Datasets:
Where the shipped data came from
Everything here is third-party, redistributed under its own terms. The MIT licence on this repository covers the code, not these files.
Recombination map — maps/decode2019_avg_hg38.bed
Halldórsson et al., Science 363:eaau1043 (2019). 4,531,535 crossovers
localised in Icelandic pedigrees, average map resolution 682 bp, native GRCh38.
Obtained as recombAvg.bw from UCSC:
https://hgdownload.soe.ucsc.edu/gbdb/hg38/recombRate/recombAvg.bw
Rebuilt by maps/build_decode2019.py, which writes cM/Mb — the source units,
so the file stays checkable against UCSC — and applies two bounds. Rates are
capped at 1,000 cM/Mb, because 34 intervals genome-wide exceed 10,000 with a
median width of one base pair: those are the artefact of localising a crossover
to a point, not hotspots, and both this ingest and msprime's own RateMap
reject rates above 1e-4 per bp. Rates are floored at 0.1 cM/Mb, because a
pedigree map reports zero where no crossover was observed, and that is sparsity
rather than an absence of recombination — 18.4% of 1 kb cells on chr1 between
20 and 40 Mb read exactly zero, while in those same cells HapMap II reports a
median 6.6% of its mean and pyrho CEU 17.0%, neither ever zero.
A pedigree map is used rather than an LD-based one on purpose. HapMap II and pyrho are inferred under a demographic model, and this project infers demography from linkage; using them would be circular.
Mutation map — maps/roulette_2023_autosomes_hg38_1000bp.bed
Seplyarskiy et al., Nature Genetics (2023). Roulette models the mutation rate
from local sequence context and reports it in 1 kb bins, with a pseudo-R² of
0.86 against observed variation. Build script in maps/build/.
Inferred human histories — templates/human_ne/
191 curves, used as the one part of the demography mixture that nobody designed, and as the calibration target for every synthetic recipe.
| set | method | source |
|---|---|---|
| 26 populations | SMC++ | Terhorst, Kamm & Song, Nature Genetics 49:303 (2017), applied to 1000 Genomes |
| 154 populations | phlash | Terhorst, Nature Genetics (2025), figure 7a |
| 7 superpopulations | phlash | the same, figure 7b |
| 2 | MSMC2 | Yoruba–French, published estimates |
| 2 | MSMC-IM | Yoruba–French, published estimates |
Times are given in generations; where a source published years, the conversion used a generation time of 29 years and the file records it.
Catalogue models — templates/stdpopsim_single_pop/
The 18 single-population demographic models in the stdpopsim catalogue
(Adrion et al., eLife 9:e54967, 2020), across 8 species: Anopheles gambiae,
Arabidopsis thaliana, Bos taurus, Drosophila melanogaster, Homo
sapiens, Mus musculus, Papio anubis and Phocoena sinus. Frozen by
build_stdpopsim_single_pop.py from stdpopsim 0.3.0.
They are frozen rather than imported at runtime because they are the held-out evaluation set: what matters is that the same 18 trajectories are available to every run on every machine, and a runtime import makes that depend on whichever version happens to be installed. Multi-population models are skipped — reading one population out of a structured model would silently turn migration into size change.
Three trajectories carry an effective size outside this project's declared band and are multiplied by one constant to fit it: A. gambiae by 0.246, the Indian M. m. castaneus by 0.197, and one Holstein model by 1.111. A uniform factor leaves the shape — which is what generalisation is measured on — bit-for-bit intact, and each factor is recorded in the file.