KTOI Ecosystem Project Relational Database : a Report Prepared by Statistical Consulting Services for KTOI Describing the Key Components and Specifications of the KTOI Relational Database

摘要

Data are the central focus of any research project. Their collection and analysis are crucial to meeting project goals, testing scientific hypotheses, and drawing relevant conclusions. Typical research projects often devote the majority of their resources to the collection, storage and analysis of data. Therefore, issues related to data quality should be of foremost concern. Data quality issues are even more important when conducting multifaceted studies involving several teams of researchers. Without the use of a standardized protocol, for example, independent data collection carried out by separate research efforts can lead to inconsistencies, confusion and errors throughout the larger project. A database management system can be utilized to help avoid all of the aforementioned problems. The centralization of data into a common relational unit, i.e. a relational database, shifts the responsibility for data quality and maintenance from multiple individuals to a single database manager, thus allowing data quality issues to be assessed and corrected in a timely manner. The database system also provides an easy mechanism for standardizing data components, such as variable names and values uniformly across all segments of a project. This is particularly an important issue when data are collected on a number of biological/physical response and explanatory variables from various locations and times. The database system can integrate all segments of a large study into one unit, while providing oversight and accessibility to the data collection process. The quality of all data collected is uniformly maintained and compatibility between research efforts ensured.