Datasets:

Larrivhan
/

NeCast-simulations

license: cc-by-4.0
pretty_name: NeCast simulations
tags:
  - population-genetics
  - demographic-inference
  - effective-population-size
  - coalescent
  - msprime
  - simulation
size_categories:
  - 100K<n<1M
configs:
  - config_name: index
    data_files: index/windows.parquet
    default: true

NeCast simulations

Simulated genotype windows used to train and test NeCast, a neural network that estimates the size history of a population from 32 diploid genomes together with recombination and mutation maps. There are 109,248 windows. Each is 30 Mb of sequence sampled from 64 diploid individuals, with sequencing-like errors already applied, and each comes with its true size history. The files the network was trained on are included, as are the code and configuration that made the windows.

What the corpus covers. a, Eight training windows drawn at random from each of the twelve families of size history, with the number of windows in each family. Each line is a window's 64 interval labels, drawn through the interval centres. b, Windows by the type of map stored with them. Every window carries sequencing-like errors except those with constant maps at the baseline rates. c, Variant sites per window, false sites included.

path	what it holds	size
`windows/000/` to `windows/027/`	the 109,248 windows, four per `.npz` file, with per-window metadata in the `.jsonl.gz` file of the same name	567 GB
`windows/receipts.jsonl.gz`	the receipt written when each file was generated, with its SHA-256	small
`index/windows.parquet`	one row per window: where it is stored, its split, how it was simulated	0.05 GB
`index/histories.parquet`	the epochs of every simulated history, by window	0.93 GB
`splits/`	the train / development / reserved split, and the windows used to train, validate and test the final network	0.2 GB
`features/`	what the final network reads, for the 104,996 train and development windows	15.7 GB
`additional/`	the same inputs for 1, 2, 4, 8 and 16 individuals, and a variant of the PSMC+ posterior	63.1 GB
`generator/`	the simulator code, the generation plan and the map scripts	small
`tools/necast_windows.py`	a reader that needs only numpy	small
`logs/`	build logs of the feature files	small
`figures/`	the figures on this card	small
`SHA256SUMS`	a checksum for every file except this card	small

The windows are grouped by generation chunk: windows/NNN/ holds chunks NNN000 to NNN999. Window sim_XXXXXXXX is member XXXXXXXX mod 4 of file fragment_{XXXXXXXX div 4}. The run planned 110,000 windows in 27,500 chunks of four; 188 chunks were not generated, so window numbers have gaps. The index lists every window that exists.

How the windows were simulated

Sequence and sample. 30 Mb, 64 diploid individuals (128 haplotypes). msprime 1.4.2 with the default coalescent with recombination (the full ancestral recombination graph), a binary mutation model, the window's true recombination and mutation maps, and no gene conversion.
Size histories. Twelve families, named in generator_recipe: constant (constant), exponential (exponential), one size change (size_event), several changes (multi_event), bottleneck (bottleneck), event on a trend (event_on_background), random walk (spectral_walk), sawtooth (sawtooth), zigzag-like (literature_sawtooth), founder chain (ooa_chain), perturbed published human histories (empirical_human_ne_template; 191 curves inferred by SMC++, phlash, MSMC2 and MSMC-IM), and the 18 single-population models of the stdpopsim catalogue (stdpopsim_single_pop). Sizes stay between 100 and 1,000,000. The realised parameters of every history are in the metadata.
Maps (map_type). A constant map at the baseline rates (constant_clean), constant rates drawn at random (constant_random_global), synthetic heterogeneous maps (synthetic_heterogeneous), or 30 Mb slices of the deCODE 2019 recombination map and the Roulette mutation map taken from the same chromosomal interval (empirical_slice).
Errors (noise_profile). mild_noise windows have genotype errors, missing calls, false variant sites and phase switches within individuals, at rates drawn per window and recorded in the metadata (noise_* fields). clean windows, which are the constant_clean ones, have none.
Maps stored with a window are the maps a method is given. Constant maps are stored exactly. Synthetic and empirical maps are stored as the true map seen through noise: a log-normal factor with mean one on short cells (observed_cell_bp), smaller for the mutation map, and for synthetic maps some hotspots left out. The noise size is in the metadata (observed_noise_sigma, and rec_slice_* and mut_slice_* for empirical slices). The true maps are not stored.
Labels. target_log10_ne gives, for each of 64 time intervals spaced evenly in log time between 50 and 500,000 generations ago, the average of log10 Ne over the interval (averaged in log time) of the simulated history. The file also stores the true number of pairwise coalescences and the total branch length in each interval.

Splits and the windows used by the final network

Windows were assigned by a hash of their identifier: 100,000 train, 5,000 development and 4,248 reserved. Four train windows have no PSMC+ output and belong to no split (split = none in the index), leaving 99,996 train windows.

The final NeCast network was fitted on 89,996 train windows. Another 500 train windows picked the training epoch (stage = validation), and 500 development windows were the test set (stage = test); both sets of 500 sit at evenly spaced quantiles of the number of variant sites. splits/stages.json lists them by sample ID and by feature row.

The 4,248 reserved windows were never used to train, select or score a network. Their PSMC+ output was computed (it is in features/psmcplus_store/), but they were left out of every other feature file. Now that they are public, they no longer serve as an untouched test set.

Reading a window

One window, sim_00000016: a perturbed published human history, slices of the deCODE and Roulette maps, and errors. a, Diploid genotypes of the 64 individuals at the 133 variant sites between 15.00 and 15.10 Mb: 0 pale grey, 1 light purple, 2 purple; missing calls are empty cells. b, The recombination and mutation maps stored with the window, averaged in 300-kb bins. c, The simulated history (grey) and its 64 interval labels (black), which are the network's targets.

from huggingface_hub import hf_hub_download
import sys

path = hf_hub_download("Larrivhan/NeCast-simulations", "windows/000/fragment_00000000.npz", repo_type="dataset")
hf_hub_download("Larrivhan/NeCast-simulations", "windows/000/fragment_00000000.jsonl.gz", repo_type="dataset")
reader = hf_hub_download("Larrivhan/NeCast-simulations", "tools/necast_windows.py", repo_type="dataset")
sys.path.insert(0, reader.rsplit("/", 1)[0])
from necast_windows import Fragment, diploid_genotypes, TIME_EDGES

frag = Fragment(path)
w = frag.window(0)
w["genotypes"].shape       # (n_sites, 128): 0/1, haplotypes 2i and 2i+1 are individual i
w["missing"].sum()         # missing calls (their genotype bit is 0)
g = diploid_genotypes(w)   # (n_sites, 64): 0, 1, 2, or -1 where a call is missing
w["recombination_map"]     # breakpoints (bp) and rates per bp per generation
w["target_log10_ne"]       # 64 labels on the intervals TIME_EDGES
w["metadata"]["generator_recipe"]

The index is a parquet table, so pandas.read_parquet("index/windows.parquet") or the datasets library will load it. Filter it to pick windows, then read only the files you need.

File format

Each fragment_XXXXXXXX.npz holds four windows; ragged arrays are concatenated, and an *_offsets array of length 5 marks where each window starts.

array	type and shape	meaning
`sample_id`	str (4)	window identifiers
`variant_positions_bp`, `variant_offsets`	uint32, int64	site positions; window k is `[off[k], off[k+1])`
`genotype_packed`	uint8 (sites, 16)	128 haplotypes, 8 per byte; haplotype h is bit `h % 8` of byte `h // 8` (`numpy.unpackbits(..., bitorder="little")`)
`missing_flat_idx`, `missing_offsets`	uint32, int64	missing calls as row-major indices into the window's (sites, 128) matrix; the genotype bit of a missing call is 0
`obs_rec_pos`, `obs_rec_rate`, `obs_rec_offsets`	float64, float32, int64	recombination map: breakpoints in bp and the rate (per bp per generation) between them; window k has one rate fewer than breakpoints, so rates for window k start at `sum(len_j - 1 for j < k)`
`obs_mut_pos`, `obs_mut_rate`, `obs_mut_offsets`	the same	mutation map
`target_log10_ne`	float32 (4, 64)	labels
`pair_coalescences_per_bin`, `branch_length_per_bin`	float32 (4, 64)	true pairwise coalescences and branch length per interval
`pair_coalescences_outside`	float64 (4, 2)	pairwise coalescences before the first and after the last interval
`sequence_length`, `n_haplotypes`, `packed_hap_bytes`	int	30,000,000; 128; 16
`source_id`, `demography_type_id`, `generator_recipe_id`	int16 (4)	codes; the names are in the metadata
`generator_schema_version`, `missing_storage`		format version (1) and the missing-call encoding

Variant sites include the false sites added as errors. Positions are sorted; a few windows have two sites at the same position.

The .jsonl.gz file has one JSON object per window with every parameter of its history, maps and errors. The index carries the same fields, flattened: nested objects become noise_*, rec_slice_*, mut_slice_* and similar columns, and short lists are stored as JSON text. The epochs of each history (demography_breaks in generations ago, demography_values and demography_growth_rates, as passed to msprime) are in index/histories.parquet as numeric lists, one row per window.

Inputs and labels. A method under test should read only the genotypes, the site positions, the stored maps, the sequence length and the sample size. Every other field describes how the window was made (the history, the errors, the map type, the labels) and is there for selecting windows and for scoring.

Feature files (`features/`)

All feature files cover the 104,996 train and development windows in one order: feature row r is window splits/stages.json["feature_sample_ids"][r], and the index gives it as feature_row. NeCast reads 32 of the 64 individuals in a window; which 32 is fixed per window by a seeded permutation and stored in features/level32/unphased_evidence_cache.npz as ladder_columns.

registry/ locates every train and development window in the fragment files (raw_index.jsonl), with a byte-exact read-back check for each one.
level32/unphased_evidence_cache.npz holds summary statistics of the 32 individuals: the folded site frequency spectrum, projected to 32 haplotypes to absorb missing calls (folded_sfs_counts); linkage disequilibrium between sites 1 to 64 sites apart, by genetic distance (ld_features, ld_pair_counts, with ld_bin_edges_morgan and ld_lags); lengths of identity-by-state tracts between pairs of individuals in 7 kinds and powers-of-two length classes (ibs_counts, ibs_bin_edges_bp); a Watterson estimate of the size level (nw_log10), which anchors the network's output; per-window context values (context); and the labels (targets). features and coordinates hold statistics over 32-kb sub-windows that the final network does not read. The network's site encoder reads the genotypes of the 32 individuals straight from the window files, found through registry/.
psmcplus_posterior/level32.npz holds PSMC+ posteriors for the same 32 individuals, decoded with the window's maps on the 64 intervals. The array is named sidecar (104,996 × 256): columns 0–63 are the mean over individuals of the posterior share of the window in each interval with a flat prior, 64–127 their standard deviation, and 128–255 the same after one refit of the prior. sidecar_present marks rows with a value.
psmcplus_store/shards/ is the PSMC+ output these posteriors were cut from: every window (reserved ones included), four random sets of individuals, and sample sizes of 1, 2, 4, 8, 16 and 32. Each shard has sample_id, features (rows, 4 sets, 6 sizes, 256), flags (the number of individuals scored in each pass), theta_cl and bin_cl (the diversity θ and the bin size in bp the decoder used) and meta (mean mutation rate, mean recombination rate, number of sites, sequence length, seconds, and four counters). builder/ has the scripts that made them and logs.tar their logs.

Additional files (`additional/`)

sample_size_levels/ has the summary statistics and PSMC+ posteriors for 1, 2, 4, 8 and 16 individuals (the first individuals of the same permutation). They were built for an earlier design in which the network read a variable number of individuals; the final network reads 32.
psmcplus_converged/ has a PSMC+ posterior whose prior was refitted five times in a row instead of once, for 13,500 windows: a 12,500-window training subset spread evenly over variant counts, and the 500 validation and 500 test windows. The scripts and checks that made it are included.

Generator (`generator/`)

generator/code/ is the simulator exactly as it ran (MIT licence), and corpus_plan.json the generation plan: configuration, seeds, and checksums of the maps and templates. The simulator shipped third-party data files: the deCODE and Roulette maps, the published human histories and the stdpopsim trajectories. They are not copied here. generator/EXCLUDED_THIRD_PARTY.tsv lists each one with its size, SHA-256 and source. The scripts that build the deCODE map and the stdpopsim trajectories are in generator/code/simulator/assets/, and generator/roulette_map/ has the script that averaged the published Roulette rates into the 1-kb map used here.

Checksums

sha256sum -c SHA256SUMS checks every file. The checksums of the window files also match the receipts written when each file was generated (windows/receipts.jsonl.gz).

Licence and sources

The files here are released under CC BY 4.0. Please also cite the sources the windows build on:

msprime: Baumdicker et al. (2022), Genetics 220:iyab229.
deCODE recombination map: Halldorsson et al. (2019), Science 363:eaau1043.
Roulette mutation rates: Seplyarskiy et al. (2023), Nature Genetics.
Human size histories used as templates: Terhorst, Kamm and Song (2017), Nature Genetics 49:303 (SMC++); Terhorst (2025), Nature Genetics 57:2570 (phlash); Wang et al. (2020), PLOS Genetics 16:e1008552 (MSMC-IM).
stdpopsim: Adrion et al. (2020), eLife 9:e54967.
PSMC+: Cousins et al. (2024), bioRxiv.

Citation

The NeCast paper is in preparation; its reference will be added here.