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1. Surface Mass Balance, GPS, Timelapse cameras and Temperature sensors data of the Malaspina Glacier, Alaska (collected Summer 2021)

   https://doi.org/10.18739/a2ms3k37k  

   Devaux-Chupin, Victor (January 2025, NSF Arctic Data Center)

   ### Access GPS, photo, and position data can be accessed and downloaded from the directory via: [http://arcticdata.io/data/10.18739/A2MS3K37K](http://arcticdata.io/data/10.18739/A2MS3K37K). ### Overview The following files gather Surface Mass Balance stakes, timelapse camera photos, Hobo TidBits temperature sensors data, Global Positioning System (GPS) measurements, and debris thickness measurements on the surface of Malaspina Glacier, specifically in its lobe. The data was collected in Summer 2021. The glacier is located on the southwestern coast of Alaska, in the Wrangell St-Elias National Park. The study was mostly focused on the lobe of this piedmont glacier. The goal of the study was to measure several glaciological variables to be later compared to satellite-derived datasets, and incorporated into an ice flow model. The research topic was the retreat of Malaspina glacier over the next decades. We identified regions of interests within the glacier's lobe, that could potentially be markers of a starting retreat. The data gathered was used for publications currently in preparation as of October 2025. This study was led under the NSF grant number #1929566. 

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2. Mass and Chemistry of Organic Horizons and Surface Mineral Soils on Watershed 1 at the Hubbard Brook Experimental Forest 1996-present

   https://doi.org/10.6073/pasta/7395cf86c134440f38d800ca59a4857b  

   Johnson, Chris E (January 2022, Environmental Data Initiative)

   {"Abstract":["This data set includes chemistry of O-horizons ("forest floor") and the 0-10 cm \nmineral soil layer in Watershed 1 at Hubbard Book. Calcium in the form of wollastonite \n(CaSiO3) was added to Watershed 1 in October 1999. The application rate was 1028 kg \nCa per ha, and the application was relatively uniform across the watershed. Pre-treatment \nforest floor surveys were completed in 1996 and 1998. The first post-treatment forest \nfloor survey was completed in 2000. This data set includes mass and thickness data for \nthe sampled layers. Chemical data include concentrations and pools of organic matter, \nC, N, Ca, Mg, K, P, Mn, Fe, Al, Cu, Pb, and Zn. Soil pH and exchangeable Al, Ca, Mg, K, \nand H are also included. Sampling is intended to continue at 4 or 5 year intervals.\n These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). \nThe HBES is a collaborative effort at the Hubbard Brook Experimental Forest, \nwhich is operated and maintained by the USDA Forest Service, Northern Research Station."]} 

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3. UConn Voter Center - Voting Bubbles with Swatches

   https://doi.org/10.5281/zenodo.15458710  

   Verma, Aayushi; Manicke, C; Ahmad, S (August 2025, Zenodo)

   We introduce the UConn Bubbles with Swatches dataset. This dataset contains images of voting bubbles, scanned from Connecticut ballots, either captured as grayscale (8 bpp) or color (RGB, 24 bpp) artifacts, and extracted through segmentation using ballot geometry. These images are organized into 4 groups of datasets. The stored file contains all data together in color and we manually convert to greyscale. Each image of a bubble is 40x50 pixels. The labels are produced from an optical lens scanner.  The first dataset, Gray-B (Bubbles), uses 42,679 images (40x50, 8 bpp) with blank (35,429 images) and filled (7,250 images) bubbles filled in by humans, but no marginal marks. There are two classes, mark and nonmark. The second dataset, RGB-B, is a 24 bpp color (RGB) version of Bubbles-Gray.  The third dataset, Gray-C (Combined), augments Gray-B with a collection of marginal marks called “swatches”, which are synthetic images that vary the position of signal to create samples close to the boundary of an optical lens scanner. The 423,703 randomly generated swatches place equal amounts of random noise throughout each image such that the amount of light is the same. This yields 466,382 labeled images. The fourth dataset, RGB-C, is a 24bpp color (RGB) version of Gray-C. The empty bubbles are bubbles that were printed by a commercial vendor. They have undergone registration and segmentation using predetermined coordinates. Marks are on paper printed by the same vendor. These datasets can be used for classification training. The .h5 has many levels of datasets as shown below.  The main dataset used for training is positional.  This is only separated into blank (non-mark) and vote (mark).  Whether the example is a bubble or a swatch is indicated by batch number.  See https://github.com/VoterCenter/Busting-the-Ballot/blob/main/Utilities/LoadVoterData.py for code that creates torch arrays for RGB-B and RGB-C. See the linked Github repo (https://github.com/VoterCenter/Busting-the-Ballot/blob/main/Utilities/VoterLab_Classifier_Functions.py) for grayscale conversion functions and other utilities.   Dataset structure: COLOR - POSITIONAL - INFORMATION / / / B/V/Q B/V/Q COLOR/POSITIONAL / / / IMAGE IMAGE B/V/Q / BACKGROUND RGB VALUES Images divided into 'batches' not all of which have dataInformation contains labels for all images. Q is the swatch data, while B and V are non-mark and mark respectively. 

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4. Outputs from a Regional Ocean Modeling System (ROMS) two-way nested model of the Mid-Atlantic Bight and Delaware Bay for 2009-2015.

   https://doi.org/10.17882/94520  

   Hunter, Elias; Fuchs, Heidi; Gerbi, Gregory; Chant, Robert; Garwood, Jessica; Wilkin, John (January 2023, SEANOE)

   {"Abstract":["This is an archive of model output from the Regional Ocean Modeling System (ROMS) with two grids and two-way nesting. The parent grid resolution (referred to as Doppio) is 7 km and spans the Atlantic Ocean off the northeast United States from Cape Hatteras to Nova Scotia. The refinement grid (referred to as Snaildel) focuses on Delaware Bay and the adjacent coastal ocean at 1 km resolution. This ROMS configuration uses turbulence kinetic energy flux and significant wave height from Simulating Waves Nearshore (SWAN) as surface boundary conditions for turbulence closure.Ocean state variables computed are sea level, velocity, temperature, and salinity. Also inclued are surface and bottom stresses, as well as vertical diffusivity of tracer and momentum. \nThe files uploaded here are examples of one time record from each of this dataset. Outputs for the full reanalysis, which comprises 14 Terabytes of data, are made available for download via a THREDDS (Thematic Real-time Environmental Distributed Data Services) web service to facilitate user geospatial or temporal sub-setting.\nThe THREDDS catalog URLs and example filenames available here, for the respective collections, are:\n\t- 12 minute snapshots of the Doppio domain 2009-2015:\nhttps://tds.marine.rutgers.edu/thredds/roms/snaildel/catalog.html?dataset=snaildel_doppio_history\n\t- 12 minute snapshots of the Snaildel domain 2009-2015:\nhttps://tds.marine.rutgers.edu/thredds/roms/snaildel/catalog.html?dataset=snaildel_snaildel_history\n \nGarwood, J. C., H. L. Fuchs, G. P. Gerbi, E. J. Hunter, R. J. Chant and J. L. Wilkin (2022). "Estuarine retention of larvae: Contrasting effects of behavioral responses to turbulence and waves." Limnol. Oceanogr. 67: 992-1005.\nHunter, E. J., H. L. Fuchs, J. L. Wilkin, G. P. Gerbi, R. J. Chant and J. C. Garwood (2022). "ROMSPath v1.0: Offline Particle Tracking for the Regional Ocean Modeling System (ROMS)." Geosci. Model Dev. 15: 4297-4311."]} 

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5. Radar Reflectivity at Whillans Ice Plain

   https://doi.org/10.5281/zenodo.11201199  

   Hills, Benjamin; Siegfried, Matthew (January 2024, Zenodo)

   {"Abstract":["This is an extracted data product for radar bed reflectivity from Whillans Ice Plain, West Antarctica. The original data are hosted by the Center for Remote Sensing of Ice Sheets (CReSIS; see associated citation below). The files here can be recalculate and are meant to be used within a set of computational notebooks here:https://doi.org/10.5281/zenodo.10859135\n\nThere are two csv files included here, each structured as a Pandas dataframe. You can load them in Python like:df = pd.read_csv('./Picked_Bed_Power.csv')\n\nThe first file, 'Picked_Bed_Power.csv' is the raw, uncorrected power from the radar image at the bed pick provided by CReSIS. There are also other useful variables for georeferencing, flight attributes, etc.\n\nThe second file, 'Processed_Reflectivity.csv' is processed from the first file. Processing includes: 1) a spreading correction; 2) an attenuation correction; and, 3) a power adjustment flight days based on compared power at crossover points. This file also has identifiers for regions including "grounded ice", "ungrounded ice", and "subglacial lakes"."]} 

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