Climate Change/Environmental

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.

We used  Sentinel-2 images to create the dataset In order to estimate sequestered carbon in the above-ground forest Biomass.  Moreover, fieldwork was completed to gather related forest biomass volume. The clipped image has a size of 1115 × 955 pixels and consists of bands 3, 4, and 8, which correspond to green, red, and near-infrared.

The dataset contains fitted three-pole Debye dielectric model parameters of 567 soil spectra. Three soils of loamy sand, sandy loam, and silt loam textures were tested. Of each soil, 20 samples of various water contents were prepared with the use of distilled water and potassium chloride solutions, 5 samples for each liquid. Air-dry samples were also prepared. Dielectric spectra were obtained with the use of a six-channel coaxial-transmission-line cell system at 9 controlled temperature steps from 0.5 to 40°C in the 0.02 – 3 GHz frequency range.

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.