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42 results for “gauge data”
Precipitation data from a standard can rain gauge at the LTER weather station, Jornada Basin, southern New Mexico, USA, 1992-ongoing
This data package contains precipitation measurements collected from a "dipstick" rain gauge (NOAA standard can type) at the LTER Weather Station in the Jornada Basin, southern New Mexico, USA. The primary purpose of this data set is to validate the LTER Weather Station tipping bucket rain gauge data. The dipstick rain gauge (DSRG) data is measured at least weekly during scheduled maintenance trips to the LTER Weather Station to maintain the evaporation pan water levels. During the summer months this may be twice a week. Additionally, DSRG data is collected after any rain event that requires the collection of the Wetfall/Dryfall precipitation buckets which are located about 10 meters from the DSRG. This is usually any amount greater than 0.02 inches. DSRG data is also collected after very small events when personnel are in the vicinity. Rain gauge records at this gauge began in 1992 and the study is ongoing.
Topological Data Analysis of Monopoles in U(1) Lattice Gauge Theory — Data Release
<div>This release contains data used to prepare the publication <a href="https://arxiv.org/abs/2403.07739">X. Crean, J. Giansiracusa and B. Lucini, Topological Data Analysis of Monopoles in U(1) Lattice Gauge Theory (2024)</a>. There exists an <a href="https://doi.org/10.5281/zenodo.10806185">accompanying software release</a> that explains in detail how to extract and use the compressed data files on a Linux distribution (or compatible environment).</div>
Probing center vortices and deconfinement in SU(2) lattice gauge theory with persistent homology — data release
<p>This release contains all data used to prepare the publication <a href="https://arxiv.org/abs/2207.13392">Probing center vortices and deconfinement in SU(2) lattice gauge theory with persistent homology</a>.</p> <p>Included are:</p> <ul> <li>The raw log output from the simulations and computed persistence images for the analysis in Section IV.B of the <a href="https://arxiv.org/abs/2207.13392">paper</a> in 'raw_data.zip'.</li> <li>The values of the action and Polyakov loop from the above logs, along with the persistence images restructured into netCDF4 format for convenience, in the files 'Nt=*_Ns=*_pis_actions_polyakovs.nc'.</li> <li>The values of the observable m_2 (as defined in the <a href="https://arxiv.org/abs/2207.13392">paper</a>) for configurations for the twisted boundary conditions analysis in netCDF4 format in 'Nt=4_Ns=12_16_20_m2.nc'.</li> <li>The example persistence diagrams used in the <a href="https://arxiv.org/abs/2207.13392">paper</a> in netCDF4 format in 'Nt=4_Ns=12_example_pds.nc'.</li> </ul>
Data and code related to the paper: "Integrated stretchable pneumatic strain gauges for electronics-free soft robots"
<p>This folder contains the raw data and Matlab scripts to reproduce the plots and supplementary movies for the paper:</p> <p>Anastasia Koivikko, Vilma Lampinen, Mika Pihlajamäki, Kyriacos Yiannacou, Vipul Sharma & Veikko Sariola, "Integrated Stretchable Pneumatic Strain Gauges for Electronics-Free Soft Robots", Communications Engineering, 1, 14 (2022).</p> <p><a href="https://doi.org/10.1038/s44172-022-00015-6">Link to the paper</a>.</p> <p>The scripts were tested on Matlab R2021a on Windows.</p> <p>Generally speaking, there is a folder containing the plotting scripts for each figure. In most cases, the folder contains scripts named <strong>plot<...>.m</strong> that recreate the actual plots. Some folders also have a scripts <strong>analyze<...>.m</strong> to analyze the data; these need to be run before the actual plotting.</p> <p>For more details, please see the paper.</p>
Monthly precipitation data from a network of standard gauges at the Jornada Experimental Range (Jornada Basin LTER) in southern New Mexico, January 1916 - ongoing
This ongoing dataset contains monthly precipitation measurements from a network of standard can rain gauges at the Jornada Experimental Range in Dona Ana County, New Mexico, USA. Precipitation physically collects within gauges during the month and is manually measured with a graduated cylinder at the end of each month. This network is maintained by USDA Agricultural Research Service personnel. This dataset includes 39 different locations but only 29 of them are current. Other precipitation data exist for this area, including event-based tipping bucket data with timestamps, but do not go as far back in time as this dataset.
On the spectrum of mesons in quenched Sp(2N) gauge theories---Data release
<p>This release contains all data and metadata used to prepare the publication <a href="https://arxiv.org/abs/2312.08465" target="_blank" rel="noopener">On the spectrum of mesons in quenched Sp(2N) gauge theories</a>.</p> <p>Included are:</p> <ul> <li>The file <code>README.md</code>, containing descriptions of the data formats used for other data in this submission.</li> <li>The raw log output for: <ul> <li>The gauge field generation</li> <li>The correlation function computation</li> <li>The Wilson flow computation</li> </ul> </li> </ul> <p>in the file <code>raw_data.zip</code>.<br>These include all numbers used in the publication (aside from fit parameters) in plaintext form. The archive contains a separate <code>README.md</code> documenting the layout of these data.</p> <ul> <li>All metadata used for the fitting and subsequent analysis of these data, in the file <code>metadata.zip</code>, in files described in more detail in the README.</li> <li>All data presented in plots and tables in the paper, in CSV format, in files described in more detail in the README.</li> <li>All input files given during the gauge field generation, in <code>input_files.zip</code>. These are compatible with<a href="https://github.com/sa2c/HiRep" target="_blank" rel="noopener">the Sp(2N) extension of HiRep</a>.</li> </ul>
Lattice studies of the Sp(4) gauge theory with two fundamental and three antisymmetric Dirac fermions—data release
<p>This dataset contains the raw data and metadata used to prepare the publication <a href="https://arxiv.org/abs/2202.05516">Lattice studies of the Sp(4) gauge theory with two fundamental and three antisymmetric Dirac fermions</a>. </p> <p>Included are:</p> <ul> <li>The raw log output from the configuration generation, correlation function calculation, and Dirac eigenvalue computation, as well as metadata describing the ensembles used for the mass spectrum calculation, in `raw_data.zip`. These include all numbers used in the publication (aside from fit parameters) in plaintext form.</li> <li>All numbers included in the above logs, restructured into HDF5 format for convenience, in `data.h5`.</li> <li>The fit parameters used to compute the spectrum, including the thermalisation length, and the plateau start and end points, in `fit_params.zip`.</li> <li>The data underlying tables 2–6 of the publication above, in CSV format.</li> </ul> <p>More details can be found in the file README.md.</p> <p>Version history:</p> <ul> <li>v1.1: Replace out_corr_48x24x24x24b6.5mas-1.01mf-0.71 due to a mistake where the wrong version of the measurement code was used.</li> <li>v1.0: Initial release</li> </ul>
Source data for "Synthetic gauge fields for phonon transport in a nano-optomechanical system"
<ul> <li>Experimental raw data for density plots in Fig 2. Each .csv contains an array, where 1st row corresponds to x_axis (mechanical frequency in MHz for panels 1,2,3,4) and first column the y_axis (optical frequency in THz for panel 1, modulation frequency in MHz for panels 2,3,4). First nonzero component is the 2nd for each array. Remaining array elements contain the z values (Thermomechanical noise spectral for panel 1, Amplitude of driven responses for panels 2,3,4). An illustrative example of plotting in an ipython notebook follows:</li> </ul> <p> %pylab inline</p> <p> A= genfromtxt('Fig2_data_modVolt=0mV_experiment.csv', delimiter=',') </p> <p> x = A[0,1:]<br> y = A[1:,0]<br> z = A[1:,1:]<br> imshow(z,aspect='auto',vmin=z.min(),vmax=z.max(),extent=[x.min(),x.max(),y.min(),y.max()],cmap='magma') </p> <ul> <li> Theoretical data for panel 4 in Fig 2, stored in a .csv with the same structure as previous.</li> <li> Raw experimental data for upper panels in Fig 3. Each .csv contains an array where 1st row corresponds to x_axis (modulation phase) and first column the y_axis (optical frequency in THz). Z values contain the experimental signal proportional to the Y optical quadrature of the transferred mode.</li> <li>Theoretical data for lower panels in Fig 3, stored in a .csv with the same structure as previous.</li> <li>Jupyter notebook to produce and plot typical data for Fig 4: phononic amplitude averaged over 100 disorder realizations, normalized to the maximum value (*extra_dependencies: Kwant Python library: <a href="https://kwant-project.org/">https://kwant-project.org/</a>).</li> </ul>
On the mixing between flavor singlets in lattice gauge theories coupled to matter fields in multiple representations - data release
<p>This release contains all data and metadata used to prepare the publication <a href="https://arxiv.org/abs/2405.05765"><em>On the mixing between flavor singlets in lattice gauge theories coupled to matter fields in multiple representations</em> [2405.05765].</a> </p> <p>If you encounter difficulties downloading the large files, we recommend using <a href="../records/11142962">zenodo-get</a>. This provides a command-line downloader for any Zenodo record. For unstable connections we recommend using it with the -w flag to generate a list all files in this Zenodo record. This can then be used with tools such as <a href="https://www.gnu.org/software/wget/">wget</a> to resume partial downloads as</p> <p><code>zenodo_get RECORD_ID_OR_DOI -w - | xargs wget -c<br>zenodo_get RECORD_ID_OR_DOI </code></p> <p>(The second line ensures that the downloads completed correctly, and that the md5 hashes match)<br><br>Further details are given in the file README.md.</p> <p>The work of EB and BL is supported in part by the EPSRC ExCALIBUR programme ExaTEPP (project EP/X017168/1). The work of EB, BL, MP, and FZ has been supported by the STFC Consolidated Grant No. ST/X000648. The work of EB has also been supported by the UKRI Science and Technology Facilities Council (STFC) Research Software Engineering Fellowship EP/V052489/1. The work of NF has been supported by the STFC Consolidated Grant No. ST/X508834/1. The work of DKH was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1B06033701). The work of DKH was further supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2021R1A4A5031460). The work of JWL is supported by IBS under the project code, IBS-R018-D1. The work of HH and CJDL is supported by the Taiwanese MoST grant 109-2112-M-009-006-MY3 and NSTC grant 112-2112-M-A49-021-MY3. The work of CJDL is also supported by Grants No. 112-2639-M-002-006-ASP and No. 113-2119-M-007-013. The work of BL and MP has been further supported in part by the STFC Consolidated Grant No. ST/T000813/1.<br>BL and MP received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program under Grant Agreement No.~813942. The work of DV is supported by STFC under Consolidated Grant No. ST/X000680/1.</p> <p>Numerical simulations have been performed on the DiRAC Extreme Scaling service at the University of Edinburgh, and on the DiRAC Data Intensive service at Leicester.<br>The DiRAC Extreme Scaling service is operated by the Edinburgh Parallel Computing Centre on behalf of the STFC DiRAC HPC Facility (www.dirac.ac.uk). This equipment was funded by BEIS capital funding via STFC capital grant ST/R00238X/1 and STFC DiRAC Operations grant ST/R001006/1. DiRAC is part of the National e-Infrastructure</p>
SU(2) gauge theory with one and two adjoint fermions towards the continuum limit—data release
<p>This package contains all data generated in preparing the publication <a href="https://arxiv.org/abs/2408.00171">SU(2) gauge theory with one and two adjoint fermions towards the continuum limit</a>. It includes four classes of data:</p> <ol> <li>Raw data, as generated from the measurement code running on HPC, in their native formats (raw_data.zip).</li> <li>Metadata around the analysis of the ensembles, in YAML format (ensembles.yaml).</li> <li>Data obtained by analysing the above data and presented in <a href="https://arxiv.org/abs/2408.00171">arXiv:2408.00171</a>, for specific ensembles, in sqlite3 format (su2.sqlite).</li> <li>The above data in (3), and additional data obtained by further analysing them, in CSV format (ensemble_results.csv and gammastar_results.csv).</li> <li>For convenience, the data in (1) above, repackaged in HDF5 format (package.h5).</li> </ol> <p>Each of these is documented in more detail in the file README.md.</p> <p>Due to their size, raw gauge configurations are not included in this package.</p> <p> </p>
Observational rainfall data of the 2021 mid-July flood event in Belgium – Part 1. Rain gauges observations
<p>From July 13th to 16th 2021, a long period of sustained and heavy rainfall affected Central Europe producing extreme rainfall amounts in western Germany, eastern Belgium, Luxembourg and The Netherlands. In Belgium, this unusual event induced massive flooding on a large part of the country and was responsible for 39 fatalities and strong damages to buildings and infrastructures.</p><p>Such extremely rare event needs to be documented as much as possible and data must be made available for further studies in hydrology, in urban planning and, more generally, in all multi-disciplinary studies aiming at identifying and understanding all factors leading to such disaster.</p><p>The observational rainfall data available for Belgium during the period from July 13th to July 16th 2021 are here shared with the scientific community. These data are twofold and provided in 2 parts:</p><p><br><strong>Part 1. </strong><a href="https://doi.org/10.5281/zenodo.7739983"><strong>Observations from high-quality rain gauges</strong></a></p><p>The dataset includes daily precipitation accumulation recorded by 323 weighing and manual rain gauges in Belgium as well as 5-min precipitation data recorded by 168 weighing rain gauges. These data were checked for possible errors and inconsistencies.</p><p>The rain gauges observations are provided in csv format in 2 files:</p><ul><li>RainGaugesData_FLOOD21_daily.csv</li><li>RainGaugesData_FLOOD21_5min.csv</li></ul><p><br><strong>Part 2. </strong><a href="https://doi.org/10.5281/zenodo.7740059"><strong>Radar-based quantitative precipitation estimation (RADFLOOD21)</strong></a></p><p>This product provides a quantitative precipitation estimation of the event at high spatial (i.e., 1 km) and temporal (i.e., 5 min and hourly) resolutions. It is obtained after a careful processing of the weather radar measurements and a merging with rain gauge measurements. The data is provided in hdf5 format. In addition, an animation of the 5-min RADFLOOD21 data is also made available.</p><p> </p><p>These data are exposed and discussed in <a href="https://hess.copernicus.org/articles/27/3169/2023/">https://hess.copernicus.org/articles/27/3169/2023/</a>. In particular, several analyses of these data are performed to describe the spatial and temporal distribution of rainfall during the event and to illustrate its exceptional character.</p><p> </p>
Daily summary precipitation data from a tipping bucket rain gauge near former Jornada Basin LTER Biodiversity study site, 1996-ongoing
This data package contains daily precipitation values from a rain gauge near the former Biodiversity study site at the Jornada Basin LTER in southern New Mexico, USA. Data collection at the Biodiversity site began in 1996 and is now complete. Data collection from the tipping bucket rain gauge near this site commenced in April 1996. The data file included here reports daily precipitation totals in millimeters (mm). Data collection from this tipping bucket rain gauge is ongoing and collected on a monthly basis (data package may be updated less frequently).
High resolution precipitation event data from a tipping bucket gauge near former Jornada Basin LTER Biodiversity study site, 1996-ongoing
This data package contains high temporal resolution values from a tipping bucket rain gauge during rain events near the former Biodiversity study site at the Jornada Basin LTER in southern New Mexico, USA. Data collection at the Biodiversity site began in 1996 and is now complete. Data collection from the tipping bucket rain gauge near this site commenced in April 1996. The data file included here reports 1-second (1997-2016) or 1-minute (2016-present) frequency precipitation data, in millimeters, from this rain gauge during precipitation events. There are no data records for rain amounts less than 0.1 mm. Data collection from this tipping bucket rain gauge is ongoing and collected on a monthly basis (data package may be updated less frequently).
Lattice investigations of the chimera baryon spectrum in the Sp(4) gauge theory---Data Release
<p>This release contains the analysis workflow used to prepare the publication <a href="https://arxiv.org/abs/2311.14663" target="_blank" rel="noopener">Lattice investigations of the chimera baryon spectrum in the Sp(4) gauge theory</a>.</p> <p>A Python code for performing the analysis and generating the plots and tables is <a href="https://doi.org/10.5281/zenodo.10929539" target="_blank" rel="noopener">uploaded to Zenodo</a>. See the README therein for details on running the code.</p> <p>For details on the data formats, see the relevant README.md files.</p> <h2>Content of directories and files:</h2> <ul> <li>README.md: This contains general information on the content of the release.</li> <li>raw_data.zip: This compressed file contains all the raw data utilized in the research outlined in arXiv:2311.14663. These data were crucial in generating the results showcased in the paper.</li> <li><span>data.h5: An HDF5 file housing the correlators derived from the raw data through the processing code, <code>generate/transform_h5.py</code>.</span></li> <li><span>metadata.zip: This archive furnishes essential metadata such as ensemble information, fitting intervals, and smearing parameters crucial for extracting masses.</span></li> <li><span>F_meson.csv: Presents the fundamental meson masses extracted via the <code>analysis/analysis_F.py</code> script.</span></li> <li><span>AS_meson.csv: Presents the antisymmetric meson masses extracted via the <code>analysis/analysis_AS.py</code> script.</span></li> <li><span>CB_mass.csv: Presents the chimera baryon masses extracted via the <code>analysis/analysis_CB.py</code> script.</span></li> <li><span>FIT_mass.csv: Offers the AIC scan results conducted through the <code>analysis/analysis_AIC.py</code> script.</span></li> <li><span>FIT_cross_fixAS.csv and FIT_cross_fixF.csv: These files provide cross-check results computed by the</span> <span><code>analysis/analysis_cross.py</code> script, specifically for fixing antisymmetric and fundamental masses, respectively.</span></li> </ul>
Gauge adjusted radar data for the Namoi Catchment in Australia
<p>Within the Horizon 2020 Project WaterSENSE (grant agreement No 870344) a modular approach was developed to provide different stakeholders with the required precipitation information. An operational high-quality rainfall grid was set up for the Namoi catchment in Australia based on rain gauge adjusted radar data. Before the gauge adjustment several correction filters were applied to the original data. The original radar and rain gauge data was obtained from the Bureau of Meteorology (https://www.openradar.io/operational-network). The gauge adjusted radar data will serve as a benchmark for alternative precipitation data.<br> </p>
Gauge structure of the Einstein field equations in Bondi-like coordinates: convergence tests data
<p>This dataset is the result of the runs performed for the paper "Gauge structure of the Einstein field equations in Bondi-like coordinates". The data can be used to reproduce the convergence plots, as well as to compare with the data obtained if one performs the same runs independently.</p> <p> </p> <p>To perform independently the runs that produce the data, the PITTNullCode included has to be used. More details on how to proceed with this can be found in the ancillary files of https://arxiv.org/abs/2111.14794 at the directory "anc/numerics/README".</p>
Problems Using Data Gloves with Strain Gauges to Measure Distal Interphalangeal Joints' Kinematics (Experimental data)
<p>Experimental data from <em>"Problems Using Data Gloves with Strain Gauges to Measure Distal Interphalangeal Joints’ Kinematics", </em>available in Sensors.</p> <p> </p> <p><strong>"PHASE I - STATIC POSTURES, FREE MOTION AND GRASPING TASKS.xlsx" </strong> contains raw data of CyberGlove data glove of 22DoF while performing experiments detailed in Phase I.</p> <p>Jonts labelled as in <a href="https://www.nature.com/articles/s41597-019-0175-6">Human hand kinematic data during feeding and cooking tasks</a>. </p> <p>Task order detailed in "PHASE I TASK ORDER.txt".</p> <p>Subjects' hand length detailed in "PHASE I SUBJECT DATA.txt".</p> <p> </p> <p><strong>"PHASE II - SOLLERMAN HAND FUNCTION TEST.xlsx" </strong> contains joint angles recorded using CyberGlove data glove of 22DoF while performing experiments detailed in Phase II.</p> <p>Jont angles and sign criteria considered as in <a href="https://www.nature.com/articles/s41597-019-0175-6">Human hand kinematic data during feeding and cooking tasks</a>.</p> <p>Subjects' hand length and laterality detailed in "PHASE II SUBJECT DATA.txt".</p> <p> </p> <p>For further information please contact authors (rodaa@uji.es).</p>
Rain gauge data used in the study "Characteristics of Precipitation and Mesoscale Convective Systems over the Peruvian Central Andes in Multi 5-Year Convection-Permitting Simulations"
<p>The rain gauge data in Peru and Brazil used in the study,</p> <p>Yongjie Huang, Ming Xue, Xiao-Ming Hu, et al. Characteristics of Precipitation and Mesoscale Convective Systems over the Peruvian Central Andes in Multi 5-Year Convection-Permitting Simulations. <em>ESS Open Archive .</em> November 14, 2023.<br><span>DOI: <a href="https://doi.org/10.22541/essoar.170000370.07634797/v1" target="_blank" rel="noopener noreferrer">10.22541/essoar.170000370.07634797/v1</a></span></p> <p><span>The original data source:</span></p> <ul> <li>The rain gauge data in Peru are available at <a href="https://piscoprec.github.io/webPISCO/en/raingauges">https://piscoprec.github.io/webPISCO/en/raingauges</a> (last access: 18 July 2021).</li> <li>The rain gauge data in Brazil are available at <a href="https://bdmep.inmet.gov.br">https://bdmep.inmet.gov.br</a> (last access: 19 January 2023).</li> </ul>
Virtual tide gauges for predicting relative sea level rise supporting data
<p>Data and results from the publication</p> <p> Hawkins R., Husson L., Choblet G., Bodin T. and Pfeffer J.,<br> "Virtual tide gauges for predicting relative sea level rise",<br> JGR: Solid Earth,<br> 2019 (submitted)<br> </p> <p>Software available from </p> <p>https://github.com/rhyshawkins/TransTessellate2D/</p> <p> </p>
On the Landau gauge ghost-gluon-vertex close to and in the conformal window - data release
<h4>On the Landau gauge ghost-gluon-vertex close to and in the conformal window</h4><p>This repository contains the data presented in "On the Landau gauge ghost-gluon-vertex close to and in the conformal window". See the file README.md for more information.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.