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1. Overwintering Fires from 2009-2010 Burns near Fairbanks, Alaska: Residual Soil Organic Layer Depth, Burn Depth and Thaw Depth Collected 2023

   https://doi.org/10.6073/pasta/8e628fc7868b891774062ef5a3c166ba  

   Walker, Xanthe; Mack, Michelle Cailin; LTER, Bonanza Creek (January 2024, Environmental Data Initiative)

   This dataset contains data from adjacent overwintering and single-season burn sites. For the overwintering fires, we targeted locations that had burned in the summers of 2009, smouldered through the winter months, and reignited in 2010. Adjacent to these overwintering sites, we identified single-season burn sites from within portions of the 2009 fires that were unaffected by overwintering. A total of seven overwintering fire sites and four single-season fire sites were sampled. Within each site, three plots were established. Data inlcudes within plot measurments of post-fire seedling composition and density, residual SOL, burn depth estimated by black spruce adventitious roots, thaw depth, and pre-fire tree species composition and estimates of combustion. This is one of three packages from this project; this one contains the soils data. 

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2. Intentional islanding of power grids with data depth

   https://doi.org/10.1109/CAMSAP.2017.8313149  

   Dey, Asim; Gel, Yulia R.; Poor, H. Vincent (December 2017, Proceedings of the 2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing)

   A new method for intentional islanding of power grids is proposed, based on a data-driven and inherently geometric concept of data depth. The utility of the new depth-based islanding is illustrated in application to the Italian power grid. It is found that spectral clustering with data depths outperforms spectral clustering with k-means in terms of k-way expansion. Directions on how the k-depths can be extended to multilayer grids in a tensor representation are outlined. 

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3. New Hampshire Soil Sensor Network: Snow depth (2012-2022)

   https://doi.org/10.6073/pasta/5e87e5edf5b8bc115cfa6b0399e905ce  

   Contosta, Alexandra R; Frey, Serita D; Perry, Apryl; Burakowski, Elizabeth (July 2025, Environmental Data Initiative)

   The goal of the New Hampshire Soil Sensor Network is to examine spatial and temporal changes in soil properties and processes as the climate changes. Data collected can also calibrate and validate models that examine how ecosystems may respond to changing climate and land use. To determine how soil processes are affected by climate change and land management, this soil sensor network measures snow depth, air temperature, soil temperature, soil volumetric water content, and soil electrical conductivity, as well as soil CO2 fluxes. This data package includes data from snow depth sensors. Data were collected at the following sites: BRT = Bartlett Experimental Forest, Bartlett, NH; BDF = Burley-Demmerit Farm, Lee, NH; DCF = Dowst Cate Forest, Deerfield, NH; HUB = Hubbard Brook Experimental Forest, Woodstock, NH; SBM = Saddleback Mountain, Deerfield, NH; THF = Thompson Farm, Durham, NH; and Trout Pond Brook, Strafford, NH. 

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4. Focal Split: Untethered Snapshot Depth from Differential Defocus

   https://doi.org/10.1109/CVPR52734.2025.02511  

   Luo, Junjie; Mamish, John; Fu, Alan; Concannon, Thomas; Hester, Josiah; Alexander, Emma; Guo, Qi (June 2025, IEEE)

   We introduce Focal Split, a handheld, snapshot depth camera with fully onboard power and computing based on depth-from-differential-defocus (DfDD). Focal Split is passive, avoiding power consumption of light sources. Its achromatic optical system simultaneously forms two differentially defocused images of the scene, which can be independently captured using two photosensors in a snapshot. The data processing is based on the DfDD theory, which efficiently computes a depth and a confidence value for each pixel with only 500 floating point operations (FLOPs) per pixel from the camera measurements. We demonstrate a Focal Split prototype, which comprises a handheld custom camera system connected to a Raspberry Pi 5 for real-time data processing. The system consumes 4.9 W and is powered on a 5 V, 10,000 mAh battery. The prototype can measure objects with distances from 0.4 m to 1.2 m, outputting 480×360 sparse depth maps at 2.1 frames per second (FPS) using unoptimized Python scripts. Focal Split is DIY friendly. A comprehensive guide to building your own Focal Split depth camera, code, and additional data can be found at https://focal-split.qiguo.org. 

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5. Snow depth survey in North Slope, Alaska, 2022

   https://doi.org/10.18739/A24746T0K  

   Ohara, Noriaki (January 2023, NSF Arctic Data Center)

   This is the field measured snow depth data using an automatic snow depth probe (magnaprobe, Snow-Hydro LCC) in April 19 - May 7, 2022 in North Slope, Alaska. The data are in csv format (comma delimited text format with geographical coordinate, WGS 84, and UTM zone 5). The goal of this research project is to quantify the role of thermokarst lake drainage and drained thermokarst lake basin (DTLB) evolution in the arctic system. The joint research team (University of Alaska, Fairbanks, University of Wyoming, and Michigan Technological University) spent several days based in Utqiagvik (Western Coastal Plain) and rest of days in Teshekpuk Lake (Central Coastal Plain). During the travel, manual snow survey was conducted using the mangaprobe to quantify the snowdrift around thermokarst lakes and other land features as complementary to the geophysical and remote sensed snowpack characterizations. 

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