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10,016 results for “Observation”
The observation of the dynamic sea level (DSL) is available from the AVISO
<p>The observation of the DSL used for the publication about formulation of a new explicit tidal scheme in ocean general circulation model by Jin et al., currently under review for GMD journal. (See https://doi.org/10.5194/gmd-2021-441).</p>
Gravity Spy Volunteer Classifications of LIGO Glitches from Observing Runs O1, O2, O3a, and O3b
<p>This dataset contains machine learning and volunteer classifications from the <a href="https://www.zooniverse.org/projects/zooniverse/gravity-spy">Gravity Spy project</a>. It includes glitches from observing runs <a href="https://doi.org/10.7935/K57P8W9D">O1</a>, <a href="https://doi.org/10.7935/CA75-FM95">O2</a>, <a href="https://doi.org/10.7935/nfnt-hm34">O3a</a> and <a href="https://doi.org/10.7935/pr1e-j706">O3b</a> that received at least one classification from a registered volunteer in the project. It also indicates glitches that are nominally retired from the project using our default set of retirement parameters, which are described below. See more details in the <a href="https://ui.adsabs.harvard.edu/abs/2017CQGra..34f4003Z/abstract">Gravity Spy Methods paper</a>. </p> <p>When a particular subject in a citizen science project (in this case, glitches from the LIGO datastream) is deemed to be classified sufficiently it is "retired" from the project. For the Gravity Spy project, retirement depends on a combination of both volunteer and machine learning classifications, and a number of parameterizations affect how quickly glitches get retired. For this dataset, we use a default set of retirement parameters, the most important of which are: </p> <ol> <li>A glitches must be classified by at least 2 registered volunteers</li> <li>Based on both the initial machine learning classification and volunteer classifications, the glitch has more than a 90% probability of residing in a particular class</li> <li>Each volunteer classification (weighted by that volunteer's confusion matrix) contains a weight equal to the initial machine learning score when determining the final probability</li> </ol> <p>The choice of these and other parameterization will affect the accuracy of the retired dataset as well as the number of glitches that are retired, and will be explored in detail in an upcoming publication (Zevin et al. in prep). </p> <p>The dataset can be read in using e.g. Pandas: <br> ```<br> import pandas as pd<br> dataset = pd.read_hdf('<a href="https://zenodo.org/api/files/512bfa79-dfbc-4af0-b563-9fdd06edcb16/retired_fulldata_min2_max50_ret0p9.hdf5?versionId=7f568823-76d9-4452-8553-c1eee5993f81">retired_fulldata_min2_max50_ret0p9.hdf5</a>', key='image_db')<br> ```<br> Each row in the dataframe contains information about a particular glitch in the Gravity Spy dataset. </p> <p><strong>Description of series in dataframe</strong></p> <ul> <li>['1080Lines', '1400Ripples', 'Air_Compressor', 'Blip', 'Chirp', 'Extremely_Loud', 'Helix', 'Koi_Fish', 'Light_Modulation', 'Low_Frequency_Burst', 'Low_Frequency_Lines', 'No_Glitch', 'None_of_the_Above', 'Paired_Doves', 'Power_Line', 'Repeating_Blips', 'Scattered_Light', 'Scratchy', 'Tomte', 'Violin_Mode', 'Wandering_Line', 'Whistle'] <ul> <li>Machine learning scores for each glitch class in the trained model, which for a particular glitch will sum to unity</li> </ul> </li> <li>['ml_confidence', 'ml_label'] <ul> <li>Highest machine learning confidence score across all classes for a particular glitch, and the class associated with this score</li> </ul> </li> <li>['gravityspy_id', 'id'] <ul> <li>Unique identified for each glitch on the Zooniverse platform ('gravityspy_id') and in the Gravity Spy project ('id'), which can be used to link a particular glitch to the <a href="https://zenodo.org/record/5649212#.YfLNjVjMLzc">full Gravity Spy dataset</a> (which contains GPS times among many other descriptors)</li> </ul> </li> <li>['retired'] <ul> <li>Marks whether the glitch is retired using our default set of retirement parameters (1=retired, 0=not retired)</li> </ul> </li> <li>['Nclassifications'] <ul> <li>The total number of classifications performed by registered volunteers on this glitch</li> </ul> </li> <li>['final_score', 'final_label'] <ul> <li>The final score (weighted combination of machine learning and volunteer classifications) and the most probable type of glitch</li> </ul> </li> <li>['tracks'] <ul> <li>Array of classification weights that were added to each glitch category due to each volunteer's classification</li> </ul> </li> </ul> <p> </p> <p>```<br> For machine learning classifications on all glitches in O1, O2, O3a, and O3b, please see <a href="https://zenodo.org/record/5649212#.YfLNjVjMLzc">Gravity Spy Machine Learning Classifications</a> on Zenodo</p> <p>For the most recently uploaded training set used in Gravity Spy machine learning algorithms, please see <a href="https://zenodo.org/record/1486046#.YZfcar3MJqs">Gravity Spy Training Set</a> on Zenodo.</p> <p>For detailed information on the training set used for the original Gravity Spy machine learning paper, please see <a href="https://zenodo.org/record/1476156#.YZfchL3MJqs">Machine learning for Gravity Spy: Glitch classification and dataset</a> on Zenodo. </p>
A hierarchical dependent double-observer method for estimating waterfowl breeding pairs abundance from helicopters
<p>We applied a dependent double-observer method for helicopter surveys and developed a hierarchical Bayesian model as a means to adjust counts of waterfowl for incomplete detection. We conducted our study using 52 plots in Labrador, Canada. A designated pair of primary observers reported counts and location of all waterfowl flocks that they detected to a pair of secondary observers, including details regarding the species, age and sex of observed birds. Secondary observers then reported any additional flocks observed by them but missed by the primary observers. The pairs of observers alternated between primary and secondary roles during the course of the survey, as well as position (front or back) within the helicopter. We used hierarchical Bayesian models to estimate detection probabilities of waterfowl flocks, as well as derive species-specific detection-corrected abundance and sex composition estimates of flocks. The hierarchical model output allowed us to derive estimates of indicated breeding pairs for each species in the survey area corrected for incomplete detection. Observers seated in the back of the helicopter had higher detection probabilities (0.89; 90% Bayesian Credible Intervals [BCI] = 0.82 – 0.95) than those in the front (0.74; 90% BCI = 0.66 – 0.83), and observer experience had a limited effect on detection. Total crew detection probabilities ranged between 0.99 (90% BCI = 0.97 – 1.00) and 0.97(90% BCI = 0.94 – 0.99), depending on the individual observers' position and role in the helicopter. Detection probabilities were higher for sea ducks and diving ducks and lower for dabbling ducks. Observers generally missed less than 5% of the total indicated pairs for all species. We recommend that detection in helicopter surveys be measured to control for observer turnover, observer experience, and aircraft-related differences in visibility.</p>
Search for merger ejecta emission in Short Gamma Ray Bursts from very late time radio observations
<p>Coalescence of inspiral binary neutron stars (BNS) system, giving rise to short Gamma Ray Bursts (GRBs), are one of the most probable candidates for Gravitational Waves (GWs). If the resultant product of the merger is a millisecond magnetar, a significant proportion of the rotational energy deposited to emerging ejecta that produce late time radio brightening from the interaction with the surrounding ambient medium. Detection of this late-time radio emission from short GRBs can have profound implications for understanding the physics of the progenitor. This study presents the deepest and an extensive search for radio emission at late times following a short GRB to date incorporating proper frequency regime, wider observation span and relativistic correction. Five short GRBs were observed with the Giant Meter Wave Radio Telescope (GMRT) at 1250, 610, and 325 MHz band $\sim$ 2 - 11 years since the burst to search for radio emission from the merger ejecta. The estimated upper limits at the burst location are used to constrain the parameters of the burst and its surrounding environment. The magnetar model, with appropriate modifications, constrains the number density of the ambient medium for these bursts to be between $10^{-4}$ - $10^{-2}$ $cm^{-3}$. Our analysis rules out a stable magnetar with an energy of $10^{53}$ erg for four out of the five GRBs in our sample.</p>
Supplementary Information for Coverage of in situ climatological observations in the world's mountains (Thornton et al.)
<p>Supplementary Information for "Coverage of in situ climatological observations in the world's mountains" (Thornton et al., Frontiers in Climate).</p>
EVEX rocket experiment: sounding rocket and C/NOFS satellite observations in equatorial regions of the ionosphere at sunset
<p>These data files support the JGR: Space Physics article by Pfaff, R. et al. (2022) entitled "Dual sounding rocket and C/NOFS satellite observations of DC electric fields and plasma density in the equatorial E and F region ionosphere at sunset".</p>
Global Meteor Network observations of Starlink re-entry 2022-02-10
<p>This dataset contains video observations by some stations of the Global Meteor Network of the re-entry of the satellite StarLink-1668 above Spain on 2022-02-10 around 19:50 UTC.</p> <p>There are several types of files:</p> <ul> <li>FF files: these are 10.24 second videos compressed in the four-frame format. They are just FITS files with four frames, containing per pixel 1) the maximum value over 256 frames 2) the frame nr (between 0 and 255) where the maximum occurred 3) the mean value of all 256 frames and 4) the RMS of the 256 values.</li> <li>FR files: compressed video recordings of detected fireballs. These can be read with the RMS software.</li> <li>MP4 files: rendered movies of combined FF and FR files for selected stations.</li> <li>Platepar-files: these contain astrometry corresponding to the FITS files. These can be interpreted by the RMS software.</li> <li>ECSV files: these contain manually picked points (with SkyFit2.py from RMS) along the track of the reentry. For each point, time and apparent coordinates are recorded. These files can be interpreted by the WesternMeteorPyLib trajectory solver.</li> <li>trajectory-points.txt: solutions from the trajectory solver.</li> <li>reentry-map-v2.png: a rendered map of the trajectory (made in QGIS).</li> </ul> <p>The files can be processed with the software in https://github.com/CroatianMeteorNetwork/RMS and https://github.com/wmpg/WesternMeteorPyLib.</p>
Observing emergent hydrodynamics in a long-range quantum magnet
<p>Here lies the experimental data and the analysis codes for the manuscript titled "Observing emergent hydrodynamics in a long-range quantum magnet" to be published in Science.</p>
The Return of the Templates: Revisiting the Galactic Center Excess with Multi-Messenger Observations
<p>We provide here the galactic diffuse emission maps used in "The Return of the Templates: Revisiting the Galactic Center Excess with Multi-Messenger Observations". </p>
Fig. 3 in Observations On The Embryonic Development Of Trichuris Sylvilagi (Nematoda, Trichuridae) Under Laboratory Conditions
Fig. 3. Changes in the size of Trichuris sylvilagi eggs during embryogenesis at the temperature of 30 ° C: а — egg length; b — egg width; с — egg plug length; d — egg plug width; е — eggshell thickness (μm). А — zygote stage; В — larval stage; the small squares in the centre corresponds to the median, the lower and upper borders of the large rectangular correspond to the first and the third quartiles, respectively, vertical line segments, directed up and down from the rectangular, correspond to minimum and maximum values; * P <0.05, ** P <0.01, *** P <0.001 — relative to the parameters of eggs at the zygote stage (n = 15).
Fig. 1 in Observations On The Embryonic Development Of Trichuris Sylvilagi (Nematoda, Trichuridae) Under Laboratory Conditions
Fig. 1. Stages of embryonic development Trichuris sylvilagi: а — zygote; b — formation of two blastomeres; с — blastomere cleavage; d — bean-shaped embryo; e — tadpole-shaped embryo; f — larva.
Fig. 3 in Observation Of Eurasian Otter'S Diel Activity Using Camera Trapping In Central-Eastern Romania
Fig. 3. The activity pattern of the otter in the three protected areas, based on the number of otter recordings at the observation sites during March 2011–April 2016.
Fig. 6 in Observation Of Eurasian Otter'S Diel Activity Using Camera Trapping In Central-Eastern Romania
Fig. 6. Seasonal activity patterns of Lutra lutra in the study area during the study period based on the number of otter crossings through the observation sites.
Thetis Baltic Sea simulation: model and observation data sets
<p>Model and observation data sets used in article "Adjoint-based optimization of a regional water elevation model".</p>
Community access to rectal artesunate for malaria (CARAMAL): a large-scale observational implementation study in the Democratic Republic of the Congo, Nigeria and Uganda
<p>Datasets underlying the publication "Community access to rectal artesunate for malaria (CARAMAL): a large-scale observational implementation study in the Democratic Republic of the Congo, Nigeria and Uganda":</p> <p><strong>Figure 6: </strong>Number of children enrolled in the Patient Surveillance System (grey bars), and percentage of these children being administered rectal artesunate (RAS), by country.</p> <p><strong>Figure 8:</strong> Overall case fatality ratio (CFR) in patients with danger signs and a positive malaria test at enrolment across the entire study period, by enrolment location and country. Data for Uganda excludes enrolments at PHCs (N=34).</p>
Fatiando a Terra Data: Bushveld, Southern Africa - Observed and preprocessed gravity
<p>This dataset contains ground gravity observations over the area that comprises the Bushveld Igenous Complex in Southern Africa, including preprocessed gravity fields such as the <em>gravity disturbance</em> and the <em>bouguer gravity disturbance</em> (topography-free gravity disturbance). In addition, the dataset contains the heights of the observation points referenced on the WGS84 reference ellipsoid and over the mean sea-level (what can be considered to be the geoid). This dataset was built upon a portion of the Southern Africa gravity compilation available through <a href="https://www.ngdc.noaa.gov/mgg/gravity/">NOAA NCEI</a>.<br> <br> <strong>Note:</strong> This is a processed and formatted version of the source dataset below. It's meant for use in documentation and tutorials of the <a href="https://www.fatiando.org">Fatiando a Terra</a> project. Please <strong>cite the original authors</strong> when using this dataset.<br> <br> <strong>Changes made: </strong></p> <ul> <li>The original data were cropped to a region bounded by 25 and 32 degrees on longitude and -27 and -23 degrees on latitude.</li> <li>Geometric observation heights were obtained by adding geoid heights to the original observation heights referenced on the mean sea-level. The geoid heights on each observation point were obtained by interpolation of the geoid available in doi: <a href="https://doi.org/10.5281/zenodo.5882205">10.5281/zenodo.5882205</a>.</li> <li>Gravity disturbances were computed by removing the normal gravity of the WGS84 ellipsoid computed through <a href="https://www.fatiando.org/boule">Boule</a>.</li> <li>Bouguer gravity disturbances were computed by forward modelling the topography using <a href="https://www.fatiando.org/harmonica">Harmonica</a> starting from the topography grid provided in doi: <a href="https://doi.org/10.5281/zenodo.6481379">10.5281/zenodo.6481379</a> and using densities of 2670 kg/m³ above the ellipsoid and 1040 - 2670 kg/m³ below the ellipsoid.</li> </ul> <p><strong>Source: </strong><a href="https://www.ngdc.noaa.gov/mgg/gravity/">NOAA NCEI</a> (gravity) and <a href="https://doi.org/10.7289/V5C8276M">ETOPO1</a> (topography)</p> <p><strong>Source license: </strong><a href="https://ngdc.noaa.gov/ngdcinfo/privacy.html">public domain</a> (gravity) and <a href="https://ngdc.noaa.gov/mgg/global/dem_faq.html#sec-2.4">public domain</a> (topography)</p> <p><strong>Repository</strong>: <a href="https://github.com/fatiando-data/bushveld-gravity">https://github.com/fatiando-data/bushveld-gravity</a></p>
Social information use about novel aposematic prey depends on the intensity of the observed cue
<p>Animals gather social information by observing the behavior of others, but how the intensity of observed cues influences decision-making is rarely investigated. This is crucial for understanding how social information influences ecological and evolutionary dynamics. For example, observing a predator's distaste of unpalatable prey can reduce predation by naïve birds, and help explain the evolution and maintenance of aposematic warning signals. However, previous studies have only used demonstrators that responded vigorously, showing intense beak-wiping after tasting prey. Therefore, here we conducted an experiment with blue tits (<em>Cyanistes caeruleus</em>) informed by variation in predator responses. First, we found that the response to unpalatable food varies greatly, with only few individuals performing intensive beak-wiping. We then tested how the intensity of beak-wiping influences observers' foraging choices using video-playback of a conspecific tasting a novel conspicuous prey item. Observers were provided social information from: (1) no distaste response, (2) a weak distaste response, or (3) a strong distaste response, and were then allowed to forage on evolutionarily novel (artificial) prey. Consistent with previous studies, we found that birds consumed fewer aposematic prey after seeing a strong distaste response, however a weak response did not influence foraging choices. Our results suggest that while beak-wiping is a salient cue, its information content may vary with cue intensity. Furthermore, the number of potential demonstrators in the predator population might be lower than previously thought, although determining how this influences social transmission of avoidance in the wild will require uncovering the effects of intermediate cue salience.</p>
Weather data (forecast and observation) at three locations in France over 2021 for Machine Learning Training
<p>The data provided data are historical weather measurement and forecast at three location in France.</p> <p>Measurements are inside files named OBS_xxx</p> <p>Forecasts are inside files names YYY_xxx, with YYY is the name of the forecast simultion (GFS0.25, WRF12km or WRF3KM).</p> <p>In the two cases, xxx is the name of the site (Site 1, Site2 or Site3).</p> <p><br> <strong>Description of OBS_xxx files:</strong><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 go:<br> from 16/04/2021 00H <br> to 31/01/2022 23H</p> <p><br> <strong>Description of YYY_xxx files:</strong><br> - One line per forecast with hourly resolution<br> - columns are: First date run (TU),forecast hour,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 hour = forecast hour from the start of the forecast date. 00 = forecast for "first date run". 01 = forecast for "First date run" + 1 hour. .... 95 = forecast for "First date run" + 95 hours.<br> For GFS0.25, forecast hour go from 00 to 95<br> For WRF12km, forecast hour go from 00 to 95<br> For WRF3m, forecast hour go from 00 to 95<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 forecast data go:<br> from 13/04/2021 00H + 72H = first forecast for the 16/04/2021 00H<br> to 31/01/2022 12H + 11H = last forecast for the 31/01/2022 23H</p>
GWTC-2.1: Deep Extended Catalog of Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run - Glitch modelling for events
<p>This material is part of several data products associated with GWTC-2.1, the deep extended catalog of compact binary coalescences observed by the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration and the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration during the first half of the third observing run. For further information, see the paper (<a href="https://dcc.ligo.org/LIGO-P2100063/public">dcc.ligo.org/LIGO-P2100063/public</a>), the related material linked from this page, and the GWTC-2.1 data release documentation (<a href="https://www.gw-openscience.org/GWTC-2.1/">www.gw-openscience.org/GWTC-2.1/</a>).</p> <p><strong>Glitch model for GWTC-2.1 events</strong></p> <p>Glitch model for events in the GWTC_2.1 catalog that used <a href="https://git.ligo.org/lscsoft/bayeswave">BayesWave</a> glitch subtraction. This includes LIGO Livingston Observatory (L1) data for the following events:</p> <ul> <li>GW190413_134308</li> <li>GW190425_081805</li> <li>GW190503_185404</li> <li>GW190513_205428</li> <li>GW190514_065416</li> <li>GW190701_203306</li> <li>GW190924_021846</li> </ul> <p>Each data file contains three channels:</p> <ol> <li>the calibrated strain data, including any glitches that are present,</li> <li>a model of the glitches, produced using the BayesWave algorithm,</li> <li>the calibrated data with the glitch model subtracted, used for parameter estimation</li> </ol> <p>For the L1 data for all events, these channels have the following names and sample rates (in Hz):</p> <ol> <li>L1:DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01 16384</li> <li>L1:DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01_glitch 16384</li> <li>L1:DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01_T1700406_v4 16384</li> </ol> <p><strong>How to download all files from this page</strong></p> <p>If you would like to download all files on this page we recommend <a href="https://gitlab.com/dvolgyes/zenodo_get">zenodo_get</a>:</p> <pre><code class="language-bash">pip install zenodo_get zenodo-get RECORD_ID_OR_DOI</code></pre> <p>where the record ID for the most recent version of this page is 6477075 and IDs for other versions can be found in the Versions section at the side of this page.</p> <p> </p> <p>For more general background on gravitational-wave data quality, try the materials from a <a href="https://www.gw-openscience.org/workshops/">GW Open Data Workshop</a> or the guide to <a href="https://doi.org/10.1088/1361-6382/ab685e">LIGO-Virgo data analysis</a>.</p>
Gravity waves in Titan's atmosphere: A comparison between linearized wave model calculations and HASI observations
<p>The data for the article "Gravity waves in Titan's atmosphere: A comparison between linearized wave model calculations and HASI observations" (GWTA). </p> <p> </p> <ol> <li>"Titan_CJP_std_chem.dat" is the background atmosphere data of Titan's atmosphere from Strobel's model. It is used in Figure 1 of the article.</li> <li>"HASI_T_p_rho_vsZ_2008.dat" is the data for Cassini-Huygens observations in Titan's atmosphere. It is used in Figure 1 of the article.</li> <li>"Mma-Program-for-GW-on-Titan.txt" is the main Mathematica program to simulate the gravity waves on Titan.</li> <li>"solutions-fun.rar" is the simulation result. This RAR file includes 174 gravity wave samples simulated with different periods and horizontal wavelengths (can be read from the subfile names after uncompressing). These gravity wave solutions are stored as InterpolatingFunction of Mathematica. The solution describes the gravity wave temperature, velocity, and density perturbations profiles from altitude 300km to 2000km. However, they are plain texts and can easily be read by any software. Figures from 2-10 are based on these data.</li> </ol> <p> </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.