Mendeley Data Showcase

The Universal Machinery Fault Diagnosis Dataset (UMFDD) is a multi signal repository created for machinery fault diagnosis. This dataset integrates multiple publicly available and experimentally collected datasets into standardized units allowing for cross domain learning and evaluation. All signals are converted into a physically consistent representation through unit normalization. Vibration signals originally measured in acceleration m/s^2 are transformed into velocity m/s while acoustic signals are converted from voltage to sound pressure and subsequently to particle velocity. This allows for comparability across different datasets. The dataset is organized into four primary fault categories 0_healthy 1_bearing_faults 2_gearbox_faults 3_induction_motor_faults Each data file is stored in CSV format with a consistent structure. Column 1 represents Source 1 and Column 2 represents Source 2. Metadata includes dataset origin, sampling frequency, and operating conditions. Signals are structured to support consistent multi source analysis across different machinery types. The dataset allows for the evaluation of machine learning models under varying operating conditions, sensor configurations, and fault types within a normalized dataset. The datasets used to create this dataset include Case Western Reserve University (CWRU) [1], University of Ottawa Electric Motor Dataset Vibration and Acoustic Faults under Constant and Variable Speed Conditions (UOEMD) [2], University of Ottawa Rolling Element Bearing Dataset (UORED) [3], Huazhong University of Science and Technology Bearing Dataset (HUST) [4], Induction Motor Vibration and Acoustic Cellular Device Dataset (IM-VACD) [5], Multi mode fault diagnosis datasets of gearbox under variable working conditions [6]. References [1] Case Western Reserve University Bearing Data Center. Bearing Data Center Dataset. Case Western Reserve University. Link: https://engineering.case.edu/bearingdatacenter/download-data-file [2] University of Ottawa Electric Motor Dataset Vibration and Acoustic Faults under Constant and Variable Speed Conditions. Mendeley Data. Link: https://data.mendeley.com/datasets/msxs4vj48g/2 [3] University of Ottawa Rolling Element Bearing Dataset Vibration and Acoustic Fault Classification. Mendeley Data. Link: https://data.mendeley.com/datasets/y2px5tg92h/5 [4] Huazhong University of Science and Technology Bearing Dataset. Mendeley Data. Link: https://data.mendeley.com/datasets/cbv7jyx4p9/3 [5] Induction Motor Vibration and Acoustic Cellular Device Dataset. Mendeley Data. Link: https://data.mendeley.com/datasets/yc8yhg5xjd/1 [6] Multi mode fault diagnosis datasets of gearbox under variable working conditions. Mendeley Data. Link: https://data.mendeley.com/datasets/p92gj2732w/2

This dataset reflects work supported by an American Heart Association award to Blanco-Suarez, E., and will be included in a forthcoming publication.

Replication file for "Financial Literacy and Financial Crime: A Regression Discontinuity Approach"

This dataset underpins the analysis presented in the article submitted to Irish Political Studies. It includes the two-mode dataset used for network analysis, the corpus of parliamentary documents analysed for discourse analysis, and an additional dataset containing attributes of the actors referenced in the debates. All data are derived from publicly available Oireachtas parliamentary records.

This data is compiled from existing datasets with focus on concrete composition and the corresponding compressive strength and RCPT values at 28 days

Processed plasma lipidomic data matrices from the BECAME-HF study supporting the manuscript “Lipidomics Identifies HFpEF Phenogroups and a High-Risk Metabolic Signature.” Files include log2-transformed, normalized, and batch-corrected lipid intensities, lipid annotations, and cluster assignments for the Belgian and Canadian cohorts. These de-identified processed data support the lipidomic phenogroup discovery, cross-cohort comparison, and minimal lipid signature analyses presented in the study.

Supplementary Materials

Carbon dioxide is an abundant and renewable C1 building block for the synthesis of value-added chemicals. Herein, we report the catalytic carboxylative cyclization of propargylamines with CO₂ using palladium(II) ferrocenyl chalcogenide complexes. Selenium-containing complexes display superior catalytic activity compared to their tellurium analogues, affording 2-oxazolidinones in good to excellent yields under relatively mild conditions. The catalytic system tolerates a variety of propargylamine substrates and operates efficiently under low CO₂ pressure. Mechanistic studies suggest that the transformation proceeds via alkyne coordination to palladium, followed by CO₂ insertion and intramolecular 5-exo-dig cyclization, which was confirmed by NMR and mass spectral studies. These results highlight the potential of ferrocenyl palladium chalcogenide complexes as efficient catalysts for CO₂ fixation and the synthesis of valuable heterocyclic compounds.

Data corresponding to a study about the effect of rearing population size of mass-reared populations on the thermal performance of the aphid parasitoid Aphidius uzbekistanicus

This is data and code related to my paper: Ecological Suboptimality in Naturalistic Foraging: Amplified Deviation from Optimality in a Mouse Model of Alzheimer Disease