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This repository contains bathymetry data for Grand Lake, Oklahoma, along with a Python script for interpolating and visualizing the lake's bottom elevation. The dataset, titled "Horse Creek Cove Bathymetric Data 2020 Survey Manuel.csv," includes depth measurements (latitude, longitude, and depth) collected using an Unmanned Surface Vehicle (USV) named "MANUEL." The provided Python script performs interpolation to estimate depths at locations not directly sampled, generating detailed bathymetric plots of the lakebed. These visualizations offer a comprehensive understanding of the underwater topography of Grand Lake.

This study highlights the most common manifestations of spiritual and emotional states among a specific group of students and teachers. The research is based on a survey conducted with students and faculty at Aldent University, who provided individual responses regarding their spiritual and emotional states.

La crónica es una tipología narrativa que combina la investigación periodística con la narración, lo que propicia a describir problemáticas, hechos y situaciones socioculturales. La crónica como voz de las problemáticas sociales, hace parte de las notas de clase para realizarse con los estudiantes como ejercicio de análisis de problemáticas sociales.

For the Good and Bad Classification of Boiled Rice project, the dataset comprises 1000 samples, evenly distributed between two classes: 500 "good" boiled rice samples and 500 "bad" boiled rice samples. The classification task aims to distinguish between the quality of boiled rice, with "good" indicating well-cooked rice and "bad" representing overcooked, undercooked, or otherwise poorly cooked rice. Dataset Composition: Total Samples: 1000 Good Rice: 500 samples Bad Rice: 500 samples Data Features: Each sample is characterized by multiple features that reflect both physical and sensory attributes of the boiled rice. These features help capture the differences in quality between the good and bad samples. The specific features may include: Moisture Content: A critical determinant of rice quality, this feature captures the water content in the boiled rice, which is expected to vary between good and bad samples. Texture Score: Measured either through machine-based or manual sensory evaluation, texture indicates firmness or stickiness. Good boiled rice typically has an ideal firmness, while bad samples may be too hard or too mushy. Color Measurement: The color of the rice, usually quantified in terms of brightness or yellowness. Overcooked rice might appear darker or yellowish, while properly boiled rice retains a whiter appearance. Aroma: The intensity of aroma could serve as a distinguishing feature, as poorly cooked rice may emit different smells, often indicating burning, overcooking, or spoilage. Cooking Time: The duration for which the rice was boiled. Too short or too long cooking times generally correlate with bad rice quality. Grain Structure: A measure of grain integrity after cooking, indicating if the grains are intact, broken, or overly sticky. Good rice samples are expected to have individual, unbroken grains, whereas bad samples may show broken or clumped grains. pH Level: pH value may influence taste and texture, which is a subtle yet useful parameter to assess the quality of boiled rice. Sensory Rating: Subjective ratings provided by testers on the overall quality of the rice. This feature can be aggregated from multiple sensory dimensions (texture, taste, aroma) and serves as a holistic quality indicator. Data Collection: The samples were obtained from controlled cooking environments, ensuring consistency in raw rice type and cooking conditions. Variability between good and bad samples was introduced intentionally by altering parameters like water-to-rice ratio, cooking time, and heat intensity. The evaluation of each sample’s quality was carried out through both objective methods (e.g., moisture analysis, texture measurement) and subjective assessments (e.g., sensory panel evaluations). Data Preprocessing: Before applying machine learning models, the dataset underwent the following preprocessing steps: Normalization/Standardization: Continuous variables such as moisture content and cooking time were normalized to ensure

To observe the effect of nanopore size rapid film (NRF) co-loaded with propolis and aloe vera extract in the treatment of wound healing. Methods: The porosity, mechanical properties, and water vapor transmittance rate (WVTR) of the materials were determined, and the effect of P-A-NRF in promoting cell proliferation and migration was evaluated, and the mechanism of wound healing in a mouse full-thickness wound model was provided. Results: P-A-NRF is rapidly film-forming within 5min, and the contact angle is 80°, which has a waterproof effect. The cell viability rate of P-A-NRF was more than 80%, which was significantly higher than that of the positive control group, and the cell migration rate reached more than 50%. In vivo experiments, the P-A-NRF group achieved a 98% healing rate at 14 days 90% in the commercially available liquid band-aid group, and 70% in the blank control group, with faster wound healing. The expression levels of IL-6, IL-1β, TNF-α, VEGF, and Hydroxyproline were measured by enzyme-linked immunoassay (ELISA), the expression levels of IL-6, IL-1β and TNF-α in the P-A-NRF group were significantly decreased compared with those in the control group and the commercially available liquid band-aid group, and the levels of VEGF and Hydroxyproline were increased. Conclusion: The film formed by P-A-NRF is uniform, soft, and malleable. It can enhance cell viability, promote cell proliferation and migration, and accelerate wound healing mainly by reducing inflammation, antibacterial, and promoting angiogenesis and collagen deposition, which is a promising wound dressing.

This data is used in data analysis for research entitled: Impact of group lending on entreprise capital formation and employment creation in Ethiopia

SPSS data file in a survey "The association between dietary inflammatory index, Polycystic Ovary Syndrome risk, and circulating oxidative stress markers: A case-control study"

The dynamic LSC70 database consists of a collection of images of Colombian Sign Language (LSC), created to support research in artificial intelligence for developing sign language recognition applications that promote the social inclusion of individuals with hearing disabilities. This database was developed with the participation of 70 volunteers and contains 47 signs in transitions of six frames. It comprises a total of 35,208 images, including signs of the alphabet, numbers, and common words. Additionally, the dataset is divided into three sections: LSC70AN, which includes full-body signs of the alphabet and numbers with a resolution of 640x480 pixels; LSC70ANH, which similarly covers alphanumeric signs but focuses on the dominant hand of the volunteers with a resolution of 120x120 pixels; and LSC70W, which encompasses common words used in basic conversations, such as greetings and identifications, also at 640x480 pixels. Each sign was recorded under uncontrolled lighting and clothing conditions using a cellphone and was later filtered to remove duplicates and enhance the quality of the database. The images are in JPG format and organized in folders by volunteer and sign, facilitating their use in research and the development of sign language recognition models.

Sorption potential of the indigenously prepared biosorbents from orange, melon and banana peels were optimized for arsenic. The responses were generated using response surface methodology (RSM), results indicated a significant model with three sources of biosorbents was also influencing the levels of arsenic sorption significantly. The high R2 value (above 90%) indicating a good reliability of model and explained greater than 90% of variability among collected data. Linear effect of biosorbent dose concentrations (mg) were observed on the sorption efficiency for As. However, the sorption efficiency reduces significantly after the interaction time of 120 minutes. Under optimized predicted model actual experiment revealed that melon peel biosorbent uptake the 96.5mg, orange peel biosorbent uptake 83.5mg, and banana peel biosorbent uptake the 80.25mg of As. The response surface methodology showed that the indigenously prepared biosorbents had a great potential for removal of arsenic from water. And the sorption potential was greater in melon peel as compared to orange peel and banana peel respectively.

Supplemental methods (*updated 9/17/24)