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This dataset comprises images of cotton leaves affected by various diseases, as well as healthy leaves. The data is meticulously organized into multiple folders, with each folder representing a specific disease or condition. The dataset includes images collected under diverse environmental conditions to enhance the robustness of machine learning models trained on it. The dataset is categorized as follows: Cotton_Healthy: Healthy cotton leaves without any visible signs of disease. Bacterial_Blight: Leaves showing symptoms of bacterial blight, characterized by dark, water-soaked spots that may enlarge over time. Fusarium_Wilt: Leaves affected by fusarium wilt, often exhibiting yellowing and wilting. Curl_Virus: Cotton leaves infected with curl virus, characterized by curling and deformation of leaves. The dataset is further stratified based on disease severity levels, including mild, moderate, severe, and critical stages. It is intended for training and validating machine learning models for automating disease detection and classification, enabling timely interventions in cotton farming.

Trace element abundances and noble gas isotope ratios of Ca-Al-rich inclusions from two CV3 chondrites (Allende and Axtell)

Metabolic data from the 22 patients included in the study

This study analyzed visits for actinic keratoses (AK) in the National Ambulatory Medical Care Survey (1995–2019), excluding those with concurrent keratinocyte carcinoma or warts. Procedure codes identified destructive treatments, while topical 5-fluorouracil and imiquimod were used to define field-directed therapy (FDT). Weighted Rao-Scott χ² tests and one-way ANOVA compared treatment utilization across survey years, patient demographics, insurance, and provider specialties. The analysis included 70 million estimated visits derived from 3,463 unweighted records, revealing increased FDT use over time and differences by insurance type and patient age.

(1) The real-time monitored data of the 270000 m3 LNG tank roof pouring stages: Including real-time stress variation data of the hoop beam and longitudinal beam during the dome pouring process of a 270000 cubic meter LNG storage tank. (2) Stability variation-different plate thicknesses: Data on the variation of buckling load factor with plate thickness (3) Stability variation-hoop beams section: Data on the variation of buckling load factor with hoop beams section amplification factor (4) Stability variation-longitudinal beams section: Data on the variation of buckling load factor with longitudinal beams section amplification factor (5) Stability variation-rise-to-span ratio: Data on the variation of buckling load factor with rise-to-span ratio amplification factor

This dataset explains the effect of sulfur dosage on natural rubber compounds and rubber vulcanizate when the rubber is used as an asphalt modifier.

The chemical species mass concentration in NR-PM1 across China and Cl- time series of six tipical observations

This dataset corresponds to the study presented in the paper "An Electromyography-Based Multi-Muscle Fatigue Model to Investigate Operational Task Performance." It comprises processed electromyography (EMG) data from eight muscles of the shoulder complex — Muscle 1: Medial Deltoid; Muscle 2: Anterior Deltoid; Muscle 3: Posterior Deltoid; Muscle 4: Supraspinatus; Muscle 5: Infraspinatus; Muscle 6: Teres Major; Muscle 7: Biceps; Muscle 8: Triceps—collected during various task conditions. The tasks were divided in static and dynamic trials. The static tasks required holding 2 and 6 lbs weights till exhaustion whereas dynamic tasks involved repetitive movement with 2 and 6 lbs, performed at two different shoulder heights: high and low

The following dataset contains: 1. Color perception in Design (Responses) Data. 2. Pie Analysis of the Color perception in Design (Responses) by Google Forms 3. Tables containing information collected for research purposes. On Colour temperature, preference and 20 Dishes of India and South Korea under 5 topics with its color palette. 4. Named Tables from Table 3.

This study presents a novel approach for determining chitosan concentration using T2 relaxation times measured by NMR relaxometry. Chitosan is widely applied in nanomaterials and biotechnology, and accurate quantification of its concentration is essential for various applications. Conventional methods, such as colorimetric and spectrophotometric techniques, often require additional chemicals, complex sample preparation, and are limited to certain concentration ranges. In contrast, our method, is straightforward, rapid, and avoids the need for additional reagents or specific temperature conditions. The concentration of chitosan is determined indirectly by the chitosan-water interface. This biointerface affects the relaxation times of the water molecules that are measured. Samples of chitosan in deionized water were analysed over a concentration range of 5–1000 mg/L using the Bruker minispec mq20 at 40°C and 21°C. T2 relaxation times were obtained using the CPMG sequence, and the data were analysed through monoexponential fitting from Bruker software, CONTIN software, and the Solver algorithm. The results demonstrated that our method produces reliable calibration curves, with the CONTIN software providing a continuous distribution of relaxation times that enables precise quantification. By applying linear regression of Absolute Area (AA) from continuous T2 distribution versus concentration, we achieved highly accurate results, making this method a strong competitor to traditional chitosan quantification techniques.