254,Goldi,+1920,9,8,3,0,0,0,0,0, ,0,9, ,0,0,100,1930,2 59 -255,Suri,+1940,2,8,3,2.75,9,1.5,3,2.5,2.5,9,9,0,0,0,3900,1947,3.5910646070265 60 -256,Herero,+1900,1, ,2,3,2.5,2,3,2,3,2,0,0,0,0,25000,1928,4.39794000867204 61 -257,Cahuilla,+1870,1,2.25,3,3,4,9,3.25,1.75,3.25,9,0,0,0,0,750,1920,2.8750612633917 62 -258,Shivwits,+1860,2,1,3,4,4,3,4,1.75,3.25,9,1,0,0,0,300,1873,2.47712125471966 63 -259,Gbari,+1885,1,3.5,3,1.5,9,1.5,2.25,2.75,2.25,9,9,1,0,0,140000,1921,5.14612803567824 64 -261,Lango,+1890,1,4.75,3,3.5,1,2.5,2.75,2.25,2.75,1,0,0,0,0,275000,1947,5.43933269383026 65 -263,Naskapi,+1890,2,1,3,4,4,3,4,1,4,9,1,0,0,0,3910,1857,3.59217675739587 66 -264,Choctaw,+1766,1,2,3,2.75,3.25,2.5,2.5,1.75,3.25,9,0,4,0,0,21500,1764,4.3324384599156 67 -265,Gurage,+1800,3.5,8,3,0,0,0,0,0, ,0,9, ,0,0,350000,1960,5.54406804435028 68 -266,Nyankole,+1850,1,1,3,1.5,1.25,1,2.5,3.75,1.25,9,0,1,1,0,520000,1959,5.7160033436348 69 -267,Shona,+1920,4,1,3,0,0,0,0,0, ,0,9, ,0,0,700000,1931,5.84509804001426 70 -268,Yukaghir,+1900,2,1.25,3,3.25,3.75,3,1.75,1.25,3.75,2,0,0,0,0,1000,1900,3 71 -269,Tahltan,+1870,9, , ,3,9,3,2.75,1.25,3.75,9,9,0,0,0,50,1885,1.69897000433602 72 -270,Tibet,+1920,9, , ,0,0,0,0,0, ,0,9, ,0,0,4000000,1915,6.60205999132796 73 -271,Uncompahgre,+1860,2,1,3,2.5,9,1.5,2.25,1.5,3.5,9,9,1,0,0, , , 74 -272,Yaruro,+1950,1.5,1,3,4,1,2.5,3,1.25,3.75,9,0,0,0,0,150,1930,2.17609125905568 75 -9,Hadza,1930,4,1,3, , , , , , , , ,0,0,0,750,1960,2.8750612633917 76 -13,Mbuti,1950,9, ,2,4,4,3,3,1,4,1,1,0,0,0, , , 77 -30,Otoro Nuba,1930,2,4,3,3,3,9,3,4,1,2,0,0,0,0,300000,1930,5.47712125471966 78 -80,Vedda,1860,4,1,3, , , , , , , , ,0,0,0,364,1856,2.56110138364906 79 -90,Tiwi,1929,2,3.5,2,3,4,3,4,3,2,9,0,0,0,0,1062,1928,3.02612451674545 80 -112,Ifugao,1910,1,5,3,4,3,3,4,1,4,9,0,0,0,0,120000,1910,5.07918124604763 81 -124,Copper Esk.,1915,9,1,3,4,4,3,4,1,4,1,1,0,0,0,750,1915,2.8750612633917 82 -134,Yurok,1850,1,3,3,4,9,3,4,9, ,1,9,0,0,0,1052,1852,3.02201573981772 83 -169,Jivaro,1920,1, ,2,4,4,3,4,4,1,1,1,0,0,0,10059,1930,4.00255480814848 84 -170,Amahuaca,1960,1,4.75,2, , , , , , , , ,0,0,0,500,1968,2.69897000433602 85 -180,Aweikoma,1932,9, ,2,4,4,3,4,1,4,1,1,0,0,0,106,1934,2.02530586526477 86 -2,Kung,1950,9,1,3,4,4,3,3,1,4,1,1,0,0,0,433,1964,2.63648789635337 87 -10,Luguru,1925,9,1,3, , , , , , , , ,0,0,0, , , 88 -11,Kikuyu,1920,3,1,3,3,3,2,3,2,3,2,0,0,0,0,795934,1929,5.90087705690327 89 -23,Tallensi,1934,4,1,3,2,1,2,2,2,3,3,0,1,0,0,29972,1921,4.47671572392606 90 -27,Masa,1910,2,8,3, , , , , , , , ,0,0,0, , , 91 -32,Mao,1939,9, ,1, , , , , , , , ,0,0,0, , , 92 -34,Masai,1900,1,4,3,3,3,3,2,2,3,2,0,1,0,0, , , 93 -39,Kenuzi Nub.,1900,2, ,2, , , , , , , , ,0,0,0,25542,1897,4.40725490056078 94 -40,Teda,1950,4, ,1,3,3,3,3,9, ,1,0,0,0,0,195000,1957,5.29003461136252 95 -52,Lapps,1950,1,1,3,3,4,2,3,3,2,2,0,0,0,0,34000,1945,4.53147891704226 96 -53,Yurak,1894,9,2.5,3, , , , , , , , ,3,0,0,9245,1897,3.96590691549519 97 -67,Lolo,1910,1,4.5,2,4,1,1,1.75,2,3,9,0,0,0,0,1800000,1943,6.25527250510331 98 -72,Lamet,1940,9, ,3,2,9,1,3,3,2,2,9,0,0,0,3806,1940,3.580468783951 99 -74,Rhade,1962,1,2.25,3, , , , , , , , ,0,0,0,115000,1962,5.06069784035361 100 -77,Semang,1925,1,1,3,3,3,3,3,1,4,9,0,0,0,0,1980,1925,3.29666519026153 101 -78,Nicobarese,1870,1,1,3, , , , , , , , ,0,0,1,6310,1901,3.80002935924413 102 -79,Andamanese,1860,1,4,3,4,4,3,3,2,3,9,1,0,0,1,5500,1859,3.74036268949424 103 -85,Iban,1950,4,1,3,3,2,3,3,1,4,1,0,0,0,0,190326,1947,5.27949812031661 104 -86,Badjau,1963,1,1,3, , , , , , , , ,0,0,0,3766,1963,3.57588031568065 105 -91,Aranda,1896,1,8,2,3,3.5,2.5,1.5,2.25,2.75,0,0,0,0,0,2000,1896,3.30102999566398 106 -92,Orokaiva,1925,2,5,3,4,3,3,4,9, ,2,0,0,0,0,9000,1925,3.95424250943932 107 -93,Kimam,1960,9,1,3, , , , , , , , ,0,0,0,7000,1960,3.84509804001426 108 -95,Kwoma,1937,3,1.75,3, , , , , , , , ,0,0,0,898,1936,2.9532763366673 109 -96,Manus,1929,4,1,3,4,3,3,4,9, ,1,0,0,0,0,30000,1928,4.47712125471966 110 -97,New Ireland,1930,4,1,3, , , , , , , , ,0,0,0,7096,1928,3.85101360682367 111 -99,Siuai,1939,3,2.5,3, , , , , , , , ,0,0,0,35000,1938,4.54406804435028 112 -101,Pentecost,1953,4,1,2, , , , , , , , ,0,0,0,400,1953,2.60205999132796 113 -109,Trukese,1947,4,1,3, , , , , , , , ,0,0,1,9185,1946,3.96307916064183 114 -118,Ainu,1880,3,2,3,2,2,2,1,2,3,3,0,0,0,0,17198,1882,4.23547794461295 115 -119,Gilyak,1890,2,3.75,2,4,4,3,4,2,3,1,1,0,0,0,4500,1890,3.65321251377534 116 -120,Yukaghir,1850,1,1.25,3, , , , , , , , ,0,0,0,707,1850,2.8494194137969 117 -121,Chukchee,1900,9,4.5,2, , , , , , , , ,0,0,0,12000,1900,4.07918124604763 118 -122,Ingalik,1885,1,1.75,3,4,4,3,4,1,4,3,1,0,0,0,451,1880,2.65417654187796 119 -125,Montagnais,1910,9,1,3, , , , , , , , ,0,0,0,75,1921,1.8750612633917 120 -126,Micmac,1650,1,1,3,3.5,3,2,2.75,2,3,9,0,0,0,0,3000,1615,3.47712125471966 121 -127,Saulteaux,1930,4,1,3,4,4,3,4,2,3,1,1,0,0,0,20000,1930,4.30102999566398 122 -128,Slave,1940,4,1,3,4,4,3,4,1,4,9,1,0,0,0,1386,1941,3.14176323027579 123 -129,Kaska,1900,9,1,3, , , , , , , , ,0,0,0,350,1900,2.54406804435028 124 -131,Haida,1875,9, ,1, , , , , , , , ,0,0,0,800,1885,2.90308998699194 125 -132,Bellacoola,1880,1,2.25,3,3,3,3,3,2,3,1,0,0,0,0,300,1922,2.47712125471966 126 -133,Twana,1860,2,1.5,3, , , , , , , , ,0,0,0,290,1858,2.46239799789896 127 -135,E. Pomo,1850,1,2.25,3,3,1,3,2.5,1.25,3.75,3,0,0,0,0,8000,1850,3.90308998699194 128 -136,Yokuts,1850,1, ,2,2,3,1,2,3,2,9,0,1,0,0,2436,1859,3.38667728396084 129 -137,Paiute,1870,1,1,3,4,4,3,3.5,1.5,3.5,1,1,0,0,0,200,1872,2.30102999566398 130 -138,Klamath,1860,1,4,3,9,1,9,4,9, ,9,9,0,0,0,676,1881,2.82994669594164 131 -139,Kutenai,1890,1,1,3,1,1.5,1,1.5,4,1,2.25,0,0,0,0,1000,1892,3 132 -141,Hidatsa,1836,2,1,3,3,3,2.5,1.75,1.75,3.25,1.5,0,0,0,0,400,1837,2.60205999132796 133 -147,Comanche,1870,1,2,2,3,3,3,2,2,3,1,0,0,0,0,15000,1870,4.17609125905568 134 -149,Zuni,1880,9,1,3, , , , , , , , ,0,0,0,1650,1884,3.21748394421391 135 -150,Havasupai,1918,4,1,3,4,4,3,4,2,3,9,1,0,0,0,177,1919,2.24797326636181 136 -151,Papago,1910,9,1,3,3,4,3,1,2,3,3,0,0,0,0,9942,1890,3.99747375880298 137 -152,Huichol,1890,4, ,2, , , , , , , , ,0,0,0,4000,1895,3.60205999132796 138 -157,Bribri,1917,9,1,3, , , , , , , , ,0,0,0, , , 139 -161,Callinago,1650,1,8,3, , , , , , , , ,0,0,0,4500,1647,3.65321251377534 140 -162,Warrau,1935,1,1,3,9,9,1,3,4,1,9,9,1,0,0,8000,1941,3.90308998699194 141 -163,Yanomamo,1965,1,5,3,4,3.5,3,2.75,2,3,1,0,0,0,0,10000,1964,4 142 -164,Carib,1932,4,1,3,3,3,3,4,2,3,1,0,0,0,0,600,1932,2.77815125038364 143 -166,Mundurucu,1850,1, ,1,3,3,3,2,2,3,1,0,0,0,0,1250,1952,3.09691001300806 144 -167,Cubeo,1939,2,3.5,3, , , , , , , , ,0,0,0,2500,1939,3.39794000867204 145 -168,Cayapa,1908,1,1,3,2,3,1,4,4,1,2,0,0,0,0,2500,1900,3.39794000867204 146 -173,Siriono,1942,1,1,3,4,3,3,4,9, ,2.75,0,0,0,0,2000,1950,3.30102999566398 147 -174,Nambicuara,1940,2,5,3,2,3,2,1,3,2,1,0,0,0,0,2500,1948,3.39794000867204 148 -175,Trumai,1938,2, ,3, , , , , , , , ,0,0,0,43,1938,1.63346845557959 149 -176,Timbira,1915,3,1,2,4,3,2,3,2,3,9,0,0,0,0, , , 150 -178,Botocudo,1884,9,4.25,3, , , , , , , , ,0,0,0,13000,1883,4.11394335230684 151 -179,Shavante,1958,2,4,3,3,4,3,1,2,3,1,0,0,0,0,2000,1958,3.30102999566398 152 -181,Cayua,1890,4, ,1, , , , , , , , ,0,0,0,3000,1945,3.47712125471966 153 -182,Lengua,1889,1,1,2, , , , , , , , ,0,0,0, , , 154 -183,Abipon,1750,1,4,3,4,3,3,4,9, ,1,0,0,0,0, , , 155 -185,Tehuelche,1870,2,3,3,4,3.5,3,4,9, ,1,0,0,0,0,1400,1870,3.14612803567824 156 -186,Yahgan,1865,1,2.75,2,4,4,3,4,1,4,9,1,0,0,0,3000,1867,3.47712125471966 157 -1,Nama,1860,1,5,3,3,1.5,2.5,1.75,2.5,2.5,9,0,4,0,0,2500,1876,3.39794000867204 158 -14,Nkundo Mongo,1930,2,3.5,3, , , , , , , , ,0,0,0,1000000,1930,6 159 -15,Banen,1935,9, ,1, , , , , , , , ,1,0,0, , , 160 -16,Tiv,1920,1,5,3,3,3,2,1,2,3,1,0,0,0,0,350000,1922,5.54406804435028 161 -17,Ibo,1935,2,3.75,3,3.5,3,2.5,1.5,1,4,9,0,0,0,0,3000000,1934,6.47712125471966 162 -20,Mende,1945,9,1.5,3,3,9,2,1,1,4,9,9,4,0,0,750000,1945,5.8750612633917 163 -22,Bambara,1902,4,8,3,2,1,1,1,2,3,2,0,1,0,0, , , 164 -24,Songhai,1940,9,1,3, , , , , , , , ,0,0,0,3500,1940,3.54406804435028 165 -25,Past. Fulani,1951,9,2.5,2, , , , , , , , ,1,0,0, , , 166 -31,Shilluk,1910,2,3.5,3,1,9,1,3,3,2,9,9,1,0,0,60000,1910,4.77815125038364 167 -35,Konso,1935,4, ,2, , , , , , , , ,3,0,0,1100,1948,3.04139268515822 168 -36,Somali,1900,1,5,2,4,4,3,1,2,3,2,1,0,0,0,50000,1880,4.69897000433602 169 -38,Bogo,1855,9, ,1, , , , , , , , ,0,0,0, , , 170 -46,Rwala,1913,2,5,3,2,1,1,2,3,2,1,0,1,0,0,35000,1913,4.54406804435028 171 -55,Abkhaz,1880,2,5,3, , , , , , , , ,0,0,0,72103,1897,4.85795333484898 172 -60,Gond,1930,4,1,3,3,2,2,2,2,3,3,0,4,0,0,3063753,1931,6.48625374951768 173 -61,Toda,1900,1,1,3, , , , , , , , ,4,0,0,805,1901,2.90579588036787 174 -64,Burusho,1934,4,1,3,1,1,1,3,4,1,3,0,1,0,0,20813,1931,4.31833468422614 175 -68,Lepcha,1937,1,1,3,3,2,2,3,3,2,3,0,1,0,0,25780,1931,4.41128291301738 176 -70,Lakher,1930,3,1.75,3,2,9,1,1,4,1,3,9,0,0,0,10000,1921,4 177 -81,Tanala,1925,3,3,3, , , , , , , , ,1,0,0, , , 178 -87,Toradja,1910,1,4,3,3,3,3,1,2,3,2,0,0,0,0,30000,1930,4.47712125471966 179 -94,Kapauku,1955,1,4.5,3,2,3,1,3,4,1,9,0,1,0,0,52500,1954,4.72015930340596 180 -98,Trobrianders,1914,4,1,3,2,3,2,2,2,3,9,0,0,0,1,8780,1921,3.9434945159061 181 -100,Tikopia,1930,4,1,3,3,1,1,1,3,2,3,0,1,0,1,1278,1929,3.10653085382238 182 -103,Ajie,1845,9, ,1, , , , , , , , ,4,0,0, , , 183 -104,Maori,1820,1,4.5,3,2,3,2,2,2,3,9,0,0,0,1,37520,1871,4.57426282970703 184 -105,Marquesans,1800,1,5,2, , , , , , , , ,0,0,1,50000,1804,4.69897000433602 185 -107,Gilbertese,1890,1,8,2, , , , , , , , ,1,0,1,1945,1905,3.28891960566173 186 -108,Marshallese,1900,4,1,2,1,1,1,2,4,1,3,0,1,0,1,11300,1886,4.05307844348342 187 -110,Yapese,1910,9,1.25,2,3,1,2,2,2,3,9,0,1,0,1,5790,1915,3.76267856372744 188 -113,Atayal,1930,2,5,3, , , , , , , , ,0,0,0,32925,1930,4.51752578363382 189 -115,Manchu,1915,2,5,3,1,0,1,1.5,3.5,1.5,9,0,0,0,0,19639671,1916,7.29313420829404 190 -123,Aleut,1778,1,5,3,3,2,2,2,2.5,2.5,2.5,0,1,0,1,13500,1750,4.13033376849501 191 -130,Eyak,1890,1,1,3,3,3,2,9,3,2,3,0,1,0,0,154,1890,2.18752072083646 192 -140,Gros Ventre,1880,1,1,3,3,3,2,2,3,2,9,0,0,0,0,1400,1875,3.14612803567824 193 -142,Pawnee,1867,1,1,3,3,9,3,3,3,2,3,9,0,0,0,3414,1861,3.53326351677871 194 -143,Omaha,1860,1,3.5,2,1,1,1,1.25,3.75,1.25,3,0,0,0,0,1076,1876,3.03181227133037 195 -148,Chiricahua,1870,1,1,2,3,3,3,3,1,4,2,0,1,0,0, , , 196 -154,Popoluca,1940,9, ,1, , , , , , , , ,5,0,0,6779,1930,3.83116563390944 197 -155,Quiche,1930,9,1,3, , , , , , , , ,5,0,0,24408,1952,4.38753219463435 198 -156,Miskito,1921,9, ,2,9,9,9,9,2,3,9,9,0,0,0,15000,1932,4.17609125905568 199 -158,Cuna,1927,1,1.25,3,3,2,2,2,1,4,2,0,3,0,0,22155,1927,4.34547175436763 200 -159,Goajiro,1947,1,3.25,3,9,3,3,3,4,1,1,9,1,0,0,18000,1948,4.25527250510331 201 -172,Aymara,1940,6,1.5,3,2,9,1,1,3,2,9,9,0,0,0,600000,1935,5.77815125038364 202 -177,Tupinamba,1550,1,5,3,9,3.5,3,4,9, ,9,9,0,0,0,27000,1600,4.43136376415899 203 -184,Mapuche,1950,4,1,3,3,2,3,3,2,3,1,0,5,0,0,200000,1950,5.30102999566398 204 -3,Thonga,1895,9, ,2, , , , , , , , ,1,1,0, , , 205 -5,Mbundu,1890,1,2,3,2.5,0,1.75,2,2.5,2.5,2,0,1,1,0, , , 206 -8,Nyakyusa,1934,4,1,3,3,2,2,2,3,2,1,0,1,1,0,84000,1931,4.92427928606188 207 -21,Wolof,1950,4,1,3, , , , , , , , ,1,1,0,30000,1949,4.47712125471966 208 -28,Azande,1905,2,5,3,2,3,3,1,4,1,2,0,1,1,0,75000,1907,4.8750612633917 209 -41,Tuareg,1900,2,5,2, , , , , , , , ,1,1,0, , , 210 -42,Riffians,1926,2,5,3,3,3,2,1,3,2,1,0, ,1,0, , , 211 -44,Hebrews,-621,2,2.5,2, , , , , , , , ,1,1,0,12500,-621,4.09691001300806 212 -48,Gheg Alban.,1910,1,4.5,3,3,1,2,1,2,3,2,0,4,1,0,814485,1927,5.91088309051412 213 -50,Basques,1940,6,1,3, , , , , , , , , ,1,0, , , 214 -51,Irish,1955,1,1,2,3,4,3,4,4,1,1,0,4,1,0, , , 215 -57,Kurd,1951,3,2.5,3,1,1,2,2,2,3,1,0,4,1,0,3000000,1950,6.47712125471966 216 -58,Basseri,1958,9,8,3,3,2,3,3,2,3,1,0,1,1,0, , , 217 -62,Santal,1940,4,1,3,3,9,2,1,3,2,2,9,4,1,0,2902266,1941,6.4627372141107 218 -65,Kazak,1885,2,4,3,2,3,2,2,3,2,2,0,0,1,0,4000000,1897,6.60205999132796 219 -69,Garo,1955,4,1,3, , , , , , , , ,5,1,0,300000,1955,5.47712125471966 220 -82,NegriSembil.,1958,2,3.25,2,3,9,2,3,3,2,1,9,5,1,0,72154,1947,4.85826041207227 221 -84,Balinese,1958,2,5,3,3,2,2,1,2,3,9,0,0,1,0,1500000,1954,6.17609125905568 222 -88,Tobelorese,1900,9, ,1, , , , , , , , ,0,1,0, , , 223 -89,Alorese,1938,4,1,3, , , , , , , , ,1,1,0,70000,1938,4.84509804001426 224 -102,Mbau Fijians,1840,1,5,3,2,9,1,1,3,2,1,9,1,1,0,150000,1840,5.17609125905568 225 -106,W. Samoans,1829,1,4.5,3,2,2,2,1,2,3,2,0,4,1,1,44502,1922,4.64837952939544 226 -111,Palauans,1873,1,5,3,3,2,1.5,1.25,3,2,9,0,4,1,1,6578,1951,3.81809386914664 227 -144,Huron,1634,2,1.75,3,3,2,3,1,2,3,2,0,4,1,0,21000,1633,4.32221929473392 228 -145,Creek,1800,2,2,2,3,2,1.75,1.5,2.75,2.25,2,0,0,1,0, , , 229 -146,Natchez,1718,1,1.25,3, , , , , , , , ,1,1,0,3500,1700,3.54406804435028 230 -153,Aztec,1520,1,4.5,3,2,2,1,1,4,1,9,0,1,1,0,300000,1520,5.47712125471966 231 -165,Saramacca,1928,3,2,3, , , , , , , , ,1,1,0,14000,1930,4.14612803567824 232 -4,Lozi,1900,1,3.5,2,2,2,1,3,3,2,1,0,4,1,0, , , 233 -6,Suku,1920,1,2.75,2,3,3,2,3,3,2,2,0,1,1,0, , , 234 -7,Bemba,1897,1,5,1, , , , , , , , ,1,1,0, , , 235 -12,Ganda,1875,1,4.5,2,1,2,1,1,4,1,9,0,1,1,0,750000,1878,5.8750612633917 236 -18,Fon,1890,1,1.5,3,3,2,1,1,4,1,9,0,1,1,0, , , 237 -19,Ashanti,1895,1,1,3, , , , , , , , ,4,1,0,406193,1921,5.60873243485518 238 -26,Hausa,1900,2,4.5,3,2,2,1,1,4,1,2,0,1,1,0, , , 239 -29,Fur,1880,1,8,2, , , , , , , , ,1,1,0, , , 240 -33,Kaffa,1905,2,8,3, , , , , , , , ,1,1,0, , , 241 -37,Amhara,1953,2,8,3,2,2,1,1,4,1,9,0,1,1,0, , , 242 -43,Egyptians,1950,2,2,3,2,2,1,2,3,2,3,0,4,1,0,15932694,1937,7.2022892152496 243 -45,Babylonians,-1750,2,8,2, , , , , , , , ,1,1,0, , , 244 -47,Turks,1950,1,1,3,4,2,2,3,1.25,3.75,9,0,5,1,0,20934670,1950,7.32086611935629 245 -49,Romans,110,2,1,2, , , , , , , , ,1,1,0,1200000,117,6.07918124604763 246 -54,Russians,1955,1,5,3, , , , , , , , ,3,1,0,111000000,1950,8.04532297878666 247 -56,Armenians,1843,9,8,3, , , , , , , , ,1,1,0,300000,1854,5.47712125471966 248 -59,Punjabi,1950,2,3.25,3,1.5,1,1,2.5,3.25,1.75,2.25,0,4.5,1,0,76450000,1951,7.88337748974834 249 -63,Uttar Prad.,1945,6,1.5,3, , , , , , , , ,4,1,0,1400,1941,3.14612803567824 250 -66,Khalka Mong.,1920,2,3,2, , , , , , , , ,4,1,0,492000,1918,5.69196510276736 251 -71,Burmese,1960,2,5,2, , , , , , , , ,4.5,1,0, , , 252 -73,Vietnamese,1930,2,5,3,2,9,1,1,3,2,9,9,4,1,0,8096000,1931,6.90827049949572 253 -75,Khmer,1292,1,2,2, , , , , , , , ,1,1,0,5000000, ,6.69897000433602 254 -76,Siamese,1955,1, ,1, , , , , , , , , ,1,0,17442689,1947,7.24161343746814 255 -83,Javanese,1955,2,5,2, , , , , , , , ,0,1,0,248625,1953,5.39554479613249 256 -114,Chinese,1936,2,4.5,3,2.75,3,2,1.5,2,3,1.5,0,1,1,0,430000000,1933,8.63346845557959 257 -116,Koreans,1947,1,5,3,2,1,1,2,3,2,3,0,1,1,0,24326327,1940,7.38607654033025 258 -117,Japanese,1950,1,1,3,3,2,2,1.75,1.5,3.5,2,0,5,1,0,83199637,1950,7.92012143146812 259 -160,Haitians,1935,9, ,2, , , , , , , , ,1,1,0, , , 260 -171,Inca,1530,1,5,2, , , , , , , , ,1,1,0,1500000,1571,6.17609125905568 261 -
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... ... @@ -1,65 +1,0 @@ 1 -* Encoding: UTF-8. 2 -FILTER OFF. 3 -USE ALL. 4 -EXECUTE. 5 - 6 -DESCRIPTIVES VARIABLES=PE_15 PE_16 PE_17 PE_18 PE_19 TL_5 TL_6 TL_15 TL_19 TL_20 TL_21 7 - /SAVE 8 - /STATISTICS=MEAN STDDEV MIN MAX. 9 - 10 -COMPUTE IN_1=MEAN(ZPE_15, ZPE_16,ZPE_17,ZPE_18,ZPE_19). 11 -VARIABLE LABELS IN_1 'Corporate-Exclusionary Index'. 12 -EXECUTE. 13 -RELIABILITY 14 - /VARIABLES=ZPE_15 ZPE_16 ZPE_17 ZPE_18 ZPE_19 15 - /SCALE('Corporate-Exclusionary Index') ALL 16 - /MODEL=ALPHA 17 - /SUMMARY=TOTAL. 18 -EXECUTE. 19 - 20 -COMPUTE IN_2b=MEAN(ZTL_5,ZTL_6,ZTL_15,ZTL_19,ZTL_20,ZTL_21). 21 -VARIABLE LABELS IN_2b 'Looseness-Tightness Index'. 22 -EXECUTE. 23 -RELIABILITY 24 - /VARIABLES=ZTL_5 ZTL_6 ZTL_15 ZTL_19 ZTL_20 ZTL_21 25 - /SCALE('Looseness-Tightness Index') ALL 26 - /MODEL=ALPHA 27 - /SUMMARY=TOTAL. 28 -EXECUTE. 29 - 30 -FACTOR 31 - /VARIABLES PE_15 PE_16 PE_17 PE_18 PE_19 TL_5 TL_6 TL_15 TL_19 TL_20 TL_21 32 - /MISSING LISTWISE 33 - /ANALYSIS PE_15 PE_16 PE_17 PE_18 PE_19 TL_5 TL_6 TL_15 TL_19 TL_20 TL_21 34 - /PRINT UNIVARIATE INITIAL EXTRACTION ROTATION 35 - /CRITERIA MINEIGEN(1) ITERATE(25) 36 - /EXTRACTION PC 37 - /CRITERIA ITERATE(25) 38 - /ROTATION VARIMAX 39 - /METHOD=CORRELATION. 40 -EXECUTE. 41 - 42 -FACTOR 43 - /VARIABLES PE_15 PE_16 PE_17 PE_18 PE_19 44 - /MISSING LISTWISE 45 - /ANALYSIS PE_15 PE_16 PE_17 PE_18 PE_19 46 - /PRINT UNIVARIATE INITIAL EXTRACTION ROTATION 47 - /CRITERIA MINEIGEN(1) ITERATE(25) 48 - /EXTRACTION PC 49 - /CRITERIA ITERATE(25) 50 - /ROTATION VARIMAX 51 - /METHOD=CORRELATION. 52 -EXECUTE. 53 - 54 -FACTOR 55 - /VARIABLES TL_5 TL_6 TL_15 TL_19 TL_20 TL_21 56 - /MISSING LISTWISE 57 - /ANALYSIS TL_5 TL_6 TL_15 TL_19 TL_20 TL_21 58 - /PRINT UNIVARIATE INITIAL EXTRACTION ROTATION 59 - /CRITERIA MINEIGEN(1) ITERATE(25) 60 - /EXTRACTION PC 61 - /CRITERIA ITERATE(25) 62 - /ROTATION VARIMAX 63 - /METHOD=CORRELATION. 64 -EXECUTE. 65 -
- OurBetterNature_ReadmeForDataFile.pdf
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... ... @@ -1,731 +1,0 @@ 1 -############################################################# 2 -####Religion and Resource Stress -- Analysis Script 01-2020#### 3 -############################################################# 4 - 5 -library(Hmisc) 6 -library(lme4) 7 -library(lmerTest) 8 -library(ggplot2) 9 -library(ggthemes) 10 -library(phia) #also loads car package 11 -library(plyr) 12 -library(lm.beta) 13 -lm.beta.lmer <- function(mod) { b <- fixef(mod)[-1]; sd.x <- apply(matrix(getME(mod,"X")[,-1]),2,sd); sd.y <- sd(getME(mod,"y")); b*sd.x/sd.y; } 14 - 15 -#G = High gods 16 -#SG = Superior gods 17 -#MS = minor spirits 18 -#Gor_SG = broader supernatural agents 19 - 20 -#re-naming data files to shorter names (arbitrary-just for easier typing in R) 21 -mor<-read.csv("DT-MoralHighGods_Long_EP.csv") 22 -ang<-read.csv("DT-GodsAndResourceStress_Long.csv") 23 - 24 - 25 -##################################### 26 -##Analyses on Newly Coded Variables## 27 -##################################### 28 - 29 -##How many overlapping language families are there? 30 -table(ang$language_family) 31 - 32 -##Dummy-coding language families 33 -ang$Afroasiatic<-ifelse(as.character(ang$language_family)=="Afro-Asiatic",1,0) 34 -ang$Afroasiatic<-ifelse(is.na(ang$Afroasiatic)==T,0,ang$Afroasiatic) 35 - 36 -ang$Austronesian<-ifelse(as.character(ang$language_family)=="Austronesian",1,0) 37 -ang$Austronesian<-ifelse(is.na(ang$Austronesian)==T,0,ang$Austronesian) 38 - 39 -ang$AtlanticCongo<-ifelse(as.character(ang$language_family)=="Atlantic-Congo",1,0) 40 -ang$AtlanticCongo<-ifelse(is.na(ang$AtlanticCongo)==T,0,ang$AtlanticCongo) 41 - 42 -ang$Indoeuropean<-ifelse(as.character(ang$language_family)=="Indo-European",1,0) 43 -ang$Indoeuropean<-ifelse(is.na(ang$Indoeuropean)==T,0,ang$Indoeuropean) 44 - 45 -ang$Austroasiatic<-ifelse(as.character(ang$language_family)=="Austroasiatic",1,0) 46 -ang$Austroasiatic<-ifelse(is.na(ang$Austroasiatic)==T,0,ang$Austroasiatic) 47 - 48 -ang$NuclearTransNewGuinea<-ifelse(as.character(ang$language_family)=="Nuclear Trans New Guinea",1,0) 49 -ang$NuclearTransNewGuinea<-ifelse(is.na(ang$NuclearTransNewGuinea)==T,0,ang$NuclearTransNewGuinea) 50 - 51 -ang$SinoTibetan<-ifelse(as.character(ang$language_family)=="Sino-Tibetan",1,0) 52 -ang$SinoTibetan<-ifelse(is.na(ang$SinoTibetan)==T,0,ang$SinoTibetan) 53 - 54 -ang$Turkic<-ifelse(as.character(ang$language_family)=="Turkic",1,0) 55 -ang$Turkic<-ifelse(is.na(ang$Turkic)==T,0,ang$Turkic) 56 - 57 -ang$Uralic<-ifelse(as.character(ang$language_family)=="Uralic",1,0) 58 -ang$Uralic<-ifelse(is.na(ang$Uralic)==T,0,ang$Uralic) 59 - 60 - 61 -##Dummy variables for separate stressors 62 -#Famine (re-coded during restructuring as 1 in variable "ResourceStress") 63 -ang$famine<-ifelse(as.character(ang$ResourceStress)=="1",1,0) 64 -#Natural Hazards (re-coded during restructuring as 2 in variable "ResourceStress") 65 -ang$naturalhazards<-ifelse(as.character(ang$ResourceStress)=="2",1,0) 66 -#Chronic Scarcity (re-coded during restructuring as 3 in variable "ResourceStress") 67 -ang$chron_scarcity<-ifelse(as.character(ang$ResourceStress)=="3",1,0) 68 -#Abundance (re-coded during restructuring as 4 in variable "ResourceStress") 69 -ang$abundance<-ifelse(as.character(ang$ResourceStress)=="4",1,0) 70 - 71 -#Recoding resource stress 72 -ang$stress2<-ifelse(ang$abundance==1,(0-ang$Stress), 73 - ifelse(ang$abundance==0,ang$Stress, 74 - NA)) 75 - 76 -#Dichotomized stress variable (primary analyses do not use this variable) 77 -ang$stress3<-ifelse(ang$chron_scarcity==1&ang$stress2<(-.11),(-1), 78 - ifelse(ang$chron_scarcity==1&ang$stress2>(-.11),1, 79 - ifelse(ang$famine==1&ang$stress2<(-.11),(-1), 80 - ifelse(ang$famine==1&ang$stress2>(-.11),1, 81 - ang$stress2)))) 82 - 83 - 84 -ang_fam<-ang[ang$famine==1,] 85 -ang_naturalhazards<-ang[ang$naturalhazards==1,] 86 -ang_abundance<-ang[ang$abundance==1,] 87 -ang_chron_scarcity<-ang[ang$chron_scarcity==1,] 88 -ang_hurthelp<-ang[is.na(ang$stress2)==F&is.na(ang$G3_Help)==F&is.na(ang$G2_Harm)==F,] 89 - 90 -##Creating datasets for each of the god variables (this will allow us to calculate R2 later) 91 -ang2<-ang[is.na(ang$G1_Associated)==F,] 92 -ang3<-ang[is.na(ang$G2_Harm)==F,] 93 -ang4<-ang[is.na(ang$G3_Help)==F,] 94 -ang5<-ang[is.na(ang$G4_Punitive)==F,] 95 -ang6<-ang[is.na(ang$SG1_Associated)==F,] 96 -ang7<-ang[is.na(ang$SG2_Harm)==F,] 97 -ang8<-ang[is.na(ang$SG3_Help)==F,] 98 -ang9<-ang[is.na(ang$SG4_Punitive)==F,] 99 -ang10<-ang[is.na(ang$MS1_Associated)==F,] 100 -ang11<-ang[is.na(ang$MS2_Harm)==F,] 101 -ang12<-ang[is.na(ang$MS3_Help)==F,] 102 -ang13<-ang[is.na(ang$MS4_Punitive)==F,] 103 - 104 -#Empty model controlling for language family (anova) (this will allow us to calculate R2 later) 105 -summary(lmer(stress2~(1|SCCS_ID),data=ang)) 106 -summary(lmer(stress2~Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+(1|SCCS_ID),data=ang)) 107 -summary(lmer(stress2~Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+(1|SCCS_ID),data=ang2)) 108 -summary(lmer(stress2~Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+(1|SCCS_ID),data=ang3)) 109 -summary(lmer(stress2~Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+(1|SCCS_ID),data=ang4)) 110 -summary(lmer(stress2~Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+(1|SCCS_ID),data=ang5)) 111 -summary(lmer(stress2~Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+(1|SCCS_ID),data=ang_hurthelp)) 112 -summary(lmer(stress2~Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+NuclearTransNewGuinea+SinoTibetan+Uralic+(1|SCCS_ID),data=ang6)) 113 -summary(lmer(stress2~Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+(1|SCCS_ID),data=ang7)) 114 -summary(lmer(stress2~Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+SinoTibetan+(1|SCCS_ID),data=ang8)) 115 -summary(lmer(stress2~Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+NuclearTransNewGuinea+(1|SCCS_ID),data=ang9)) 116 -summary(lmer(stress2~Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+Turkic+NuclearTransNewGuinea+Austroasiatic+SinoTibetan+(1|SCCS_ID),data=ang10)) 117 -summary(lmer(stress2~Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+NuclearTransNewGuinea+(1|SCCS_ID),data=ang11)) 118 -summary(lmer(stress2~Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+NuclearTransNewGuinea+(1|SCCS_ID),data=ang12)) 119 -summary(lmer(stress2~Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+NuclearTransNewGuinea+(1|SCCS_ID),data=ang13)) 120 - 121 -################################################################# 122 -###PART A: Central Analyses on Stressors and Religious Beliefs### 123 -################################################################# 124 - 125 -##For the analyses below, you can get the R squared by comparing... 126 -#...the variance explained at the society level with the variance explained by language family alone 127 - 128 -#Resource Stress and Gods Associated with Weather# Table 2, Model 1 129 -summary(model1<-lmer(stress2~G1_Associated+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+(1|SCCS_ID),data=ang)) 130 -lm.beta.lmer(model1) 131 - 132 -##Secondary analyses looking at specific types of resource stress# Table S4, Model S1 133 -#looking at chronic scarcity 134 -summary(model1b<-lmer(stress2~G1_Associated*famine+G1_Associated*naturalhazards+G1_Associated*abundance+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+(1|SCCS_ID),data=ang)) 135 -#looking at natural hazards 136 -summary(model1c<-lmer(stress2~G1_Associated*famine+G1_Associated*chron_scarcity+G1_Associated*abundance+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+(1|SCCS_ID),data=ang)) 137 -#Looking at abundance 138 -summary(model1d<-lmer(stress2~G1_Associated*famine+G1_Associated*naturalhazards+G1_Associated*chron_scarcity+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+(1|SCCS_ID),data=ang)) 139 -#Looking at famine 140 -summary(model1e<-lmer(stress2~G1_Associated*chron_scarcity+G1_Associated*naturalhazards+G1_Associated*abundance+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+(1|SCCS_ID),data=ang)) 141 - 142 -#Resource Stress and Gods that Harm with Weather# 143 -summary(model2<-lmer(stress2~G2_Harm+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+(1|SCCS_ID),data=ang)) 144 -lm.beta.lmer(model2) 145 - 146 -##Secondary analyses looking at specific types of resource stress # Table S4, Model S2 147 -#Looking at Chronic scarcity, then natural hazards, then abundance, then famine 148 -summary(model2b<-lmer(stress2~G2_Harm*famine+G2_Harm*naturalhazards+G2_Harm*abundance+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+(1|SCCS_ID),data=ang)) 149 -summary(model2c<-lmer(stress2~G2_Harm*famine+G2_Harm*chron_scarcity+G2_Harm*abundance+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+(1|SCCS_ID),data=ang)) 150 -summary(model2d<-lmer(stress2~G2_Harm*famine+G2_Harm*naturalhazards+G2_Harm*chron_scarcity+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+(1|SCCS_ID),data=ang)) 151 -summary(model2e<-lmer(stress2~G2_Harm*chron_scarcity+G2_Harm*naturalhazards+G2_Harm*abundance+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+(1|SCCS_ID),data=ang)) 152 - 153 -#Resource Stress and Gods that Help with Weather# Table 2, Model 3 154 -summary(model3<-lmer(stress2~G3_Help+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+(1|SCCS_ID),data=ang)) 155 -lm.beta.lmer(model3) 156 - 157 -##Secondary analyses looking at specific types of resource stress# Table S4, Model S3 158 -#Looking at Chronic scarcity, then natural hazards, then abundance, then famine 159 -summary(model3b<-lmer(stress2~G3_Help*famine+G3_Help*naturalhazards+G3_Help*abundance+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+(1|SCCS_ID),data=ang)) 160 -summary(model3c<-lmer(stress2~G3_Help*famine+G3_Help*chron_scarcity+G3_Help*abundance+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+(1|SCCS_ID),data=ang)) 161 -summary(model3d<-lmer(stress2~G3_Help*famine+G3_Help*naturalhazards+G3_Help*chron_scarcity+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+(1|SCCS_ID),data=ang)) 162 -summary(model3e<-lmer(stress2~G3_Help*chron_scarcity+G3_Help*naturalhazards+G3_Help*abundance+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+(1|SCCS_ID),data=ang)) 163 - 164 -#Resource Stress and Gods that Punish with Weather# Table 2, Model 4 165 -summary(model4<-lmer(stress2~G4_Punitive+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+(1|SCCS_ID),data=ang)) 166 -lm.beta.lmer(model4) 167 - 168 -##Secondary analyses looking at specific types of resource stress# Table S4, Model S4 169 -#Looking at Chronic scarcity, then natural hazards, then abundance, then famine 170 -summary(model4b<-lmer(stress2~G4_Punitive*famine+G4_Punitive*naturalhazards+G4_Punitive*abundance+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+(1|SCCS_ID),data=ang)) 171 -summary(model4c<-lmer(stress2~G4_Punitive*famine+G4_Punitive*chron_scarcity+G4_Punitive*abundance+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+(1|SCCS_ID),data=ang)) 172 -summary(model4d<-lmer(stress2~G4_Punitive*famine+G4_Punitive*naturalhazards+G4_Punitive*chron_scarcity+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+(1|SCCS_ID),data=ang)) 173 -summary(model4e<-lmer(stress2~G4_Punitive*chron_scarcity+G4_Punitive*naturalhazards+G4_Punitive*abundance+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+(1|SCCS_ID),data=ang)) 174 - 175 -#Modeling Helping and Harming Together# Table 2, Model 5 176 -summary(model5<-lmer(stress2~G3_Help+G2_Harm+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+(1|SCCS_ID),data=ang_hurthelp)) 177 -vif(model5) ##Look at Multicollinearity, since the predictors are highly correlated 178 - 179 -#Resource Stress and Superior Gods Associated With Weather# Table 3, Model 6 180 -summary(model6<-lmer(stress2~SG1_Associated+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+NuclearTransNewGuinea+SinoTibetan+Uralic+(1|SCCS_ID),data=ang)) 181 - 182 -##Secondary analyses looking at specific types of resource stress# Table S5, Model S5 183 -#Looking at Chronic scarcity, then natural hazards, then abundance, then famine 184 -summary(model6b<-lmer(stress2~SG1_Associated*famine+SG1_Associated*naturalhazards+SG1_Associated*abundance+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+Indoeuropean+NuclearTransNewGuinea+SinoTibetan+Uralic+(1|SCCS_ID),data=ang)) 185 -summary(model6c<-lmer(stress2~SG1_Associated*famine+SG1_Associated*chron_scarcity+SG1_Associated*abundance+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+Indoeuropean+NuclearTransNewGuinea+SinoTibetan+Uralic+(1|SCCS_ID),data=ang)) 186 -summary(model6d<-lmer(stress2~SG1_Associated*famine+SG1_Associated*naturalhazards+SG1_Associated*chron_scarcity+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+Indoeuropean+NuclearTransNewGuinea+SinoTibetan+Uralic+(1|SCCS_ID),data=ang)) 187 -summary(model6e<-lmer(stress2~SG1_Associated*chron_scarcity+SG1_Associated*naturalhazards+SG1_Associated*abundance+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+Indoeuropean+NuclearTransNewGuinea+SinoTibetan+Uralic+(1|SCCS_ID),data=ang)) 188 - 189 -#Resource Stress and Superior Gods that Harm With Weather# Table 3, Model 7 190 -summary(model7<-lmer(stress2~SG2_Harm+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+(1|SCCS_ID),data=ang)) 191 - 192 -##Secondary analyses looking at specific types of resource stress# Table S5, Model S6 193 -#Looking at Chronic scarcity, then natural hazards, then abundance, then famine 194 -summary(model7b<-lmer(stress2~SG2_Harm*famine+SG2_Harm*naturalhazards+SG2_Harm*abundance+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+(1|SCCS_ID),data=ang)) 195 -summary(model7c<-lmer(stress2~SG2_Harm*famine+SG2_Harm*chron_scarcity+SG2_Harm*abundance+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+(1|SCCS_ID),data=ang)) 196 -summary(model7d<-lmer(stress2~SG2_Harm*famine+SG2_Harm*naturalhazards+SG2_Harm*chron_scarcity+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+(1|SCCS_ID),data=ang)) 197 -summary(model7e<-lmer(stress2~SG2_Harm*chron_scarcity+SG2_Harm*naturalhazards+SG2_Harm*abundance+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+(1|SCCS_ID),data=ang)) 198 - 199 -#Resource Stress and Superior Gods that Help With Weather# Table 3, Model 8 200 -summary(model8<-lmer(stress2~SG3_Help+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+SinoTibetan+(1|SCCS_ID),data=ang)) 201 - 202 -##Secondary analyses looking at specific types of resource stress# Table S5, Model S7 203 -#Looking at Chronic scarcity, then natural hazards, then abundance, then famine 204 -summary(model8b<-lmer(stress2~SG3_Help*famine+SG3_Help*naturalhazards+SG3_Help*abundance+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+SinoTibetan+(1|SCCS_ID),data=ang)) 205 -summary(model8c<-lmer(stress2~SG3_Help*famine+SG3_Help*chron_scarcity+SG3_Help*abundance+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+SinoTibetan+(1|SCCS_ID),data=ang)) 206 -summary(model8d<-lmer(stress2~SG3_Help*famine+SG3_Help*naturalhazards+SG3_Help*chron_scarcity+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+SinoTibetan+(1|SCCS_ID),data=ang)) 207 -summary(model8e<-lmer(stress2~SG3_Help*chron_scarcity+SG3_Help*naturalhazards+SG3_Help*abundance+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+SinoTibetan+(1|SCCS_ID),data=ang)) 208 - 209 -#Resource Stress and Superior Gods that Punish With Weather# Table 3, Model 9 210 -summary(model9<-lmer(stress2~SG4_Punitive+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+NuclearTransNewGuinea+(1|SCCS_ID),data=ang)) 211 - 212 -##Secondary analyses looking at specific types of resource stress# Table S5, Model S8 213 -#Looking at Chronic scarcity, then natural hazards, then abundance, then famine 214 -summary(model9b<-lmer(stress2~SG4_Punitive*famine+SG4_Punitive*naturalhazards+SG4_Punitive*abundance+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+NuclearTransNewGuinea+(1|SCCS_ID),data=ang)) 215 -summary(model9c<-lmer(stress2~SG4_Punitive*famine+SG4_Punitive*chron_scarcity+SG4_Punitive*abundance+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+NuclearTransNewGuinea+(1|SCCS_ID),data=ang)) 216 -summary(model9d<-lmer(stress2~SG4_Punitive*famine+SG4_Punitive*naturalhazards+SG4_Punitive*chron_scarcity+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+NuclearTransNewGuinea+(1|SCCS_ID),data=ang)) 217 -summary(model9e<-lmer(stress2~SG4_Punitive*chron_scarcity+SG4_Punitive*naturalhazards+SG4_Punitive*abundance+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+NuclearTransNewGuinea+(1|SCCS_ID),data=ang)) 218 - 219 -#Resource Stress and Minor Spirits Associated With Weather# Table 4, Model 10 220 -summary(model10<-lmer(stress2~MS1_Associated+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+Turkic+NuclearTransNewGuinea+Austroasiatic+SinoTibetan+(1|SCCS_ID),data=ang)) 221 - 222 -##Secondary analyses looking at specific types of resource stress# Table S6, Model S9 223 -#Looking at Chronic scarcity, then natural hazards, then abundance, then famine 224 -summary(model0b<-lmer(stress2~MS1_Associated*famine+MS1_Associated*naturalhazards+MS1_Associated*abundance+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+Turkic+NuclearTransNewGuinea+Austroasiatic+SinoTibetan+(1|SCCS_ID),data=ang)) 225 -summary(model0c<-lmer(stress2~MS1_Associated*famine+MS1_Associated*chron_scarcity+MS1_Associated*abundance+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+Turkic+NuclearTransNewGuinea+Austroasiatic+SinoTibetan+(1|SCCS_ID),data=ang)) 226 -summary(model0d<-lmer(stress2~MS1_Associated*famine+MS1_Associated*naturalhazards+MS1_Associated*chron_scarcity+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+Turkic+NuclearTransNewGuinea+Austroasiatic+SinoTibetan+(1|SCCS_ID),data=ang)) 227 -summary(model0e<-lmer(stress2~MS1_Associated*chron_scarcity+MS1_Associated*naturalhazards+MS1_Associated*abundance+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+Turkic+NuclearTransNewGuinea+Austroasiatic+SinoTibetan+(1|SCCS_ID),data=ang)) 228 - 229 -#Resource Stress and Minor Spirits That Harm With Weather# Table 4, Model 11 230 -summary(model11<-lmer(stress2~MS2_Harm+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+NuclearTransNewGuinea+(1|SCCS_ID),data=ang)) 231 - 232 -##Secondary analyses looking at specific types of resource stress# Table S6, Model S10 233 -#Looking at Chronic scarcity, then natural hazards, then abundance, then famine 234 -summary(model11b<-lmer(stress2~MS2_Harm*famine+MS2_Harm*naturalhazards+MS2_Harm*abundance+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+NuclearTransNewGuinea+(1|SCCS_ID),data=ang)) 235 -summary(model11c<-lmer(stress2~MS2_Harm*famine+MS2_Harm*chron_scarcity+MS2_Harm*abundance+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+NuclearTransNewGuinea+(1|SCCS_ID),data=ang)) 236 -summary(model11d<-lmer(stress2~MS2_Harm*famine+MS2_Harm*naturalhazards+MS2_Harm*chron_scarcity+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+NuclearTransNewGuinea+(1|SCCS_ID),data=ang)) 237 -summary(model11e<-lmer(stress2~MS2_Harm*chron_scarcity+MS2_Harm*naturalhazards+MS2_Harm*abundance+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+NuclearTransNewGuinea+(1|SCCS_ID),data=ang)) 238 - 239 - 240 -#Resource Stress and Minor Spirits That Help With Weather# Table 4, Model 12 241 -summary(model12<-lmer(stress2~MS3_Help+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+NuclearTransNewGuinea+(1|SCCS_ID),data=ang)) 242 - 243 -##Secondary analyses looking at specific types of resource stress# Table S6, Model S11 244 -#Looking at Chronic scarcity, then natural hazards, then abundance, then famine 245 -summary(model12b<-lmer(stress2~MS3_Help*famine+MS3_Help*naturalhazards+MS3_Help*abundance+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+NuclearTransNewGuinea+(1|SCCS_ID),data=ang)) 246 -summary(model12c<-lmer(stress2~MS3_Help*famine+MS3_Help*chron_scarcity+MS3_Help*abundance+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+NuclearTransNewGuinea+(1|SCCS_ID),data=ang)) 247 -summary(model12d<-lmer(stress2~MS3_Help*famine+MS3_Help*naturalhazards+MS3_Help*chron_scarcity+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+NuclearTransNewGuinea+(1|SCCS_ID),data=ang)) 248 -summary(model12e<-lmer(stress2~MS3_Help*chron_scarcity+MS3_Help*naturalhazards+MS3_Help*abundance+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+NuclearTransNewGuinea+(1|SCCS_ID),data=ang)) 249 - 250 -#Resource Stress and Minor Spirits That Punish With Weather# Table 4, Model 13 251 -summary(model13<-lmer(stress2~MS4_Punitive+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+NuclearTransNewGuinea+(1|SCCS_ID),data=ang)) 252 - 253 -##Secondary analyses looking at specific types of resource stress# Table S6, Model S12 254 -#Looking at Chronic scarcity, then natural hazards, then abundance, then famine 255 -summary(model13b<-lmer(stress2~MS4_Punitive*famine+MS4_Punitive*naturalhazards+MS4_Punitive*abundance+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+NuclearTransNewGuinea+(1|SCCS_ID),data=ang)) 256 -summary(model13c<-lmer(stress2~MS4_Punitive*famine+MS4_Punitive*chron_scarcity+MS4_Punitive*abundance+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+NuclearTransNewGuinea+(1|SCCS_ID),data=ang)) 257 -summary(model13d<-lmer(stress2~MS4_Punitive*famine+MS4_Punitive*naturalhazards+MS4_Punitive*chron_scarcity+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+NuclearTransNewGuinea+(1|SCCS_ID),data=ang)) 258 -summary(model13e<-lmer(stress2~MS4_Punitive*chron_scarcity+MS4_Punitive*naturalhazards+MS4_Punitive*abundance+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+NuclearTransNewGuinea+(1|SCCS_ID),data=ang)) 259 - 260 -################################################# 261 -##For the Existing SCCS Moralizing God Variable## 262 -################################################# 263 - 264 -##Dummy-coding language family (there are more language families represented in these data) 265 -mor$Afroasiatic<-ifelse(as.character(mor$language_family)=="Afro-Asiatic",1,0) 266 -mor$Afroasiatic<-ifelse(is.na(mor$Afroasiatic)==T,0,mor$Afroasiatic) 267 - 268 -mor$Austronesian<-ifelse(as.character(mor$language_family)=="Austronesian",1,0) 269 -mor$Austronesian<-ifelse(is.na(mor$Austronesian)==T,0,mor$Austronesian) 270 - 271 -mor$AtlanticCongo<-ifelse(as.character(mor$language_family)=="Atlantic-Congo",1,0) 272 -mor$AtlanticCongo<-ifelse(is.na(mor$AtlanticCongo)==T,0,mor$AtlanticCongo) 273 - 274 -mor$Indoeuropean<-ifelse(as.character(mor$language_family)=="Indo-European",1,0) 275 -mor$Indoeuropean<-ifelse(is.na(mor$Indoeuropean)==T,0,mor$Indoeuropean) 276 - 277 -mor$Algic<-ifelse(as.character(mor$language_family)=="Algic",1,0) 278 -mor$Algic<-ifelse(is.na(mor$Algic)==T,0,mor$Algic) 279 - 280 -mor$Arawakan<-ifelse(as.character(mor$language_family)=="Arawakan",1,0) 281 -mor$Arawakan<-ifelse(is.na(mor$Arawakan)==T,0,mor$Arawakan) 282 - 283 -mor$AthabaskanEyakTlingit<-ifelse(as.character(mor$language_family)=="Athabaskan-Eyak-Tlingit",1,0) 284 -mor$AthabaskanEyakTlingit<-ifelse(is.na(mor$AthabaskanEyakTlingit)==T,0,mor$AthabaskanEyakTlingit) 285 - 286 -mor$Chibchan<-ifelse(as.character(mor$language_family)=="Chibchan",1,0) 287 -mor$Chibchan<-ifelse(is.na(mor$Chibchan)==T,0,mor$Chibchan) 288 - 289 -mor$SinoTibetan<-ifelse(as.character(mor$language_family)=="Sino-Tibetan",1,0) 290 -mor$SinoTibetan<-ifelse(is.na(mor$SinoTibetan)==T,0,mor$SinoTibetan) 291 - 292 -mor$Tupian<-ifelse(as.character(mor$language_family)=="Tupian",1,0) 293 -mor$Tupian<-ifelse(is.na(mor$Tupian)==T,0,mor$Tupian) 294 - 295 -mor$Turkic<-ifelse(as.character(mor$language_family)=="Turkic",1,0) 296 -mor$Turkic<-ifelse(is.na(mor$Turkic)==T,0,mor$Turkic) 297 - 298 -mor$Uralic<-ifelse(as.character(mor$language_family)=="Uralic",1,0) 299 -mor$Uralic<-ifelse(is.na(mor$Uralic)==T,0,mor$Uralic) 300 - 301 -mor$UtoAztecan<-ifelse(as.character(mor$language_family)=="Uto-Aztecan",1,0) 302 -mor$UtoAztecan<-ifelse(is.na(mor$UtoAztecan)==T,0,mor$UtoAztecan) 303 - 304 -mor$Dravidian<-ifelse(as.character(mor$language_family)=="Dravidian",1,0) 305 -mor$Dravidian<-ifelse(is.na(mor$Dravidian)==T,0,mor$Dravidian) 306 - 307 -mor$EskimoAleut<-ifelse(as.character(mor$language_family)=="Eskimo-Aleut",1,0) 308 -mor$EskimoAleut<-ifelse(is.na(mor$EskimoAleut)==T,0,mor$EskimoAleut) 309 - 310 -mor$Mande<-ifelse(as.character(mor$language_family)=="Mande",1,0) 311 -mor$Mande<-ifelse(is.na(mor$Mande)==T,0,mor$Mande) 312 - 313 -mor$Salishan<-ifelse(as.character(mor$language_family)=="Salishan",1,0) 314 -mor$Salishan<-ifelse(is.na(mor$Salishan)==T,0,mor$Salishan) 315 - 316 -table(mor$language_family) 317 - 318 -##Dummy coding stressors 319 -#Famine=1 under ResourceStress Variable 320 -mor$famine<-ifelse(as.character(mor$ResourceStress)=="1",1,0) 321 -#Natural Hazards=2 under ResourceStress Variable 322 -mor$naturalhazards<-ifelse(as.character(mor$ResourceStress)=="2",1,0) 323 -#Chronic Scarcity=3 under ResourceStress Variable 324 -mor$chron_scarcity<-ifelse(as.character(mor$ResourceStress)=="3",1,0) 325 -#Abundance=4 under ResourceStress Variable 326 -mor$abundance<-ifelse(as.character(mor$ResourceStress)=="4",1,0) 327 - 328 -#Reverse-coding abundance to be able to merge it with other stressors 329 -mor$stress2<-ifelse(mor$abundance==1,(0-mor$Stress), 330 - ifelse(mor$abundance==0,mor$Stress, 331 - NA)) 332 - 333 -#Dichotomized stress variable (not necessary for this analysis) 334 -mor$stress3<-ifelse(mor$chron_scarcity==1&mor$stress2<(-.11),(-1), 335 - ifelse(mor$chron_scarcity==1&mor$stress2>(-.11),1, 336 - ifelse(mor$famine==1&mor$stress2<(-.11),(-1), 337 - ifelse(mor$famine==1&mor$stress2>(-.11),1, 338 - mor$stress2)))) 339 - 340 -##Creating datasets for each of the god variables (this can be used to compute R2 later) 341 -mor2<-mor[is.na(mor$MHG_PresAbs)==F,] 342 -mor3<-mor[is.na(mor$HG_Pres_MoralNoYes)==F,] 343 - 344 -#Empty model (anova) (this can be used to compute R2 later) 345 -summary(fit1<-lmer(stress2~Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+(1|SCCS_ID),data=mor)) 346 -summary(fit1<-lmer(stress2~Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+(1|SCCS_ID),data=mor2)) 347 -summary(fit1<-lmer(stress2~Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+(1|SCCS_ID),data=mor3)) 348 - 349 - 350 -##For the analyses below, you can get the R squared by comparing... 351 -#...the variance explained at the society level with the variance explained by language family alone 352 - 353 -#Association between presence of moralizing high gods and resource stress# Table 5, Model 14 354 -summary(model14<-lmer(stress2~MHG_PresAbs+Social_Complexity+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+Algic+Arawakan+AthabaskanEyakTlingit+Chibchan+SinoTibetan+Tupian+Turkic+Uralic+UtoAztecan+Dravidian+EskimoAleut+Mande+Salishan+(1|SCCS_ID),data=mor)) 355 -summary(model14<-lmer(scale(stress2)~scale(MHG_PresAbs)+scale(Social_Complexity)+scale(Afroasiatic)+scale(Austronesian)+scale(AtlanticCongo)+scale(Indoeuropean)+scale(Algic)+scale(Arawakan)+scale(AthabaskanEyakTlingit)+scale(Chibchan)+scale(SinoTibetan)+scale(Tupian)+scale(Turkic)+scale(Uralic)+scale(UtoAztecan)+scale(Dravidian)+scale(EskimoAleut)+scale(Mande)+scale(Salishan)+(1|SCCS_ID),data=mor)) 356 -##Extract effect size from model on line 355, since beta.lmer is having trouble with this model 357 - 358 -##Secondary analyses looking at specific types of resource stress# Table S7, Model S13 359 -#Looking at Chronic scarcity, then natural hazards, then abundance, then famine 360 -summary(model14b<-lmer(stress2~MHG_PresAbs*famine+MHG_PresAbs*naturalhazards+MHG_PresAbs*abundance+Social_Complexity+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+Algic+Arawakan+AthabaskanEyakTlingit+Chibchan+SinoTibetan+Tupian+Turkic+Uralic+UtoAztecan+Dravidian+EskimoAleut+Mande+Salishan+(1|SCCS_ID),data=mor)) 361 -summary(model14c<-lmer(stress2~MHG_PresAbs*famine+MHG_PresAbs*chron_scarcity+MHG_PresAbs*abundance+Social_Complexity+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+Algic+Arawakan+AthabaskanEyakTlingit+Chibchan+SinoTibetan+Tupian+Turkic+Uralic+UtoAztecan+Dravidian+EskimoAleut+Mande+Salishan+(1|SCCS_ID),data=mor)) 362 -summary(model14d<-lmer(stress2~MHG_PresAbs*famine+MHG_PresAbs*naturalhazards+MHG_PresAbs*chron_scarcity+Social_Complexity+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+Algic+Arawakan+AthabaskanEyakTlingit+Chibchan+SinoTibetan+Tupian+Turkic+Uralic+UtoAztecan+Dravidian+EskimoAleut+Mande+Salishan+(1|SCCS_ID),data=mor)) 363 -summary(model14e<-lmer(stress2~MHG_PresAbs*chron_scarcity+MHG_PresAbs*naturalhazards+MHG_PresAbs*abundance+Social_Complexity+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+Algic+Arawakan+AthabaskanEyakTlingit+Chibchan+SinoTibetan+Tupian+Turkic+Uralic+UtoAztecan+Dravidian+EskimoAleut+Mande+Salishan+(1|SCCS_ID),data=mor)) 364 - 365 - 366 -#Association between presence of moralizing high gods amongst societies that have high gods and resource stress# Table 5, Model 15 367 -summary(model15<-lmer(stress2~HG_Pres_MoralNoYes+Social_Complexity+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+Algic+Arawakan+AthabaskanEyakTlingit+Chibchan+SinoTibetan+Tupian+Turkic+Uralic+UtoAztecan+(1|SCCS_ID),data=mor)) 368 -summary(model15<-lmer(scale(stress2)~scale(HG_Pres_MoralNoYes)+scale(Social_Complexity)+scale(Afroasiatic)+scale(Austronesian)+ 369 - scale(AtlanticCongo)+scale(Indoeuropean)+scale(Algic)+scale(Arawakan)+scale(AthabaskanEyakTlingit)+ 370 - scale(Chibchan)+scale(SinoTibetan)+scale(Tupian)+scale(Turkic)+scale(Uralic)+scale(UtoAztecan)+(1|SCCS_ID),data=mor)) 371 -##Extract effect size from model on line 368, since beta.lmer is having trouble with this model 372 - 373 -##Secondary analyses looking at specific types of resource stress# Table S7, Model S14 374 -#Looking at Chronic scarcity, then natural hazards, then abundance, then famine 375 -summary(model15b<-lmer(stress2~HG_Pres_MoralNoYes*famine+HG_Pres_MoralNoYes*naturalhazards+HG_Pres_MoralNoYes*abundance+Social_Complexity+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+Algic+Arawakan+AthabaskanEyakTlingit+Chibchan+SinoTibetan+Tupian+Turkic+Uralic+UtoAztecan+(1|SCCS_ID),data=mor)) 376 -summary(model15c<-lmer(stress2~HG_Pres_MoralNoYes*famine+HG_Pres_MoralNoYes*chron_scarcity+HG_Pres_MoralNoYes*abundance+Social_Complexity+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+Algic+Arawakan+AthabaskanEyakTlingit+Chibchan+SinoTibetan+Tupian+Turkic+Uralic+UtoAztecan+(1|SCCS_ID),data=mor)) 377 -summary(model15d<-lmer(stress2~HG_Pres_MoralNoYes*famine+HG_Pres_MoralNoYes*naturalhazards+HG_Pres_MoralNoYes*chron_scarcity+Social_Complexity+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+Algic+Arawakan+AthabaskanEyakTlingit+Chibchan+SinoTibetan+Tupian+Turkic+Uralic+UtoAztecan+(1|SCCS_ID),data=mor)) 378 -summary(model15e<-lmer(stress2~HG_Pres_MoralNoYes*chron_scarcity+HG_Pres_MoralNoYes*naturalhazards+HG_Pres_MoralNoYes*abundance+Social_Complexity+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+Algic+Arawakan+AthabaskanEyakTlingit+Chibchan+SinoTibetan+Tupian+Turkic+Uralic+UtoAztecan+(1|SCCS_ID),data=mor)) 379 - 380 -########################################################################### 381 -###PART B: Central Analyses on Stressors, Religious Beliefs, and Sharing### 382 -########################################################################### 383 - 384 - 385 -mor<-read.csv("DT-MoralHighGods_EP.csv") 386 -ang<-read.csv("DT-GodsAndResourceStress.csv") 387 - 388 -library(Kendall) 389 - 390 -##Dummy-coding language family information 391 -mor$Afroasiatic<-ifelse(as.character(mor$language_family)=="Afro-Asiatic",1,0) 392 -mor$Afroasiatic<-ifelse(is.na(mor$Afroasiatic)==T,0,mor$Afroasiatic) 393 -mor$Austronesian<-ifelse(as.character(mor$language_family)=="Austronesian",1,0) 394 -mor$Austronesian<-ifelse(is.na(mor$Austronesian)==T,0,mor$Austronesian) 395 -mor$AtlanticCongo<-ifelse(as.character(mor$language_family)=="Atlantic-Congo",1,0) 396 -mor$AtlanticCongo<-ifelse(is.na(mor$AtlanticCongo)==T,0,mor$AtlanticCongo) 397 -mor$Indoeuropean<-ifelse(as.character(mor$language_family)=="Indo-European",1,0) 398 -mor$Indoeuropean<-ifelse(is.na(mor$Indoeuropean)==T,0,mor$Indoeuropean) 399 -mor$Afroasiatic<-ifelse(as.character(mor$language_family)=="Afro-Asiatic",1,0) 400 -mor$Afroasiatic<-ifelse(is.na(mor$Afroasiatic)==T,0,mor$Afroasiatic) 401 -mor$Austronesian<-ifelse(as.character(mor$language_family)=="Austronesian",1,0) 402 -mor$Austronesian<-ifelse(is.na(mor$Austronesian)==T,0,mor$Austronesian) 403 -mor$AtlanticCongo<-ifelse(as.character(mor$language_family)=="Atlantic-Congo",1,0) 404 -mor$AtlanticCongo<-ifelse(is.na(mor$AtlanticCongo)==T,0,mor$AtlanticCongo) 405 -mor$Indoeuropean<-ifelse(as.character(mor$language_family)=="Indo-European",1,0) 406 -mor$Indoeuropean<-ifelse(is.na(mor$Indoeuropean)==T,0,mor$Indoeuropean) 407 -mor$Algic<-ifelse(as.character(mor$language_family)=="Algic",1,0) 408 -mor$Algic<-ifelse(is.na(mor$Algic)==T,0,mor$Algic) 409 -mor$Arawakan<-ifelse(as.character(mor$language_family)=="Arawakan",1,0) 410 -mor$Arawakan<-ifelse(is.na(mor$Arawakan)==T,0,mor$Arawakan) 411 -mor$AthabaskanEyakTlingit<-ifelse(as.character(mor$language_family)=="Athabaskan-Eyak-Tlingit",1,0) 412 -mor$AthabaskanEyakTlingit<-ifelse(is.na(mor$AthabaskanEyakTlingit)==T,0,mor$AthabaskanEyakTlingit) 413 -mor$Chibchan<-ifelse(as.character(mor$language_family)=="Chibchan",1,0) 414 -mor$Chibchan<-ifelse(is.na(mor$Chibchan)==T,0,mor$Chibchan) 415 -mor$SinoTibetan<-ifelse(as.character(mor$language_family)=="Sino-Tibetan",1,0) 416 -mor$SinoTibetan<-ifelse(is.na(mor$SinoTibetan)==T,0,mor$SinoTibetan) 417 -mor$Tupian<-ifelse(as.character(mor$language_family)=="Tupian",1,0) 418 -mor$Tupian<-ifelse(is.na(mor$Tupian)==T,0,mor$Tupian) 419 -mor$Turkic<-ifelse(as.character(mor$language_family)=="Turkic",1,0) 420 -mor$Turkic<-ifelse(is.na(mor$Turkic)==T,0,mor$Turkic) 421 -mor$Uralic<-ifelse(as.character(mor$language_family)=="Uralic",1,0) 422 -mor$Uralic<-ifelse(is.na(mor$Uralic)==T,0,mor$Uralic) 423 -mor$UtoAztecan<-ifelse(as.character(mor$language_family)=="Uto-Aztecan",1,0) 424 -mor$UtoAztecan<-ifelse(is.na(mor$UtoAztecan)==T,0,mor$UtoAztecan) 425 -mor$Dravidian<-ifelse(as.character(mor$language_family)=="Dravidian",1,0) 426 -mor$Dravidian<-ifelse(is.na(mor$Dravidian)==T,0,mor$Dravidian) 427 -mor$EskimoAleut<-ifelse(as.character(mor$language_family)=="Eskimo-Aleut",1,0) 428 -mor$EskimoAleut<-ifelse(is.na(mor$EskimoAleut)==T,0,mor$EskimoAleut) 429 -mor$Mande<-ifelse(as.character(mor$language_family)=="Mande",1,0) 430 -mor$Mande<-ifelse(is.na(mor$Mande)==T,0,mor$Mande) 431 -mor$Salishan<-ifelse(as.character(mor$language_family)=="Salishan",1,0) 432 -mor$Salishan<-ifelse(is.na(mor$Salishan)==T,0,mor$Salishan) 433 - 434 -ang$Afroasiatic<-ifelse(as.character(ang$language_family)=="Afro-Asiatic",1,0) 435 -ang$Afroasiatic<-ifelse(is.na(ang$Afroasiatic)==T,0,ang$Afroasiatic) 436 -ang$Austronesian<-ifelse(as.character(ang$language_family)=="Austronesian",1,0) 437 -ang$Austronesian<-ifelse(is.na(ang$Austronesian)==T,0,ang$Austronesian) 438 -ang$AtlanticCongo<-ifelse(as.character(ang$language_family)=="Atlantic-Congo",1,0) 439 -ang$AtlanticCongo<-ifelse(is.na(ang$AtlanticCongo)==T,0,ang$AtlanticCongo) 440 -ang$Indoeuropean<-ifelse(as.character(ang$language_family)=="Indo-European",1,0) 441 -ang$Indoeuropean<-ifelse(is.na(ang$Indoeuropean)==T,0,ang$Indoeuropean) 442 -ang$NuclearTransNewGuinea<-ifelse(as.character(ang$language_family)=="Nuclear Trans New Guinea",1,0) 443 -ang$NuclearTransNewGuinea<-ifelse(is.na(ang$NuclearTransNewGuinea)==T,0,ang$NuclearTransNewGuinea) 444 -ang$SinoTibetan<-ifelse(as.character(ang$language_family)=="Sino-Tibetan",1,0) 445 -ang$SinoTibetan<-ifelse(is.na(ang$SinoTibetan)==T,0,ang$SinoTibetan) 446 -ang$Uralic<-ifelse(as.character(ang$language_family)=="Uralic",1,0) 447 -ang$Uralic<-ifelse(is.na(ang$Uralic)==T,0,ang$Uralic) 448 -ang$Turkic<-ifelse(as.character(ang$language_family)=="Turkic",1,0) 449 -ang$Turkic<-ifelse(is.na(ang$Turkic)==T,0,ang$Turkic) 450 -ang$Austroasiatic<-ifelse(as.character(ang$language_family)=="Austroasiatic",1,0) 451 -ang$Austroasiatic<-ifelse(is.na(ang$Austroasiatic)==T,0,ang$Austroasiatic) 452 - 453 - 454 -##Creating single stressor variable 455 -ang$ZSCCS_Abundance_imp_rev<-(0-ang$ZSCCS_Abundance_imp) 456 -mor$ZSCCS_Abundance_imp_rev<-(0-mor$ZSCCS_Abundance_imp) 457 -ang_stress<-data.frame(ang$ZFamine,ang$ZNatural_Hazards,ang$ZChronic_Scarcity,ang$ZSCCS_Abundance_imp_rev) 458 -ang$stress<-rowMeans(ang_stress,na.rm=T) 459 -mor_stress<-data.frame(mor$ZFamine,mor$ZNatural_Hazards,mor$ZChronic_Scarcity,mor$ZSCCS_Abundance_imp_rev) 460 -mor$stress<-rowMeans(mor_stress,na.rm=T) 461 - 462 -##Regressions on Food Sharing 463 -summary(model16<-lm(S3_Resolved_IA_TR~stress+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean,data=ang)) 464 -lm.beta(model16) 465 -confint(lm.beta(model16),level = .95) 466 -summary(model17<-lm(S3_Resolved_IA_TR~stress+G3_Help+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean,data=ang)) 467 -lm.beta(model17) 468 -confint(lm.beta(model17),level = .95) 469 -summary(model18<-lm(S3_Resolved_IA_TR~stress+G2_Harm+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean,data=ang)) 470 -lm.beta(model18) 471 -confint(lm.beta(model18),level = .95) 472 -summary(model19<-lm(S3_Resolved_IA_TR~stress+G4_Punitive+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean,data=ang)) 473 -lm.beta(model19) 474 -confint(lm.beta(model19),level = .95) 475 -summary(model20<-lm(S3_Resolved_IA_TR~HG_Pres_MoralNoYes+Social_Complexity+stress+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+Arawakan+SinoTibetan+Uralic+UtoAztecan,data=mor)) 476 -lm.beta(model20) 477 -confint(lm.beta(model20),level = .95) 478 - 479 -##Just regressions for labor sharing 480 -summary(model21<-lm(LS4_Res_IA_2~stress+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean,data=ang)) 481 -lm.beta(model21) 482 -confint(lm.beta(model21),level = .95) 483 -summary(model22<-lm(LS4_Res_IA_2~stress+G3_Help+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean,data=ang)) 484 -lm.beta(model22) 485 -confint(lm.beta(model22),level = .95) 486 -summary(model23<-lm(LS4_Res_IA_2~stress+G2_Harm+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean,data=ang)) 487 -lm.beta(model23) 488 -confint(lm.beta(model23),level = .95) 489 -summary(model24<-lm(LS4_Res_IA_2~stress+G4_Punitive+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean,data=ang)) 490 -lm.beta(model24) 491 -confint(lm.beta(model24),level = .95) 492 -summary(model25<-lm(LS4_Res_IA_2~HG_Pres_MoralNoYes+Social_Complexity+stress+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+Arawakan+SinoTibetan+Tupian+Turkic+Uralic+UtoAztecan,data=mor)) 493 -lm.beta(model25) 494 -confint(lm.beta(model25),level = .95) 495 - 496 -#making social complexity and moral high god interaction term 497 -mor$HGPres_x_Complex=mor$HG_Pres_MoralNoYes * mor$Social_Complexity 498 - 499 -summary(model20a<-lm(S3_Resolved_IA_TR~HG_Pres_MoralNoYes+Social_Complexity+stress+ HGPres_x_Complex+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+Algic+Arawakan+AthabaskanEyakTlingit+Chibchan+SinoTibetan+Tupian+Turkic+Uralic+UtoAztecan,data=mor)) 500 -summary(model20a) 501 -summary(lm.beta(model20a)) 502 -summary.lm.beta(model20a) 503 -confint(lm.beta(model20a),level = .95) 504 - 505 -summary(model25a<-lm(LS4_Res_IA_2~HG_Pres_MoralNoYes+Social_Complexity+stress+ HGPres_x_Complex+Afroasiatic+Austronesian+AtlanticCongo+Indoeuropean+Algic+Arawakan+AthabaskanEyakTlingit+Chibchan+SinoTibetan+Tupian+Turkic+Uralic+UtoAztecan,data=mor)) 506 -summary(model25a) 507 -lm.beta(model25a) 508 -summary.lm.beta(model25a) 509 -confint(lm.beta(model25a),level = .95) 510 - 511 -########################### 512 -##Monte Carlo Simulations## 513 -########################## 514 -##Model involving Resource Stress, Helping Gods, and Food Sharing 515 -require(MASS) 516 -a=0.9401 517 -b=-0.2995 518 -rep=20000 519 -conf=95 520 -pest=c(a,b) 521 -acov <- matrix(c( 522 - 0.07160976, 0, 523 - 0, 0.04109729 524 -),2,2) 525 -acov 526 -mcmc <- mvrnorm(rep,pest,acov,empirical=FALSE) 527 -ab <- mcmc[,1]*mcmc[,2] 528 -low=(1-conf/100)/2 529 -upp=((1-conf/100)/2)+(conf/100) 530 -LL=quantile(ab,low) 531 -UL=quantile(ab,upp) 532 -LL4=format(LL,digits=4) 533 -UL4=format(UL,digits=4) 534 -################################################ 535 -# The number of columns in the histogram can # 536 -# be changed by replacing 'FD' below with # 537 -# an integer value. # 538 -################################################ 539 -hist(ab,breaks='FD',col='skyblue',xlab=paste(conf,'% Confidence Interval ','LL',LL4,' UL',UL4), 540 - main='Distribution of Indirect Effect') 541 - 542 -##Model involving Resource Stress, Harming Gods, and Food Sharing 543 -require(MASS) 544 -a=1.194 545 -b=-0.69148 546 -rep=20000 547 -conf=95 548 -pest=c(a,b) 549 -acov <- matrix(c( 550 - 0.11048976, 0, 551 - 0, 0.0482022 552 -),2,2) 553 -mcmc <- mvrnorm(rep,pest,acov,empirical=FALSE) 554 -ab <- mcmc[,1]*mcmc[,2] 555 -low=(1-conf/100)/2 556 -upp=((1-conf/100)/2)+(conf/100) 557 -LL=quantile(ab,low) 558 -UL=quantile(ab,upp) 559 -LL4=format(LL,digits=4) 560 -UL4=format(UL,digits=4) 561 -################################################ 562 -# The number of columns in the histogram can # 563 -# be changed by replacing 'FD' below with # 564 -# an integer value. # 565 -################################################ 566 -hist(ab,breaks='FD',col='skyblue',xlab=paste(conf,'% Confidence Interval ','LL',LL4,' UL',UL4), 567 - main='Distribution of Indirect Effect') 568 - 569 -##Model involving Resource Stress, Punitive Gods, and Food Sharing 570 -require(MASS) 571 -a=0.2929 572 -b=-0.3853 573 -rep=20000 574 -conf=95 575 -pest=c(a,b) 576 -acov <- matrix(c( 577 - 0.08988004, 0, 578 - 0, 0.05175625 579 -),2,2) 580 -mcmc <- mvrnorm(rep,pest,acov,empirical=FALSE) 581 -ab <- mcmc[,1]*mcmc[,2] 582 -low=(1-conf/100)/2 583 -upp=((1-conf/100)/2)+(conf/100) 584 -LL=quantile(ab,low) 585 -UL=quantile(ab,upp) 586 -LL4=format(LL,digits=4) 587 -UL4=format(UL,digits=4) 588 -################################################ 589 -# The number of columns in the histogram can # 590 -# be changed by replacing 'FD' below with # 591 -# an integer value. # 592 -################################################ 593 -hist(ab,breaks='FD',col='skyblue',xlab=paste(conf,'% Confidence Interval ','LL',LL4,' UL',UL4), 594 - main='Distribution of Indirect Effect') 595 - 596 -##Model involving Resource Stress, Moralizing Gods, and Food Sharing 597 -require(MASS) 598 -a=0.468225 599 -b=-0.286069 600 -rep=20000 601 -conf=95 602 -pest=c(a,b) 603 -acov <- matrix(c( 604 - 0.03375671, 0, 605 - 0, 0.03394769 606 -),2,2) 607 -mcmc <- mvrnorm(rep,pest,acov,empirical=FALSE) 608 -ab <- mcmc[,1]*mcmc[,2] 609 -low=(1-conf/100)/2 610 -upp=((1-conf/100)/2)+(conf/100) 611 -LL=quantile(ab,low) 612 -UL=quantile(ab,upp) 613 -LL4=format(LL,digits=4) 614 -UL4=format(UL,digits=4) 615 -################################################ 616 -# The number of columns in the histogram can # 617 -# be changed by replacing 'FD' below with # 618 -# an integer value. # 619 -################################################ 620 -hist(ab,breaks='FD',col='skyblue',xlab=paste(conf,'% Confidence Interval ','LL',LL4,' UL',UL4), 621 - main='Distribution of Indirect Effect') 622 - 623 -##Model involving Resource Stress, Helping Gods, and Labor Sharing 624 -require(MASS) 625 -a=0.9401 626 -b=0.08712 627 -rep=20000 628 -conf=95 629 -pest=c(a,b) 630 -acov <- matrix(c( 631 - 0.07160976, 0, 632 - 0, 0.076016 633 -),2,2) 634 -mcmc <- mvrnorm(rep,pest,acov,empirical=FALSE) 635 -ab <- mcmc[,1]*mcmc[,2] 636 -low=(1-conf/100)/2 637 -upp=((1-conf/100)/2)+(conf/100) 638 -LL=quantile(ab,low) 639 -UL=quantile(ab,upp) 640 -LL4=format(LL,digits=4) 641 -UL4=format(UL,digits=4) 642 -################################################ 643 -# The number of columns in the histogram can # 644 -# be changed by replacing 'FD' below with # 645 -# an integer value. # 646 -################################################ 647 -hist(ab,breaks='FD',col='skyblue',xlab=paste(conf,'% Confidence Interval ','LL',LL4,' UL',UL4), 648 - main='Distribution of Indirect Effect') 649 - 650 -##Model involving Resource Stress, Harming Gods, and Labor Sharing 651 -require(MASS) 652 -a=1.194 653 -b=0.1561 654 -rep=20000 655 -conf=95 656 -pest=c(a,b) 657 -acov <- matrix(c( 658 - 0.11048976, 0, 659 - 0, 0.18198756 660 -),2,2) 661 -mcmc <- mvrnorm(rep,pest,acov,empirical=FALSE) 662 -ab <- mcmc[,1]*mcmc[,2] 663 -low=(1-conf/100)/2 664 -upp=((1-conf/100)/2)+(conf/100) 665 -LL=quantile(ab,low) 666 -UL=quantile(ab,upp) 667 -LL4=format(LL,digits=4) 668 -UL4=format(UL,digits=4) 669 -################################################ 670 -# The number of columns in the histogram can # 671 -# be changed by replacing 'FD' below with # 672 -# an integer value. # 673 -################################################ 674 -hist(ab,breaks='FD',col='skyblue',xlab=paste(conf,'% Confidence Interval ','LL',LL4,' UL',UL4), 675 - main='Distribution of Indirect Effect') 676 - 677 -##Model involving Resource Stress, Punitive Gods, and Labor Sharing 678 -require(MASS) 679 -a=0.2929 680 -b=0.09117 681 -rep=20000 682 -conf=95 683 -pest=c(a,b) 684 -acov <- matrix(c( 685 - 0.08988004, 0, 686 - 0, 0.05847208 687 -),2,2) 688 -mcmc <- mvrnorm(rep,pest,acov,empirical=FALSE) 689 -ab <- mcmc[,1]*mcmc[,2] 690 -low=(1-conf/100)/2 691 -upp=((1-conf/100)/2)+(conf/100) 692 -LL=quantile(ab,low) 693 -UL=quantile(ab,upp) 694 -LL4=format(LL,digits=4) 695 -UL4=format(UL,digits=4) 696 -################################################ 697 -# The number of columns in the histogram can # 698 -# be changed by replacing 'FD' below with # 699 -# an integer value. # 700 -################################################ 701 -hist(ab,breaks='FD',col='skyblue',xlab=paste(conf,'% Confidence Interval ','LL',LL4,' UL',UL4), 702 - main='Distribution of Indirect Effect') 703 - 704 -##Model involving Resource Stress, Moralizing Gods, and Labor Sharing 705 -require(MASS) 706 -a=0.468225 707 -b=0.188035 708 -rep=20000 709 -conf=95 710 -pest=c(a,b) 711 -acov <- matrix(c( 712 - 0.03375671, 0, 713 - 0, 0.02658661 714 -),2,2) 715 -mcmc <- mvrnorm(rep,pest,acov,empirical=FALSE) 716 -ab <- mcmc[,1]*mcmc[,2] 717 -low=(1-conf/100)/2 718 -upp=((1-conf/100)/2)+(conf/100) 719 -LL=quantile(ab,low) 720 -UL=quantile(ab,upp) 721 -LL4=format(LL,digits=4) 722 -UL4=format(UL,digits=4) 723 -################################################ 724 -# The number of columns in the histogram can # 725 -# be changed by replacing 'FD' below with # 726 -# an integer value. # 727 -################################################ 728 -hist(ab,breaks='FD',col='skyblue',xlab=paste(conf,'% Confidence Interval ','LL',LL4,' UL',UL4), 729 - main='Distribution of Indirect Effect') 730 - 731 -
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... ... @@ -1,22 +1,0 @@ 1 -Case_Number,Case_Name,NGA,SigTempChng,Data_Source,CV_1,DV_1,DV_2,DV_3,DV_4_1,DV_4_2,DV_4_3,DV_1rec,DV_2rec,DV_3rec,DV_4_1rec,DV_4_2rec,DV_4_3rec,M486,M536,TempDiff,ZIV_2_1,ZIV_2_2,ZIV_2_3,ZIV_2_4,ZIV_2_5,ZIV_2_6,ZIV_3_1,ZIV_3_2,ZIV_3_3,ZIV_3_4,ZIV_3_5,ZDV_1rec,ZDV_2rec,ZDV_3rec,ZDV_4_1rec,ZDV_4_2rec,ZDV_4_3rec,ZCE_Index,ZTL_Full_Index,ZDV_Index,SC_Index 2 -ARO-1,Point Peninsula,Ontario,2,1,1,3,3,3,3,3,3,1,1,1,1,1,1,-.016927127,-.760167645,.743240518,-.840819091235472,-.0979589689408756,-.974679434480896,-.374384741177081,-1.40046075367382,.255474412755014,-1.85691899342652,-1.90418387514612,-1.62894084480133,-1.57974550321319,-1.46864503505568,-.971324245748044,-.521749194749951,-.724490058419616,-1.04197614450346,-1.25139191908843,-.959797958537171,-1.68768685032857,-.572138096125522,-.911788253507778,6 3 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Empire,Ganges,1,3,3,3,3,4,4,2,4,1,1,2,2,2,2,-.113309933,-.474442349,.361132415,.210204772808869,.881630720467889,2.16422309957864E-016,.873564396079853,1.22540315946459,.255474412755014,.795822425754221,.916829273218502,.180993427200148,.850632194037869,.0772971071081939,-.971324245748044,-.521749194749951,.592764593252413,.260494036125864,.0658627325836015,.239949489634293,.564314885463787,.57437957692937,-.055667098150304,10 20 -ARO-19,Byzantine,Upper Egypt,1,2,3,1,5,5,2,3,2,3,3,3,2,1,2,-.142966804,-.383099953,.240133149,.210204772808869,.881630720467889,.974679434480897,.873564396079853,.350115188418455,1.10705578860506,.795822425754221,.916829273218502,-.723973708800591,.850632194037869,.0772971071081939,1.31414456777677,2.45967477524977,1.91001924492444,.260494036125864,-1.25139191908843,.239949489634293,.383321458263639,.732875050143504,.822148365770451,14 21 -ARO-20,Jenne-jeno III,Lower 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- Social resilience.csv
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- Author
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... ... @@ -1,22 +1,0 @@ 1 -Case_Number,Case_Name,NGA,SigTempChng,Data_Source,CV_1,DV_1,DV_2,DV_3,DV_4_1,DV_4_2,DV_4_3,DV_1rec,DV_2rec,DV_3rec,DV_4_1rec,DV_4_2rec,DV_4_3rec,M486,M536,TempDiff,ZIV_2_1,ZIV_2_2,ZIV_2_3,ZIV_2_4,ZIV_2_5,ZIV_2_6,ZIV_3_1,ZIV_3_2,ZIV_3_3,ZIV_3_4,ZIV_3_5,ZDV_1rec,ZDV_2rec,ZDV_3rec,ZDV_4_1rec,ZDV_4_2rec,ZDV_4_3rec,ZCE_Index,ZTL_Full_Index,ZDV_Index,SC_Index 2 -ARO-1,Point Peninsula,Ontario,2,1,1,3,3,3,3,3,3,1,1,1,1,1,1,-.016927127,-.760167645,.743240518,-.840819091235472,-.0979589689408756,-.974679434480896,-.374384741177081,-1.40046075367382,.255474412755014,-1.85691899342652,-1.90418387514612,-1.62894084480133,-1.57974550321319,-1.46864503505568,-.971324245748044,-.521749194749951,-.724490058419616,-1.04197614450346,-1.25139191908843,-.959797958537171,-1.68768685032857,-.572138096125522,-.911788253507778,6 3 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Empire,Ganges,1,3,3,3,3,4,4,2,4,1,1,2,2,2,2,-.113309933,-.474442349,.361132415,.210204772808869,.881630720467889,2.16422309957864E-016,.873564396079853,1.22540315946459,.255474412755014,.795822425754221,.916829273218502,.180993427200148,.850632194037869,.0772971071081939,-.971324245748044,-.521749194749951,.592764593252413,.260494036125864,.0658627325836015,.239949489634293,.564314885463787,.57437957692937,-.055667098150304,10 20 -ARO-19,Byzantine,Upper Egypt,1,2,3,1,5,5,2,3,2,3,3,3,2,1,2,-.142966804,-.383099953,.240133149,.210204772808869,.881630720467889,.974679434480897,.873564396079853,.350115188418455,1.10705578860506,.795822425754221,.916829273218502,-.723973708800591,.850632194037869,.0772971071081939,1.31414456777677,2.45967477524977,1.91001924492444,.260494036125864,-1.25139191908843,.239949489634293,.383321458263639,.732875050143504,.822148365770451,14 21 -ARO-20,Jenne-jeno III,Lower 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- Supplement_Data_April2018.csv
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(Masa),1910,Africa,27,-0.275713877,3,2,1,1,0,1,1,0,1,2,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,0,NA,NA,0,1,NA,NA 29 -28,Atlantic-Congo,Azande,1905,Africa,31,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA 30 -29,Furan,Fur (Darfur),1880,Circum-Mediterranean,37,-0.68770535,2,2,1,1,1,0,1,0,1,2,1,NA,NA,1,NA,1,NA,NA,NA,NA,0,1,NA,NA,NA,1,1,NA,0,NA,NA,NA,0,NA,NA,NA 31 -30,Heibanic,Otoro Nuba,1930,Africa,28,2.097049838,4,4,4,1,0,1,1,1,1,2,1,0,1,0,1,1,NA,1,NA,NA,NA,1,NA,NA,NA,NA,NA,NA,0,0,0,1,0,1,0,1 32 -31,Nilotic,Shilluk,1910,Africa,33,NA,NA,NA,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA 33 -32,Blue Nile Mao,Mao,1939,Africa,25,NA,NA,2,1,NA,NA,NA,NA,NA,NA,NA,1,0,1,1,1,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1,NA,NA,NA,NA,NA,NA,NA,NA,NA 34 -33,Ta-Ne-Omotic,Kaffa (Kafa),1905,Circum-Mediterranean,35,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA 35 -34,Nilotic,Masai,1900,Africa,19,NA,3,4,NA,NA,NA,NA,NA,NA,NA,NA,1,NA,0,1,NA,1,1,NA,NA,NA,0,1,1,NA,NA,NA,1,1,NA,NA,NA,NA,NA,NA,NA,NA 36 -35,Afro-Asiatic,Konso,1935,Circum-Mediterranean,34,NA,2,NA,1,1,NA,1,NA,NA,NA,NA,1,0,1,0,1,1,1,1,NA,NA,NA,NA,NA,NA,1,NA,1,NA,NA,NA,0,1,NA,NA,NA,NA 37 -36,Afro-Asiatic,Somali,1900,Circum-Mediterranean,28,NA,2,2,NA,1,0,1,0,1,1,1,1,0,1,0,1,1,1,NA,NA,NA,NA,1,1,NA,0,NA,1,1,NA,NA,1,1,NA,NA,1,1 38 -37,Afro-Asiatic,Amhara,1953,Circum-Mediterranean,42,-1.053104578,2,1,1,1,0,0,1,1,0,1,1,0,1,1,1,1,1,NA,NA,1,0,1,1,NA,0,1,1,1,0,0,0,0,0,0,0,1 39 -38,Afro-Asiatic,Bogo,1855,Circum-Mediterranean,27,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA 40 -39,Nubian,Kenuzi 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-51,Indo-European,Irish,1932,Circum-Mediterranean,47,NA,NA,NA,2,1,0,1,1,1,1,2,1,0,1,1,1,1,1,1,NA,NA,1,0,1,NA,1,1,0,1,0,0,0,1,0,1,0,0 53 -52,Uralic,Lapps,1950,Circum-Mediterranean,18,NA,1,1,NA,1,1,0,1,0,1,2,1,1,1,0,2,1,1,NA,0,1,NA,1,1,NA,NA,NA,1,1,0,0,NA,NA,0,0,NA,NA 54 -53,Uralic,Yurak Samoyed,1894,East Eurasia,18,0.865075841,3,4,2,1,NA,NA,1,NA,NA,NA,1,NA,NA,0,NA,1,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1,NA,NA,NA,NA,0,1,NA,NA 55 -54,Indo-European,Russians,1955,Circum-Mediterranean,48,NA,NA,4,1,1,1,0,1,0,1,2,1,0,NA,0,NA,1,NA,NA,NA,NA,NA,0,NA,NA,NA,NA,0,NA,0,1,NA,NA,0,1,NA,NA 56 -55,Abkhaz-Adyge,Abkhaz,1880,Circum-Mediterranean,36,NA,NA,3,1,1,0,1,1,0,1,2,1,NA,NA,1,NA,1,1,1,NA,NA,NA,0,1,NA,NA,NA,1,1,NA,NA,0,1,1,1,NA,NA 57 -56,Indo-European,Armenians,1843,Circum-Mediterranean,43,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA 58 -57,Indo-European,Kurd,1951,Circum-Mediterranean,43,NA,1,1,NA,1,0,NA,NA,NA,NA,NA,1,0,0,1,0,1,1,NA,NA,NA,0,0,NA,NA,NA,1,1,NA,0,0,NA,NA,NA,NA,NA,NA 59 -58,Indo-European,Basseri,1958,East Eurasia,25,-1.099696823,1,2,1,1,1,1,0,0,2,2,1,0,0,1,0,1,NA,0,NA,NA,1,1,NA,NA,NA,1,1,NA,0,0,0,NA,NA,NA,NA,NA 60 -59,Indo-European,Punjabi (West),1950,East Eurasia,45,0.499676613,3,3,2,1,0,1,1,1,1,2,1,0,1,1,1,1,1,NA,NA,0,0,0,1,NA,1,1,1,1,NA,NA,1,1,1,1,1,1 61 -60,Dravidian,Gond,1938,East Eurasia,27,-1.465096051,1,1,1,1,1,0,0,1,1,1,1,0,1,0,1,1,1,NA,NA,0,NA,1,1,NA,1,NA,1,1,0,NA,NA,NA,NA,NA,0,1 62 -61,Dravidian,Toda,1900,East Eurasia,19,NA,NA,NA,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA 63 -62,Austroasiatic,Santal,1940,East Eurasia,31,NA,NA,2,3,1,NA,NA,NA,1,NA,NA,1,0,0,1,0,1,NA,NA,NA,NA,0,1,NA,NA,NA,1,1,NA,NA,NA,NA,NA,NA,NA,0,1 64 -63,Indo-European,Uttar Pradesh,1945,East Eurasia,50,NA,NA,2,NA,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,0,NA,NA,NA,NA,NA,NA 65 -64,Burushaski,Burusho,1934,East Eurasia,33,NA,NA,1,2,1,1,0,1,1,1,2,1,NA,NA,NA,NA,NA,1,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,NA,NA,1,0,0,1 66 -65,Turkic,Kazak,1885,East Eurasia,30,0.294680982,3,3,1.5,1,0,1,1,1,1,2,1,NA,NA,0,NA,1,1,NA,NA,NA,NA,0,1,NA,NA,NA,1,1,0,0,0,0,0,NA,0,NA 67 -66,Mongolic,Khalka Mongols,1920,East Eurasia,32,NA,2,4,NA,1,1,1,1,0,2,3,1,0,1,NA,1,NA,1,NA,NA,1,NA,NA,1,NA,1,NA,NA,1,0,NA,1,1,1,1,NA,NA 68 -67,Sino-Tibetan,Lolo,1910,East Eurasia,35,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA 69 -68,Sino-Tibetan,Lepcha,1937,East Eurasia,32,-1.465096051,1,1,1,1,0,NA,1,1,NA,NA,1,0,NA,1,NA,1,1,NA,NA,NA,1,1,1,NA,NA,1,1,1,NA,NA,NA,NA,0,1,0,1 70 -69,Sino-Tibetan,Garo,1955,East Eurasia,31,NA,NA,NA,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA 71 -70,Sino-Tibetan,Lakher,1930,East Eurasia,31,NA,3,3,NA,1,NA,1,NA,NA,NA,NA,1,0,1,0,1,1,0,NA,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,NA,NA,NA,NA,NA 72 -71,Sino-Tibetan,Burmese,1965,East Eurasia,48,NA,NA,NA,1,1,0,0,1,1,0,1,1,1,0,1,1,1,1,NA,0,NA,1,NA,1,1,NA,1,1,1,NA,NA,NA,NA,0,1,0,1 73 -72,Austroasiatic,Lamet,1940,East Eurasia,19,0.134277385,3,2,2,NA,NA,NA,NA,NA,NA,NA,1,0,1,0,1,1,NA,NA,NA,0,NA,0,NA,NA,1,NA,1,NA,NA,NA,NA,NA,NA,NA,NA,NA 74 -73,Austroasiatic,Vietnamese,1930,East Eurasia,47,NA,1,4,NA,1,0,1,0,0,1,1,1,0,NA,1,NA,1,1,NA,NA,NA,NA,0,0,NA,NA,NA,1,NA,0,0,0,1,0,0,NA,NA 75 -74,Austronesian,Rhade,1962,East Eurasia,29,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA 76 -75,Austroasiatic,Khmer,1292,East Eurasia,46,NA,1,NA,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA 77 -76,Tai-Kadai,Siamese,1955,East Eurasia,50,-0.734297595,1,3,1,1,0,1,1,1,1,2,1,0,1,1,1,1,NA,NA,NA,0,0,0,NA,NA,1,1,1,NA,0,0,0,1,0,1,0,NA 78 -77,Austroasiatic,Semang,1925,East Eurasia,12,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA 79 -78,Austroasiatic,Nicobarese,1870,East Eurasia,27,NA,NA,NA,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA 80 -79,Great Andamanese,Andamanese,1860,East Eurasia,14,NA,NA,NA,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA 81 -80,Indo-European,Vedda,1860,East Eurasia,16,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA 82 -81,Austronesian,Tanala,1925,East Eurasia,27,NA,1,NA,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA 83 -82,Austronesian,Negri Sembilan,1958,East Eurasia,45,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA 84 -83,Austronesian,Javanese,1954,Insular Pacific,49,NA,1,3,NA,1,0,1,1,1,1,2,1,0,0,1,0,1,NA,NA,NA,NA,1,0,NA,NA,NA,1,1,NA,0,0,0,1,1,1,0,1 85 -84,Austronesian,Balinese,1958,Insular Pacific,48,NA,NA,2,1,1,0,1,0,1,1,1,1,0,0,1,0,0,0,NA,NA,NA,0,NA,NA,NA,NA,1,NA,NA,0,0,0,1,NA,NA,0,1 86 -85,Austronesian,Iban,1950,Insular Pacific,23,NA,NA,2,NA,1,0,0,1,1,0,1,1,0,1,1,1,1,NA,NA,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,0,0,NA,NA,1,0,0,0 87 -86,Austronesian,Badjau,1963,Insular Pacific,15,NA,NA,1,1,1,0,1,0,1,1,1,1,0,0,1,0,1,NA,NA,NA,NA,0,1,NA,NA,NA,0,1,NA,0,0,0,0,NA,NA,0,1 88 -87,Austronesian,Toradja,1910,Insular Pacific,28,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA 89 -88,North Halmahera,Tobelorese,1900,Insular Pacific,31,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA 90 -89,Timor-Alor-Pantar,Alorese,1938,Insular Pacific,30,NA,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA 91 -90,Tiwi,Tiwi,1929,Insular Pacific,12,-1.465096051,1,1,1,1,0,1,0,1,1,1,1,0,1,0,1,1,1,NA,NA,1,NA,1,1,NA,0,NA,NA,0,0,0,0,NA,NA,NA,NA,NA 92 -91,Pama-Nyungan,Aranda,1896,Insular Pacific,13,NA,2,2,NA,1,NA,1,NA,NA,NA,NA,1,NA,NA,1,NA,1,NA,NA,NA,NA,0,0,NA,NA,NA,NA,NA,NA,NA,NA,1,NA,NA,NA,NA,NA 93 -92,Nuclear Trans New Guinea,Orokaiva,1925,Insular Pacific,20,NA,1,NA,2,1,0,1,1,1,1,2,1,1,0,0,1,1,1,NA,0,NA,NA,1,1,NA,NA,NA,NA,1,0,0,0,1,0,1,1,1 94 -93,Kolopom,Kimam,1960,Insular Pacific,22,NA,3,4,NA,1,1,0,1,0,1,2,1,0,1,0,1,1,NA,NA,NA,1,NA,NA,NA,NA,NA,NA,NA,NA,0,0,NA,NA,0,1,NA,NA 95 -94,Nuclear Trans New Guinea,Kapauku,1955,Insular Pacific,29,NA,1,NA,1,1,0,1,0,1,1,1,1,NA,NA,NA,NA,1,1,NA,NA,NA,NA,1,1,NA,NA,NA,1,1,0,0,0,0,NA,NA,0,0 96 -95,Sepik,Kwoma,1960,Insular Pacific,28,NA,1,NA,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA 97 -96,Austronesian,Manus,1937,Insular Pacific,26,-1.465096051,1,1,1,1,0,1,0,1,1,1,1,0,NA,0,NA,1,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,NA,0,0,NA,0,NA,NA,1,1 98 -97,Austronesian,New Ireland,1930,Insular Pacific,26,NA,NA,NA,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA 99 -98,Austronesian,Trobrianders,1914,Insular Pacific,26,1.277067314,4,4,2,1,0,1,1,0,1,2,1,0,1,1,1,1,NA,NA,NA,1,NA,1,NA,NA,1,NA,0,NA,NA,NA,0,1,0,NA,NA,NA 100 -99,South Bougainville,Siuai,1939,Insular Pacific,27,NA,3,NA,1,1,1,0,1,1,1,2,1,0,1,0,1,1,1,NA,NA,NA,NA,NA,0,NA,NA,NA,1,1,0,1,NA,NA,0,0,0,0 101 -100,Austronesian,Tikopia,1930,Insular Pacific,28,0.865075841,3,4,2,1,1,1,0,0,2,2,1,1,1,1,2,1,1,NA,0,1,NA,1,NA,NA,1,NA,0,0,0,0,0,0,NA,NA,NA,NA 102 -101,Austronesian,Pentecost,1953,Insular Pacific,23,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA 103 -102,Austronesian,Mbau Fijians,1840,Insular Pacific,32,NA,NA,1,1,1,0,1,0,1,1,1,1,NA,NA,0,NA,1,NA,NA,NA,NA,NA,0,NA,NA,NA,NA,1,NA,NA,NA,0,NA,NA,NA,NA,1 104 -103,Austronesian,Ajie,1845,Insular Pacific,26,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA 105 -104,Austronesian,Maori,1820,Insular Pacific,24,1.275067104,3,4,3,1,1,1,1,1,2,3,1,0,1,0,1,1,1,NA,NA,1,NA,1,1,NA,NA,NA,1,1,0,0,0,NA,1,1,1,1 106 -105,Austronesian,Marquesans,1800,Insular Pacific,27,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA 107 -106,Austronesian,Western Samoans,1829,Insular Pacific,28,0.043093106,2,4,1,1,0,1,0,1,1,1,1,NA,NA,0,NA,1,1,NA,NA,NA,NA,NA,1,NA,NA,NA,1,1,NA,NA,0,1,NA,NA,0,1 108 -107,Austronesian,Gilbertese,1890,Insular Pacific,29,NA,NA,NA,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA 109 -108,Austronesian,Marshallese,1900,Insular Pacific,29,0.499676613,3,3,2,1,0,1,1,1,1,2,1,1,0,NA,1,0,1,NA,0,NA,NA,NA,1,1,NA,NA,NA,1,NA,NA,1,1,0,NA,0,NA 110 -109,Austronesian,Trukese,1947,Insular Pacific,28,NA,NA,2,NA,1,1,1,0,0,2,2,1,1,1,1,2,1,1,1,0,1,NA,NA,1,0,0,1,1,1,0,0,0,1,NA,NA,NA,NA 111 -110,Austronesian,Yapese,1910,Insular Pacific,32,NA,NA,4,NA,1,0,1,0,1,1,1,1,NA,NA,1,NA,1,1,NA,NA,NA,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,0,NA,NA,1,1 112 -111,Austronesian,Palauans,1947,Insular Pacific,29,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA 113 -112,Austronesian,Ifugao,1910,Insular Pacific,31,-0.734297595,1,3,1,1,0,1,1,0,1,2,1,0,1,0,1,1,1,1,NA,0,NA,1,NA,NA,1,NA,NA,NA,NA,NA,0,0,0,0,NA,NA 114 -113,Austronesian,Atayal,1930,Insular Pacific,27,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA 115 -114,Sino-Tibetan,Chinese,1936,East Eurasia,50,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA 116 -115,Tungusic,Manchu,1915,East Eurasia,36,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA 117 -116,Koreanic,Koreans,1947,East Eurasia,48,0.132277175,2,2,3,1,0,1,0,1,1,1,1,NA,NA,0,NA,1,1,NA,NA,NA,NA,0,1,NA,NA,NA,1,1,NA,NA,0,1,NA,NA,0,1 118 -117,Japonic,Japanese,1950,East Eurasia,50,NA,1,2,NA,1,0,0,0,1,0,0,1,0,1,1,1,1,1,NA,NA,0,1,0,NA,NA,NA,1,1,NA,0,0,0,0,0,0,1,1 119 -118,Ainu,Ainu,1880,East Eurasia,17,-0.643113316,2,1,2,1,0,1,1,1,1,2,1,0,1,0,1,1,1,NA,NA,1,NA,1,NA,NA,1,NA,1,NA,0,0,0,1,0,0,1,1 120 -119,Nivkh,Gilyak,1890,East Eurasia,19,0.911668086,4,3,2,1,0,1,1,1,1,2,1,NA,1,0,NA,1,1,1,NA,0,NA,0,1,NA,1,NA,1,1,NA,NA,0,0,0,0,0,0 121 -120,Yukaghir,Yukaghir,1850,East Eurasia,17,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA 122 -121,Chukotko-Kamchatkan,Chukchee,1900,East Eurasia,18,-0.322306122,2,3,1,1,NA,NA,1,NA,NA,NA,1,0,1,1,1,1,1,NA,NA,1,NA,1,1,NA,1,NA,1,1,NA,NA,NA,NA,0,0,NA,NA 123 -122,Athapaskan-Eyak-Tlingit,Ingalik,1885,North America,19,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA 124 -123,Eskimo-Aleut,Aleut,1800,North America,21,NA,3,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA 125 -124,Eskimo-Aleut,Copper Eskimo,1915,North America,16,1.277067314,4,4,2,1,1,NA,NA,NA,NA,NA,1,1,1,NA,2,NA,1,NA,0,1,NA,NA,1,0,1,NA,NA,1,0,NA,NA,NA,NA,NA,NA,NA 126 -125,Algic,Montagnais,1910,North America,15,NA,3,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA 127 -126,Algic,Micmac,1650,North America,17,NA,2,NA,2,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA 128 -127,Algic,Saulteaux,1930,North America,20,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA 129 -128,Athapaskan-Eyak-Tlingit,Slave,1940,North America,18,NA,NA,NA,2,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA 130 -129,Athapaskan-Eyak-Tlingit,Kaska,1900,North America,14,NA,2,NA,2,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA 131 -130,Athapaskan-Eyak-Tlingit,Eyak,1890,North America,16,NA,1,1,NA,1,1,1,1,0,2,3,1,1,0,0,1,1,1,NA,0,NA,NA,1,NA,1,NA,NA,1,NA,0,0,0,1,0,1,NA,NA 132 -131,Haida,Haida,1875,North 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... ... @@ -1,26 +1,0 @@ 1 -The nine files here provide all the data and supporting information for Peter N. Peregrine "Social resilience to climate change during the Late Antique Little Ice Age: A replication study." 2 - 3 -Codebook. This is the codebook for the project for which this study is only one part. As such there are many variables in the codebook not present in the analyzed data. 4 - 5 -It should be noted that the dependent variables in the codebook are all scored on a five-point scale. These were re-coded into a three-point scale for analysis using the following formula: 6 - 7 -1=3 (Much) 8 -2=2 (Some) 9 -3=1 (None) 10 -4=2 (Some) 11 -5=3 (Much) 12 - 13 -Social Change Data (csv) contains all the data used in the analysis in csv format. Again, not all the variables listed in the codebook are included, only those used in the study. In addition, however, are both the original and recoded versions of the dependent variables, as well as the standardized versions of the independent variables used to create the Corporate-Exclusionary and Looseness-Tightness indexes. 14 - 15 -Social Change Data (SPSS) contains the data used in the analysis in SPSS format. This format provides full variable titles and score labels. 16 - 17 -SPSS Output (pdf) and (SPSS) are identical. They latter is an SPSS output file. All the analyses reported in the article are given in these files, indcluding the code used to create the indices, the factor analyses of those indices, and the full Bayesian analyses. 18 - 19 -The Raw Coding Data Excel file contains all the supporting information for each coded value. 20 - 21 -The Raw Coding Data Bibliography Excel file contains the bibliographic notes for each piece of supporting information in the Raw Coding Data file. 22 - 23 -The Raw Coding Data RDF file contains both the supporting information for each coded value and linked bibliographic notes in RDF triplestore format. 24 - 25 -The Raw Coding Data RDF Ontology file contains the ontology associated with the Raw Coding Data RDF triplestore file. 26 -
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- rx1s_Combined_ann.xlsx
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... ... @@ -1,188 +1,0 @@ 1 -id,socname,Revised.latitude,Revised.longitude,Variable..Variance.in.monthly.temperature.means,fam_orig,oth_orig,chrs_orig,FC1A,SCCS_Abundance_imp,SCCS_Stability_imp,DepGath_v203,DepHunt_v204,DepFish_v205,DepAnHusb_v206,DepAg_v207,IntensityCultivation_v232,EASubDIvCorr_Rev,SubType,util_rev 2 -1,Nama_Hottentot,-27.5,17,10.59263669,NA,2,NA,0,-1.110426279,-0.151609738,1,3,1,5,0,1,8,Mixed,6 3 -2,Kung_Bushmen,NA,NA,NA,NA,4,3,NA,NA,NA,NA,NA,NA,NA,NA,1,NA,NA,NA 4 -3,Thonga,NA,NA,NA,3,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,3,NA,NA,NA 5 -4,Lozi,NA,NA,NA,NA,NA,1,NA,NA,NA,NA,NA,NA,NA,NA,5,NA,NA,NA 6 -5,Mbundu,NA,NA,NA,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,3,NA,NA,NA 7 -6,Suku,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,3,NA,NA,NA 8 -7,Bemba,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,3,NA,NA,NA 9 -8,Nyakyusa,-9.5,34,7.994229965,1,2,1,0,1.359407527,-0.40857176,0,1,1,2,6,5,8,Intensive_Agr,3 10 -9,Hadza,-3.75,35.18,2.786357605,1,3,1,0,1.038279333,-0.167191931,6,4,0,0,0,1,4,HG,2 11 -10,Luguru,NA,NA,NA,NA,NA,3,NA,NA,NA,NA,NA,NA,NA,NA,3,NA,NA,NA 12 -11,Kikuyu,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,5,NA,NA,NA 13 -12,Ganda,0.33,32.5,1.036121133,3.5,2,NA,1,1.039498936,0.521125447,0,1,1,1,7,5,6,Intensive_Agr,8 14 -13,Mbuti,1.5,28.33,0.294615692,1,1,1,1,1.406768811,0.566510396,3,7,0,0,0,1,4,HG,3 15 -14,Nkundo_Mongo,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,3,NA,NA,NA 16 -15,Banen,NA,NA,NA,NA,NA,1,NA,NA,NA,NA,NA,NA,NA,NA,3,NA,NA,NA 17 -16,Tiv,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,3,NA,NA,NA 18 -17,Ibo,NA,NA,NA,NA,NA,1,NA,NA,NA,NA,NA,NA,NA,NA,3,NA,NA,NA 19 -18,Fon,NA,NA,NA,1,NA,1,NA,NA,NA,NA,NA,NA,NA,NA,3,NA,NA,NA 20 -19,Ashanti,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,3,NA,NA,NA 21 -20,Mende,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,3,NA,NA,NA 22 -21,Wolof,13.75,-15.33,3.044509334,2,3,3,0,-0.871992271,0.754006776,0,0,1,3,6,3,6,Horticulturalist,6 23 -22,Bambara,12.5,-7,6.772644427,NA,3,NA,0,-0.292002963,0.396749301,2,0,1,2,5,5,10,Agr_past,4 24 -23,Tallensi,10.66,-0.57,3.811400769,2,4,2,0,-0.011906629,0.47055019,1,0,0,2,7,5,6,Intensive_Agr,4 25 -24,Songhai,16.58,-1.67,29.92172658,NA,2,NA,0,-1.541326519,0.228921442,0,0,2,3,5,5,8,Agr_past,2 26 -25,Pastoral_Fulani,15,7.5,20.945364,3,3,2,0,-1.301598485,0.235338483,0,0,0,9,1,2,2,Pastoralist,4 27 -26,Hausa,10.5,7.5,2.752774799,4,4,2,0,0.313676203,0.386406705,1,0,0,3,6,6,6,Intensive_Agr,1 28 -27,Massa_Masa,NA,NA,NA,3,2,1,NA,NA,NA,NA,NA,NA,NA,NA,6,NA,NA,NA 29 -28,Azande,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,3,NA,NA,NA 30 -29,Fur_Darfur,13.5,25.5,12.98935722,2,2,1,0,-1.242422091,0.158305561,0,0,1,3,6,5,6,Intensive_Agr,1 31 -30,Otoro_Nuba,11.33,30.67,4.111629945,4,4,4,0,-0.724205135,0.59044116,0,2,0,2,6,5,8,Intensive_Agr,1 32 -31,Shilluk,NA,NA,NA,NA,NA,1,NA,NA,NA,NA,NA,NA,NA,NA,3,NA,NA,NA 33 -32,Mao,9.27,34.67,2.312127458,NA,2,1,0,-0.001028103,0.702238174,1,1,1,1,6,3,8,Horticulturalist,2 34 -33,MISSING_Kafa_Kaffa,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,5,NA,NA,NA 35 -34,Masai,-3.5,36.75,3.28556643,3,4,NA,0,1.083773715,-0.374438321,0,1,0,9,0,1,2,Pastoralist,2 36 -35,Konso,5.25,37.5,3.600164802,2,NA,1,0,0.026704249,0.272530062,0,0,0,2,8,5,4,Intensive_Agr,1 37 -36,Somali,9,47.25,6.748995934,2,2,NA,0,-1.305540471,0.484723755,0,0,0,9,1,5,2,Pastoralist,5 38 -37,Amhara,12.5,37.75,3.842058505,2,1,1,0,-0.339668453,0.268001654,0,0,1,3,6,5,6,Intensive_Agr,6 39 -38,Bogo,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,2,NA,NA,NA 40 -39,Kenuzi_Nubians,NA,NA,NA,3.5,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,6,NA,NA,NA 41 -40,Teda,20.5,17.5,45.56660607,3,4,4,0,-2.876038126,0.360967326,2,0,0,3,5,6,8,Agr_past,5 42 -41,Tuareg,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,6,NA,NA,NA 43 -42,Riffians,34.92,-3.25,35.37451605,2,2,3,0,-1.36930785,-0.19552153,0,1,1,3,5,5,8,Agr_past,8 44 -43,Egyptians,24.75,33,55.98120443,NA,4,2,0,-2.66273082,0.304432609,0,0,1,3,6,6,6,Intensive_Agr,1 45 -44,Hebrews,NA,NA,NA,2,2,1,NA,NA,NA,NA,NA,NA,NA,NA,5,NA,NA,NA 46 -45,Babylonians,NA,NA,NA,NA,2,NA,NA,NA,NA,NA,NA,NA,NA,NA,6,NA,NA,NA 47 -46,Rwala_Bedouin,33.25,38.5,80.1892964,3,4,NA,0,-2.214468868,-0.28498785,1,1,0,8,0,1,4,Pastoralist,2 48 -47,Turks,39.33,34.25,72.20179292,3,4,3,0,-0.411152163,-0.99179557,0,0,0,4,6,5,4,Intensive_Agr,3 49 -48,Gheg_Albanians,NA,NA,NA,NA,NA,2,NA,NA,NA,NA,NA,NA,NA,NA,5,NA,NA,NA 50 -49,Romans,NA,NA,NA,2,NA,1,NA,NA,NA,NA,NA,NA,NA,NA,5,NA,NA,NA 51 -50,Basques,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,5,NA,NA,NA 52 -51,Irish,53.5,-10,8.422231814,1,1,1,0,-0.030757432,-0.275576766,0,1,0,4,5,5,6,Agr_past,6 53 -52,Lapps,68.7,21.5,91.64819994,1,1,NA,0,-0.766077663,-1.598770422,0,2,2,6,0,1,8,Pastoralist,7 54 -53,Yurak_Samoyed,68,51.5,144.9889046,3,4,2,0,-0.976789891,-1.751425819,0,3,3,4,0,1,8,PrimarilyHG,9 55 -54,Russians,52.67,41.33,133.6090259,NA,4,1,0,0.241671524,-1.818936595,0,0,0,3,7,6,4,Intensive_Agr,3 56 -55,Abkhaz,43.13,40.77,50.15138085,NA,3,1,0,0.085190164,-0.723003519,1,0,0,6,3,5,6,Pastoralist,3 57 -56,Armenians,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,5,NA,NA,NA 58 -57,Kurd,36.5,44.5,120.0464559,1,1,NA,0,-1.252771783,-0.512474938,0,0,1,4,5,6,6,Agr_past,5 59 -58,Basseri,29,53,88.36224178,1,2,1,0,-1.957393684,-0.029256674,0,1,0,8,1,2,4,Pastoralist,3 60 -59,Punjabi_West,32.5,74,82.82722977,3,3,2,0,-1.213571865,0.153850258,0,0,0,2,8,5,4,Intensive_Agr,1 61 -60,Gond,19.63,80.92,18.28860998,1,1,1,1,-0.213627236,0.349559862,2,1,0,2,5,3,10,Agr_past,5 62 -61,Toda,NA,NA,NA,NA,NA,1,NA,NA,NA,NA,NA,NA,NA,NA,1,NA,NA,NA 63 -62,Santal,23.5,87.17,33.0366254,NA,2,3,0,-0.441295394,0.305015625,0,1,1,1,7,6,6,Intensive_Agr,6 64 -63,Uttar_Pradesh,25.92,83,50.37229997,NA,2,NA,0,-0.804549287,0.354458988,0,0,0,2,8,6,4,Intensive_Agr,1 65 -64,Burusho,36.43,74.58,135.0992607,NA,1,2,0,-0.639041621,-1.096099997,0,0,0,4,6,6,4,Intensive_Agr,3 66 -65,Kazak,42.5,75.5,119.7090915,3,3,1.5,0,-0.296912891,-1.496695491,0,1,0,8,1,2,4,Pastoralist,3 67 -66,Khalka_Mongols,47.17,96.08,243.6511698,2,4,NA,0,-0.20565455,-1.860169063,0,1,0,8,1,2,4,Pastoralist,3 68 -67,Lolo,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,5,NA,NA,NA 69 -68,Lepcha,27.5,89,39.42342866,1,1,1,0,1.213261184,-0.499568395,0,1,0,3,6,5,6,Intensive_Agr,3 70 -69,Garo,NA,NA,NA,NA,NA,1,NA,NA,NA,NA,NA,NA,NA,NA,3,NA,NA,NA 71 -70,Lakher,22.33,93,18.32906196,3,3,NA,1,0.497973312,0.24868449,0,2,1,1,6,3,8,Horticulturalist,4 72 -71,Burmese,22,95.67,16.7040508,NA,NA,1,0,0.025559785,0.20171909,0,0,0,2,8,6,4,Intensive_Agr,3 73 -72,Lamet,20,100.67,20.45249536,3,2,2,1,1.227529072,-0.243632926,1,2,0,2,5,3,10,Agr_past,3 74 -73,Vietnamese,20.5,106.25,31.54052685,1,4,NA,0,0.391418033,-0.121134706,0,0,2,2,6,6,8,Intensive_Agr,4 75 -74,Rhade,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,3,NA,NA,NA 76 -75,Khmer,NA,NA,NA,1,NA,1,NA,NA,NA,NA,NA,NA,NA,NA,5,NA,NA,NA 77 -76,Siamese,14,100.85,8.550653512,1,3,1,0,0.041075496,0.370618684,0,0,1,1,8,6,4,Intensive_Agr,4 78 -77,Semang,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1,NA,NA,NA 79 -78,Nicobarese,NA,NA,NA,NA,NA,1,NA,NA,NA,NA,NA,NA,NA,NA,4,NA,NA,NA 80 -79,Andamanese,NA,NA,NA,NA,NA,1,NA,NA,NA,NA,NA,NA,NA,NA,1,NA,NA,NA 81 -80,Vedda,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1,NA,NA,NA 82 -81,Tanala,NA,NA,NA,1,NA,1,NA,NA,NA,NA,NA,NA,NA,NA,6,NA,NA,NA 83 -82,Negri_Sembilan,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,6,NA,NA,NA 84 -83,Javanese,-7.7,112.22,0.434914972,1,3,NA,0,0.25272541,1.370815876,0,0,0,2,8,6,4,Intensive_Agr,1 85 -84,Balinese,-8.5,115.33,0.554782468,NA,2,1,0,-0.430263798,1.664369789,0,0,0,3,7,6,4,Intensive_Agr,1 86 -85,Iban,2,113,NA,NA,2,NA,1,0.931190849,1.483953279,0,0,2,2,6,3,8,Horticulturalist,4 87 -86,Badjau,5,120,0.219546455,NA,1,1,0,NA,NA,0,0,8,0,2,4,4,PrimarilyHG,2 88 -87,Toradja,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,3,NA,NA,NA 89 -88,Tobelorese,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,3,NA,NA,NA 90 -89,Alorese,NA,NA,NA,1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,3,NA,NA,NA 91 -90,Tiwi,-11.38,131,1.691451885,1,1,1,1,NA,NA,5,3,2,0,0,1,8,HG,5 92 -91,Aranda,-24.25,133.5,36.607207,2,2,NA,0,-1.38039561,-0.117636998,6,4,0,0,0,1,4,HG,5 93 -92,Orokaiva,-8.5,148,0.905342429,1,NA,2,1,0.695596149,1.052952599,1,1,2,1,5,4,10,Mixed,8 94 -93,MISSING_Kimam,-7.5,138.5,0.768917344,3,4,NA,0,-0.76201153,1.813735279,2,1,1,0,6,5,8,Intensive_Agr,7 95 -94,Kapauku,-4,136,0.360267781,1,NA,1,1,0.342954258,1.619022709,0,1,1,2,6,3,8,Horticulturalist,11 96 -95,Kwoma,NA,NA,NA,1,NA,1,NA,NA,NA,NA,NA,NA,NA,NA,4,NA,NA,NA 97 -96,Manus,-2.17,147.17,0.160421822,1,1,1,0,-1.266562874,2.358705991,0,0,9,1,0,2,2,HG,2 98 -97,New_Ireland,NA,NA,NA,NA,NA,1,NA,NA,NA,NA,NA,NA,NA,NA,4,NA,NA,NA 99 -98,Trobrianders,-8.54,151.01,0.810055509,4,4,2,1,-1.737923154,2.054854381,1,0,3,1,5,4,8,Mixed,4 100 -99,Siuai,-6.76,155.45,0.2837929,3,NA,1,1,-0.483457234,2.034076463,2,1,0,2,5,4,10,Mixed,4 101 -100,Tikopia,-12.29,168.83,0.642731268,3,4,2,1,NA,NA,0,0,5,0,5,4,4,Mixed,7 102 -101,Pentecost,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,4,NA,NA,NA 103 -102,Mbau_Fijians,-18,178.58,2.821134649,NA,1,1,1,NA,NA,1,0,5,0,4,4,6,PrimarilyHG,6 104 -103,Ajie,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,6,NA,NA,NA 105 -104,Maori,-35.33,174.17,9.378743708,3,4,3,1,NA,NA,3,1,2,0,4,3,10,PrimarilyHG,12 106 -105,Marquesans,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,4,NA,NA,NA 107 -106,Western_Samoans,-13.94,-171.77,0.623528039,2,4,1,1,NA,NA,0,0,3,1,6,4,6,Horticulturalist,2 108 -107,Gilbertese,NA,NA,NA,NA,NA,1,NA,NA,NA,NA,NA,NA,NA,NA,4,NA,NA,NA 109 -108,Marshallese,6.28,169.49,0.082967644,3,3,2,1,NA,NA,1,0,5,0,4,4,6,PrimarilyHG,4 110 -109,Trukese,7.34,151.6,0.086232883,NA,2,NA,1,NA,NA,0,0,5,0,5,4,4,Mixed,6 111 -110,Yapese,9.52,138.16,0.151060734,NA,4,NA,1,NA,NA,0,0,4,1,5,4,6,Mixed,7 112 -111,Palauans,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,4,NA,NA,NA 113 -112,Ifugao,16.83,121.17,3.017132284,1,3,1,1,-0.047927208,1.079881631,0,2,1,1,6,6,8,Intensive_Agr,4 114 -113,Atayal,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,3,NA,NA,NA 115 -114,Chinese,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,6,NA,NA,NA 116 -115,Manchu,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,5,NA,NA,NA 117 -116,Koreans,37.6,126.42,100.1607204,2,2,3,0,-0.010504153,-0.77598861,0,0,2,2,6,6,8,Intensive_Agr,1 118 -117,Japanese,34.67,133.67,67.50235953,1,2,NA,0,-0.028266043,-0.309220543,0,0,0,1,9,6,2,Intensive_Agr,1 119 -118,Ainu,42.83,143,108.1514634,2,1,2,0,-0.103598293,-1.17498514,2,3,4,0,1,3,10,HG,10 120 -119,Gilyak,53,142,229.6694279,4,3,2,0,0.032461396,-1.825106828,2,3,5,0,0,1,8,HG,4 121 -120,Yukaghir,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1,NA,NA,NA 122 -121,Chukchee,66.5,179.6,158.9184121,2,3,1,0,NA,NA,0,2,3,5,0,1,8,Mixed,5 123 -122,Ingalik,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1,NA,NA,NA 124 -123,Aleut,NA,NA,NA,3,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,1,NA,NA,NA 125 -124,Copper_Eskimo,68.72,-112.53,287.366131,4,4,2,0,-1.33027634,-1.615649901,0,4,6,0,0,1,4,HG,7 126 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