Produce a collection of E4S community learning resources - initial ideas

While E4S already has some resources for the community, we want to provide a holistic, community driven collection of resources for many types of stakeholders.

Here are some initial thoughts about what we. might do:

High-Level Plan for an E4S Learning Resources Collection

This document outlines a structured, scalable plan for creating a coherent and sustainable collection of learning resources for the E4S community. The plan is designed to align with DOE-style ecosystem stewardship, heterogeneous audiences, and the practical realities of HPC and AI software adoption.

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1. Clarify Purpose and Scope

Primary Objectives

• Lower the barrier to entry for new E4S users.
• Accelerate effective use of E4S software in production and research settings.
• Build shared vocabulary and mental models across the ecosystem.
• Reinforce E4S as the curated pathway from research software to deployable capability.

Explicit Non-Goals

• Replacing package-level documentation.
• Serving as a full academic curriculum.
• Providing vendor-specific training (unless explicitly labeled).

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2. Define Target Audiences and Learning Paths

Audience Personas

1. Newcomers / Explorers

   • Graduate students, postdocs, new staff.
   • Goal: Understand what E4S is and why it matters.

2. Application Developers

   • Domain scientists and research programmers.
   • Goal: Achieve performance portability, correctness, and sustainability.

3. Software Engineers / Infrastructure Experts

   • CI/CD, packaging, containers, deployment specialists.
   • Goal: Ensure reproducibility, integration, and scaling.

4. Facility and Program Stakeholders

   • DOE programs, center leads, vendors.
   • Goal: Assess ecosystem health, adoption, and return on investment.

Learning Paths

• Curated, milestone-based paths tailored to each audience.
• Focused on progression rather than time spent.

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3. Organize Content into Progressive Tiers

Tier 0: Orientation (5–15 minutes)

• What is E4S?
• How the ecosystem fits together.
• Why E4S matters for modern HPC and AI.

Tier 1: Quick Starts (30–60 minutes)

• Install via Spack.
• Run a simple example.
• Use containers.
• Minimal “hello world” workflows with real tools.

Tier 2: Core Competencies (2–6 hours)

• Performance portability concepts.
• Build and dependency management.
• Debugging, profiling, and correctness.
• Reproducibility and environment management.

Tier 3: Advanced and Integrative Topics

• Mixed and low-precision techniques.
• HPC–AI workflows.
• Scaling to leadership-class systems.
• Application–facility–vendor co-design.

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4. Establish a Canonical Topic Taxonomy

Learning resources should be organized around stable concepts rather than transient tools.

Example Topic Families

• Programming models
• Math libraries
• Data, I/O, and workflows
• Performance and correctness tools
• Build, packaging, and deployment
• AI-for-Science integration
• Sustainability and governance

Each resource should clearly state:

• Prerequisites
• Learning outcomes
• Placement within the taxonomy

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5. Choose Resource Types Deliberately

Avoid over-reliance on a single content format.

Recommended Resource Mix

• Short written guides (Markdown, Jupyter-friendly)
• Hands-on tutorials (repository-based)
• Recorded talks with timestamps
• Conceptual explainers (architecture, tradeoffs)
• Case studies grounded in real applications and challenges

Each resource should answer:

“What problem does this help me solve?”

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6. Integrate with Existing E4S Infrastructure

The learning collection should not form a parallel ecosystem.

Leverage Existing Assets

• E4S release structure and product families
• Spack environments and recipes
• Containers and CI artifacts
• Existing tutorials and documentation (curated, not duplicated)

Design principle: Learning resources should point into the ecosystem, not away from it.

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7. Governance and Contribution Model

Learning content must scale socially as well as technically.

Core Principles

• Lightweight contribution process.
• Clear quality standards and editorial voice.
• Named maintainers for each topic family.

Contribution Roles

• Curators (learning-path builders)
• Content authors
• Reviewers
• Infrastructure maintainers

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8. Incentives and Signals of Progress

Motivation and recognition matter.

Possible Incentives

• Completion badges tied to learning paths.
• “E4S-ready” signals for contributors and practitioners.
• Recognition in E4S release notes or community calls.

Badges should reflect demonstrated capability, not attendance.

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9. Delivery Platform Strategy

Start simple while designing for future growth.

Short-Term Approach

• Markdown-first content.
• Website-hosted with GitHub-native workflows.
• Clear navigation by audience and tier.

Longer-Term Opportunities

• Interactive notebooks.
• Automated tutorial validation.
• Analytics to identify friction points in learning paths.

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10. Success Metrics

Define success criteria early and measure consistently.

Quantitative Metrics

• Resource usage by tier and audience.
• Learning-path completion rates.
• Adoption signals such as downloads, citations, and reuse.

Qualitative Metrics

• User feedback and testimonials.
• Facility and project endorsements.
• Evidence of reduced onboarding friction.

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11. Phased Rollout Plan

Phase 1: Pilot

• Focus on a single audience.
• Deliver one complete learning path.
• Produce 5–10 high-quality resources.

Phase 2: Expansion

• Fill gaps across tiers and topics.
• Add cross-links and case studies.

Phase 3: Ecosystem Integration

• Align learning content with E4S releases.
• Tie learning paths to community milestones.

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Closing Framing

E4S Learning is not training material—it is ecosystem infrastructure.

This framing emphasizes durability, reuse, and community stewardship rather than one-off instruction.