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ZENODO
Software . 2024
License: CC BY
Data sources: ZENODO
ZENODO
Software . 2024
License: CC BY
Data sources: Datacite
ZENODO
Software . 2024
License: CC BY
Data sources: Datacite
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Software for the paper "Cultural transmission, networks, and clusters among Austronesian-speaking peoples

Authors: Macdonald, Joshua; Blanco-Portillo, Javier; Feldman, Marcus; Ram, Yoav;

Software for the paper "Cultural transmission, networks, and clusters among Austronesian-speaking peoples

Abstract

Abstract With its linguistic and cultural diversity, Austronesia is important in the study of evolutionary forces that generate and maintain cultural variation. By analyzing publicly available datasets, we have identified four classes of cultural features in Austronesia and distinct clusters within each class. We hypothesized that there are differing modes of transmission and patterns of variation in these cultural classes and that geography alone would be insufficient to explain some of these patterns of variation. We detected relative differences in the verticality of transmission and distinct patterns of cultural variation in each cultural class. There is support for pulses and pauses in the Austronesian expansion, a west-to-east increase in isolation with explicable exceptions, and correspondence between linguistic and cultural outliers. Our results demonstrate how cultural transmission and patterns of variation can be analyzed using methods inspired by population genetics. Software version, package, and license informationThese files include all of the data nessecary to generate the results of our paper as well as useful python/matlab functions. Data files are organized by file type. Be sure to check the beginning of the program files for data and package dependicies.For the raw unprocessed data as well as feature encodings download the original data from dplace: https://github.com/D-PLACE All code in this repository is available under a Creative Commons International 4.0 license with attribution. Authors wishing to modify this code for their own purposes should cite the version of this work archived in Zenodo. The MATLAB scripts in this repository use only base MATLAB install modules and were written using release R2020b. We have tested the code with release R2023b and found no compatibility issues. The packages used in this notebook are dirichlet, pandas, numpy, seaborn, matplotlib, py-pcha, MNE, panel, scipy, sklearn, and sys. We have tested this software with python 3.10.14 using versions 0.9, 2.2.2, 1.26.4,0.13.2, 3.9.2, 0.1.3, 1.7.1, 1.4.5, 1.13.1, and 1.5.2 of these packages respectively and found no compatibility issues. Authors Joshua C. Macdonald, Javier Blanco-Portillo, Marcus W. Feldman, and Yoav Ram Corresponding authors contactYR: yoavram-AT-tauex.tau.ac.il, MWF: mfeldman-AT-stanford.edu

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average