Carneiro's global dataset of cultural traits in ethnographic and historical societies: a digital edition.

IF 7.2 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2026-10-02 DOI:10.1038/s41597-026-08212-0

Václav Hrnčíř, Robert Forkel, Russell D Gray

{"title":"Carneiro's global dataset of cultural traits in ethnographic and historical societies: a digital edition.","authors":"Václav Hrnčíř, Robert Forkel, Russell D Gray","doi":"10.1038/s41597-026-08212-0","DOIUrl":null,"url":null,"abstract":"<p><p>The late Robert L. Carneiro was a prominent anthropologist who devoted his life to the study of political evolution and the evolution of complex societies. In the 1960s and 1970s, he and his colleagues created a cross-cultural dataset for their Scale Analysis project, collecting ethnographic and historical data on hundreds of cultural traits in more than a hundred societies worldwide. Although Carneiro presented the results of the project in several papers, the dataset itself has not yet been published. Here we present a digital version of two editions of the dataset, based on Carneiro's documentation stored in the archives of the American Museum of Natural History. The 4th edition contains 127 societies and 354 cultural traits, and the 6th edition features 74 societies and 618 traits. Both represent unique sets of coded cross-cultural information that can be used as a comparative source to other cross-cultural datasets, as a starting point for one's own collection of cross-cultural data, and for quantitative testing of cultural evolutionary hypotheses.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"13 1","pages":""},"PeriodicalIF":7.2000,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13633262/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-026-08212-0","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}

引用次数: 0

Abstract

The late Robert L. Carneiro was a prominent anthropologist who devoted his life to the study of political evolution and the evolution of complex societies. In the 1960s and 1970s, he and his colleagues created a cross-cultural dataset for their Scale Analysis project, collecting ethnographic and historical data on hundreds of cultural traits in more than a hundred societies worldwide. Although Carneiro presented the results of the project in several papers, the dataset itself has not yet been published. Here we present a digital version of two editions of the dataset, based on Carneiro's documentation stored in the archives of the American Museum of Natural History. The 4th edition contains 127 societies and 354 cultural traits, and the 6th edition features 74 societies and 618 traits. Both represent unique sets of coded cross-cultural information that can be used as a comparative source to other cross-cultural datasets, as a starting point for one's own collection of cross-cultural data, and for quantitative testing of cultural evolutionary hypotheses.

查看原文本刊更多论文

卡内罗关于人种学和历史社会文化特征的全球数据集：数字版。

已故的罗伯特·l·卡内罗（Robert L. Carneiro）是一位杰出的人类学家，他毕生致力于研究政治演变和复杂社会的演变。在20世纪60年代和70年代，他和他的同事为他们的规模分析项目创建了一个跨文化数据集，收集了全球100多个社会中数百种文化特征的民族志和历史数据。尽管卡内罗在几篇论文中展示了该项目的结果，但数据集本身尚未发表。在这里，我们展示了两个版本数据集的数字版本，这些数据集是基于卡内罗保存在美国自然历史博物馆档案中的文件。第4版收录了127个社会和354个文化特征，第6版收录了74个社会和618个文化特征。两者都代表了独特的编码跨文化信息集，可以作为其他跨文化数据集的比较来源，作为自己收集跨文化数据的起点，并用于文化进化假设的定量测试。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.