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Excel file containing Tables S1–S3, each provided in a separate tab. Tables S1 and S2 present differential gene expression analyses from bulk RNA sequencing of mouse hearts treated with isoproterenol (ISO) or lipopolysaccharide (LPS). Related RNA-seq data are available in GEO accession GSE307900. Table S3 presents differential chromatin accessibility analyses from bulk ATAC sequencing of bone marrow–derived hematopoietic stem and progenitor cells (HSPCs) from mice treated with ISO or LPS, focusing on loci associated with genes implicated in clonal hematopoiesis. Related ATAC-seq data are available in GEO accession GSE305274.

The search focused on pot experiments examining biochar’s impact on the chemical and biological properties of grey desert and aeolian sandy soils.

DRG samples (n = 5 per group) were analyzed using mass spectrometry in both positive and negative ion modes to identify metabolic alterations. Thirty milligrams of DRG tissue were placed in 2-ml centrifuge tubes for metabolite extraction. A total of 900 μl of extraction solution (methanol: water = 4 : 1) was added to each sample. The DRG tissues were pulverized using a cold tissue grinder for 6 minutes (−10°C, 50 Hz) to ensure thorough homogenization. Following this, the samples underwent low-temperature ultrasonic treatment for 30 minutes (5°C, 40 kHz) to enhance metabolite extraction. The mixtures were left to stand at −20°C for 30 minutes and then centrifuged at 12,000 rpm for 15 minutes at 4°C. The resulting supernatants were transferred into sample vials with insert tubes for further analysis. Metabolomic profiling was conducted using a Q-Exactive HF mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA), as described in previous methodologies . Initial data-dependent acquisition was performed with a single full-scan at a resolution of 60,000, targeting hydrophilic metabolites with mass-to-charge ratios ranging from 60 to 900. Lipid profiling was subsequently performed within an extended mass-to-charge ratio range of 300 to 1,200. For further structural elucidation, 10 consecutive tandem mass spectrometry scans were conducted in high-energy collision dissociation mode. Stringent quality control measures were implemented throughout the metabolomic study to ensure the accuracy and reliability of the data generated.

The Dual-Task Emotion–Authenticity Facial Expression Dataset (GFFD-2025) is a carefully curated collection of facial images created to support research in emotion recognition and authenticity detection. Unlike traditional emotion datasets, it focuses not only on identifying which emotion a person expresses but also on whether the expression is genuine or acted, contributing to studies in artificial intelligence, affective computing, and human–computer interaction. A total of 2,224 raw facial images were initially collected from voluntary participants. After quality assessment and manual verification, a subset was refined and curated for further research. The dataset repository includes approximately 1,900 raw facial images and around 1,500 cropped and augmented images, representing the cleaned and extended version of the original collection. The dataset covers seven primary emotions: Angry, Disgust, Fear, Happy, Neutral, Sad, and Surprise; each subdivided into two authenticity categories: Genuine and Fake (Acted). Images were captured under controlled indoor conditions to ensure consistent lighting, neutral backgrounds, and stable face positioning. Genuine expressions were elicited via emotional recall or audiovisual stimuli, while fake expressions were intentionally acted. All data collection sessions were supervised by a certified psychologist to ensure ethical compliance and emotional validity. Images were reviewed and labeled following micro-expression research principles, considering subtle cues such as eye involvement, facial symmetry, muscle tension, and temporal dynamics to distinguish genuine from acted expressions. Curated images were standardized to 224×224 pixels for compatibility with common deep learning frameworks. To enhance dataset diversity and model robustness, images underwent preprocessing and augmentation, including rotation (±30°), width and height shifts (0.2), shear (0.15), zoom (0.2), horizontal flipping, random brightness and contrast adjustments, and normalization to the [0,1] range. This dataset offers a practical benchmark for research in emotion recognition, authenticity detection, human behavior analysis, multitask learning, and explainable AI, enabling development of models sensitive to subtle psychological authenticity cues. Data collection and labeling were conducted at Daffodil International University, Dhaka, Bangladesh, under strict ethical guidelines with informed consent from all participants. Sessions were supervised to ensure participant comfort and authenticity. Supervisor: Md. Mizanur Rahman Lecturer, Department of Computer Science and Engineering Daffodil International University, Dhaka, Bangladesh Email: mizanurrahman.cse@diu.edu.bd Data Collectors: Sarah Tasnim Diya (Email: diya15-5423@diu.edu.bd) Most. Jannatul Ferdos (Email: ferdos15-5453@diu.edu.bd) Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh.

This repository contains a Stata workflow for processing, cleaning, and merging LM (Labor Management) forms filed by labor unions, specifically LM-2, LM-3, LM-4, and LM-5 forms from the U.S. Department of Labor. Please, cite the following paper if you use this code and/or the cleaned data: Carlos F. Avenancio-León, Alessio Piccolo, Roberto Pinto, Resilience in collective bargaining, Journal of Financial Economics, Volume 173, 2025.

This data is for FTIR spectroscopy, RSM modelling, the influence of factors on adsorption, isotherm, kinetic and thermodynamic modelling, and all tables and figures. FTIR spectra derived from FTIR-RSB (pristine biochar), KRSB (KOH-modified biochar) and KRSB-Pb (modified biochar after Pb adsorption). The FTIR spectra revealed oxygen-containing functional groups (-OH, -COO⁻, C–O–C) that facilitated complexation and precipitation. Five factors (Dose, initial Pb(II) concentration, contact time, temperature, and pH) were used in RSM modeling and optimisation. The resulting model was adequate with high R2 and low p-value. The adsorption followed dual Langmuir-Temkin isotherm models and pseudo-second-order kinetic model. The Pb(II) adsorption was endothermic with negative gibb's free energy.

Raw data of XPS of Zn0.2Co0.8-xNixFe2O4 (x = 0.2, 0.4, 0.6, and 0.8) nanoparticles , prepared through the sol-gel auto-combustion technique.

Dataset Overview: It contains 2991 high-resolution images of eggplant leaves. Images were collected from the Changao, Paragram, Ashulia, Dhaka, and Narsingdi regions of Bangladesh. Data was gathered between October 20 and November 2, 2024, over a period of 13 days. Classes: Cercospora: 628 images Curl: 284 images Flea Beetles: 84 images Hadda Beetles: 530 images Healthy: 188 images LeafhopperJassids: 38 images Magnesium Deficiency: 50 images Phomposist Blast: 218 images TMV (Tobacco Mosaic Virus): 356 images Tobacco Caterpillar: 452 images Verticillium Wilt: 163 images Purpose: Supports the advancement of automated agricultural disease detection systems. It aims to assist in the early detection and management of eggplant leaf diseases. Enables the development of reliable diagnostic tools using machine learning and image processing techniques. Promotes better crop management, increased yield, and reduced pesticide use, contributing to sustainable agricultural practices. Serves as a resource for developing and evaluating image-based disease recognition models and deep learning applications in agriculture.

This dataset includes clinical and experimental data of patients with atrial fibrillation and stroke from March 2018 to May 2025.

Supplementary TableS1-S5