Ecology
The dataset aims to facilitate research in the optimization of the carbon footprint of recipes. Consisting of 30 Excel files processed through various Python scripts and Jupyter notebooks, the dataset serves as a versatile resource for both performance analysis and environmental impact assessment. The unique attribute of this dataset lies in its ability to calculate representative values of carbon footprint optimization through multiple algorithmic implementations.
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This study first gathered together protein sequences of kinases found for yeasts along with associated annotations and aspects of their features. Data for these yeast kinases were obtained using the Yeast Kinome database (https://yeastkinome.org/, retrieved 04/08/2021. Protein sequences corresponding to each of the following five kinase genes from S.
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Photo identification (photoID) is a non-invasive technique devoted to the identification of individual animals using photos, and it is based on the hypothesis that each specimen has unique features useful for its recognition. This technique is particularly suitable to study highly mobile and hard to detect marine species, such as cetaceans. These animals play a key role in marine biodiversity conservation because they maintain the stability and health of marine ecosystems due to their apical role as top predators in food webs.
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This dataset is the outcome of an observation on Hyssop germination under Lead (Pb) tension and Gixberlin Acid based hormonal priming. Pb tension levels are 0, 25, 50,75, and 100 mg/L, respectively in this study. Gixberlin acid priming is done under 0, 50,100, 150, and 200 mg/L and each scenario is repeated four times during this study. Mean Germination Time (MGT), Root Length( RL) and Shoot Length (SL) have been measured. Moreover, different enzyme levels including superoxide dismutase, catalase, ascorbate peroxidase, and Guaiacol Peroxidase.
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The Paddy Doctor dataset contains 16,225 labeled paddy leaf images across 13 classes (12 different paddy diseases and healthy leaves). It is the largest expert-annotated visual image dataset to experiment with and benchmark computer vision algorithms. The paddy leaf images were collected from real paddy fields using a high-resolution (1,080 x 1,440 pixels) smartphone camera. The collected images were carefully cleaned and annotated with the help of an agronomist.
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This dataset is in support of my research paper - Short Circuit Analysis of 666 Wh Li-Ion NMC
Faults and datasets can be copied to submit in fire cause investigation reports or thesis. The simulation is run for 20 hours (72000 seconds) of simulation time for each fault of 100 faults.
PrePrint : (Make sure you have read Caution.)
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This dataset supports a review and an in-depth analysis on the environmental impacts of integrated circuits (ICs). The paper is currently under review.
We gathered data from foundry reports, industry roadmaps, scientific literature, and commercial state-of-the-art LCA databases. All assumptions are detailed.
More information can be found on the GitHub repository : https://github.com/ThibaultPirson/environmental-footprint-IC.
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This <file> was prepared along with the establishment of the reference intervals for the hematological and biochemical parameters of the juvenile Visayan warty pig (Sus cebifrons negrinus). The determination of the reference intervals of the juvenile Visayan warty pig was significant, as these reference intervals have never been established for the purpose of assessing the health of this critically endangered species. The file contains six separate sheets featuring the raw and transformed data, as well as the calculated reference interval.
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This dataset consists of 1878 labeled images of flowers from blackberry trees from the specie Rubus L. subgenus Rubus Watson. These are white flowers with five petals that blossom in the spring through summer. The images were collected using an Intel RealSense D435i camera inside a greenhouse.
This images were inicially collected to support a robotic autonomous pollination project.
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This dataset was prepared to aid in the creation of a machine learning algorithm that would classify the white blood cells in thin blood smears of juvenile Visayan warty pigs. The creation of this dataset was deemed imperative because of the limited availability of blood smear images collected from the critically endangered species on the internet. The dataset contains 3,457 images of various types of white blood cells (JPEG) with accompanying cell type labels (XLSX).
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