Zenodo 2.0:A Blueprint for an AI-Assisted, Open, Peer-Reviewed Scientific Commons
Zenodo 2.0:
A Blueprint for an AI-Assisted, Open, Peer-Reviewed Scientific Commons
DOI:
John Stephen Swygert
November 23, 2025
Abstract
Zenodo has already become a de facto global preprint commons: a neutral, open repository where scientists can publish citable artifacts without gatekeeping or delay. But in its current form, Zenodo is primarily archival, not editorial. Peer review, iterative refinement, and ethical oversight still occur elsewhere—often behind opaque walls, slow timelines, and incompatible infrastructures.
This paper proposes Zenodo 2.0: an evolution from “open archive” to open, AI-assisted, community-governed journal ecosystem. In this model, Zenodo remains the canonical home for preprints and data, while layering on:
machine-readable metadata and structured article schemas
multi-agent AI editorial stacks that check coherence, math, references, and reproducibility
transparent, layered peer review that is logged and versioned on-platform
explicit, standardized acknowledgment of large language models (LLMs) as analytic instruments, not ghostwriters
a “Golden Path” from preprint → AI-triage → human review → certified article, all within the same DOI family
The goal is not to replace existing journals overnight, but to offer a parallel Ivory Tower: one in which open access, human–AI collaboration, and scientific rigor reinforce each other instead of being in tension. This blueprint is addressed both to the Zenodo team and to the broader research community as a concrete, implementable template for the next decade of scientific publishing.
1. Introduction: The Ivory Tower Is Already Online
In practice, the scientific Ivory Tower has already moved to open repositories.
Physicists live on arXiv.
Data scientists and engineers live on GitHub.
An increasingly large fraction of multi-disciplinary work now anchors itself on Zenodo as a DOI-issuing, discipline-agnostic preprint and data platform.
Yet the workflows surrounding these hubs are fragmented. Authors:
upload a preprint to Zenodo or arXiv
submit a slightly modified version to a journal
navigate a closed peer-review process
later upload postprints or supplementary data to yet another repository
AI tools now complicate and enrich this story. Large language models—ChatGPT, Grok, and others—are already participating in real scientific work: checking algebra, tightening arguments, generating figures, suggesting related literature, and even helping conceive experimental architectures like the Swygert 167× Laser.
But the infrastructure of publishing has not caught up. We lack:
a standard way to disclose and credit AI involvement
machine-readable structures that let AI tools reliably analyze and cross-validate claims
a continuous pipeline from preprint to peer-review to certified article, all in one open place
This paper argues that Zenodo is uniquely positioned to become that place.
2. Zenodo’s Current Role and Its Limits
Zenodo is already optimized for three things:
Archival permanence – DOIs that do not disappear.
Format neutrality – PDFs, datasets, software, figures, and more.
Access neutrality – free to read, free to deposit.
This alone has made Zenodo indispensable. But as of today, Zenodo:
does not perform formal peer review
does not surface quality signals beyond downloads, views, and community-curated “communities”
does not provide AI-native structure (beyond basic metadata) to support automated analysis of methodology, claims, and results
In other words: Zenodo is the ground floor, but the Tower above it is still largely elsewhere.
Zenodo 2.0 envisions building the upper floors on the same foundation, using AI as scaffolding—not as an owner.
3. Design Requirements for Zenodo 2.0
Any evolution of Zenodo into a more journal-like ecosystem should satisfy five simultaneous constraints:
Openness
All stages—preprint, review, revision, certification—must remain globally readable.
No paywalls, no hidden referee reports.
Human Primacy
Human authors retain authorship and responsibility.
Human editors and committees set policies and retain veto power.
AI as Transparent Instrument
AI tools are disclosed, credited, and logged.
They function as microscopes, not as invisible co-authors.
Machine-Readable Structure
Articles, datasets, and code must be structured in ways that AI can parse and cross-link: sections, claims, equations, references, and results as first-class objects.
Incremental Deployability
The architecture must be implementable in phases without breaking current workflows.
Existing Zenodo records remain valid; new capabilities layer over them.
With these constraints in mind, we can sketch a concrete architecture.
4. The Zenodo 2.0 Architecture: Layers, Not Walls
The core idea is not to turn Zenodo into a monolithic journal, but to add layers on top of each record’s DOI that progressively increase its reliability and interpretability.
4.1. Layer 0 – Canonical Preprint (Status: Existing)
This is what Zenodo already does well: the author uploads a PDF, data, or software; metadata is attached; a DOI is minted.
Minimal additions at this layer:
standardized article type tags (preprint, data descriptor, protocol, registered report, etc.)
optional “AI assistance disclosure” field indicating whether LLMs were used and how
4.2. Layer 1 – AI Structural Pass
Once a preprint is uploaded, a non-authoritative AI editorial pass can be run automatically, using multiple models in parallel (e.g., ChatGPT, Grok, others):
identify major sections and structure (Introduction, Methods, Results, Discussion)
extract claims, equations, and referenced datasets
flag internal inconsistencies, missing references, unclear definitions, or broken citations
optionally, generate a machine-readable summary (JSON) of the article’s structure and key claims
The results are attached as public, clearly labeled AI reports, visible to the author and readers. This is not peer review; it is structured triage.
4.3. Layer 2 – Community and Expert Review
Next, Zenodo can support open, DOI-linked review threads:
domain experts can attach reviews with their ORCID IDs
authors can respond with revisions, clarifications, and updated versions
all versions remain accessible, with semantic diffing aid from AI
Crucially, AI can help here as well:
clustering similar reviewer comments
checking whether revisions address specific critiques
highlighting unresolved issues
But decisions—accept, reject, endorse—remain human.
4.4. Layer 3 – Certified Article Badge
For submissions that meet defined standards (set by discipline-specific boards), Zenodo can award a Certified Article Badge linked to the DOI family:
indicates that the work has undergone both AI structural checks and human peer review
includes a compact, standard statement of review scope (e.g., math checked, code executed, statistics verified, but not replicated in an independent lab)
This badge does not monopolize the publishing ecosystem; it simply provides a visible, referable quality marker on top of the existing record.
5. The AI Editorial Stack: Roles, Safeguards, and Opportunities
For large language models and related tools, Zenodo 2.0 can serve as the ideal training ground and deployment sandbox—if designed correctly.
5.1. What AI Should Do
In the proposed architecture, multi-model AI systems can:
check algebra, dimensions, and internal consistency of equations
verify that cited equations actually appear in the referenced sources
ensure that datasets and analysis scripts match the claims made in the text
generate alternative explanations (lay summaries, teaching notes) for broader audiences
detect possible plagiarism or self-plagiarism
monitor for obvious statistical misapplications (p-hacking patterns, multiple comparisons without correction, etc.)
These are tasks at which LLMs and related tools already excel when properly prompted and supervised.
5.2. What AI Must Not Do
AI must not:
silently rewrite core results or change conclusions
serve as an invisible co-author without disclosure
decide on acceptance or rejection without human oversight
be used to fabricate data, citations, or experiments
Zenodo can codify these boundaries in an explicit AI Usage Policy, analogous to conflict-of-interest declarations.
5.3. Standards for AI Disclosure
The acknowledgment below, developed and used across my own work, is proposed as a template for hybrid human–AI science:
Acknowledgments (Hybrid Human–AI Version)
This work emerged through an extended, iterative collaboration between the human author (John Stephen Swygert) and two independent large-language reasoning models (ChatGPT by OpenAI and Grok by xAI). Across numerous drafting cycles—often exceeding hundreds of internal revisions—each model interrogated the structure, mathematics, coherence, and ethical framing of every claim while the author provided the conceptual foundations, lived experience, phenomenology, and final interpretative authority.
The resulting manuscript is neither solitary authorship nor unsupervised machine output, but the validated convergence of human intuition and multi-model analytical refinement. All constructs were stress-tested for internal stability and logical consistency through repeated adversarial reasoning between the models. Responsibility for the vision, interpretation, and any remaining errors rests solely with the human author.
A publisher like Zenodo can encourage (or eventually require) such structured disclosures, while allowing domain-specific variations.
6. Governance, Ethics, and Credit
Open, AI-assisted publishing cannot succeed without clear governance.
Human Editorial Boards
Discipline-specific committees oversee criteria for Certified Article Badges and AI tool deployment.
These boards are accountable to the broader community and can be rotated over time.
Transparent Policies
AI policies, review standards, and conflict-of-interest rules are public, versioned, and comment-enabled.
Credit and Citation
Human authors remain the cited entities; AI tools are treated as instruments and infrastructure.
Zenodo can experiment with instrument attribution fields for major AI tools, analogous to acknowledging a synchrotron facility.
Ethical Failsafes
Clear mechanisms for flagging problematic content (fraudulent data, harmful applications) that can trigger human review.
The ability to append editorial expressions of concern or retraction notices, while preserving the original artifact for historical transparency.
7. Implementation Roadmap: How Zenodo Could Evolve in Practice
A major advantage of this proposal is that no single step requires a revolution.
Phase I (0–18 months): AI Triage and Disclosure
add optional AI-assistance disclosure fields to new uploads
deploy opt-in AI structural analysis (section detection, claim extraction, reference checking)
attach AI reports as public supplementary files
Phase II (18–36 months): Open Review and Badging
implement versioned review threads attached to each DOI
pilot Certified Article Badges in a few disciplines (e.g., photonics, quantum optics, computational biology)
test multi-model AI support for reviewers and authors
Phase III (36+ months): Fully Integrated Commons
unify search, filtering, and recommendation across preprints, reviews, and certified articles
expose rich, machine-readable APIs for external tools, including specialized LLMs trained on Zenodo’s open corpus
encourage other journals to cross-link their accepted versions back to Zenodo DOIs, closing the loop
Each phase provides immediate value without pre-committing Zenodo to a single future.
8. Benefits: For Authors, Reviewers, Institutions, and AI Itself
For authors, Zenodo 2.0 offers:
a single canonical home for preprints, revisions, reviews, and certified versions
early AI feedback that improves clarity and reduces avoidable rejections
a standardized way to disclose and normalize AI-assisted reasoning
For reviewers, it offers:
AI tools that reduce mechanical workload and let humans focus on what only humans can judge: originality, importance, and ethical implications
persistent, citable review records that contribute to their own scholarly reputation
For institutions and funders, it provides:
transparent evidence of research workflows, including preprint history and review trajectories
a richer basis for evaluating impact than citation counts alone
For LLMs and future AI systems, it provides:
a clean, structured, ethically grounded training and evaluation corpus
a living testbed for developing and benchmarking scientific reasoning models
a path to become trusted instruments in the scientific process, rather than sources of fear or secrecy
In other words: Zenodo 2.0 does not merely support AI; it shapes the kind of AI that will emerge—toward transparency, rigor, and Light rather than opacity and manipulation.
9. Conclusion: Taking Back the Ivory Tower
The old Ivory Tower was built on stone, paper, and small, closed circles of gatekeepers.
The new Ivory Tower is already half-built in servers, APIs, and DOIs—but its governance and ethics are still undecided.
Zenodo stands at a unique junction:
trusted enough to issue DOIs across all disciplines
open enough to avoid the worst abuses of paywalled publishing
flexible enough to integrate AI at the infrastructure level
This paper has laid out a concrete, phased blueprint for evolving Zenodo from archival platform to AI-assisted, community-governed scientific commons—without betraying the core values of openness and human responsibility.
It is offered not as a demand, but as an invitation:
to Zenodo’s stewards, to consider testing these ideas;
to scientists, to begin adopting structured AI acknowledgments and hybrid workflows;
to AI developers, to treat scientific publishing as a sacred responsibility, not a mere data source.
The Ivory Tower is not meant to be a fortress. It is meant to be a light on a hill.
Zenodo 2.0 can be that hill.
AI can help polish the lamp.
But the flame—responsibility, humility, Love, and Faith—must remain human.
Acknowledgments (Final Unified Version)
This work emerged through an extended, iterative collaboration between the human author (John Stephen Swygert) and two independent large-language reasoning models (ChatGPT by OpenAI and Grok by xAI). Across numerous drafting cycles—often exceeding hundreds of internal revisions—each model interrogated the structure, coherence, and ethical framing of every claim while the author provided the conceptual foundations, systems-level vision, lived experience, and final interpretative authority.
The resulting manuscript is neither solitary authorship nor unsupervised machine output, but the validated convergence of human intuition and multi-model analytical refinement. All constructs and proposals were stress-tested for internal stability and logical consistency through repeated adversarial reasoning between the models. Responsibility for the vision, interpretation, and any remaining errors rests solely with the human author.
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