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10,016 results for “Observation”
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>
Data from: Direct observational evidence of strong CO2 uptake in the Southern Ocean
<p>These are the eddy covariance air-sea CO2 flux dataset, the subsampled CO2 flux products, and the neural network-based interpolation of the SOCCOM-weighted and SOCAT plus SOCCOM datasets. These datasets are used in the article 'Direct observational evidence of strong CO2 uptake in the Southern Ocean'.</p>
Observation of topological frequency combs
<p>On-chip generation of optical frequency combs using nonlinear ring resonators has enabled numerous applications of combs that were otherwise limited to mode-locked lasers. Nevertheless, on-chip frequency combs have relied predominantly on single-ring resonators. Here, we experimentally demonstrate the generation of a novel class of frequency combs, the topological frequency combs, in a two-dimensional lattice of hundreds of ring resonators that hosts fabrication-robust topological edge states with linear dispersion. By pumping these edge states, we demonstrate the generation of a nested frequency comb that shows oscillation of multiple edge state resonances across $\approx$40 longitudinal modes and is spatially confined at the lattice edge. Our results provide an opportunity to explore the interplay between topological physics and nonlinear frequency comb generation in a commercially available nanophotonic platform.</p>
Fig. 6 in Observations on the mortality of olive ridley sea turtles (Lepidochelys olivacea) and associated factors along Ganjam coast, east coast of India
Fig. 6 — Association of beach elevation with turtle mortality
Fig. 5 in Observations on the mortality of olive ridley sea turtles (Lepidochelys olivacea) and associated factors along Ganjam coast, east coast of India
Fig. 5 — Changes in the Rushikulya river mouth during the last two decades
Fig. 4 — A dead olive ridley with a in Observations on the mortality of olive ridley sea turtles (Lepidochelys olivacea) and associated factors along Ganjam coast, east coast of India
Fig. 4 — A dead olive ridley with a possible hit mark on its carapace
Fig. 1 in Observations on the mortality of olive ridley sea turtles (Lepidochelys olivacea) and associated factors along Ganjam coast, east coast of India
Fig. 1 — Study area map with survey locations
Data from U and Th zonation in apatite observed by synchrotron X–ray fluorescence tomography and implications for the (U–Th)/He system Sousa et al 2024
<p>Data from </p> <p><span>U and Th zonation in apatite observed by synchrotron X–ray fluorescence tomography and implications for the (U–Th)/He system</span></p> <p><span>Sousa et al</span></p> <p><span>Geochronology</span></p>
Carbon dioxide (CO2) variations across India: Synthesis of observations and model simulations
<p>India is the 3rd largest emitter of fossil fuel carbon dioxide (CO<sub>2</sub>), making it essential to understand CO<sub>2</sub> dynamics to manage carbon emissions. This study examines CO<sub>2</sub> variability and its dynamics based on observations at eleven observational sites in India, satellite observations and model simulations. Results reveal distinct diurnal and seasonal patterns, along with an overall increasing trend in CO<sub>2</sub>. Deeper (shallower) seasonal cycle amplitudes (SCA) observed over northern (southern) part of India are due to the influence of the monsoon system, seasonal climate and terrestrial biosphere. The lowest SCA is observed over the high-altitude site, Hanle in north India (7.4 ppm), followed by the coastal sites, Pondicherry (8.0 ppm) and Thumba (8.4 ppm) in south India. Deepest SCA, 26.7 ppm, is observed at Mohali with one of the peaks observed in November attributed to crop residue burning activities in the Indo-Gangetic Plain. We have used an Atmospheric Chemistry Transport Model (ACTM) to simulate spatial and temporal variations in CO<sub>2</sub>. While the ACTM generally reproduces diurnal variability in January, it fails to capture the CO<sub>2 </sub>minima in July. The model simulates seasonal patterns at Thumba reasonably well, whereas underestimates the SCA at Gadanki. Satellite observations of column CO<sub>2</sub> (XCO<sub>2</sub>) show higher values (410–414 ppm) during the pre-monsoon season, while they remain lower (407–410 ppm) during winter and post-monsoon seasons during 2014–2024. Mean XCO<sub>2</sub> trend (2.41–2.46 ppm yr<sup>-1</sup>) and growth rate variations are similar to Mauna Loa observations.</p>
Diagnosis of tuberculosis infection in children with a novel skin test and the traditional tuberculin skin test: an observational study
<p>Codebook of a minimal data set that can be accessed through the MF-DAC of the Universtiy Basel, Switzerland. </p>
Bromoform observations in the HalOcAt database
<p>These two files contain atmospheric and oceanic observations of Bromoform from the HalOcAt database (https://halocat.geomar.de/).</p> <p>The data are daily mean averages which are used for model validation in the publication "Natural marine bromoform emissions in the fully coupled ocean-atmosphere-model NorESM2" (https://doi.org/10.5194/esd-2024-3)</p> <p> </p>
Observations of moose behaviour and foraging choices in summer
<p>Understanding how the nutritional properties of food resources drive foraging choices is important for the management and conservation of wildlife populations. For moose (<em>Alces alces</em>), recent experimental and observational studies during the winter have shown macronutrient balancing between available protein (AP) and highly metabolizable macronutrients (total non-structural carbohydrates [TNC] and lipids).</p> <p>Here, we combined the use of continuous-recording camera collars with plant nutrient analyses and forage availability measurements to obtain a detailed insight into the food and nutritional choices of three wild moose in Norway over a five-day period in summer.</p> <p>We found that moose derived their macronutrient energy primarily from carbohydrates (74.2%), followed by protein (13.1%), and lipids (12.7%). Diets were dominated by deciduous tree browse (71%). Willows (<em>Salix </em>spp.) were selected for and constituted 51% of the average diet. Moose consumed 25 different food items during the study period of which nine comprised 95% of the diet. Moose tightly regulated their intake of protein to highly metabolizable macronutrients (AP:TNC+lipids). They did this by feeding on foods that most closely matched the target macronutrient ratio such as <em>Salix</em> spp., or by combining nutritionally imbalanced foods (complementary feeding) in a non-random manner that minimized deviations from the intake target. The observed patterns of macronutrient balancing aligned well with the findings of winter studies.</p> <p>Differential feeding on nutritionally balanced downy birch (<em>Betula pubescens</em>) leaves versus imbalanced twigs+leaves across moose individuals indicated that macronutrient balancing may occur on as fine a scale as foraging bites on a single plant species. Utilized forages generally met the suggested requirement thresholds for the minerals calcium, phosphorus, copper, molybdenum, and magnesium but tended to be low in sodium. Our findings offer new insights into the foraging behavior of a model species in ungulate nutritional ecology and contribute to informed decision-making in wildlife and forest management.</p>
Hypervelocity star observations constrain the Galactic Centre
<p>Full candidate list and observational log accompanying the publication Hypervelocity star observations constrain the Galactic Centre.</p>
WiFeS observation of southern nearby Type Ia supernova host galaxies
<p>This repository contains all the reduced and spatially-binned spectral data used in this work. Refer to the README for more detailed information. </p>
Addressing Observational Gaps in Aerosol Parameters using Machine Learning: Implications to Aerosol Radiative Forcing
<p>This dataset represents Aerosol Optical Depth (AOD), Single Scattering Albedo (SSA), and Absorption Parameter (AP) data over Kanpur, India, sourced from AERONET with initial data gaps of approximately 37%, 62%, and 58% respectively. To reduce these gaps, XGBoost, a machine learning model trained with reanalysis and satellite datasets, was employed with optimized hyperparameter tuning. Using AERONET data for training, XGBoost effectively addressed gaps, improving AOD by 10%, SSA by 23%, and AP by 21%.</p>
iSALE Datasets of "Evidence for magnetized ejecta deposits on the Moon based on observations of demagnetized craters"
<p>The input data (*.inp) is used for iSALE impact simulation for the four craters (Chaplygin, Keeler, Gauss, and Fermi) and the output data (*.dat) are its result.</p>
In-situ observations of surface Chlorophyll-a (HPLC) in the Western Antarctic Peninsula during 2008-2018 (GOAL-FURG)
<p>This dataset contains observations of in-situ chlorophyll-a (mg/m3) along the Western Antarctic Peninsula from 2008 to 2018. All data included in this dataset were collected by the Brazillian High Latitude Oceanography Group (GOAL), based at the Federal University of Rio Grande (FURG), aboard research vessels from the Brazillian Navy.</p> <p>All chlorophyll-a samples were collected at 5 metres depth. Chlorophyll-a concentration was determined through High Pressure Liquid Chromatography. This dataset has been collected thanks to a 10 year effort of sampling by GOAL in the Western Antarctic Peninsula. As such, many researchers, students, and crew members contributed to this valuable dataset. Therefore, we ask that users acknowledge the use of the dataset.</p> <p>Please consult Ferreira et al. 2024 for more information on how the data was collected and analysed.</p>
Datasets used in "A 23-Year Severe Hail Climatology Using GridRad MESH Observations"
<p>These datasets are those used in the Murillo et al. (2021) publication in the AMS journal of Monthly Weather Review. For any questions, please email Dr. Murillo at emurillo@ucar.edu.</p>
CLAMPS NWC/RIL Observations
<p>CLAMPS observations from CLAMPS1 during a local/internal deployment to restart operations after COVID19 pause of deployments. CLAMPS2 did not deploy. These datasets include boundary-layer profiles of wind, temperature, and humidity collected from the CLAMPS system. </p> <p>These data can be accessed on the <a href="https://data.nssl.noaa.gov/thredds/catalog/FRDD/CLAMPS/campaigns/PBLtops/catalog.html">NOAA National Severe Storms THREDDS Archive</a>. The readme contained here provides information about how to access and available resources for users. </p> <p>After a full pause of all observation activity due to the COVID19 pandemic, the CLAMPS1 facility was deployed just outside the National Weather Center in Norman, OK to restart observation practices. The location is between the National Weather Center and the OU Radar Innovations Laboratory and directly adjacent to the KOUN NWS-Norman Sounding release location. Observations are generally continuous throughout the period, though some non-standard scan and operations modes may be included as a result of tests conducted by the operations team during the deployment. </p>
PBL Tops CLAMPS observations
<p>CLAMPS observations from both CLAMPS1 and CLAMPS2 during PBL Tops: Evaluating Polarimetric Retrievals of Boundary Layer Height Using State-of-the-Art Boundary Layer Profiling, a project supported by OU/CIWRO's Director's Discretionary Funds. Details about the instruments onboard CLAMPS can be found on the <a href="https://apps.nssl.noaa.gov/CLAMPS/instrumentation.php">NSSL CLAMPS</a> webpage. These datasets include boundary-layer profiles of wind, temperature, and humidity collected from the CLAMPS systems.</p> <p>These data can be accessed on the <a href="https://data.nssl.noaa.gov/thredds/catalog/FRDD/CLAMPS/campaigns/PBLtops/catalog.html">NOAA National Severe Storms THREDDS Archive</a>. The readme contained here provides information about how to access and available resources for users. </p> <p>Observations cover a 4-week observation period, split into two 2-week blocks. During block 1, each CLAMPS will deploy near KTLX (Norman, OK) at a radar-adjacent site (0-5 km) and a radar-distant site at Kessler Atmospheric and Ecologial Field Station (10-20 km), respectively. During block 2, the radar-distant CLAMPS will reposition to a radar-adjacent site at KSHV (Shreveport, LA). The observational approach providess one continuous 4-week time series within 5 km of KTLX. By moving the second CLAMPS platform between the two blocks (from Kessler Station to KSHV), the data can be used to evaluate differences due to distance from radar and the local climatology or land-use characteristics, which may modify expected boundary-layer evolution. </p> <p>Owing to the sparse distribution of atmospheric profilers, numerous studies have sought to use radar detection of Bragg scattering to observe the evolution of planetary boundary layer (BL) height, including efforts using the operational NWS WSR-88D network by Banghoff et al. (2018; hereafter B18) using. The B18 method uses quasi-vertical profiles obtained from azimuthal averaging (Ryzhkov et al. 2016) and offers the promise of national, real-time estimates of BL height. However, the B18 method was only initally validated using twice-daily radiosonde observations, thus obscuring its efficacy for retrieving BL height estimates during periods of evolution and complex structure (e.g., morning/evening transitions, residual layers, etc.). The observations in this dataset were collected in an effort to (1) validate the polarimetric B18 method using specialized independent measurements from both CLAMPS; (2) extend validation times to include important BL evolution periods beyond synoptic sampling times and identify failure periods and regimes; (3) contextualize and interpret signatures observed in B18 using novel near-continuous data (e.g., overnight signatures); and (4) quantify the skill of the B18 method.</p> <p>Banghoff, J.R., D.J. Stensrud, and M.R. Kumjian, 2018: Convective Boundary Layer Depth Estimation from S-Band Dual-Polarization Radar. J. Atmos. Oceanic Technol., 35, 1723–1733.</p> <p>Ryzhkov, A., P. Zhang, H. Reeves, M. Kumjian, T. Tschallener, S. Trömel, and C. Simmer, 2016: Quasi-Vertical Profiles—A New Way to Look at Polarimetric Radar Data. J. Atmos. Oceanic Technol., 33, 551–562.</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
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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.