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20 results for “trace water”
A multi-level network tool to trace wasted water from farm to fork and backward
<p>NETFLOW - Network-based 13 Evaluation Tool for Food LOss and Waste<br>V 0.1</p> <p>####################################################################################################################################################</p> <p><br>Authors:<br>Francesco Semeria - Politecnico di Torino - francesco.semeria@polito.it<br>Marta Tuninetti - Politecnico di Torino<br>Luca Ridolfi - Politecnico di Torino</p> <p>####################################################################################################################################################</p> <p>CONTENT OF THIS ARCHIVE</p> <p>The listed files contain output data from the NETFLOW tool and assess the impact on water resources of food loss and waste (FLW) for wheat an its main derived products (flour, bran, pasta and bread).</p> <p>In particular, they quantify such impact offering two perspectives: <br> 1. supply-side, from FLW associated to food consumption backwards to the countries of production;<br> 2. utilisation-side, from the countries of production forward to the countries where FLW occurs.</p> <p>It should be noted that the two perspectives allow to identify two different aspects of the FLW issue.</p> <p><br>List of files:</p> <p>data_fig2_ita_supply_vw.xlsx = output data regarding the supply network of Italy.<br>data_fig3_usa_utilisation_vw.xlsx = output data regarding the utilisation network of the United States.<br>data_fig4_global_supply_vw.xlx = output data regarding the global supply network.</p> <p><br>Modelling scripts are currently available upon request.</p>
Isotopes and related data associated with water tracing with environmental DNA in a high-Alpine catchment
<p>Isotopes and related data associated with water tracing with environmental DNA in a high-Alpine catchment<br> Prepared by Natalie Ceperley, February 2020. </p> <p><br> All methods associated with this data are available in the manuscript: Elvira Mächler, Anham Salyani, Jean-Claude Walser, Annegret Larsen, Bettina Schaefli, Florian Altermatt, and Natalie Ceperley. 2019. Water tracing with environmental DNA in a high-Alpine catchment, Hydrology and Earth System Sciences. https://doi.org/10.5194/hess-2019-551. <br> Related data sets are and will be published in the Vallon de Nant Community on Zenodo. Associated sequencing data are publicly available on European Nucleotide Archive (Mächler et al., 2020). </p> <p>All isotope data analyzed in the laboratory of Torsten W. Vennemann at the University of Lausanne. </p> <p> </p> <p><br> All Files:<br> ▪ NaN - No measurement or sample<br> ▪ Details regarding measurement are available in paper or supplement. </p> <p>Files: <br> 1) climate_hydro_2017_daily.csv <br> ⁃ 16 columns: <br> ⁃ 1. day of year with January 1, 2017 = 1<br> ⁃ 2-5. Q: daily mean, min, max, and baseflow discharge as measured at outlet (location ER/MR), in liters / day <br> ⁃ 6. P: mean mm of rain across catchment per day<br> ⁃ 7. SR: total solar radiation per day in W/hr/m2 as median of 4 meteorological stations<br> ⁃ 8-10. SCA: mean, min, and max snow covered area on days with satellite imagery available for whole catchment area, in %<br> ⁃ 11-13. water temperature, mean, min, and max, at outlet (location ER/MR), in degrees C<br> ⁃ 14-16. air temperature, mean, min, and max at 4 meteorological stations, in degrees C</p> <p>2) delta-18-O_permil.csv <br> ⁃ stable isotopes of water (delta 18-O) in per mil<br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p> <p>3) delta-2-H_permil.csv <br> ⁃ stable isotopes of water (delta 2-H) in per mil<br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p> <p>4) dqdt_outlet_prev48hrs.csv<br> - dq/dt determined at the outlet for the previous 48 hours at sampling moment (TimeOfSamples_HR.csv) for each sampling site<br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p> <p><br> 5) ednasamplecount.csv <br> - this is the tally of samples (1 sample includes 4 replicates)<br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p> <p>6) electricalconductivity_instrument.csv <br> ⁃ Code: <br> 108 - post-analyzed using a glass bodied 6 mm probe in the laboratory (Jenway 4510, Staffordshire, UK). <br> 102 - hand measurement with WTW (multi-3510 with a IDS-tetracon-925, Xylem Analytics, Germany)<br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p> <p><br> 7) electricalconductivity_uScm.csv <br> - this is the electrical conductivity in micro siemens per cm, according to the instruments coded in electricalconductivity_instrument.csv<br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p> <p>8) LC-excess.csv <br> - this is the line control execss from the meteoric water line as determined by the samples in the file: precipitationistopemetadata.csv<br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p> <p>9) locations.csv <br> ⁃ Location codes used in other files. <br> - Coordinates in CH1903 / LV03 and WGS 84 (lat/lon). Elevation in m. asl. </p> <p>10) precipitationisotopemetadata.csv <br> - This is the sampling information for the isotope data that was used to calculate the meteoric water line. <br> - The full data set will become available in a subsequent publication on Zenodo linked to the same community. <br> - 4 columns: <br> - 1. code: rain (1) or snow (2)<br> - 2. collection date and time<br> - 3. elevation in m. asl. <br> - 4. in the case of rain, this is the depth of collection in mm (area normalized volume), in the case of snow, this is the mean depth below the surface that the sample was taken from in cm. <br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p> <p>11) sampledates.csv <br> - These are the sample dates in day, month, year and day of year corresponding to the rows in other files</p> <p>12) stationlocations.csv<br> - These are the locations of four meteorological stations and discharge measurement station. <br> - Coordinates in CH1903 / LV03 and WGS 84 (lat/lon). Elevation in m. asl. </p> <p>13) TimeOfSamples_HR.csv <br> - This is the time of the sample in hours and decimals correspond to minutes past hour<br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p> <p>14) watertemperature_degC.csv <br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)<br> - measure in degrees C<br> - instrument in watertemperature_instrument.csv</p> <p>15) watertemperature_instrument.csv <br> ⁃ Code: <br> 1 = hand measurement with WTW (multi-3510 with a IDS-tetracon-925, Xylem Analytics, Germany)<br> 2 = HOBO Pendant Temperature/Light Data Logger 64K - UA-002-64", Onset (Bourne, MA, USA)<br> 3 = Continually logging WTW (IDS-tetracon-325, Xylem Analytics, Germany)<br> 4 = Continually logging (10min) HOBO U24-001 Conductivity, Onset (Bourne, MA, USA) <br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p>
Data accompanying the manuscript "Biogeochemical cycling of trace elements and nutrients in ferruginous waters – constraints from a deep oligotrophic ancient lake", published in Limnology and Oceanography (doi: 10.1002/lno.12687)
<p>CTD and geochemical data accompanying the publication: Biogeochemical cycling of trace elements and nutrients in ferruginous waters – constraints from a deep oligotrophic ancient lake in Limnology & Oceanography (doi: 10.1002/lno.12687).</p>
Tracing and visualisation of contributing water sources in a model of flood inundation: video supplement
<p>These are video supplement files to Wilson & Coulthard (2021), produced using version 1.8f-WS of CAESAR-Lisflood software, <a href="https://doi.org/10.5281/zenodo.5541122">available on Zenodo here</a>. For a full description of the methodology and case studies, please refer to the paper which is available here: <a href="https://doi.org/10.5194/gmd-2021-340">https://doi.org/10.5194/gmd-2021-340</a>.</p> <p>Video animations (no audio) for the following case studies are included:</p> <p>1. <strong>Carlisle, United Kingdom</strong> (carlisleanimation-sourcetracing.avi and carlisleanimation-depthonly.avi):</p> <ul> <li>Simulation of the January 2005 flood event at the confluence of the Rivers Caldew, Petteril and Eden, using a 5 m grid.</li> <li>Both water source tracing and depth only versions are provided.</li> <li>In the water tracing version, blue colours represent flows from the River Eden, reds are from the River Petteril and greens are from the River Caldew; darker shades represent deeper water. Available on YouTube here: <a href="https://youtu.be/xOtOi06cXvA">https://youtu.be/xOtOi06cXvA</a></li> <li>In the depth only version, darker shades of blue represent deeper water, with no information about the water source in a grid cell. Available on YouTube here: <a href="https://youtu.be/aFz-sPRGHVE">https://youtu.be/aFz-sPRGHVE</a></li> </ul> <p>2. <strong>Avon-Heathcote estuary in Christchurch, New Zealand</strong> (avonheathcoteanimation.avi):</p> <ul> <li>Simulation for July 2017, which included a high flow event on 22 July, using a model grid of 10 m.</li> <li>Blue colours represent flows from tide, reds are from the River Avon and greens are from the Heathcote River; darker shades represent deeper water.</li> <li>Available on YouTube here: <a href="https://youtu.be/Fczr5tczzXU">https://youtu.be/Fczr5tczzXU</a></li> </ul> <p>3. <strong>Amazon </strong>(amazonanimation.avi):</p> <ul> <li>Simulation at the confluence of the Solimões (mainstem Amazon) and Purus rivers in the central Amazon, Brazil, for the period of 1 October 2013 through December 2014, using a ~270 m model grid.</li> <li>Red colours are from the Solimões, green colours are from the Purus; darker shades represent deeper water.</li> <li>Available on YouTube here: <a href="https://youtu.be/PknAL_8fd1I">https://youtu.be/PknAL_8fd1I</a></li> </ul> <p>4. <strong>Planar slope</strong> (planaranimation.avi):</p> <ul> <li>A simple test case consisting of a 2000 x 1000 m planar slope (0.001 m/m), with walls added at 250 m intervals across the slope, each of which has several gaps through which water can flow. Model grid was 5 m.</li> <li>Eight water sources were traced in total, with three visualised in the animation: red = source 2, green = source 4, blue = source 6. Depths are shown in the middle plot.</li> <li>Available on YouTube here: <a href="https://youtu.be/DTw8ysJtx8o">https://youtu.be/DTw8ysJtx8o</a></li> </ul> <p>Please feel free to use these animations, under the terms of the CC-BY-4.0 license. Please provide a link back to this site and a citation to Wilson & Coulthard (2021).</p> <p>Reference:</p> <p>Wilson, M. D. and Coulthard, T. J.: Tracing and visualisation of contributing water sources in the LISFLOOD-FP model of flood inundation, Geosci. Model Dev. Discuss. [preprint], <a href="https://doi.org/10.5194/gmd-2021-340">https://doi.org/10.5194/gmd-2021-340</a>, in review, 2021</p>
Dataset: The SPIKE II experiment - Tracing the water balance
<p>This repository holds data collected during the “SPIKE II” tracer experiment. The experiment was carried out on a large vegetated lysimeter (2.5 m<sup>3</sup>) planted with two willow trees (clones) (<em>Salix viminalis</em>) within the EPFL campus (CH), in Switzerland. SPIKE II took place from May 10 to June 29 in 2018. This composite dataset contain stable isotopic composition (δ<sup>2</sup>H and δ<sup>18</sup>O) of more than 900 water samples of precipitation, soil water, bulk soil collected at different depths in the soil profile, xylem from willow, and leakage flow in the bottom of the lysimeter. The dataset comprises environmental conditions and water fluxes recorded during the experiment. This includes: meteorological conditions, soil moisture and tension, evapotranspiration in the lysimeters, and tree transpiration recorded at high resolution. Finally, the repository holds tree hydraulic and growth measurements and root traits.</p> <p>Specifically, this dataset contains six files:</p> <ul> <li>“METADATA_spikeII.txt” contains specific information about each recorded variable and data point collected throughout the experiment.</li> <li>“spike.hydrometric.II.csv” contains information about meteorological and soil conditions, evapotranspiration fluxes, and tree stem radius, including growth and tree water deficit.</li> <li>"spike.isotopes.II.csv” contains stable isotope data.</li> <li>“fineroots_spike.II.csv” contains root traits information.</li> <li>“events_chronology.csv” summarizes the main events that occurred during SPIKE II.</li> <li>“Figure1_SpikeII_Aerial_Image.PNG” illustrates the location and spatial display of the experiment at the EPFL campus.</li> </ul> <p>This data repository was used in the following SPIKE II publications:</p> <p>Nehemy, M. F., Benettin, P., Asadollahi, M., Pratt, D., Rinaldo, A., & McDonnell, J. J. (2021). Tree water deficit and dynamic source water partitioning. <em>Hydrological Processes</em>, <em>35</em>(1), e14004. doi:10.1002/hyp.14004</p> <p>Benettin, P., Nehemy, M. F., Cernusak, L. A., Kahmen, A., & McDonnell, J. J. (2021). On the use of leaf water to determine plant water source: A proof of concept. <em>Hydrological Processes</em>, <em>35</em>(3), e14073. doi:10.1002/hyp.14073</p> <p>Benettin, P., Nehemy, M. F., Asadollahi, M., Pratt, D., Bensimon, M., McDonnell, J. J., & Rinaldo, A. (2021). Tracing and closing the water balance in a vegetated lysimeter. <em>Water Resources Research</em>, 57, e2020WR029049. doi:org/10.1029/2020WR029049</p> <p>For any further inquiry, please contact Magali Nehemy or Paolo Benettin.</p> <p>We thank Kim Janzen for assistance with laser and mass spec analysis. We thank the Laboratory of Ecohydrology at EPFL (ECHO/IIE/ENAC/EPFL) for assistance throughout the experiment. We also thank Pierre Queloz and Scott Allen for precious help, Gabriel Cotte and Torsten Vennemann from University of Lausanne (CH) for the collection and analysis of atmospheric vapor samples. This research was supported by the American Geophysical – Horton Research Grant 2019 awarded to MFN, an NSERC CREATE in Water Security and an NSERC Discovery Grant to JJM, AR and PB thank ENAC school at EPFL for financial support and acknowledge the Swiss National Science Foundation grant number CRSII5\_186422.</p> <p> </p>
Zonal mean of atmospheric water vapour and water vapour perturbation by emitted trace gases of hypersonic aircraft
<p>This short movie (no sound) shows two figures with time steps of five days over a period of fourteen years (2000-2014). On the left the atmospheric mixing ratio of water vapour is presented in parts per million. On the right the perturbation of stratospheric water vapour is depicted in parts per million. The perturbation is created by emitted water vapour of hypersonic aircraft flying at high altitudes (35 km). Over the years the accumulation of water vapour up to equilibrium is shown.</p>
Рис. 1. Раковины Mya truncata (А–Г) (Белое море) и Laternula elliptica (А'–Г') (Зал. Прюдс): А, А' – обЩий вид; Б, Г' – внутреннЯЯ поверхность левых створок; В, В' – вид хондрофора со стороны дорсального краЯ; Г, Б' – внутреннЯЯ поверхность правых створок. ОбоЗначениЯ: пК – передний край раковины; ЗК – Задний край; дК – дорсальный край; м – макушка; мщ – макушечнаЯ (умбональнаЯ) Щель; Кс – концентрическаЯ скульптура; сКп – складки периостракума; хр – хондрофор; ппЛ – поддерживаюЩаЯ пластинка; син – синус; ОмЗ – отпечаток мускула-ЗамыкателЯ. Fig. 1. Shells of Mya truncata (А–Г) (White Sea) and Laternula elliptica (А'–Г') (Prydz Bay): A, A' – general view; Б, Г' – internal view of left valves; В, В' – dorsal view on chondrophores; Г, Б' – internal view of right valves. Notes: пК – anterior margin; ЗК – posterior margin; дК – dorsal margin; м – umbo; мщ – umbonal crack; Кс – concentric sculpture; сКп – periostracal wrinkles; хр – chondrophore; ппЛ – buttress; син – sinus; ОмЗ – trace of retractor muscle. in Species of warm-water origin Laternula elliptica (King, 1832) (Mollusca: Bivalvia: Laternulidae), a widespread mollusk in recent Antarctica
Рис. 1. Раковины Mya truncata (А–Г) (Белое море) и Laternula elliptica (А'–Г') (Зал. Прюдс): А, А' – обЩий вид; Б, Г' – внутреннЯЯ поверхность левых створок; В, В' – вид хондрофора со стороны дорсального краЯ; Г, Б' – внутреннЯЯ поверхность правых створок. ОбоЗначениЯ: пК – передний край раковины; ЗК – Задний край; дК – дорсальный край; м – макушка; мщ – макушечнаЯ (умбональнаЯ) Щель; Кс – концентрическаЯ скульптура; сКп – складки периостракума; хр – хондрофор; ппЛ – поддерживаюЩаЯ пластинка; син – синус; ОмЗ – отпечаток мускула-ЗамыкателЯ. Fig. 1. Shells of Mya truncata (А–Г) (White Sea) and Laternula elliptica (А'–Г') (Prydz Bay): A, A' – general view; Б, Г' – internal view of left valves; В, В' – dorsal view on chondrophores; Г, Б' – internal view of right valves. Notes: пК – anterior margin; ЗК – posterior margin; дК – dorsal margin; м – umbo; мщ – umbonal crack; Кс – concentric sculpture; сКп – periostracal wrinkles; хр – chondrophore; ппЛ – buttress; син – sinus; ОмЗ – trace of retractor muscle.
Datasets and code for Rapljenović et al. 2024: Adsorption of trace metals onto different plastics during long-term exposure in an estuarine environment: influence of time, stratified water column, and specific surface area
<p>This repository contains datasets and R code to reproduce results from Rapljenović et al. 2024: Adsorption of trace metals onto different plastics during long-term exposure in an estuarine environment: influence of time, stratified water column, and specific surface area.</p>
Supplementary material for "Tracing emerging contaminants from the Baltic Sea and North Sea in fjord waters in southern Norway with rare earth elements as far-field tracers"
<p><span>Dataset presented and discussed in the manuscript of the research article “</span><span>Tracing emerging contaminants from the Baltic Sea and North Sea in fjord waters in southern Norway with rare earth elements as far-field tracers</span><span><span>” by Zocher et al. The manuscript will be submitted to <em>Environmental Pollution</em> and was prepared by the following authors: </span></span></p> <p> </p> <p><span><span>Anna-Lena Zocher (1), Tomasz Maciej Ciesielski (2,3), Stefania Piarulli (4), Julia Farkas (4) and Michael Bau (1). </span></span></p> <p><span> </span></p> <p><span><span>(1) School of Science, Constructor University, Bremen, Germany</span></span></p> <p><span><span>(2) Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway</span></span></p> <p><span><span>(3) </span></span><span><span>Department of Arctic Technology, The University Centre in Svalbard (UNIS), Longyearbyen, Norway</span></span></p> <p><span><span>(4) SINTEF Ocean, Trondheim, Norway</span></span></p> <p> </p> <p><span>This work was conducted within the ELEMENTARY project, and we appreciate funding from the Norwegian Research Council (grant No. 301236).</span></p>
Decoding the metabolic response of Escherichia coli for sensing trace heavy metals in water
<p>As: Raman spectra from E. coli lysate sample after exposing to As in DI water</p> <p>Cr: Raman spectra from E. coli lysate sample after exposing to Cr in DI water</p> <p>As_TapWater: Raman spectra from E. coli lysate sample after exposing to As in tap water</p> <p>WasteWater_FineTune_Dataset: Raman spectra from E. coli lysate sample after exposing to As in waste water</p> <p>WasteWater 'Unknow' Dataset: Raman spectra from E. coli lysate sample after exposing to waste water</p>
Trace element composition of drinking water in Almaty
<p>Water is an important component of all life on Earth, and water pollution with heavy metals can lead to detrimental consequences for public health. The purpose of this study was to determine the health risks caused by trace elements present in the drinking water supply systems of Almaty City. As part of this research, the elemental composition of 78 drinking water samples taken in winter, summer, and autumn of 2023 in different areas of the city was studied.Based on the data obtained, drinking water contamination indices were calculated for heavy metal groups, and the degree of water suitability for drinking purposes was assessed for each sampling point.</p>
Tracing the imprint of river runoff on Arctic water mass transformation [dataset]
<p>This dataset contains the underlying data for the manuscript Lambert et al., Tracing the imprint of river runoff<br> variability on Arctic water mass transformation, submitted to JGR-Oceans</p> <p>-----------------------------------<br> Both files contain variables with the general notation:<br> S..., which are the cumulative salt fluxes;<br> S..2, which are the salinity-transformation fluxes;<br> T..., which are the cumulative heat fluxes; and<br> T..2, which are the temperature-transformation fluxes.</p> <p>-----------------------------------<br> In the file crfdata.nc, the variable names contain:<br> slrx: surface salinity restoring term<br> emp: evaporation-precipitation, small en neglected in the manuscript<br> rnf: river runoff<br> ice: ice melt<br> brnx: brine rejection including the penetration into subsurface layers<br> qns: nonsolar surface heat flux<br> qswx: heat flux due to shortwave radiation including the penetration into subsurface layers<br> fsiso/ftiso: isopycnal diffusion of salt/heat<br> fsdia/ftdia: diapycnal diffusion of salt/heat<br> sec: advection across the collective Arctic gateways</p> <p>Each variable is of size [4,12,nS] or [4,12,nT] where nS is the number of salinity bins, equal to the length of variable S<br> and nT is the number of temperature bins, equal to the length of variable T</p> <p>The first dimension is ordered as follows:<br> 0: delta_s, the equilibrium response to a 30% increase in total Arctic river runoff<br> 1: tau_s, the e-folding time scale of this response in months<br> 2: std, the standard deviation of the control value<br> 3: ctrl, the average control value</p> <p>The second dimension indicates the calendar month</p> <p>-----------------------------------------<br> In the file pp2.nc, the variable names contain:<br> slrx: surface salinity restoring term<br> rnf: river runoff<br> ice: ice melt<br> brnx: brine rejection including the penetration into subsurface layers<br> qns: nonsolar surface heat flux<br> qswx: heat flux due to shortwave radiation including the penetration into subsurface layers<br> adv: advection across the collective Arctic gateways<br> dif: total isopycnal + diapyncal diffusion</p> <p>Each variable is of size [2,nS] or [2,nT]</p> <p>The first dimension is:<br> 0: explained model variance between 0 and 1<br> 1: explained model variance where correlations with p>.05 equal NaN</p>
Trace element composition of drinking water in Almaty
Open the record for dataset details and reuse information.
Soil trace metals: Effects of Various Nutrients and Water on Vegetation
This experiment was conducted within the fenced areas of fields A, B, C, and D. The purpose of this experiment was to determine the effect of various nutrients on vegetation. There were eight different nutrients and a control. The different nutrients were N, P, K, Ca, Mg, Na, H2O, and a combination of trace metals. There were 36 plots in each field, 4 replicates of the 9 treatment levels. The plots were 1 meter by 4 meters and were laid out in a 4 by 9 grid. The grid was divided into 4 quarters and treatments were assigned with a randomized block design.
Data in support of manuscript submitted to JGR-Planets "Strong variability of Martian water ice clouds during dust storms revealed from ExoMars Trace Gas Orbiter/NOMAD"
<p>These files contain data underlying figures for the submission version of:</p> <p>Strong variability of Martian water ice clouds during dust storms revealed from ExoMars Trace Gas Orbiter/NOMAD</p> <p>which was submitted to JGR: Planets for review. </p> <p>The access to the dataset will be closed until the final version of the paper is accepted.</p>
Smart temperature tracing using heat and cold water in India - VIDEO
<p>With following the enclosed link to YouTube, you are invited to watch a video about smart temperature tracing with hot and cold water injections in India:</p> <p><a href="https://www.youtube.com/watch?v=cx6s4cGj1sc">https://www.youtube.com/watch?v=cx6s4cGj1sc</a></p>
222Rn tracing groundwater–lake water exchange in the transitional lake
<p>There are three tables including continuous monitoring data, profile radon activity data and sediments experiment data. The continuous monitoring table includes the time of continuous monitoring, radon in the profile, lake water temperature, and wind speed. Lake water volume, sediment mass, overlying water radon activity, wet density, porosity, pore water radon activity, and radon released per unit volume of sediment are listed in the sediment experimental data table.</p>
Data from: First assessments of trace metal fluxes from the Pacific to the Arctic - high resolution 2021 summer measurements show surprisingly high influence of the Alaskan Coastal Water
<p>Trace metals (manganese, iron, nickel, copper, zinc, and cadmium) are essential micronutrients for phytoplankton and can be used as tracers of oceanic processes. The supply of trace metals to the Western Arctic was thought to be dominated by macronutrient-rich Pacific waters entering through the Bering Strait and modified by uptake and regeneration on the Chukchi Shelf. However, the first high resolution (~6km) trace metal measurements in the strait (July 2021) show large variability in trace metal concentrations across the strait and a close relationship with salinity. The previously unsampled Alaskan Coastal Water has unexpectedly high trace metal concentrations, while the macronutrient-rich Anadyr Water has surprisingly low trace metal concentrations. We make the first estimates of trace metal flux from the Pacific to the Arctic through the Bering Strait and find they are elevated despite the comparatively small volume transport and, for some metals, exceed the Arctic to Atlantic export.</p>
Data from: First assessments of trace metal fluxes from the Pacific to the Arctic - high resolution 2021 summer measurements show surprisingly high influence of the Alaskan Coastal Water
Open the record for dataset details and reuse information.
Water Soluble Vitamins and Trace Elements Loss in Hemodiafiltration Patients
ClinicalTrials.gov study NCT05099185. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.