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19,393 results for “water”
Data for manuscript titled 'Impact of urbanization and drought on river water quality, case study of nutrient levels in Cuenca and Giron (Azuay, Ecuador)'
<p>The uploaded zip-file entails the data obtained through four field campaigns performed in the province of Azuay (Ecuador) in the period July 2023 - May 2024, which is used as a basis for the manuscript titled 'Impact of urbanization and drought on river water quality, case study of nutrient levels in Cuenca and Giron (Azuay, Ecuador)' that was submitted to a Special Issue in the journal Water in 2024. The study aimed at illustrating the impact of urbanisation and drought on the abiotic water conditions of the rivers passing through the studied urban areas.</p> <p>The data includes a subfolder with data obtained from an external website (https://generacioncsr.celec.gob.ec/graficasproduccion/) and aligns with the folder structure of the GitHub-repository that contains the analysis scripts (to be added when the manuscript is accepted). The data file only contains the baseline data, while results can be obtained through running the R-scripts in the GitHub-repository. Additional comments on the analyses are also provided in the analysis scripts.</p> <p><strong>DATA COLLECTION</strong></p> <p>Information on the locations was collected prior to the first field campaign (July 2023) and confirmed in the field (and corrected when necessary). The following variables were registered: Date & Time, Coordinates (latitude and longitude, in WGS84 format), Altitude (in meters above mean sea level), Distance (to a fixed location downstream; being the province border), and Category (River or Stream).</p> <p>Information on the physicochemical conditions was collected directly in the field with a <strong>Horiba U-52</strong> multiprobe. The following variables were registered: Temperature, pH, Electrical conductivity (reference at 25 °C), Oxygen level (as concentration), and Turbidity (in NTU).</p> <p>At each site, a bucket was rinsed thrice with prevailing surface water and subsequently filled with a water sample of the top of the water column. The multiprobe was rinsed with this sample water and then submerged in the bucket, followed by continuous stirring (to avoid a decrease of the oxygen levels) until the readings stabilised. After stabilisation, readings were recorded on a separate data sheet prior to being digitalised.</p> <p>Information on the nutrient levels was obtained through the collection of water samples in the field and the subsequent analysis in the laboratory. The following nutrients were selected: ammonium, nitrate, nitrite, and orthophosphate. For the analyses, <strong>Merck test kits</strong> (equivalent to USEPA analyses) were used in combination with a Genesys UV-VIS spectrophotometer (Thermofisher).</p> <p><strong>In the field</strong>, a bucket was rinsed thrice with prevailing surface water and subsequently filled with a water sample of the top of the water column. A polyethylene syringe was rinsed thrice with sample water and subsequently filled prior to being fitted with a 0.45 µm PES filter. About 100 mL of sampled water was filtered and collected in a 250 mL polyethylene bottle that was rinsed with the first 5 mL of filtered water. The bottle was stored in a cooling box and transported to the laboratory.</p> <p><strong>In the laboratory</strong>, the 250 mL bottle was stored at 4 °C until analysis. Within 36 hours, concentrations of ammonium, nitrate, nitrite, and orthophosphate were determined <strong>in triplicate</strong>. More specifically, the following test kits were used to determine said nitrogen and phosphorus concentrations (with quantification range between brackets):</p> <ul> <li>Ammonium: 1.14752.0001 (0.05-3.00 mgN/L)</li> <li>Nitrate: 1.14773.0001 (2-20 mgN/L)</li> <li>Nitrite: 1.14776.0001 (0.02-1.00 mgN/L)</li> <li>Orthophosphate: 1.14848.0001 (0.05-5.00 mgP/L)</li> </ul> <p>Regarding the <strong>spectrophotometric determination</strong>, all analyses were complemented with a blank and a standard with a known concentration of each individual nutrient component. For each nutrient, a specific wavelength was used and the resulting absorbance was converted to the associated nutrient concentration through known factors (similar to the use of calibration curves), after setting the absorbance of the blank as reference absorbance (i.e. a concentration of 0 mg/L). All of the analyses were performed with plastic 1-cm cuvettes during the first campaign, while 5-cm cuvettes were used in the remaining three campaigns due to low nutrient levels (except for nitrate, for which an analysis through 5-cm cuvettes is not supported by the used test kits).</p>
Yangtze River Basin Water Reservoir (YWR) dataset
<p>The Yangtze River Basin Water Reservoir (YWR) dataset, developed by multi-source satellite remote sensing data, provides monthly time series data for 443 reservoirs (with a total storage capacity of 276.51 km³) in the Yangtze River Basin (YRB) from 1990 to 2023, including area and storage data for all 443 reservoirs and water level data for 175 reservoirs. This dataset is associated with the study: Wang et al., "Advanced monitoring of reservoirs in the Yangtze River Basin from 1990 to 2023 using multi-source satellite remote sensing", Journal of Remote Sensing, under review, 2025.</p>
Input Dataset for Estimating Continuous Soil Water Retention Curves Using Physics-Informed Neural Networks
<p>This dataset was used as input to a physics-informed neural network (PINN) model developed to estimate continuous soil water retention curves (SWRCs). It includes basic soil properties such as particle-size distribution (sand, silt, clay), organic carbon content (OC), bulk density (BD), and measurements of soil water retention at various matric potentials. These inputs allow the model to learn the relationship between soil properties and water retention, via both data and embedded physical constraints. This data set consists of 4,200 Danish soil samples with measurements spanning the wet and dry ends of the SWRC. </p>
Indicative distribution map for Ecosystem Functional Group M2.1 Epipelagic ocean waters
<p>This archive contains indicative distribution maps and profiles for <strong>M2.1 Epipelagic ocean waters</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Indicative distribution map for Ecosystem Functional Group M2.3 Bathypelagic ocean waters
<p>This archive contains indicative distribution maps and profiles for <strong>M2.3 Bathypelagic ocean waters</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Indicative distribution map for Ecosystem Functional Group M2.2 Mesopelagic ocean water
<p>This archive contains indicative distribution maps and profiles for <strong>M2.2 Mesopelagic ocean water</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Indicative distribution map for Ecosystem Functional Group M2.4 Abyssopelagic ocean waters
<p>This archive contains indicative distribution maps and profiles for <strong>M2.4 Abyssopelagic ocean waters</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Indicative distribution map for Ecosystem Functional Group SF2.1 Water pipes and subterranean canals
<p>This archive contains indicative distribution maps and profiles for <strong>SF2.1 Water pipes and subterranean canals</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Structure and dynamics of water confined in cylindrical nanopores with varying hydrophobicity
<p>Supporting data for Phil. Trans. R. Soc. A 379: 20200403 (2021)</p> <p><a href="https://doi.org/10.1098/rsta.2020.0403">http://doi.org/10.1098/rsta.2020.0403</a></p>
Dataset for the adjustment of a wave forecasting system for the deep waters of the South Atlantic Ocean and for the southern coast of Brazil: Numerical Wave Experiment in the South of Brazil (NWESB).
<p>This dataset corresponds to the input files of the test domains used for the simulations of the coupled GFS (Global Forecast System) and WAVEWATCH III models in the waters of the South Atlantic Ocean and in waters of the Brazilian Southeastern during the passage of a cold front and the presence of strong pressure gradient between a low-pressure system and a high-pressure system. In the files generated by WAVEWATCH III, wave fields are presented from 2016-03-25 14:00:00, which is the date from when the model it stabilizes. Also contained in this dataset are the files of the GFS model wind fields, the bathymetry files (eTOPO1) and the files of the bathymetry entries in WAVEWATCH III.</p> <p>All files with suffix 2 correspond to the geographic region 70°W to 4°W longitude and 55°S to 13°S latitude and all files with suffix 3 correspond to the geographic region 70°W at 20°W longitude and 55°S at 13°S latitude.</p> <p><strong>ww3-2.inp</strong> and <strong>Bathymetry2.ascii</strong> are the input configuration files for WAVEWATCH III bathymetry and bathymetry (in ASCII format) respectively for the WW3-2 domain. <strong>gfs-2.nc</strong> is the input file of the winds obtained from the outputs of the GFS model (in NetCDF format) for the WW3-2 domain. <strong>ww3-2.nc</strong> is the WAVEWATCH III model output file with the simulated waves for the WW3-2 domain.</p> <p><strong>ww3-3.inp</strong> and <strong>Bathymetry3.ascii</strong> are the input configuration files for WAVEWATCH III bathymetry and bathymetry (in ASCII format) respectively for the WW3-3 domain. <strong>gfs-3.nc</strong> is the input file of the winds obtained from the outputs of the GFS model (in NetCDF format) for the WW3-3 domain. <strong>ww3-3.nc</strong> is the WAVEWATCH III model output file with the simulated waves for the WW3-3 domain.</p> <p>The GFS model files contain data every 6 hours and the WAVEWATCH III model files contain data every 1 hour. All files have a spatial resolution of 0.25° (27.78 km).</p> <p> </p> <p><strong>Other data that complement this dataset:</strong></p> <p><strong><a href="https://figshare.com/articles/figure/Complementary_figures_of_Parameter_adjustments_of_the_GFS_WAVEWATCH_III_coupled_models_in_Southern_Brazil/16726375"><em>Complementary figures of Parameter adjustments of the GFS – WAVEWATCH III coupled models in Southern Brazil.</em></a></strong></p> <p><em><strong><a href="https://figshare.com/articles/dataset/Dataset_for_the_adjustment_of_a_wave_forecasting_system_for_the_deep_waters_of_the_South_Atlantic_Ocean_and_for_the_southern_coast_of_Brazil_Output_files_in_GrADS_format_/16767058">Dataset for the adjustment of a wave forecasting system for the deep waters of the South Atlantic Ocean and for the southern coast of Brazil (Output files in GrADS format).</a></strong></em></p> <p> </p> <p> </p>
GFN2-xTB structures of iCOM adsorbed on a cluster model of water molecules derived from a periodic model of crystalline ice
<p>This dataset contains the atomic coordinates in the <a href="http://www.moldraw.unito.it/">.</a>xyz format of the GFN2-xTB optimized structures of 20 iCOMs adsorbed at the surface of a cluster of 84 water molecules mimicking the periodic model of crystalline water icy grain as described by Ferrero, S.; Zamirri, L., Ceccarelli, C.; Witzel, A.; Rimola, A.; Ugliengo, P. ApJ, (2020) 904:11. For all considered structures we also provided a specific file in the Gaussian format with the computed harmonic frequencies. Each file can be easily converted in input for the variety of quantum mechanical programs, like VASP, QE, Gaussian 16 etc.</p> <p> </p> <p> </p>
Crop-specific global fertilizer application rates from "Closing yield gaps through nutrient and water management"
<p>Crop-specific global maps of N, P2O5, and K2O fertilizer application rates circa the year 2000 from the following paper:</p> <p>Mueller, ND, JS Gerber, M Johnston, DK Ray, N Ramankutty, and JA Foley. 2012. Closing yield gaps through nutrient and water management. <em>Nature</em> <strong>490</strong>: 254–257</p> <p>Data are provided at five arc-minute resolution and are saved as netcdf files. Fertilizer application rates are estimated from reconciling various national and subnational data sources. See the Supplementary Information from the 2012 paper for a full description of data sources and methods. Data quality for each grid cell is described in a map layer. Files containing the text "totalcons" sum nutrient consumption across crops per grid cell, using crop harvested areas from Monfreda et al. 2008 Global Biogeochemical Cycles. For maize, wheat, and soybean N application rates, additional maps and csv files (containing the text "politboundaries") identify the political units around the world containing unique information. Crops and crop group categories are consistent with those utilized in Monfreda et al. 2008 Global Biogeochemical Cycles.</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>
Live cribwall + slope grating + fascines drainage system - soil-water dynamics
<p>Dataset containing raw time series (August 2022) for soil-water dynamics -i.e., volumetric soil moisture, matric suction, soil-pore water pressure, and soil temperature - retrieved from a live, vegetated cribwall+slope grating+fascines drainage system built in Catterline, Scotland. NBS intervention built to restore a landslide taking place in February 2021. </p>
Data sources for the groundwater depletion manuscript in Water Resources Research
<p>Here, you can access the source files for the figures (and tables) of the publication (see reference).</p> <p>Basically, you find the model output (WaterGAP 2.2a) for global scaled groundwater storage, total water storage, baseflow, groundwater recharge (diffuse and below surface water bodies) and a table where location of grid cell and belonging continental area (e.g. to convert values into km³) is given. In addition, an Excel-File for the diagram of HPA (Figure 2) is accessible.</p> <p>First part of the file name represents the model variant (IRR100, IRR100_S, IRR70_S, NOUSE_S, for details see the manuscript), then the variable name and unit is given (Total Water Storages [mm], groundwater storage [mm], Qb (baseflow) [mm], Rg (diffuse groundwater recharge) [mm], Rg_swb (groundwater recharge below surface water bodies) [mm]). File format is a zipped netCDF. The table "lat_lon_cont_area.txt" contains the ArcID (internal grid cell number), coordinates and the continental area which is used for WaterGAP calculations.</p> <p>Original data description: https://www.uni-frankfurt.de/49903932/6__GW_depletion</p>
Deleterious effects of thermal and water stresses on life history and physiology: a case study on woodlouse
<p>Datasets and R source code of the article Depeux C, Branger A, Moulignier T, Moreau J, Lemaître J-F, Dechaume-Moncharmont F-X, Laverre T, Paulhac H, Gaillard J-M, Beltran-Bech S (2023) Deleterious effects of thermal and water stresses on life history and physiology: a case study on woodlouse. <strong><em>Peer Community Journal</em></strong> 3:e7 http://dx.doi.org/<a href="https://doi.org/10.24072/pcjournal.228">10.24072/pcjournal.228</a></p> <p>This article previously appeared as preprint Depeux C, Branger A, Moulignier T, Moreau J, Lemaître J-F, Dechaume-Moncharmont F-X, Laverre T, Pauhlac H, Gaillard J-M, Beltran-Bech S (2022) Deleterious effects of thermal and water stresses on life history and physiology: a case study on woodlouse. <em><strong>bioRxiv</strong>, 2022.09.26.509512 </em> https://doi.org/10.1101/2022.09.26.509512</p> <p><em>Peer-reviewed and recommended by <strong>Peer Community in Ecology</strong>: </em> Belsare A (2022) An experimental approach for understanding how terrestrial isopods respond to environmental stressors. <em>Peer Community in Ecology, 100506. </em><a href="https://doi.org/10.24072/pci.ecology.100506"><strong>https://doi.org/10.24072/pci.ecology.100506</strong></a></p>
Water stable isotope, temperature and electrical conductivity dataset (snow, ice, rain, surface water, groundwater) from a high alpine catchment (2019-2021).
<p>Data collected in the Otemma forefield in Switzerland (45°56’03”N,7°24’42”) from July 2019 to October 2021.<br> Data were collected by the research teams of Bettina Schaefli<sup>2</sup> and Stuart N. Lane<sup>1</sup>.</p> <p><sup>1</sup> Institute of Earth Surface Dynamics (IDYST), University of Lausanne, 1015 Lausanne, Switzerland</p> <p><sup>2</sup> Institute of Geography (GIUB), University of Bern, 3012 Bern, Switzerland</p> <p>For further information, please contact:</p> <ul> <li>tom.muller.1@unil.ch</li> </ul> <p><strong>Description of the dataset</strong></p> <p>This dataset contains water stable isotope (δ<sup>2</sup>H, δ<sup>17</sup>O, δ<sup>18</sup>O), water temperature and water electrical conductivity (EC) measurements collected from the Otemma glacier catchment.</p> <p>All water isotope samples were collected directly from the source and stored in 12 mL amber glass vials with an air-tight caps. River samples were first collected with an automatic ISCO 6712 portable water sampler with 1L open plastic bottles and transferred in 12 mL vials every one to two weeks. All isotope analysis were performed using a Wavelength-Scanned Cavity Ring Down Spectrometer (Picarro 2140-I, Santa Clara, California, USA) and expressed relative to the international Vienna Standard Mean Ocean Water (VSMOW) standards.</p> <p>All EC and water temperature measurements were performed with a WTW Multi 3510 IDS logger with a IDS TetraCon® 925 probe.</p> <p>The dataset contains measurements performed at various locations within the catchment. A total of approximately 1500 measurements are provided. In the dataset each point correspond to a measurement station (column "<strong>Station</strong>") which we classified in specific class of water (column "<strong>Type</strong>") as follows :</p> <ul> <li><strong>Stream </strong>: samples collected at three locations, from the glacier snout, after a small outwash plain and 2km downstream.</li> <li><strong>Tributary </strong>: 5 hillslopes tributaries originating from small seasonal overland flow or small springs at the base of the morainic hillslope. Those tributaries were monitored weekly. In addition, a few other seasonal lateral streams were sampled in various locations (Type: Other tributaries).</li> <li><strong>Bedrock </strong>: A few exfiltrations directly leaking out of the bedrock outcrop were sampled.</li> <li><strong>Ice </strong>: Ice was sampled either as surface ice (small cores 5 cm deep), as deeper cores (5 to 8m deep) or as meltwater from supraglacial gullies. All solid ice samples were melted at ambiant air temperature in air-tight plastic bags before being transferred into 12 mL vials.</li> <li><strong>Snow </strong>: The snowpack was sampled either at the surface (0 to 5cm) or at about 20 cm depth. Where possible, meltwater leaking from the snowpack was sampled. At 3 locations in 2021, we dug snowpits from which we sampled snow at different layers with depth. All solid snow samples were melted at ambiant air temperature in air-tight plastic bags before being transferred into 12 mL vials</li> <li><strong>Rain </strong>: Rainwater was mostly sampled at our camp site at 2450 m. asl. Rainwater samples represent single rain events which are identified by dry periods of at least one day long.</li> <li><strong>Groundwater </strong>: shallow (2 to 3 meters) fully-screened groundwater wells were installed in the outwash plain and water sampled monthly in the snow-free season.</li> </ul> <p>- GPS coordinates are provided with each point (Swiss coordinate system CH1903+ / LV95<strong> (EPSG: 2056)).</strong></p> <p>- Dates are provided in local timezone (GMT+1 with daylight saving time) and in UTC date format.</p> <p>- Analyitcal error from the Picarro spectrometer is reported as 1 standard deviation.</p> <p>More information can be accessed in the corresponding publication by Müller et al. (to be published in 2023).</p> <p><strong>Data files</strong></p> <ul> <li><em>Otemma_isotope_EC_T_2019_2021.csv</em> : file containing all data with GPS coordinates</li> <li> <p><em>isotope_locations_Otemma.jpg</em> : an overview of the locations of each measurement point</p> </li> <li> <p><em>Otemma_Isotopes_2019-2020.html </em>: interactive plots of all datasets (δ<sup>2</sup>H, EC, temperature), classified by Type.</p> </li> </ul>
Mass of wastes discharged directly from vessels to the water column
<p>Data set of mass discharge of selected pollutants from shipping in the European region. Created in the framework of the project Evaluation, control and Mitigation of the EnviRonmental impacts of shippinG Emissions (EMERGE).</p> <p>To determine the mass of discharged pollutants, AIS-based ship emission modelling of discharge volumes is combined with results of water effluent analysis. For the discharge volumes, the Ship Traffic Emission Assessment Model (STEAM) is used. The total discharge volume of wastes is comprised of five waste streams: open/close scrubber, grey, black and ballast water. For the content of pollutants in the five waste streams, a bibliographic database of waste stream pollutant concentrations compiled during the project is used.</p>
Data and code from "Water availability is a stronger driver of soil microbial processing of organic nitrogen than tree species composition"
<p>#### Data description<br> Data from large scale, long-term tree diversity experiment in southwestern France (<a href="https://sites.google.com/view/orpheeexperiment/home">ORPHEE</a>), additionally manipulating water contraint. Variables presented are soil nitrogen cycling rates measured using isotope pool dilutions.</p> <p>Companion paper is found here:</p> <p>Maxwell TL, Augusto L, Tian Y, Wanek W & Fanin N (2023). Water availability is a stronger driver of soil microbial processing of organic nitrogen than tree species composition. <em>European Journal of Soil Science</em>. <a href="https://doi.org/10.1111/ejss.13350">https://doi.org/10.1111/ejss.13350</a></p> <p>#### Metadata<br> Soil sampling: July 2020<br> Maxwell_ShortComm_Data.csv data description</p> <p>ID: unique identifier per sample<br> Block: numbered 1-6. Blocks 1,3,6 are control (unirrigated), Blocks, 2,4,5 are irrigated<br> Plot: numbered plot according to the ORPHEE design. Plot 1 = BP, Plot 5 = PP, Plot 9 = BP_PP<br> Espece: species ID. BP = pure birch (<em>Betula pendula</em>), PP = pure pine (<em>Pinus pinaster</em>), BP_PP (50% mixed birch-pine)<br> Rep: sample replicate, 3 replicates per plot<br> Sample name: long unique identifier per sample. Concatenation of Block, Plot, and Espece<br> PD: gross protein depolymerization rates (micrograms nitrogen per grams dry soil per day = µg N g-1 d-1)<br> AAU: gross free amino acid uptake rates (µg N g-1 d-1)<br> Cmicrobial_ug_g: microbial biomass carbon (µg C g-1)<br> Nmicrobial_ug_g: microbial biomass nitrogen (µg N g-1)<br> MRT_FAA_hrs: mean residence time of free amino acids (hours)<br> FAA_ugN_g: free amino acids (µg N g-1)<br> Moisture_percent: soil moisture percent (%)<br> N_nonfumige_ug_g: nitrogen from non fumigated soils, i.e. extractable N (µg N g-1)<br> C_nonfumige_ug_g: nitrogen from non fumigated soils, i.e. extractableC (µg C g-1)</p>
Dataset for: Water column dynamics control nitrite-dependent anaerobic methane oxidation by Candidatus 'Methylomirabilis' in stratified lake basins
<p>Dataset containing treated 16S rRNA amplicon sequence data, accompanying the manuscript "Water column dynamics control nitrite-dependent anaerobic methane oxidation by Candidatus ‘Methylomirabilis’ in stratified lake basins" </p> <p>Files: </p> <p>- Mapping file</p> <p>- ASV table</p> <p>- Refseq file</p> <p>-Tree file</p> <p>- Relative abundances of dominant methanotrophs in the water column of Lake Lugano North Basin (used to create Fig. 6c)</p> <p>- Multi annual dataset of water chemistry data of the Lake Lugano North Basin</p> <p> </p> <p> </p> <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.