Data Collection

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Data collection refers to performance, use, and user data collection that is used to establish performance baselines and standards that build a predictable track record and trust. This is essential for benchmarking, creating revenue models, producing production engineering documents, creating specific value propositions, developing marketing based on specific economic benefits, and other purposes. Performance transparency is required for any product to previde regenerative solutions at scale.


HintLightbulb.png Hint:

  1. Start a wiki page named ProjectName_Data_Collection_Form
  2. Prior to running a machine or testing a product - and even prior to beginning the design and build - write down all the things you want to observe or collect data on. Define test protocols, the measurement tools that you will need, and document processes by which data was taken. A good starting point for parameters that you may want to measure corresponds to all the values that can be obtained from Basic Calculations. Comparing Basic Calculations to actual data points is a good test of whether we understand how something actually works - whether our mental model of the workings matches reality.
  3. Work on getting results under different conditions, by varying run parameters, working materials, ambient conditions, etc.
  4. Test the limits by running the artifact to its limits of speed, power, accuracy, temperature, etc.
  5. Log data points using loggers, such as for temperature, power use, flow rate, etc.
  6. Write down, document, or otherwise embed all process definitions and results, such as videos of the first runs, real life functioning working, pictures, test data points, and any observations


Defining Data Collection protocols is part of Test Driven Design. In pertinent part:

Test driven design refers to a design process that performs tests, validation so, proofs, verifications, assessments and rapid prototypes on an early and ongoing basis - prior to an actual build. The purpose is to speed up development by avoiding unworkable paths that have already been demonstrated by others; to discover various insights that inform further design; and to reduce costs by doing as much analysis and calculation as possible to find out those things that a physical prototype would show -prior to using more expensive materials and build processes to build an actual prototype.