Democratizing pointe shoe information through open data and transparent research tools. A comprehensive data filtering platform for informed dance decisions.
I built Data Pointes Lab as a passion project and personally developed this custom software and maintain all aspects of the platform. I self-fund all costs related to Data Pointes Lab, including cloud database servers, web hosting, Azure resource subscriptions, and other infrastructure expenses.
Open pointe shoe database. Transparent research tools. Built by dancers, for dancers.
Uses transparent mathematical algorithms to analyze shoe relationships and similarities across 8+ factors. Every calculation is explainable. No AI guessing - pure data filtering and similarity matching tools.
Most pointe shoe information is scattered across different sources and inconsistent, making comparisons difficult. We're changing that.
This platform was developed to help dancers understand their options and make informed decisions based on transparent data in addition to consultation with professional fitters or teachers.
Hi, I'm Danielle Heymann. I'm a data/computer scientist, engineer, and ballet dancer (and always student) for most of my life. I built Data Pointes Lab because a tool and database like this didn't exist, and I wanted it for myself and the dance community.
I'm open-sourcing the pointe shoe database to democratize this information—so dancers, parents, teachers, fitters, and students can access and use it.
My goal with Data Pointes Lab is to make pointe shoe data open and accessible, so every dancer and supporter can make informed choices.
I founded a small software company for my apps and projects, Heymann Apps LLC, to develop thoughtful tools that blend technology with creative and educational pursuits.
M.S. Computer Science, B.S. Industrial & Systems Engineering. Ballet is a lifelong passion.
Privacy Policy, Disclaimers & Safety Guidelines
Always consult qualified professionals before:
🔓 Accountless System: Data Pointes Lab operates without user accounts or login requirements. This ensures maximum privacy while providing full access to our data filtering tools.
🍪 Cookie Consent: We comply with GDPR/CCPA requirements by only loading Google Analytics after you explicitly consent via our cookie banner. You can decline analytics cookies and still use all platform features. Consent choices are stored locally and can be changed at any time.
📋 Complete Privacy Policy: For detailed privacy policy information including COPPA compliance, privacy contact details, and policy update procedures, see our Terms of Use.
Last updated: August 2, 2025
Testing program guidelines and beta participation terms (also serving as general terms of use)
You're helping shape the future of pointe shoe data accessibility through community collaboration and feedback.
By using this platform, you agree to the following terms:
🔓 No Account System: Data Pointes Lab operates without user accounts, ensuring maximum privacy while providing full access to data filtering tools.
For privacy inquiries, data corrections, or deletion requests, contact us at:
By submitting comments, you agree to:
2-Step Moderation Process: Comments are automatically screened using Heymann Apps' custom AI moderation system (custom software that is GPT-powered) for content screening. All comments currently require manual admin approval before becoming visible to ensure community standards are maintained.
Content Ownership: You retain ownership of comment content but grant Data Pointes Lab permission to display, analyze, and use comments for platform improvement, research, and community insights (always anonymized for analysis purposes). Inappropriate content, spam, or harmful comments will be removed.
📋 Complete Terms & Privacy Policy: For detailed terms including age requirements, COPPA compliance, privacy contact information, and policy updates, see our comprehensive Beta Terms of Use section.
This platform is not intended for children under the age of 13.
We may update this Privacy Policy and Terms of Use from time to time as technologies or laws evolve. Here's how we handle updates:
🗃️ Open Data Commitment: Our pointe shoe database is open source and freely available for research, commercial use, and community benefit.
License: Creative Commons Attribution 4.0 International (CC BY 4.0)
GitHub Repository: 🔗 View Full License on GitHub
Quick Summary: You can use this data for anything, including commercial purposes, as long as you give proper credit.
This database and dataset are licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
When using this data, you must provide appropriate credit. Please use this attribution format:
"Data Pointes Lab Database" by Danielle Heymann (Data Pointes Lab, a product of Heymann Apps LLC) is licensed under CC BY 4.0. Original source: https://github.com/4dh/datapointeslab-database
Under CC BY 4.0, you are free to:
Commercial use is explicitly permitted under this license. You may:
Only requirement: Proper attribution as specified above.
In academic papers:
Data from "Data Pointes Lab Database" by Danielle Heymann (Data Pointes Lab, https://datapointeslab.com), licensed under CC BY 4.0. Available at: https://github.com/4dh/datapointeslab-database
In mobile apps:
Pointe shoe data © Danielle Heymann (Data Pointes Lab, https://datapointeslab.com), CC BY 4.0. Source: https://github.com/4dh/datapointeslab-database
In modified datasets:
Adapted from "Data Pointes Lab Database" by Danielle Heymann (Data Pointes Lab, https://datapointeslab.com), licensed under CC BY 4.0. Original source: https://github.com/4dh/datapointeslab-database. This version includes modifications to [describe changes].
In software documentation:
This application uses data from the "Data Pointes Lab Database" by Danielle Heymann (Data Pointes Lab, https://datapointeslab.com), licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Original source: https://github.com/4dh/datapointeslab-database
This data is provided "as-is" without warranty of any kind, express or implied. The contributors make no guarantees regarding the accuracy, completeness, or fitness for any particular purpose of the information contained in this database.
Note: This license applies specifically to the database and data files. The recommendation algorithm and application code may be subject to different licensing terms.
For the complete license text, visit: https://creativecommons.org/licenses/by/4.0/legalcode
By using Data Pointes Lab, you acknowledge that you have read, understood, and agree to these terms.
Remember: Data Pointes Lab is a data filtering platform to explore similarities and possibilities, not a replacement for the irreplaceable expertise of qualified teachers, fitters, and medical professionals.
Community-Driven: Your respectful participation in comments and feedback helps build a valuable resource for the entire dance community.
Last updated: August 2, 2025
Platform architecture, data structures, and research methodologies
Data Pointes Lab is a comprehensive pointe shoe data platform featuring interactive visualizations, sizing tools, and relationship analysis. The platform focuses on transparency, data accuracy, and user-friendly exploration tools.
The site is built around four core features that work together to provide comprehensive pointe shoe information:
The Corps de Data section provides access to our complete database:
The Relévé Relatives page provides an interactive network visualization that shows relationships between pointe shoes based on their technical characteristics.
The Relevé Relatives network uses a sophisticated, transparent mathematical model to calculate similarity scores between pointe shoes. Every calculation is explainable and user-controllable.
Where S is the final similarity score [0,1], wᵢ are user-adjustable weights, and Sᵢ are individual similarity components.
Each component uses a specialized calculation method appropriate to its data type:
Categorical: either identical manufacturing DNA or not.
Ordered categorical: Narrow(1) → Medium(2) → Wide(3) → Very Wide(4), etc.
Ordered categorical: Narrow(1) → Medium(2) → Wide(3), etc.
Multi-value: shoes can have multiple shank options (Soft, Medium, Hard).
Multi-value: shoes can offer multiple vamp lengths.
Matrix-based: Egyptian↔Greek(0.7), Egyptian↔Square(0.3), etc.
Matrix-based: Traditional↔Enhanced(0.6), Advanced↔Composite(0.8), etc.
For shoes with multiple values in a single attribute (e.g., "Soft, Medium" shank strength), the system uses the Jaccard similarity coefficient:
Shoe A: ["Soft", "Medium"]
Shoe B: ["Medium", "Hard"]
The foot shape calculations use a carefully designed similarity matrix based on ballet anatomy research:
Below is a simplified example showing how shoes connect in the network based on similarity:
The Size Variations page provides intelligent cross-brand size conversion with automatic padding adjustments and comprehensive sizing guidance.
The tool includes seven padding types with precise size and width adjustments:
The system includes specialized knowledge for major brands:
The Barre Graphs page provides interactive data visualizations for exploring pointe shoe characteristics across the entire database.
Platform development history and feature announcements
Where is the conductor?
This release deepens our commitment to responsible and ethical technology. Every update prioritizes user privacy, data transparency, and community safety while fostering meaningful engagement through improved conversation tools and enhanced moderation systems.
STEP THROUGH. STAY AWAKE. LOOK CLOSER.
Recommendation Algorithm Retirement - Removed the shoe recommendation system as it did not align with our commitment to responsible and explainable technology. The algorithm lacked sufficient confidence levels and transparency needed for such personal recommendations.
This release reflects our commitment to responsible and ethical technology. Rather than providing potentially misleading recommendations, we now offer powerful tools that let users explore the data themselves with full transparency into how similarities and relationships are calculated.
WHEN THE ENSEMBLE DEMANDS INDIVIDUAL VOICES
WOODLAND CREATURES DANCING THROUGH ALGORITHM UPDATES WITH MYTHICAL PRECISION
Community contributors and beta testing volunteers
Dancers, teachers, and dance community members who volunteered to test the research platform during development.
Olivia Schieber • Priyanka P • Ellie C • Christine Reynolds • Alice S • Nina Basu • Connie J • Anonymous ballet student • Naama Heymann • Mitchell Heymann