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6,741 results for “rates”
Exemple of a valorization network based on a 10% collection rate from potential biowaste in the Grand Lyon territory (France).
<p>This study, conducted in the frame of the H2020 DECISIVE project, aims at developing a method to design a decentralized and small-scale AD (mAD) network in urban and peri-urban areas. A mixed integer linear program (MILP) was set up based on the proposed system. It aims at minimizing the impacts of the biowaste and the digestate transportation by minimizing their payload-distances while taking into account notably the technical constraints of the newly developed micro-AD. A Geographic Information System (GIS) based methodology was developed to feed the MILP model with very fine-scale data required to optimized a proximity treatment system. The method allows to locate and estimate the biowaste generation and to locate the digestate outlets, the agricultural areas, and to estimate the maximal amount of digestate usable. The candidate sites for mAD are identified with a GIS multi-criteria analysis that includes the environmental regulations, some urban planning rules and the site accessibility and heat outlet valorization. The method developed is successfully applied in the territory of The Grand Lyon Metropole (534 km²), located in France. The MILP model succeeds at providing a solution even with the very large problem studied (≥10<sup>8</sup> possible combination). Different scenarios can be easily tested to meet the potential needs of the stakeholder: the quantity of biowaste to treat, the type of sources to target, the synergy with the current treatment solution, etc.</p> <p>The data are provided through the open, non-proprietary GeoPackage files (GPKG) commonly recognized in GIS tools. A complementary xml file describe the metadata in compliance with the Inspire directive. A complementary pdf file describe the fields of the datasets.</p> <p> </p>
Cisplatin enhances cell stiffness and decreases invasiveness rate in prostate cancer cells by actin accumulation: Confocal and atomic force microscopy
<p><strong>Summary</strong></p> <p>Dataset of imaging data related to the publication Raudenska, M., Kratochvilova, M., Vicar, T., Gumulec, J., Balvan, J., Polanska, H. Pribyl, J. & Masarik, M.:Cisplatin enhances cell stiffness and decreases invasiveness rate in prostate cancer cells by actin accumulation. <em>Scientific Reports </em><strong>2019, </strong>9, 1660</p> <p>This dataset includes image data of <em>atomic force microcopy</em> (Young modulus) and <em>confocal microscopy</em>(staining of F-actin and β-tubulin) of prostate cell lines PNT1A, 22Rv1, and PC-3. </p> <p><strong>Materials and Methods</strong></p> <p><em>Cells, cell culture conditions</em></p> <p>Cells confluent up to 50–60% were washed with a FBS-free medium and treated with a fresh medium with FBS and required antineoplastic drug concentration (IC50 concentration for the particular cell line). The cells were treated with 93 µM (PC-3), 38 µM (PNT1A), and 24 µM (22Rv1) of cisplatin (Sigma-Aldrich, St. Louis, Missouri), respectively. IC50 concentrations used for treatment with docetaxel (Sigma-Aldrich, St. Louis, Missouri) were 200nM for PC-3, 70nM for PNT1A, and 150nM for 22Rv1. </p> <p><em>Long-term zinc (II) treatment of cell cultures</em></p> <p>Cells were cultivated in the constant presence of zinc(II) ions. Concentrations of zinc(II) sulphate in the medium were increased gradually by small changes of 25 or 50 µM. The cells were cultivated at each concentration no less than one week before harvesting and their viability was checked before adding more zinc. This process was used to select zinc resistant cells naturally and to ensure better accumulation of zinc within the cells (accumulation of zinc is usually poor during the short-term treatment of prostate cancer cells). Total time of the cultivation of cell lines in the zinc(II)-containing media exceeded one year. Resulting concentrations of zinc(II) in the media (IC50 for the particular cell line) were 50 µM for the PC-3 cell line, 150 µM for the PNT1A cell line, and 400 µM for the 22Rv1 cell line. The concentrations of zinc(II) in the media and FBS were taken into account. </p> <p><em>Actin and tubulin staining</em></p> <p>β-tubulin was labeled with anti- β tubulin antibody [EPR1330] (ab108342) at a working dilution of 1/300. The secondary antibody used was Alexa Fluor® 555 donkey anti-rabbit (ab150074) at a dilution of 1/1000. Actin was labeled with Alexa Fluor™ 488 Phalloidin (A12379, Invitrogen); 1 unit per slide. For mounting Duolink® In Situ Mounting Medium with DAPI (DUO82040) was used. The cells were fixed in 3.7% paraformaldehyde and permeabilized using 0.1% Triton X-100. </p> <p><em>Confocal microscopy</em></p> <p>The microscopy of samples was performed at the Institute of Biophysics, Czech Academy of Sciences, Brno, Czech Republic. Leica DM RXA microscope (equipped with DMSTC motorized stage, Piezzo z-movement, MicroMax CCD camera, CSU-10 confocal unit and 488, 562, and 714 nm laser diodes with AOTF) was used for acquiring detailed cell images (100× oil immersion Plan Fluotar lens, NA 1.3). Total 50 Z slices was captured with Z step size 0.3 μm.</p> <p><em>Atomic force microscopy</em></p> <p>We used the bioAFM microscope JPK NanoWizard 3 (JPK, Berlin, Germany) placed on the inverted optical microscope Olympus IX‑81 (Olympus, Tokyo, Japan) equipped with the fluorescence and confocal module, thus allowing a combined experiment (AFM‑optical combined images). The maximal scanning range of the AFM microscope in X‑Y‑Z range was 100‑100‑15 µm. The typical approach/retract settings were identical with a 15 μm extend/retract length, Setpoint value of 1 nN, a pixel rate of 2048 Hz and a speed of 30 µm/s. The system operated under closed-loop control. After reaching the selected contact force, the cantilever was retracted. The retraction length of 15 μm was sufficient to overcome any adhesion between the tip and the sample and to make sure that the cantilever had been completely retracted from the sample surface. Force‑distance (FD) curve was recorded at each point of the cantilever approach/retract movement. AFM measurements were obtained at 37°C (Petri dish heater, JPK) with force measurements recorded at a pulling speed of 30 µm/s (extension time 0.5 sec).</p> <p>The Young's modulus (E) was calculated by fitting the Hertzian‑Sneddon model on the FD curves measured as force maps (64x64 points) of the region containing either a single cell or multiple cells. JPK data evaluation software was used for the batch processing of measured data. The adjustment of the cantilever position above the sample was carried out under the microscope by controlling the position of the AFM‑head by motorized stage equipped with Petri dish heater (JPK) allowing precise positioning of the sample together with a constant elevated temperature of the sample for the whole period of the experiment. Soft uncoated AFM probes HYDRA-2R-100N (Applied NanoStructures, Mountain View, CA, USA), i.e. silicon nitride cantilevers with silicon tips are used for stiffness studies because they are maximally gentle to living cells (not causing mechanical stimulation). Moreover, as compared with coated cantilevers, these probes are very stable under elevated temperatures in liquids – thus allowing long-time measurements without nonspecific changes in the measured signal.</p> <p><em>Image analysis</em></p> <p>Fluorescence microscopy data were analyzed in ImageJ 1.52h and Python 3.7.1 as follows: cells were manually segmented using actin fluorescence channel, two regions were created for analysis: whole cell and cell periphery, lining a 4 μm thick region around cell border and including most of periphery actin cytoskeleton. In these two regions following parameters were measured for both actin and tubulin fluorescence: Integrated intensity, median intensity, and following regions were measured to describe cell morphology: Cell area, Maximum caliper (max feret diameter), roundness, and aspect ratio. Moreover, stress fibers were manually segmented in every cell and following parameters were measured: number of fibers per cell, feret angle of fiber, integrated intensity, fiber length, mean intensity. Next, a standard deviation of feret angles of individual fibers was calculated relatively to mean of feret angle using a circstd function from scipy package for Python.</p> <p><strong>Identification of files</strong></p> <p><em>Microscopy data</em></p> <p>Files are separated into individual zip files. The dataset of <em>confocal microscopy </em>is separated based on treatments: untreated control, docetaxel-treated cells, cisplatin-treated cells, zinc-treated cells. Filenames actin_tubulin_Zstack_cisplatin.zip, actin_tubulin_Zstack_untreated_control.zip, actin_tubulin_Zstack_zinc.zip, actin_tubulin_Zstack_docetaxel.zip. Files included in these ZIP archives are named as follows: "cellline_treatment_FOV". Files are 3-layer 16bit tiff files with layer sequence as follows: F-Actin (Phalloidin)/b-tubulin/Hoechst 33342. The dataset contains 242 FOVs of three cell line types/three treatments + one control, files are Z-stacks made of 50 slices.</p> <p>The dataset of <em>atomic force microscopy </em>(AFM) is included in one ZIP archive "AFM_YoungModulus_SetpointHeight.zip", which includes data on Young modulus and Setpoint Height of cell lines 22Rv1, PNT1A and PC-3 and treatments zinc, docetaxel, cisplatin (+control), i.e. identical like for confocal microscopy. The file naming is as follows: "AFM_cellline_treatment_FOV_Youngmodulus.tif" for Young modulus and "AFM_cellline_treatment_FOV_setpointheight.tif" for setpoint height. The data are filtered 32-bit tiff images, where the pixel value correspond to cell stiffness (young modulus) in Pa or setpoint height in m.</p> <p><em>Confocal microscopy analysis files</em></p> <p>Following files are csv tables including image analysis of actin/tubulin staining captured by confocal microscope:</p> <p>Cytoskeleton_fluo_analysis_Cell_Cell_periphery_morphology.csv: table includes analyzed data for actin and tubulin staining in following cellular regions: cell, cell periphery. Standard ImageJ parameters regarding intensity and morphology included.</p> <p>Cytoskeleton_fluo_analysis_Fibers.csv: table includes results of manual segmentation and consequent analysis of actin stres fibers in the cells. Apart from standard ImageJ parameters, also number of stress fibers per cell and standard deviation of fiber angle relative to the cell mean angle (for details see methods) are included.</p>
Dataset _ Influence of the seasonal variation of environmental conditions on biogas upgrading in an outdoors pilot scale high rate algal pond
<p>This is the dataset used for the publication of the journal article title<em> “</em><strong>Influence of the seasonal variation of environmental conditions on biogas upgrading in an outdoors pilot scale high rate algal pond”. </strong>In this dataset there is all the information collected in the experimentation process.</p>
Bedrock radioactivity influences the rate and spectrum of mutation - Orthologous genes
<p>Alignments of the 2490 orthologous genes used in the article "Natural Bedrock radioactivity influences the rate and spectrum of mutation" to estimate the mutational spectrum and synonymous substitution rate.</p> <p>To compute accurate synonymous substitution rate, we removed genes with short sequences (<half of the alignment) and genes strongly supporting another phylogeny using ProfileNJ <a href="https://paperpile.com/c/Klqlpb/W5sS">(Noutahi et al. 2016)</a> with a bootstrap threshold of 90%, resulting in a subset of 769 genes listed in the file "List_769_1-to-1_orthologs_EvolutionRate.txt".</p> <p>Transcriptome paired-end reads used to define these orthologous genes have been deposited to the European Nucleotide Archive and are available under the study ID PRJEB14193.</p> <p>Sequences were aligned with Prank<a href="https://paperpile.com/c/Klqlpb/pilh"> (Löytynoja & Goldman 2008)</a> using a codon model and sites ambiguously aligned were removed with Gblocks <a href="https://paperpile.com/c/Klqlpb/c5kb">(Castresana 2000)</a>.</p>
Mars Odyssey Neutron Spectrometer Time Series of Corrected Cateogry 1 Counting Rates, 2002-2017
<p>Ubinned time series of derived neutron data from the Mars Odyssey Neutron Spectrometer (MONS) Category 1 data, from 200 - 2017.</p>
Data in New $^{63}$Ga(p,$\gamma$)$^{64}$Ge and $^{64}$Ge(p,$\gamma$)$^{65}$As reaction rates corresponding to the temperature regime of thermonuclear X-ray bursts
<p>Abstract quoted from <a href="https://doi.org/10.1103/PhysRevC.110.065804" target="_blank" rel="noopener">Physical Review C 110 (2024) 065804</a> [<a href="https://arxiv.org/abs/2406.14624">arXiv:2406.14624</a>] </p> <p>We compute the $^{63}$Ga(p,$\gamma$)$^{64}$Ge and $^{64}$Ge(p,$\gamma$)$^{65}$As thermonuclear reaction rates using the latest experimental input supplemented with theoretical nuclear spectroscopic information. The experimental input consists of the latest proton thresholds of $^{64}$Ge and $^{65}$As, and the nuclear spectroscopic information of $^{65}$As, whereas the theoretical nuclear spectroscopic information for $^{64}$Ge and $^{65}$As are deduced from the full <em>pf</em>-shell space configuration-interaction shell-model calculations with the GXPF1A Hamiltonian. Both thermonuclear reaction rates are determined with known uncertainties at the energies that correspond to the Gamow windows of the temperature regime relevant to type I x-ray bursts, covering the typical temperature range of the thermonuclear runaway of the GS 1826$-$24 periodic bursts and SAX J1808.4$-$3658 photospheric radius expansion bursts. </p>
Dataset: Environmental Gamma Dose Rate Measurements using CZT Detectors
<h1>Scope</h1> <p>This dataset compiles the raw and partely processed data for the manuscript <em>Environmental Gamma Dose Rate Measurements using CZT</em><br><em>Detectors </em>by Sebastian Kreutzer, Loïc Martin, Didier Miallier, and Norbert Mercier. </p> <h1>Dataset structure</h1> <ul> <li>00_Measurement_Data: All original spectra recorded with a Kromek GR1+ and Kromek RayMon10 GR1 detector. All files have the file ending .spe</li> <li>10_GEANT4 modelling results as .xlsx and .ods + simulation code in ZIP file</li> <li>20_System_Calibrations: The final detector calibration results as .rda (external representation of R objects)</li> <li>30_R_Scripts: A compact version of R scripts used to derive the results in the manuscript as HTML and Quatro file</li> <li>40_RadionuclideComposition_WH2024: Raw data from the radionuclide measurements of sample WH2024 (.ud, .pdf)</li> <li>60_RadionuclideComposition_Flossi: Radionuclide concentrations of the granute block Flossi extracted from an unpublished Diplom thesis by Uwe Rieser (1991)</li> <li>70_Heidelberg_Dataset4gamma: Dataset for the R package 'gamma' with nuclide concentration results for Weiße-Hohl, Flossi and calculated dose rates </li> <li>80_Strain_Relief_3D_printing: Construction files for printing the strain relief adapter for the GR1. Please note that all files in this subfolder are subject to CC BY-NC licence conditions, excluding commercial use. </li> </ul> <p> </p>
Ensemble of ice shelf basal melt rates and ocean properties for tipped-over continental shelves
<p><strong>Summary</strong><strong>:</strong></p> <p>This dataset contains the reference and tipped states from several model configurations developed at the <a href="https://www.awi.de/en/">Alfred Wegener Institute (AWI)</a> and the <a href="https://www.ige-grenoble.fr/?lang=en">Institut des Géosciences de l’Environnement (IGE)</a>. They were gathered here in the context of the <a href="https://www.tipaccs.eu">TiPACCs European project</a> and constitute a useful ensemble of reference and tipped ocean–ice-shelf simulations that <strong>can be used to feed ice-sheet simulations or to train melt parameterizations</strong>.</p> <p>The simulations produced by AWI are based on the <a href="https://fesom.de">FESOM</a> global ocean–sea-ice model using either Z- or Sigma- coordinates and all show a cold-to-warm tipping point for Filchner-Ronne Ice Shelf. The two sets of simulations produced by IGE are based on the <a href="https://www.nemo-ocean.eu">NEMO</a> ocean–sea-ice model. They include a global configuration showing a cold-to-warm tipping point for Ross Ice Shelf, and regional Amundsen Sea configuration showing a warm-to-warmer transition (likely not a proper tipping point). </p> <p>The files include 3-dimensional and sea-floor ocean temperatures and salinities, ice-shelf melt rates, as well as topographic and grid data. All variables are interpolated onto the common 8km stereographic grid that was used to provide ocean forcing in ISMIP6 (<a href="https://doi.org/10.5194/tc-14-2331-2020">Nowicki et al. 2020</a>).</p> <p>We provide the reference state and the anomaly, so that the tipped state is:</p> <ul> <li><em>Tipped = Reference + Anomaly</em></li> </ul> <p>To have an overview of the reference and tipped states, have a look at these figures:</p> <ul> <li><em>figure_ref_and_anomalies_1.pdf</em></li> <li> <p><em>figure_ref_and_anomalies_2.pdf</em></p> </li> <li> <p><em>figure_seafloor_temp_zooms.pdf</em></p> </li> </ul> <p> </p> <p>_______________________________________________</p> <p><strong>Detailed Data Description</strong><strong>:</strong></p> <p> </p> <ul> <li><strong>reference_high_FESOM_sigma_AWI_TiPACCs.nc</strong> <ul> <li>contact: Ralph Timmermann <a href="mailto:ralph.timmermann@awi.de">ralph.timmermann@awi.de</a>, Verena Haid <a href="mailto:verena.haid@awi.de">verena.haid@awi.de</a></li> <li>model: FESOM1.4, sigma-coordinates (global with refined grid around Antarctica)</li> <li>atmospheric forcing: HadCM3 20C</li> <li>provided average: 1990-1999 (10-year mean)</li> <li>more: <a href="https://doi.org/10.1007/s10236-013-0642-0">Timmermann and Hellmer (2013)</a></li> </ul> </li> </ul> <p> </p> <ul> <li><strong>reference_low_FESOM_sigma_AWI_TiPACCs.nc</strong> <ul> <li>contact: Ralph Timmermann <a href="mailto:ralph.timmermann@awi.de">ralph.timmermann@awi.de</a>, Verena Haid <a href="mailto:verena.haid@awi.de">verena.haid@awi.de</a></li> <li>model: FESOM1.4, sigma-coordinates (global with refined grid around Antarctica)</li> <li>atmospheric forcing: HadCM3 20C</li> <li>provided average: 1990-1999 (10-year mean)</li> <li>more: <a href="https://doi.org/10.5194/os-13-765-2017">Timmermann and Goeller (2017)</a></li> </ul> </li> </ul> <p> </p> <ul> <li><strong>reference_FESOM_z_AWI_TiPACCs.nc</strong> <ul> <li>contact: Verena Haid <a href="mailto:verena.haid@awi.de">verena.haid@awi.de</a></li> <li>model: FESOM1.4, Z-coordinates (global with refined grid around Antarctica)</li> <li>atmospheric forcing: ERA Interim</li> <li>provided average: 2008-2017 (10-year mean), i.e. model year 30-39</li> <li>more: same mesh as <a href="https://doi.org/10.5194/tc-13-2317-2019">Gürses et al. (2019)</a></li> </ul> </li> </ul> <p> </p> <ul> <li><strong>reference_NEMO4_eORCA025.L121_IGE_TiPACCs.nc</strong> <ul> <li>contact: Pierre Mathiot <a href="mailto:pierre.mathiot@univ-grenoble-alpes.fr">pierre.mathiot@univ-grenoble-alpes.fr</a></li> <li>model: NEMO-4.0, eORCA025.L121 (Global, 1/4°, 121 vertical levels)</li> <li>atmospheric forcing: JRA55do</li> <li>provided average: 2<sup>nd</sup> cycle of 1989-1998 (10-year mean); we first run 1979-2018, and we redo 1979-1998 starting from the 2018 state.</li> <li>more: <a href="https://pmathiot.github.io/NEMOCFG/docs/build/html/simu_eORCA025_OPM021.html">https://pmathiot.github.io/NEMOCFG/docs/build/html/simu_eORCA025_OPM021.html</a></li> </ul> </li> </ul> <p> </p> <ul> <li><strong>reference_NEMO3_AMUXL12.L75_IGE_TiPACCs.nc</strong> <ul> <li>contact: Nicolas Jourdain <a href="mailto:nicolas.jourdain@univ-grenoble-alpes.fr">nicolas.jourdain@univ-grenoble-alpes.fr</a></li> <li>model: NEMO-3.6, AMUXL12.L75 (Amundsen, 1/12°, 75 vertical levels)</li> <li>atmospheric forcing: MAR (<a href="https://doi.org/10.5194/tc-14-229-2020">Donat-Magnin et al. 2020</a>)</li> <li>provided average: 1989-2009 (21-year mean)</li> <li>more: similar model set-up as <a href="https://doi.org/10.1016/j.ocemod.2018.11.001">Jourdain et al. (2019)</a>.</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>anomaly_high_FESOM_sigma_AWI_TiPACCs.nc</strong> <ul> <li>continuation of reference_high_FESOM_sigma_AWI_TiPACCs.nc</li> <li>forced with HadCM3 A1B</li> <li>provided average: 2190-2199 (10-year mean)</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>anomaly_low_FESOM_sigma_AWI_TiPACCs.nc</strong> <ul> <li>continuation of reference_low_FESOM_sigma_AWI_TiPACCs.nc</li> <li>forced with HadCM3 A1B</li> <li>provided average: 2190-2199 (10-year mean)</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>anomaly_high_FESOM_z_AWI_TiPACCs.nc</strong> <ul> <li>same model set-up as reference_FESOM_z_AWI_TiPACCs.nc</li> <li>atmospheric forcing south of 60°S HadCM3 A1B starting 2050, otherwise ERA Interim starting 1979</li> <li>provided average: model year 69-78 (10-year mean), i.e. 2008-2017 of 2<sup>nd</sup> 39yr-cycle</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>anomaly_medium_FESOM_z_AWI_TiPACCs.nc</strong> <ul> <li>same model set-up as reference_FESOM_z_AWI_TiPACCs.nc</li> <li>atmospheric forcing: ERA Interim modified with a strong imprint of the seasonal cycle of HadCM3 A1B 2070-2089</li> <li>provided average: model year 69-78 (10-year mean), i.e. 2008-2017 of 2<sup>nd</sup> 39yr-cycle</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>anomaly_low_FESOM_z_AWI_TiPACCs.nc</strong> <ul> <li>same model set-up as reference_FESOM_z_AWI_TiPACCs.nc</li> <li>atmospheric forcing: manipulated ERA Interim with prolongued summer and shorter, milder winter south of 50°S, additional modification of winds in Weddell Sea region</li> <li>provided average: model year 108-117 (10-year mean), i.e. 2008-2017 of 3<sup>rd</sup> 39yr-cycle</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>anomaly_NEMO4_eORCA025.L121_IGE_TiPACCs.nc</strong> <ul> <li>similar to reference_NEMO4_eORCA025.L121_IGE_TiPACCs.nc</li> <li>perturbation of the model parameters: Different iceberg distribution and different sea-ice–ocean drag and snow conductivity on sea-ice, leading to less sea-ice production in the eastern Ross Sea.</li> <li>More: <a href="https://pmathiot.github.io/NEMOCFG/docs/build/html/simu_eORCA025_OPM020.html">https://pmathiot.github.io/NEMOCFG/docs/build/html/simu_eORCA025_OPM020.html</a></li> </ul> </li> </ul> <p> </p> <ul> <li><strong>anomaly_NEMO3_AMUXL12.L75_IGE_TiPACCs.nc</strong> <ul> <li>similar to reference_NEMO3_AMUXL12.L75_IGE_TiPACCs.nc</li> <li>perturbation of atmospheric forcing: MAR forced by the CMIP5 multi-model anomaly under the RCP8.5 scenario (<a href="https://doi.org/10.5194/tc-15-571-2021">Donat-Magnin et al. 2021</a>).</li> <li>provided average: 2080-2100 (21-year average)</li> </ul> </li> </ul> <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>
The Data Related to Interfacial Shift Keying Allows a High Information Rate in Molecular Communication
<p>This dataset is related to a method for molecular communication in fluids described on "Fluorescent nanoparticles for reliable communication among implantable medical devices," Carbon, vol. 190, pp. 262-275, Apr. 2022, by Federico Calì, Luca Fichera, Giuseppe Trusso Sfrazzetto, Giuseppe Nicotra, Gianfranco Sfuncia, Elena Bruno, Luca Lanzanò, Ignazio Barbagallo, Giovanni Li-Destri, Nunzio Tuccitto; doi: 10.1016/J.CARBON.2022.01.016. <br> The dataset is linked to the manuscript entitled "Interfacial Shift Keying Allows a High Information Rate in Molecular Communication: Methods and Data" by F. Calì, G. Li-Destri, and N. Tuccitto submitted to IEEE Transactions on Molecular, Biological, and Multi-Scale Communications (T-MBMC).<br> The data, including elapsed time (s), starting from the injection and fluorescence intensity (a.u.), is given in tab-separated values format as .txt files. When present, a column includes the intensity subtracted for the baseline and the subtracted and normalized intensity. In all cases, the baseline was obtained by performing a linear fit between 10 and 110 s and subtracting the line obtained from the entire dataset.<br> </p>
Base rates of food safety practices in European households: Summary data from the SafeConsume Household Survey
<p>This data set contains estimates of the base rates of 550 food safety-relevant food handling practices in European households. The data are representative for the population of private households in the ten European countries in which the SafeConsume Household Survey was conducted (Denmark, France, Germany, Greece, Hungary, Norway, Portugal, Romania, Spain, UK).</p> <p><em>Sampling design</em></p> <p>In each of the ten EU and EEA countries where the survey was conducted (Denmark, France, Germany, Greece, Hungary, Norway, Portugal, Romania, Spain, UK), the population under study was defined as the private households in the country. Sampling was based on a stratified random design, with the NUTS2 statistical regions of Europe and the education level of the target respondent as stratum variables. The target sample size was 1000 households per country, with selection probability within each country proportional to stratum size.</p> <p><em>Fieldwork</em></p> <p>The fieldwork was conducted between December 2018 and April 2019 in ten EU and EEA countries (Denmark, France, Germany, Greece, Hungary, Norway, Portugal, Romania, Spain, United Kingdom). The target respondent in each household was the person with main or shared responsibility for food shopping in the household. The fieldwork was sub-contracted to a professional research provider (Dynata, formerly Research Now SSI). Complete responses were obtained from altogether 9996 households.</p> <p><em>Weights</em></p> <p>In addition to the SafeConsume Household Survey data, population data from Eurostat (2019) were used to calculate weights. These were calculated with NUTS2 region as the stratification variable and assigned an influence to each observation in each stratum that was proportional to how many households in the population stratum a household in the sample stratum represented. The weights were used in the estimation of all base rates included in the data set.</p> <p><em>Transformations</em></p> <p>All survey variables were normalised to the [0,1] range before the analysis. Responses to food frequency questions were transformed into the proportion of all meals consumed during a year where the meal contained the respective food item. Responses to questions with 11-point Juster probability scales as the response format were transformed into numerical probabilities. Responses to questions with time (hours, days, weeks) or temperature (C) as response formats were discretised using supervised binning. The thresholds best separating between the bins were chosen on the basis of five-fold cross-validated decision trees. The binned versions of these variables, and all other input variables with multiple categorical response options (either with a check-all-that-apply or forced-choice response format) were transformed into sets of binary features, with a value 1 assigned if the respective response option had been checked, 0 otherwise.</p> <p><em>Treatment of missing values</em></p> <p>In many cases, a missing value on a feature logically implies that the respective data point should have a value of zero. If, for example, a participant in the SafeConsume Household Survey had indicated that a particular food was not consumed in their household, the participant was not presented with any other questions related to that food, which automatically results in missing values on all features representing the responses to the skipped questions. However, zero consumption would also imply a zero probability that the respective food is consumed undercooked. In such cases, missing values were replaced with a value of 0.</p>
Novel estimates of the leaf relative uptake rate of carbonyl sulfide from optimality theory
<p>Data and Matlab scripts for repeating the analysis presented in the paper. In addition, global monthly climatological LRUs are provided at 0.05° resolution for the period 2001-2010 as nc-files. </p>
The effect of solvent on convectively-driven silica particle assembly: Decoupling surface tension,viscosity, and evaporation rate
<p>Dataset associated with 'The effect of solvent on convectively-driven silica particle assembly: Decoupling surface tension, viscosity, and evaporation rate’.</p> <p>The data is based on the figures below, published in the linked article (see the doi).</p> <p><strong>- Figure 1. S</strong>egmented and raw images of dip-coated films. <strong>(images, .TIF)</strong></p> <p><strong>- Figure 2. </strong>Calculated surface coverages <strong>(data, .csv)</strong></p> <p><strong>- Figure 3. </strong>Rheology on SiO<sub>2</sub>-iPrOH-Glycerol mixtures & SEM micrographs of particle films. <strong>(data, .csv; images, .TIF)</strong></p> <p><strong>- Figure 4. </strong>SEM micrographs of silica helices films. <strong>(images, .TIF)</strong></p> <p><strong>- Figure S1. </strong>Measured evaporated masses of each solvent as a function of time. <strong>(data, .csv)</strong></p> <p><strong>- Figure S2. </strong>TEM micrographs of SiO2 seeds and measured particle diameters. <strong>(data, .csv; images, .TIF)</strong></p> <p><strong>- Figure S3. </strong>TEM micrographs of SiO particles and measured particle diameters. <strong>(data, .csv; images, .TIF)</strong></p> <p><strong>- Figure S4. </strong>Calculated solvent fractions as a function of time. <strong>(data, .csv)</strong></p> <p><strong>- Figure S5.</strong> Rheology of i-PrOH-glycerol mixtures.<strong> (data, .csv)</strong></p>
Moita-Montijo Bay saltmarsh and tidal flat change rates (1958-2018)
<p>Saltmarsh and tidal flat change rates were determined for the period of 1958 to 2018, for the Moita-Montijo Bay (Tagus Estuary, Portugal). Rates were determined only for the exposed saltmarsh. No rates were calculated for sheltered saltmarsh (saltmarsh inside abandoned saltpans and similar structures). Saltmarsh margins were delineated in ArcMap, using, as the carthographic basis, orthorectified aerial photgraphs from 1958 and orthophotomaps from 1995 and 2018 (from Direção Geral do Território (DGT)).</p> <p>Tidal flat change rates were determined for the period of 1977 to 2018, in the north and south banks of the Montijo Channel (Moita-Montijo Bay, Tagus Estuary, Portugal). Rates were determined only for the Montijo Channel margins. The tidal flat margins were delineated in ArcMap, using, as the carthographic basis, orthorectified aerial photgraphs from 1977 and orthophotomaps from 1995 and 2018 (from DGT).</p> <p>The Digital Shoreline Analysis System (DSAS) in ArcMap was used to calculate the End Point Rates (EPR) between the margins (See Himmelstoss et al. (2018) for details on how to the EPR is calculated using DSAS).</p> <p>This dataset was produced in the scope of the Project Planta II*. Further details can be consulted in Martins et al. (2023).</p> <p> </p> <p>Dataset:</p> <ul> <li>Saltmarsh change rates in the Moita-Montijo Bay (1958 to 1995) (Tagus Estuary, Portugal) = rates_smarsh_moitamontijobay_1958_1995.shp</li> <li>Saltmarsh change rates in the Moita-Montijo Bay (1995 to 2018) (Tagus Estuary, Portugal) = rates_smarsh_moitamontijobay_1995_2018.shp</li> <li>Tidal flat change rates in the north bank of the Montijo Channel (1977 to 1995) (Moita-Montijo Bay, Tagus Estuary, Portugal) = rates_tf_nb_moitamontijobay_1977_1995.shp</li> <li>Tidal flat change rates in the north bank of the Montijo Channel (1995 to 2018) (Moita-Montijo Bay, Tagus Estuary, Portugal) = rates_tf_nb_moitamontijobay_1995_2018.shp</li> <li>Tidal flat change rates in the south bank of the Montijo Channel (1977 to 1995) (Moita-Montijo Bay, Tagus Estuary, Portugal) = rates_tf_sb_moitamontijobay_1977_1995.shp</li> <li>Tidal flat change rates in the south bank of the Montijo Channel (1995 to 2018) (Moita-Montijo Bay, Tagus Estuary, Portugal) = rates_tf_sb_moitamontijobay_1995_2018.shp</li> </ul> <p>Please cite the paper where this dataset is discussed as Martins, D., Alves da Silva, A., Duarte, J., Canário, J., & Vieira, G. (2023). Changes in vessel traffic disrupt tidal flats and saltmarshes in the Tagus Estuary, Portugal. <em>Estuaries and Coasts</em>. DOI: <a href="https://doi.org/10.1007/s12237-023-01198-7">https://doi.org/10.1007/s12237-023-01198-7</a></p> <p>*Project Planta II - PTDC/CTA-GQU/31208/2017, co-funded by the European Regional Development Fund (ERDF), through the financing Regional Operational Program of Lisbon, and by the Portuguese Science and Technology Foundation (FCT), through the national funds (PIDDAC).</p>
Trajectory-Aware Rate Adaptation for Aerial Networks Simulation Results
<p><strong>Introduction</strong></p> <p>Even though the concept of ubiquitous wireless connectivity is becoming a reality, there are scenarios where wireless communications coverage is insufficient or does not exist. Considering natural and man-made disaster scenarios, communications infrastructures may be damaged and become unavailable. In temporary crowded events, the existing infrastructure may not have been designed to cope with the additional traffic demand, resulting in overload. In maritime scenarios, environmental monitoring activities using autonomous vehicles will take place in offshore zones, typically not in range of existing onshore communications infrastructures.</p> <p>Flying networks, composed of Unmanned Aerial Vehicles (UAV), are emerging as a flexible and cost-effective solution to provide on-demand wireless connectivity in such scenarios. UAVs have the possibility to operate virtually everywhere, and the growing payload capacity makes them suitable platforms to carry wireless communications hardware, playing the role of mobile base stations, access points or relay nodes. A flying network may typically be composed of a fleet of UAVs, organized in a multi-tier topology with so-called Flying Edge Nodes (FENs) and Flying Gateways (FGWs) <a href="https://doi.org/10.1016/j.adhoc.2022.103000">[1]</a>. FENs can play the role of Flying Access Points that provide the access network to the users on the ground, or the role of Flying Sensor Nodes that can perform video surveillance missions. The FENs forward the traffic to the FGWs, that act as relay nodes and are responsible for forwarding the traffic to/from the backhaul (BKH) network and ultimately to/from the Internet.</p> <p>The flying network concept brings up new challenges. The flying nodes need to be properly positioned and their wireless link configuration dynamically adjusted in order to ensure the Quality of Service (QoS) expected by the end users. In addition, these scenarios are typically highly unpredictable due to the varying locations as well as the concentration/dispersion of end-users and their movements regarding direction and velocity - e.g., vehicles or pedestrians. Therefore, a static wireless link configuration and UAV positioning are not adequate. State of the art work has been mainly focused on the optimal positioning of the flying nodes, having most of the wireless link parameters statically configured with default values. The Rate Adaptation challenge is well-known in fixed or low mobility IEEE 802.11 networks, and Minstrel High Throughput (HT) <a href="https://lwn.net/Articles/376765">[2]</a> is the default Wi-Fi rate adaptation algorithm used in the Linux kernel since the IEEE 802.11n version. However, few works propose solutions designed to consider the characteristics of other communications environments, such as flying and vehicular networks <a href="https://doi.org/10.1007/s11276-020-02295-2">[3]</a>. To the best of our knowledge, solutions that use the node trajectory information to predict the wireless channel conditions and perform rate adaptation are yet to be developed.</p> <p>The main contribution of this paper is the Trajectory-Aware Rate Adaptation (TARA) algorithm. TARA takes advantage of knowing the trajectory of all nodes in the flying network to estimate future changes in the wireless link quality and perform rate adaptation accordingly. The network performance improvement achieved with TARA was evaluated using ns-3 <a href="https://doi.org/10.1007/978-3-642-12331-3_2">[4]</a>. The simulation results presented in this dataset show significant throughput gains when compared with conventional rate adaptation algorithms.</p> <p><strong>Folder Organization</strong></p> <p>The following dataset presents the results of the TARA Paper, organized in different folders for each Rate Adaptation Algorithm, as well as the random seeds that were used to obtain such results:</p> <p><strong>Naming Convention:</strong></p> <ul> <li>Rate Adaptation Algorithm<strong> </strong> <ul> <li><strong>tara </strong>– Trajectory-Aware Rate Adaptation</li> <li><strong>min </strong>– MinstrelHTWifiManager</li> <li><strong>id </strong>– IdealWifiManager</li> </ul> </li> </ul> <p><strong>Folder Content: </strong></p> <ul> <li><em>distances.csv - </em><strong>Distances between nodes</strong> <ul> <li>Column 1 – <strong>Simulation Time </strong>(seconds)</li> <li>Column 2 – <strong>Distance between BKH and FGW</strong> (meters)</li> <li>Column 3 – <strong>Distance between FEN and FGW </strong>(meters)</li> </ul> </li> <li><em>positions.csv</em> <em>- </em><strong>Current 3D position of nodes</strong> <ul> <li>Column 1 – <strong>Simulation Time </strong>(seconds)</li> <li>Column 2 – <strong>BKH x </strong>(meters)</li> <li>Column 3 – <strong>BKH y </strong>(meters)</li> <li>Column 4 – <strong>BKH z </strong>(meters)</li> <li>Column 5 – <strong>FEN x </strong>(meters)</li> <li>Column 6 – <strong>FEN y </strong>(meters)</li> <li>Column 7 – <strong>FEN z </strong>(meters)</li> <li>Column 8 – <strong>FGW x </strong>(meters)</li> <li>Column 9 – <strong>FGW y </strong>(meters)</li> <li>Column 10 – <strong>FGW z </strong>(meters)</li> </ul> </li> <li><em>throughput.csv</em> - <strong>Link Specific Throughput, at MAC layer level</strong> <ul> <li>Column 1 – <strong>Simulation Time </strong>(seconds)</li> <li>Column 2 – <strong>Relay Link (BKH - FGW), Throughput measured in BKH </strong>(Mbit/second)</li> <li>Column 3 – <strong>Access Link (FEN - FGW), Throughput measured in FEN </strong>(Mbit/second)</li> <li>Column 4 – <strong>Relay Link (BKH - FGW), Throughput measured in FGW </strong>(Mbit/second)</li> <li>Column 5 – <strong>Access Link (FEN - FGW), Throughput measured in FGW </strong>(Mbit/second)</li> </ul> </li> </ul>
Preference ratings and 32 magnitude frequency response curves
<p>This repository comprises two CSV files: PreferenceRatings and MagnitudeFrequencyResponses. The former includes preference ratings obtained from 56 naive assessors (30 Danish---DK, 26 Japanese---JP) of 32 headphone curves over several music programs in several trials. The latter includes the magnitude frequency response curves evaluated by the assessors, expressed as gains of a 30-band graphic equalizer whose bands are centered between 31 Hz and 25 kHz. The curves were either derived from eight popular closed circumaural headphones, measured with a Brüel & Kjær Head and Torso Simulator 5128C, or otherwise obtained from the literature. The details of the methods, results, etc. are published in [1].</p> <p>[1] G. Ravizza, J. Villegas, T. Stegenborg-Andersen, and C. P. Volk, “An over-ear headphone target curve for Brüel & Kjær head and torso simulator type 5128 measurements,” in Proc. 155 Audio Eng. Soc. Conv., Oct 2023.</p> <p> </p>
Data publication supplementing "Novel nanoindentation strain rate sweep method for continuously investigating the strain rate sensitivity of materials at the nanoscale"
<p>This data publication contains the results of nanoindentation tests on Fused silica, nanocrystalline nickel, a nanocrystalline FeCr alloy, a bulk metallic glass, the superplastic alloy Zn-22%Al and single crystalline aluminum as well as the method files developed for the G200 nanoindenter. It supplements the publication "Novel nanoindentation strain rate sweep method for continuously investigating the strain rate sensitivity of materials at the nanoscale". The materials are described in more detail in the respective publication. The data publication takes over the sample naming convention from the related publication. </p><p>Nanoindentation measurement were performed by H. Holz at the Max-Planck Institut für Eisenforschung GmbH, Max-Planck-Straße 1, 40237 Düsseldorf, Germany using a G200 nanoindenter (KLA, Milpitas, CA, USA), equipped with a modified Berkovich diamond indenter tip of the type 171-561-500 with a serial number of C-0040446 from Synton MDP (Nidau, Switzerland). Constant strain rate tests, strain rate jump tests, strain rate sweep tests and strain rate sweep reversal tests were performed on each material in Continuous Stiffness Measurement (CSM) mode. The maximum indentation depth was 2200 nm, the CSM amplitude 2 nm and the CSM frequency 45 Hz. Strain rates were varied within the range 0.001 – 0.1 s-1. Further information on the test protocol can be found in the corresponding publication.</p><p>The subfolder "Nanoindentation data" contains the raw data for all valid indents as output by the NanoSuite © software v 7.1.7 and converted to the semicolon-separated format. The naming convention for the folder in which the CSV files are located in gives first the used material, then the method used with additional information to the parameters inputted for the method such as strain rate and indentation depth all separated by an underscore. An example can be "FS_CSR_01s-1" for a constant strain rate tests performed on fused silica with a strain rate target of 0.1 s-1 or "Nc-Ni_SweepReversal_005s-1_0005s-1" for a sweep reversal test performed on the nanocrystalline nickel sample with a targeted initial and ending strain rate of 0.05 s-1 and a strain rate target at which the strain rate direction gets reversed of 0.005 s-1. The CSV files are named either "Results" giving the average results of each test, "Required Inputs" giving information about the parameters used for the experiments, "Inputs Editable Post Test" giving information about the analysis parameters to obtain the results from, and "Test XXX" which include the Raw data of the corresponding test number. In each file the first row gives the data description e.g., "Time", the second row the physical unit e.g., "s" for seconds and from the third row the measured values.</p><p>The Nano Suite method files to perform the experiments on KLA G200 instruments is provided in the folder "G200 methods". The method for the strain rate sweep experiments is called "Strain Rate Sweep.msm" and the method for the strain rate sweep reversal experiments "Strain Rate Sweep Reversal.msm". This method is provided as is and shall be used at your own risk. The authors explicitly decline responsibility for any physical or immaterial damage resulting from the use of this method. Should minor issues occur, some feedback to the authors would be greatly appreciated.</p>
Consumption rates of tethered live and dead pinfish and dried squid in mudflat and seagrass habitats in the Upper Laguna Madre in 2018.
These data were recorded during surveys of a tethering experiment conducted on June 7, 2018, during which 180 tethered prey were deployed in two habitat types (seagrass and mudflat) in the Upper Laguna Madre, Texas, USA (27.544006, -97.285912). In each habitat, the following prey types were deployed: squidpops (1 cm2 discs of dried squid (Duffy et al. 2015) attached to 5 cm tethers, n=50), live (n=20) and dead (n=20) pinfish (Lagodon rhomboides, 4 cm fork length, attached to 20 cm tethers) at midday in each habitat. The presence/absence of tethered prey on each stake was observed and recorded after 1 hour and 24 hours. The rate of decay (i.e., disappearance or consumption rate of tethered prey over time) was calculated as the slope of an exponential model fit to discrete observations of the presence/absence of tethered prey over time. Additional environmental variables (temperature, salinity) were also recorded at the site.
Groundwater-derived nutrient fluxes and offshore mixing rates along the New Jersey coast 21-23
Radium isotopes are natural tracers useful for studying the magnitude of groundwater discharge and the transport and fate of nutrients in the coastal ocean. We collected radium and nutrient samples from groundwater and surface waters along the southern New Jersey coast to calculate the flux of groundwater-derived nutrients and coastal mixing rates. These data serve as baselines for assessing future changes in the magnitude and quality of groundwater discharge driven by human activity and climate change.
Microbial Sulfate Reduction Rates in Soils in the Napa River Watershed, California, USA, 2021-2023
Agricultural sulfur (S) inputs are a major source of anthropogenic S to the environment, often stimulating microbial sulfate reduction (MSR) in downstream environments, which can drive a cascade of unintended ecosystem consequences. This dataset includes samples collected throughout the Napa River Watershed, California, USA, where routine, high S applications to vineyards are common. Sampling was focused confirming the presence of microbial sulfate reduction (MSR) within upland soils, challenging the conventional view that this process is restricted to fully saturated environments. We collected soil samples for sulfate concentrations, sulfate reduction rates (SRRs), and organic C content (Loss On Ignition, LOI) under three different hydrological conditions: while soils were moist (November 2021), dry (January/February 2022), and saturated (January/February 2023). We collected samples from a variety of different land cover types, including vineyard, vineyard stream, grassland, forest, forest stream, and wetland.
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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.