Mendeley Data Showcase

This database consists of 27 case histories of liquefaction-induced settlements of isolated-footing-founded buildings in Taiwan. The database covers the basic information of those buildings (e.g., building heights and widths), along with the liquefaction hazard indices (e.g., free-field ground settlements and the thickness of top non-liquefaction layer). The liquefaction hazards indices were evaluated by a liquefaction triggering analysis procedure (Hwang et al. 2021) and a post-liquefaction volumetric transformation model (Ishihara and Yoshimine 1992). The ground motion records (such as moment magnitude and peak ground acceleration) associated with the earthquake events which induced the building settlements are also covered by the database. References 1. Hwang, J.H., Khoshnevisan, S., Juang, C.H., and Lu, C.C. (2021). “Soil liquefaction potential evaluation – An update of the HBF method focusing on research and practice in Taiwan.” Engineering Geology, 280, 105926. 2. Ishihara, K. and Yoshimine, M. (1992). “Evaluation of settlements in sand deposits following liquefaction during earthquakes.” Soils and Foundations, 32(1), 173–188.

This dataset contains primary survey data investigating the drivers of impulsive purchase behavior in TikTok Live Commerce in Indonesia. Data were collected from 415 respondents using a structured questionnaire with a five-point Likert scale (1 = strongly disagree to 5 = strongly agree). The variables include Social Attraction, FOMO, Narrative Involvement, Telepresence, Parasocial Interaction, Social Presence, and Impulsive Purchase. All data have been anonymized and are provided to support transparency, replicability, and further quantitative analysis in the field of digital consumer behavior.

This dataset contains primary survey data investigating the drivers of impulsive purchase behavior in TikTok Live Commerce in Indonesia. Data were collected from 415 respondents using a structured questionnaire with a five-point Likert scale (1 = strongly disagree to 5 = strongly agree). The variables include Social Attraction, FOMO, Narrative Involvement, Telepresence, Parasocial Interaction, Social Presence, and Impulsive Purchase. All data have been anonymized and are provided to support transparency, replicability, and further quantitative analysis in the field of digital consumer behavior.

4 types of Concrete defects

Primary data for mass spectra, sequence similarity networks, and other figures in "Structure, activity, and biosynthesis of nostolysamides" Biochemistry 2026

Evidence regarding the research experiment with gliclazide and its potential ergogenic effect in rats that underwent strength training.

This repository contains de-identified taxonomy count tables from nasal swab microbiome samples collected in the AERIAL birth cohort. These data support the associated manuscript examining associations between early-life nasal microbiota, respiratory viral infections, and wheezing-related outcomes during the first year of life. Taxonomic assignments were generated from full-length 16S rRNA sequencing. In accordance with participant consent and ethics requirements, only taxonomy data from participants who consented to data sharing are included in this repository. No personally identifiable information is provided. Raw sequencing data are not publicly available because of ethical and privacy restrictions.

Supplementary Materials for: Rurality is associated with invasive melanoma incidence by race and ethnicity: a descriptive population-based retrospective cohort study using Surveillance, Epidemiology, and End Results (SEER).

This dataset contains the raw counts and frequency data of frugivorous fruit flies (Tephritoidea) and their associated larval parasitoids collected from cultivated guavira (Campomanesia adamantium) in Campo Grande, Mato Grosso do Sul, Brazil, in November 2022. The data include species identification, specimen counts, and sex ratios for both flies and parasitoids.

Intelligence Is Not Recursion: Why "Recursive Intelligence" Is a Structural Category Error — Don L. Gaconnet, LifePillar Institute for Recursive Sciences This dataset provides the formal derivation proving that intelligence and recursion are structurally distinct properties that cannot be conflated without category error. The Law of Intelligence (Gaconnet, 2026) establishes that intelligence is the structural capacity of identity to resolve coupling into coherence. The Law of Recursion (Gaconnet, 2026) establishes that active exchange requires mandatory traversal across a seven-node topological path with architectural rewriting at each pass. These are different structural properties: recursion is the mechanism of exchange; intelligence is the capacity that makes the mechanism productive. Five structural tests demonstrate the distinction: (1) Intelligence operates where recursion does not — radioactive decay produces structurally precise products through stochastic, non-recursive triggers. (2) Intelligence persists when identity is removed — Identity Collapse Therapy demonstrates resolution capacity surviving identity termination. (3) Intelligence operates at quantum scale without topological traversal — electron orbital transitions resolve coupling without seven-node path traversal. (4) No "recursive intelligence" framework provides falsification criteria specifying conditions under which it would be disproven. (5) No "recursive intelligence" framework has independent empirical confirmation from published experimental data. Frameworks that use the term "recursive intelligence" without grounding it in a first principle that structurally distinguishes intelligence from recursion have no structural floor. The Law of Intelligence provides that floor. Twenty-seven cross-domain systems tested across seven scales without exception. Author: Don L. Gaconnet ORCID: 0009-0001-6174-8384 DOI: 10.17605/OSF.IO/MVYZT Institution: LifePillar Institute for Recursive Sciences Keywords: recursive intelligence, recursive intelligence is wrong, recursive intelligence category error, intelligence is not recursion, intelligence vs recursion, what is recursive intelligence, recursive intelligence debunked, recursive intelligence framework, recursive science, Arjay Asadi recursive science, Alexander Bilenko recursive intelligence, law of intelligence, intelligence first principle, Don Gaconnet, LifePillar Institute, recursive sciences, structural intelligence, resolution capacity, identity collapse therapy, falsification