Mendeley Data Showcase

This repository contains a fully de-identified public-use analytic dataset derived from a cross-sectional secondary analysis of routinely collected police records from Chile. The public dataset does not include raw police statements, transcripts, narrative text, audio, direct identifiers, or any other source material from individual case files. Instead, it contains only numeric variables derived from prior text processing with LIWC-22 (v1.3.0), including 51 psycholinguistic indicators across seven dimensions and total word count, together with non-identifying analytic variables required for reproducibility. The underlying study examined 250 cases from 2017–2021, grouped by victim age category (<14 years vs ≥14 years), and evaluated between-group differences in LIWC-derived linguistic markers. LIWC-22 was applied to the original statements before public release, and the repository therefore provides only derived quantitative metrics, not language samples or recoverable verbal content. The shared files are intended exclusively to support transparency, reproducibility, and secondary methodological work in forensic psycholinguistics. Because the repository contains only de-identified, derived numeric outputs, it does not permit reconstruction of the original statements or re-identification of individual participants. These materials should not be interpreted as evidence of guilt, credibility, or investigative priority, but as reproducible research data documenting group-level linguistic associations within a police-investigation sample..

This study examines the concept of place through the lens of ageism by employing neuroarchitectural approaches to investigate visual complexity. Therefore, it develops a detailed understanding of how older adults perceive visual complexity in urban environments using an instrument called CLARITY. The study utilizes a mixed-methods strategy, analysing the visual complexity of the selected urban environments using CLARITY and conducting semi-structured interviews together with a rating scale. For this purpose, older adults evaluated the visual complexity of selected urban environments and provided information on their overall well-being.

## Description This dataset presents a novel multi-dialect Arabic scam and legitimate telephone call conversation corpus designed for training and evaluating scam detection models. The dataset addresses a critical gap in Arabic-language fraud detection research, where no publicly available scam call datasets currently exist. The dataset contains 448 annotated conversations covering nine Arabic dialects: Modern Standard Arabic (MSA), Egyptian, Gulf, Jordanian, Saudi, Yemeni, Sudanese, Iraqi, and Syrian. Each conversation simulates a realistic telephone interaction structured as a multi-turn dialogue between a caller and a receiver over five utterance turns (three caller turns and two receiver turns). ## Data Structure The Excel file contains 18 columns per conversation: | Column | Type | Description | |--------|------|-------------| | conversation_id | String | Unique identifier (CONV_0001 to CONV_0448) | | full_conversation | String | Complete conversation text with speaker labels | | caller_turn_1 | String | First caller utterance | | receiver_turn_1 | String | First receiver response | | caller_turn_2 | String | Second caller utterance | | receiver_turn_2 | String | Second receiver response | | caller_turn_3 | String | Third caller utterance | | label | String | Binary class label: scam or not_scam | | category | String | Fine-grained category (23 categories) | | dialect | String | Arabic dialect (9 dialects) | | urgency_score | Integer | Time pressure intensity (0–5) | | sensitive_info_requests | Integer | Confidential data solicitation (0–2) | | financial_pressure_score | Integer | Monetary demands intensity (0–5) | | threat_score | Integer | Threat/intimidation level (0–3) | | impersonation_score | Integer | Identity deception level (0–2) | | conversation_length | Integer | Total characters in conversation | | word_count | Integer | Total words in conversation | | label_binary | Integer | Binary encoding: 1 = scam, 0 = not_scam | | File | Description | |------|-------------| | arabic_scam_dataset_complete.xlsx | Complete text dataset with 448 conversations, labels, categories, dialects, risk scores, and metadata (18 columns) | | audio_dataset/scam/*.wav | Synthesized audio files for scam conversations (16 kHz, mono, WAV) | | audio_dataset/not_scam/*.wav | Synthesized audio files for legitimate conversations (16 kHz, mono, WAV) | --- ## License CC BY 4.0 (Creative Commons Attribution 4.0 International)

This data is on DAO governance proposals from Snapshot's GraphQL API. The unit of observation is an individual governance proposal, for proposals submitted between July 2022 and December 2024

Background: Indolent systemic mastocytosis (ISM), a clonal mast cell disease driven by the KIT D816V mutation, can often cause debilitating dermatologic symptoms. Objective: Assess improvement of ISM-related skin manifestations after treatment with avapritinib, a highly selective KIT D816V inhibitor, vs placebo in Part 2 of the PIONEER study (NCT03731260). Methods: Patients with moderate-to-severe ISM received avapritinib 25 mg once daily (n=141) or placebo (n=71). Endpoints included skin lesion area and pigmentation at week 24, skin mast cell burden, and change in symptoms. Results: Mean percent reduction in lesional surface area was −36.6% with avapritinib vs −1.8% with placebo in the most affected area; 86% vs 0% had improved skin lesion color. Mean percent change in skin mast cell burden decreased with avapritinib (−22.1%) vs placebo (10.1%). Avapritinib vs placebo significantly improved skin symptom domain score (mean change −7.2 vs −2.8; p<.0001), including the individual skin symptoms itching, flushing, and spots. Avapritinib was well tolerated. Limitations: Photography was optional, so analysis population for lesion area and color were smaller (n=111) than the overall study population (n=212). Conclusion: Avapritinib treatment improved dermatologic symptoms, decreased skin lesion size, normalized skin lesion color, and reduced skin mast cell burden in patients with ISM. These data are the supplemental material for the primary publication of PIONEER Skin Assessments in JAAD.

G protein-coupled receptors (GPCRs) mediate a variety of signaling pathways and are among the most common pharmacological targets. While advances in structural biochemistry have provided deep functional insights into dozens of key receptors, many of the 800+ human GPCRs remain understudied. In the following, we introduce a versatile “deep receptor scanning” platform that can be used to experimentally characterize 767 human GPCRs and 174 known GPCR splice variants in parallel. We quantitatively characterize the relative abundance of receptor transcripts, their translational efficiency, and the plasma membrane expression of each receptor in the context of a recombinant pool of HEK293T cells expressing individual GPCRs. We then employ machine learning to identify specific structural features that modulate GPCR expression. This experimental platform and informatic approach are compatible with a variety of assays and can be used to efficiently explore the biochemical and pharmacological properties of the GPCRome.

This replication package provides all the publicly available data, code, andinstructions required to reproduce the main empirical results of the above-mentionedresearch paper. The package is designed to facilitate transparency and enableindependent verification of all statistical findings reported in the study.

This repository contains the processed simulation data, selected trajectories, analysis outputs, and figure-generation scripts associated with the manuscript: Glassy interphases reinforce elastomeric nanocomposites by enhancing percolation-driven volume expansion under strain In filled elastomers, nanoparticle additives can dramatically increase stiffness and toughness, yet the molecular origins of this reinforcement have remained debated for decades. A prominent idea is that strong polymer–particle attractions create “glassy bridges” that directly cement particles into a load-bearing network. The data and scripts in this repository accompany a molecular simulation study showing a different picture: glassy interphases do not primarily reinforce by directly supplying elongational cohesion. Instead, they amplify a more fundamental mechanism in which competition between particulate and elastomeric networks increases volume expansion under strain, thereby activating large bulk-modulus contributions to the response. The repository is organized around two main directories. The `data/` directory contains processed simulation outputs, selected input files, and helper scripts for reproducing simulations and post-processing analyses. The `figures/` directory contains figure-specific plotting scripts and source files for all main-text and supporting-information figures. Simulation datasets are organized by filler structure, filler loading, and filler–polymer attraction strength. For full repository documentation, directory structure, software prerequisites, and detailed workflow instructions, see the top-level `README.md`. Associated manuscript: https://arxiv.org/abs/2509.04755

Data for article: Acidogenic fermentation of tequila vinasse: Impact of operational pH on metabolic profile and microbial communities