cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.18739/a24j0b021
title: Global Vegetation Greenness Normalized Difference Vegetation Index (NDVI) from Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies-3rd Generation (GIMMS-3G+), 1981-2022

description: This dataset holds the Global Inventory Modeling and Mapping Studies-3rd Generation V1.2 (GIMMS-3G+) data for the Normalized Difference Vegetation Index (NDVI). NDVI was based on corrected and calibrated measurements from Advanced Very High Resolution Radiometer (AVHRR) data with a spatial resolution of 0.0833 degree and global coverage for 1982 to 2022. Maximum NDVI values are reported within twice monthly compositing periods (two values per month). The dataset was assembled from different AVHRR sensors and accounts for various deleterious effects, such as calibration loss, orbital drift, and volcanic eruptions. The data are provided in NetCDF format. This dataset provides one of the longest continuous records of global vegetation productivity. It has been widely used by the remote sensing community and has supported many scientific publications.
DOI: 10.18739/a24q7qs09
title: Understand and forecast long-term variations of in-situ geophysical and geomechanical characteristics of degrading permafrost in the Arctic - continuously observed ground temperatures, Utqiagvik, Alaska, 2022-2023

description: This project is part of the NSF United States Department of Agriculture-National Institute of Food and Agriculture (NIFA)-jointly-funded Signals in the Soil (SitS) program whose goal is to support collaborative research on dynamic soil processes and soil formation through advances in sensor systems and predictive, process-based and mechanistic modeling.
 This dataset provides ground temperature data in the active layer and near-surface permafrost to provide a baseline for assessing the future changes in the near-surface temperatures in the natural environment at Utqiagvik, Alaska. Collected ground temperature data are intended to help researchers ground-truth measurements of geophysical and geomechanical properties measured using a 1.5-kilometer-long fiber-optic distributed acoustic sensing array. The project outcomes will enable realistic evaluation of the performances of infrastructure in Arctic Alaska and improve the design of more robust infrastructure in the Arctic.
DOI: 10.18739/a24q7qs1s
title: Aerosol Measurements at Utqiagvik, Alaska, from 2022 to 2027 for Sustaining The Utqiagvik Aerosol Record of Decades (STUARD)

description: ### Access

Dataset can be accessed and downloaded from the 'ADC' directory via: [http://arcticdata.io/data/10.18739/A24Q7QS1S](http://arcticdata.io/data/10.18739/A24Q7QS1S) 

### Overview
 
The Arctic is warming faster than any other place on Earth, with sea ice declining rapidly and sources of sea spray and biogenic aerosol emissions changing by consequence. Utqiagvik is at the forefront of this change, abutting one of the largest areas of sea ice loss. This change will have far-reaching impacts to both the environment and the community. Because this change has happened largely in the last decade, now is an important time to both document that change and to continue a data record that will allow for a characterization of the New Arctic, as climate is already altering the Arctic landscape forever. The longest and most complete record of aerosol properties in the American Arctic is that of Utqiagvik, making this unique location serve as a regional record of changes in atmospheric aerosol properties. This dataset will extend the baseline measurements of this Arctic aerosol record, including and continuing the 15-year record of submicron inorganic components (Quinn et al., 2009; Quinn et al., 2002), re-instituting the 2-year record of organic components collected a decade ago (Frossard et al., 2011; Shaw et al., 2010), enhancing the chemical analysis with sulfur isotopes to improve interpretation of emission sources (Kunasek et al., 2010; Thiemens & Lin, 2019), continuing particle number size distribution measurements (Freud et al., 2017), and re-starting cloud condensation nuclei measurements (Schmale, Henning, et al., 2018).
DOI: 10.18739/a2513tx9k
title: Minirhizotron fine root data from a treeline snowfence experiment near the Agashashok River, northwest Alaska (2019-2021)

description: Snow is an important insulator of Arctic soils during winter and may be an important source of soil moisture in summer. Changes in snow depth are likely to affect fine root growth and mortality via changes in soil temperature, moisture, and/or nutrient availability, which could alter aboveground growth and reproduction of Arctic vegetation, including white spruce (Picea glauca), a dominant Arctic treeline species in North America. In this study, we explored the drivers of fine root dynamics at three contrasting treelines in the western Brooks Range of Alaska. We increased snow depth relative to control plots using snowfences, and we used minirhizotrons to estimate fine root growth, standing crop, and overwinter mortality. Nearly all fine roots were observed in the upper 25 centimeters (cm), with the greatest fine root density occurring in the top 10 cm. Experimental deepening of winter snowpacks led to warmer soils throughout the winter. However, deeper snowpacks did not affect growing season soil moisture. After five years of treatment, deeper winter snowpacks reduced fine root standing crop with no significant effects on overwinter fine root mortality. Using structural equation modelling, we tested for pathways by which experimental deepening of winter snowpacks may have affected soil microclimates which, in turn, may have affected fine root growth and standing crop. Warmer soils in late winter were associated with warmer soils in early and mid-summer. Warmer early summer soils may have promoted early root growth. However, warmer July soils were associated with reduced fine root growth and smaller fine root standing crops. We hypothesize that experimental deepening of winter snowpacks improved plant access to soil nutrients, resulting in reduced investment in fine roots, potentially leaving additional resources available to support aboveground growth and reproduction. Our results suggest one mechanism by which deeper snowpacks could promote advance of the northern treeline.
DOI: 10.18739/a2599z381
title: National Oceanic and Atmospheric Administration/ National Snow and Ice Data Center (NOAA/NSIDC) Climate Data Record of Passive Microwave Sea Ice Concentration, Version 4

description: This data set provides a Climate Data Record (CDR) of sea ice concentration from passive microwave data. The CDR algorithm output is a rule-based combination of ice concentration estimates from two well-established algorithms: the NASA Team (NT) algorithm (Cavalieri et al. 1984) and NASA Bootstrap (BT) algorithm (Comiso 1986). The CDR is a consistent, daily and monthly time series of sea ice concentrations from 25 October 1978 through the most recent processing for both the north and south polar regions. All data are on a 25 kilometer (km) x 25 km grid. Note: A near-real-time version of this data set also exists to fill the gap between the time that this data set is updated through to the present. The data set is called the Near-Real-Time National Oceanic and Atmospheric Administration/ National Snow and Ice Data Center (NOAA/NSIDC) Climate Data Record of Passive Microwave Sea Ice Concentration (https://nsidc.org/data/g10016).
 Citation: Meier, W. N., F. Fetterer, A. K. Windnagel, and S. Stewart. 2021. NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 4. [Indicate subset used]. Boulder, Colorado USA. NSIDC: National Snow and Ice Data Center https://doi.org/10.7265/efmz-2t65. [Date Accessed].
DOI: 10.18739/a25d8ng9c
title: Pond water salinity along the Kashunk River in the Yukon-Kuskokwim (Y-K) Delta of western Alaska, summer 2023

description: During September 2022, Typhoon Merbok flooded a large area of the Yukon-Kuskokwim Delta (Y-K Delta) in western Alaska. This flood had the potential to alter the salinity of the numerous ponds and small water bodies in the region. We surveyed pond water salinity during early to mid-summer in 2023 along the Kashunuk river in the central Y-K Delta. Ponds were sampled along transects perpendicular to the river extending up to 500 meters (m) from the river at 26 sample points between the mouth and ca. 50 km inland. Data reported is pond water salinity in parts per thousand at multiple ponds per sample transect
DOI: 10.15468/dl.rjytfb
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Tiliqua rugosa (Gray, 1825))",
 "Country is one of (Afghanistan, Armenia, Azerbaijan, Bahrain, Bangladesh, Bhutan, Brunei Darussalam, Cambodia, China, Cyprus, Georgia, Hong Kong, India, Indonesia, Iran (Islamic Republic of), Iraq, Israel, Japan, Jordan, Kazakhstan, Korea (Democratic People’s Republic of), Korea, Republic of, Kuwait, Kyrgyzstan, Lao People’s Democratic Republic, Lebanon, Macao, Malaysia, Maldives, Mongolia, Myanmar, Nepal, Oman, Pakistan, Palestine, State of, Philippines, Qatar, Saudi Arabia, Singapore, Sri Lanka, Syrian Arab Republic, Tajikistan, Thailand, Timor-Leste, Türkiye, Turkmenistan, United Arab Emirates, Uzbekistan, Viet Nam, Yemen)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016830-250525065834625/datasets/export for details.
DOI: 10.15468/dl.7tu4dt
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Tiliqua rugosa (Gray, 1825))",
 "Country is one of (Anguilla, Antigua and Barbuda, Argentina, Aruba, Bahamas, Barbados, Belize, Bolivia (Plurinational State of), Bonaire, Sint Eustatius and Saba, Bouvet Island, Brazil, Cayman Islands, Chile, Colombia, Costa Rica, Cuba, Curaçao, Dominica, Dominican Republic, Ecuador, El Salvador, Falkland Islands (Malvinas), French Guiana, Grenada, Guadeloupe, Guatemala, Guyana, Haiti, Honduras, Jamaica, Martinique, Mexico, Montserrat, Nicaragua, Panama, Paraguay, Peru, Puerto Rico, Saint Barthélemy, Saint Kitts and Nevis, Saint Lucia, Saint Martin (French part), Saint Vincent and the Grenadines, Sint Maarten (Dutch part), South Georgia and the South Sandwich Islands, Suriname, Trinidad and Tobago, Turks and Caicos Islands, Uruguay, Venezuela (Bolivarian Republic of), Virgin Islands (British), Virgin Islands (U.S.))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016831-250525065834625/datasets/export for details.
DOI: 10.15468/dl.crq3as
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Tiliqua rugosa (Gray, 1825))",
 "Country is one of (Bermuda, Canada, Greenland, Saint Pierre and Miquelon, United States of America)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016829-250525065834625/datasets/export for details.
DOI: 10.18739/a25h7bw3s
title: Assessment of Plant Functional Types on Burn Severity in Interior Alaska, 2017-2020

description: Wildfires are increasing in both frequency and severity across boreal forests, making remote observations of burn severity paramount for above and belowground fuel monitoring, modeling, and risk management. However, traditional burn severity metrics such as the Normalized Burn Ratio (NBR) are often unilaterally applied across burn scars, despite evidence suggesting burn severity may vary with vegetation structure and composition. We present data to calculate a new, scalable integrative vegetation-fire index (IntFire) that incorporates plant functional type (PFT) specific sensitivities to burn for improving severity assessments. The IntFire index was developed using ninety-four nonzero composite burn index (CBI) observations measured across four upland and lowland boreal forest fires (total burned area: 909 square kilometers) in central Alaska. We mapped pre-fire PFT cover using 3 meter resolution PlanetScope imagery, synthsesizing very high-resolution WorldView-2 and NASA G-LiHT imagery to inform fine-scale classifications. Linear and logarithmic regression models were used to correlate each PFT with its best performing burn severity metric then integrated into IntFire. We demonstrate the scalability of IntFire by applying the optimal regression models to both fine and meso-scale optical products (i.e., PlanetScope and Harmonized Landsat/Sentinel, HLS) using derived and spectrally unmixed PFT maps.
DOI: 10.18739/a25m62870
title: Alaskan river discharge, temperature, and climate data for a climate reference (1990-2021) and at mid-century (2034-2065)

description: ### Access

NetCDF files in this dataset can be accessed and downloaded from the 'ADC' directory via: [https://arcticdata.io/data/10.18739/A25M62870/](https://arcticdata.io/data/10.18739/A25M62870/)

### Overview

The Regional Arctic System Model, the combined Weather Research & Forecasting Model and the Community Terrestrial Systems Model for climate and land surface processes, mizuRoute for river routing, and the River Basin Model for river temperature, was used to generate high-resolution spatial and temporal data for 49 major Alaskan river basins. This modeling framework was applied to compare Alaskan hydrology between historical (1990-2021) and mid-century (2035-2064) periods across six future scenarios. These scenarios include six dynamically downscaled projections: two pseudo-global warming simulations based on historical meteorology, and four directly downscaled global climate models under the Shared Socioeconomic Pathway (SSP) SSP2-4.5 and SSP3-7.0 emission pathways. The climate data encompass variables such as snowpack, evapotranspiration, precipitation (rain and snow), groundwater, river temperature and discharge, as well as heat flux to the ocean.
DOI: 10.18739/a25m6288g
title: Integrated Field and Synthetic Aperture Radar-Based Dataset for Arctic Lake Ice and Under-Ice Water Salinity Classification, Northern Alaska, 2024

description: This dataset provides a comprehensive, field-validated Synthetic Aperture Radar (SAR) dataset for Arctic lake ice classification, with a particular emphasis on under-ice water salinity. It includes in situ measurements from 104 lakes (132 measurement sites) across northern Alaska collected in May 2024, capturing data on lake ice thickness, snow depth, lake depth, and specific conductance of unfrozen water beneath the ice. These field observations are integrated with multi-season Sentinel-1 SAR imagery from early winter (January) to late winter (May), along with additional geospatial datasets such as Interferometric Synthetic Aperture Radar (IfSAR)-derived elevation models and summer ice-off timing. The dataset enables improved differentiation of bedfast and floating ice lakes, particularly identifying lakes with brackish to saline water that were previously misclassified as bedfast ice lakes using traditional SAR-based remote sensing approaches. This resource supports research in permafrost stability, Arctic hydrology, climate change impacts, and winter water resource availability. This work was supported by grants from the U.S. National Science Foundation (OPP-2336164 and OPP-2336165) and the European Research Council project No. 951288 (Q-Arctic). Additional support was provided under a Broad Agency Announcement award from ERDC-CRREL, PE 0603119A.
DOI: 10.18739/a25q4rn8z
title: DARTS: Multi-year database of AI detected retrogressive thaw slumps (RTS) in hotspots of the circum-arctic permafrost region - v1.1

description: This dataset, called DARTS, comprises footprints of retrogressive thaw slump (RTS) identified and quantified using an automated deep learning approach in RTS hotspots across the Arctic and Subarctic permafrost regions. We utilized multispectral PlanetScope imagery with a spatial resolution of ~3 meters (m), complemented by ArcticDEM (Digital Elevation Model) and derived datasets, including slope, relative elevation, and Landsat-derived change trends.
 The dataset covers an area of 1.6 million square-kilometers (km²), with at least one coverage between 2021 and 2023, and provides annual coverage for approximately 900,000 km². In several highly active key sites, such as Banks Island, Peel Plateau, and Novaya Zemlya, we extended the data frequency and temporal coverage to 2018-2023. We mapped a total of more than 43,000 individual RTS and ALD, many of them multiple times. We offer two levels of datasets; Level 1: RTS footprints per image with timestamps; and Level 2: annually aggregated RTS footprints. Essential metadata includes image footprints, dataset coverage, timestamps, and model-specific information.
 To enhance reproducibility and further use, the training labels, processing code, and model checkpoints are publicly available. This version, v1.1, is the revised first openly accessible release. The dataset will be maintained and continuously updated in both spatial and temporal extent. It can be used for mapping and quantifying RTS, analyzing spatio-temporal patterns of RTS dynamics, or serving as input for landscape dynamics models.
DOI: 10.18739/a25x25f6k
title: Organic geochemistry of Lake Agassiz sediment cores from southwest Ontario and northern Minnesota in 2023

description: This dataset presents the basic organic geochemistry of Lake Agassiz sediments composing the majority of six long cores from extent lakes in southwestern Ontario and northern Minnesota. Analyses include dry weight density, organic carbon (C) percent (%) , nitrogen (N) percent (%), carbon-13 (13C) (per mil (‰)) and nitrogen-15 (15N) (per mil). These data were used to calculate variable rates of organic carbon accumulation in sediments resulting in part from changes in glacial meltwater inputs to Lake Agassiz. Organic C/N ratios and stable isotope values were also used to infer potential changes in organic matter sources to this lake's sediments over time.
DOI: 10.18739/a2610vt7v
title: A global high resolution coastline database from satellite imagery (2009 - 2023)

description: ### Access

Full dataset can be accessed and downloaded from the directory via: [https://arcticdata.io/data/10.18739/A2610VT7V/](https://arcticdata.io/data/10.18739/A2610VT7V/).

### Overview

This research is motivated by the limited resolution of existing global coastline datasets and the growing availability of high-resolution multispectral satellite imagery. We retrieve coastlines using a water probability algorithm, which stacks water masks generated from the Normalized Difference Water Index (NDWI) method. This dataset provides global coastlines, water probability maps, and intertidal zones derived from multispectral images captured by Maxar satellites (2009–2023) at a high spatial resolution of 2 meters (m). Coastlines represent the median tidal height of image acquisitions, with modeled tidal heights included. The intertidal zones derived from water probability maps represent dynamic regions sensitive to tidal variations. These high-resolution products support applications in coastal resource management, sea level rise analysis, and coastal habitat loss and migration.
DOI: 10.18739/a26688m5x
title: Chemical, elemental, and isotopic measurements of the upper 8000 years of the archived REnland ice CAP (RECAP) ice core from the Renland ice cap in east-central Greenland; 2022-2024

description: The overall objective of this research was to develop and interpret detailed records of Arctic lead pollution during the past ~8000 years to better understand the development, history, and impacts of mining and metallurgy from the late Neolithic period to present. The dataset contains high-resolution (30 millimeters [mm] average) measurements of elements, chemical species, and water isotopic ratios in the upper ~520 meters [m] of the archived REnland ice CAP (RECAP) ice core. Included are lead and other parameters such as the rare-earth-element cerium, non-sea-salt calcium, sulfur and sodium used for chronology development and interpretation of the lead record. The core was collected in 2015 from the Renland Ice Cap in east-central Greenland. No archive was available for the upper 99.6 m so the most recent part of the record corresponds to 116 years before 1950 (yb1950) or 1833 current era (CE).
DOI: 10.18739/a2697006p
title: Near-Real-Time Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager/Sounder (SSMIS) Daily Polar Gridded Sea Ice Concentrations, Version 2

description: This Near-Real-Time Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager/Sounder (SSMIS) Daily Polar Gridded Sea Ice Concentrations (NRTSI) data set provides sea ice concentrations for both the Northern and Southern Hemispheres. The near-real-time passive microwave brightness temperature data that are used as input to this data set are acquired with the SSMIS on board the DMSP satellites. Investigators generate sea ice concentrations from these data using the NASA Team algorithm. These NRTSI data are primarily meant to provide a best estimate of current ice conditions based on information and algorithms available at the time the data are acquired.