Dataset
clinical-trials
Metadata for hundreds of thousands of clinical trials in English, French and Spanish, structured in Parquet with study-level fields such as phase, condition, sponsor and status (Apache 2.0). Trial starts and phase transitions lead clinical development, so this works as an early indicator of biotech pipeline activity.
Data preview
Dataset card
The clinical-trials dataset is a Parquet compilation of structured metadata and semantic embeddings drawn from ClinicalTrials.gov, distributed on the Hugging Face Hub under an unstated license. It contains roughly 542,000 records in a single train split and is tagged for English, French, and Spanish language coverage.
What this dataset contains
Each row represents one study registered on ClinicalTrials.gov and includes identifying codes, descriptive titles, status, phase, and sponsor information. The dataset covers study design attributes such as study_type, phases, enrollment_count, and healthy_volunteers, alongside timeline fields like study_first_submit_date, start_date, primary_completion_date, and completion_date. Eligibility and narrative content are preserved in fields such as eligibility_criteria, brief_summary, detailed_description, and study_population. Organisational metadata includes lead_sponsor_name, lead_sponsor_class, org_study_id_info, and expanded_access_info.
Structure and fields
The dataset ships as a single train split of 541,897 tabular records. The schema pairs an nct_id string key with timestamp fields (updated_at, start_date, primary_completion_date, completion_date, last_update_submit_qc_date), numeric fields (enrollment_count, minimum_age, maximum_age), and boolean flags such as healthy_volunteers and hasExpandedAccess. Categorical fields cover overall_status, study_type, phases, std_ages, and sex, while nested structs encode org_study_id_info, last_update_post_date_struct, and study_first_post_date_struct. Per the README, domain-specific models can be used to generate additional embedding columns for fields like eligibility_criteria.
How analysts use it
The dataset is designed for machine learning workflows such as question answering, feature extraction, and text classification over clinical trial narratives, using the pre-computable embeddings referenced in the README. For investment analysts, the records could support tracking of sponsor activity via lead_sponsor_name and lead_sponsor_class, monitoring pipeline progression through phases and overall_status, and sizing recruitment via enrollment_count. Timing fields such as primary_completion_date and completion_date enable event-driven screens of upcoming study readouts.
Documentation & references 1
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SEC EDGAR 10-K & 10-Q financial statements for 2,600+ NASDAQ companies.
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