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Exposing more historical data in public sick events
Currently the exposed public retrospectively labeled sick events only give access to a ~2-week window before and after the event.
Additional historical data (3-6+ months worth, possibly even more) is necessary for most prediction and detection models, so that a model, particularly time series analyses models, may feasibly learn what "normal"/baseline looks like. This would also help identify and filter out innate time trends & seasonality within the data (such as temperature & menstrual cycles within Oura data).
A possible user-centric solution would allow the user to select how much data they're like to share within privacy settings. Some considerations around the default settings should be had as well.
The text was updated successfully, but these errors were encountered:
@madprime and I discussed this and thought that 3 months would be a good cut-off between data sharing concerns and usefulness of the data.
Implementing this would require a bit more work, as currently we expose the same json that is used for the interactive data visualization for the public data. But I don't think we want to change our graphs to include 3 months of historic data, as it shifts the scale (plus the visualizations with as many points get sluggish).
So for the implementation we would need the following:
Create a new model to save the json data that should be exposed through the public API
Extend the data source parsers in a way that they create a second object for public data, creating
Add a new view for displaying that data through the public data API
Exposing more historical data in public sick events
Currently the exposed public retrospectively labeled sick events only give access to a ~2-week window before and after the event.
Additional historical data (3-6+ months worth, possibly even more) is necessary for most prediction and detection models, so that a model, particularly time series analyses models, may feasibly learn what "normal"/baseline looks like. This would also help identify and filter out innate time trends & seasonality within the data (such as temperature & menstrual cycles within Oura data).
A possible user-centric solution would allow the user to select how much data they're like to share within privacy settings. Some considerations around the default settings should be had as well.
The text was updated successfully, but these errors were encountered: