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1,899 results for “Running”
Data and list of commands required to run the Mefite d'Ansanto gas dispersion case with VIGIL v1.3.7
<p>This dataset include input file to run a probabilistic hazard assessment of CO2 dispersion in the Mefite d'Ansanto area, Southern Italy. </p> <p>Alongside the input file, pre-retrieved meteorological data for the 1000 simulations run in this study are available in the folder "simulations". These can be used to reproduce the results presented in the manuscript [EDIT] by means of the VIGIL commands listed in the file "commands.txt". In this case, after the simulations.tar.gz is decompressed into the folder "simulations", the user can avoid running weather.py (see the first command in commands.txt) and should not modify nor cancel the file days_list.txt. A completely new run is instead performed if all the VIGIL scripts are executed. </p> <p>The archive "selected_outputs.tar" contains all the statistical outputs (ECDF and persistence) of the Mefite hazard study in .grd format and the related plots as produced by VIGIL. Part of these outputs have been used to produce the figures of the manuscript. </p>
Ozone 3D field from IRS OSSE Control run - June 2019
<p>In the frame of Vittorioso's PhD, an Observing System Simulation Experiment has bee set up to evaluate the future benefit of the geostationary IRS sounder on ozone field over Europe. The full set-up is composed of a Nature Run (reality), a Control Run (no assimilation) and an Assimilation Run (assimilation or IRS) over the months June to August 2019.<br>This dataset is Ozone 3D field from the control run for June 2019. One file per day including hourly fields, gathered in 3 tar files.</p>
Ozone 3D field from IRS assimilation run - July 2019
<p>In the frame of Vittorioso's PhD, an Observing System Simulation Experiment has bee set up to evaluate the future benefit of the geostationary IRS sounder on ozone field over Europe. The full set-up is composed of a Nature Run (reality), a Control Run (no assimilation) and an Assimilation Run (assimilation or IRS) over the months June to August 2019.<br>This dataset is Ozone 3D field from the assimilation run for July 2019. One file per day including hourly field, all days in one tar file.</p>
Ozone 3D field from IRS OSSE Control run - July 2019
<p>In the frame of Vittorioso's PhD, an Observing System Simulation Experiment has bee set up to evaluate the future benefit of the geostationary IRS sounder on ozone field over Europe. The full set-up is composed of a Nature Run (reality), a Control Run (no assimilation) and an Assimilation Run (assimilation or IRS) over the months June to August 2019.<br>This dataset is Ozone 3D field from the control run for July 2019. One file per day including hourly fields, gathered in 3 tar files.</p>
Ozone 3D field from IRS assimilation run - August 2019
<p>In the frame of Vittorioso's PhD, an Observing System Simulation Experiment has bee set up to evaluate the future benefit of the geostationary IRS sounder on ozone field over Europe. The full set-up is composed of a Nature Run (reality), a Control Run (no assimilation) and an Assimilation Run (assimilation or IRS) over the months June to August 2019.<br>This dataset is Ozone 3D field from the assimilation run for August 2019. One file per day including hourly field, distributed in 3 tar files.</p>
Ozone 3D field from IRS OSSE Control run - August 2019
<p>In the frame of Vittorioso's PhD, an Observing System Simulation Experiment has bee set up to evaluate the future benefit of the geostationary IRS sounder on ozone field over Europe. The full set-up is composed of a Nature Run (reality), a Control Run (no assimilation) and an Assimilation Run (assimilation or IRS) over the months June to August 2019.<br>This dataset is Ozone 3D field from the control run for August 2019. One file per day including hourly fields, gathered in 3 tar files.</p>
Ozone 3D field from IRS OSSE Nature run - June 2019
<p>In the frame of Vittorioso's PhD, an Observing System Simulation Experiment has bee set up to evaluate the future benefit of the geostationary IRS sounder on ozone field over Europe. The full set-up is composed of a Nature Run (reality), a Control Run (no assimilation) and an Assimilation Run (assimilation or IRS) over the months June to August 2019.<br>This dataset is Ozone 3D field from the nature run for June 2019. One file per day including hourly fields, gathered in 3 tar files.</p>
Ozone 3D field from IRS OSSE Nature run - August 2019
<p>In the frame of Vittorioso's PhD, an Observing System Simulation Experiment has bee set up to evaluate the future benefit of the geostationary IRS sounder on ozone field over Europe. The full set-up is composed of a Nature Run (reality), a Control Run (no assimilation) and an Assimilation Run (assimilation or IRS) over the months June to August 2019.<br>This dataset is Ozone 3D field from the nature run for August 2019. One file per day including hourly fields, gathered in 3 tar files.</p>
Ozone 3D field from IRS OSSE Nature run - July 2019
<p>In the frame of Vittorioso's PhD, an Observing System Simulation Experiment has bee set up to evaluate the future benefit of the geostationary IRS sounder on ozone field over Europe. The full set-up is composed of a Nature Run (reality), a Control Run (no assimilation) and an Assimilation Run (assimilation or IRS) over the months June to August 2019.<br>This dataset is Ozone 3D field from the nature run for July 2019. One file per day including hourly fields, gathered in 3 tar files.</p>
Model runs - Reconciling the EU forest, biodiversity, and climate strategies
<p>These are the model runs for the paper "Reconciling the EU forest, biodiversity, and climate strategies" published in Global Change Biology in 2024, by Gregor et al.</p> <p>The data can be used to re-run the optimizations.</p>
Example data for running wrmXpress modules
<p>Example/test datasets for wrmXpress</p> <ul> <li>Motility (96-well plate, individual wells) - <em>Brugia malayi</em> microfilaria: 20210819-p01-NJW_753</li> <li>Motility (96-well plate, whole plate) - <em>Brugia pahangi</em> adults: 20220622-p02-KTR</li> <li>Motility (24-well plate, whole plate) - <em>Brugia pahangi</em> adults: 20220527-p02-KTR</li> <li>Feeding - <em>Caenorhabditis elegans</em> adults: 20210823-p01-KJG_795</li> <li>Worm size - <em>Caenorhabditis elegans</em>: 20220408-p01-MGC_1351</li> <li>Viability (CellTox) - <em>Brugia malayi</em> microfilaria: 20210917-p15-NJW_913</li> <li>Fecundity (with adults) - <em>Schistosoma mansoni</em>: 20220722-p04-JDC_1606</li> <li>Fecundity (without adults) - <em>Schistosoma mansoni</em>: 20220722-p06-JDC_1608</li> <li>Fecundity - <em>Brugia pahangi</em>: 20210906-p01-NJW_857</li> <li>Tracking - <em>Schistosoma mansoni</em> miracidia: 20240307-p01-RVH</li> </ul>
WRF Model Output for the 3 km grid of the Hurricane Nature Run, days 08-07-2005 through 08-09-2005
<p>This is WRF model output for the Hurricane Nature Run presented in Nolan et al. (2013). The tar file contains gzipped netcdf files each with 6 model outputs at 30 minute intervals.</p>
WRF Model Output for the 3 km grid of the Hurricane Nature Run, days 08-04-2005 to 08-06-2005
<p>This is WRF model output for the Hurricane Nature Run presented in Nolan et al. (2013). The tar file contains gzipped netcdf files each with 6 model outputs at 30 minute intervals.</p>
Dataset from the paper "The boring history of GAIA BH3 from isolated binary evolution": I - initial conditions & run scripts
<p>This repository contains the initial condition files and the run scripts to reproduce the results in <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240417568I/abstract" target="_blank" rel="noopener">Iorio et al., 2024</a>.</p> <h2>Content</h2> <h3>Files XXX.ic</h3> <p>Ascii files containing the initial conditions of 2e7 binary systems. The files do not contain a header, and the columns report (from left to right):</p> <ol> <li>Mass of the primary [Msun]</li> <li>Absolute metallicity of the primary </li> <li>Initial spin (angular velocity over critical angular velocity)</li> <li>Supernova model for the primary</li> <li>Initial age of the star (Myr or special keywords)</li> <li>Mass of the secondary [Msun]</li> <li>Absolute metallicity of the secondary</li> <li>Initial spin (angular velocity over critical angular velocity)</li> <li>Supernova model for the secondary</li> <li>Initial age of the star (Myr or special keywords)</li> <li>Initial semimajor axis [Rsun]</li> <li>Initial Eccentricity </li> <li>Ending time or condition of the simulation [Myr or special keyword]</li> <li>Output frequency [Myr or special]</li> <li>Random seed</li> </ol> <p>Some columns contains the special placeorder keyword XXX. In this case, the values are undefined and they are set at runtime using the value on the runscripts. For additional information see the SEVN <a href="https://gitlab.com/sevncodes/sevn/-/blob/a4753f1177aab076da2641afc497a06006b30267/resources/SEVN_userguide.pdf">Userguide</a>.</p> <p>The initial conditions are:</p> <ul> <li><a href="../api/records/11617742/draft/files/SEVNIC20M_qmin0005.in/content" target="_blank" rel="noopener noreferrer">SEVNIC20M_qmin0005.in,</a> initial conditions used in all the simuations of <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240417568I/abstract" target="_blank" rel="noopener">Iorio et al., 2024</a> except for the model fiducial_ICthermal (see Table 1) e partially for the model fiducial2E8 (see below).</li> <li><a href="../api/records/11617742/draft/files/SEVNIC20M_qmin0005_ethermal.in/content" target="_blank" rel="noopener noreferrer">SEVNIC20M_qmin0005_ethermal.in</a>, initial conditions for the simulation set fiducial_ICthermal</li> <li>SEVNIC20M_X.in (where X ranges from 1 to 9), initial conditions (split in different chuncks) used for the simulation set fiducial2e8. This set is also complemetned by the <a href="../api/records/11617742/draft/files/SEVNIC20M_qmin0005.in/content" target="_blank" rel="noopener noreferrer">SEVNIC20M_qmin0005.in</a> initial conditions. </li> </ul> <h3>Runscripts</h3> <p>The runscripts are contained in the archive runscripts.zip. See the README in the archive for additional information. </p> <h2>Notes</h2> <p>The code used in <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240417568I/abstract" target="_blank" rel="noopener">Iorio et al., 2024</a> is SEVN (<a href="https://ui.adsabs.harvard.edu/abs/2023MNRAS.524..426I/abstract">Iorio et al., 2023</a>) version 2.10.1 (commit <a href="https://gitlab.com/sevncodes/sevn/-/tree/a4753f1177aab076da2641afc497a06006b30267">a4753f11</a>). </p>
Results of Bet-and-Run Strategies with Different Decision Makers on the Traveling Salesman Problem and the Minimum Vertex Cover Problem
<p><em><strong>Results of Bet-and-Run Strategies with Different Decision Makers on the Traveling Salesman Problem and the Minimum Vertex Cover Problem</strong></em></p> <p><strong>1. Introduction</strong></p> <p>In this repository, we provide the implementation and results of an improved generic Bet-and-Run strategy for black-box optimization.<br> The goal our new Bet-and-Run method is to obtain the best possible results within a given time budget <em>T</em> using a given black-box optimization algorithm.<br> If no prior knowledge about problem features and algorithm behavior is available, the question about how to use the time budget most efficiently arises. We propose to first start <em>n>=1</em> independent runs of the algorithm during an initialization budget <em>T1<T</em>, pausing these runs, then apply a decision maker <em>D</em> to choose <em>1<=m<n</em> runs from them (consuming <em>T2>=0</em> time units in doing so), and then continuing these runs for the remaining <em>T3=T-T1-T2</em> time units.</p> <p>In previous bet-and-run strategies, the decision maker <em>currentBest</em> would simply select the run with the best-so-far results at negligible time.<br> We propose using more advanced methods and test several different approaches, including neural networks trained or polynomials fitted on the current trace of the algorithm to predict which run may yield the best results if granted the remaining budget.<br> Applying this implementation to run "virtual experiments," one can find that this approach can yield better results than the previous methods, but also find that the `currentBest` method is a very reliable and robust baseline approach.</p> <p>Here you can find the results of such experiments on the Traveling Salesman Problem and the Minimum Vertex Cover Problem. Both betAndRun_tsp.tar.xz and betAndRun_vertex_cover.tar.xz are extracted in the scale of 30 GiB of size.</p> <p><strong>2. Copyright</strong></p> <p>The source code in this repository is under MIT License and is published in the most recent version at http://github.com/thomasWeise/betAndRun, while the results are under the Creative Commons Attribution 4.0 License.</p> <p>The code on bet-and-run (mainly under cn.edu.hfuu.iao.betAndRun) is jointly developed by Dr. Thomas Weise (http://iao.hfuu.edu.cn), tweise@hfuu.edu.cn, tweise@gmx.de) and Dr. Markus Wagner (http://cs.adelaide.edu.au/~markus/, markus.wagner@adelaide.edu.au).</p> <p>The <em>jpack</em> (http://github.com/marmakoide/jpack) code for Artificial Neural Networks, Linear Algebra, and Evolution Strategies (e.g., CMA-ES) has originally been developed by Dr. Alexandre Devert (http://www.marmakoide.org, marmakoide@hotmail.fr, and http://github.com/marmakoide), who kindly granted us the permission to include it in our repository. The code published here is a slightly modified version of his code, but the copyright and authorship remains entirely with Dr. Devert, who provides it under the MIT license at GitHub under http://github.com/marmakoide/jpack. Please contact Dr. Devert for any questions, in particular regarding licensing and (re-)distribution.</p>
Μονή Θάρρι Moni Thari, Ρόδος Rodos. Spring and water channel running east of the church.
<p>Μονή Θάρρι Moni Thari, Ρόδος Rodos. Spring and water channel running east of the church.</p>
RAM-SCB Model Description Runs
<p>This archive contains data files from two simulations of the Ring current Atmosphere interactions<br> Model with Self Consistent magnetic field (RAM-SCB).</p> <p><br> These simulations were performed for, and used in, the paper<br> “Improved simulations of the inner magnetosphere during high geomagnetic activity with the<br> RAM-SCB model”, M.A. Engel, S.K. Morley, M.G. Henderson, V.K. Jordanova, J.R.<br> Woodroffe, R. Mahfuz<br> </p>
Database required to run pHash.
<p>This database contains minimum hash values based on 12,576 plasmids from RefSeq.</p>
Experimental runs data
<p>Pressure-temperture-time and Raman spectra data (txt files) of the experimental runs shown in the manuscript</p>
CWL run of Alignment Workflow (CWLProv 0.6.0 Research Object)
<p>This dataset folder is a <strong>CWLProv Research Object</strong> that captures the <a href="http://commonwl.org/">Common Workflow Language</a> execution provenance, see <a href="https://w3id.org/cwl/prov/0.6.0">CWLProv 0.6.0</a> or use the <a href="https://pypi.org/project/cwlprov/">cwlprov Python tool</a> to explore.</p> <p> </p> <p>The CWL <a href="https://w3id.org/cwl/view/git/923ae325fa3a6d3ee3871539c65afa06ac86ac54/aligner/sbg-alignment-cwl/topmed-alignment.cwl">alignment workflow</a> included in this case study is designed by <a href="https://github.com/DataBiosphere">Data Biosphere</a>. It adapts the alignment pipeline originally developed at Abecasis Lab, The University of Michigan. This <a href="https://github.com/FarahZKhan/topmed-workflows/tree/cwlprov_testing">workflow</a> is part of NIH Data Commons initiative and comprises of four stages.</p> <p>First step, <strong><em>Pre-align</em></strong><em>,</em> accepts a <em>Compressed Alignment Map</em> (CRAM) file (a compressed format for BAM files developed <em>by European Bioinformatics Institute</em> (EBI)) and human genome reference sequence as input and using underlying software utilities of <strong>SAMtools</strong> such as <em>view</em>, <em>sort</em> and <em>fixmate</em> returns a list of fastq files which can be used as input for the next step.</p> <p>The next step <strong><em>Align</em></strong> also accepts the human reference genome as input along with the output files from <em>Pre-align</em> and uses <strong>BWA-mem</strong> to generate aligned reads as BAM files. <strong>SAMBLASTER</strong> is used to mark duplicate reads and <strong>SAMtools</strong> view to convert read files from SAM to BAM format.</p> <p>The BAM files generated after <em>lign</em> are sorted with <strong><em>SAMtool sort</em></strong>'.</p> <p>Finally, these sorted alignment files are merged to produce single sorted BAM file using SAMtools merge in <strong><em>Post-align</em></strong> step.</p> <p> </p> <p><strong>Steps to reproduce</strong></p> <p>This analysis was run using a 16-core Linux cloud instance with 64GB RAM and pre-installed docker.</p> <ol> <li> <p>Install <strong>gsutils</strong><br> </p> <pre><code class="language-bash">export CLOUD_SDK_REPO="cloud-sdk-$(lsb_release -c -s)" echo "deb http://packages.cloud.google.com/apt $CLOUD_SDK_REPO main" | \ sudo tee -a /etc/apt/sources.list.d/google-cloud-sdk.list curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | \ sudo apt-key add - sudo apt-get update && sudo apt-get install google-cloud-sdk</code></pre> <p> </p> </li> <li> <p>Get the data and make the analysis environment ready:<br> </p> <pre><code class="language-bash">git clone https://github.com/FarahZKhan/topmed-workflows.git cd topmed-workflows git checkout cwlprov_testing cd aligner/sbg-alignment-cwl # this is a custom script download google bucket files from json files and create a local json # it needs gsutil to be installed though git clone https://github.com/DailyDreaming/fetch_gs_frm_json.git # Wait... this should download ~18Gb. python2.7 fetch_gs_frm_json/dl_gsfiles_frm_json.py topmed-alignment.sample.json </code></pre> <p> </p> </li> <li> <p>Run the following commands to create the CWLProv Research Object:</p> <pre><code class="language-bash">time cwltool --no-match-user --provenance alignmnentwf0.6.0 --tmp-outdir-prefix=/CWLProv_workflow_testing/intermediate_temp/temp --tmpdir-prefix=/CWLProv_workflow_testing/intermediate_temp/temp topmed-alignment.cwl topmed-alignment.sample.json.new zip -r alignment_0.6.0_linux.zip alignment_0.6.0_linux sha256sum alignment_0.6.0_linux.zip > alignment_0.6.0_linux.zip.sha25</code></pre> <p> </p> </li> </ol>
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.