Dataset
clinicaltrials-gov-scraper-sample-data
An API-based ClinicalTrials.gov scraper that retrieves over 585,000 study records including phase, sponsor, conditions, interventions, enrollment, eligibility, and sites, filterable by condition, sponsor, and status.
Data preview
Dataset card
clinicaltrials-gov-scraper-sample-data is a small sample dataset published by logiover, derived from the official ClinicalTrials.gov API via an Apify scraper. It is intended as a static preview of trial records and explicitly does not represent current counts or timestamps.
What this dataset contains
The sample contains a subset of clinical-trial records covering items such as nctId, briefTitle, officialTitle, overallStatus, studyType, phases, enrollmentCount, leadSponsor, sponsorClass, conditions, interventions, startDate, completionDate, firstPostedDate, lastUpdatePostedDate, locations, hasResults, and a scrapedAt timestamp. The full underlying scraper is described as covering 585K+ studies with status, phase, sponsor, conditions, interventions, enrollment, eligibility and locations. The sample itself is under 1K rows (100 in the train split), distributed as Parquet, and carries a CC-BY-4.0 license tag.
Structure and fields
Records are delivered as Parquet with a single train split of 100 rows and a flat tabular schema. Text fields such as briefTitle, officialTitle, organization, conditions, interventions, locations, and url are stored as large_string, enrollmentCount as int64, healthyVolunteers and hasResults as bool, and date fields (startDate, completionDate, primaryCompletionDate, firstPostedDate, lastUpdatePostedDate, scrapedAt) as large_string. Identification is anchored by the nctId column, with sponsor and collaborator relationships captured in leadSponsor, sponsorClass, and collaborators.
How analysts use it
The dataset is positioned as a shape-of-the-data preview for anyone evaluating the ClinicalTrials.gov scraper before running it at scale, supporting quick schema checks and pipeline prototyping in pandas, polars, or MLCroissant. For healthcare and biotech investment work it can be used to prototype filters by condition, sponsor, or overallStatus, to track phase progression (phases) and enrollment sizing (enrollmentCount), and to monitor study start and completion timelines (startDate, completionDate). Because it is a static snapshot with drift over time, it should be treated as a development aid rather than a live clinical-trials feed.
Documentation & references 1
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