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1,121 results for “weathering”
Algal growth, bumblebee colony and individual development, bee behavior and yield of oilseed rape under a trophic cascade and extreme weather
<p><span>Trophic cascades in the aquatic environment constitute important mechanisms for improving water quality. However, how the presence or non-presence of these trophic cascades may affect interactions across the aquatic-terrestrial interface remains poorly investigated. Pollinators such as bees may be especially vulnerable to changes in water resource quality induced by trophic cascades. Understanding how aquatic trophic cascades affect bees and pollination becomes even more pressing under ongoing climate change due to increased physiological demands for water under extreme weather events.</span><span>In a novel field experiment combining terrestrial and aquatic mesocosms, we aimed to test how changes in water quality induced by an aquatic trophic cascade </span><span>affected foraging and growth of bumblebee colonies as well as foraging of solitary bees. While we expected fish predation to reduce top-down control of zooplankton on phytoplankton and thereby, indirectly, induce increased growth of toxic cyanobacteria</span><span>, we instead found the trophic cascade to induce the formation of algal surface mats that bumblebees used to access water under a severe heat wave and drought. This access to water was associated with higher bumblebee colony reproductive success, growth and weight compared to control colonies with no trophic cascade induced (and hence no algal surface mats). We also found marginal </span><span>but non-significant</span><span> effects on oilseed rape yield, but surprisingly with higher yields in the control treatment where bumblebees could not access water.</span><span>Our results provide new insights on how aquatic trophic cascades can lead to unpredicted ecological interactions across the aquatic-terrestrial interface facilitated by climate change. Our study highlights the importance of water for the fitness of terrestrial ecosystem service providers under altered environmental conditions.</span></p>
Baliles Center (Hull Springs) Weather Data from 2021-10-28 to 2021-12-04
<p>General Metadata for Hull Springs Farm 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>HSF_weather_YYYY-MM-DD_metadata.txt</code></pre> <p>Where YYYY-MM-DD is the date that the sampling period ended.</p> <p>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 Hull Springs Farm near the "Yellow House" (38.121683, -76.666781) 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>NOTE: The the TIMESTAMP variable is incorrect. The correct time is in the TIMESTAMP.corr variable. See the metadata file for more information.</p> <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>
Data of manuscript "Forecasting day-ahead 1-minute irradiance variability from Numerical Weather Predictions" submitted to Solar Energy
<p>This is the data corresponding to manuscript "Forecasting day-ahead 1-minute irradiance variability from Numerical Weather Predictions" by Kreuwel et al., 2022, submitted to Solar Energy.</p> <p> </p> <p>The file `basic_stats.tar.gz` contains a broad set of standard statistics of surface meteorology and vertical profiles. The file `sw_flux_dn_xy.tar.gz` contains spatial cross sections of downwelling shortwave radiation.</p>
2021_Fires_weather_station_data
<p>Data from the closest weather station to each fire. The file is an Excel file. The table fields are fire name, weather station name, day, hour, average temperature (°C), maximum temperature(°C), minimum temperature (°C), average relative humidity (%), precipitation (mm), wind speed (10 m, km/h), wind direction (10 m, degrees), wind gusts (10 m, km/h), pressure (hPa), radiation (W/m).</p> <p>Source: Meteo.cat, Servei Meteorològic de Catalunya</p>
Data from: Detecting sub-micron space weathering effects in lunar grains with synchrotron infrared nanospectroscopy
<p>Space weathering processes induce changes to the physical, chemical, and optical properties of space-exposed soil grains. For the Moon, space weathering causes reddening, darkening, and diminished contrast in reflectance spectra over visible and near-infrared wavelengths. The physical and chemical changes responsible for these optical effects occur on scales below the diffraction limit of traditional far-field spectroscopic techniques. Recently developed super-resolution spectroscopic techniques provide an opportunity to understand better the optical effects of space weathering on the sub-micrometer length scale. This paper uses synchrotron infrared nanospectroscopy to examine depth-profile samples from two mature lunar soils in the mid-infrared, 1500–700 cm<sup>-1</sup> (6.7–14.3 µm). Our findings are broadly consistent with prior bulk observations and theoretical models of space weathered spectra of lunar materials. These results provide a direct spatial link between the physical/chemical changes in space-exposed grain surfaces and spectral changes of space-weathered bodies.</p>
Influence of weather on gobbling activity of male wild turkeys
<p>Gobbling activity of Eastern wild turkeys (<em>Meleagris gallopavo silvestris</em>; hereafter, turkeys) has been widely studied, focusing on drivers of daily variation. Weather variables are widely believed to influence gobbling activity, but results across studies are contradictory and often equivocal, leading to uncertainty in the relative contribution of weather variables to daily fluctuations in gobbling activity. Previous works relied on road-based auditory surveys to collect gobbling data which limits data consistency, duration, and quantity due to logistical difficulties associated with human observers and restricted sampling frames. Development of new methods using autonomous recording units (ARUs) allows researchers to collect continuous data in more locations for longer periods of time, providing the opportunity to delve into factors influencing daily gobbling activity. We used ARUs from 1 March to 31 May to detail gobbling activity across multiple study sites in the southeastern United States during 2014 – 2018. We used state-space modeling to investigate the effects of weather variables on daily gobbling activity. Our findings suggest rainfall, greater wind speeds, and greater temperatures negatively affected gobbling activity, whereas increasing barometric pressure positively affected gobbling activity. Therefore, when using daily gobbling activity to make inferences relative to gobbling chronology, reproductive phenology, and hunting season frameworks stakeholders should recognize and consider the potential influences of extended periods of inclement weather. </p>
Associated data for: Disease and weather induce rapid shifts in a rangeland ecosystem mediated by a keystone species (Cynomys ludovicianus)
<p><span>Habitat loss and changing climate have direct impacts on native species but can also interact with disease pathogens to influence wildlife communities. In the North American Great Plains, black-tailed prairie dogs (<em>Cynomys ludovicianus</em>) are a keystone species that create important grassland habitat for numerous species and serve as prey for predators, but lethal control driven by agricultural conflict has severely reduced their abundance. Novel disease dynamics caused by epizootic plague (<em>Yersinia pestis</em>) within prairie dog colonies have further reduced prairie dog abundances, in turn destabilizing associated wildlife communities. We capitalized on a natural experiment </span><span>—</span><span> collecting data on prairie dog distributions, vegetation structure, avian abundance, and mesocarnivore and ungulate occupancy before (2015–2017) and after (2018–2019) a plague event in northeastern Wyoming, USA. Plague decimated black-tailed prairie dog populations in what was then the largest extant colony complex, reducing colony cover in the focal area from over 10,000 ha to less than 50 ha. We documented dramatic declines in mesocarnivore occupancy and raptor abundance post-plague, with probability of occupancy or abundance approaching zero in species that rely on prairie dogs for a high proportion of their diet (e.g., ferruginous hawk [<em>Buteo regalis</em>], American badger [<em>Taxidea taxus</em>], and swift fox [<em>Vulpes velox</em>]). Following the plague outbreak, abnormally high precipitation in 2018 hastened vegetation recovery from prairie dog disturbance on colonies where constant herbivory had formerly maintained shortgrass structure necessary for certain colony-associates. As a result, we observed large shifts in avian communities on former prairie dog colonies, including near-disappearance of mountain plovers (<em>Charadrius montanus</em>) and increases in mid-grass associated songbirds (e.g., lark bunting [<em>Calamospiza melanocorys</em>]). Our research highlights how precipitation can interact with disease-induced loss of a keystone species to induce drastic and rapid shifts in wildlife communities. Although grassland taxa co-evolved with high spatiotemporal variation, fragmentation of remaining North American rangelands paired with higher-than-historical variability in climate and disease dynamics are likely to destabilize these systems in the future. </span></p>
Pre-generated network files for "Intersecting near-optimal spaces: European power systems with more resilience to weather variability"
<p>These are network files that can be used to investigate the impacts of weather variability on the European power system using PyPSA-Eur as in <a href="https://github.com/aleks-g/intersecting-near-opt-spaces/tree/v1.0">https://github.com/aleks-g/intersecting-near-opt-spaces/tree/v1.0</a>. Find more information about the approach in the README of that repository.</p> <p>These network files are a shortcut to reproduce the results and use a fixed configuration ("v1.0"). For other configurations, it may be necessary to download ERA5 reanalysis cutouts (more on this in the git repository).</p> <p>Instructions can be found in the git repository.</p>
Data from: Learning to predict spatio-temporal movement dynamics from weather radar networks
<p>This dataset contains the following:</p> <ul> <li><strong>data</strong>: <ul> <li><em>preprocessed</em>: hourly European weather radar data (here: <em>radar</em>) and aggregated simulation outputs (here: <em>abm</em>), combined with ERA5 reanalysis data and Voronoi tessellation details</li> <li><em>shapes: </em>geographical shapes used for plotting</li> </ul> </li> <li><strong>results</strong><em>:</em> trained models and corresponding results for both simulated data (here: <em>abm</em>) and weather radar data (here: <em>radar</em>).</li> <li><strong>figures</strong><em>: </em>final figures presented in our paper "Learning to predict spatio-temporal movement dynamics from weather radar networks" to summarize the results</li> </ul> <p>The corresponding code used to train and evaluate models is archived here: <a href="https://doi.org/10.5281/zenodo.6921595">10.5281/zenodo.6921595</a>.</p>
Data and code for "A decade of monitoring micropollutants in urban wet-weather flows: what did we learn?"
<p><strong>Data and code for publication</strong></p> <p><em>Lena Mutzner, Viviane Furrer, Hélène Castebrunet, Ulrich Dittmer, Stephan Fuchs, Wolfgang Gernjak, Marie-Christine Gromaire, Andreas Matzinger, Peter Steen Mikkelsen, William R. Selbig, Luca Vezzaro,<br> A decade of monitoring micropollutants in urban wet-weather flows: what did we learn?<br> Water Research, 2022.<br> https://doi.org/10.1016/j.watres.2022.118968</em></p> <p><strong>Data</strong></p> <p>Micropollutants concentrations in µg/l (incl. heavy metals) for 77 wet-weather discharge sites (36 combined sewer overflows, 41 stormwater outlets). The provided data set is a collection of raw data sets based on publications referenced below. The data description is provided in the folder Data\AA_DataDescription.txt</p> <p><strong>Abstract</strong></p> <p>Urban wet-weather discharges from combined sewer overflows (CSO) and stormwater outlets (SWO) are a potential pathway for micropollutants (trace contaminants) to surface waters, posing a threat to the environment and possible water reuse applications. Despite large efforts to monitor micropollutants in the last decade, the gained information is still limited and scattered. In a metastudy we performed a data-driven analysis of measurements collected at 77 sites (683 events, 297 detected micropollutants) over the last decade to investigate which micropollutants are most relevant in terms of 1) occurrence and 2) potential risk for the aquatic environment, 3) estimate the minimum number of data to be collected in monitoring studies to reliably obtain concentration estimates, and 4) provide recommendations for future monitoring campaigns. We highlight micropollutants to be prioritized due to their high occurrence and critical concentration levels compared to environmental quality standards. These top-listed micropollutants include contaminants from all chemical classes (pesticides, heavy metals, polycyclic aromatic hydrocarbons, personal care products, pharmaceuticals, and industrial and household chemicals). Analysis of over 30,000 event mean concentrations shows a large fraction of measurements (> 50%) were below the limit of quantification, stressing the need for reliable, standard monitoring procedures. High variability was observed among events and sites, with differences between micropollutant classes. The number of events required for a reliable estimate of site mean concentrations (error bandwidth of 1 around the “true value) depends on the individual micropollutant. The median minimum number of events is 7 for CSO (2 to 31, 80%-interquantile) and 6 for SWO (1 to 25 events, 80%-interquantile). Our analysis indicates the minimum number of sites needed to assess global pollution levels and our data collection and analysis can be used to estimate the required number of sites for an urban catchment. Our data-driven analysis demonstrates how future wet-weather monitoring programs will be more effective if the consequences of high variability inherent in urban wet-weather discharges are considered.</p>
Impact of weather and hydro variability on the operation of the Bolivian power system.
<p>This DataSet is the support for the study of the Impact of climate variability on the hydroelectric generation of the Bolivian electrical system, for which a hydrological model (WEAP) and unit commitment and optimal dispatch model (DispaSet) have been used. The study provides information about the flexibility of the electrical system, by the variation of the hydropower generation through the time series of historical inflows, in terms of power generation, total system costs, consumption, and carbon dioxide emissions.</p>
Differential effects of weather, plant phenology and predators on the seasonal variation of aphids on cabbage
<p>Raw field data of aphids and predator (Syrphid, spiders, and coccinellids) densities on cabbage, and weather data in two agroecological zones of Ghana, collected over two years for five cropping seasons. Data was collected on field-grown cabbage weekly after two weeks of transplanting, till when cabbages were matured. The destructive sampling method according to Hughes, 1963 was adopted by randomly sampling the third, fourth, and fifth expanded leaves of 20 randomly selected cabbage plants. Aphids and predators present were visually identified, counted, and recorded. </p>
Weathered Rocking Chair
**MESH PARAMETERS** **Object Description:** Digitized weathered, wooden rocking chair; Real-life approximate dimensions 34.5"x26.75"x48.5" **Watertight:** Yes <br> **TEXTURE PARAMETERS** **Count:** 1 **Size:** 8,192 x 8,192 **Delighting:** Yes Source: Objaverse 1.0 / Sketchfab
Unsupervised detection of Large-scale Weather Patterns in the Northern Hemisphere via Markov State Modelling: from Blockings to Teleconnections
<p><em><span>Data Source</span></em></p> <p>This dataset is derived from the <em><span>NCEP-NCAR Reanalysis 1</span> data provided by the NOAA PSL, Boulder, Colorado, USA, from their website at <a href="https://psl.noaa.gov/">https://psl.noaa.gov</a></em>., a robust atmospheric dataset that includes a wide range of climatic measurements essential for comprehensive climate analysis. The original data can be accessed at the NOAA Physical Sciences Laboratory website: https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html.</p> <p> </p>
Can ingredients based forecasting be learned? Disentangling a random forest's severe weather predictions
<p>Machine learning (ML)-based models have been rapidly integrated into forecast practices across the weather forecasting community in recent years. While ML tools introduce additional data to forecasting operations, there is a need for explainability to be available alongside the model output, such that the guidance can be transparent and trustworthy for the forecaster. This work makes use of the algorithm tree interpreter (TI) to disaggregate the contributions of meteorological features used in the Colorado State University Machine Learning Probabilities (CSU-MLP) system, a random forest-based ML tool that produces real-time probabilistic forecasts for severe weather using inputs from the Global Ensemble Forecast System v12. TI feature contributions are analyzed in time and space for CSU-MLP day-2 and 3 individual hazard (tornado, wind, and hail) forecasts and day-4 aggregate severe forecasts over a 2-yr period. For individual forecast periods, this work demonstrates that feature contributions derived from TI can be interpreted in an ingredients-based sense, effectively making the CSU-MLP probabilities physically interpretable. When investigated in an aggregate sense, TI illustrates that the CSU-MLP system's predictions use meteorological inputs in ways that are consistent with the spatiotemporal patterns seen in meteorological fields that pertain to severe storms climatology. This work concludes with a discussion on how these insights could be beneficial for model development, real-time forecast operations, and retrospective event analysis.</p>
Dataset for the manuscript "Amapari Marker Band Metal-Enrichments: Potential Mechanisms and Implications for Surface and Subsurface Water and Weathering in Gale crater"
<p>The dataset for the main manuscript contains three CSV files: list of long distance remote micro images (LDRMIs), the ChemCam chemical data, and the alpha particle X ray spectrometer (APXS) chemical data. The LDRMI csv lists two sets of data, one for the sol (Mars solar day), sequence identifier, and target name of LDRMIs that target the Amapari Marker Band (AMB). These are represented as green points on manuscript Figure 1. The second list are for LDRMI images that did not specifically target the AMB, but contain the AMB, and these are orange points on manuscript Figure 1. The ChemCam data file has metadata for each observation point in a ChemCam target: file name, spacecraft clock, observation number, stratigraphic member, rock type, target distance, and total of all spectral channels. Chemical information includes: the oxide chemistry (oxide wt%), uncertainty (RMSEP accuracy and shot-to-shot precision), minor element quantification, and minor element uncertainty (ppm), preliminary sulfate composition (without uncertainty and only provided up to sol 3778), peak areas for some elements (Cu, Zn, and Cr). The oxide sum includes all oxides and elements for which we have quantitative data. Spectral unmixing qualitative values are listed for each element. Derived data includes the corrected MgO, Na<sub>2</sub>O, and CaO in both mol and oxide wt% (values before and after correction are provided), and the calculated alteration index. Derived data is only calculated for AMB bedrock targets (does not include veins or mixed vein-rock targets with elevated CaO). The APXS data sheet provides sol, target name, oxide data, and uncertainties for the drill tailings observations, and the derived data for each target.</p>
Raw data for publication: Weathering of Wood Modified with Acetic Anhydride – Physical, Chemical, and Aesthetical Evaluation
<p>Color.xlsx</p> <p>This file contains CIE Lab* color coordinates measured on the surface of wood samples.</p> <p>Weather data.xlsx</p> <p>This file contains daily local weather conditions in San Michelle, Italy during the natural weathering test.</p> <p>Gloss.xlsx</p> <p>This file contains the gloss value measured on the surface of wood samples.</p> <p>contact angle and surface energy.xlsx</p> <p> This file contains dynamic contact angle and surface energy measured on the surface of wood samples.</p> <p>TGA.xlsx</p> <p>This file contains the Thermogravimetric (TG) and derivative thermogravimetric (DTG) data of the wood samples.</p> <p>Surface roughness.xlsx</p> <p>This file contains the roughness value of wood samples.</p>
Front Data for Scalable GPU-Enabled Creation of Three Dimensional Weather Fronts
<p>Frontal Polylines for 2016 created with the network described in:</p> <p>https://doi.org/10.5194/wcd-3-113-2022</p> <p> </p> <p>The work is supported by the project ``Big Data in Atmospheric Physics<br>(BINARY)'', funded by the Carl Zeiss Foundation (grant P2018-02-003).</p>
Pedestrian street crossing in difficult weather conditions (Pedestrian-DVS)
<p>Pedestrian-DVS is a dataset of video frames showing pedestrians crossing the street obtained from CARLA simulation environment.</p> <p>The dataset consists of two subsets, showing scenes in good and bad weather conditions. The good weather subset consists of 117 videos, while bad weather subset contains 81 videos. Each video consists of 900 frames.</p> <p>Standard RGB frames are paired with corresponding frames extracted from recordings of dynamic vision sensor. Each frame is labeled using the following format: <frame_number-label.format>. A frame is labeled as positive if, in a given frame, the pedestrian is crossing the street.</p> <p>For more information, please visit the repository with code used to analyze this dataset or contact the authors.</p> <p>Contact email:</p> <p>szmazurek@agh.edu.pl</p>
Weather data (forecast and observation) at 48 locations in France for beginning of 2024 for Machine Learning Training
<p>The data provided data are historical weather measurement and forecast at 48 locations in France and its boundary.</p> <p>Measurements are inside files named MES_YYYY.csv with YYYY is the id code of the station.</p> <p>The file "Station_list.csv" contains the list of the 45 locations with the id code, the name and then the latitude and longitude.</p> <p><br>Forecasts are inside files named XXX_YYYY.csv with YYYY the id code corresponding of the location of the grid ouput close to the associated observation location.<br>XXX is the id of the numerical forecast:<br> "GFS0.25-Complet" for GFS file at 0.25° resolution<br> "LEXIS" for WRF produced by EVEREST project using the LEXIS chain<br> "WRF3KM-Complet" for WRF at 3km resolution produced by NUMTECH<br> "WRF12KM-Complet" for WRF at 12km resolution produced by NUMTECH</p> <p><br>Description of MES-YYYY files:<br>- One line per measurement with hourly resolution<br>- columns are: Date(TU),Temperature2m_degC,WindSpeed10m_m/s,WindDirection10m_m/s<br> Date = date of measurement in TU and format DD/MM/YYYY HH:MM<br> Temperature2m_degC = air temperature at 2m height in °Celsius<br> WindSpeed10m_m/s = wind speed at 10m height in m/s<br> WindDirection10m_deg = wind direction at 10m height in deg. (0 or 360 = wind from north to south, 45°=wind from east to east, ....)<br>If measurement is not available for a specific hour for one parameter, the value "-999" is used.</p> <p>The observation data gocfrom 28/01/2024 00HTU to 17/03/2024 23HTU</p> <p><br>Description of XXX_YYYY forecast files:<br>- One line per forecast with hourly resolution<br>- columns are: First date run (TU),Forecast date,Temperature2m_degC,WindSpeed10m_m/s,WindDirection10m_m/s<br> First date run (TU) = date of start of the forecast in TU and format DD/MM/YYYY HH:MM. HH could be 00 and 12 according to the cycle of forecast start.<br> Forecast date = date of the forecast in TU and format DD/MM/YYYY HH:MM. HH go from 00 to 23. <br> Temperature2m_degC = air temperature at 2m height in °Celsius<br> WindSpeed10m_m/s = wind speed at 10m height in m/s<br> WindDirection10m_deg = wind direction at 10m height in deg. (0 or 360 = wind from north to south, 45°=wind from east to east, ....)<br>If forecast is not available for a specific hour for one parameter, the value "-999" is used.</p> <p>The forecast data go from 28/01/2024 00HTU to 17/03/2024 23HTU</p>
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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.