摘要翻译：
大型系统生物学项目可以包括几个工作组，通常位于不同的国家。综述了系统生物学中现有的数据标准以及大型分布式研究项目中生成的数据的管理、存储、交换和集成，从实用的角度说明了不同方法的利弊，并对现有的软件--开放源码软件和商业软件--以及相关文献进行了广泛的综述，使读者能够决定哪种数据管理方法最适合自己的特殊需要。重点是工作流系统和基于制表符的格式的使用。这种格式的数据可以使用工作的实验生物学家熟悉的电子表格程序轻松地查看和编辑。介绍了如何使用工作流对自己或公共数据库中的数据进行标准化访问，以及操作程序的标准化。本体论和语义网技术在数据管理中的使用将在下一篇论文中讨论。
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英文标题：
《Data management in systems biology I - Overview and bibliography》
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作者：
Gerhard Mayer
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最新提交年份：
2009
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Databases        数据库
分类描述：Covers database management, datamining, and data processing. Roughly includes material in ACM Subject Classes E.2, E.5, H.0, H.2, and J.1.
涵盖数据库管理、数据挖掘和数据处理。大致包括ACM学科类E.2、E.5、H.0、H.2和J.1中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Data Structures and Algorithms        数据结构与算法
分类描述：Covers data structures and analysis of algorithms. Roughly includes material in ACM Subject Classes E.1, E.2, F.2.1, and F.2.2.
涵盖数据结构和算法分析。大致包括ACM学科类E.1、E.2、F.2.1和F.2.2中的材料。
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一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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英文摘要：
  Large systems biology projects can encompass several workgroups often located in different countries. An overview about existing data standards in systems biology and the management, storage, exchange and integration of the generated data in large distributed research projects is given, the pros and cons of the different approaches are illustrated from a practical point of view, the existing software - open source as well as commercial - and the relevant literature is extensively overview, so that the reader should be enabled to decide which data management approach is the best suited for his special needs. An emphasis is laid on the use of workflow systems and of TAB-based formats. The data in this format can be viewed and edited easily using spreadsheet programs which are familiar to the working experimental biologists. The use of workflows for the standardized access to data in either own or publicly available databanks and the standardization of operation procedures is presented. The use of ontologies and semantic web technologies for data management will be discussed in a further paper.
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PDF链接：
https://arxiv.org/pdf/0908.0411