NAHPU (NAtural History Project Utility) is a cross-platform application designed for cataloging natural history specimens. It modernizes data recording and management for field and museum work.
Visit our website to learn more: nahpu.app
- Cross-Platform: Use Nahpu on Android, iOS, Windows, macOS, and Linux.
- Field and Museum Ready: Designed for both field data collection and museum curation workflows.
- Rich Data Capture: Record a wide range of specimen data, including images, measurements, and collection details.
- Data Export: Export your data to CSV and other formats for use in other applications.
- Offline First: Works offline; we plan to allow syncing your data when you have an internet connection.
- Birds 🦅
- Mammals 🐿️
- Herpetofauna (in development) 🐍
- Fishes (in development) 🐠
- Paleo Vertebrates (planned) 🦣
The NAHPU project's main challenges are the multi-platform nature of the application and ensuring the app works under the constraint of mobile devices in remote field conditions. Some of key technologies used to overcome these challenges include:
- Flutter: For the cross-platform user interface.
- Rust: For performance-critical native code, integrated with Flutter Rust Bridge.
- Drift: For the local SQLite project database.
- redb: For user configuration and export preset storage through the NAHPU API.
- Riverpod: For state management.
- Typst: For the template system that powers NAHPU document exports.
Many other open source libraries were used in the development of NAHPU. A full list of these libraries can be found in the app manifests in NAHPU and NAHPU_API repositories.
We welcome contributions from the community! Whether you're a developer, a designer, or a user, you can help make Nahpu better.
Please read our CONTRIBUTING.md file for details on how to get started with setting up the project and our contribution guidelines.
Nahpu is licensed under the MIT License. See the LICENSE file for more details.
NAHPU is a collaborative project among museum and computer scientists. Many thanks to the following people for their contributions and feedback: Darwin Morales-Martínez, Diego J. Elias, Spenser J. Babb-Biernacki, Jessie L. Williamson, Jocelyn P Colella, Nicholas A. Mason, Katrina Derieg, Ellie Dripps, Alivia Hartz, Eleanor Hoeger, Gabriella Linsalata, Lazaro Lopez, John Mewherter, Fritz Pichardo Marcano, Heidi Stevens, Marisa Surovy, Rose Wilhoyt, Litsa Wooten, and Lucas H. DeCicco. Funding for this project was provided by the National Science Foundation (DEB-1754393 and DEB-2244754) and the Alfred L. Gardner and Mark S. Hafner Mammalogy Fund. Various other funding sources may have supported this project through the work of the contributors. Human-Augmented Analytics Group at the Georgia Institute of Technology provided logistical support for collaboration and development.