TrialPanorama-filtered

Filtered version of TrialPanorama/TrialPanorama-database. This release removes any rows where study_id overlaps with IDs present in the task CSVs (NCT IDs and PubMed IDs).

Source

• Original dataset: TrialPanorama/TrialPanorama-database
• Task CSVs used for overlap IDs:
  • task1_status_transition.csv
  • task2_completion_termination.csv
  • task3_publication_emergence.csv
  • task4_serious_ae.csv

Filtering rules

• NCT IDs are collected from CSV columns whose headers contain nct (case-insensitive).
• PubMed IDs are collected from CSV columns whose headers contain pmid (case-insensitive).
• Cells are tokenized on whitespace, commas, semicolons, and |.
• A row is removed when study_id matches an overlapping NCT ID (^NCT\d+$) or an overlapping PubMed ID (^\d+$).
• Rows with missing or empty study_id are retained.
• Files without a study_id column are copied unchanged.

Overlap counts from task CSVs

• NCT IDs: 39,779
• PubMed IDs: 2,635

Files included

All parquet files from the source dataset are retained with identical schemas. Only row-level deletions are applied where study_id is present.

Subsplit summaries

adverse_events.parquet

Adverse event terms linked to studies (with MedDRA identifiers and seriousness).

• Rows (original): 5,400,796
• Rows (filtered): 5,329,726
• Rows removed: 71,070
• Filter applied: yes
• Columns: study_id, study_source, adverse_event_name, adverse_event_description, is_serious, meddra_id, meddra_id_type

adverse_events_part_2.parquet

Continuation of adverse events.

• Rows (original): 332,068
• Rows (filtered): 328,348
• Rows removed: 3,720
• Filter applied: yes
• Columns: study_id, study_source, adverse_event_name, adverse_event_description, is_serious, meddra_id, meddra_id_type

biomarkers.parquet

Biomarker mentions associated with studies.

• Rows (original): 256,034
• Rows (filtered): 254,298
• Rows removed: 1,736
• Filter applied: yes
• Columns: study_id, study_source, biomarker_type, biomarker_name, biomarker_genes

biomarkers_part_2.parquet

Continuation of biomarkers.

• Rows (original): 84,035
• Rows (filtered): 78,416
• Rows removed: 5,619
• Filter applied: yes
• Columns: study_id, study_source, biomarker_type, biomarker_name, biomarker_genes

conditions.parquet

Conditions linked to studies with MeSH identifiers.

• Rows (original): 6,069,464
• Rows (filtered): 6,005,009
• Rows removed: 64,455
• Filter applied: yes
• Columns: study_source, study_id, condition_name, condition_mesh_id, condition_mesh_type

conditions_part_2.parquet

Continuation of conditions.

• Rows (original): 1,982,348
• Rows (filtered): 1,773,112
• Rows removed: 209,236
• Filter applied: yes
• Columns: study_source, study_id, condition_name, condition_mesh_id, condition_mesh_type

disposition.parquet

Disposition/intervention groups and subject counts.

• Rows (original): 2,512,148
• Rows (filtered): 2,490,343
• Rows removed: 21,805
• Filter applied: yes
• Columns: study_id, intervention_type, group_type, intervention_name, intervention_description, study_source, number_of_subjects

disposition_part_2.parquet

Continuation of disposition.

• Rows (original): 765,033
• Rows (filtered): 689,812
• Rows removed: 75,221
• Filter applied: yes
• Columns: study_id, intervention_type, group_type, intervention_name, intervention_description, study_source, number_of_subjects

drug_moa.parquet

Drug mechanism-of-action and target activity annotations (not study-specific).

• Rows (original): 19,378
• Rows (filtered): 19,378
• Rows removed: 0
• Filter applied: no
• Columns: drug_name, drug_moa_id, target_name, target_class, accession, gene, swissprot, act_value, act_unit, act_type, act_comment, act_source, relation, moa, moa_source, act_source_url, moa_source_url, action_type, tdl, organism

drugs.parquet

Drug interventions linked to studies with normalization and approvals.

• Rows (original): 1,028,836
• Rows (filtered): 1,022,795
• Rows removed: 6,041
• Filter applied: yes
• Columns: study_id, study_source, drug_moa_id, drug_name, rx_normalized_name, rx_source, rx_mapping_id, drugbank_name, drugbank_id, drugbank_mesh_id, drug_source, fda_approved, ema_approved, pmda_approved, drug_description

drugs_part_2.parquet

Continuation of drugs.

• Rows (original): 287,165
• Rows (filtered): 269,798
• Rows removed: 17,367
• Filter applied: yes
• Columns: study_id, study_source, drug_moa_id, drug_name, rx_normalized_name, rx_source, rx_mapping_id, drugbank_name, drugbank_id, drugbank_mesh_id, drug_source, fda_approved, ema_approved, pmda_approved, drug_description

endpoints.parquet

Primary endpoints with domain and subdomain.

• Rows (original): 1,071,103
• Rows (filtered): 1,059,876
• Rows removed: 11,227
• Filter applied: yes
• Columns: study_id, study_source, primary_endpoint, primary_endpoint_domain, primary_endpoint_subdomain

endpoints_part_2.parquet

Continuation of endpoints.

• Rows (original): 368,043
• Rows (filtered): 320,051
• Rows removed: 47,992
• Filter applied: yes
• Columns: study_id, study_source, primary_endpoint, primary_endpoint_domain, primary_endpoint_subdomain

outcomes.parquet

Study outcomes and termination reasons.

• Rows (original): 365,521
• Rows (filtered): 357,876
• Rows removed: 7,645
• Filter applied: yes
• Columns: study_id, study_source, overall_status, outcome_type, why_terminated

outcomes_part_2.parquet

Continuation of outcomes.

• Rows (original): 159,027
• Rows (filtered): 128,112
• Rows removed: 30,915
• Filter applied: yes
• Columns: study_id, study_source, overall_status, outcome_type, why_terminated

relations.parquet

Entity-to-entity relation edges (not study-specific).

• Rows (original): 940,582
• Rows (filtered): 940,582
• Rows removed: 0
• Filter applied: no
• Columns: head_id, head_type, tail_id, tail_type, relation_type

results.parquet

Aggregated results with population/interventions/outcomes text.

• Rows (original): 3,746,215
• Rows (filtered): 3,735,669
• Rows removed: 10,546
• Filter applied: yes
• Columns: study_id, study_source, population, interventions, outcomes, group_type

results_part_2.parquet

Continuation of results.

• Rows (original): 726,383
• Rows (filtered): 722,595
• Rows removed: 3,788
• Filter applied: yes
• Columns: study_id, study_source, population, interventions, outcomes, group_type

studies.parquet

Study-level metadata (titles, recruitment status, phase, accrual, demographics).

• Rows (original): 1,010,037
• Rows (filtered): 1,002,045
• Rows removed: 7,992
• Filter applied: yes
• Columns: study_id, study_source, trial_type, title, abstract, sponsor, start_year, recruitment_status, phase, actual_accrual, target_accrual, min_age, max_age, min_age_unit, max_age_unit, sex, healthy_volunteers

studies_part_2.parquet

Continuation of studies.

• Rows (original): 322,104
• Rows (filtered): 290,738
• Rows removed: 31,366
• Filter applied: yes
• Columns: study_id, study_source, trial_type, title, abstract, sponsor, start_year, recruitment_status, phase, actual_accrual, target_accrual, min_age, max_age, min_age_unit, max_age_unit, sex, healthy_volunteers

Quick start

The easiest way to download the dataset to your local is to use huggingface-cli. The specific command you can use is

huggingface-cli download zifeng-ai/TrialPanorama-database --local-dir LOCAL_DIR --repo-type dataset

where LOCAL_DIR should be replaced with the target directory you want to save your dataset to.

Update history

• Aug.4 2025: updated tables with the full set of studies

Dataset website: https://ryanwangzf.github.io/projects/trialpanorama

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TrialPanorama

TrialPanorama is a large-scale, structured database and benchmark designed to support AI-driven tasks in clinical trial workflows—including systematic review and trial design. This repo hosts the database of clinical trials. To conduct benchmarking experiments on trial design and systematic review asks, check this dataset instead: https://huggingface.co/datasets/zifeng-ai/TrialPanorama-benchmark

Dataset Overview

• Aggregates over 1M clinical trial records from ClinicalTrials and PubMed
• Captures standardized elements such as trial setups, interventions, conditions, biomarkers, outcomes, and links to biomedical ontologies (e.g. DrugBank, MedDRA).
• Structured into multiple conceptual clusters and tables (e.g. trial-level attributes, protocol design, results, links)

Benchmark Suite

The dataset supports a suite of 8 benchmark tasks across two domains:

• Systematic Review:

  • Study search
  • Study screening
  • Evidence summarization

• Trial Design:

  • Arm design
  • Eligibility criteria
  • Endpoint selection
  • Sample size estimation
  • Trial completion assessment

Data Schema (Major Tables)

• studies — core metadata (e.g. study_id, intervention type, sponsor type, start year, trial phase, recruitment status)

• protocols, interventions, conditions, biomarkers, outcomes, results — each standardized to a unified schema

Use Cases

• Developing and evaluating LLMs and ML models for clinical trial-related tasks
• Benchmarking AI capabilities in trial search, screening, design, and summarization
• Conducting meta-analyses and exploring evidence synthesis across disease areas
• Enabling interoperability with biomedical ontologies to support richer clinical trial reasoning

Citation

If you use TrialPanorama, we appreciate your citation:

@article{wang2025trialpanorama,
  title={TrialPanorama: Database and Benchmark for Systematic Review and Design of Clinical Trials},
  author={Wang, Zifeng and Jin, Qiao and Lin, Jiacheng and Gao, Junyi and Pradeepkumar, Jathurshan and Jiang, Pengcheng and Danek, Benjamin and Lu, Zhiyong and Sun, Jimeng},
  journal={arXiv preprint arXiv:2505.16097},
  year={2025}
}