Distribution of trial registry numbers within full-text PubMed Central - full dataset of discovered links

Linking registered clinical trials with their published results continues
to be a challenge. A variety of natural language processing
(NLP)-based and machine learning-based models have been developed to
assist users in identifying these connections. Articles from the PubMed
Central full-text collection were scanned for mentions of
ClinicalTrials.gov and international clinical trial registry identifiers.
We analyzed the distribution of trial registry numbers within
sections of the articles and characterized their publication type
indexing and other metrics. Three supporting files are included herein: a
pdf containing supplementary figures pertaining to the distribution of
registry numbers found within the full text of articles, a csv dataset
providing the registry numbers discovered and the corresponding XML path
location within the document, and an example Python script to locate
registry identifiers within an XML article document. It should be noted
that the purpose of this study is to summarize clinical trial mentions
within publications and specific registries or other nominative
information contained in this dataset may contain errors.