Data Collection

About

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.

Protocol

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

Protocol for Extreme Manufacturing Workshops

Data collection should include several points - with procedures and protocols automated via video monitoring, remote control, AI, image processing, and other IT tools.

• Drone time lapse - build progress tracking via automated flight paths taken at hourly or shorter intervals during any build. Intervals should be as short as allowed by automated landing/charging pads.
• Participation tracking - video image processing which counts the number of participants in realtime, and can be accessed in a leaderboard or scorecard on an app. Data is recorded for improvement.
• Quality Control - remote quality control procedures should be developed for allowing remote participants to verify results, or automated via image processing.
• Testimonials - a camera station for testimonials should be set up in the workspace for promoting future events - with honest eedback for both positive features and improvement points - with OSE's commitment to publish transparently for improvement points consistent with the OSE Mission, OSE Vision, Roadmap, and OSE Model of Change. Note that OSE will not publish testimonials that do not adhere to these guiding points - ie, OSE will publish only mission-aligned and operations-aligned feedback.

Notes

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.

Links

• Use this for quick data collection into a form - Data Collection Form
• Open Source PV System Data Collection
• Seed Eco-Home 4 Data Collection