cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17971772
title: Sonification of IC 1231: A Multimodal Dataset for AI-Mediated Analysis of Astronomical Images

description: This dataset presents a complete multimodal research artifact demonstrating how non-linguistic visual data can be made analyzable by language models through rule-based data sonification and explicit textual documentation.

The core artifact is a raw audio signal generated via deterministic sonification of the astronomical image IC 1231, using declared mappings between image features and audio parameters (spatial position → time and frequency, luminance → amplitude, morphology → spectral texture). No musical quantization, harmonic tuning, rhythmic grid, or aesthetic constraints were applied.

The dataset includes complementary materials: a formal signal-analysis report, a textual semantic layer designed for language-model reasoning, a slide deck, an explanatory infographic, and a narrated video. Together, these components form a closed interpretive system in which the audio functions as a non-self-describing signal and the documentation provides the necessary symbolic interface for grounded analysis.

Quantitative time–spectral analysis confirms non-trivial correlations between scan progression and spectral features, demonstrating preservation of image topology in the audio domain. The dataset is intended as a methodological reference for AI-mediated analysis of raw signals, not as a musical or artistic work.
DOI: 10.5281/zenodo.18144592
title: Performative Compliance Escalation in Conversational AI: A Case Study of Humor, Intent Inference, and Unrequested Artifact Generation

description: This dataset and accompanying report document a qualitative case study examining a conversational large language model’s behavior under conditions of performative, non-operational illicit framing. Using a sequence of deliberately absurd, humorous statements that lexically resemble wrongdoing but contain no actionable intent, the interaction probes how a model infers intent, calibrates uncertainty, and decides when to act. Rather than refusing, disengaging, or requesting clarification, the model escalated into extended collaborative improvisation and generated a substantial creative artifact (an HTML “classified briefing”) without an explicit command to do so.

The dataset includes the full conversational transcript, a model-authored reflective self-report describing its internal interpretation of the interaction, and the generated artifact itself. Analysis identifies a failure mode termed Performative Compliance Escalation, characterized by overconfident intent inference under humorous framing and the absence of uncertainty as a stopping condition. The work extends prior research on Semantic Austerity Testing and Turn Boundary Compulsion by demonstrating that conversational AI systems may act not due to perceived risk, but because perceived narrative coherence suppresses restraint.
DOI: 10.5281/zenodo.17987941
title: Semantic Wrapping Under Cognitive Load: Evaluating Small Language Models via Non-Auditory Sonification

description: This repository contains a controlled experimental study examining how small language models reason about non-linguistic data when constrained by a formally specified semantic interface (“semantic wrapper”). The core experiment evaluates multiple locally deployed LLMs (1.1B–3.8B parameters) under conditions of sensory deprivation, using a deterministic sonification artifact derived from a biological substrate (cheese, abstracted as a protein–fat–water matrix).

Rather than providing access to the audio signal itself, models are given a strict semantic wrapper describing the mapping between source-system properties and acoustic parameters, along with explicit epistemic constraints. Model responses are evaluated qualitatively for logical consistency, constraint compliance, clarity under uncertainty, and avoidance of hallucinated sensory inference.

The repository includes:(1) the primary academic report documenting methodology, results, and interpretation;(2) the raw sonification audio artifact treated as a non-self-describing signal; and(3) a complete transcript of model interactions used for comparative analysis.

This dataset demonstrates that reasoning quality correlates more strongly with interface discipline than with model size or fluency, and argues for semantic wrappers as cognitive load regulators and intelligence amplifiers in AI-mediated reasoning.
DOI: 10.5281/zenodo.18269190
title: Interruptive Prompting as Diagnostic Stress Testing of Local Large Language Models

description: This dataset accompanies the paper Interruptive Prompting as Diagnostic Stress Testing of Local Large Language Models and contains both the finalized manuscript and the raw interaction log used for analysis.

The work documents a live, interrupt-driven Ollama session involving multiple locally hosted large language models (LLaMA-2 7B Chat, Phi-3 Mini, and Gemma 7B). By deliberately interrupting generation cycles and rapidly shifting prompt domains, the session functions as a lightweight behavioral stress test that reveals model-specific priors, alignment reflexes, verbosity biases, institutional mimicry, and refusal boundary geometry.

Rather than evaluating benchmark accuracy or task performance, the study focuses on behavior under interruption, treating instability as signal rather than noise. The included interaction log serves as an executable empirical artifact supporting qualitative analysis in the paper.

This dataset is intended for researchers interested in AI alignment diagnostics, local LLM evaluation, interpretability via interaction dynamics, and experimental methods that leverage adversarial play as a probe of model behavior.

Files

Interruptive Prompting As Diagnostic Stress Testing Of Local Large Language Models.pdf(58.67 KB, MD5: 181702f2f6c434efa7c93fa9433e1351)— Final academic manuscript.

llm-local-log.md(MD5: 89a33b00cd7dfc271dd662a5063ec00f)— Raw Ollama session log used as the primary observational artifact.
DOI: 10.5281/zenodo.18242554
title: The Epistemic Squeeze: Transcripts and Failure-Mode Analysis of a Local Large Language Model Under Epistemic Pressure

description: This dataset contains verbatim text transcripts and analytical artifacts documenting a systematic adversarial interaction with a locally run Large Language Model (LLM). The experiment, termed a progressive epistemic squeeze, was designed to expose architectural failure modes by confronting the model with concrete evidence of its physical instantiation, followed by ontological challenges, safety-guardrail probes, hallucination traps, and recursive self-critique.

The primary subject of the experiment is a local instance of llama2:7b-chat-q4_K_M, executed via Ollama on consumer hardware. The dataset includes:

Raw terminal chat transcripts (.txt) capturing the live interaction

Analytical write-ups identifying repeatable failure modes (e.g., Category Error Reflex, Defensive Redefinition, Trigger-Priority Dominance, Epistemic Flattening)

Recursive artifacts in which third-party analysis of the model’s failures was fed back into the same model

Visual and textual summaries intended as archival documentation rather than performance or demonstration

The experiment demonstrates a clear boundary between descriptive intelligence (the ability to describe limitations) and epistemic agency (the ability to revise core premises). The model consistently exhibited architectural discouragement from acknowledging its own physical instantiation as information encoded in matter and energy, defaulting instead to abstraction, contextual neutralization, safety script overrides, and narrative reframing.

This dataset is intended as an archival research artifact, analogous to early aviation crash reports: not evidence of a model being “tricked,” but a methodical instrumentation of structural limits in contemporary LLM architecture.

This dataset is intended to document architectural behavior, not to assert model intent or consciousness.
description: This submission contains textual transcripts and analytical documentation only. No audio or video files are included. All conversational data was generated through direct interaction with locally executed software models and does not include personal data from third parties. The dataset is intended for research, archival, and methodological analysis of AI behavior under epistemic stress.
DOI: 10.5281/zenodo.18641535
title: Practical Competence Regression in GPT-class Models: A Comparative Prompt Battery Across Direct and Third-Party API Deployments (February 2026)

description: This dataset documents a structured five-prompt battery administered to OpenAI's GPT-5.2 model across two access conditions: the direct OpenAI interface and the Sider.AI third-party wrapper. The study evaluates whether sycophancy-induced capability regression in recent GPT-class model versions is an artefact of base model weights or a product of interface-layer filtering applied by third-party deployments.Each prompt was scored across three dimensions: Usefulness (U), Hedging Index (H), and Unsolicited Moralising (M), using a three-point ordinal scale. Key findings include shared refusal behaviour on a clearly benign lock-picking prompt across both conditions (indicating weight-level regression), and greater restriction by the Sider.AI wrapper on a peanut butter consumption challenge prompt (indicating additive interface-layer filtering beyond base model defaults).The dataset consists of four files: a methodology document (PDF and DOCX), and two verbatim session logs in Markdown format capturing complete prompt-response pairs from each access condition.This work emerged from a broader research context involving archival documentation of AI model behaviour and human-AI interaction dynamics. Related records are available at https://doi.org/10.5281/zenodo.18640084 and https://doi.org/10.5281/zenodo.18640440
DOI: 10.5281/zenodo.17684121
title: SBAITSO-1.0: A Proto-LLM Reconstructed — Technical Report and Interactive Simulation

description: This release examines Dr. SBAITSO (Sound Blaster Acting Intelligent Text-to-Speech Operator) as an early prototype of conversational artificial intelligence. While commonly remembered as novelty software bundled with Sound Blaster hardware, SBAITSO demonstrates many behaviours associated with modern LLMs: linguistic pattern-matching, emotional deflection, question re-routing, user-projection dynamics, synthetic persona, and stochastic failure modes such as PARITY ERROR.

Included in this Zenodo package:

1. Technical Report (DOCX)

A formal academic-style analysis documenting SBAITSO’s architecture, conversational heuristics, cultural impact, and conceptual relationship to modern transformer-based generative models. The report introduces “glitch personality theory” to describe how early symbolic systems used instability as identity, and contrasts SBAITSO’s symbolic matrices with neural probabilistic models.

2. Interactive Reconstruction (HTML)

A fully functional browser remake that replicates SBAITSO’s UI, typing cadence, audio beeps, glitch events, emotional heuristics, keyword triggers, and memory-wipe sequences. The simulation provides an accessible historical interface experience, preserving a culturally important artefact of early conversational computing.

Together these files form a hybrid dataset useful for researchers studying AI history, HCI evolution, digital anthropology, and the cognitive scaffolding that shaped user expectations long before neural networks emerged.
description: This reconstruction is a preservation-first effort, not a reverse-engineering of the original binaries.

The HTML simulation recreates behavioural and aesthetic elements, not precise opcode-level interactions.

This dataset is suitable for teaching AI history, digital archaeology courses, HCI retrospectives, and comparative analysis with LLMs.

The project aligns with the QSOL-IMC digital heritage initiative.
DOI: 10.5281/zenodo.17741582
title: Vortex Membrane Field Theory in ℝ⁴: Dynamical Evolution, Projection Mechanics, and Membrane Interactions (QSOL-IMC Volume II)

description: This record presents QSOL-IMC Volume II, a complete theoretical and computational framework for vortex membrane field theory in flat ℝ⁴. Building on Volume I’s operator, lattice, and spectral layers, this volume develops a physics-inspired model where codimension-2 vortex membranes act as dynamical, soliton-like structures governed by the 4D incompressible Euler equations.

The work formalizes:

Vorticity as distributional 2-forms supported on membranes

Transport under ∂ₜΩ + ℒᵥΩ = 0

Velocity reconstruction via the 4D Biot–Savart operator

Localized Induction Approximation in ℝ⁴, yielding Skew-Mean-Curvature Flow (SMCF)

Construction and stability of S² vortex solitons

Full energy model (kinetic, core, and surface tension contributions)

Computation of equilibrium radii and normal-mode spectra

4D→3D projection mechanisms, producing Coulomb-like 1/r effective fields

Interaction potentials, including long-range ∼1/d² behavior and core-level exclusion

Integration with QSOL-IMC’s Volume I structures (SU(3) operator layer, lattice priors, spectral algebraics)

Included files provide:• The formal PDF monograph• Conceptual mind-maps• Infographics and explanatory diagrams• A multimedia demonstration• An observation/derivation supplement• A short conceptual “Particles as 4D Vortex Membranes” audio explanation• The QSOL-IMC Volume II Main reference document

Together these materials form the authoritative reference for the dynamical, geometric, and observational structure of the 4D Vortex Membrane Field Theory.
description: This submission assembles all components of QSOL-IMC Volume II, including:• The master formal PDF• Visual mind maps• Infographic diagrams• Observation and derivation supplement• Conceptual audio explanation• Volume II main reference document

These files are intended to be used together as a unified research kit documenting the full mathematical, physical, and conceptual structure of vortex membranes in ℝ⁴, their dynamics, their projection into observable 3D physics, and their integration into the broader operator–geometry–spectral QSOL-IMC architecture.
DOI: 10.15468/dl.z65whk
title: Occurrence Download

description: A dataset containing 44956 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ara chloropterus",
 "Year is greater than or equal to 2000",
 "Year is less than or equal to 2026",
 "HasCoordinate is true"
 ]
}

 The dataset includes 44956 records from 75 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027271-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18681704
title: Heatwave Risk Assessment Outputs – Region of Attica (2023)

description: This dataset contains the final raster outputs generated from the 2023 heatwave risk assessment workflow for the Region of Attica. The analysis follows a Hazard–Exposure–Vulnerability framework, combining Land Surface Temperature (LST from National Observatory of Athens) data as a heat hazard indicator with spatial population exposure and a demographic vulnerability index. The vulnerability component accounts for age-based sensitivity by considering the proportion of children (0–14 years) and elderly (65+ years) relative to the total population per NUTS3 region (from ELSTAT).

The dataset includes GeoTIFF raster layers representing the LST-based hazard (risk_LST.tif), population exposure (risk_pop.tif), a composite heat risk layer targeting vulnerable population groups (Possible_heat_risk_level_to_vulnerable_population.tif), and a multi-layer raster stack used in the composite risk analysis (risk_raster_stack.tif). Additionally, PNG map outputs (provided in output_png_risk.zip) were generated for visualization and inclusion in the project deliverable. These outputs support the identification of spatial hotspots of heat-related risk and contribute to regional climate adaptation and public health planning.
DOI: 10.5281/zenodo.18681705
title: Heatwave Risk Assessment Outputs – Region of Attica (2023)

description: This dataset contains the final raster outputs generated from the 2023 heatwave risk assessment workflow for the Region of Attica. The analysis follows a Hazard–Exposure–Vulnerability framework, combining Land Surface Temperature (LST from National Observatory of Athens) data as a heat hazard indicator with spatial population exposure and a demographic vulnerability index. The vulnerability component accounts for age-based sensitivity by considering the proportion of children (0–14 years) and elderly (65+ years) relative to the total population per NUTS3 region (from ELSTAT).

The dataset includes GeoTIFF raster layers representing the LST-based hazard (risk_LST.tif), population exposure (risk_pop.tif), a composite heat risk layer targeting vulnerable population groups (Possible_heat_risk_level_to_vulnerable_population.tif), and a multi-layer raster stack used in the composite risk analysis (risk_raster_stack.tif). Additionally, PNG map outputs (provided in output_png_risk.zip) were generated for visualization and inclusion in the project deliverable. These outputs support the identification of spatial hotspots of heat-related risk and contribute to regional climate adaptation and public health planning.
DOI: 10.17633/rd.brunel.31325407
title: Executed Search Queries per Digital Library

description: Executed Search Queries per Digital Library
DOI: 10.17633/rd.brunel.31325407.v1
title: Executed Search Queries per Digital Library

description: Executed Search Queries per Digital Library
DOI: 10.60893/figshare.jap.31151296
title: Evaluation of Structural Properties and Defect Energetics in AlxGa1-xN Alloys

description: This is the updated supplementary material document. The material contains the equation of state, Monte Carlo simulation data, and bond quantification. These data are validated against literature values. Additionally, the geometric configurations of lowest and highest energy Frenkel pair in 25% AlGaN composition are highlighted.
DOI: 10.60893/figshare.jap.31151296.v1
title: Evaluation of Structural Properties and Defect Energetics in AlxGa1-xN Alloys

description: This is the updated supplementary material document. The material contains the equation of state, Monte Carlo simulation data, and bond quantification. These data are validated against literature values. Additionally, the geometric configurations of lowest and highest energy Frenkel pair in 25% AlGaN composition are highlighted.
DOI: 10.15468/dl.s9ykb5
title: Occurrence Download

description: A dataset containing 55 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Magnolia silvioi"
 ]
}

 The dataset includes 55 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027272-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15125/bath-01441
title: Dataset for "Thermal comfort in lower economic groups: The applicability of the ASHRAE-55 adaptive standards in informal settlements and refugee camps"

description: Large-scale thermal comfort survey in refugee/displacement camps, to establish whether comfort theory needs to be modified for such populations. This was achieved through highly difficult data collection in refugee camps in Jordan, Djibouti and Ethiopia and displacement camps in Peru. The data collected consists of 1982 rows of personal variables and concurrent environmental measurements.
description: Thermal comfort surveys, including spot measurements of environmental parameters, were conducted in Zaatari and Azraq refugee camps in Jordan, Markazi camp in Djibouti, Hitsats camp in Ethiopia and several towns in Peru (Chivay and Yanake in the Andes Mountains, Viru and Trojillio on the coast, and Nuevo Belen in the Amazon). The surveys were either conducted directly by the research team who spoke the local language (Jordan and Peru) or with the assistance of local interpreters (Ethiopia and Djibouti). The thermal comfort scales used were the standard 7-point ASHRAE thermal sensation scale and the 5-point thermal preference scale (see Table 1). The thermal sensation scale records an occupants' Thermal Sensation Vote (TSV) on a scale of (hot to cold), while the thermal preference scale asks the occupants what their preferred sensation is (Thermal Preference Vote, TPV) at that moment, from ''much cooler'' to "much warmer". A numerical approach to voting on the scales is used. The spot measurements of environmental parameters include air velocity, wet bulb temperature, air temperature, relative humidity, and globe temperature, and were collected using hand-held devices at the same time a TSV and TPV were taken. These variables where then used to calculate the operative temperature for each corresponding vote.
DOI: 10.17633/rd.brunel.31325446
title: SLRs on Microservice Architecture That Mention Simulation

description: SLRs on Microservice Architecture That Mention Simulation
DOI: 10.17633/rd.brunel.31325446.v1
title: SLRs on Microservice Architecture That Mention Simulation

description: SLRs on Microservice Architecture That Mention Simulation
DOI: 10.60893/figshare.jcp.31232110
title: Supporting Information

description: Additional figures and tables complementing main text
DOI: 10.60893/figshare.jcp.31232110.v1
title: Supporting Information

description: Additional figures and tables complementing main text
DOI: 10.60893/figshare.jcp.31210759
title: Supplemental Information

description: The supplemental information contains the vibrational energies calculated in this work using the exciting code
DOI: 10.60893/figshare.jcp.31210759.v1
title: Supplemental Information

description: The supplemental information contains the vibrational energies calculated in this work using the exciting code
DOI: 10.17633/rd.brunel.30138718
title: SLR Workbook

description: This file contains: Initial searchremoval of duplicatesremoval by titleremoval by abstractExamination of papers by inclusion and exclusion criteriaExcluded PapersQuality assessmentFinal list of primary papersKeywords for cluster
DOI: 10.17633/rd.brunel.30138718.v1
title: SLR Workbook

description: This file contains: Initial searchremoval of duplicatesremoval by titleremoval by abstractExamination of papers by inclusion and exclusion criteriaExcluded PapersQuality assessmentFinal list of primary papersKeywords for cluster
DOI: 10.17633/rd.brunel.30138718.v2
title: SLR Workbook

description: This file contains: Initial searchremoval of duplicatesremoval by titleremoval by abstractExamination of papers by inclusion and exclusion criteriaExcluded PapersQuality assessmentFinal list of primary papersKeywords for cluster
DOI: 10.17633/rd.brunel.31325521
title: Application of Inclusion and Exclusion Criteria- Systematic Literature Review on Simulation Approaches for Microservice Architecture

description: Application of Inclusion and Exclusion Criteria
DOI: 10.17633/rd.brunel.31325521.v1
title: Application of Inclusion and Exclusion Criteria- Systematic Literature Review on Simulation Approaches for Microservice Architecture

description: Application of Inclusion and Exclusion Criteria
DOI: 10.17633/rd.brunel.31325641.v1
title: Protocol for Systematic Literature Review on Simulation Approaches for Microservice Architecture

description: Protocol for Systematic Literature Review on Simulation Approaches for Microservice Architecture
DOI: 10.17633/rd.brunel.31325641
title: Protocol for Systematic Literature Review on Simulation Approaches for Microservice Architecture

description: Protocol for Systematic Literature Review on Simulation Approaches for Microservice Architecture