One challenge provided by CoSSci comparative analysis is to discern, in complex networks of coded relationships among societies and variables, mappable spatial and environmental effects and phylogenetic historical trees that help reveal evolutionary adaptations and causal effects of environmental and sociocultural processes. The goal here, using controls for autocorrelation and complete imputation of missing data, is to achieve understanding of scientific and humanistic principles that can produce research findings by students and researchers using canonical datasets such as the Standard Cross-Cultural Sample (n=186), Ethnographic Atlas (n=1270), Binford Foragers (n=339) among others. This aspect of study begins with a single dependent variable and an initial set of independent variables and expands to networks of variables that help to uncover causal effects. In a second complex network domain of study CoSSci uniquely provides for large or massive networks k-cohesive subset analysis which has been shown to have causal effects for a myriad of cooperative phenomena. R software allows analysis of large-scale kinship networks. Telmo's CNRS synthetic tools parse kin networks into parameters that can be entered into the comparative databases as causal or effect variables in comparative analysis.

Researchers should cite this work as follows:

• (2024), "UCI Complex Social Science Gateway (CoSSci)," https://sciencegateways.org/resources/ucicomplexsocialsciencegatewaycossci.

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1. Atlas
2. CNRS
3. CoSSci
4. k-cohesive