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Datasets:
genomic-benchmarks
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BEACON

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100K<n<1M
Tags:
genomics
benchmark
dna
rna
regression
License:
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The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 3 new columns ({'library', 'library_index', 'proximal_isoform_proportion'}) and 1 missing columns ({'label'}).

This happened while the csv dataset builder was generating data using

gzip://Isoform_train.csv::hf://datasets/genomic-benchmarks/BEACON@adb095d6b0c629167c15866582bc2c72e28dfb90/v1/Isoform/Isoform_train.csv.gz, ['hf://datasets/genomic-benchmarks/BEACON@adb095d6b0c629167c15866582bc2c72e28dfb90/v1/CRISPROnTarget/CRISPROnTarget_train.csv.gz', 'hf://datasets/genomic-benchmarks/BEACON@adb095d6b0c629167c15866582bc2c72e28dfb90/v1/Isoform/Isoform_train.csv.gz', 'hf://datasets/genomic-benchmarks/BEACON@adb095d6b0c629167c15866582bc2c72e28dfb90/v1/MeanRibosomeLoading/MeanRibosomeLoading_train.csv.gz']

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1848, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._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
              id: string
              row_index: int64
              library: string
              seq: string
              proximal_isoform_proportion: double
              library_index: int64
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 998
              to
              {'id': Value('string'), 'row_index': Value('int64'), 'seq': Value('string'), 'label': Value('float64')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1850, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
                  ...<4 lines>...
                  )
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 3 new columns ({'library', 'library_index', 'proximal_isoform_proportion'}) and 1 missing columns ({'label'}).
              
              This happened while the csv dataset builder was generating data using
              
              gzip://Isoform_train.csv::hf://datasets/genomic-benchmarks/BEACON@adb095d6b0c629167c15866582bc2c72e28dfb90/v1/Isoform/Isoform_train.csv.gz, ['hf://datasets/genomic-benchmarks/BEACON@adb095d6b0c629167c15866582bc2c72e28dfb90/v1/CRISPROnTarget/CRISPROnTarget_train.csv.gz', 'hf://datasets/genomic-benchmarks/BEACON@adb095d6b0c629167c15866582bc2c72e28dfb90/v1/Isoform/Isoform_train.csv.gz', 'hf://datasets/genomic-benchmarks/BEACON@adb095d6b0c629167c15866582bc2c72e28dfb90/v1/MeanRibosomeLoading/MeanRibosomeLoading_train.csv.gz']
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

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.

id
string
row_index
int64
seq
string
label
float64
BEACON:CRISPROnTarget:v1:train:0000000
0
TTCACTGCGCAGCCTGGCATTGG
0.261026
BEACON:CRISPROnTarget:v1:train:0000001
1
TTCATTTACTGACTTCAGATTGG
0.088202
BEACON:CRISPROnTarget:v1:train:0000002
2
AAAGAGGTCGAAGATGTAGCGGG
0.103584
BEACON:CRISPROnTarget:v1:train:0000003
3
CAAGATTGATCAGAAAGCTGTGG
0.274124
BEACON:CRISPROnTarget:v1:train:0000004
4
TTCACTGCCAGTTCTCCGCGTGG
0.315928
BEACON:CRISPROnTarget:v1:train:0000005
5
CTCCTGGATAACAACGTTGATGG
0.084915
BEACON:CRISPROnTarget:v1:train:0000006
6
TTTCTTGTTCTCCACCAAGGTGG
0.218952
BEACON:CRISPROnTarget:v1:train:0000007
7
AGTCCCACCACCTCGAACTCTGG
0.160351
BEACON:CRISPROnTarget:v1:train:0000008
8
AGGTGAAGATCATGCTGCCCTGG
0.198797
BEACON:CRISPROnTarget:v1:train:0000009
9
CCTGACGCCCGAGCAGCAGGAGG
0.227806
BEACON:CRISPROnTarget:v1:train:0000010
10
GATCGCACCACATCAATGATGGG
0.166885
BEACON:CRISPROnTarget:v1:train:0000011
11
CATCTTGCATGGCCCGAGTGCGG
0.57097
BEACON:CRISPROnTarget:v1:train:0000012
12
CAAAGGATACCTCGCTAGCTAGG
0.082736
BEACON:CRISPROnTarget:v1:train:0000013
13
ACAACTCAGCCCTGCTCATGAGG
0.157668
BEACON:CRISPROnTarget:v1:train:0000014
14
GCAGTTCCCGGGCGGCCTTGCGG
0.200919
BEACON:CRISPROnTarget:v1:train:0000015
15
AAGCGGAGCCAACATGCCAGTGG
0.252417
BEACON:CRISPROnTarget:v1:train:0000016
16
CTCTCTGGTATGCCATGATCAGG
0.145544
BEACON:CRISPROnTarget:v1:train:0000017
17
TCAACATTGCTCGACAGATGCGG
0.551833
BEACON:CRISPROnTarget:v1:train:0000018
18
GCCGTATTTCTACTGCGACGAGG
0.370816
BEACON:CRISPROnTarget:v1:train:0000019
19
TGCCAGAAAGATCAGTGACATGG
0.168139
BEACON:CRISPROnTarget:v1:train:0000020
20
TCCCTCCGCCTCACCATTCCTGG
0.337911
BEACON:CRISPROnTarget:v1:train:0000021
21
GCGGCAGCGAGTTGAACTTTAGG
0.076207
BEACON:CRISPROnTarget:v1:train:0000022
22
TACTTCCGCTTCTTGTGGTAGGG
0.087978
BEACON:CRISPROnTarget:v1:train:0000023
23
GACACAGTATAAGAAGGGCAAGG
0.170165
BEACON:CRISPROnTarget:v1:train:0000024
24
CTATGACCTCGACTACGACTCGG
0.39672
BEACON:CRISPROnTarget:v1:train:0000025
25
CATAACTGCCACGCAGGAGTTGG
0.23552
BEACON:CRISPROnTarget:v1:train:0000026
26
AGCTCTTCTGCTGAAGAATTTGG
0.168442
BEACON:CRISPROnTarget:v1:train:0000027
27
ATAGCAAGTGCAAGGTCAAATGG
0.230712
BEACON:CRISPROnTarget:v1:train:0000028
28
GGAAGTAACACGCACCAGCTTGG
0.223934
BEACON:CRISPROnTarget:v1:train:0000029
29
GATTTCATCAAGTTCGACACTGG
0.208165
BEACON:CRISPROnTarget:v1:train:0000030
30
GCACTGTCTTGCTCAGAGGCCGG
0.159818
BEACON:CRISPROnTarget:v1:train:0000031
31
CACGATGGCGACCTCGGGCTCGG
0.241564
BEACON:CRISPROnTarget:v1:train:0000032
32
GACCATAACTGATGATGTGCGGG
0.38378
BEACON:CRISPROnTarget:v1:train:0000033
33
AAGGAGGCTGCTGTCGATTAAGG
0.038199
BEACON:CRISPROnTarget:v1:train:0000034
34
AGGCTCCAAACAGGCATGCGAGG
0.192965
BEACON:CRISPROnTarget:v1:train:0000035
35
CAGATCTTGATGCCCAACATTGG
0.296192
BEACON:CRISPROnTarget:v1:train:0000036
36
CAGCACTACATACCTTCTGGCGG
0.266641
BEACON:CRISPROnTarget:v1:train:0000037
37
ACAGGAGGCGGTAAAGGAGGTGG
0.177645
BEACON:CRISPROnTarget:v1:train:0000038
38
CATCCGAACCAAGGACATTGAGG
0.203144
BEACON:CRISPROnTarget:v1:train:0000039
39
ACTCTGAAGATGATCAGACAAGG
0.256291
BEACON:CRISPROnTarget:v1:train:0000040
40
AAATGAAACTGAAGATCATTTGG
0.088729
BEACON:CRISPROnTarget:v1:train:0000041
41
TCTCTGGACGGCTACGGCGTAGG
0.378189
BEACON:CRISPROnTarget:v1:train:0000042
42
GTGGTCAGTGAACTCCTGATAGG
0.165785
BEACON:CRISPROnTarget:v1:train:0000043
43
AGACATGACCAGTGCTGGTAGGG
0.190526
BEACON:CRISPROnTarget:v1:train:0000044
44
CACCTCCATAGGCAGTGCCAAGG
0.03724
BEACON:CRISPROnTarget:v1:train:0000045
45
GATGGAGATGAGGCTTCCAGTGG
0.388117
BEACON:CRISPROnTarget:v1:train:0000046
46
CAAGTTGGGCCGCTTGGTCAAGG
0.258271
BEACON:CRISPROnTarget:v1:train:0000047
47
TGAAACGGTATAATCGGAATCGG
0.158353
BEACON:CRISPROnTarget:v1:train:0000048
48
GCAATTCTTCACCAGGGTCTTGG
0.231043
BEACON:CRISPROnTarget:v1:train:0000049
49
CCGCCAAGTCCTTCAAGTTCTGG
0.238571
BEACON:CRISPROnTarget:v1:train:0000050
50
TTCACCTGGAACAAGCGGAGTGG
0.285399
BEACON:CRISPROnTarget:v1:train:0000051
51
AACTGCTGTGCCCAGTTACGAGG
0.216071
BEACON:CRISPROnTarget:v1:train:0000052
52
GATCTTTGCTGGCAAGCAGCTGG
0.038268
BEACON:CRISPROnTarget:v1:train:0000053
53
TTCCCTCCTCTAGGAGCTCGAGG
0.062328
BEACON:CRISPROnTarget:v1:train:0000054
54
TTCTGGGCAACCTCCTCTTCTGG
0.203331
BEACON:CRISPROnTarget:v1:train:0000055
55
GGGAGTCATGGTGGCCAAGAAGG
0.406111
BEACON:CRISPROnTarget:v1:train:0000056
56
CTGTCAGATCCTTTGGCATCCGG
0.138137
BEACON:CRISPROnTarget:v1:train:0000057
57
AGGTCTGTGTGCCATTGCCCAGG
0.40533
BEACON:CRISPROnTarget:v1:train:0000058
58
ATAAGGCAAAGAGGAACTGCTGG
0.456121
BEACON:CRISPROnTarget:v1:train:0000059
59
TTCAGGACACTGGTCTCACTGGG
0.125132
BEACON:CRISPROnTarget:v1:train:0000060
60
ACAGAGGTCCTCAAGACCCACGG
0.166413
BEACON:CRISPROnTarget:v1:train:0000061
61
ATGGTGAATGCCAAATTTGAAGG
0.230471
BEACON:CRISPROnTarget:v1:train:0000062
62
GCCGAAAGGAAAGAAGGCCAAGG
0.233893
BEACON:CRISPROnTarget:v1:train:0000063
63
GTGGCCCTCTGGACAAGTGGCGG
0.163562
BEACON:CRISPROnTarget:v1:train:0000064
64
CGCGGACGGGTTGTGCCTGGAGG
0.306166
BEACON:CRISPROnTarget:v1:train:0000065
65
ACGCTCGTCACCAGGACCCAAGG
0.219989
BEACON:CRISPROnTarget:v1:train:0000066
66
GAGTCTCACTCTCACTCAGGTGG
0.129476
BEACON:CRISPROnTarget:v1:train:0000067
67
GAGTGCCCTTCTGATGAATGTGG
0.38891
BEACON:CRISPROnTarget:v1:train:0000068
68
CTTTGTATGCCCAGGGAAGGAGG
0.311082
BEACON:CRISPROnTarget:v1:train:0000069
69
TCCCAGGTACTCACGGGAGTTGG
0.322198
BEACON:CRISPROnTarget:v1:train:0000070
70
CAGGAAGTTGAGCCTCTGGAAGG
0.360092
BEACON:CRISPROnTarget:v1:train:0000071
71
CACCTTGAGACGGTCCAGAGCGG
0.312546
BEACON:CRISPROnTarget:v1:train:0000072
72
GCGGTAGGAACATGGCGGATCGG
0.258647
BEACON:CRISPROnTarget:v1:train:0000073
73
ATTTACAAACTGGCCAAGAAGGG
0.11295
BEACON:CRISPROnTarget:v1:train:0000074
74
GCGTTTGATCATCTCGTCCGTGG
0.18641
BEACON:CRISPROnTarget:v1:train:0000075
75
GTTGCTTCCATAACCAATGTTGG
0.209436
BEACON:CRISPROnTarget:v1:train:0000076
76
GTTAGATTTACCTTCTATGATGG
0.317617
BEACON:CRISPROnTarget:v1:train:0000077
77
GAAACAAATACACCCATTGAAGG
0.378216
BEACON:CRISPROnTarget:v1:train:0000078
78
TGGTAGCGAGCGCGGGCACCAGG
0.086223
BEACON:CRISPROnTarget:v1:train:0000079
79
GAAGGACGAGCTGGAGCGTGTGG
0.294816
BEACON:CRISPROnTarget:v1:train:0000080
80
GCTGGCATCACTTTGAGGGAGGG
0.272578
BEACON:CRISPROnTarget:v1:train:0000081
81
ACAGCCGGGTACCTTGAACCTGG
0.468763
BEACON:CRISPROnTarget:v1:train:0000082
82
GTTTCCGCTTTGTGGCCACATGG
0.030038
BEACON:CRISPROnTarget:v1:train:0000083
83
AGCAACATTTCCGCCGCCAAAGG
0.237188
BEACON:CRISPROnTarget:v1:train:0000084
84
CCACGCGGTCCTCCTCCGAGAGG
0.352917
BEACON:CRISPROnTarget:v1:train:0000085
85
TCACCTGGCTGTCAACTTCTTGG
0.105846
BEACON:CRISPROnTarget:v1:train:0000086
86
CTCCCGCAGCCCGAGATGATCGG
0.169247
BEACON:CRISPROnTarget:v1:train:0000087
87
TGCCCTTTCACTTCAGAAACAGG
0.160247
BEACON:CRISPROnTarget:v1:train:0000088
88
CTACATCGACAACCTCATGGCGG
0.300987
BEACON:CRISPROnTarget:v1:train:0000089
89
CTGGGCACTCACATCTTGCATGG
0.204216
BEACON:CRISPROnTarget:v1:train:0000090
90
ATGCAGTCCAGGGCAGTAGATGG
0.11751
BEACON:CRISPROnTarget:v1:train:0000091
91
GACATCCGTGTCCGTGTAAAGGG
0.077445
BEACON:CRISPROnTarget:v1:train:0000092
92
AGCCAAGGAAGCCGCAGAACAGG
0.057558
BEACON:CRISPROnTarget:v1:train:0000093
93
AGAGAAGAAGCAGAGACTGTTGG
0.245997
BEACON:CRISPROnTarget:v1:train:0000094
94
AGGCACCTCGCATGCCTGTTTGG
0.111745
BEACON:CRISPROnTarget:v1:train:0000095
95
GGGTGAAGAACTTCGGGATCTGG
0.226545
BEACON:CRISPROnTarget:v1:train:0000096
96
TATATGCGAATCTATAAGAAAGG
0.393617
BEACON:CRISPROnTarget:v1:train:0000097
97
TGCAGGCAGAATGCTGGTGACGG
0.259684
BEACON:CRISPROnTarget:v1:train:0000098
98
AGGTGCGAGTTACACCAACCAGG
0.219402
BEACON:CRISPROnTarget:v1:train:0000099
99
TAGACAGTCACTTCGGTCCCTGG
0.19495
End of preview.

BEACON, curated

BEACON, as published in quality-curated genomic benchmarks - one format, fixed row order, a permanent ID on every row. 3 datasets, 9 files, 321,983 rows, one gzipped CSV per split, all Homo sapiens.

Getting the data

Two packages are the way in: genomic-benchmarks-data for people, genomic-benchmarks-data4agents for agents, the same functions either way. They resolve the URL, check the checksum, and carry each dataset's QC results, which this repository does not.

pip install genomic-benchmarks-data

from genomic_benchmarks_data import load_dataset

df = load_dataset("BEACON", "CRISPROnTarget", "train")

A number measured on these files belongs on the leaderboard, which is what makes two of them comparable, and which prints what QC found about a dataset above every table.

Datasets

dataset task train val test
CRISPROnTarget Regression 1453 207 416
Isoform Regression 145463 33170 49755
MeanRibosomeLoading Regression 76319 7600 7600

Files are v1/<dataset>/<dataset>_<split>.csv.gz, where v1 is the version of the data. A dataset whose sequences change appears under a new prefix rather than being rewritten here.

Licence and provenance

Every row has an identity

Each file carries two columns the upstream release does not:

  • id - <collection>:<dataset>:<version>:<split>:<row index>, the index zero-padded to 7 digits.
  • row_index - the row's 0-based position in this file.

Row order is fixed within a major version, so an id names the same sequence for as long as that version exists - which is what makes a reported number traceable to what it was computed on. The corpus metadata carries the SHA-256 of every split file, so a copy can be checked against the audited one.

Citation

Cite the collection's own publication (https://doi.org/10.52202/079017-2949) for the data, and Automated quality control for genomic sequence benchmarks reveals pervasive single-feature separability and train-test leakage for the curation and the audit.
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