data from: Genomic architecture controls multivariate adaptation to climate change

All data used for the 2024 study "Genomic architecture controls multivariate adaptation to climate change" by Drew E. Terasaki Hart and Ian J. Wang.

The study presents simulation results (created using [Geonomics](https://geonomics.readthedocs.io/)) across 18 simulation scenarios of interest, defined by the complete cross of 3 values of linkage (strong, weak, independent), 3 values of polygenicity (low, moderate, high) and 2 values of genotypic redundancy (low and high).

The archived data is a single zipfile comprising two subdirectories:

`output_100_final_for_analysis`, which contains all simulation outputs from each of the 100 main and null (i.e., control) iterations run for the high-genotypic-redundancy scenarios, and all identical outputs for the lo1-genotypic-redundancy scenarios:

3 CSV files:

a table of directional gene flow data, for all 9 main and 9 null scenarios for the given iteration's redundancy level (filename formatted as 'output_PID-_DIR.csv')

 a table of summary statistic time series, for all 9 main and 9 null scenarios for the given iteration's redundancy level (filename formatted as 'output_PID-_TS_DATA.csv'

a table of summary statistics (single values per iteration, rather than time series), for all 9 main and 9 null scenarios for the given iteration's redundancy level (filename formatted as 'output_PID-_TS_DATA.csv'

a set of 180 directories (with names formatted as 'GNX_mod-_L_G_its0_randID_'), generated by Geonomics, containing the automated CSV and VCF outputs produced by Geonomics.

`analysis` contains all analysis outputs (processed and summarized data, statistical tests, and figures) derived from that data and used to produce the results presented in the paper.