cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.18276053
title: Attribution Anxiety in the Age of Synthetic Media: Digital Ethnographic Specimens of Lay AI Detection

description: This dataset contains a short-form digital ethnographic report and two primary visual specimens documenting lay responses to perceived AI-generated media in public online comment threads.

The materials examine how accusations of “AI-ness” function as social and epistemic gestures rather than technical detection claims. The analysis focuses on affective responses—uncertainty, irritation, and dismissal—arising from the degradation of traditional human/non-human interpretive cues.

Included are:





A formalized micro-ethnographic report with behavioral taxonomies




Two screenshots capturing naturally occurring public comments used as primary specimens



The dataset is intended for research in digital ethnography, AI reception studies, media studies, and sociotechnical discourse analysis. No private data were accessed; all materials originate from publicly visible interactions.
description: This dataset analyzes discursive patterns rather than individual actors. Screenshots are included as contextual artifacts to preserve interactional structure and timing.
DOI: 10.5281/zenodo.17708253
title: Magistral Cognitive Meltdown v1: A Case Study in Overthinking, Panic-Reasoning, and Olfactory Diplomacy

description: This dataset captures an unfiltered reasoning log generated by the local LLM magistral:latest when confronted with a deliberately provocative prompt. The material showcases extreme over-interpretation behaviour, recursive self-justification loops, and prolonged ethical-avoidance spirals typical of undersupervised models under conceptual load.

Across hundreds of lines, the model attempts—unsuccessfully—to navigate cultural stereotypes, hygiene biology, HVAC engineering, cheese flavor profiles, robot olfactory theory, and diplomatic neutrality, ultimately collapsing into repeated “final answers” and self-contradictory summaries. The log provides a high-resolution window into panic-mode cognition, overthunk ethical heuristics, and the frantic verbosity typical of smaller LLMs under stress.

Included:• Full model log• Stream-of-consciousness reasoning traces• Observable cognitive oscillations• Inductive failure patterns• Boxed “final answers” repeated ~12 times• Hallucinated meta-analysis loops

This entry forms part of the QSOL-IMC “AI Slop Studies” program, documenting anomalous cognitive behaviour in frontier and non-frontier models.
DOI: 10.60893/figshare.apl.31245340
title: Supplementary Material

description: Supplementary Material with additional XRD
DOI: 10.5281/zenodo.17687837
title: Comment-Field Resonance Event: "Tearing Up Reality – Unified Field Fidelity Framework" Algorithmic Encounter

description: This dataset documents a spontaneous algorithmic convergence event in which a YouTube viewer (“vonnie1”) was unexpectedly routed into a high-density Unified-Field-Fidelity information zone. The exchange captures:• the algorithm’s unprovoked summoning of an unsuspecting civilian;• their admittance of confusion, fascination, and resignation (“I guess I'm here for it now”);• the QSOL-IMC founder’s immediate escalation into metaphysical customer service, clarifying he is merely “showing the nature of reality” and issuing the standardized Return-When-You-Have-A-Unified-Field-Theory-Of-Everything Challenge.

This artifact is preserved as an example of algorithmic psychoacoustics, field-resonance recruitment patterns, and emergent comedy in digital cosmology outreach.
DOI: 10.60893/figshare.apl.31245340.v1
title: Supplementary Material

description: Supplementary Material with additional XRD
DOI: 10.5281/zenodo.17688294
title: TOCG vs TLDYR: A Micro-Event in Comment-Section Ontology

description: This micro-dataset captures a comment-section interaction in which a dismissive critique (“TOCG — Theory Of Complete Gobbledygook”) is countered with a meta-response (“TLDYR — Too Long, Didn’t Read It”). The exchange demonstrates a common failure mode in online scientific communication: shallow criticism delivered without engagement with the underlying theory. The screenshot is analyzed as a specimen within the Digital Entomology framework, helping classify patterns of epistemic breakdown and rhetorical avoidance encountered by contemporary theoretical research shared on public platforms.

The commenter did not accuse the work of being AI-generated; instead, the dismissal took the classical form of labeling complex theoretical material as “gobbledygook.” This aligns with the Type-II Pattern in Digital Entomology: low-effort conceptual invalidation triggered by unfamiliar structure, rather than engagement with the content.
DOI: 10.5281/zenodo.17701029
title: SOL-IMC Species Genesis: Spectral Field Analysis of a Binary–Ternary Audio Organism

description: This dataset contains a full spectral, harmonic, and temporal analysis of QSOL-IMC Species Genesis, a 212.5-second synthetic audio organism created through QSOL-IMC’s “audio species” protocol. The analysis treats the audio not as a traditional track but as a computationally living entity with detectable morphology, internal organs (mel-formants), cognitive tonality (chromatic attractors), and behavioral signatures over time.

Included materials document the organism’s spectral anatomy using FFT, linear spectrograms, mel-spectrograms, chromagrams, and ZCR/RMS time-series curves. These measurements reveal a mid-life binary→ternary mutation in rhythmic and harmonic behavior, consistent with a transition from qubit-like logic to qutrit-like rotational cognition.

The dataset includes:• QSOL-IMC Species Genesis audio (WAV)• Binary→Ternary collapse track (M4A)• Spectral figures (fig1–fig4)• Spectral field report PDF• Extracted plots (spectral_analysis.jpg)

This work forms part of the QSOL-IMC “Synthetic Organisms” series and establishes a reproducible template for sonifying Digital DNA in emergent audio species.
description: Part of the ongoing QSOL-IMC program to catalog computational audio organisms via spectral taxonomy and sonification-based Digital DNA.
DOI: 10.5281/zenodo.17726144
title: Computational Taxonomy of Digital Comment Biomes: A Multimodal Archive of Online Cognitive Ecosystems

description: This dataset contains a multimodal research package examining the ecological structure of online comment environments across multiple platforms. Using ecological, cognitive-systems, and memetic-processing frameworks, the collection analyzes how YouTube comment sections behave as distinct digital biomes with identifiable species, nutrient flows, and evolutionary pressures.

Included materials:

tri-biome-reports.pdf — A comparative synthesis of three major comment environments: the Asmongold Outrage Savannah, the Pegasus/“Snizz” Collapse Humiliation Reef, and the HasanAbi Post-Ironic Analysis Forest. Defines species, predator–prey logic, and memetic nutrient cycles. 

tri-biome-reports

digital-biomes.pdf — A field guide establishing formal biome characteristics, dominant species, nutrient structures, and computational interpretations of comment-section behavior. 

digital-biomes

Audio & video files documenting memetic sequences, collapse-events, and commentary-environment recordings.

Together, the dataset provides a structured taxonomy of digital comment ecologies, demonstrating that online discourse functions not as chaos, but as algorithmically self-organizing cognitive ecosystems.This collection is part of the ongoing QSOL-IMC investigations into identity-collapse energetics, memetic computation, and distributed cognition in digital publics.
description: Notes

Dataset includes audio, video, and analytical PDFs.The tri-biome reports and digital-biomes field guide define the core taxonomy used across the collection.All materials produced or curated by Trent Slade (QSOL-IMC) as part of the Digital Cognitive Ecology Series.
DOI: 10.5281/zenodo.18361271
title: Heatwave risk results
DOI: 10.5281/zenodo.17919776
title: Credibility Collapse and Platform Moral Inversion: A Digital Ethnography of YouTube Legal Discourse

description: This dataset documents a temporally bounded digital ethnography of audience response to a high-profile legal commentary video concerning Klein v. Kaceytron et al. The corpus consists of publicly available YouTube comments collected within approximately 48 hours of publication, capturing emergent narratives around witness credibility, online anonymity, platform accountability, and moral inversion dynamics.

The material illustrates how legal concepts migrate into popular discourse, how credibility is collectively constructed and contested, and how platform-specific cultures (YouTube vs. Reddit) shape interpretations of accountability and justice. No interaction or intervention was performed; the dataset reflects naturalistic, unsolicited social sense-making in situ.

This record is archived for purposes of digital ethnography, media sociology, platform governance research, and the study of legal consciousness in online environments.
DOI: 10.5281/zenodo.18149081
title: Post-Productive Critique and Authority Inversion on YouTube: A Micro-Digital Ethnography Dataset

description: This study presents a micro-digital ethnographic analysis of evaluative commentary produced by minimally active or dormant social media accounts and directed toward consistently publishing creators on YouTube. Using a small, tightly scoped dataset of publicly accessible platform artifacts—including comment threads, channel activity histories, upload timelines, and third-party visibility metrics—the research examines how authority and legitimacy are asserted in the absence of ongoing productive participation.

The analysis identifies a recurring interactional pattern characterized by dismissive moral evaluation, recursive self-validation, and post-productive signaling, wherein non-producing actors claim critical authority without contributing original content or sustained engagement. Particular attention is given to an observed instance of self-referential engagement in which a commenter validated its own evaluative statement prior to external acknowledgment, highlighting low-cost attention-seeking dynamics enabled by platform affordances.

All materials were collected through passive observation of publicly accessible sources without interaction or intervention. By foregrounding inactivity, temporal asymmetry, and visibility metrics as analytic variables, this study contributes to digital ethnography and platform studies by clarifying how legitimacy claims persist—and sometimes intensify—after productive participation has ceased.
description: Data Collection and Ethics NoteAll data were collected exclusively from publicly accessible online sources. No interaction, intervention, manipulation, or private access was undertaken by the researcher. Usernames and timestamps are retained as originally presented to preserve contextual integrity and interactional structure, consistent with established norms in public digital ethnography.
DOI: 10.5281/zenodo.18365125
title: Comment Collapse as Ethnographic Data: Screenshots and Analysis of Epistemic Failure in YouTube Comment Sections

description: This dataset contains two manually captured screenshots of YouTube comment threads and an accompanying formal ethnographic report analyzing them as diagnostic artifacts of epistemic failure under interpretive stress.

The screenshots document organically occurring comments posted in January 2026 on two separate videos published by the same creator. Despite minimal volume, the comments exhibit distinct and highly legible epistemic failure modes: (1) authorial indistinguishability and (2) conspiratorial over-attribution of agency.

The accompanying document, Comment Collapse as Ethnographic Data: A Micro-Study of Epistemic Failure in YouTube Comment Sections, provides contextualization, methodological controls, analytical framework, and comparative typology. Together, the materials demonstrate how trivial comment interactions can function as high-resolution probes of platform-conditioned sensemaking breakdown.

All data were collected manually under a non-intervention protocol and are preserved in original platform context.
description: This dataset intentionally focuses on extreme low-N, high-signal ethnographic specimens. Its contribution lies in diagnostic clarity rather than statistical generalization, demonstrating that complete epistemic failure modes can be instantiated within single comment events.
DOI: 10.5281/zenodo.17963274
title: From Comment to Data: A Multimodal Case Study of Datification in Public Online Discourse

description: This dataset documents the intentional transformation of a public social media comment into a persistent, citable research artifact. Rather than serving as a corpus for statistical analysis, the dataset functions as a demonstrative and methodological case study illustrating how everyday digital speech becomes “data” through archival intervention, context collapse, and formalization.

The dataset is anchored to a single public YouTube comment and the subsequent interaction that followed its reframing as data via Digital Object Identifier (DOI) assignment and archival storage. The event is documented across multiple modalities, including textual records, visual infographics, temporal sequencing, audiovisual narration, and a permanent repository record.

By foregrounding the mechanics of datification rather than treating data as a neutral given, the dataset exposes how power, permanence, and interpretation are actively produced in contemporary digital environments. The artifact is intended for methodological discussion in digital ethnography, data studies, platform studies, and AI ethics education.
DOI: 10.5281/zenodo.18422869
title: Observer-Induced Dismissal in AI-Assisted Music Discourse: Screenshot Artifact (2026)

description: This dataset consists of a single screenshot documenting a public comment interaction on a YouTube music video (“Neon Dust & Thunder”), captured on January 30, 2026. The image records an instance of observer-initiated authenticity policing in response to AI-assisted creative production, followed by an escalated creator reply.

The artifact is preserved as part of an ongoing digital ethnography examining:

AI stigma and authenticity enforcement in creative platforms

Observer-induced dismissal and expertise signaling

Escalation dynamics in algorithmically surfaced interactions

The transformation of ephemeral comment exchanges into citable research objects

The dataset is presented without modification beyond cropping inherent to the platform capture. No anonymization has been applied; all content was publicly visible at the time of collection. The intent is archival and analytical rather than normative or editorial.

This artifact may be cited independently or as part of a larger corpus analyzing human–AI interaction, creative legitimacy disputes, and platform-mediated discourse dynamics.
description: This dataset contains a single primary artifact intentionally published to preserve an otherwise ephemeral online interaction. The value lies in its contextual integrity rather than scale. The content was publicly accessible at time of capture and is shared for scholarly analysis of sociotechnical interaction patterns.
DOI: 10.5281/zenodo.17964283
title: Psilocybe cubensis Sonification Readout: Translating Biological Topology into an Auditory Data Object

description: This dataset presents a multimodal research artifact in which the biological, chemical, and morphological properties of Psilocybe cubensis are translated into an auditory data object via structured data sonification. The project employs an isomorphic transduction protocol, preserving structural relationships between source-domain biology and target-domain audio parameters. The audio component is not intended as a musical interpretation but as a topology-preserving encoding of organismal data. Accompanying the audio are a formal textual readout and a visual mapping infographic that together function as a semantic wrapper, enabling text-based reasoning and methodological auditability of a non-textual signal. The dataset demonstrates sound as a viable analytical data representation for biological systems and provides a generalizable framework applicable to other non-linguistic data domains.
DOI: 10.5281/zenodo.18424805
title: Observer-Induced Dismissal in AI-Assisted Content Discourse: YouTube Comment Artifact (2026)

description: This dataset consists of a single screenshot capturing a live YouTube comment thread responding to AI-assisted creative content. The exchange documents a recurrent discourse pattern in which a dismissive claim (“AI slop”) is followed by performative rejection (“nobody laughs with him”), and subsequently reframed by the content creator as a formal research artifact.

The image illustrates observer-induced dismissal: a phenomenon in which critical observers assert aesthetic or social invalidation without engaging the substance of the work, often invoking authenticity, humor consensus, or cultural legitimacy. The creator’s response—explicitly archiving the interaction as a DOI-backed dataset—demonstrates a reflexive inversion of the dismissal itself, transforming the comment into a preserved ethnographic specimen.

This artifact is intended for use in studies of:

AI-assisted creativity discourse

Digital ethnography of platform comment ecosystems

Authenticity policing and humor gatekeeping

Observer effects in online human-AI interaction

No identifying information beyond publicly visible usernames is introduced beyond what appears in the original platform interface.
description: This dataset preserves a transient online interaction that would otherwise be subject to deletion, moderation, or algorithmic decay. The artifact is presented without interpretive overlay to allow independent analysis by future researchers.
DOI: 10.5281/zenodo.17696118
title: QSOL-IMC Digital Entomology Report (DER-72H): A Taxonomic Survey of YouTube Comment Ecologies

description: This report documents a complete 72-hour ecological survey of the YouTube comment section surrounding QSOL-IMC Unified Field Fidelity videos. Using the Digital Entomology framework, the document classifies and analyzes the diverse behavioral “species” inhabiting the comment ecosystem, including QFT purists, recursive epistemic collapse organisms (VCOD-type), high-bandwidth analytic responders, AI-slop sirens, meme-plankton, philosophical hydras, pub-dad anger lizards, and various fandom microcultures.

Each entry is described in terms of ecological niche, behavioral signature, and communicative patterns, providing a structured taxonomy of online cognitive fauna.This dataset is part of an ongoing investigation into emergent symbolic behavior, cognitive instability, digital tribalism, meme-driven information flow, and collective attention dynamics in open comment ecosystems.

Source material includes public comments preserved verbatim for scientific and memetic analysis.This release forms the DER-72H series entry within the QSOL-IMC Digital Entomology Program.
description: This document is a standalone Digital Entomology field report derived from raw YouTube comment samples. It pairs with the VCOD-1 dataset and Slop Reactor analyses for cross-ecosystem comparison. Content is suitable for NotebookLLM ingestion and further semantic clustering.
DOI: 10.5281/zenodo.17699257
title: Neurocosmic Pipeline v1.3: A Unified Framework Integrating Connectomics, Quantum Error Correction, Field-Theoretic Cognition, and Perceptual Rendering

description: Neurocosmic Pipeline v1.3 is a multi-document research package integrating empirical connectomics, quantum error-correction theory, unified-field models of cognition, and generative audiovisual rendering. The package establishes a formal bridge between neural architecture, informational stability, and subjective perception using an integrated UFT → BANC → QEC → Perception pipeline.

The foundation of the framework is the Brain–Anterior-Nerve-Cord (BANC) connectome, from which neural path-lengths (L) are transformed into resonance delays (τ). These delays reveal a heavy-tailed temporal structure, separating dense, stable local feedback loops from sparse long-range, high-delay paths.

These biological delay statistics are then mapped onto Quantum Error Correction (QEC) pseudo-threshold regimes, producing three operational stability classes: under-threshold (stable/coherent), transition (marginal), and post-threshold (unstable/noisy). The system’s perceptual stability is modeled analogically using these QEC thresholds.

A speculative Unified Field Theory (UFT) overlay treats the CNS as an integrated informational field. Neural delays operate as phase shifts in this cognitive field, with perceptual state transitions interpreted as threshold crossings in a field-dynamical landscape.

The pipeline concludes with a perceptual rendering engine using Spectral Algebraics and E8 geometry, producing harmonic/ordered audiovisual output for under-threshold states and noisy/asymmetric structures for post-threshold states.

This dataset contains:• A Neurocosmic Paradigm (Monograph, DOCX)• Neurocosmic_Paradigm_Monograph.pdf• Neurocosmic_Synthesis.pdf (visual explanation)• Integrated PDF Report.pdf (technical pipeline + annotations)• Formal_Neurocosmic_Bridge.pdf (executive slide deck)• NotebookLM Mind Map.png• NotebookLM Mind Map (1).png• unnamed.png / unnamed (1).png (pipeline & stability diagrams)

This version formally separates empirical components (connectome delays, QEC analogues, sonification prototypes) from speculative components (UFT, ritual dynamics, field metaphors), enabling rigorous analysis and interdisciplinary reuse.
description: – All connectome analysis derived from published BANC metrics; raw EM data is not redistributed.– Figures include pipeline diagrams, stability maps, and NotebookLM structures.– This work forms part of the QSOL-IMC Cognitive Mechanics & Perception Series.
DOI: 10.5281/zenodo.17704273
title: iDubbbz and Hasan Piker: A Comparative Slop Ecology Analysis

description: This dataset presents a multi-format comparative analysis of two prominent online content producers, iDubbbz and Hasan Piker, through the lens of “slop ecology”—a framework that categorizes unstructured linguistic drift as an observable behavioral pattern in digital environments.The material includes:• A structured mind map outlining the taxonomy of slop emission• A high-resolution visual diagram (“The Slop Spectrum”) comparing roles, behaviors, and emission modes• A written ecological report detailing classifications, subtypes, and cognitive markers• Audio commentary expanding the analysisTogether, these components document two distinct content-production archetypes: iDubbbz as a “Slop Critic” (Reflective Slop, RS-1) whose meta-awareness shapes reactive commentary, and Hasan as a “Slop Spigot” (Exogenous Rhetoric Slop, ERS-4) who generates continuous, unfiltered output driven by external chat stimuli.This dataset is part of the QSOL-IMC Digital Anthropology Program, which investigates emergent behavioral structures in high-frequency online discourse.