READ ME ARTICLE: Social network architecture and the tempo of cumulative cultural evolution AUTHORS: Cantor M, Chimento M, Smeele SQ, He P, Papageorgiou D, Aplin LM, Farine DR. CONTACT: Mauricio Cantor 1,2 Michael C. Chimento 3 Simeon Q. Smeele 3,4 Damien R. Farine 5,6,7,8 1 Department for the Ecology of Animal Societies, Max Planck Institute of Animal Behavior, Konstanz, Germany 2 Departamento de Ecologia e Zoologia, Universidade Federal de Santa Catarina, Florianópolis, Brazil 3 Cognitive & Cultural Ecology Research Group, Max Planck Institute of Animal Behavior, Radolfzell, Germany 4 Department of Human Behavior, Ecology and Culture, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany 5 Department of Collective Behaviour, Max Planck Institute of Animal Behavior, Konstanz, Germany 6 Department of Biology, University of Konstanz, Konstanz, Germany 7 Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany 8 Department of Evolutionary Biology and Environmental Science, University of Zurich, Switzerland DESCRIPTION: Here we provide the data used in this article to generate the results and the figures. These data were generated using two agent-based models, and here we saved data form 5,000 model runs. The Python and R code to replicate these data are available at https://github.com/simeonqs/Social_network_architecture_and_the_tempo_of_cumulative_cultural_evolution FILE: df_TTC_m1.Rda This file contains the outputs from agent-based model 1 in Python on the Time to Recombination (TTC). This is a 345000 by 18 data frame, with the number of the simulation, the time step at which recombination was reached, the type of social network architecture, the population size, the social connectivity (degree), the agents' identification, their discovery and innovation levels, from two cultural lineages and the network properties (density, degree, clustering, modularity, mean distance) FILE: df_TTC_m2.Rda This file contains the outputs from agent-based model 2 in Python on the Time to Recombination (TTC). This is a 345000 by 18 data frame, with the number of the simulation, the time step at which recombination was reached, the type of social network architecture, the population size, the social connectivity (degree), the agents' identification, their discovery and innovation levels, from two cultural lineages and the network properties (density, degree, clustering, modularity, mean distance) FILE: df_TTC_TTD_m2.Rda This file contains the outputs from agent-based model 2 in Python on the Time to Recombination (TTC) and Time to Diffusion (TTD). This is a 345000 by 13 data frame, with the number of the simulation, the type of social network architecture, the population size, the social connectivity (degree), the time (epoch) at which TTC was reached, the time (epoch) at which TTD was reached, the difference between these two (delta TTC and TTD), and the network properties (density, degree, clustering, modularity, mean distance) FILE: results_ABM1_2020-08-14_13_06_41.RData This file contains the outputs (object 'timings_all') from agent-based model 1 in R on the Time to Recombination (TTC). This is a 7500 by 8 data frame, with the type of social network architecture, the population size, the social connectivity (degree), the iteration, the time (epoch) at which TTC was reached, and the innovation type. FILE: results_ABM1_div_2020-08-14_13_06_41.RData This file contains the outputs (object 'div') from agent-based model 1 in R on the diversity of cultural products. This is a 348440 by 6 data frame, with the combined type of social network architecture, population size, and social connectivity (degree), the time step, the product type (medicin), the cultural lineage of that product, the proportion of the population that achieved that product, and the progress score of the product. FILE: props_all.RData This file contains the outputs (object 'props_all') from agent-based model 1 in R on the diversity of cultural products. This is a 15946240 by 9 data frame, with the type of social network architecture, the population size, the social connectivity (degree), the iteration, the time (epoch) at which TTC was reached, the time step, the product type (medicin) with the cultural lineage of that product (A or B), the proportion of the population that achieved that product.