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10,016 results for “Observation”
Baltimore Ecosystem Study LTER site, station NOAA Station 8574680, Baltimore, MD, study of mean sea level: the arithmetic mean of hourly heights observed over the National Tidal Datum Epoch (as defined by NOAA) in units of meter on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Baltimore Ecosystem Study LTER (BES) contains mean sea level: the arithmetic mean of hourly heights observed over the National Tidal Datum Epoch (as defined by NOAA) measurements in meter units and were aggregated to a yearly timescale.
Georgia Coastal Ecosystems site, station NOAA Station 8670870, Ft. Pulaski, GA, study of mean sea level: the arithmetic mean of hourly heights observed over the National Tidal Datum Epoch (as defined by NOAA) in units of meter on a monthly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Georgia Coastal Ecosystems (GCE) contains mean sea level: the arithmetic mean of hourly heights observed over the National Tidal Datum Epoch (as defined by NOAA) measurements in meter units and were aggregated to a monthly timescale.
Georgia Coastal Ecosystems site, station NOAA Station 8670870, Ft. Pulaski, GA, study of mean sea level: the arithmetic mean of hourly heights observed over the National Tidal Datum Epoch (as defined by NOAA) in units of meter on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Georgia Coastal Ecosystems (GCE) contains mean sea level: the arithmetic mean of hourly heights observed over the National Tidal Datum Epoch (as defined by NOAA) measurements in meter units and were aggregated to a yearly timescale.
The Effects of Climate Downscaling Technique and Observational Dataset on Modeled Ecological Responses: Supporting Data Tables
These data have been prepared as a supplement to Pourmokhtarian et al. (2016; full citation below), where complete details on methods can be found. We evaluated three downscaling methods: the delta method (or the change factor method); monthly quantile mapping (Bias Correction-Spatial Disaggregation, or BCSD); and daily quantile regression (Asynchronous Regional Regression Model, or ARRM). Additionally, we trained outputs from four atmosphere-ocean general circulation models (AOGCMs) (CCSM3, HadCM3, PCM, and GFDL-CM2.1) driven by higher (A1fi) and lower (B1) future emissions scenarios on two sets of observations (1/8th degree resolution grid vs. individual weather station) to generate the high-resolution climate input for the forest biogeochemical model PnET-BGC (8 ensembles of 6 runs). This dataset consists of three files - 1) a zip archive file of all raw daily downscaled AOGCMs (csv format; years 1960-2099; delta method 2012-2099 only) which were used as input for PnET-BGC model, 2) a zip archive file of all PnET-BGC output files for each model run (csv format; years 1000-2100), and 3) a pdf document file that describes the content of the input and output files. Data were also used from the following Hubbard Brook longterm datasests: Daily Streamflow Watershed 6: http://dx.doi.org/10.6073/pasta/727ee240e0b1e10c92fa28641bedb0a3 Chemistry of Streamwater at the Hubbard Brook Experimental Forest, Watershed 6: http://dx.doi.org/10.6073/pasta/2ec152b0ab1d4e64aa40f4aa9bc492ac Daily Precipitation Watershed 6: http://dx.doi.org/10.6073/pasta/17c8ff8b160bf7893ef39f75a02652e5 Daily Maximum/Minimum Temperature Data: http://dx.doi.org/10.6073/pasta/2a4ab5522ce15f28196a6035802b09e8 Daily Solar Radiation Data: http://dx.doi.org/10.6073/pasta/2fa098a5aa191c64e622b253c0fee5af These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest
National Weather Service Daily Observations at Kalamazoo Hospital (1887 to 1987)
Dataset Abstract Weather Observations taken between June 1887 and April 1987 at the Kalamazoo Hospital NWS station. Comments: This data has not been verified; there are errors. If you come across inconsistent data please send a message stating your observation to the LTER data manager. Datatable Abstracts The daily record of the NWS station at the Kalamazoo Hospital. Flag Codes: * 1 Missing * 2 Trace * 3 Estimated * 4 Amount included in subsequent observation * 5 Estimated trace amount * 9 Miscellaneous NOTE: The date represents time intervals from 16:00 thru 16:00 e.g.minimum temperature data for 02-may-1987 is the minimum temperature recorded from 30-apr- 1987:16:00 to 01-may-1987:16:00. This date corresponds to date noted at the end of the day interval. (01-may-1987:24:00:00 = 02-may- 1987:00:00:00). This data has not been verified; there are errors. If you come across inconsistent data please send a message stating your observation to the <a mailto='lter.data.manager@kbs.msu.edu'>LTER data manager</a>. The weather data from the Kalamazoo Hospital Station formatted for climdb
Observational data and numerical experimental files for subglacial discharge plume in Bowdoin Fjord, northwestern Greenland
<p>The dataset includes observational data and all the files used in the numerical experiments. Additionally, this includes the codes to generate the figures and analysis. Please see Ohashi et al. (2019) for details (<a href="https://www.ocean-sci-discuss.net/os-2019-33/">https://www.ocean-sci-discuss.net/os-2019-33/</a>).</p>
LiF data pack supporting "Observation of diffraction contrast in scanning helium microscopy"
<p>All the experimental data from the paper is included in this data pack including both the images and the z scans. </p> <p>Images from the paper are presented in three different formats with filenames scan00*****:</p> <p>1) .png file which is an unprocessed image of the raw data. <br> 2) .gwy file which is an unprocessed version of the data that can be opened in Gwyddion<br> 3) .mat file which contains all the measured data that can be opened in MATLAB</p> <p>The .mat file contains the most information and requires some explanation to easily understand.<br> The data is saved in a structure named meas, the key fields are as follows:</p> <p>meas.target - A matrix of the target position for each pixel, with a column for each of the x,y coordinates<br> meas.inputs - Input parameters used in the scanning program to collect the data<br> meas.initial_pos - Position of each the three stages at the start of the scan. The 3rd coordinate is the z scan and does not change throughout the scan<br> meas.data - A vector of the measured current at each pixel in Amps<br> meas.image - A matrix that represents the acquired image</p> <p>The remaining fields are explained at the bottom of the readme.</p> <p>The z scans are saved Z*****.mat and can be opened in MATALB.<br> The key variables in the data files are:</p> <p>z_position - Raw z coordinate of the positioner in nm<br> current_z - Measured current at each point in Amps<br> m - Horizontal scan gradient (approximately 1)<br> m2 - Vertical scan gradient (approximately 0)<br> step_pos - x coordiate at z_position=0<br> step_pos_v - y coordinate at z_position=0</p> <p>The x,y position at each point in the scan can then be obtained from<br> x=step_pos + m*(z_position)<br> y=step_pos_v + m2*(z_position)</p> <p><br> The true z distance between the pinhole and the sample in nm can be calculated from z=z_position-4295000</p> <p>The included data in the pack contains:</p> <p>Images presented in the paper:<br> scan001357<br> scan001361<br> scan001398<br> scan001415</p> <p>Z000006.mat - z scan of flat region<br> Z000008.mat - z scan of heavily distorted region<br> Z0000012.mat - z scan of lightly distorted region<br> Z000013.mat - z scan of HOPG</p> <p>z_scan_movie.avi - Video illustrating how the images vary with z<br> scan001401-scan001413 - Data to produce the video</p> <p>In addition, the analysis scripts have been included for reference. <br> The scripts can be used to regnerate the figures in the paper.</p> <p><br> The remaining fields in the meas structure are:<br> meas.date_time - Start date and time of the scan<br> meas.type - Scan pattern of the image. Always raster_jump<br> meas.p1 - Unused pressure<br> meas.p2 - Unused pressure<br> meas.p3 - Unused pressure<br> meas.p_srce - Source pressure at the start of the scan<br> meas.p_diff - Differential stage pressure at the start of the scan<br> meas.p_smpl - Sample pressure at the start of the scan<br> meas.p_det - Detector pressure at the start of the scan<br> meas.N_dwell - Number of current measurements taken at each pixel<br> meas.comments - comments (unused)<br> meas.det_params - Detector parameters used in the scan<br> meas.labtemp - A vector of the labtemp at each pixel (unused)<br> meas.return_state - Error status <br> </p>
E-THEMIS Thermal Anomaly Simulation Observations
<p>This dataset contains 100,000 simulated observations of Europa with the E-THEMIS instrument. Each observation was generated by randomly selecting (1) one of 46 planned Europa flybys by the Europa Clipper from the 17F12_V2 trajectory and (2) the distance of the spacecraft to Europa (±50,000 km from closest approach). We used the KRC thermal model to simulate the Europa surface temperature as a function of latitude, longitude, and local solar time (Sun position). We simulated the appearance of Europa within the E-THEMIS field of view given the Europa radius of 1591 km. We used ray tracing to determine, given the surface temperature model and the relative positions of Europa and the spacecraft, the amount of thermal radiation from Europa that a given pixel would collect. We used Monte Carlo integration over 100 uniformly random sub-pixel locations within each pixel.</p> <p>We injected artificial thermal anomalies into the simulated Europa observations by selecting a random radius between 1 m and 25 km and random temperature between 75 K and 275 K, then modifying the modeled surface temperature. We used an instrument model that converts temperature to digital numbers to generate the final E-THEMIS data product.</p> <p>The ethemis_dn_to_temp.npz file contains the mapping from digital number to temperature in each of the three E-THEMIS bands. Each observation is also a <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.lib.format.html#module-numpy.lib.format">NPY-formatted</a> file containing the following entries:</p> <ul> <li>event_time: the simulated time at which the observation was acquired</li> <li>trial: the trial identifier (should match the file name)</li> <li>samples: the number of samples used in the Monte Carlo integration</li> <li>anomaly_radius_m: the (circular) anomaly radius in meters</li> <li>temperature: the temperature of the anomaly</li> <li>band1_dn: the digital number for each pixel in the observation (similar entries exist for the other bands)</li> <li>band1_mask: a mask specifying the fraction of each pixel containing an anomaly (similar entries exist for the other bands)</li> </ul> <p>The extracted dataset is roughly 18 Gb in size.</p>
Comparison between high-speed video observation of sprites and broadband sferic measurements
<p>The dataset here includes high-speed video, low-light video and broadband magnetic sferics (LF, VLF, ULF) for cases analyzed in the paper "Comparison between high-speed video observation of sprites and broadband sferic measurements".</p>
Global Dataset of Ecohydrological Parameters Inferred from Satellite Observations
<p>This dataset contains global maps of</p> <p>(1) ecohydrological parameters for a theoretical model of the probability distribution of soil saturation;<br> (2) convergence, uncertainty and goodness-of-fit diagnostics; and<br> (3) soil water stress and uptake indexes,</p> <p>associated with analysis in: Bassiouni, M., S.P. Good, C.J. Still, and C.W. Higgins (2020), Plant water uptake thresholds inferred from satellite soil moisture. Geophysical Research Letters. <a href="https://doi.org/10.1029/2020GL087077">https://doi.org/10.1029/2020GL087077 </a></p> <p>All variable descriptions and units are included in the .nc metadata.</p> <p>Code associated with this dataset are publicly available:<br> Probabilistic Inference of Ecohydrological Parameters (PIEP): <a href="http://doi.org/10.5281/zenodo.1257718">http://doi.org/10.5281/zenodo.1257718</a>.<br> Data Management for Global PIEP: <a href="http://doi.org/10.5281/zenodo.3235820">http://doi.org/10.5281/zenodo.3235820</a></p> <p><strong>Abstract</strong><br> Empirical functions are widely used in hydrological, agricultural, and earth system models to parameterize plant water uptake. We infer soil water potentials at which uptake is downregulated from its maximum rate and at which uptake is zero, in biomes with < 60% woody vegetation at 36-km grid resolution. We estimate thresholds through Bayesian inference using a stochastic water balance framework to construct theoretical soil moisture probability distributions consistent with satellite surface soil moisture. The global median Nash–Sutcliffe efficiency between empirical soil moisture distributions derived from satellite soil moisture observations and best-fit theoretical distributions using inferred parameters is 0.8. Spatially variable thresholds capture location-specific vegetation and climate characteristics and can be connected to biome-level water uptake strategies.</p>
Data for publication "Evaluation of equivalent black carbon (EBC) source apportionment using observations from Switzerland between 2008 and 2018"
<p>Data to accompany "<a href="https://www.atmos-meas-tech-discuss.net/amt-2019-351/"><em>Evaluation of equivalent black carbon (EBC) source apportionment using observations from Switzerland between 2008 and 2018</em></a>" publication in <em>Atmospheric Measurement Techniques</em>. This repository contains observational data and two helper tables containing monitoring site information and instrument location data. All files are .csv files, with UTF-8 encoding, and are self describing. The time zone used for analysis was UTC + 1, tz database/Olson string "Etc/GMT+1".</p>
Supplementary Information to: Increasing the spatial resolution of cloud property retrievals from Meteosat SEVIRI by use of its high–resolution visible channel: Evaluation of candidate approaches with MODIS observations
<p>This repository contains the Python programmes and datasets used for</p> <p>the research described in the following paper:<br> https://doi.org/10.5194/amt-2019-334</p> <p>It has been prepared as supplementary information to the final paper.<br> <br> Note that the actual Cloud Property Retrieval (CPP) which would be<br> required for full reproducability of the results cannot be made<br> available by the authors, that the code included here is based on Python2, and that<br> paths to the dataset have to be adapted in the code.<br> <br> The repository consists of three separate parts/directories:<br> <br> * method: Python routines for generating the cloud property retrieval<br> input based on the Meteosat and MODIS data<br> <br> * analysis: Python routines for analysing the different experiments<br> from the CPP outputs, producing the figures and calculating the<br> comparison statistics<br> <br> * datasets: the various input and output datasets used for the<br> analyses of the paper</p>
Figure 2 in Field and laboratory observations on reproductive aspects of Pseudopaludicola ameghini (Copeı 1887) (Leptodactylidae: Leiuperinae)
Figure 2. Behaviours of the male and female during egg laying in Pseudopaludicola ameghini: (a) the male inserts his feet in a space between the legs and vent of the female; (b) the female lays an egg (white arrow) and the male begins to relax his hind limbs; (c) the female rubs her feet against one another.
Figure 7 in Digging with 'hands': observations of food capture in the flying gurnard Dactylopterus volitans (Linnaeusı 1758)
Figure 7. Above: still image collected from video Sequence 1. Fish in process of turning left after digging with left anterior pectoral fin, raising suspension cloud. Below: line drawing of outline of fish and its fins.
Figure 6 in Digging with 'hands': observations of food capture in the flying gurnard Dactylopterus volitans (Linnaeusı 1758)
Figure 6. Above: still image collected from video Sequence 2 (0.27 s later than the image shown in Figure 4). Short white arrows indicate movements of the two anterior pectoral fins (left anterior pectoral fin largely obscured). The right anterior pectoral fin is moving backwards and laterally with the anterior fin rays against the substratum. Long white arrows indicate movements of the large posterior pectoral fins, both of which were moving forwards and laterally, not in contact with the substratum. Below: line drawing of outline of fish and its fins.
Figure 5 in Digging with 'hands': observations of food capture in the flying gurnard Dactylopterus volitans (Linnaeusı 1758)
Figure 5. Above: still image collected from video Sequence 2. Fish viewed from above and slightly behind. White arrows indicate movements of the two anterior pectoral fins (right fin clearer than left; not quite in synchrony). The right anterior pectoral fin is moving forwards/medially and the anterior fin rays are moving downwards. Below: line drawing of outline of fish and its fins.
Mitigating flicker noise in high-precision photometry. I - Characterization of the noise structure, impact on the inferred transit parameters, and predictions for CHEOPS observations
<p>This repository contains the publicly available artificial exoplanet transit light curves that has been generated in HMI observations.</p> <p>Descriptions of the data are encapsulated in the READme.dat file. For further details, please report to Sec. 3 of the paper (arXiV link: <a href="https://arxiv.org/abs/2003.07707">https://arxiv.org/abs/2003.07707</a>).</p> <p>If you find these synthetic data useful in your own research please cite the Sulis. et al., (2020) paper.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Data for 'Impact of Satellite Observations on Forecasting Sudden Stratospheric Warmings'
<p>These data were used for making plots in the manuscript entitled 'Impact of Satellite Observations on Forecasting Sudden Stratospheric Warmings'. DOI: 10.1029/2019GL086233. Data format is NetCDF.</p>
Aggregated frequencies of transcription initiations observed in FANTOM5 CAGE data on GRCm38, including alignments with low mapping qualities
<p><strong>Overview</strong></p> <p>Aligned reads of the FANTOM5 CAGE data have been used after filtering (ones with mapping quality less than 20 or percent identity less than 85% were discarded) for general purpose, resulting in the data set consisting of only the reads aligned with confidence. The filtering process made possible to interpret the data without ambiguity, however it also limited interpretation of paralogous or duplicated regions within the genome. Here all of the 5'-ends of the CAGE read alignments, including the ones with low mapping quality, were counted. The counts in the individual profiles were aggregated and summed up. </p> <p>This data set is produced for mouse, in the same way to the one for human data set http://doi.org/10.5281/zenodo.1410835</p> <p> </p> <p><strong>Data files</strong></p> <p>The resulting data files are formatted as bigWig (https://genome.ucsc.edu/FAQ/FAQformat.html#format6.1). '*.fwd.bw' and '*.rev.bw' represent forward and reverse strand on the genome, respectively. </p> <p> </p> <p><strong>Methods</strong></p> <p>The BAM files under https://fantom.gsc.riken.jp/5/datafiles/reprocessed/mm10_v7/basic/ were subjected to 5'-end counting by bedtools v2.27.1 (https://github.com/arq5x/bedtools2), followed by conversion into bigWig with jksrc v366 (http://hgdownload.cse.ucsc.edu/admin/).</p>
CNR global observation-based OMEGA3D quasi-geostrophic vertical and horizontal ocean currents (1993-2018): validation subset.
<p>A subset of the Global Ocean Multi Observations OMEGA3D product is provided here for comparison with independent in situ observations and model data. OMEGA3D product has been developed by the Consiglio Nazionale delle Ricerche in the framework of the Copernicus Monitoring Environment Marine Service (https://doi.org/10.25423/cmcc/multiobs_glo_phy_w_rep_015_007). It consists of 26 year of 3D quasi-geostrophic vertical and horizontal ocean currents (January 1993-December 2018) provided over a regular grid at 1/4° horizontal resolution, with 75 non-uniformly spaced vertical levels between the surface and 1500 m depth. The subset provided here includes horizontal velocities at 15 m and 1000 m depths (that can be used for validation with independent velocity estimates from SVP drifters and Argo floats displacements, respectively) and vertical velocities at 100 m depth (for comparison with model re-analyses). OMEGA3D product is delivered with a weekly sampling (representative of each Wednesday). The velocities are obtained by solving a Q-vector formulation of the Omega equation that explicitly considers the effect of both geostrophic advection and upper layer turbulent mixing as described by KPP parameterization. Omega forcings are estimated from the CMEMS observation-based ARMOR3D temperature and salinity multi-year data and ERA-Interim surface fluxes. </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.