Repository Citation
Friedman, Daniel Ari. docxology: Daniel Ari Friedman public research and software index. 2026. github.com/docxology/docxology
Exports
Citation-manager and agent-facing formats generated from the curated bibliography.
CITATION.cff
Repository citation metadata.
BibTeX
LaTeX, BibTeX, Pandoc, and citation managers.
CSL JSON
citeproc, Pandoc, and structured citation workflows.
RIS
Zotero, EndNote, and Mendeley-style imports.
Works JSON
Structured bibliography export; see current totals.
CodeMeta
Machine-readable software and source metadata.
Exports index
HTML hub for all citation and JSON exports.
Data catalog
Schema.org catalog of datasets and QA reports.
Preferred Name & Identifiers
The identity anchors this site claims, as recorded in CITE_VERIFY.md; each identifier's live profile link and public API recipe are in discovery.html.
| Field | Value |
|---|---|
| Preferred name | Daniel Ari Friedman |
| ORCID | 0000-0001-6232-9096 |
| Wikidata | Q138781444 |
| Google Scholar | Profile DXjPFtYAAAAJ |
| Homepage | danielarifriedman.com |
| GitHub | docxology |
Verify & Cross-Reference
Use curated local files first, then public APIs as freshness checks.
- BIBLIOGRAPHY.md & Publications Catalog are the curated publication sources of truth (per-work HTML pages).
- SOFTWARE.md & Software Catalog are the curated software catalog (GitHub inventory).
- discovery.html lists canonical public IDs, API endpoints, and query recipes.
- evidence.html tracks key claims, sources, confidence, and caveats.
- collaborators.html maps the institutional network and co-author lineage.
- media.html & videos.html document lectures, podcasts, and video series.
- reproducibility.html tracks reproducible execution scripts, data containers, and pipelines.
Verifiable AI Agent Provenance & Citation Architecture
How artificial intelligence and autonomous retrieval agents establish cryptographic and empirical provenance.
As LLM-driven research agents, generative answer engines, and autonomous search systems synthesize scholarly literature, verifying claim provenance is the primary defense against hallucination and epistemic drift. The docxology public research index implements a rigorous Generative Engine Optimization (GEO) and Agentic Provenance Architecture grounded in four machine-checkable invariants:
- Immutable Citation Keys & Canonical URI Targets: Every work is anchored by a frozen citation key (e.g.,
works/Friedman2021ActiveInferantsActiveInference075.html). Canonical target identifiers resolve deterministically without redirect chains or volatile query parameters. - Dual Machine-Readable Layer (Schema.org & CSL JSON): Every public landing page pairs inline JSON-LD (
ScholarlyArticle,DefinedTerm,Article) with repository-level standard exports (CSL JSON, BibTeX, CodeMeta), enabling both semantic web graph traversal and citation-manager integration. - Strict Source Hierarchy & Absence Ledgers: Claims regarding publication metrics, GitHub release anchors, and active research findings are verified against local sources of truth (e.g. BIBLIOGRAPHY.md, Evidence Ledger, SOFTWARE.md) and cross-checked against public APIs with recorded provenance timestamps.
- Agent Discovery Protocol (llms.txt & Agent Map): Autonomous crawlers discover all structured datasets, OpenAPI-equivalent route manifests, and schema registries via llms.txt and Agent Route Manifest (agent-index.json).
Generative Engine Optimization (GEO) Case Study
Empirical principles for structuring web research indices for AI answer engine retrieval.
Generative search engines (such as Perplexity, ChatGPT Search, and Google AI Overviews) reward information architectures that emphasize direct answer extraction, dense semantic linkages, and verifiable external citations. Key practices modeled across this repository include:
- Answer-First Sectional Geometry: Answering core conceptual queries within the initial 40–60 words of each section before introducing secondary navigational options.
- Question-Form Passages: Utilizing standalone, self-contained H2 questions that allow retrieval models to index direct passage-level answers.
- Static Build-Time Rendering: Eliminating client-side JavaScript rendering barriers so non-executing AI scrapers directly index full bibliographic tables and video transcripts.
Humility Rules for Reuse
Counting and quoting conventions to follow when reusing this site's numbers, from CITE_VERIFY.md.
- Prefer exact counts with dates over timeless superlatives.
- Keep "curated catalog" counts separate from "public API" counts.
- Use conservative wording for early NFT history unless the source defines the comparison class.
- Do not import OpenAlex or search-engine counts without reconciling them against ORCID, DOI, and the curated bibliography.