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KNMI-LENTIS large ensemble time slice dataset description
<p><strong>1. Contents </strong></p> <ul> <li><strong>Available variables in KNMI-LENTIS</strong> <ul> <li>request-overview-CMIP-historical-including-EC-EARTH-AOGCM-preferences.txt</li> </ul> </li> <li><strong>Where is the data deposited on the ECWMF's tape storage (section 4)</strong> <ul> <li>LENTIS_on_ECFS.zip </li> </ul> </li> <li><strong>Data of all variables for 1 year for 1 ensemble member (section 5)</strong> <ul> <li>tree_of_files_one_member_all_data.txt</li> <li>{AERmon,Amon,Emon,LImon,Lmon,Ofx,Omon,SImon,fx,Eday,Oday,day,CFday,3hr,6hrPlev,6hrPlevPt}.zip</li> </ul> </li> </ul> <p><strong>2. Description of this Zenodo dataset</strong></p> <p>This Zenodo dataset pertains to the full KNMI-LENTIS dataset: a large ensemble of simulations with the Global Climate Model EC-Earth3. The periods are for the present-day period (2000-2009) and a future +2K period (2075-2084 following SSP2-4.5). KNMI-LENTIS has 1600 simulated years for both the two climates. This level of sampled climate variability allows for robust and in-depth research into extreme events. The available variables are listed in the file <strong>request-overview-CMIP-historical-including-EC-EARTH-AOGCM-preferences.txt. </strong>All variables are cmorised following CMIP6 data format convention. Further details on the variables and their output dimensions is available via the<a href="https://clipc-services.ceda.ac.uk/dreq/mipVars.html"> following search tool</a>. The total size of KNMI-LENTIS is 128 TB. KNMI-LENTIS is stored at the <a href="https://www.ecmwf.int/en/computing/our-facilities/data-handling-system">high performance storage system of the ECMWF (ECFS)</a>. </p> <p>The Global Climate Model that is used for generating this Large Ensemble is EC-Earth3 - VAREX project branch <a href="https://svn.ec-earth.org/ecearth3/branches/projects/varex">https://svn.ec-earth.org/ecearth3/branches/projects/varex</a> (access restricted to ECMWF members).</p> <p>The goal of this Zenodo dataset is :</p> <ol> <li>to provide an accurate description and example of how the KNMI-LENTIS dataset is organised. </li> <li>to describe in which servers the data are deposited and how to gain access to the data for future users</li> <li>to provide links to related git repositories and other content relating to the KNMI-LENTIS production</li> </ol> <p><strong>3. How KNMI-LENTIS is </strong><strong>organised</strong></p> <p>KNMI-LENTIS consists of 2 times 160 runs of 10 years. All simulations have a unique ensemble member label that reflects the forcing, and how the initial conditions are generated. The initial conditions have two aspects: the parent simulation from which the run is branched (macro perturbation, there are 16), and the seed relating to a particular micro-perturbation in the initial three-dimensional atmosphere temperature field (there are 10). The ensemble member label thus is a combination of: </p> <ul> <li>forcing (<em>h</em> for present-day/historical and <em>s</em> for +2K/SSP2-4.5)</li> <li>parent ID (number between 1 and 16)</li> <li>micro perturbation ID (number between 0 and 9)</li> </ul> <p>In this Zenodo dataset we publish 1 year from 1 member to give insight into the type of data and metadata that is representative of the full KNMI-LENTIS dataset. The published data is year 2000 from member <em>h010</em>. See Section 4 </p> <p>Further, all KNMI-LENTIS simulations are labeled per the CMIP6 convention of variant labelling. A variant label is made from four components: the realization index <em>r</em>, the initialization index <em>i</em>, the physics index <em>p</em> and the forcing index <em>f</em>. Further details on CMIP6 variant labelling be found in <a href="https://pcmdi.llnl.gov/CMIP6/Guide/modelers.html">The CMIP6 Participation Guidance for Modelers</a>. In the KNMI-LENTIS data set, the forcing is reflected in the first digit of the realization index <em>r </em>of the variant label. For the historical simulations, the one thousands (r1000-r1999) have been reserved. For the SSP2-4.5 the five thousands (r5000-r5999) have been reserved. The parent is reflected in the second and third digit of the realization index <em>r</em> of the variant label (r?01?-r?16?). The seed is reflected in the fourth digit of the realization index <em>r</em>: (r???0-r???9). The seed is also reflected in the initialization index <em>i</em> of the variant label (i0-i9), so this is double information. The physics index <em>p5</em> has been reserved for the ECE3p5 version: all KNMI-LENTIS simulations have the <em>p5</em> label. The forcing index <em>f </em>of the variant label is kept at 1 for all KNMI-LENTIS simulations. As an example, variant label r5119i9p5f1 refers to: the 2K time slice with parent 11 and randomizing seed number 9. The physics index is 5, meaning the run is done with the ECE3p5 version of EC-Earth3. </p> <p><strong>4. Where is the data deposited on the ECWMF's tape storage</strong></p> <p>In this Zenodo folder, there are several text files and several netcdf files. The text files provide</p> <p>Data from KNMI-LENTIS is deposited in the <a href="https://www.ecmwf.int/en/computing/our-facilities/data-handling-system">ECMWF ECFS tape storage system</a>. Data can be freely downloaded by to those who have access to the ECMWF ECFS. Else, the data can be made available by the authors upon request. </p> <p>The way the dataset is organised is detailed in <strong>LENTIS_on_ECFS.zip. </strong>This contains details on all available KNMI-LENTIS files, in particular details for how these are filed in ECFS. The files on ECFS are tar zipped per ensemble member & variable: these contain 10 years of ensemble member data (10 separate netcdf files). The location on ECFS of the tar-zipped files that are listed in the various text files in this Zenodo dataset is</p> <p>ec:/nklm/LENTIS/ec-earth/cmorised_by_var/ </p> <pre><code class="language-bash">#!/bin/bash #------------------- # script to write out LENTIS details on ECFS #------------------- for freq in AERmon Amon Emon LImon Lmon Ofx Omon SImon fx Eday Oday day CFday 3hr 6hrPlev 6hrPlevPt; do for scen in hxxx sxxx; do els -l ec:/nklm/LENTIS/ec-earth/cmorised_by_var/${scen}/${freq}/* >> LENTIS_on_ECFS_${scen}_${freq}.txt done done</code></pre> <p>Further, part of the data will be made publicly available from the <a href="https://esgf-node.llnl.gov/projects/cmip6/">Earth System Grid Federation (ESGF) data portal.</a> We aim to upload most of the monthly variables for the full ensemble. As search terms use <strong>EC-Earth</strong> for model and <strong>p5</strong> for physical index to locate the KNMI-LENTIS data. </p> <p><strong>5. Data of all variables for 1 year for 1 ensemble member</strong></p> <p>The netcdf files of the data of 1 year from 1 member <em>h010 </em>are published here to give insight into the type of data and metadata that is representative of the full KNMI-LENTIS dataset. The data are in zipped folders per output frequencies: AERmon, Amon, Emon, LImon, Lmon, Ofx, Omon, SImon, fx, Eday, Oday, day, CFday, 3hr, 6hrPlev, 6hrPlevPt. The text file <strong>request-overview-CMIP-historical-including-EC-EARTH-AOGCM-preferences.txt </strong> gives an overview of variables available per output frequency. the text files <strong>tree_of_files_one_member_all_data.txt </strong>gives an overview of the files in the zipped folders. </p> <p><strong>6. Related links</strong></p> <p>The production of the KNMI-LENTIS ensemble was funded by the KNMI (Royal Dutch Meteorological Institute) multi-year strategic research fund <a href="https://www.knmi.nl/research/weather-climate-models/projects/mso-climate-variability-and-extremes-varex">KNMI MSO Climate Variability And Extremes (VAREX)</a></p> <p>GitHub repository corresponding to this Zenodo dataset: <a href="https://github.com/lmuntjewerf/KNMI-LENTIS_dataset_description.git">https://github.com/lmuntjewerf/KNMI-LENTIS_dataset_description.git </a></p> <p>Github repository for KNMI-LENTIS production code: <a href="https://github.com/lmuntjewerf/KNMI-LENTIS_production_script_train.git">https://github.com/lmuntjewerf/KNMI-LENTIS_production_script_train.git</a></p>
Data of publication: "Many-Body Radiative Decay in Strongly Interacting Rydberg Ensembles"
<p>The uploaded files contain the data of the simulations presented in the figures in <a href="https://doi.org/10.1103/PhysRevLett.129.243202">https://doi.org/10.1103/PhysRevLett.129.243202</a>.</p>
PFVN-synth: Synthesized Violin-Piano Ensemble Dataset
<p>A PFVN-synth dataset contains realistic instrumental triplet audios of piano, violin, and their mixture by rendering MIDI files with virtual instruments using musical scores of 45 different pieces by 23 classical composers with a total duration of 7 hours. All MIDI files were collected on the <a href="https://musescore.com">MuseScore website</a>. All tracks are rendered into monaural audio files with the standard CD quality: 44.1kHz, 16-bit. We used commercial virtual instruments to synthesize piano and violin. Specifically, we used ‘Bösendorfer Grand Piano’ in Apple Logic Pro and ‘SWAM Violin V3’ by Audio Modeling.</p> <p>It is divided into a train set containing 32 pieces with a duration of 5.8 hours, a validation set containing 3 pieces with a duration of 30 minutes, and a test set containing 10 pieces with a duration of 50 minutes so that the composers are not biased to the split sets.</p>
Supplementary material for "Marker and source-marker reprogramming of Most Permissive Boolean networks and ensembles with BoNesis"
<p>This archive contains supplementary material for the paper <a href="https://doi.org/10.48550/arXiv.2207.13307">Marker and source-marker reprogramming of Most Permissive Boolean networks and ensembles with BoNesis</a> by Loïc Paulevé (2023):</p> <ul> <li>sources of the executable paper in Markdown and Jupyter notebook formats</li> <li>benchmark dataset</li> <li>scripts for converting the executable paper in LaTeX for arXiv deposit</li> </ul>
WACCM-X Northern Hemisphere Winter Ensemble
<p>Simulation output from the Whole Atmosphere Community Climate Model with thermosphere-ionosphere eXtension (WACCM-X) for 40 Northern Hemisphere winters. The simulations were performed as free-running simulations with constant geomagnetic and solar activity. Included output is the Northern Annular Mode (NAM), zonal mean temperature and zonal wind, residual circulation, atomic oxygen to molecular nitrogen ratio (O/N2), migrating semidiurnal tide (SW2), and the SW2 component of TEC. The data files are in support of the publication “Influence of Stratosphere Polar Vortex Variability on the Mesosphere, Thermosphere, and Ionosphere”</p>
Data and code to next-generation ensemble projections reveal higher climate risks for marine ecosystems
<p>Data products: <strong>Tittensor et al. (2021). Next-generation ensemble projections reveal higher climate risks for marine ecosystems, Nature Climate Change. DOI: <a href="https://doi.org/10.1038/s41558-021-01173-9">https://doi.org/10.1038/s41558-021-01173-9</a> </strong></p> <p>This data was produced using R scripts available on the GitHub repository <a href="https://github.com/Fish-MIP/CMIP5vsCMIP6">https://github.com/Fish-MIP/CMIP5vsCMIP6</a>, and was used for analysis and plotting in Tittensor et al. (2021). These R scripts are also available here as CMIP5vsCMIP6_code.zip </p> <p>Data_CMIP5.Rdata and Data_CMIP6.RData include all data used to produce global maps of percentage change in total consumer biomass. </p> <p>Data_trends_CMIP5.Rdata and Data_trends_CMIP6.RData include all data used to produce temporal trends of percentage change in total consumer biomass. </p> <p>Data_inputs_CMIP5.Rdata and Data_trends_CMIP6.RData include all data used to produce global maps of percentage change in phytoplankton biomass, zooplankton biomass, net primary production and sea surface temperature. </p> <p>Data_trends_inputs_CMIP5.Rdata and Data_trends_CMIP6.RData include all data used to produce temporal trends of percentage change in phytoplankton biomass, zooplankton biomass, net primary production and sea surface temperature.</p> <p>The suffix _reducedModelSet refers to the case when only the subset of Fish-MIP models in Lotze et al. (2019) - Global ensemble projections reveal trophic amplification of ocean biomass declines with climate change, PNAS, DOI: https://doi.org/10.1073/pnas.1900194116 - are considered. This data was used to produce some of the supplementary figures in Tittensor et al. (2021).</p> <p>Please contact Derek Tittensor (derek.tittensor@dal.ca), Camilla Novaglio (camilla.novaglio@gmail.com), or Julia Blanchard (julia.blanchard@utas.edu.au) for data interpretation and use. </p>
CMIP6 derived ensemble of global vapor pressure deficit, potential evapotranspiration, and reference evapotranspiration
<p>Climate change induced trends in long-term aridity—via changes to atmospheric water demand for have the potential to impact surface water availability across the globe by altering efficiency by which precipitation is converted to runoff. Quantification of aridity requires estimates of evaporative demand, often using vapor pressure deficit, potential evapotranspiration, and/or reference evapotranspiration, but no comprehensive estimate of these climate variables exists to date from the Coupled Model Intercomparison Project 6 (CMIP6). Here we present global monthly estimates of the Penman-Monteith short grass reference evapotranspiration, its advective and radiation components, Priestley-Taylor potential evapotranspiration, and vapor pressure deficit from 16 CMIP6 general circulation models (GCM) for the historical period and four future emission scenarios ranging from low to high projected emissions. The purpose of this dataset is to offer structured and well-documented estimates of historical and future projected evaporative demand derived from the state-of-the-science CMIP6 climate models for use in hydrologic and ecological analyses. We produce a single file for all monthly values of each variable for individual GCM/emission scenario combination gridded at the given GCMs native resolution. Produced alongside all of the files are descriptions of each of the variables and generated python scripts that contain the functions used to estimate vapor pressure deficit, potential evapotranspiration, and reference evapotranspiration.</p>
Animations of Tropospheric Signatures Preceding and Following Sudden Stratospheric Warmings in Extended-Range Ensemble Forecasts
<p>Animations of Tropospheric Signatures Preceding and Following Sudden Stratospheric Warmings in Extended-Range Ensemble Forecasts</p>
Anechoic and IR Convolution-based Auralization Data Compilation Ensemble (AIRCADE)
<p><strong>AIRCADE</strong> is a data-compilation ensemble, primarily intended to serve as a resource for researchers in the field of dereverberation, particularly for data-driven approaches. It comprises <strong><a href="https://zenodo.org/record/1188976#.ZDhNTHbMJPY">speech and song samples</a></strong>, together with <strong><a href="https://zenodo.org/record/3371780#.ZDhOC3bMJPZ">acoustic guitar sounds</a></strong>, with original annotations pertinent to emotion recognition and Music Information Retrieval (MIR). Moreover, it includes a selection of <strong><a href="https://www.openair.hosted.york.ac.uk/">Impulse Response (IR) samples</a></strong> with varying Reverberation Time (RT) values, providing a wide range of conditions for evaluation. This data-compilation can be used together with provided Python scripts (available on <strong><a href="http://github.com/TulioChiodi/AIRCADE">GitHub</a></strong>), for generating auralized data ensembles in different sizes: <em>tiny</em>, <em>small</em>, <em>medium</em> and <em>large</em>. Additionally, the provided metadata annotations also allow for further analysis and investigation of the performance of dereverberation algorithms under different conditions. All data is licensed under <strong><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode">Creative Commons Attribution 4.0 International License</a></strong>.</p> <p><strong>About the sizeable versions:</strong></p> <p>The data-compilation is hosted here at <strong><a href="https://zenodo.org/record/7818761#.ZD7ON3bMJPa">Zenodo</a></strong>, with an approximate total file size of 1.3 GB. For simplicity, all samples in our data-compilation were renamed, e.g., <em>guitar_0000</em>, <em>rir_0000</em>, <em>song_0000</em>, <em>speech_0000</em>, and so on. The ensemble versions are available in different sizes, from a <em>tiny</em> version, with limited data, to a <em>large</em> version, with almost 300,000 samples. This allows users to choose the most suitable version for their specific research needs. The following table illustrates the differences between all versions, detailing the number of song, speech, guitar, IR and auralized samples in each one, together with their respective total file size and duration.</p> <table align="center"> <caption>Number of anechoic, IR and resultant auralized data samples, together with their respective total duration and file size for each ensemble version</caption> <tbody> <tr> <td><strong>Version</strong></td> <td><strong>Tiny</strong></td> <td><strong>Small</strong></td> <td><strong>Medium</strong></td> <td><strong>Large</strong></td> </tr> <tr> <td>Song samples</td> <td>100</td> <td>500</td> <td>1,012</td> <td>1,012</td> </tr> <tr> <td>Speech samples</td> <td>100</td> <td>500</td> <td>1,012</td> <td>1,440</td> </tr> <tr> <td>Guitar samples</td> <td>100</td> <td>500</td> <td>1,012</td> <td>2,004</td> </tr> <tr> <td>IR samples</td> <td>5</td> <td>9</td> <td>33</td> <td>65</td> </tr> <tr> <td>Auralized samples</td> <td>1,500</td> <td>13,500</td> <td>100,188</td> <td>289,640</td> </tr> <tr> <td>Total duration</td> <td>3.2 h</td> <td>30.41 h</td> <td>221.77 h</td> <td>658.08 h</td> </tr> <tr> <td>Total file size (required)</td> <td>1.1 GB</td> <td>10.5 GB</td> <td>76.6 GB</td> <td>227.5 GB</td> </tr> </tbody> </table> <p>For more information, please refer to our data paper on <strong><a href="https://arxiv.org/abs/2304.09318">ArXiv</a></strong>.</p> <p><strong>Citation</strong>:</p> <p>If you find <strong>AIRCADE </strong>useful in your research, please cite:</p> <blockquote> <pre>@misc{chiodi2023aircade, title={AIRCADE: an Anechoic and IR Convolution-based Auralization Data-compilation Ensemble}, author={Túlio Chiodi and Arthur dos Santos and Pedro Martins and Bruno Masiero}, year={2023}, eprint={2304.09318}, archivePrefix={arXiv}, primaryClass={eess.AS} }</pre> </blockquote> <p><strong>Acknowledgement</strong>:</p> <p>This work was partially supported by the <strong><a href="https://fapesp.br/">São Paulo Research Foundation (FAPESP)</a></strong>, grants #2017/08120-6 and #2019/22795-1.</p>
Supporting data for the publication "Emission ensemble approach to improve the development of multi-scale emission inventories"
<p>This dataset includes the source IDL code as well as the three emission inventory aggregated emission datasets necessary to perform the analysis presented in the publication: "Emission ensemble approach to improve the development of multi-scale emission inventories (GMD)"</p>
Flaring Latitudes in Ensembles of Low Mass Stars
<p>Simulated data, and aggregated results necessary to recreate the results and figures in the forthcoming paper, Ilin et al. 2023: "Flaring Latitudes in Ensembles of Low Mass Stars" (link TBD). The double timestamped files are simulated mean and standard deviation values for ensembles of randomly oriented stars, split into the training and the validation data sets. Their configurations are listed in the _all_runs file. You can find the aggregated mean and standard deviation values in _all_means_std. The flare parameter fits from Fig. 1 are under _a_from_ed and fwhm_from_ed_a. The fit parameters from Table 2 in the paper are in fit_paratemers. The results for the Okamoto et al. (2021) G dwarf flares are in okamoto2021_table.</p>
Arabic news credibility on Twitter using sentiment analysis and ensemble learning
<p>Arabic news credibility on Twitter using sentiment analysis and ensemble learning.</p> <p> </p> <p>WHAT IS IT?</p> <p>-----------</p> <p>an Arabic news credibility model on Twitter using sentiment analysis and ensemble learning.</p> <p>Here we include the Collected dataset and the source code of the proposed model written in Python language and using Keras library with Tensorflow backend.</p> <p> </p> <p>Required Packages</p> <p>------------------</p> <ol> <li>Keras (<a href="https://keras.io/">https://keras.io/</a>).</li> <li>Scikit-learn (<a href="http://scikit-learn.org/)">http://scikit-learn.org/)</a></li> <li>Imnlearn (<a href="https://imbalanced-learn.org/stable/">imbalanced-learn documentation — Version 0.10.1</a>)</li> </ol> <p> </p> <p> </p> <p>To Run the model</p> <p>---------------</p> <p>One data file is required to run the model which are:</p> <p> </p> <ol> <li>The data that were used are the collected dataset in the file, set the path of the required data file in the code.</li> </ol> <p> </p> <p>The dataset</p> <p>---------------</p> <ol> <li>There are the dataset file with all features, you can choose the features that you need and apply it on the model.</li> <li>There are a description file that describe each feature in the news credibility dataset</li> <li>The file Tweet_ID contains the list of tweets id in the dataset.</li> <li>The annotated replies based on credibility is provided.</li> </ol> <p> </p> <p> </p> <p> </p> <p> </p> <p>CONTACTS</p> <p>--------</p> <ul> <li>If you want to report bugs or have general queries email to <duha_atif@yahoo.com></li> </ul> <p> </p> <p> </p> <p> </p>
Extended ensemble molecular dynamics study of ammonia–cellulose I complex crystal models: free-energy landscape and atomistic pictures of ammonia diffusion in the crystalline phase.
<p>The data deposited here accompany the manuscript "Extended ensemble molecular dynamics study of ammonia–cellulose I complex crystal models: free-energy landscape and atomistic pictures of ammonia diffusion in the crystalline phase" and include the molecular dynamics trajectories and the AMBER topology (parm) files. Detailed file contents are summarized in the README file.</p>
Characterizing the Folding Transition State Ensembles in the Energy Landscape of an RNA Tetraloop - available data.
<p>Trajectory file and scripts necessary to generate an ELViM [Oliveira, A. B.; Yang, H.; Whitford, P. C.; Leite, V. B. P. JCTC, 2019, 15, p.6482] projection of the conformational space for the GCAA tetraloop.</p>
Data set for the ensemble postprocessing of 2m surface temperature forecasts in Germany for 24 hours lead time
<p>Full data set for the ensemble postprocessing of 2m surface temperature forecasts at 462 observation stations in Germany for 24 hours lead time in the years 2015-2020. The data set is provided in .Rdata format supported by the statistical software <a href="https://www.r-project.org">R</a>. The ensemble forecasts are retrieved from <a href="https://www.ecmwf.int">ECMWF</a> and the observation data from the <a href="https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/hourly/air_temperature/historical/BESCHREIBUNG_obsgermany_climate_hourly_tu_historical_de.pdf">German Weather Service</a> (<a href="https://www.dwd.de/">DWD</a>). <br> <br> For more information about the data set see: <a href="https://github.com/jobstdavid/paper_gamvinereg">https://github.com/jobstdavid/paper_gamvinereg</a></p>
Model output from Snow Ensemble Uncertainty Project (SEUP) as used in Seasonal Snow Predictability Derived from Early-Season Snow in North America
<p>The files provided here are the output from the median peak snow water equivalent (peak_SWE.mat), 1 December snow water equivalent (Dec1_SWE.mat), and 1 January snow water equivalent (Jan1_SWE.mat) model simulations for the Noah-MP run with MERRA-2 forcing, as used in Lundquist et al. (2023) and described in Kim et al. (2021). We also include the model grid's latitude, longitude and elevation data (SEUPlatlon.mat), and example code for plotting the data (Plotmodeldata.m) as in the Lundquist et al. (2023) paper. </p> <p>Kim, R. S., Kumar, S., Vuyovich, C., Houser, P., Lundquist, J., Mudryk, L., et al. (2021). Snow Ensemble Uncertainty Project (SEUP): Quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling. <em>The Cryosphere, 15</em>(2), 771-791.</p> <p>Lundquist, J. D., R. S. Kim, M. Durand, and L. R. Prugh, 2023, Seasonal Peak Snow Predictability Derived from Early-Season Snow in North America, Geophysical Research Letters, (submitted 2023)</p>
Data set for the ensemble postprocessing of 2m surface temperature forecasts in Germany for five different lead times
<p>Full data set for the ensemble postprocessing of 2m surface temperature forecasts at 462 observation stations in Germany for the lead times 24, 48, 72, 96 and 120 hours in the years 2015-2020. The data set is provided in .RData format supported by the statistical software <a href="https://www.r-project.org">R</a>. The ensemble forecasts are retrieved from <a href="https://www.ecmwf.int">ECMWF</a> and the observation data from the <a href="https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/hourly/air_temperature/historical/BESCHREIBUNG_obsgermany_climate_hourly_tu_historical_de.pdf">German Weather Service</a> (<a href="https://www.dwd.de/">DWD</a>). <br> <br> For more information about the data set see: <a href="https://github.com/jobstdavid/paper_tsEMOS">https://github.com/jobstdavid/paper_tsEMOS</a></p>
Ensemble Structure of the N-terminal Domain (1-267) of FUS in a Biomolecular Condensate
<p>Primary double electron-electron resonance (DEER) EPR data, restraint files, and weighted ensembles for the N-terminal domain (1-267) of the protein fused in sarcoma (FUS) in the denatured state (3 M urea), the dispersed state, and the condensed state after liquid-liquid phase separation. The dispersed and condensed state ensembles are based on measurements on the same biphasic samples. An unrestrained ensemble with 5000 conformers based on residue-specific Ramachandran angle distributions is included as well. Modelling was performed with MMMx (https://github.com/gjeschke/MMMx). </p>
PARALLEL G-QUADRUPLEX FOLDS VIA MULTIPLE PATHS INVOLVING G-TRACT STACKING AND STRUCTURING FROM COIL ENSEMBLE
<p>Data from all-atom molecular dynamics simulations of DNA G-quadruplex and various G-hairpins: input files (Gromacs and Amber), stripped trajectory files (reactive trajectories and reference replicas for G4, and reactive trajectories, and reference replicas for selected hairpin simulations), metadynamics bias files, and the ΔG_fold calculation protocol.</p>
Standard Error Estimates from ARRI ensemble GAM model outputs
<p><strong>Summary (Purpose)</strong></p> <p>Basal area per acre (BAA) standard error estimate (SEE) for all trees, pine trees, and non-pine trees across three diameter at breast height (DBH) size classes, 2- to 10-inch, 10- to 14-inch, and 14+ inch. Models were informed by relative density rasters from 2018 Light Detection and Ranging (lidar) point clouds.</p> <p><strong>Description</strong></p> <p>The GAM_SEE_rasters are modelled basal area per acre standard error estimate single band rasters. The units for the rasters’ are square foot per acre. Rasters are divided into three tree species groups: 'All' trees, 'Pine' trees (defined as trees of the genus<em> Pinus)</em>, and 'No-Pine' trees, and three size classes: LT 10 for trees with DBH between 2- and 10-inches, 10-14 for trees with DBH between 10- and 14- inch DBH, and GT 14 for trees with DBH greater than 14-inches.</p> <p>Ensemble generalized additive models (GAM) of estimated tree basal area per DBH class were created from Restore field plots and relative density canopy cover rasters, or RDCC (St. Peter, et al., 2023). This ensemble GAM model was created using the R script detailed in Hogland, 2021. The parameters used were 0.75 for the percent of data used to train the model (selected using random sampling with replacement), 50 models, and using gaussian family. The estimated BAA for each of the 50 ensemble GAM models for each cell were averaged (mean) to produce the GAM estimate, additionally the variability between these estimates was used to create the standard error estimate (SEE) for each cell. </p> <p> </p> <p>The 246 Restore field plots used to train the GAM model of basal area include measurements of all trees within four non-overlapping 9m radius circular subplots within a 36m square plot. Tree diameter at breast height (DBH), species, count, and condition measurements were recorded. Measurements were summarized to the plot and DBH (square inches) was converted to basal area per acre (square feet per acre) using the formula 0.005454 * DBH^2. Restore field plots were measured in the Spring of 2018. The RDCC metrics are 5m resolution multiband rasters produced by applying a custom r software function that uses the r software’s ‘lidR’ package to produce forest metrics summarized from Light Imaging Detection and Ranging (LiDAR) point clouds. ARSA LiDAR is a combination of three collections, Block 2 and 3 were collected in early 2018 and has a NPS of 0.7-m using a Riegl VQ-1560i lidar system. Leon county LiDAR data has a nominal pulse spacing (NPS) of 0.35-m and was acquired between February 05, 2018 and April 25, 2018 using the Leica ALS80 HP SN8137 and SN8235 lidar systems. Choctawhatchee data was acquired in early 2017, using the Riegl LMS-Q1560 lidar system and has a NPS of 0.7-m. Rasters were generated in their vendor provided spatial projection before being reprojected to UTM Zone 16.</p> <p> </p> <p>The 5m resolution RDCC bands were summarized to 40x40m to correspond with the area of our plots and used as predictor variables in the models of all trees basal area. Each 5m pixel value represents the estimated standard error of the basal area per acre estimate as if it was the center of a 40x40m (8x8 cells) plot surrounding that pixel. </p> <p> </p> <p>Reference:</p> <p>Hogland, J. (2021). Ensemble Generalized Additive Models (EGAM). Retrieved from Jupyter Notebook: <a href="https://colab.research.google.com/drive/1GnRagruTUCoPJQZSkZ2vMKS9aAKgnhEw?usp=sharing">https://colab.research.google.com/drive/1GnRagruTUCoPJQZSkZ2vMKS9aAKgnhEw?usp=sharing</a></p> <p>St. Peter, Joseph, Drake, Jason, Medley, Paul, & Ibeanusi, Victor. (2023). Relative Density Canopy Cover Outputs for Leon Lidar data in the Florida Panhandle 2018 [Data set]. In Remote Sensing (Vol. 13, Number 23, p. 4763). Zenodo. https://doi.org/10.5281/zenodo.8222114</p> <p><strong>Credits</strong></p> <p>This dataset was built by Joseph St. Peter of FAMU’s Center for Spatial Ecology and Restoration using 2018 LiDAR data funded by Leon County, Northwest Florida Water Management District, US Geological Survey and the USDA Forest Service and processed using the r package lidR. Restore plots were funded by the Gulf Coast Ecosystem Restoration Council (RESTORE Council) through an interagency agreement with the USDA Forest Service (17-IA-11083150-001) for the Apalachicola Tate’s Hell Strategy 1 project.</p> <p><strong>Use Limitations</strong></p> <p>This spatial data is based on various data collection and processing techniques as well as on modeling or interpretation. While this data uses the most current and complete information available at the time of production, spatial data and derivative products may vary in accuracy. Spatial data are often developed from sources of differing accuracy which may be accurate only at certain scales. This data has been quality checked but may contain spurious errors or be incomplete or inappropriate for certain uses. Spatial data products used for purposes other than those for which they were created, may yield inaccurate or misleading results. The USDA Forest Service, Florida A&M University, and the Center for Spatial Ecology & Restoration (CSER) reserves the right to correct, update, modify, remove or replace GIS products at any time and without notification. This data may not be distributed without written permission from the Center for Spatial Ecology & Restoration (CSER) at Florida A&M University, and/or the USDA Forest Service.</p>
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