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2026.03.19 Community Meeting

Andrew Woods edited this page Mar 28, 2026 · 3 revisions

Call-in Details

Attendees

  1. Andrew Woods
  2. Ross Spencer
  3. Seth Erickson
  4. John Kunze
  5. Julian M. Morley
  6. Neil Jefferies
  7. Simeon Warner

Agenda

  1. Welcome
  2. News from the community
  3. OCFL Community Engagement Use Case
  4. Introductions and Open Topics

Recording

AI Summary

Prompt: Provide concise summary of this meeting with salient takeaways.

Summary

This was an open-ended OCFL community call focused on OCFL v2 progress, implementation concerns, and future-facing topics (AI, testing, sustainability), with side discussions on persistent identifiers and community engagement.


Key Takeaways

1) OCFL is at a real “inflection point”

  • Movement toward OCFL v2 is the primary driver of current activity.
  • Implementers (e.g., Basel’s GoCFL) are actively refactoring to support both v1 and v2, with testing and error handling still evolving.
  • Some institutions are waiting for v2 maturity (e.g., packaging support) before adopting or expanding usage.

Implication:
v2 readiness is gating broader adoption and tooling alignment.


2) Error handling + testability are emerging gaps

  • “Error handling” surfaced as an interest area but lacks clear shared direction.
  • Strong interest in a language-agnostic test suite:
    • Go beyond validation → include operation sequences (create/update/delete)
    • Define expected end-state on disk after operations
  • Challenge: OCFL defines storage outcomes, not APIs, making automation harder.

Implication:
A standardized behavioral test corpus could become critical infrastructure—especially for:

  • interoperability
  • onboarding new implementations
  • AI-assisted development workflows

3) AI is influencing design thinking (early but real)

  • File systems are becoming relevant again because AI agents interact well with filesystem abstractions.
  • Ideas explored:
    • Mount OCFL objects via FUSE for agent access
    • Use read-only + overlay (copy-on-write) for safe AI interaction
  • Strong emphasis on:
    • Test suites enabling agents to self-validate code
    • Tension in cultural heritage community about AI adoption

Implication:
OCFL’s filesystem-oriented design may become a strategic advantage in AI-native workflows.


4) OCFL works well in cloud/object storage (with care)

  • Harvard reports successful use of OCFL on S3-compatible storage, including migration workflows.
  • Key nuance:
    • Requires thoughtful object naming and layout strategy
  • Real-world validation example:
    • OCFL validation caught data loss during S3 copy, preventing silent corruption.

Implication:
OCFL’s storage abstraction is portable, but operational discipline matters.


5) Sustainability of preservation infrastructure is a major concern

  • Repeated failures of preservation organizations (e.g., DPN, SPN) highlight systemic risk.
  • Skepticism about:
    • traditional org structures
    • “preservation as a business model”
  • ARK Alliance presented as an alternative:
    • low-cost, decentralized, no formal entity
  • Related ideas:
    • blockchain / decentralized storage (e.g., Arweave) as persistence layers

Implication:
There is growing concern that institutional models—not just technology—are the weak point in long-term preservation.


6) Persistent identifiers: low-friction approaches exist

  • ARKs can be layered onto existing permalink systems with minimal effort (even “one-line config”).
  • Value framed as:
    • signaling persistence, not enforcing it

Implication:
Adoption barriers for PIDs may be more cultural than technical.


7) Community engagement needs adjustment

  • Attendance is inconsistent; current cadence may be too frequent.
  • Suggestions:
    • Move to quarterly meetings
    • Add short user showcases (10–15 min) to attract participation

Implication:
OCFL may be “quietly successful” infrastructure—but risks low visibility and engagement.


Bottom Line

  • Technically: OCFL is stable, useful, and evolving—v2 + better testing are the next major steps.
  • Strategically: Its filesystem model aligns well with both cloud storage and emerging AI workflows.
  • Organizationally: The biggest uncertainty is not the spec—it’s sustainable ecosystem governance and engagement.

New Action Items

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Previous Action Items

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