| Summary: | This dataset containssea-ice floe segmentation products derived from 78 Medea images from the United StatesGeological Survey (USGS) Global Fiducials Library (GFL) acquired in the Arctic's Canada Basin from 1999 to 2014. Sea-ice floe segmentation is the identification of individual sea ice floes in imagery for the purpose of retrieval of unique properties of those individual floes, such as their size or shape. The product was developed for and used in the analysis of the accompanying paper, Denton and Timmermans (2022) . The product was created using an algorithm written by Alexis Denton (Yale University) in tandem for the manuscript analysis. The script is also available at github.com/dentonaa/sea-ice-floe-segmentation . This image product was developed to contribute a novel, multidecadaldataset of identified sea icecharacteristics,namely floe size, to the Arctic science community. If you use this product in your work or research or for any other reason, credit the authors hereand cite the code DOI issued by Zenodo. The DOI badge to the right points to the latest released version of the repository. The development of this product and the research presented in the manuscript was funded by the Office of Naval Research as a part of their Multi-University Research Initiative (MURI) Mathematics and Data Science for Physical Modeling and Prediction of Sea Ice. To learn more about the work of the Sea Ice MURI, please visit https://seaicemuri.org/ . For further details about the dataset, please see Denton and Timmermans (2022) . A full description of the product files is as follows: This dataset is comprised of the sea-ice floe segmentation products derived from 78 MEDEA imagesacquired at various locationsin the Canada Basin from 1999 through 2014, and one ReadMe file. For each sea-ice floe segmented image, there are 3 files. Each of the 3 files starts with the naming convention, "sifloeseg_MEDEAImageName_segmentationParameters_", followed by a filename ending designated by the type of segmentation data stored, as ...
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