摘要：

【目的】 对生物医学领域数据论文的出版现状进行系统调研和分析,为数据论文的出版提供建议。【方法】 检索Web of Science数据库文献类型为“data paper”和PubMed数据库文献类型为“dataset”的数据论文,收集124种出版生物医学数据论文的期刊,调研17个数据论文模板,提炼生物医学领域的期刊对数据论文类型、数据论文模板、数据存储以及同行评审的要求,归纳当前生物医学领域数据论文的发展现状与问题。【结果】 生物医学领域的期刊主要将数据论文作为出版的文章类型之一并设立专栏。数据论文主要为描述数据集或描述数据库,涵盖data report、data note、data descriptor、database等多个文章类型,同一出版商旗下的不同期刊所采用的类型也有差异。数据论文模板主要要求作者提交与数据收集、数据存储和数据重用相关的信息。Frontiers、Springer Nature、Pensoft为期刊制定了统一的数据存储方法,一些期刊则各自制定数据存储方法。数据论文需要进行同行评审,一些期刊专门针对数据论文规定了需要评审的内容。【结论】 生物医学数据论文仍存在主体内容结构尚不明晰、数据存储与重用管理不够规范、数据质量评价标准和方法不够全面等问题,我国生物医学数据论文正大量流失到国外。建议我国鼓励生物医学期刊开设数据论文独立专栏,规范数据论文内容,优化数据存储与重用,探索多元化和标准化的同行评审机制。

关键词: 数据论文, 生物医学, 数据论文模板, 数据存储, 同行评审

Abstract:

[Purposes] Conduct a systematic investigation and analysis of the current publishing status of biomedical data papers, and provide corresponding suggestions for the publication of data papers.[Methods] Data papers were retrieved from the Web of Science database for the document type of “data paper” and the PubMed database for the document type of “dataset”. Subsequently, 124 journals publishing biomedical data papers and 17 data paper templates were collected. Requirements related to data paper types, template structures, data storage, and peer-review process were extracted to summarize the current landscape and challenges in biomedical data paper development.[Findings] In the biomedical field, journals predominantly categorize data papers as one of their published article types and establish dedicated sections for them. Data papers primarily describe datasets or databases and encompass various article types such as data reports, data notes, data descriptors, and database papers, with classifications differing even among journals under the same publisher. Data paper templates typically require authors to submit information related to data collection, storage, and reuse. Publishers like Frontiers, Springer Nature, and Pensoft have implemented unified data storage policies across their journals, while several journals develop their own storage guidelines. Peer review is mandatory for data papers, and some journals tailored specific guidelines for evaluating data papers.[Conclusions] Biomedical data papers still face challenges such as unclear structural frameworks for content, insufficient standardization in data storage and reuse management, and incomplete criteria and methodologies for data quality assessment. Furthermore, a significant portion of China’s biomedical data papers are being published overseas. To address these issues, it is recommended that China incentivize biomedical journals to establish dedicated sections for data papers, standardize their content requirements, optimize data storage and reuse practices, and explore diversified and standardized peer-review process.

Key words: Data paper, Biomedical science, Data paper template, Data storage, Peer review