Acknowledgments¶

This training program synthesizes excellent open science materials from multiple sources. We are deeply grateful to the creators and contributors of the following projects:

Primary Source Materials¶

CyVerse FOSS (Foundational Open Science Skills)¶

The CyVerse FOSS program provided substantial content for Lessons 1 and 2, particularly:

• Open science definitions and frameworks
• The six pillars of open science
• FAIR and CARE data principles
• Data lifecycle and management practices
• Data management plan guidance

Source: foss.cyverse.org

Repository: github.com/CyVerse-learning-materials/foss

Contributors: CyVerse Science Team, including Jason Williams, Tyson Swetnam, Jeffrey Gillan, and many community contributors

License: CC-BY 4.0

NCEMS Pre-Summit FOSS Training¶

The NCEMS Pre-Summit training provided refined content on:

• Open science motivations and applications
• Data management best practices
• Prompt engineering and AI tool usage
• Integration of open science with modern research practices

Source: ncems.github.io/pre-summit-foss

Repository: github.com/NCEMS/pre-summit-foss

Contributors: Tyson Swetnam, Nicole Lazar, and the NCEMS community

License: CC-BY 4.0

Introduction to GPT Workshop¶

Substantial content for Lesson 3 on AI ethics and responsible AI use came from:

• Ethics of artificial intelligence frameworks
• Bias and discrimination in AI systems
• Transparency and accountability considerations
• Responsible use of AI tools in research
• Prompt engineering fundamentals

Source: tyson-swetnam.github.io/intro-gpt

Repository: github.com/tyson-swetnam/intro-gpt

Contributors: Tyson Swetnam

License: CC-BY 4.0

Awesome Open Science¶

Resources and community connections drew from:

• Curated lists of open science tools
• Repository and platform recommendations
• Community networks and organizations
• Open science definitions and frameworks

Source: tyson-swetnam.github.io/awesome-open-science

Repository: github.com/tyson-swetnam/awesome-open-science

Contributors: Tyson Swetnam

License: CC-BY 4.0

Additional Influences¶

The Turing Way¶

Inspiration for documentation structure, accessibility, and community-driven open science practices.

Source: book.the-turing-way.org

License: CC-BY 4.0

The Carpentries¶

Pedagogical approach emphasizing hands-on learning and practical skills development.

Source: carpentries.org

License: CC-BY 4.0

Foster Open Science¶

Framework for understanding open science education and training needs.

Source: fosteropenscience.eu

License: CC-BY 4.0

Technical Infrastructure¶

MkDocs Material¶

This website is built with Material for MkDocs, an outstanding documentation theme.

Project: squidfunk.github.io/mkdocs-material

Creator: Martin Donath (@squidfunk)

License: MIT

MkDocs¶

Static site generator that powers this documentation.

Project: mkdocs.org

License: BSD-2-Clause

Python-Markdown Extensions¶

Enhanced Markdown features through pymdown-extensions.

Project: facelessuser.github.io/pymdown-extensions

License: MIT

Content Attribution¶

All content in this training is derived from openly licensed sources and adapted for educational purposes. Specific attributions:

Lesson 1: Foundations of Open Science

• Core framework from CyVerse FOSS Lesson 1
• Policy context from NCEMS Pre-Summit Training
• Community resources from Awesome Open Science
• Additional examples and activities created for this training

Lesson 2: Modern Data Management

• Data lifecycle and principles from CyVerse FOSS Lesson 2
• Practical examples from NCEMS Pre-Summit Training
• FAIR and CARE principles with expanded examples
• DMP guidance synthesized from multiple sources

Lesson 3: AI Ethics

• Primary content from Intro to GPT ethics modules
• Bias framework synthesized from multiple AI ethics sources
• Practical research scenarios created for this training
• Updated policy landscape as of 2025

Individual Contributors¶

Special thanks to:

• Tyson Swetnam - Original content creation, curation, and instruction across all source materials
• Jason Williams - CyVerse FOSS program development and open science leadership
• Jeffrey Gillan - CyVerse FOSS content development and geospatial expertise
• Nicole Lazar - NCEMS training design and statistical perspectives
• CyVerse Science Team - Ongoing development of open science training materials
• NCEMS Community - Feedback and refinement of training content

Community Acknowledgments¶

This training benefits from broader open science communities:

• UNESCO - Open Science framework and recommendations
• Center for Open Science - FAIR principles and research integrity
• Global Indigenous Data Alliance - CARE principles for data sovereignty
• Research Data Alliance - Data management standards and practices
• AI ethics researchers - Frameworks for responsible AI development and use

Institutional Support¶

Development of source materials was supported by:

• University of Arizona
• CyVerse (NSF DBI-0735191, DBI-1265383, DBI-1743442)
• NCEMS (National Computational Environmental Modeling Science)
• NSF - Various grants supporting open science infrastructure

License and Reuse¶

This training is licensed under Creative Commons Attribution 4.0 International License (CC-BY 4.0).

When reusing this material:

Suggested Citation:

Swetnam, T.L. (2025). DUST 2025: Open Science Training. https://tswetnam.github.io/dust-2025

Attribution Requirements:

You must give appropriate credit to:

1. This training (DUST 2025)
2. Original source materials (CyVerse FOSS, NCEMS, etc.)
3. Indicate if changes were made
4. Provide a link to the license

Example Attribution:

Adapted from "DUST 2025: Open Science Training" by Tyson Swetnam (CC-BY 4.0), which synthesizes materials from CyVerse FOSS, NCEMS Pre-Summit Training, and other open science resources.

Contributing¶

We welcome contributions to improve this training:

• Report issues: github.com/tswetnam/dust-2025/issues
• Suggest improvements: Submit pull requests
• Share feedback: Email tswetnam@arizona.edu

All contributors will be acknowledged in future versions.

Updates and Maintenance¶

This training will be updated to reflect:

• Evolving open science practices
• New tools and resources
• Policy changes
• Community feedback
• Emerging AI ethics considerations

Check the repository for the latest version and change history.

Thank You¶

Most importantly, thank you to:

• All open science practitioners who share their work openly
• Instructors and educators who teach these principles
• Researchers implementing open practices despite institutional barriers
• You - for investing time in learning and practicing open science

By working together, we strengthen the foundation of transparent, reproducible, and accessible research for everyone.

Questions or corrections? Open an issue on GitHub

Last updated: 2025-10-14