The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
K: int64
arm: string
config_id: string
data_arm: string
dataset: string
g: int64
method: string
mu: double
mu_source: string
n: int64
options: struct<>
r: double
regions: list<item: string>
child 0, item: string
rep: int64
schema: string
settings: struct<file: string, method_call_sha256: string>
child 0, file: string
child 1, method_call_sha256: string
stage: string
utc: string
lock_of_record_sha256: string
status: string
host: string
lock_sha256: string
slurm_job_id: string
lock: string
drift: struct<>
compared_keys: int64
parts: list<item: string>
child 0, item: string
to
{'compared_keys': Value('int64'), 'drift': {}, 'host': Value('string'), 'lock': Value('string'), 'lock_of_record_sha256': Value('string'), 'lock_sha256': Value('string'), 'parts': List(Value('string')), 'schema': Value('string'), 'slurm_job_id': Value('string'), 'status': Value('string'), 'utc': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 544, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 3052, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 558, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 361, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
K: int64
arm: string
config_id: string
data_arm: string
dataset: string
g: int64
method: string
mu: double
mu_source: string
n: int64
options: struct<>
r: double
regions: list<item: string>
child 0, item: string
rep: int64
schema: string
settings: struct<file: string, method_call_sha256: string>
child 0, file: string
child 1, method_call_sha256: string
stage: string
utc: string
lock_of_record_sha256: string
status: string
host: string
lock_sha256: string
slurm_job_id: string
lock: string
drift: struct<>
compared_keys: int64
parts: list<item: string>
child 0, item: string
to
{'compared_keys': Value('int64'), 'drift': {}, 'host': Value('string'), 'lock': Value('string'), 'lock_of_record_sha256': Value('string'), 'lock_sha256': Value('string'), 'parts': List(Value('string')), 'schema': Value('string'), 'slurm_job_id': Value('string'), 'status': Value('string'), 'utc': Value('string')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
NeCast benchmark
The simulated benchmark on which the NeCast paper compares NeCast with phlash, SMC++, FitCoal and MSMC2: the true histories, the simulated windows, the exact inputs each method received, every estimate, the scores, the timing runs, eight-window joint fits and twelve scenarios with population structure.
Contents
| path | what it holds | size |
|---|---|---|
histories/ |
the 33 size histories: definition and true curve of each | small |
windows/{a1,a2,b}/<history>/ |
the simulated windows of each setting | 2.0 GB |
necast_inputs/{a1,a2,b}/<history>/ |
summary statistics and PSMC+ posteriors NeCast read | 0.15 GB |
method_inputs/{a1,a2}/<history>/ |
the files given to phlash, SMC++, FitCoal and MSMC2 | 7.0 GB |
estimates/<method>/{a1,a2}/<history>/ |
every fit, with its settings and receipt; estimates/necast/ also covers b |
1.8 GB |
map_variants/<history>/<variant>/ |
setting b run by NeCast with five versions of the maps |
5.0 GB |
scores/{a1,a2,b}/<history>/ |
per-window scores and every method's curve on the 64 intervals | small |
results/ |
the tables behind the paper's benchmark figures (main/, supplement/) |
small |
timing/ |
the timing runs on CPU and GPU nodes | 0.5 GB |
joint_fits/ |
phlash fitted to eight windows at once, and NeCast averaged over the same eight | small |
structure/ |
12 scenarios with population structure, 11 replicates each | 7.4 GB |
figures/ |
the figure on this card | small |
SHA256SUMS |
a checksum for every file except this card | small |
The three settings
All windows are 30 Mb long, hold 64 diploid individuals, and were made with the same simulator
as the NeCast training data (msprime 1.4.2, the coalescent with recombination), with seeds that
no training window used. Every method read the same 32 individuals of each window; they are
listed in each input's meta.json (columns).
| setting | histories | rates and maps | errors | methods |
|---|---|---|---|---|
a1 |
33 | uniform, μ = 1.25 × 10⁻⁸ and r = 10⁻⁸ per bp per generation | none | NeCast, phlash, SMC++, FitCoal, MSMC2 |
a2 |
12 | as a1 |
genotype errors, missing calls, false sites and phase switches at the levels of the training data, added to the a1 windows |
the same |
b |
33 | slices of the deCODE 2019 recombination map and the Roulette mutation map | as a2 |
NeCast |
Each history folder holds 12 windows; 8 are scored, and the scores list which ones. In setting
b, NeCast read each window five times (map_variants/): with the stored maps (v1_true),
with both maps flat at the genome-wide means (v3_flat_genomewide) or at the window's own means
(v3b_flat_rowmean), with the recombination map and a flat mutation map
(v4_rec_observed_mut_genomewide), and with the mutation map and a flat recombination map
(v5_mut_observed_rec_genomewide).
The 33 histories fall into five families: 15 human and other realistic models (empirical human
curves, out-of-Africa models and the stdpopsim single-population models), 7 bottlenecks, 6 steps
and slow changes, 3 constant sizes and 2 oscillations. histories/<name>/definition.json gives
each one; trajectories.csv and histories/scenarios_v2_files/v2_all_targets.npz give the truth.
histories/scenario_ids.json lists the histories in each setting.
The 33 histories, as the 64-interval curves every method is scored against, ordered by
family: human and other realistic models, bottlenecks, steps and slow changes, constant sizes,
oscillations. The 12 marked a2 are also in setting a2; all 33 are in a1 and b.
Methods and settings
| folder | method | setting |
|---|---|---|
estimates/necast/ |
NeCast, final weights | as released in Larrivhan/NeCast |
estimates/phlash/ |
phlash | its defaults |
estimates/smcpp/ |
SMC++ | the setting tune_d4_rp3, chosen on two tuning histories, at the default stopping tolerance |
estimates/smcpp_converged/ |
SMC++ | the same, with stopping tolerance 10⁻⁶ (supplement) |
estimates/fitcoal/ |
FitCoal | its defaults |
estimates/msmc2/ |
MSMC2 | time patterning 1*2+20*1+1*10 (supplement) |
A fit folder (<history>/K1/g<window>/n32/) holds the estimated curve (curve.npz: time
points t_left, sizes ne, the method's version and runtime), the fit settings
(fit_spec.json), a receipt, and the method's own output where it has one (for phlash, its
posterior samples). NeCast's predictions.npz holds log10 Ne on the 64 intervals for the 8
scored windows, with the truth and the interval edges.
Inputs given to the methods
method_inputs/<setting>/<history>/r<window>/n32/ holds one window as each method reads it:
view.bcf (32 diploid individuals), multihetsep.txt (MSMC2), phlash_contig.npz (phlash) and
sfs_unfolded.npy, with meta.json recording the individuals and checksums. Where two sites
share a position, the first is exported and the others dropped, since the methods require
distinct positions.
Scores
scores/<setting>/<history>/v2score_20260917/ holds the main scores and v2supp_20260918/ the
supplement (converged SMC++ and MSMC2): model_metrics.csv and tool_metrics.csv (the RMSE of
log10 Ne over the 64 intervals, m1_rmse64, and further measures), tool_curves64.npz (every
method's curve on the 64 intervals) and runtime.tsv. truth/ has the true curves. The 64
intervals run from 50 to 500,000 generations ago, evenly spaced in log time.
results/main/ and results/supplement/ hold the tables built from these scores
(scores_rows.tsv, summary_by_scenario.tsv, summary_by_family.tsv, paired_contrasts.tsv,
B_map_effect.tsv, timing_C1_C2.tsv, curves_bundle.json), each with a checksum manifest.
Joint fits and structure
joint_fits/ has phlash fitted to eight windows of a history at once, with its scripts and
summary (results/E3_summary.json). structure/ has the 12 structure scenarios (a panmictic
control, island models, changing migration, admixture and ancient structure): the windows, the
phlash fits, NeCast's runs, and the results (phlash_and_results/results/).
Licence and sources
The files here are released under CC BY 4.0. They were made with msprime (Baumdicker et al. 2022, Genetics 220:iyab229), the deCODE 2019 map (Halldorsson et al. 2019, Science 363:eaau1043), the Roulette mutation rates (Seplyarskiy et al. 2023, Nature Genetics) and stdpopsim (Adrion et al. 2020, eLife 9:e54967), and compare phlash (Terhorst 2025, Nature Genetics 57:2570), SMC++ (Terhorst, Kamm and Song 2017, Nature Genetics 49:303), FitCoal (Hu et al. 2023, Science 381:979) and MSMC2 (Schiffels and Wang 2020, Methods Mol Biol 2090:147).
Citation
The NeCast paper is in preparation; its reference will be added here.
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