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6,334 results for “Directivity”
Dataset for: The Effect of Loading Direction on Slip and Twinning in an Irradiated Zirconium Alloy
<p><strong>This is the dataset used in the following publication: </strong></p> <p>R. Thomas, D. Lunt, M. D. Atkinson, J. Quinta da Fonseca, M. Preuss, F. Barton, J. O'Hanlon, and P. Frankel, "The Effect of Loading Direction on Slip and Twinning in an Irradiated Zirconium Alloy," in <em>Zirconium in the Nuclear Industry: 19th International Symposium</em>, ed. A. Motta and S. Yagnik (West Conshohocken, PA: ASTM International, 2021), 233-261. <a href="https://doi.org/10.1520/STP162220190027">https://doi.org/10.1520/STP162220190027</a>.</p> <p><strong>Contained in this dataset are:</strong></p> <p>A Jupyter notebook which uses the open-source DefDAP Python package (https://github.com/MechMicroMan/DefDAP) to open enclosed HRDIC, EBSD and image data for non-irradiated and 0.1 dpa proton irradiated Zircaloy-4 deformed to ~3% strain, along the rolling direction and transverse direction.</p> <p>Please use the 'develop' version of DefDAP: https://github.com/MechMicroMan/DefDAP/tree/f6b5d6ec33db9a45089fada17026432645044d2f</p> <p><strong>Publication abstract:</strong></p> <p>In this study, deformation experiments together with high-resolution digital image correlation were used to quantify the effect of proton irradiation on strain localization in Zircaloy-4 loaded along the rolling and transverse directions. Significant increases in strain heterogeneity were measured in the irradiated material compared to the nonirradiated material. This was a result of confinement of slip to channels in the irradiated material, which contain high effective shear strain values, with almost no strain in the regions between channels. The active slip systems in the material were also determined by comparing experimental slip trace angles from high-resolution digital image correlation with theoretical slip trace angles determined using grain orientation from electron backscatter diffraction. An increased amount of pyramidal and wavy basal slip, as well as tension twinning, were observed in the sample loaded along the transverse direction, compared to the sample loaded along the rolling direction, due to crystallographic texture. No significant change in slip system activity was observed as a result of 0.1 dpa proton irradiation, despite the dramatic change in slip pattern. The findings provide further insight into the role of irradiation on deformation behavior and provide quantitative data on slip system activation, for as-received and irradiated Zircaloy-4, against which to validate models.</p>
Vertical profiles of urban wind speed, wind direction and turbulence measured by LiDAR on campus of University College Cork, Ireland
<p><strong>Vertical Profiles of Urban wind speed, wind direction and turbulence measured by LiDAR on campus of University College Cork, Ireland</strong></p> <p>=================================</p> <p>README version 1.3, 21/07/2022</p> <p>==================================</p> <p>Contact info:</p> <p>Paul Leahy, University College Cork</p> <p>paul.leahy@ucc.ie | +353 21 4902017</p> <p>================================</p> <p> </p> <p><strong>Contents</strong></p> <p><strong>1. Measurement location and time period</strong></p> <p><strong>2. What is measured (brief description)</strong></p> <p><strong>3. Instrumentation</strong></p> <p><strong>4. CSV file detailed descriptions</strong></p> <p>================================</p> <p> </p> <p><strong>1. Measurement location and time period: </strong></p> <p>North roof of Kane Building, University College Cork (UCC), Ireland.</p> <p>Lat 51 d 53 m 34 s N.</p> <p>Long 8 d 29 m 39 s W.</p> <p>Roof is c. 39 m above sea level, and c. 26 m above ground level (ground level reference point is the car park West of the UCC Kane Building).</p> <p>The measurements were taken over a time period of several months in the years 2013 / 2014.</p> <p>=================================</p> <p><strong>2. What is measured (brief description):</strong></p> <p>* LiDAR Wind speed (horizontal and vertical), wind direction, turbulence intensity at 5 altitudes; reference point (0 m) for these altitudes is the top of the LiDAR instrument c. 1.2 m above roof level.</p> <p>* Air temperature, atmospheric pressure, relative humidity.</p> <p>* Wind speed and direction from an ultrasonic anemometer mounted on top of the instrument (c. 1.2 m above roof level).</p> <p>* 10-minute average values (2 files) and high-resolution (c. 23 sec) data (1 file) are provided.</p> <p>See 'CSV file detailed description' below for detailed information.</p> <p>* Diagnostic information.</p> <p>=================================</p> <p><strong>2.1 Surrounding terrain:</strong></p> <p>Surrounding area is urban/suburban. The aspect is northerly.</p> <p>To the West: 2-5 storey buildings, open spaces, suburban.</p> <p>To the South: 2-3 storey buildings, open spaces, trees, river.</p> <p>To the East: 2-3 storey buildings, open spaces.</p> <p>To the North: A higher section of the Kane Building roof (47 m asl), 1-3 storey buildings, suburban.</p> <p>=================================</p> <p><strong>3. Instrumentation:</strong></p> <p>ZephIR 175 continuous wave wind profiling LiDAR with integrated sonic anemometer, temperature, humidity, air temperature pressure sensors and GPS.</p> <p>=================================</p> <p><strong>4. CSV files detailed description:</strong></p> <p><strong>4.1 Data on 10-minute averages:</strong></p> <p>Filename 05092013-03122013_10min_res.csv contains:</p> <p>10 minute averaged data from 05/09/2013 to 03/12/2013.</p> <p>Measurement altitudes: 148 m, 90 m, 69 m, 44 m, 19m above instrument level.</p> <p> </p> <p>Filename 03122013-07082014_10min_res.csv contains:</p> <p>10 minute averaged data from: 03/12/2013 to 07/08/2014.</p> <p>Measurement altitudes: 148 m, 90 m, 50 m, 35 m, 15 m above instrument level.</p> <p>Note: from 19/06/2014 onwards, LiDAR data missing (MET data continues).</p> <p> </p> <p>The first two rows contain header information.</p> <p>Row 1 contains location information (GPS record)) and the measurement altitudes for wind speeds.</p> <p>Sample GPS record: N51535775W8296590 = 51 d 53.5775 m North; 8 d 29.6590 m West.</p> <p>Row 2 contains the data column headers including units.</p> <p> </p> <p>Wind speeds at each altitude are recorded:</p> <p>No of Packets (= number of scan units averaged over) []</p> <p>Wind direction (mean) [deg]</p> <p>Horizontal wind speed (mean) & standard deviation [m/s]</p> <p>Vertical wind speed (mean) & standard deviation [m/s]</p> <p>Horizontal variance [m^2/s^2] </p> <p>Horizontal min [m/s] </p> <p>Horizontal max [m/s] </p> <p>TI (turbulence intensity) []</p> <p> </p> <p>Other meteorological data:</p> <p>Air temperature [<sup>o</sup>C]</p> <p>Pressure [mbar]</p> <p>Rel. Humidity [%]</p> <p>Rain indicator [unitless] Higher values indicate more rain during the averaging interval.</p> <p>Wind Speed [m/s] (column 'MET Wind Speed' measured at the top of the instrument by the ultrasonic anemometer)</p> <p>Wind direction [deg] (column 'MET Direction' measured at the top of the instrument by the ultrasonic anemometer).</p> <p> </p> <p>Other housekeeping and diagnostic data:</p> <p>Instrument tilt [deg]</p> <p>Instrument bearing [deg]</p> <p>GPS data [degrees N, degrees W]</p> <p>Battery voltage [V] </p> <p>Optics, electronics and battery temperature [<sup>o</sup>C]</p> <p> </p> <p>=====================================================</p> <p> </p> <p><strong>4.2 Data with high time resolution (~23 s):</strong></p> <p> </p> <p>Filename 05092013-11112013_23s_res.csv contains:</p> <p>High resolution data from 05/09/2013 to 11/11/2013</p> <p>Measurement altitudes: 148 m, 90 m, 69 m, 44 m, 19m.</p> <p> </p> <p>Note on time resolution:</p> <p>The time resolution of processed wind measurements is c. 3 seconds per wind level, and around 8 seconds to reset to the first level. A full wind profile measurement at 5 altitudes therefore takes around (5 x 3) + 8 = 23 s to complete.</p> <p>The raw scanning resolution of the instrument is higher than this, as each wind measurement is an average of several values.</p> <p> </p> <p>Row 1 contains location information (lat, long) and the vertical measurement levels for wind speeds.</p> <p>Row 2 contains the data column headers including units.</p> <p> </p> <p>Wind speeds at each altitude are recorded:</p> <p>No of Packets (= scan units averaged over) []</p> <p>Wind direction (mean) [deg]</p> <p>Horizontal wind speed (mean) & standard deviation [m/s]</p> <p>Vertical wind speed (mean) & standard deviation [m/s]</p> <p>Horizontal variance [m^2/s^2] not defined as measurement interval is too short.</p> <p>Horizontal min [m/s] not defined as measurement interval is too short. </p> <p>Horizontal max [m/s] not defined as measurement interval is too short. </p> <p>TI (turbulence intensity) [] not defined as measurement interval is too short.</p> <p> </p> <p>Other meteorological data:</p> <p>Air temperature [<sup>o</sup>C]</p> <p>Pressure [mbar]</p> <p>Rel. Humidity [%]</p> <p>Rain indicator [unitless] Higher values indicate more rain during the scanning interval.</p> <p>Wind Speed [m/s] (column 'MET Wind Speed' measured at the top of the instrument by the ultrasonic anemometer)</p> <p>Wind direction [deg] (column 'MET Direction' measured at the top of the instrument by the ultrasonic anemometer.</p> <p> </p> <p>Other housekeeping and diagnostic data:</p> <p>Instrument tilt [deg]</p> <p>Instrument bearing [deg]</p> <p>GPS data [degrees N, degrees W]</p> <p>Battery voltage [V] </p> <p>Optics, electronics and battery temperature [<sup>o</sup>C]</p> <p> </p> <p>=====================================================</p> <p><strong>4.3 Quality control indicators:</strong></p> <p> </p> <p>9998 atmospheric conditions which adversely affect LiDAR wind speed measurements e.g. fog</p> <p>9999 high quality wind speed measurement not possible e.g. very low wind speed or obscuration of optical path</p> <p>Status Flag 'Green' => good</p> <p>=======================================================</p> <p> </p>
Selected data(s) from : Femtosecond direct laser writing of silver clusters in phosphate glasses for x-ray spatially-resolved dosimetry
<p>The data selected is based on the figures below, published in the linked article (see the doi).</p> <p><strong>- Figure 1.</strong> Microscopy fluorescence image of ARGOi glass sample (excitation at 365 nm) of laser-inscribed structures for the different writing irradiances at two different depths: (<strong>a</strong>) structures at 150 µm below the glass front surface, (<strong>b</strong>) structures at 550 µm below the glass front surface, and at 150 µm from the glass rear surface. <strong>(Only picture)</strong></p> <p>- <strong>Figure 2.</strong> (<strong>a</strong>) Transparent color before irradiation (ARGO glass sample), (<strong>b</strong>) yellow color after X-ray irradiation with 222 Gy (ARGO* glass sample). <strong>(Only picture)</strong></p> <p><strong>- </strong> <strong>Figure 3.</strong> (<strong>a</strong>) Absorption spectra of the ARGO and ARGO* glass sample after various X-ray doses and the difference absorption coefficient spectrum for 222 Gy vs. pristine. (<strong>b</strong>) Fit of the radiation-induced spectrum (difference between 222 Gy and pristine) considering Gaussian energy contributions for ARGO and ARGO*. (<strong>c</strong>) Absorption spectra for the GPN and GPN* glasses for X-ray doses from 5 mGy to 3 kGy [<a href="https://www.mdpi.com/2227-9040/10/3/110/htm#B39-chemosensors-10-00110">39</a>]. (<strong>d</strong>) The difference absorption coefficient spectra between different doses conditions for GPN and GPN* [<a href="https://www.mdpi.com/2227-9040/10/3/110/htm#B39-chemosensors-10-00110">39</a>]. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure3_2022-03-03_V01. <strong>Figure 3</strong></li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure3_Datas_2022-03-03_V01. Datas : <strong>wavelength, effective absorption coefficient (cm-1)</strong></li> </ol> <p>- <strong>Figure 4.</strong> Micro-luminescence of GPN* glass performed on the optically polished glass side: (<strong>a</strong>) integrated fluorescence intensity at different depths, (<strong>b</strong>) normalized spectrum evolution with depth for the 500 Gy dose [<a href="https://www.mdpi.com/2227-9040/10/3/110/htm#B39-chemosensors-10-00110">39</a>]. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure4_2022-03-03_V01. Figure 4</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure4_Datas_2022-03-03_V01. Datas</li> </ol> <p>- <strong>Figure 5.</strong> Estimated depth-dependent profiles in absolute values of the linear absorption coefficient at 405 nm. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure5_2022-03-03_V01. Figure 5</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure5_Datas_2022-03-03_V01. Datas : <strong>sample depth (mm) ; scaled linear absorption coefficient profile at 405 nm (mm-1)</strong></li> </ol> <p>- <strong>Figure 6.</strong> (<strong>a</strong>) X-ray energy spectra simulated by SpekPy for each irradiation facility, normalized by integral. (<strong>b</strong>) Geant4-simulated dose inside each sample, normalized by the surface dose; filled areas show uncertainties at 95% confidence. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure6_2022-03-03_V01. Figure 6</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure6_Datas_2022-03-03_V01. Datas : <strong>ARGO 100KV_dose ; GPN-20KV_dose ; GPN-32KV_dose</strong></li> </ol> <p>- <strong>Figure 7.</strong> Radio-photoluminescence measurement of the GPNi* glass for the inscribed structure [<a href="https://www.mdpi.com/2227-9040/10/3/110/htm#B39-chemosensors-10-00110">39</a>]. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure7_2022-03-03_V01. Figure 7</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure7_Datas_2022-03-03_V01. Datas : <strong>wavelength ; relative intensity a.u.</strong></li> </ol> <p>- <strong>Figure 8.</strong> Normalized RPL spectra excited at 325 nm: (<strong>a</strong>) for the ARGO (pristine—right axis) and ARGO* (X-ray irradiation at 222 Gy—left axis) glasses collected around 150 µm below the surface, (<strong>b</strong>,<strong>c</strong>) for the highest DLW irradiance structure for ARGOi and ARGOi* in the front- and the rear-inscribed surfaces, respectively. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure8_2022-03-03_V01. Figure 8</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure8_Datas_2022-03-03_V01. Datas : <strong>inscribed glass...</strong></li> </ol> <p>- <strong>Figure 9.</strong> (<strong>a</strong>) Differential linear absorption coefficient of the laser-inscribed structures (11 TW/cm<sup>2</sup>) for the two planes after irradiation at 222 Gy X-ray dose in the ARGOi* glass sample. (<strong>b</strong>) Average differential absorption of the inscribed structures for all DLW irradiance (as from <a href="https://www.mdpi.com/2227-9040/10/3/110/htm#fig_body_display_chemosensors-10-00110-f009">Figure 9</a>a). (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure9_2022-03-03_V01. Figure 9</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure9_Datas_2022-03-03_V01. Datas : <strong>integrated differential linear absoprtion percentage ; irradiance (TW/cm2)</strong></li> </ol> <p>- <strong>Figure 10.</strong> (<strong>a</strong>) Phase image under white light illumination of the laser inscribed structure (11 TW/cm<sup>2</sup>) before irradiation. (<strong>b</strong>) Optical path difference determined from the phase image. (<strong>c</strong>) The refractive index modification Δ<em>n</em> as a function of laser irradiance before/after 222 Gy-dose for the two planes in ARGOi, ARGOi* glass sample. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure10_2022-03-03_V01. Figure 10</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure10_Datas_2022-03-03_V01. Datas : <strong>refractive index modification ; irradiance (TW/cm2), Error bar</strong></li> </ol> <p><strong>- Figure 11.</strong> Comparison between calculated and measured Δ<em>n</em>ˆ after irradiation for a decrease in the initial value of <em>N</em><em>α</em>3 by 0.48%: (<strong>a</strong>,<strong>c</strong>) the real part Δ<em>n</em> for the front and rear surfaces, respectively; (<strong>b</strong>,<strong>d</strong>) their imaginary counterparts Δ<em>κ</em>, respectively. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure11_2022-03-03_V01. Figure 11</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure11_Datas_2022-03-03_V01. Datas : <strong>rear surface...</strong></li> </ol> <p><strong>- Figure 12.</strong> Integrated measure of the amplitude of fluorescence intensity for the different laser irradiance before and after 222 Gy-dose for the two planes in ARGOi and ARGOi* glass sample. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure12_2022-03-03_V01. Figure 12</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure12_Datas_2022-03-03_V01. Datas : <strong>integrated measure of the amplitude of fluorescence intensity ; Irradiance (TW/cm2) ; Error bar </strong></li> </ol> <p><strong>- Figure 13.</strong> (<strong>a</strong>) Composite FLIM and fluorescence intensity microscopy images of the laser-induced structure (11 TW/cm<sup>2</sup>) before and after irradiation for an emission at 425 nm from the front surface; the color-code represents the mean lifetime obtained by FAST-FLIM algorithm (color scale from 0 to 31 ns); inset: luminescence intensity only (grey-scale from 0 to 45 counts). (<strong>b</strong>) Same composite FLIM and luminescence intensity images for an emission at 510 nm. (<strong>c</strong>) Luminescence decays in arbitrary units for the emission at 425 nm of the same structure before and after irradiation for the two surfaces, and fitting curves thereof using three exponential decay functions. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure13_2022-03-03_V01. Figure 13</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure13_Datas_2022-03-03_V01. Datas : <strong>fluorescence intensity (arbitrary units) ; time (ms)</strong></li> </ol> <p>- <strong>Figure 14.</strong> Dose-dependent evolution of the amplitude ratio of extracted spectral bands for (<strong>a</strong>) the GPNi* glass sample for DLW irradiance of 13.4 TW/cm<sup>2</sup> at 160 µm below the glass surface, (<strong>b</strong>) the ARGOi and ARGOi* glass sample for DLW irradiance of 11 TW/cm<sup>2</sup> at 550 µm below the glass surface (rear surface). (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure14_2022-03-03_V01. Figure 14</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure14_Datas_2022-03-03_V01. Datas : <strong>ratio of amplitudes of spectral bands ; doses (gy)</strong>.</li> </ol>
Output files corresponding to "Direct groundwater discharge and vulnerability to hidden nutrient loads along the Great Lakes coast of the United States"
<p>This dataset corresponds to the output files that were produced for the study reported in:</p> <p>Knights, Deon, Kevin C. Parks, Audrey H. Sawyer, Cédric H. David, Trevor N. Browning, Kelsey M. Danner, and Corey D. Wallace, (2017), Direct groundwater discharge and vulnerability to hidden nutrient loads along the Great Lakes coast of the United States, <em>Journal of Hydrology,</em> 554, 331-341</p> <p><strong>Data sources</strong></p> <p>The following sources were used to produce files in this dataset:</p> <ul> <li>The National Hydrography Dataset Plus (NHDPlus) Version 2, obtained from http://www.horizon-systems.com/nhdplus/NHDplusV2_data.php. Region used is: Great Lakes (04)</li> <li>The second phase of the North American Land Data Assimilation System (NLDAS2), obtained from ftp://hydro1.sci.gsfc.nasa.gov/data/s4pa/NLDAS. Model outputs used are: NLDAS_MOS0125_MC.002, NLDAS_NOAH0125_MC.002, and NLDAS_VIC0125_MC.002.</li> <li>The United States 2011 National Land Cover Database (NLCD 2011), obtained from: http://www.mrlc.gov/nlcd2011.php.</li> </ul> <p> </p> <p><strong>Description of files</strong></p> <p>The files in this dataset contain are described below:</p> <ul> <li><em>Flowlines</em>: This folder contains a shapefile (<em>GL_coastcatchment_NHDflowline</em>) with the coastline of the Contiguous United States as described by NHDPlus V2, and was merged from a subsample of all river reaches available in the region used. </li> <li><em>Catchment</em>: This folder contains a shapefile (GL_coastcatchment_polygon) with the contributing catchments of NHDPlus V2 corresponding to the above coastline, and was merged from a subsample of all catchments available in the region used. </li> <li><em>Centroid</em>: This folder contains a shapefile (GL_coastcatchment_centroid) with the centroids of the above catchments. </li> <li><em>DischargeVulnerabilities.csv</em>. This .csv file contains the following data (units are in parentheses): <ul> <li>COMID: Unique feature identifier in NHDPlusV2 ().</li> <li>Length_km: Length of coastline feature (km).</li> <li>Area_sqkm: Area of coastal catchment feature (km<sup>2</sup>).</li> <li>Infiltration_kgsqm: Average annual infiltrating runoff for REACHCODE (kg/m<sup>2</sup>)</li> <li>REACHCODE: Reach identifier in NHDPlusV2; reaches can include multiple features; Submarine Groundwater Discharge (SGD) is computed by reach, not feature ().</li> <li>RLength_km: Total length of coastline accumulated by REACHCODE (km).</li> <li>RArea_sqkm: Total area of coastal catchment accumulated by REACHCODE (km<sup>2</sup>).</li> <li>RInfiltration_kgsqm: Average annual infiltrating runoff for REACHCODE (kg/m<sup>2</sup>)</li> <li>DGWD: Average annual direct groundwater discharge for REACHCODE (m<sup>2</sup>/y).</li> <li>Vulnerable_percent: Percentage of reach area with developed or agricultural land use in 2011 (%).</li> <li>Vulnerable: Vulnerability to coastal contamination (0- not vulnerable; 1-vulnerable)</li> </ul> </li> </ul> <p> </p>
Supporting Data for the paper titled "The Intensity, Directionality and Statistics of Underwater Noise from Melting Icebergs"
<p>The dataset contains:</p> <p>a) 13 audio files, with names including date, track number and channel; format: WAV files</p> <p>b) data from magnetic compass used to calculate noise directionality; format: txt files with lines containing date, time and magnetic direction (degrees)</p> <p>c) GPS tracks of the boat and attached acoustic buoy; format: txt files with NMEA codes</p> <p>d) GPS tracks around each iceberg tracked; format: txt files with UTM coordinates</p> <p>The study was founded by National Science Centre Poland grant no. 2013/11/N/ST10/01729 and partially supported within statutory activities No 3841/E-41/S/2018 of the Ministry of Science and Higher Education of Poland. Partial support for this work was also provided by US Office of Naval Research, Grant No. N00014-17-1-2633.</p> <p>Corresponding author: Oskar Glowacki, oglowacki@igf.edu.pl</p>
Direct observation of electron density reconstruction at the metal-insulator transition in NaOsO3
<p>Open access data set for manuscript "Direct observation of electron density reconstruction at the metal- insulator transition in NaOsO3" published in Physical Review B, 98, 115116 (2018)</p>
Dataset Comparison of MRI-based automated segmentation methods and functional neurosurgery targeting with direct visualization of the Ventro-intermediate thalamic nucleus at 7T
<p>Scientific Reports - Nature - DOI : 10.1038/s41598-018-37825-8</p> <p>##################################<br> "Comparison of MRI-based automated segmentation methods and functional neurosurgery targeting with direct visualization of the Ventro-intermediate thalamic nucleus at 7T"<br> ##################################</p> <p>E. Najdenovska*, C. Tuleasca*, J. Jorge, P. Maeder, J.P. Marques, T. Roine, D. Gallichan, J.-P. Thiran, M. Levivier, and M. Bach Cuadra</p> <p>*Equally contributed authors</p> <p><br> Copyright (c) - All rights reserved. University of Lausanne. 2018.</p> <p><br> To reproduce the analyses presented in the referred study, in this repository you could find the MR images acquired from nine young healthy subjects (YS1-YS5), four elderly healthy subject (ES1-ES4) and two drug-resistant tremor patients treated treated with Vim radiosurgery by Gamma Knife (P1 and P2).</p> <p>The provided dataset includes the following NifTI files:</p> <p>- MPRRAGE @3T<br> - DWI @3T (together with the corresponding bvals and bvecs)<br> - MP2RAGE @7T<br> - SWI @7T<br> - binary masks of the manual delineation of both left and right Vim respectively that were done on the SWI (as NifTI files as well).</p> <p>Additionally, for the young cohort (YS1-YS5) we include as well the images used for building the quadrilateral of Guiot:<br> - T2-w @3T<br> - T2 CISS @3T</p> <p>For the patients (P1 and P2), a follow-up MPRAGE (acquired at 3T) with Gadolinium enhancement is also provided.</p> <p>——————————————<br> Notes:<br> 1. For YS3 MP2RAGE at 7T is missing, instead MPRAGE at 3T was used</p> <p>2. The code performing the thalamic nuclei clustering could be found in Zenodo (DOI: 10.5281/zenodo.123768)</p>
Research data supporting "Block copolymer-directed single diamond hybrid structures derived from X-ray nanotomography"
<p>Research data supporting "Block copolymer-directed single diamond hybrid structures derived from X-ray nanotomography"</p>
Geometric Frustration Directs the Self-assembly of Nanoparticles with Crystallized Ligand Bundles
<p>This is the supporting dataset of the publication "Geometric Frustration Directs the Self-assembly of Nanoparticles with Crystallized Ligand Bundles".</p> <p><a href="https://doi.org/10.1021/acs.jpcb.4c04562">https://doi.org/10.1021/acs.jpcb.4c04562</a></p> <p>The description of the dataset can be found in the file README.txt</p>
Direct imaging of carbohydrate stereochemistry structural dataset
<p>Dataset includes. </p> <ol> <li>Training data for the NequIP model (all_4NPxG_mod_E.extxyz) including structures, energies and force components.</li> <li>Zip file (4NPxG_training_run.zip) containing training parameters (config.yaml) and metrics (.csv files) and the deployed NequIP model (deployed_model.pth) used for minima hopping in the corresponding study.</li> <li>Bayesian Optimization Structure Search results for conformers (alpha/beta-4-Nitrophenyl-D-Galacturonide_opt.extxyz) and isolated adsorbates (alpha/beta_isolated_adsorbates_opt.extxyz) on Au(111). DFT relaxed structures.</li> <li>CREST NADG conformers (crest_conformers_alpha.extxyz). </li> <li>Initial monolayer structure relaxations (4NPaG/4NPbG_monolayer_relaxation_every_fifth.extxyz). Every fifth geometry from the relaxation.</li> <li>Results from NequIP minima hopping for monolayer structures as trajectory files (alpha/beta_minima_hopping_nequip.traj). Contains also protonated NADG structures (alpha_protonated_minima_hopping_nequip.traj).</li> <li>Final NADG and NBDG monolayer structures with Hartree potentials and STM images simulated with FHI-aims (Final_NADG/NBDG_hartree_potential.cube, Final_NADG/NBDG_stm_01.cube) with the z-maps (Final_NADG/NBDG_stm_z_map.cube) for creating STM image contrast.</li> </ol>
Mass of wastes discharged directly from vessels to the water column
<p>Data set of mass discharge of selected pollutants from shipping in the European region. Created in the framework of the project Evaluation, control and Mitigation of the EnviRonmental impacts of shippinG Emissions (EMERGE).</p> <p>To determine the mass of discharged pollutants, AIS-based ship emission modelling of discharge volumes is combined with results of water effluent analysis. For the discharge volumes, the Ship Traffic Emission Assessment Model (STEAM) is used. The total discharge volume of wastes is comprised of five waste streams: open/close scrubber, grey, black and ballast water. For the content of pollutants in the five waste streams, a bibliographic database of waste stream pollutant concentrations compiled during the project is used.</p>
Dataset for Min-Deviation-Flow in Bi-directed Graphs for T-Mesh Quantization
<p>Dataset for the paper <a href="https://www.algohex.eu/publications/bimdf-quantization/"><strong>Min-Deviation-Flow in Bi-directed Graphs for T-Mesh Quantization</strong></a>, <em>Martin Heistermann, Jethro Warnett, David Bommes</em>, ACM Transactions on Graphics, Volume 42, Issue 4 (2023), <a href="https://doi.org/10.1145/3592437">DOI 10.1145/3592437</a></p> <p>Both archive files (.zip and .tar.zst) have identical contents.</p> <p>Contents:</p> <ul> <li>Results of running various algorithmus variants on the "300" dataset <ul> <li>Intermediate data (e.g., patch decompositions)</li> <li>Final meshes</li> <li>Logfiles</li> <li>Metadata in json format (e.g., utilized settings, achieved energies, detailed runtimes)</li> </ul> </li> <li>Meshes used in all figures</li> </ul> <p> </p>
QTL summary statistics from the DIRECT consortium
<p>These are the complete summary statistics for DIRECT genotype-phenotypes associations (QTLs). The project performed genotype-phenotype associations for gene expression (RNAseq), targeted proteins (Olink), targeted metabolites (Biocrates) and untargeted metabolites (Metabolon) derived from 3,029 blood and plasma samples from the DIRECT cohort. This submission includes supplementary files and nominal pvalues (as uncorrected pvalues) for all associations included in the manuscript. Trans associations included are typically limited to pvalues <1e-04. Network tables are also included, with information to load and use Cytoscape to visualize them. This is version 2, some files were missing on version 1.</p>
Pathways to enhance electrochemical CO2 reduction identified through direct pore-level modeling (data for figures)
<p>This is the data used to create the figures in the article "Pathways to enhance electrochemical CO2 reduction identified through direct pore-level modeling".</p> <p>Published in EES Catalysis</p> <p>DOI: 10.1039/d3ey00122a<br> Evan Johnson<br> Etienne Boutin<br> Shuo Liu<br> Sophia Haussener</p> <p><br> Additional notes are given in the "ReadMe.txt" file.</p>
HURRECON Model for Estimating Hurricane Wind Speed, Direction, and Damage (R and Python)
The HURRECON model estimates wind speed, wind direction, enhanced Fujita scale wind damage, and duration of EF0 to EF5 winds as a function of hurricane location and maximum sustained wind speed. Results may be generated for a single site or an entire region. Hurricane track and intensity data may be imported directly from the US National Hurricane Center's HURDAT2 database. HURRECON is available in R and Python. The R version is available on CRAN as HurreconR. The model is an updated version of the original HURRECON model written in Borland Pascal for use with Idrisi (see HF025). New features include support for: (1) estimating wind damage on the enhanced Fujita scale, (2) importing hurricane track and intensity data directly from HURDAT2, (3) creating a land-water file with user-selected geographic coordinates and spatial resolution, and (4) creating plots of site and regional results. The model equations for estimating wind speed and direction, including parameter values for inflow angle, friction factor, and wind gust factor (over land and water), are unchanged from the original HURRECON model. For more details and sample datasets, see the project website on GitHub (https://github.com/hurrecon-model).
Enclosure/exclosure experiments in a montane Puerto Rican stream examining direct and indirect effects of two dominant taxa of atyid (Atyidae) shrimp, Atya lanipes Holthius and Xiphocaris elongata Guerin-Meneville (Shrimp/ Algae/ Oecologia (1993))
Freshwater shrimp dominate the faunal biomass of many tropical headwater streams: however, their role in community organization is unclear. Enclosure/exclosure experiments in a montane Puerto Rican stream examined direct and indirect effects of two dominant taxa of atyid (Atyidae) shrimp, Atya lanipes Holthius and Xiphocaris elongata Guerin-Meneville. Both shrimp taxa caused significant reductions in sediment cover on rock substrata, reducing sedimentation and enhancing algal biovolume on clay tiles in cages. When tiles incubated in shrimp exclosures for 2 wks were placed outside of cages, atyid shrimp removed 100% of sediment cover within a 30 min. observation period. Atyid shrimp appear to play an important role in stream recovery after high discharge events in rapidly removing sediments and detritus deposited on benthic substrata in pools. We evaluated the mechanism by which A. lanipes influences algae and benthic insects by comparing patterns of algal biomass, taxonomic composition and shrimp-presence treatments both with and without manual sediment removal. The shrimp exclusion treatment without manual sediment removal had significantly lower algal biomass and greater sedimentation than all other treatments. The treatment in which shrimp were excluded but sediment was manually removed, however, accrued almost the same algal biovolume as the shrimp enclosure treatment, supporting the hypothesis that sediment removal enhances the biovolume of understory algal taxa. Algal community composition was similar between stream bottom bedrock exposed to natural densities of shrimp and all experimental treatments for both Atya and Xiphocaris: a diatom community strongly dominated (78-95%) by the adnate taxon, Achnanthes lanceolata Breb ex. Kutz. Atyid shrimp are important in determining the distribution and abundance of benthic insects through both direct and indirect effects. Sessile, retreat-building chironomid larvae (Chironomidae: Diptera) are negatively affected by bo
PIE LTER 15-minute Wind speed and direction in the lower Plum Island Sound at the Ipswich Bay Yacht Club pier, Ipswich, MA, year 2024.
Wind speed and direction measurements for 2024 at Ipswich Bay Yacht Club, Ipswich, MA. Wind speed is measured every 5 seconds and reported as an average in 15 minute intervals. Maximum wind speed is also reported for each 15 minute interval with a timestamp. Wind direction is measured every 15 minutes.
PIE LTER 15-minute Wind speed and direction in the lower Plum Island Sound at the Ipswich Bay Yacht Club pier, Ipswich, MA, year 2025.
Wind speed and direction measurements for 2025 at Ipswich Bay Yacht Club, Ipswich, MA. Wind speed is measured every 5 seconds and reported as an average in 15 minute intervals. Maximum wind speed is also reported for each 15 minute interval with a timestamp. Wind direction is measured every 15 minutes.
FIGURE 1 from conference paper AIP Proceedings 2145 "Solid state synthesis of CdS quantum dots through laser direct writing"
<p>FIGURE 1 of the conference paper "Solid state synthesis of CdS quantum dots through laser direct writing"</p>
FIGURE 4 from conference paper AIP Proceedings 2145 "Solid state synthesis of CdS quantum dots through laser direct writing"
<p>The dataset includes two files: the word file describe the type of sample and the procedures used to pick up the data; the excel file includes the raw data used to obtain the plots of figure 4.</p> <p>in the following is reported the description of figure 4 as wrote in the paper.</p> <p>PL emission of CdDBX QDs synthetized with single pulses at 355 nm, 10 ps and pulse energy varied between 4.67 to 7.6 μJ. (a), (b) and (c) are respectively the emission in blue, green and red range as defined in methodology. Graph (d) shows the change of the ratio between the red and green emission from the same points.</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.