DUST 2026: Open Science Training¶

What you will learn¶

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

• Explain the core principles and practices of open science, the nine Gold Standard Science tenets, and the federal policies that now require them
• Apply FAIR data principles, and CARE principles for Indigenous data, to your research projects
• Write a data management and sharing plan in the 2026 NIH and NSF formats
• Recognize and mitigate bias in AI systems, including large language models and agents
• Use AI tools ethically and responsibly in your research, and disclose that use correctly
• Navigate the 2026 policy landscape around open science, publication costs, and AI
• Leverage modern tools and platforms for reproducible research

Who should take this training¶

• Graduate students and postdoctoral researchers in the UA DUST Center and the UNM METALS Center
• Researchers working with mine waste, arsenic, uranium, and metal-mixture exposure data
• Scientists working with Navajo Nation, Pueblo of Laguna, and other tribal partners
• Early-career faculty in environmental health, toxicology, and public health
• Anyone preparing NIH Superfund Research Program proposals or budgeting publication costs under 2026 rules
• Teams collaborating on multi-site field studies with hazardous materials

Training structure¶

Each in-person lesson follows the same learner-centered structure:

Every lesson pairs the 50-minute lecture with a self-paced homework page (about 90 to 120 minutes) that holds the full material in twelve modules with checkpoints. Any lesson can be taken with an AI assistant as a lecture, a tutorial, or a quiz: see Learn with an AI tutor. The site's Accessibility page describes support for screen-reader users, deaf and hard-of-hearing learners, and learners whose first language is not English.

Prerequisites: basic familiarity with research processes, a computer with an internet connection, and a willingness to discuss. No prior technical expertise is required.

How to use this site¶

Use the top navigation to move between lessons and resources. Each lesson stands on its own but builds on the previous one, so we recommend completing them in order. Throughout the lessons you will find:

What's new in 2026¶

• Two centers, one training. Every example now pairs Arizona arsenic and mine-tailings research with UNM METALS uranium and metal-mixture research on tribal lands.
• The 2026 policy landscape. Zero-embargo public access is in force at NIH, DOE, EPA, USGS, NSF, and USDA; the NIH data management and sharing plan has a new 2026 format; NIH rules on AI-written applications and AI in peer review are covered in Lesson 3.
• Data rescue. Lesson 2 uses the 2025 removal of EPA's EJScreen, and the community mirrors that replaced it, to teach why preservation matters for environmental-justice data.
• Indigenous data sovereignty. CARE principles, Navajo Nation research review requirements, and Local Contexts are treated as core practice, not a footnote.
• Agentic AI. Lesson 3 covers what changes when an AI can browse, run code, and act on your behalf, with current energy and water figures and a Southwest data-center angle.
• Repaired and refreshed links, current article-processing charges, openRxiv and arXiv governance changes, and updated tool recommendations.
• Gold Standard Science. Lesson 1 maps the nine tenets of Executive Order 14303 to open-science practices and follows the agencies' 2025 implementation plans and September 2026 annual reports.
• Lecture plus homework. Each lesson is now a 50-minute in-person lecture with a self-paced companion page, so the session covers the highlights and the depth is done at home.
• Learn with an AI tutor. Each lesson declares its objectives, key terms, delivery modes, and accessibility profile in machine-readable form; prompts for Claude, ChatGPT, Gemini, and NotebookLM turn a lesson into a lecture, a tutorial, or a quiz.
• Accessibility. Plain-language summaries, text descriptions of every figure, glossaries, keyboard-operable quizzes, and guidance for blind, deaf, and multilingual learners; see Accessibility.
• Built for people and for AI agents. The site is an Open Knowledge Format bundle with llms.txt, llms-full.txt, per-page Markdown (add index.md to any page address, or use the "View this page as Markdown" button), raw source at raw.githubusercontent.com/UNM-CARC/dust-2026/main/docs/, and schema.org LearningResource records; see For AI agents.

License¶

This work is licensed under a Creative Commons Attribution 4.0 International License. You may share and adapt the material for any purpose, with attribution. See Credits and attribution for sources and how to cite.

Browse the site¶

• Lessons - The three 50-minute lessons and their self-paced homework pages.
• About this site - Who the training is for, resources, credits, the AI tutor guide, accessibility, guidance for AI agents, and the update log.
• llms.txt - Linked outline of every page for AI agents, with the Markdown twin and raw GitHub source of each; llms-full.txt is the whole site in one file.

Machine-readable versions of this page: Markdown twin · raw source on GitHub · llms.txt · llms-full.txt (whole site). See For AI agents.