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507 results for “refinement”
German weather services (DWD) multi annual meteorological rasters for the climate period 1991-2020 refined to 25m grid
<h1>Overview</h1> <p>These are two multi-annual raster products from the german weather service, that got refined from a 1km grid to a 25m grid, by using a local regression model.</p> <p>The base rasters from DWD are:</p> <ul> <li>HYRAS precipitation</li> <li>REGNIE precipitation</li> <li>DWD-grid (precipitation, potential evapotranspiration and temperature 2m above ground)</li> </ul> <p>To refine the grids the Copernicus DEM with a resolution of 25m got used. For every cell a linear regression model got created, by selecting the multi-annual rasters value and the elevation, from the original digital elevation model that was used by the DWD to create the raster, in a certain window around the cell. This window was at least 2 cells around the considered cell, so 5x5=25 cells. If the standard deviation of the elevation in this window was less than 4m, more neighbooring cells are considered until a maximum of 13x13=169 cells are considered. This widening of the window was necessary for flat regions to get a reasonable regression model.</p> <p>Out of these combinations of elevation and climate parameter a linear regression model was build. These regression models are then applied to the finer digital elevation model with its 25m resolution from Copernicus.</p> <p>The following image illustrates the generation of the refined rasters on a small example window:</p> <p></p>
Data to "Humans Can Visually Judge Grasp Quality and Refine Their Judgments Through Visual and Haptic Feedback"
<p>This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p>Maiello, G.^, Schepko, M.^, Klein, L. K., Paulun, V. C., and Fleming, R. W. (2021) Humans Can Visually Judge Grasp Quality and Refine Their Judgments Through Visual and Haptic Feedback. Front. Neurosci. 14:591898.<br> doi: 10.3389/fnins.2020.591898</p> <p>A preprint version of the manuscript is available at: https://doi.org/10.1101/2020.08.11.246173</p>
Galápagos Archipelago Refined Analysis Validation data
<p>This dataset includes (near) surface data variables from the <a href="https://data.klima.tu-berlin.de/GAR/">GAR</a> dataset for the model validation period from 2022-04-01 to 2023-03-31.</p> <p>As this data is part of the GAR dataset, please find additional data at <a href="https://data.klima.tu-berlin.de/GAR/">https://data.klima.tu-berlin.de/GAR/</a></p> <p>The <a href="https://www.unidata.ucar.edu/software/netcdf/">netCDF</a> format is self-describing, so that all needed metadata are included within the files.</p> <p>The file names are composed with the following structure:</p> <p><model-setup>_<horizontal-resolution>_<time-resolution>_<variable-name>.nc</p> <p>The shorthands in the file names represent the following:</p> <p><strong>MM</strong> = Name of the model setup, described in Schmidt et al. (unpublished)</p> <p><strong>d02km</strong> = domain with a grid spacing of 2 km</p> <p><strong>2d</strong> = spatial dimensions (2d data, single level)</p> <p><strong>3d_press</strong> = spatial dimensions (3d data, pressure level)</p> <p><strong>d</strong> = time frequency of the data (daily)</p> <p><strong>m</strong> = time frequency of the data (monthly)</p> <p><strong>y</strong> = time frequency of the data (yearly)</p> <p><strong>psfc</strong> = surface (sfc) pressure</p> <p><strong>q2</strong> = water vapor mixing ratio (qv) st 2 m</p> <p><strong>q</strong> = mixing ratio</p> <p><strong>prcp</strong> = total precipitation (step-wise)</p> <p><strong>et</strong> = actual evapotranspiration (step-wise)</p> <p><strong>t2</strong> = temperature (temp) at 2 m</p> <p><strong>theta</strong> = potential temperature</p> <p><strong>sh2 </strong>= specific humidity at 2 m</p> <p><strong>rh2 </strong>= relative humidity at 2 m</p> <p><strong>u10</strong> = 10 m u-wind component</p> <p><strong>v10</strong> = 10 m v-wind component</p> <p><strong>ws10</strong> = 10 m wind speed</p> <p><strong>w</strong> = w-wind component</p> <p><strong>wd10</strong> = 10 m wind direction</p> <p><strong>hgt </strong>= surface height</p> <p><strong>landmask </strong>= landmask</p> <p> </p> <p>The data is in accordance with the <a href="https://cfconventions.org/">CF Conventions</a> CF-1.8</p>
Cervix_Refining_6
This is the process of making a carafe Bontemps. The step depicted is called "cervix_refining"(from the Mingei project).
Cervix_Refining_2
This is the process of making a carafe Bontemps. The step depicted is called "cervix_refining"(from the Mingei project).
Cervix_Refining_5
This is the process of making a carafe Bontemps. The step depicted is called "cervix_refining"(from the Mingei project).
Cervix_Refining_8
This is the process of making a carafe Bontemps. The step depicted is called "cervix_refining"(from the Mingei project).
Cervix_Refining_7
This is the process of making a carafe Bontemps. The step depicted is called "cervix_refining"(from the Mingei project).
Cervix_Refining_1
This is the process of making a carafe Bontemps. The step depicted is called "cervix_refining"(from the Mingei project).
Cervix_Refining_4
This is the process of making a carafe Bontemps. The step depicted is called "cervix_refining"(from the Mingei project).
Cervix_Refining_3
This is the process of making a carafe Bontemps. The step depicted is called "cervix_refining"(from the Mingei project).
Refined Land cover for Beijing, Shanghai, Ningbo in China and Paris Region, Velika Gorica, Aarhus in Europe under different scenarios in 2030
<p>Europe and China Refined Land Cover 2030 (ECRLC2030) was derived from historical landcover observations, natural geographical, location, and socio-economic factors and the Conversion of Land Use and its Effects at Small Regional Extent model (CLUE-S). With a spatial resolution of 60m, landcover under three different scenarios were simulated: the business-as-usual scenario (BAU), the market-liberal scenario (MLS), and the ecological protection scenario (EPS).</p>
Refinements for Bragg coherent X-ray diffraction imaging: Electron backscatter diffraction alignment and strain field computation
<p>Here we present the final crystal reconstructions and analysis scripts for the paper titled "Refinement for Bragg coherent X-ray diffraction imaging: Electron backscatter diffraction alignment and strain field computation" published in Journal of Applied Crystallography, 55, 2022. Please see the README file for more information.</p>
A large data-set of CASP protein refinement simulations for machine-learning
<p>The uploaded trajectory data originates from our own laboratory's refinement method in CASP11 and CASP12 for which the reference crystal structure is available in the PDB. In total the trajectory data consists of 904 trajectories with 3419 ns cumulative simulation time and 1,709,704 snapshots with a delta t =2 ps from 42 different protein systems.</p> <p><strong>File Overview</strong></p> <ul> <li><strong>trajectory_data_pdbs.tar.gz :</strong> contains the PDB files of the different trajectories as well as the starting model and reference crystal structure for each target</li> <li><strong>casp_normalized_all_data_final.csv.gz : </strong>contains the trajectory features calculated for each snapshot from the trajectory PDBs</li> <li><strong>cv_folds.csv : </strong>contains the 7 fold cross-validation assignment used to assess the performance of the model<br> </li> </ul> <p> </p> <p> </p> <p> </p>
IPCC Climate Zones (from the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories)
<p><strong>Description</strong></p> <p>These data (re)create spatial data for the 2019 IPCC Climate Zones, shown in <em>Figure 3A.5.1</em> of <a href="https://www.ipcc-nggip.iges.or.jp/public/2019rf/pdf/4_Volume4/19R_V4_Ch03_Land%20Representation.pdf">Chapter 3: Consistent Representation of Lands</a> in <a href="https://www.ipcc-nggip.iges.or.jp/public/2019rf/vol4.html">Volume 4: Agriculture, Forestry and Other Land Use</a> of the <a href="https://www.ipcc-nggip.iges.or.jp/public/2019rf/index.html">2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories</a>. I recreated these data because I could not readily identify the data in a spatial format online, a problem which has previously been noted by ESDAC, who produced a <a href="https://esdac.jrc.ec.europa.eu/content/support-renewable-energy-directive#tabs-0-description=1">spatial version of <em>Figure 3A.5.1</em> from the original 2006 guidelines</a>.</p> <p>Resolution: 0.5 arc degree</p> <p>CRS: lon/lat WGS 84</p> <p><strong>If you use these data please ensure you also cite the IPCC</strong> - Calvo Buendia, E et al. (2019). 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. IPCC, Switzerland.</p> <p> </p> <p><strong>Methods</strong></p> <p>The data were derived using the classification scheme shown in <em>Figure 3A.5.2</em> based on the gridded Climate Research Unit (CRU) Time Series (TS) monthly climate data (<a href="https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.3711">Harris et al., 2014</a>) for the period from 1985 to 2015 following the methods described in <em>Annex 3A.5 Default climate and soil classifications </em>of the above Chapter. All data were processed in <em>R</em> version 4.2.1, with the packages <a href="https://cran.r-project.org/web/packages/elevatr/index.html"><em>elevatr</em></a> (v0.4.2), <a href="https://cran.r-project.org/web/packages/lubridate/index.html"><em>lubridate</em></a> (v1.8.0), <a href="https://cran.r-project.org/web/packages/magrittr/index.html"><em>magrittr</em></a> (v2.0.3), and <a href="https://cran.r-project.org/web/packages/terra/index.html"><em>terra</em></a> (v1.6-7)<em> </em>attached. The full session info is included as a <em>.txt</em> file. As these methods are not exhaustively described in the Annex, the following assumptions were made:</p> <ul> <li><a href="http://http://dx.doi.org/10.5285/c311c7948e8a47b299f8f9c7ae6cb9af">CRU TS3.25</a> was used as the most recently published data (published on 2017-09-22) that could have been incorporated into the Refinement. Other possibilities include CRU TS3.24 (which are the first data to include 2015), or CRU TS4.00 or CRU TS4.01 (both of which were published in parallel to 3.24 and 3.25). These data were all investigated, and CRU TS3.25 produced results that were the most visually similar to the published <em>Figure 3A.5.1</em> (though non-identical).</li> <li>As the methods did not mention a preferred elevation data source, the <a href="https://cran.r-project.org/web/packages/elevatr/index.html"><em>elevatr</em></a> R package was used to obtain data at zoom level 2 (approx resolution of 0.15 arc degree), that was then resampled to match the 0.5-degree resolution of the CRU data. These data originally come from the <a href="https://www.ngdc.noaa.gov/mgg/global/global.html">ETOPO1 global relief model</a>.</li> </ul> <p> </p> <p><strong>Known discrepancies</strong></p> <ul> <li>The distribution of Tropical Wet and Tropical Moist in South America does not exactly match the original data.</li> <li>There are small discrepancies in Tropical Montane classifications (likely arising from the use of a different elevation layer). These are most noticeable in, but not restricted to, Africa.</li> <li>The classification of Boreal Dry, Polar Dry, and Polar Moist in northern Russia and (to a lesser extent) in northern Canada does not exactly match the original data.</li> <li>There are a small number of Cool Temperate Dry pixels in the UK, and Warm Temperate Dry pixels around Brittany which do not occur in the original data.</li> </ul> <p> </p> <p><strong>Disclaimer</strong></p> <p><strong>I am not affiliated with the IPCC in any way</strong>, I just needed spatial data of the Climate Zones, and could not readily identify any online. This is a problem which has previously been noted by ESDAC, who produced a <a href="https://esdac.jrc.ec.europa.eu/content/support-renewable-energy-directive#tabs-0-description=1">spatial version of <em>Figure 3A.5.1</em> from the original 2006 guidelines</a>.</p> <p> </p> <p><strong>File description</strong></p> <ul> <li><em>README.html</em> - ~this description file.</li> <li><em>IPCC_Climate_Zones_ts_3.25.tif</em> - the output Climate Zones map at 0.5-arc degree resolution based on the CRU TS3.25 data.</li> <li><em>IPCC_Climate_Zones_colour_map.clr </em>- a colour map file to render the output map with the same colours as in the IPCC 2019 Refinement figure.</li> <li><em>IPCC_Climate_Zones_ts_3.25.png</em> - an image file of the output Climate Zones map.</li> <li><em>ipcc_climate_zones_2019.R</em> - the script used to produce these data.</li> <li><em>session_info.txt</em> - the R session info.</li> </ul>
Paired refinement under control of PAIREF - examples
<p>This archive contains example runs of PAIREF - a tool for macromolecular crystallographers performing the paired refinement protocol.</p> <p>Program website: https://pairef.fjfi.cvut.cz/</p> <p>Results of six data sets presented in the paper "Paired refinement under control of PAIREF" by M. Maly, K. Diederichs, J. Dohnalek, P. Kolenko (2020) are included.</p> <ul> <li>simulated data for lysozyme from Gallus gallus (SIM)</li> <li>measured data for thermolysin from Bacillus thermoproteolyticus (TL)</li> <li>measured data for cysteine-bound complex of cysteine dioxygenase from Rattus norvegicus (CDO)</li> <li>measured data for endothiapepsin from Cryphonectria parasitica in complex with fragment B53 (EP)</li> <li>measured data for interferon gamma from Paralichthys olivaceus (POLI)</li> <li>measured data for bilirubin oxidase from Myrothecium verrucaria (BO)</li> </ul>
X-ray diffraction images used for refinement of cytochrome cL from Methylobacterium extorquens.
<p>X-ray diffraction images for cytchrome cL from <em>M. extorquens</em> extending to 1.6 Angstroms resolution that were collected at ID14-2 at the ESRF (Grenoble) in April 2001. This dataset was used for high resolution refinement of the structure. </p>
Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures
<p>This repository holds all of the raw data generated by my (Modern) Fortran code for a paper "Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures".</p><p>The (Modern) Fortran code solves the multi-group neutron diffusion equation using a novel IGA-based spatial discretisations.</p>
Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of an Interior-Penalty Scheme for a Discontinuous Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures
<p>This repository holds all of the raw data generated by my (Modern) Fortran code for a paper "Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of an Interior-Penalty Scheme for a Discontinuous Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures".</p><p>The (Modern) Fortran code solves the multi-group neutron diffusion equation using a novel IGA-based spatial discretisations.</p>
Europe and China Refined Land cover (ECRLC) (10m)
<p>Europe and China Refined Land cover (ECRLC) V2 is a 10 m Sentinel 1 & 2-based land cover database for Paris Region (Europe), Aarhus (Europe), Velika Gorica (Europe), Beijing (China), Shanghai (China), and Ningbo (China) for 2020.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.