More Like this

1. High-resolution predictions of ground ice content for the Northern Hemisphere permafrost region

   https://doi.org/10.5194/essd-2022-144  

   Karjalainen, Olli; Aalto, Juha; Kanevskiy, Mikhail Z; Luoto, Miska; Hjort, Jan (September 2022, Copernicus Publications)

   Ground ice content of the Arctic soils largely dictates the effects of climate change-induced permafrost degradation and top ground destabilization. The current circumarctic information on ground ice content is overly coarse for many key applications, including assessments of hazards to Arctic infrastructure, while detailed data are restricted to very few regions. This study aims to address these gaps by presenting spatially comprehensive data on pore and segregated ground ice content across the Northern Hemisphere permafrost region at a 1-km resolution. First, ground ice content datasets (n=437 and 380 1-km grid cells for volumetric and gravimetric ice content, respectively) were compiled from field observations over the permafrost region. Spatial estimates of ground ice content in the near-surface permafrost north of the 30th parallel north were then produced by relating observed ground ice content to physically relevant environmental data layers of climate, soil, topography, and vegetation properties using a statistical modelling framework. The produced data show that ground ice content varies substantially across the permafrost region. The highest ice contents are found on peat-dominated Arctic lowlands and along major river basins. Low ice contents are associated with mountainous areas and many sporadic and isolated permafrost regions. The modelling yields relatively small prediction errors (a mean absolute error of 13.6 % volumetric ice content) over evaluation data and broadly congruent spatial distributions with earlier regional-scale studies. The presented data allow the consideration of ground ice content in various geomorphological, ecological, and environmental impact assessment applications at a scale that is more relevant than previous products. The produced ground ice data are available in the supplement for this study and at Zenodo https://doi.org/10.5281/zenodo.7009875 (Karjalainen et al., 2022). 

   more » « less
2. Interannual variability of Arctic sea ice concentration and area for six climate model large ensembles and four observational datasets, 1979-2020

   https://doi.org/10.18739/A2H98ZF3T  

   Wyburn-Powell, Christopher (January 2022, NSF Arctic Data Center)

   This dataset includes statistically resampled monthly time series data of Arctic sea ice area and gridded data for March and September for sea ice concentration for a selection of large ensemble climate models and observational datasets. Arctic sea ice concentrations and areas are resampled from all available members of six coupled climate models from the Coupled Model Intercomparison Project 5 (CMIP5). These six models are: The second generation Canadian Earth System Model (CanESM2), The Community Earth System Mode version 1 (CESM1), The Commonwealth Scientific and Industrial Research Organisation Global Climate Model Mark 3.6 (CSIRO MK3.6), The Geophysical Fluid Dynamics Laboratory Coupled Climate Model version 3 (GFDL CM3), Geophysical Fluid Dynamics Laboratory Earth System Model version 2 with Modular Ocean Model version 4.1 (GFDL ESM2M), Max Planck Institute Earth System Model version 1 (MPI ESM1). The Four observational datasets are The Hadley Centre Sea Ice and Sea Surface Temperature data set version 1 (HadISST1), The National Oceanic and Atmospheric Administration and National Snow and Ice Data Center Climate Data Record Version 4 (CDR), The The National Aeronautics and Space Administration Team Algorithm (NT), and the The National Aeronautics and Space Administration Bootstrap Team Algorithm (BT). The sea ice area data is resampled 10,000 times and then the standard deviation of those resamplings is calculated, which can be considered analagous to interannual variability of sea ice area (SIA). The standard deviation (sigma) and mean (mu) of these data represent the variability and typical values respectively of interannual variability found in each ensemble member or observational dataset. Sea ice concentration is resampled 1000 times with the same standard deviation and mean metrics for sea ice concentration. This dataset was created to evaluate climate model projections of Arctic sea ice interannual variability and is used in the article Wyburn-Powell, Jahn, England (2022), Modeled Interannual Variability of Arctic Sea Ice Cover is Within Observational Uncertainty, Journal of Climate, https://doi.org/10.1175/JCLI-D-21-0958.1. This work was conducted at the University of Colorado Boulder from 2020-2022. The figures from the Journal of Climate article can be reproduced from the following datasets. The code used to create the datasets can be located at https://www.doi.org/10.5281/zenodo.6687725. - Figure 1: Sigma_obs_SIA.nc - Figure 2: Sigma_obs_SIA.nc, Mu_obs_SIA.nc, Sigma_mem_SIA.nc, Mu_mem_SIA.nc - Figure 3: Sigma_mem_varying_time_periods_1965_2066_03.nc, Sigma_LE_varying_time_periods_1965_2066_03.nc, Sigma_LE_varying_time_periods_1970_2040_09.nc, Sigma_obs_varying_time_periods_1953_2020.nc - Figure 4: Sigma_obs_SIA.nc, Sigma_mem_SIA.nc - Figure 5: Sigma_obs_SIA.nc - Figure 6: <model_name>_resampled_0<month>_individual.nc, <observational_dataset>_resampled_individual_1979_2020_03_09.nc - Figure 7: Sigma_obs_SIA.nc, Mu_obs_SIA.nc, Sigma_mem_SIA.nc, Mu_mem_SIA.nc - Figure 8: <model_name>_resampled_0<month>_individual.nc, <observational_dataset>_resampled_individual_1979_2020_03_09.nc - Figure 9: Sigma_mem_SIA.nc, Sigma_LE_SIA.nc 

   more » « less
3. Sequencing data discovery with MetaSeek

   https://doi.org/10.1093/bioinformatics/btz499  

   Hoarfrost, Adrienne; Brown, Nick; Brown, C. Titus; Arnosti, Carol; Wren, ed., Jonathan (June 2019, Bioinformatics)

   Abstract SummarySequencing data resources have increased exponentially in recent years, as has interest in large-scale meta-analyses of integrated next-generation sequencing datasets. However, curation of integrated datasets that match a user’s particular research priorities is currently a time-intensive and imprecise task. MetaSeek is a sequencing data discovery tool that enables users to flexibly search and filter on any metadata field to quickly find the sequencing datasets that meet their needs. MetaSeek automatically scrapes metadata from all publicly available datasets in the Sequence Read Archive, cleans and parses messy, user-provided metadata into a structured, standard-compliant database and predicts missing fields where possible. MetaSeek provides a web-based graphical user interface and interactive visualization dashboard, as well as a programmatic API to rapidly search, filter, visualize, save, share and download matching sequencing metadata. Availability and implementationThe MetaSeek online interface is available at https://www.metaseek.cloud/. The MetaSeek database can also be accessed via API to programmatically search, filter and download all metadata. MetaSeek source code, metadata scrapers and documents are available at https://github.com/MetaSeek-Sequencing-Data-Discovery/metaseek/. 

   more » « less
4. Locations of beaded streams in the Pan Arctic, 2023 (Harlan et al 2023 Remote Sensing of Environment)

   https://doi.org/10.18739/A21Z41V46  

   Gleason, Colin (January 2023, NSF Arctic Data Center)

   Here we observe and predict the location of beaded stream catchments throughout the pan-Arctic domain by combining the location of known beaded streams with recent advances in computer vision and high-resolution (3 meter (m)) satellite imagery. Specifically, we use the location of known existing beaded streams to classify potential river catchments as beaded or non-beaded, then download high resolution imagery across those regions, and use the latest You-Only-Look-Once (YOLO) object detection algorithm to identify beaded streams throughout the pan-Arctic, estimating 138,500 ± 43,700 beaded catchments globally, occurring in an estimated one third of all pan-Arctic catchments. In the largest dataset of beaded streams to date (Arp et al., 2015), only 375 catchments that contain beaded streams were identified, thus our estimate significantly expands our current understanding of the location and prevalence of Arctic beaded streams. Data is accessible through the alternate identifier link on Zenodo: https://doi.org/10.5281/zenodo.7223256 

   more » « less
5. Towards building a trustworthy pipeline integrating Neuroscience Gateway and Open Science Chain

   https://doi.org/10.1093/database/baae023  

   Sivagnanam, S.; Yeu, S.; Lin, K.; Sakai, S.; Garzon, F.; Yoshimoto, K.; Prantzalos, K.; Upadhyaya, D_P; Majumdar, A.; Sahoo, S_S; et al (April 2024, Database)

   Abstract When the scientific dataset evolves or is reused in workflows creating derived datasets, the integrity of the dataset with its metadata information, including provenance, needs to be securely preserved while providing assurances that they are not accidentally or maliciously altered during the process. Providing a secure method to efficiently share and verify the data as well as metadata is essential for the reuse of the scientific data. The National Science Foundation (NSF) funded Open Science Chain (OSC) utilizes consortium blockchain to provide a cyberinfrastructure solution to maintain integrity of the provenance metadata for published datasets and provides a way to perform independent verification of the dataset while promoting reuse and reproducibility. The NSF- and National Institutes of Health (NIH)-funded Neuroscience Gateway (NSG) provides a freely available web portal that allows neuroscience researchers to execute computational data analysis pipeline on high performance computing resources. Combined, the OSC and NSG platforms form an efficient, integrated framework to automatically and securely preserve and verify the integrity of the artifacts used in research workflows while using the NSG platform. This paper presents the results of the first study that integrates OSC–NSG frameworks to track the provenance of neurophysiological signal data analysis to study brain network dynamics using the Neuro-Integrative Connectivity tool, which is deployed in the NSG platform. Database URL: https://www.opensciencechain.org. 

   more » « less