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59 results for “river surface”
Total dissolved nitrogen (TDN), dissolved organic carbon (DOC), radiocarbon (14C-DOC), and stable carbon (13C-DOC) of surface waters from the Canning River watershed, 2019 and 2021
Sites along the Canning River mainstem and contributing streams near the Kavik River Camp, Alaska, were visited to track changes in stream and river total dissolved nitrogen (TDN) concentration, dissolved organic carbon (DOC) concentration, and the stable carbon (13C) and radiocarbon (14C) isotopic composition of DOC across transitions between the Brooks Range, Brooks foothills, and Arctic Coastal Plain. The dataset also includes water samples collected from lakes, springs, groundwater, and streams and rivers outside the Canning River watershed. Water samples were collected in late April and early August 2019 and in late July and early August 2021. Data include measurements of individual samples for TDN (milligrams nitrogen per liter), DOC (milligrams carbon per liter), carbon-13 of DOC (reported as delta-13C, per mil), carbon-14 of DOC (reported as fraction modern), and analytical error in the fraction modern values. Additional water chemistry data for these samples can be found in Koch et al. (2024). References: Koch, J. C., Connolly, C. T., Repasch, M., Best, H. R., Couvillion, C. S., Hunt, A. (2024). [Dataset] Hydrochemistry and age date tracers from springs, streams, and rivers in the Arctic National Wildlife Refuge, 2019-2022, U.S. Geological Survey data release, https://doi.org/10.5066/P95CXJIT.
Surface water and flooding dynamics data set based on seasonally continuous Landsat data (1986-2011) in a dryland river basin
<p>Animations of the data are available here: <a href="https://doi.org/10.5281/zenodo.2438110">https://doi.org/10.5281/zenodo.2438110</a></p> <p>If you are using this data set, please cite the following publication:</p> <p>Tulbure, M.G. and M. Broich (2018). Spatiotemporal patterns and effects of climate and land use on surface water extent dynamics in a dryland region with three decades of Landsat satellite data. Science of the Total Environment. https://www.sciencedirect.com/science/article/pii/S0048969718347466 </p> <p>The data represent statistically validated surface water and flooding extent dynamics derived from seasonally continous Landsat TM/ETM+ data and random forest models, and summarised to the maximum extent of surface water per season between 1986-2011 over Australia's Murray-Darling Basin. The overall accuracy was over 99% and producer's accuracy for water 87% +/- 3%. </p> <p>The method is described in the following publication: <br> Tulbure, M.G., M. Broich, S.V. Stehman, A. Kommareddy. (2016). Surface water extent dynamics from three decades of seasonally continuous Landsat time series at subcontinental scale in a semi-arid region. Remote Sensing of Environment. 178: 142-157</p> <p>URL: https://www.sciencedirect.com/science/article/pii/S0034425716300621 </p> <p>Data are provided in GeoTIFF format per season per year. File naming convention is as follows:<br> yy_inund_freq_season_SamplingMethod. For example, "99_inund_freq_winter_max" will represent inundation frequency for winter 1999 resampled using a maximum resampling method. </p> <p>Inundation frequency represents the number of times a pixel has been flagged as flooded out of the times that pixel had valid observations * 100. Valid observation exclude no data values and clouds. The valid range of inundation frequency is 0-100 [%], with 255 indicating no data values. Data type is eight bit unsigned integer (uint8). </p> <p>The data were resampled to 120m resolution to reduce file size. The resampling methods used include max (e.g. selects the max value of all non-NODATA contributing 30m pixels) and mean (median and min can be provided upon request). If you are unsure which resampling to use, you may want to start with the mean. </p>
Open Surface Drifter Data - Pirita river
<p>This dataset contains the surface drifter tracks collected in Pirita River (Estonia) to test the open drifter presented in the publication https://doi.org/10.3390/s22249918</p>
National Park Service - South Florida/Caribbean Inventory & Monitoring Network - SARI SET Surface Water level data from Salt River Bay National Historical Park and Ecological Preserve, St. Croix, US Virgin Islands.
Surface water level data (m) was collected in Salt River Bay National Historic Park and Ecological Preserve (SARI) by the South Florida/Caribbean Inventory and Monitoring Network (SFCN) as part of the Soil Elevation Table (SET) vital sign monitoring program. Water level data collected from 2017 to 2024 is included in this dataset. The water level data was collected using HOBOware Onset Water Level Data Loggers. This data-package is complete.
Water Depths and Water Temperatures near Soil Surface from Shark River Slough, Everglades National Park (FCE LTER), Florida, USA, October 2000 - ongoing
Water depth (from October 2000 to present) and water temperature (from September 2021 to present) are recorded at least hourly at SRS1c (not active), SRS1d, SRS2, SRS3, SRS4, SRS5, and SRS6. Water depth is measured with pressure water level loggers (Infinities USA or HOBO) that record water height relative to the local soil surface. Water temperature near soil surface is measured with HOBO loggers. Note by IM (2021): The water meters at some of the SRS sites have been moved over the years as boardwalks have been reconstructed. There is no set survey datum for these sites, so it is impossible to correct the data to an actual datum. For hydrologic applications, it may be better to use water level data from USGS stations.
River Surface Reflectance Database (RiverSR)
<p><strong>RiverSR database (River Surface Reflectance) v1.1.0</strong></p> <p>This database contains Landsat 5, 7, and 8 Level 1 Collection 1 surface reflectance from all rivers in the contiguous USA that are ~60 meters wide or greater. The surface reflectance values across bands (red, green, blue, nir, swir1, swir1) represent the median reflectance of pixels detected as water within each Landsat scene that are within the boundaries of each reach represented by NHDPlusV2 centerlines. Surface reflectance is therefore geo-referenced to river center lines with network topology (NHDPlusV2) for quick geospatial analysis.</p> <p><strong>Files:</strong></p> <p>1) Metadata (riverSR_v1.1_metadata.docx): Description of all data files associated with this repository. </p> <p>2) Surface reflectance database (riverSR_usa_v1.1.feather). Feather files are text files readable in R and python with the feather package and this table is joinable to nhdplusv2_modified_v1.0.shp based on the "ID" column and to the original NHDplusV2 flowlines with the "COMID" column.</p> <p>3) Shapefile of river centerlines to which the reflectance data can be attached (nhdplusv2_modified_v1.0.shp).</p> <p>4) Shapefile of the reach polygons associated with each nhdplusv2_modified reach. (nhdplusv2_polygons.shp).</p> <p>5) The reach IDs of original and new NHDplusV2 centerlines. (COMID_ID.csv).</p> <p> </p> <p> </p> <p> </p>
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>
GRWSE-global river water surface elevation from sentinel-3
<p>This dataset includes time series of Water Surface Elevation (WSE) of large rivers at over 3000 virtual stations. The WSE time series were created using Sentinel-3A and Sentinel-3B altimetry data. </p>
Surface water and flooding dynamics based on seasonally continuous Landsat data (1986-2011) in a dryland river basin (monthly, seasonally, and yearly animations)
<p>The animations provided here are part of the following publication:<br> Tulbure, M.G. and M. Broich (2018). Spatiotemporal patterns and effects of climate and land use on surface water extent dynamics in a dryland region with three decades of Landsat satellite data. Science of the Total Environment. https://www.sciencedirect.com/science/article/pii/S0048969718347466</p> <p>Please refer to the above mentioned publication for a description of the data and interpretation of the patterns.</p> <p>The animations are based on statistically validated surface water and flooding extent dynamics data derived from seasonally continous Landsat TM/ETM+ and random forest models from 1986 to 2011 over Australia's Murray-Darling Basin. The overall accuracy was over 99% and producer's accuracy for water 87% +/- 3%. </p> <p>The method is described in the following publication: <br> Tulbure, M.G., M. Broich, S.V. Stehman, A. Kommareddy. (2016). Surface water extent dynamics from three decades of seasonally continuous Landsat time series at subcontinental scale in a semi-arid region. Remote Sensing of Environment. 178: 142-157 and available here: https://www.sciencedirect.com/science/article/pii/S0034425716300621 </p>
LAGOS-US NETWORKS v1.0: Data module of surface water networks characterizing connections among lakes, streams, and rivers in the conterminous U.S
Knowing the degree of surface water connectivity among aquatic ecosystems can help scientists better understand and predict the movement of materials and biota across ecosystems. Methods to quantify surface water networks that include lake and stream connections at broad spatial scales are rare because it is difficult to balance accurate estimates of surface water connectivity and computational challenges. The LAGOS-US NETWORKS (NETS) module contains surface connectivity metrics for lake networks across the conterminous United States. We applied a graph theory approach to identify lake networks (i.e. a set of lakes connected by streams either upstream, downstream, or both) created from the medium resolution NHD lakes, streams, and rivers and subsequently derive surface water connectivity metrics for lakes and networks. Using this approach, we created a total of 898 networks that include 86,511 lakes. The NETS module includes a table with metrics for connections between lakes (both upstream and downstream), dams, network position, and whole networks. NETS also includes a flow table and bidirectional and unidirectional distance tables that provide the distances between every pair of connected lakes.
Abiotic monitoring of physical characteristics in porewaters and surface waters of mangrove forests from the Shark River Slough and Taylor Slough, Everglades National Park (FCE LTER), South Florida, USA, December 2000 - ongoing
Data on porewater salinity, temperature, conductivity, pH and redox have been collected to help explain patterns found in porewater nutrient concentrations that were sampled in the same plots. See knb-lter-fce.1171 (https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-fce&identifier=1171) for related porewater-nutrient-concentration data.
Data archive for journal paper "Assimilation of Sentinel-1 Backscatter into a Land Surface Model with River Routing and Its Impact on Streamflow Simulations in Two Belgian Catchments"
<p>The datasets archived here include data assimilation results presented in the journal paper, "Assimilation of Sentinel-1 Backscatter into a Land Surface Model with River Routing and Its Impact on Streamflow Simulations in Two Belgian Catchments" (https://doi.org/10.1175/JHM-D-22-0198.1). The output was produced by combining land surface modeling (Noah-MP with HYMAP river routing) and Sentinel-1 backscatter data, applying a 1D Ensemble Kalman Filter using the NASA Land Information System. We provide Netcdf daily output files for 6 different experiments</p><p>- OLfd and OLgw: model-only (open-loop, OL) for two different model settings (fd: free drainage and gw: SIMTOP groundwater option) <br>- DASMfd and DASMgw: data assimilation (DA) with soil moisture (SM) updating for two different model settings (fd: free drainage and gw: SIMTOP groundwater option) <br>- DASMLAIfd and DASMLAIgw: data assimilation (DA) with soil moisture (SM) and leaf area index (LAI) updating for two different model settings (fd: free drainage and gw: SIMTOP groundwater option) </p><p>Each experiment directory contains five subdirectories (DAOBS, EnKF, ROUTING, RTM, SURFACEMODEL) with corresponding outputs as described in https://nasa-lis.github.io/LISF/LIS_users_guide/LIS_users_guide.html</p>
Water surface occurrence and recurrence from the article "Amazon's 2023 Drought: Sentinel-1 Reveals Extreme Rio Negro River Contraction"
<p>This data package contains the 10 m spatial resolution occurrence and recurrence water surface masks from the article "Amazon's 2023 Drought: Sentinel-1 Reveals Extreme Rio Negro River Contraction" . These maps have been produced with Sentinel-1 images (10 m) and a Deep Learning method for image segmentation called U-net, methods and data are fully described in the article. Water surface occurrence is computed for the period 2022-2023 and indicates the percentage of time that a pixel is classified as water (100%: always water, 0%: never water, and values between 0 and 100 indicate seasonality). Water surface recurrence is computed for the period 2022-2023 and indicates the number of times that a pixel was classified as a water surface, i.e., 35 indicates that the pixel was classified 35 times as a water surface during the 2022-2023 period. The total size of the dataset is 158 Mo and is distributed in two Geotiffs, one for the water surface occurence and one for the water surface. When using this dataset, please cite the original article <a href="https://doi.org/10.3390/rs16061056">https://doi.org/10.3390/rs16061056</a></p>
Global river density, seasonal and surface water occurrence and upstream area at 250 m in the Goode Homolosine projection
<p>Several layers describing density of surface water / streams projected to the <a href="https://en.wikipedia.org/wiki/Goode_homolosine_projection">Good Homolosine projection</a>. List of layers included:</p> <ul> <li>hyd_log1p.upstream.area_merit.hydro_m = Upstream Drainage Area based on the <a href="http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_Hydro">MERIT Hydro</a>,</li> <li>hyd_river.density_gloric_p = rasterized <a href="https://www.hydrosheds.org/page/gloric">Global River Classification (GLORIC)</a> DB,</li> <li>lcv_water.occurance_jrc.surfacewater_p = Surface Water based on the JRC's <a href="https://global-surface-water.appspot.com/">Global Surface Water</a>,</li> <li>lcv_water.seasonal_probav.glc.lc100_p = Seasonal Inland Water probability based on the <a href="https://lcviewer.vito.be/">Copernicus LC100 map</a>,</li> <li>lcv_wetlands.cw_upmc.wtd_c = composite wetland (CW) map based on <a href="https://doi.org/10.1594/PANGAEA.892657">Tootchi et al. (2019)</a>,</li> <li>Goode_Homolosine_domain_250m.tif = map domain prepared by <a href="https://doi.org/10.5281/zenodo.1475152">Luís de Sousa</a>,</li> <li>tiles_GH_100km_land.gpkg = 100 km x 100 km tiling system covering the land mass,</li> </ul> <p>Important notes: Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/input_layers/WaterDensity">here</a></strong>. Antartica is not included. Reprojecting maps to Goode Homolosine projection can be cumbersome and small amount of artifacts at the edges of the map can be anticipated.</p> <p>These maps were develop in connection to the <a href="http://www.OpenLandMap.org">OpenLandMap.org</a> initiative.</p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>hyd = theme: hydrology and water dynamics,</li> <li>log1p.upstream.area = variable: log(X+1)*10 of the upstream area,</li> <li>merit.hydro = determination method: MERIT Hydro,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b0..0cm = vertical reference: surface,</li> <li>2017 = time reference: period 2017,</li> <li>v0.1 = version number: 0.1,</li> </ul>
Belham River Valley Digital Surface Model (1 metre) - March 2019 - Pleiades Photogrammetry (co-registered)
<p>This tiff contains a Digital Surface Model (DSM) of the Belham River Valley in Montserrat, Eastern Caribbean. This was generated via leverage of Pleiades tri-stereo imagery throught the DSM-OPT software platform. This DSM has been co-registered to a LiDAR DSM (generated in 2010, vertical error 0.15 m) and found to have a vertical error of 2.3 m.</p>
Atmospheric Rivers Contribute to Summer Surface Buoyancy Forcing in the Atlantic Sector of the Southern Ocean
<p>These are the Wave glider data used in the analysis and creation of figures in Edholm et al. 2022: <em>Atmospheric Rivers Contribute to Summer Surface Buoyancy Forcing in the Atlantic Sector of the Southern Ocean</em> in support of open-code, transparency, and repeatability.</p> <p>Abstract:</p> <p>Atmospheric rivers (ARs) dominate moisture transport globally; however, it is unknown what impact ARs have on surface ocean buoyancy. This study explores the surface buoyancy gained by ARs using high-resolution surface observations from a Wave Glider deployed in the subpolar Southern Ocean (54°S, 0°E) between 19 December 2018 and 12 February 2019 (55 days). When ARs combine with storms, the associated precipitation is significantly enhanced (189%). In addition, the daily accumulation of AR-induced precipitation provides a buoyancy gain to the surface ocean equivalent to warming by surface heat fluxes. Over the 55 days, ARs accounted for 47% of the total precipitation equating to 10% of the summer surface ocean buoyancy gain. This study indicates that ARs play an important role in the summer precipitation over the subpolar Southern Ocean and that they can alter the upper-ocean buoyancy budget from synoptic to seasonal timescales.</p>
A framework for estimating global river discharge from the Surface Water and Ocean Topography satellite mission example data
<p>These files contain the Confluence pipeline outputs, prior information (SOS) and Simulated SWOT shape files from the example in the "A framework for estimating global river discharge from the Surface Water and Ocean Topography satellite mission" manuscript. </p>
High-resolution surface wind observations over complex terrain: Big Southern Butte, Salmon River Canyon, Birch Creek
<p>This dataset contains high-resolution wind observations from three field campaigns that took place during 2010-2014 at Big Southern Butte, Salmon River Canyon, and Birch Creek, Idaho. There are three SQLite databases containing 30-s averaged 3-m wind speed, wind direction, and wind gust data from 30-90 cup-and-vane anemometers over a period of 2-4 months at each field site.</p>
Burned soil surface radiocarbon values for moss macrofossils plucked from the Anaktuvuk River Fire sites
We used radiocarbon dating of the organic soil surface remaining post-fire to examine whether the fire burned into ancient and likely irreplaceable soil C pools. Suprisingly, it did not; all radiocarbon dates from burned soil surfaces contained bomb carbon, setting the maximum age of the burned soil surfaces at ~50 years.
Yukon River Basin Fire and Permafrost Study: Elevation of soil surface and permafrost table along transects with different fire disturbance regimes (2009-2012)
Two 100 - 200 m transects were established on hilly loess deposits in the Yukon Flats near Boot Lake to monitor annual changes in the permafrost table and thaw settlement under different fire disturbance regimes. One was located in an area that burned around 1925; the other transect was located in a 2009 burn. Both transects were partially affected by a fire in 1967. This dataset includes the elevations of the ground surface, permafrost surface, water table, and surface organic thickness measurements. Note that there was compaction of the organic layer due to human disturbance along the portion of the transect that burned in only 1925. These transects are associated with the burned and unburned silty upland sites detailed in related datasets.
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.