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5,155 results for “resolution”
High-resolution projections of evapotranspiration and water availability for Europe under climate change
<p>Europe-wide high-resolution (1 km) gridded data of estimates of monthly and annual potential evapotranspiration (ET0), annual actual evapotranspiration (AET0) and water availability for a climate normal period largely preceding an anthropogenic warming signal (1961-1990) and for two CMIP5 multimodel future projections (2011-2040 and 2041-2070). In the ET0 calculation, the monthly and annual heat index <em>I</em> and annual <em>α</em> parameter were estimated following the Thornthwaite method, and AET0 was calculated using the Budyko approach.</p> <p>For citations and more details, please refer to "High-resolution projections of evapotranspiration and water availability for Europe under climate change" by Ştefan Dezsi, Marcel Mândrescu, Dănuţ Petrea, Praveen Kumar Rai, Andreas Hamann, Mărgărit-Mircea Nistor, published in <em>International Journal of Climatology</em> (<a href="https://doi.org/10.1002/joc.5537">https://doi.org/10.1002/joc.5537</a>)</p>
Historical climate model output of ECHAM5-wiso from 1871-2011 at T106 resolution
<p>Historical climate model simulation of the isotope-enabled ECHAM5-wiso model from the years 1871 to 2011 at T106 (1 degree) resolution. The model code was provided by Martin Werner of AWI. The simulations were designed and run by Nathan Steiger on the Yellowstone supercomputer. The boundary conditions were interpolated HadISST fields. The simulations also included updated fractionation factors (an option within the ECHAM5-wiso Fortran code). All variables here are at monthly resolution in netcdf format. Please <a href="http://www.ldeo.columbia.edu/~nsteiger/contact.html">contact</a> Nathan Steiger if you have any questions about the simulation. In addition to the data citation, please also cite the following reference for where the data were first published: Steiger, N.J., E.J. Steig, S.G. Dee, G.H. Roe, and G.J. Hakim, (2017): <em>Climate reconstruction using data assimilation of water-isotope ratios from ice cores.</em> Journal of Geophysical Research: Atmospheres, doi:10.1002/2016JD026011.</p> <p>Standard variables include: ECHAM5 T106 orography, 2 m temperature, surface pressure, mean sea level pressure, vertically integrated water vapor, total precipitation, evaporation, soil moisture, relative humidity, specific humidity, atmospheric stream function at 200 hPa, geopotential height at 500 hPa, windspeed at 10 m, and u-velocity wind at 200 hPa. Moisture variables are given at the surface (lowest atmospheric level).</p> <p>Isotope variables include: d18O and dD of total precipitation, d18O and dD of evaporation, d18O and dD of snow fall, d18O and dD of seasonal snow cover, d18O and dD of snow on glaciers, d18O and dD of soil moisture, and specific humidity of water isotopes.</p>
Images and supporting data for high-resolution μCT of a mouse embryo using a compact laser-driven x-ray betatron source
<p>A high resolution x-ray CT scan of an embryonic mouse sample was performed with the betatron x-ray source produced by a laser wakefield accelerator. This data deposition includes all of the raw images of the mouse sample, information regarding their indexing, featured slices of the tomogram and some further raw data regarding the x-ray source characterisation.</p>
High-resolution glomerular responses to a large variety of odorants in the mouse olfactory bulb
<p>Imaging of glomerular responses using intrinsic optical signal and synaptopHluorin. </p> <p>Find the software here: <a href="https://doi.org/10.5281/zenodo.3383874">https://doi.org/10.5281/zenodo.3383874</a></p> <p>The paper is here: </p> <p>Soelter, J., Schumacher, J., Spors, H., Schmuker, M.: Computational exploration of molecular receptive fields in the olfactory bulb reveals a glomerulus-centric chemical map. <em>Sci Rep</em> 10, 77 (2020). <a href="https://doi.org/10.1038/s41598-019-56863-4">https://doi.org/10.1038/s41598-019-56863-4</a></p>
High Resolution Retinal Blood Vessels Datasets of Diabetic Retinopathy Patients
<p>This dataset contains 89 high resolution image files of blood vessels extracted from publicly available retina images available in DIARETDB1</p>
WRF output. Resolution: 12 km. Domain: Eastern North America
<p>Derived products from WRF simulations at 12 km resolution. LBC: ERA-Interim. Output every 10-min. Output level: 3rd model level </p> <p>Details given in: Pryor S.C. et al. (2018): Inter-annual variability of wind climates and wind turbine annual energy production. Manuscript published in Wind Energy Science.</p> <p>5 netcdf files;</p> <p>Pryoretal_WES2018_part1.nc contains information shown in Figure 3. Included variables; lat, lng, medianWSHH, CI(medianWSHH), vectors of data in WT grid cells and in grid cells without WT.</p> <p>Pryoretal_WES2018_part2.nc contains information shown in Figure 4. Included variables; Weibull distribution fits by wind speed bin and year, and FFT of output from TX, IA, and NY.</p> <p>Pryoretal_WES2018_part3.nc contains information shown in Figure 5. Included variables; lat, lng, 50th and 90th percentile WI, AEP and difference, vectors of data in WT grid cells and in grid cells without WT.</p> <p>Pryoretal_WES2018_part4.nc contains information shown in Figure 6.Included variables; lat, lng, 50th and 90th percentile AEP and difference, vectors of data in WT grid cells and in grid cells without WT.</p> <p>Pryoretal_WES2018_part5.nc contains information shown in Figure 7. Included variables; lat, lng, time of minimum, time of maximum.</p> <p> </p>
Climatic dissimilarity data for North America at 1 km resolution.
<p>The raster data layers contained in this archive represent climatic dissimilarity as the distance in multivariate climate space (represented by the 1st and 2nd axes of a principal components analysis based on 11 bioclimatic variables) between current (1981-2010) and future (either 2041-2070 ("2055") or 2071-2100 ("2085")) time periods. Ensemble data are based on mean projections from 15 CMIP5 models (CanESM2, ACCESS1.0, IPSL-CM5A-MR, MIROC5, MPI-ESM-LR, CCSM4, HadGEM2-ES, CNRM-CM5, CSIRO Mk 3.6, GFDL-CM3, INM-CM4, MRI-CGCM3, MIROC-ESM, CESM1-CAM5, GISS-E2R) that were chosen to represent all major clusters of similar AOGCMs. Projections from 8 of these 15 GCMs were also used to generate dissimilarity values based on individual GCM projections. Input bioclimatic data was developed by statistical downscaling using the ClimateNA software developed by T. Wang. Additional description of the data can be found at https://adaptwest.databasin.org/pages/climatic-dissimilarity.</p>
Spatial patterns of uncertainty in climate exposure metrics for North America at 1km resolution
<p>The data provided below represents the degree of uncertainty or variation between 8 individual general circulation models (GCM) for three metrics commonly used to assess the intensity of exposure to climate change. The three exposure metrics (forward and backward <a href="https://adaptwest.databasin.org/pages/adaptwest-velocitywna">climatic velocity</a> and <a href="https://adaptwest.databasin.org/pages/climatic-dissimilarity">local climatic dissimilarity</a>) were calculated based on the first two principal components (PC) scores derived from <a href="https://adaptwest.databasin.org/pages/climatic-dissimilarity">11 different climate variables</a>. Frameworks and heuristics supporting climate adaptation for conservation often rely on projections of climate change or climate exposure. However, projections of climate change vary among alternative GCM outputs, different emissions scenarios, and different future time periods. The potential for these model predictions to vary geographically presents a source of uncertainty in assigning climate-informed conservation strategies to landscapes. Regions with high agreement among predictions could be more confidently assigned a climate-informed strategy, whereas regions with less agreement among predictions may require a more cautious approach. More information on the data can be found at https://adaptwest.databasin.org/pages/uncertainty-climate-metrics.</p>
Ruthwell Cross 3D Model High Resolution (112M poly count)
<p>This is a very High-Resolution 3D model the Ruthwell Cross, developed as part of the ongoing Visionary Cross project. This model has a 112M poly count.</p> <p>This record contains:</p> <ul> <li>An xml record: <a href="http://zenodo.org/record/1490878/_Ruthwell_3DModel_112M_Metadata.xml">_Ruthwell_3DModel_112M_Metadata.xml</a></li> <li> A 3D Model of the Ruthwell cross: <a href="https://zenodo.org/record/1490878/cross_ColorMapped_112M.ply">cross_ColorMapped_112M.ply</a> </li> <li>A 2D thumbnail: <a href="https://zenodo.org/record/1490878/Ruthwell_Cross00.png">Ruthwell_Cross00.png</a></li> </ul> <pre>The DOI for this version of the record is <a href="https://zenodo.org/record/1490878">10.5281/zenodo.1490878</a>. The DOI <a href="https://10.5281/zenodo.1233638">10.5281/zenodo.1233638</a> always points to the latest version of this record.</pre> <p>This file in .ply format is best viewed using 3DHOP, but can also be viewed using 3D rendering software like MeshLab.</p> <p>For individual panels and other related material go to The Visionary Cross community at: https://zenodo.org/communities/the_visionary_cross/</p>
Dataset of 100 simulated root images, with different resolutions
<p>All images were analysed with WinRhizo<sup>TM</sup> 2013, Regent method (Régent Instruments Inc 2013) and ImageJ (1.51j8, Schneider, Rasband, & Eliceiri, 2012) with the macro IJ_Rhizo (IJ_Rhizo_v0beta). We conducted batch analyses with the grey level thresholds set to automatic at all four resolutions (200, 400, 800 and 1200 DPI). We further analysed the images at 800 dpi and 1200 dpi with the threshold value manually set to 144. We considered this value suitable after visual inspection of 10 images at different thresholds using the threshold slider of WinRhizo<sup>TM</sup>. Since no debris was simulated, we used no automatic debris correction. For WinRhizo<sup>TM</sup>, the boundaries of diameter classes were set from 0.1 to 1.9 mm (with an increment of 0.1 mm).</p> <p>The dataset contains:</p> <p>- the root images in jpg format (images.zip)</p> <p>- the root architecture data in rsml format (rsml.zip)</p> <p>- the ground-truth value for each image (all_root_data_2D.csv)</p> <p>- the ground-truth value for each root in each image (ground_truth_data_2D.csv)</p> <p>- the estimation given by WinRhizo and IJ_Rhizo (all_analyses_2D.txt and all_analyses_fixed.txt)</p> <p>- the different codes used for the analysis (*.R)</p> <p>Dataset used in : Accuracy of image analysis tools for functional root traits: A comment on Delory et al., 2017 from Rose and Lobet (2019), Methods in Ecology and Evolution. </p> <p>The images in this dataset are a subset of the dataset here: https://doi.org/10.5281/zenodo.208214</p>
WRF-Chem output. Resolution: 24 km. Domain: Eastern North America
<p>Derived products from WRF-Chem simulations at 24 km resolution.</p> <p>Details given in: Crippa P. et al. (2019): Sensitivity of simulated aerosol properties over eastern North America to WRF-Chem parameterizations. Manuscript published in the Journal of Geophysical Research.</p> <p>14 netcdf files:</p> <p>Crippaetal_JGR2019_AOD.nc contains monthly mean AOD cloud screened for each ensemble member.</p> <p>Crippaetal_JGR2019_net_radiation.nc contains monthly mean surface net radiation for each ensemble member.</p> <p>Crippaetal_JGR2019_NO3_fine.nc contains monthly mean NO3 in the fine aerosol mode for each ensemble member.</p> <p>Crippaetal_JGR2019_SO4_fine.nc contains monthly mean SO4 in the fine aerosol mode for each ensemble member.</p> <p>Crippaetal_JGR2019_PM25_daily.nc contains daily PM<sub>2.5 </sub>concentrations for each ensemble member.</p> <p>Crippaetal_JGR2019_O3_daily.nc contains daily mean surface ozone concentrations in the fine aerosol mode for each ensemble member.</p> <p>Crippaetal_JGR2019_PBLH.nc contains monthly mean planetary boundary layer height for each ensemble member regridded on the MERRA-2 resolution</p> <p>Crippaetal_JGR2019_T2.nc contains monthly mean surface temperature at 2m for each ensemble member regridded on the MERRA-2 resolution</p> <p>Crippaetal_JGR2019_QPBL.nc contains monthly mean specific humidity in the PBL for each ensemble member regridded on the MERRA-2 resolution</p> <p>Crippaetal_JGR2019_PPT.nc contains monthly total precipitation for each ensemble member regridded on the MERRA-2 resolution</p> <p>Crippaetal_JGR2019_lat_WRF-Chem.nc contains the latitude coordinates of the WRF-Chem grid</p> <p>Crippaetal_JGR2019_lon_WRF-Chem.nc contains the longitude coordinates of the WRF-Chem grid</p> <p>Crippaetal_JGR2019_lat_MERRA2.nc contains the latitude coordinates of the MERRA2 grid</p> <p>Crippaetal_JGR2019_on_MERRA2.nc contains the longitude coordinates of the MERRA2 grid</p>
A High-resolution Mosaic of the Neutral Hydrogen in the M81 Triplet
<p>This dataset shows the distribution of neutral hydrogen in and around the M81 galaxy triplet (M81, M82, NGC 3077) and consists of a 3° × 3°, 105-pointing, high-resolution neutral hydrogen (H I) mosaic obtained with the Very Large Array C and D arrays. The data are described in the paper by <a href="http://adsabs.harvard.edu/abs/2018ApJ...865...26D">de Blok et al. (2018)</a>.</p> <p>Here we provide the following data products:</p> <p><strong>Cubes:</strong></p> <ul> <li>the natural-weighted cube of the VLA C+D mosaic: <em>m81.nat.cube.fits</em></li> <li>the robust-weighted cube of the VLA C+D mosaic: <em>m81.rob.cube.fits</em></li> <li>the natural-weighted cube using only D-array-like baselines: <em>m81_D.nat.cube.fits</em></li> <li>the natural-weighted and zero-spacing corrected data cube of the VLA C+D array and GBT single-dish data from <a href="http://adsabs.harvard.edu/abs/2011AJ....141....9C">Chynoweth et al. (2011)</a>: <em>m81.zero.cube.fits</em></li> </ul> <p><strong>Moment maps:</strong></p> <ul> <li>natural-weighted zeroth (column density), first (velocity field) and second (velocity dispersion) moment maps of the VLA C+D mosaic: <em>m81.nat.mom[0,1,2].fits</em></li> <li>robust-weighted zeroth (column density), first (velocity field) and second (velocity dispersion) moment maps of the VLA C+D mosaic: <em>m81.rob.mom[0,1,2].fits</em></li> <li>natural-weighted zeroth, first and second moment maps of the "D-array" mosaic: <em>m81_D.nat.mom[0,1,2].fits</em></li> <li>zero-spacing corrected natural-weighted integrated HI map (zeroth-moment) of VLA C+D and GBT data: <em>m81.zero.mom0.fits</em></li> </ul> <p><strong>Acknowledgements:</strong></p> <p>If you make use of these data please cite the original paper:</p> <p><a href="http://adsabs.harvard.edu/abs/2018ApJ...865...26D">de Blok et al. (2018) </a>- de Blok, W.J.G., Walter, F., Ferguson, A.M.N., et al. 2018, ApJ, 865, 26 (<a href="https://doi.org/10.3847/1538-4357/aad557">10.3847/1538-4357/aad557</a>)</p> <p> </p>
A high-frequency and high-resolution image time series of the Gornergletscher - Swiss Alps - derived from repeated UAV surveys
<p>This dataset is based on aerial photographs of the Gornergletscher glacial system (Switzerland) collected during ten intensive UAV surveys carried out approximately every two weeks throughout the summer 2017.</p> <p>The final products consist in a series of 10 cm resolution ortho-images, Digital Elevation Models of the glacier surface, and Matching Maps that can be used to quantify ice surface displacements.</p>
Predicted soil water content (volumetric %) for 33kPa and 1500kPa suctions at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution
<p>Migrated to: <a href="https://doi.org/10.5281/zenodo.2629589">https://doi.org/10.5281/zenodo.2629589</a></p>
High resolution Sea Surface Wind retrieval over coastal Protected Areas by means of Sentinel-1 data
<p><br> The algorithm used, i.e. SARWIND LG-Mod ver. v4.01 (see reference below), is aimed at producing the Sea Surface Wind (SSW), i.e. Speed and Direction, from a single co-polarized (VV or HH) SAR image. We used EW (Extended Wide) and IW (Interferometric Wide) Swath Mode GRD (Ground Range, Multi-Look, Detected) HR (High Resolution) Sentinel-1 images, with pixel spacings of 40m x 40m and 10m x 10m (azimuth x range) respectively. Associated auxiliary products were obtained from ESA SNAP 5.0 release. SSW fields were provided for the two coastal Protected Areas (PAs) named Camargue and Wadden Sea.</p> <p>Each output folder of the SARWIND LG-Mod results contains useful plots and the estimated SSW field, provided in the file 'SAR_Sigma0_pp_decimationL2P2Tn_gradientOptSobel_LGMod_Results.txt' (pp = VV or HH; n = smoothing/decimation level), which is in the sub-folder 'LG-Mod_Theoretical_Results/Results_MEdegTHxx.xxx_Fisher (where xx.xxx is the final threshold applied). This txt file reports the following 19 columns:</p> <p><br> 1) LAT; 2) LON; 3) AZI; 4) RNG; [Location of the centre of the processed AOI]</p> <p>5) REF_U; 6) REF_V; 7) REF_W; 8) REF_D; [ECMWF reference wind, as U/V components and speed/direction]</p> <p>9) SAR_U; 10) SAR_V; 11) SAR_W; 12) SAR_D; [SARWIND LG-Mod wind estimates, as U/V components and speed/direction]</p> <p>Both REF_D and SAR_D are wind directions (expressed in degrees) with respect to the geographic North (0°=North, 90°=East, 180°=South, 270°=West), that the wind is blowing to.<br> Both REF_W and SAR_W are wind speeds (expressed in m/s).<br> Regarding REF_U/SAR_U and REF_V/SAR_V, note that a positive U component represents wind blowing to the East; a positive V component represents wind blowing to the North.</p> <p>13) SceneCentre_TrueHeading_FF; [Mean angle formed between the geographical South-North direction and the SAR azimuth direction (wrt the centre of the SAR Full-Frame image)]</p> <p>SceneCentre_TrueHeading_FF is a positive clockwise angle. In particular: SceneCentre_TrueHeading_FF is in ]180,360[ [deg].<br> Thus:<br> Descending Pass <-> SceneCentre_TrueHeading_FF is in ]180,270[ [deg]<br> Ascending Pass <-> SceneCentre_TrueHeading_FF is in ]270,360[ [deg]</p> <p>14) ROI_Npoints_UnUsablePointsMasked; [Number of samples used for each SARWIND LG-Mod wind estimation]</p> <p>15) MeanIncAng; 16) MeanNRCS; [Mean incident angle (expressed in degrees) and NRCS of the ROI]</p> <p>17) MeanResultantLength; 18) Alpha2_Est; [Fisher's formula parameters]</p> <p>19) MEdeg [Margin of Error, i.e. accuracy of each wind direction estimate, between 0° and 45°]</p> <p>The accuracy MEdeg is given by the semi-width of the confidence interval, with a confidence level (1-α) fixed, which is assigned to the wind direction estimate. Consequently, lower MEdeg values correspond to better estimates. And, if MEdeg == 45°, wind estimates must be discharged.</p> <p><br> Finally, note also that you can cut an entire row when [SAR_U SAR_V SAR_W SAR_D] == [NaN NaN NaN NaN] (typically, this happens for 'land pixels').</p> <p>% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %<br> % %<br> % REFERENCES: %<br> % %<br> % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %<br> % %<br> % The algorithm SARWIND LG-Mod is based on the Ph.D thesis below: %<br> % %<br> % [1] Rana, Fabio Michele (2016) "Exploitation of Satellite %<br> % Synthetic Aperture Radar Data for Geophysical Parameters Retrieval over %<br> % Land and Ocean". Unpublished Ph.D thesis. Politecnico di Bari. %<br> % %<br> % Some applications of the method are described in the following papers: %<br> % %<br> % [2] Fabio M. Rana, Maria Adamo, Guido Pasquariello, Giacomo De Carolis, %<br> % and Sandra Morelli, "LG-Mod: A Modified Local Gradient (LG) Method to %<br> % Retrieve SAR Sea Surface Wind Directions in Marine Coastal Areas," %<br> % Journal of Sensors, vol. 2016, Article ID 9565208, 7 pages, 2016. %<br> % doi:10.1155/2016/9565208. %<br> % %<br> % [3] Rana, F. M., Adamo, M., & Blanda, P. (2018, July). %<br> % LG-Mod Multi-Scale Approach for Sar Sea Surface Wind Directions %<br> % Retrieval. In IGARSS 2018-2018 IEEE International Geoscience and Remote %<br> % Sensing Symposium (pp. 3216-3219). IEEE. %<br> % %<br> % [4] Rana, F. M., Adamo, M., Lucas, R., & Blonda, P. (2019). Sea surface %<br> % wind retrieval in coastal areas by means of Sentinel-1 and numerical %<br> % weather prediction model data. Remote Sensing of Environment, 225, %<br> % 379-391. %<br> % %<br> % Suggestions and comments are always welcome. %<br> % Thanks in advance, %<br> % Fabio Michele Rana %<br> % %<br> % MOB: (+39) 3804114171 %<br> % E-MAILS: fabiomichele.rana@gmail.com; fabiomichele.rana@iia.cnr.it %<br> % %<br> % SKYPE: fabiomichelerana %<br> % %<br> % SARWIND_LG-Mod_v4.01, 2014-2019 %<br> % Author: Fabio M. Rana %<br> % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %<br> </p>
Oatmeal Data: Experimental cone-beam tomographic data for techniques to improve image resolution
<p><sub>This submission contains supplementary material to A.A. Hendriksen et al., (2019). Throughout the description, we mention this publication as the "referred paper". </sub></p> <p> </p> <p><strong>Description Summary</strong></p> <p>"Oatmeal Data" is a collection of experimental tomographic data of a plastic jar filled with store-bought mixed-size flake oatmeal. The dataset was collected to test methods specifically developed for improving image resolution in a reconstructed volume (primarily for machine learning).</p> <p>As well as the raw (unprocessed) tomographic data, this submission includes training and test data, weights and geometry parameters, and a text file (Makefile) to reproduce the results presented in the referred paper. The full list of contents is given below. </p> <p> </p> <p><strong>Apparatus</strong></p> <p>The dataset is acquired using the custom-built and highly flexible CT scanner, FleX-ray Laboratory, developed by XRE NV and located at CWI. This apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1944-by-1536 pixels, 14-bit, flat detector panel. </p> <p> </p> <p><strong>Sample Information</strong></p> <p>The sample consists of a thin-walled plastic far filled with store-bought mixed-sized oatmeal flakes. This sample was chosen due to its repeating/self-similar structure. This self-similarity is an important factor and is exploited in the algorithm for improving image resolution, presented in the referred paper.</p> <p> </p> <p><strong>Experimental Plan</strong></p> <p>Three experiments at two magnification levels were performed, which are denoted by <strong>Zoom1</strong> and <strong>Zoom4</strong>: <em>Zoom1</em> is the dataset collected with magnification factor of 1.09, with the plastic jar filled with oatmeal fully in view. <em>Zoom4 - centre</em> is the sample scanned at magnification of 4.38, with the field of view aligned with the centre of the same (i.e. the centre of the original view in Zoom1). <em>Zoom4 - top</em> is where the tube and the detector are positioned to show the top of the sample (surface level of the oatmeal flakes is visible at the top of the field of view).</p> <p>The datasets were collected over a 360° in circular and continuous motion with 2000 projections distributed evenly over the full circle. The upload includes the raw data collected following the experimental plan, which includes additional projections for normalization: one dark-field (closed shutter) and two (pre- and post-) flat-field (open shutter) images. Each dataset is packaged with the full list of data and scan settings files (in .txt format). These files contain the tube settings, scan geometry and full list of motor positions at the start of the scan. In addition, ASTRA geometry format file is included for each data in the geometries folder. </p> <p> </p> <p><strong>List of Contents</strong></p> <p>The contents of the submission (sorted into folders) is given below.</p> <ul> <li><strong>data</strong>: The raw (uncorrected) datasets (including dark and flat fields) for <ul> <li><em>Zoom1,</em></li> <li><em>Zoom4-centre,</em></li> <li><em>Zoom4-top.</em></li> </ul> </li> <li><em><strong>geometries</strong></em>: The set of parameters needed for the ASTRA forward and back projectors.</li> <li><em><strong>test</strong></em>: Test dataset compiled as detailed in the referred paper, or in <a href="https://ahendriksen.github.io/on_the_fly/readme.html">On-the-fly GitHub documentation.</a></li> <li><em><strong>train</strong></em>: Training dataset as detailed in the referred paper, or in <a href="https://ahendriksen.github.io/on_the_fly/readme.html">On-the-fly GitHub documentation.</a></li> <li><em><strong>weights</strong></em>: Weights as detailed in the referred paper, or in <a href="https://ahendriksen.github.io/on_the_fly/readme.html">On-the-fly GitHub documentation.</a></li> <li><em><strong>Makefile</strong></em>: File containing steps to reproduce results presented in the referred paper.</li> </ul> <p> </p> <p><strong>Additional Links</strong></p> <p>These datasets are produced by the <a href="https://www.cwi.nl/research/groups/computational-imaging">Computational Imaging group</a> at Centrum Wiskunde & Informatica (CI-CWI). For any useful Python/MATLAB scripts for the FleX-ray datasets, we refer the reader to our group's <a href="http://github.com/cicwi">GitHub page</a>.</p> <p> </p> <p><strong>Contact Details</strong></p> <p>For more information or guidance in using these dataset, please get in touch with </p> <ul> <li>s.b.coban [at] cwi.nl</li> <li>allard.hendriksen [at] cwi.nl</li> </ul> <p> </p>
Dataset and Data treatment for Data mining Raman Microspectroscopic Responses of Cells to Drugs in Vitro using Multivariate Curve Resolution-Alternating Least Squares
<p><strong>Matlab scripts for the simulation and treatment of Raman datasets obtained from time dependent experiments Using MCR-ALS.</strong></p> <p> </p> <p><strong>- SIMULATED DATA: </strong>Simulated data is obtained by adding spectra of artificially generated responses (weighted considering artificially generated time profiles) to an experimental cell spectrum (Initial component) Three Different Scenarios are generated. </p> <p>Spectral and time profiles are obtained from here: </p> <p> </p> <p><strong>- EXPERIMENTAL DATA: </strong>DOX dataset obtained from here</p> <p>https://doi.org/10.1002/jbio.201800328</p> <p> </p> <p> </p> <p><strong>- DATA ANALYSIS INSTRUCTIONS</strong></p> <p>Run <em>datatreatment.m</em></p>
TERENO-preAlpine observatory and ScaleX 2016 campaign data set associated with HESS paper "High-resolution fully-coupled atmospheric–hydrological modeling: a cross-compartment regional water and energy cycle evaluation"
<p>netCDF Dataset, that holds processed hourly station observations for the period 2016-06-01 to 2016-10-31.</p> <p>dimensions:<br> time = 3672 ;<br> stations = 6 ;<br> name_strlen = 6 ;<br> depth = 3 ;<br> height = 201 ;<br> variables:<br> double time(time) ;<br> time:standard_name = "time" ;<br> time:long_name = "time of measurement" ;<br> time:units = "hours since 2016-06-01 00:00:00" ;<br> time:timezone = "UTC" ;<br> time:calendar = "proleptic_gregorian" ;<br> double lat(stations) ;<br> lat:standard_name = "latitude" ;<br> lat:long_name = "station_latitude" ;<br> lat:units = "degrees_north" ;<br> double lon(stations) ;<br> lon:standard_name = "longitude" ;<br> lon:long_name = "station_longitude" ;<br> lon:units = "degrees_east" ;<br> double elev(stations) ;<br> elev:standard_name = "altitude" ;<br> elev:long_name = "station_altitude" ;<br> elev:units = "m ASL" ;<br> double height(height) ;<br> height:standard_name = "altitude" ;<br> height:long_name = "station_altitude" ;<br> height:units = "m ASL" ;<br> double depth(depth) ;<br> depth:standard_name = "soil_depth" ;<br> depth:long_name = "soil sensor depth" ;<br> depth:units = "cm" ;<br> char station_name(name_strlen, stations) ;<br> station_name:long_name = "station_name" ;<br> station_name:cf_role = "timeseries_id" ;<br> double T(time, stations) ;<br> T:_FillValue = -9999. ;<br> T:standard_name = "temperature" ;<br> T:long_name = "2m air temperature" ;<br> T:units = "degree_Celsius" ;<br> T:source = "TERENO-preAlpine" ;<br> double Q(time, stations) ;<br> Q:_FillValue = -9999. ;<br> Q:standard_name = "mixing_ratio" ;<br> Q:long_name = "2m mixing ratio" ;<br> Q:units = "g kg-1" ;<br> Q:source = "TERENO-preAlpine" ;<br> double ET_i(time, stations) ;<br> ET_i:_FillValue = -9999. ;<br> ET_i:standard_name = "evapotranspiration_intensive" ;<br> ET_i:long_name = "lysimeter evapotranspiration intensive management" ;<br> ET_i:units = "g kg-1 h-1" ;<br> ET_i:source = "TERENO-preAlpine" ;<br> double ET_e(time, stations) ;<br> ET_e:_FillValue = -9999. ;<br> ET_e:standard_name = "evapotranspiration_extensive" ;<br> ET_e:long_name = "lysimeter evapotranspiration extensive management" ;<br> ET_e:units = "g kg-1 h-1" ;<br> ET_e:source = "TERENO-preAlpine" ;<br> double LvE_cor(time, stations) ;<br> LvE_cor:_FillValue = -9999. ;<br> LvE_cor:standard_name = "latent_heat_flux" ;<br> LvE_cor:long_name = "energy balance corrected flux tower latent heat flux" ;<br> LvE_cor:units = "W m-2" ;<br> LvE_cor:source = "TERENO-preAlpine" ;<br> double HTs_cor(time, stations) ;<br> HTs_cor:_FillValue = -9999. ;<br> HTs_cor:standard_name = "sensible_heat_flux" ;<br> HTs_cor:long_name = "energy balance corrected flux tower sensible heat flux" ;<br> HTs_cor:units = "W m-2" ;<br> HTs_cor:source = "TERENO-preAlpine" ;<br> double GHF(time, stations) ;<br> GHF:_FillValue = -9999. ;<br> GHF:standard_name = "ground_heat_flux" ;<br> GHF:long_name = "flux tower ground heat flux" ;<br> GHF:units = "W m-2" ;<br> GHF:positive = "up" ;<br> GHF:source = "TERENO-preAlpine" ;<br> double SW(time, stations) ;<br> SW:_FillValue = -9999. ;<br> SW:standard_name = "short_wave_radiation" ;<br> SW:long_name = "downward short wave radiation" ;<br> SW:units = "W m-2" ;<br> SW:source = "TERENO-preAlpine" ;<br> double LW(time, stations) ;<br> LW:_FillValue = -9999. ;<br> LW:standard_name = "long_wave_radiation" ;<br> LW:long_name = "downward long wave radiation" ;<br> LW:units = "W m-2" ;<br> LW:source = "TERENO-preAlpine" ;<br> double VWC_25(time, depth) ;<br> VWC_25:_FillValue = -9999. ;<br> VWC_25:standard_name = "volumetric_water_content" ;<br> VWC_25:long_name = "DE-Fen SoilNet volumetric water content first quartile" ;<br> VWC_25:units = "vol. %" ;<br> VWC_25:source = "TERENO-preAlpine" ;<br> double VWC_50(time, depth) ;<br> VWC_50:_FillValue = -9999. ;<br> VWC_50:standard_name = "volumetric_water_content" ;<br> VWC_50:long_name = "DE-Fen SoilNet volumetric water content second quartile" ;<br> VWC_50:units = "vol. %" ;<br> VWC_50:source = "TERENO-preAlpine" ;<br> double VWC_75(time, depth) ;<br> VWC_75:_FillValue = -9999. ;<br> VWC_75:standard_name = "volumetric_water_content" ;<br> VWC_75:long_name = "DE-Fen SoilNet volumetric water content third quartile" ;<br> VWC_75:units = "vol. %" ;<br> VWC_75:source = "TERENO-preAlpine" ;<br> double T_prof(time, height) ;<br> T_prof:_FillValue = -9999. ;<br> T_prof:standard_name = "temperature_profile" ;<br> T_prof:long_name = "DE-Fen HATPRO spline interpolated temperature profile" ;<br> T_prof:units = "K" ;<br> T_prof:source = "scaleX campaign 2016" ;<br> double A_prof(time, height) ;<br> A_prof:_FillValue = -9999. ;<br> A_prof:standard_name = "humidity_profile" ;<br> A_prof:long_name = "DE-Fen HATPRO spline interpolated absolute humidity profile" ;<br> A_prof:units = "kg m-3" ;<br> A_prof:source = "scaleX campaign 2016" ;<br> double PRW(time) ;<br> PRW:_FillValue = -9999. ;<br> PRW:standard_name = "precipitable_water" ;<br> PRW:long_name = "DE-Fen HATPRO column precipitable water" ;<br> PRW:units = "kg m-2" ;<br> PRW:source = "scaleX campaign 2016" ;</p> <p>// global attributes:<br> :history = "2019-09-12: File created." ;<br> :institution = "Karlsruhe Institute of Technology (KIT) - Campus Alpin, Institute for Meteorology and Climate Research" ;<br> :Contact_person = "Benjamin Fersch (benjamin.fersch@kit.edu)" ;<br> :Author = "Benjamin Fersch (benjamin.fersch@kit.edu)" ;<br> :source = "https://www.tereno.net, https://scalex.imk-ifu.kit.edu" ;<br> :Conventions = "CF-1.6" ;<br> :License = "Creative Commons Attribution Non Commercial Share Alike 4.0 International" ;</p> <p> </p>
Sensitivity experiments with EC-Earth3-Veg at low resolution
<p>piControl and abrupt4xCO2 experiments with the T159 configuration of EC-Earth3-Veg. Each file contains the annual global mean from one experiment, created with ecm. The details of each experiment are listed in the included README. </p>
Characterization of heteroatom distributions in the polar fraction of North Sea oils using high-resolution mass spectrometry
<p>Supplementary data for <a href="https://doi.org/10.1016/j.petrol.2019.106563">10.1016/j.petrol.2019.106563</a></p> <p>Mass spectra were measured on a Q Exactive HF at 240k@200 m/z resolution in nanospray-ESI direct infusion using a Advion TriVersa NanoMate source. Broadband mass spectra were generated from SIM-segments using dimspy (https://github.com/computational-metabolomics/dimspy). Peaks were annotated using Formularity v.1.0.0 (10.1021/acs.analchem.7b03318) after internal calibration using a homologous CHN series (identified from preliminary KMD/KM plots). All plots were generated using python 3.6.6 and the plotly graphing library (https://plot.ly/python/).</p> <p> </p>
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