Data source

Data from ClinicalTrials.gov (NLM/NIH), delivered clean.

3 datasets pulled from ClinicalTrials.gov (NLM/NIH)'s releases, checked field by field and shipped the way you want them — daily, weekly, or hourly, your call.

  • 3 datasets
  • 1 industry
  • Real rows on request

What Datadory delivers from ClinicalTrials.gov (NLM/NIH)

3
Biotechnology Global - studies registered from 200+ countri… · Records span 1999-present

ClinicalTrials.gov

Biotechnology Primarily United States (FDA-regulated produc… · Drug adverse event records reach back to 2004…

openFDA — FDA Regulatory Data on Drugs, Devices and Food

Biotechnology

ChEMBL

Pick a catch, see the rows.

Name any ClinicalTrials.gov (NLM/NIH) dataset and we send real rows from it — not a screenshot of rows. 1,744 datasets. Pick your catch.

Get a sample

API, files, or your warehouse. Daily, weekly, or hourly.

Straight answers about ClinicalTrials.gov (NLM/NIH) data

How many ClinicalTrials.gov (NLM/NIH) datasets does Datadory deliver?

One, and it is the registry itself: ClinicalTrials.gov in our Biotechnology slice, scoring 10 out of 10 on our rubric. It holds 599,549 registered studies as of August 2026 - interventional trials, observational studies and expanded-access records together - each organized into the same named protocol modules from identification through references.

What can one row of this data tell you?

A complete development-program position on a single key. NCT00429442 reads: withdrawn Phase 3 simvastatin trial in relapsing multiple sclerosis, lead sponsor named outright, enrollment planned rather than achieved. Status, phase, sponsor, condition and enrollment arrive on the same row shape whether a study is two decades old or filed last week.

Does every record include posted results?

No, and the distinction is explicit rather than guessed. A hasResults boolean marks the subset that posted result data; every record still carries the outcome-measure module documenting planned primary and secondary measures with their time frames. Filter on the flag when result-level analysis is the goal.

Which questions is this source best at answering?

Sponsor pipeline views built by joining leadSponsor.name to phase and status codes; site-density and feasibility questions answered from the locations module without a geocoding project; enrollment benchmarking against ACTUAL versus ANTICIPATED counts; condition mapping rolled up the MeSH hierarchy. All four run as filters, not reconstructions.