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10,553 results for “measurements”
PIE LTER measurements of water column depth at 15 minute intervals in the Parker River near Rt 1A bridge, Newbury, MA, year 2001. Water depths are relative to the sonde pressure transducer and not associated with a datum.
PIE LTER, year 2001,15 minute readings of water column depth in the lower Parker River Estuary at Fernalds Marina bulkhead off Rt. 1A., Newbury, MA. Water depths are relative to the sonde pressure transducer and not associated with a datum.
PIE LTER measurements of water column depth at 15 minute intervals in the Parker River near Rt 1A bridge, Newbury, MA, year 2002. Water depths are relative to the sonde pressure transducer and not associated with a datum.
PIE LTER, year 2002, 15 minute readings of water column depth in the lower Parker River Estuary at Fernalds Marina bulkhead off Rt. 1A., Newbury, MA. Water depths are relative to the sonde pressure transducer and not associated with a datum.
Spectrometer measurements of snow and bare ground targets and simultaneous measurements of snow conditions
<p>The dataset holds measurements of reflected sunlight spectrum from snow and different snow free targets (moss, lichen, rock surface, ground vegetation). The purpose of the measurements has been to create a dataset to better understand the effect of snow cover characteristics and different bare ground targets on reflected sunlight, and to relate this information to observed at satellite reflectances from snow covered and partially snow covered boreal forests. The instrument in the measurements was Field Spec Pro JR from Analytical Spectral Devices Inc. Additionally to the 237 bands measured between 350-2500 nm, the dataset contains the following parameters from the measurement sites: id, location, time of measurement, target type (snow, bare ground etc.), cloudiness (in octas), snow depth (cm), snow temperature at 5 cm (degree C), snow temperature half way through the snow pack (degree C), soil temperature (degree C), air temperature (degree C), average grain size (mm), grain type, snow water content (subjective), snow patchiness (%) in the surrounding area, visible impurities in snow yes/no and land cover.</p>
Measurement of 139La(p,x) cross sections from 35-60 MeV by stacked-target activation
<p>This repository contains all raw gamma-ray spectra analyzed for the present manuscript, as well as calibration spectra. Further details and analysis code are available on reasonable request. </p> <p>A stacked-target of natural lanthanum foils (99.9119% 139La) was irradiated using a 60 MeV proton beam at the LBNL 88-Inch Cyclotron. 139La(p,x) cross sections are reported between 35–60 MeV for nine product radionuclides. The primary motivation for this measurement was the need to quantify the production of 134Ce. As a positron-emitting analogue of the promising medical radionuclide 225Ac, 134Ce is desirable for in vivo applications of bio-distribution assays for this emerging radio-pharmaceutical. The results of this measurement were compared to the nuclear model codes TALYS, EMPIRE and ALICE (using default parameters), which showed significant deviation from the measured values.</p>
Free-field sensitivity of four electro-acoustic measuring chains at 0° incidence angle in the frequency range 0.25 kHz to 100 kHz
<p>This dataset contains calibration data of the free-field sensitivity of four electro-acoustic measuring chains at 0° incidence angle in the frequency range 0.25 kHz to 100 kHz. Each of the four channels consisted of a ¼'' externally polarized free-field measurement microphone of the condenser type GRAS 40 BF, a ¼'' preamplifier GRAS 26AC, a power module GRAS 12AQ and an FFT analyzer Ono Sokki CF-9400. The calibration data was acquired in the laboratory of the Physikalisch-Technische Bundesanstalt (PTB).</p>
SSARA A-LIFE measurement data
<p>This dataset contains the measurement data of the SSARA-P polarized sun and sky photometer during the A-LIFE field campaign in Cyprus during April 2017.</p> <p>The data is provided in several stages of preprocessing:</p> <ul> <li><em>L0</em>: raw instrument data</li> <li><em>L1</em>: calibrated measurements</li> <li><em>L2</em>: Aerosol Optical Thickness (AOT) derived from direct sun measurements</li> </ul> <p>The datasets are also split for three different observation geometries:</p> <ul> <li><em>direct</em>: direct sun observation</li> <li><em>almuc</em>: almucantar geometry (scan at solar elevation)</li> <li><em>pplane</em>: principle plane scan (hemispheric scan through sun and zenith)</li> </ul>
PetrocShelley/Measured-solid-state-and-sub-cooled-liquid-vapour-pressures-of-nitroaromatics-using-KEMS-Data-Set: Measured-solid-state-and-sub-cooled-liquid-vapour-pressures-of-nitroaromatics-using-KEMS-Data-Set
<p>All data files for the Measured solid state and sub-cooled liquid vapour pressures of nitroaromatics using Knudsen effusion mass spectrometry by Shelley et al.</p>
Raw data for: Pressure and inertia sensing drifters for glacial hydrology flow path measurements
<p>Raw data for paper</p> <p>Title: Pressure and inertia sensing drifters for glacial hydrology flow path measurements</p> <p>Authors: A.Alexander, M.Kruusmaa, J.A. Tuhtan, A.J. Hodson, T.V. Schuler, A. Kääb</p> <p>Journal: The Cryosphere</p> <p>Year, 2020</p>
Measuring Software Testability Modulo Test Quality - Replication Package
<p>This repository represents the replication package for the paper <em>Measuring Software Testability Modulo Test Quality</em>.</p> <p>It includes the dataset and the Jupyter Notebook we used for the analysis in our paper.</p>
Raw SNR data for Manuscript "GPS Interferometric Reflectometry : Using a Low Cost Antenna to Measure Water Levels"
<p>Raw GPS L1 SNR (and ancillary) data for an experiment to use a low-cost GPS antenna/receiver to measure water levels using the GNSS - Interferometric Reflectometry technique.</p> <p>The data were recorded at the RNLI lifeboat station in Sligo, Ireland (N 54<sup>o </sup>18' 17.8'', W 8<sup>o</sup> 34' 5.4'' ) using a Globalsat BU353S4 USB puck that uses a SirfStar IV receiver with patch antenna (2018 data) and a Maestro A2200A SirfStar IV module (2019 data). Both systems were mounted to a radio mast at around 16m above sea level.</p> <p>The data are stored in daily files with the naming convention sligDDD0.YY.TNR.gz where DDD is the Day of Year and YY is the year in short format (18,19). Each file is gzipped. </p> <p>The files are flat text files with fixed width columns in the following order</p> <p>1) PRN GPS satellite code</p> <p>2) Elevation (degrees)</p> <p>3) Azimuth (degrees)</p> <p>4) Seconds of Day</p> <p>5) change in elevation angle with time (degrees/second) : needed for reflector height change corrections</p> <p>6) Blank</p> <p>7) S1 SNR signal (dB-Hz)</p> <p>8) Blank reserved for S2 SNR signal</p> <p>9) Blank reserved for S5 SNR signal</p>
CHAMP and Swarm solar activity- and height-scaled polar cap plasma density measurements
<p>Solar activity- and height-adjusted plasma density measurements in the polar cap (i.e., above 80° latitude in Modified Apex<sub>110</sub> coordinates) from the Swarm and CHAMP satellites. covering the entire CHAMP mission period (2002–2009) and the Swarm mission period from launch through February 2020.</p> <p>Plasma density measurements are scaled to a nominal solar activity level of <<em>F</em>10.7><sub>27</sub> = 80 sfu, and an altitude of 500 km, as described in Hatch et al. (submitted to JGR: Space Physics; <a href="https://www.essoar.org/doi/abs/10.1002/essoar.10502854.1">ESSOAr pre-print</a>) </p> <p>This dataset was prepared as a part of the "Swarm+ Coupling High-Low Atmosphere Interactions: Ion Outflow" project (<a href="https://swarmoutflow.w.uib.no/">project website</a>) (<a href="https://eo4society.esa.int/projects/swarm-coupling-high-low-atmosphere-interactions-ion-outflow/">ESA website</a>), and is funded by European Space Agency Contract #4000126731.</p> <p>Data are stored in HDF5 format as a Python Pandas dataframe. They can be loaded into Python via the following.</p> <pre><code class="language-python">import pandas as pd df = pd.read_hdf('CHAMP_Swarm_polarcap_adjDensity.hdf',key='df')</code></pre> <p>The data columns are</p> <ul> <li>'NeAdj' : Solar activity- and height-adjusted plasma density (cm<sup>-3</sup>)</li> <li>'a110lat' : Modified Apex<sub>110</sub> latitude (deg)</li> <li>'a110lon' : Modified Apex<sub>110</sub> longitude (deg)</li> <li>'mlt' : Modified Apex<sub>110</sub> magnetic local time</li> <li>'h_km' : satellite altitude (km)</li> <li>'gclat' : geocentric latitude (deg)</li> <li>'gclon' : geocentric longitude (deg)</li> <li>'sat' : satellite identifier (string, one of 'A', 'B',' 'C', or 'CHAMP')</li> </ul>
Hourly non-gridded volcanic ash properties retrieved from SEVIRI measurements for the Eyjafjallajökull 2010 eruption
<p>- Publishing date:<br> 14.05.2020</p> <p>- Title:<br> Hourly non-gridded volcanic ash properties retrieved from SEVIRI<br> measurements for the Eyjafjallajökull 2010 eruption </p> <p>- Authors of data set:<br> Arve Kylling (aky@nilu.no), NILU - Norwegian Institute for Air Research<br> Espen Sollum, NILU - Norwegian Institute for Air Research</p> <p>- Description:<br> Ash satellite detection and retrievals were made using infrared<br> measurements by SEVIRI on board the MSG-2 satellite. MSG-2 is<br> geostationary, centred at approximately 0N latitude, and has a 70<br> degree view coverage (Schmetz et al., 2002). Pixel resolution is 3 ×<br> 3 km at nadir, while at the edge of the coverage it increases to 10<br> × 10 km. Observations are available every 15 min. Pixels are<br> identified as containing ash if the brightness temperature<br> difference (BTD) between the SEVIRI 10.8 and 12.0 μm channels<br> (Prata, 1989) is below a certain threshold value, here −0.5 K. The<br> BTDs have been adjusted for water vapour absorption using the approach of<br> Yu et al. (2002). Ash clouds give negative BTDs, ice give positive<br> BTDs, and BTDs of water clouds are closer to zero. The ash mass<br> loading and effective ash particle radius are retrieved as described<br> in Kylling et al. (2015). The retrieval is based on a modification<br> of the Bayesian optimal estimation technique used by Francis et<br> al. (2012). We assume andesite ash with refractive index from Pollack<br> et al. (1973), spherical ash particles, and a lognormal size<br> distribution. The lognormal size distribution is described by the<br> geometric mean radius and the geometric standard deviation. The data<br> set includes retrievals for geometric standard deviation of 1.5,<br> 1.75, 2.0, and 2.25, which is a subset of the values used by Francis<br> et al. (2012). The data set has been used by Steensen et al. (2017).</p> <p> Data comes as hourly files broadly covering Iceland, Europe and the<br> surrounding oceans. The files are in bzip2 netcdf-format which<br> should be self-explanatory. </p> <p>- Version:<br> 1.0</p> <p>- Language:<br> English</p> <p>- Keywords<br> Volcanic ash, remote sensing, SEVIRI, Eyjafjallajökull 2010</p> <p>- Additional notes<br> None</p> <p>- Access right:<br> Open access</p> <p>- License:<br> CC BY-SA 4.0 </p> <p>- Funding:<br> Partly funded by the Norwegian ash project financed by the Norwegian<br> Ministry of Transport and Communications and Avinor. </p> <p>- References:<br> Francis, P. N., Cooke, M. C., and Saunders, R.W.: Retrieval of<br> physical properties of volcanic ash using Meteosat: A case study<br> from the 2010 Eyjafjallajokull eruption, J. Geophys. Res. Atmos.,<br> 117, D00U09, https://doi.org/10.1029/2011JD016788, 2012.</p> <p> Kylling, A., Kristiansen, N., Stohl, A., Buras-Schnell, R., Emde,<br> C., and Gasteiger, J.: A model sensitivity study of the impact of<br> clouds on satellite detection and retrieval of volcanic ash, Atmos. <br> Meas. Tech., 8, 1935-1949, https://doi.org/10.5194/amt-8-1935-<br> 2015, 2015.<br> <br> Pollack, J. B., Toon, O. B., and Khare, B. N.: Optical properties of<br> some terrestrial rocks and glasses, Icarus, 19, 372-389,<br> https://doi.org/10.1016/0019-1035(73)90115-2, 1973. </p> <p> Prata, A. J.: Observations of volcanic ash clouds in the 10-12 um<br> window using AVHRR/2 data, Int. J. Remote Sens., 10, 751-761,<br> 1989.</p> <p> Schmetz, J., Pili, P., Tjemkes, S., and Just, D.: An introduction to<br> Meteosat second generation (MSG), B. Am. Meteorol. Soc., 83,<br> 977-992, 2002.<br> <br> Steensen, B. M., Kylling, A., Kristiansen, N. I., and Schulz, M.:<br> Uncertainty assessment and applicability of an inversion method for<br> volcanic ash forecasting, Atmos. Chem. Phys., 17, 9205-9222,<br> https://doi.org/10.5194/acp-17-9205-2017, 2017. </p> <p> Yu, T., Rose, W. I., and Prata, A. J.: Atmospheric correction for<br> satellite-based volcanic ash mapping and retrievals using "split<br> window" IR data from GOES and AVHRR, J. Geophys. Res. Atmos., 107,<br> https://doi.org/10.1029/2001JD000706, 2002. </p>
[Dataset] FP-Redemption: Measuring Browser Fingerprinting Adoption for the Sake of Web Security
<p>Full dataset for the paper "FP-Redemption: Measuring Browser Fingerprinting Adoption for the Sake of Web Security"</p> <p>5 files are provided:</p> <ul> <li>dataset.csv. The raw elements collected when browsing the web. Each entry corresponds to one attribute being accessed with one parameter combination by one script on one webpage. A single attribute with the same parameters can be accessed several times. It is represented with the key <em>nbTimes</em></li> <li>domainTags.csv: For each website, it provides its category and country tag.</li> <li>webpageTags.csv: For each webpage, it provides its type.</li> <li>fingerprinters.zip/<filenumber>.js: Our fingerprinters. Out of the 199 we requested, 7 are missing, leading in 192 js files.</li> <li>mapping.csv. 3 columns CSV file: <ul> <li>The first one lists the 199 fingerprinters detected by our algorithm.</li> <li>The second one gives the <filenumber> used to link a fingerprinter and its file in the directory.</li> <li>The third one gives the groups the fingerprinters belongs to. By default, each fingerprinter belongs to his own group. However, several fingerprinters are belonging to the same group as we evaluate there were duplicates. Thus, the number of distinct groups corresponds to the distinct fingerprinters we measured in our dataset: 169.</li> </ul> </li> </ul>
Measured and modelled significant wave height time series at the Bothnian Sea Wave buoy in the Baltic Sea
<p>Significant wave height data at the location of FMI's wave buoy in the Bothnian Sea, Baltic Sea (61 degrees 8' N, 20 degrees 14' E). Contains 2011-2019 wave buoy observations, 1965-2005 SWAN modelled data (Björkqvist et al. 2018), and 1979-2013 WAM modelled data (Tuomi et al. 2019).</p>
Data set: Can ocean community production and respiration be determined by measuring high-frequency oxygen profiles from autonomous floats?
<p>Relevant autonomous float data for <a href="https://doi.org/10.5194/bg-17-4119-2020">Gordon et al. (2020)</a>. Following a similar structure to the Argo network's "synthetic" profile files, one file per float is produced with all relevant variables (temperature, salinity, chlorophyll, backscatter, dissolved oxygen) on a common depth and time grid. The Electro-Magnetic Autonomous Profiling Explorer (EM-APEX) floats were deployed in the northern Gulf of Mexico in May 2017 - see <a href="https://doi.org/10.1109/CWTM43797.2019.8955168">Shay et al. (2019)</a> for more information. </p> <p>The data published here contains a timestamp for each data point. Another version of this data which contains some additional variables is hosted on <a href="https://data.gulfresearchinitiative.org/data/R5.x275.281:0001">GRIIDC</a>, but does not contain a timestamp for each data point, but rather for each profile. </p>
Tree measurements and summaries of the field plots used to develop Rojo and Montero (1996) yield tables for Pinus sylvestris L. in central Spain
<p>Tree measurements for principal trees and trees marked for thinning and summaries of the Pinus silvestris L. plots measured for the construction of Rojo and Montero (1996) Pinus sylvestris L. yield tables for central Spain. PRM_Functions.R contains R functions implementing parameter recovery methods to transform Rojo and Montero (1996) Pinus sylvestris L. yield tables into a diameter distribution model.</p> <p><strong>Trees.csv: </strong>Comma separated file with headers in the first row. Each record represents a measured tree. Fields:</p> <ul> <li>"PlotID": Identifier of the plot where the tree was measured</li> <li>"Type": Code indicating if the tree was marked for thinning.</li> <li>"ID_tree" Tree_Identifier</li> <li>"DBH1": First Diameter at breast height measurement for the tree.(mm)</li> <li> "DBH2" Second diameter at breast height measurement for the tree. The second measurement was taken in the direction perpendicular to the first measurement. (mm)</li> <li>"DBHmean": Mean of DBH 1 and DBH 2 <strong>and converted to cm</strong> (cm)</li> </ul> <p><strong>Plot_summaries.csv: </strong>Comma separated file with headers in the first row. Data digitized from Annex II of Rojo and Montero (1996). Each record contains different forest attributes of the plot. Fields:</p> <ul> <li>"PlotID": Identifier of the plot where the tree was measured</li> <li>"Age": Age of the plot determined from tree cores (Years)</li> <li>"Ho" Assman Dominant height for the plot (meters)</li> <li>"SiteIndex": Site index for the plot in meters. Site index is defined as the dominant height in meters measured or expected for the plot for an Age of 100 years.</li> <li>"MeanH" Mean tree height (m)</li> <li>"Dg" Quadratic mean diameter (cm)</li> <li>"Do" Dominant diameter. Mean diameter of the 100 largest trees of a hectare (cm)</li> <li>"N" Stand density (trees per hectare)</li> <li>"G" Plot basal area (m<sup>2</sup>/ha)</li> <li>"V" Total plot volume per unit area (m<sup>3</sup>/ha)</li> <li>"DeltaV" Periodic increment of merchantable volume (m<sup>3</sup>/ha)</li> <li>"Bark" Average percentage of total volume that is Bark. (%)</li> </ul> <p><strong>PRM_Functions.R: </strong>R functions to solve parameter recovery systems of equations based on mean and quadratic mean diameter and dominant diameter, quadratic mean diameter and stand density. Details provided as comments.</p> <p><strong>References</strong></p> <p>Rojo Alberto, Montero G (1996) El pino silvestre en la Sierra de Guadarrama: historia y selvicultura de los Pinares de Cercedilla, Navacerrada y Valsain. Ministerio de Agricultura, Pesca y Alimentación, Secretaria General Tecnica, Centro de Publicaciones, Madrid</p>
ESA-WOC North Atlantic Sea Surface Salinity maps from a multivariate combination of satellite and in situ surface measurements (2010-2018)
<p>We deliver here the daily sea surface salinity level 4 (SSS L4) product developed in the framework of the European Space Agency World Ocean Circulation project (ESA-WOC), covering the period 2010-2018. This product was obtained by adapting to a 1/10° North Atlantic grid the multidimensional optimal interpolation algorithm used within the Copernicus Marine Environment Monitoring Service to retrieve the global SSS multi-year dataset (<a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id: MULTIOBS_GLO_PHY_REP_015_002, dataset_id: dataset-sss-ssd-rep-weekly). This algorithm interpolates SMOS observations and in situ SSS observations considering a space-time-thermal decorrelation function, estimated by including information from high-pass filtered daily SST data (Droghei et al., 2016; Buongiorno Nardelli, 2012). Here, we ingested the L3OS 2Q debiased daily valid ocean salinity values product from SMOS satellite, produced and disseminated by the Centre Aval de Traitement des Données SMOS (CATDS, 2017), OSTIA SST data (CMEMS, <a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id=SST_GLO_SST_L4_REP_OBSERVATIONS_010_011) and CORA5.2 surface salinity values (<a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id: INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b, doi: 10.17882/46219TS1, Szekely et al., 2019) as input data, and used CMEMS weekly SSS dataset to build our background field (linearly interpolating it in time between the two closest analysis dates, and upsizing to the 1/10° grid through a cubic spline). All other interpolation parameters were set as in Droghei et al. (2018). </p> <p> </p> <p><em>References:</em></p> <p>Buongiorno Nardelli, B.: A Novel Approach for the High-Resolution Interpolation of In Situ Sea Surface Salinity, J. Atmos. Ocean. Technol., 29(6), 867–879, doi:10.1175/JTECH-D-11-00099.1, 2012.</p> <p>CATDS (2017). CATDS-PDC L3OS 2Q - Debiased daily valid ocean salinity values product from SMOS satellite. CATDS (CNES, IFREMER, LOCEAN, ACRI). http://dx.doi.org/10.12770/12dba510-cd71-4d4f-9fc1-9cc027d128b0</p> <p>Droghei, R., Buongiorno Nardelli, B. and Santoleri, R.: Combining in-situ and satellite observations to retrieve salinity and density at the ocean surface, J. Atmos. Ocean. Technol., 33, 1211–1223, doi:10.1175/JTECH-D-15-0194.1, 2016.</p> <p>Droghei, R., Buongiorno Nardelli, B. and Santoleri, R.: A New Global Sea Surface Salinity and Density Dataset From Multivariate Observations (1993–2016), Front. Mar. Sci., 5(March), 1–13, doi:10.3389/fmars.2018.00084, 2018.</p> <p>Szekely, T., Gourrion, J., Pouliquen, S. and Reverdin, G.: The CORA 5.2 dataset for global in situ temperature and salinity measurements: Data description and validation, Ocean Sci., 15(6), 1601–1614, doi:10.5194/os-15-1601-2019, 2019.</p>
Developing a deep Learning network to retrieve ocean hydrographic profiles in the North Atlantic from combined satellite and in situ measurements: test datasets.
<p>We provide here the datasets used for the test and assessment of a deep learning algorithm which is presently candidate for the development of a daily 3D ocean product covering the North Atlantic at 1/10° resolution, over the 2010-2018 period, as part of the European Space Agency World Ocean Circulation project (ESA-WOC). The method is based on a stacked Long Short-Term Memory neural network, coupled to a Monte-Carlo dropout approach, and allows to project satellite-derived sea surface temperature, sea surface salinity and absolute dynamic topography data at depth after training with sparse co-located in situ vertical hydrographic profiles (Buongiorno Nardelli, 2020, doi:<a href="https://www.researchgate.net/deref/http%3A%2F%2Fdx.doi.org%2F10.3390%2Frs12193151?_sg%5B0%5D=0xE-347r7Hvb80klJcEo811AhUiXq-twG_E6l4yB-BfIKkVtW-lVLGcO02mTFkUczvozYYI0WCPyUBFR3kzWNGGZKg.ftvLheFrzHIJriO4qW2bdxalvR_TWt3MpwUfvto3EemhRgvDRGwJ9Mdy4Xr0IcGCfICivf4j-VqTgKxVvXRogA">10.3390/rs12193151</a>). </p> <p>The test dataset presented here includes different sets of co-located temperature and salinity vertical profiles: </p> <ul> <li>in situ observations extracted from the quality controlled Argo and CTD profiles produced by Copernicus Marine Environment Monitoring Service CORA 5.2 (<a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id: INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b, doi: 10.17882/46219TS1, Szekely et al., 2019) and interpolated through a spline on a regularly spaced vertical grid (with 10 m intervals);</li> <li>climatological profiles extracted from World Ocean Atlas 2013 optimally interpolated monthly fields (Locarnini et al., 2013; Zweng et al., 2013), interpolated through a spline on a regularly spaced vertical grid (with 10 m intervals), upsized to a 1/10° horizontal grid through a cubic spline and linearly interpolated in time between the central day of each month;</li> <li>synthetic profiles obtained through three different techniques: multivariate EOF reconstruction, a 2 layer feed-forward network (with 1000 units in each hidden layer) and a stacked LSTM network (with 2 LSTM layers and 35 hidden units)</li> </ul> <p><em>References:</em></p> <p>Buongiorno Nardelli, B.: A Deep Learning network to retrieve ocean hydrographic profiles from combined satellite and in situ measurements, 2020, <em>submitted</em>.</p> <p>Locarnini, R. A., Mishonov, A. V., Antonov, J. I., Boyer, T. P., Garcia, H. E., Baranova, O. K., Zweng, M. M., Paver, C. R., Reagan, J. R., Johnson, D. R., Hamilton, M. and Seidov, D.: World Ocean Atlas 2013. Vol. 1: Temperature., S. Levitus, Ed.; A. Mishonov, Tech. Ed.; NOAA Atlas NESDIS, 73(September), 40, doi:10.1182/blood-2011-06-357442, 2013.</p> <p>Szekely, T., Gourrion, J., Pouliquen, S. and Reverdin, G.: The CORA 5.2 dataset for global in situ temperature and salinity measurements: Data description and validation, Ocean Sci., 15(6), 1601–1614, doi:10.5194/os-15-1601-2019, 2019.</p> <p>Zweng, M. M., Reagan, J. R., Antonov, J. I., Mishonov, A. V., Boyer, T. P., Garcia, H. E., Baranova, O. K., Johnson, D. R., Seidov, D. and Bidlle, M. M.: World Ocean Atlas 2013, Volume 2: Salinity, NOAA Atlas NESDIS, 119(1), 227–237, doi:10.1182/blood-2011-06-357442, 2013.</p> <p> </p>
Demonstration of 100 Gbit/s active measurements in dynamically provisioned optical paths
<p>New techniques, based on Software-Defined Networks, are used to deploy optical paths dynamically. This demonstration shows how active measurements at 100 Gbit/s are performed to check before operation that the performance requirements are met in terms of capacity, delay or packet loss.</p>
York Archaeological Trust 1981.7.33205.SY102 3D Archaeological Use-wear Raw Measurements
<p>This dataset contains the 51 raw measurements/scans taken before the mesh creation step for object 1981.7.33205.SY102 in the collections of York Archaeological Trust.</p> <p>1981.7.33205.SY102 is a Mortarium in standard Ebor oxidised fabric (M3) (Monaghan 1997, 1028) from Coppergate.</p> <p>Data was captured using a Zeiss Comet L3D 2 5M at 100 FOV to enable the analysis of use-wear on archaeological objects. The model is scaled in millimetres.</p> <p>This dataset consists of 105,524,551 points - additional metadata included in associated spreadsheet.</p>
ScienceDex guides
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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.