Screenshots
Watch trailer on Steam ↗First-Person Data Explorer
Star Weaver's Production · Published by Star Weaver's Production
Positive — 90% positive (10 reviews)
Last updated 28 Sept 2026
In First-Person Data Explorer, datasets become immersive 3D environments where numbers shape color, size, movement, and more. Load, generate, or edit your data during gameplay, and turn raw values into something you can see, experience - and explore.
£2.65
Lowest price we've ever recorded
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Price history
Simulation, Early Access · 2025 · More under £5 games
Dive into First-Person Data Explorer and transform raw data into vivid, interactive environments. This early access simulation from Star Weaver's Production invites you to load, generate, or edit your datasets, turning them into explorable 3D worlds.
About this game
First-Person Data Explorer is an experimental, sandbox-style environment where datasets are translated into physical features like color, size, motion and more. The goal is to let you experience your data in fun and creative ways.
Players can choose from different graphing types, each of which visualizes the data dynamically in real time. Data is loaded from Jsonl and CSV files, which players can create, modify, or select during gameplay. Each Explorer has a few simple guidelines for the data it reads, and players are encouraged to write scripts or choose datasets that fit those formats - with examples provided to help you get started.
The game is packaged with python and includes Pandas and NumPy, but you can also load scripts and datasets from any directory of your choice.
System Requirements
Will my PC run this? →Minimum
- Requires a 64-bit processor and operating system
- OS: Windows 10 64-bit
- Processor: Quad-core Intel or AMD 2.5 GHz or faster
- Memory: 4 GB RAM
- Graphics: Direct X 11 or Direct X 12 compatible graphics card
- DirectX: Version 11
- Storage: 10 GB available space
Recommended
- Requires a 64-bit processor and operating system
Supported Languages
*languages with full audio support
Latest news
AnnouncementsOfficial
First-Person Data Explorer Will Become a Paid Game on August 24
Hello Everyone,I wanted to share an important update about First-Person Data Explorer.On August 24th, 2026, FPDE will change from being a free game to a paid game. Its price will be $2.99 USD, with Steam regional pricing other currencies.If you already have FPDE, nothing will change regarding your access. You will still be able to play it without purchasing it again. Also, until the change takes effect, anyone can grab it for free.FPDE has grown enormously throughout Early Access, and this change is part of preparing the game for its official 1.0 release and support its continued development. It will remain a growing game with updates in the future!Thank you for everyone who has played FPDE, shared feedback, or followed its development. Building this game has been a long and deeply meaningful journey - I am excited to bring it into its next chapter.Read full article on Community Announcements (First-Person Data Explorer)Patch NotesOfficial
🎵 Melody Update for The Prop Explorer 🎵
Data Becomes Melody in the Prop Explorer Sound Update - live on Steam! How can data sing? The (very) experimental system uses two deterministic random-projection passes and a K-Means clustering pass to turn each row of data into a stable 16-step musical fingerprint (one bar of melody). First Random Projection is for Pitch: Each row’s normalized feature values are projected into 16 pitch scores. These are bucketed into harmonic frequencies: C, E, G, or high C. Second Random Projection is for Rhythm: A separate 16-step projection decides which notes are heard. The four strongest rhythm scores become the audible steps for that prop. K-Means Clustering is for Octave: Finally, props are grouped into octaves using 5-group K-Means clustering. Rows are clustered by similarity, then each cluster’s centroid average determines whether that group is assigned to a lower or higher octave. The result is a spatial data ensemble where each row gets its own musical voice. Similar rows tend to have similar melodies and voices.Read full article on Community Announcements (First-Person Data Explorer)Patch NotesOfficial
Bar Explorer Update
The Bar Explorer received a major update! Updates include: - Auto Grouping from Dataset Labeling - Custom Grouping (make a new group, drag and drop columns/rows into it) - Group Aggregation + Color Mapping - Row/Column sorting by values or totals - Random projection coloring (see hidden structure in your data) - Similarity clustering (nearest insertion ordering)Read full article on Community Announcements (First-Person Data Explorer)Patch NotesOfficial
Prop Explorer Update 🔵
The Prop explorer treats your CSV's rows as objects and its Columns as Properties.Drag and drop Rows into the world (or pull all) and use a number of marking options to transform each object based on their row(s) values to (hopefully) reveal similarities, groups and trends.Options include:Unique column value selectionRandom Projection ColoringSimilarity NetworksK-Means ClusteringBehavior mapping (sort of like dancing)Percentile GroupingValue Sequencing (color/size)Read full article on Community Announcements (First-Person Data Explorer)AnnouncementsOfficial
⛲️ The Neural Fountain ⛲️
⛲️ The Neural Fountain is Live!! ⛲️Point-cloud diffusion training — inside First-Person Data Explorer.• Train on your own point clouds • See which neurons respond to which shapes • Step through training and watch reconstruction improve in real time Point Cloud Diffusion ExplorerThe Neural Fountain is a visualization of point-cloud diffusion training.As the model trains, the explorer shows:(1) how active each neuron is on average, and(2) how strongly each neuron’s connections are being updated through backpropagation.(3) how accurately the model reconstructs each shape at the current training step.Importantly, activations and gradients are tracked separately for each shapecategory in the dataset. This allows the fountain to display how the networkresponds differently to each type of geometry during training.What you’re seeing• Activation Fountain (arching particles) Each vertical bar and band of particles corresponds to one neuron in a layer. The particle color intensity represents that neuron’s average activation strength for the currently emphasized shape. During training, each neuron produces an activation value for every point. To keep the visualization readable, the explorer computes a per-shape, per-neuron summary: - group batch samples by shape label - take the absolute value of each activation - average across all points of that shape - store one magnitude per neuron, per shape These values are tracked independently for each shape category, allowing you to see which neurons respond most strongly to different geometries when switching between shape color mappings. Particle colors correspond to the shape category selected in the UI.• Gradient Strands (horizontal lines) These represent learning pressure — how strongly the optimizer is pushing each neuron’s connections to change for a specific shape. Backpropagation produces gradients for every weight in the network. Rendering each connection individually would be visually overwhelming, so the explorer computes a...Read full article on Community Announcements (First-Person Data Explorer)
