Data journals can be great companions to hard sensor data. You can think of them as annotations to the data – what was happening when the sensor recorded that high or low point? Often this is particularly helpful in flagging false-positives and outliers on the data. The key to journalling is to keep the annotations as focused as possible. These annotations will also likely be individual to the type of measurement the campaign is focused on. For example, in an environmental sensing campaign, observation of personal and physical effects – such as mood changes or impairment at different stages – might help establish a pattern between the data captured and the effects on the participants. These are things the sensor alone cannot capture.

Format MethodTimeframe Duration of sensing stageGroup Size AllFacilitation Level MediumRequired Materials Pen and paper


  • By the end of the scoping and planning stages, many anecdotal, personal annoyances will have likely been recorded. Consider how a journal might track the frequency of these events.
  • Community Level Indicators are a useful starting point for data journalling. Consider how participants might record the indicators they are tracking ‘in the wild’, and how this process might be more engaging. For example, does it have to be written down? Perhaps it can be sketched, photographed or visualised in some other way.
  • Consider what the common false flags are with your chosen data capture process. From sudden environmental shifts, wildlife and tampering, to regular or random events, there are many helpful annotations participants can contribute in order to ensure more accurate data collection. Whether the high spike in the noise sensor was their own pet or a passing ambulance, annotations like these help us make sense of the data.
  • Gauge the participants’ engagement level and attitude, but do not ask for too much, and keep expectations reasonable. Complex data entry tasks can lead to invalidating errors, making participants frustrated and more ikely to abandon the process.


Sensors can be very effective at capturing hard, quantitative data. But to truly make sense of data, we often need context that even sensors cannot detect.


Keep in mind the guiding question while implementing this method: what are the important things that the sensor cannot detect?
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