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..., These datasets and files are the results of scanning 6,901,686 XML documents within the Pubmed Central Open Access article datasets available at: https://ftp.ncbi.nlm.nih.gov/pub/pmc/ Each registry identifier match is represented by a row in the xmlScanOutput.csv file, along with PubMed identifiers, file information, XML path information, and several computed columns including a validation that an NCT number exists within ClinicalTrials.gov, a generalized article section, and publication types from multiple indexing sources. Summaries within the Distribution_of_Trial_Registry_Numbers_Additional_File.pdf were generated by counting distinct PMID values within the csv file across various groups., , # Distribution of trial registry numbers within full-text PubMed Central - full dataset of discovered links [https://doi.org/10.5061/dryad.dbrv15fb1](https://doi.org/10.5061/dryad.dbrv15fb1) This data set contains a table with every combination of publication ID, registry number, XML path, and section of the publication discovered in the Full-Text scanning of PubMed Central articles. ## Description of the data and file structure #### **Distribution\_of\_Trial\_Registry\_Numbers\_Additional\_File.pdf** This document contains charts and summaries of the trial registry numbers found from the XML document scanning process. The explicit criteria for locating registry identifiers and designating article sections are provided in this document and may be useful for further research and refinement. #### **Distribution\_of\_Trial\_Registry\_Numbers\_ScanOutput.zip** This zip archive contains a comma-separated file named \"xmlScanOutput.csv\" that contains all rows of registry numbers and art...将已注册临床试验与其已发表结果进行关联始终是一项难题。目前已开发出多种基于自然语言处理（Natural Language Processing，NLP）与机器学习的模型，以辅助用户识别此类关联。本研究对PubMed Central全文馆藏文献进行了扫描，以检索提及ClinicalTrials.gov及国际临床试验注册库标识符的内容。我们分析了文章各章节中试验注册编号的分布情况，并对其发表类型索引及其他指标进行了特征刻画。 本文附带3份支持性文件：其一为PDF文档，包含与文章全文中检测到的注册编号分布相关的补充图表；其二为逗号分隔值（Comma-Separated Values，CSV）数据集，提供了已发现的注册编号及其在文档中的XML（eXtensible Markup Language）路径位置；其三为示例Python脚本，用于在XML格式的文章文档中定位注册标识符。需要说明的是，本研究的目的为……本数据集及相关文件来自对PubMed Central开放获取文章数据集（可从https://ftp.ncbi.nlm.nih.gov/pub/pmc/获取）内的6,901,686份XML文档的扫描结果。 xmlScanOutput.csv文件中的每一行代表一条注册标识符匹配记录，同时包含PubMed标识符（PubMed Identifier，PMID）、文件信息、XML路径信息，以及多列计算所得的指标，包括验证某NCT编号是否存在于ClinicalTrials.gov数据库中、泛化后的文章章节，以及来自多个索引源的发表类型。Distribution_of_Trial_Registry_Numbers_Additional_File.pdf中的统计摘要，通过对CSV文件中不同PMID值按各类分组进行计数生成。 # PubMed Central全文临床试验注册编号分布——已发现关联的完整数据集 [https://doi.org/10.5061/dryad.dbrv15fb1](https://doi.org/10.5061/dryad.dbrv15fb1) 本数据集包含一张表格，收录了在PubMed Central文章全文扫描过程中发现的所有组合项，包括发表ID、注册编号、XML路径及文章所属章节。 ## 数据与文件结构说明 #### **Distribution_of_Trial_Registry_Numbers_Additional_File.pdf** 本文档包含从XML文档扫描流程中检测到的临床试验注册编号的相关图表与统计摘要。本文档中明确给出了定位注册标识符及标注文章章节的具体标准，可用于后续研究与方法优化。 #### **Distribution_of_Trial_Registry_Numbers_ScanOutput.zip** 该压缩归档包含一个名为"xmlScanOutput.csv"的逗号分隔值文件，其中收录了所有注册编号与文章……