Self-Mapping App
The guided entry point for visitors who want to experience the method directly.
The strongest public story is simple: map the self, export the data, inspect the structure. This page is where the self-mapping app, the companion dashboard, screenshots, teaser video, specifications, and future open resources can live together as one ecosystem.
The guided entry point for visitors who want to experience the method directly.
The analytics layer that turns mapped selves into interpretable cohort summaries, visual profiles, and exploratory structure.
It is still the best opening image because it feels most distinctive at a glance. Keep it on the homepage hero, then use the app screens and dashboard views to deepen the story immediately below.
Exploratory network of identity co-occurrence with drill-down details for selected identities, paired with cross-period descriptive analytics and emerging profile-based metrics.
This is the image that makes people stop. It should remain the anchor visual for the ecosystem.
This order gives visitors a natural progression from approachable entry, to structured input, to exploratory visualization, to analytic credibility.





Lead with the visual hook, then immediately soften the experience with the polished onboarding screen.
These make the platform concrete by showing how identity data are entered and how they become readable outputs.
Use the radar/profile views for visitors who want to see that the ecosystem is visually strong and analytically ambitious.
This view shows range. It makes clear that the dashboard can move beyond one visualization into distributions, period comparisons, and cohort-level interpretation.
This view signals that the platform is developing distinctive constructs and not merely re-displaying a legacy index.
Cut the first public video around the journey rather than the theory. Let the theory ride underneath the sequence.
Use the tools page to showcase the interface, then direct serious visitors to a resources layer that makes the literature, specifications, and open-science direction easier to inspect.
Highlight the classic theory and measurement papers that explain why self-complexity still matters and why the field is ready for a reboot.
Give researchers a place to inspect formulas, operational definitions, and notes about how the ecosystem handles legacy and newer metrics.
Make it obvious that the platform is part of a broader research program, not just a polished demo.