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229 results for “Gauge”
Precipitation measurements from historic and current standard, storage and recording rain gauges at the Andrews Experimental Forest, 1951 to present
Andrews Forest precipitation has been measured continuously using various rain gage types since 1951. Most of these rain gages are standard (non-recording) gages with 7.5 or 8 inch orifices or large capacity storage gages intended for sites with limited access collected irregularly over longer intervals. Recording rain gages have also been established to collect higher temporal resolutions (e.g., 5 minute or 15 minute) and also used as a means of parsing (“prorating”) these periodic interval measurements from these standard and storage gages into daily totals. This data set includes an inventory of all rain gages that have operated within the Andrews as well as one site in the nearby Wildcat RNA and one in the town of Blue River. The inventory includes information regarding the date range of operation, gage location, type of gage, the rain network within which it was established, general availability of data and descriptive notes. A second table includes all of the raw measurement data for these non-recording gages over every interval where data were taken, and additionally includes the corresponding recording gage and its measurement total used to prorate data into a daily record. A third table includes the prorated daily data for all of these standard and storage gages as well as the true daily totals for two recording rain gages. A fourth table includes high temporal resolution for one early recording gage at Forks and the Mack Creek recording gage. Note that while precipitation data associated with the 6 benchmark stations are included in this rain gage inventory (Entity 1), the daily and high temporal resolution data for these sites were available through a separate meteorological data set, database code MS001, until 2025. In 2025, the benchmark station data was migrated here and will be combined with the Forks and Mack Creek data.
Graduated rain gauge (GRG) precipitation observations from 21 sites at the Jornada Basin LTER site, 1989-ongoing
This dataset contains long-term precipitation measurements from graduated rain gauges (GRGs) at 21 sites in the Jornada Basin of southern New Mexico, USA. Gauges are located on the Jornada Experimental Range (JER) and the Chihuahuan Desert Rangeland Research Center (CDRRC), and this set of gauges includes all 15 net primary production (NPP) study sites monitored by the Jornada Basin LTER program. At each site a 4 inch diameter cylindrical graduated rain gauge (11" x 0.01" capacity) is mounted on a 4x4 inch diameter redwood post or a wooden exclosure post next to gate at or near each site. For NPP sites, the primary collection is made on the day that monthly hydroprobe soil water content measurements are made. This enables correlation of precipitation with belowground soil water content. Additional data collections during the month may be made in coordination with other studies. Observations at each site come primarily from GRGs. However, at some sites in the NPP study, GRGs were not installed until later, and the nearest available rain gauge in the area has been used to gapfill the precipitation record prior to installation (details in methods section). Rain gauge identity and field measurement date is recorded with each observation in the data file. Other gauge types that may be listed are the Standard Can Gauge (DSRG or dipstick rain gauge), Belfort Weigh Bucket Rain Gauge (WBRG), and Qualimetrics Tipping Bucket Rain Gauge (TBRG). Data collection is ongoing for all 21 gauges in this dataset.
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>
Water levels at tide gauges from: Reconstruction of hourly coastal water levels and counterfactuals without sea level rise for impact attribution
<p>Data to reproduce the analysis of the Hourly Coastal water levels with Counterfactual (HCC) dataset, presented in the publication "<strong>Reconstruction of hourly coastal water levels and counterfactuals without sea level rise for impact attribution</strong>" published in Earth System Science Data (ESSD). </p><p>Note that in this repository, water levels are only provided tide gauge locations which were used for the analysis presented in the paper. The full Hourly Coastal water levels with Counterfactual (HCC) dataset is published in the <a href="https://doi.org/10.48364/ISIMIP.749905">ISIMIP repository</a>.</p><h2>File Descriptions</h2><h4>HCC_analysis_and_plots.ipynb</h4><p>This jupyter-notebook contains all scripts to produce the plots presented in the paper. Make sure that all necessary python packages are installed. The script assumes all netCDF files from this repository to be stored in a sub-directory called "data".</p><h3>hcc_gesla3_99pctl_surge_2011_2015.nc</h3><p>Extreme surge levels from 2011-2015 at 999 GESLA-3 tide gauge stations with at least 90 percent of data in the considered period. As astronomical tides are removed from the modeled and observed water levels to yield the surge component. The file also contains monthly relative water levels and monthly geocentric water levels from 1900-2015 from the HCC dataset.</p><h4>Variables:</h4><ul><li><i>observed_99pctl_surge_level_anomaly</i> -- 99th percentile of daily maximum surge level anomalies from 2011-2015</li><li><i>hcc_99pctl_surge_level_anomaly -- </i>HCC surge level anomalies at the same time steps as <i>observed_99pctl_surge_level_anomaly</i></li><li><i>hcc_counterfactual_99pctl_surge_level_anomaly</i> -- HCC counterfactual surge levels at the same time steps as <i>observed_99pctl_surge_level_anomaly</i></li><li><i>hcc_water_level_monthly</i> – Monthly relative water level from 1900-2015</li><li><i>hcc_geocentric_water_level_monthly</i> – Monthly geocentric water level from 1900-2015</li></ul><h3>hcc_hr_psmsl_water_level_monthly_1900_2015.nc</h3><p>Monthly water levels at 663 PSMSL tide gauge stations of at least 20 year length and with at least 30 percent data coverage in the 1993-2012 period. The file contains data from the HCC, HR and PSMSL datasets. To align PSMSL and HR with HCC, the 1993-2012 average from PSMSL and HR is removed from each of those datasets respectively and the 1993-2012 average of HCC is added. The average is calculated only over all time steps where the associated observational record has valid data.</p><h4>Variables:</h4><ul><li><i>hcc_water_level_monthly</i> – Monthly relative water level from the HCC dataset</li><li><i>hr_aligned_water_level_monthly</i> -- Monthly relative water level from the HR dataset, aligned with <i>hcc_water_level_monthly</i></li><li><i>psmsl_aligned_water_level_monthly</i> -- Monthly relative water level from the PSMSL database, aligned with <i>hcc_water_level_monthly</i></li></ul><h3>hcc_codec_hr_gesla3_water_level_hourly_monthly_1979_2015.nc</h3><p>Hourly water levels at 1040 GESLA-3 tide gauge stations which have at least 30 percent of valid observations between 1979 and 2015. The file contains data from the HCC, CoDEC, HR and GESLA-3 datasets. The different records are not vertically aligned.</p><h4>Variables:</h4><ul><li><i>gesla3_water_level_hourly</i> -- Hourly relative water level from the GESLA3 database</li><li><i>hcc_water_level_hourly</i> -- Hourly relative water level from the HCC dataset</li><li><i>codec_water_level_hourly</i> -- Hourly relative water level from the CoDEC dataset</li><li><i>hr_water_level_monthly</i> -- Monthly relative water level from the HR dataset</li></ul><h3> </h3><h3>hcc_gesla3_water_level_hourly_2011_2015.nc</h3><p>Water levels from the HCC and GESLA-3 datasets, only for tide gauge stations with a complete record in the period 2011-2015 and associated HCC grid points.</p><h4>Variables:</h4><ul><li><i>gesla3_water_level_hourly</i> -- Hourly relative water level from the GESLA3 database</li><li><i>hcc_water_level_hourly</i> -- Hourly relative water level from the HCC dataset</li></ul><h3>slr_ds_psmsl_selected.nc</h3><p>Linear estimates of relative sea level rise from 1900 to 2015. Data is provided at 663 PSMSL tide gauge stations of at least 20 year length and with at least 30 percent data coverage in the 1993-2012 period. Estimates are calculated for the HCC, HR and PSMSL datasets.</p><h4>Variables:</h4><ul><li><i>psmsl_rslr, psmsl_rslr_lower, psmsl_rslr_upper</i> -- Relative sea level rise for PSMSL with lower and upper bounds for a 95 percent confidence interval</li><li><i>hcc_long_rslr, hcc_long_rslr_lower, hcc_long_rslr_upper </i>-- Relative sea level rise for HCC with lower and upper bounds for a 95 percent confidence interval</li><li><i>hr_rslr, hr_rslr_lower, hr_rslr_upper</i> -- Relative sea level rise for HR with lower and upper bounds for a 95 percent confidence interval</li></ul><h3>reg_mask_xr.nc</h3><p>Split of the world into 7 ocean basins: Indian Ocean - South Pacific, Northwest Pacific, East Pacific, South Atlantic, Subtropical North Atlantic, Subpolar North Atlantic West and Subpolar North Atlantic East.</p><h4>Variables:</h4><p><i>reg_mask</i> – Float value, representing the ocean basins</p><p> </p>
CFD simulation and measurements of effect of wind on non-catching rain gauge
<p>Simulation_dataset file shows the results of a CFD simulation of the measurements of a Thies laser precipitation monitor under different conditions of wind.</p> <p>Wind_tunnel_dataset shows the results of the model validation using an actual wind tunnel.</p>
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>
Dataset for "Light Scalar Meson and Decay Constant in SU(3) Gauge Theory with Eight Dynamical Flavors"
<p><strong>Decoding File Names</strong>: Consider the file name f8l24t48b48m00889_S0.csv. We will break down the meaning of the various pieces of the filename</p> <ul> <li>"f8" means 8 Dirac flavors.</li> <li>"l24t48" means 24<sup>3</sup>×48 lattice.</li> <li>"b48" means beta=4.8, related to the inverse bare gauge coupling.</li> <li>"m00889" means fermion mass m=0.00889.</li> <li>"S" means flavor-singlet scalar meson. Other options are "P" for flavor non-singlet pseudoscalar meson and "C" for flavor non-singlet scalar meson.</li> <li>"0" an integer from 0 to 4 proportional to the squared length of the spatial momentum vector of the correlation function.</li> </ul> <p><strong>Columns of the CSV files</strong>: Each line of the CSV file should contain 41 entries, separated by commas. Refer to the Eq. (8) which defines model A in the accompanying paper to understand the physical interpretation of these parameters.</p> <ol> <li>Model number: 1 is model A, 2 is model B, 3 is model C.</li> <li>n<sub>max</sub>: the number of non-oscillating states in the fit.</li> <li>j<sub>max</sub>: the number of oscillating states in the fit.</li> <li>t<sub>min</sub>: the minimum t value used in the fit.</li> <li>t<sub>max</sub>: the maximum t value used in the fit.</li> <li>𝜒<sup>2</sup> of the fit.</li> <li><span class="math-tex">\(\log\ p\left(\left.M\right|D\right)\)</span>: log of model probability used in Bayesian model averaging.</li> <li>fit value for c<sub>0</sub> (model A) or <span class="math-tex">\(\overline{c}_0\)</span> (model B).</li> <li>fit error for c<sub>0</sub> (model A) or <span class="math-tex">\(\overline{c}_0\)</span> (model B).</li> <li>fit value for c<sub>1</sub>.</li> <li>fit error for c<sub>1</sub>.</li> <li>fit value for c<sub>2</sub>.</li> <li>fit error for c<sub>2</sub>.</li> <li>fit value for c<sub>3</sub>.</li> <li>fit error for c<sub>3</sub>.</li> <li>fit value for c<sub>4</sub>.</li> <li>fit error for c<sub>4</sub>.</li> <li>fit value for <span class="math-tex">\(c_1^\prime\)</span>.</li> <li>fit error for <span class="math-tex">\(c_1^\prime\)</span></li> <li>fit value for <span class="math-tex">\(c_2^\prime\)</span>.</li> <li>fit error for <span class="math-tex">\(c_2^\prime\)</span>.</li> <li>fit value for <span class="math-tex">\(c_3^\prime\)</span>.</li> <li>fit error for <span class="math-tex">\(c_3^\prime\)</span>.</li> <li>fit value for <span class="math-tex">\(c_4^\prime\)</span>.</li> <li>fit error for <span class="math-tex">\(c_4^\prime\)</span>.</li> <li>fit value for <span class="math-tex">\(\log(E_2 - E_1)\)</span>.</li> <li>fit error for <span class="math-tex">\(\log(E_2-E_1)\)</span>.</li> <li>fit value for <span class="math-tex">\(\log(E_3-E_2)\)</span>.</li> <li>fit error for <span class="math-tex">\(\log(E_3-E_2)\)</span>.</li> <li>fit value for <span class="math-tex">\(\log(E_4-E_3)\)</span>.</li> <li>fit error for <span class="math-tex">\(\log(E_4-E_3)\)</span>.</li> <li>fit value for <span class="math-tex">\(\log(E_2^\prime - E_1^\prime)\)</span>.</li> <li>fit error for <span class="math-tex">\(\log(E_2^\prime - E_1^\prime)\)</span>.</li> <li>fit value for <span class="math-tex">\(\log(E_3^\prime - E_2^\prime)\)</span>.</li> <li>fit error for <span class="math-tex">\(\log(E_3^\prime - E_2^\prime)\)</span>.</li> <li>fit value for <span class="math-tex">\(\log(E_4^\prime - E_3^\prime)\)</span>.</li> <li>fit error for <span class="math-tex">\(\log(E_4^\prime - E_3^\prime)\)</span>.</li> <li>fit value for <span class="math-tex">\(\log(E_1)\)</span>.</li> <li>fit error for <span class="math-tex">\(\log(E_1)\)</span>.</li> <li>fit value for <span class="math-tex">\(\log(E_1^\prime)\)</span>.</li> <li>fit error for <span class="math-tex">\(\log(E_1^\prime)\)</span>.</li> </ol>
Annual bedload accumulation from sediment basin surveys in small gauged watersheds in the Andrews Experimental Forest, 1957 to present
Sediment debris basins are established within the Andrews Experimental Forest as part of paired watershed experiments examining differences in streamflow and nutrient chemistry due to timber harvest. Basins are constructed below the stream gaging station in each of five basins, and these basins and the deposits of sediment within them are re-surveyed or emptied annually to measure bedload sediment production. Basins are measured on Watersheds 1, 2 (control) and 3 beginning with wateryear 1957 and on Watersheds 9 (control) and 10 beginning wateryear 1974. Data collection is ongoing at an annual time step. Data provided include the watershed name, wateryear, survey method, watershed area, annual bedload volume and accumulation rate. These data display both the chronic production of sediment, as well as pulsed, episodic bedload from landslides within the contributing basins.
Stream discharge and bedload accumulation in gauged watersheds at the South Umpqua Experimental Forest, Coyote Creek, 1963 to 1981 and 2001 to present
Stream discharge is collected on four small watersheds in the Coyote Creek drainage within the South Umpqua Experimental forest in the southwest Oregon Cascades. Stream discharge data was started in October 1963 and discontinued in June 1981 (discontinued April 1985 on Watershed 4). Stream discharge measurement was resumed in December 2000 on all four watersheds. Watersheds 1, 2, and 3 were harvested with differing silvicultural methods in summer 1971, and watershed 4 is the control. High resolution temporal data is provided as well as daily, monthly and annual summary data. Streamflow data by sampling intervals are also provided from 1970 to 1981 when stream water chemistry data were being collected. Annual bedload accumulation totals from each of the four watersheds is also provided beginning 2001.
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.
Magnetism and anomalous transport in the Weyl semimetal PrAlGe: Possible route to axial gauge fields
<p>The file ManuscriptDataFiles.7z contains the raw experimental data from which the figures are made in the manuscript entitled "Magnetism and anomalous transport in the Weyl semimetal PrAlGe: Possible route to axial gauge fields" that appeared in npj Quantum Materials <strong>5</strong>, 5 (2020).</p> <p>Paper Abstract: In magnetic Weyl semimetals, where magnetism breaks time-reversal symmetry, large magnetically sensitive anomalous transport responses are anticipated that could be useful for topological spintronics. The identification of new magnetic Weyl semimetals is therefore in high demand, particularly since in these systems Weyl node configurations may be easily modified using magnetic fields. Here we explore experimentally the magnetic semimetal PrAlGe, and unveil a direct correspondence between easy-axis Pr ferromagnetism and anomalous Hall and Nernst effects. With sizes of both the anomalous Hall conductivity and Nernst effect in good quantitative agreement with first principles calculations, we identify PrAlGe as a system where magnetic fields can connect directly to Weyl nodes via the Pr magnetization. Furthermore, we find the predominantly easy-axis ferromagnetic ground state co-exists with a low density of nanoscale textured magnetic domain walls. We describe how such nanoscale magnetic textures could serve as a local platform for tunable axial gauge fields of Weyl fermions.</p>
Supplementary Dataset for "Extracting near-field seismograms from ocean-bottom pressure gauge inside the focal area: application to the 2011 Mw 9.1 Tohoku-Oki earthquake"
<p>Datasets S1 contains the results obtained by the analysis in this study, such as the spatial and temporal configuration of the basis functions. Dataset S2 contains the ocean-bottom pressure gauge data used in this study.</p> <p>The manuscript is available at: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL091664</p> <p> </p> <p> </p> <div> </div>
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>
Disorder-free localization transition in a two dimensional lattice gauge theory
<p>Data files for Figs 2 and 3 from the paper "Disorder-free localization transition in a two dimensional lattice gauge theory".</p>
Salt gauging and stage-discharge curve, Avançon de Nant, outlet Vallon de Nant catchment
<p>This data set contains the salt gaugings completed over the period 2016 - 2017 to establish a stage-discharge curve at the <a href="https://s.geo.admin.ch/7834d541d2">outlet of the Vallon de Nant catchment</a>. The data set contains also the latest version of the estimated stage-discharge curve, compared to the theoretical curve estimated from the geometric properties of the weir.</p> <p>The salt gaugings have been obtained independently by two different research groups from</p> <ul> <li>Institute of Earth Surface Dynamics (<a href="https://www.unil.ch/idyst/en/home/menuinst/research-topics/water-and-elemental-cycles/catchment-hydrology.html">IDYST</a>), Faculty of Geosciences and Environement (FGSE), University of Lausanne (UNIL),</li> <li>Stream Biofilm and Ecosystem Research Laboratory (<a href="https://sber.epfl.ch/">SBER</a>), School of Architecture, Environmental and Civil Engineering (ENAC), Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland,</li> </ul> <p>The stream gauging station itself was constructed thanks to a joint funding by the Swiss Federal Institute for Forest, Snow and Landscape (WSL), University of Lausanne (chair of Prof. S. Lane) and ETH Zürich (chair of Prof. J. Kirchner).</p> <p>The gauging station is maintained by the <a href="http://www.wsl.ch/en/about-wsl/organization/research-units/mountain-hydrology.html">Mountain Hydrology and Mass Movements research uni</a>t of the Swiss Federal Institute for Forest, Snow and Landscape (WSL) and by the<a href="https://www.unil.ch/idyst/en/home.html"> Institute of Earth Surface Dynamics</a> of University of Lausanne.</p> <p> </p>
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>
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.