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722 results for “elevation data”
PIE LTER surface elevation table (SET) pin height data from twelve marsh sites in northeast Massachusetts.
Surface elevation table (SET) measurements from 26 SETs at 9 marsh sites in the Plum Island Sound Long-Term Ecological Research Site in the Great Marsh, Massachusetts. SET measurements are useful for determining the relative elevation change of marsh sediments. Precise measurements of sediment elevation in marshes is useful for determining rates of elevation change in response to changes in sea level.
Surface Elevation Data for the Upper Phillips Creek Marsh at the Virginia Coast Reserve 1998-2025
This dataset contains data from Surface Elevation Tables (SETs), Root SETs (RSET), and Marker Horizons (MH) located at the Virginia Coast Reserve. NOTE: These research plots are highly sensitive to disturbance and should not be approached by anyone not engaged in taking measurements. Mark Brinson, Robert Christian and Linda Blum conceived the experimental design, installed (1997), and measured (1997-2011) the SET and MH. After Mark Brinson died in 2011, Robert Christian and Linda Blum continued SET and MH measurements through 2017. VCR Staff and Keryn Gedan assumed responsibility for SET and MH measurements subsequent to 2017. Linda Blum and Pat Willis installed the RSET (2003). Pat was responsible for measuring the RSET between (2003-2005). Between 2005-2017, Linda Blum was responsible for measuring the RSET in addition to the SET and MH. Subsequent to 2017, Cora Johnson Baird and Keryn Gedan assumed responsibility for RSET measurements.
Surface Elevation Table (SET) data (pin heights) from control and fertilized plots at Spartina alterniflora, S. patens and Typha sp. marshes, Plum Island Ecosystems LTER, MA (1999-2025).
A Surface Elevation Table (SET) is used to measure changes in the elevation of the marsh surface at three long term marsh fertilization experimental research sites. The sites include one Typha-dominated brackish marsh, one Spartina alterniflora-dominated salt marsh, and one S. patens-dominated salt marsh. Sites are located on the Rowley and upper Parker Rivers in the Plum Island Ecosystems (PIE) LTER site.
Biome Transition Along Elevational Gradients in New Mexico (SEON) Study: Flux Tower Seasonal Biomass and Seasonal and Annual NPP Data at the Sevilleta National Wildlife Refuge, New Mexico
The varied topography and large elevation gradients that characterize the arid and semi-arid Southwest create a wide range of climatic conditions - and associated biomes - within relatively short distances. This creates an ideal experimental system in which to study the effects of climate on ecosystems. Such studies are critical given that the Southwestern U.S. has already experienced changes in climate that have altered precipitation patterns (Mote et al. 2005), and stands to experience dramatic climate change in the coming decades (Seager et al. 2007; Ting et al. 2007). Climate models currently predict an imminent transition to a warmer, more arid climate in the Southwest (Seager et al. 2007; Ting et al. 2007). Thus, high elevation ecosystems, which currently experience relatively cool and mesic climates, will likely resemble their lower elevation counterparts, which experience a hotter and drier climate. In order to predict regional changes in carbon storage, hydrologic partitioning and water resources in response to these potential shifts, it is critical to understand how both temperature and soil moisture affect processes such as evapotranspiration (ET), total carbon uptake through gross primary production (GPP), ecosystem respiration (Reco), and net ecosystem exchange of carbon, water and energy across elevational gradients. We are using a sequence of six widespread biomes along an elevational gradient in New Mexico -- ranging from hot, arid ecosystems at low elevations to cool, mesic ecosystems at high elevation to test specific hypotheses related to how climatic controls over ecosystem processes change across this gradient. We have an eddy covariance tower and associated meteorological instruments in each biome which we are using to directly measure the exchange of carbon, water and energy between the ecosystem and the atmosphere. This gradient offers us a unique opportunity to test the interactive effects of temperature and soil moisture on ecosystem proces
Surface Elevation (SET) Data for Brownsville Forest at the Virginia Coastal Reserve, 2019-2025
This dataset contains data from Surface Elevation Tables (SETs) located in the Brownsville Forest at the Virginia Coastal Reserve. These research plots were set up in 2019 as a part of a larger forest disturbance project. Marker Horizons and Shallow SETs were installed in 2020 but have not yet been measured. Tyler Messerschmidt has made all measurements of these SETs
Plant species percent cover data: BioCON : Biodiversity, Elevated CO2, and N Enrichment
BioCON (Biodiversity, CO2, and Nitrogen) is an ecological experiment started in 1997 at the University of Minnesota's Cedar Creek Ecosystem Science Reserve. BioCON's goal is to explore the ways in which plant communities will respond to three environmental changes that are known to be occurring on a global scale: increasing nitrogen deposition, increasing atmospheric CO2, and decreasing biodiversity. Why Biodiversity, CO2, and Nitrogen? While there are many uncertainties in global change biology, there are also some well documented facts. Some of these are: 1. The amount of carbon dioxide (CO2) in the atmosphere is rising. Since the industrial revolution, the CO2 concentration in the atmosphere has increased from approximately 275 parts per million (ppm) to about 378 ppm today. This has been largely the result of fossil fuel burning. It is expected that CO2 levels will continue to rise, and that by the year 2050 these levels will be approximately 550 ppm. CO2 is the raw material for photosynthesis and is known to affect plant growth and development. 2. The amount of nitrogen moving through terrestrial ecosystems has increased in the recent past. While natural "background" levels of nitrogen fixation have remained constant, human additions to the system through fertilizer production and fossil fuel use have increased dramatically. Nitrogen is a key nutrient for plant growth and plays a critical role in plant community structure and composition in many environments. 3. Biodiversity levels are falling. While the research and data are not as complete as they are for CO2 and nitrogen, data indicate that the number of species globally, is being reduced. Perhaps more important for ecosystem function, diversity levels on local to regional scales have fallen due to land use change, biotic invasion and many other drivers. While much is known about how each of these factors affects ecosystem functioning, many questions remain. There is also little data on how these issues affe
Long-Term Elevation Plots (LTEP) (Altitudinal transects vegetation data along three rivers in the Luquillo Experimental Forest)
The composition of plant communities changes with elevation in the Luquillo Experimental Forest (LEF). The goal of this project is to document the patterns of these changes, and in particular, to determine whether the distributions of individual species are independent of one another, or whether they are related, in either a congruent or a hierarchical manner. Thirty-two permanent vegetation plots, each 50m by 20m are being established in the LEF, with 5 plots along the Icacos river, 11 along the Mamayes river and 16 along the Sonadora stream. The plots were established at every 100m in elevation, starting at 200m above sea level. All woody, free-standing stems greater than 1cm dbh were marked, identified and mapped into 5x5 subquadrats. We anticipated that gradient analysis will show whether the distributions of species are coincident or independent, enabling us to evaluate whether separate, genuine plant communities exist in the LEF. Because the plots are permanent, we also expected that they allow us to better evaluate how different vegetation types, at different elevations, respond to large scale disturbances, especially hurricanes. 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 Forestry, USDA Forest Service.
Terrestrial gastropods abundance data along an elevational gradient within the Sonadora River watershed
The data set includes 3 files that contain abundance data for terrestrial gastropods along an elevational gradient within the Sonadora River watershed. Two files (1 and 2) contain data from the same transect but differ in the year during which they were collected (2007 and 2008). The third file (3) contains data from a separate elevational transect (sites were located at the same elevation as in files 1 and 2) in palm dominated forest within the same watershed that was collected during the same time period in 2008 as data from file 2. Note: Plots at 250 m of elevation were not sampled in 2008 on either transect and a plot at elevation 750 m in the palm transect was never sampled. 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 Forestry, USDA Forest Service.
Elevation Gradient (EG) Soil Microbial diversity FAME and TRFLP data
Soil fungal communities respond to multiple abiotic and biotic factors that change along elevation gradients. The limited information available on fungi and microbial processes along elevation gradients is primarily from temperate areas and very few from tropical regions. This study documents changes in fungal and bacterial diversity, and abundance and composition of microbial functional groups along a subtropical elevation gradient. 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 Forestry, USDA Forest Service.
Geospatial data for Luquillo Mountains, Puerto Rico: Mean annual precipitation, elevation, watershed outlines, and rain gage locations
The data archive is here: https://doi.org/10.5066/F74F1PM2 please use this DOI when citing this data set. These geospatial data sets were developed as part of a new analysis of all known current and historical rain gages in the Luquillo Mountains, Puerto Rico published in the journal article Murphy, S.F., Stallard, R.F., Scholl, M.A., Gonzalez, G., and Torres-Sanchez, A.J., 2017, Reassessing rainfall in the Luquillo Mountains, Puerto Rico: Local and global ecohydrological implications: PLOS One 12(7): e0180987, p. 1-26, https://doi.org/10.1371/journal.pone.0180987. That article provides a revised map of mean annual precipitation developed using elevation regression functions and residual interpolation, and that map is presented here in a raster file. Most previous forest- and watershed-wide estimates of precipitation (and evapotranspiration, as inferred by a water balance) have assumed that precipitation increases consistently with elevation in the Luquillo Mountains; therefore, precipitation in leeward Luquillo watersheds has been overestimated by up to 40%.Because the Luquillo Mountains often serve as a wet tropical archetype in global assessments of basic ecohydrological processes, these revised estimates are relevant to regional and global assessments of runoff efficiency, hydrologic effects of reforestation, geomorphic processes, and climate change. \<para\> 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 Forestry, USDA Forest Service.\</para\>
Rainfall and ion composition data from multiple weather stations along an elevation gradient in northeastern Puerto Rico (2009-2018)
The data archive is here: https://doi.org/10.2737/RDS-2021-0013 please use this DOI when citing this dataset. Rainfall and ionic composition data were collected at 21 sites along the elevational gradient of the Luquillo Mountains, in Puerto Rico. Stations were selected along the east coast of the island and follow the steep slope of the mountains until the highest peaks. Rainfall data were collected every two weeks and are provided in this data publication as monthly rainfall from January 2009 through May 2019. Also included are pH and conductivity which are provided monthly starting roughly in November 2011 and continue through May 2019. Monthly ionic composition data from rainwater samples collected during the last two weeks of each month are also included from January 2009 through December 2017. 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 Forestry, USDA Forest Service.
Luquillo Experimental Forest atmospheric and high and mid elevation weather data.
The data archive is here:https://doi.org/10.2737/RDS-2022-0050 please use this DOI when citing this dataset. This data publication contains daily means from laser ceilometer data from Sabana, ozone data from Bisley, and meteorological data collected from Bisley and the mountain top station of East Peak, all located on the Luquillo Experimental Forest (El Yunque National Forest) in Puerto Rico. Atmospheric data include: mean cloud observed frequency, mean lowest height cloud (cloud base), mean daytime mixing layer height observed frequency, mean lowest mixing layer height from hours 7am and 7pm only, and mean daytime mixing layer height collected from February 2013 through April 2021. Also included is mean ozone amount collected from April 2008 through early April 2021. The cloud and mixing layer frequency and low values were calculated using the Automated Surface Observing System (ASOS) method employed at airports in the area. Weather data include high elevation East Peak mean northeast wind speed (wind rose quartiles 45° to 90°), mean southeast wind speed (wind rose quartiles 90° to 135°), and mean total wind speed measured from October 2009 through 2020 (and a few months in 2021). Additional weather data collected from January 2008 through mid October 2020 include mid elevation Bisley mean precipitation, mean relative humidity, and mean temperature. 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 Forestry, USDA Forest Service.
Northeastern Puerto Rico open-canopy and under-canopy temperature and moisture data on an elevational gradient
The data archive is here:https://doi.org/10.2737/RDS-2022-0051 please use this DOI when citing this dataset. This data publication contains monthly means of temperature and moisture data collected from August 2006 through September 2021 from 22 locations along an elevational gradient, from 0 to 1045 meters, in Northeastern Puerto Rico. The higher elevational data are in the Luquillo Experimental Forest (El Yunque National Forest). Five kinds of data are included: air temperature and precipitation measured in the open (not under canopy) at 20 locations, air temperature and soil temperature both measured under the canopy at all 22 locations, and soil moisture under the canopy at 4 locations. Two locations have 3 sites each, measured in different canopy types at the same location. The other 20 locations have one site each, making a total of 26 measurement sites. Data are provided as monthly averages at each site. 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 Forestry, USDA Forest Service.
Shorelines, elevation transects and environmental data for Drew Point, Ak study area, 2019-2022
<p>This record consists of the shorelines and their derived products, elevation transects and environmental data used for the Cryosphere preprint: <em>Multiple modes of shoreline change along the Alaskan Beaufort Sea observed using ICESat-2 altimetry and satellite imagery</em>. There are three primary datasets:</p> <p><em>ERA-5 </em>contains hourly output ERA5 reanalysis dataset (Hersbach et al., 2020, https://doi.org/10.24381/cds.adbb2d47) from a single pixel (-153.83, 70.87) from May 1st through November 30th for 2019, 2020, 2021, and 2022. It also contians a list of derived open water days for each year.</p> <p><em>ICESat-2 </em>contains the subsetted ATL03 photons (Neuman et. al, 2023, https://doi.org/10.5067/ATLAS/ATL03.006) for RGT 137 ground tracks 1r, 2r and 3r on 07 April 2019, 04 January 2020, 02 July 2021, and 31 December 2021, elevation profiles derived from SlideRule (Shean et al., 2023), and shoreline boundaries picked from the elevation profiles and Planet imagery. It also includes cross-shore transects used to project ICESat-2-derived changes into the local shoreline-perpindicular direction.</p> <p><em>Planet_shorelines</em> contains shorelines drived from 3m multispectral imagery from Planet Labs (Planet Team, 2024) as well as their derivatiev products. This included shorelines used for year-to-year change (<em>annual_coastlines</em>) and for uncertainty analysis (<em>uncertainty_coastlines</em>). It also includes the derived year-to year-change <em>(shoreline_change),</em> the baseline and cross-shore transects used to derived these change estiamtes, and a table with our derived annual shoreline change estimates compared with previously published estimates in this region.</p> <p>More detailed information on each datasets can be found in each folder's README file.</p>
WINTERC-G: a global upper mantle thermochemical model from coupled geophysical–petrological inversion of seismic waveforms, heat flow, surface elevation and gravity satellite data
<p>WINTERC-G: A global, temperature and compositional model of the lithosphere<br> and upper mantle.<br> Version: v5.4, December 2020, J. Fullea, S. Lebedev, Z. Martinec, N. Celli<br> <br> Contact: Javier Fullea (jfullea@ucm.es)<br> Facultad de Fisica,<br> Universidad Complutense de Madrid (UCM),<br> Spain<br> ////////<br> Geophysics Section,<br> Dublin Institute for Advanced Studies<br> Dublin, Ireland<br> </p> <p>TYPE:<br> This contains files with:<br> i) the model directly on the triangular grid solved for in the surface wave inversion.</p> <p> ii) an interpolated grid at 0.5 deg lateral resolution for the density and density discontinuities used in the gravity field data inversion<br> </p> <p>If you have any questions regarding the methodology or the construction<br> of the model, please contact the authors. If you use the model, we would<br> request that you cite the reference indicated below, and appreciate<br> your feedback regarding the model and its application.</p> <p>Citation:</p> <p>Fullea, J., Lebedev, S., Martinec, Z., & Celli, N. L. (2021). WINTERC-G: mapping the upper mantle thermochemical heterogeneity from coupled geophysical–petrological inversion of seismic waveforms, heat flow, surface elevation and gravity satellite data. Geophysical Journal International, 226(1), 146-191.</p> <p>*******************************<br> Summary: construction of the model.<br> WINTERC-G is a Waveform tomography and Gravity (geoid and gravity anomalies and gradiometric measurements<br> from ESA's GOCE mission) INversion model of the TEmpeRature and Composition of the lithosphere and upper mantle at<br> global scale. WINTERC-G is based on upon the integrated geophysical-petrological<br> approach LitMod (Afonso et al., 2008; Fullea et al. 2009) and, hence, all<br> relevant mantle rock physical properties modelled (seismic velocities and density) are<br> computed within a thermodynamically self-consistent framework allowing for a direct<br> parameterization in terms of the temperature and composition of the lithosphere-upper<br> mantle. The inversion is a two-step procedure. In a first step, we invert surface-wave, Rayleigh and Love<br> fundamental mode dispersion curves from a high resolution global dataset measured using waveform inversion,<br> along with surface heat flow and elevation (isostasy) for temperature and crustal structure<br> using a point-wise, non-linear, gradient-search inversion<br> over a triangular grid with an average 225 km lateral inter-knot spacing. In a second step we<br> use a fully parallelized spherical harmonic formalism to invert satellite gravity field data in<br> order to refine the initial crustal density and mantle composition distributions from the step 1<br> for a fixed temperature field.</p> <p>The parameter space in step 1 includes crust (densities and S-wave velocities for a three-layered crust)<br> and mantle variables (the depth of the thermal Lithosphere-Athenosphere-Boundary,<br> the thickness of the sublithospheric thermal buffer, the sublithospheric temperatures at 3 different<br> equispaced nodes down to 400 km, the lithospheric and sublithospheric mantle compositon, and<br> the the radial anisotropy at the 3 crustal layers and at 56, 80, 110, 150, 200, 260, 330,<br> and 400 km depths.</p> <p>The parameter space in step 2 is defined by the average crustal density, and the<br> mantle composition in the lithosphere and sublithosphere.<br> We use the output crustal density from step 1 as the<br> initial value in step 2 inversion. Mantle densities are derived based on the output temperature<br> field from step 1 (kept fixed) and the bulk mantle composition inversion variables.</p> <p> </p> <p> </p> <p>*******************************</p> <p>This archive contains the following files:<br> README (this file)<br> WINTERC-G_Vp-Vs.lis (triangular grid)<br> WINTERC-G_rad_anis_Vs.lis (triangular grid)<br> WINTERC-G_Temperature.lis (triangular grid)<br> WINTERC-G_Density.lis (triangular grid)<br> WINTERC-G_LAB.lis (triangular grid)<br> WINTERC_T_rho_1D.z (1D average model of temperature and density)<br> rho_*_out.xyz (0.5 deg egular grid for gravity field)<br> ETOPO2_km_continental.xyz (0.5 deg egular grid for gravity field)<br> ETOPO2_km_depth_Ice.xyz (0.5 deg egular grid for gravity field)<br> ETOPO2_km_depth_Bed.xyz (0.5 deg egular grid for gravity field)<br> Global_Moho_WINTERC-G.xyz (0.5 deg egular grid for gravity field)</p> <p><br> Files in the triangular grid with an average 225 km lateral inter-knot spacing (12232 grid points):</p> <p>* WINTERC-G_Vp-Vs.lis: Vp and Vs (in km/s) in all model columns with a vertical grid step of 2 km<br> Format for each column:<br> #Column number longitude latitude depth(km, <0 downwards) Vp (km/s) Vs(km/s)<br> 5640 93.72 4.135 -5.0 3.91 2.11</p> <p><br> * WINTERC-G_rad_anis_Vs.lis: radial anisotropy, (Vsh-Vsv)/Vs_iso (in %) in all model columns with a vertical grid step of 2 km<br> Format for each column:<br> #Column number longitude latitude depth(km, <0 downwards) anisotropy (%)</p> <p>* WINTERC-G_Temperature.lis: temperature (in ºC) in all model columns with a vertical grid step of 2 km<br> Format for each column:<br> #Column number longitude latitude depth (km, <0 downwards) T (ºC) dT (%) dT(K) <br> 6437 297.20 -2.524 -259.000 1431.9 -1.91 -27.9<br> The anomalies dT are in % and K with respect to the 1D model in WINTERC_T_rho_1D.z (column 2).</p> <p>* WINTERC-G_Density.lis: density (in kg/m3) in all model columns with a vertical grid step of 2 km<br> Format for each column:<br> #Column number longitude latitude depth(km, <0 downwards) rho (kg/m3) drho(%) drho(kg/m3)<br> The anomalies drho are in % and kg/m3 with respect to the 1D model in WINTERC_T_rho_1D.z (column 3).</p> <p>* WINTERC_T_rho_1D.z: 1D average model of temperature (column 2 in ºC) and density (column 3 in kg/m3) with a vertical grid step of 2 km <br> 5.00000000 0.0000000000000000 6.0259973839110526<br> 3.00000000 0.0000000000000000 38.960571309690394<br> 1.00000000 0.33634006819423840 174.42296045978722<br> -1.00000000 3.8888495253719624 1692.8437489147236<br> -3.00000000 23.974111923225379 1863.8834351235944<br> -5.00000000 47.727920701943034 2568.2414495590924<br> -7.00000000 89.633398074381162 2819.8386016341910<br> -9.00000000 137.01489361657013 2839.5325893195904<br> -11.0000000 182.35233447017222 2897.6600872935287<br> -13.0000000 224.46247069572485 2945.2036923862997<br> -15.0000000 260.63395547331390 3069.6809340323475<br> -17.0000000 292.28175449521456 3132.4574175461721<br> -19.0000000 322.29571965406632 3145.5747337463940<br> -21.0000000 351.58698283375054 3157.2401512748038<br> -23.0000000 380.30002225705056 3177.0000420059773<br> -25.0000000 408.50259805632055 3183.6651032398490<br> -27.0000000 436.22632217636487 3190.9586785996116<br> -29.0000000 463.48733903170023 3198.9369509456310<br> -31.0000000 490.29841705549831 3209.7229872383764<br> -33.0000000 516.71149258457456 3221.6329506091679<br> ...</p> <p>Files in the interpolated regular grid at 0.5 deg lateral resolution used for gravity field data inversion:</p> <p> * rho_c_out.xyz: average crustal density<br> * rho_submoho_out.xyz: mantle density below the Moho discontinuity<br> * rho_*_out.xyz: mantle density defined at different model depths: 20, 35, 56, 80, 110, 150, 200, 260, 330 and 400 km.</p> <p> Format for the density files:<br> # longitude latitude density (kg/m3)<br> <br> Files containing layer discontinuities:</p> <p> * ETOPO2_km_continental.xyz: surface elevation including ice sheet and 0 in marine areas (km, <0 upwards)</p> <p> * ETOPO2_km_depth_Ice.xyz: surface elevation including ice sheet (km, >0 downwards, <0 above sea level)</p> <p> * ETOPO2_km_depth_Bed.xyz: bedrock surface elevation without ice sheet (km, >0 downwards, <0 above sea level)</p> <p> * Global_Moho_WINTERC-G.xyz: crust-mantle discontinuity depth (km, >0 downwards)</p> <p> Format for the discontinuity files:<br> # longitude latitude depth (km)<br> <br> <br> The gravity field in WINTERC-G is computed using an spherical harmonic formalism and a model discretization<br> in 13 layers with laterally varying density. The first 7 layers are characterized by top and bottom boundaries with laterally varying radius whereas the last 6 layers are defined by top and bottom boundaries with constant radius:</p> <p>1/ Water: from ETOPO2_km_continental.xyz to ETOPO2_km_depth_Ice.xyz with rho=1030 kg/m3 (constant vertically)</p> <p>2/ Ice: from ETOPO2_km_depth_Ice.xyz to ETOPO2_km_depth_Bed.xyz with rho=910 kg/m3 (constant vertically)</p> <p>3/ Crust: from ETOPO2_km_depth_Bed to Global_Moho_WINTERC-G.xyz with rho=rho_c_out.xyz (constant vertically)</p> <p>4/ submoho-20km: from Global_Moho_WINTERC-G.xyz to z_20km (file with 20 km everywhere except where z_moho>20km) with rho=rho_submoho_out.xyz (top) and rho=rho_20km_out.xyz (bottom)</p> <p>5/ 20km-36km: from z_20km (file with 20 km everywhere except where z_moho>20km) to z_36km (file with 36 km everywhere except where z_moho>36km) with rho=rho_20km_out.xyz (top) and rho=rho_36km_out.xyz (bottom)</p> <p>6/ 36km-56km: from z_36km (file with 36 km everywhere except where z_moho>36km) to z_56km (file with 56 km everywhere except where z_moho>56km) with rho=rho_36km_out.xyz (top) and rho=rho_56km_out.xyz (bottom)</p> <p>7/ 56km-80km: from z_56km (file with 56 km everywhere except where z_moho>56km) to 80 km depth with rho=rho_56km_out.xyz (top) and rho=rho_80km_out.xyz (bottom)</p> <p>The next 6 layers are computed using the constant radius option:</p> <p>8/ 80km-110km: from z=80km to z=110 km with rho=rho_80km_out.xyz (top) and rho=rho_110km_out.xyz (bottom)</p> <p>9/ 110km-150km: from z=110km to z=150 km with rho=rho_110km_out.xyz (top) and rho=rho_150km_out.xyz (bottom)</p> <p>10/ 150km-200km: from z=150km to z=200 km with rho=rho_150km_out.xyz (top) and rho=rho_200km_out.xyz (bottom)</p> <p>11/ 200km-260km: from z=200km to z=260 km with rho=rho_200km_out.xyz (top) and rho=rho_260km_out.xyz (bottom)</p> <p>12/ 260km-330km: from z=260km to z=330 km with rho=rho_260km_out.xyz (top) and rho=rho_330km_out.xyz (bottom)</p> <p>13/ 330km-400km: from z=330km to z=400 km with rho=rho_330km_out.xyz (top) and rho=rho_400km_out.xyz (bottom)</p> <p> </p> <p> </p>
Data from: Elevated fires during COVID-19 lockdown and the vulnerability of protected areas
<p><strong>Related article:</strong> Johanna Eklund, Julia P G Jones, Matti Räsänen, Jonas Geldmann, Ari-Pekka Jokinen, Adam Pellegrini, Domoina Rakotobe, O. Sarobidy Rakotonarivo, Tuuli Toivonen, and Andrew Balmford. Elevated fires during COVID-19 lockdown and the vulnerability of protected areas. Nature Sustainability (2022) https://doi.org/10.1038/s41893-022-00884-x.</p> <p><strong>In this dataset:</strong></p> <p>This dataset contains information about monthly fire incidence and precipitation for the protected areas of Madagascar from January 2012 to December 2020. The fire data is sourced from NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fire product and the precipitation data from the Global Precipitation Measurement (GPM) mission (for years 2016-2020) and its predecessor The Tropical Rainfall Measuring Mission (TRMM) (for years 2011-2015) at spatial resolution 10 km. The fire and precipitation data was overlayed with the protected area polygons of the June 2020 release of the World Database of Protected Areas. For sources and more details on how the data was compiled see the related article. The data can be used to inspect temporal dynamics of wildfires inside protected areas and for informing adaptive protected area management and planning.</p> <p><strong>Please cite this dataset as:</strong></p> <p>Johanna Eklund, Julia P G Jones, Matti Räsänen, Jonas Geldmann, Ari-Pekka Jokinen, Adam Pellegrini, Domoina Rakotobe, O. Sarobidy Rakotonarivo, Tuuli Toivonen, and Andrew Balmford. Elevated fires during COVID-19 lockdown and the vulnerability of protected areas. Nature Sustainability (2022) https://doi.org/10.1038/s41893-022-00884-x.</p> <p><strong>Column names</strong></p> <p>NAME: Name of protected area</p> <p>Fires_sum: Number of observed fires (VIIRS)</p> <p>Month: Month</p> <p>Year: Year</p> <p>Precipitation: Precipitation (mm)</p> <p>Plag_1:Plag_12: Precipitation during previous month; 2 months ago; 3 months ago…12 months ago</p> <p>YEAR_CREAT: Year of establishment of protected area</p> <p>Biome: Biome</p> <p>REP_AREA: Area of protected area (km<sup>2</sup>)</p> <p>Fires_per_km2: Fires per km<sup>2</sup></p> <p>Prec_acc_12m: Accumulated precipitation during the last 12 months</p> <p>fBiome: Biome as factor</p> <p>fNAME: Name as factor</p> <p>sPrecipitation: Precipitation (scaled; see Methods section of article)</p> <p>sPlag_1: Precipitation in previous month (scaled; see Methods section of article)</p> <p>sPrec_acc_12m: Accumulated precipitation during the last 12 months (scaled; see Methods section of article)</p> <p>Pred_Zinb_1a: Predicted fires (see Methods section of article)</p> <p>Diff_Zinb_1a: Difference: Observed fires - predicted fires</p> <p>Year_pred: Year for prediction</p> <p><strong>License</strong><br> Creative Commons Attribution 4.0 International.</p>
Data supporting: Combined stress of an insecticide and heatwaves or elevated temperature induce community and food web effects in a Mediterranean freshwater ecosystem
<p>Data used to obtain the results of the research paper entitled: "Combined stress of an insecticide and heatwaves or elevated temperature induce community and food web effects in a Mediterranean freshwater ecosystem", published in the journal "Water Research". The data derives from an outdoor (meso-) cosm experiment in Spain (Imdea Water, Alcala de Henares) in which the transportable temperature and heatwave control device (TENTACLE) was used to investigate the multiple stressors effects of two different climate change scenarios related to temperature (i.e., elevated temperature and reoccurring heatwaves) in combination with the neonicotinoid insecticide imidacloprid.</p>
LeConte Bay Seafloor Elevation Data
<p>Multibeam data of LeConte Bay seafloor from the inner fjord.</p> <p>These are processed multibeam sonar data from LeConte Bay, southeast Alaska. The data come from five cruises grouped into five files:<br> 1) Aug 1999 and Sep 2000 (20-m resolution) - 1 file;<br> 2) August 2016 (20-m and 4-m resolution) - 2 files;<br> 3) May 2017 (2-m resolution) - 1 file;<br> 4) Sep 2018 (2-m resolution) - 1 file.</p> <p>The datasets collected in 2016-2018 originally included ice-face data, as reported in Sutherland et al., 2019 (DOI 10.1126/science.aax3528). The data presented here include only the seafloor data, exclusive of the ice face. They are referenced to WGS84, UTM zone 8 (meters easting/northing), and mean sea level.</p> <p>Reference and more details: Eidam, E.F., Sutherland, D.A., Duncan, D., Kienholz, C., Amundson, J.M., Motyka, R.J. Morainal bank evolution and fjord infilling during a tidewater glacier stillstand, on seasonal to decadal timescales. In prep for JGR-Earth Surface, September 2019.</p>
NMNH Botany Elevation Data: Dinoflagellates
Elevation data from specimens in the collections of the Smithsonian National Museum of Natural History Botany Department.<p></p>
NMNH Botany Elevation Data: Mins
Elevation data from specimens in the collections of the Smithsonian National Museum of Natural History Botany Department.<p></p>
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.