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1,413 results for “wetting”
Estimated field scale sediment loss on the North Wyke Farm Platform in typical and extreme wet winters
<p>Based on monitored runoff and turbidity at 15-minute intervals from the North Wyke Fam Platform - a UK National Bioscience Research Infrastructure (NBRI), sediment loss during both typical and more extreme wet winters (December - February, inclusive) over the past decade (2012-2013, 2013-2014, 2015-2016, 2019-2020 and 2023-2024) from 5 grassland field catchments and 5 recently converted arable field catchments was estimated, including uncertainty ranges. Daily rainfall totals for the corresponding winter periods are also included.</p>
GSDM-WBT: Global station-based daily maximum wet-bulb temperature data for 1981-2020
<p>The wet-bulb temperature integrates the temperature and humidity to comprehensively describe the thermal environment and the energy regulation of human bodies. Daily maximum wet-bulb temperature is an important indicator to be used for research on extreme humid heat. GSDM-WBT is a new dataset of global station-based daily maximum wet-bulb temperature, which was produced through calculating wet-bulb temperature, data quality control, infilling missing values and homogenisation based on the HadISD station-based observations and the NCEP-DOE reanalysis data. GSDM-WBT covers the complete daily series of 1834 stations around the world from 1981 to 2020. We provide the NetCDF files of GSDM-WBT for each station and one compressed file containing all data.</p>
Data used for doi.org/10.1029/2012JD018338 : Baars et al, 2012: Aerosol profiling with lidar in the Amazon Basin during the wet and dry season
<p><span>This is the data whoch ahs been used for the publication:</span></p> <p><span><span>Baars, H.</span></span><span>, <span>A. Ansmann</span>, <span>D. Althausen</span>, <span>R. Engelmann</span>, <span>B. Heese</span>, <span>D. Müller</span>, <span>P. Artaxo</span>, <span>M. Paixao</span>, <span>T. Pauliquevis</span>, and <span>R. Souza</span> (<span>2012</span>), <span>Aerosol profiling with lidar in the Amazon Basin during the wet and dry season</span>, <em>J. Geophys. Res.</em>, <span>117</span>, D21201, doi:<a title="Link to external resource: 10.1029/2012JD018338" href="https://doi.org/10.1029/2012JD018338" target="_blank" rel="noopener">10.1029/2012JD018338</a>.</span></p> <p><span>For each of the Figures in the Publication the underlaying data is provided in a respective folder.</span></p> <p><span>The raw data (i.e,. the analyzed lidar data for several case during the one-year campaign in 2008) is provided separately.</span></p> <p><span>As the time of data creation is more than 10 years ago, the data description does not comply to current standards. Thus, in case of any questions, please contact the first author.</span></p> <p> </p> <p> </p> <p> </p>
Wet snow cover for Gran Paradiso
<p>Series of wet snow cover area maps derived from Sentinel-1 using the algorithm proposed in Nagler T, Rott H, Ripper E, Bippus G, Hetzenecker M. Advancements for snowmelt monitoring by means of Sentinel-1 SAR. Remote Sensing. 2016 Apr 20;8(4):348.</p>
Datasets for figures in Implementation and evaluation of Wet Bulb Globe Temperature within non-urban environments in the Community Land Model version 5
<p>The files contain 4 scripts and 6 netcdf files. </p> <p>"laborCap_200400.ncl" uses "Lancet_LRF.nc" to create Figure 1.</p> <p>Script "world_plot_ensemble_Avg.I2000.csh", drives a NCL script, "plot_modern.I2000.WBGT.ncl" to make figures 3 and 4, using the netcdf files, "I2000_PR_22_x1_60_5.exceed.WBGT.20yrs.75_99.nc," "I2000_PR_22_x1_60_5.exceed.WBGT_BG_R.20yrs.75_99.nc," "I2000_PR_22_x1_60_5.exceed.WBGT_BC_R.20yrs.75_99.nc," and "I2000_PR_22_x1_60_5.exceed.WBGT_AC_R.20yrs.75_99.nc."</p> <p>"heatmap.wbgt.v4.org.ncl" uses netcdf "I2000_PR_23_Chicago_x1_60_1.11-17.Chicago.allvars.nc" to create figures 5-7. </p>
Dynamic strength characterisation of wet granular ice layers
<p>Dataset for the research paper "Dynamic strength characterisation of wet granular ice layers" submitted to the International Journal of Impact Engineering.</p>
Odonata Assemblage in Metropolitan Area of Puerto Rico (2018-2019, Wet and Dry Season)
Data comes from a project that aimed to identify the effects of urbanization on stream habitat quality and associated odonate assemblages in Puerto Rico. In this study, 16 streams along a rural to urban gradient in the San Juan Metropolitan Area were sampled. Each stream was characterized using the Stream Visual Assessment Protocol (SVAP) for Puerto Rico and by analyzing their surrounding land cover. A 100-m segment of each stream was surveyed to assess adult odonate richness and abundance during the rainy and dry seasons. Adults were identified visually, and their abundance was recorded. Data is a product of: Maldonado-Benítez, Mariani-Ríos & Ramírez (2022): Effects of urbanization on Odonata assemblages in tropical island streams in San Juan, Puerto Rico. International Journal of Odonatology, 25, 31–42 doi:10.48156/1388.2022.1917163
NEON Biorepository Wet Deposition Collection (repackaging of occurrences published by the NEON Biorepository Data Portal)
This collection contains wet deposition samples collected during precipitation events at NEON terrestrial and aquatic sites (NEON sample class: wdp_collection_in.chemSubsampleID). Samples are collected in a climate controlled wet deposition collector located at the tower top of terrestrial sites, and at the meteorologic tower of select aquatic sites. The automated assembly detects precipitation with an optical sensor and opens to collect wet deposition during all rain events. This allows for all types of precipitation to enter the glass collection bottles located within the enclosure. Once precipitation has ceased (as detected by the optical precipitation detector), the retractable lid closes until the next precipitation event is detected. Every two weeks samples are retrieved. A portion of the sample is filtered and sent to an analytical facility for analysis of major ions, pH, and conductivity. The remaining (unused) portion of the sample is not filtered and archived at 4 degrees Celsius for five years. Samples are stored in plastic Nalgene bottles, either PP or HDPE. See related links below for protocols and NEON related data products. Please note that associated datasets include important remarks and notes from the analysis laboratory about the condition of each sample (for example, if debris or contaminants were observed in the sample).
Climate data from Arctic LTER Toolik Inlet Wet Sedge site, Toolik Field Station, Alaska 2012 to 2018.
Two Figaro TGS 2600 sensors were installed at the Toolik Wet Sedge site in late June 2012 to 2018. The TGS 2600 was the first low-cost solid state sensor that shows a weak response to ambient levels of methane (i.e., range ~1.8–2.7 µmol mol–1). Reference CH4 mole fractions were measured by a Fast Methane Analyser during the years 2012–2016 which was replace by a Fast Greenhouse Gas Analyzer (Los Gatos Research, Inc., San Jose, CA, U.S.A.) in 2016. Data were used in the Eugster et al. (2020) study on Long-term reliability of the Figaro TGS 2600 solid-state methane sensor under low Arctic conditions at Toolik lake, Alaska (doi:10.5194/amt-2019-402)
Long-term record of wetted area, substrate type, and plant features in Sycamore Creek, Arizona, USA (2010-2020)
The primary objective of this project is to understand how long-term climate variability and change influence the structure and function of desert streams via effects on hydrologic disturbance regimes. Climate and hydrology are intimately linked in arid landscapes; for this reason, desert streams are particularly well suited for both observing and understanding the consequences of climate variability and directional change. Researchers try to (1) determine how climate variability and change over multiple years influence stream biogeomorphic structure (i.e., prevalence and persistence of wetland and gravel-bed ecosystem states) via their influence on factors that control vegetation biomass, and (2) compare interannual variability in within-year successional patterns in ecosystem processes and community structure of primary producers and consumers of two contrasting reach types (wetland and gravel-bed stream reaches). This dataset was collected to understand changes of biota patch cover in different sites and reaches of Sycamore Creek and the transition of different algal and plant communities in post-flood succession.
Canopy opening increased leaf shredding arthropods and nutrient mineralization but not mass loss in a wet tropical forest
Canopy opening increased leaf shredding arthropods and nutrient mineralization but not mass loss in a wet tropical forest Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Throughfall and wet deposition N data for C1, 2001.
A field study at a Rocky Mountain spruce-fir-pine forest was undertaken to obtain measures of canopy nitrogen uptake. Wet deposition, dry deposition, and throughfall fluxes of ammonium and nitrate were measured during the 2001 growing season. Estimation of CNU, for both ammonium and nitrate, was obtained by subtracting throughfall (TF) flux from the sum of wet deposition (WD) and dry deposition (DD): CNU = WD+DD-TF. Total dissolved nitrogen was also determined for TF and WD samples. The organic nitrogen flux in WD and TF was determined by subtracting the inorganic N flux (sum of ammonium and nitrate fluxes) from total dissolved N fluxes.
De computer de wet laten herschrijven…?!
<p>De computer de wet laten herschrijven…?!</p> <p> </p> <p><strong>Als Researcher-in-Residence houd ik mij bezig met wetteksten uit de periode ± 1500-1800. De boeken waarin deze teksten zijn verzameld zijn niet goed leesbaar gedigitaliseerd en dus is het systematisch doorzoeken lastig. Daarom trainen we binnen het <em>‘Entangled Histories’</em>-project de computer om de teksten beter te herkennen én we gebruiken <em>machine learning</em> om de teksten in categorieën te plaatsen. Hoe we dit doen, leg ik je in deze lunchlezing van de Weetfabriek uit.</strong></p> <p> </p> <p>Annemieke Romein houdt zich als historica bezig met vroegmoderne regelgeving. Waar zij voorheen alles met de hand moest categoriseren, is het <em>Entangled Histories</em>-project op zoek naar mogelijkheden om dit door de computer te laten overnemen. In deze lunchlezing zal Annemieke laten zien met wat voor soort teksten het projectteam (Sara Veldhoen en Michel de Gruijter en zijzelf) aan de slag is gegaan en wat het doel van het onderzoek is.</p> <p> </p> <p>KB Onderzoeksweek: Weetfabrieksessie.</p>
Hydraulic Properties of Expansive Yazoo Clay subjected to wet dry cycles
<p>The data file includes the investigation of the behavior of yazoo clay under different wet-dry cycles. The investigation was conducted using laboratory testing and numerical analysis.</p>
Dataset for Recurrent Rossby wave packets modulate the persistence of dry and wet spells across the globe
<p>This dataset is used in the following study: "Recurrent Rossby wave packets modulate the persistence of dry and wet spells across the globe."</p> <p>Dataset includes:</p> <ul> <li>Dry and wet spells for the Northern and the Southern Hemisphere respectively.</li> <li>The output of the statistical model for each season (MJJASO/NDJFMA) and for each spell type (dry/wet) for both the hemispheres (NH/SH).</li> </ul> <p>File naming used belongs to mainly two categories: one for naming spell file, and second for naming the output file from the statistical model. An example from each category is explained below. The rest of the files follow the same naming style.</p> <ol> <li><em>Spell file</em>;<strong> NH_1.0mm_dry_spells_all_months_gap_1_days_no_spell2_check.nc</strong>: Northern Hemisphere 1.0mm threshold dry spell for all months with a gap of 1 day</li> <li><em>Statisical model output file</em>; <strong>NH_weibull_MJJASO_drythresh_1_min_spell_count_40_time_steps_min_spell_length_5D_1D_N_1980_2016.nc</strong>: Northern Hemisphere Weibull model output for MJJASO using dry threshold of 1.0 mm with a minimum spell count of 40 time-steps and a minimum spell length of 5 days for the period 1980-2016</li> </ol> <p>This dataset alone is sufficient for reproducing the analysis presented in the study. Open source tools like Python, R, etc can be used to read '.nc' file type. Additional code help in the form of Jupyter notebooks reproducing figures made from this dataset can be viewed <a href="https://github.com/avatar101/RRWPS-extremes">here on GitHub.</a></p>
LSD4WSD : An Open Dataset for Wet Snow Detection with SAR Data and Physical Labelling
<p><strong>LSD4WSD V2.0</strong></p><p><strong>L</strong>earning <strong>S</strong>AR <strong>D</strong>ataset for <strong>W</strong>et <strong>S</strong>now <strong>D</strong>etection - Full Analysis Version. </p><p>The aim of this dataset is to provide a basis for automatic learning to detect wet snow. It is based on Sentinel-1 SAR GRD satellite images acquired between August 2020 and August 2021 over the French Alps. The new version of this dataset is no longer simply restricted to a classification task, and provides a set of metadata for each sample.</p><p>Modification and improvements of the version 2.0.0 :</p><ul><li><i>Number of massif:</i> add 7 new massif to cover the all Sentinel-1 images (cf `info.pdf`).</li><li><i>Acquisition:</i> add images of the descending pass in addition to those originally used in the ascending pass.</li><li><i>Sample: </i>reduction in the size of the samples considered to 15 by 15 to facilitate evaluation at the central pixel.</li><li><i>Sample: </i>increased density of extracted windows, with a distance of approximately 500 meters between the centers of the windows.</li><li><i>Sample:</i> removal of the pre-processing involving the use of logarithms.</li><li><i>Sample:</i> removal of the pre-processing involving the normalisation.</li><li><i>Labels:</i> new structure for the labels part: dictionary with keys: `topography`, `metadata` and `physics`.</li><li><i>Labels:</i> `physics`: addition of direct information from the CROCUS model for 3 simulations: Liquid Water Content, snow height and minimum snowpack temperature.</li><li><i>Labels:</i> `topography`: information on the slope, altitude and average orientation of the sample.</li><li><i>Labels:</i> `metadata` : information on the date of the sample, the mountain massif and the run (ascending or descending).</li><li><i>Dataset</i>: removal of the train/test split*</li></ul><p>*We leave it up to the user to use the Group Kfold method to validate the models using the alpine massif information.</p><p>Finally, it consists of 2467516 samples of size 15 by 15 by 9. For each sample, the 9 metadata are provided, using in particular the <a href="https://www.umr-cnrm.fr/spip.php?article265&lang=en">Crocus</a> physical model:</p><ul><li>topography:<ul><li>elevation (meters) (average),</li><li>orientation (degrees) (average),</li><li>slope (degrees) (average),</li></ul></li><li>metadata:<ul><li>name of the alpine massif,</li><li>date of acquisition,</li><li>type of acquisition (ascending/descending),</li></ul></li><li>physics<ul><li>Liquid Water Content (km/m2),</li><li>snow height (m),</li><li>minimum snowpack temperature (Celsius degree).</li></ul></li></ul><p>The 9 channels are in the following order:</p><ul><li>Sentinel-1 polarimetric channels: VV, VH and the combination C: VV/VH in linear,</li><li>Topographical features: altitude, orientation, slope</li><li>Polarimetric ratio with a reference summer image: VV/VVref, VH/VHref, C/Cref**</li></ul><p>** The reference image selected is that of August 9th 2020, as a reference image without snow (cf. <a href="https://ieeexplore.ieee.org/document/842004">Nagler&al</a>)</p><p>An overview of the distribution and a summary of the sample statistics can be found in the file info.pdf.</p><p>The data is stored in .hdf5 format with gzip compression. We provide a python script to read and request the data. The script is dataset_load.py. It is based on the h5py, numpy and pandas libraries. It allows to select a part or the whole dataset using requests on the metadata. The script is documented and can be used as described in the README.md file</p><p>The processing chain is available at the following <a href="https://github.com/Matthieu-Gallet/LSD4WSD-dataset"><strong>Github</strong></a> address.</p><p>The authors would like to acknowledge the support from the National Centre for Space Studies (CNES) in providing computing facilities and access to SAR images via the PEPS platform.</p><p>The authors would like to deeply thank Mathieu Fructus for running the Crocus simulations.</p><p><strong>Erratum :</strong></p><p>In the dataloader file, the name of the "aquisition" column must be added twice, see the correction below.:</p><blockquote><p>dtst_ld = Dataset_loader(path_dataset,shuffle=False,descrp=["date","massif","aquisition","aquisition","elevation","slope","orientation","tmin","hsnow","tel",],) </p></blockquote><p>If you have any comments, questions or suggestions, please contact the authors: </p><ul><li>matthieu.gallet@univ-smb.fr</li><li>fatima.karbou@meteo.fr</li><li>abdourrahmane.atto@univ-smb.fr</li><li>emmanuel.trouve@univ-smb.fr</li></ul>
Potential near-term wetting of the Southwestern United States if the Eastern Pacific cooling trend reverses
<p>Data for the manuscript "Potential near-term wetting of the Southwestern United States if the Eastern Pacific cooling trend reverses."</p>
Figure 3 in A new genus of millipedes (Diplopoda: Polydesmida: Dalodesmidae) from wet forests in southern Victoria, with brief remarks on the Victorian Polydesmida
Figure 3. Victoriombrus acanthus sp. nov., male, paratype. Anterior (left) and mesal (right) views of right gonopod (specimen from NMV K-8849). Dashed line indicates course of prostatic groove; for clarity, the continuation of the groove to the solenomere tip is not indicated.
Figure 2 in A new genus of millipedes (Diplopoda: Polydesmida: Dalodesmidae) from wet forests in southern Victoria, with brief remarks on the Victorian Polydesmida
Figure 2. Victoriombrus acanthus sp. nov., male, paratype. Posterior view of gonopods in situ (specimen from NMV K-8849). Scale bar = 0.5 mm.
Figure 1. Victoriombrus spp. details. A, V in A new genus of millipedes (Diplopoda: Polydesmida: Dalodesmidae) from wet forests in southern Victoria, with brief remarks on the Victorian Polydesmida
Figure 1. Victoriombrus spp. details. A, V. acanthus sp. nov., male paratype from NMV K-8849; B, V. seminudus sp. nov., male paratype from NMV K-8856. 1, Antenna; 2, posterior leg on somite 6; 3, posterior leg on somite 12; 4, dorsal outline view of somite 12; 5, posterior outline view of somite 12. Scale bar = 1.0 mm; setation and antennal cones not shown.
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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)
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DANDI Archive for NWB datasets
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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.