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1,121 results for “weathering”
Distinct New Particle Formation and Growth Events under Heatwave Weather of Summer 2022 at Urban Chongqing, Southwest China
Open the record for dataset details and reuse information.
Data for A History of Open Weather in New Zealand (HOWNZ)
<p>Data for A History of Open Weather in New Zealand (HOWNZ): an open access 1 km resolution monthly 1910-2019 time-series of interpolated temperature and rainfall grids with associated uncertainty</p> <p>There is data for both:</p> <ul> <li>nni: the variable estimate from natural neighbour interpolation</li> <li>unc: the uncertainty of the variable estimate</li> </ul> <p>for four weather variables:</p> <ul> <li>rain: total rainfall</li> <li>tavg: mean air temperature</li> <li>tmin: mean daily minimum air temperature</li> <li>tmax: mean daily maximum air temperature</li> </ul> <p>and these variables help form the file names alongside the year and month.</p> <p>So for example, nni-rain-1910-01.tif contains the natural neighbour interpolated values for total rainfall in January 1910, and unc-tavg-1979-05.tif contains the uncertainty values for mean air temperature in May 1979.</p> <p>For storage efficiency when archiving the data, all data value were multiplied by 10 so that they could be stored as 16 bit integer files. Therefore, <strong>all the data needs to be divided by 10</strong> to restore the actual data values before the data is used for any analysis.</p>
Lancer Park (Environmental Education Center) Weather Data from 2021-10-19 to 2021-11-19
<p>General Metadata for Lancer Park Environmental Education Center Atmospheric Sampling Station</p> <p>Files</p> <p>Specific metadata for each deployment can be found as text files with the file format of:</p> <pre><code>LP_weather_YYYY-MM-DD_metadata.txt</code></pre> <p>Where YYYY-MM-DD is the date that the sampling period ended.</p> <p>Metadata File Created</p> <ul> <li>2019-10-27 by KF</li> </ul> <p>File Modified</p> <p>Description</p> <p>These data are from a weather station installed at the Longwood University Environmental Education Center at Lancer Park (37.308189, -78.402768) as part of the Longwood Environmental Observatory.</p> <p>All data are CC-BY and should be cited using the DOI available at <a href="https://zenodo.org/communities/leo/">https://zenodo.org/communities/leo/</a></p> <p>Station Specifics</p> <p>The specific at each site are:</p> <pre><code>* Barometric Pressure (mmHg) - Campbell Scientific CS100 Barometric Pressure Sensor * Light Flux Density (kW/m^2) - Campbell Scientific Pyranometer CS300 * Light Total Flux (kJ/m^2) - Campbell Scientific Pyranometer CS300 * Rainfall (mm) - Texas Electronics TE525 Tipping Bucket * Temperature (dC) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor * Relative Humidity (%) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor * Wind Speed (m/s) - RM Young 05103 Wind Speed and Direction Sensor * Wind Direction (degrees from true N) - RM Young 05103 Wind Speed and Direction Sensor * Data Collection - Campbell Scientific CR200 Data Logger * The sensors are sampled every 15 minutes</code></pre> <p>Measurement Parameters, units, and Variable Names</p> <pre><code>* DATE - the date that the record was collected (YYYY-MM-DD) * TIMESTAMP - the date and time that the record was collected (YYYY-MM-DD HH:MM:SS) * RECORD.x - a unique identifying number provided from the data logger for table 1 from the original downloaded data. * BattV_Avg - The average battery voltage (Volts) * BattV - The battery voltage at the time of the sampling (Volts) * BP_mmHg_Avg - The average barometric pressure over the sampling interval (mmHg) * BP_mmHg_Std - The standard deviation of the average barometric pressure (mmHg) * BP_mmHg - The barometric pressure at the time of the sampling (mmHg) * Rain_mm_Tot - The total rainfall during the sampling interval (mm) * AirTC_Avg - The average air temperature during the sampling interval (dC) * AirTC_Std - The standard deviation of the average air temperature (dC) * AirTC - the air temperature at the time of the sampling (dC) * RH - the relative humidity at the time of the sampling (%) * RH_Min - the minimum relative humidity recorded (%) * RH_TMn - the time that the minimum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS) * RH_Max - the maximum relative humidity recorded (%) * RH_TMx - the time that the maximum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS) * RECORD.y - a unique identifying number provided from the data logger for table 1 from the original downloaded data. * BattV_Min - the minimum battery voltage (Volts) * SlrkW_Avg - the average light flux density during the sampling interval (kW/m^2) * SlrkW_Std - the standard deviation of the average light flux density (kW/m^2) * SlrkW - the light flux density at the time of the sampling (kW/m^2) * SlrMJ_Tot - the total light flux (MJ/m^2) * WS_ms_Avg - the average wind speed during the sampling interval (m/s) * WS_ms_Std - the standard deviation of the average wind speed (m/s) * WS_ms - the wind speed at the time of the sampling event (m/s) * WindDir - the wind direction at the time of the sampling event (degrees from true N) * WS_ms_S_WVT - the average wind speed over the previous 15 minutes (m/s) * WindDir_D1_WVT - the average wind direction over the previous 15 minutes (degrees from true N) * WindDir_SD1_WVT - the standard deviation of the average wind direction over the previous 15 minutes (degrees from true N)</code></pre>
Trends in weather conditions favor generalist over specialist species in rear-edge alpine bird communities
<p>Using the community of six passerine species breeding in the alpine zone of the Cantabrian mountains (NW Iberian Peninsula), we sought to document the changes in bird abundance across the elevational gradient during the last decade, evaluate the relationship between bird abundance and local climatic conditions (i.e. weather conditions), and discuss the mechanisms by which these conditions might be mediating the observed abundance trends in a global warming context. We estimated bird abundance at the home range level using point count transects and distance sampling during the breeding season, and fitted generalized linear mixed models to describe their temporal trends across the elevational gradient. We used a structural equation modeling approach to estimate the direct, indirect and total effects of weather and temporal variables, while considering the correlations and causal relationships among them and with the elevational gradient.</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Kortrijk Kennedy Park, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Kortrijk Kenny Park (50° 48' 2"N 3°16'13" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in recent past climate, Sint-Katelijne-Waver, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Sint-Katelijne-Waver (51°3'25"N 4°11'24" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the recent past period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Digital research data from: Biochar stability in a highly weathered sandy soil under four years of continuous corn production
<p>Biochar is being considered a climate change mitigation tool by increasing soil organic carbon contents (SOC), however, questions remain concerning its longevity in soil. We applied 30,000 kg ha−1 of biochars to plots containing a Goldsboro sandy loam (Fine-loamy, siliceous, sub-active, thermic Aquic Paleudults) and then physically disked all plots. Thereafter, the plots were agronomically managed under 4 years (Y) of continuous corn (Zea Mays, L.) planting. Annually, incremental soil along with corresponding bulk density samples were collected and SOC concentrations were measured in topsoil (down to 23-cm). The biochars were produced from Lodgepole pine (Pinus contorta) chip (PC) and Poultry litter (PL) feedstocks. An untreated Goldsboro soil (0 biochar) served as a control.</p>
Calculation results of the interaction between wet-dry weathering and bedload induced abrasion
<p>Calculation results of the interaction between wet-dry weathering and bedload induced abrasion</p>
Network Analysis to Identify Critical Links for Relief Activities During Extreme Weather Events
<p>As one of the principal lifeline systems, transportation networks are crucial for evacuation and delivering essential resources and services during the response and recovery phases of extreme weather events and must remain intact to enhance regional resiliency. The conventional evaluation measures that estimate the vulnerability or criticality of road network based on travel time or link volumes do not capture the community impacts due to disruptions. This study seeks to develop a framework to evaluate road network infrastructure criticality during extreme weather events by introducing measures that evaluate the vulnerability of roads users, rather than the physical aspects of link importance. The research develops an innovative approach that integrates three important concepts including hurricane evacuation behavior, community impacts, and road criticality to identify the critical links. Results show that the critical links for vulnerable populations during evacuation do not always align with conventional link-based measures. This highlights the importance of using a performance measure that takes the social vulnerability of road users into consideration when identifying the criticality of a road network and planning for fortification of links to avoid irreversible consequences for vulnerable population groups. Furthermore, decision-making that considers the risks to different communities may lead to a more effective distribution of resources and help support a timely and safe evacuation from disaster events by strengthening the preservation of critical infrastructure links.</p>
Wildfire impacts on land-surface properties: implications for weather forecasting
<p>The WRF simulations to study widlfire impacts on land-surface properties. </p>
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result
<p>Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result / 15 Days / Created By Jihyeon Yoon(somehowme@gmail.com, flyingtext@nate.com)</p>
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result / 15 Days / Created By Jihyeon Yoon(somehowme@gmail.com, flyingtext@nate.com)
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result / 15 Days / Created By Jihyeon Yoon(somehowme@gmail.com, flyingtext@nate.com)
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result / 15 Days / Created By Jihyeon Yoon(somehowme@gmail.com, flyingtext@nate.com)
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result / 15 Days / Created By Jihyeon Yoon(somehowme@gmail.com, flyingtext@nate.com)
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result / 15 Days / Created By Jihyeon Yoon(somehowme@gmail.com, flyingtext@nate.com)
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result / 15 Days / Created By Jihyeon Yoon(somehowme@gmail.com, flyingtext@nate.com)
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result / 15 Days / Created By Jihyeon Yoon(somehowme@gmail.com, flyingtext@nate.com)
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result / 10 Days / Created By Jihyeon Yoon(somehowme@gmail.com, flyingtext@nate.com) Latest prediction can be available on map at https://sonagi-weather.web.app Prediction is based on the observation data of - Korea Meteorological Administration API
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result / 10 Days / Created By Jihyeon Yoon(somehowme@gmail.com, flyingtext@nate.com) Latest prediction can be available on map at https://sonagi-weather.web.app Prediction is based on the observation data of - Korea Meteorological Administration API
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.