Start with the public tools
- Use the browser Lab to move example check-ins and see the descriptive summary change.
- Read the research note for the scientific basis and limitations.
- Run the public protocol for synthetic edge cases, expected boundaries and the reviewer checklist.
What the view describes
Evidence carried with each observation
Each observation keeps the fields needed to decide whether it is comparable and eligible for the requested purpose:- dimension identifier and version;
- value and declared scale;
- observed time and recorded time;
- Consent reference and version;
- source and source version;
- quality state and weight; and
- optional context references that do not contain Session content.
When a measure is withheld
The view returns a clear empty or not-interpretable state when:- too few eligible check-ins are available;
- the available period is too short;
- timing is too irregular for lag-one autocorrelation;
- the series has no variance;
- no return is observed inside the available window;
- Consent does not permit the read; or
- the request crosses an Organization boundary.
Time and reconstruction
observed_at records when an observation applied. recorded_at records when
the system learned it. A read at a knowledge cutoff includes only observations
whose recorded_at value is at or before that cutoff. This allows a reviewer to
reconstruct what the system could have shown at that time.
Access and review
The public Lab uses example data. The authenticated Platform preview remains
feature-gated, and Affective Dynamics is not currently a public REST resource.
Use the Proving Ground protocol for integration-independent evaluation.
Reproduce the public boundary
Published sources
- Jahng, Wood and Trull (2008) on variability, temporal dependency and successive differences.
- Kuppens, Allen and Sheeber (2010) on emotional inertia.
- Houben, Van Den Noortgate and Kuppens (2015) on short-term emotion dynamics and well-being.
- Dejonckheere and colleagues (2019) on incremental value beyond mean and variance.
- McNeish and colleagues (2021) on measurement in intensive longitudinal data.
- Schneider and colleagues (2023) on reliability and sampling error in within-person dynamics.