Welcome to DUST 2025 Open Science Training¶

About This Training¶

Welcome to the DUST 2025 Open Science Training program, designed specifically for graduate students and researchers at the University of Arizona DUST Superfund Research Center. This comprehensive training consists of three carefully designed 50-minute lessons that will guide you through the fundamentals of modern open science practices, data management, and ethical considerations in artificial intelligence.

As an environmental health researcher studying mine tailings, arsenic exposure, lung injury, or phytoremediation, open science practices are essential for advancing public health and environmental remediation. This training will help you manage complex environmental datasets, share findings with affected communities, and use AI tools ethically in environmental health research.

What You Will Learn¶

This training program covers three critical areas of modern research practice:

Lesson 1: Foundations of Open Science¶

Discover what open science means, why it matters, and how to implement its core principles in your research. Learn about the six pillars of open science and understand how transparency and accessibility can accelerate scientific discovery.

Duration: 50 minutes Level: Beginner to Intermediate

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Lesson 2: Modern Data Management for Computational Research¶

Master the essential practices of research data management, including the data lifecycle, FAIR principles, and practical tools for organizing, documenting, and sharing your data. Learn how proper data management saves time, prevents errors, and amplifies research impact.

Duration: 50 minutes Level: Beginner to Intermediate

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Lesson 3: Ethics and Artificial Intelligence¶

Explore the ethical dimensions of AI in research, including bias, discrimination, transparency, and responsible use of AI tools. Develop critical thinking skills to evaluate AI systems and learn best practices for ethical AI integration in your work.

Duration: 50 minutes Level: Beginner to Intermediate

Start Lesson 3

Learning Objectives¶

By the end of this training, you will be able to:

• Explain the core principles and practices of open science
• Apply FAIR data principles to your research projects
• Create and implement a data management plan
• Recognize and mitigate bias in AI systems
• Use AI tools ethically and responsibly in your research
• Navigate the policy landscape around open science and AI
• Leverage modern tools and platforms for reproducible research

Who Should Take This Training?¶

This training is designed for:

• DUST Superfund graduate students studying environmental health, toxicology, and remediation
• Graduate students and postdoctoral researchers in environmental sciences
• Researchers working with mine waste contamination and human health data
• Scientists studying arsenic exposure, lung disease, or phytoremediation
• Early-career faculty in environmental health and public health
• Anyone preparing NIH Superfund Research Program proposals
• Teams collaborating on multi-site field studies with hazardous materials

Training Structure¶

Each lesson follows a consistent, learner-centered structure:

Introduction (5 minutes)¶

Set the context, activate prior knowledge, and preview learning objectives

Core Concepts (25 minutes)¶

Deep dive into essential principles with examples, demonstrations, and explanations

Hands-on Activity (15 minutes)¶

Apply what you have learned through practical exercises and group discussions

Wrap-up (5 minutes)¶

Review key takeaways, assess understanding, and preview next steps

Prerequisites¶

• Basic familiarity with research processes
• Access to a computer with internet connection
• Willingness to engage in discussions and activities
• No prior technical expertise required

How to Use This Site¶

Navigation¶

Use the top navigation bar to access different lessons and resources. Each lesson is self-contained but builds on previous concepts, so we recommend completing them in order.

Interactive Elements¶

Throughout the lessons, you will find:

Code Examples¶

Code blocks include syntax highlighting and copy buttons for easy use:

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# Example: Creating a simple data management function
def organize_data(input_file, output_dir):
    """
    Organize research data following FAIR principles
    """
    # Implementation here
    pass

Assessment Questions¶

Each lesson includes self-assessment questions to check your understanding:

Getting Help¶

If you have questions or need clarification:

1. Review the lesson materials carefully
2. Check the Additional Resources page
3. Discuss with your instructor or peers
4. Open an issue on our GitHub repository

Acknowledgments¶

This training draws on excellent materials from several open science initiatives:

• CyVerse FOSS - Foundational Open Science Skills
• NCEMS Pre-Summit Training
• Introduction to GPT Workshop
• Awesome Open Science

See the Acknowledgments page for detailed attribution.

License¶

This work is licensed under a Creative Commons Attribution 4.0 International License.

You are free to:

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

Under the following terms:

• Attribution - You must give appropriate credit, provide a link to the license, and indicate if changes were made

Ready to Begin?¶

Choose your starting point:

Last updated: 2025-10-14