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Raw data in the manuscript by Yuan et al. to be published by Geochimica et Cosmochimica Acta as "Gallium isotope fractionation during granite weathering: Insights from two profiles in contrasting climatic conditions".

This dataset examines how U.S. electoral cycles influence automation adoption trends in the public and private sectors. Drawing on a mixed-method approach, the data combine longitudinal quantitative analysis of automation rates over the period 2000–2020 with qualitative insights from semi-structured interviews with 30 decision-makers. Significant increases in public sector automation are seen after the election, given the policy for modernization by an incoming administration, while private sector investments in automation are much lower during the pre-election period due to uncertainty over future regulations. The current study fills an important gap between political economy and technological adoption by providing empirical evidence and practical recommendations for policymakers and business executives. These drivers include regulatory uncertainty and an electoral mandate; a dataset on the trends, regression analyses, and thematic insights was drawn upon.

Dataset of stable isotope data for precipitation in the global from 1961 to 2023.

Baseline dataset for SpaceThumb study.

Datasets

Measured enviromental forcing and lake-water temperature regulation for 20 m from Agamon Hula, Isreal.

Ács AR, Ion MC, Miok K, Laza AV, Pitic A, Robnik-Šikonja M, Pârvulescu L (2024) Threats assessment of the endemic Idle Crayfish (Austropotamobius bihariensis Pârvulescu, 2019): Lessons from long-term monitoring. Aquatic Conservation: Marine and Freshwater Ecosystems 34, e70033 (https://doi.org/10.1002/aqc.70033)

The Multi-Weather-Based Pothole Detection Dataset is a comprehensive collection of images designed to aid in developing and evaluating deep learning models for detecting road surface anomalies, particularly potholes, under diverse environmental conditions. Images captured under normal weather, and rainy conditions include variations in lighting, such as daytime, twilight, and nighttime settings. We tried to add high-quality images to ensure the clarity of road surface details. It facilitates the detection of small and partially obscured potholes. In the dataset, potholes are precisely annotated with bounding boxes. This dataset is meticulously curated to reflect weather scenarios, ensuring robust and adaptable pothole detection systems.

Data supporting the research on the U-shaped association between environmental rating divergence and corporate greenwashing

A Comprehensive dataset on diseases affecting rose leaves: contains 3,113 high-resolution images and 15,500 augmented images (resized to 640x480) of rose leaves, categorized by disease type to assist in identification and control strategy research. The dataset is designed for training deep learning and machine learning models and studying plant pathology, pest management, and disease prevention in rose cultivation. The images were taken from October 30 - November 6, 2024 , approximately 8 days. Total Original Images: 3,113 images Total Augmented Images: 15,500 images Resolution: Resized to 640x480 pixels Categories: Four disease classes Class 1: Black Spot Class 2: Healthy Leaf Class 3: Dry Leaf Class 4: Leaf Holes Image Count per Class: At least 300 images per category in original data Purpose: Disease detection, research in plant pathology, and development of control strategies