Nemosyne is an experimental framework for investigating immersive data visualization. We're testing a hypothesis: does embodied, navigable 3D space improve data comprehension compared to 2D? Help us find the answer.
This interactive VR scene demonstrates our experimental spatial data framework. We're testing whether the Datumplane mapping (X=relationships, Y=hierarchy, Z=time) improves comprehension compared to traditional 2D layouts.
Research Question: Does embodied navigation through 3D data space improve recall and pattern recognition? See our methodology →
Create immersive data experiences that leverage spatial computing for deeper understanding and intuitive manipulation.
Bind real-world datasets to interactive 3D objects. Manipulate complex data through natural hand gestures and spatial interactions.
Stream live data into VR environments. Watch as your data updates in real-time, responding to changes in the physical world.
Multi-user VR environments where teams can simultaneously interact with shared data artefacts, regardless of physical location.
Built on open web standards. Runs in any modern browser without plugins. Compatible with Quest, HoloLens, and mobile AR.
Extensible architecture with reusable artefact components. Build custom data representations or use pre-built templates.
Connect to REST APIs, GraphQL, WebSockets, or IoT streams. Visualize everything from financial data to sensor networks.
Create interactive VR objects with declarative syntax. Bind data, define interactions, and render in minutes.
Explore working demonstrations of Nemosyne visualizations. Each example includes source code and live VR scenes.
Single crystal with basic interactions — the simplest starting point.
8-node microservices network visualization with force-directed layout.
Monthly revenue visualization using spatial bar artefacts.
24-hour activity patterns plotted on a temporal spiral layout.
Hierarchical file system visualization with tree layout.
Game development level editor with NavMesh integration.
Nemosyne makes design assumptions that need validation. We're treating the framework as a platform for answering seven critical research questions.
Question: Does embodied, navigable 3D space produce better data comprehension than 2D alternatives, and for which task types?
Hypothesis: 3D advantages emerge for topology-reading tasks (finding bridges between clusters), not value-reading tasks (precise comparisons).
Question: Are the X/Y/Z axis assignments (relationships/hierarchy/time) congruent with human spatial intuition, or arbitrary?
Experiment: Present the same dataset in multiple axis configurations and measure comprehension speed and accuracy.
Question: Can we reliably distinguish structural-level topology (scale-free networks, continuous fields) from schema-level patterns?
Challenge: Extend constraint programming approaches (Draco system) to the 3D design space.
Question: Does encoding information in a self-constructed, navigable VR space improve recall compared to 2D representations?
Hypothesis: Self-generated spatial contexts produce stronger encoding than imposed ones (generation effect + contextual reinstatement).
Question: When multiple users navigate the same data space simultaneously, how do shared reference frames and private viewpoints interact?
Problem: In VR, users can have literally inverted spatial reference frames. "Look at this" becomes ambiguous.
Question: At what point does node density in 3D space cause the visualization to become illegible regardless of frame rate?
Focus: Effective field of relevant perception, depth as a data channel (vergence-accommodation conflict), clutter thresholds.
Question: When is a 3D form a better encoding than a colour channel or a label? What conditions make arbitrary shapes read as meaningful icons?
Framework: Apply semiotics (Peirce's icon/index/symbol) and affordance theory (Gibson, Norman) to 3D glyph design.
"The project has good aesthetic sense and clear philosophical direction. What it needs is the discipline to test whether the philosophy actually holds when humans try to use it."
Join the Research DiscussionThese domains represent our hypotheses for where immersive data visualization may provide advantages. We're seeking collaborators to help validate (or refute) these use cases.
Monitor factory floor sensors as interactive 3D models. Spot anomalies by observing colour and scale changes in real-time.
Navigate market data as spatial landscapes. Walk through trading volumes and manipulate time-series in 3D space.
Handle molecular structures or astronomical data at scale. Collaborate with remote teams in shared virtual labs.
Manipulate MRI and CT scans as volumetric artefacts. Annotate and discuss cases with specialists worldwide.
Urban data visualization—traffic flows, energy grids, and population density as manipulable city models.
Interactive learning artefacts. Students manipulate historical artifacts or mathematical concepts in VR.