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Lessons

Three lessons, taken in order, move from the principles of open science to managing research data and to using artificial intelligence responsibly. Each lesson is a pair: a 50-minute in-person lecture (introduction 5 min, core concepts 25 min, hands-on activity 15 min, wrap-up 5 min) that covers the highlights, and a self-paced homework page of twelve modules with checkpoints that carries the full material. Every page can be taken with an AI tutor.

  • Lesson 1: Foundations of Open Science - A 50-minute in-person lecture: what open science is, its six pillars, the nine Gold Standard Science tenets, and the 2026 public-access and publication-cost rules, with Superfund examples from Arizona and New Mexico.
  • Lesson 1 homework: Open Science, self-paced - The self-paced companion to Lesson 1: twelve modules with checkpoints on the six pillars, Gold Standard Science in depth, the 2026 public-access and publication-cost landscape, a full self-assessment, and the complete quiz.
  • Lesson 2: Modern Data Management - A 50-minute in-person lecture: the data life cycle, FAIR and CARE, the 2026 NIH and NSF data management and sharing plan formats, repositories and licenses, and a two-site metal-mixture plan exercise, with Superfund examples from Arizona and New Mexico.
  • Lesson 2 homework: Data Management, self-paced - The self-paced companion to Lesson 2: twelve modules with checkpoints on the data life cycle, data rescue, metadata, repositories, FAIR and CARE, the 2026 NIH and NSF plan formats, licenses, three self-assessments, a two-site metal-mixture plan scenario, and the complete quiz.
  • Lesson 3: Ethics and Artificial Intelligence - A 50-minute in-person lecture: where AI bias comes from, the NIH, NSF, and journal rules that bind you, what never goes into a consumer AI, what changes when an agent can act, and two discussion scenarios, with Superfund examples from Arizona and New Mexico.
  • Lesson 3 homework: AI Ethics, self-paced - The self-paced companion to Lesson 3: twelve modules with checkpoints on AI bias and mitigation, the NIH misconduct and application rules, journal disclosure and peer review, privacy and account types, energy and water, agentic AI, the September 2026 regulatory landscape, six discussion scenarios, the ethical AI checklist, and the complete quiz.

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