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8,924 results for “formation”
Microbial Observatory at North Temperate Lakes LTER High-resolution temporal and spatial dynamics of microbial community structure in freshwater bog lakes 2005 - 2009 original format (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/349/4. The abstract below was extracted from the Level 0 data package and is included for context: The North Temperate Lakes - Microbial Observatory seeks to study freshwater microbes over long time scales (10+ years). Observing microbial communities over multiple years using DNA sequencing allows in-depth assessment of diversity, variability, gene content, and seasonal/annual drivers of community composition. Combining information obtained from DNA sequencing with additional experiments, such as investigating the biochemical properties of specific compounds, gene expression, or nutrient concentrations, provides insight into the functions of microbial taxa. Our 16S rRNA gene amplicon datasets were collected from bog lakes in Vilas County, WI, and from Lake Mendota in Madison, WI. Ribosomal RNA gene amplicon sequencing of freshwater environmental DNA was performed on samples from Crystal Bog, North Sparkling Bog, West Sparkling Bog, Trout Bog, South Sparkling Bog, Hell’s Kitchen, and Mary Lake. These microbial time series are valuable both for microbial ecologists seeking to understand the properties of microbial communities and for ecologists seeking to better understand how microbes contribute to ecosystem functioning in freshwater.
Xylem Embolism Formation, Refilling and Water Storage in Tree Trunks at Harvard Forest 2012
Trunks of large trees play an important role in whole-plant water balance but technical difficulties have limited most hydraulic research to small stems, leaves and roots. To investigate the dynamics of water-related processes in tree trunks, such as winter embolism refilling, xylem hydraulic vulnerability, and water storage, volumetric water content (VWC) in the main stem was monitored continuously using frequency domain moisture sensors in adult Betula papyrifera trees from early spring through the beginning of winter. An air injection technique was developed to estimate hydraulic vulnerability of the trunk xylem. Trunk VWC increased in early spring and again in autumn concurrent with root pressure during both seasons. Diurnal fluctuations and a gradual decrease in trunk VWC through the growing season were observed, which, in combination with VWC increase after significant rainfall events and depletion during periods of high water demand, indicate the importance of stem water storage in both short-and long-term water balance. Comparisons between the trunk air injection results and conventional branch hydraulic vulnerability curves showed no evidence of “vulnerability segmentation” between the main stem and small branches in B. papyrifera. Measurements of VWC following air injection, together with evidences from air injection and xylem dye perfusion, indicate that embolized vessels can be refilled by active root pressure but not in the absence of root pressure. The precise, continuous and non-destructive measurement of wood water content using frequency domain sensors provides an ideal way to probe many hydraulic processes in large tree trunks that are otherwise difficult to investigate.
Nonstructural Carbon, Phenology and Wood Formation in Three Tree Species at Harvard Forest 2017-2019
This data set comprises various observations and measurements across the 2017 to 2019 growing season for seven red maple (Acer rubrum), eight red oak (Quercus rubra), and six white pine (Pinus strobus) in the Prospect Hill Tract of Harvard Forest. The observations include spring and fall leaf phenology and basic allometry, such as diameter at breast height and height. For the leaf phenology, we followed the protocol from John O’Keefe (HF003). Measurements include wood growth data from weekly microcores and a three time characterisation of growing season nonstructural carbon concentrations (soluble sugars and starch) for stems and leaves. Additionally, stem CO2 efflux was measured once a month for the 2018 growing season and weekly for the 2019 growing season.
Lake ice clearance and formation data for Green Lakes Valley, 1968 - ongoing.
Records were based on intermittent observation of the extent of ice cover on Silver Lake, Lake Albion, and Green Lakes 1-5, dependent on observers present for other reasons (or in some cases from photos or satellite data). Dates of freeze-up (formation), breakup (first melt or open water), and lake-ice clearance were recorded, along with visual estimates of percent ice cover at various dates prior to complete meltout in spring. The latter estimates are only included starting in 2019.
Microbial Observatory at North Temperate Lakes LTER High-resolution temporal and spatial dynamics of microbial community structure in freshwater bog lakes 2005 - 2009 original format
The North Temperate Lakes - Microbial Observatory seeks to study freshwater microbes over long time scales (10+ years). Observing microbial communities over multiple years using DNA sequencing allows in-depth assessment of diversity, variability, gene content, and seasonal/annual drivers of community composition. Combining information obtained from DNA sequencing with additional experiments, such as investigating the biochemical properties of specific compounds, gene expression, or nutrient concentrations, provides insight into the functions of microbial taxa. Our 16S rRNA gene amplicon datasets were collected from bog lakes in Vilas County, WI, and from Lake Mendota in Madison, WI. Ribosomal RNA gene amplicon sequencing of freshwater environmental DNA was performed on samples from Crystal Bog, North Sparkling Bog, West Sparkling Bog, Trout Bog, South Sparkling Bog, Hell’s Kitchen, and Mary Lake. These microbial time series are valuable both for microbial ecologists seeking to understand the properties of microbial communities and for ecologists seeking to better understand how microbes contribute to ecosystem functioning in freshwater.
Pre-processed (in Detectron2 and YOLO format) planetary images and boulder labels collected during the BOULDERING Marie Skłodowska-Curie Global fellowship
<p>This database contains 4976 planetary images of boulder fields located on Earth, Mars and Moon. The data was collected during the BOULDERING Marie Skłodowska-Curie Global fellowship between October 2021 and 2024. The data was already splitted into train, validation and test datasets, but feel free to re-organize the labels at your convenience. </p> <p>For each image, all of the boulder outlines within the image were carefully mapped in QGIS. More information about the labelling procedure can be found in the following manuscript (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023JE008013">https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023JE008013</a>). This dataset differs from the previous dataset included along with the manuscript <a href="https://zenodo.org/records/8171052">https://zenodo.org/records/8171052</a>, as it contains more mapped images, especially of boulder populations around young impact structures on the Moon (cold spots). In addition, the boulder outlines were also pre-processed so that it can be ingested directly in YOLOv8.</p> <p>A description of what is what is given in the README.txt file (in addition in how to load the custom datasets in Detectron2 and YOLO). Most of the other files are mostly self-explanatory. Please see previous dataset or manuscript for more information. If you want to have more information about specific lunar and martian planetary images, the IDs of the images are still available in the name of the file. Use this ID to find more information (e.g., M121118602_00875_image.png, ID M121118602 ca be used on https://pilot.wr.usgs.gov/). I will also upload the raw data from which this pre-processed dataset was generated (see <a href="https://zenodo.org/records/14250970">https://zenodo.org/records/14250970</a>).</p> <p>Thanks to this database, you can easily train a Detectron2 Mask R-CNN or YOLO instance segmentation models to automatically detect boulders. </p> <p><strong>How to cite:</strong></p> <p>Please refer to the "how to cite" section of the readme file of <a href="https://github.com/astroNils/YOLOv8-BeyondEarth" target="_blank" rel="noopener">https://github.com/astroNils/YOLOv8-BeyondEarth.</a></p> <p><strong>Structure:</strong></p> <pre><code>. └── boulder2024/ ├── jupyter-notebooks/ │ └── REGISTERING_BOULDER_DATASET_IN_DETECTRON2.ipynb ├── test/ │ └── images/ │ ├── <image_name>_image.png │ ├── ... │ └── labels/ │ ├── <image_name>_image.txt │ ├── ... ├── train/ │ └── images/ │ ├── <image_name>_image.png │ ├── ... │ └── labels/ │ ├── <image_name>_image.txt │ ├── ... ├── validation/ │ └── images/ │ ├── <image_name>_image.png │ ├── ... │ └── labels/ │ ├── <image_name>_image.txt │ ├── ... ├── detectron2_inst_seg_boulder_dataset.json ├── README.txt ├── yolo_inst_seg_boulder_dataset.yaml</code></pre> <p> </p> <pre><code>detectron2_inst_seg_boulder_dataset.json</code></pre> <p>is a json file containing the masks as expected by Detectron2 (see <a href="https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html">https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html</a> for more information on the format). In order to use this custom dataset, you need to register the dataset before using it in the training. There is an example how to do that in the jupyter-notebooks folder. You need to have detectron2, and all of its depedencies installed. </p> <pre><code>yolo_inst_seg_boulder_dataset.yaml</code></pre> <p>can be used as it is, however you need to update the paths in the .yaml file, to the test, train and validation folders. More information about the YOLO format can be found here (<a href="https://docs.ultralytics.com/datasets/segment/">https://docs.ultralytics.com/datasets/segment/</a>).</p>
Cirrus formation regimes - Data driven identification and quantification of mineral dust effect
<p>This repository contains the data for the paper: </p> <p>Authors: Kai Jeggle , David Neubauer , Hanin Binder and Ulrike Lohmann<br>Titel: Cirrus formation regimes - Data driven identification and quantification of mineral dust effect<br>Date: 2024</p> <p>Note that the scripts can be found in the accompanying code repository (https://github.com/tabularaza27/cloud_clustering)<br><br>Contents:<br><br>├── cirrus_cloud_trajectories.ftr<br>├── cluster_input_data.ftr<br>├── cluster_models<br>│ └── temperature_clustering_k4_12<br>│ ├── cloud_ids.npy<br>│ ├── model_params.json<br>│ └── trained_model.hdf5</p> <p>│ └── temperature_clustering_k4_24<br>│ ├── cloud_ids.npy<br>│ ├── model_params.json<br>│ └── trained_model.hdf5</p> <p>├── cluster_predictions.ftr<br>└── readme.txt<br><br>For more info, please have a look at the <em>readme.txt</em><br><br>This is an updated version of the data, containing updated models and predictions based on the Journal revisions</p>
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>
Data for: Diminutive temnospondyls from the lower and middle Fremouw Formation (Lower Triassic) of Antarctica
<p>This dataset contains the supporting data for the journal article, "Diminutive temnospondyls from the lower and middle Fremouw Formation (Lower Triassic) of Antarctica." Included are the phylogenetic character matrix (in .nex and .tnt formats) that was analyzed in TNT, the resultant 18 MPTs (.tre) recovered by the analysis, the skull length measurement data sourced from the literature for capitosaurs (.csv), a list of references used to source this measurement data (.pdf), and a README file with more metadata and details (.txt). </p>
Spectral reflectance data of Mercury's surface collected by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument during orbital observations of the NASA MESSENGER mission between 2011 and 2015 resampled to a [55399 × 396] tabular data format.
<p>MASCS is a three sensor point spectrometer with a spectral coverage from 200 nm to 1450 nm.<br> Single spectra are resamples in to steps to a format useful for our ML application : a datacube with ~400 spectral channel covering the whole surface of Mercury.<br> The final dataset has dimension [N×M] where N is the number of grid cells (360 × 180 = 64, 800) and M is the number of spectral features (396).<br> Due to the incomplete coverage and data filtering, some grid cells are empty.<br> After removing these empty cells, the size of the dataset is [55399 × 396].</p> <p>This specific product is stored as a gzip compressed json, where each element is a grid cell.<br> We are in the process to publish a complete pipeline to produce this product from RAW data on https://github.com/epn-ml/MESSENGER-Mercury-Surface-Cassification-Unsupervised_DLR/ .</p> <p>Spectral reflectance data of Mercury’s surface collected by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument during orbital observations of the NASA MESSENGER mission between 2011 and 2015.<br> MASCS is a three sensor point spectrometer with a spectral coverage from 200 nm to 1450 nm.<br> Single spectra are resamples in to steps to a format useful for our ML application : a datacube with ~400 spectral channel covering the whole surface of Mercury.<br> The final dataset has dimension [N×M] where N is the number of grid cells (360 × 180 = 64, 800) and M is the number of spectral features (396).<br> Due to the incomplete coverage and data filtering, some grid cells are empty.<br> After removing these empty cells, the size of the dataset is [55399 × 396].</p> <p>0. Pre-filtering<br> We used the most recent dataset that had large-scale photometric corrections and thus was almost free from observation geometry effects.<br> However, extreme geometry are still present and are typically associated with high noise and some residual instrumental effects.<br> Based on our empirical tests, we filtered out observations with an emission/incidence angle ≥80∘.<br> We also calculated the median value per wavelength and per cell grid when constructing the global hyperspectral data cube and filtered out observations falling under the 2nd percentile and above 99.9th percentile to clean some residual geometry effects.<br> With this approach we create an effective noise filter while retaining enough observations to be able to analyse the entirety of the surface of the planet.</p> <p>1. Spectral resmpling<br> Unprocessed MASCS spectra could have 512 or 256 channes, depending on binning.<br> We resampled the data in the spectral dimension to a common wavelength range from 260 nm to 1052 nm with a 4 nm resolution (2 nm spectral sampling), resulting in 396 spectral channels.<br> This approach slightly oversamples the original 4.77 nm spectral resolution and removes some points from the original 200-1050 nm range.<br> The resulting data matrix is expressed in tabular form, with each row representing a single grid cell or pixel on the surface.<br> The elements of each row are the spectral reflectance values from the VIS instrument at 396 (resampled) wavelengths.</p> <p>2. Spatial resmpling<br> The whole dataset of ∼ 5 million spectra is resampled to a planet-wide rectangular grid of 1×1deg in the latitudinal band between ± 80.<br> The cell longitudinal size varies between ∼ 40 km at the equator to a minimum of ∼ 10 km at ±80∘.<br> Thus, the area spanned by each grid cell depends on the latitude. However, the same is true for the acquisition process, where higher spatial resolution is reached near the equator and lower resolution at the poles.</p>
Nuclear Magnetic Resonance values for the Eptachori, Pentalofos and Tsotyli formations in West Macedonia
<p>The data comprises work under the Project Pilot Strategy GA No. 101022664, funded by the European Union. </p> <p>The work relates to rock samples collected in 2022 in West Macedonia, Greece. For full details, please refer to the following:</p> <ol> <li>Tsotyli formation: <a href="https://app.geosamples.org/sample/igsn/IE5770001">https://app.geosamples.org/sample/igsn/IE5770001</a> - <strong>WGS84 Lat : 40.3075, </strong><strong>WGS84 Long : 21.3354</strong></li> <li>Pentalofos formation: <a href="https://app.geosamples.org/sample/igsn/IE5770002">https://app.geosamples.org/sample/igsn/IE5770002</a> - <strong>WGS84 Lat : 40.1332,</strong> <strong>WGS84 Long : 21.1997</strong></li> <li>Eptachori formation: <a href="https://app.geosamples.org/sample/igsn/IE5770003">https://app.geosamples.org/sample/igsn/IE5770003</a> - <strong>WGS84 Lat : 40.1332, </strong><strong>WGS84 Long : 21.1997</strong></li> </ol> <p>The focus of the work is related to CO2 storage in appropriate saline aquifers in West Macedonia. The bulk samples were shipped to IFP Energies for porosity and permeability laboratory investigation conducted by Nuclear Magnetic Resonance techniques. </p> <p>Further to the raw data from the NMR, a depiction of the latter is provided in the corresponding figures</p> <p> </p>
Simulated galaxy cluster data at z=0 demonstrating the entropy core problem with the SWIFT-EAGLE galaxy formation model
<p>Cluster simulated with the SWIFT hydrodynamic code with the Ref SWIFT-EAGLE model. This dataset contains the redshift 0 snapshot and the VELOCIraptor halo catalogue.</p> <p>Paper reference: https://arxiv.org/abs/2210.09978</p>
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.