Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
5,155
datasets available to search
ShareScore release 0.9.0
Dataset results
5,155 results for “resolution”
Global surface water quality data from 1980 - 2019, derived from the dynamical surface water quality model (DynQual) at 30 arcmin spatial resolution
<p>Global ~50km (30 arcmin) surface water quality data from the dynamical surface water quality model (DynQual) from 1980-2019, with annual, monthly and daily temporal resolution. Simulations are made following the ISIMIP3a protocol (https://protocol.isimip.org/#/ISIMIP3a).</p> <p>Output data includes:</p> <ul> <li>Salinity; as indicated by TDS concentrations (mg l-1)</li> <li>Organic pollution; as indicated by BOD concentrations (mg l-1)</li> <li>Pathogen/bacterial pollution; as indicated by FC concentrations (cfu 100ml-1)</li> </ul> <p>Simulations were originally made at 5-arcmin resolution and aggregated to 30 arcmin 0.5 degree by summing the in-stream (routed) loadings and channel storage over the aggregated area (at daily, monthly and annual timesteps), and subsequently calculating in-stream concentrations. Please note the aggregation technique is provisional and thus the data is subject to change.</p> <p>Note. A minimum discharge threshold of 0.1 m3 s-1 was used when computing TDS, BOD and FC concentrations, as uncertainties in absolute values of water availabilities have large impacts on resulting in-stream concentrations. Concentrations in these gridcells are assigned as NA.</p> <p>Hydrology and water quality simulations made at DynQuals native spatial resolution (5 arcmin) can be found at: <a href="https://zenodo.org/records/14673871">https://zenodo.org/records/14673871</a>.</p>
Global surface water quality datasets under uncertain climate and socio-economic change, derived from the dynamical surface water quality model (DynQual) at 5 arcmin spatial resolution
<pre>Global ~10km (5 arcmin) surface water quality data from the dynamical surface water quality model (DynQual) from 2005-2100, with annual and monthly temporal resolution. Simulations are made under three combined climate and socio-economic scenarios (SSP1-RCP2.6; SSP3-RCP7.0 and SSP5-RCP8.5) and using five general circulation model (GFDL-ESM4; UKESM1-0-LL; MPI-ESM1-2-hr; IPSL-CM6A-LR and MRI-ESM2-0), following the ISIMIP3b protocol (<a href="https://protocol.isimip.org/#/ISIMIP3b">https://protocol.isimip.org/#/ISIMIP3b</a>). Output data are provided at annual and monthly temporal resolution over WorldClim time periods (2005-2020; 2021-2040; 2041-2060; 2061-2080; 2081-2100). Output data includes: - Discharge (m<sup>3</sup> s<sup>-1</sup>) - Water temperature (K)<br>- Total dissolved solids (TDS) load (g s<sup>-1</sup>)<br>- Biological oxygen demand (BOD) load (g s<sup>-1</sup>)<br>- Fecal coliform (FC) load (million cfu s<sup>-1</sup>) - Salinity; as indicated by TDS concentrations (mg l<sup>-1</sup>) - Organic pollution; as indicated by BOD concentrations (mg l<sup>-1</sup>) - Pathogen/bacterial pollution; as indicated by FC concentrations (cfu 100ml<sup>-1</sup>)<br><br>Note. A minimum discharge threshold of 0.1 m<sup>3</sup> s<sup>-1</sup> was used when computing TDS, BOD and FC concentrations, as uncertainties in absolute values of water availabilities have large impacts on resulting in-stream concentrations. Concentrations in these gridcells are assigned as NA.<br><br>Full time series of these variables at 30 arcmin (0.5 degree) can be found at: <a href="https://zenodo.org/records/14677534">https://zenodo.org/records/14677534</a>.</pre>
paragraphs of the resolutions of the States General of the Dutch Republic (1576-1796)
<p><strong>Update 2025-03-24:</strong> A new version of the paragraphs has been released, with better date recognition and document segmentation (separately, a new version of the recognied named entties has been released https://doi.org/10.5281/zenodo.15074645). </p> <p>This TSV file contains all the plain text paragraphs of all the resolutions of the States General of the Dutch Republic (1576-1796). These were created as part of the project REPUBLIC (REsolutions PUBLished In a Computational environment) which is funded by NWO grant 175.2017.024</p> <p>There are a total of 1,062,723 paragraphs from 692,156 resolutions, making up a corpus of 129,688,306 words (according to Unix word count). The recognised named entities are available in a separate repository (https://doi.org/10.5281/zenodo.14577243)</p> <p>The file contains six columns:</p> <ul> <li>`session_date`: the date on which the resolution (decision) was reached.</li> <li>`resolution_id`: the identifier of the resolution.</li> <li>`para_id`: the identifier of the paragraph in the resolution.</li> <li>`line_start`: the identifier of the first line in the paragraph. This identifier contains the scan ID and the x,y,w,h coordinates of the line in the scan, so it the resolution text can be traced to where it starts in the scan. </li> <li>`line_end`: the identifier of the last line in the paragraph. This identifier contains the scan ID and the x,y,w,h coordinates of the line in the scan, so it the resolution text can be traced to where it ends in the scan (which is not necessarily the same scan as the first line, as paragraphs can cross scan boundaries). </li> <li>`text`: the text of the paragraph.</li> </ul> <p> </p> <p>For more information on how this was generated, see the following publications:</p> <p>Marijn Koolen, Rik Hoekstra, Joris Oddens, Ronald Sluijter, Rutger van Koert, Ger Brouwer en Hennie Brugman, ‘<a href="https://dl.acm.org/doi/pdf/10.1145/3575864">The Value of Preexisting Structures for Digital Access Modelling the Resolutions of the Dutch States General</a>’, <em>Journal of Computing and Cultural Heritage </em>16:1 (2023). https://dl.acm.org/doi/10.1145/3575864</p> <p>Marijn Koolen en Rik Hoekstra, ‘<a href="https://ceur-ws.org/Vol-3290/long_paper5740.pdf">Detecting Formulaic Language Use in Historical Administrative Corpora</a>’, in: F. Karsdorp, A. Lassche, en K. Nielbo eds., <em>Proceedings of the Computational Humanities Research Conference 2022 </em>(Antwerpen 2022) 127-151. <em>Proceedings http://ceur-ws. org ISSN</em>, <em>1613</em>, 0073. https://ceur-ws.org/Vol-3290/long_paper5740.pdf</p> <p>Koolen, M., Hoekstra, R., Oddens, J., & Sluijter, R. (2023). '<a href="https://ceur-ws.org/Vol-3558/paper9465.pdf">Formulas and decision-making: the case of the states general of the Dutch Republic</a>' in: F. Karsdorp, A. Lassche, en K. Nielbo eds., <em>Proceedings of the Computational Humanities Research Conference 2023 </em>(Paris 2023) 772-798. <em>Proceedings http://ceur-ws. org ISSN</em>, <em>1613</em>, 0073. https://ceur-ws.org/Vol-3558/paper9465.pdf</p> <p>Rutger van Koert, Stefan Klut, Tim Koornstra, Martijn Maas en Luke Peters, ‘<a href="https://link.springer.com/chapter/10.1007/978-3-031-70645-5_6">Loghi: An End-to-End Framework for Making Historical Documents Machine-Readable</a>’,<em> Document Analysis and Recognition – ICDAR 2024 Workshops</em> (Cham: Springer 2024) 73-88. https://link.springer.com/chapter/10.1007/978-3-031-70645-5_6</p>
ChinaWheat30L: Long-term winter wheat maps of China at 30-m resolution from 2000 to 2023
<p>This is the long-term winter wheat map product of China at 30-m resolution from 2000 to 2023 (ChinaWheat30L). The product was generated using a knowledge-guided machine learning approach and the integration of satellite remote sensing and environmental datasets.</p>
Monthly averaged lightning data extracted from 1-year EMAC simulation including LCC-lightning (between 1 March, 2017 and 28 February, 2018, T42L90MA resolution).
<p>About Dataset<br> Monthly averaged lightning data extracted from 1-year EMAC simulation (between 1 March, 2017 and 28 February, 2018, T42L90MA resolution).</p> <p>Authors: Francisco J. Perez-Invernon, Heidi Huntrieser, Patrick Joeckel and Francisco J. Gordillo-Vazquez</p> <p>Description of the data<br> P_cth.tar: Lightning parameterization based on cloud top height. Scaling factor is applied.<br> L_cth.tar: Lightning parameterization based on cloud top height and modified over the oceans. A scaling factor of 0.571 have to be applied.<br> G_updr: Lightning parameterization based on updraft velocity. Scaling factor is applied.<br> A_prec: Lightning parameterization based on convective precipitation. A scaling factor of 0.76 have to be applied.<br> A_updr: Lightning parameterization based on Updraft strength at 440~hPa. A scaling factor of 0.01618 have to be applied.<br> P_cth + A_prec: Lightning parameterization based on cloud top height and updraft velocity. A scaling factor of 1.13 have to be applied.</p> <p>File format: netcdf</p> <p> Example:<br> <br> netcdf LCC_2017_______20170201_0000_mmlb_PaR_T {<br> dimensions:<br> time = UNLIMITED ; // (1 currently)<br> lon = 128 ;<br> lat = 64 ;<br> tbnds = 2 ;<br> variables:<br> double time(time) ;<br> time:long_name = "time" ;<br> time:bounds = "time_bnds" ;<br> time:units = "day since 2017-01-01 00:00:00" ;<br> time:calendar = "gregorian" ;<br> double YYYYMMDD(time) ;<br> YYYYMMDD:long_name = "time" ;<br> YYYYMMDD:units = "days as %Y%m%d.%f" ;<br> YYYYMMDD:calendar = "gregorian" ;<br> double dt(time) ;<br> dt:long_name = "delta_time" ;<br> dt:units = "s" ;<br> double nstep(time) ;<br> nstep:long_name = "current time step" ;<br> float lon(lon) ;<br> lon:long_name = "longitude" ;<br> lon:units = "degrees_east" ;<br> float lat(lat) ;<br> lat:long_name = "latitude" ;<br> lat:units = "degrees_north" ;<br> float aps(time, lat, lon) ;<br> aps:long_name = "surface pressure" ;<br> aps:units = "Pa" ;<br> aps:representation = "GP_2D_HORIZONTAL" ;<br> aps:grid_type = "gaussian" ;<br> aps:table = 128 ;<br> aps:code = 134 ;<br> aps:REFERENCE_TO = "g3b: aps" ;<br> aps:coordinates = "lon lat" ;<br> aps:cell_methods = "time: point" ;<br> float aps_ave(time, lat, lon) ;<br> aps_ave:long_name = "surface pressure" ;<br> aps_ave:units = "Pa" ;<br> aps_ave:representation = "GP_2D_HORIZONTAL" ;<br> aps_ave:grid_type = "gaussian" ;<br> aps_ave:table = 128 ;<br> aps_ave:code = 134 ;<br> aps_ave:REFERENCE_TO = "g3b: aps" ;<br> aps_ave:coordinates = "lon lat" ;<br> aps_ave:cell_methods = "time: mean" ;<br> float fpscg(time, lat, lon) ;<br> fpscg:long_name = "CG flash frequency" ;<br> fpscg:units = "1/s" ;<br> fpscg:REFERENCE_TO = "lnox_PaR_T_gp: fpscg" ;<br> fpscg:coordinates = "lon lat" ;<br> fpscg:cell_methods = "time: point" ;<br> float fpscg_ave(time, lat, lon) ;<br> fpscg_ave:long_name = "CG flash frequency" ;<br> fpscg_ave:units = "1/s" ;<br> fpscg_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpscg" ;<br> fpscg_ave:coordinates = "lon lat" ;<br> fpscg_ave:cell_methods = "time: mean" ;<br> float fpsic(time, lat, lon) ;<br> fpsic:long_name = "IC flash frequency" ;<br> fpsic:units = "1/s" ;<br> fpsic:REFERENCE_TO = "lnox_PaR_T_gp: fpsic" ;<br> fpsic:coordinates = "lon lat" ;<br> fpsic:cell_methods = "time: point" ;<br> float fpsic_ave(time, lat, lon) ;<br> fpsic_ave:long_name = "IC flash frequency" ;<br> fpsic_ave:units = "1/s" ;<br> fpsic_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpsic" ;<br> fpsic_ave:coordinates = "lon lat" ;<br> fpsic_ave:cell_methods = "time: mean" ;<br> float fpsm2cg(time, lat, lon) ;<br> fpsm2cg:long_name = "CG flash density" ;<br> fpsm2cg:units = "1/s/m2" ;<br> fpsm2cg:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2cg" ;<br> fpsm2cg:coordinates = "lon lat" ;<br> fpsm2cg:cell_methods = "time: point" ;<br> float fpsm2cg_ave(time, lat, lon) ;<br> fpsm2cg_ave:long_name = "CG flash density" ;<br> fpsm2cg_ave:units = "1/s/m2" ;<br> fpsm2cg_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2cg" ;<br> fpsm2cg_ave:coordinates = "lon lat" ;<br> fpsm2cg_ave:cell_methods = "time: mean" ;<br> float fpsm2ic(time, lat, lon) ;<br> fpsm2ic:long_name = "IC flash density" ;<br> fpsm2ic:units = "1/s/m2" ;<br> fpsm2ic:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2ic" ;<br> fpsm2ic:coordinates = "lon lat" ;<br> fpsm2ic:cell_methods = "time: point" ;<br> float fpsm2ic_ave(time, lat, lon) ;<br> fpsm2ic_ave:long_name = "IC flash density" ;<br> fpsm2ic_ave:units = "1/s/m2" ;<br> fpsm2ic_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2ic" ;<br> fpsm2ic_ave:coordinates = "lon lat" ;<br> fpsm2ic_ave:cell_methods = "time: mean" ;<br> float fpslcc10(time, lat, lon) ;<br> fpslcc10:long_name = "LCC(>10 ms) flash frequency" ;<br> fpslcc10:units = "1/s" ;<br> fpslcc10:REFERENCE_TO = "lnox_PaR_T_gp: fpslcc10" ;<br> fpslcc10:coordinates = "lon lat" ;<br> fpslcc10:cell_methods = "time: point" ;<br> float fpslcc10_ave(time, lat, lon) ;<br> fpslcc10_ave:long_name = "LCC(>10 ms) flash frequency" ;<br> fpslcc10_ave:units = "1/s" ;<br> fpslcc10_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpslcc10" ;<br> fpslcc10_ave:coordinates = "lon lat" ;<br> fpslcc10_ave:cell_methods = "time: mean" ;<br> float fpslcc20(time, lat, lon) ;<br> fpslcc20:long_name = "LCC(>20 ms) flash frequency" ;<br> fpslcc20:units = "1/s" ;<br> fpslcc20:REFERENCE_TO = "lnox_PaR_T_gp: fpslcc20" ;<br> fpslcc20:coordinates = "lon lat" ;<br> fpslcc20:cell_methods = "time: point" ;<br> float fpslcc20_ave(time, lat, lon) ;<br> fpslcc20_ave:long_name = "LCC(>20 ms) flash frequency" ;<br> fpslcc20_ave:units = "1/s" ;<br> fpslcc20_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpslcc20" ;<br> fpslcc20_ave:coordinates = "lon lat" ;<br> fpslcc20_ave:cell_methods = "time: mean" ;<br> float fpsm2lcc10(time, lat, lon) ;<br> fpsm2lcc10:long_name = "LCC(>10 ms) flash density" ;<br> fpsm2lcc10:units = "1/s/m2" ;<br> fpsm2lcc10:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2lcc10" ;<br> fpsm2lcc10:coordinates = "lon lat" ;<br> fpsm2lcc10:cell_methods = "time: point" ;<br> float fpsm2lcc10_ave(time, lat, lon) ;<br> fpsm2lcc10_ave:long_name = "LCC(>10 ms) flash density" ;<br> fpsm2lcc10_ave:units = "1/s/m2" ;<br> fpsm2lcc10_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2lcc10" ;<br> fpsm2lcc10_ave:coordinates = "lon lat" ;<br> fpsm2lcc10_ave:cell_methods = "time: mean" ;<br> float fpsm2lcc20(time, lat, lon) ;<br> fpsm2lcc20:long_name = "LCC(>20 ms) flash density" ;<br> fpsm2lcc20:units = "1/s/m2" ;<br> fpsm2lcc20:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2lcc20" ;<br> fpsm2lcc20:coordinates = "lon lat" ;<br> fpsm2lcc20:cell_methods = "time: point" ;<br> float fpsm2lcc20_ave(time, lat, lon) ;<br> fpsm2lcc20_ave:long_name = "LCC(>20 ms) flash density" ;<br> fpsm2lcc20_ave:units = "1/s/m2" ;<br> fpsm2lcc20_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2lcc20" ;<br> fpsm2lcc20_ave:coordinates = "lon lat" ;<br> fpsm2lcc20_ave:cell_methods = "time: mean" ;<br> float fpssprite(time, lat, lon) ;<br> fpssprite:long_name = "Sprites flash frequency" ;<br> fpssprite:units = "1/s" ;<br> fpssprite:REFERENCE_TO = "lnox_PaR_T_gp: fpssprite" ;<br> fpssprite:coordinates = "lon lat" ;<br> fpssprite:cell_methods = "time: point" ;<br> float fpssprite_ave(time, lat, lon) ;<br> fpssprite_ave:long_name = "Sprites flash frequency" ;<br> fpssprite_ave:units = "1/s" ;<br> fpssprite_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpssprite" ;<br> fpssprite_ave:coordinates = "lon lat" ;<br> fpssprite_ave:cell_methods = "time: mean" ;<br> float fpsm2sprite(time, lat, lon) ;<br> fpsm2sprite:long_name = "Sprites flash density" ;<br> fpsm2sprite:units = "1/s/m2" ;<br> fpsm2sprite:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2sprite" ;<br> fpsm2sprite:coordinates = "lon lat" ;<br> fpsm2sprite:cell_methods = "time: point" ;<br> float fpsm2sprite_ave(time, lat, lon) ;<br> fpsm2sprite_ave:long_name = "Sprites flash density" ;<br> fpsm2sprite_ave:units = "1/s/m2" ;<br> fpsm2sprite_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2sprite" ;<br> fpsm2sprite_ave:coordinates = "lon lat" ;<br> fpsm2sprite_ave:cell_methods = "time: mean" ;<br> float bps(time, lat, lon) ;<br> bps:long_name = "BJ flash frequency" ;<br> bps:units = "1/s" ;<br> bps:REFERENCE_TO = "bluejetbPaR_T_gp: bps" ;<br> bps:coordinates = "lon lat" ;<br> bps:cell_methods = "time: point" ;<br> float bps_ave(time, lat, lon) ;<br> bps_ave:long_name = "BJ flash frequency" ;<br> bps_ave:units = "1/s" ;<br> bps_ave:REFERENCE_TO = "bluejetbPaR_T_gp: bps" ;<br> bps_ave:coordinates = "lon lat" ;<br> bps_ave:cell_methods = "time: mean" ;<br> float bpsm2(time, lat, lon) ;<br> bpsm2:long_name = "BJ flash density" ;<br> bpsm2:units = "1/s/m2" ;<br> bpsm2:REFERENCE_TO = "bluejetbPaR_T_gp: bpsm2" ;<br> bpsm2:coordinates = "lon lat" ;<br> bpsm2:cell_methods = "time: point" ;<br> float bpsm2_ave(time, lat, lon) ;<br> bpsm2_ave:long_name = "BJ flash density" ;<br> bpsm2_ave:units = "1/s/m2" ;<br> bpsm2_ave:REFERENCE_TO = "bluejetbPaR_T_gp: bpsm2" ;<br> bpsm2_ave:coordinates = "lon lat" ;<br> bpsm2_ave:cell_methods = "time: mean" ;<br> double time_bnds(time, tbnds) ;<br> time_bnds:long_name = "time bounds" ;<br> time_bnds:units = "days since 2017-01-01T00:00:00Z" ;<br> time_bnds:cell_methods = "time: point"</p>
Data for: "High-resolution Soil Moisture Evolution in Hyper-arid Regions: A Comparison of InSAR, SAR, Microwave, Optical, and Data Assimilation Systems in the southern Arabian Peninsula"
<p>Data accompanying the publication: High-resolution Soil Moisture Evolution in Hyper-arid Regions: A Comparison of InSAR, SAR, Microwave, Optical, and Data Assimilation Systems in the southern Arabian Peninsula. For filenames starting with T: Exponential fit parameters time0 and mag0 for InSAR coherence data. they are binary files, where fit = a*exp(-b*x); a = -log(mag0); b = 1/time0. timeerr contains the uncertainty of the time0 parameter, and maghigh/maglow contain the high and low uncertainty for the mag0 parameter, respectively. For for each frame or overlap region (T101, T28, T130, T28_T101, T130_T28), there is a vrt file (T..._20180524.time0.vrt), which is the metadata file applicable to all files of the same frame. Files starting with mags_times: Exponential fit parameters for ASCAT/SMAP/GLDAS data. the same parameters (time0, timeerr, mag0, maghigh, maglow) can be found in these matlab structure files. In addition, the .mat files contain the offset parameter and related uncertainty, as well as lat/lon information. </p>
High-resolution scans of impact spatter patterns
<p>High-resolution scans of spatter patterns used in the following peer-reviewed research article<br> <br> Article Title: Do impact spatters depend on impact velocity, impact energy or impactor shape?<br> DOI : 10.1007/s00348-021-03341-1<br> Journal: Experiments in Fluids<br> Publisher: Springer<br> Authors: R Faflak and D Attinger</p> <p><br> Spatial resolution is 600 pixel per inch (a flatbed scanner A3 Epson Expression 11000XLwas used)<br> Images are oriented so that gravity points downwards<br> The file format is <br> impactor material_impactor shape_height of free fall_impactor mass in gram_<br> For instance the following file <br> PP,Al_F_h30_m644,7(2)_stitch_CLEAN.tiff<br> means<br> impactor material is Polypropylene and Aluminum<br> impactor shape is Flat<br> free fall height is 30 cm<br> impactor mass in gram is 644.7g</p> <p>A summary description of the impact setup and of the impact parameters is in the Adobe PDF document "description of geometry and parameters.pdf"</p>
Fast and accurate spectral estimation axial super resolution optical coherence tomography
<p>This depository contains the data and code underlying the results of the publication '<em>Fast and accurate spectral estimation axial super resolution optical coherence tomography</em>' in Optics Express (doi.org/<em>10.1364</em>/<em>OE</em>.<em>439761). </em>The reader is free to use the scripts and data in this depository, as long as the manuscript is correctly cited in their work. For further questions, please contact the corresponding author.</p> <p><strong>Description of the code and datasets</strong></p> <p>Table 1 describes the Matlab scripts and functions in this depository that were used in the publication. For reproducing the figures of the publication, refer to the scripts <em>SE_OCT_figure(..).m</em>. For understanding the method and applying it on other datasets from the reader, <em>Bscan_reconstruction.m </em>and <em>Cscan_reconstruction.m</em> are the most convenient scripts to start with. For simulating OCT data as presented in the publication, <em>OCT_simulations.m</em> could be applied. Details on the variables and parameters, such as number of iterations, grid interpolation factor and number of data chunks are commented on in the code itself and should be understandable with the publication as reference. </p> <p>Table 2 describes the datasets that have been used for the publication and are free for the readers to be used with their methods. Table 3 then gives a brief explanation of the variables that are contained in the dataset <em>.mat</em> files.</p> <table> <caption>Table 1. The Matlab scripts in this depository with brief description.</caption> <thead> <tr> <th scope="col">script name</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>Bscan_reconstruction.m</td> <td>This script loads a B-scan .mat file and applies the four processing methods FBW-DFT, PBW-DFT, AR, RFIAA on the data. </td> </tr> <tr> <td>Cscan_resonstruction.m</td> <td>This script loads a 3Ddata .mat file and applies the four processing methods FBW-DFT, PBW-DFT, AR, RFIAA on the data. </td> </tr> <tr> <td>oct_ar.m</td> <td>This function applies the auto-regressive spectral estimation on the input data.</td> </tr> <tr> <td>oct_iaa.m</td> <td>This function applies RFIAA or FIAA on the input data.</td> </tr> <tr> <td>fiaa_oct.m</td> <td>This function is called within oct_iaa.m for applying FIAA (without the recursive scheme) and within rfiaa_oct.m for the first line. This function applies FIAA on a single A-scan.</td> </tr> <tr> <td>rfiaa_oct.m</td> <td>This function is called within oct_iaa.m for applying RFIAA (with the recursive scheme) on a chunk of data. It initializes the first line of the chunk with fiaa_oct.m, and then it applies rec_fiaa_oct.m with the initialization taken from the previous scanline.</td> </tr> <tr> <td>rec_fiaa_oct.m</td> <td>This function applies RFIAA on a single A-scan, taking the initialization from the previous scanline as extra input parameter. </td> </tr> <tr> <td>RayleighThreshold.m</td> <td>This function automatically determines the lower limit of the dynamic range for plotting an OCT image. It fits a Rayleigh distribution on the input data (preferably noise, but also a full image could be used) and returns a threshold in dB. </td> </tr> <tr> <td>morgenstemning.m</td> <td>This function defines the colormap as used in the publication.</td> </tr> <tr> <td>Bscan_reconstruction_function.m</td> <td>This function takes the interference OCT signal, reference spectra and reconstruction parameters as input and returns the reconstructed images according to the four methods in the publication. This function is used in the scripts for reproducing the figures in the publication. It follows the same structure as the script <em>Bscan_reconstruction.m.</em></td> </tr> <tr> <td>SE_OCT_figure3.m</td> <td>This script does the processing for and plots figure 3 in the manuscript. For this script, the .zip file <em>wedge_simulation_data </em>needs to be unpacked and placed as folder in the folder where this script is executed.</td> </tr> <tr> <td>SE_OCT_figure4.m</td> <td>This script reproduces figure 4 in the publication.</td> </tr> <tr> <td>SE_OCT_figure5.m</td> <td>This script reproduces figure 5 in the publication.</td> </tr> <tr> <td>SE_OCT_figure6.m</td> <td> <p>This script reproduces figure 6 in the publication</p> </td> </tr> <tr> <td>OCT_simulations.m</td> <td>This script reproduces the OCT simulations as described in the publication. As the noise is random, any new realization might slightly differ from the data in the publication.</td> </tr> </tbody> </table> <p> </p> <table> <caption>Table 2. The OCT datasets contained in this depository with a brief description. Table 3 describes the variables that are contained in each of these datasets.</caption> <thead> <tr> <th scope="col">dataset name</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>wedge_Bscan_data.mat</td> <td>Experimental data from the wedge phantom as visualized in figure 3 of the publication. No spectrum averaging is applied.</td> </tr> <tr> <td>wedge_simulation_data.zip</td> <td>This zipped folder contains 16 simulation datasets with different noise levels, which form the basis of Figure 3 (f) in the publication.</td> </tr> <tr> <td>interfaces_simulation_Bscan_data.mat</td> <td>This file contains the simulation data for 8 interfaces with decreasing intensity and forms the basis of Figure 4 in the publication.</td> </tr> <tr> <td>layered_phantom_Bscan_data.mat</td> <td>This file contains the experimental data from the layered phantom, as used in Figure 4 (c-d) in the publication. No spectrum averaging is applied.</td> </tr> <tr> <td>onion_Bscan_data.mat</td> <td>This file contains the experimental data from the onion sample as used in Figure 5 in the publication. No spectrum averaging is applied.</td> </tr> <tr> <td>skin_Bscan_data.mat</td> <td>This file contains the experimental data from the skin sample as used in Figure 5 in the publication. No spectrum averaging is applied.</td> </tr> <tr> <td>intralipid_Bscan_data.mat</td> <td>This file contains the experimental data from the intralipid sample as used in Figure 6 in the publication. No spectrum averaging is applied.</td> </tr> <tr> <td>speckle_simulation_Bscan_data.mat</td> <td>This file contains simulation data for 3 speckle regions as used in Figure 6 in the publication.</td> </tr> <tr> <td>reference_spectrum.mat</td> <td>This file just contains a spectrum from the used experimental setup which is used as input for the simulations.</td> </tr> <tr> <td>onion_3Ddata.mat</td> <td>This file contains 3D data of the onion sample, which is used for visualization 1. The OCT spectra are obtained from averaging 8 spectra from the experimental setup.</td> </tr> <tr> <td>skin_3Ddata.mat</td> <td>This file contains 3D data of the skin sample, which is used for visualization 2. The OCT spectra are obtained from averaging 8 spectra from the experimental setup.</td> </tr> </tbody> </table> <table> <caption>Table 3. This table contains the variables in the .mat files and their description.</caption> <thead> <tr> <th scope="col">variable name</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td><em>iRawdata</em></td> <td>OCT interference spectra interpolated to a linear grid in k-domain, before subtracting the reference spectrum</td> </tr> <tr> <td><em>sk</em></td> <td>the reference spectrum, interpolated to a linear grid in k-domain</td> </tr> <tr> <td><em>phasep</em></td> <td>4 polynomial coeficients, which can be used in 'polyval' to correct for dispersion</td> </tr> <tr> <td><em>sizeX</em></td> <td>the lateral size of the scan in mm</td> </tr> <tr> <td><em>sizeY</em></td> <td>(only for 3D datasets) the lateral size in the direction perpendicular to x in mm</td> </tr> <tr> <td><em>sizeZ</em></td> <td>the axial field of view (one-sided) before range reduction in mm </td> </tr> <tr> <td><em>ROIp</em></td> <td>the best axial region of interest for this dataset to apply RFIAA on a reduced reconstruction range (in pixels of the DFT reconstruction without zero-padding)</td> </tr> </tbody> </table> <p> </p>
Supporting dataset for the paper : " Hydro-geomorphic metrics for high resolution fluvial landscape analysis"
<p>This repository contains all the original data supporting the results of Bernard et al., 2021: "Consistent hydro-geomorphic indicators for high resolution topographic analysis".<br> The parameter used to perform hydraulic simulations are also available.<br> </p>
High-resolution spatialization for estimation of precipitation in the Cordillera Blanca, Peru
<p>Supplementary materials for the article "High-resolution spatialization for estimation of precipitation in the Cordillera Blanca, Peru"</p>
Deep-Learning-Based Harmonization and Super-Resolution of Near-Surface Air Temperature from CMIP6 Models (1850-2100)
<p>A long-term (1850-2100) monthly air temperature (tas) product with a spatial resolution of 0.5 degree. This is a merged product from 31 CMIP6 models using the Deep-learning model which reduce bias, spatial downscaling and data merge at the same time,. To facilitate user-friendly access and download the dataset is stored individually for each year in a separate file. These files contain one historical data (1850-2014) , four future scenarios data during 2015-2100 (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) and four future scenarios data in Australia . The dataset is stored in NetCDF format, containing the variable tas, representing air temperature, produced in centigrade (℃) as a unit. There are three dimensions included in the dataset: longitude, latitude, and time, with the longitude ranging from -179.75E to 179.75E, the latitude from -89.75N to 89.75N. </p>
EcoDes-DK15: High-resolution ecological descriptors of vegetation and terrain derived from Denmark's national airborne laser scanning data set
<p><strong>Eighteen high-resolution ecological descriptors of vegetation and terrain for Denmark "EcoDes-DK15"</strong></p> <p>The data are derived from the nationwide airborne laser scanning / LiDAR campaign of Denmark from 2014-2015 provided by the Danish Agency for Data Supply and Efficiency.</p> <p><strong>Update: EcoDes-DK15 v1.1.0 (4 Dec. 2021)</strong></p> <p>Following the recommendations and feedback during the first round of peer-review, we updated the EcoDes-DK processing pipeline and EcoDes-DK15 data set. The key changes are:</p> <ul> <li>New version of the source data optimised to contain only point data collected before the end of 2015. The source data for EcoDes-DK15 v1.0.0 unintentionally contained data from 2018. The new source data is documented <a href="https://github.com/jakobjassmann/ecodes-dk-lidar/blob/master/documentation/source_data/readme.md">here</a>.</li> <li>New "date_stamp_*" auxiliary variables that illustrate the survey dates for the vegetation points in each cell. See updated descriptor documentation <a href="https://github.com/jakobjassmann/ecodes-dk-lidar/blob/master/documentation/descriptors.md">here</a>.</li> <li>Re-scaling of "solar_radiation" variable to MJ per 100 m<sup>2</sup> per year.</li> </ul> <p><strong>Detailed documentation for the data set can be found in the accompanying manuscript and GitHub repository:</strong></p> <p>Assmann, J. J., Moeslund, J. E., Treier, U. A., and Normand, S.: EcoDes-DK15: High-resolution ecological descriptors of vegetation and terrain derived from Denmark's national airborne laser scanning data set, Earth Syst. Sci. Data Discuss. [preprint], <a href="https://doi.org/10.5194/essd-2021-222">https://doi.org/10.5194/essd-2021-222</a>, in review, 2021<strong><em>.</em></strong></p> <p><a href="https://github.com/jakobjassmann/ecodes-dk-lidar">https://github.com/jakobjassmann/ecodes-dk-lidar</a></p> <p>Files are compressed using bzip2 and tar archiving. The compressed archives can be extracted using commonly available archiving tools (for example <a href="https://www.7-zip.org/">7z </a>on Windows, the archiving tool on macOS and bz2 on Linux). </p> <p>A small example "teaser" subset (5 MB) of the data set, covering the Husby Klit area from Figure 7 in the manuscript, can be found <a href="https://github.com/jakobjassmann/ecodes-dk-lidar/blob/master/manuscript/figure_7/EcoDes-DK15_teaser.zip">here</a>.</p> <p><strong>Abstract (from manuscript)</strong></p> <p>Biodiversity studies could strongly benefit from three-dimensional data on ecosystem structure derived from contemporary remote sensing technologies, such as Light Detection and Ranging (LiDAR). Despite the increasing availability of such data at regional and national scales, the average ecologist has been limited in accessing them due to high requirements on computing power and remote-sensing knowledge. We processed Denmark’s publicly available national Airborne Laser Scanning (ALS) data set acquired in 2014/15 together with the accompanying elevation model to compute 70 rasterized descriptors of interest for ecological studies. With a grain size of 10 m, these data products provide a snapshot of high-resolution measures including vegetation height, structure and density, as well as topographic descriptors including elevation, aspect, slope and wetness across more than forty thousand square kilometres covering almost all of Denmark’s terrestrial surface. The resulting data set is comparatively small (~94 GB, compressed 16.8 GB) and the raster data can be readily integrated into analytical workflows in software familiar to many ecologists (GIS software, R, Python). Source code and documentation for the processing workflow are openly available via a code repository, allowing for transfer to other ALS data sets, as well as modification or re-calculation of future instances of Denmark’s national ALS data set. We hope that our high-resolution ecological vegetation and terrain descriptors (EcoDes-DK15) will serve as an inspiration for the publication of further such data sets covering other countries and regions and that our rasterized data set will provide a baseline of the ecosystem structure for current and future studies of biodiversity, within Denmark and beyond.</p> <p><strong>Acknowledgements (from manuscript)</strong></p> <p>We would like to thank Andràs Zlinszky for his contributions to earlier versions of the data set, Charles Davison for feedback regarding data use and handling, as well as Matthew Barbee and Zsófia Koma for sharing their insights on the source data merger and Zsófia’s script to generate summary statistics for the different versions of the DHM point clouds. Funding for this work was provided by the Carlsberg Foundation (Distinguished Associate Professor Fellowships) and Aarhus University Research Foundation (AUFF-E-2015-FLS-8-73) to Signe Normand (SN). This work is a contribution to SustainScapes – Center for Sustainable Landscapes under Global Change (grant NNF20OC0059595 to SN).</p>
Predictors and predictand for "Repeatable high-resolution statistical downscaling through deep learning"
<p>Predictors and predictand for "Repeatable high-resolution statistical downscaling through deep learning". Predictors from the ERA5 reanalysis and predictand from ReKIS (https://rekis.hydro.tu-dresden.de). Data is saved in ".rda" format, to be read from R.</p>
Capturing features of hourly-resolution energy models through statistical annual indicators
<p>Dear colleagues,</p> <p>This is the official repository of the Task 7.4 of H2020 Locomotion project. Feel free to use our data by citing this work and comment about our work by referencing the main authors of it. The article explaining this work is under revision. it will be referenced as soon as posible.</p> <p><strong>Python scripts </strong>("create_inputs.txt" and "run_simulations.txt") creates the input files for EnergyPLAN. The second one runs iteratively EnergyPLAN to generate the outputs of combinations (which are saved in the "EU_Iterate_case.xlsx" file). Hourly distributions of demands and supply technologies are contained in the RAR file ("EUdist.rar") and "EU_start_v2_noFlex.txt" initialize the starting configuration of the European energy system. Those files are required to run EnergyPLAN. The <strong>PowerPoint file</strong> ("EnergyPLAN_instructions.pptx") explains the procedure to carry out the runs of combinations in Python/Excel.</p> <p>In case you couldn't properly do the combinations, the<strong> Excel file</strong> ("EU.xlsx") saves this information, so the steps of the approach could be followed from this point with the Excel file. We have used Power Query (Excel) to prepare the data for the next step of building the regression models.</p> <p>The <strong>Matlab file (</strong>"CreateRegressionModels.m"<strong>)</strong> automatically generates the regression models for the European region of WILIAM (official model of the Locomotion project).</p> <p>Best regards,</p> <p>Gonzalo.</p>
Fig. 28 Acroteriobatus leucospilus, BMNH 1905.6.8.12 in Resolution of the Acroteriobatus leucospilus species complex, with a redescription of A. leucospilus (Norman, 1926) and descriptions of two new western Indian Ocean species of Acroteriobatus (Rhinopristiformes, Rhinobatidae)
Fig. 28 Acroteriobatus leucospilus, BMNH 1905.6.8.12, subadult male syntype, 416 mm TL, in total dorsal view. The photograph was taken and kindly provided by Kevin Webb
Fig. 26 in Resolution of the Acroteriobatus leucospilus species complex, with a redescription of A. leucospilus (Norman, 1926) and descriptions of two new western Indian Ocean species of Acroteriobatus (Rhinopristiformes, Rhinobatidae)
Fig. 26 Acroteriobatus stehmanni sp. nov., ZMH 25553, adultmale holotype, 597 mm TL, radiograph of cranium and snout in dorsal view
Fig. 35 Acroteriobatus leucospilus, SAIAB 34588 in Resolution of the Acroteriobatus leucospilus species complex, with a redescription of A. leucospilus (Norman, 1926) and descriptions of two new western Indian Ocean species of Acroteriobatus (Rhinopristiformes, Rhinobatidae)
Fig. 35 Acroteriobatus leucospilus, SAIAB 34588, gravid female, 690 mm TL, close-up of mouth-nasal region. Photograph courtesy Marsha Englebrecht ©
Fig. 31 in Resolution of the Acroteriobatus leucospilus species complex, with a redescription of A. leucospilus (Norman, 1926) and descriptions of two new western Indian Ocean species of Acroteriobatus (Rhinopristiformes, Rhinobatidae)
Fig. 31 Acroteriobatus leucospilus, underwater photograph showing the vivid live coloration. The photograph was taken and kindly provided by Dennis King
Fig. 19 in Resolution of the Acroteriobatus leucospilus species complex, with a redescription of A. leucospilus (Norman, 1926) and descriptions of two new western Indian Ocean species of Acroteriobatus (Rhinopristiformes, Rhinobatidae)
Fig. 19 Acroteriobatus stehmanni sp. nov., ZMH 25553, adult male holotype, 597 mm TL, head in dorsal view. Scale bar: 5 cm
Fig. 18 in Resolution of the Acroteriobatus leucospilus species complex, with a redescription of A. leucospilus (Norman, 1926) and descriptions of two new western Indian Ocean species of Acroteriobatus (Rhinopristiformes, Rhinobatidae)
Fig. 18 Acroteriobatus stehmanni sp. nov., ZMH 25560, juvenile male paratype, 201.4 mm TL, in total dorsal view. Scale bar: 5 cm
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.