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1,568 results for “slope”
Cone production of upper slope conifers in the Cascade Range of Oregon and Washington, 1959 to 2022
Seed supply is a key feature of tree population dynamics, and seed production may be indicative of environmental and biological drivers. This study examines cone production in upper-slope, true fir-hemlock forests of the Pacific Northwest, starting in 1959 to the present. Annual surveys of cone counts of Abies spp. (A. amabilis, A. concolor, A. grandis, A. lasiocarpa, A. magnifica, A. procera), Pinus spp. (P. engelmannii, P. lamberti, P. monticola), and Tsuga spp. (T mertsiana) have been conducted at sixty-one plots in 37 locations in nine national forests in Washington and Oregon (originally 10 national forests, but Mt. Baker and Snoqualimie were combined). At each site, a visual count is made of cone production in each of a number (20-30) trees in a stand of one tree species. At some plots, additional trees were added in the 1980s. Primary data include numbers of counts per tree per year, periodic measurements of tree diameter, and the names of the sites. These data illustrate the periodicity of cone production cycles, as well as longer trends associated with climate change and variability in the region.
Net Carbon Exchange of a Young Upper-Slope Deciduous Forest at Harvard Forest LPH Tower 2002-2010
This data set contains sensible heat exchange, water vapor exchange and carbon exchange as well as environmental data for a deciduous forest dominated by red oak (Quercus rubra). It is 1.1 km WNW of the EMS tower where continuous eddy covariance measurements began in 1992 (see HF004). The High Deciduous site is about 385 m a.s.l., or 35 m higher in elevation than the EMS, which is situated in a relatively low area near a stream. The forest near this eddy covariance tower is broadly similar in species composition to the EMS site, but it is younger and shorter in stature. The site was cleared for pasture, but not deeply plowed or planted, in the 18th and19th centuries. Agriculture on the site was abandoned near the end of the 19th century. The forest within 200 to 300 m of the eddy covariance tower to the NW, W, SW, and S burned in an intense fire in 1957, which left few or no surviving trees.
Biogeochemistry data set for Imnavait Creek Weir on the North Slope of Alaska 2002-2024.
Data file containing biogeochemical data of water samples collected in Imnavait Creek, North Slope of Alaska. Sample site descriptors include a unique assigned number (sortchem), site, date, time, depth, distance (downstream), and elevation. Values of variables measured in the field include temperature, conductivity, pH. Chemical analysis for samples include alkalinity, dissolved organic carbon (DOC), inorganic and total dissolved nutrients particulate carbon, nitrogen, and phosphorus, cations and anions.
Hourly weather data from the Arctic LTER Wet Sedge Inlet Experimental plots from 1994 to present, Toolik Field Station, North Slope, Alaska.
Hourly weather data from the Arctic Tundra LTER wet sedge experimental site at Toolik Lake. The following parameters are measured every minute and averaged every hour: control plot air temperature and relative humidity at 3 meters and greenhouse plot air temperature and relative humidity at 1 meters (inside the greenhouse).
Biogeochemistry data set for soil waters, streams, and lakes near Toolik Lake on the North Slope of Alaska, 2012 through 2020
Data file of the biogeochemistry of samples collected at various sites near Toolik Lake, North Slope of Alaska. Sample site descriptors include a unique assigned number (sortchem), site, date, time, depth, distance (downstream from a reference location), elevation, treatment, date-time, category, and water type (lake, surface, soil). Physical measures collected in the field include temperature (water, soil, well water), conductivity, pH, and average thaw depth in soil. Chemical analyses for the sample include alkalinity; dissolved inorganic and organic carbon (DIC and DOC); dissolved gases CO2 and CH4; inorganic and total dissolved nutrients (NH4, PO4, NO3, TDN, TDP); particulate carbon, nitrogen, and phosphorus (PC, PN, and PP); cations (Ca, Mg, Na, K, and Si); and anions (SO4 and Cl).
Model simulated hydrological estimates for the North Slope drainage basin, Alaska, 1980-2010
Estimates of runoff, river discharge, snow water equivalent (SWE), subsurface runoff, and soil temperatures are drawn from the Permafrost Water Balance Model (PWBM). The simulation and derived data span the period 1980-2010. The model was forced with daily gridded meteorological data obtained from the Modern-Era Retrospective analysis for Research and Applications (MERRA) reanalysis (version 5.2.0). The estimates of total runoff (daily), soil temperature (daily), subsurface runoff (monthly), and SWE (monthly) are expressed on a spatial grid (N=312; 25x25 km EASE-Grid version 1, Northern Hemisphere) over the North Slope drainage basin, with the coastline extending from Utqiagvik (formerly Barrow) to just west of the Mackenzie River delta. River discharge, calculated as a volume flux of runoff at each grid cell, was routed through the river network defined on a simulated topological network (STN). Archived files contain discharge flux through the grid cell representing the outlet of each of forty-two basins defined across the region on the 25 km resolution EASE-Grid. Details of the PWBM, forcing variables, model validation and results of analysis are described in Rawlins et al. (2019).
Hourly weather data from the Arctic LTER Moist Acidic Tussock Experimental plots from 2011 to present, Toolik Filed Station, North Slope, Alaska.
Hourly weather data from the LTER Moist Acidic Tussock Experimental plots. The station was installed in 1990 in block 2 of the Toolik LTER experimental moist acidic tussock plots. The plots are located on a hillside near Toolik Lake (68 38' N, 149 36'W). Global solar radiation, photosynthetic active radiation, unfrozen precipitation, air temperature, relative humidity, wind speed, and wind direction are measured at 3 meters. Additional sensors in greenhouses and shade houses plots measure air temperature, relative humidity and photosynthetic active radiation during the growing season. The sensors are read every minute and averaged or totaled every hour.
Biomass in wet sedge tundra near the Atigun River crossing of the Dalton Highway, North Slope AK, 1982.
Biomass in wet sedge tundra near the Atigun River crossing of the Dalton Highway, North Slope AK. There were three harvests; Late May-early June; Late July-early August; Late August-early September. See Shaver and Chapin (Ecological Monographs, 61, 1991 pp.1-31).
Soil temperature data collected from the Arctic LTER wet sedge experimental site Toolik Field Station North Slope, Alaska from 1994 to 2020
Soil temperature data collected every 4 hours from a wet sedge site at the Arctic Tundra LTER site at Toolik Lake. Temperatures are measured every 3 minutes and averaged every 4 hours in control, nitrogen alone, phosphorus alone, nitrogen and phosphorus, and greenhouse experimental plots soil temperatures.
Air temperature, relative humidity, soil temperatures and soil moisture for Arctic Long Term Experimental Research (ARC LTER) heath experimental plots, Toolik Field Station, North Slope Alaska for 2001-2024-09-22.
Air temperature and relative humidity at 3 meters, soil temperatures at 2 depths, 5 and 10 cm, canopy temperatures and soil moisture at 10 cm were measured in an Arctic Long Term Experimental Research (ARC-LTER) heath tundra site (DHT89) at Toolik Lake Field Station, North slope, Alaska. Only control and nutrient addition (nitrogen plus phosphorus ) treatments plots soils were measured. Note: In version 1 the moisture columns were mixed up. The fractional volumetric water columns were actually the period frequency of the wave of the sensor (Campbell Scientific CS616). Version 3 adds calculated percent moisture corrected for organic soil.
FCE Redlands 2008 Slope Mosaic, Miami-Dade County, South Florida
Urban growth models have increasingly been used by planners and policy makers to visualize, organize, understand, and predict urban growth. However, these models reveal a wide disparity in their attention to policy factors. Some urban growth models capture few if any specific policy effects (e.g.,as model variables), while others integrate certain policies but not others. Since zoning policies are the most widely used form of land use control in the United States, their conspicuous absence from so many urban growth models is surprising. This research investigated the impacts of zoning on urban growth by calibrating and simulating a cellular automaton urban growth model, SLEUTH, under two conditions in a South Florida location. The first condition integrated restrictive agricultural zoning into SLEUTH, while the other ignored zoning data. Goodness of fit metrics indicate that including the agricultural zoning data improved model performance. The results further suggest that agricultural zoning has been somewhat successful in retarding urban growth in South Florida. Ignoring zoning information is detrimental to SLEUTH performance in particular, and urban growth modeling in general.
IRIS: ICESat-2 River Surface Slope
<p><strong>ICESat-2 River Surface Slope (IRIS)</strong></p> <p>When using this data please cite<strong> </strong><em>Scherer D., Schwatke C., Dettmering D., Seitz F.</em>: <strong>ICESat-2 river surface slope (IRIS): A global reach-scale water surface slope dataset</strong>. Scientific Data, 10(1), 359, <a href="https://doi.org/10.1038/s41597-023-02215-x">10.1038/s41597-023-02215-x</a>, 2023.</p> <p>A detailed description of the methodology and validation is published in <em>Scherer D., Schwatke C., Dettmering D., Seitz F. </em>: <strong>ICESat-2 Based River Surface Slope and Its Impact on Water Level Time Series From Satellite Altimetry</strong>. Water Resources Research, <a href="http://doi.org/10.1029/2022WR032842">10.1029/2022WR032842</a>, 2022.</p> <p><strong>1. Summary</strong><br>The unique multibeam lidar altimeter of ICESat-2 is used to measure reach-scale water surface slope (WSS) every time the spacecraft’s orbit crosses a reach. The method of deriving WSS from simultaneous ICESat-2 ATL13 (<em>Jasinski et al., 2021</em>) observations is described in detail and validated in <em>Scherer et al. </em>(2022). In this ICESat-2 River Surface Slope (IRIS) dataset, we provide the minimum, average, and maximum slope derived with three different approaches (across, along, and combined) per reach. Additionally, we give the standard deviation and epochs of the derived WSS data. The reaches are defined by the SWOT River Database (SWORD, <em>Altenau et al., 2021</em>).</p> <p>An interactive map is available at <a href="https://dahiti.dgfi.tum.de/en/products/water-surface-slope/.">DAHITI</a>.</p> <p><strong>2. Version History</strong></p> <p>IRIS <strong>v0</strong>: Only includes the reaches studied in Scherer et al. (2022).<br>Based on ICESat-2 ATL13 v5, Cycle 1-13 (October 2018 to October 2021) and SWORD Version v1.</p> <p>IRIS <strong>v1</strong>: Global coverage (limited by ICESat-2 data availability and cloud cover).<br>Based on ICESat-2 ATL13 v5, Cycle 1-16 (October 2018 to August 2022) and SWORD Version v2.</p> <p>IRIS <strong>v2</strong>: Global coverage with 6,083 additional reaches and 92,347 more observations compared to v1.<br>Based on ICESat-2 ATL13 <strong>v6</strong>, Cycle 1-19 (October 2018 to April 2023) and SWORD Version v15.</p> <p>IRIS <strong>v2.1</strong>: Based on ICESat-2 ATL13 v6, Cycle 1-19 (October 2018 to April 2023) and <strong>SWORD Version v16</strong>.</p> <p>IRIS <strong>v2.2</strong>: 3,251 additional reaches and 58,862 new observations compared to v2.1.<br>Based on ICESat-2 ATL13 v6, Cycle 1-<strong>20</strong> (October 2018 to <strong>August</strong> 2023) and SWORD Version v16.</p> <p>IRIS <strong>v2.3</strong>: 1,595 additional reaches and 32,590 new observations compared to v2.2.<br>Based on ICESat-2 ATL13 v6, Cycle 1-<strong>21</strong> (October 2018 to <strong>October </strong>2023) and SWORD Version v16.</p> <p>IRIS <strong>v2.6</strong>: 2,755 additional reaches and 362,136 new observations compared to v2.3.<br>Based on ICESat-2 ATL13 v6, Cycle 1-<strong>23</strong> (October 2018 to <strong>May 2024</strong>) and SWORD Version v16.</p> <p>IRIS <strong>v2.9</strong>: 2,485 additional reaches and 184,549 new observations compared to v2.6.<br>Based on ICESat-2 ATL13 v6, Cycle 1-<strong>24</strong> (October 2018 to <strong>August 2024</strong>) and SWORD Version v16.<br>Fixed some broken geometries in the gpkg data.</p> <p>IRIS <strong>v3.0</strong>: Based on ICESat-2 ATL13 v6, Cycle 1-24 (October 2018 to August 2024) and <strong>SWORD Version</strong> <strong>v17</strong>.</p> <p>IRIS <strong>v3.2</strong>: 1,370 additional reaches and 210,951 new observations compared to v3.0.<br>Based on ICESat-2 ATL13 v6, Cycle 1-<strong>26</strong> (October 2018 to <strong>December 2024</strong>) and SWORD Version v17.</p> <p><strong>3. Data Format and Variable Description</strong></p> <p>From Version 2.6, <strong>IRIS is also available as GeoPackage</strong>.<br>The IRIS data is stored in a single NetCDF4 file which is structured in a single group containing the following variables:<br><strong><em>reach_id</em></strong>:<br>The SWORD reach identifier [-]<br><strong><em>lon</em></strong>:<br>Approx. centroid longitude of the SWORD reach [degrees east]<br><strong><em>lat</em></strong>:<br>Approx. centroid latitude of the SWORD reach [degrees north]<br><strong><em>across_flag, along_flag, combined_flag:</em></strong><br>Flags indicating whether ICESat-2 [across/along/combined] slope is available (1) for the reach or not (0) [-]<br><strong><em>avg_across_slope, avg_along_slope, avg_combined_slope:</em></strong><br>Average (median) ICESat-2 [across/along/combined] slope for the reach [mm/km]<br><strong><em>min_across_slope, min_along_slope, min_combined_slope:</em></strong><br>Minimum ICESat-2 [across/along/combined] slope for the reach [mm/km]<br><strong><em>max_across_slope, max_along_slope, max_combined_slope:</em></strong><br>Maximum ICESat-2 [across/along/combined] slope for the reach [mm/km]<br><strong><em>std_across_slope, std_along_slope, std_combined_slope:</em></strong><br>ICESat-2 [across/along/combined] slope standard deviation for the reach [mm/km]<br><strong><em>n_across_slope, n_along_slope, n_combined_slope:</em></strong><br>Number of days with ICESat-2 [across/along/combined] slope observations for the reach [-]<br><strong><em>min_date_across_slope, min_date_along_slope:, min_date_combined_slope:</em></strong><br>First date of ICESat-2 [across/along/combined] slope observations for the reach [days since 2000-01-01]<br><strong><em>max_date_across_slope, max_date_along_slope:, max_date_combined_slope:</em></strong><br>Latest date of ICESat-2 [across/along/combined] slope observations for the reach [days since 2000-01-01]</p> <p><strong>4. References</strong></p> <p><em>Scherer D., Schwatke C., Dettmering D., Seitz F.</em>: <strong>ICESat-2 river surface slope (IRIS): A global reach-scale water surface slope dataset</strong>. Scientific Data, 10(1), 359, <a href="https://doi.org/10.1038/s41597-023-02215-x">10.1038/s41597-023-02215-x</a>, 2023<br><em>Scherer D., Schwatke C., Dettmering D., Seitz F. (2022): <strong>ICESat-2 Based River Surface Slope and Its Impact on Water Level Time Series From Satellite Altimetry</strong>, Water Resources Research, https://doi.org/10.1029/2022WR032842</em><br><em>Jasinski M., Stoll J., Hancock D., Robbins J., Nattala J., Morison J., Jones B., Ondrusek M., Pavelsky T.M., Parrish C. and the ICESat-2-Science-Team (2021). <strong>ATLAS/ICESat-2 L3A Inland Water Surface Height</strong>, Version 5. [Dataset]</em><br><em>Altenau E.H., Pavelsky T.M., Durand, M.T., Yang X., Frasson, R.P.d.M., Bendezu, L. (2021): <strong>SWOT River Database (SWORD)</strong> [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3898569</em></p>
Indicative distribution map for Ecosystem Functional Group M3.1 Continental and island slopes
<p>This archive contains indicative distribution maps and profiles for <strong>M3.1 Continental and island slopes</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Live cribwall + slope grating + fascines drainage system - soil-water dynamics
<p>Dataset containing raw time series (August 2022) for soil-water dynamics -i.e., volumetric soil moisture, matric suction, soil-pore water pressure, and soil temperature - retrieved from a live, vegetated cribwall+slope grating+fascines drainage system built in Catterline, Scotland. NBS intervention built to restore a landslide taking place in February 2021. </p>
Warm Core Ring Trajectories in the Northwest Atlantic Slope Sea (2000-2010)
<p>This dataset consists of weekly trajectory information of Gulf Stream Warm Core Rings from 2000-2010. This work builds upon Silver et al. (2022a) ( <a href="https://doi.org/10.5281/zenodo.6436380">https://doi.org/10.5281/zenodo.6436380</a>) which contained Warm Core Ring trajectory information from 2011 to 2020. Combining the two datasets a total of 21 years of weekly Warm Core Ring trajectories can be obtained. An example of how to use such a dataset can be found in Silver et al. (2022b).</p> <p>The format of the dataset is similar to that of Silver et al. (2022a), and the following description is adapted from their dataset. This dataset is comprised of individual files containing each ring’s weekly center location and its area for 374 WCRs present between January 1, 2000 and December 31, 2010. Each Warm Core Ring is identified by a unique alphanumeric code 'WEyyyymmddA', where 'WE' represents a Warm Eddy (as identified in the analysis charts); 'yyyymmdd' is the year, month and day of formation; and the last character 'A' represents the sequential sighting of the eddies in a particular year. Continuity of a ring which passes from one year to the next is maintained by the same character in the first sighting. For example, the first ring in 2002 having a trailing alphabet of 'F' indicates that five rings were carried over from 2001 which were still observed on January 1, 2002. Each ring has its own netCDF (.nc) filename following its alphanumeric code. Each file contains 4 variables, “Lon”- the ring center’s weekly longitude, “Lat”- the ring center’s weekly latitude, “Area” - the rings weekly size in km<sup>2</sup>, and “Date” in days - representing the days since Jan 01, 0000. </p> <p>The process of creating the WCR tracking dataset follows the same methodology of the previously generated WCR census (Gangopadhyay et al., 2019, 2020). The Jenifer Clark’s Gulf Stream Charts used to create this dataset are 2-3 times a week from 2000-2010. Thus, we used approximately 1560 Charts for the 10 years of analysis. All of these charts were reanalyzed between 75° and 55°W using QGIS 2.18.16 (2016) and geo-referenced on a WGS84 coordinate system (Decker, 1986). </p> <p> </p> <p>Silver, A., Gangopadhyay, A, & Gawarkiewicz, G. (2022a). Warm Core Ring Trajectories in the Northwest Atlantic Slope Sea (2011-2020) (1.0.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.6436380">https://doi.org/10.5281/zenodo.6436380</a></p> <p>Silver, A., Gangopadhyay, A., Gawarkiewicz, G., Andres, M., Flierl, G., & Clark, J. (2022b). Spatial Variability of Movement, Structure, and Formation of Warm Core Rings in the Northwest Atlantic Slope Sea. <em>Journal of Geophysical Research: Oceans</em>, <em>127</em>(8), e2022JC018737. <a href="https://doi.org/10.1029/2022JC018737">https://doi.org/10.1029/2022JC018737</a> </p> <p>Gangopadhyay, A., G. Gawarkiewicz, N. Etige, M. Monim and J. Clark, 2019. An Observed Regime Shift in the Formation of Warm Core Rings from the Gulf Stream, Nature - Scientific Reports, <a href="https://doi.org/10.1038/s41598-019-48661-9.%20www.nature.com/articles/s41598-019-48661-9">https://doi.org/10.1038/s41598-019-48661-9. www.nature.com/articles/s41598-019-48661-9</a>.</p> <p>Gangopadhyay, A., N. Etige, G. Gawarkiewicz, A. M. Silver, M. Monim and J. Clark, 2020. A Census of the Warm Core Rings of the Gulf Stream (1980-2017). Journal of Geophysical Research, Oceans, 125, e2019JC016033. https://doi.org/10.1029/2019JC016033.</p> <p>QGIS Development Team. QGIS Geographic Information System (2016).</p> <p>Decker, B. L. World Geodetic System 1984. World geodetic system 1984 (1986).</p> <p> </p>
Landscape classes of combinations of elevation, slope angle, and aspect, for the Ilirney Lake System Region, Chukotka, Russia
<p>The elevation was accessed for the area of interest in 90 m spatial resolution from the TanDEM-X 90 m digital elevation model (DEM) product (Krieger et al, 2013). Prior to spatial topographical parameters extraction, the DEM was resampled from the 90-m cell spacing to a 30-m resolution. The result was classified into 589 different possible combinations of elevation, slope angle, aspect. For the classification we used the possible combinations of elevation, slope, and aspect which were grouped into the following categories:</p> <p>Elevation:</p> <ul> <li>0-400 m</li> <li>400-450m</li> <li>450-500m</li> <li>500-600m</li> <li>600-650m</li> <li>650-700m</li> <li>700-1000m</li> <li>1000-1500m</li> </ul> <p>Slope:</p> <ul> <li>0-2°</li> <li>2-4°</li> <li>4-6°</li> <li>6-8°</li> <li>8-10°</li> <li>10-12°</li> <li>12-16°</li> <li>16-18°</li> <li>18-20°</li> <li>20-25°</li> <li>25-50°</li> </ul> <p>Aspect:</p> <ul> <li>0-45°</li> <li>45-90°</li> <li>90-135°</li> <li>135-180°</li> <li>180-225°</li> <li>225-270°</li> <li>270-315°</li> <li>315-360°</li> </ul> <p>Format: Geotiff; projection UTM58N and 30x30 m tiles; extent: 642010.1, 654910.1, 7462218, 7492908 m (xmin, xmax, ymin, ymax)</p>
3DPC of a gypsum slope in Finestrat, Alicante (Spain)
<p>3DPC of a gypsum slope in Finestrat, Alicante (Spain).</p> <p>Two different ground-based LiDARs were used during a 5-year time-span: (a) an Ilris-3D and a Ilris 3D long range were used for the first and the second field surveys, respectively, and (b) a Leica C10 laser scanner was used for the third and fourth field surveys.</p> <p> </p> <table> <thead> <tr> <th scope="col">Parameters</th> <th scope="col">2011 February</th> <th scope="col">2012 August</th> <th scope="col">2014 July </th> <th scope="col">2016 January</th> </tr> </thead> <tbody> <tr> <td>Relative time span (days) </td> <td> 0</td> <td>553 </td> <td>1256 </td> <td>1822</td> </tr> <tr> <td>LiDAR model </td> <td>Ilris 3D</td> <td>Ilris 3D (long range) </td> <td>Leica C10 </td> <td>Leica C10 </td> </tr> <tr> <td>Mean distance of scanning (m) </td> <td>276</td> <td>276</td> <td>120</td> <td>133</td> </tr> <tr> <td>Acquisition velocity (points/s)</td> <td> 2500 </td> <td>10,000 </td> <td> 50,000</td> <td>50,000</td> </tr> </tbody> </table> <p>The datasets were used for this paper:</p> <p>Tomás, R., Abellán, A., Cano, M. et al. A multidisciplinary approach for the investigation of a rock spreading on an urban slope. Landslides 15, 199–217 (2018). https://doi.org/10.1007/s10346-017-0865-0</p>
Monthly maps of Warm Core Ring Occupancy and occurrences of Salinity Maximum Intrusions in the Slope Sea (1990-2019)
<p>This dataset presents two important variables across the shelfbreak in the Northwest Atlantic: (i) Warm Core Ring Occupancy in the Slope sea proximate to the shelfbreak; and (ii) locations of Salinity maximum intrusions in the shelf. Monthly fields of both of these fields together are presented for the period 1990-2019 with file name format <em>smax_ring_mm_yyyy.jpg. </em>The gray and red dots on the shelf represent locations of profiles taken from the Ecosystem Monitoring Program’s (EcoMon) hydrographic data (available from the National Centers for Environmental Information World Ocean Database accessible at <a href="http://www.ncei.noaa.gov/products/world-ocean-database">www.ncei.noaa.gov/products/world-ocean-database</a>). Red dots show locations of profiles which contained mid-depth salinity maximum intrusions, gray dots are profiles without any mid-depth salinity maximum intrusion. Profiles with intrusions were identified using the methodology of Gawarkiewicz et al., 2022. The ring occupancy was calculated from a Warm Core Ring Tracking dataset with ring tracks from 2000-2010 and 2011- 2020 available from Zenodo (<a href="https://doi.org/10.5281/zenodo.6436380">https://doi.org/10.5281/zenodo.6436380</a>, <a href="https://doi.org/10.5281/zenodo.7406675">https://doi.org/10.5281/zenodo.7406675</a>) and ring trajectories from 1978 through 1999 available from the Bedford Institute of Oceanography, Canada. To calculate the ring occupancy the region was sub-divided into 0.1 by 0.1 degree bins. Ring trajectories and approximate geographical range (calculated from the ring area, assuming the ring is a perfect circle) were overlain on this region and the days rings are present in each bin are counted in units of ring days. A ring day is the presence of one single ring in a bin during a given day. These ring day counts were converted to percentages, dividing by days in the given month and multiplying by 100. For more details see Silver et al., 2022 and Salois et al., 2023. From these figures one can see the spatial relationship between Warm Core Rings and Salinity Maximum Intrusions, with clusters of intrusions occurring in areas adjacent to high ring occupancy. </p> <p>An animation of two particular years is also presented in <em>movie_smax_ring_1993_2012.gif</em> to highlight this relationship: Low ring year (1993) leading to fewer Smax intrusion and high ring year (2012) leading to more intrusions.</p> <p> </p> <p>Gawarkiewicz, G., Fratantoni, P., Bahr, F., & Ellertson, A. (2022). Increasing Frequency of Mid‐Depth Salinity Maximum Intrusions in the Middle Atlantic Bight. <em>Journal of Geophysical Research: Oceans</em>, <em>127</em>(7), e2021JC018233. <a href="https://doi.org/10.1029/2021JC018233">https://doi.org/10.1029/2021JC018233</a></p> <p>Silver, A., Gangopadhyay, A., Gawarkiewicz, G., Andres, M., Flierl, G., & Clark, J. (2022). Spatial Variability of Movement, Structure, and Formation of Warm Core Rings in the Northwest Atlantic Slope Sea. <em>Journal of Geophysical Research: Oceans</em>, <em>127</em>(8), e2022JC018737. <a href="https://doi.org/10.1029/2022JC018737">https://doi.org/10.1029/2022JC018737</a> </p> <p>Salois, S. L., Hyde, K. J., Silver, A., Lowman, B. A., Gangopadhyay, A., Gawarkiewicz, G., ... & Lapp, M. (2023). Shelf break exchange processes influence the availability of the northern shortfin squid, Illex illecebrosus, in the Northwest Atlantic. <em>Fisheries Oceanography</em>. <a href="https://doi.org/10.1111/fog.12640">https://doi.org/10.1111/fog.12640</a> </p>
Anaktuvuk River fire scar canopy reflectance spectra from the 2008-2014 growing seasons, North Slope Alaska.
The Anaktuvuk River Fire occurred in 2007 on the North Slope of Alaska. In 2008, three eddy covariance towers were established at sites represent ing unburned tundra, moderately burned tundra, and severely burned tundra. During the 2008-2014 growing seasons, canopy vegetation within the footprint of each of these towers was scanned with a handheld spectrophotometer several times throughout the growing season. Average reflectance spectra per site and collection day are presented here.
Biogeochemistry data set for soil waters, streams, and lakes near Toolik on the North Slope of Alaska.
Data file describing the biogeochemistry of samples collected at various sites near Toolik Lake, North Slope of Alaska. Sample site descriptors include a unique assigned number (sortchem), site, date, time, depth, distance (downstream), elevation, treatment, date-time, category, and water type (lake, surface, soil). Physical measures collected in the field include temperature (water, soil, well water), conductivity, pH, average thaw depth, well height, discharge, stage height, and light (lakes). Chemical analysis for the sample include alkalinity; dissolved organic carbon (DOC); inorganic and total dissolved nutrients (NH4, PO4, NO3, TDN, TDP); particulate carbon, nitrogen, and phosphorus (PC, PN, and PP); cations (Ca, Mg, Na, K); anions (SO4 and Cl); silica and oxygen.
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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.