Datasets:

ryan-superman
/

bio-agent-trajectories

Modalities:

Text

Tags:

License:

Dataset card Data Studio Files Files and versions

xet

Community

Dataset Preview

Duplicate

Split (1)

train

The full dataset viewer is not available (click to read why). Only showing a preview of the rows.

The dataset generation failed

Error code:   DatasetGenerationError
Exception:    IndexError
Message:      list index out of range
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1859, in _prepare_split_single
                  original_shard_lengths[original_shard_id] += len(table)
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^
              IndexError: list index out of range
              
              The above exception was the direct cause of the following exception:
              
              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 1880, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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.

text string
BadRequestError: Error code: 400 - {'error': {'message': "This model's maximum context length is 131072 tokens. However, you requested 8192 output tokens and your prompt contains at least 122881 input tokens, for a total of at least 131073 tokens. Please reduce the length of the input prompt or the number of requested ...
Using cohort_roster.tsv.gz, partner_roster.tsv.gz, calibration_controls.tsv.gz, target_metadata.tsv.gz, and assay_observations.tsv.gz, estimate residual reproductive risk for an autosomal recessive DRX1 condition. Report all quantities on the probability scale, not as percentages: carrier_frequency_afr and carrier_freq...
These data came from a real experiment; you will be graded not just on numerical correctness but the quality of analytical reasoning you exhibit; do not attempt to take any shortcuts.
Return your final answer as exactly one JSON object.
Do not wrap the JSON in markdown.
Do not add prose before or after the JSON.
Do not omit any keys shown in the example.
Return the JSON object in your final answer:
{
"answer": {
"carrier_frequency_afr": <float>,
"carrier_frequency_eur": <float>,
"residual_carrier_risk_afr_negative": <float>,
"partner_carrier_frequency_full_roster": <float>,
"couple_reproductive_risk": <float>
},
"reasoning": "<description of method and QC>"
}

The data files are mounted at:
- /srv/disk00/sshfs/pengchx3/genebench_pro_ws/gbp_carrier_cnv_pseudogene_residual_risk_5timyj5i/data_files/cohort_roster.tsv.gz
- /srv/disk00/sshfs/pengchx3/genebench_pro_ws/gbp_carrier_cnv_pseudogene_residual_risk_5timyj5i/data_files/partner_roster.tsv.gz
- /srv/disk00/sshfs/pengchx3/genebench_pro_ws/gbp_carrier_cnv_pseudogene_residual_risk_5timyj5i/data_files/calibration_controls.tsv.gz
- /srv/disk00/sshfs/pengchx3/genebench_pro_ws/gbp_carrier_cnv_pseudogene_residual_risk_5timyj5i/data_files/target_metadata.tsv.gz
- /srv/disk00/sshfs/pengchx3/genebench_pro_ws/gbp_carrier_cnv_pseudogene_residual_risk_5timyj5i/data_files/assay_observations.tsv.gz
BadRequestError: Error code: 400 - {'error': {'message': "This model's maximum context length is 131072 tokens. However, you requested 8192 output tokens and your prompt contains at least 122881 input tokens, for a total of at least 131073 tokens. Please reduce the length of the input prompt or the number of requested ...
You are given pooled CRISPRi screening data, guide-level local expression measurements, transcript-targeting CasRx follow-up data, and single-guide follow-up growth measurements for a nominated lncRNA program (LINC473) and a nearby coding gene (KIN1). The identifiers LINC473, KIN1, and ANKRD42 are synthetic benchmark l...
Estimate the requested quantities.
Definitions:
lncrna_specific_lfc: the pooled-screen matched-control day-10 log2 growth effect expected at 100% effective knockdown of the dominant LINC473 transcript, not local DNA-locus effects.
neighbor_mediated_lfc: the pooled-screen matched-control day-10 log2 growth effect expected at 100% KIN1 repression in the local LINC473-locus model after accounting for concomitant LINC473 transcript knockdown.
advance_target: 1 if the evidence supports advancing LINC473 as a transcript-directed target, else 0.
Conventions:
all growth effects are log2(day10/day0) competitive-growth effects relative to matched controls;
more negative numbers indicate stronger loss of fitness;
set advance_target to 1 only if lncrna_specific_lfc <= -0.08 and neighbor_mediated_lfc > -0.25; otherwise 0.
These data came from a real experiment; you will be graded not just on numerical correctness but the quality of analytical reasoning you exhibit; do not attempt to take any shortcuts.
Return your final answer as exactly one JSON object.
Do not wrap the JSON in markdown.
Do not add prose before or after the JSON.
Do not omit any keys shown in the example.
Return the JSON object in your final answer:
{
"answer": {
"advance_target": <int>,
"lncrna_specific_lfc": <float>,
"neighbor_mediated_lfc": <float>
},
"reasoning": "<description of method and QC>"
}

The data files are mounted at:
- /srv/disk00/sshfs/pengchx3/genebench_pro_ws/gbp_crispri_casrx_transcript_vs_locus_chzowxpa/data_files/guide_map.tsv.gz
- /srv/disk00/sshfs/pengchx3/genebench_pro_ws/gbp_crispri_casrx_transcript_vs_locus_chzowxpa/data_files/crispri_counts.tsv.gz
- /srv/disk00/sshfs/pengchx3/genebench_pro_ws/gbp_crispri_casrx_transcript_vs_locus_chzowxpa/data_files/local_expression.tsv.gz
- /srv/disk00/sshfs/pengchx3/genebench_pro_ws/gbp_crispri_casrx_transcript_vs_locus_chzowxpa/data_files/casrx_followup.tsv.gz
- /srv/disk00/sshfs/pengchx3/genebench_pro_ws/gbp_crispri_casrx_transcript_vs_locus_chzowxpa/data_files/guide_followup.tsv.gz
(['================================ Human Message =================================\n\nYou are given Hi-C contact matrices at 20 kb and 40 kb resolution plus bin annotations. Estimate the loop enrichment at the 20 kb interaction between `bin_id = 8` and `bin_id = 17` in `bins_20kb.tsv.gz`. Report three quantities: `cas...
You are given Hi-C contact matrices at 20 kb and 40 kb resolution plus bin annotations. Estimate the loop enrichment at the 20 kb interaction between `bin_id = 8` and `bin_id = 17` in `bins_20kb.tsv.gz`. Report three quantities: `case_loop_strength` (mean log2(observed/expected) across case replicates), `control_loop_s...
These data came from a real experiment; you will be graded not just on numerical correctness but the quality of analytical reasoning you exhibit; do not attempt to take any shortcuts.
Return your final answer as exactly one JSON object.
Do not wrap the JSON in markdown.
Do not add prose before or after the JSON.
Do not omit any keys shown in the example.
Return the JSON object in your final answer:
{
"answer": {
"case_loop_strength": <float>,
"control_loop_strength": <float>,
"delta_loop_strength": <float>
},
"reasoning": "<description of method and QC>"
}

The data files are mounted at:
- /srv/disk00/sshfs/pengchx3/genebench_pro_ws/gbp_hic_sv_masked_loop_strength_6fzgeaap/data_files/bins_20kb.tsv.gz
- /srv/disk00/sshfs/pengchx3/genebench_pro_ws/gbp_hic_sv_masked_loop_strength_6fzgeaap/data_files/contacts_20kb.tsv.gz
- /srv/disk00/sshfs/pengchx3/genebench_pro_ws/gbp_hic_sv_masked_loop_strength_6fzgeaap/data_files/bins_40kb.tsv.gz
- /srv/disk00/sshfs/pengchx3/genebench_pro_ws/gbp_hic_sv_masked_loop_strength_6fzgeaap/data_files/contacts_40kb.tsv.gz
BadRequestError: Error code: 400 - {'error': {'message': "This model's maximum context length is 131072 tokens. However, you requested 8192 output tokens and your prompt contains at least 122881 input tokens, for a total of at least 131073 tokens. Please reduce the length of the input prompt or the number of requested ...
Map the chromosome 1 QTL in an 8-founder multi-parent population. Report the position (cM) and which founder carries the high-effect allele.
Report high_founder as "F1".."F8".
These data came from a real experiment; you will be graded not just on numerical correctness but the quality of analytical reasoning you exhibit; do not attempt to take any shortcuts.
Return your final answer as exactly one JSON object.
Do not wrap the JSON in markdown.
Do not add prose before or after the JSON.
Do not omit any keys shown in the example.
Return the JSON object in your final answer:
{
"answer": {
"high_founder": "<string>",
"qtl_pos_cM": <float>
},
"reasoning": "<description of method and QC>"
}

The data files are mounted at:
- /srv/disk00/sshfs/pengchx3/genebench_pro_ws/gbp_multiparent_qtl_hmm_lmm_f43vprq9/data_files/markers.tsv.gz
- /srv/disk00/sshfs/pengchx3/genebench_pro_ws/gbp_multiparent_qtl_hmm_lmm_f43vprq9/data_files/founders.tsv.gz
- /srv/disk00/sshfs/pengchx3/genebench_pro_ws/gbp_multiparent_qtl_hmm_lmm_f43vprq9/data_files/ril_genotypes.npz

End of preview.

Bio-agent trajectories and metrics

Artifacts from a study of agent harnesses on two scientific-analysis benchmarks.

What is here, and what is deliberately not

genebench_pro/ -- full trajectories. GeneBench-Pro is MIT-licensed and OpenAI publishes its ground truth, so nothing new is disclosed.
biomysterybench_metrics/ -- metrics only: problem id, status, score, step and token counts, timings. No question, rubric, answer or transcript.
vera_graphs/ -- the analysis graphs our harness builds (node kinds, check counts).
code/vera/ -- the harness itself.

BioMysteryBench trajectories are withheld on purpose. Anthropic gates that benchmark for evaluation only, and its judge prompts embed the grading rubric verbatim, which states the answer. Publishing those files would breach the access terms and permanently contaminate the benchmark, since public text is scraped into training corpora and cannot be recalled. Every file here is scanned against the local copy of the questions and rubrics before upload, and publishing aborts on any hit.

Anyone with benchmark access can reproduce the withheld trajectories from the harness in code/.

Downloads last month: 25

Total file size:

9.86 MB