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1,265 results for “discharges”
Clinical Dataset for Artificial Intelligence-Driven Predictive Modeling for Home Discharge in Neurological and Orthopedic Conditions
<p><span>In recent years, the fusion of the medical and computer science domains has gained significant traction in the scientific research landscape. Progress in both fields has enabled the generation of a vast amount of data used for making predictions and identifying interesting clusters and pathways. The Machine Learning model's application in the medical domain is one of the most compelling and challenging topics to explore, bridging the gap between Artificial Intelligence (AI) and healthcare. The combination of AI and medical information offers the possibility to create tools that can benefit both healthcare providers and physicians. This enables the enhancement of rehabilitation therapy and patient care. In the rehabilitation context, this work provides an alternative perspective: prediction of patients’ home discharge upon completing the rehabilitation protocol. Demographic and clinical data were collected from electronic Medical Record. </span></p> <p><span>The original analysis dataset includes clinical and demographic data of adults admitted to the neurology and orthopedic departments of a rehabilitation hospital in Italy from January 2015 to August 2022. The completion of patients’ ADR-r form</span><span> resulted in the collection of data for 10520 individuals, whose information is distributed across 120 initial features. We anonymized dataset rows. Clinical data, including the primary reason for rehabilitation, any associated medical conditions, impairments, and admission/discharge mBi scores were collected. </span></p> <p><span>A legend file is enclosed to explain variable labels.</span></p>
Sentinel-1 snow depth assimilation to improve river discharge estimates in the western European Alps
<p>This data set contains model output presented in the following paper: I. Brangers, H. Lievens, A. Getirana, and G. J. M. De Lannoy. (2024). Sentinel-1 snow depth assimilation to improve river discharge estimates in the western European Alps. Water Resources Research. Under review.</p> <div>The model simulations were carried out in NASA's Land Information System (LIS), using the NoahMP v3.6 land surface model, forced with ERA5. The land surface model was coupled to the HyMAP routing algorithm to produce streamflow estimates. The data contains model results for the western European Alps for the period of 2015-2021 for two seperate cases. 1) The OL run: model run without assimilation of external observations; and 2) DA run: model run with the assimilation of Sentinel-1 snow depth observations.</div> <div> </div> <div>The zip-folders contain netcdf files for each day of the simulation period, for 1) the river discharge (_ROUTING), </div> <div>2) land surface model variables such as snow depth and SWE (_SURFACEMODEL_yyyy) grouped per year, and 3) variables related to the data assimilation such as the spread and innovations (_EnKF).</div>
Dataset accompanying the publication: River mouth hydrodynamics: the role of the outlet geometry and transient tidal and river discharge conditions on the jet structure
<p>Delft3D model input files for all numerical scenarios tested. Subfolders contents:</p> <p>00_example: files to run simulation 111 (see Table 1 in the original manuscript). The remaining cases can be simulated using the correct combination of files contained in the following subfolders.</p> <p>01_grid: grid files for the different geometries defined</p> <p>02_bathymetry: bathymetry files for the different geometries defined</p> <p>03_Boundary conditions: files containing the node information where the boundary conditions are defined for each case.</p> <p>04_bct: files containing the time series for the boundary conditions</p> <p>05_bch: files containing the information on the harmonic conditions defined at the offshore boundary condition.</p> <p>06_Discharge: files containing the river discharge conditions (the hydrographs)</p> <p>07_Initial: files containing the initial conditions used to reduce the spin-up interval of the simulations.</p> <div> <div> <div> <div> <p>Delft3D source codes are downloadable from the Deltares model repository (https://oss. deltares.nl/web/delft3d).</p> </div> </div> </div> </div>
Behaviour of dissolved inorganic salts in the cooling water of a nuclear power plant open recirculation system and formation of water discharge
<p>The main problem in the operation of nuclear power plants is the scale formation of mineral impurities in an open recirculating system. However, water discharge from an open recirculating system into water bodies can lead to changes in the chemical equilibrium of wastewater components and requires constant monitoring. The purpose of this study was to analyse the behaviour of dissolved inorganic salts in water in an open recirculating system during water treatment using the example of the Rivne Nuclear Power Plant. Moreover, the analysis impact of their discharge with return water in the Styr River. The dissolved inorganic salt concentration has a significant impact on the efficiency of the system and the environment of an open recirculating system power plant. Altogether, each dissolved inorganic salt component was analysed separately using standard measurement methods, using statistical methods of data processing, and correlation analysis. In addition, the annual discharge of the dissolved inorganic salts components was calculated and the amount of discharge was assessed for compliance with the maximum discharge limit. Thus, the influence of the formation of the dissolved inorganic salts and changes in their concentration value during the discharge of returned water into a natural water body was analysed.</p>
Global Daily River Discharge product at 0.5° resolution from ISBA-CTRIP simulations based on ERA5-GPCC forcing from 1950 to 2023
<p>This product provides <strong>global daily river discharge at a resolution of 0.5 degree from 1950 to 2023</strong>. It is derived from an offline simulation conducted with the <a href="http://www.umr-cnrm.fr/spip.php?article1092&lang=en" target="_blank" rel="noopener">ISBA-CTRIP</a> global land surface modeling system (<a href="https://doi.org/10.1029/2018MS001545" target="_blank" rel="noopener"><em>Decharme et al. </em>2019</a>) embedded in the <a href="http://www.umr-cnrm.fr/spip.php?article145&lang=en" target="_blank" rel="noopener">SURFEX</a> version 8 modeling platform. It was designed for use in large-scale hydrological applications, as well as in the the <a href="http://www.umr-cnrm.fr/cmip6/spip.php?rubrique8" target="_blank" rel="noopener">CNRM climate models</a> that participate in <a href="https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6" target="_blank" rel="noopener">CMIP6</a> but also in large scale hydrological applications. The model is driven by meteorological forcing data (temperature, precipitation, humidity, winds, etc) derived from the <a href="https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5" target="_blank" rel="noopener">ERA5</a> global atmospheric reanalysis, but where montly precipitation is corrected to align with the <a href="https://www.dwd.de/EN/ourservices/gpcc/gpcc.html" target="_blank" rel="noopener">GPPC</a> observed precipitation product. GPCC <a href="https://opendata.dwd.de/climate_environment/GPCC/html/fulldata-monthly_v2022_doi_download.html">Full Data Monthly Product Version 2022</a> at 0.5 degree resolution are used from 1950 to 2020. For the last three years (2021 to 2023), we used the GPCC <a href="https://opendata.dwd.de/climate_environment/GPCC/html/gpcc_monitoring_v2022_doi_download.html" target="_blank" rel="noopener">Monitoring Product Version 2022</a>, which is only available at 1 degree resolution, and then interpolated at 0.5 degree using a conservative remapping.</p>
Fig. 5 in Spatial Distribution of Suspended Solids During Short-Term High River Discharge in the Bay of Koper, Northern Adriatic Sea
Fig. 5: Velocity profile at comparison site 2 after a 24-hour simulation on 8th June 2011.
Global database of river width, slope, catchment area, meander wavelength, sinuosity, and discharge
<p><strong>1.Summary</strong></p> <p>This document describes the database that accompanies the article written by the authors of this dataset and accepted by Geophysical Research Letters (doi: 10.1029/2019GL082027).The database is distributed as a set of shapefiles, containing polylines that define the geometry of river centerlines located between 60°N and 56°S, with attributes described below. The shapefiles are organized according to continent and further broken into major basins to allow for manageable file sizes. A more complete dataset is available in the netCDF format upon request (please email Renato Frasson at renato.prata.de.moraes.frasson@jpl.nasa.gov).</p> <p>This database was partially funded by the Algorithm Definition Team contract to the Ohio State University, University of North Carolina at Chapel Hill, and Remote Sensing Solutions, Inc.</p> <p><strong>2.Polyline geometry</strong></p> <p>The centerline geometry is defined by sets of points located approximately every 30 m based on the Global River Widths from Landsat (GRLW) database (Allen & Pavelsky, 2015; 2018). Each line describes a meander and features the following attributes.</p> <p><strong>3.Attribute description</strong></p> <ul> <li><strong>SegmentID:</strong> identification number of the river segment (segments are parts of a river delimited by confluences).</li> <li><strong>lakeFlag:</strong> 0 – river, 1 – lake, 2 – river under the influence of tide, 3 – canal, 4 – unable to connect GRWL with HydroSHEDs, 5 – dam, -9999 – no data.</li> <li><strong>Width:</strong> average width in the meander, disregarding small river widths assigned to locations undetected by Landsat but known to be inundated. Locations where no width could be produced are marked as -9999.</li> <li><strong>Elevation:</strong> mean elevation from SRTM (90m) per river meander in meters. SRTM pixels are assigned to equally spaced points (every ~30m) over the river centerlines using the nearest neighbor approach. The average elevation of all valid points per meander is reported here. Locations where no elevation could be produced are marked as -9999.</li> <li><strong>Slope:</strong> water surface slope in centimeter per kilometer. Slope is initially computed over 10 km reaches, then used to compute optimum reach lengths using a modified version of the equation proposed by LeFavour and Alsdorf (2005) in the form of RL=2σ /S, where RL is the optimum reach length, σ is the height uncertainty (5.51 m from LeFavour and Alsdorf, 2005) and S the initial slope estimate. Final slopes are computed over the optimum reach lengths using elevations assigned to the 30 m river points using either classic linear regression or the Theil-Sen estimator depending on which method produces the best coefficient of determination. Locations where no slope could be produced are marked as -9999.</li> <li><strong>Meandwave:</strong> Meander wavelength in meters. This is computed by first smoothing the 30 m resolution river centerlines using a 5-point moving average and then identifying inflection points on the smoothed river centerlines. Finally, the meander wavelength takes the value of twice the distance between consecutive inflection points according to the definition given by Leopold and Wolman (1960).</li> <li><strong>Sinuosity:</strong> Dimensionless sinuosity of each river meander computed the ratio of the length between meander endpoints measured along the river centerline to half the meander wavelength as defined by Leopold and Wolman (1960).</li> <li><strong>catch_area:</strong> Catchment area was derived from flow direction and corresponding flow accumulation grids based on HydroSHEDS (Lehner<em> et al.</em>, 2008). The flow accumulation grid describes, for any location (i.e. pixel), the number of upstream raster pixels that drain to that particular location. We translated flow accumulation given in number of pixels into catchment area (in m<sup>2</sup>) by multiplying the number of pixels flowing to a location by the average area of SRTM pixels according to the latitude of the centroid of the river segment.</li> <li><strong>QWBM:</strong> mean annual flow estimated with the water balance model WBMsed (Cohen<em> et al.</em>, 2014).</li> <li><strong>Strpwr_len:</strong> stream power normalized by width (W/m).</li> <li><strong>Strpwr_are:</strong> stream power normalized by area (W/m<sup>2</sup>).</li> </ul> <p><strong>Acknowledgements</strong></p> <p>Use of this database should be acknowledged appropriately.</p> <p>The WBM data used in this database were provided by Dr. Albert Kettner at INSTAAR, University of Colorado at Boulder.</p> <p><strong>References</strong></p> <p>Allen, G. H., and T. M. Pavelsky (2015), Patterns of river width and surface area revealed by the satellite-derived north american river width data set, <em>Geophysical Research Letters</em>, <em>42</em>(2), 395-402, doi: 10.1002/2014gl062764.</p> <p>Allen, G. H., and T. M. Pavelsky (2018), Global extent of rivers and streams, <em>Science</em>, doi: 10.1126/science.aat0636.</p> <p>Cohen, S., A. J. Kettner, and J. P. M. Syvitski (2014), Global suspended sediment and water discharge dynamics between 1960 and 2010: Continental trends and intra-basin sensitivity, <em>Glob. Planet. Change</em>, <em>115</em>, 44-58, doi: https://doi.org/10.1016/j.gloplacha.2014.01.011.</p> <p>LeFavour, G., and D. Alsdorf (2005), Water slope and discharge in the amazon river estimated using the shuttle radar topography mission digital elevation model, <em>Geophysical Research Letters</em>, <em>32</em>(17), doi: 10.1029/2005gl023836.</p> <p>Lehner, B., K. Verdin, and A. Jarvis (2008), New global hydrography derived from spaceborne elevation data, <em>EOS, TRANSACTIONS, AMERICAN GEOPHYSICAL UNION</em>, <em>89</em>(10), 93-94, doi: doi:10.1029/2008EO100001.</p> <p>Leopold, L. B., and M. G. Wolman (1960), River meanders, <em>Geological Society of America Bulletin</em>, <em>71</em>(6), 769-793, doi: 10.1130/0016-7606(1960)71[769:RM]2.0.CO;2.</p> <p> </p> <p> </p>
Influence of Dust Particles on Spatial Distributions of Particles and Fluxes in Positive Column of Glow Discharge.
<p><strong>Figures obtained from numerical simulation and discribe the influence of dust particles on spatial distributions of particles and fluxes in positive column of glow discharge. Table contain a list of reactions used in model. Dataset will be published in the article "Influence of Dust Particles on Spatial Distributions of Particles and Fluxes in Positive Column of Glow Discharge" </strong>C. Yuan, R. Tian, D. V. Bogdanov, E. A. Bogdanov, A. A. Kudryavtsev and Z. Zhou</p>
Water discharge rates at SAFE Project experimental streams
<b>Description: </b><p>Data for calculating stream discharge in SAFE's experimental rivers</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/115"><b>Effects of retaining riparian forest zones of differing width on stream hydrology following conversion to oil palm</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3479171">here</a></p><p><b>Files: </b>This consists of 1 file: Template_hydro.xlsx</p><p><b>Template_hydro.xlsx</b></p><p>This file contains dataset metadata and 4 data tables:</p><ol><li><p><b>Stream level data for estimating discharge</b> (described in worksheet StreamLevels)</p><p>Description: Five-minute recordings of stream height Each recording has 10 separate values</p><p>Number of fields: 14</p><p>Number of data rows: 141659</p><p>Fields: </p><ul><li><b>Site</b>: Site of datalogger (Field type: location)</li><li><b>Datetime</b>: Date and time of measurement (Field type: datetime)</li><li><b>Distance1</b>: Distance between datalogger and water surface (Field type: numeric)</li><li><b>Distance2</b>: Distance between datalogger and water surface (Field type: numeric)</li><li><b>Distance3</b>: Distance between datalogger and water surface (Field type: numeric)</li><li><b>Distance4</b>: Distance between datalogger and water surface (Field type: numeric)</li><li><b>Distance5</b>: Distance between datalogger and water surface (Field type: numeric)</li><li><b>Distance6</b>: Distance between datalogger and water surface (Field type: numeric)</li><li><b>Distance7</b>: Distance between datalogger and water surface (Field type: numeric)</li><li><b>Distance8</b>: Distance between datalogger and water surface (Field type: numeric)</li><li><b>Distance9</b>: Distance between datalogger and water surface (Field type: numeric)</li><li><b>Distance10</b>: Distance between datalogger and water surface (Field type: numeric)</li><li><b>LoggerBattery</b>: Battery level of the datalogger; max = 4.20V (Field type: numeric)</li><li><b>LoggerTemp</b>: Temperature of the datalogger (Field type: numeric)</li></ul></li><li><p><b>Salt dilution experiments to estimate discharge rates</b> (described in worksheet SaltDilution)</p><p>Description: Salt dilution experiments to estimate discharge rates. Experiments were repeated at each stream on multiple dates. Salt was added 2.5 minutes into each experiment. Details about sites and conditions relating to the experiments are in the 'SaltDilutionMeta' worksheet.</p><p>Number of fields: 6</p><p>Number of data rows: 14620</p><p>Fields: </p><ul><li><b>Site</b>: SAFE stream where dilution was carried out (Field type: location)</li><li><b>Date</b>: Date of dilution experiment (Field type: date)</li><li><b>Treatment</b>: Was salt added or not? (Field type: categorical)</li><li><b>Time</b>: Time since measurements began (Field type: numeric)</li><li><b>Conductivity</b>: Conductivity (Field type: numeric)</li><li><b>SaltConc</b>: Salt concentration (Field type: numeric)</li></ul></li><li><p><b>Calibration for salt dilution experiment</b> (described in worksheet SaltDilutionCalib)</p><p>Description: Calibrating salt concentration to conductivity measuments for salt dilution gauging</p><p>Number of fields: 3</p><p>Number of data rows: 8</p><p>Fields: </p><ul><li><b>Concentration</b>: Salt concentration (Field type: numeric)</li><li><b>Conductivity</b>: Conductivity (Field type: numeric)</li><li><b>Temp</b>: Water temperature (Field type: numeric)</li></ul></li><li><p><b>Salt dilution experiment metadata</b> (described in worksheet SaltDilutionMeta)</p><p>Description: Metadata giving context for salt dilution experiment data in 'SaltDilution' worksheet</p><p>Number of fields: 16</p><p>Number of data rows: 23</p><p>Fields: </p><ul><li><b>Site</b>: Stream on which salt dilution took place (Field type: location)</li><li><b>Date</b>: Date experiment was conducted (Field type: date)</li><li><b>Treatment</b>: Was salt added or not? (Field type: categorical)</li><li><b>SaltAmount</b>: Amount of salt added (Field type: numeric)</li><li><b>Start</b>: Start time (Field type: time)</li><li><b>End</b>: End time (Field type: time)</li><li><b>InitConducRelease</b>: Conductivity at start of experiment at location where salt was poured in (Field type: numeric)</li><li><b>InitConducRecord</b>: Conductivity at start of experiment at location where measurements were recorded (Field type: numeric)</li><li><b>EndConducRelease</b>: Conductivity at end of experiment at location where salt was poured in (Field type: numeric)</li><li><b>EndConducRecord</b>: Conductivity at end of experiment at location where measurements were recorded (Field type: numeric)</li><li><b>ReleaseLat</b>: Latitude of location where salt was released (Field type: numeric)</li><li><b>ReleaseLong</b>: Longitude of location where salt was released (Field type: numeric)</li><li><b>RecordLat</b>: Latitude of location where conductivity was recorded (Field type: numeric)</li><li><b>RecordLong</b>: Longitude of location where conductivity was recorded (Field type: numeric)</li><li><b>Distance between poured and measured (m)</b>: Distance between location where salt was released and conductivity was recorded (Field type: numeric)</li><li><b>Video</b>: Link to video of conductivity metre (Field type: id)</li></ul></li></ol><p><b>Date range: </b>2019-01-24 to 2019-07-01</p><p><b>Latitudinal extent: </b>4.6498 to 4.7317</p><p><b>Longitudinal extent: </b>117.5413 to 117.6233</p>
Water level and Discharge data at Trung Trang and Hon Dau stations, Vietnam
<p>This dataset contains hourly water level data at two stations in the Red River Delta, Hon Dau, located near the coast, and Trung Trang, located in the Van Uc estuary. Hourly discharge data at the Trung Trang station are also included in this dataset.</p>
Supporting data ocean model GMD submission: From Weather Data to River Runoff: Leveraging Spatiotemporal Convolutional Networks for Comprehensive Discharge Forecasting
<p>Ocean model salinity data used for the comparison of the ConvLSTM river runoff model and the original E-HYPE based model simulations.</p>
At-a-station hydraulic geometry exponent b derived from Landsat river width and discharge observation
<h2>Description</h2> <p>Global at-a-station hydraulic geometry exponent b dataset derived from Landsat river width (Feng et al., 2022) and discharge observation (Lin et al., 2019).</p> <p> </p> <p>For more details, please refer to:</p> <div> </div> <p><span>Zimin Yuan, Peirong Lin, Xiwei Guo, Kai Zhang, Hylke E. Beck. Revisiting At-a-Station Hydraulic Geometry Using Discharge Observations and Satellite-Derived River Widths. <em>J Remote Sens.</em> 2024;4:0271. DOI:<a href="https://doi.org/10.34133/remotesensing.0271">10.34133/remotesensing.0271</a></span></p> <p> </p> <h2>Contacts</h2> <ul> <li>Zimin Yuan, <a href="mailto:ziminyuan@pku.edu.cn">ziminyuan@pku.edu.cn</a></li> <li>Peirong Lin, <a href="mailto:peironglinlin@pku.edu.cn" target="_blank" rel="noopener">peironglinlin@pku.edu.cn</a></li> </ul> <p> </p>
Satellite derived discharge for 27 river sections
<p>Discharge time series derived from remote sensing data with upper and lower uncertainty for 27 studied rivers.</p> <p>The filename is based on the upstream and downstream SWORD v15 node_id used in the computation:<br>River_DownstreamNodeID_Upstream_NodeID_discharge.csv</p> <p>When using this data please cite <em>Scherer D., Schwatke C., Dettmering D., Seitz F.</em>: <strong>Monitoring river discharge from space: An optimization approach with uncertainty quantification for small ungauged rivers. </strong>Remote Sensing of Environment. In Review.</p>
Study data for the journal article "Mental health of individuals at increased suicide risk after hospital discharge and initial findings on the usefulness of a suicide prevention project in Central Switzerland"
<p>Anonymized raw data of our cross-sectional survey study.</p> <p>id = study participant ID; se1-se10 = questions of the General Self-Efficacy Scale; sm1-sm5 = questions of the Self-Management Self-Test; hl1-hl12 = questions of the Health Literacy Questionnaire (Swiss version); prisms = question on the utilization of the PRISM-S technique; sp1-sp4 = questions on the utilization and perceived usefulness of the personal safety plan; app1-app7 = questions on the utilization and perceived usefulness of the SERO app; ensa = question on the participation in ensa courses; sex-income = sociodemographic questions</p>
silo discharge of mixtures of soft and hard grains in a 3D silo
<p>This dataset contains experimental data as well as simulation data for the study of granular mixtures of hard frictional beads and soft low-friction beads flowing in a cylindrical silo. The data processing is described in readme.txt respectively. The corresponding scripts are included in the folders.</p> <p>The discharge of granular mixtures of hard frictional beads and soft low-friction beads was studied in a cylindrical silo through experiments and simulations. Flow rate depends on fill height for 100% soft grains but remains constant for 100% hard grains. Mixing the two types of grains causes an abrupt transition: adding just 20% hard grains to soft grains changes the flow behavior significantly, it reduces the slope of the flow rate curve by 50-70%. Simulations show that this is due to the stress sensitivity near the orifice. Additional tests reveal increased dissipation with more hard grains, though not enough to explain the sharp flow rate change.</p> <p> </p>
Data from: Discharge communication for chronic disease patients in three hospitals in India
OBJECTIVES <p>Poor discharge communication is associated with negative health outcomes in high-income countries. However, quality of discharge communication has received little attention in India and many other low and middle-income countries. Primary Objective To investigate verbal and documented discharge communication for chronic non-communicable disease (NCD) patients. Secondary objective To explore the relationship between quality of discharge communication and health outcomes.</p> METHODS: <p>Design Prospective study.</p> <p><strong>Setting:</strong> Three public hospitals in Himachal Pradesh and Kerala states, India.</p> <p><strong>Participants:</strong> 546 chronic NCD (chronic respiratory disease, cardiovascular disease or diabetes) patients. Piloted questionnaires were completed at admission, discharge and Five and eighteen-week follow-up covering health status, health-seeking behaviour and healthcare information exchange practices. Logistic regression was used to explore the relationship between quality of discharge communication and health outcomes.</p> <p><strong>Outcome Measures:</strong></p> <p><strong>Primary:</strong> Patient recall and experiences of verbal and documented discharge communication.</p> <p><strong>Secondary:</strong> Death, hospital readmission and self-reported deterioration of NCD/s.</p> RESULTS <p>All patients received discharge notes, which were predominantly on minimally structured sheets of paper (71%); 31% of notes contained all of the following information required for facilitating continuity of care: diagnosis, medication information, lifestyle advice, and follow-up instructions. Patient reports indicated notable variations in verbal information provided during discharge consultations; 50% received ongoing treatment/management information and 23% received lifestyle advice. Within 18 weeks of follow-up, 25 (5%) patients had died, 69 (13%) had been readmitted and 62 (11%) reported that their chronic NCD/s had deteriorated. Significant associations were found between low-quality documented discharge communication and death (AOR=3.00; 95% CI 1.27,7.06) and low-quality verbal discharge communication and self-reported deterioration of chronic NCD/s (AOR=0.46; 95% CI 0.25,0.83) within 18-weeks of follow-up.</p> CONCLUSIONS <p>Sub-optimal discharge practices may be compromising the continuity and safety of chronic NCD patient care. Structured protocols, documents and training are required to improve discharge communication, healthcare integration and overall NCD management.</p>
Electrocharging face masks with corona discharge treatment
<p></p><p>We detail an experimental method to electrocharge N95 facepiece respirators and face masks (FMs) made from a variety of fabrics (including non-woven polymer and knitted cloth) using corona discharge treatment (CDT). We present practical designs to construct a CDT system from commonly available parts and detail calibrations performed on different fabrics to study their electrocharging characteristics. After confirming the post-CDT structural integrity of fabrics, measurements showed that all non-woven polymer electret and only some knitted cloth fabrics are capable of charge retention. Whereas polymeric fabrics follow the well-known isothermal charging route, ion adsorption causes electrocharging in knitted cloth fabrics. Filtration tests demonstrate improved steady filtration efficiency in non-woven polymer electret filters. On the other hand, knitted cloth fabric filters capable of charge retention start with improved filtration efficiency which decays in time over up to 7 h depending on the fabric type, with filtration efficiency tracking the electric discharge. A rapid recharge for a few seconds ensures FM reuse over multiple cycles without degradation.</p><p></p>
Houska_et_al_Dataset_Identifying the drivers of discharge and instream nitrate concentrations using Random Forest
<p>Dataset and Python code to perform a Random Forest regression analysis on mesured discharge and nitrate concentration data at sixteen points within the Sschwingbach Earth Observatory (SEO) located in Hüttenberg, Hesse, Germany.</p>
The sensitivity of primary productivity in Disko Bay, a coastal Arctic ecosystem to changes in freshwater discharge and sea ice cover
<p>Data for figure 4 in <a href="https://doi.org/10.5194/egusphere-2022-916">https://doi.org/10.5194/egusphere-2022-916</a></p>
Data Set: Balanced Magnetic Antenna for Partial Discharge Measurements in Gas-Insulated Substations
<p>Data set for the publication named: Balanced Magnetic Antenna for Partial Discharge Measurements in Gas-Insulated Substations. Each header corresponds to the figure and legend.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.