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About This Training

Overview

The DUST 2025 Open Science Training program is a comprehensive educational resource designed specifically for the University of Arizona DUST Superfund Research Center community. This training equips environmental health researchers with essential skills for conducting modern, transparent, and reproducible science in the context of mine waste contamination, toxicology, and environmental remediation research.

Why Open Science Matters for Superfund Research

As researchers studying hazardous waste sites, arsenic exposure, and environmental health impacts, open science practices are critical for:

  • Community Impact - Sharing findings transparently with communities affected by mine tailings
  • Reproducibility - Ensuring toxicology and exposure studies can be validated and built upon
  • Collaboration - Facilitating multi-institutional research on complex environmental health problems
  • Compliance - Meeting NIH Superfund Research Program data sharing requirements
  • Environmental Justice - Making research accessible to policymakers and affected populations
  • Scientific Integrity - Documenting methods for studies involving hazardous materials and vulnerable populations

Training Philosophy

Our approach is grounded in several core beliefs:

Learning by Doing

Each lesson balances conceptual understanding with hands-on activities. We believe that skills are best developed through practice, reflection, and application to real-world scenarios.

Accessibility First

Open science should be accessible to all researchers, regardless of technical background, career stage, or institutional resources. We have designed these materials to be:

  • Free and openly licensed
  • Self-paced with clear structure
  • Jargon-free where possible, with explanations where not
  • Practical and immediately applicable

Continuous Improvement

This training is a living resource. We welcome feedback, suggestions, and contributions from the community. Open an issue on GitHub or submit a pull request to help improve these materials.

Who Created This?

This training was developed by synthesizing excellent materials from multiple open science initiatives:

  • CyVerse FOSS - Foundational Open Science Skills program
  • NCEMS Pre-Summit Training - Open science training for NCEMS community
  • Intro to GPT Workshop - AI and prompt engineering fundamentals
  • Awesome Open Science - Curated resources for open science tools

See the Acknowledgments page for detailed attribution and credits.

How to Use This Training

For Individual Learners

Work through the three lessons sequentially at your own pace. Each lesson takes approximately 50 minutes and includes:

  • Clear learning objectives
  • Core concepts with examples
  • Hands-on activities for practice
  • Self-assessment questions
  • Additional resources for deeper learning

Set aside dedicated time for each lesson and complete the activities to maximize learning.

For Instructors

These materials can be used for:

  • Workshops - Three 50-minute lessons or a half-day intensive
  • Course modules - Integrate into methods courses or research seminars
  • Lab training - Onboard new lab members to open science practices
  • Professional development - Departmental or institutional training programs

All materials are licensed CC-BY 4.0, allowing you to adapt and remix as needed for your context.

Teaching Tips:

  • Encourage discussion during activities
  • Adapt examples to your discipline
  • Share your own experiences with open science
  • Create space for questions and concerns
  • Follow up with resources specific to your field

For Research Groups

Use these lessons to:

  • Establish shared practices and standards for DUST research projects
  • Create data management protocols for environmental samples and exposure data
  • Develop ethical guidelines for AI use in environmental health research
  • Build open science culture across toxicology, remediation, and epidemiology teams
  • Prepare for NIH Superfund Research Program grant requirements
  • Document protocols for handling sensitive location data from contaminated sites

Consider working through lessons together as a group, discussing how to apply concepts to mine waste studies, phytoremediation experiments, and community health research.

Technical Implementation

This website is built with:

  • MkDocs - Static site generator for project documentation
  • Material for MkDocs - Modern theme with powerful features
  • GitHub Pages - Free hosting for open source projects
  • GitHub Actions - Automated building and deployment

The entire source is available on GitHub, allowing you to:

  • View the source markdown files
  • Propose improvements or corrections
  • Fork the repository to create your own version
  • Learn how to build similar documentation sites

Accessibility

We strive to make these materials accessible to all learners:

  • Semantic HTML structure for screen readers
  • Sufficient color contrast
  • Keyboard navigation support
  • Alternative text for images
  • Clear, readable fonts

If you encounter accessibility barriers, please let us know so we can improve.

Privacy

This website:

  • Does not collect personal information
  • Does not use authentication or accounts
  • Uses Google Analytics for aggregate usage statistics
  • Does not place tracking cookies (beyond analytics)
  • Is hosted on GitHub Pages (subject to GitHub's privacy policy)

License

All content is licensed under Creative Commons Attribution 4.0 International License.

You are free to:

  • Share - copy and redistribute in any medium or format
  • Adapt - remix, transform, and build upon the material

Under these terms:

  • Attribution - Give appropriate credit and indicate changes
  • No additional restrictions - Cannot apply legal or technological measures that restrict others

Contact

For questions, suggestions, or issues:

Version History

Version 1.0 (January 2025)

  • Initial release with three complete lessons
  • Open Science foundations
  • Data management best practices
  • AI ethics and responsible use

Future versions will incorporate community feedback and evolving best practices.

Last updated: 2025-10-14