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The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
$schema: string
$id: string
title: string
description: string
type: string
additionalProperties: bool
required: list<item: string>
child 0, item: string
properties: struct<record_id: struct<type: string, minLength: int64, pattern: string, description: string>, sche (... 6897 chars omitted)
child 0, record_id: struct<type: string, minLength: int64, pattern: string, description: string>
child 0, type: string
child 1, minLength: int64
child 2, pattern: string
child 3, description: string
child 1, schema_version: struct<type: string, const: string, description: string>
child 0, type: string
child 1, const: string
child 2, description: string
child 2, source_identifiers: struct<type: string, additionalProperties: bool, description: string, required: list<item: string>, (... 879 chars omitted)
child 0, type: string
child 1, additionalProperties: bool
child 2, description: string
child 3, required: list<item: string>
child 0, item: string
child 4, properties: struct<title: struct<type: string, minLength: int64>, authors: struct<type: string, items: struct<ty (... 766 chars omitted)
child 0, title: struct<type: string, minLength: int64>
child 0, type: string
child 1, minLength: int64
child 1, authors: struct<type: string, items: struct<type: string, minLength: int64>>
child 0, type: string
child 1, items: struct<type: s
...
child 3, detailed_synthesis: string
child 4, verbatim_quote: string
child 7, biomedical_entities: struct<drugs_compounds_and_chemicals: list<item: string>, genes_proteins_and_pathways: list<item: st (... 104 chars omitted)
child 0, drugs_compounds_and_chemicals: list<item: string>
child 0, item: string
child 1, genes_proteins_and_pathways: list<item: string>
child 0, item: string
child 2, diseases_and_phenotypes: list<item: string>
child 0, item: string
child 3, organisms_models_and_cell_lines: list<item: string>
child 0, item: string
source_identifiers: struct<title: string, authors: list<item: string>, affiliations: list<item: string>, pmid_or_doi: st (... 326 chars omitted)
child 0, title: string
child 1, authors: list<item: string>
child 0, item: string
child 2, affiliations: list<item: string>
child 0, item: string
child 3, pmid_or_doi: string
child 4, pmid: string
child 5, pmcid: string
child 6, doi: string
child 7, journal: string
child 8, publication_date: timestamp[s]
child 9, download_url: string
child 10, pdf_url: string
child 11, license: string
child 12, cited_by_count: int64
child 13, grants_and_funding: list<item: string>
child 0, item: string
child 14, trial_and_data_accessions: list<item: string>
child 0, item: string
child 15, keywords_and_mesh: list<item: string>
child 0, item: string
child 16, domain_category: string
to
{'record_id': Value('string'), 'schema_version': Value('string'), 'source_identifiers': {'title': Value('string'), 'authors': List(Value('string')), 'affiliations': List(Value('string')), 'pmid_or_doi': Value('string'), 'pmid': Value('string'), 'pmcid': Value('string'), 'doi': Value('string'), 'journal': Value('string'), 'publication_date': Value('timestamp[s]'), 'download_url': Value('string'), 'pdf_url': Value('string'), 'license': Value('string'), 'cited_by_count': Value('int64'), 'grants_and_funding': List(Value('string')), 'trial_and_data_accessions': List(Value('string')), 'keywords_and_mesh': List(Value('string')), 'domain_category': Value('string')}, 'abstract': {'full_text': Value('string'), 'sections': List({'heading': Value('string'), 'text': Value('string')})}, 'extraction': {'background_and_problem_statement': {'prior_state_of_knowledge': Value('string'), 'identified_knowledge_gap': Value('string'), 'problem_statement_and_hypothesis': Value('string'), 'scientific_rationale': Value('string'), 'verbatim_quote': Value('string')}, 'methodology_and_study_design': {'study_type_and_design': Value('string'), 'model_system_or_population': Value('string'), 'sample_size_and_allocation': Value('string'), 'interventions_dosing_and_comparators': Value('string'), 'experimental_protocols_and_assays': Value('string'), 'statistical_methods_and_software': Value('string'), 'verbatim_quotes': List(Value('string'))}, 'quantitative_measurements_and_statistics': List({'metric_name': Val
...
ons': Value('string'), 'acknowledged_study_limitations': Value('string'), 'confounding_variables_and_biases': Value('string'), 'verbatim_quote': Value('string')}, 'underlying_assumptions': List({'assumption': Value('string'), 'category': Value('string'), 'impact_if_violated': Value('string')}), 'referenced_papers_and_literature_synthesis': List({'ref_id': Value('string'), 'citation_summary': Value('string'), 'relationship_to_current_study': Value('string'), 'detailed_synthesis': Value('string'), 'verbatim_quote': Value('string')}), 'biomedical_entities': {'drugs_compounds_and_chemicals': List(Value('string')), 'genes_proteins_and_pathways': List(Value('string')), 'diseases_and_phenotypes': List(Value('string')), 'organisms_models_and_cell_lines': List(Value('string'))}}, 'cited_references': List({'ref_id': Value('string'), 'ref_number': Value('string'), 'pmid': Value('string'), 'pmcid': Value('string'), 'doi': Value('string'), 'title': Value('string'), 'authors': List(Value('string')), 'journal': Value('string'), 'year': Value('string'), 'volume': Value('string'), 'pages': Value('string'), 'raw_citation': Value('string')}), 'verification_audit': {'total_evidence_quotes': Value('int64'), 'verified_verbatim_quotes': Value('int64'), 'grounding_fidelity_percent': Value('float64'), 'numerical_drift_detected': Value('bool'), 'unmatched_numbers': List(Value('null')), 'cited_references_parsed_count': Value('int64'), 'abstract_present': Value('bool')}, 'extracted_at': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1835, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 621, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
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
$schema: string
$id: string
title: string
description: string
type: string
additionalProperties: bool
required: list<item: string>
child 0, item: string
properties: struct<record_id: struct<type: string, minLength: int64, pattern: string, description: string>, sche (... 6897 chars omitted)
child 0, record_id: struct<type: string, minLength: int64, pattern: string, description: string>
child 0, type: string
child 1, minLength: int64
child 2, pattern: string
child 3, description: string
child 1, schema_version: struct<type: string, const: string, description: string>
child 0, type: string
child 1, const: string
child 2, description: string
child 2, source_identifiers: struct<type: string, additionalProperties: bool, description: string, required: list<item: string>, (... 879 chars omitted)
child 0, type: string
child 1, additionalProperties: bool
child 2, description: string
child 3, required: list<item: string>
child 0, item: string
child 4, properties: struct<title: struct<type: string, minLength: int64>, authors: struct<type: string, items: struct<ty (... 766 chars omitted)
child 0, title: struct<type: string, minLength: int64>
child 0, type: string
child 1, minLength: int64
child 1, authors: struct<type: string, items: struct<type: string, minLength: int64>>
child 0, type: string
child 1, items: struct<type: s
...
child 3, detailed_synthesis: string
child 4, verbatim_quote: string
child 7, biomedical_entities: struct<drugs_compounds_and_chemicals: list<item: string>, genes_proteins_and_pathways: list<item: st (... 104 chars omitted)
child 0, drugs_compounds_and_chemicals: list<item: string>
child 0, item: string
child 1, genes_proteins_and_pathways: list<item: string>
child 0, item: string
child 2, diseases_and_phenotypes: list<item: string>
child 0, item: string
child 3, organisms_models_and_cell_lines: list<item: string>
child 0, item: string
source_identifiers: struct<title: string, authors: list<item: string>, affiliations: list<item: string>, pmid_or_doi: st (... 326 chars omitted)
child 0, title: string
child 1, authors: list<item: string>
child 0, item: string
child 2, affiliations: list<item: string>
child 0, item: string
child 3, pmid_or_doi: string
child 4, pmid: string
child 5, pmcid: string
child 6, doi: string
child 7, journal: string
child 8, publication_date: timestamp[s]
child 9, download_url: string
child 10, pdf_url: string
child 11, license: string
child 12, cited_by_count: int64
child 13, grants_and_funding: list<item: string>
child 0, item: string
child 14, trial_and_data_accessions: list<item: string>
child 0, item: string
child 15, keywords_and_mesh: list<item: string>
child 0, item: string
child 16, domain_category: string
to
{'record_id': Value('string'), 'schema_version': Value('string'), 'source_identifiers': {'title': Value('string'), 'authors': List(Value('string')), 'affiliations': List(Value('string')), 'pmid_or_doi': Value('string'), 'pmid': Value('string'), 'pmcid': Value('string'), 'doi': Value('string'), 'journal': Value('string'), 'publication_date': Value('timestamp[s]'), 'download_url': Value('string'), 'pdf_url': Value('string'), 'license': Value('string'), 'cited_by_count': Value('int64'), 'grants_and_funding': List(Value('string')), 'trial_and_data_accessions': List(Value('string')), 'keywords_and_mesh': List(Value('string')), 'domain_category': Value('string')}, 'abstract': {'full_text': Value('string'), 'sections': List({'heading': Value('string'), 'text': Value('string')})}, 'extraction': {'background_and_problem_statement': {'prior_state_of_knowledge': Value('string'), 'identified_knowledge_gap': Value('string'), 'problem_statement_and_hypothesis': Value('string'), 'scientific_rationale': Value('string'), 'verbatim_quote': Value('string')}, 'methodology_and_study_design': {'study_type_and_design': Value('string'), 'model_system_or_population': Value('string'), 'sample_size_and_allocation': Value('string'), 'interventions_dosing_and_comparators': Value('string'), 'experimental_protocols_and_assays': Value('string'), 'statistical_methods_and_software': Value('string'), 'verbatim_quotes': List(Value('string'))}, 'quantitative_measurements_and_statistics': List({'metric_name': Val
...
ons': Value('string'), 'acknowledged_study_limitations': Value('string'), 'confounding_variables_and_biases': Value('string'), 'verbatim_quote': Value('string')}, 'underlying_assumptions': List({'assumption': Value('string'), 'category': Value('string'), 'impact_if_violated': Value('string')}), 'referenced_papers_and_literature_synthesis': List({'ref_id': Value('string'), 'citation_summary': Value('string'), 'relationship_to_current_study': Value('string'), 'detailed_synthesis': Value('string'), 'verbatim_quote': Value('string')}), 'biomedical_entities': {'drugs_compounds_and_chemicals': List(Value('string')), 'genes_proteins_and_pathways': List(Value('string')), 'diseases_and_phenotypes': List(Value('string')), 'organisms_models_and_cell_lines': List(Value('string'))}}, 'cited_references': List({'ref_id': Value('string'), 'ref_number': Value('string'), 'pmid': Value('string'), 'pmcid': Value('string'), 'doi': Value('string'), 'title': Value('string'), 'authors': List(Value('string')), 'journal': Value('string'), 'year': Value('string'), 'volume': Value('string'), 'pages': Value('string'), 'raw_citation': Value('string')}), 'verification_audit': {'total_evidence_quotes': Value('int64'), 'verified_verbatim_quotes': Value('int64'), 'grounding_fidelity_percent': Value('float64'), 'numerical_drift_detected': Value('bool'), 'unmatched_numbers': List(Value('null')), 'cited_references_parsed_count': Value('int64'), 'abstract_present': Value('bool')}, 'extracted_at': Value('string')}
because column names don't match
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 1379, 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 956, 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 1702, 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 1887, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
record_id string | schema_version string | source_identifiers dict | abstract dict | extraction dict | cited_references list | verification_audit dict | extracted_at string |
|---|---|---|---|---|---|---|---|
med_rec_PMC13475465 | biology_schema_synth_v1.3 | {
"title": "Exploring variables leading to major postmarketing label changes in leading regulatory authorities: a retrospective cohort study in Israel.",
"authors": [
"Vishkautzan A",
"Vexberg MH",
"Berkovitch M",
"Ashkenazi S",
"Weiss I",
"Hershkowitz R",
"Gorelik E",
"Maayan H",
... | {
"full_text": "Objective: The US Food and Drug Administration (FDA) and the European Medicines Agency (EMA) are leading benchmarks for reliance approval pathways, while many regulatory authorities streamline their review processes by referencing these authorities’ evaluations and decisions. Major postmarketing (MPM)... | {
"background_and_problem_statement": {
"prior_state_of_knowledge": "National health regulatory authorities such as the Israel Ministry of Health (MOH) commonly employ reliance pathways referencing benchmark agencies—specifically the US Food and Drug Administration (FDA) and European Medicines Agency (EMA)—to acc... | [
{
"ref_id": "R1",
"ref_number": "1",
"pmid": "28342075",
"pmcid": "",
"doi": "",
"title": "Evaluation of Pre-marketing Factors to Predict Post-marketing Boxed Warnings and Safety Withdrawals.",
"authors": [
"Schick A",
"Miller KL",
"Lanthier M",
"Dal Pan G",
... | {
"total_evidence_quotes": 21,
"verified_verbatim_quotes": 21,
"grounding_fidelity_percent": 100,
"numerical_drift_detected": false,
"unmatched_numbers": [],
"cited_references_parsed_count": 20,
"abstract_present": true
} | 2026-11-10T15:38:06.973249+00:00 |
med_rec_PMC13598462 | biology_schema_synth_v1.3 | {
"title": "Global, regional, and national prevalence of second-hand smoke and attributable disease burden in 204 countries and territories, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023.",
"authors": [
"GBD 2023 Second-hand smoke Collaborators."
],
"affiliations": [],
"pmid... | {
"full_text": "Background: Second-hand smoke (SHS) exposure remains a major source of morbidity and mortality among non-smokers. This study presents the first dedicated Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) publication to systematically quantify SHS prevalence and evaluate its effect as a... | {
"background_and_problem_statement": {
"prior_state_of_knowledge": "Second-hand smoke (SHS) combines unfiltered, incompletely combusted sidestream smoke with exhaled mainstream smoke and contains higher per-unit concentrations of toxicants and carcinogens than active smoking. There is no safe level of exposure, ... | [
{
"ref_id": "R1",
"ref_number": "1",
"pmid": "39537242",
"pmcid": "",
"doi": "",
"title": "Association between second-hand smoke exposure and lung cancer risk in never-smokers: a systematic review and meta-analysis.",
"authors": [
"Possenti I",
"Romelli M",
"Carreras G"... | {
"total_evidence_quotes": 17,
"verified_verbatim_quotes": 17,
"grounding_fidelity_percent": 100,
"numerical_drift_detected": false,
"unmatched_numbers": [],
"cited_references_parsed_count": 42,
"abstract_present": true
} | 2026-11-10T15:38:06.994700+00:00 |
med_rec_PMC13575558 | biology_schema_synth_v1.3 | {
"title": "Biomarker Analysis from Patients with Metastatic PDAC Treated with TGFβ Antibody NIS793 plus Abraxane + Gemcitabine versus Abraxane + Gemcitabine Alone in a Phase II, Open-Label, Randomized Study.",
"authors": [
"Pelletier M",
"Yang J",
"Joshi M",
"Grauel A",
"Abecunas C",
"Altze... | {
"full_text": "Purpose: Transforming growth factor β (TGFβ) plays a dual role in cancer, acting as a tumor suppressor early in the disease but promoting progression and immune evasion when dysregulated. In pancreatic ductal adenocarcinoma (PDAC), TGFβ-driven desmoplasia fosters chemoresistance and immunosuppression,... | {
"background_and_problem_statement": {
"prior_state_of_knowledge": "Transforming growth factor β (TGFβ; isoforms TGFβ1–3) acts early as an epithelial tumor suppressor but later drives desmoplasia, cancer-associated fibroblast (CAF) activation, extracellular matrix (ECM) deposition, angiogenesis, and T-cell exclu... | [
{
"ref_id": "R1",
"ref_number": "1",
"pmid": "23396013",
"pmcid": "",
"doi": "",
"title": "Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples.",
"authors": [
"Cibulskis K",
"Lawrence MS",
"Carter SL",
"Sivachenko A",
"J... | {
"total_evidence_quotes": 19,
"verified_verbatim_quotes": 19,
"grounding_fidelity_percent": 100,
"numerical_drift_detected": false,
"unmatched_numbers": [],
"cited_references_parsed_count": 43,
"abstract_present": true
} | 2026-11-10T15:38:07.015296+00:00 |
med_rec_PMC13424262 | biology_schema_synth_v1.3 | {"title":"CYP2C19 Genotyping to Guide Clopidogrel Prescribing for Neurovascular Indications: A Surve(...TRUNCATED) | {"full_text":"Dual antiplatelet therapy (DAPT) with aspirin and clopidogrel is commonly used for sec(...TRUNCATED) | {"background_and_problem_statement":{"prior_state_of_knowledge":"Dual antiplatelet therapy (DAPT) wi(...TRUNCATED) | [{"ref_id":"R1","ref_number":"1","pmid":"19106084","pmcid":"","doi":"","title":"Cytochrome p-450 pol(...TRUNCATED) | {"total_evidence_quotes":18,"verified_verbatim_quotes":18,"grounding_fidelity_percent":100.0,"numeri(...TRUNCATED) | 2026-11-10T15:38:07.027819+00:00 |
med_rec_PMC13468982 | biology_schema_synth_v1.3 | {"title":"Medication-related examination questions in clinical courses within undergraduate medical (...TRUNCATED) | {"full_text":"Purpose: Pharmacotherapy is a core competence for physicians and students’ learning (...TRUNCATED) | {"background_and_problem_statement":{"prior_state_of_knowledge":"Pharmacotherapy is a core clinical (...TRUNCATED) | [{"ref_id":"R1","ref_number":"1","pmid":"31454058","pmcid":"","doi":"","title":"[A new international(...TRUNCATED) | {"total_evidence_quotes":22,"verified_verbatim_quotes":22,"grounding_fidelity_percent":100.0,"numeri(...TRUNCATED) | 2026-11-10T15:38:07.044571+00:00 |
med_rec_PMC13689281 | biology_schema_synth_v1.3 | {"title":"Unintended Pregnancies and Breastfeeding Studies during Central Nervous System Drug Develo(...TRUNCATED) | {"full_text":"Pregnant and breastfeeding individuals are routinely excluded from clinical drug devel(...TRUNCATED) | {"background_and_problem_statement":{"prior_state_of_knowledge":"Pregnant and breastfeeding individu(...TRUNCATED) | [{"ref_id":"R1","ref_number":"1","pmid":"26488521","pmcid":"","doi":"","title":"ACOG Committee Opini(...TRUNCATED) | {"total_evidence_quotes":21,"verified_verbatim_quotes":21,"grounding_fidelity_percent":100.0,"numeri(...TRUNCATED) | 2026-11-10T15:38:07.067932+00:00 |
med_rec_PMC13574604 | biology_schema_synth_v1.3 | {"title":"Predicting clinically significant medication errors: Development and validation of a risk (...TRUNCATED) | {"full_text":"Background: Medication error reporting systems capture large volumes of incidents, yet(...TRUNCATED) | {"background_and_problem_statement":{"prior_state_of_knowledge":"Medication errors drive roughly $42(...TRUNCATED) | [{"ref_id":"B1","ref_number":"1","pmid":"","pmcid":"","doi":"","title":"World Health Organization Me(...TRUNCATED) | {"total_evidence_quotes":23,"verified_verbatim_quotes":23,"grounding_fidelity_percent":100.0,"numeri(...TRUNCATED) | 2026-11-10T15:38:07.090576+00:00 |
med_rec_PMC13520735 | biology_schema_synth_v1.3 | {"title":"Effect of Evolocumab on Grafts Post-Coronary Artery Bypass Surgery: Post-Hoc Analysis of N(...TRUNCATED) | {"full_text":"Background: Graft failure remains a significant clinical problem.\nObjectives: This st(...TRUNCATED) | {"background_and_problem_statement":{"prior_state_of_knowledge":"Coronary artery bypass graft (CABG)(...TRUNCATED) | [{"ref_id":"R1","ref_number":"1","pmid":"41211925","pmcid":"","doi":"","title":"Evolocumab in Patien(...TRUNCATED) | {"total_evidence_quotes":24,"verified_verbatim_quotes":24,"grounding_fidelity_percent":100.0,"numeri(...TRUNCATED) | 2026-11-10T15:38:07.110235+00:00 |
med_rec_PMC13541161 | biology_schema_synth_v1.3 | {"title":"Patterns in biomedical research funding in India: An analysis of ICMR small and intermedia(...TRUNCATED) | {"full_text":"Background and objectives: The Indian Council of Medical Research (ICMR) funds small a(...TRUNCATED) | {"background_and_problem_statement":{"prior_state_of_knowledge":"India faces a distinctive epidemiol(...TRUNCATED) | [{"ref_id":"ref1","ref_number":"1","pmid":"25664236","pmcid":"PMC4318649","doi":"10.1016/j.gheart.20(...TRUNCATED) | {"total_evidence_quotes":18,"verified_verbatim_quotes":18,"grounding_fidelity_percent":100.0,"numeri(...TRUNCATED) | 2026-11-10T15:38:07.123973+00:00 |
med_rec_PMC13708324 | biology_schema_synth_v1.3 | {"title":"MDL-001: An Oral, Direct-Acting Broad-Spectrum Antiviral for Influenza-like Illness (ILI) (...TRUNCATED) | {"full_text":"Background/Objectives: Endemic viral respiratory illnesses caused by influenza, RSV, a(...TRUNCATED) | {"background_and_problem_statement":{"prior_state_of_knowledge":"Viral respiratory illnesses (VRIs)(...TRUNCATED) | [{"ref_id":"B1-pharmaceuticals-19-01639","ref_number":"1","pmid":"40003946","pmcid":"PMC11855616","d(...TRUNCATED) | {"total_evidence_quotes":26,"verified_verbatim_quotes":26,"grounding_fidelity_percent":100.0,"numeri(...TRUNCATED) | 2026-11-10T15:38:07.149458+00:00 |
End of preview.
OpenBioMed-Evidence
OpenBioMed-Evidence is a verbatim-grounded biomedical and clinical research evidence corpus structured for scientific literature analysis, quantitative evidence synthesis, and domain-specific reasoning.
Each record represents a single peer-reviewed empirical research publication decomposed into canonical bibliographic metadata, structured abstracts, study methodology, quantitative outcomes, mechanistic pathways, explicit assumptions, cited-literature synthesis, and source quotations subjected to automated matching and numerical consistency checks.
Release Note: Dataset records are curated incrementally in validated collection cycles; additional records are appended as curation progresses.
Domain Scope
Coverage spans primary empirical research across:
- Clinical Pharmacology & Therapeutics: Dosing regimens, PK/PD endpoints, adverse events, drug–drug precautions, and postmarketing pharmacovigilance.
- Oncology, Immunology & Translational Medicine: Clinical trials, biomarker stratification, signaling pathways, and molecular assays.
- Epidemiology, Neurology, Cardiology & Toxicology: Cohort/comparative-risk studies, regression modeling, effect sizes, and safety thresholds.
Record Structure & Schema (biology_schema_synth_v1.3)
Every JSONL record conforms to the formal JSON Schema defined in schema.json (biology_schema_synth_v1.3) across five primary sections:
source_identifiers: Canonicalpmid,pmcid,doi,title,authors,affiliations,journal,publication_date,download_url,pdf_url,license,cited_by_count,grants_and_funding,trial_and_data_accessions(e.g.,NCT,GSE),keywords_and_mesh, anddomain_category.abstract: Completefull_textabstract alongside original section splits (sections).extraction:background_and_problem_statement— Prior state of knowledge, identified gap, formal hypothesis, scientific rationale, and exactverbatim_quote.methodology_and_study_design— Study classification, population/model criteria, sample size & arm allocation, interventions/dosing/comparators, assay protocols, statistical software/models, andverbatim_quotes.quantitative_measurements_and_statistics— List of measured metrics with arm/subgroup comparators, exactvalue_with_units, baseline/reference values,statistical_significance(p-values, CIs, HRs/ORs/RRs), interpretation, andverbatim_quote.key_results_and_resolution— Primary outcomes, secondary/subgroup findings, negative/null results, mechanistic pathway, translational implications, andverbatim_quotes.precautions_limitations_and_safety— Graded adverse events/toxicities, contraindications/precautions, study limitations, confounders/biases, andverbatim_quote.underlying_assumptions— Structured assumptions categorized by domain (biological_surrogate,model_translatability,statistical_model,measurement_validity,clinical_generalizability) paired withimpact_if_violated.referenced_papers_and_literature_synthesis— Contextual links keyed byref_idintocited_references, specifyingrelationship_to_current_study,detailed_synthesis, and supportingverbatim_quote.biomedical_entities— Normalized lists of drugs/compounds, genes/proteins/pathways, diseases/phenotypes, and organisms/models/cell lines.
cited_references: Complete structured bibliography (ref_id,ref_number,pmid,pmcid,doi,title,authors,journal,year,volume,pages,raw_citation).verification_audit: Quantitative integrity metrics per record (verified_verbatim_quotes,total_evidence_quotes,grounding_fidelity_percent,numerical_drift_detected,unmatched_numbers,cited_references_parsed_count,abstract_present).
Data Quality Notes
Initial manual audits identified limitations that automated verification may not detect, including lost negative signs in uncertainty intervals, conflicting values between abstracts and main-text tables, and causal wording that overstates observational findings. Therefore, 100% verbatim quote matching does not guarantee numerical-semantic accuracy or scientific validity. Records should be checked against their original publications before downstream use.
Licensing
Because OpenBioMed-Evidence aggregates open-access publications from multiple publishers and appends new records incrementally, no single blanket license covers all underlying source publications. See LICENSE for full details.
Each record preserves its authoritative source publication license in source_identifiers.license (such as cc by, cc by-nc, cc by-nc-sa, and cc by-nc-nd) alongside canonical attribution (title, authors, journal, doi, pmid, pmcid, download_url).
Downstream users should respect each record's license terms and filter on source_identifiers.license if a uniform subset is needed:
# Example: filter to commercially permissive CC BY records only
cc_by_records = [r for r in records if r["source_identifiers"]["license"].lower() == "cc by"]
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