A Database for Agroecological Research Data: I. Data Model
A Database for Agroecological Research Data: I. Data Model
复制标题
农业生态研究数据数据库：一、数据模型
DOI：
10.2134/agronj1999.00021962009100010009x
复制
发表时间：
1999
期刊：
影响因子：
--
通讯作者：
J. Baker
中科院分区：
文献类型：
--
作者：
F. K. Evert;E. Spaans;Scott Krieger;J. Carlis;J. Baker
农业生态学实验的数据通常存储在一系列记录最少的计算机文件中，其他信息则输入田间或实验室书籍。由于这种数据碎片化和缺乏适当的文档，信息可能会丢失，数据操作通常很麻烦，容易出错，并且难以自动化。现代数据库技术有可能解决这些问题。将实验数据存储在数据库中解决了碎片化的问题，因为所有数据都在数据库中;通过在数据库的设计过程中明确不同信息项之间的关系解决了文档化的问题;通过现代数据库管理系统提供的强大查询语言解决了操作问题。作为在农业生态研究中使用的普遍适用的数据库的建设的第一步，我们使用了一种形式化的方法来设计一个数据模型，明确地描述了类型的信息（实体），人们可能想要记住的实验和这些实体之间的关系。这里描述的数据模型由40个实体和54个关系组成。这些实体分为五类：㈠实验，包括统计设计; ㈡进行测量的物体; ㈢测量规程和设备; ㈣测量; ㈤实地作业。我们详细描述了如何从几种常见类型的测量信息存储使用建议的数据模型，并得出结论，数据模型充分描述了信息，科学家在农业生态学科需要记住他们的实验。
Data from agroecological experiments are typically stored in a collection of minimally documented computer files, with additional information entered into field or lab books. As a result of this fragmentation of data and lack of proper documentation, information may be lost and data manipulation is generally cumbersome, error-prone, and hard to automate. Modern database technology has the potential to resolve these issues. Storing experiment data in a database solves the problem of fragmentation because all data are in the database; the problem of documentation is solved by making the relations between different items of information explicit during the design of the data-base : and the problem of manipulation is solved by the powerful query languages available with modern database management systems. As a first step in the construction of a generally applicable database for use in agroecological research, we used a formal method to design a data model that explicitly describes the types of information (entities') one may want to remember about experiments and the relationships between these entities. The data model described here consists of 40 entities and 54 relationships. The entities are classified in five categories: (i) experiments, including statistical design; (ii) objects on which measurements are made; (iii) measurement protocol and equipment: (iv) measurements; and (v) field operations. We describe in detail how the information from several common types of measurements is stored using the proposed data model and conclude that the data model adequately describes the information that scientists in agroecological disciplines need to remember about their experiments.