cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.13182596
title: Binary black-hole simulation SXS:BBH:2117

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13182597
title: Binary black-hole simulation SXS:BBH:2117

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.15468/dl.vr6ej8
title: Occurrence Download

description: A dataset containing 60 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "GadmGid is CHN.29_1",
 "TaxonKey is Lophura leucomelanos (Latham, 1790)"
 ]
}

 The dataset includes 60 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046000-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13182598
title: Binary black-hole simulation SXS:BBH:2108

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13182599
title: Binary black-hole simulation SXS:BBH:2108

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13182600
title: Binary black-hole simulation SXS:BBH:2123

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13182601
title: Binary black-hole simulation SXS:BBH:2123

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5440/1545775
title: Expansion of high-latitude deciduous forests driven by interactions between climate warming and fire: Modeling Archive

description: This Modeling Archive is in support of an NGEE Arctic publication. More information in the related references. The dataset is a model (ecosys) output used in a study that examined the potential future shifts in high-latitude vegetation types (PFTs) driven by changes in 21st century climate and fire. The study highlights the importance of mechanistically representing differences in plant functional traits on current and possible 21st century trajectories of Alaskan PFTs. This dataset package includes 13 files in pdf, csv, and nc formats.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
DOI: 10.5440/1783190
title: Full Spectrum, 350 - 2500 nm, Leaf and Canopy Spectral Reflectance, Seward Peninsula, Alaska, 2017

description: Full-range (350 - 2500 nm) leaf and canopy reflectance spectra of various Arctic tundra ecosystem endmembers, including species-level leaf reflectance, canopy-scale species endmember spectra, plot-scale spectra, and transect spectra as well as non-vegetated surface (NVS) spectra. The datasets were collected at the three core NGEE-Arctic watersheds, Kougarok, Teller, and Council within the larger Seward Peninsula, Alaska region. The data were collected in the months of July and August of 2017 using a full-range Spectra Vista Corporation (SVC) HR-1024i spectroradiometer. Leaf-level spectra were collected with the original SVC leaf clip/plant probe connected to the spectrometer through a 1.15 meter long fiber optic cable, while canopy-scale reflectance was collected with an 8-degree field-of-view (FOV) foreoptic lens. All spectral measurements were collected as calibrated surface radiance and converted to surface reflectance using a 99.99% reflective Spectralon white reference standard. For those canopy spectra collected with associated functional trait data, the FOV of the instrument was positioned to include the same leaves harvested for functional trait measurements, including leaf mass per area (LMA) and foliar carbon and nitrogen content (see associated dataset). This data package includes 26 files in a variety of formats including processed canopy and leaf spectra (*.csv), processed dGPS locations (*.csv and *.kmz), digital photographs of spectral targets (*.jpg) and raw data from spectroradiometer and dGPS instruments (compressed as tar.gz). Metadata files include data descriptions (_dd.csv) for tabular data and a key to species symbols used in data files. All included files are listed and described in NGA110_flmd.csv.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
DOI: 10.5440/1783191
title: Full spectrum (350-2500 nm) canopy spectral reflectance, Seward Peninsula, Alaska, 2018

description: Full-range (350-2500 nm) canopy reflectance spectra of various Arctic tundra ecosystem endmembers, including canopy-scale species endmember spectra, plot-scale spectra, and transect spectra as well as non-vegetated surface (NVS) spectra. The datasets were collected at the three core NGEE-Arctic watersheds, Kougarok, Teller, and Council, as well as the supplemental Kougarok Mile 80 site within the larger Seward Peninsula, Alaska region. The data were collected in July 2018 using a full-range Spectra Vista Corporation (SVC) HR-1024i spectroradiometer. Canopy-scale reflectance was collected with an 8-degree field-of-view (FOV) foreoptic lens. All spectral measurements were collected as calibrated surface radiance and converted to surface reflectance using a 99.99% reflective Spectralon white reference standard. For those canopy spectra collected with associated functional trait data, the FOV of the instrument was positioned to include the same leaves harvested for functional trait measurements, including leaf mass per area (LMA) and foliar carbon and nitrogen content (see associated dataset). Species included in the spectra and functional trait dataset include Alnus viridis, Betula nana, Betula glandulosa, Arctous alpina, and Salix pulchra. This data package includes 35 files in a variety of formats including processed canopy spectra (*.csv), processed dGPS locations (*.csv and *.kmz), digital photographs of spectral targets (*.jpg) and raw data from spectroradiometer and dGPS instruments (compressed as tar.gz). Metadata files include data descriptions (_dd.csv) for tabular data and a key to species symbols used in data files. All included files are listed and described in NGA208_flmd.csv.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
DOI: 10.5281/zenodo.13182602
title: Binary black-hole simulation SXS:BBH:2143

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13182603
title: Binary black-hole simulation SXS:BBH:2143

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.25584/2229733
title: Global Soil Virus (GSV) Atlas

description: Historically neglected by microbial ecologists, soil viruses are now thought to be critical to global biogeochemical cycles. However, our understanding of their global distribution, activities, and interactions with the soil microbiome remains limited. Here, we present the Global Soil Virus (GSV) Atlas, a comprehensive dataset from 2,953 soils that comprises 616,935 uncultivated virus genomes (UViGs) and 38,508 unique viral operational taxonomic units (vOTUs). Rarefaction curves from the GSV Atlas indicate that most soil viral diversity remains unexplored, further underscored by high spatial turnover and low rates of shared vOTUs across samples. By examining genes associated with biogeochemical functions, we also demonstrate the viral potential to impact soil carbon and nutrient cycling. This study represents the most extensive characterization of soil viral diversity to date and provides a foundation for developing testable hypotheses regarding the role of the virosphere in the soil microbiome and global biogeochemistry.
DOI: 10.5281/zenodo.13182604
title: Binary black-hole simulation SXS:BBH:2124

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13182605
title: Binary black-hole simulation SXS:BBH:2124

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13182608
title: Binary black-hole simulation SXS:BBH:2101

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13182609
title: Binary black-hole simulation SXS:BBH:2101

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13182610
title: Binary black-hole simulation SXS:BBH:2158

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13182611
title: Binary black-hole simulation SXS:BBH:2158

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.6084/m9.figshare.26488198
title: A curated dataset of the Human Diseases Unknowns Network(UnknownNet)

description: This is a curated dataset of the Human Diseases Unknowns Network (UnknownNet). It encompasses a collection of sentences that express unknowns (unknowns statements), identified through the application of Natural Language Processing (NLP) techniques from publications in health and biological sciences journals indexed in the Nature Index. Additionally, the dataset includes entities extracted from disease-related 'unknown' statements at the sentence level, all of which contribute to the construction of UnknownNet."<br>
DOI: 10.5440/2368782
title: LiDAR Point Cloud Data from the 2018 NGEE Arctic UAS Campaign at the Kougarok 64 Field Site, Seward Peninsula, Alaska

description: Airborne remote sensing data collected from Los Alamos National Laboratory's (LANL) heavy-lift unoccupied aerial system (UAS) hexacopter platform operated by NGEE Arctic scientists from the EES-14 group at Los Alamos National Laboratory. These data were collected in July 2018 at a field site near mile marker 64 along the Kougarok road (Nome-Taylor Highway) between Nome, Alaska and Taylor, Alaska. A DJI Matrice 600 Pro Airframe and Routescene UAV LiDARSystem was used to collect LiDAR data. The LiDAR data has undergone basic post-processing using Routescene LidarViewer Pro software to create point cloud data (.laz files). This data package contains point clouds (.laz), processing metadata files (json.lvp), and post-processed kinematic files (.csv). Ancillary aircraft data, flight mission parameters, weather conditions, raw LiDAR data, and RGB imagery can be found in NGA298.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
DOI: 10.5281/zenodo.13182612
title: Binary black-hole simulation SXS:BBH:2140

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13182613
title: Binary black-hole simulation SXS:BBH:2140

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.6084/m9.figshare.26488219
title: The dataset and R code for the paper "Soil respiration response to decade-long warming modulated by soil moisture in a boreal forest" in Nature Geoscience, 2024

description: Data and R code for soil respiration under experimental warming in boreal forests from the project named "The Boreal Forest Warming at an Ecotone in Danger (B4WarmED)".
DOI: 10.5281/zenodo.13182619
title: Binary black-hole simulation SXS:BBH:2134

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13182620
title: Binary black-hole simulation SXS:BBH:2134

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.15468/dl.9wekft
title: Occurrence Download

description: A dataset containing 4558 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hapalopsittaca Ridgway, 1912"
 ]
}

 The dataset includes 4558 records from 53 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046001-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s2nu2w
title: Occurrence Download

description: A dataset containing 51 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "GadmGid is CHN.29_1",
 "TaxonKey is Pernis ptilorhynchus (Temminck, 1821)"
 ]
}

 The dataset includes 51 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046002-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15485/2425965
title: Volumetric Soil Moisture Measurements at the Teller 27 Site, Seward Peninsula, Alaska, 2022-2023

description: The Teller 27 watershed on the Seward Peninsula, Alaska, has been well characterized by the NGEE Arctic project. The study site is underlain by discontinuous permafrost that is thawing as the climate warms. As a result, the site is experiencing a short-term wetting trend as a perched water table above the remaining permafrost provides plant available water during the growing season. Soil moisture patterns drive microbial activity and plant species compositions including plant density and height. This study aimed to understand how soil moisture patterns were influenced by tundra microtopography. To accomplish this, we placed a strategic network of soil moisture sensors in micro-highs, micro-lows, and control areas under different vegetation types within the Teller 27 watershed from summer of 2022 through fall of 2023. This data was used in conjunction with other soil data from the Teller 27 watershed to gain a more comprehensive understanding of soil moisture patterns in a rapidly thawing discontinuous permafrost region. This dataset includes six *.csv files: four of time series soil moisture data, one of field soil moisture data, and one of site conditions. The dataset also includes one *.kml file of the watershed and the soil moisture sensor sites as well as this user guide to provide details on data collection and processing methods. NGEE Arctic Project Summary The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
DOI: 10.7799/2425969
title: Reference Site Condition Datasets for Floating Wind Arrays in the United States

description: Floating offshore wind farm design is highly site-specific, requiring detailed information about the specific conditions of a project area for realistic design studies. Unfortunately, publicly available site condition data for potential floating offshore wind project sites in the United States is scarce. To support U.S. offshore wind research, we developed reference site condition datasets, including metocean and seabed information, for four potential floating wind project areas in the U.S.: Humboldt Bay, Morro Bay, the Gulf of Maine, and the Gulf of Mexico. These datasets were compiled using publicly available data. Our metocean analysis, covering wind, waves, and surface currents, utilized measurement data from 2000 to 2020. Sources included the National Renewable Energy Laboratory’s National Offshore Wind Dataset for wind data, National Data Buoy Center buoys for wave data, and the High Frequency Radar Network for surface currents. These data were integrated into hourly time series used to compute extreme return periods up to 500 years, monthly statistics, and joint probability clusters for fatigue analysis. Soil conditions were evaluated using the usSEABED database and bathymetry grids were interpolated from the NCEI Digital Elevation Model Global Mosaic. Further information on the datasets and how they were created can be found in: Biglu, M., M. Hall, E. Lozon, S. Housner. 2024. Reference Site Conditions for Floating Wind Arrays in the United States. Golden, CO: National Renewable Energy Laboratory (NREL). NREL/TP-5000-89897. The data are also available at: https://github.com/FloatingArrayDesign/SiteConditions The content of each dataset is as follows: _NOW23_wind.txt: Hourly NOW-23 wind data up to a height of 400 meter. _metocean_1hr.txt: Hourly time series including wind, wave, surface current and temperature data. _Summary.xlsx: Metocean data, including extreme values, joint probability distributions and monthly statistics. _usSEABED_soil.csv: Extract of the usSEABED database for this specific site. _bathymetry_200m.txt (and 500m, 1000m): Gridded seabed depth data.
DOI: 10.15468/dl.4mvad7
title: Occurrence Download

description: A dataset containing 31 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "GadmGid is CHN.29_1",
 "TaxonKey is Trochalopteron elliotii J.Verreaux, 1870"
 ]
}

 The dataset includes 31 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046003-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13182617
title: Binary black-hole simulation SXS:BBH:2089

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13182618
title: Binary black-hole simulation SXS:BBH:2089

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13182622
title: Binary black-hole simulation SXS:BBH:2092

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13182623
title: Binary black-hole simulation SXS:BBH:2092

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13182626
title: Binary black-hole simulation SXS:BBH:2153

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13182627
title: Binary black-hole simulation SXS:BBH:2153

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13182628
title: Binary black-hole simulation SXS:BBH:2137

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13182629
title: Binary black-hole simulation SXS:BBH:2137

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13182634
title: Binary black-hole simulation SXS:BBH:2086

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13182635
title: Binary black-hole simulation SXS:BBH:2086

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13182638
title: Binary black-hole simulation SXS:BBH:2094

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13182639
title: Binary black-hole simulation SXS:BBH:2094

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13182644
title: Binary black-hole simulation SXS:BBH:2148

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13182645
title: Binary black-hole simulation SXS:BBH:2148

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13182640
title: Binary black-hole simulation SXS:BBH:2131

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13182641
title: Binary black-hole simulation SXS:BBH:2131

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13182648
title: Binary black-hole simulation SXS:BBH:2107

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13182649
title: Binary black-hole simulation SXS:BBH:2107

description: Simulation of a black-hole binary system evolved by the SpEC code.