The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
n_system_types_tested: int64
n_significant_bh_q10: int64
n_observations_total: int64
n_genomes_with_cas_and_other: int64
n_permutations: int64
cell_Y0_S0: int64
cell_Y1_S1_selftargeting: int64
masked_bp: int64
cell_Y0_S1_informative: int64
P(S=1): double
selftargeting_depletion_ratio: double
n_element_hits: int64
n_elements: int64
n_crispr_arrays_masked: int64
min_identity: double
cell_Y1_S0: int64
P(Y=1): double
expected_Y1S1_under_independence: double
n_pairs: int64
min_cov: double
epsilon_decoy_false_match_rate: double
n_decoy_hits: int64
n_hosts: int64
to
{'n_hosts': Value('int64'), 'n_elements': Value('int64'), 'n_pairs': Value('int64'), 'P(Y=1)': Value('float64'), 'P(S=1)': Value('float64'), 'cell_Y1_S1_selftargeting': Value('int64'), 'cell_Y1_S0': Value('int64'), 'cell_Y0_S1_informative': Value('int64'), 'cell_Y0_S0': Value('int64'), 'expected_Y1S1_under_independence': Value('float64'), 'selftargeting_depletion_ratio': Value('float64'), 'epsilon_decoy_false_match_rate': Value('float64'), 'n_element_hits': Value('int64'), 'n_decoy_hits': Value('int64'), 'n_crispr_arrays_masked': Value('int64'), 'masked_bp': Value('int64'), 'min_identity': Value('float64'), 'min_cov': Value('float64')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, 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 127, 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 483, 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 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, 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 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, 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 343, 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 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
n_system_types_tested: int64
n_significant_bh_q10: int64
n_observations_total: int64
n_genomes_with_cas_and_other: int64
n_permutations: int64
cell_Y0_S0: int64
cell_Y1_S1_selftargeting: int64
masked_bp: int64
cell_Y0_S1_informative: int64
P(S=1): double
selftargeting_depletion_ratio: double
n_element_hits: int64
n_elements: int64
n_crispr_arrays_masked: int64
min_identity: double
cell_Y1_S0: int64
P(Y=1): double
expected_Y1S1_under_independence: double
n_pairs: int64
min_cov: double
epsilon_decoy_false_match_rate: double
n_decoy_hits: int64
n_hosts: int64
to
{'n_hosts': Value('int64'), 'n_elements': Value('int64'), 'n_pairs': Value('int64'), 'P(Y=1)': Value('float64'), 'P(S=1)': Value('float64'), 'cell_Y1_S1_selftargeting': Value('int64'), 'cell_Y1_S0': Value('int64'), 'cell_Y0_S1_informative': Value('int64'), 'cell_Y0_S0': Value('int64'), 'expected_Y1S1_under_independence': Value('float64'), 'selftargeting_depletion_ratio': Value('float64'), 'epsilon_decoy_false_match_rate': Value('float64'), 'n_element_hits': Value('int64'), 'n_decoy_hits': Value('int64'), 'n_crispr_arrays_masked': Value('int64'), 'masked_bp': Value('int64'), 'min_identity': Value('float64'), 'min_cov': Value('float64')}
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.
LEDGER data
Processed data for LEDGER, a latent-variable model that separates exposure from establishment when estimating which mobile genetic elements a bacterial genome will accept. Code lives at bryanc5864/LEDGER.
The core problem: when an element is missing from a genome, the host either never met it or met it and rejected it. Regressing observed presence on host features estimates the product of those two things, and exposure tracks the same phylogeny as the defense repertoire, so susceptibility is never separately identified. LEDGER uses CRISPR spacers as a record of encounter that survives regardless of the outcome.
Contents
simulation/ 5,551 per-fit records from every simulation sweep (JSONL, one object per fit)
genomes/
annotations/ DefenseFinder 3.0.0 systems tables for 1,200 P. aeruginosa genomes
matrices/ host x defense-system presence matrices, split chromosomal/extrachromosomal
crispr/ 27,620 CRISPR spacers (MinCED), pooled and per genome
elements/ Y and S matrices: 1,200 hosts x 2,589 spacer-derived elements
clusters/ ANI lineage clusters at three thresholds
metadata/ NCBI assembly metadata and the stratified cohort definition
results/ exclusion-restriction audit, CRISPR proximity test, real-data fit output
figures/ every figure in the paper, PNG and PDF
main.pdf the paper
The two matrices
genomes/elements/Y.tsv.gz and S.tsv.gz are the observables the model consumes. Rows are the
1,200 genomes, columns the 2,589 elements. S[i,j] = 1 means host i carries a CRISPR spacer
targeting element j, so it demonstrably encountered it. Y[i,j] = 1 means the element (strictly,
a >= 95% identity match to the protospacer, outside any CRISPR array) is resident in that genome.
Three things about this data are worth knowing before using it:
- The informative cell, encounter without establishment, holds 25,099 pairs.
- Self-targeting pairs (spacer present and element resident) are depleted 0.54x relative to independence, which is what the model predicts, since a spacer against a resident element is self-targeting and gets purged.
- The false-match rate is zero: 2,589 dinucleotide-shuffled decoy sequences produced no hits at all against the same 1,200 genomes under identical search settings.
CRISPR arrays were masked out of the genomes before searching. Without that step every spacer
matches its own array, Y becomes identical to S, and the informative cell is empty.
Simulation records
simulation/*.jsonl is one JSON object per model fit: the full configuration, every metric, the
realised marginal rates and the convergence status. Nothing is aggregated. Three audit scripts in
the code repository recompute all 181 published numbers from these records.
Superseded runs are kept rather than deleted and are named accordingly, including a permutation control that turned out not to be a null and a threat sweep whose arms were confounded by a shared random stream.
Raw genomes
Not mirrored here. They are public NCBI assemblies; make genomes in the code repository
re-downloads them (about 18 GB) and reproduces everything above.
License
MIT, Bryan Cheng, 2026.
- Downloads last month
- 5