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Essential Design Considerations for Data Management in Ecological Research

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This document outlines key design considerations for storing and managing ecological data, emphasizing the importance of planning before data collection. It discusses various database structures, such as horizontal (flat file) and vertical tables, and their implications for flexibility, null values, and data typing. Fundamental concepts like normalization, primary keys, and the difference between natural and surrogate keys are explained. By understanding these principles, researchers can create robust data management systems that enhance their legacy and support effective data analysis.

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Essential Design Considerations for Data Management in Ecological Research

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  1. Table Design Considerations GEOG 5880/ ECOL 5620 Spring 2011 Instructor: Dr. Shannon E. Albeke http://sites.google.com/site/albekeecoinfo/courses/data-managment

  2. Basic Design Considerations • THINK about how you might store your data • Especially if you haven’t collected it yet • This may become a part of your legacy, think big! • Requires some imagination • Study design = Database design? • Include Spatial information? • Points, lines, polygons or raster • Data types (again!) • Text, Long Integer, Double, Date/Time…maybe Memo

  3. Basic Design Considerations • Normalization – set of methods to break down tables to their constituent parts (i.e. each table represents 1 ‘thing’ only) • Primary Key – a column containing a value that makes the row of data unique (can be multiple columns…called a Composite key) • Best to use Natural Key vs Surrogate Key (auto #) • Child Key is found in the related table, linking the two tables together through a common value

  4. Horizontal Table Structure • Also known as Flat File • Positives • Most intuitive • 1 record contains all of the attributes describing the ‘thing’ • Matches format for most Reports, Stats packages, GIS • Negatives • Not flexible when adding new fields • Null values (what do they mean?) • Dummy values • Require many lookup tables

  5. Vertical Table Structure • Stores data going down instead of across • Can use Crosstab Query to make flat • Positives • Flexible, can easily add in new Attributes (field) • No Null values stored in the table • Fewer lookup tables • I find it easier to calculate statistics on the data • Negatives • Loss of strong data typing (general field type (text) to store all information (Number, Text, DateTime) • Less control over data

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