Zaghian, Soheil · Mohammadzadeh, Ali · Ahmadi, S. Ali · et al.

S12-Coasts is a global, multimodal, patch-based geospatial dataset designed for coastline and water-body segmentation tasks. The dataset consists of 6,637 georeferenced image patches , each with a spatial size of 512 × 512 pixels , paired with automatically generated binary water masks . Data modalities Sentinel-2 (optical): Multi-band optical imagery providing spectral information suitable for discriminating water, land, and coastal features under cloud-free or low-cloud conditions. Sentinel-1 (SAR): Synthetic Aperture Radar imagery enabling robust water detection under all-weather and day/night conditions and complementing optical observations in cloudy or turbid environments. Each patch contains co-registered Sentinel-1 and Sentinel-2 observations covering the same spatial extent, enabling multimodal learning and data fusion approaches. Reference labels Reference water masks were generated automatically using the Ensemble Object-Based Clustering (EOBC) framework. EOBC integrates multiple independent shoreline and water-body vector datasets, including: NOAA shoreline datasets (CUSP), OpenStreetMap (OSM) water and coastline features, HydroLAKES lake polygons. These vectors are fused with satellite imagery through object-based clustering and consensus rules to derive binary water / non-water labels. This fully automated approach removes the need for manual annotation while ensuring spatial consistency across regions.

Khedr, Walid I.

This artifact contains the finalized synthetic benchmark corpus, derived decision windows, validation manifests, result artifacts, curated replay code, Colab/GPU result archives, Raspberry Pi 5 profiling outputs, human quality-audit materials, deployment-boundary diagnostics, and manuscript source/PDF for the accompanying paper on edge-oriented behavioral authentication in multi-agent LLM systems. Version 1.0.4 refreshes the reproducibility package with venue-neutral paper, code, experiment, and artifact paths. The reviewer-facing archive now stages edge-efficiency materials under artifacts/edge_efficiency/ , scripts under code/edge_efficiency/ , and RP5 power protocols under experiments/rp5_power/ . It also includes paper-local artifact mirrors so the manuscript can be rebuilt from the extracted package. The package is designed for reproducible evaluation replay rather than bit-identical regeneration of the synthetic conversations. The archived corpus is the citable benchmark object. Scenario planning and turn generation used GPT-4.1, whose outputs may change over time; generation scripts are included as provenance and extension tools.

CORUH, Uğur

Benchmark pack accompanying the journal submission 'A template-preserving AST-based LaTeX-to-DOCX compiler with native Office Math ML equation output' (Coruh, 2026). Every numerical figure in the manuscript is either reproducible from the deposit (single-tool corpus) or auditable from the canonical CSV / JSON snapshots it carries (four-pipeline matrix and the structural-presence regression check). Contents. A single 188 MB zip with 2907 entries: the production binary (latex2docx 1.0.4 Windows installer), the 40-class publisher template matrix, the 39 feature-coverage mockups, the 203 graded body documents, the per-stem pdflatex reference-PDF cache (~45 MB), benchmark runner scripts, metric modules and the canonical CSV / JSON snapshots from the run reported in the manuscript. A step-by-step reproduction guide ( BENCHMARK_GUIDE.md ) is included at the root of the unpacked archive. Reproducibility scope. The single-tool corpus (R_surv, M_edit, P_fix, T_build, V_sim) reproduces end-to-end from the bundled binary against the bundled corpus. The four-pipeline benchmark matrix additionally requires Pandoc 3.5, make4ht and plastex on the local PATH; the corresponding CSV / JSON snapshots are bundled so reviewers can audit every figure without re-running the matrix. Access. This record is published under Restricted access . The DOI and landing page are publicly resolvable; the files require a Zenodo secret access link issued by the corresponding author. During peer review the link is supplied to the journal's editorial office under separate cover for distribution to assigned reviewers; after publication, the corresponding author continues to issue secret links to qualified researchers on reasonable e-mail request ( ugur.coruh@erdogan.edu.tr ). See LICENSE-NOTES.md for the full access conditions. Licensing (subject to access conditions above). Depositor-authored documentation, scripts and measurement CSV / JSON snapshots: CC-BY-4.0 with attribution. Forty publisher LaTeX classes: upstream LPPL . Bundled bin/*.exe binaries: proprietary, commercially licensed under a 30-day evaluation licence anchored to the binary's build date (commercial licence and current evaluation builds via https://coruhtech.github.io/latex2docx-toolkit/ ). Re-distribution of the deposit, or of any component in isolation, requires the corresponding author's written authorisation. Authoritative per-component disclosure is in LICENSE-NOTES.md . Integrity. SHA-256 manifest in SHA256SUMS.txt at the deposit root. The zip itself hashes to 33651f0071c6ee1866e9ae10de317ce2349c228e7ff8e11df6218d965e8804a3 .

lin, chen · Bin, Yang · Haoran, Shi · et al.

This dataset contains quality-controlled SNP and InDel genotype calls from whole-genome resequencing of 299 purebred Large White pigs. The dataset includes 100 individuals sequenced at 30× depth (merged genome-wide VCFs) and 199 individuals sequenced at 10× depth (chromosome-split VCFs). All variants were called against the *Sus scrofa* Sscrofa11.1 reference genome and filtered using standard GATK hard-filtering criteria: QD < 2.0, QUAL < 30.0, FS > 200.0, ReadPosRankSum < -20.0, call rate < 90%, MAF < 0.05. This data supports the findings of the study "Whole-genome resequencing identifies RASAL2 as a candidate gene for feed efficiency in Large White pigs".

Gómez Pedraza, Mauricio Alexander · Meneses-Báez, Alba Lucía · Serrano García, Maria Fernanda · et al.

Esta base de datos corresponde al manuscrito ' Validación de la Escala Multidimensional de Perfeccionismo Infantil (EMPI) en estudiantes colombianos y mexicanos' y recopila datos de 433 estudiantes de educación básica de ambos países.

Aniceto, Fabricio David Simplicio

This dataset provides multi-temporal UAV-derived Digital Elevation Models (DEMs) and orthomosaics of an erosion feature located at the entrance of the IFPE Campus Cabo de Santo Agostinho, Pernambuco, Brazil. The dataset is based on aerial imagery acquired with a DJI Air 2S UAV during four survey campaigns carried out in September 2023, July 2024, August 2025, and February 2026. The images were processed in Agisoft Metashape Professional Edition following standard photogrammetric procedures to produce georeferenced elevation models and orthomosaics for erosion monitoring and terrain analysis.

SERKAN, CANTÜRK

eplication package containing the panel dataset (18 emerging markets, 2003-2024), R replication scripts, output tables and figures, online appendix, and full documentation. Companion to the manuscript submitted to Environmental Economics and Policy Studies.

Di Lorenzo, Giovanni · Severini, Francesco · Toma, Luciano · et al.

This record contains the raw high-resolution source videos acquired with Insta360 Ace Pro 2 cameras as part of the MOSMON-Larvae dataset. MOSMON-Larvae is a multispecies mosquito-larvae dataset developed to support computer-vision research on larval detection, tracking, motion analysis, and automated vector surveillance. The videos in this record were collected under controlled indoor laboratory conditions while preserving naturalistic aquatic variability, including changes in illumination, water turbidity, reflections, larval density, container configuration, and camera viewpoint. The recordings include mosquito larvae from medically relevant species represented in the MOSMON-Larvae acquisition campaign, including Aedes albopictus , Aedes aegypti , Culex pipiens , and Anopheles stephensi , depending on the individual recording. Larvae were primarily recorded at developmental stages 3-4. Species identity and acquisition conditions are encoded in the original filenames. The files provided in this record are raw video recordings only. They have not been cropped, resized, annotated, re-encoded, or otherwise preprocessed. This release is intended to provide reproducible access to the original video material and to support downstream tasks such as frame extraction, temporal detection, larval tracking, behavior analysis, video-based benchmarking, and extension of the MOSMON-Larvae dataset. This record contains 22 MP4 video files uploaded with shortened filenames: video_001.mp4 to video_022.mp4 filename_mapping.tsv: mapping between the shortened Zenodo filenames and the original acquisition filenames checksums.sha256: SHA256 checksums for file-integrity verification, if provided video_recap_report & video_recap_table: brief video recap with original acquisition filenames The original filenames contain structured acquisition metadata, including recording date, project identifier, species, larval stage, camera model, recording resolution and frame rate, lens or distortion configuration, lighting condition, camera position, container type, and water depth. Users should consult filename_mapping.tsv to recover the original filename and associated metadata for each video. Videos were acquired using commercially available Insta360 Ace Pro 2 action-camera hardware in a controlled indoor laboratory environment. During acquisition, the camera optics were positioned underwater to record mosquito larvae directly within aquatic containers. Recording conditions varied across sessions to capture realistic visual variability, including illumination changes, turbidity, reflections, larval density, container geometry, and camera placement.The recordings are provided as MP4 files encoded with HEVC/H.265 compression and contain no audio streams. Resolution and frame rate vary across videos and are indicated in the original filenames when available. Recommended use - These raw videos are suitable for: mosquito-larvae detection in video sequences; larval tracking and motion-analysis research; temporal object-detection benchmarking; frame extraction for additional annotation campaigns; domain-adaptation studies between still-image and video-based larval surveillance; reproducibility checks of the MOSMON-Larvae acquisition workflow. For machine-learning experiments, users should account for temporal correlation between adjacent frames. When extracting frames for model training and evaluation, video-disjoint splits are recommended to reduce leakage between temporally related samples. File integrity: If checksums.sha256 is included, downloaded files can be verified with: sha256sum -c checksums.sha256 Related dataset - This record is associated with the broader project of the MOSMON-Larvae dataset, which includes high-resolution annotated images, COCO and YOLO bounding-box annotations, extracted frames, metadata, and processing scripts. This Zenodo record specifically provides the raw Insta360 Ace Pro 2 video subset. Citation - If you use these videos, please cite this Zenodo record and the associated MOSMON-Larvae dataset or paper when available.

Reza Asadi

Melt pool images with related annotations in LW-DED processes with Stainless Steel (SS) and low carbon steel (S355) substrates and Inconel 625 wire material are provided for instance segmentation methods. The annotation of all images in the dataset was carried out utilizing the "labelme" library, which is a Python package. This library offers an image annotation tool and various functionalities for reading, writing, and converting the resulting annotations in JSON format. As the complementary data, 3D scanned data as well as microscopy images of the cross-sections of the deposited beads on the mentioned substrates and wire feedstock are provided in the dataset.

Stabla, Paweł

The dataset contains data collected during a project financed by National Centre Science (Poland) - UFracture analysis of filament wound composite structures regarding the influence of mosaic pattern (Miniatura 8, Decision No. DEC-2024/08/X/ST8/01480) The datasat consists of the xlsx and csv files gathered during the experimental campaign.

D'Alelio, Domenico · Bellardini, Daniele · Russo, Luca · et al.

Grant Agreement: 101082021 Project Acronym: MARCO-BOLO Project Title: MARine COastal BiOdiversity Long-term Observations Deliverable Number: D2.2 Work Package Number: WP2 Deliverable Title: Datasets, databases and softwares/pipelines facilitating the implementation of eDNA-based monitoring Analysis of the V9 region of 18S rDNA in the environmental DNA from seawater through amplicon sequencing. Samples were collected monthly between September 2021 and December 2023, across different sampling sites within the context of the NEREA observatory in the Gulf of Naples (Italy). Seawater was collected at discrete depths, filtered through 0.45 micron filters, and the environmental DNA was extracted from and amplified by PCR. Plankton metabarcoding raw reads were registered in the European Nucleotide Archive (ENA), under the umbrella project permanent identifier PRJEB74649 ( https://identifiers.org/ena.embl:PRJEB74649 ). The raw eukaryotic metabarcoding sequencing data files - V9-18S (FastQ format) are available on BioProject ID PRJEB98195.

Casotti, Raffaella · Eliso, Maria Concetta · Tramontin, Eugenia · et al.

Grant Agreement: 101082021 Project Acronym: MARCO-BOLO Project Title: MARine COastal BiOdiversity Long-term Observations Deliverable Number: D2.2 Work Package Number: WP2 Deliverable Title: Datasets, databases and softwares/pipelines facilitating the implementation of eDNA-based monitoring Analysis of the V4-V5 regions of 16S rDNA in the environmental DNA from seawater through amplicon sequencing. Samples were collected monthly between September 2021 and December 2023, across different sampling sites within the context of the NEREA observatory in the Gulf of Naples (Italy). Seawater was collected at discrete depths, filtered through 0.45 micron filters, and the environmental DNA was extracted and amplified by PCR. Plankton metabarcoding raw reads were registered in the European Nucleotide Archive (ENA), under the umbrella project permanent identifier PRJEB74649 ( https://identifiers.org/ena.embl:PRJEB74649 ). The raw prokaryotic metabarcoding sequencing data files - 16S (FastQ format) are available on BioProject ID PRJEB100988

Eliso, Maria Concetta · Di Tuccio, Viviana · Annona, Giovanni · et al.

Grant Agreement: 101082021 Project Acronym: MARCO-BOLO Project Title: MARine COastal BiOdiversity Long-term Observations Deliverable Number: D2.2 Work Package Number: WP2 Deliverable Title: Datasets, databases and softwares/pipelines facilitating the implementation of eDNA-based monitoring This dataset provides the metadata and results obtained from droplet digital PCR (ddPCR) assays used to detect and quantify Engraulis encrasicolus eDNA, following the procedures described in the associated protocol ( https://doi.org/10.5281/zenodo.17670154 ). The file "Data and Metadata_ddPCR_MBO_Stazione Zoologica A.Dohrn.csv" contains quantitative measurements of E. encrasicolus mitochondrial DNA in seawater samples analyzed via ddPCR. Samples were collected monthly between January 2020 and August 2021 at LTER-MareChiara sites within the NEREA Observatory in the Gulf of Naples (Italy).

De Volder, Carolina · Kippes, Romina

Este conjunto de datos contiene las respuestas anonimizadas de una encuesta realizada en julio de 2025 a equipos editoriales de revistas científicas argentinas y chilenas incluidas en el Catálogo 2.0 de Latindex. La encuesta fue desarrollada en el marco de una investigación orientada a analizar la incorporación de recursos multimediales en los procesos de edición, publicación y post publicación de revistas científicas, así como las estrategias de circulación digital empleadas por los equipos editoriales.

KOÇ, Halil · UNAL, Yavuz

Aegeaeobuthus gibbosus Image Dataset for Few-Shot Sex Classification This dataset contains 198 high-resolution images of 99 individual scorpions of the species Aegeaeobuthus gibbosus (Brullé, 1832), collected from multiple locations in Turkey. Each individual is photographed from two anatomical views (dorsal and ventral) under controlled laboratory conditions. The dataset is designed to support few-shot learning research on sex classification in venomous arthropods, where specimen collection is constrained by safety, ecological, and logistical factors. Dataset composition: Female: 50 individuals × 2 views = 100 images Male: 49 individuals × 2 views = 98 images Total: 99 individuals, 198 images Directory structure: female-dorsal/ - 50 dorsal-view images of female specimens female-ventral/ - 50 ventral-view images of female specimens male-dorsal/ - 49 dorsal-view images of male specimens male-ventral/ - 49 ventral-view images of male specimens female.txt and male.txt - dorsal-ventral pairing files (one individual per line, in the format DORSAL_ID-VENTRAL_ID ) README.md - full documentation Important - individual-level splits: Because dorsal and ventral images of the same specimen are highly correlated, train/test splits must be performed at the individual level (not the image level) to avoid data leakage. The pairing files are provided to support correct individual-level splitting. Image acquisition: Laboratory setting, controlled lighting, white background, JPEG format. Ethical note: Aegeaeobuthus gibbosus is not a protected species under Turkish wildlife regulations; no special collection permit was required. This dataset accompanies the manuscript "Few-Shot Learning for Sex Classification in Venomous Scorpions: A Framework for Data-Scarce Biological Research" (under review). The associated code repository will be made available upon publication.

Bacciaglia, Antonio · Ceruti, Alessandro · Liverani, Alfredo

This repository contains the dataset and supporting files used in the study "Neural Networks for Direct Material Distribution in Frequency Topology Optimization". The dataset was generated through 10,000 automated frequency-based topology optimization simulations performed using the Bi-directional Evolutionary Structural Optimization (BESO) method on a two-dimensional cantilever beam benchmark. The simulations include randomized optimization parameters, loading conditions, mesh discretizations, and geometric constraints represented by circular no-design regions (holes). The repository contains: problem_configs.csv : input parameters and configuration data for each topology optimization simulation, including optimization settings, loading conditions, mesh characteristics, and hole geometry parameters. beso_results.csv : output performance indicators associated with each simulation, including optimization metrics and computational time. beso_topologies.mat : binary topology matrices generated by the BESO algorithm, representing the optimized material distributions. dataset_cantilever_hole_2026_v4.mat : processed dataset used for neural network training, including resized topology representations and associated input/output data. The dataset was created to support research on machine learning-assisted topology optimization, surrogate modeling, engineering design automation, and data-driven structural optimization. In particular, it was used to train and validate a multi-head neural network capable of directly predicting optimized material distributions and performance indicators, subsequently integrated into a Bayesian Optimization framework. The benchmark problem consists of a frequency-constrained topology optimization of a cantilever beam with variable concentrated masses and randomly generated no-design regions. The objective is to minimize structural mass while preserving the first natural frequency. Researchers are encouraged to use this dataset for benchmarking, reproducing the results presented in the associated publication, developing alternative machine learning models, or investigating data-driven approaches for topology optimization and engineering design. If you use this dataset in your research, please cite the associated publication. Keywords: Topology Optimization, BESO, Frequency Optimization, Neural Networks, Bayesian Optimization, Machine Learning, Engineering Design, Structural Optimization, Additive Manufacturing, Data-Driven Design.

Muhammad, Reyvansyah · Sitompul, Joshua Pieres Haryakim · Halim, Erwin · et al.

Artificial intelligence (AI) has emerged as one of the most revolutionary technologies impacting our everyday lives as well as industry. Nonetheless, there has been a significant rise in the demand for high-end technology like the random access memory (RAM) due to advancements in AI, leading to price surges unseen before. In this study, the impact of increased RAM prices due to advances in AI on user perceptions, attitudes, and behavior will be analyzed. The increased hardware costs raise several accessibility and sustainability issues despite the benefits offered by AI, such as efficiency. Price Value will be considered to include the economic aspect of technology acceptance within this research, using the Unified Theory of Acceptance and Use of Technology (UTAUT2) framework. A quantitative methodology was applied, and data collection took place between May and June 2026 through online surveys targeted at respondents familiar with RAM and generative AI technologies. Ten hypotheses connecting behavioral intention and usage behavior to social influence, trust, usefulness, cost benefit, performance expectation, and habit were analyzed using structural equation modeling (SEM). As evidenced by the findings, although the usefulness of AI is perceived to be very high, increasing costs associated with RAM significantly influence AI adoption behavior, resulting in decreased intentions for users to remain loyal to AI. It seems evident that affordability plays an important role in sustaining AI adoption, whereas trust and social influence remain strong determinants of behavioral intentions towards AI. This finding further underlines the importance of striking a balance between cost and technology to ensure equal AI adoption.

Svab, Marek · Svabova, Martina · Pohořelý, Michael · et al.

This dataset contains experimental data acquired during the evaluation of micropollutant removal from raw water using granulated activated carbon (GAC) project SS01020063, alongside laboratory sorption tests utilizing model organic dyes. To further characterize the structural and sorption properties of the carbon surfaces under operational conditions, multi-component equilibrium sorption experiments were conducted using selected model dyes as surrogates for organic contaminants. The data serves to clarify the fundamental mechanisms governing targeted pollutant separation, identify key molecular parameters influencing sorption capacity, and evaluate the efficiency of the thermal reactivation process for water treatment applications.

Grinko, Anastasiya

Dataset of the publication "Single-cell RNA-seq using UltraMarathonRT expands the known transcriptome". This is the updated version of the previous BioArxiv preprint "Single-cell RNA-seq using UltraMarathonRT expands the known transcriptome", https://doi.org/10.1101/2025.10.06.680646 Included is the following data: R scripts with code for every main and supplemental figure Bash scripts for the preprocessing of the data Count tables obtained after featureCounts Prepared RDS objects GENCODE 47 annotation files used in the study GENCODE47 + SIRVOme annotation file used in the study Scripts for TSO concatemer counting Raw fastq files are found here: HeLa S3 : https://doi.org/10.5281/zenodo.20812770 K562 : https://doi.org/10.5281/zenodo.20812231