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.
657
datasets available to search
ShareScore release 0.9.0
Dataset results
657 results for “July”
Orthophoto mosaic of the July 1974 lava flow of Kīlauea
<p><span>This 20 cm-resolution orthophoto mosaic was produced from digital images taken during a helicopter overflight in January 2018 during a mission by the Hawaiian Volcano Observatory and reconstructed using <em>Pix4D Mapper v.3.2.14. </em>Reference CRS is EPSG:32605.</span></p>
Thermal Flight Data for Forchheim, July 20, 2022, OPTRIS PI-450
<p>Georeferenced orthomosaic of the thermal flight campaign in raster format</p>
Raw temperature measurements from SmartSantander sensors reported between January 1st 2021 and July 31st 2022
<p>This is a smart city domain dataset, and more specifically a environmental one generated within the framework of the SmartSantander research testbed.</p> <p>It contains raw temperature measurements reported by SmartSantander sensors deployed in the spanish city of Santander, covering a period of 17 months between January 1st 2021 and July 31st 2022, and comprising more than 24 million data points. The dataset includes not only the temperature dimension but also spatial and temporal information, as well as the specific device identifier and some labels to differentiate between static/mobile and indoor/outdoor devices.</p> <p>As is common with large-scale sensor deployments, there are occasional sensor malfunctions, which have deliberately not been filtered out of this raw dataset.</p>
Ozone 3D field from IRS assimilation run - July 2019
<p>In the frame of Vittorioso's PhD, an Observing System Simulation Experiment has bee set up to evaluate the future benefit of the geostationary IRS sounder on ozone field over Europe. The full set-up is composed of a Nature Run (reality), a Control Run (no assimilation) and an Assimilation Run (assimilation or IRS) over the months June to August 2019.<br>This dataset is Ozone 3D field from the assimilation run for July 2019. One file per day including hourly field, all days in one tar file.</p>
Ozone 3D field from IRS OSSE Control run - July 2019
<p>In the frame of Vittorioso's PhD, an Observing System Simulation Experiment has bee set up to evaluate the future benefit of the geostationary IRS sounder on ozone field over Europe. The full set-up is composed of a Nature Run (reality), a Control Run (no assimilation) and an Assimilation Run (assimilation or IRS) over the months June to August 2019.<br>This dataset is Ozone 3D field from the control run for July 2019. One file per day including hourly fields, gathered in 3 tar files.</p>
Ozone 3D field from IRS OSSE Nature run - July 2019
<p>In the frame of Vittorioso's PhD, an Observing System Simulation Experiment has bee set up to evaluate the future benefit of the geostationary IRS sounder on ozone field over Europe. The full set-up is composed of a Nature Run (reality), a Control Run (no assimilation) and an Assimilation Run (assimilation or IRS) over the months June to August 2019.<br>This dataset is Ozone 3D field from the nature run for July 2019. One file per day including hourly fields, gathered in 3 tar files.</p>
Physical and Tsfel Features for 40s Waveforms (Updated 15 July, 2024)
<p>Physical and Tsfel Features for four classes of events that dominate the seismicity in the pacific northwest. Before extracting the features, following processing was applied - </p> <ul> <li>trimming (first arrival - 10, first arrival +100)</li> <li>detrending</li> <li>resampling to 100 Hz</li> <li>cosine taper (10%)</li> <li>bandpass filtered (1-10 Hz)</li> <li>normalizing by maximum</li> <li>resampling to 50 Hz</li> </ul>
Physical and Tsfel Features for 150s waveforms (Updated 15 July, 2024)
<p>Physical and Tsfel Features for four classes of events that dominate the seismicity in the pacific northwest. Before extracting the features, following processing was applied - </p> <ul> <li>trimming (first arrival - 50, first arrival +100)</li> <li>detrending</li> <li>resampling to 100 Hz</li> <li>cosine taper (10%)</li> <li>bandpass filtered (1-10 Hz)</li> <li>normalizing by maximum</li> <li>resampling to 50 Hz</li> </ul> <p> </p>
LPL11 - July 2024
<p>Measurements of LPL11 (Frontera Rural Park) in July 2024</p> <p>El Hierro</p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL11">info</a></p>
LPL1 - July 2024
<p>Measurements of LPL1 (Teide NP) in July 2024</p> <p>Tenerife</p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL1">info</a></p>
LPL8 - July 2024
<p>Measurements of LPL8 (Timanfaya NP) in July 2024</p> <p>Lanzarote</p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL8">info</a></p>
LPL1 - July 2024
<p>Measurements of LPL1 (Teide NP) in July 2024</p> <p>Tenerife</p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL1">info</a></p>
SG - July 2024
<p>Measurements of SG (Global) in July 2024</p> <p></p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/SG">info</a></p>
LPL5 - July 2024
<p>Measurements of LPL5 (Tejeda) in July 2024</p> <p>Gran Canaria</p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL5">info</a></p>
LPL4 - July 2024
<p>Measurements of LPL4 (Ilha do Corvo) in July 2024</p> <p>Corvo</p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL4">info</a></p>
Sub-hourly output over Tropical Western Pacific for June, July, August 1997
<p>To evaluate the simulation of deep convection over the Tropical Western Pacific (TWP) in the Canadian Atmospheric Model version 4.3 (CanAM4.3) we compare sub-hourly output against output from the super-parameterized Community Atmospheric Model version 5 (spCAM5). Both models used observed sea-surface temperatures, sea-ice distribution and atmospheric gases and simulated the period January through August, 1997. Model output related to deep convection for CanAM4.3 is saved every 15 minutes over the region bounded by 150 and 170 E and 0 to 10 N while the spCAM5 output was saved every 10 minutes over the same region. The CanAM4.3 data includes results from a single simulation and from a 5 member ensemble of CanAM4.3 simulations.</p> <p><br> <em>Relevant publications:</em></p> <p><br> <strong>Description of CanAM:</strong></p> <p><br> von Salzen, K., Scinocca, J. F., McFarlane, N. A., Li, J., Cole, J. N. S., Plummer, D., Verseghy, D., Reader, M. C., Ma, X., Lazare, M., and Solheim, L.: The Canadian Fourth Generation Atmospheric Global Climate Model (CanAM4). Part I: representation of physical processes. Atmosphere Ocean, 51, 104-125, doi:10.1080/07055900.2012.755610, 2013.</p> <p><strong>Description of CAM5 and spCAM5: </strong></p> <p>Khairoutdinov, M. F. and Randall, D. A.: A cloud resolving model as a cloud parameterization in the NCAR community climate system model: preliminary results. Geophys, Res. Lett., 28: 3617–3620, 2001.</p> <p>Khairoutdinov, M. F. and Randall, D. A.: Cloud resolving modeling of the ARM summer 1997 IOP: Model formulation, results, uncertainties, and sensitivities, J. Atmos. Sci., 60, 607–625, 2003.</p> <p>Neale, R. B., and Coauthors.: Description of the NCAR Community Atmosphere Model (CAM 5.0). NCAR Tech. Note TN-35 486, 274 pp, 2012.</p> <p><strong>Description of simulations and analysis: </strong></p> <p>Mitovski T.,Cole, J. N. S., McFarlane, N. A., von Salzen, K., and Zhang, G. J.: Convective response to large-scale forcing in the Tropical Western Pacific simulated by spCAM5 and CanAM4.3. submitted to GMD, July 2018.</p> <p> </p>
Courage registry - open dataset 1.1 (July 2019)
<p>The package contains the Linked Data of the COURAGE registry (http://cultural-opposition.eu/registry/) built by the COURAGE H2020 project. The dataset contains collections, featured items, groups, organizations and persons for the better understanding of the cultural heritage of dissent in the former socialist countries.</p>
Photos from DH2019 DH & Lib SIG Preconference workshop on 8 July 2019 in KB National Library of the Netherlands
<p>A selection of photos taken at the DH2019 DH & Lib SIG Preconference workshop on 8 July 2019 in KB National Library of the Netherlands</p>
Underwater-sound records in glacier fjords (Inglefield Bredning, Baffin Bay, NW Greenland, Denmark), 19-28 July 2019
<p>Acoustic data (.wav) recorded by 2 hydrophones suspended from boats in Inglefield Bredning and Bowdoin fjords (Baffin Bay, NW Greenland, Denmark) in July 2019 for underwater soundscape documentation (narwhal vocalizations and environmental sources). </p> <p>*******************</p> <p>First set-up had a hydrophone AQH-020 by AquaSound Inc. (20Hz – 20kHz) connected to Amplifier Aquafeeler III (SQE-1001B, 50dB gain) by AquaSound Inc. and a recorder PCM-M10 by Sony (44.1 kHz, 16 bit, auto-mode). Recording depth was about 6.6 m, except a record collected on July 27, 2019 at 15:56:33 (depth was about 0.5 m).</p> <p>Channels: 2 (but records are only at “Left”/1 channel; the “Right”/2 is electric noise).</p> <p>File name: sony.YYMMDDhhmmss.wav</p> <p>Note that the strongest regularly-spaced impulsive sounds in two files (sony.190719130533.WAV, sony.190719132214.WAV) are seemingly not due to a whale nearby, but due to repetitive impacts of the hydrophone with a ballast-rope in strong current. This issue was fixed by adjusting the rope length, after which the sound was gone.</p> <p>*******************</p> <p>Second set-up had a hydrophone SoundTrap SD3000 by Ocean Instruments NZ (20Hz – 60kHz), integrated with amplifier and recorder, sampling at 96 kHz, 16 bit. Signal-to-pressure conversion constant was 176.2 dB for this particular device (ID number 5146, at High-Gain mode). Recording depth was about 10.8 m, except records collected on July 20, 2019 between 00:44:47 and 08:44:47 (depth was about 1 m). </p> <p>Channels: 1</p> <p>File name format: 5146.YYMMDDhhmmss.wav</p> <p>*******************</p> <p>Coordinates for each record by each set-up are shown below.</p> <p> </p> <p>Geographic position of each measurement with <strong>SoundTrap</strong> is as the following:</p> <p>Date, Record Start Time(UTC), lon, lat,</p> <p> </p> <p>19 July 2019, 12:55:17, 77.474752, -68.660610 </p> <p>19 July 2019, 13:19:53, 77.488215, -68.597227 </p> <p>19 July 2019, 14:19:53, 77.485103, -68.574985 </p> <p>19 July 2019, 16:19:41, 77.523033, -68.403958 </p> <p>19 July 2019, 19:06:57, 77.618543, -68.564536 </p> <p> </p> <p>20 July 2019, 00:44:47, 77.548649, -68.550752 </p> <p>20 July 2019, 13:02:59, 77.527026, -68.534285</p> <p>20 July 2019, 14:30:26, 77.487211, -68.483834</p> <p>20 July 2019, 15:10:13, 77.495954, -68.658084 </p> <p> </p> <p>*******************</p> <p>Geographic position of each measurement with <strong>Sony-AquaSound</strong> is as the following:</p> <p>Date, Record Start Time(UTC), lon, lat,</p> <p> </p> <p>19 July 2019, 13:05:33, 77.474752, -68.660610 </p> <p>19 July 2019, 13:22:14, 77.488215, -68.597227 </p> <p>19 July 2019, 16:11:54, 77.523033, -68.403958 </p> <p>19 July 2019, 19:09:11, 77.618543, -68.564536</p> <p> </p> <p>20 July 2019, 13:04:42, 77.527026, -68.534285</p> <p>20 July 2019, 14:01:42, 77.505033, -68.553648</p> <p>20 July 2019, 15:11:10, 77.495954, -68.658084 </p> <p> </p> <p>21 July 2019, 22:17:04, 77.675334, -68.636040 </p> <p>21 July 2019, 22:19:53, 77.671904, -68.639090</p> <p> </p> <p>22 July 2019, 00:12:34, 77.525553, -68.442136 </p> <p> </p> <p>27 July 2019, 13:28:58, 77.617588, -68.597946</p> <p>27 July 2019, 14:13:59, 77.667788, -68.643976 </p> <p>27 July 2019, 15:56:33, 77.665487, -68.778195 </p> <p>27 July 2019, 17:03:04, 77.672426, -68.658150 </p> <p>27 July 2019, 17:22:16, 77.669874, -68.657587 </p> <p>27 July 2019, 17:59:39, 77.676941, -68.664817 </p> <p>27 July 2019, 19:44:32, 77.668669, -68.656365 </p> <p>27 July 2019, 21:57:07, 77.628218, -68.637347</p> <p>27 July 2019, 23:07:12, 77.625571, -68.616875</p> <p> </p> <p>28 July 2019, 00:02:41, 77.619299, -68.595757</p>
Western Peloponnese Seismological Data Acquisition - July 2016 to May 2017
<p><strong>2016_Data_Temporary_Network:</strong><br> Station Type: <em>3-Components Short Period</em><br> Format: <em>MSEED </em><br> Location: <em>Western Peloponnese</em><br> Deployment Time: <em>July to December 2016</em></p> <p><strong>2017_Data_Temporary_Network:</strong><br> Station Type: <em>3-Components Short Period</em><br> Format: <em>MSEED </em><br> Location: <em>Western Peloponnese</em><br> Deployment Time: <em>January to May 2017</em></p> <p><strong>Stations_Positions_DDMM: </strong>Stations Locations in Degree Minute format.<br> <br> <strong>Resp_Files_Temporary_Network:</strong> Response files for all stations 3 Components</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.