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7,169 results for “30”
CoastSeg: Shoreline data at 30-m spatial resolution for 5x5 degree regions of the world, in geoJSON format.
<p><em><strong>CoastSeg: global 30-m shoreline in 5x5 degree chunks</strong></em></p> <p>Data fields:</p> <ol> <li>MEAN_SIG_WAVEHEIGHT</li> <li>TIDAL_RANGE</li> <li>CHLOROPHYLL</li> <li>TURBIDITY</li> <li>TEMP_MOISTURE</li> <li>EMU_PHYSICAL</li> <li>REGIONAL_SINUOSITY</li> <li>GHM</li> <li>MAX_SLOPE %</li> <li>OUTFLOW_DENSITY</li> <li>ERODIBILITY</li> <li>LENGTH_GEO</li> <li>ch_label</li> <li>river_label</li> <li>sinuosity_label</li> <li>slope_label</li> <li>tidal_label</li> <li>turbid_label</li> <li>wave_label</li> <li>CSU_Descriptor</li> <li>CSU_ID</li> </ol> <p>The data originally come from https://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/USGSEsriGlobalCoastalSegmentsv1.mpk</p> <p>The data are described in the following publication</p> <p>Roger Sayre, Suzanne Noble, Sharon Hamann, Rebecca Smith, Dawn Wright, Sean Breyer, Kevin Butler, Keith Van Graafeiland, Charlie Frye, Deniz Karagulle, Dabney Hopkins, Drew Stephens, Kevin Kelly, Zeenatul Basher, Devon Burton, Jill Cress, Karina Atkins, D. Paco Van Sistine, Beverly Friesen, Rebecca Allee, Tom Allen, Peter Aniello, Irawan Asaad, Mark John Costello, Kathy Goodin, Peter Harris, Maria Kavanaugh, Helen Lillis, Eleonora Manca, Frank Muller-Karger, Bjorn Nyberg, Rost Parsons, Justin Saarinen, Jac Steiner & Adam Reed (2019) A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units, Journal of Operational Oceanography, 12:sup2, S47-S56, DOI: <a href="https://doi.org/10.1080/1755876X.2018.1529714">10.1080/1755876X.2018.1529714</a></p> <p>Metadata file (each file listed alongside the bounds in WGS84 latitude/longitude): global_5x5grid.geojson</p>
CoastSeg: Shoreline data at 30-m spatial resolution for 5x5 degree regions of the world, in geoJSON format. Version 2.
<p><em><strong>CoastSeg: global 30-m shoreline in 5x5 degree chunks</strong></em></p> <p>Data fields:</p> <ol> <li>MEAN_SIG_WAVEHEIGHT</li> <li>TIDAL_RANGE</li> <li>CHLOROPHYLL</li> <li>TURBIDITY</li> <li>TEMP_MOISTURE</li> <li>EMU_PHYSICAL</li> <li>REGIONAL_SINUOSITY</li> <li>GHM</li> <li>MAX_SLOPE %</li> <li>OUTFLOW_DENSITY</li> <li>ERODIBILITY</li> <li>LENGTH_GEO</li> <li>ch_label</li> <li>river_label</li> <li>sinuosity_label</li> <li>slope_label</li> <li>tidal_label</li> <li>turbid_label</li> <li>wave_label</li> <li>CSU_Descriptor</li> <li>CSU_ID</li> </ol> <p>The data originally come from https://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/USGSEsriGlobalCoastalSegmentsv1.mpk</p> <p>The data are described in the following publication</p> <p>Roger Sayre, Suzanne Noble, Sharon Hamann, Rebecca Smith, Dawn Wright, Sean Breyer, Kevin Butler, Keith Van Graafeiland, Charlie Frye, Deniz Karagulle, Dabney Hopkins, Drew Stephens, Kevin Kelly, Zeenatul Basher, Devon Burton, Jill Cress, Karina Atkins, D. Paco Van Sistine, Beverly Friesen, Rebecca Allee, Tom Allen, Peter Aniello, Irawan Asaad, Mark John Costello, Kathy Goodin, Peter Harris, Maria Kavanaugh, Helen Lillis, Eleonora Manca, Frank Muller-Karger, Bjorn Nyberg, Rost Parsons, Justin Saarinen, Jac Steiner & Adam Reed (2019) A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units, Journal of Operational Oceanography, 12:sup2, S47-S56, DOI: <a href="https://doi.org/10.1080/1755876X.2018.1529714">10.1080/1755876X.2018.1529714</a></p> <p>Metadata file (each file listed alongside the bounds in WGS84 latitude/longitude): global_5x5grid.geojson</p>
FORMS: Forest Multiple Source height, wood volume, and biomass maps in France at 10 to 30 m resolution based on Sentinel-1, Sentinel-2, and GEDI data with a deep learning approach.
<p>The products can be vizualized at <a href="https://martinschwartz0.users.earthengine.app/view/forms-height-biomass-volume-viewer">https://martinschwartz0.users.earthengine.app/view/forms-height-biomass-volume-viewer</a></p> <p>- FORMS-H: Canopy height map of France at 10 m resolution. The units are in centimeter (10^-2 m).</p> <p>- FORMS-B: Above-ground biomass density map of France at 30 m resolution. The units are in Mg ha-1</p> <p>- FORMS-V: Wood volume density map of France at 30 m resolution. The units are in m3 ha-1</p> <p>Please refer to the paper <a href="https://doi.org/10.5194/essd-15-4927-2023">https://doi.org/10.5194/essd-15-4927-2023</a> for further details.</p>
All three types of otoliths of Cyprinus carpio (total length=30 cm); left/right asterisci and lapilli from top and bottom view, and one sagittus.
<p>The image shows all three types of otoliths of <em>Cyprinus carpio</em> (total length=30 cm): left/right <em>asterisci</em> and <em>lapilli </em>from top and bottom view, and one sagittus. </p>
CoastSeg: 30-m atlas of the coastal shoreline attributes of California, in geoJSON format.
<p><strong>CoastSeg: 30-m atlas of the coastal shoreline attributes of California, in geoJSON format.</strong></p> <p>This is a shoreline atlas of California at 30m resolution, to support analysis of CoastSat/CoastSeg-derived shoreline time-series and other shoreline data, and miscellaneous analyses of coastal shoreline data. The dataset consists of a GeoJSON files containing a 30-m shoreline estimate for California, based on an analysis of 2014 Landsat imagery (Sayre et al., 2019). This shoreline vector has been attributed with the following fields that may be useful in analyses of shoreline patterns and regional variability:</p> <ol> <li>MEAN_SIG_WAVEHEIGHT (m)</li> <li>TIDAL_RANGE (m)</li> <li>CHLOROPHYLL (mg/L)</li> <li>TURBIDITY</li> <li>TEMP_MOISTURE (descriptive)</li> <li>EMU_PHYSICAL (descriptive)</li> <li>REGIONAL_SINUOSITY</li> <li>GHM</li> <li>MAX_SLOPE (%)</li> <li>OUTFLOW_DENSITY</li> <li>ERODIBILITY (descriptive)</li> <li>LENGTH_GEO</li> <li>ch_label (descriptive)</li> <li>river_label (descriptive)</li> <li>sinuosity_label (descriptive)</li> <li>slope_label (descriptive)</li> <li>tidal_label (descriptive)</li> <li>turbid_label (descriptive)</li> <li>wave_label (descriptive)</li> <li>CSU_Descriptor (descriptive)</li> <li>CSU_ID</li> <li>elevation (m)</li> <li>aspect (degrees N)</li> <li>slope (degrees)</li> </ol> <p>Fields 1 to 21 inclusive originally come from raw data https://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/USGSEsriGlobalCoastalSegmentsv1.mpk, which is described in Sayre et al (2019)</p> <p>Fields 22 and 24 come from raw data originally in the U.S. Geological Survey Elevation Derivatives for National Applications (EDNA) database (https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-elevation-derivatives-national), accessed through Earth Explorer and processed in QGIS.</p> <p>The figure shows distributions of selected quantities. A python script to reproduce this plot is provided</p> <p>A subset of numeric-only variables and descriptive-only variables has also been prepared and made available. A CSV version of the full dataset is also provided</p> <p> </p> <p><strong>References</strong></p> <ol> <li>Roger Sayre, Suzanne Noble, Sharon Hamann, Rebecca Smith, Dawn Wright, Sean Breyer, Kevin Butler, Keith Van Graafeiland, Charlie Frye, Deniz Karagulle, Dabney Hopkins, Drew Stephens, Kevin Kelly, Zeenatul Basher, Devon Burton, Jill Cress, Karina Atkins, D. Paco Van Sistine, Beverly Friesen, Rebecca Allee, Tom Allen, Peter Aniello, Irawan Asaad, Mark John Costello, Kathy Goodin, Peter Harris, Maria Kavanaugh, Helen Lillis, Eleonora Manca, Frank Muller-Karger, Bjorn Nyberg, Rost Parsons, Justin Saarinen, Jac Steiner & Adam Reed (2019) A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units, Journal of Operational Oceanography, 12:sup2, S47-S56, DOI: <a href="https://doi.org/10.1080/1755876X.2018.1529714">10.1080/1755876X.2018.1529714</a></li> <li><a href="https://doi.org/10.5066/F7TD9VTQ">Elevation Derivatives for National Applications (EDNA) Seamless Three-Dimensional Hydrologic Database Digital Object Identifier (DOI) number: /10.5066/F7TD9VTQ</a></li> </ol> <p> </p>
30 m resolution global forest burned area dataset 2018
<p>Global forest burned area data produced based on the high-precision global burned area product GABAM.The product was projected in a Geographic (Lat/Long) projection at 0.00025<sup>°</sup>(approximately 30 meters) resolution, with the WGS84 horizontal datum and the EGM96 vertical datum, consisting of 10° x 10° tiles spanning the range 180W–180E and 80N–60S.</p>
30 m resolution global forest burned area dataset 2016
<p>Global forest burned area data produced based on the high-precision global burned area product GABAM.The product was projected in a Geographic (Lat/Long) projection at 0.00025<span>°</span> (approximately 30 meters) resolution, with the WGS84 horizontal datum and the EGM96 vertical datum, consisting of 10° x 10° tiles spanning the range 180W–180E and 80N–60S.</p>
30 m resolution global forest burned area dataset 2014
<p>Global forest burned area data produced based on the high-precision global burned area product GABAM.The product was projected in a Geographic (Lat/Long) projection at 0.00025°(approximately 30 meters) resolution, with the WGS84 horizontal datum and the EGM96 vertical datum, consisting of 10° x 10° tiles spanning the range 180W–180E and 80N–60S.</p><p>contacts : zhangzhaoming@aircas.ac.cn / zhangzm@radi.ac.cn</p>
30 m resolution global forest burned area dataset 2020
<p>Global forest burned area data produced based on the high-precision global burned area product GABAM.The product was projected in a Geographic (Lat/Long) projection at 0.00025°(approximately 30 meters) resolution, with the WGS84 horizontal datum and the EGM96 vertical datum, consisting of 10° x 10° tiles spanning the range 180W–180E and 80N–60S.</p><p>contacts : zhangzhaoming@aircas.ac.cn / zhangzm@radi.ac.cn</p>
Cothran, R. D., F. Radarian, and R. A. Relyea. 2011. Altering aquatic food webs with a global insecticide: Arthropod-amphibian links in mesocosms that simulate wetland communities. Journal of the North American Benthological Society 30:893-912.
Pesticides play a critical role in maximizing yields of economically important crops and minimizing the human health threats of disease-carrying pests, but they often have collateral effects on nontarget species. We used a mesocosm study to address how the most commonly used insecticide in the USA, malathion, applied at low, ecologically relevant concentrations (20 and 110 mg/L) affects species interactions in aquatic communities. Unlike many community ecotoxicology studies, our study assessed how malathion affects both consumptive and nonconsumptive effects of predators. We also considered how the vertical distribution of predator cues and malathion (caused by potential stratification) affects species interactions. We found no evidence for vertical stratification of malathion, a result suggesting that exposure to the pesticide was uniform throughout the water column. Malathion was lethal to some primary consumers (cladocerans) at both concentrations and to top predators (dragonflies) at the highest concentration (110 mg/L). These lethal effects initiated density-mediated indirect effects in both cases. Malathion also may have decreased dragonfly foraging efficiency, resulting in increased tadpole survival (trait-mediated indirect effect), which decreased the resources used by tadpoles (periphyton). Collectively, our results show that malathion alters species interactions. However, we suggest that the degree to which pesticides affect aquatic communities will depend strongly on the species composition of communities. Therefore, the community-level consequences of pesticide exposure are likely to vary across the ecological landscape.
30 meter digital elevation model (DEM) clipped to the Andrews Experimental Forest, 1996
Elevation Model for the HJ Andrews Experimental Forest (30 meter DEM). This dataset includes the raw DEM, and several value added products. The products are contour lines, aspect, percent slope, and a hill shade for relief mapping.
Site, environmental and stand data across an age since fire range of 6-338 years for 30 sites in northern Yukon and central Alaska
This dataset describes site level environmental and stand attributes of 30 black spruce stands across northern Yukon and central Alaska. Sites were selected to cover a broad age range since fire (6 to 338 years). Site level descriptions include: latitude, longitude, elevation, slope, aspect, soil characteristics (organic layer depth, pH, active layer depth, moisture classification, texture), average tree age, density and stand basal area. <br><br> **In Prep Puplication** Viglas, Jayme, Carissa Brown, and Jill Johnstone. Stand age effects on seed productivity of north
Samples of individual trees (cones, age) within sites across an age since fire range of 6-338 years for 30 sites in northern Yukon and central Alaska
This dataset describes individual trees within site attributes including cohort of cones (2006-2009), whether cones are opened or closed, the number of cones and seeds and viability of each cohort and the measured age of two perpendicular sections at the base of tree. <br><br> **In Prep Puplication** Viglas, Jayme, Carissa Brown, and Jill Johnstone. Stand age effects on seed productivity of north
Monthly sea-level summary data for the Fort Pulaski, Georgia, water level station (NOAA/NOS CO-OPS ID 8670870) from 01-Jul-1935 to 30-Jun-2006
Monthly mean water levels based on MLLW (mean lower low water) datum in meters were acquired from the NOAA/NOS Center for Operational Oceanographic Products and Services web site (http://tidesandcurrents.noaa.gov/) for station ID 8670870 (Fort Pulaski, Georgia). Selected date/time and data columns were extracted from the CO-OPS web pages, standardized and documented using GCE-LTER metadata templates. This data set covers the period from 01-Jul-1935 to 30-Jun-2006
Jornada Basin LTER Nutrient and Ecosystem impacts of Aeolian Transport Study (NEAT) Block 1 meteorological station: 30-minute summary data: 2019 - ongoing
30-minute summary data at NEAT Block-1 met station. A met station consisting of a 10-meter mast is installed on the northwest corner of the site. Air temperature, wind direction and wind speed sensors are mounted on the mast. A vertical wind profile is measured with anemometers at 0. 45m, 0.90m, 1.90m, 4.40m and 10m on the mast. A wind vane is installed at 2.50m and 8.50m. Precipitation is measured. This climate station is operated by the Jornada LTER Program. This is an ONGOING dataset.
Luquillo Experimental Forest Canopy Trimming Experiment CTE2 2015-2020 30-minute abiotic data
The data archive is here: https://doi.org/10.2737/RDS-2021-0028 please use this DOI when citing this dataset. This data publication contains 30-minute values for abiotic field data from 3 treated and 3 control plots from the Canopy Trimming Experiment (CTE) located near El Verde Field Station in the Luquillo Experimental Forest (El Yunque National Forest), Puerto Rico collected from 2015 through 2020. In December of 2014 (CTE2), in 0.09 hectare (ha) square plots near the El Verde Field Station the forest canopy was trimmed and the canopy debris was littered to the forest floor. The plot size and trim amounts were based on the patch disturbance after the two most recent hurricanes before 2017, both category 3 hurricanes at the location of El Verde: Hugo in September 1989, and Georges in September 1998. Data were collected in the inner 0.04 ha quadrants of the 0.09 ha trimmed plots to minimize edge effects. Each plot was made up of 16 subplots with different data types collected in each subplot. There were 3 sets of control and treated plots, with each set near El Verde field station. Field data include 30-minute: solar radiation, soil profile volumetric water content, shallow soil volumetric water content, canopy leaf saturation, litter leaf saturation, air temperature, soil temperature, air relative humidity, vapor pressure, and throughfall. Field data were collected by one automatic sensor in each plot inside a designated subplot, except: soil profile volumetric water content, canopy leaf saturation, and litter leaf saturation; each were collected in three subplots. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical
Year 2006, 10, 15 or 30 minute measurements of stage, water temperature, conductivity in a small headwater stream draining draining a mainly wetland catchment (49% wetlands/swamp + 36% forest), Bear Meadow Brook, draining Cedar Swamp, Reading, MA.
Year 2006, continuous measurements, every 10, 15 or 30 minutes, were made of stage, water temperature, conductivity in a small headwater stream, Bear Meadow Brook , Cedar Swamp, Reading MA, draining a mainly wetland catchment (49% wetland + 36% wetland). Discharge is determined from stage using discharge vs stage regressions.
Year 2000, 30 minute interval, water quality measurements of water column temperature, salinity, oxygen, depth, and turbidity in the upper Parker River Estuary at Middle Road Bridge
Year 2000, Continuous, 30 minute time interval, water quality measurements of water column temperature, salinity, conductivity, oxygen, depth, and turbidity in the upper Parker River Estuary at Middle Road Bridge, Newbury, MA.
HLS4ML LHC Jet dataset (30 particles)
<p>Dataset of high-pT jets from simulations of LHC proton-proton collisions</p> <p>Prepared for FastML/HLS4ML studies: https://fastmachinelearning.org</p> <p>Includes: High level features (see https://arxiv.org/abs/1804.06913)</p> <p>Images: jet images with up to 30 particles/jet (see https://arxiv.org/abs/1908.05318)</p> <p>List: list of jet features with up to 30 particles/jet (see https://arxiv.org/abs/1908.05318)</p>
ePSproc: ABCO, orb 30 ioinzation (E)
<p>ABCO, orb 30 ioinzation (E) - photoionization calculations with ePolyScat (ePS) + ePSproc.<br> <br> *Web version*: <a href="https://phockett.github.io/ePSdata/ABCO/ABCO_1-50eV_orb30_E.html">https://phockett.github.io/ePSdata/ABCO/ABCO_1-50eV_orb30_E.html</a><br> <br> For more details of the calculations, see readme.txt, or:</p> <ul> <li><a href="https://phockett.github.io/ePSdata/about.html">About ePSdata</a></li> <li><a href="http://epsproc.readthedocs.io/en/latest/about.html">About ePSproc</a></li> <li><a href="http://www.chem.tamu.edu/rgroup/lucchese/ePolyScat.E3.manual/manual.html">About ePS</a></li> </ul>
ScienceDex guides
Understand access before you commit
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.