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9,113 results for “Sampling”
Zooplankton sample inventory for Northeast U.S. Shelf Long Term Ecological Research (NES-LTER) Transect cruises, ongoing since 2018
This dataset provides an inventory of physical samples collected from zooplankton bongo and/or ring net tows conducted during the Northeast U.S. Shelf Long-Term Ecological Research (NES-LTER) Transect cruises, ongoing since 2018. The NES-LTER transect, located south of Martha’s Vineyard, Massachusetts, comprises standard stations L1-L11 and extends 150 km offshore. Dedicated NES-LTER cruises target all four seasons: winter, spring, summer, and fall. Samples are collected via oblique tows with a 61-cm bongo net fitted with 150- and 335-micron mesh nets, as well as a ring net with a 20-micron mesh net. During earlier spring and fall cruises along the NES-LTER Transect, in collaboration with the Ocean Observatories Initiative, samples were collected through vertical tows using a ring net with a 150-micron mesh. Samples collected are distributed among various laboratories for DNA metabarcoding, stable isotopes analysis, and morphological identification.
Abundance, biovolume, and biomass of Synechococcus, eukaryote pico- and nano- phytoplankton, and heterotrophic bacteria from flow cytometry for water column bottle samples on NES-LTER Transect cruises, ongoing since 2018
These data represent the abundance, biovolume, and biomass of prokaryotic phytoplankton, eukaryotic pico- and nano- phytoplankton, and heterotrophic bacteria from discrete flow cytometry samples collected during the Northeast U.S. Shelf Long-Term Ecological Research (NES-LTER) Transect cruises, ongoing since 2018. Samples were collected and preserved from the water column at multiple depths using Niskin bottles on a CTD rosette system along the NES-LTER transect, and analyzed post cruise. Cells were identified and enumerated from the flow cytometry data files based on their scattering, SYBR (525 nm), phycoerythrin (575 nm) and chlorophyll (680 nm) fluorescence signals. Gating was completed manually in the Attune NXT software interface.
Dissolved inorganic nutrients from NES-LTER Transect cruises, including 4 macro-nutrients from water column bottle samples, ongoing since 2017
Dissolved inorganic nutrients are measured from water column bottle samples taken on NES-LTER Transect cruises (ongoing since 2017) and include nitrate + nitrite, ammonium, silicate, and phosphate. Sampling frequency is approximately seasonal. Samples were filtered, frozen, then processed at the Woods Hole Oceanographic Institution's Nutrient Analytical Facility. Each sample may have up to 2 replicates, indicated by a or b in the data table. These macro-nutrients are analyzed in seawater using a colorimetric assay in which light absorbance is measured versus known standards, and final concentrations are calculated in micromole per liter. Some of the data are from cruises in collaboration with the Ocean Observatories Initiative (OOI).
Size-fractionated chlorophyll from water column bottle samples collected during NES-LTER Transect cruises, ongoing since 2017.
Size-fractionated chlorophyll a and phaeopigments are measured from discrete bottle samples collected during CTD-rosette casts on Northeast U.S. Shelf Long-Term Ecological Research (NES-LTER) Transect cruises (ongoing since 2017), as a proxy for phytoplankton biomass. Sampling frequency is approximately seasonal. Samples were processed by three lab groups using different methods. Size fractions in addition to whole seawater (>0.7 micron) include >5 , <10, >10, and >20 microns with some samples pre-filtered <200 microns. Pigments were analyzed using fluorometers in which fluorescence was measured versus a blank and a standard, and final concentrations were calculated in micrograms per liter (or mg per cubic meter). Some of the data are from cruises in collaboration with the Ocean Observatories Initiative (OOI).
SBC LTER: Reef: Benthic Composition Experiment: recruitment sampling
These data quantify the richness and abundance of mobile invertebrates settling and recruiting on brushes across 5 sampling sites (Arroyo Quemado, Naples, Isla Vista, Mohawk, Carpentaria) along the Santa Barbara Coast and 2 sites at Santa Cruz Island (San Pedro Point, and Cavern Point). Sampling began December 2021 and is conducted seasonally every 3 months. Data are collected from brushes that are attached to randomly chosen rock plates within two circular plots at each sampling site. Plot 1 represents the control plot with no giant kelp removal and plot 2 represents the kelp clearing plot where all giant kelp are removed. Additionally, understory algae are removed seasonally from half of the rock slates for both plots (Plot 1 rock plates #1-6 and Plot 2 rock plates #13-18). Recruitment is measured as a proportion of the number of individuals found on the recruitment brushes.
SBC LTER: pH time series: Water-sample pH and CO2 system chemistry, ongoing since 2011
Data are a time-series of pH and carbonate chemistry in manually-collected sea water samples from sixteen near-shore locations and 20 locations along the Santa Barbara Channel, California, USA, and intended to benchmark data from moored pH instruments. Data collection began in June 2011 and is ongoing. Time-series for pH sensors deployed in the field (e.g., SeaFET) are available elsewhere. Discrete seawater samples were collected by hand or using a Niskin bottle. In the laboratory, samples were analyzed for pH (total scale) and total alkalinity. Two parameters were used to calculate the full suite of carbonate chemistry parameters using the CO2calc algorithms. Program outputs are included for laboratory conditions as input, and adjusted for situ temperature. See Methods for more information, e.g., Lunden, et al. Data were collected by a consortium of research groups with the intent to leverage their collective assets and expertise. These groups (and sponsors) include Santa Barbara Coastal LTER (NSF), OMEGAS Project (NSF), Passow Lab (Calif. State Water Board), Hofmann Lab (NSF, CINMS, US NPS).
Effects of Multiple Resource Additions on Community and Ecosystem Processes: NutNet NPP Quadrat Sampling at the Sevilleta National Wildlife Refuge, New Mexico
Two of the most pervasive human impacts on ecosystems are alteration of global nutrient budgets and changes in the abundance and identity of consumers. Fossil fuel combustion and agricultural fertilization have doubled and quintupled, respectively, global pools of nitrogen and phosphorus relative to pre-industrial levels. In spite of the global impacts of these human activities, there have been no globally coordinated experiments to quantify the general impacts on ecological systems. This experiment seeks to determine how nutrient availability controls plant biomass, diversity, and species composition in a desert grassland. This has important implications for understanding how future atmospheric deposition of nutrients (N, S, Ca, K) might affect community and ecosystem-level responses. This study is part of a larger coordinated research network that includes more than 40 grassland sites around the world. By using a standardized experimental setup that is consistent across all study sites, we are addressing the questions of whether diversity and productivity are co-limited by multiple nutrients and if so, whether these trends are predictable on a global scale.
Sevilleta LTER Vegetation Sample Catalog- Ground Samples for Chemical Analysis
Several long-term studies at the Sevilleta LTER measure net primary production (NPP) across ecosystems and treatments. Net primary production is a fundamental ecological variable that quantifies rates of carbon consumption and fixation. Estimates of NPP are important in understanding energy flow at a community level as well as spatial and temporal responses to a range of ecological processes. The NPP weight data (SEV 157) is obtained by harvesting a series of covers for species observed during plot sampling. These species are always harvested from habitat comparable to the plots in which they were recorded. This data is then used to make volumetric measurements of species and build regressions correlating biomass and volume. From these calculations, seasonal biomass and seasonal and annual NPP are determined. These sampled are then vouchered for use to do analyses of inorganic and organic components such as carbon, nitrogen, and phosphorous as well as and other macro and micro nutrients and organic components such as cellulose and lignin.
Consumer Front Plant Trait Sampling in Two Virginia Coast Salt Marshes, 2018
A consumer front forms when dense aggregations of herbivores form at the edge of a resource. The front then propagates through the ecosystem in search of additional resources. In U.S. Atlantic salt marshes, the purple marsh crab, Sesarma reticulatum, creates consumer fronts as it grazes the smooth cordgrass, Spartina alterniflora. Sesarma fronts typically form at the heads of tidal creeks and create distinct zonation between the low marsh, tall-form Spartina zones and the high marsh, short-form Spartina zones, with a denuded band of mudflat in between. Over time, Sesarma consumer fronts are moving directionally inland towards the short-form zone and away from the tall-form zone. This movement inland allows for tall-form Spartina to revegetate, preventing further marsh loss. However, it remains unknown why these consumer fronts are moving inland. To test the hypothesis that plant traits (i.e., nutritional quality, palatability) are driving the Sesarma consumer front inland, we collected Spartina from consumer fronts at 8 unique creekheads across two marsh systems on the Eastern Shore of Virginia (4 consumer fronts at Upper Phillips Creek and 4 at Upshur Creek). Spartina was collected from 15 replicate quadrats (0.0625m^2) from the tall-form low marsh zones (TSA) and from the short-form low marsh zones (SSA) at each creekhead. The short-form zone was delineated into two additional zones, an interior (SSA-I) and an exterior (SSA-E), to assess if there were any differences in plant traits between Spartina being actively grazed (SSA-E, adjacent to consumer front) and those that have not been grazed (SSA-I, 2 meters from consumer front). Collected Spartina plants were then processed for a series of plant traits that can influence herbivore preference.
Galaxy Zoo 2: Images from Original Sample
<p>The Galaxy Zoo team regularly receives requests for subject images for various versions of Galaxy Zoo, in order to facilitate other investigations, e.g. machine learning projects. This repository is an updated attempt to provide those in a way that is useful to the wider community.</p> <p>The images here are meant to be used with the data tables available at <a href="http://data.galaxyzoo.org">data.galaxyzoo.org</a>. They are the "original" sample of subject images in Galaxy Zoo 2 (Willett et al. 2013, MNRAS, 435, 2835, DOI: <a href="https://doi.org/10.1093/mnras/stt1458">10.1093/mnras/stt1458</a>) as identified in Table 1 of Willett et al. and also in Hart et al. (2016, MNRAS, 461, 3663, DOI: <a href="https://doi.org/10.1093/mnras/stw1588">10.1093/mnras/stw1588</a>). The original GZ2 subjects also gave the option to view an inverted version of the subject image; these inverted images are not provided but are easily reproducible from the included subject images. </p> <p><strong>If you use this dataset, please cite</strong> Willett et al. (2013) as the general data release and <em>also</em> cite the DOI for this dataset; if you use the updated debiased tables from Hart et al. (2016) please cite that as well.</p> <p>There are 243,434 images in total. This is off by about 0.08% from the total count in the tables - it's not clear what the cause of the discrepancy is, but we don't think the missing images have any particular sampling bias, so this sample should be useful for research.</p> <p>The images are available in a single zip file (<strong>images_gz2.zip</strong>).</p> <p>The most recent and reliable source for morphology measurements is "GZ2 - Table 1 - Normal-depth sample with new debiasing method – CSV" (from Hart et al. 2016), which is available at <a href="https://data.galaxyzoo.org">data.galaxyzoo.org</a>. To cross-reference the images with Table 1, this sample includes another CSV table (<strong>gz2_filename_mapping.csv</strong>) which contains three columns and 355,990 rows. The columns are:</p> <ul> <li><strong>objid</strong>: the Data Release 7 (DR7) object ID for each galaxy. This should match the first column in Table 1.</li> <li><strong>sample</strong>: string indicating the subsampling of the galaxy. </li> <li><strong>asset_id</strong>: an integer that corresponds to the filename of the image in the zipped file linked above.</li> </ul> <p>As an example row:</p> <p>587722981742084144,original,16</p> <p>The galaxy is 587722981741363294, which is in Table 1 and was identified by GZ2 volunteers as a barred spiral galaxy with a mild bulge and two tightly-wound arms (morphology='Sc2t'). It is in the original GZ2 sample, and can be found in the zipped file as 16.jpg. </p> <p>The overlap between the set of images, the attached table, and Table 1 is not 100%; there are a few rows in the tables that don't have a corresponding image. Again, it's not clear what the exact reason is for this, but we suggest just dropping any missing rows/images from your analysis unless you have a need for analyzing specific subjects. If you do need a 100% complete sample, you can obtain the missing images directly from SDSS. </p> <p>Based on spot checks the mappings between asset ID and DR7 object ID appear correct, but we strongly suggest that you pick some random images and verify on your own that the image seems to match the label/classifications that are listed in Table 1. </p> <p>If you have any issues using this dataset, please contact the Galaxy Zoo team, in particular Brooke Simmons (b.simmons@lancaster.ac.uk). Should Dr Simmons be unavailable, try contacting Karen Masters or Chris Lintott.</p> <p>- the GZ team, 5 Dec 2019<br> </p>
Ionic composition of particulate matter (PM10) from high-volume sampling over the Southern Ocean during the austral summer of 2016/2017 on board the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>Aerosol particles originate from a variety of sources (Tomasi and Lupi, 2017). Information on particle chemical composition can be utilized to access particle origin. During the Antarctic Circumnavigation Expedition (ACE) cruise around the Southern Ocean, off-line filter sampling of ambient air was performed. Filters were stored on the ship (at -20 degrees C) and after the cruise concluded analysed at Leibniz-Institute for Tropospheric Research (TROPOS) concerning ionic composition of sampled material. Here, we give mass concentrations for inorganic ions (chloride, sodium, potassium, magnesium, calcium, ammonium, nitrate, sulphate, and bromide), organic constituents (methane-sulfonic acid and oxalate), and total filter load of particles with a mobility diameter smaller 10 micrometers (PM10) for each 24 hour-sampled filter.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ACESPACE_particulate_matter_pm10_ionic_composition_highvolume.csv, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This ionic composition of particulate matter (PM10) from high-volume sampling dataset during ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Supplementary data for Nested sampling cross-checks using order statistics
<p>This is the raw data for tables 1 and 2 in <a href="https://arxiv.org/abs/2006.03371">Nested sampling cross-checks using order statistics</a>. The file names for the MultiNest results in table 1 are:</p> <pre><code>MN_{PROBLEM}_{number of dimensions}d_efr_{MultiNest efr parameter}.txt</code></pre> <p>The file names for the PolyChord results in table 2 are:</p> <pre><code>PC_{PROBLEM}_{number of dimensions}d_nr_{PolyChord number of repeats}.txt</code></pre> <p>where PROBLEM specifies one of four test functions described in Appendix C</p> <ul> <li>gaussian = Gaussian</li> <li>mixture = Gaussian-log-gamma mixture</li> <li>rosenbrock = Rosenbrock function</li> <li>shells = Gaussian shells</li> </ul> <p>Each file contains 100 rows (corresponding to 100 runs) and 8 columns</p> <ol> <li>log evidence</li> <li>error log evidence</li> <li>KS statistic from test on all iterations</li> <li>number of iterations</li> <li>p-value from all iterations</li> <li>Greatest KS statistic from tests on chunks of iterations</li> <li>Iteration of start of chunk at which greatest KS statistic occurred</li> <li>Bonferroni corrected p-value from greatest KS statistic from tests on chunks of iterations</li> </ol>
Posterior samples of a dynamical fit for planetary system HD128311
<p>This dataset contains the posterior samples from a dynamical fit of the multi-planetary system HD128311. See <a href="https://doi.org/10.1093/mnrasl/slu202">https://doi.org/10.1093/mnrasl/slu202</a> for a description of the fit. The dataset is one numpy array, consisting of</p> <ul> <li>500 walkers </li> <li>1250 generations</li> <li>3 bodies (1 star + 2 planets)</li> <li>7 parameters per body (mass, x, y, z, vx, vy, vz)</li> </ul>
Ice Nucleating Particle number concentration from low-volume sampling over the Southern Ocean during the austral summer of 2016/2017 on board the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract </strong></p> <p>Ice nucleating particles (INP) are a subclass of atmospheric aerosol particles, which can force heterogeneous freezing of cloud droplets at temperatures above -38 degrees C. In contrast, ice particles form from cloud droplets at temperatures below -38 degrees C due to homogeneous freezing, without INP. Due to their abundance, these particles can affect micro-physical properties of clouds, while acting as INP. During the Antarctic Circumnavigation Expedition (ACE) around the Southern Ocean, off-line filter sampling was performed. Filters were stored on the ship and analysed after the cruise at Leibniz-Institute for Tropospheric Research (TROPOS) concerning INP abundance. Here, we give INP number concentrations for sampling of 8 hour periods.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ACESPACE_ice_nucleating_particles_frozen_fraction_from_lowvolume_filters.csv, data file, comma-separated values</li> <li>ACESPACE_ice_nucleating_particles_number_concentration_from_lowvolume_filters.csv, data file, comma-separated values</li> <li>data_file_header_frozen_fraction.txt, metadata, text format</li> <li>data_file_header_number_concentration.txt, metadata, text format</li> <li>README.txt, metadata, text format</li> <li>change_log.txt, metadata, text format</li> </ul> <p><strong>Change log</strong></p> <p>v1.1 - data files updated</p> <ul> <li>change dataset title to reflect low-volume sampling method</li> <li>addition of INP number concentration data from different temperatures</li> <li>addition of fraction of frozen droplets data</li> <li>addition of field blank filter data</li> <li>create separate data_file_header files</li> <li>add change log</li> </ul> <p>v1.0 - initial release of dataset</p> <p> </p>
The role of injection method on residual trapping at the pore-scale in continuum-scale samples: segmented data
<p>The experiments in this work explore the role of a variable injection rate on gas saturation and residual trapping. There are 2 experiments in this work H2L (high to low injection rate) and L2H (low to high injection rate). The workflow for processing the micro-CT images to get the segmented images is described in [1]. </p><p>The following scans are included in this repository NB. all data for this repository is segmented micro-CT data.: </p><ol><li>Dry scan prior to experiment = merged_binning_2_38_1927</li><li>H2L during high flow = merged_segmented_flow_09_h2lh_merged</li><li>H2L during low flow = merged_segmented_flow_11_h2ll_2_merged</li><li>H2L at the end of drainage (no flow) =merged_segmented_flow_16_dra1_pd5_merged</li><li>H2L at the end of imbibition (no flow) =merged_segmented_flow_21_imb1_pi1_merged</li><li>L2H during low flow = merged_segmented_flow_29_2_l2hl_merged</li><li>L2H during high flow = merged_segmented_flow_30_l2hh_merged</li><li>L2H at the end of drainage (no flow) =merged_segmented_flow_31_dra2_pd1_merged</li><li>L2H at the end of imbibition (no flow) =merged_segmented_flow_33_imb2_pi1_merged</li></ol>
Humic acid like concentration in seawater samples, collected from the trace metal rosettes in the Southern Ocean during the austral summer of 2016/2017, on board the Antarctic Circumnavigation Expedition.
<p><strong>Dataset abstract</strong></p> <p>Humic acid like concentration (abbreviated HA) measured with respect to the Suwannee River Fulvic acid standards (µmol SRFA equivalent per litre).</p> <p>Seawater samples were collected from trace metal rosette (TMR) deployments at different depths in the water column during the Antarctic Circumnavigation Expedition (ACE). Humic acid like data from legs 1 and 2, from TMR cast numbers 3 to 16, were analysed by electrochemistry following standard additions of Suwannee River Fulvic Acid (standard 1, IHSS). This data is to support iron ligands and iron bioavailability as well as hydrolysable saccharides (TPZT) data, also collected during ACE.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_humics_data.csv, data file, comma-separated values</li> <li>ace_humics_data_visual_summary.png, metadata, portable network graphics</li> <li>data_file_header.txt, metadata, text format</li> <li>README.txt, metadata, text format</li> </ul> <p><strong>Dataset license</strong></p> <p>This humics dataset from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Replication data for: "The hapax / type ratio: an indicator of minimally required sample size in productivity studies?"
<p>The dataset accompanies the scientific article "The hapax / type ratio: an indicator of minimally required sample size in productivity studies?" and can be used to reproduce the findings presented in this article. This dataset consists of two components, namely (i) the corpus data involving the Dutch semi-copular verb "raken" and (ii) an R analysis script to reproduce the computational steps.</p>
Near-field images and cross-section of guided modes in a laser-inscribed double-tracks waveguide in TZN:Ag glass sample
<p><strong>Raw images were captured</strong> with a Thorlabs beam monitoring camera, while the waveguides were injected at 633 nm.<br> The fours cross-sections were computed from these raw images.<br> These files are new data from the co-authors among those presented in the review publication "Materials 2020, 13, 3846" (DOI: 10.3390/ma13173846.<br> <strong>Extracted, centered and scaled horizontal cross-sections are given in "Fig12-b-c_final.xlsx"</strong></p> <p>Sample name : TZN:Ag.</p> <p> </p>
Historical Sea Surface Temperature (SST) data and thermal stress indices of the Tara Pacific Expedition's coral reef sampling sites, from May 1st 2002 to August 31st 2018.
<p>The Tara Pacific expedition (2016-2018) sampled coral ecosystems at 111 sampling sites around 32 islands in the Pacific Ocean, and sampled the surface of oceanic waters at 249 locations, resulting in the collection of nearly 58,000 samples (Gorsky et al. 2019, Planes et al. 2019, Flores et al. 2020). The expedition was designed to systematically study corals, fish, plankton, and seawater, and included the collection of samples for advanced biogeochemical, molecular, and imaging analysis.</p> <p>Here we provide a high-resolution historical dataset that spans from 2002 to each sites’ sampling date and gives an overview of past climate variability and heatwaves experienced by corals sampled at each site. Ocean skin temperature (11 and 12 µm spectral bands longwave algorithm) was extracted from 1km resolution level-2 MODIS-Aqua and MODIS-Terra from 2002 to the sampling date and from level-2 VIIRS-SNPP from 2012 to the sampling date. Day and night overpasses were used to maximize data recovery. Following recommendations from NASA Ocean Color (OB.DAAC), only SST products of quality 0 and 1 were used. The 9 closest pixels to the sampling sites of each scene were extracted. All the extracted pixels from the 3 satellites were then averaged daily to obtain daily SST averages and standard deviations time series for each sampling site, from 2002 to the sampling date.</p> <p>Each time series was first averaged on a Julian day basis to provide a seasonal average. This yearly seasonal average was triplicated and concatenated into a 3-year seasonal cycle to apply a digital low pass filter on the middle year without generating artifacts. A digital low pass filter (filter order 3, pass band ripple 0.1; “filfilt” function in matlab) with 36 Julian days windows was applied to the concatenated time series to remove high frequency noise. The middle year was then extracted from the concatenated time series to recover the seasonal cycle. The sea surface temperature anomaly was calculated as the SST minus the seasonal cycle over the full time series. Considering the short periods of missing data (mean of the 95th percentile of the duration of consecutive days with missing data: 9.8 ± 4.1 days), the missing values in the SST and SST anomaly time series were linearly interpolated in order to calculate thermal stress indices. The SST anomaly frequency was calculated as the number of days over the past 52 weeks when the SST anomaly is greater than or equal to 1 °C. Thermal stress indices relevant to coral reef health were then calculated using methodology developed for the Coral Reef Temperature Anomaly Database (CoRTAD) data base (Saha et al. 2019). Events of cold temperature accumulation were also reported to cause bleaching and mortality (Lirman et al. 2011; González-Espinosa & Donner 2020), therefore, the same set of indices were calculated for cold stress adapting the CoRTAD method, but using the minimum weekly climatologies.</p> <p>A condensed table containing single values associated with each sampling site was created ('TaraPacific_SST_timeseries_mean_products') extracting the minimum, maximum, sum, averages, standard deviations, and value recorded at the sampling day of each of these indices (detailed in the readme file provided with the dataset 'README_TaraPacific_historical_SST.md'). Additional metrics of the last heating and cooling events as well as the time of recovery is also provided to represent the state of thermal stress at the day of sampling.</p>
Data files belonging to the paper "Dealing with clustered samples for assessing map accuracy by cross-validation"
<p>Mapping of environmental variables often relies on map accuracy assessment through cross-validation with the data used for calibrating the underlying mapping model. When the data points are spatially clustered, conventional cross-validation leads to optimistically biased estimates of map accuracy. Several papers have promoted spatial cross-validation as a means to tackle this over-optimism. Many of these papers blame spatial autocorrelation as the cause of the bias and propagate the widespread misconception that spatial proximity of calibration points to validation points invalidates classical statistical validation of maps. In the paper related to these data, we present and evaluate alternative cross-validation approaches for assessing map accuracy from clustered sample data. </p> <p> </p> <p>The study area is western Europe, constrained in the north at 52° latitude and at -10° and 24° longitude The projection is IGNF:ETRS89LAEA (Lambert azimuthal equal area projection).</p> <p> </p> <p><strong>Files:</strong></p> <p>agb.tif = above ground biomass (AGB) map from version 3 of the 2017 CCI-Biomass product (<a href="https://catalogue.ceda.ac.uk/uuid/5f331c418e9f4935b8eb1b836f8a91b8">https://catalogue.ceda.ac.uk/uuid/5f331c418e9f4935b8eb1b836f8a91b8</a>)<br> AGBstack.tif = covariates used for predicting AGB<br> aggArea.tif = coarse grid used for simulation in the model-based methods<br> ocs.tif = soil organic carbon stock (OCS) map (0-30 cm) from Soilgrids (<a href="https://www.isric.org/explore/soilgrids">https://www.isric.org/explore/soilgrids</a>)<br> OCSstack.tif = covariates used for predicting OCS<br> strata.xxx = 100 compact geo-strata (ESRI shape) created with the spcosa package; used for generating clustered samples<br> TOTmask.tif = mask of the area covered by the covariates</p> <p> </p> <p><strong>Details and data sources of the covariates in AGBstack.tif and OCSstack.tif:</strong></p> <table> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Source</strong></p> </td> <td> <p><strong>Note</strong></p> </td> </tr> <tr> <td> <p>ai</p> </td> <td> <p>Aridity Index</p> </td> <td> <p><a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a></p> </td> <td>Version 2.1</td> </tr> <tr> <td> <p>bio1</p> </td> <td> <p>Mean annual air temperature [°C]</p> </td> <td><a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a></td> <td>Version 2.1</td> </tr> <tr> <td> <p>bio5</p> </td> <td> <p>Mean daily maximum air temperature of the warmest month [°C]</p> </td> <td><a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a></td> <td>Version 2.1</td> </tr> <tr> <td> <p>bio7</p> </td> <td> <p>Annual range of air temperature [°C]</p> </td> <td><a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a></td> <td>Version 2.1</td> </tr> <tr> <td> <p>bio12</p> </td> <td> <p>Annual precipitation [kg/m<sup>2</sup>]</p> </td> <td><a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a></td> <td>Version 2.1</td> </tr> <tr> <td> <p>bio15</p> </td> <td> <p>Precipitation seasonality [kg/m<sup>2</sup>]</p> </td> <td><a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a></td> <td>Version 2.1</td> </tr> <tr> <td> <p>gdd10</p> </td> <td> <p>Growing degree days heat sum above 10°C</p> </td> <td><a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a></td> <td>Version 2.1</td> </tr> <tr> <td> <p>clay</p> </td> <td> <p>Clay content [g/kg] of the 0-5cm layer</p> </td> <td> <p><a href="https://soilgrids.org/">https://soilgrids.org/</a></p> <p> </p> </td> <td> <p>Only used for AGB</p> </td> </tr> <tr> <td> <p>sand</p> </td> <td> <p>Sand content [g/kg] of the 0-5cm layer</p> </td> <td><a href="https://soilgrids.org/">https://soilgrids.org/</a></td> <td>as above</td> </tr> <tr> <td> <p>pH</p> </td> <td> <p>Acidity (Ph(water)) of the 0-5cm layer</p> </td> <td><a href="https://soilgrids.org/">https://soilgrids.org/</a></td> <td>as above</td> </tr> <tr> <td> <p>glc2017</p> </td> <td> <p>Landcover 2017</p> </td> <td> <p><a href="https://land.copernicus.eu/global/products/lc">https://land.copernicus.eu/global/products/lc</a>, reclassified to: closed forest, open forest, natural non-forest veg., bare & sparse veg. cropland, built-up, water</p> </td> <td> <p>Categorical variable</p> </td> </tr> <tr> <td> <p>dem</p> </td> <td> <p>Elevation</p> </td> <td> <p><a href="https://www.eea.europa.eu/data-and-maps/data/copernicus-land-monitoring-service-eu-dem">https://www.eea.europa.eu/data-and-maps/data/copernicus-land-monitoring-service-eu-dem</a></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>cosasp</p> </td> <td> <p>Cosine of slope aspect</p> </td> <td> <p>Computed with the terra package from elevation</p> </td> <td>Computed @25m resolution; next aggregated to 0.5km</td> </tr> <tr> <td> <p>sinasp</p> </td> <td> <p>Sine of slope aspect</p> </td> <td>Computed with the terra package from elevation</td> <td>as above</td> </tr> <tr> <td> <p>slope</p> </td> <td> <p>Slope</p> </td> <td>Computed with the terra package from elevation</td> <td>as above</td> </tr> <tr> <td> <p>TPI</p> </td> <td> <p>Topographic position index</p> </td> <td>Computed with the terra package from elevation</td> <td>as above</td> </tr> <tr> <td> <p>TRI</p> </td> <td> <p>Terrain ruggedness index</p> </td> <td>Computed with the terra package from elevation</td> <td>as above</td> </tr> <tr> <td> <p>TWI</p> </td> <td> <p>Topographic wetness index</p> </td> <td> <p>Computed with SAGA from 500m resolution (aggregated) dem</p> </td> <td> </td> </tr> <tr> <td> <p>gedi</p> </td> <td> <p>Forest height</p> </td> <td> <p><a href="https://glad.umd.edu/dataset/gedi">https://glad.umd.edu/dataset/gedi</a></p> </td> <td> <p>Zone: NAFR</p> </td> </tr> <tr> <td> <p>xcoord</p> </td> <td> <p>X coordinate</p> </td> <td> <p>Using a mask created from the other covariates</p> </td> <td> </td> </tr> <tr> <td> <p>ycoord</p> </td> <td> <p>Y coordinate</p> </td> <td>Using a mask created from the other covariates</td> <td> </td> </tr> <tr> <td> <p>Dcoast</p> </td> <td> <p>Distance from coast</p> </td> <td> <p>Using a land mask created from the other covariates</p> </td> <td> </td> </tr> </tbody> </table> <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.