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10,016 results for “Observation”
Derived data supporting the analysis of surface albedo changes from Mars 2020 observations: Probabilistic distribution of the Amplitude Spectral Densities of Supercam microphone recordings and Monte-Carlo dust devil simulations.
<p>These files contain derived data used in the analysis submitted for publication in Journal of Geophysical Research: Planets, entitled "Dust Lifting Through Surface Albedo Changes at Jezero Crater, Mars" by Vicente-Retortillo et al. The article was initially submitted on November 14, 2022, and the revised version on March 1, 2023.</p> <p>Files include the derived data and information needed to generate Figures 4 (Microphone_Data.mat and Plot_ASD_from_Microphone_Data) and 6 (remaining files) of the article.</p>
Investigating the geometrical and optical properties of the persistent stratospheric aerosol layer observed over a Southern European lidar station during 2019
<p>Simulted aerosol extinction at 550nm profiles over Thessaloniki by the IFS-CB05-BASCOE-GLOMAP system.</p>
Optical polarimetric observations of the TDE AT 2020mot with RoboPol
<p>The dataset contains raw FITS images of the tidal disruption event <a href="https://www.wis-tns.org/object/2020mot">AT 2020mot</a> (Gaia20ead) and raw FITS images of a polarization standard stars that can be used for the instrumental polarization correction. The dataset was obtained with the <a href="http://robopol.org/">RoboPol</a> optical polarimeter in the R-band mounted at the 1.3 m telescope of the Skinakas Observatory, Greece. The data were collected between 7 September and 31 October 2020.</p>
Observational rainfall data of the 2021 mid-July flood event in Belgium – Part 2. Radar product RADFLOOD21
<p>From July 13th to 16th 2021, a long period of sustained and heavy rainfall affected Central Europe producing extreme rainfall amounts in western Germany, eastern Belgium, Luxembourg and The Netherlands. In Belgium, this unusual event induced massive flooding on a large part of the country and was responsible for 39 fatalities and strong damages to buildings and infrastructures.</p><p>Such extremely rare event needs to be documented as much as possible and data must be made available for further studies in hydrology, in urban planning and, more generally, in all multi-disciplinary studies aiming at identifying and understanding all factors leading to such disaster.</p><p>The observational rainfall data available for Belgium during the period from July 13th to July 16th 2021 are here shared with the scientific community. These data are twofold and provided in 2 parts:</p><p><br><strong>Part 1. </strong><a href="https://doi.org/10.5281/zenodo.7739983"><strong>Observations from high-quality rain gauges</strong></a></p><p>The dataset includes daily precipitation accumulation recorded by 323 weighing and manual rain gauges in Belgium as well as 5-min precipitation data recorded by 168 weighing rain gauges. These data were checked for possible errors and inconsistencies.</p><p>The rain gauges observations are provided in csv format in 2 files:</p><ul><li>RainGaugesData_FLOOD21_daily.csv</li><li>RainGaugesData_FLOOD21_5min.csv</li></ul><p><br><strong>Part 2. </strong><a href="https://doi.org/10.5281/zenodo.7740059"><strong>Radar-based quantitative precipitation estimation (RADFLOOD21)</strong></a></p><p>This product provides a quantitative precipitation estimation of the event at high spatial (i.e., 1 km) and temporal (i.e., 5 min and hourly) resolutions. It is obtained after a careful processing of the weather radar measurements and a merging with rain gauge measurements. The data is provided in hdf5 format. In addition, an animation of the 5-min RADFLOOD21 data is also made available.</p><p> </p><p>These data are exposed and discussed in <a href="https://hess.copernicus.org/articles/27/3169/2023/">https://hess.copernicus.org/articles/27/3169/2023/</a>. In particular, several analyses of these data are performed to describe the spatial and temporal distribution of rainfall during the event and to illustrate its exceptional character.</p><p> </p>
Supplementary data for the manuscript entitled "Evolution of the convective boundary layer in a WRF simulation nested down to 100 m resolution during a cloud-free case of LAFE 2017 and comparison to observations" (JGR Atmospheres)
<p>This dataset contains additional material to reproduce the simulation and some of the figures of the manuscipt entitled "Evolution of the convective boundary layer in a WRF simulation nested down to 100 m resolution during a cloud-free case of LAFE 2017 and comparison to observations" in the Journal of Geophysical Reasseach - Atmospheres.</p>
Data and Supplemental Data from Radwin et al. "Southwestern Australia Acid-Saline Mineralogy: Observations from Reflectance Spectroscopy"
<p>These data are part of Radwin et al., "Southwestern Australia Acid-Saline Mineralogy: Observations from Reflectance Spectroscopy", submitted to IEEE TGRS in 2022. This work was supported in part by the National Aeronautics and Space Administration through the Solar System Workings program grant 80NSSC19K0552, and by the National Science Foundation grants from the Sedimentary Geology and Paleobiology Program (EAR-0719822, EAR-0719838 and EAR-0719892).</p> <p>Included in this dataset are ten separate downloadable files:</p> <p>1) RADWIN_2022_ACID_SALINE_SPECTRA.xlsx is an Excel spreadsheet containing all of the continuum-removed spectra utilized and an appendix organizing the data.</p> <p>2) RADWIN_2022_ACID_SALINE_POLYFIT_IRON_SPECTRA.xlsx is an Excel spreadsheet containing all of the polynomial fit continuum-removed iron spectra between ~800-1200 nm.</p> <p>3) RADWIN_2022_HYMAP_DATA.rar is a folder containing Envi files (with associated image metadata, such as bad-band-list) for the HyMap imagery of Lake Brown and the Twin Lakes region.</p> <p>4) RADWIN_2022_WA_SEDS_XRF_DATA.xlsx contains supplemental pXRF data.</p> <p>5) Radwin_Supplemental_XRD_LA1_Data.xlsx contains supplemental XRD data from the Lake Aerodrome LA2 core dataset (LA1 on filename is a typo).</p> <p>6) SAM Classification Maps.zip contains the classification map files.</p> <p>7) SAM Rule Images & Stats.zip contains rule images and class statistics from the SAM classification maps.</p> <p>8) SAM_Processing_Log.xlsx contains the specifications used for the SAM classification maps when using ENVI.</p> <p>9) SAM_Training_Data_Shapefiles.zip contains shapefiles that designate the training class data for the 7 classification map classes.</p> <p>10) XRF_Spectra_Matched_Sample_Photos_For_Reference.docx contains photos of the samples that were analyzed with pXRF.</p> <p> </p>
Fig. 1 in Nouvelles observations de quelques Carabes rares en Suisse
Fig. 1. Amara gebleri (12,9 mm) (A), Leistus terminatus (9,0 mm.) (B), Agonum viridicupreum (9,3 mm) (C) et Elaphrus uliginosus (11,1 mm) (D), quatre espèces très rares en Suisse, capturées en 2014. (Photos Jessica Litman)
Surface wave observations in 100% Umf fluidised granular current
<p>High speed video frames taken at 500 fps, looking down at the upper surface of a granular flow being supplied with 100% of the minimum fluidisation velocity. Shows development and evolution of surface waves as the current propagates and comes to a halt. Flume base is 15 cm wide. Current is comprised of 45-90 micron soda lime ballotini.</p>
Surface wave observations in 60% Umf fluidised granular current
<p>High speed video frames taken at 500 fps, looking down at the upper surface of a granular flow being supplied with 60% of the minimum fluidisation velocity (Umf). Shows development and evolution of surface waves as the current propagates and comes to a halt. Flume base is 15 cm wide. Current is comprised of 45-90 micron soda lime ballotini.</p>
Surface wave observations in 90% Umf fluidised granular current
<p>High speed video frames taken at 500 fps, looking down at the upper surface of a granular flow being supplied with 90% of the minimum fluidisation velocity. Shows development and evolution of surface waves as the current propagates and comes to a halt. Flume base is 15 cm wide. Current is comprised of 45-90 micron soda lime ballotini.</p>
Tahoe rain or snow precipitation phase observations
<p>These data include observations of rain, snow, and mixed precipitation from the Tahoe Rain or Snow citizen science project. Included with each observation is a set of ancillary variables, including latitude and longitude, elevation, modeled meteorological data, and additional info. Please see the metadata file for the description and units of each data column.</p> <p>For more info, please see Jennings et al. (2023) and Arienzo et al. (2021):</p> <ul> <li>Jennings, Keith S., Monica M. Arienzo, Meghan Collins, Benjamin Hatchett, Anne W. Nolin, and Graeme Aggett. "Crowdsourced Data Highlight Precipitation Phase Partitioning Variability in Rain-Snow Transition Zone." Earth and Space Science (2023). <a href="https://doi.org/10.1029/2022EA002714">https://doi.org/10.1029/2022EA002714</a> </li> <li>Arienzo, Monica M., Meghan Collins, and Keith S. Jennings. "Enhancing engagement of citizen scientists to monitor precipitation phase." Frontiers in Earth Science 9 (2021): 617594. <a href="https://doi.org/10.3389/feart.2021.617594">https://doi.org/10.3389/feart.2021.617594</a> </li> </ul> <p>For the code used to process these data: <a href="https://github.com/SnowHydrology/MountainRainOrSnow/tree/tahoe_ros">https://github.com/SnowHydrology/MountainRainOrSnow/tree/tahoe_ros</a></p>
Simultaneous observations of atmospheric vertical potential gradient from coastal Antarctic Stations Bharati and Maitri
<p><strong>Simultaneous dual station observations of the atmospheric electric potential gradient (PG) at Bharati and Maitri studied for the period 2014-2016, bring out a new regional diurnal pattern of fair-weather PG for the coastal Antarctic region, perhaps the ubiquitous characteristics of the PG for the coastal Antarctic region. It is a broad minimum in the Carnegie-type PG variation. The surface wind distorts the fair-weather diurnal pattern of PG over Bharati more significantly than at Maitri. Katabatic wind effect over Bharati PG than Maitri PG is the sharper slope gradient of the ice sheet over Bharati than over Maitri as a consequence the katabatic wind streamlines drain over the Lambert glacier, located close to Bharati station. The wind speed significantly affects the Bipolar Air Ion Concentration (BAIC) by accumulation and dispersion. The concentration is maximum when the wind speed is minimum. Obviously, the PG is minimum during these hours. This particular signature distorts the expected global pattern of the PG at Bharati. Data quality is improved by measuring the PG using the EFM flush mounted with ground EFM rather than from an elevated site. Perhaps this position reduces the wind effects on the PG and favours the detection of globally representative data.</strong></p>
Data files for Atmospheric Gravity Wave and Instability Observations from the International Space Station using the Near InfraRed Airglow Camera (NIRAC)
<p>The files in this set are data obtained from the NIRAC airglow imager on the International Space Station. The files are named for a JGR paper by J. Hecht et al. entitled Atmospheric Gravity Wave and Instability Observations from the International Space Station using the Near InfraRed Airglow Camera (NIRAC). These files are for plots in Figures 5,7,10,11,17,18, and 19 in the submitted paper. The files are published here so as to be available for review. This paper should appear in JGR Atmospheres sometime in late 2023 or early 2024. The files that are text files are meant to be read with IDL as discussed in the readme file. </p>
Air-Sea fluxes of CO2 in the Indian Ocean between 1985 and 2018: A synthesis based on Observation-based surface CO2, hindcast and atmospheric inversion models.
<p>This data set contains 14 hindcast models (CCSM-WHOI.nc, CESC_ETHZ.nc, CNRM-ESM2-1.nc, EC_Earth3.nc, FESOM_REcoM_LR.nc, MOM6_Princeton.nc, MPIOM_HAMOCC.nc; MRI-ESM2-1.nc, NorESM-OC1.2.nc, ORCA1-LIM3-PISCES.nc, ORCA025-EOMAR.nc, Plankotom12, INCOIS-BIO-ROMS.nc, ROMS-NYUAD.nc), nine empirical models (CMEMS-LSCE-FFNN.nc, CSIRML6.nc, Jena-MLS.nc, JMAMLR.nc, Spco2_LDEO_HPD.nc, SOMFNN.nc, NIES-MLR3.nc, UOEX-WAT20.nc, OceanSODAETHZ.nc) and CO2 flux climatology data (CO2_Climatology.nc). This data set also has two atmospheric inversion models output and those are - MACTM (MACTM.nc) and CAMSv20r1 (CMSv20r1.zip format and inside the zip folder files are .nc format).</p>
Early Evolution of the Stratospheric Aerosol Plume Following the 2022 Hunga Tonga-Hunga Ha'apai Eruption: Lidar Observations from Reunion Island (21°S, 55°E)
<p>Lidar dataset of the first week of measurements of the Hunga Tonda volcanic plume recorded above Reunion Island.</p>
Preliminary biohybrid experiments with the Behavioral Observation & Biohybrid Interaction framework (featuring the LureBot)
<p>This is the dataset that corresponds to preliminary biohybrid interaction experiments conducted with the Behavioral Observation & Biohybrid Interaction (BOBI) framework, featuring the LureBot.</p> <p> </p> <p>The directory contains:</p> <ol> <li>Trajectories of fish and/or robot agents for different scenarios (<filename>.dat).</li> <li>A robot index file (<filename>_ridx.dat) that corresponds to each trajectory file, that contains the robot's ID (starting from 0) in the experiment, or a negative ID in the case of fish-only experiments.</li> </ol> <p> </p> <p>A brief explanation of the directory's contents:</p> <ul> <li>[Open-loop dynamics] <strong>Circular</strong> trajectories <ul> <li><strong>Disc-shaped</strong>: 1-hour-long trajectories of the LureBot with a disc-shaped lure performing a circular trajectory while a single H. rhodostomus is in the tank.</li> <li><strong>Biomimetic</strong>: 1-hour-long trajectories of the LureBot with a biomimetic lure performing a circular trajectory while a single H. rhodostomus is in the tank.</li> </ul> </li> </ul> <p> </p> <ul> <li>[Open-loop dynamics] <strong>Eightfold rose</strong> trajectories <ul> <li><strong>Disc-shaped</strong>: 1-hour-long trajectories of the LureBot with a disc-shaped lure performing an eightfold rose trajectory while a single H. rhodostomus is in the tank.</li> <li><strong>Biomimetic</strong>: 1-hour-long trajectories of the LureBot with a biomimetic lure performing an eightfold rose trajectory while a single H. rhodostomus is in the tank.</li> </ul> </li> </ul> <p> </p> <ul> <li>[Closed-loop dynamics] <strong>Biomimetic Interaction Model</strong> trajectories <ul> <li><strong>single_agent</strong>: <ul> <li><strong>Fish</strong>: 1-hour-long trajectories of a single H. rhodostomus interacting with the wall alone.</li> <li><strong>Biomimetic</strong>: 1-hour-long trajectories of a single robot following a biomimetic model.</li> </ul> </li> <li><strong>pair_agents</strong>: <ul> <li><strong>Fish-only</strong>: 1-hour-long trajectories of a pair of H. rhodostomus interacting with the wall and with each other.</li> <li><strong>Disc-shaped</strong>: 1-hour-long trajectories of the LureBot with a disc-shaped lure biomimetically interacting with a single H. rhodostomus.</li> <li><strong>Biomimetic</strong>: 1-hour-long trajectories of the LureBot with a biomimetic lure biomimetically interacting with a single H. rhodostomus.</li> </ul> </li> <li><strong>group5_agents</strong>: <ul> <li><strong>Fish-only</strong>: 1-hour-long trajectories of 5 H. rhodostomus interacting with the wall and with each other.</li> <li><strong>Biomimetic</strong>: 1-hour-long trajectories of the LureBot with a biomimetic lure biomimetically interacting with 4 H. rhodostomus.</li> </ul> </li> </ul> </li> </ul>
FIG. 1 in Observations on the taxonomy and distribution of Epipterygium atlanticum Hanusch and E. tozeri (Grev.) Lindb. (Mniaceae Schwägr.) in mainland Portugal
FIG. 1. — Areolation in mid-leaf, lateral leaves:A-C, Epipterygium tozeri s.s.; A, Celorico de Bastos,LISU[LISU 239535]; B, Gouveia, Ribamondego,LISU[LISU 244744]; C, Ribeira de Seixe, Herb. Porley; D-F, Epipterygium atlanticum Hanusch; D, Monchique, Picota summit, Herb. Porley; E, Rabaçal, LISU[LISU 246865]; F, Odemira, LISU[LISU 204992]. Scale bars: 100 µm.
FIG. 2 in Observations on the taxonomy and distribution of Epipterygium atlanticum Hanusch and E. tozeri (Grev.) Lindb. (Mniaceae Schwägr.) in mainland Portugal
FIG. 2. — Quantitative characters and measurements (mean values in µm) of the 125 Portuguese studied samples of Epipterygium atlanticum Hanusch and E. tozeri s.s.
FIG. 3. — A in Observations on the taxonomy and distribution of Epipterygium atlanticum Hanusch and E. tozeri (Grev.) Lindb. (Mniaceae Schwägr.) in mainland Portugal
FIG. 3. — A, distribution of Epipterygium atlanticum Hanusch and E. tozeri s.s. in mainland Portugal based on revised LISU and samples from Algarve (Herb. Porley). Intensity of shading corresponds to altitudinal data (0 to 1993); B, box plots showing the variation (0 to 900 m) in locality elevation for the two taxa.
Gap-filled Multivariate Observations of Global Land-climate Interactions
<p>The NETCDF encompasses monthly time series spans the years 1995-2020 globally at 0.5-degree resolution with gap-free estimates of nine variables, gap-filled using the CLIMFILL (CLIMate data gapFILL) framework [1,2]</p> <p>The nine variables are:</p> <p>- surface layer soil moisture from the Climate Change Initiative (CCI) of the European Space Agency (ESA),</p> <p>- land surface temperature and</p> <p>- diurnal temperature range from the Moderate Resolution Imaging Spectroradiometer (MODIS),</p> <p>- precipitation from the Global Precipitation Measurement (GPM),</p> <p>- terrestrial water storage from the Gravity Recovery and Climate Experiment (GRACE),</p> <p>- ESA-CCI burned area,</p> <p>- ESA-CCI snow cover fraction, </p> <p>- two-meter temperature and precipitation from the Climate Research Unit (CRU).</p> <p> </p> <p>Note: this dataset is only validated and tested for the use cases in the accompanying study (DOI to come). Please have caution using the data for analysis that might include trends in high latitude, in regions where the variable has high fraction of missing values, or in mountainous regions.</p> <p> </p> <p>References:</p> <p>[1] Bessenbacher, V., Seneviratne, S.I. and Gudmundsson, L. (2022): CLIMFILL v0.9: a framework for intelligently gap filling Earth observations. Geosci. Model Dev., 15, 4569–4596, 2022 <a href="https://doi.org/10.5194/gmd-15-4569-2022">https://doi.org/10.5194/gmd-15-4569-2022</a></p> <p>[2] <a href="https://github.com/climachine/climfill/releases/tag/1.0">https://github.com/climachine/climfill/releases/tag/1.0</a></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.