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177 results for “CMS”
CMS 2011A Simulation | Pythia 6 QCD1800-inf | pT > 375 GeV | MOD HDF5 Format
<p>Simulated QCD jets from the <a href="http://doi.org/10.7483/OPENDATA.CMS.CX2X.J3KW">Simulated QCD 1800-<span class="math-tex">\(\infty\)</span> Dataset of the CMS 2011 Open Data</a> reprocessed into the MOD HDF5 format. Jets are provided at generator (truth) level in the GEN files and after GEANT4 detector simulation in the SIM files (which also contain associated GEN jets to facilitate studies involving both types of jets). Jets are selected from the hardest two anti-kT R=0.5 jets in events passing the Jet300 High Level Trigger (only relevant for SIM) and are required to have <span class="math-tex">\(p_T^\text{jet}>375\)</span> GeV, where <span class="math-tex">\(p_T^\text{jet}\)</span> includes a jet energy correction factor (again, only relevant for SIM). GEN jets contain truth-level particles with kinematic and PDG ID information, and SIM jets contain Particle Flow Candidates (PFCs) with kinematic, PDG ID, and vertex information. Additionally, jets have metadata describing their kinematics and provenance in the original CMS AOD files.</p> <p>For additional details about the dataset, please see the accompanying paper, Exploring the Space of Jets with CMS Open Data. There, jets were further restricted to have <span class="math-tex">\(|\eta^\text{jet}|<1.9\)</span> to ensure tracking coverage and (in the case of SIM) have "medium" quality to reject fake jets.</p> <p>The supported method for downloading, reading, and using this dataset is through the <a href="https://energyflow.network">EnergyFlow Python package</a>, which has additional documentation about how to read and use this and related datasets. Should any problems be encountered, please <a href="https://github.com/pkomiske/EnergyFlow/issues">submit an issue on GitHub</a>.</p> <p>For reference, the other corresponding datasets of simulated jets available on Zenodo are:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3341500">SIM/GEN QCD Jets 170-300 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341498">SIM/GEN QCD Jets 300-470 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341419">SIM/GEN QCD Jets 470-600 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3364139">SIM/GEN QCD Jets 600-800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341413">SIM/GEN QCD Jets 800-1000 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341502">SIM/GEN QCD Jets 1000-1400 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341770">SIM/GEN QCD Jets 1400-1800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341772">SIM/GEN QCD Jets 1800-<span class="math-tex">\(\infty\)</span> GeV</a></li> </ul> <p>There is an associated dataset of jets recorded by the CMS detector available on Zenodo:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3340205">CMS 2011A Jets, pT > 375 GeV</a></li> </ul>
CMS 2011A Simulation | Pythia 6 QCD 1400-1800 | pT > 375 GeV | MOD HDF5 Format
<p>Simulated QCD jets from the <a href="http://doi.org/10.7483/OPENDATA.CMS.RC9V.B5KX">Simulated QCD 1400-1800 Dataset of the CMS 2011 Open Data</a> reprocessed into the MOD HDF5 format. Jets are provided at generator (truth) level in the GEN files and after GEANT4 detector simulation in the SIM files (which also contain associated GEN jets to facilitate studies involving both types of jets). Jets are selected from the hardest two anti-kT R=0.5 jets in events passing the Jet300 High Level Trigger (only relevant for SIM) and are required to have <span class="math-tex">\(p_T^\text{jet}>375\)</span> GeV, where <span class="math-tex">\(p_T^\text{jet}\)</span> includes a jet energy correction factor (again, only relevant for SIM). GEN jets contain truth-level particles with kinematic and PDG ID information, and SIM jets contain Particle Flow Candidates (PFCs) with kinematic, PDG ID, and vertex information. Additionally, jets have metadata describing their kinematics and provenance in the original CMS AOD files.</p> <p>For additional details about the dataset, please see the accompanying paper, Exploring the Space of Jets with CMS Open Data. There, jets were further restricted to have <span class="math-tex">\(|\eta^\text{jet}|<1.9\)</span> to ensure tracking coverage and (in the case of SIM) have "medium" quality to reject fake jets.</p> <p>The supported method for downloading, reading, and using this dataset is through the <a href="https://energyflow.network">EnergyFlow Python package</a>, which has additional documentation about how to read and use this and related datasets. Should any problems be encountered, please <a href="https://github.com/pkomiske/EnergyFlow/issues">submit an issue on GitHub</a>.</p> <p>For reference, the other corresponding datasets of simulated jets available on Zenodo are:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3341500">SIM/GEN QCD Jets 170-300 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341498">SIM/GEN QCD Jets 300-470 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341419">SIM/GEN QCD Jets 470-600 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3364139">SIM/GEN QCD Jets 600-800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341413">SIM/GEN QCD Jets 800-1000 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341502">SIM/GEN QCD Jets 1000-1400 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341770">SIM/GEN QCD Jets 1400-1800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341772">SIM/GEN QCD Jets 1800-<span class="math-tex">\(\infty\)</span> GeV</a></li> </ul> <p>There is an associated dataset of jets recorded by the CMS detector available on Zenodo:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3340205">CMS 2011A Jets, pT > 375 GeV</a></li> </ul>
CMS 2011A Simulation | Pythia 6 QCD 1000-1400 | pT > 375 GeV | MOD HDF5 Format
<p>Simulated QCD jets from the <a href="http://doi.org/10.7483/OPENDATA.CMS.96U2.3YAH">Simulated QCD 1000-1400 Dataset of the CMS 2011 Open Data</a> reprocessed into the MOD HDF5 format. Jets are provided at generator (truth) level in the GEN files and after GEANT4 detector simulation in the SIM files (which also contain associated GEN jets to facilitate studies involving both types of jets). Jets are selected from the hardest two anti-kT R=0.5 jets in events passing the Jet300 High Level Trigger (only relevant for SIM) and are required to have <span class="math-tex">\(p_T^\text{jet}>375\)</span> GeV, where <span class="math-tex">\(p_T^\text{jet}\)</span> includes a jet energy correction factor (again, only relevant for SIM). GEN jets contain truth-level particles with kinematic and PDG ID information, and SIM jets contain Particle Flow Candidates (PFCs) with kinematic, PDG ID, and vertex information. Additionally, jets have metadata describing their kinematics and provenance in the original CMS AOD files.</p> <p>For additional details about the dataset, please see the accompanying paper, Exploring the Space of Jets with CMS Open Data. There, jets were further restricted to have <span class="math-tex">\(|\eta^\text{jet}|<1.9\)</span> to ensure tracking coverage and (in the case of SIM) have "medium" quality to reject fake jets.</p> <p>The supported method for downloading, reading, and using this dataset is through the <a href="https://energyflow.network">EnergyFlow Python package</a>, which has additional documentation about how to read and use this and related datasets. Should any problems be encountered, please <a href="https://github.com/pkomiske/EnergyFlow/issues">submit an issue on GitHub</a>.</p> <p>For reference, the other corresponding datasets of simulated jets available on Zenodo are:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3341500">SIM/GEN QCD Jets 170-300 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341498">SIM/GEN QCD Jets 300-470 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341419">SIM/GEN QCD Jets 470-600 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3364139">SIM/GEN QCD Jets 600-800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341413">SIM/GEN QCD Jets 800-1000 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341502">SIM/GEN QCD Jets 1000-1400 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341770">SIM/GEN QCD Jets 1400-1800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341772">SIM/GEN QCD Jets 1800-<span class="math-tex">\(\infty\)</span> GeV</a></li> </ul> <p>There is an associated dataset of jets recorded by the CMS detector available on Zenodo:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3340205">CMS 2011A Jets, pT > 375 GeV</a></li> </ul>
CMS 2011A Simulation | Pythia 6 QCD 800-1000 | pT > 375 GeV | MOD HDF5 Format
<p>Simulated QCD jets from the <a href="http://doi.org/10.7483/OPENDATA.CMS.S3D5.KF2C">Simulated QCD 800-1000 Dataset of the CMS 2011 Open Data</a> reprocessed into the MOD HDF5 format. Jets are provided at generator (truth) level in the GEN files and after GEANT4 detector simulation in the SIM files (which also contain associated GEN jets to facilitate studies involving both types of jets). Jets are selected from the hardest two anti-kT R=0.5 jets in events passing the Jet300 High Level Trigger (only relevant for SIM) and are required to have <span class="math-tex">\(p_T^\text{jet}>375\)</span> GeV, where <span class="math-tex">\(p_T^\text{jet}\)</span> includes a jet energy correction factor (again, only relevant for SIM). GEN jets contain truth-level particles with kinematic and PDG ID information, and SIM jets contain Particle Flow Candidates (PFCs) with kinematic, PDG ID, and vertex information. Additionally, jets have metadata describing their kinematics and provenance in the original CMS AOD files.</p> <p>For additional details about the dataset, please see the accompanying paper, Exploring the Space of Jets with CMS Open Data. There, jets were further restricted to have <span class="math-tex">\(|\eta^\text{jet}|<1.9\)</span> to ensure tracking coverage and (in the case of SIM) have "medium" quality to reject fake jets.</p> <p>The supported method for downloading, reading, and using this dataset is through the <a href="https://energyflow.network">EnergyFlow Python package</a>, which has additional documentation about how to read and use this and related datasets. Should any problems be encountered, please <a href="https://github.com/pkomiske/EnergyFlow/issues">submit an issue on GitHub</a>.</p> <p>For reference, the other corresponding datasets of simulated jets available on Zenodo are:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3341500">SIM/GEN QCD Jets 170-300 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341498">SIM/GEN QCD Jets 300-470 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341419">SIM/GEN QCD Jets 470-600 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3364139">SIM/GEN QCD Jets 600-800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341413">SIM/GEN QCD Jets 800-1000 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341502">SIM/GEN QCD Jets 1000-1400 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341770">SIM/GEN QCD Jets 1400-1800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341772">SIM/GEN QCD Jets 1800-<span class="math-tex">\(\infty\)</span> GeV</a></li> </ul> <p>There is an associated dataset of jets recorded by the CMS detector available on Zenodo:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3340205">CMS 2011A Jets, pT > 375 GeV</a></li> </ul>
CMS 2011A Simulation | Pythia 6 QCD 470-600 | pT > 375 GeV | MOD HDF5 Format
<p>Simulated QCD jets from the <a href="http://doi.org/10.7483/OPENDATA.CMS.BKTD.SGJX">Simulated QCD 470-600 Dataset of the CMS 2011 Open Data</a> reprocessed into the MOD HDF5 format. Jets are provided at generator (truth) level in the GEN files and after GEANT4 detector simulation in the SIM files (which also contain associated GEN jets to facilitate studies involving both types of jets). Jets are selected from the hardest two anti-kT R=0.5 jets in events passing the Jet300 High Level Trigger (only relevant for SIM) and are required to have <span class="math-tex">\(p_T^\text{jet}>375\)</span> GeV, where <span class="math-tex">\(p_T^\text{jet}\)</span> includes a jet energy correction factor (again, only relevant for SIM). GEN jets contain truth-level particles with kinematic and PDG ID information, and SIM jets contain Particle Flow Candidates (PFCs) with kinematic, PDG ID, and vertex information. Additionally, jets have metadata describing their kinematics and provenance in the original CMS AOD files.</p> <p>For additional details about the dataset, please see the accompanying paper, Exploring the Space of Jets with CMS Open Data. There, jets were further restricted to have <span class="math-tex">\(|\eta^\text{jet}|<1.9\)</span> to ensure tracking coverage and (in the case of SIM) have "medium" quality to reject fake jets.</p> <p>The supported method for downloading, reading, and using this dataset is through the <a href="https://energyflow.network">EnergyFlow Python package</a>, which has additional documentation about how to read and use this and related datasets. Should any problems be encountered, please <a href="https://github.com/pkomiske/EnergyFlow/issues">submit an issue on GitHub</a>.</p> <p>For reference, the other corresponding datasets of simulated jets available on Zenodo are:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3341500">SIM/GEN QCD Jets 170-300 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341498">SIM/GEN QCD Jets 300-470 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341419">SIM/GEN QCD Jets 470-600 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3364139">SIM/GEN QCD Jets 600-800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341413">SIM/GEN QCD Jets 800-1000 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341502">SIM/GEN QCD Jets 1000-1400 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341770">SIM/GEN QCD Jets 1400-1800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341772">SIM/GEN QCD Jets 1800-<span class="math-tex">\(\infty\)</span> GeV</a></li> </ul> <p>There is an associated dataset of jets recorded by the CMS detector available on Zenodo:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3340205">CMS 2011A Jets, pT > 375 GeV</a></li> </ul>
CMS 2011A Simulation | Pythia 6 QCD 300-470 | pT > 375 GeV | MOD HDF5 Format
<p>Simulated QCD jets from the <a href="http://doi.org/10.7483/OPENDATA.CMS.X3XQ.USQR">Simulated QCD 300-470 Dataset of the CMS 2011 Open Data</a> reprocessed into the MOD HDF5 format. Jets are provided at generator (truth) level in the GEN files and after GEANT4 detector simulation in the SIM files (which also contain associated GEN jets to facilitate studies involving both types of jets). Jets are selected from the hardest two anti-kT R=0.5 jets in events passing the Jet300 High Level Trigger (only relevant for SIM) and are required to have <span class="math-tex">\(p_T^\text{jet}>375\)</span> GeV, where <span class="math-tex">\(p_T^\text{jet}\)</span> includes a jet energy correction factor (again, only relevant for SIM). GEN jets contain truth-level particles with kinematic and PDG ID information, and SIM jets contain Particle Flow Candidates (PFCs) with kinematic, PDG ID, and vertex information. Additionally, jets have metadata describing their kinematics and provenance in the original CMS AOD files.</p> <p>For additional details about the dataset, please see the accompanying paper, Exploring the Space of Jets with CMS Open Data. There, jets were further restricted to have <span class="math-tex">\(|\eta^\text{jet}|<1.9\)</span> to ensure tracking coverage and (in the case of SIM) have "medium" quality to reject fake jets.</p> <p>The supported method for downloading, reading, and using this dataset is through the <a href="https://energyflow.network">EnergyFlow Python package</a>, which has additional documentation about how to read and use this and related datasets. Should any problems be encountered, please <a href="https://github.com/pkomiske/EnergyFlow/issues">submit an issue on GitHub</a>.</p> <p>For reference, the other corresponding datasets of simulated jets available on Zenodo are:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3341500">SIM/GEN QCD Jets 170-300 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341498">SIM/GEN QCD Jets 300-470 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341419">SIM/GEN QCD Jets 470-600 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3364139">SIM/GEN QCD Jets 600-800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341413">SIM/GEN QCD Jets 800-1000 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341502">SIM/GEN QCD Jets 1000-1400 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341770">SIM/GEN QCD Jets 1400-1800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341772">SIM/GEN QCD Jets 1800-<span class="math-tex">\(\infty\)</span> GeV</a></li> </ul> <p>There is an associated dataset of jets recorded by the CMS detector available on Zenodo:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3340205">CMS 2011A Jets, pT > 375 GeV</a></li> </ul>
CMS 2011A Simulation | Pythia 6 QCD 170-300 | pT > 375 GeV | MOD HDF5 Format
<p>Simulated QCD jets from the <a href="http://doi.org/10.7483/OPENDATA.CMS.WKRR.DCJP">Simulated QCD 170-300 Dataset of the CMS 2011 Open Data</a> reprocessed into the MOD HDF5 format. Jets are provided at generator (truth) level in the GEN files and after GEANT4 detector simulation in the SIM files (which also contain associated GEN jets to facilitate studies involving both types of jets). Jets are selected from the hardest two anti-kT R=0.5 jets in events passing the Jet300 High Level Trigger (only relevant for SIM) and are required to have <span class="math-tex">\(p_T^\text{jet}>375\)</span> GeV, where <span class="math-tex">\(p_T^\text{jet}\)</span> includes a jet energy correction factor (again, only relevant for SIM). GEN jets contain truth-level particles with kinematic and PDG ID information, and SIM jets contain Particle Flow Candidates (PFCs) with kinematic, PDG ID, and vertex information. Additionally, jets have metadata describing their kinematics and provenance in the original CMS AOD files.</p> <p>For additional details about the dataset, please see the accompanying paper, Exploring the Space of Jets with CMS Open Data. There, jets were further restricted to have <span class="math-tex">\(|\eta^\text{jet}|<1.9\)</span> to ensure tracking coverage and (in the case of SIM) have "medium" quality to reject fake jets.</p> <p>The supported method for downloading, reading, and using this dataset is through the <a href="https://energyflow.network">EnergyFlow Python package</a>, which has additional documentation about how to read and use this and related datasets. Should any problems be encountered, please <a href="https://github.com/pkomiske/EnergyFlow/issues">submit an issue on GitHub</a>.</p> <p>For reference, the other corresponding datasets of simulated jets available on Zenodo are:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3341500">SIM/GEN QCD Jets 170-300 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341498">SIM/GEN QCD Jets 300-470 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341419">SIM/GEN QCD Jets 470-600 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3364139">SIM/GEN QCD Jets 600-800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341413">SIM/GEN QCD Jets 800-1000 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341502">SIM/GEN QCD Jets 1000-1400 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341770">SIM/GEN QCD Jets 1400-1800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341772">SIM/GEN QCD Jets 1800-<span class="math-tex">\(\infty\)</span> GeV</a></li> </ul> <p>There is an associated dataset of jets recorded by the CMS detector available on Zenodo:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3340205">CMS 2011A Jets, pT > 375 GeV</a></li> </ul>
CMS 2011A Open Data | Jet Primary Dataset | pT > 375 GeV | MOD HDF5 Format
<p>A dataset of 1,785,625 jets from the <a href="http://doi.org/10.7483/OPENDATA.CMS.UP77.P6PQ">Jet Primary Dataset of the CMS 2011A Open Data</a> reprocessed into the MOD HDF5 format. Jets are selected from the hardest two anti-kT R=0.5 jets in events passing the Jet300 High Level Trigger and are required to have <span class="math-tex">\(p_T^\text{jet}>375\)</span> GeV, where <span class="math-tex">\(p_T^\text{jet}\)</span> includes a jet energy correction factor. Particle Flow Candidates (PFCs) for each jet are provided and include information about the PFC kinematics, PDG ID, and vertex. Additionally, jets have metadata describing their kinematics and provenance in the original CMS AOD files.</p> <p>For additional details about the dataset, please see the accompanying paper, Exploring the Space of Jets with CMS Open Data. There, jets were further restricted to have <span class="math-tex">\(|\eta^\text{jet}|<1.9\)</span> to ensure tracking coverage and have "medium" quality to reject fake jets.</p> <p>The supported method for downloading, reading, and using this dataset is through the <a href="https://energyflow.network">EnergyFlow Python package</a>, which has additional documentation about how to read and use this and related datasets. Should any problems be encountered, please <a href="https://github.com/pkomiske/EnergyFlow/issues">submit an issue on GitHub</a>.</p> <p>There are corresponding datasets of simulated jets organized by hard parton <span class="math-tex">\(\hat p_T\)</span> also available on Zenodo:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3341500">SIM/GEN QCD Jets 170-300 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341498">SIM/GEN QCD Jets 300-470 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341419">SIM/GEN QCD Jets 470-600 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3364139">SIM/GEN QCD Jets 600-800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341413">SIM/GEN QCD Jets 800-1000 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341502">SIM/GEN QCD Jets 1000-1400 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341770">SIM/GEN QCD Jets 1400-1800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341772">SIM/GEN QCD Jets 1800-<span class="math-tex">\(\infty\)</span> GeV</a></li> </ul>
CMS 2011A Simulation | Pythia 6 QCD 600-800 | pT > 375 GeV | MOD HDF5 Format
<p>Simulated QCD jets from the <a href="http://doi.org/10.7483/OPENDATA.CMS.EJT7.KSAY">Simulated QCD 600-800 Dataset of the CMS 2011 Open Data</a> reprocessed into the MOD HDF5 format. Jets are provided at generator (truth) level in the GEN files and after GEANT4 detector simulation in the SIM files (which also contain associated GEN jets to facilitate studies involving both types of jets). Jets are selected from the hardest two anti-kT R=0.5 jets in events passing the Jet300 High Level Trigger (only relevant for SIM) and are required to have <span class="math-tex">\(p_T^\text{jet}>375\)</span> GeV, where <span class="math-tex">\(p_T^\text{jet}\)</span> includes a jet energy correction factor (again, only relevant for SIM). GEN jets contain truth-level particles with kinematic and PDG ID information, and SIM jets contain Particle Flow Candidates (PFCs) with kinematic, PDG ID, and vertex information. Additionally, jets have metadata describing their kinematics and provenance in the original CMS AOD files.</p> <p>For additional details about the dataset, please see the accompanying paper, Exploring the Space of Jets with CMS Open Data. There, jets were further restricted to have <span class="math-tex">\(|\eta^\text{jet}|<1.9\)</span> to ensure tracking coverage and (in the case of SIM) have "medium" quality to reject fake jets.</p> <p>The supported method for downloading, reading, and using this dataset is through the <a href="https://energyflow.network">EnergyFlow Python package</a>, which has additional documentation about how to read and use this and related datasets. Should any problems be encountered, please <a href="https://github.com/pkomiske/EnergyFlow/issues">submit an issue on GitHub</a>.</p> <p>For reference, the other corresponding datasets of simulated jets available on Zenodo are:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3341500">SIM/GEN QCD Jets 170-300 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341498">SIM/GEN QCD Jets 300-470 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341419">SIM/GEN QCD Jets 470-600 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3364139">SIM/GEN QCD Jets 600-800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341413">SIM/GEN QCD Jets 800-1000 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341502">SIM/GEN QCD Jets 1000-1400 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341770">SIM/GEN QCD Jets 1400-1800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341772">SIM/GEN QCD Jets 1800-<span class="math-tex">\(\infty\)</span> GeV</a></li> </ul> <p>There is an associated dataset of jets recorded by the CMS detector available on Zenodo:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3340205">CMS 2011A Jets, pT > 375 GeV</a></li> </ul> <p>Changes:</p> <ul> <li>v1 - Uploaded missing file.</li> </ul>
CMS DoubleMuParked dataset from 2012 in simple little endian binary format
<p>The Muon.bin file contains in a binary data little-endian format the dataset from Ref [1]. This dataset contains about 60 millon data events from the CMS detector taken in 2012 during Run B and C.</p> <p>The file format is described in the companion file Muon.txt.</p> <p>[1] Wunsch, Stefan; (2019). DoubleMuParked dataset from 2012 in NanoAOD format reduced on muons. CERN Open Data Portal. DOI:<a href="http://doi.org/10.7483/OPENDATA.CMS.LVG5.QT81">10.7483/OPENDATA.CMS.LVG5.QT81</a></p>
OmniFold Weights | CMS 2011A Open Data | Jet Primary Dataset | pT 375-700 GeV
<p>Unfolding weights corresponding to a selection of jets from the <a href="https://doi.org/10.5281/zenodo.3340205">Jet Primary Dataset of the CMS 2011A Open Data in MOD HDF5 format</a> and associated simulated datasets. The unfolding is performed in a high-dimensional manner using the <a href="https://arxiv.org/abs/1911.09107">OmniFold</a> method, which can unfold all observables simultaneously. <a href="https://arxiv.org/abs/1810.05165">Particle Flow Networks</a> are used in Step 1 and Step 2 of the OmniFold method to process the full phase space information. The datasets and neural networks were accessed/built via the <a href="https://energyflow.network/">EnergyFlow Python package</a>. An upcoming version of the package will contain an example/demo demonstrating how to use these weights.</p> <p>The phase space selections for the data, sim, and gen datasets (using the terminology of the OmniFold paper) are:</p> <ul> <li>data: <span class="math-tex">\(p_T^{\rm jet}\in [375, 700]\)</span> GeV, <span class="math-tex">\(|\eta^{\rm jet}|<2.4\)</span>, jet quality <span class="math-tex">\(\ge\)</span> 2</li> <li>sim: <span class="math-tex">\(p_{T,\text{corr}}^{\rm jet} \in [375, 700]\)</span> GeV, <span class="math-tex">\(|\eta^{\rm jet}| < 2.4\)</span>, gen jet matched ('gen_jet_pts != -1' in EnergyFlow), jets from the <a href="https://doi.org/10.5281/zenodo.3341500">170</a> and <a href="https://doi.org/10.5281/zenodo.3341772">1800</a> MC datasets are excluded</li> <li>gen: Matched to sim jet</li> </ul> <p>The omnifold_weights.npz file contains two arrays, 'wssim' corresopnding to the Step 1 weights <span class="math-tex">\(\omega_n\)</span>, and 'wsgen' corresponding to the Step 2 weights <span class="math-tex">\(\nu_n\)</span>, for iteration <span class="math-tex">\(n\)</span>. The shape of each of these arrays is (6, 16489054), with the first axis being the iteration axis and the second axis being the event axis. There are 5 iterations, but 6 sets of weights in each array, with the 0th entry being the starting weights.</p>
CMS Open Data 2012 datasets for dimuon exercises
<p>These datasets are a subset of the CMS Open data with 2021 data-taking conditions for education purposes.</p> <p>In this version, the data and simulation files are compressed into one big file for easy access. They are stored in two different formats (CSV and PKL) with the same content, therefore just use one of them.</p> <p>Once unzipped:</p> <p>- Data files, starting with output_data_CMS_Run2012B, correspond to 4429.37 /pb of data collected by the CMS Experiment. They are a subset of the dataset on reference [1].</p> <p>- Simulation files, starting with output_sim_CMS_MonteCarlo2012, are a subset of the dataset referenced on [2]. The number of generated events in this case is 30458871, and the cross section is 3503.71.</p> <p>All the files were processed with a modified version of the AOD2NanoAODOutreachTool [3]. The small modifications are related to the number of triggers stored, and some objects like taus were removed.</p> <p> </p> <p>--------------------------------------------------------</p> <p>[1] CMS collaboration (2017). DoubleMuParked primary dataset in AOD format from Run of 2012 (/DoubleMuParked/Run2012B-22Jan2013-v1/AOD). CERN Open Data Portal. DOI:<a href="http://doi.org/10.7483/OPENDATA.CMS.YLIC.86ZZ">10.7483/OPENDATA.CMS.YLIC.86ZZ</a></p> <p>[2] Wunsch, Stefan; (2019). DYJetsToLL dataset in reduced NanoAOD format for education and outreach. CERN Open Data Portal. DOI:<a href="http://doi.org/10.7483/OPENDATA.CMS.SRRA.2GON">10.7483/OPENDATA.CMS.SRRA.2GON</a></p> <p>[3] https://github.com/cms-opendata-analyses/AOD2NanoAODOutreachTool</p>
Laser Response in ECAL Crystals in CMS Detector
<p>The dataset contains the Laser responses of the Lead-Tungstate crystals in the Electromagnetic Calorimeter (ECAL) of the CMS Experiment recorded during the Run 2 (2016-2018) of LHC running. The datasets consists of two tar folders: one corresponding to the "plus" side of the detector and one corresponding to the "minus" side. Each folder contains files in csv format, each file corresponding to the histories of all crystals in each "ieta" ring. The detailed description of the columns can be found under the section names "dataset" on Github pages at https://fair-umn.github.io/fair_ecal_monitoring.</p>
Growth and fitness measures of cytoplasmic male sterility (CMS) of Physa acuta
Open the record for dataset details and reuse information.
Knowledge representation of a CMS Analysis
<p>The diagram shows a mindmap representing all the pieces of knowledge that need to be collected to preserve an analysis of the CMS experiment in the CERN Analysis Preservation system. This diagram provides the basis for the metadata schema used in CAP.</p>
Simulated and Reconstructed Jets with CMS and DELPHES
<p>We provide data used for fast simulation studies. The subject of the study is simulated <a href="../records/3341419">CMS single light-jet data</a>. The data is split by the parton pT range in slices 470-600 GeV, 600-800 GeV, 800-1000 GeV, 1000-1400 GeV, 1400-1800 GeV, and > 1800 GeV. The file names are indicated by the lower bound. Each file contains the branch with stable input particles before detector simulation and the branch with the reconstructed particle flow candidates jet. A 1 GeV cut is applied to the particle flow candidates.</p> <p>Our model was trained on two slices covering the 800 - 1400 GeV region and evaluated on all slices. Each train, validation, and test file contains 1.7 M, 240 k, and 480 k jets. </p> <p>For comparison, we provide the detector simulation obtained with <a href="https://arxiv.org/abs/1307.6346">DELPHES 3.5</a> for the same set of truth-level events in each jet energy slice. We used the CMS card with pileup and an average of 6.35 pileup vertices per event. The particle flow objects are clustered into anti-kT R-0.5 jets and only constituents of the leading jet in each event are kept. Each file contains 250k jets.</p>
56th CIRP Conference on Manufacturing Systems, CIRP CMS '23, South Africa - Enhancing SLM Stainless Steel: Model-Based Parameter Optimization
<p> SLM (selective laser melting) has faced challenges in ensuring consistent mechanical properties for various geometries in the past. However, the authors have developed a system that solves this issue by calculating the optimal process parameters for each scan vector on each layer. This approach ensures consistent melt pool geometry, derived from the most robust in conduction welding mode. The results from the single melt-line experiments and the presented thermal conduction solver demonstrate the effectiveness of this approach in achieving a stable melt pool geometry in stainless steel (1.4404 or 316L). Furthermore, a rigorous testing regimen has been implemented to evaluate the system under different changing boundary conditions, to further demonstrate its usefulness. By utilizing this system, manufacturers will achieve consistent and high-quality mechanical properties for small lot sizes, including spare and obsolescent parts.</p>
Higgs to 4 Leptons with CMS Open data from the Large Hadron Collider
<p>Dataset for MIT 8.S50 online class details to process it can be found here: <br> https://github.com/mit-physics-data/psets/tree/main/pset2 </p>
Design Of A Condition Monitoring System (CMS) For A Gas Compressor Using Shell Bonny Terminal As A Case Study
<p><strong>Subcontracted</strong> <strong>Researcher</strong> <strong>&</strong> <strong>Field</strong> <strong>Electrical</strong> <strong>and</strong> <strong>Electronics</strong> <strong>Engineer</strong>, <em>Shell</em> <em>Bonny</em> <em>Terminal, Nigeria</em>— 2013</p> <p>I served as a Hooks Electric subcontracted researcher and field electrical and electronics engineer at Shell Bonny Terminal, spearheading a groundbreaking project to design and construct a state-of-the-art software condition monitoring system (CMS) for a gas compressor. In addition to my technical responsibilities, I also took charge of integrating this innovative design into the existing SCADA (Supervisory Control and Data Acquisition) infrastructure of the plant.</p> <p>Working collaboratively with a team of four talented engineers, I successfully managed and coordinated their efforts throughout the project. Together, we leveraged our collective expertise to develop and implement a fully functional software condition monitoring system. This collaborative approach ensured that our project benefitted from diverse perspectives and skill sets, leading to a comprehensive and robust solution.</p> <p>Utilizing my proficiency in LabVIEW, C#, native C, Java and ActiveX library, I led the development of a cutting-edge data acquisition (DAQ) device for CM system. This device seamlessly captured sensor readings from the live gas compressor in real-time, while the systemic algorithm I designed skillfully tracked false alarms using fallback sensors. By monitoring critical parameters such as ambient and self-temperature, axial speed, vibration, inflow pressure, and outflow pressure, we were able to enhance the system's reliability and performance.</p> <p>Furthermore, I effectively integrated the software condition monitoring system into the plant's existing SCADA infrastructure, ensuring seamless communication and compatibility with the overall plant monitoring and control system. This integration facilitated centralized data management and provided comprehensive insights into the gas compressor's operation, enabling proactive maintenance and efficient decision-making.</p> <p>By successfully managing the team and overseeing the integration process, I ensured that our project aligned seamlessly with the plant's existing infrastructure, resulting in an optimized and harmonized system.</p> <p>Through our collective efforts, we delivered a highly sophisticated and fully integrated software condition monitoring system that significantly improved the management and performance of the gas compressor at Shell Bonny Terminal.</p> <p>I have curated all the resource files that will enable anyone replicate our work. This is shared under the CC-by-4 license.</p> <p>The LabVIEW codes are documented in Work.zip file and all manuals and equipment specifications have been uploaded. I have created a test bench to help anyone quickly test the LabVIEW software in simulation mode. </p> <p>I have also provided a slide presentation that summarizes our work, including methodologies and results.</p>
CMS High Granularity Calorimeter Trigger Cell Simulated Dataset (Part 1)
<p>The dataset consists of simulated events of electron-positron pairs (<em>e</em><sup>+</sup><em>e</em><sup>−</sup>) with flat transverse momentum <em>p</em><sub>T</sub> distribution <em>p</em><sub>T</sub> ∈ [1,200] GeV, with Phase 2 conditions, 200 pileup, V11 geometry, HLT TDR Summer20 campaign The <a href="https://cmsweb.cern.ch/das/request?input=dataset%3D%2FDoubleElectron_FlatPt-1To100%2FPhase2HLTTDRSummer20ReRECOMiniAOD-PU200_111X_mcRun4_realistic_T15_v1-v2%2FGEN-SIM-DIGI-RAW-MINIAOD&instance=prod/global">original dataset (CMS-internal)</a>.</p> <p>This derived dataset in ROOT format contains generator-level particle and simulated detector information. More information about how the dataset is derived is available at this <a href="https://twiki.cern.ch/twiki/bin/viewauth/CMS/HGCALTriggerPrimitivesSimulation">TWiki (CMS-internal)</a>.</p> <p>A description of each variable is below.</p> <table> <thead> <tr> <th>Variable</th> <th>Description</th> <th>Type</th> </tr> </thead> <tbody> <tr> <td><code>run</code></td> <td>Run number</td> <td><code>int</code></td> </tr> <tr> <td><code>event</code></td> <td>Event number</td> <td><code>int</code></td> </tr> <tr> <td><code>lumi</code></td> <td>Luminosity section</td> <td><code>int</code></td> </tr> <tr> <td><code>gen_n</code></td> <td>Number of primary generated particles</td> <td><code>int</code></td> </tr> <tr> <td><code>gen_PUNumInt</code></td> <td>Number of pileup interactions</td> <td><code>int</code></td> </tr> <tr> <td><code>gen_TrueNumInt</code></td> <td>Number of true interactions</td> <td><code>float</code></td> </tr> <tr> <td><code>vtx_x</code></td> <td>Simulated primary vertex <em>x</em> position in cm</td> <td><code>float</code></td> </tr> <tr> <td><code>vtx_y</code></td> <td>Simulated primary vertex <em>y</em> position in cm</td> <td><code>float</code></td> </tr> <tr> <td><code>vtx_z</code></td> <td>Simulated primary vertex <em>z</em> position in cm</td> <td><code>float</code></td> </tr> <tr> <td><code>gen_eta</code></td> <td>Primary generated particle pseudorapidity <em>η</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>gen_phi</code></td> <td>Primary generated particle azimuthal angle <em>ϕ</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>gen_pt</code></td> <td>Primary generated particle transverse momentum <em>p</em><sub>T</sub> in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>gen_energy</code></td> <td>Primary generated particle energy in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>gen_charge</code></td> <td>Initial generated particle charge</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>gen_pdgid</code></td> <td>Primary generated particle PDG ID</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>gen_status</code></td> <td>Primary generated particle generator status</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>gen_daughters</code></td> <td>Primary generated particle daughters (empty)</td> <td><code>vector<vector<int>></code></td> </tr> <tr> <td><code>genpart_eta</code></td> <td>Primary and secondary generated particle pseudorapidity <em>η</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_phi</code></td> <td>Primary and secondary generated particle azimuthal angle <em>ϕ</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_pt</code></td> <td>Primary and secondary generated particle transverse momentum <em>p</em><sub>T</sub> in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_energy</code></td> <td>Primary and secondary generated particle energy in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_dvx</code></td> <td>Primary and secondary generated particle decay vertex <em>x</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_dvy</code></td> <td>Primary and secondary generated particle decay vertex <em>y</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_dvz</code></td> <td>Primary and secondary generated particle decay vertex <em>z</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_ovy</code></td> <td>Primary and secondary generated particle original vertex <em>y</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_ovz</code></td> <td>Primary and secondary generated particle original vertex <em>z</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_mother</code></td> <td>Primary and secondary generated particle parent particle index (-1 indicates no parent)</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>genpart_exphi</code></td> <td>Primary and secondary generated particle azimuthal angle <em>ϕ</em> extrapolated to the corresponding HGCAL coordinate</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_exeta</code></td> <td>Primary and secondary generated particle pseudorapidity <em>η</em> extrapolated to the corresponding HGCAL coordinate</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_exx</code></td> <td>Primary and secondary generated particle decay vertex <em>x</em> extrapolated to the corresponding HGCAL coordinate</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_exy</code></td> <td>Primary and secondary generated particle decay vertex <em>y</em> extrapolated to the corresponding HGCAL coordinate</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_fbrem</code></td> <td>Primary and secondary generated particle decay vertex <em>z</em> extrapolated to the corresponding HGCAL coordinate</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_pid</code></td> <td>Primary and secondary generated particle PDG ID</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>genpart_gen</code></td> <td>Index of associated primary generated particle</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>genpart_reachedEE</code></td> <td>Primary and secondary generated particle flag: <code>2</code> indicates that the particle reached the HGCAL, <code>1</code> indicates the particle reached the barrel calorimeter, and <code>0</code> indicates other cases</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>genpart_fromBeamPipe</code></td> <td>Deprecated variable, always true</td> <td><code>vector<bool></code></td> </tr> <tr> <td><code>genpart_posx</code></td> <td>Primary and secondary generated particle position <em>x</em> coordinate in cm</td> <td><code>vector<vector<float>></code></td> </tr> <tr> <td><code>genpart_posy</code></td> <td>Primary and secondary generated particle position <em>y</em> coordinate in cm</td> <td><code>vector<vector<float>></code></td> </tr> <tr> <td><code>genpart_posz</code></td> <td>Primary and secondary generated particle position <em>z</em> coordinate in cm</td> <td><code>vector<vector<float>></code></td> </tr> <tr> <td><code>ts_n</code></td> <td>Number of trigger sums</td> <td><code>int</code></td> </tr> <tr> <td><code>ts_id</code></td> <td>Trigger sum ID</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>ts_subdet</code></td> <td>Trigger sum subdetector</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>ts_zside</code></td> <td>Trigger sum endcap (plus or minus endcap)</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>ts_layer</code></td> <td>Trigger sum layer ID</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>ts_wafer</code></td> <td>Trigger sum wafer ID</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>ts_wafertype</code></td> <td>Trigger sum wafer type: 0 indicates fine divisions of wafer with 120 <em>μ</em>m thick silicon, 1 indicates coarse divisions of wafer with 200 <em>μ</em>m thick silicon, and 2 indicates coarse divisions of wafer with 300 <em>μ</em>m thick silicon</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>ts_data</code></td> <td>Trigger sum ADC value</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>ts_pt</code></td> <td>Trigger sum transverse momentum in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>ts_mipPt</code></td> <td>Trigger sum energy in units of transverse MIP</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>ts_energy</code></td> <td>Trigger sum energy in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>ts_eta</code></td> <td>Trigger sum pseudorapidity <em>η</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>ts_phi</code></td> <td>Trigger sum azimuthal angle <em>ϕ</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>ts_x</code></td> <td>Trigger sum <em>x</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>ts_y</code></td> <td>Trigger sum <em>y</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>ts_z</code></td> <td>Trigger sum <em>z</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_n</code></td> <td>Number of trigger cells</td> <td><code>int</code></td> </tr> <tr> <td><code>tc_id</code></td> <td>Trigger cell unique ID</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>tc_subdet</code></td> <td>Trigger cell subdetector ID (EE, EH silicon, or EH scintillator)</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>tc_zside</code></td> <td>Trigger cell endcap (plus or minus endcap)</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>tc_layer</code></td> <td>Trigger cell layer number</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>tc_waferu</code></td> <td>Trigger cell wafer <em>u</em> coordinate; <em>u</em>-axis points along − <em>x</em>-axis</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>tc_waferv</code></td> <td>Trigger cell wafer <em>v</em> coordinate; <em>v</em>-axis points at 60 degrees with respect to <em>x</em>-axis</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>tc_wafertype</code></td> <td>Trigger cell wafer type: <code>0</code> indicates fine divisions of wafer with 120 <em>μ</em>m thick silicon, <code>1</code> indicates coarse divisions of wafer with 200 <em>μ</em>m thick silicon, and <code>2</code> indicates coarse divisions of wafer with 300 <em>μ</em>m thick silicon)</td> <td> </td> </tr> <tr> <td><code>tc_cellu</code></td> <td>Trigger cell <em>u</em> coordinate within wafer; <em>u</em>-axis points along − <em>x</em>-axis</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>tc_cellv</code></td> <td>Trigger cell <em>v</em> coordinate within wafer; <em>v</em>-axis points at 60 degrees with respect to <em>x</em>-axis</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>tc_data</code></td> <td>Trigger cell ADC data at 21-bit precision after decoding from 7-bit encoding</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>tc_uncompressedCharge</code></td> <td>Trigger cell ADC data at full precision before compression</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>tc_compressedCharge</code></td> <td>Trigger cell ADC data compressed into 7-bit encoding</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>tc_pt</code></td> <td>Trigger cell transverse momentum <em>p</em><sub>T</sub> in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_mipPt</code></td> <td>Trigger cell energy in units of transverse MIPs</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_energy</code></td> <td>Trigger cell energy in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_simenergy</code></td> <td>Trigger cell energy from simulated particles in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_eta</code></td> <td>Trigger cell pseudorapidity <em>η</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_phi</code></td> <td>Trigger cell azimuthal angle <em>ϕ</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_x</code></td> <td>Trigger cell <em>x</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_y</code></td> <td>Trigger cell <em>y</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_z</code></td> <td>Trigger cell <em>z</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_cluster_id</code></td> <td>ID of the 2D cluster in which the trigger cell is clustered</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>tc_multicluster_id</code></td> <td>ID of the 3D cluster in which the trigger cell is clustered</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>tc_multicluster_pt</code></td> <td>Transverse momentum <em>p</em><sub>T</sub> in GeV of the 3D cluster in which the trigger cell is clustered</td> <td><code>vector<float></code></td> </tr> </tbody> </table>
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.