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135 results for “Arctic Region”
FIG. 6. — A in Archaeofaunal signatures of specialized bowhead whaling in the Western Canadian Arctic: a regional study
FIG. 6. — A, Faunal frequencies (%NISP) for sites on the East Channel of the Mackenzie River; B., faunal frequencies (%NISP) for sites
FIG. 7 in Archaeofaunal signatures of specialized bowhead whaling in the Western Canadian Arctic: a regional study
FIG. 7. — Artefact frequencies of whale bone artefacts as a percentage of total organic artefacts from coastal contexts in the Mackenzie Delta Region. Black bars represent presumptive whaling sites, while grey bars represent non-whaling sites.
FIG. 8 in Archaeofaunal signatures of specialized bowhead whaling in the Western Canadian Arctic: a regional study
FIG. 8. — Relative meat contribution of taxa in the presumptive bowhead whaling assemblages based on % weight of total edible tissue, using techniques applied in Friesen & Arnold (1995).
FIG. 3 in Archaeofaunal signatures of specialized bowhead whaling in the Western Canadian Arctic: a regional study
FIG. 3. — Locations of bowhead whale aggregation areas (after Martell et al.1984; Fraker et al. 1978, and Hardwood & Smith 2002).
Subcomplexes of the Regional Security Complex in the Arctic
<p>Subcomplexes of the Regional Security Complex in the Arctic -a map showing the distribution of subcomplexes within the Regional Security Complex in the Arctic. The map was used to create a model of the Regional Security Complex in the Arctic.</p> <p>The map was drafted as a part of research supported by Poland’s National Centre for Science under the grant entitled: The adaptation of the regional security complex in the face of climate change: the example of the Arctic, with number: UMO-2019/35/N/HS5/00578.</p>
Total numbers per square meter and taxa of insects taken during a survey of headwater streams in the Toolik Lake region during the summer of 2001, Arctic LTER 2001.
A Surber sampler (25 X 25 cm frame fitted with a 243 um mesh net) was used to sample invertebrates on a single date at each site. Five replicates were taken from at least two riffles at each site. Samples were preserved in 4% formaldehyde and transported to Orono, Maine, where invertebrates were removed by hand under 15X magnification and then identified and counted. All values are the mean of five replicates and have been converted to individuals per square meter.
CH4 top-down emissions from three high-latitude Arctic regions.
<p>This dataset contains top-down emissions from three high-latitude Arctic regions (the North Slope of Alaska, the East Siberian Lowlands and the Taymyr Peninsula) using three different priors. Emissions were used in the publication "Ward et al., 2024 - Increasing methane emissions and widespread cold-season release from high-Arctic regions detected through atmospheric measurements" in review with JGR:Atmospheres. </p> <p>Top-down emissions were derived using the RHIME inverse model from the University of Bristol's Atmopsheric Chemistry Research Group. For more details on the model, inputs and region definitions please see the above publication.</p>
Figure 3 in Towards a revision of the genus Halectinosoma (Copepoda, Harpacticoida, Ectinosomatidae): new species from the North Atlantic and Arctic regions
Figure 3. Halectinosoma mandibularis sp. nov. Female holotype: A, P1; B, P2; C, P3; D, P4.
Figure 6 in Towards a revision of the genus Halectinosoma (Copepoda, Harpacticoida, Ectinosomatidae): new species from the North Atlantic and Arctic regions
Figure 6. Halectinosoma latisetifera sp. nov. Female holotype: A, P1; B, P2; C, P3; D, P4.
The prediction data analyzed in the article: "An improved regional coupled modeling system for Arctic sea ice simulation and prediction: a case study for 2018"
<p>The outputs of seasonal predictions with the Coupled Arctic Prediction System version 1 analyzed in the article, "An improved regional coupled modeling system for Arctic sea ice simulation and prediction: a case study for 2018", including:</p> <p>Sea ice concentration (SIC)</p> <p>Sea ice thickness (SIT)</p> <p>Sea surface temperature (SST)</p> <p>Ice mass budget diagnostics</p> <p>Accumulated downward shortwave radiation at the surface (ASWDN)</p> <p>Accumulated downward longwave radiation at the surface (ALWDN)</p> <p>Near surface air temperature (T2) </p> <p>Temperature and salinity profile of the upper ocean under sea ice </p>
Supporting Data for "Regional Sensitivity Patterns of Arctic Ocean Acidification Revealed With Machine Learning"
<p>This repository contains additional model simulation data used in the following paper:</p> <p>Krasting et al., 2022: Regional sensitivity patterns of Arctic Ocean acidification revealed with machine learning. <em>Communications Earth & Environment</em>.</p> <p><strong>Description of data files in this repository:</strong></p> <ol> <li>GFDL-CM4.c_ant.nc (42M) - NetCDF file of anthropogenic carbon inventory for 3 historical simulation ensemble members performed with the NOAA GFDL-CM4 climate model </li> <li>GFDL-ESM4.c_ant.nc (12M) - NetCDF file of anthropogenic carbon inventory for 3 concentration-driven historical simulation ensemble members performed with the NOAA GFDL-ESM4 Earth system model</li> <li>GFDL-ESM4e.c_ant.nc (12M) - NetCDF file of anthropogenic carbon inventory for 3 emission-driven historical simulation ensemble members performed with the NOAA GFDL-ESM4 Earth system model</li> </ol> <p>Notes:</p> <ul> <li>Anthropogenic carbon was calculated by vertically-integrating the dissolved inorganic carbon tracer (dissic) simulated at year 2002 and subtracting from the corresponding year of the preindustrial control simulation</li> <li>Results are provided on the models' native tripolar grids. Supporting grid metrics are provided in each NetCDF file</li> <li>All other model simulation data used in Krasting et al. 2022 is available publicly through the Earth System Grid Federation.</li> </ul> <p> </p>
Phase picker models and training data for paper "Deep learning models for regional phase detection on seismic stations in Northern Europe and the European Arctic"
<p>This ZIP file includes tensorflow models for seismic phase detection. Please see how to use these models here: https://github.com/NorwegianSeismicArray/tphasenet</p> <p>The HDF5 files includes waveforms and labels which are part of the training data set (only NORSAR event catalogue and station ARA0).</p>
Fig. 5. A in Evidence Supporting the Concept of a Regionalized Distribution of Testate Amoebae in the Arctic
Fig. 5. A hypothetical model for explaining the species diversity in the Arctic.
The prediction data analyzed in "Seasonal Arctic sea ice prediction using a newly developed fully coupled regional model with the assimilation of satellite sea ice observations"
<p>The outputs of seasonal predictions with the new modeling system analyzed in the article including:</p> <p>Sea ice concentration (SIC)</p> <p>Sea ice thickness (SIT)</p> <p>Sea surface temperature (SST)</p> <p>Near surface air temperature (T2) </p>
Code for "An increasing Arctic-boreal CO2 sink offset by wildfires and source regions"
<p>This repository includes the codes used to produce the results in Virkkala et al. (2024) in review. "An increasing Arctic-boreal CO2 sink offset by wildfires and source regions" (preprint: https://doi.org/10.1101/2024.02.09.579581). In short, there are R codes used to train random forest models, estimate importance scores and partial dependences for the variables, assess model predictive performance and uncertainties, predict (i.e. upscale) with the models and summarize model outputs using R version 4.2. Note that some of the datasets and results (e.g. monthly geospatial predictors or upscaling outputs) were not included in the repository due to their large file size (hundreds of GBs). Therefore, some of the paths to the files included in the codes are not working. These additional files are available from the corresponding author upon request. </p> <p><strong>Folder structure:</strong></p> <p><em>flux_upscaling_data</em>: includes the model training data, i.e. in-situ data as well as environmental data extracted from geospatial datasets</p> <p><em>abcflux_modeling: </em>includes the R analysis scripts in the codes-folder</p> <p><em>abcflux_modeling_bigfiles: </em>includes the key results and figures produced by the scripts</p> <p><strong>More details from avirkkala@woodwellclimate.org and from manuscript:</strong></p> <div>Anna-Maria Virkkala, Brendan M. Rogers, Jennifer D. Watts, Kyle A. Arndt, Stefano Potter, Isabel Wargowsky, Edward A. G. Schuur, Craig See, Marguerite Mauritz, Julia Boike, Syndonia M. Bret-Harte, Eleanor J. Burke, Arden Burrell, Namyi Chae, Abhishek Chatterjee, Frederic Chevallier, Torben R. Christensen, Roisin Commane, Han Dolman, Bo Elberling, Craig A. Emmerton, Eugenie S. Euskirchen, Liang Feng, Mathias Goeckede, Achim Grelle, Manuel Helbig, David Holl, Järvi Järveoja, Hideki Kobayashi, Lars Kutzbach, Junjie Liu, Ingrid Liujkx, Efrén López-Blanco, Kyle Lunneberg, Ivan Mammarella, Maija E. Marushchak, Mikhail Mastepanov, Yojiro Matsuura, Trofim Maximov, Lutz Merbold, Gesa Meyer, Mats B. Nilsson, Yosuke Niwa, Walter Oechel, Sang-Jong Park, Frans-Jan W. Parmentier, Matthias Peichl, Wouter Peters, Roman Petrov, William Quinton, Christian Rödenbeck, Torsten Sachs, Christopher Schulze, Oliver Sonnentag, Vincent St.Louis, Eeva-Stiina Tuittila, Masahito Ueyama, Andrej Varlagin, Donatella Zona, and Susan M. Natali. An increasing Arctic-boreal CO<sub>2</sub> sink offset by wildfires and source regions.</div> <div>bioRxiv 2024.02.09.579581; doi: https://doi.org/10.1101/2024.02.09.579581</div>
FIG. 1 in Archaeofaunal signatures of specialized bowhead whaling in the Western Canadian Arctic: a regional study
FIG. 1. — Mackenzie Inuit (Siglit) main winter villages and territories (after Betts 2009:Fig. 2).
FIG. 2 in Archaeofaunal signatures of specialized bowhead whaling in the Western Canadian Arctic: a regional study
FIG. 2. — Bowhead whale mandible eroding from bank at McKinley Bay, Northwest Territories.
Data from: Taxonomy, biogeography and DNA barcodes of Geodia species (Porifera, Demospongiae, Tetractinellida) in the Atlantic boreo-arctic region
Geodia species north of 60°N in the Atlantic appeared in the literature for the first time when Bowerbank described Geodia barretti and G. macandrewii in 1858 from western Norway. Since then, a number of species have been based on material from various parts of the region: G. simplex, Isops phlegraei, I. pallida, I. sphaeroides, Synops pyriformis, G. parva, G. normani, G. atlantica, Sidonops mesotriaena (now called G. hentscheli), and G. simplicissima. In addition to these 12 nominal species, four species described from elsewhere are claimed to have been identified in material from the northeast Atlantic, namely G. nodastrella and G. cydonium (and its synonyms Cydonium muelleri and Geodia gigas). In this paper, we revise the boreo-arctic Geodia species using morphological, molecular, and biogeographical data. We notably compare northwest and northeast Atlantic specimens. Biological data (reproduction, biochemistry, microbiology, epibionts) for each species are also reviewed. Our results show that there are six valid species of boreo-arctic Atlantic Geodia while other names are synonyms or mis-identifications. Geodia barretti, G. atlantica, G. macandrewii, and G. hentscheli are well established and widely distributed. The same goes for Geodia phlegraei, but this species shows a striking geographical and bathymetric variation, which led us to recognize two species, G. phlegraei and G. parva (here resurrected). Some Geodia are arctic species (G. hentscheli, G. parva), while others are typically boreal (G. atlantica, G. barretti, G. phlegraei, G. macandrewii). No morphological differences were found between specimens from the northeast and northwest Atlantic, except for G. parva. The Folmer cytochrome oxidase subunit I (COI) fragment is unique for every species and invariable over their whole distribution range, except for G. barretti which had two haplotypes. 18S is unique for four species but cannot discriminate G. phlegraei and G. parva. Two keys to the boreo-arctic Geodia are included, one based on external morphology, the other based on spicule morphology.
FIGURE 6 in Description of Chaetocladius (Chaetocladius) spiridonovi sp. nov. from the Russian Arctic Region (Diptera, Chironomidae)
FIGURE 6. Maximum likelihood (ML) tree (-Ln likelihood = 7822.92) of the genus Chaetocladius and one out- group related to Hydrobaenus conformis (Holmgren, 1869) inferred from the cytochrome c oxidase I (COI) nucleotide sequence data (658 bp). Numbers are bootstrap support of 1000 replicates, bootstrap values only on branches that were supported in more than 60 % of the bootstrap replicates. Specimens obtained in this study are in bold.
FIGURES 1–5 in Description of Chaetocladius (Chaetocladius) spiridonovi sp. nov. from the Russian Arctic Region (Diptera, Chironomidae)
FIGURES 1–5. Male adult of of Chaetocladius spiridonovi sp. nov., 1-2, hypopygium in dorsal and ventral view; 3, anal point in lateral view; 4, gonostylus; 5, tibial spur of hind tibia.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.