多学科通用型开放科学数据共享平台对比研究——以Zenodo和ScienceDB为例

doi:10.19788/j.issn.2096-6369.000063

Abstract

Abstract:

The purpose of this study is to explore the similarities and differences between two representative multidisciplinary general-purpose scientific data platforms—Zenodo and ScienceDB—in terms of functionality, services, and community collaboration, and to propose their respective strengths and potential areas for improvement. The significance of this research lies in providing references for the optimization and improvement of scientific research data platforms, promoting the efficient management and utilization of scientific data, thereby contributing to the advancement of scientific research. The study employs a comparative analysis method to delve into the characteristics and differences of Zenodo and ScienceDB in aspects such as data storage capacity, sharing mechanisms, user interface design, technical support, community interaction, and data security and privacy protection. During the analysis process, a detailed comparison was made between the two platforms in terms of data submission and description, metadata requirements, data services, data statistics, and community services, to assess their service capabilities and features in the field of scientific data management. Zenodo enjoys a high reputation internationally with its user-friendly interface, flexible technical architecture, and robust community functions, while ScienceDB provides strong support for scientific data sharing in China and globally by adhering to the FAIR principles and emphasizing data governance. Both platforms have their advantages but also have room for improvement. Zenodo can further enhance its localized data services, and ScienceDB can learn from Zenodo's experience in community management to improve user experience. Ultimately, the continuous development and optimization of both platforms will jointly promote the progress of scientific research and the dissemination of knowledge.

Key words: scientific data platforms, Zenodo, ScienceDB, multidisciplinary, data sharing

HE HaoYu, HOU ChunMei, SUN LiWei, CHI XiuLi, YE XiYan. A Comparative Analysis of Multidisciplinary General-Purpose Scientific Data Platforms: Taking Zenodo and ScienceDB as Examples[J].Journal of Agricultural Big Data, 2025, 7(2): 193-200.

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References 18

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	Zenodo	ScienceDB
登录方式	1. ORCID账号 2. Github账号 3. 邮箱注册登录	1. ScienceDB平台提供的登录界面 2.第三方账号，如中国科技云、Github、ORCID、Google账号等 3. 社交媒体账号，如微信、微博等
数据提交方式	支持在线上传数据，基于网络的提交工具Banklt提交数据	浏览器在线上传和FTP客户端在线上传
数据大小	最多50 GB，可以一次性增加配额至200 GB	没有数据量的限制，用户可以上传任意大小的数据集
数据格式	对数据格式没有任何限制，如PDF、PNG、JPG、ZIP、HDF5、XML、DOCX、GZ、JSON 和 TXT 等	提供了推荐和非推荐文件格式的列表，如推荐使用Unicode文本(.txt)、XML(.xml)、PDF/A(.pdf)、CSV(.csv)、JPEG (.jpg,.jpeg)等格式，而非推荐格式包括非Unicode文本、SGML(.sgml)、ODT(.odt)、MS Word(.doc,.docx)等
许可证	默认采用 Creative Commons Attribution 4.0 International (CC-BY) 许可证	CC-BY 4.0 许可协议
数据审核	社区可以设置审核机制，保证跨学科研究的质量和标准	形式审核，包括数据上传情况、数据可访问性以及数据可用性声明等
语言	所有语言	中文和英文

	Zenodo	ScienceDB
数据引用跟踪	DOI系统	DOI和CSTR标识
数据使用追溯	提供关于上传记录的使用情况的统计数据	提供数据集的使用情况统计
数据影响力分析	展示数据集的影响力，如被社交媒体、新闻或政策文件提及的情况	提供数据影响力分析，如数据集被引用的次数、在学术界的影响力等
开放的API	提供开放的API	提供开放的API
图表和可视化	某些服务可能包括图表和可视化工具	目前不支持
数据集评分	不直接统计数据	每2年评选1次“ScienceDB科学数据奖”

A Comparative Analysis of Multidisciplinary General-Purpose Scientific Data Platforms: Taking Zenodo and ScienceDB as Examples

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