Mendeley Data Showcase

Supplementary Material 1 Full myxomycete occurrence records. This record-level dataset contains all 492 collection records, including taxon, acquisition method, dates, moist chamber information, substrate type and substrate species, pH, elevation, geographic coordinates, coordinate precision, collector(s), determiner(s), reserve name, forest address, forest district, forest site type, degradation degree, moisture variant and Natura 2000 habitat code. Records distinguish field-survey collections from moist chamber culture records and should be interpreted as collection incidences rather than direct measures of field abundance. Supplementary Material 1.1 Monthly field sampling effort in Gązwa and Dębowo Nature Reserves and meteorological context during the study period. The table summarises the number of field outings per reserve for each sampled month, together with mean monthly air temperature, monthly precipitation totals and mean monthly relative humidity. Supplementary Material 1.2 Substrate-level alpha-diversity metrics and taxon incidence totals. For each substrate code, the file reports taxon richness, Shannon diversity, the Gini–Simpson index and Pielou’s evenness, together with host tree, substrate material, ecological substrate class and, where available, mean soaking-water pH. It also includes total incidence values for each recorded taxon. Supplementary Material 1.3 Summaries of richness and diversity by ecological substrate class, together with the exploratory Wilcoxon comparison between lignicolous and non-lignicolous substrates. The file reports class-level means and standard deviations for diversity metrics and the test output for the richness comparison. Supplementary Material 1.4 Univariate Gaussian linear model relating taxon richness to mean soaking-water pH for tree-linked substrates with pH data. The file includes model estimates and summary statistics used to describe the richness–pH relationship. Supplementary Material 1.5 Non-metric multidimensional scaling ordination scores for substrate codes. The ordination is based on Bray–Curtis dissimilarities calculated from presence–absence data and includes NMDS coordinates together with substrate metadata. Supplementary Material 1.6 Combined records of slime moulds by host tree and substrate type. The file summarises observed taxon–substrate associations across both reserves and both acquisition methods. Supplementary Material 1.7 Mean soaking-water pH values from moist chamber cultures. The file summarises pH measurements for tree-linked substrate combinations used in the culture-based part of the study. Supplementary Material 1.8 Moist chamber cultures established from tree-linked substrates in Gązwa and Dębowo Nature Reserves. The file documents culture identifiers, substrate combinations and cultivation metadata.

SOD3D is a testing dataset built to evaluate the impact of an SOD stage applied to 3D reconstruction. It consists of images of 28 objects of different sizes, shapes and textures, and their processed versions. Starting with 36 original photographs taken for every object from various points of view, a manually segmented ground truth is included for every original image as a baseline for SOD algorithm evaluation. Furthermore, saliency maps generated with four SOD algorithms, one symbolic, based on the brain programming methodology, and three sub-symbolic, based on DL techniques, are included. Additionally, binarized versions of every saliency map are included to evaluate the performance of the SOD algorithms. Finally, we include a set of image overlays masking the original images with the proto-objects. The repository’s root contains a directory for every object. Inside these are a folder for the original images and their processed versions. Inside every object’s folder, there is a total of six folders that contain the original images, manually segmented ground truth, saliency maps generated by every SOD algorithm, the proto-objects that derive from the saliency maps, the overlays generated from masking the original images with the proto-objects and the final reconstructions in the form of point clouds resulting for every object after applying every proposed technique. The Original and GroundTruth folders contain the raw images and their manually segmented versions. Then, the SaliencyMap and ProtoObject folders share the same structure, where an independent directory is reserved for every SOD algorithm. Similarly, Overlay includes a folder for the data derived from every SOD algorithm, with the addition of a folder called GroundTruth, which contains the data derived from processing the original images using the manually segmented ground truth, to serve as a reference. Finally, the Reconstruction folder contains the available 3D reconstructions in the form of poing clouds for every applied technique.

This dataset contains the implementation of Tumor Volume-Aware Normalization (TVAN), a multivariate radiomic normalization framework designed to remove tumor volume–dependent effects from PET radiomic features while preserving biologically meaningful heterogeneity information. The dataset includes both simulated heterogeneous tumor datasets and longitudinal preclinical PET imaging datasets derived from MyC-CAP prostate tumor-bearing mice imaged across four time points (T1–T4). The repository contains: (i) the original radiomic feature datasets, (ii) TVAN-corrected radiomic feature datasets, (iii) feature-wise tumor volume sensitivity coefficients (β), (iv) feature-ranking outputs integrating volume independence and biology preservation metrics, and (v) the Python implementation of the TVAN framework. TVAN operates by estimating feature-specific tumor volume sensitivity coefficients using regression in log-transformed feature space and removing the volume-associated component from each radiomic feature representation. The framework is intended to improve robustness, interpretability, and longitudinal consistency of PET radiomic biomarkers by minimizing size-driven confounding effects.

Quadruple sulfur isotope data, estimation values of fractionation factors for microbial sulfate reduction and initial sulfate isotopic compositions and compiled published data used for figures.

This repository provides the data and model used in the analysis presented in the manuscript listed above. The study develops an environmentally extended multi-regional input-output (MRIO) model to estimate the economic, energy, environmental, and employment impacts—including indirect rebound effects (RE)—of electricity efficiency improvements in Portugal. The analysis is conducted across three regions (Coast Centro, Interior Centro, and Rest of Portugal) and compares efficiency gains occurring at the household (consumption) and firm (production) levels. The model is fully implemented in Microsoft Excel, using interlinked worksheets and standard formulas. No external scripts or macros are required.

The values of forces and Specific cutting pressure for each test condition based on RSM BBD design for Mg-4Zn Si3N4 nanocomposites

This dataset contains the ecological, spatial, and remote sensing data used for species distribution modeling (SDM) of water frog lineages in Anatolia/Turkey. The data includes species occurrence records, environmental predictors, and R scripts used for statistical analysis and model evaluation. The predictive modeling was performed using Maxent to assess habitat suitability. This repository aims to ensure the reproducibility of the methodology and findings presented in the manuscript entitled as "Spatial Distribution and Ecological Drivers of Vertebral Stripe Polymorphism in Anatolian Water Frogs Complex (Genus Pelophylax) in Türkiye."

Supplemental Material for ALM Paper

This dataset contains all materials required to reproduce the experimental results reported in the paper "Enhanced Memetic Algorithm with Adaptive Local Search for Area-Optimized VLSI Standard-Cell Placement" (submitted to IETE Journal of Research, 2026). Contents: 1. ema_source_code.zip — Python implementation of the EMA algorithm, benchmark generator, ablation study scripts, MCNC circuit loader, and OpenROAD integration adapter. 2. benchmarks.zip — 16 JSON benchmark files: 10 synthetic instances (3–49 modules), 3 scalability instances (100–500 modules), and 3 MCNC/GSRC soft-module circuits (apte, xerox, hp). 3. results_tables.xlsx — Raw 30-run experimental results for all benchmark instances (Tables 2–7 of the paper), including mean fitness, bounding-box area, HPWL, 95% confidence intervals, and runtime. 4. README.txt — Full documentation covering algorithm parameters, file formats, usage instructions, reproducibility notes, and citation information. All Python code uses only the standard library (Python 3.9+, no third-party packages required). Fixed random seeds are used throughout; all results are reproducible by running the provided scripts. ISPD 2005/2006 benchmark circuits (adaptain, bigblue1, adaptec2, newblue2) are not included due to contest licensing but are freely available from the ISPD contest archive (https://ispd.cc).

Structural Transition and Quality-Differentiated Intra-Industry Trade in Automotive Parts: Evidence from Korea’s Trade with the U.S., China, and Japan