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497 results for “campaign”
Vertical profiling of the electrical properties of charged desert dust during the pre-ASKOS campaign: Dataset
<p>The zipped files contain the datasets used to produce Figure 1 of the following conference proceedings paper:</p> <p>Vasiliki Daskalopoulou, George Hloupis, Sotirios A. Mallios, Ilias Makrakis, Evangelos Skoubris, Maria Kezoudi, Zbigniew Ulanowski, & Vassilis Amiridis. (2021, July 6). <em>Vertical profiling of the electrical properties of charged desert dust during the pre-ASKOS campaign</em>. 15th International Conference on Meteorology, Climatology and Atmospheric Physics (COMECAP 2021), Ioannina, Greece. https://doi.org/10.5281/zenodo.5076042</p> <p>The repository contains overall:</p> <ol> <li>the ground-based JCI 131 Fieldmill Electrometer data that were acquired during the campaign (<a href="https://zenodo.org/api/files/b9a97eac-3bc2-4468-a43d-efa270ce8d95/Fieldmill_Ion_counter_Cyprus_campaign.rar">Fieldmill_Ion_counter_Cyprus_campaign.rar</a>)</li> <li>Data from an Alphalab Air Ion counter co-located with the fieldmill (<a href="https://zenodo.org/api/files/b9a97eac-3bc2-4468-a43d-efa270ce8d95/Fieldmill_Ion_counter_Cyprus_campaign.rar">Fieldmill_Ion_counter_Cyprus_campaign.rar</a>)</li> <li>Data from the five MiniMill electrometers that were launched (<a href="https://zenodo.org/api/files/b9a97eac-3bc2-4468-a43d-efa270ce8d95/MiniMills_Cyprus_campaign.rar">MiniMills_Cyprus_campaign.rar</a>)</li> <li>Data from the two of the charge sensors that were launched (<a href="https://zenodo.org/api/files/b9a97eac-3bc2-4468-a43d-efa270ce8d95/Charge_sensors_Cyprus_campaign.rar">Charge_sensors_Cyprus_campaign.rar</a>)</li> <li>Data from the eleven ion counters that were launched, tethered together with the MiniMills or the charge sensors (<a href="https://zenodo.org/api/files/b9a97eac-3bc2-4468-a43d-efa270ce8d95/Ion_counters_Cyprus_campaign.rar">Ion_counters_Cyprus_campaign.rar</a>)</li> <li>A campaign calendar with the launches schedule (<a href="https://zenodo.org/api/files/b9a97eac-3bc2-4468-a43d-efa270ce8d95/Cyprus_campaign_November2019_calendar.pdf">Cyprus_campaign_November2019_calendar.pdf</a>).</li> </ol>
Data for: Multi-campaign ship and aircraft observations of marine cloud condensation nuclei, and droplet concentrations
<p class="MsoNormal"><span>In-situ marine cloud droplet number concentrations (CDNCs), cloud condensation nuclei (CCN), and CCN proxies, based on particle sizes and optical properties, are accumulated from seven field campaigns, ACTIVATE, NAAMES, CAMP2EX, ORACLES, SOCRATES, MARCUS, and CAPRICORN2. Each campaign involves aircraft measurements, ship-based measurements, or both. Measurements are collected over the North and Central Atlantic, Indo-Pacific, and Southern Oceans, representing a range of clean to polluted conditions in various climate regimes. With the large range of environmental conditions sampled, this collection of data is ideal for testing satellite remote detection methods of CDNC and CCN in marine environment. Remote measurement methods are key to expanding the available data, in these difficult to reach regions of the Earth, and improving our understanding of aerosol-cloud interactions. Additional particle composition and continental tracers are included to identify potential contributing CCN source. Several of these campaigns, include both High Spectral Resolution Lidar and polarimetric imaging measurements that will be the basis for the next generation of space-based remote sensors and, thus, can be utilized as satellite surrogates.</span></p>
TEAMx-PC22 (TEAMx pre-campaign 2022) – DWD Radiosonde lauches and drone measurements at Brannenburg
<p>This dataset contains data from 9 radiosonde launches and 6 drone ascents of an IOP on 18./19.7.2022 during the TEAMx pre-campaign 2022. More details about TEAMx can be found at <a href="http://www.teamx-programme.org/">http://www.teamx-programme.org</a>.</p> <p><strong>DATA SET DESCRIPTION</strong></p> <p><strong>1. Measurement location and measured variables</strong></p> <p>Radiosonde launches and drone measurements were conducted at the site of Brannenburg (456m MSL) at the following coordinates:</p> <table> <thead> <tr> <th scope="col"> </th> <th scope="col">Coordinates</th> <th scope="col">Instrument</th> <th scope="col">Measured Variables</th> <th scope="col">Time of sounding [UTC]</th> </tr> </thead> <tbody> <tr> <td>Radiosonde</td> <td> <p>47.742376 N</p> 12.121785 E</td> <td>Radiosonde Vaisala RS41</td> <td>GPH, WDIR, WSPEED, T, DT, potT, P</td> <td> <p>18.07. 12:03</p> <p>18.07. 13:45</p> <p>18.07. 16:45</p> <p>18.07. 19:45</p> <p>18.07. 22:45</p> <p>19.07. 01:45</p> <p>19.07. 04:42</p> <p>19.07. 07:46</p> 19.07. 08:28</td> </tr> <tr> <td>Drone</td> <td> <p>47.742840 N</p> <p>12.121808 E</p> </td> <td>Drone DJI Mavic pro, iMETXQ2</td> <td>T, RH, P</td> <td> <p>18.07. 12:45</p> <p>18.07. 13:45</p> <p>18.07. 16:45</p> <p>18.07. 22:45</p> 19.07. 01:45</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p>
TEAMx-PC22 (TEAMx pre-campaign 2022) – DWD Doppler wind lidar data set (SLXR172)
<p>This dataset contains data measured by DWD with a Doppler Wind Lidar SLXR172 during the TEAMx pre-campaign 2022. More details about TEAMx can be found at <a href="http://www.teamx-programme.org/">http://www.teamx-programme.org</a>.</p> <p><strong>DATA SET DESCRIPTION</strong></p> <p><strong>1. Measurement location and time period </strong></p> <p>Measurements with the SLXR172 were collected at the site of Brannenburg (47.741547 N / 12.122187 E / 456 m MSL) between 15.June – 19 October 2022.</p> <p><strong>2. Measurement setup</strong></p> <p>During the measurement period, two different scanning modes were applied:</p> <p>15. June - 26. July 2022 and 13. August – 19. October 2022 (VAD_CSM).</p> <ul> <li><strong>VAD (velocity-azimuth display) scans in </strong><strong>continuous scanning mode</strong><strong> : </strong>These scans were conducted at an elevation angle of 35°. Azimuth angle interval of the CSM data sampling was about 1.1°. </li> </ul> <p>27.July – 12. August 2022 (VAD_RHI)</p> <ul> <li><strong>VAD scans in step-stare mode: </strong>Step-stare scans were conducted at an elevation angle of 35° and with azimuth steps of 15°.</li> <li><strong>RHI (</strong><strong>range-height indicator) scans into the Inn Valley</strong>; The RHI scans were performed for 10 azimuth angles from 151° to 160° and covered elevation angles from 3° to 51°.</li> </ul> <p> </p> <p><strong><em>3. Data processing, corrections and filter</em></strong></p> <p>For <strong><em>VAD scans in continuous scanning mode</em></strong> the processed wind fields are provided. The data have not been corrected. The data can be filtered using the parameters R<sup>2 </sup>(coefficient of determination), CN (condition number) and NVRAD (number of radial velocities) as described in Päschke (2015):</p> <p>R<sup>2</sup>> 0.95 and CN<10 and NVRAD>12 </p> <p>Please note that in the postprocessing of the VAD CSM scans, the R<sup>2</sup> filter criterion was set to R<sup>2</sup>>0 in order to include all data and therefore might also include scans where the assumptions of homogeneity are not fulfilled. The parameter qwind is therefore not meaningful due to this configuration and should not be used to filter the data. We recommend the use of the above criterion from Päschke.</p> <p>For scans from the <strong><em>VAD scans in step stare mode</em></strong> as well as the <strong><em>RHI scans</em></strong> the raw data files are provided. They have not been corrected nor filtered.</p> <p><strong>4. Data file structure</strong></p> <p>The data are provided in NetCDF format. File names contain date and time information in UTC. The following wildcard characters are used in the file examples below: yyyy - year; mm - month, dd - day; HH - hour, MM - minute, `SS` - second. Files are sorted in monthly folders.</p> <p>The data are provided in two zip-files.</p> <ul> <li>VAD_CSM contains the processed wind fields from 15. June - 26. July 2022 and from 13. August – 19. October 2022)</li> <li>VAD+RHI the raw data files for 27.July – 12. August 2022.</li> </ul> <p>Raw data files of the VAD CSM scans can be provided upon request.</p> <p><strong>5. Contact</strong></p> <p>Contact Katrin.sedlmeier(at)dwd.de.at for any questions regarding the data set.</p> <p><strong>6. References</strong></p> <p>Päschke, E., Leinweber, R., and Lehmann, V.: An assessment of the performance of a 1.5 μm Doppler lidar for operational vertical wind profiling based on a 1-year trial, Atmos. Meas. Tech., 8, 2251–2266, https://doi.org/10.5194/amt-8-2251-2015, 2015.</p>
PALM Model System v 6.0 input and configuration files for coupled large eddy simulations of land surface heterogeneity effects and diurnal evolution of late summer and early autumn atmospheric boundary layers during the CHEESEHEAD19 field campaign
<p>Namelist, configuration and forcing files for the PALM Model System 6.0 revision number 21.10-rc.2 used for the numerical simulations Coupled Large Eddy Simulations of land surface heterogeneity induced atmospheric boundary layer response during the CHEESEHEAD19 field campaign.</p>
Datasets for BIo4A Deliverable D2.10 bio4a.eu - 1st Lysimeters Campaign
<p>Datasets of biochar, leachate and nitrogen forms from experiments in lysimeters in climatic chambers in controlled conditions with barley in soil amended with biochar, corresponding to deliverable D2.10 of H2020 project BIO4A (www.bio4a.eu).</p>
Datasets for BIo4A Deliverable D2.10 bio4a.eu - 2nd Lysimeters Campaign
<p>Datasets of biochar, soil humidity, biomass from experiments in lysimeters in climatic chambers in controlled conditions with barley in soil amended with biochar, corresponding to deliverable D2.10 of H2020 project BIO4A (www.bio4a.eu).</p>
WALL-E polarization lidar measurements from Cyprus campaign 11/2019
<p>WALL-E lidar measurements during the Cyprus observational campaign, on November 2019.The description of the WALL-E lidar system design and calibration procedures can be found herein:</p> <p>Tsekeri, A., Amiridis, V., Louridas, A., Georgoussis, G., Freudenthaler, V., Metallinos, S., Doxastakis, G., Gasteiger, J., Siomos, N., Paschou, P., Georgiou, T., Tsaknakis, G., Evangelatos, C., and Binietoglou, I.: Polarization lidar for detecting dust orientation: system design and calibration, Atmos. Meas. Tech., 14, 7453–7474, https://doi.org/10.5194/amt-14-7453-2021, 2021.</p> <ul> <li>Orientation flag measurements</li> <li>Measurements for the lidar system calibration</li> </ul>
NASA's Airborne Topographic Mapper (ATM) airborne waveform and ground calibration data for the Arctic Spring campaign 2016
<p>The Airborne Topographic Mapper (ATM) was a scanning lidar developed and used by NASA for observing the Earth’s topography for several scientific applications, foremost of which was the measurement of changing Arctic and Antarctic ice sheets, glaciers and sea ice. ATM measured topography to an accuracy of better than 5 centimeters by incorporating measurements from GPS (global positioning system) receivers and inertial navigation system (INS) attitude sensors.</p> <p>In pressurized aircraft the transmitted laser pulse travels thru the aircraft’s optical window close to the scan mirror. The optical delay fiber that is necessary to separate the transmit pulse and window reflection as well as other system components introduce a laser time-of-flight range bias that needs to be determined from ground calibration measurements. This data set includes ATM airborne waveform data from the T2 lidar, as well as the ground test data and true ranges for the Arctic Spring campaign 2016.</p> <p>A collection of MATLAB® functions to read ground test waveform and airborne waveform data is available at: <a href="https://doi.org/10.5281/zenodo.6341229">https://doi.org/10.5281/zenodo.6341229</a></p> <p><strong><strong>See also:</strong> </strong></p> <ul> <li>NASA's Airborne Topographic Mapper (ATM) ground calibration data for waveform data products: <a href="https://doi.org/10.5281/zenodo.7225936">https://doi.org/10.5281/zenodo.7225936</a></li> <li>User guide for NASA's Airborne Topographic Mapper HDF5 waveform data products:<a href="https://doi.org/10.5281/zenodo.7246097"> https://doi.org/10.5281/zenodo.7246097</a></li> <li>Collection of MATLAB® functions for working with ATM (Airborne Topographic Mapper, laser altimetry data products in HDF5 waveform format: <a href="https://github.com/mstudinger/ATM-waveform-tools">https://github.com/mstudinger/ATM-waveform-tools</a></li> <li>Airborne Topographic Mapper (ATM) Bathymetry Toolkit (MATLAB® functions): <a href="https://doi.org/10.5281/zenodo.6341229">https://doi.org/10.5281/zenodo.6341229</a></li> <li>All ATM data products are freely available at the National Snow and Ice Data Center (NSIDC) at <a href="https://nsidc.org/data/icebridge">https://nsidc.org/data/icebridge</a> and can also be downloaded from the NASA Earthdata portal at <a href="https://earthdata.nasa.gov/">https://earthdata.nasa.gov/</a></li> <li>The ILATMW1B airborne waveform data is available at NSIDC: <a href="https://nsidc.org/data/ILNSAW1B/versions/1">https://nsidc.org/data/ILNSAW1B/versions/1</a> (narrow swath) <a href="https://nsidc.org/data/ILATMW1B/versions/1">https://nsidc.org/data/ILATMW1B/versions/1</a> (wide swath)</li> </ul>
Data for: Multi-campaign ship and aircraft observations of marine cloud condensation nuclei, and droplet concentrations
Open the record for dataset details and reuse information.
Depth profiles of water column dissolved methane, density and biomass of zooplankton (specifically, Chaoborus spp.), and measurements of methane extracted from Chaoborus spp. during two sampling campaigns in 2016
Depth profiles of water column dissolved methane (CH4), density and biomass of zooplankton (specifically, Chaoborus spp.), and measurements of methane extracted from Chaoborus spp. were collected from Beaverdam Reservoir, Vinton, VA, USA during two, 24-hr intensive sampling campaigns in fall 2016 (3-4 August and 16-17 September). The sampling campaign was designed to test the hypothesis that Chaoborus spp. can transport CH4 from the hypolimnion to the epilimnion of lakes and reservoirs as they migrate to the surface waters from the benthos at night in search of zooplankton prey, and results of the project are published in Carey et al. 2018. During both sampling events, depth profiles of water column methane and Chaoborus spp. density were collected at noon, dusk, midnight, 2 a.m., dawn, and noon the following day at a 1 m resolution. In addition, during the September sampling event depth profiles were collected 1 hour prior to and after dusk. Methane gas was extracted from live Chaoborus individuals at every depth and time point immediately after collection via gentle centrifugation in distilled water following McGinnis et al. 2017. REFERENCES: Carey, C.C., R.P. McClure, J.P. Doubek, M.E. Lofton, N.K. Ward, and D. Scott. 2018. Chaoborus spp. transport CH4 from the sediments to the surface waters of a eutrophic reservoir, but their contribution to water column CH4 concentrations and diffusive efflux is minor. Environmental Science & Technology. 52:1165-1173. DOI 10.1021/acs.est.7b04384 McGinnis, D. F., Flury, S., Tang, K. W., Grossart, H.-P. 2017. Porewater methane transport within the gas vesicles of diurnally migrating Chaoborus spp.: an energetic advantage. Scientific Reports. 7(44478) DOI 10.1038/srep44478
25cm NDVI data from UAV campaign at Niwot Ridge Saddle Catchment, 2017
Data collected as part of unmanned aerial vehicle (UAV)/drone campaign during Summer 2017. Investigating snow depth variability and spatiotemporal variations and controls on vegetation productivity within the Niwot Ridge LTER Saddle Catchment.
Geodetic snow depth from UAV campaign at Niwot Ridge, 2017
Geodetic snow depth for of Niwot Ridge from UAV campaign in June 2017. Data collected as part of unmanned aerial vehicle (UAV)/drone campaign during Summer 2017, investigating snow depth variability and spatiotemporal variations and controls on vegetation productivity within the Niwot Ridge LTER Saddle
5cm multispectral imagery from UAV campaign at Niwot Ridge, 2017
Data collected as part of unmanned aerial vehicle (UAV)/drone campaign during Summer 2017. Investigating snow depth variability and spatiotemporal variations and controls on vegetation productivity within the Niwot Ridge LTER Saddle Catchment.
Campaigning in S. Iraq, Nineveh relief
Panels 17-19, panel 8 and panels 2-6 Panel 17 - 19 dimensions: Gypsum wall panel relief in three parts: depicting campaigning in Southern Iraq. Assyrian soldiers are bringing in heads, prisoners, and captive women for the counting of the spoil by two scribes, obviously differing in race as in language, who are recording on tablet and parchment (?) the booty taken. Height: 99.06 centimetres Width: 387.99 centimetres Panel 8: Gypsum wall panel relief: scene carved in four registers; above middle horizontal band, a wooded hill, possibly parkland in Nineveh, with irrigation canals fed from an aqueduct. Height: 208.3 centimetres Width: 129.54 centimetres Panels 2-6: Gypsum wall panel relief: depicting cattle and Assyrian soldier escorting prisoners through palm grove towards clerks with scroll and writing materials. Height: 96.52 centimetres Width: 154.94 centimetres Source: Objaverse 1.0 / Sketchfab
EIRSAT-1 Test Campaign and Flight Dataset for Anomaly Detection
<p><span>We have curated a unique dataset derived from EIRSAT-1, Ireland's inaugural domestically produced satellite, as a testing and validation resource for these ML models and the future development cycle of AI-enabled small satellites. This dataset consists of a training set developed during ground testing and containing artificial anomalies induced to train satellite operators, a validation dataset containing real anomalies encountered during the qualification campaign, and an early flight test dataset collected since the satellite was launched on December 1<sup>st</sup>, 2023. This paper presents an in-depth analysis of the efficacy of these ML techniques when applied to the EIRSAT-1 dataset, offering insights into their potential to revolutionize the domain of satellite operations through enhanced autonomy and responsiveness. This study not only showcases the capabilities of these ML techniques in an operational environment but also sets the stage for future research and development in autonomous satellite systems.</span></p>
Roman Army Campaigning spreadsheet
<p><strong>UPDATED August 2025 </strong>with improvements and minor bug fixes. Most importantly the spreadsheet is now in LibreOffice Calc .ods format. The spreadsheet had always been developed in LibreOffice and then in the past migrated to .xlsx format. Unfortunately some of the new graphical elements (depictions of the rampart-ditch and the camp in plan view) and some complex operations no longer work seemlessly in MS Excel formats, thence the LibreOffice .ods format.</p> <p><strong>UPDATED January 2024</strong> with dynamic charts of marching camp defence profiles and camp plans. These simple 2D representations are updated as the user changes values in the spreadsheet giving a visual confirmation of appropriateness.</p> <p>Spreadsheet calculations for campaigning Roman armies of the early Imperial period. The spreadsheet is also available at www.bandaarcgeophysics.co.uk/arch_intro.html . It allows the definition of army sizes, march rates and distances, and the building of camp defences (ditch, rampart and palisade) before calculating over 70 parameters and metrics associated with a Roman marching camps, e.g internal roads. Calculated camps sizes are cross-matched to extant camps allowing comparisons.</p> <p>Observations from the use of the spreadsheet, together with instructions on how to use, are available in an accompanying essay, "Early Imperial Roman army campaigning: observations on marching metrics, energy expenditure and the building of marching camps", available at www.bandaarcgeophysics.co.uk/arch_intro.html or from Zenodo.org ( https://zenodo.org/record/838275#.ZGSOIHbMIuU ) or Academia.edu (search 'steve kaye').</p>
Processed data for a Slocum ocean glider campaign in the Gulf of Lion, Mediterranean Sea, 2021
<p>The data set was measured in the Gulf of Lion by a Slocum glider in February 2021. It includes processed data from a CTD and a turbulence microstructure package.</p>
2DVD dataset for GMD publication - Simulated prognostic approach of graupel density in a bulk-type cloud microphysics scheme and evaluation during the ICE-POP field campaign
<p>This archive contains the 2DVD measurement of graupel particles used in the GMD paper "Simulated prognostic approach of graupel density in a bulk-type cloud microphysics scheme and evaluation during the ICE-POP field campaign".</p><p>For each identified graupel particle, the following are included:</p><ul><li>Volume-equivalent diameter (mm)</li><li>Density (g cm-3)</li><li>Fall velocity (m s-1)</li></ul>
Aerosol, Temperature and Water Vapor profiling during the BIOSPHERE Athens Campaign - NTUA - Level 3 - June 2023
<p>June 2023 - Level 3 of the lidar data obtained by the EOLE and DEPOLE lidar systems in the National Technical University of Athens (NTUA), during the EURAMET European Partnership on Metrology (EPM) project BIOSPHERE.</p>
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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.