MASCDB, a database of images, descriptors and microphysical properties of individual snowflakes in free fall

<strong>Dataset overview</strong> This dataset provides data and images of snowflakes in free fall collected with a Multi-Angle Snowflake Camera (MASC) The dataset includes, for each recorded snowflakes: A triplet of gray-scale images corresponding to the three cameras of the MASC A large quantity of geometrical, textural descriptors and the pre-compiled output of published retrieval algorithms as well as basic environmental information at the location and time of each measurement. The pre-computed descriptors and retrievals are available either individually for each camera view or, some of them, available as descriptors of the triplet as a whole. A non exhaustive list of precomputed quantities includes for example: Textural and geometrical descriptors as in <em>Praz et al 2017</em> Hydrometeor classification, riming degree estimation, melting identification, as in <em>Praz et al 2017</em> Blowing snow identification, as in <em>Schaer et al 2020 </em> Mass, volume, gyration estimation<em>, as in Leinonen et al 2021</em> <strong>Data format and structure</strong> The dataset is divided into four <em>.parquet</em> file (for scalar descriptors) and a <em>Zarr</em> database (for the images). A detailed description of the data content and of the data records is available here. <strong>Supporting code</strong> A python-based API is available to manipulate, display and organize the data of our dataset. It can be found on GitHub. See also the code documentation on ReadTheDocs. <strong>Download notes</strong> All files available here for download should be stored in the same folder, if the python-based API is used <em>MASCdb.zarr.zip</em> must be unzipped after download <strong>Version</strong> 0.3 - a new campaign is added to the dataset ("Remoray-2022") 0.2 - rename of variables. Variable precision (digits) standardized 0.1 - first upload