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1,392 results for “accumulation”
Annual Maxima of Station-based Rainfall Data over Different Accumulation Durations
<p><strong>Description</strong></p> <p>These data were used in the study "Flexible and Consistent Quantile Estimation for Intensity-Duration-Frequency Curves" (Fauer et al., 2021). Rainfall data were collected from stations by the German Meteorological Service (DWD) and Wupperverband (corrected data). Raw time series data from the German Meteorological Service is publicly available under https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/. Only the annual precipitation maxima over different durations are published here.</p> <p><strong>Files</strong></p> <ul> <li><strong>yearMax.csv:</strong> This file contains aggregated rainfall data over different durations and for different stations.</li> <li><strong>meta.csv:</strong> This file contains additional information of the different stations such as longitude, latitude, altitude, temporal resolution (m=minutely, h=hourly, d=daily), group. The same group is assigned to stations which have a distance of less than 250 meters and can be treated as one station.</li> </ul> <p><br> <strong>Abstract of the according study</strong></p> <p>We suggest a flexible parametric model for describing intensity duration frequency relationships (IDF-curves) in a consistent way, i.e., without crossing of different quantiles for a wide range of durations (1 min to 5 days). The model is based on the duration-dependent formulation of the generalized extreme value distribution (GEV). The original model shows a power-law like behaviour for the quantiles for a wide range of durations and takes care of a deviation from this scaling relation (curvature) for small durations. We extend the model with two features: i) different power-law exponents for different quantiles (multiscaling) and ii) deviation from the power-law for large durations (flattening). Based on the quantile skill score, we investigate the performance of the resulting flexible model with respect to the benefit of the individual features (curvature, multiscaling, flattening) with simulated and empirical data. We provide detailed information on the duration and probability ranges for which specific features or a systematic combination of features leads to improvements for stations in a case study area in the Wupper catchment (Germany). Our results show that allowing curvature or multiscaling improves the model only for very short or long durations, respectively, but leads to disadvantages in modeling the other duration ranges. In contrast, allowing flattening on average leads to an improvement for medium durations between 1 hour and 1 day without affecting other duration regimes. Overall, the new parametric form offers a flexible and performant model for consistently describing IDF relations over a wide range of durations.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We would like to thank the German Weather Service (DWD), and the Wupperverband, especially Marc Scheibel, for maintaining the station-based rainfall gauge and providing us with data.</p>
Supplementary material 1: Accumulation Curve Data from: DNA Barcoding of the parasitoid wasp subfamily Doryctinae (Hymenoptera: Braconidae) from Chamela, Mexico - Biodiversity Data Journal 3: e5109 (18 May 2015) https://doi.org/10.3897/BDJ.3.e5109
Table containing the Process ID of specimens sampled and Barcode Index Number (BIN) used for the species accumulation curve.
accumulator 600ml empty p0 40bar p1 60bar
<p>This is a dataset publication with multiple measurements seperated into different .mat files created at the Institut für Fluidsystemtechnik (Chair of Fluid Systems) at the Technische Universität Darmstadt (Technical University of Darmstadt (https://ror.org/05n911h24)).</p> <p>The experimental data include dynamic characterisations of gas accumulators. The data were obtained on a dynamic uni-axial test machine. Each measurement-file contains the measurement data at a monofrequent harmonic excitation.</p> <p>This dataset publication in this version doesn't contain an RDF (Resource Description Framework) 'METADATA.ttl' file in the RDF Turtle format that would contain metadata of the measurements. This decision is made, since the information model marked a interim stage to FAIR measurement data and is currently under major reconstruction.</p> <p>The different measurement files in the .mat format contain different time series and corresponding metadata. The various measurements differ in various parameters (e.g. excitation frequency, excitation amplitude, ambient temperature, etc.).</p> <p>If you have further questions, please feel free to contact info*AT*fst.tu-darmstadt.de .<br>Thank you.</p> <p>Yours sincerely<br><br></p> <p><strong>CHANGELOG:</strong><br>1. This version contains the same data inside the .mat files but with updated/corrected metadata<br><br></p> <p><strong>LICENSE NOTICE:</strong><br>This dataset is licensed under the 'CC-BY-4.0' License (https://creativecommons.org/licenses/by/4.0/)</p>
hydraulic accumulator 600ml PURfoamPPI60 p0 40bar p1 60bar 20degreeCelsius
<p>This is a dataset publication with multiple measurements seperated into different .mat files created at the Institut für Fluidsystemtechnik (Chair of Fluid Systems) at the Technische Universität Darmstadt (Technical University of Darmstadt (https://ror.org/05n911h24)).</p> <p>The experimental data include dynamic characterisations of gas accumulators. The data were obtained on a dynamic uni-axial test machine. Each measurement-file contains the measurement data at a monofrequent harmonic excitation.</p> <p>This dataset publication in this version doesn't contain an RDF (Resource Description Framework) 'METADATA.ttl' file in the RDF Turtle format that would contain metadata of the measurements. This decision is made, since the information model marked a interim stage to FAIR measurement data and is currently under major reconstruction.</p> <p>The different measurement files in the .mat format contain different time series and corresponding metadata. The various measurements differ in various parameters (e.g. excitation frequency, excitation amplitude, ambient temperature, etc.).</p> <p>If you have further questions, please feel free to contact info*AT*fst.tu-darmstadt.de .<br>Thank you.</p> <p>Yours sincerely<br><br></p> <p><strong>CHANGELOG:</strong><br>1. This version contains the same data inside the .mat files but with updated/corrected metadata<br><br></p> <p><strong>LICENSE NOTICE:</strong><br>This dataset is licensed under the 'CC-BY-4.0' License (https://creativecommons.org/licenses/by/4.0/)</p>
hydraulic accumulator 600ml PURfoamPPI80 p0 40bar p1 60bar 20degreeCelsius
<p>This is a dataset publication with multiple measurements seperated into different .mat files created at the Institut für Fluidsystemtechnik (Chair of Fluid Systems) at the Technische Universität Darmstadt (Technical University of Darmstadt (https://ror.org/05n911h24)).</p> <p>The experimental data include dynamic characterisations of gas accumulators. The data were obtained on a dynamic uni-axial test machine. Each measurement-file contains the measurement data at a monofrequent harmonic excitation.</p> <p>This dataset publication in this version doesn't contain an RDF (Resource Description Framework) 'METADATA.ttl' file in the RDF Turtle format that would contain metadata of the measurements. This decision is made, since the information model marked a interim stage to FAIR measurement data and is currently under major reconstruction.</p> <p>The different measurement files in the .mat format contain different time series and corresponding metadata. The various measurements differ in various parameters (e.g. excitation frequency, excitation amplitude, ambient temperature, etc.).</p> <p>If you have further questions, please feel free to contact info*AT*fst.tu-darmstadt.de .<br>Thank you.</p> <p>Yours sincerely</p> <p> </p> <p><strong>CHANGELOG:</strong><br>1. This version contains the same data inside the .mat files but with updated/corrected metadata</p> <p> </p> <p><strong>LICENSE NOTICE:</strong><br>This dataset is licensed under the 'CC-BY-4.0' License (https://creativecommons.org/licenses/by/4.0/)</p>
Data set: "Higher Antarctic ice sheet accumulation and surface melt rates revealed at 2 km resolution"
<p>This data set includes the materials required to reproduce the figures and tables presented in the study: "Higher Antarctic ice sheet accumulation and surface melt rates revealed at 2 km resolution". The data consist of:</p><p> </p><p><strong>ASCII files:</strong></p><ol><li><strong>SMB-ANT-Sectors-1979-2021-RACMO2.3p2-ERA5-2km.txt</strong>: time series of Antarctic sector-integrated annual SMB<strong> (Gt per year)</strong> from ERA5-forced RACMO2.3p2 at 27 km, statistically downscaled to 2 km resolution (1979-2021).</li><li><strong>Melt-ANT-Sectors-1979-2021-RACMO2.3p2-ERA5-2km.txt:</strong> time series of Antarctic sector-integrated annual surface melt (Gt per year) from ERA5-forced RACMO2.3p2 at 27 km, statistically downscaled to 2 km resolution (1979-2021).</li><li><strong>Melt-ANT-Sectors-1950-2014-RACMO2.3p2-CESM2-HIST-2km.txt:</strong> time series of Antarctic sector-integrated annual surface melt from CESM2-forced RACMO2.3p2 historical reconstruction (HIST), statistically downscaled to 2 km resolution (1950-2014).</li><li><strong>Melt-ANT-Sectors-2015-2099-RACMO2.3p2-CESM2-SSP126-2km.txt:</strong> time series of Antarctic sector-integrated annual surface melt from CESM2-forced RACMO2.3p2 SSP1-2.6 projection (SSP126), statistically downscaled to 2 km resolution (2015-2099).</li><li><strong>Melt-ANT-Sectors-2015-2099-RACMO2.3p2-CESM2-SSP245-2km.txt:</strong> time series of Antarctic sector-integrated annual surface melt from CESM2-forced RACMO2.3p2 SSP2-4.5 projection (SSP245), statistically downscaled to 2 km resolution (2015-2099).</li><li><strong>Melt-ANT-Sectors-2015-2099-RACMO2.3p2-CESM2-SSP585-2km.txt:</strong> time series of Antarctic sector-integrated annual surface melt from CESM2-forced RACMO2.3p2 SSP5-8.5 projection (SSP585), statistically downscaled to 2 km resolution (2015-2099).</li></ol><p>Antarctic sectors include the Antarctic Peninsula (APIS), the West Antarctic ice sheet (WAIS), the East Antarctic ice sheet (EAIS), the grounded Antarctic ice sheet (AIS), the floating ice shelves (Ice shelves), and the whole of Antarctica (ANT) including both the AIS and Ice shelves. The APIS, WAIS, EAIS and AIS sectors include land ice from neighbouring Antarctic islands.</p><p> </p><p><strong>Netcdf files:</strong></p><ol><li><strong>smb_rec.1979-2021.BN_RACMO2.3p2_ANT27_ERA5-3h.AIS.2km.YY.nc: </strong>map of annual SMB (kg per m² or mm w.e. per year) from ERA5-forced RACMO2.3p2 at 27 km, statistically downscaled to 2 km resolution, covering the whole of Antarctica (1979-2021).</li><li><strong>snowmelt.1979-2021.BN_RACMO2.3p2_ANT27_ERA5-3h.AIS.2km.YY.nc: </strong>map of annual surface melt (kg per m² or mm w.e. per year) from ERA5-forced RACMO2.3p2 at 27 km, statistically downscaled to 2 km resolution, covering the whole of Antarctica (1979-2021).</li><li><strong>snowmelt.1950-2014.BN_RACMO2.3p2_ANT27_CESM2_HIST.AIS.2km.YY.nc: </strong>map of annual surface melt (kg per m² or mm w.e. per year) from CESM2-forced RACMO2.3p2 historical reconstruction (HIST), statistically downscaled to 2 km resolution, covering the whole of Antarctica (1950-2014).</li><li><strong>snowmelt.2015-2099.BN_RACMO2.3p2_ANT27_CESM2_SSP126.AIS.2km.YY.nc: </strong>map of annual surface melt (kg per m² or mm w.e. per year) from CESM2-forced RACMO2.3p2 SSP1-2.6 projection (SSP126), statistically downscaled to 2 km resolution, covering the whole of Antarctica (2015-2099).</li><li><strong>snowmelt.2015-2099.BN_RACMO2.3p2_ANT27_CESM2_SSP245.AIS.2km.YY.nc: </strong>map of annual surface melt (kg per m² or mm w.e. per year) from CESM2-forced RACMO2.3p2 SSP2-4.5 projection (SSP245), statistically downscaled to 2 km resolution, covering the whole of Antarctica (2015-2099).</li><li><strong>snowmelt.2015-2099.BN_RACMO2.3p2_ANT27_CESM2_SSP585.AIS.2km.YY.nc: </strong>map of annual surface melt (kg per m² or mm w.e. per year) from CESM2-forced RACMO2.3p2 SSP5-8.5 projection (SSP585), statistically downscaled to 2 km resolution, covering the whole of Antarctica (2015-2099).</li><li><strong>ANT_masks.2km.nc</strong>: file including the grounded AIS mask (AIS), Antarctic sectors mask (Sectors), floating ice shelves mask (Shelves), surface elevation down-sampled from REMA (Topography), latitude and longitude on the 2 km grid<strong>. </strong>The sector mask includes: 0 – Ocean, 1 – APIS, 2 – WAIS, 3 – EAIS, 4 – APIS islands, 5 – WAIS islands, 6 – EAIS islands, and 7 – ice shelves.<strong> </strong></li></ol><p>The projection used for statistical downscaling is Polar Stereographic South (EPSG:3031) with a spatial resolution of 2 km x 2 km. </p><p> </p><p><strong>Additional data: </strong>The gridded, daily downscaled SMB data sets from the ERA-forced RACMO2.3p2 simulation, and the CESM2-forced RACMO2.3p2 projections under a low-end SSP1-2.6, moderate SSP2-4.5 and high-end SSP5-8.5 warming scenario are freely available from the authors upon request and without conditions (contact: bnoel@uliege.be). Besides SMB, the data sets include total precipitation (snow and rain), snowfall, total melt (snow and ice), runoff, refreezing and retention, drifting snow erosion, and total sublimation (surface and drifting snow) at 2 km horizontal resolution. </p><p> </p><p><strong>Abstract:</strong> Antarctic ice sheet (AIS) mass loss is predominantly driven by increased solid ice discharge, but its variability is governed by surface processes. Snowfall fluctuations control the surface mass balance (SMB) of the grounded AIS, while meltwater ponding can trigger ice shelf collapse potentially accelerating discharge. Surface processes are essential to quantify AIS mass change, but remain poorly represented in climate models typically running at 25-100 km resolution. Here we present SMB and surface melt products statistically downscaled to 2 km resolution for the contemporary climate (1979-2021) and low, moderate and high-end warming scenarios until 2100. We show that statistical downscaling modestly enhances contemporary SMB (3%), which is sufficient to reconcile modelled and satellite mass change. Furthermore, melt strongly increases (46%), notably near the grounding line, in better agreement with in-situ and satellite records. The melt increase persists by 2100 in all warming scenarios, revealing higher surface melt rates than previously estimated.</p>
Species‑specific influence of powdery mildew mycelium on the efficiency of PM accumulation by urban greenery - Data
<p>Dataset of article: Przybysz, A., Nawrocki, A., Mirzwa-Mróz, E. <em>et al.</em> Species-specific influence of powdery mildew mycelium on the efficiency of PM accumulation by urban greenery. <em>Environ Sci Pollut Res</em> (2023). https://doi.org/10.1007/s11356-023-28371-6</p>
Discrimination accumulates over time
<p>alt-text:</p> <p> </p> <p>The left figure (bar graph) shows the career progression of a privileged person who is promoted on their potential ie. forgiven for their lack of experience, and has not had to deal with discrimination. This shows y-axis as career progression and x-axis as time. The bar graph starts at level 6 and continues to grow steadily til it gets to level 14 in career progression. </p> <p>The right figure (bar graph) shows the career progression of a marginalised person who has to prove their experience ie. punished for their lack of experience, and has had to leave roles due to discrimination. This shows y-axis as career progression and x-axis as time. The bar graph starts at level 6 but due to discrimination the person is forced to leave (signified by a snake) and then drops to level 5 on the next unit of time. In this unit of time the person is forced out due to discrimination (again signified by a snake) and has to find a sideways move. Their career then grows until it hits another “snake” that forces it back from level 7 to level 6. It then grows back to level 8.</p> <p>This shows how experience can be slowed when you are faced with discrimination and that expereince can be highly correlated with opportunity or lack of discrmiination and not on ability.</p>
Fig. 2. Species accumulation curves for 45 in Mammal inventories in Seasonal Neotropical Forests: traditional approaches still compensate drawbacks of modern technologies
Fig. 2. Species accumulation curves for 45 sampling days for each method used to sample mammals in Serra do Japi Biological Reserve, JundiaÍ, State of SÃo Paulo, Brazil in July and August 2009 and January and February 2010.
Fig. 3 in ATP accumulation in early resting cyst formation towards cryptobiosis in Colpoda cucullus
Fig. 3. Measurement of the relative amount of ATP per 10,000 cells in vegetative cells and cells at 12 h after the onset of encystment induction. The columns and attached bars represent the means and standard errors of 9 identical replicates, respectively. Double asterisks represent significant differences at p <0.01.
Fig. 2 in ATP accumulation in early resting cyst formation towards cryptobiosis in Colpoda cucullus
Fig. 2. Relative gene expression of ATP synthase beta chain by real time PCR analysis. The columns and attached bars represent the means and standard errors of 4 identical replicates, respectively. Double asterisks represent significant differences at p <0.01.
Fig. 1 in ATP accumulation in early resting cyst formation towards cryptobiosis in Colpoda cucullus
Fig. 1. Visualization of the mitochondrial membrane potential of Colpoda vegetative cells using a Mito PT assay kit; the results from cells at 0 h and cells at 1–24 h after the onset of encystment induction are shown. Differential interference microscopic observation (A) and fluorescence microscopic observation (B). Cells that contain mitochondria with polarized inner membranes show orange fluorescence, whereas those with depolarized mitochondria show green fluorescence. The bar represents 100 μm.
Modulation of fracture healing by the transient accumulation of senescent cells
<p>Senescent cells have detrimental effects across tissues with aging but may have beneficial effects on tissue repair, specifically on skin wound healing. However, the potential role of senescent cells in fracture healing has not been defined. Here, we performed an in silico analysis of public mRNAseq data and found that senescence and senescence-associated secretory phenotype (SASP) markers increased during fracture healing. We next directly established that the expression of senescence biomarkers increased markedly during murine fracture healing. We also identified cells in the fracture callus that displayed hallmarks of senescence, including distension of satellite heterochromatin and telomeric DNA damage; the specific identity of these cells, however, requires further characterization. Then, using a genetic mouse model (Cdkn2aLUC) containing a Cdkn2aInk4a-driven luciferase reporter, we demonstrated transient in vivo senescent cell accumulation during callus formation. Finally, we intermittently treated young adult mice following fracture with drugs that selectively eliminate senescent cells ('senolytics', Dasatinib plus Quercetin), and showed that this regimen both decreased senescence and SASP markers in the fracture callus and significantly accelerated the time course of fracture healing. Our findings thus demonstrate that senescent cells accumulate transiently in the murine fracture callus and, in contrast to the skin, their clearance does not impair but rather improves fracture healing.</p>
Fig. 2 in Accumulation Of Heavy Metals By Small Mammals The Background And Polluted Territories Of The Urals
Fig. 2. The dendrogram is obtained for element analysis (Cu+Zn+Cd) in small mammals from natural populations in the background zone (Bcg) and polluted territories (Imp). The results of cluster analysis confirmed the statistically significant differences in heavy metals total accumulation in three species of small mammals.
Thermal demagnetization data of Risica et al. (Deposit-derived block-and-ash flows: the hazard posed by perched temporary tephra accumulations on volcanoes; 2018 Fuego disaster, Guatemala)
<p>Thermal demagnetization data (repository data) of Risica et al. "Deposit-derived block-and-ash flows: the hazard posed by perched temporary tephra accumulations on volcanoes; 2018 Fuego disaster, Guatemala".</p>
Dataset: A conflict between spatial selection and evidence accumulation in area LIP
<p>This dataset (packaged as the zip file cdots_share.zip) accompanies the article titled "A conflict between spatial selection and evidence accumulation in area LIP" by JA Seideman, TR Stanford, and E Salinas, in Nature Communications (2022).</p> <p>The experimental results in the paper are based on behavioral and single-neuron data collected from two subjects during performance of visuomotor tasks, as described in the article. The trial-by-trial data arrays (matrices inside .mat files) included here are the basis for most analyses reported in the article. </p> <p>In addition to the trial-wise data matrices, the dataset includes Matlab functions and scripts (*.m files) used to analyze the data and generate figures in the article. Instructions and specifics are detailed in the README file. </p>
Data_Sediment accumulation rates and carbonate fluxes of deep-sea sediments in the southern Gulf of Mexico
<p>We present the mass and carbonate annual fluxes, collected by two sediment traps at 1000 m depth, located in the western and southern deep-water region of the Gulf of Mexico, and the total mass and carbonate accumulation rates from 48 sediment cores retrieved from continental slopes and the abyssal plain of the southern Gulf of Mexico (sGM). We also presented the conventional and calibrated radiocarbon age analyzed in planktic foraminifera (> 250 μm size fraction) in sediment cores from the southern Gulf of Mexico, collected in the XIXIMI-7 cruise (May 2019).</p>
Magnetoencephalographic spectral fingerprints differentiate evidence accumulation from saccadic motor preparation in perceptual decision-making
<p>This repository contains the dataset of the paper "Magnetoencephalographic spectral fingerprints differentiate evidence accumulation<br> from saccadic motor preparation in perceptual decision-making".</p> <p>The dataset contains the MEG power spectrum of 16 subjects in the source space of a perceptual decision making experiment.</p>
1-km Antarctic net snow accumulation predictions
<p><strong>Overview</strong></p> <p>We provide static predictions of net snow accumulation over the Antarctic Ice Sheet derived using a machine learning approach. Here, we train random forest models to predict variability in net accumulation using atmospheric variables and topographic characteristics as predictors at 1 km resolution. Observations of net snow accumulation from both in situ and airborne radar data provide the input observable targets needed to train the random forest models. The data file includes all the predictors and predictands, an independent stake transect used for evaluation, four random forest gridded accumulation anomalies and their uncertainties, and a combined accumulation anomalies and its uncertainty.</p> <p>For a thorough description of how the predictions were generated generated see Medley et al. (2022). </p>
DATASET: In situ measurements of meltwater flow through snow and firn in the accumulation zone of the SW Greenland Ice Sheet
<p>This repository contains all the data and code used to analyse these data related to the paper "In situ measurements of meltwater flow through snow and firn in the accumulation zone of the SW Greenland Ice Sheet" by Clerx et al. (2022), published in "The Cryosphere".</p>
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