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ShareScore release 0.9.0

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660 results for “Cannabis”

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zenodo40/100

Dataset: Aurora Cannabis Inc. (ACB) Stock Performance

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

opencc-zeroJun 2024View details →

zenodo40/100

Dataset: IM Cannabis Corp. (IMCC) Stock Performance

opencc-zeroJun 2024View details →

zenodo40/100

Figura 1 in Artropofauna asociada al cultivo de Cannabis sativa L., 1753 (Urticales: Cannabaceae) medicinal en Antioquia, Colombia

Figura 1. Proporción de órdenes y familias de insectos, método de captura utilizado, hábito del insecto y etapa fenológica del cultivo de Cannabis sativa medicinal en Colombia. / Proportion of insect orders and families, capture method used, insect habit and phenological stage of medicinal Cannabis sativa crop in Colombia.

opencc-by-4.0Dec 2023View details →

zenodo40/100

Figuras 3a-r in Artropofauna asociada al cultivo de Cannabis sativa L., 1753 (Urticales: Cannabaceae) medicinal en Antioquia, Colombia

Figuras 3a-r. Insectos recolectados en un cultivo de Cannabis sativa. / Insect collected in a Cannabis sativa crop. a) Notiobia sp. b) Platynus sp. c) Labarrus pseudolividus. d) Spodoptera cf. ornithogalli. e) Agrius cingulata. f) Xylophanes tersa. g) Polyglypta costata. h) Chinavia sp. i) Agallia sp. j) Linepithema sp. k) Pheidole sp. l) Campsomeris sp. m) Eucelatoria sp. n) Palpada pos. prietorum. o) Sarcofahrtiopsis sp. p) Kopis sp. q) Brachybaenus rubrinervosus. r) Orphulella concinnula.

opencc-by-4.0Dec 2023View details →

zenodo40/100

Quantification of Fatty Acids in Hemp Seeds (Cannabis sativa L.) and Yield Prediction Using Machine Learning for Soxhlet and Ultrasound Extraction Methods

<p>This study focuses on the quantification of fatty acids present in hemp seeds (Cannabis sativa L.) cultivated in the Ecuadorian Andes using Soxhlet and ultrasound extraction methods. The aim is to evaluate and compare the extraction efficiency of these two techniques. Furthermore, machine learning models are applied to predict extraction yields based on experimental conditions. Using locally cultivated seeds provides valuable insights into the influence of regional agro-climatic conditions on the chemical composition. The integration of predictive algorithms offers a novel approach to optimizing the extraction process, enhancing both precision and efficiency. The findings could contribute to developing sustainable extraction methods for high-value bioactive compounds in the food and pharmaceutical industries.</p>

opencc-by-4.0Oct 2024View details →

zenodo40/100