PubMed Research Data Product: Biomedical Citations
收藏资源简介：
The PubMed Research Database by The Data Developers provides the PubMed corpus as a powerful, query-ready relational database in your data environment. This data product eliminates the complexities of API management and XML parsing, allowing your team to move beyond simple searches and perform large-scale, structured analysis of biomedical literature. This product is more than a list of articles; it is a deeply structured database that connects literature to its critical context. You can immediately query and join tables to access: - **Core Article Data:** Comprehensive citation information including the article_title ,abstract text ,journal_title , and publication dates from thearticles table. - **Detailed Author & Affiliation Data:** Granular author details, including last_name ,fore_name ,orcid , and theiraffiliation text for tracking institutional research output, available in the authors table. - **Funding and Grant Information:** Track research funding with detailed grant_id ,agency , andcountry data from thegrants table. - **Systematic Topic Indexing:** Leverage the NLM's controlled vocabulary with descriptor_name andqualifier_name from themesh_headings table for powerful, systematic searches. - **Linked Chemical & Substance Data:** Connect articles directly to chemical substances via registry_number andsubstance_name from thechemicals table. - **Complete Citation Networks:** Map the flow of scientific discovery and identify influential papers by analyzing the citation links within the references table. **Key Value & Features:** - **Perform Systematic Literature Reviews:** Go beyond simple keyword searching by using the structured mesh_headings table to perform precise, repeatable, and comprehensive literature analysis. - **Analyze Research Funding Landscapes:** Join articles and grants data to understand which agencies are funding specific research areas, track competitor funding, and identify key opinion leaders. - **Map Institutional & Competitive Output:** Use author affiliation data to benchmark research output from academic institutions and commercial competitors, find collaborators, and track scientific trends. - **Build Powerful Knowledge Graphs:** Utilize the rich relational links between articles ,authors ,chemicals , and citationreferences to model the entire landscape of biomedical research. This product is essential for teams in Medical Affairs, Competitive Intelligence, Health Economics and Outcomes Research (HEOR), R&D Strategy, and academic institutions that need to derive deep insights from the world's biomedical literature.
PubMed Research Data Product: Biomedical Citations
概述
- 提供PubMed语料库作为关系型数据库，支持大规模NLP和AI驱动的生物医学发现。
- 消除API管理和XML解析的复杂性，支持结构化分析生物医学文献。
核心数据表
- articles表：包含文章标题(article_title)、摘要(abstract)、期刊名称(journal_title)、出版日期等。
- authors表：包含作者姓氏(last_name)、名字(fore_name)、ORCID(orcid)、所属机构(affiliation)。
- grants表：包含资助信息(grant_id、agency、country)。
- mesh_headings表：包含NLM控制词汇(descriptor_name、qualifier_name)。
- chemicals表：包含化学物质信息(registry_number、substance_name)。
- references表：包含文章引用关系。
关键价值与功能
- 执行系统性文献综述
- 分析研究资助格局
- 绘制机构和竞争性研究成果图
- 构建强大的知识图谱
适用领域
- 医学事务
- 竞争情报
- 健康经济学与结果研究(HEOR)
- 研发战略
- 学术机构
商业需求
- 生命科学商业化：识别关键意见领袖(KOLs)
- 真实世界数据(RWD)：提供科学背景
- 市场准入与HEOR：加速价值档案创建
- 竞争情报与研发战略：监控竞争对手活动
- 数据驱动的资助申请：加强提案
数据字典(部分)
ARTICLES表
- ABSTRACT: 文章内容摘要
- ARTICLE_TITLE: 文章完整标题
- JOURNAL_TITLE: 期刊名称
- DOI: 数字对象标识符
- PUBLICATION_DAY: 出版日
使用示例
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识别精神分裂症领域关键意见领袖 sql SELECT a.last_name, a.fore_name, a.affiliation, COUNT(DISTINCT a.pubmed_id) AS publication_count FROM authors a JOIN mesh_headings mh ON a.pubmed_id = mh.pubmed_id WHERE mh.descriptor_name = Schizophrenia GROUP BY a.last_name, a.fore_name, a.affiliation ORDER BY publication_count DESC LIMIT 20;
-
识别顶级资助研究领域 sql SELECT mh.descriptor_name, COUNT(DISTINCT g.pubmed_id) AS funded_publication_count FROM grants g JOIN mesh_headings mh ON g.pubmed_id = mh.pubmed_id WHERE g.agency ILIKE %National Institutes of Health% OR g.agency ILIKE %NIH% GROUP BY mh.descriptor_name ORDER BY funded_publication_count DESC LIMIT 20;
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在高影响力期刊中发现新兴关键词 sql SELECT k.keyword_text, COUNT(*) AS keyword_frequency FROM keywords k JOIN articles a ON k.pubmed_id = a.pubmed_id WHERE (a.journal_title ILIKE %Nature% OR ...) AND NOT EXISTS (...) GROUP BY k.keyword_text
技术信息
- 更新频率：每月
- 时间覆盖范围：最近1个月
- 地理覆盖范围：全球
- 云区域可用性：AWS US East (N. Virginia)
提供商信息
- 提供商：The Data Developers LLC
- 销售联系：marketplace.snowflake@thedatadevelopers.com
- 支持联系：support.marketplace@thedatadevelopers.com
