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1,899 results for “Running”
Top topics from word2vec run of EFSA outputs
<p>List of neural word embeddings extracted from EFSAs outputs and their closest neighbours, which have a distance < 0.3 to the original word.</p> <p>These group of words form a congruent groups which for a human seems to represent a topic.</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Dry run for Eclipse data gathering. Method, Doppler and amplitude vs time
<p>The station here is setup to receive two frequencies simultaneously. SpectrumLab is setup to do simultaneous FFT calculations and tabulate Doppler Shift and signal amplitude data for each frequency. The radio used is a K3 in the CW mode with AGC off. The main and subrx of the K3 are identical and phase locked. The antenna is a multiband Vee antenna facing north averaging 15' high. FFT parameters are adjusted for a bin width of 0.186 Hz with the SL peak detection algorithm turned on to determine the peak frequency within the bin. 75% overlap. A data point is obtained every 1.3 seconds. Doppler shift is determined via the heterodyne method differencing the station pitch and the reference oscillator (offset in freq). Both oscillators are locked to a Rubidium standard.</p> <p>The attached plots are the station setup, Doppler shifts for CHU 14670 kHz and 15000 kHz, and the amplitudes of each. </p> <p> </p>
Simulated Detection and Localization in LIGO's HL-Configuration for O4a Run, and LIGO/Virgo/KAGRA's HLVK-Configuration for O4b and O5 Runs (November 2023 edition)
<p>We have conducted a simulation of the <strong>HL</strong>-configuration deployed during the ongoing <strong>O4a</strong> run and the <strong>HLVK-</strong>configuration planned for <strong>O4b </strong>and O5, as outlined in the observation scenarios, which can also be accessed at the following <a href=" https://doi.org/10.5281/zenodo.7026209"> https://doi.org/10.5281/zenodo.7026209. </a></p>
Ice run on Oder River, 2021-02-19
<p>During on-site investigations along the Oder River in February 2021, a sudden ice run of about 20 km in length was captured using an UAV.</p>
Automatic message sequence chart creation from simulation run of the parametric colored Petri net model of the Chandy-Lamport algorithm with four processes
<p><span>The video shows the creation of the message sequence chart from a simulation run of the proposed parametric colored Petri net model of the Chandy-Lamport algorithm using the CPN tool with four constituting processes. The picture shows the resulting message sequence chart. </span></p> <p><strong><span>Message Sequence Chart of Parametric Model With 4 Processes via Automatic Simulation Run_SuppInfo.mp4</span></strong><span>: This video shows the automatic generation of a message sequence chart of the proposed parametric colored Petri net model of the Chandy-Lamport distributed global snapshot algorithm using the CPN tool version 4.0.0. The model's number of constituting processes is parametric and was set to four. The video was generated using the authors' updated CPN tool extension server. The automatic simulation run of the model has been used to create this video. The CPN tool randomly selects the enabled transition at each step in an automatic simulation run.</span></p> <p><strong><span>Picture of Message Sequence Chart of Parametric Model With 4 Processes_SuppInfo.png:</span></strong><span> This picture shows the automatically generated message sequence chart of the proposed parametric colored Petri net model of the Chandy-Lamport algorithm that is visible in the above video clip. The number of constituting processes was set to four. </span></p>
BATSRUS_Low_MA_Run_Bowers
<p>BATSRUS simulation of an ICME event at Mercury, described in the manuscript titled "MESSENGER Observations of a Possible Alfvén Wing at Mercury under Low-Alfvénic Mach Number Solar Wind Conditions Associated with an ICME Impact"</p>
GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run — Candidate data release
<p>This material is part of several data products associated with GWTC-3, the third Gravitational-Wave Transient Catalog from the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration, the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration, and the <a href="https://gwcenter.icrr.u-tokyo.ac.jp/en/">KAGRA</a> Collaboration. For more information, see the paper (<a href="https://dcc.ligo.org/LIGO-P2000318/public">dcc.ligo.org/LIGO-P2000318/public</a>), the related material linked from this page, and the GWTC-3 data release documentation (<a href="https://www.gw-openscience.org/GWTC-3/">www.gw-openscience.org/GWTC-3/</a>).</p> <p><strong>Candidate data release</strong></p> <p>Data associated with candidates in GWTC-3. These are gravitational-wave candidates from the the third observing run (O3) of the Advanced LIGO and Advanced Virgo detectors that pass a false alarm rate threshold of 2/day. We upload a tar file (search_data.tar.gz) containing all the data and a python notebook (GWTC3_search_data.ipynb) that provides a description on how to use the files contained in the dataset.</p> <p>Associated with each candidate are the search analysis results and a localization (assuming that the source is astrophysical). Four search analysis pipelines have been used: the templated-based <a href="https://lscsoft.docs.ligo.org/gstlal/">GstLAL</a>, <a href="https://doi.org/10.1088/1361-6382/abe913">MBTA</a> and <a href="https://pycbc.org/">PyCBC</a>, plus the template-free <a href="https://gwburst.gitlab.io/">cWB</a>. Localizations from the template-based pipelines are calculated using <a href="https://lscsoft.docs.ligo.org/ligo.skymap/bayestar/index.html">Bayestar</a>, while cWB candidates are calculated by cWB itself.</p> <p>This release is primarily composed of results from the second part of O3 (O3b), but also includes a subset of results from the first part (O3a). A similar release was made for the previous <a href="https://doi.org/10.5281/zenodo.5117761">GWTC-2.1</a> that contained candidates from the first part of O3 (O3a) from GstLAL, MBTA and PyCBC. We include updated probabilities of astrophysical origin for these candidates: each search analysis has a o3a_pastro directory that contains these results. Since GWTC-2.1 did not include cWB results, this release includes cWB O3a candidates in addition to O3b: the cWB directory contains a subdirectory called o3a_events that contains the O3a results.</p> <p>The probability of astrophysical origin is calculated assuming a compact binary coalescence source, which may not always be appropriate for the template-free cWB analysis.</p> <p> </p> <p>For more general background on gravitational-wave search analysis and sky maps, try the materials from a <a href="https://www.gw-openscience.org/workshops/">GW Open Data Workshop</a> or the <a href="https://doi.org/10.1088/1361-6382/ab685e">guide to LIGO–Virgo data analysis</a>.</p>
GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run — Glitch modelling for events
<p>This material is part of several data products associated with GWTC-3, the third Gravitational-Wave Transient Catalog from the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration, the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration, and the <a href="https://gwcenter.icrr.u-tokyo.ac.jp/en/">KAGRA</a> Collaboration. For more information, see the paper (<a href="https://dcc.ligo.org/LIGO-P2000318/public">dcc.ligo.org/LIGO-P2000318/public</a>), the related material linked from this page, and the GWTC-3 data release documentation (<a href="https://www.gw-openscience.org/GWTC-3/">www.gw-openscience.org/GWTC-3/</a>).</p> <p><strong>Glitch model for GWTC-3 events</strong></p> <p>Glitch model for events in the GWTC-3 catalog that used either <a href="https://git.ligo.org/lscsoft/bayeswave">BayesWave</a> glitch subtraction or linear noise subtraction.</p> <p>Each data file for events processed with BayesWave contain three channels:</p> <ol> <li>The calibrated strain data, including any glitches that are present,</li> <li>A model of the glitches, produced using the BayesWave algorithm,</li> <li>The calibrated data with the glitch model subtracted, used for parameter estimation.</li> </ol> <p>Each data file for events processed with linear noise subtraction contain one channel:</p> <ol> <li>The calibrated data with the glitch linearly subtracted, used for parameter estimation.</li> </ol> <p>LIGO Hanford data for events GW191109_010717, GW191113_071753, GW191127_050227, and GW191219_163120 was generated with BayesWave. The names and sample rates (in Hz) of the channels in these files are</p> <ol> <li>H1:DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01 16384</li> <li>H1:DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01_glitch 16384</li> <li>H1:DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01_T1700406_v4 16384</li> </ol> <p>LIGO Livingston data for events GW191109_010717, GW191219_16312, GW200105_162426, and GW200115_042309 was generated with BayesWave. The names and sample rates (in Hz) of the channels in these files are</p> <ol> <li>L1:DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01 16384</li> <li>L1:DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01_glitch 16384</li> <li>L1:DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01_T1700406_v4 16384</li> </ol> <p>Virgo data for event GW191105_143521 was generated with BayesWave. The names and sample rates (in Hz) of the channels in this file are</p> <ol> <li>V1:Hrec_hoft_16384Hz 16384</li> <li>V1:Hrec_hoft_16384Hz_glitch 16384</li> <li>V1:Hrec_hoft_16384Hz_T1700406_v4 16384</li> </ol> <p>LIGO Livingston data for event GW200129_065458 was generated with linear noise subtraction. The name and sample rate (in Hz) of the channel in this file is</p> <ol> <li>L1:DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01_P1800169_v4 16384</li> </ol> <p><strong>How to download all files from this page</strong></p> <p>If you would like to download all files on this page, we recommend <a href="https://gitlab.com/dvolgyes/zenodo_get">zenodo_get</a>:</p> <pre><code class="language-bash">pip install zenodo_get zenodo-get RECORD_ID_OR_DOI </code></pre> <p>where the record ID for the most recent version of this page is 5546679 and IDs for other versions can be found in the Versions section at the side of this page.</p> <p> </p> <p>For more general background on gravitational-wave data quality, try the materials from a <a href="https://www.gw-openscience.org/workshops/">GW Open Data Workshop</a> or the <a href="https://doi.org/10.1088/1361-6382/ab685e">guide to LIGO–Virgo data analysis</a>.</p>
Initial Files of Examples for Running Adaptive RiD
<p>The initial files of all examples for running adaptive RiD, including peptoid, chignolin and three cases in CASP13. The force filed files are from charmm36.</p>
GWTC-2.1: Deep Extended Catalog of Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run - Candidate Data Release
<p>Data associated with candidates in <a href="https://dcc.ligo.org/LIGO-P2100063/public">GWTC-2.1</a>. These are gravitational-wave candidates from the first half of the third observing run (O3a) of the Advanced LIGO and Virgo detectors that pass a false alarm rate threshold of 2/day. We upload a tar file (search_data_GWTC2p1.tar.gz) containing all the data and a python notebook (search_data.ipynb) which provides description on how to use the files contained in the dataset.</p>
Gravity Spy Volunteer Classifications of LIGO Glitches from Observing Runs O1, O2, O3a, and O3b
<p>This dataset contains machine learning and volunteer classifications from the <a href="https://www.zooniverse.org/projects/zooniverse/gravity-spy">Gravity Spy project</a>. It includes glitches from observing runs <a href="https://doi.org/10.7935/K57P8W9D">O1</a>, <a href="https://doi.org/10.7935/CA75-FM95">O2</a>, <a href="https://doi.org/10.7935/nfnt-hm34">O3a</a> and <a href="https://doi.org/10.7935/pr1e-j706">O3b</a> that received at least one classification from a registered volunteer in the project. It also indicates glitches that are nominally retired from the project using our default set of retirement parameters, which are described below. See more details in the <a href="https://ui.adsabs.harvard.edu/abs/2017CQGra..34f4003Z/abstract">Gravity Spy Methods paper</a>. </p> <p>When a particular subject in a citizen science project (in this case, glitches from the LIGO datastream) is deemed to be classified sufficiently it is "retired" from the project. For the Gravity Spy project, retirement depends on a combination of both volunteer and machine learning classifications, and a number of parameterizations affect how quickly glitches get retired. For this dataset, we use a default set of retirement parameters, the most important of which are: </p> <ol> <li>A glitches must be classified by at least 2 registered volunteers</li> <li>Based on both the initial machine learning classification and volunteer classifications, the glitch has more than a 90% probability of residing in a particular class</li> <li>Each volunteer classification (weighted by that volunteer's confusion matrix) contains a weight equal to the initial machine learning score when determining the final probability</li> </ol> <p>The choice of these and other parameterization will affect the accuracy of the retired dataset as well as the number of glitches that are retired, and will be explored in detail in an upcoming publication (Zevin et al. in prep). </p> <p>The dataset can be read in using e.g. Pandas: <br> ```<br> import pandas as pd<br> dataset = pd.read_hdf('<a href="https://zenodo.org/api/files/512bfa79-dfbc-4af0-b563-9fdd06edcb16/retired_fulldata_min2_max50_ret0p9.hdf5?versionId=7f568823-76d9-4452-8553-c1eee5993f81">retired_fulldata_min2_max50_ret0p9.hdf5</a>', key='image_db')<br> ```<br> Each row in the dataframe contains information about a particular glitch in the Gravity Spy dataset. </p> <p><strong>Description of series in dataframe</strong></p> <ul> <li>['1080Lines', '1400Ripples', 'Air_Compressor', 'Blip', 'Chirp', 'Extremely_Loud', 'Helix', 'Koi_Fish', 'Light_Modulation', 'Low_Frequency_Burst', 'Low_Frequency_Lines', 'No_Glitch', 'None_of_the_Above', 'Paired_Doves', 'Power_Line', 'Repeating_Blips', 'Scattered_Light', 'Scratchy', 'Tomte', 'Violin_Mode', 'Wandering_Line', 'Whistle'] <ul> <li>Machine learning scores for each glitch class in the trained model, which for a particular glitch will sum to unity</li> </ul> </li> <li>['ml_confidence', 'ml_label'] <ul> <li>Highest machine learning confidence score across all classes for a particular glitch, and the class associated with this score</li> </ul> </li> <li>['gravityspy_id', 'id'] <ul> <li>Unique identified for each glitch on the Zooniverse platform ('gravityspy_id') and in the Gravity Spy project ('id'), which can be used to link a particular glitch to the <a href="https://zenodo.org/record/5649212#.YfLNjVjMLzc">full Gravity Spy dataset</a> (which contains GPS times among many other descriptors)</li> </ul> </li> <li>['retired'] <ul> <li>Marks whether the glitch is retired using our default set of retirement parameters (1=retired, 0=not retired)</li> </ul> </li> <li>['Nclassifications'] <ul> <li>The total number of classifications performed by registered volunteers on this glitch</li> </ul> </li> <li>['final_score', 'final_label'] <ul> <li>The final score (weighted combination of machine learning and volunteer classifications) and the most probable type of glitch</li> </ul> </li> <li>['tracks'] <ul> <li>Array of classification weights that were added to each glitch category due to each volunteer's classification</li> </ul> </li> </ul> <p> </p> <p>```<br> For machine learning classifications on all glitches in O1, O2, O3a, and O3b, please see <a href="https://zenodo.org/record/5649212#.YfLNjVjMLzc">Gravity Spy Machine Learning Classifications</a> on Zenodo</p> <p>For the most recently uploaded training set used in Gravity Spy machine learning algorithms, please see <a href="https://zenodo.org/record/1486046#.YZfcar3MJqs">Gravity Spy Training Set</a> on Zenodo.</p> <p>For detailed information on the training set used for the original Gravity Spy machine learning paper, please see <a href="https://zenodo.org/record/1476156#.YZfchL3MJqs">Machine learning for Gravity Spy: Glitch classification and dataset</a> on Zenodo. </p>
Replication Package for ROSDiscover: Statically Detecting Run-Time Architecture Misconfigurations in Robotics Systems
<p><strong>Replication Package for ROSDiscover: Statically Detecting Run-Time Architecture Misconfigurations in Robotics Systems</strong></p> <p>This is the replication package for the paper, ROSDiscover: Statically Detecting Run-Time Architecture Misconfigurations in Robotics Systems, which has been accepted at the International Conference on Software Architecture (ICSA), 2021. A preprint of the paper is included in this replication package (paper.pdf).</p> <p>This artifact is archived on Zenodo with the following DOI: <a href="https://doi.org/10.5281/zenodo.5834633">https://doi.org/10.5281/zenodo.5834633</a></p> <p>The study associated with this artifact was carried out by the following investigators:</p> <ul> <li><a href="http://christimperley.co.uk">Christopher S. Timperley</a> (Carnegie Mellon University)</li> <li><a href="https://tobiasduerschmid.github.io">Tobias Dürschmid</a> (Carnegie Mellon University)</li> <li><a href="https://www.cs.cmu.edu/~schmerl">Bradley Schmerl</a> (Carnegie Mellon University)</li> <li><a href="http://www.cs.cmu.edu/~garlan">David Garlan</a> (Carnegie Mellon University)</li> <li><a href="https://clairelegoues.com">Claire Le Goues</a> (Carnegie Mellon University)</li> </ul> <p>If you have any questions regarding the research or the replication package, you should contact Christopher, Tobias, or Bradley.</p> <p><strong>Abstract</strong></p> <p>Robot systems are growing in importance and complexity. Ecosystems for robot software, such as the Robot Operating System (ROS), provide libraries of reusable software components that can be configured and composed into larger systems. To support compositionality, ROS uses late binding and architecture configuration via “launch files” that describe how to initialize the components in a system. However, late binding often leads to systems failing silently due to misconfiguration, for example by misrouting or dropping messages entirely.</p> <p>In this paper we present ROSDiscover, which statically recovers the run-time architecture of ROS systems to find such architecture misconfiguration bugs. First, ROSDiscover constructs component level architectural models (ports, parameters) from source code. Second, architecture configuration files are analyzed to compose the system from these component models and derive the connections in the system. Finally, the reconstructed architecture is checked against architectural rules described in first-order logic to identify potential misconfigurations.</p> <p>We present an evaluation of ROSDiscover on real world, off-the-shelf robotic systems, measuring the accuracy, effectiveness, and practicality of our approach. To that end, we collected the first data set of architecture configuration bugs in ROS from popular open-source systems and measure how effective our approach is for detecting configuration bugs in that set.</p>
The height-dependent delayed ionospheric response to solar EUV - artificial run
<p>These artificial runs use the TIE-GCM v2.0 model in its 2.5 × 2.5 configuration as the real condition model run. In order to converge to stable initial condition the TIE GCM runs for 30 days prior to the final simulation starting at 21 September 2010. This initial run was configured with default parameters. Both model runs start at 21 October 2010 and are calculated with default parameters except for the switched off auroral parameterization as well as the switched off high-latitude potential model. The influence of noise in solar activity is reduced by applying an artificial noise free sinusoidal time series for the F10.7 input.</p>
GEOS CCM free-running simulation data of the Pacific-Northwest pyrocumulonimbus Event-like aerosol injection, SWIRL selection
<p>Data from GEOS CCM free running simulation of the Pacific-Northwest pyrocumulonimbus Event. </p> <p>The simulations have been designed and performed by Sampa Das and Peter R. Colarco at the NASA Center for Climate Simulations; the computing resources supporting the simulations shown in this work were provided by the NASA High-End Computing (HEC) Program through the NASA Center for Climate Simulation (NCCS) at the Goddard Space Flight Center.</p> <p>The datasets stored here have been generated by Giorgio Doglioni starting from the whole results of the simulations.</p> <p>The files are in hdf5 format and their structure and content is described in the README file. </p> <p> </p>
DeepLabCut network trained to track mouse 'front' body parts during rotarod running (front-view)
<p>DeepLabCut (https://github.com/DeepLabCut/) (Mathis et al., 2018; Nath et al., 2019) was used for tracking body parts of mice in an open field arena or in the rotarod. DeepLabCut 2.1.8.2 (local version on Windows with CPU, using the GUI) and 2.1.10.2 (google colab to train the network) were used using default parameters and the pretrained resnet50 network with imgaug augmentation. Frames were extracted with the k-means method and outlier frames with the jump method. <em>Rotarod, front camera: </em>29 frames from 18 videos (10 fps) were extracted for a total of 520 labeled pictures. 12 body parts (left, right and mid snout, left/right top/bottom ears, left/right eyes, headmount, left/right foot), 4 corners of the rotarod and 4 points on the rotarod wheels were manually labeled and linked to each other using skeletons. A neural network was trained using these images for 225K iterations (train error: 1.58, test error: 1.63). 20 outlier frames were extracted from each video and relabeled. An additional 20 images from 20 new videos with different recording conditions were labeled. The network was then refined for 331K iterations (from scratch) (train error: 2.34, test error: 5.49). This process was repeated a second time when adding 20 new videos (400 frames) and the network trained to a final 402K (train error: 2.64, test error: 3.98). For this last batch, brightness/contrast were too low to detect body features; brightness/contrast were thus enhanced using custom-written Python scripts. Relevant videos were analyzed at each of the 3 steps, for a total of 152 videos.</p> <p><em>Used to analyze videos for a publication (Labouesse et al., Nature Communications 2023).</em></p> <p><em>Network not included in the final publication.</em></p>
DeepLabCut network trained to track mouse 'back' body parts during rotarod running (back-view)
<p>DeepLabCut (https://github.com/DeepLabCut/) (Mathis et al., 2018; Nath et al., 2019) was used for tracking body parts of mice in an open field arena or in the rotarod. DeepLabCut 2.1.8.2 (local version on Windows with CPU, using the GUI) and 2.1.10.2 (google colab to train the network) were used using default parameters and the pretrained resnet50 network with imgaug augmentation. Frames were extracted with the k-means method and outlier frames with the jump method. <em>Rotarod, back camera:</em> 20 images from 9 videos (10 fps) were extracted for a total of 180 labeled pictures. 5 body parts (2 paws, 2 ankles, tail base), 4 corners of the rotarod, 2 points on the rotarod wheels and 4 points in a flashing LED (indicating timestamps) were manually labeled. A neural network was trained using these images for 80K iterations. 20 images from 14 videos with different recording conditions were labeled. The network was trained to 200K iterations (from scratch) (train error: 3.00, test error: 3.75). Relevant videos were analyzed at each step, for a total of 152 videos.</p> <p><em>Used to analyze videos for a publication (Labouesse et al., Nature Communications 2023)</em></p>
GWTC-2.1: Deep Extended Catalog of Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run - Glitch modelling for events
<p>This material is part of several data products associated with GWTC-2.1, the deep extended catalog of compact binary coalescences observed by the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration and the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration during the first half of the third observing run. For further information, see the paper (<a href="https://dcc.ligo.org/LIGO-P2100063/public">dcc.ligo.org/LIGO-P2100063/public</a>), the related material linked from this page, and the GWTC-2.1 data release documentation (<a href="https://www.gw-openscience.org/GWTC-2.1/">www.gw-openscience.org/GWTC-2.1/</a>).</p> <p><strong>Glitch model for GWTC-2.1 events</strong></p> <p>Glitch model for events in the GWTC_2.1 catalog that used <a href="https://git.ligo.org/lscsoft/bayeswave">BayesWave</a> glitch subtraction. This includes LIGO Livingston Observatory (L1) data for the following events:</p> <ul> <li>GW190413_134308</li> <li>GW190425_081805</li> <li>GW190503_185404</li> <li>GW190513_205428</li> <li>GW190514_065416</li> <li>GW190701_203306</li> <li>GW190924_021846</li> </ul> <p>Each data file contains three channels:</p> <ol> <li>the calibrated strain data, including any glitches that are present,</li> <li>a model of the glitches, produced using the BayesWave algorithm,</li> <li>the calibrated data with the glitch model subtracted, used for parameter estimation</li> </ol> <p>For the L1 data for all events, these channels have the following names and sample rates (in Hz):</p> <ol> <li>L1:DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01 16384</li> <li>L1:DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01_glitch 16384</li> <li>L1:DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01_T1700406_v4 16384</li> </ol> <p><strong>How to download all files from this page</strong></p> <p>If you would like to download all files on this page we recommend <a href="https://gitlab.com/dvolgyes/zenodo_get">zenodo_get</a>:</p> <pre><code class="language-bash">pip install zenodo_get zenodo-get RECORD_ID_OR_DOI</code></pre> <p>where the record ID for the most recent version of this page is 6477075 and IDs for other versions can be found in the Versions section at the side of this page.</p> <p> </p> <p>For more general background on gravitational-wave data quality, try the materials from a <a href="https://www.gw-openscience.org/workshops/">GW Open Data Workshop</a> or the guide to <a href="https://doi.org/10.1088/1361-6382/ab685e">LIGO-Virgo data analysis</a>.</p>
Running the validation assistant tool on pesticide dossiers in IUCLID
<p>Before submitting a dossier, <strong>applicants</strong> are recommended to use the Validation Assistant tool to check the dossier is technically complete. It is important to resolve all validation assistant quality warnings as this will support the Admissibility Check of the RMS/EMS. The <strong>RMS/EMS</strong> should also run the validation assistant report before declaring the admissibility of a dossier to make sure the dossier is complete.</p> <p>If the report shows a business rule failure (anything starting with BR, e.g. BR_PPP_033) this will prevent the applicant from successfully submitting the dossier and therefore must be resolved.</p> <p>This demonstration is supporting RMS/EMS and applicants on how to run the validation assistant tool and extract the report. </p>
VisMetHack2022: Visualizing winds and surface variables from the ECMWF IFS 1-km nature run
<p><strong>Overview</strong></p> <p>This data collection was contributed to the <a href="https://events.ecmwf.int/event/305/">Visualisation Hackathon 2022</a> (#VisMetHack2022), in conjunction with the Using <a href="https://events.ecmwf.int/event/296/">ECMWF's Forecasts (UEF2022</a>) workshop.</p> <p>The European Center for Medium-Range Weather Forecasts (ECMWF) and the Oak Ridge National Laboratory (ORNL) are pleased to announce access to the data collection from global 1-km nature run (NR) simulations using the Integrated Forecast System (IFS) with explicit convection. We invite you to join us in exploring this precursor to a digital twin of the earth!</p> <p>The NR simulations reveal unprecedented detail of the earth’s atmosphere [1], and the then outgoing Editor-in-Chief of AGU JAMES commended the project as one of “stunning ambitions,” enabled by computational capacity at scale [2]. The project also won the <em>2020 HPCwire Readers Choice Award </em>for Best Use of HPC in Physical Sciences. </p> <p>A set of two NR seasonal simulations have been completed, one corresponding to the northern hemispheric winter months (NDJF) and the other for the North Atlantic tropical cyclone season (ASO). The project used the Summit supercomputer at the Oak Ridge Leadership Computing Facility (OLCF). The simulations were facilitated with an INCITE award from the US Department of Energy Office of Science. </p> <p>For the first seasonal run of four months (NDJF), the hydrostatic IFS model was initialized at 00Z on 1 November 2018. The NR for the TC season (AS) was initialized at 00Z on 1 August 2019. The NR simulations were constrained only by sea surface temperatures (SST) at the lower boundary. The IFS output was saved every 3 hours.</p> <p>After feedback and interest from the scientific community, the simulations were rerun for four specific extreme events, with output every 15 minutes. The special cases include a tropical cycle and three severe storm events over the continental USA.</p> <p><strong>NR Data for visualizing winds</strong></p> <p>A small subset from the 1-km IFS NR collection is make available for #VisMetHack22. This subset is extracted from the tropical cyclone area in the North Atlantic from the ASO simulations. The 912 model time steps correspond to 97935 to 111600 in minutes since the NR reference time 2019-08-01 00:00:00. The time increment is 15 minutes, corresponding to the output frequency.</p> <p>The following variables are provided for #VisMetHack2022:<br> </p> <table> <tbody> <tr> <td> <p>Short Name</p> </td> <td> <p>Parameter ID</p> </td> <td> <p>Units</p> </td> <td> <p>Long Name</p> </td> </tr> <tr> <td> <p>10u</p> </td> <td> <p>165</p> </td> <td> <p>m/s</p> </td> <td> <p>10 metre U wind component</p> </td> </tr> <tr> <td> <p>10v</p> </td> <td> <p>166</p> </td> <td> <p>m/s</p> </td> <td> <p>10 metre V wind component</p> </td> </tr> <tr> <td> <p>2t</p> </td> <td> <p>167</p> </td> <td> <p>K</p> </td> <td> <p>2 metre temperature</p> </td> </tr> <tr> <td> <p>i10fg</p> </td> <td> <p>228029</p> </td> <td> <p>m/s</p> </td> <td> <p>Instantaneous 10 metre wind gust</p> </td> </tr> <tr> <td> <p>msl</p> </td> <td> <p>151</p> </td> <td> <p>Pa</p> </td> <td> <p>Mean sea level pressure</p> </td> </tr> <tr> <td> <p>xtprate</p> </td> <td> <p>99999</p> </td> <td> <p>kg m**-2 s**-1</p> </td> <td> <p>Total instantaneous precipitation rate. Summation of convective and large scale rain and snowfall rates. </p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Data processing</strong></p> <ol> <li> <p>The native model output was in the form of data objects consisting of GRIB1/2 16-bit AEC compressed messages. The messages were extracted from the FDB database instances into one or more files.</p> </li> <li> <p>The files containing the GRIB messages were interpolated to 0.02 x 0.02 a regular latitude-longitude grid using ECMWF Meteorological Interpolation and Regridding (MIR), and then written out to files as GRIB messages.</p> </li> <li> <p>The MIR output files were extracted to the area of interest (AOI) from global fields, and converted to Netcdf-4 (NC).</p> </li> <li> <p>The metadata in NC4 files were selectively edited or added.</p> </li> <li> <p>Finally, the NC4 files were compressed to reduce volume using the ncks utility from Netcdf Operators (NCO), with lossless L1 compression.</p> </li> <li> <p>The variable ‘xtprate’ was calculated by a summation of instantaneous and large scape rainfall and snowfall rates.</p> </li> </ol> <p><strong>Contact</strong></p> <p>Valentine Anantharaj <<a href="mailto:vga@ornl.gov">vga@ornl.gov</a>> or <vga1.ornl@gmail.com> </p> <p>Samuel Hatfield <<a href="mailto:Samuel.Hatfield@ecmwf.int">Samuel.Hatfield@ecmwf.int</a>></p> <p> </p> <p><strong>Citation and references</strong></p> <p>Please cite the following manuscript as well as the DOI provided by Zenodo:</p> <p>[1] Wedi, N. P., Polichtchouk, I., Dueben, P., Anantharaj, V. G., Bauer, P., Boussetta, S., et al. (2020). A baseline for global weather and climate simulations at 1 km resolution. Journal of Advances in Modeling Earth Systems, 12, e2020MS002192. <a href="https://doi.org/10.1029/2020MS002192">https://doi.org/10.1029/2020MS002192</a></p> <p>[2] Anantharaj, V., Hatfield, S. and Vukovic, Milana (2022). VisMetHack2022: Visualizing winds and surface variables from the ECMWF IFS 1-km nature run. https://doi.org/10.5281/zenodo.6633929</p> <p><strong>Acknowledgements</strong></p> <p>This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725.</p> <p>ECMWF also benefited from collaborations funded via ESCAPE-2 (No. 800897), MAESTRO (No. 801101), EuroEXA (No. 754337), and ESiWACE-2 (No. 823988) projects funded by the European Union's Horizon 2020 future and emerging technologies and the research and innovation programmes. </p>
LEMMIv2 config files for instances and runs, July 2022
<p>All yaml configuration files to replicate the content of the https://lemmi.ezlab.org benchmark using the standalone app. See the README.md file.</p>
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