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229 results for “Gauge”
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>
Satellite-based precipitation estimates using a dense rain gauge network over the Southwestern Brazilian Amazon: Implication for identifying trends in dry season rainfall
<h1>Satellite-based precipitation estimates using a dense rain gauge network over the Southwestern Brazilian Amazon.</h1>
Spectral response of disorder-free localized lattice gauge theories
<p>Raw data for all figures in the manuscript "Spectral response of disorder-free localized lattice gauge theories"</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>
Dataset and scripts for "Non-zero temperature study of spin 1/2 charmed baryons using lattice gauge theory"
<p><strong>charmJ12Scripts</strong></p> <p>A set of scripts and folders to reproduce the analysis and plots in the spin 1/2 charm baryon paper which can be found at <a href="https://doi.org/10.1140/epja/s10050-024-01261-2">EPJA</a></p> <p> </p> <p>This repository includes the raw correlator data, the scripts and software used to analyse them as well as a script which can be run in order to reproduce the entire analysis, and particularly the figures in the manuscript.</p> <p> </p> <p><strong>correlators</strong></p> <p>Correlators from openqcd-fastsum-hadspec are zipped in the correlators folder. These are unzipped automatically by the script. The correlators are plain text files.</p> <p> </p> <p><strong>output</strong></p> <p>Analysis output is placed here. You do not need to look here in order to see the figures in the paper</p> <p> </p> <p><strong>code</strong></p> <p>The python code and scripts that do the analysis. There is some modularity here with the libraries in the lib folder</p> <p> </p> <p><strong>paperPlots</strong></p> <p>The plots from the paper will be generated here. They are not supplied with this repo as they can be found in the paper</p> <p> </p> <p><strong>plotXYData</strong></p> <p>The x-y and y-error data of each plot in the paper. Only 'scatter' style data is included. This is generated by the run script, but also supplied herein. It will be overwritten by the runscript</p> <p> </p> <p><strong>run</strong></p> <p>The folder where the main script needed to run all the analysis is.</p> <p> </p> <p><strong>Conda Notes</strong></p> <p>Install your favourite conda solution, such as <a href="https://docs.conda.io/en/latest/miniconda.html">https://docs.conda.io/en/latest/miniconda.html</a></p> <p> </p> <p>Switch to a faster environment solver</p> <p>This is optional, but likely will solve the dependencies much much faster. See <a href="https://www.anaconda.com/blog/a-faster-conda-for-a-growing-community">https://www.anaconda.com/blog/a-faster-conda-for-a-growing-community</a> <code>conda update -n base conda</code> <code>conda install -n base conda-libmamba-solver</code> <code>conda config --set solver libmamba</code></p> <p> </p> <p>Install Environment</p> <p><code>conda env create -f environment.yml</code></p> <p> </p> <p>Activate/Use</p> <p><code>conda activate charm</code></p> <p> </p> <p>Update (w. new packages)</p> <ol> <li>Edit <code>environment.yml</code></li> <li>Deactivate conda environment with <code>conda deactivate</code></li> <li>Update conda environment with <code>conda env update -f=environment.yml</code></li> </ol>
Annual summaries of daily observations from the USGS Streamflow Gauging Station on the Altamaha River near Doctortown, Georgia, for 1932 to 2004
Daily averaged river discharge data were obtained from the United States Geological Survey for streamflow gauging station USGS 02226000 on the Altamaha River near Doctortown, Georgia. Processed tabular data were downloaded from the USGS real-time web server (http://waterdata.usgs.gov/nwis/) by the Georgia Coastal Ecosystems LTER project, documented, and standardized to metric units. Missing values of mean discharge were estimated by cubic spline interpolation to fill in data gaps of five or fewer consecutive days. Annual summary statistics were then calculated from daily values aggregated by year.
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).
Daily precipitation from a network of weighing rain gauges on the Jornada Experimental Range (Jornada Basin LTER), 1976-2011.
This completed dataset contains daily precipitation measurements from a network of weighing rain gauges (Belfort Universal Precipitation Gauges, Series 5-780) at 58 locations on the Jornada Experimental Range in Dona Ana County, New Mexico, USA between January 1976 and December 2011. Locations and the dates during which data were collected were generally project-oriented. Temporal coverage per location is quite variable and ranges between 1,647 and 13,024 days. The network was maintained by USDA Agricultural Research Service personnel. After 2011 the remaining weighing rain gauges were replaced by tipping bucket gauges and those data are available separately.
Gauging Size Resolved Ambient Particulate Matter Concentration Solely Using Biometric Observations: A Machine Learning and Causal Approach
<p>Notebook and data to accompany the (unpublished) paper titled "Gauging Size Resolved Ambient Particulate Matter Concentration Solely Using Biometric Observations: A Machine Learning and Causal Approach". This work expands a previous study, relating particulate matter concentrations and short-term biometric features across multiple participants. </p><p>Github link: https://github.com/mi3nts/DUEDARE_multiple_participants</p>
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>
The use of GRDC gauging stations for calibrating large-scale hydrological models
<p>The Global Runoff Data Centre provides time series of observed discharges that are very valuable for calibrating and validating the results of hydrological models. We address a common issue in large-scale hydrology which, though investigated several times, has not been satisfactorily solved. Grid-based hydrological models need to fit the reported station location to the river network depending on the resolution, to compare simulated discharge with observed discharge. We introduce an Intersection over Union ratio approach to selected station locations on a coarser grid scale, reducing the errors in assigning stations to the wrong basin. We update the 10-year-old database of watershed boundaries with additional stations based on a high-resolution (3 arcseconds) river network, and we provide source codes and high- and low-resolution watershed boundaries.</p> <p>Same as release on Github: https://github.com/iiasa/CWATM_grdc_calibration_stations/releases/tag/V1.0</p>
Strain gauge platforms: Time-lapse microscopy dataset of engineered cardiac microbundles
<p>This dataset is a "part I" extension of the "<a href="https://doi.org/10.5061/dryad.5x69p8d8g">Engineered cardiac microbundle time-lapse microscopy image dataset</a>" and contains 732 experimental time-lapse image sequences of beating hiPSC-based cardiac microbundles using microbundle strain gauge platforms [1] ("Type1"). In the "part II" extension, we included 808 experimental time-lapse image sequences of beating hiPSC-based cardiac microbundles using FibroTUG platforms [2] ("Type2"). </p> <p>References:</p> <p>[1] Zhang, K., Cloonan, P. E., Sundaram, S., Liu, F., Das, S. L., Ewoldt, J. K., ... & Chen, C. S. (2021). Plakophilin-2 truncating variants impair cardiac contractility by disrupting sarcomere stability and organization. <em>Science Advances</em>, <em>7</em>(42), eabh3995.</p> <p>[2] DePalma, S. J., Davidson, C. D., Stis, A. E., Helms, A. S., & Baker, B. M. (2021). Microenvironmental determinants of organized iPSC-cardiomyocyte tissues on synthetic fibrous matrices. <em>Biomaterials science</em>, <em>9</em>(1), 93-107.</p>
Hydrological Station Uttendorf (ÖBB): water gauge measurement
<p>The Austrian Hydrographic Service operates a basic measuring network for recording precipitation, determining flow and water levels in rivers and lakes and monitoring groundwater levels in Austria. This data is organised into precipitation, discharge and groundwater level classes and is accessible via ehyd.gv.at</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>
Tohoku Tsunami, March 11 2011: GoogleEarth-Screenshot of Saip tide gauge station at 10:08 hours CET
<p>GoogleEarth-Screenshot of Saip tide gauge station on March 11 at 10:08 hours CET. Tide gauge station live data provided from Marine Obs by Program - National Data Buoy Center - NOAA (<a href="https://deref-gmx.net/mail/client/_W-lmx-gCeY/dereferrer/?redirectUrl=http%3A%2F%2Fwww.ndbc.noaa.gov%2Fkml%2Fmarineobs_by_pgm.kml">www.ndbc.noaa.gov/kml/marineobs_by_pgm.kml</a>).</p>
Tohoku Tsunami, March 11 2011: GoogleEarth-Screenshot of Wake tide gauge station at 10:07 hours CET
<p>GoogleEarth-Screenshot of Wake tide gauge station on March 11 at 10:07 hours CET. Tide gauge station live data provided from Marine Obs by Program - National Data Buoy Center - NOAA (<a href="https://deref-gmx.net/mail/client/_W-lmx-gCeY/dereferrer/?redirectUrl=http%3A%2F%2Fwww.ndbc.noaa.gov%2Fkml%2Fmarineobs_by_pgm.kml">www.ndbc.noaa.gov/kml/marineobs_by_pgm.kml</a>).</p>
ScienceDex guides
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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.