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These pages are a collection of my ideas on data modeling, from the experience and philosophy behind the design, to practical implementation considerations. This is a 'big picture' perspective of what is required for a corporate scale project. Small 'citizen science' projects will need only a fraction of this, but good projects do tend to grow; so it is best to understand the full scope, and then trim off those parts that are not needed for the current project. If you know your growth path, you know where and how to add parts as they become needed.

These pages were originally created to document the MVP (Minimal Viable Product), a DIY hydroponic growth chamber, and were posted on the MIT OpenAg website (which no longer exists). As such, they often reflect those needs which consisted of Environmental Observations (temperature, humidity), Agronomic Observations (leaf length, width, ...) and Agronomic Activities (planting, thinning, harvesting, ...). There has been some re-working to reflect the more limited needs of water quality monitoring. Some links may still lead off to the futureag blog.

Background

Personal experience and development of this data model.

Building for Findability

Most projects start by collecting the data they have, which is often for immediate (operational needs). Often technology projects capture what is available, without thinking about how this data will be used in the future. My approach is to first think through how the data will be used, the reports and questions that will be asked, and then to structure the data to meet these needs.

Three uses of Data

Data is used by three main groups:

• Operational (day to day business)
• Administration (how fast, how good)
• Analytic (patterns and correlations)

Activity Template: StAT Pattern

This is the standard data pattern that is customized for particular data needs. It is flexible for many applications, and supports growth into new areas.

Template Application

One template, multiple uses

Data Structures - physical data storage

• Data Capture
• Data Exchange
• Reporting

Derived Data

Meta-Data

Data about data

Smart Rock data exchange specification (draft)