Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
10,016
datasets available to search
ShareScore release 0.9.0
Dataset results
10,016 results for “Observation”
Hourly surface ozone observations across China
<p><span>Hourly surface ozone observations across China (2014-2022) obtained from the China National Environmental Monitoring Center (CNEMC; </span><span><a href="https://air.cnemc.cn:18007/"><span>https://air.cnemc.cn:18007/</span></a></span><span>). The file<em> <strong>Station_information.csv</strong></em> lists basic information of the observation stations, such as city location, latitude, longitude, and elevation (m).<strong> </strong>The archive <strong><em>Hourly_surface_ozone.zip</em> </strong>includes columns for date, hour (Beijing time: UTC+8), air pollution type and station codes, with ozone unit being </span><span>μ</span><span>g/m<sup>3</sup>. More latest observation archive could be found at </span><span><a href="https://quotsoft.net/air/"><span>https://quotsoft.net/air/</span></a></span><span>.</span></p>
Calibrated and uncalibrated projection data from the paper "Assessing observational constraints on future European climate in an out-of-sample framework"
<p>Individual uncalibrated and calibrated projections for each of the five methods (A-E) in the following folders:</p> <p>MethodA_proj/</p> <p>MethodB_proj/</p> <p>MethodC_proj/</p> <p>MethodD_proj/</p> <p>MethodE_proj/</p> <p> </p> <p>Also included are the out-of-sample data from the "pseudo-observations" (taken from CMIP6 models) used for the verification (see paper for full details):</p> <p>FUTUREverif/</p>
Observation of Gravitational Waves from the Coalescence of a 2.5-4.5 Msun Compact Object and a Neutron Star --- Data Release
<p>This data release contains the analysis results and data behind the figures of the GW230529 discovery paper (<a href="https://urldefense.com/v3/__https://dcc.ligo.org/LIGO-P2300352/public/__;!!Dq0X2DkFhyF93HkjWTBQKhk!W4i4x3JfGgemcFsnnEYP5qxiknddvrG1LWpTLjs_JGK907kTrEBkS8o6i5T6RUFMX0v04jCPhtTq9K2SLcv_4g$" target="_blank" rel="nofollow noreferrer noopener">https://dcc.ligo.org/LIGO-P2300352/public/</a>). Strain data for this event (the L1:GDS-CALIB_STRAIN_CLEAN_AR channel) can be downloaded on GWOSC (<a href="https://doi.org/10.7935/6k89-7q62" target="_blank" rel="noopener">https://doi.org/10.7935/6k89-7q62</a>).</p> <p>The PESummary metafile containing the parameter estimation posterior samples for all analyses performed in the paper (<strong>posterior_samples.h5</strong>) and skymap fits file (<strong>skymap_combined_PHM_high_spin.fits</strong>) for the preferred parameter estimation analysis (high-spin, combined samples using binary black hole waveforms) can be downloaded directly as individual files.</p> <p>The other analysis results are grouped by type: rates, populations, searches, and tidal. The <strong>figure_scripts.tar.gz</strong> file contains all the paper figures in jpeg format along with a Jupyter notebook to reproduce them and additional required helper scripts. Example code for working with the individual result files is given in the <strong>PaperPlots.ipynb</strong> notebook included in this tar file.</p> <p>In brief, the <strong>rates.tar.gz</strong> file contains two files that each include a subset of the rates probability distributions shown in Fig. 3 of the paper. The <strong>populations.tar.gz</strong> file contains all the data behind Figs. 4-8, with subdirectories for each of the three population analyses considered in the paper: Binned Gaussian Process, NSBH-pop, and Power-Law + Dip + Break. In addition to the data behind the figures, the Power-Law + Dip + Break subdirectory additionally includes two *result.json files for the hyper-parameter posterior samples. These files have the same format as the corresponding NSBH-pop *result.json files and can be manipulated in the same way, as shown in the figures notebook.</p> <p>The <strong>searches.tar.gz</strong> file contains the data behind Figs. 9-11 for each of the three search pipelines whose results are included in the paper. Finally, the <strong>tidal.tar.gz</strong> file contains the four probability distributions plotted in Fig. 14. All other figures are produced only using the posterior_samples.h5 file.</p>
Observational dataset for "Using Satellite and ARM Observations to Evaluate Cold Air Outbreak Cloud Transitions in E3SM Global Storm-Resolving Simulations"
<p>This observational dataset include the DOE ARM ground based observations and satellite for the paper titled “Using Satellite and ARM Observations to Evaluate Cold Air Outbreak Cloud Transitions in E3SM Global Storm-Resolving Simulations” on GRL.</p> <p>For the original data source, all ARM observational data sets used in this study are publicly available from the ARM data archive site (https://adc.arm.gov/discovery/#/results/iopShortName::amf2019comble/datastream::anxarmbeatmM1.c1/datastream::anxarmbecldradM1.c1/datastream::anxarsclkazr1kolliasM1.c0). MODIS MOD06 L2 cloud product are publicly available from (https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MOD06 L2, DOI:10.5067/MODIS/MOD06 L2.061).</p> <p>CloudSat products can be ordered from the CloudSat Data Processing center (https://www.cloudsat.cira.colostate.edu/order/). To download CloudSat data, a new user must first create an account by filling out the signup form (https://www.cloudsat.cira.colostate.edu/accounts/signup/).</p>
Global anthropogenic NOx emissions from 2019 to 2022 based on satellite NO2 observations and GEOS-Chem model
<p><a href="../api/records/10947114/draft/files/emissions.nc/content" target="_blank" rel="noopener noreferrer">emissions.nc</a><a href="10052904"> includes the monthly data of global anthropogenic nox emissions from 2019 to 2022, using the GEOS-Chem model combined with TROPOMI satellite observation data</a></p>
Observation of strong backscattering in valley-Hall photonic topological interface modes
<p><span>Dataset required to reproduce the figures in the main text and supplementary information of: Rosiek, C.A., Arregui, G., Vladimirova, A. <em><span>et al.</span></em> Observation of strong backscattering in valley-Hall photonic topological interface modes. <em><span>Nature Photonics</span></em> <strong><span>17</span></strong>, 386–392 (2023).</span></p> <p><span>Execution of scripts to generate the figures tested on Windows with Matlab 2020b or newer. COMSOL models generated and solved on version 6.0. Unzip folder to access all files.</span></p> <p><span>Scripts are named according to the associated figures of the main manuscript:</span></p> <p><span> - fig1_transmittances.m generates the graphs of Fig. 1.</span></p> <p><span> - fig2_pti_dispersion.m generates the graphs of Fig. 2.</span></p> <p><span> - fig3etc_main_analysis.m (which calls subroutine fig3etc_main_analysis_sub.m) generates graphs of Fig. 3 and 4 as well as Supplementary Figures S6 and S11.</span></p> <p><span> - </span><span>fig5c_fffits.m and fig5d_ffmap.m generates plots of Fig. 5c and d, respectively. fig5_load_images.m performs preprocessing required to run fig5c_fffits.m and fig5d_ffmap.m from the raw data alone.</span></p> <p><span> - fig_s10_w1_loss_analysis.m generates the graphs of Fig. S10.</span></p> <p><span>Note that the data analysis also makes use of data derived from COMSOL simulations. The COMSOL MPH simulation files are contained within the subfolders of contrib.</span></p> <p><span>Contact chanro@dtu.dk for any inquiry on the contents.</span></p> <p> </p>
Plate 2 in Observation on the re-occurrence of Nicobar spiny shrew (Crocidura nicobarica Miller, 1902): A critically endangered mammal of Great Nicobar Island, India
Plate 2. The skull and dentition of Crocidura nicobarica in Dorsal, ventral, microscopic view.
Figure 1 in Observation on the re-occurrence of Nicobar spiny shrew (Crocidura nicobarica Miller, 1902): A critically endangered mammal of Great Nicobar Island, India
Figure 1. Field photograph of Crocidura nicobarica Miller, 1902.
HRRR Convective Objects for "Comparing Distributions of Overshooting Convection in HRRR Forecasts to Observations"
<p>Data files for all convective objects identified from High-Resolution Rapid Refresh (HRRR) model forecasts for the Contiguous United States initialized at 06 UTC every day of May and July 2021. Convection was defined in the following two ways, one corresponding to each file uploaded here:</p> <ul> <li>Meeting the criteria for convection defined by the Storm Labelling in Three-Dimensions (SL3D; Starzec et al. 2017) algorithm</li> <li>Satisfying two vertical velocity thresholds (2 m/s at 4 km altitude and 5 m/s at 8 km altitude)</li> </ul>
AUV-Based Multi-Sensor Dataset: Forward-Looking Camera (FLC) and Forward-Looking Sonar (FLS) Observations in the Red Sea
<p><strong>Context</strong></p> <p>This dataset is the first part of a dataset collection comprised of forward-looking sonar (FLS) and forward-looking camera (FLC) underwater images. The entire data was collected during the years 2021-2023 using 2 underwater vehicles in both the Red Sea and the Mediterranean along the Israeli shoreline, depicting both man-made and natural underwater environments. The data is part of a research project aimed at developing fusion models for improved obstacle detection and navigation in autonomous underwater vehicles.</p> <p><strong>Content</strong></p> <p>This dataset consists of FLC and FLS images and their metadata, collected by the ALICE-AUV. Both sensors were installed in the front payload section in a configuration having aligned fields of view to achieve matching pairs of data. The data was collected to train and evaluate a complete perception and obstacle avoidance framework.</p> <p>A series of diving sessions were performed in the Red Sea, off the coast of Eilat, Israel. The experiments focused on two main sites: A "Sunboat" shipwreck and the Eilat-Ashkelon Pipeline Company (EAPC) pier pillars. The "Sunboat" shipwreck is a 40-meter long vessel resting at a depth of approximately 12 meters, with the surrounding seabed at a depth of 18-24 meters. This dataset contains approximately 8,000 FLC-FLS sample pairs from the first session conducted at the "Sun boat" shipwreck site on September 3, 2023. The data was recorded at depths ranging from 10 to 15 meters.</p> <p>The dataset is organized into separate sessions, each representing a specific dive or experiment. Within each session, the data is further categorized into modalities: camera (FLC images), sonar (FLS images), and navigation (dead reckoning data). The navigation data is derived from a combination of GPS, DVL, and IMU sensors, providing estimated positions when GPS is unavailable. Inside each modality directory, you will find the corresponding data files in PNG format for images and CSV format for navigation data. The file names follow a sequential numbering scheme (e.g., 00001.png, 00002.png, etc.). Each modality directory also contains a CSV file (e.g., camera.csv) that maps each data file to its respective timestamp. Additionally, the samples.json file documents the relationship between uni-modal and multi-modal samples, allowing for easy association of data from different modalities.</p> <p>By providing synchronized and aligned camera and sonar imagery, along with corresponding navigation data, this dataset enables researchers to explore novel algorithms and techniques for multi-modal sensor fusion in the context of autonomous underwater vehicles.</p> <p><strong>Technical Details</strong></p> <ul> <li>Sonar: Blueprint Oculus M1200d <ul> <li>Operating frequency: 1.2 MHz (low frequency mode)</li> <li>Maximum range: 40 m (set to 20 m for this dataset)</li> <li>Horizontal aperture: 130°</li> <li>Vertical aperture: 20°</li> <li>Number of beams: 512</li> <li>Angular resolution: 0.6°</li> <li>Beam separation: 0.25°</li> <li>Image resolution: 902x497 pixels</li> <li>Coordinate system: Polar</li> </ul> </li> <li>Camera: Allied-Vision Manta G-917 <ul> <li>Image dimensions: 3384x2710 pixels (downscaled to 1692x1355 for this dataset)</li> <li>Sensor type: CCD Progressive</li> <li>Sensor bit depth: 12-bit</li> <li>Captured bit depth: 8-bit</li> <li>Camera model: Pinhole with Plumb Bob (Brown–Conrady) distortion coefficients</li> <li>Focal length (fx, fy): (1638.36157, 1641.95202)</li> <li>Principal point (cx, cy): (1705.03529, 1380.27954)</li> <li>Radial distortion coefficients (k1, k2, k3): (-0.124823, 0.048851, 0.000000)</li> <li>Tangential distortion coefficients (p1, p2): (0.000259, -0.002945)</li> </ul> </li> <li>Navigation: <ul> <li>Data format: CSV</li> <li>Contains fused dead reckoning data based on GPS, DVL, and IMU sensors</li> <li>Columns: <ul> <li>timestamp: Unix timestamp (seconds)</li> <li>latitude: Latitude (degrees)</li> <li>longitude: Longitude (degrees)</li> <li>altitude: Altitude (meters)</li> <li>yaw: Yaw angle (degrees)</li> <li>pitch: Pitch angle (degrees)</li> <li>roll: Roll angle (degrees)</li> <li>velocity_x: Velocity along the x-axis (meters per second)</li> <li>velocity_y: Velocity along the y-axis (meters per second)</li> <li>velocity_z: Velocity along the z-axis (meters per second)</li> <li>depth: Depth (meters)</li> </ul> </li> </ul> </li> <li>Frame rate: 2 Hz for both sonar and camera</li> </ul> <p>More datasets from this collection will be uploaded in the future, and a link to access them will be provided on this page.</p> <p><strong>Acknowledgements</strong></p> <p>The data in this repository is part of the DeeperSense project that received funding from the European Commission, Program H2020-ICT-2020-2 ICT-47-2020, Project Number: 101016958.</p>
FIG. 2 in Expertum est a nobis pluries: observation des oiseaux d'eau et expérimentation dans le De arte venandi cum avibus de Frédéric II de Hohenstaufen (1194-1250)
FIG. 2. — Page du De arte venandi cum avibus, ms. Vatican, Vat. lat. 1071, f. 18.
Idealized wave data in support of Directional Breaking Kinematics Observations from 3D Stereo Reconstruction of Ocean Waves
<p>You will find the WaveWatchIII data output from idealized solutions of Romero 2019, ST4 and ST6</p> <p>Data are in Netcdf format and include metadata.</p> <p>Each file corresponds to a duration-limited solution with constant wind speed of 13 m/s</p>
Observed and forecasted pollutant concentrations in the center of Sao Paulo
<p>This dataset corresponds to the observed and forecasted pollutant concentrations analyzed in an article submitted to <em><strong>Earth's Future</strong></em>, which is entitled: <em><strong>Air quality forecasts with observation-based scaling of anthropogenic emissions for urban agglomerations. </strong></em></p> <p>The pollutants analyzed are carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2) and particulate matter concentrations (PM2.5 and PM10).</p> <p>Forecasted concentrations are presented for three regional simulations (F-REF, F-DAY and F-HOUR) and two flobal simulations (WACCM and CAMS) for the next day (day_p1 is d+1) and for the day after the next day (day_p2 is d+2). </p> <p>The observed data are interpolated in the center of Sao Paulo, following the methodology presented in <a href="https://doi.org/10.1029/2022JD038179">https://doi.org/10.1029/2022JD038179</a></p>
Supporting Information for "First Results of Terrestrial Gamma-ray Flash observations by Insight-HXMT"
<p>The compressed file contains data for 282 HXMT Terrestrial Gamma-ray Flashes (TGFs), spanning from T0-1 second to T0+1 second. Below is a description of the data format included in the file.</p> <p>[HXMT 1K Science Data Description]</p> <p>1. The "Time" column represents the Mission Elapsed Time (MET) in seconds for HXMT, starting from 2012-01-01T00:00:00.</p> <p>2. The "Det_ID" column indicates the detector index for the 18 HXMT/HE NaI/CsI detectors, ranging from 00 to 17.</p> <p>3. The "Channel" column refers to the energy channel for HXMT/HE NaI/CsI detectors, which spans from 0 to 255. Data filtering often uses "Channel>30" to exclude lower energy events.</p> <p>4. The "Pulse_Width" column measures the pulse width of the detector signal in milliseconds. For HXMT/HE data, a Pulse Width value of N translates to N/48 ms. To filter the data, "Pulse_Width>75" is commonly used.</p> <p>5. The "ACD" column presents data from the anticoincidence detectors of the HXMT/HE Anti-Coincidence Detector (ACD). This data consists of an array of 18 boolean values, each corresponding to a specific ACD. For instance, if the first value in the array is 1, it indicates that the event was simultaneously detected by ACD-01; conversely, a 0 in the second position signifies that ACD-02 did not detect the event at the same time. Typically, events detected by any ACD are excluded from data analysis. Conversely, events are included in data analysis when all ACD values are 0.</p> <p>6. The "Event_Type" column describes the operational modes of the detector: 0 for normal-gain mode and 1 for low-gain mode.</p> <p>Details on the NaI/CsI and ACD detector installation positions and the relationship between energy and channel for both normal-gain and low-gain modes can be found in Song et al. 2022.</p>
Model Datafiles for Observed seasonal changes in Martian hydrogen chloride consistent with heterogeneous chemistry on atmospheric dust and ice
<p>Datafiles produced by the 1-D photochemistry model used to create the publication "Observed seasonal changes in Martian hydrogen chloride consistent with heterogeneous chemistry on atmospheric dust and ice" - Taysum et al. 2024.</p> <p> </p> <p>matching_orbits_v2.txt : Lists the orbital parameters and water vapour / aerosol file names corresponding to each of the 77 ACS MIR HCl observations that we study.</p> <p> </p> <p><strong>MCD_GlobaMaps.zip</strong> : .nc files containing model output where the model is ran at Longitude 0 degrees, across Ls 0 --> 360 in intervals of 30 degrees, and latitudes 60 S to 60 N in intervals of 15 degrees.</p> <ul> <li>LowTau : model runs with the chlorine heterogeneous chemistry scheme active. <ul> <li>MY34/ : Runs driven with MY34 climatology from the MCDv6.1</li> <li>StandardClim/ : Runs driven with Standard Climatology from the MCDv6.</li> </ul> </li> <li>No_Chlorine : model runs with no chlorine tracers in the model chemistry. <ul> <li>MY34/ : Runs driven with MY34 climatology from the MCDv6.1</li> <li>StandardClim/ : Runs driven with Standard Climatology from the MCDv6.1</li> <li> </li> </ul> </li> </ul> <p><strong>ACS_Comparisons.zip </strong>: .nc files containing the model output for specific ACS MIR HCl observations in Mars Year 34.</p> <ul> <li>LowTau : model runs with the chlorine heterogeneous chemistry scheme active. <ul> <li>MY34/ : Runs driven with MY34 climatology from the MCDv6.1</li> <li>StandardClim/ : Runs driven with Standard Climatology from the MCDv6.1</li> <li>TGO/ : Runs driven with MY34 climatology, and TGO measured water ice, dust, and H2O vapor profiles, driven at the position of the ACS-TIRVIM aerosol retrievals corresponding to the approximately colocated ACS MIR HCl observation</li> </ul> </li> <li>No_Chlorine : model runs with no chlorine tracers in the model chemistry. <ul> <li>MY34/ : Runs driven with MY34 climatology from the MCDv6.1</li> <li>StandardClim/ : Runs driven with Standard Climatology from the MCDv6.1</li> <li>TGO/ : Runs driven with MY34 climatology, driven at the position of the ACS-TIRVIM aerosol retrievals corresponding to the approximately colocated ACS MIR HCl observation</li> </ul> </li> </ul>
Dataset for the manuscript "Observation of time-reversal symmetry breaking in the band structure of altermagnetic RuO2" in Science Advances Vol. 10, No. 5
<p>Dataset for publication "Observation of time-reversal symmetry breaking in the band structure of altermagnetic RuO2" in Science Advances Vol. 10, No. 5, https://doi.org/10.1126/sciadv.adj4883.</p> <p>The details corresponding to the dataset of the figures are given in a readme file in the corresponding folders.</p>
Figure 1 in Revisiting Potter Cove, King George Island, Antarctica, 12 years later: new observations of marine benthic diatoms
Figure 1: Map of sampling locations in Potter Cove, King George Island, Antarctica.
Data for the publication: Surging process and mechanism of small glaciers in the Qilian mountains revealed by long-term and dense remote sensing observations
<p>This repository contains the data and results associated to the publication submitted entitled "Surging process and mechanism of small glaciers in the Qilian mountains revealed by long-term and dense remote sensing observations".</p> <p>The results and data contain:</p> <ul> <li>Raw and processed ASTER DEM time series data stored in netcdf format (<em>Hala_surges_aster**.nc</em>): </li> </ul> <ol> <li>Raw DEM stack composed of 56 ASTER DEM.</li> <li>Processed DEM stacks generated by LOWESS-ALPS-REML workflow in each step.</li> </ol> <ul> <li>Multi-temporal elevation change maps stored in geotiff format:</li> </ul> <ol> <li>multi-temporal elevation change results calculated from different DEMs during different period (<em>Hala_surges_[sensor]_[period]_dh_final.tif</em>).</li> <li>Elevation difference map of SRTM-X and SRTM-C DEMs for estimation penetration depth difference ( <br><em>strm-c_x_n37_39_e96_e98_pentration_dh_final.tif</em>)</li> </ol> <ul> <li>Flow velocity time-series result processed by TICOI package stored in netcdf format:</li> </ul> <ol> <li>Irregular-sampling time-series inverted flow velocity results, represted by pixel-wise cumulative displacements ( <br><em>Hala_surges_LS7_LS8_ticoi_flow_angle_refine_velo_invert_ticoi.nc</em>)</li> <li>Regular-sampling time-series flow velocity results, interpolated to 30 days interval from the inverted results ( <br><em>Hala_surges_LS7_LS8_ticoi_flow_angle_refine_velo_interp_ticoi.nc</em>)</li> </ol>
Reconstructing Ocean Subsurface Temperature and Salinity with Satellite Observations
<p> Ocean data are crucial for ocean science and climate change research. While moored buoys and Argo floats can provide data on ocean temperature and salinity from the surface to the deep ocean, their spatial and temporal distribution is sparse and discontinuous. In recent years, satellite-based ocean observations have been widely used. These observations offer high spatial resolution and temporal continuity but are often limited to surface ocean quantities. In this study, we develop a novel algorithm to infer ocean subsurface temperature and salinity using satellite observations of ocean surface properties. Different from current prevalent machine learning methods, the algorithm proposed is efficient and interpretable. The resultant dataset has a global coverage with a high spatial resolution (0.25°x0.25°) and has been validated against in-situ observations with satisfactory accuracy.</p>
Data for: Seasonal differences in observed versus modeled new particle formation at two European boreal stations
<p>Model dataset variables produced from the IFS and TM5 modules in EC-Earth3 version 3.4.0 which contains the control case and four experiments with the NPF lookup table. This paper is under review.</p> <p>The files contain:</p> <ul> <li>NetCDF files from TM5 general output for each simulation at the two location grid points.</li> </ul> <p>File-name description of the EC-Earth3 experiments: "Hyyt" = Hyytiälä, "Mossa" = Hyltemossa.</p> <p>"ricc2" = CLUST-HIGH case</p> <p>"dlpno" = CLUST-Low case</p> <p>"bono" = CLUST-Low+Riccobono case</p> <p>"ctrl" = Model control run case</p> <p>"nonpf" = No NPF case</p> <ul> <li>Compressed tar file of NetCDF data from IFS output for all four simulations. All IFS data have been averaged to monthly means from 3-hourly grib datasets.</li> </ul> <p> </p> <ul> <li>ADCHEM base-case particle number size distributions from Hyytiälä and Hyltemossa.</li> </ul>
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.