The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: TypeError
Message: Couldn't cast array of type
struct<chr4p_w00: struct<outputs: struct<ACB_A: struct<path: string, set_doubletons: int64, set_fixed_alt_dropped: int64, set_monomorphic_ref_dropped: int64, set_singletons: int64, sha256: string, sites_kept: int64>, ACB_B: struct<path: string, set_doubletons: int64, set_fixed_alt_dropped: int64, set_monomorphic_ref_dropped: int64, set_singletons: int64, sha256: string, sites_kept: int64>, BEB_A: struct<path: string, set_doubletons: int64, set_fixed_alt_dropped: int64, set_monomorphic_ref_dropped: int64, set_singletons: int64, sha256: string, sites_kept: int64>, CDX_A: struct<path: string, set_doubletons: int64, set_fixed_alt_dropped: int64, set_monomorphic_ref_dropped: int64, set_singletons: int64, sha256: string, sites_kept: int64>, CEU_A: struct<path: string, set_doubletons: int64, set_fixed_alt_dropped: int64, set_monomorphic_ref_dropped: int64, set_singletons: int64, sha256: string, sites_kept: int64>, CEU_B: struct<path: string, set_doubletons: int64, set_fixed_alt_dropped: int64, set_monomorphic_ref_dropped: int64, set_singletons: int64, sha256: string, sites_kept: int64>, CHB_A: struct<path: string, set_doubletons: int64, set_fixed_alt_dropped: int64, set_monomorphic_ref_dropped: int64, set_singletons: int64, sha256: string, sites_kept: int64>, CHB_B: struct<path: string, set_doubletons: int64, set_fixed_alt_dropped: int64, set_monomorphic_ref_dropped: int64, set_singletons: int64, sha256: string, sites_kept: int64>, CHS_A: struct<path: string, set_doubletons: int64,
...
256: string, sites_kept: int64>, PUR_B: struct<path: string, set_doubletons: int64, set_fixed_alt_dropped: int64, set_monomorphic_ref_dropped: int64, set_singletons: int64, sha256: string, sites_kept: int64>, STU_A: struct<path: string, set_doubletons: int64, set_fixed_alt_dropped: int64, set_monomorphic_ref_dropped: int64, set_singletons: int64, sha256: string, sites_kept: int64>, STU_B: struct<path: string, set_doubletons: int64, set_fixed_alt_dropped: int64, set_monomorphic_ref_dropped: int64, set_singletons: int64, sha256: string, sites_kept: int64>, TSI_A: struct<path: string, set_doubletons: int64, set_fixed_alt_dropped: int64, set_monomorphic_ref_dropped: int64, set_singletons: int64, sha256: string, sites_kept: int64>, TSI_B: struct<path: string, set_doubletons: int64, set_fixed_alt_dropped: int64, set_monomorphic_ref_dropped: int64, set_singletons: int64, sha256: string, sites_kept: int64>, YRI_A: struct<path: string, set_doubletons: int64, set_fixed_alt_dropped: int64, set_monomorphic_ref_dropped: int64, set_singletons: int64, sha256: string, sites_kept: int64>, YRI_B: struct<path: string, set_doubletons: int64, set_fixed_alt_dropped: int64, set_monomorphic_ref_dropped: int64, set_singletons: int64, sha256: string, sites_kept: int64>>, panel_counts: struct<inaccessible_dropped: int64, multi_record_positions: int64, multi_record_records_dropped: int64, non_acgt_dropped: int64, records_in_span: int64, records_kept_panel: int64>, region: string, seconds_total: double>>
to
{'chr2p_w00': {'outputs': {'ACB_A': {'path': Value('string'), 'set_doubletons': Value('int64'), 'set_fixed_alt_dropped': Value('int64'), 'set_monomorphic_ref_dropped': Value('int64'), 'set_singletons': Value('int64'), 'sha256': Value('string'), 'sites_kept': Value('int64')}, 'ACB_B': {'path': Value('string'), 'set_doubletons': Value('int64'), 'set_fixed_alt_dropped': Value('int64'), 'set_monomorphic_ref_dropped': Value('int64'), 'set_singletons': Value('int64'), 'sha256': Value('string'), 'sites_kept': Value('int64')}, 'BEB_A': {'path': Value('string'), 'set_doubletons': Value('int64'), 'set_fixed_alt_dropped': Value('int64'), 'set_monomorphic_ref_dropped': Value('int64'), 'set_singletons': Value('int64'), 'sha256': Value('string'), 'sites_kept': Value('int64')}, 'CDX_A': {'path': Value('string'), 'set_doubletons': Value('int64'), 'set_fixed_alt_dropped': Value('int64'), 'set_monomorphic_ref_dropped': Value('int64'), 'set_singletons': Value('int64'), 'sha256': Value('string'), 'sites_kept': Value('int64')}, 'CHS_A': {'path': Value('string'), 'set_doubletons': Value('int64'), 'set_fixed_alt_dropped': Value('int64'), 'set_monomorphic_ref_dropped': Value('int64'), 'set_singletons': Value('int64'), 'sha256': Value('string'), 'sites_kept': Value('int64')}, 'CHS_B': {'path': Value('string'), 'set_doubletons': Value('int64'), 'set_fixed_alt_dropped': Value('int64'), 'set_monomorphic_ref_dropped': Value('int64'), 'set_singletons': Value('int64'), 'sha256': Value('string'), 'sites_kep
...
'), 'set_fixed_alt_dropped': Value('int64'), 'set_monomorphic_ref_dropped': Value('int64'), 'set_singletons': Value('int64'), 'sha256': Value('string'), 'sites_kept': Value('int64')}, 'STU_A': {'path': Value('string'), 'set_doubletons': Value('int64'), 'set_fixed_alt_dropped': Value('int64'), 'set_monomorphic_ref_dropped': Value('int64'), 'set_singletons': Value('int64'), 'sha256': Value('string'), 'sites_kept': Value('int64')}, 'STU_B': {'path': Value('string'), 'set_doubletons': Value('int64'), 'set_fixed_alt_dropped': Value('int64'), 'set_monomorphic_ref_dropped': Value('int64'), 'set_singletons': Value('int64'), 'sha256': Value('string'), 'sites_kept': Value('int64')}, 'TSI_A': {'path': Value('string'), 'set_doubletons': Value('int64'), 'set_fixed_alt_dropped': Value('int64'), 'set_monomorphic_ref_dropped': Value('int64'), 'set_singletons': Value('int64'), 'sha256': Value('string'), 'sites_kept': Value('int64')}, 'TSI_B': {'path': Value('string'), 'set_doubletons': Value('int64'), 'set_fixed_alt_dropped': Value('int64'), 'set_monomorphic_ref_dropped': Value('int64'), 'set_singletons': Value('int64'), 'sha256': Value('string'), 'sites_kept': Value('int64')}}, 'panel_counts': {'inaccessible_dropped': Value('int64'), 'multi_record_positions': Value('int64'), 'multi_record_records_dropped': Value('int64'), 'non_acgt_dropped': Value('int64'), 'records_in_span': Value('int64'), 'records_kept_panel': Value('int64')}, 'region': Value('string'), 'seconds_total': Value('float64')}}
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 2315, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2161, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<chr4p_w00: struct<outputs: struct<ACB_A: struct<path: string, set_doubletons: int64, set_fixed_alt_dropped: int64, set_monomorphic_ref_dropped: int64, set_singletons: int64, sha256: string, sites_kept: int64>, ACB_B: struct<path: string, set_doubletons: int64, set_fixed_alt_dropped: int64, set_monomorphic_ref_dropped: int64, set_singletons: int64, sha256: string, sites_kept: int64>, BEB_A: struct<path: string, set_doubletons: int64, set_fixed_alt_dropped: int64, set_monomorphic_ref_dropped: int64, set_singletons: int64, sha256: string, sites_kept: int64>, CDX_A: struct<path: string, set_doubletons: int64, set_fixed_alt_dropped: int64, set_monomorphic_ref_dropped: int64, set_singletons: int64, sha256: string, sites_kept: int64>, CEU_A: struct<path: string, set_doubletons: int64, set_fixed_alt_dropped: int64, set_monomorphic_ref_dropped: int64, set_singletons: int64, sha256: string, sites_kept: int64>, CEU_B: struct<path: string, set_doubletons: int64, set_fixed_alt_dropped: int64, set_monomorphic_ref_dropped: int64, set_singletons: int64, sha256: string, sites_kept: int64>, CHB_A: struct<path: string, set_doubletons: int64, set_fixed_alt_dropped: int64, set_monomorphic_ref_dropped: int64, set_singletons: int64, sha256: string, sites_kept: int64>, CHB_B: struct<path: string, set_doubletons: int64, set_fixed_alt_dropped: int64, set_monomorphic_ref_dropped: int64, set_singletons: int64, sha256: string, sites_kept: int64>, CHS_A: struct<path: string, set_doubletons: int64,
...
256: string, sites_kept: int64>, PUR_B: struct<path: string, set_doubletons: int64, set_fixed_alt_dropped: int64, set_monomorphic_ref_dropped: int64, set_singletons: int64, sha256: string, sites_kept: int64>, STU_A: struct<path: string, set_doubletons: int64, set_fixed_alt_dropped: int64, set_monomorphic_ref_dropped: int64, set_singletons: int64, sha256: string, sites_kept: int64>, STU_B: struct<path: string, set_doubletons: int64, set_fixed_alt_dropped: int64, set_monomorphic_ref_dropped: int64, set_singletons: int64, sha256: string, sites_kept: int64>, TSI_A: struct<path: string, set_doubletons: int64, set_fixed_alt_dropped: int64, set_monomorphic_ref_dropped: int64, set_singletons: int64, sha256: string, sites_kept: int64>, TSI_B: struct<path: string, set_doubletons: int64, set_fixed_alt_dropped: int64, set_monomorphic_ref_dropped: int64, set_singletons: int64, sha256: string, sites_kept: int64>, YRI_A: struct<path: string, set_doubletons: int64, set_fixed_alt_dropped: int64, set_monomorphic_ref_dropped: int64, set_singletons: int64, sha256: string, sites_kept: int64>, YRI_B: struct<path: string, set_doubletons: int64, set_fixed_alt_dropped: int64, set_monomorphic_ref_dropped: int64, set_singletons: int64, sha256: string, sites_kept: int64>>, panel_counts: struct<inaccessible_dropped: int64, multi_record_positions: int64, multi_record_records_dropped: int64, non_acgt_dropped: int64, records_in_span: int64, records_kept_panel: int64>, region: string, seconds_total: double>>
to
{'chr2p_w00': {'outputs': {'ACB_A': {'path': Value('string'), 'set_doubletons': Value('int64'), 'set_fixed_alt_dropped': Value('int64'), 'set_monomorphic_ref_dropped': Value('int64'), 'set_singletons': Value('int64'), 'sha256': Value('string'), 'sites_kept': Value('int64')}, 'ACB_B': {'path': Value('string'), 'set_doubletons': Value('int64'), 'set_fixed_alt_dropped': Value('int64'), 'set_monomorphic_ref_dropped': Value('int64'), 'set_singletons': Value('int64'), 'sha256': Value('string'), 'sites_kept': Value('int64')}, 'BEB_A': {'path': Value('string'), 'set_doubletons': Value('int64'), 'set_fixed_alt_dropped': Value('int64'), 'set_monomorphic_ref_dropped': Value('int64'), 'set_singletons': Value('int64'), 'sha256': Value('string'), 'sites_kept': Value('int64')}, 'CDX_A': {'path': Value('string'), 'set_doubletons': Value('int64'), 'set_fixed_alt_dropped': Value('int64'), 'set_monomorphic_ref_dropped': Value('int64'), 'set_singletons': Value('int64'), 'sha256': Value('string'), 'sites_kept': Value('int64')}, 'CHS_A': {'path': Value('string'), 'set_doubletons': Value('int64'), 'set_fixed_alt_dropped': Value('int64'), 'set_monomorphic_ref_dropped': Value('int64'), 'set_singletons': Value('int64'), 'sha256': Value('string'), 'sites_kept': Value('int64')}, 'CHS_B': {'path': Value('string'), 'set_doubletons': Value('int64'), 'set_fixed_alt_dropped': Value('int64'), 'set_monomorphic_ref_dropped': Value('int64'), 'set_singletons': Value('int64'), 'sha256': Value('string'), 'sites_kep
...
'), 'set_fixed_alt_dropped': Value('int64'), 'set_monomorphic_ref_dropped': Value('int64'), 'set_singletons': Value('int64'), 'sha256': Value('string'), 'sites_kept': Value('int64')}, 'STU_A': {'path': Value('string'), 'set_doubletons': Value('int64'), 'set_fixed_alt_dropped': Value('int64'), 'set_monomorphic_ref_dropped': Value('int64'), 'set_singletons': Value('int64'), 'sha256': Value('string'), 'sites_kept': Value('int64')}, 'STU_B': {'path': Value('string'), 'set_doubletons': Value('int64'), 'set_fixed_alt_dropped': Value('int64'), 'set_monomorphic_ref_dropped': Value('int64'), 'set_singletons': Value('int64'), 'sha256': Value('string'), 'sites_kept': Value('int64')}, 'TSI_A': {'path': Value('string'), 'set_doubletons': Value('int64'), 'set_fixed_alt_dropped': Value('int64'), 'set_monomorphic_ref_dropped': Value('int64'), 'set_singletons': Value('int64'), 'sha256': Value('string'), 'sites_kept': Value('int64')}, 'TSI_B': {'path': Value('string'), 'set_doubletons': Value('int64'), 'set_fixed_alt_dropped': Value('int64'), 'set_monomorphic_ref_dropped': Value('int64'), 'set_singletons': Value('int64'), 'sha256': Value('string'), 'sites_kept': Value('int64')}}, 'panel_counts': {'inaccessible_dropped': Value('int64'), 'multi_record_positions': Value('int64'), 'multi_record_records_dropped': Value('int64'), 'non_acgt_dropped': Value('int64'), 'records_in_span': Value('int64'), 'records_kept_panel': Value('int64')}, 'region': Value('string'), 'seconds_total': Value('float64')}}Need 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 1000 Genomes analyses
NeCast's analyses of the 1000 Genomes Project: size histories of 25 populations under four map inputs, and the comparison of the low-coverage (phase 3) and 30× call sets. Everything between the public call sets and the published curves is here: the windows, the groups of individuals, the genotype slices and extractions, every NeCast input and estimate, the phlash fits of the call-set comparison, and the result tables.
Contents
| path | what it holds | size |
|---|---|---|
reference/ |
sample panel, accessibility masks, centromere and gap tables, download list with checksums, the scripts that fetched them | 0.15 GB |
windows/ |
the 51 windows of 30 Mb accessible sequence on chromosomes 1–18 | 0.80 GB |
samples/ |
the populations' unrelated individuals and the groups of 32 used in each run | small |
genotypes_30x/ |
per-window VCF slices of the 30× call set and the genotypes extracted from them | 61.4 GB |
populations_25/ |
the 25-population analysis under four map inputs, per chromosome, and its results | 48.5 GB |
low_coverage/ |
phase 3 slices and extractions, NeCast and phlash on both call sets, and the comparison | 85.6 GB |
figures/ |
the figure on this card | small |
SHA256SUMS |
a checksum for every file except this card | small |
Data and windows
a, The 51 windows on GRCh38, alternately purple and light purple, with the number on each
chromosome; grey blocks are centromeres. Chromosomes 19–22 hold no window. b, NeCast's
estimates with both maps for YRI, CEU, GIH, CHB and PEL, read as an inverse coalescence rate
(IICR). Each thin line is one window, averaged over the population's groups of 32, and the thick
line is their mean. The values are in populations_25/results/curves_bundle4.json.
- Call set. The phased high-coverage (30×) call set (Byrska-Bishop et al. 2022), GRCh38.
The original VCFs are not copied here;
reference/DOWNLOADS.jsongives their addresses, sizes and SHA-256, andreference/scripts/the download script. - Populations. 25 of the 26 populations. Each dataset holds 64 unrelated individuals, as a simulated training window holds 64 diploid genomes; ASW has 61 and was left out, since admitting relatives would inflate recent coalescence. NeCast reads disjoint groups of 32 of these individuals: two groups in 18 populations and one in seven, 43 analyses per window.
- Windows. 30 Mb of accessible sequence under the strict mask, within chromosome arms, with
gaps of at most 1 Mb: 51 windows on chromosomes 1–18. Chromosomes 19–22 hold no such block.
Accessible intervals are joined into one 30 Mb coordinate.
windows/windows_chrN.jsonlists each window's intervals. - Sites. Biallelic SNVs only; positions with more than one record dropped. This call set has no missing calls and leaves out variants seen once in its full sample. The reference allele is taken as ancestral.
- Rates. Mutation rate 1.29 × 10⁻⁸ per bp per generation. The recombination map is the deCODE 2019 average map and the mutation map the Roulette model; both are cut to each window's accessible intervals.
The 25-population analysis (populations_25/)
NeCast was run on the same rows with four map inputs:
| folder | recombination | mutation |
|---|---|---|
no_map |
flat at the genome-wide mean | flat |
mutation_map |
flat at the genome-wide mean | Roulette, its shape rescaled to the window's mean rate |
recombination_map |
deCODE 2019 | flat |
both_maps |
deCODE 2019 | Roulette, rescaled as above |
Each populations_25/chrN/<map input>/ holds rows/ (the windows in NeCast's input format,
with REAL_ROWS.json naming the window, population, group and individuals of every row),
inputs/ (summary statistics and PSMC+ posteriors), estimates/ (NeCast's predictions), and,
for the two runs made directly on the extracted rows, range_checks/ (each row's inputs
compared with the range of the training data) and tables/ (per-row predictions as rows.csv,
rows.jsonl and predictions_log10_iicr.npz). Each dataset also holds a few simulated rows
that are not part of the analysis; they come from decoy_sources/.
populations_25/results/ has the population curves and contrasts: curves_bundle4.json (every
curve per population, window and group, and the per-interval 95% intervals),
per_population4.tsv, per_population_decade4.tsv, per_window4.tsv and per_bin_ci4.tsv.
A population's curve is the mean over its windows and groups; intervals are percentile
bootstraps over windows, paired across map inputs (4,000 resamples). run_scripts/ has the
drivers and collectors that produced them.
Low-coverage and 30× call sets (low_coverage/)
For YRI, CEU, CHB, GIH and PEL, the first group of 32 individuals and the 51 windows were extracted from the phase 3 call set (Auton et al. 2015; low coverage, mean depth 7.4×; GRCh38 release 20190312, biallelic SNVs), with the same filters, masks and maps.
phase3_vcf_slices/andphase3_extracted/: the phase 3 slices and extracted genotypes.necast_phase3/chrN/: NeCast with both maps on the phase 3 data; the 30× runs are those inpopulations_25/chrN/both_maps/.phlash/: phlash fitted per window on both call sets (tasks_p3.tsv,tasks_nygc.tsv).necast_checks/: two checks made before the 30× runs were reused for the comparison. A re-run of chromosome 16 reproduced the 30× run. NA18498, one of the YRI individuals that fill a dataset to 64 but are not in the analysed group, is absent from phase 3 and was replaced by NA18520 in both call sets; re-extracting chromosome 2 with this change left every YRI prediction unchanged (30x_extracted_fill/holds that extraction).analysis/: the scripts, the singleton tally and the results (results/E2_summary.json,results/R6_necast_vs_phlash.json).
File formats
Windows in NeCast's input format use the layout of the training data (four windows per .npz,
genotypes bit-packed); see the card of Larrivhan/NeCast-simulations and its reader
tools/necast_windows.py. Predictions are log10 Ne on 64 intervals from 50 to 500,000
generations ago, evenly spaced in log time.
Paths in provenance files
Logs, plans and other provenance files were written on the machines that ran the jobs. Absolute paths in them use
placeholders: $WORK and $DATA for the working directories, $SW for installed software and ~ for the home
directory. Checksums recorded inside the files match the released files.
Licence and sources
The files here are released under CC BY 4.0. They derive from the 1000 Genomes Project, whose
data are public: the 30× call set (Byrska-Bishop et al. 2022, Cell 185:3426) and phase 3
(1000 Genomes Project Consortium 2015, Nature 526:68). The maps are the deCODE 2019 map
(Halldorsson et al. 2019, Science 363:eaau1043) and the Roulette mutation rates (Seplyarskiy
et al. 2023, Nature Genetics). The full maps are not copied here: reference/EXCLUDED_THIRD_PARTY.tsv
gives their checksums, and generator/ in Larrivhan/NeCast-simulations has the scripts that
build them. phlash: Terhorst (2025), Nature Genetics 57:2570.
Citation
The NeCast paper is in preparation; its reference will be added here.
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