This repository documents the systems engineering collaboration between ElevateCS and the CSU Biology Teaching Collection. We are currently in the early stages of identifying and designing a system to assist with anatomical specimen classification and deviation detection — starting with a focus on animal skulls and other taxidermy.
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Project Managers: Sabrina Ornelas & Jackson Castleberry
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Main Systems & Software Engineering Team: Deon Ornelas, Parker Volesky, Osarumen Okhiku, Ethan Wise, Kyle Smith Hanna
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CSU Facutly Advisor: Fabio De Abreu Santos
To develop a tool that allows students or instructors to analyze and identify deviations in biological specimens using computer vision, feature mapping, or 3D data — enhancing learning in anatomy and comparative biology.
We are currently in the concept development and requirements analysis phase.
- ✅ Stakeholder interviews underway
- 🛠️ Early research into potential inputs (e.g., skull dimensions, photos, CT scans)
- 🧠 Brainstorming possible system architecture and feature sets
- 📋 Exploring datasets and classification approaches
We are applying model-based systems engineering principles throughout:
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Stakeholder & context definition
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Functional requirements and constraints
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Interface planning
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V-model alignment for future implementation and testing
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Possible languages: Python, OpenCV, TensorFlow (pending)
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Tools: Visio or Lucidchart (for diagrams), Jupyter notebooks for exploration
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ElevateCS Systems & Software Engineering Team
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Biology Teaching Collection faculty & curators
This project is intended for:
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Biology educators and instructors: Seeking interactive or enhanced tools to support teaching comparative anatomy, particularly through the Biology Teaching Collection’s skull and taxidermy specimens.
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Students in biology or anatomy courses: Interested in exploring hands-on ways to analyze and compare animal skeletal features using digital tools.
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Systems engineering students and collaborators: Following the development of a real-world interdisciplinary project from concept through testing, using model-based systems engineering principles.
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Developers and data scientists: Interested in applying computer vision, classification, and imaging techniques to natural science collections.
We welcome contributors from all backgrounds — whether you're into biology, computer science, systems engineering, UX, or just curious about interdisciplinary projects!
Since we are currently in the early stages of development, here are some great ways to get involved:
Summarize relevant papers on skull morphology or comparative anatomy
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Explore similar educational tools or datasets
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Investigate machine learning or computer vision techniques for shape/feature detection
✍️ Assist With Documentation Help refine stakeholder requirements
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Take notes during meetings or interviews
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Assist with creating system diagrams or flowcharts
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Suggest ideas for features or UI/UX
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Help test classification or analysis prototypes once they’re developed
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Document edge cases, limitations, or ethical considerations
Once development begins:
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We’ll open GitHub Issues for tasks and ideas
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A CONTRIBUTING.md file will be added with setup instructions and code guidelines
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Code reviews will be open and collaborative — we’re here to learn together!
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Feel free to open an Issue with a question, or email us at [email protected] if you're interested in becoming a collaborator.
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Email us at [email protected] or talk to any ElevateCS club officer
No problem! We’re happy to walk you through making your first contribution — just reach out!
📁 docs/
└── requirements.md
└── stakeholder_notes.md
└── system_diagram_sketch.vsdx
📁 research/
└── relevant_papers/
└── comparative_anatomy_notes.md
📁 prototype/
└── (empty for now)
📁 images/
└── concept sketches, early system diagrams