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  • LINF_280015300
    U6 snRNA-associated Sm-like protein LSm5 Leishmania infantum (strain JPCM5)
    • Dataset
    • Text
  • On-orbit Electrical Power System Dataset of 1U CubeSat constellation for Machine Learning Models
    This dataset contains on-orbit data samples of the Electrical Power System (EPS) from 4 different 1U CubeSats belonging to the BIRDS constellation. The EPS is responsible for providing uninterrupted power to the overall satellite both during sunlight and eclipse. The satellites are based on the BIRDS open-source standardized bus designed by Kyutech for research and education. BIRDS bus was used for six satellites that were delivered to International Space Station (ISS) onboard the Cygnus resupply spacecraft launched by Antares rocket and released into ISS orbit (altitude 400 km, inclination: 51.6º, duration: 92.6 min). The dataset contains the data of voltage (mV), current (mA) and temperature (Celsius) of the battery and solar panels attached to 5 sides of the satellite. This data is collected by the onboard computer every 90 seconds in nominal operation or every 10 seconds in fast sampling mode. The data is downloaded from the satellite memory by the ground station operators. The dataset contains one file per satellite, that includes data from solar panels and batteries since their deployment into orbit until the end of its life for the UGUISU, RAAVANA, and NEPALISAT satellites, the first two showing a failure in one of their panels during more than two years of operation on-orbit. The TSURU satellite dataset includes data since its deployment into orbit and will continue to be collected until the end of its life. The dataset generated will be useful for 1U CubeSat, such as BIRDS platform, users and satellite developers by using it as a reference to compare the behaviour of their Electric Power System under different operating scenarios and align their missions according to the available power on-orbit. At the same time, the dataset can help computer science researchers to build and validate new models for fault diagnosis and outlier detection.
    • Tabular Data
    • Dataset
  • Pothole Mix
    This dataset for the semantic segmentation of potholes and cracks on the road surface was assembled from 5 other datasets already publicly available, plus a very small addition of segmented images on our part. To speed up the labeling operations, we started working with depth cameras to try to automate, to some extent, this extremely time-consuming phase. The main dataset is composed of 4340 (image,mask) pairs at different resolutions divided into training/validation/test sets with a proportion of 3340/496/504 images equal to 77/11/12 percent. This is the dataset used in the SHREC2022 competition and it is the dataset that allowed us to train the neural networks for semantic segmentation capable of obtaining the nice images and videos that you have probably already seen. Along the main dataset we also release a set of RGB-D videos consisting of 797 RGB clips and as many clips with their disparity maps, captured with the excellent OAK-D cameras we won for being finalists at the OpenCV AI Competition 2021. In an effort to achieve (semi-)automatic labeling for these clips, we postprocessed the disparity maps using classic CV algorithms and managed to obtain 359 binary mask clips. Obviously these masks are not perfect (they cannot be by definition, otherwise the problem of automatic road damage detection would not exist), but nonetheless we believe they are an excellent starting point to create, for example, new data augmentations (creating potholes on "intact road images" belonging to other standard road datasets) or to be used as textures in the creation of 3D scenes from which to extract large amounts of images/masks for the training of neural networks. You can have a preview of what you will find in these clips by watching this video showing the overlay of images and binary masks: http://deeplearning.ge.imati.cnr.it/genova-5G/video/pothole-mix-videos/pothole-mix-rgb-d-overlay-videos-concat.html Please take a look at the readme file inside the main dataset zipfile to have some more details about the single sub-datasets and their sources.
    • Other
    • Dataset
    • File Set
  • LINF_280016700
    Ubiquitin family Leishmania infantum (strain JPCM5)
    • Dataset
    • Text
  • Supplementary Material_JDS2021_21601
    Supplementary tables and figures for Manuscript "Low-density SNP panel for efficient imputation and genomic selection of milk production and technological traits in dairy sheep."
    • Tabular Data
    • Dataset
    • Document
  • LINF_280015400
    P27 protein Leishmania infantum (strain JPCM5)
    • Dataset
    • Text
  • Datasets for "Accumulation of methylmercury in the Antarctic surface ocean: the potential role of phytoplankton uptake"
    This dataset provide data of measured total Methylmercury concentrations and the corresponding marine chemical and physical parameters in Ross and Amundsen Seas.
    • Tabular Data
    • Dataset
  • ​Department of Physics “G. Occhialini”
    Research data related to the ​​Department of Physics “G. Occhialini” of the University of Milano - Bicocca
    • Collection
  • Resistance training preserves bone in vegans
    supplementary files
    • Dataset
    • Document
  • Early Warning Model Dataset for Corporate Segment
    In dataset, 324 corporate customers were taken as reference. Dataset were created from information's between January 2018 and December 2021 time intervals. Financial ratios are calculated by taking financial information of these customers from balance sheet and income statement items.
    • Tabular Data
    • Dataset
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