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10,699 results for “easting”
Water Body Checklists 2019: East Siberian Sea Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the East Siberian Sea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
Water Body Checklists: East Siberian Sea Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the East Siberian Sea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
The Interseismic Seismicity of the East Anatolian Fault Between 2007-2012 and Aftershock Locations of the 2020 Mw6.8 Sivrice Earthquake
<p>The two files include the seismicity along the Eastern Anatolian Fault In Turkey between 2007 and 2012 and the Aftershocks of the January 24, 2020 Mw6.8 Sivrice (Elazığ) earthquake.</p>
Model projection of the effect of climate change and fishing pressure on key species of the South East Asia Seas
<p>The dataset contain Projection from the Size-Spectra Bioclimatic Envelop Model (SS-DBEM), this work was part of the GCRF Blue communities Programme (www.blue-communities.org). The model provides distribution and abundance and/or biomass of fish and other species of commercial interest under climate change and fishing pressure. The model outputs are yearly abundance/biomass on a 0.5-by-0.5 degree grid, covering the period from 2000 to 2098. Further description of the model and relevant references are listed in the following file: Guide-fish-model-output-use.docx</p> <p>The model was run under two climate scenario: RCP4.5 and RCP8.5, with different combinations of fishing pressure expressed as the Maximum Sustainable Yield (MSY) for the following values: 0 (no fishing, climate change alone will cause variation in fish biomass), 1 (sustainable fishing), 2, 3 (overfishing), and, 4 (overfishing with destructive practice). The intent is not to reproduce current fishing level but to provide a range of scenarios with which the future of fisheries can be explored.</p> <p>We projected fish species that were identified as key in the South East Asia seas region by our regional partners.The full list is provided in document: Fish-list-modelguide.xlsx</p> <p>There are 4 zip files that contain the model outputs of in either abundance (number of fish) or biomass grams of fish) for the two climate scenario. For example Biomass-RCP45.zip will contain model outputs in biomass for projections under RCP4.5 and all MSY. within the zip files are .csv files of the outputs for each species under the 5 MSY (0 to 4), the individual file names identify the species (identified by a 6digit code), the output provided (abundance or biomass), the RCP (8.5 or 4.5), and the MSY (0, 1, 2, 3, or 4). For example the file labelled 600107-Abundance-rcp85-msy4.csv contains the outputs for species 600107 (Skipjack tuna, <em>Katsuwonnus pelamis</em>), as abundance, under RCP8.5 with MSY4. Headers indicate what is in each column (latitude, longitude and year).</p> <p> </p> <p>Note: some knowledge of Python, R, or a similar software is recommended to ensure easy of use.</p>
Power production from the U.S. east coast offshore wind lease areas
<p>The accompanying file include information regarding the set up of WRF simulations of power production and wake extents from offshore wind lease areas along the U.S. east coast, and also data presented in figures in the "Wind power production from the U.S. east coast offshore lease areas" paper and the associated MATLAB data processing code.</p> <p>The US Department of Energy Office of Science (DE-SC0016605), the US Department of Energy Office of Energy Efficiency and Renewable Energy and New York State Energy Research and Development Authority via the National Offshore Wind Research and Development consortium (147505) funded this research. This research was enabled by computational resources supported by the U.S. National Science Foundation via the Extreme Science and Engineering Discovery Environment (XSEDE) (award TG-ATM170024) and ACI-1541215, and those of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.</p>
CTD Profiles from the HDMS Lauge Koch cruise to East Greenland fjords, August 2018
<p>Greenland fjords are currently undergoing large ecosystem changes due to unprecedented melting of the Greenland Ice Sheet (GrIS). The rapidly increasing discharge of meltwater and ice not only influences circulation patterns and stratification of the water column, but it also introduces large fluxes of allochthonous carbon and nutrients into the Greenland coastal environment, as well as transports large quantities of inorganic particles and suspended sediments which could limit light availability to primary producers. However, data is still limited for most Greenland fjord systems and the east coast of Greenland is especially understudied. During this cruise we investigated 3 different fjord systems of East Greenland in August 2018 aboard the HDMS Lauge Koch. We aimed to describe the physical, chemical and biological variability from glaciers to the shelf. This data set consists of 84 CTD profiles that were obtained with a Seabird SBE25 conductivity, temperature, depth (CTD) instrument. In addition to measuring pressure, conductivity, and temperature, the CTD recorded chlorophyll-a fluorescence, photosynthetically available radiation (PAR), dissolved oxygen, turbidity and pH. The instrument was factory calibrated before the cruise. The CTD recorded variables at 16 Hz and the raw data were processed using Seabird standard workflow to produce 0.1 m binned profiles using the downcast data only. This dataset compiles all CTD variables measured from all profiles into a single comma separated CSV file. </p> <p>We would like to thank the captain and crew of HDMS Lauge Koch for excellent collaboration. The cruise was funded by the Danish Center for Marine Research and by the EU Horizon2020 funded project INTAROS (grant no. 727890) and the Danish Cooperation for Environment in the Arctic.</p> <p> </p> <p> </p> <p> </p>
Remote Sensing based Sea Surface partial pressure of CO2 (pCO2) and air-sea CO2 flux (FCO2) in the East China Sea (2003-2019)
<p>Based on <em>in situ</em> seawater <em>p</em>CO<sub>2</sub> data collected on 51 cruises/legs over the past two decades, a satellite retrieval algorithm for seawater <em>p</em>CO<sub>2</sub> was developed by combining the semi-mechanistic algorithm and machine learning method (MeSAA-ML). MeSAA-ML introduces semi-analytical parameters, including the temperature-dependent seawater <em>p</em>CO<sub>2</sub> (<em>p</em>CO<sub>2,therm</sub> ) and upwelling index (<em>UI<sub>SST</sub></em>), to characterise the combined effect of atmospheric CO<sub>2</sub> forcing, thermodynamic effects, and multiple mixing processes on seawater <em>p</em>CO<sub>2</sub>. Additionally, considering the biological effects and various sub-regional features, multiple ocean colour parameters were also used as inputs in XGBoost, the best-selected machine learning algorithm. Independent cruise-based data were used to validate the satellite-derived <em>p</em>CO<sub>2</sub>, which achieved excellent performance in this complicated marginal sea, with low root mean square error (RMSE=19.6 μatm) and mean absolute percentage deviation (APD=4.12%). Air-sea CO2 fluxes are calculated based on retrieved seawater <em>p</em>CO<sub>2</sub>. </p>
Graph 6: Stagings of Heiner Müller's Plays in Germany (East and West).
<p>Graph 6 shows the number of stagings of Heiner Müller’s plays in East and West Germany from 1957-1990.</p>
IPAAST East Heslerton FDEM
<p>FDEM Dualem21Hs dataset constituting a case study in interoperable archaeological prospection and precision agricultural data at the Landscape Research Centre's site 070 and site 120 at Manor Farm in East Heslerton in context of the IPAAST-CZO project (British Academy - Knowledge Frontiers Programme: kf5210407) by UGlasgow.</p> <p>Contains:<br> -Unprocessed point data: all_orig.csv<br> -Processed point data: all.csv<br> -Interpolated data data of elevation, horizontal coplanar (HCP) coil pair at 0.5 (HCPH), 1 (HCP1) and 2 m (HCP2) transmitter-receiver separation and perpendicular (PRP) coil pair at 0.6 (PRPH), 1.1 (PRP1) and 2.1 m (PRP2) separation in respective (geo)tif files<br> -Scope notes regarding applications of EMI data in precision agricultural management decisions & data in precision agriculture potentially relevant to the interpretation of EMI datasets in archaeology</p>
Remote Sensing based Sea Surface partial pressure of CO2 (pCO2) and air-sea CO2 flux (FCO2) in the East China Sea (2003-2019)
<p>Based on <em>in situ</em> seawater <em>p</em>CO<sub>2</sub> data collected on 51 cruises/legs over the past two decades, a satellite retrieval algorithm for seawater <em>p</em>CO<sub>2</sub> was developed by combining the semi-mechanistic algorithm and machine learning method (MeSAA-ML). MeSAA-ML introduces semi-analytical parameters, including the temperature-dependent seawater <em>p</em>CO<sub>2</sub> (<em>p</em>CO<sub>2,therm</sub> ) and upwelling index (<em>UI<sub>SST</sub></em>), to characterise the combined effect of atmospheric CO<sub>2</sub> forcing, thermodynamic effects, and multiple mixing processes on seawater <em>p</em>CO<sub>2</sub>. Additionally, considering the biological effects and various sub-regional features, multiple ocean colour parameters were also used as inputs in XGBoost, the best-selected machine learning algorithm. Independent cruise-based data were used to validate the satellite-derived <em>p</em>CO<sub>2</sub>, which achieved excellent performance in this complicated marginal sea, with low root mean square error (RMSE=19.6 μatm) and mean absolute percentage deviation (APD=4.12%). Air-sea CO2 fluxes are calculated based on retrieved seawater <em>p</em>CO<sub>2</sub>. </p>
Curated GWAS summary statistics on East Asian ancestry on 19 blood count traits and glycemic traits
<p>Genome wide curated summary statistics on 19 blood count traits and glycemic traits</p> <p>File format is the inittable format intended to be used with the Joint Analysis of Summary Statistics (JASS), which allows to perform multi-trait GWAS:</p> <p>https://gitlab.pasteur.fr/statistical-genetics/jass</p> <p>GWAS of hematological traits originate from Chen et al paper and were downloaded from the GWAS Catalog (<a href="https://www.ebi.ac.uk/gwas/publications/32888493#study_panel">https://www.ebi.ac.uk/gwas/publications/32888493#study_panel</a>). GWAS of glycemic traits come from the <a href="https://www.zotero.org/google-docs/?S1MIfx">(18)</a> study downloadable from GWAS Catalog (<a href="https://www.ebi.ac.uk/gwas/publications/34059833">https://www.ebi.ac.uk/gwas/publications/34059833</a>).</p> <p>Full description of the method used to derive this dataset can be found in </p>
Supplementary Material no. 2 to the manuscript: Reemission of inorganic pollution from permafrost? – a freshwater hydrochemistry study in the lower Kolyma basin (North-East Siberia)
<p>A dataset on the inorganic chemistry of permafrost-related creeks and ice, thermokarst lakes and the Kolyma river and its tributaries in late July 2021.<br> Companion dataset to the manuscript: "Reemission of inorganic pollution from permafrost? – a freshwater hydrochemistry study in the lower Kolyma basin (North-East Siberia)".<br> Current abstract of the manuscript (prior to peer review):</p> <p>Permafrost regions are under particular pressure from climate change resulting in widespread landscape changes, which impact also freshwater chemistry. We investigated a snapshot of hydrochemistry in various freshwater environments in the lower Kolyma river basin (North-East Siberia, continuous permafrost zone) to explore the mobility of metals, metalloids and non-metals resulting from permafrost thaw. Particular attention was focused on heavy metals as contaminants potentially released from the secondary source in the permafrozen Yedoma complex. Permafrost creeks represented the Mg-Ca-Na-HCO<sub>3</sub>-Cl-SO<sub>4</sub> ionic water type (with mineralisation in the range 600-800 mg/L), while permafrost ice and thermokarst lake waters were the HCO<sub>3</sub>-Ca-Mg type. Multiple heavy metals (As, Cu, Co, Mn and Ni) showed much higher dissolved phase concentrations in permafrost creeks and ice than in Kolyma and its tributaries, and only in the permafrost samples and one Kolyma tributary have we detected dissolved Ti or Hg. In thermokarst lakes, several metal and metalloid dissolved concentrations increased with water depth (Fe, Mn, Ni and Zn - in both lakes; Al, Cu, K, Sb, Sr and Pb in either lake), reaching 1370 µg/L Cu, 4610 µg/L Mn, and 687 µg/L Zn in the bottom water layers. Permafrost-related waters were also enriched in dissolved phosphorus (up to 512 µg/L in Yedoma-fed creeks). The impact of permafrost thaw on river and lake water chemistry is a complex problem which needs to be considered both in the context of legacy permafrost shrinkage and the interference of the deepening active layer with newly deposited antropogenic contaminants.</p>
Buoy and Other Sensors Data at the South East Coast of Iceland (Höfn area) on Nov 20 - 24, 2020
<p>This dataset contains the information about sea state and environmental parameters in the area of tidal inlet near Höfn city, South East coast of Iceland. Buoy data is given with original half-hour time spacing. Wind and tidal sensors' data are given with the original 10 min time spacing. All of the given parameters did not undergo any re-interpolation, filtering or any other processing.</p> <p>structure of the "Buoy_data_20201120T010100_to_20201124T125900.csv" file</p> <pre><code>Comma-separated values'(.csv) table (217 rows 3 columns) with the the following fields: 1st column: time in the format dd-Mmm-yyyy HH:MM:SS; 2nd column: Significant wave height (Hs) in meters (m); 3rd column Peak wave period in seconds (s).</code></pre> <p>structure of the "Sea_level_tide_wind_data_20201120T010100_to_20201124T125900.csv" file (originally data was obtained from the <a href="http://vedur.mogt.is/harbor/index.php?action=ReadingsDrillDown&harborid=4&stationid=1011">web page</a> of Weather information system MogT ehf. Hornafjörður, Iceland - the corresponding table fields' names are doubled in Icelandic for convenience) </p> <pre><code>Comma-separated values'(.csv) table (649 rows 5 columns) with the the following fields: 1st column: time in the format dd-mm-yyyy HH:MM:SS; 2nd column: observed sea level (Sjávarhæð) in meters (m); 3rd column astronomical tide (Flóðatafla) in meters (m); 4th column wind speed (Vindur) in meters per second (m/s); 5th column wind direction (Vindátt) in degrees (deg).</code></pre> <p>The corresponding data chunks are published after the kind allowance of the data owners: Vignir Júlíusson and Greipur Sigurðsson.</p> <p> </p>
Subglacial valleys and ridges of Highland A, East Antarctica
<p>This dataset contains 3 GIS shapefiles which delineate a landscape buried beneath the ice in East Antarctica. The files are: PlateauBoundaries (a polygon shapefile outlining the extent of 3 blocks of ancient land surface), RADARSATRidges (a line shapefile delineating inferred mountain ridgelines inside each block of ancient landsurface), and RADARSATValleys (a line shapefile delineating inferred valley thalwegs inside each block of ancient landsurface). The landscape was mapped from RADARSAT satellite data of the surface of the East Antarctic Ice Sheet. This dataset links to a 2023 paper by the same authors entitled 'An ancient river landscape preserved beneath the East Antarctic Ice Sheet'.</p>
Dataset for "Adjoint Waveform Tomography for Crustal and Upper Mantle Structure the Middle East and Southwest Asia for Improved Waveform Simulations Using Openly Available Broadband Data"
<p>This dataset contains the MESWA (Middle East and Southwest Asia) seismic model and auxiliary data used in the creation of the model (Rodgers, 2023). MESWA is a three-dimensional model of the seismic properties of crust and upper mantle of the Middle East and Southwest Asia. The MESWA model is provided in NetCDF format (readable by for example, <em>xarray</em>, Hoyer & Hamman, <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0057">2017</a>) and HDF5 format for viewing with <em>ParaView</em> (Ahrens et al., <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0002">2005</a>) and interaction with <em>Salvus</em> (Afanasiev et al., <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0001">2019</a>). </p> <p> </p> <p>Also included are the earthquake source parameters for all 327 Global Centroid Moment Tensor events considered in this study in ASCII text format. Also included are lists of the selected 192 inversion events and 66 validation events in ASCII text format. Lastly, we include a list of all receivers used in the creation and validation of MESWA. This is a simple ASCII file with the event name and receiver name (composed of the network_code and station_code).</p> <p> </p> <p>The following table provides a listing of the files in the dataset:</p> <table> <tbody> <tr> <td> <p><strong>File</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>MESWA.nc</p> </td> <td> <p>MESWA model in NetCDF format</p> </td> </tr> <tr> <td> <p>MESWA.h5</p> </td> <td> <p>MESWA model in HDF5 format, used by Salvus</p> </td> </tr> <tr> <td> <p>MESWA.xmdf</p> </td> <td> <p>Auxiliary file for MESWA.h5, used to import model into Paraview</p> </td> </tr> <tr> <td> <p>events_project.csv</p> </td> <td> <p>Table of event source parameters for all 327 events considered in the project</p> </td> </tr> <tr> <td> <p>inversion_events_192.csv</p> </td> <td> <p>Table of 192 inversion events </p> <p>(ASCII comma separated value)</p> </td> </tr> <tr> <td> <p>validation_events_66.csv</p> </td> <td> <p>Table of 66 validation events </p> <p>(ASCII comma separated value)</p> </td> </tr> <tr> <td> <p>events_receivers_inversion.csv</p> </td> <td> <p>Table of waveform (event-receiver-channel) data used in the inversion (ASCII comma separated value)</p> </td> </tr> <tr> <td> <p>events_receivers_validation.csv</p> </td> <td> <p>Table of waveform (event-receiver-channel) data used in the validation (ASCII comma separated value)</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p><strong>References</strong></p> <p>Afanasiev, M, C Boehm, M van Driel, L Krischer, M Rietmann, DA May, MG Knepley, and A Fichtner (2019). Modular and flexible spectral-element waveform modelling in two and three dimensions, <em>Geophys. J. Int.</em>, 216(3), 1675–1692, doi: 10.1093/gji/ggy469</p> <p> </p> <p>Ahrens, J., Geveci, B., & Law, C. (2005). Paraview: An end-user tool for large data visualization. <em>The Visualization Handbook</em>, 717(8). <a href="https://doi.org/10.1016/b978-012387582-2/50038-1">https://doi.org/10.1016/b978-012387582-2/50038-1</a></p> <p> </p> <p>Hoyer, S., & Hamman, J. (2017). Xarray: N-D labeled arrays and datasets in Python. <em>Journal of Open Research Software</em>, 5(1). <a href="https://doi.org/10.5334/jors.148">https://doi.org/10.5334/jors.148</a></p> <p> </p> <p>Rodgers, A. (2023). Adjoint Waveform Tomography for Crustal and Upper Mantle Structure the Middle East and Southwest Asia for Improved Waveform Simulations Using Openly Available Broadband Data, technical report, LLNL-TR- 851939.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>This project was support by Lawrence Livermore National Laboratory’s Laboratory Directed Research and Development project 20-ERD-008 and the National Nuclear Security Administration. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-MI-852402</p> <p> </p>
Dataset for the Diet of the grey wolf Canis lupus in Roztocze and Solska Forest, south-east Poland
<p>The dataset for the article: Mysłajek R.W., Stachyra P., Figura M., Nędzyńska-Stygar M., Stefański R., Korga M., Kwiatkowska I., Stępniak K.M., Tołkacz K., Nowak S. 2022. Diet of the grey wolf <em>Canis lupus</em> in Roztocze and Solska Forest, south-east Poland. Journal of Vertebrate Biology 71: 22040. DOI: <a href="https://doi.org/10.25225/jvb.22040">10.25225/jvb.22040</a></p>
Blarina brevicauda populations in three different habitats in east-central Illinois, 1972 to 1997.
The population demography of the northern short-tailed shrew, Blarina brevicauda, was monitored monthly from 1972-1997 in bluegrass, alfalfa, and tallgrass prairie habitats in east-central Illinois. Blarina brevicauda were incidentally collected as part of a 25-year prairie vole (Microtus ochrogaster) and meadow vole (M. pennsylvanicus) trapping study. The study sites were located in the University of Illinois Biological Research Area (Phillips Tract) and Trelease Prairie. Animals were trapped with wooden multiple-capture live-traps. Over the span of 25 years, three 3-day trapping sessions monthly were conducted to cover the three habitats. All live shrews were toe-clipped at first capture for individual identification. Animals known to be present on study site, but not captured, were included in totals for that month. While trap mortality of shrews was high in this study (40%), the data obtained were sufficient for analysis of many aspects of the demography of the species.
Optimizing targeted conservation for disproportionate benefits in the East Big Creek watershed, Iowa, 2019.
This dataset supports spatial conservation planning in agricultural landscapes by providing field-level information on the placement, cost, and ecological performance of perennial conservation practices in the East Big Creek watershed, central Iowa. The dataset includes modeled configurations of prairie strips, riparian buffers, and nutrient-removal wetlands, with associated estimates of nitrate-nitrogen reduction and biodiversity enhancement. These estimates were derived using open-source geospatial tools and publicly available data, including the Agricultural Conservation Planning Framework (ACPF) and a modified Healthy Farm Index (HFI). The dataset enables users to explore how small-scale changes in land use can yield substantial environmental benefits, including improved water quality and increased biodiversity, while considering cost constraints. It is intended to support conservation practitioners, land use planners, and researchers in evaluating trade-offs and synergies among multiple ecosystem services in working landscapes.
Krummholz island soil C and N data for East of Tvan, 1994.
Previous work has shown that passage of Engelmann spruce (Picea engelmannii) and subalpine fir (Abies lasiocarpa) tree islands across tundra lowers the soil carbon and nitrogen storage capacity of the top 15cm of soil (A horizon) (Pauker and Seastedt 1996). This data forms part of a study which seeks to further examine how changes in carbon storage capacity are associated with changes in soil physical properties. Soils were collected in 1994. We sought to determine whether there were changes in natural abundance of C and N isotopes associated with previously reported changes in C and N content. Any such changes in natural abundance of C and N isotopes may indicate differences in inputs to the ecosystem or differences in ecosystem processes that may account for the differences in N and C content that have been observed. In addition to measuring ratios of natural isotopes of C and N, we also measured C and N contents to provide a comparison with previous studies. To convert % N and C values to kgs of N and C per m2, we used bulk density values, averaged across sampling dates that had been previously measured for these sites. Metadata for these data are available at: https://portal.lternet.edu/nis/mapbrowse?scope=knb-lter-nwt&identifier=131 When N and C soil contents were considered on a percentage basis, levels at windward sites were significantly lower than those of undisturbed tundra, but levels directly beneath krummholz were not significantly different from tundra soils. Bulk density was significantly lower under krummholz than other sites. Thus when N and C amounts were expressed on a volume (Kg/m2, to a depth of 15cm), they were significantly lower in both krummholz and windward sites than in undisturbed tundra. D13C values of soils under krummholz were significantly less negative under krummholz than from windward or undisturbed tundra sites. A less negative d13C value is indicative of less discrimination and may be attributable to either vegetation differences
Krummholz island soil inorganic and organic property data for East of Tvan, 1995 - 1996.
Previous work has shown that passage of Engelmann spruce (Picea engelmannii) and subalpine fir (Abies lasiocarpa) tree islands across tundra lowers the soil carbon and nitrogen storage capacity of the top 15cm of soil (A horizon) (Pauker and Seastedt 1996). This study shows that levels of KCl extractable ammonium and percent organic matter were also significantly higher in the A horizon of undisturbed tundra sites compared with soils underneath or immediately adjacent to (windward or leeward) the krummholz. The response of soil KCl extractable NO3- also showed this trend but was not statistically significant. Holtmeier and Broll (1992) suggested that the depletion of organics and nutrients following the passage of tree island may be associated with a reduced clay content. We analyzed a subsample of these soils for cation exchange capacity (CEC) and texture. We did not find significantly lower clay content in soils under or adjacent to krummholz compared with those from undisturbed tundra. In fact, percent clay was greater in krummholz and windward sites than in tundra sites. The percent clay of windward sites was significantly greater in windward soils than either krummholz or tundra soils. Clearly, depletion of organics following the passage of tree islands does not appear to be associated with a depleted clay content. We found that CEC was very highly significantly correlated with percent organic content (using percent organic data only from the subset of soils on which CEC was measured). Therefore, the CEC content of these soils would appear to be strongly associated with the organic content but not with the clay content. Percent soil moisture was significantly higher directly underneath the krummholz compared with the other sites. There was no effect of treatment on pH. We also investigated whether the organic matter lost in association with krummholz colonization (i.e. 1m from the tree) was replenished as tundra vegetation recolonized in the wake of the tree is
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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.