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NAHPU is a collaborative natural-history project. Contributions can improve the software, the data workflows, or the experience of the people who use it in the field, laboratory, museum, and archive. You do not need to be a programmer to contribute. Practical domain knowledge and careful testing are just as valuable as code.

Contributor Useful contributions Coding required First step
Scientist or domain expert Field testing, taxonomy review, data-model feedback, and workflow validation No Use NAHPU with a realistic workflow and report your findings
Programmer Flutter, Rust API, documentation tooling, bug fixes, and features Usually Choose the repository and read its setup guide
Documentation contributor Corrections, examples, and usage guidance No Follow the documentation contribution guide
Tester or reviewer Platform testing, regression reports, sample files, and accessibility feedback No Test a supported workflow and report reproducible results
Tool or design contributor Utilities, integrations, and UI or accessibility improvements Varies Open a proposal before building a separate tool

Your knowledge of natural-history work can shape NAHPU even if you never write code. Try NAHPU during fieldwork, laboratory cataloging, or museum curation and tell us where the software matches (or fails to match) your real workflow.

Useful contributions include:

  • Reviewing terminology, taxonomy, required fields, units, controlled vocabularies, and specimen workflows.
  • Validating exports, backups, templates, Darwin Core mappings, and data-import behavior.
  • Providing synthetic or anonymized sample datasets and realistic workflow scenarios.
  • Proposing new taxon groups or domain-specific workflows.
  • Identifying confusing screens, missing information, or steps that are hard to complete in the field.

Start with the usage guides, best practices, and data import and export contributor guidance. Domain experts who are collaborating on a new catalog format should also read Adding a taxon group.

When reporting a problem, include the platform, NAHPU version, a short description of the workflow, steps to reproduce it, the expected result, and the actual result. Screenshots or sample files are helpful when they do not expose sensitive information.

Please do not publish private information, protected locality coordinates, permit-restricted data, personal information, or identifiable specimen records. Use synthetic or anonymized data and remove sensitive metadata before sharing a sample or screenshot.

Choose the repository that owns the behavior you want to change. Some features cross repository boundaries, so read the architecture guidance before moving logic between them.

  1. Search existing issues and open an issue or proposal before substantial work.
  2. Select the repository and read its contribution and setup instructions.
  3. Create a focused branch from the repository’s documented target branch.
  4. Keep the change narrow and preserve the repository’s architectural boundaries.
  5. Add or update tests and documentation.
  6. Run the checks specific to the repository and the files you changed.
  7. Submit a pull request describing the problem, approach, tests, and screenshots where relevant.
  8. Respond to review feedback and update the documentation when behavior or a workflow changes.

Use the existing architecture, code conventions, persistence guidance, NAHPU API guidance, troubleshooting guide, and responsible LLM development guidance rather than duplicating their details in an issue or pull request.

Correct errors, improve examples, clarify usage instructions, and document workflows that are difficult to discover. Follow the documentation contribution guide for editing pages, local development, and review. This contributor guidance is intentionally English-only.

Translation contributions should target usage documentation so that people can learn to use NAHPU in their preferred language. We only use English for contributor guidance, mainly because the section will often changes due to code refactoring and new feature addition.

Test supported platforms and realistic field, laboratory, and collection workflows. Regression testing, accessibility checks, import and export validation, and sample files that reproduce a problem are all useful. Report reproducible results with the platform, app version, steps, expected result, actual result, and relevant screenshots or anonymized files.

Build standalone utilities, taxonomy resources, integrations, or other supporting software. See the tools guidance before starting, especially when a tool may belong in the NAHPU GitHub organization. Open a proposal first if the tool would duplicate or substantially affect an existing NAHPU workflow.

Contribute responsive layouts, readable labels, clear error messages, localization-ready content, keyboard and screen-reader support, and accessible workflows across supported devices. A short recording, screenshot, or concrete example can make a design or accessibility issue much easier to evaluate.

Every contribution can follow the same basic path:

  1. Choose a contribution path.
  2. Read the relevant guide.
  3. Try to reproduce or describe the problem using a concrete workflow.
  4. Open an issue or proposal, or contact the maintainer when the work is scientific or collaborative rather than a direct code change.
  5. Prepare a focused contribution.
  6. Validate it with tests, sample data, screenshots, or a documentation build.
  7. Submit it for review.
  8. Update the affected documentation when behavior or a workflow changes.

GitHub issues and pull requests are the normal path for repository changes. Use the project maintainer’s contact page for research collaborations, domain-review coordination, institutional data, or proposals that do not yet map cleanly to an issue.

  • Keep reviews and communication respectful, specific, and constructive.
  • Take human responsibility for every submitted change, including AI-assisted work; review and understand the result before submitting it.
  • Follow permits, institutional policies, data licenses, and collection ethics.
  • Use synthetic or anonymized test data whenever real data could expose sensitive information.
  • Prefer focused changes supported by reproducible evidence.
  • Scientists are welcome to contribute without becoming programmers first.