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21,983 results for “UNITE”
Three-dimensional subnanoscale imaging of unit cell doubling due to octahedral tilting and cation modulation in strained perovskite thin films
<p>Transmission electron microscopy data used in the journal publication <a href="https://doi.org/10.1103/PhysRevMaterials.3.063605">"Three-dimensional subnanoscale imaging of unit cell doubling due to octahedraltilting and cation modulation in strained perovskite thin films"</a></p> <p><strong>Data files</strong></p> <p>There are two data types:</p> <ul> <li>Scanning TEM (STEM) diffraction patterns acquired with a Medipix3 detector (Merlin): m004_LSMO_LFO_STO_medipix.hdf5 <ul> <li>Acquired on a probe corrected Jeol ARM200CF</li> <li>Acceleration voltage: 200 kV</li> <li>Convergence semi-angle: 20.4 mrad (calibrated using the SrTiO<sub>3</sub> substrate HOLZ ring)</li> <li>Detector calibration: 1.357 mrad per pixel (calibrated using the SrTiO<sub>3</sub> substrate HOLZ ring)</li> </ul> </li> <li>Atomic resolution STEM data, both annular dark field (ADF) and annular bright field (ABF), which were acquired simultaneously: s007_ADF.hdf5, s007_ABF.hdf5</li> </ul> <p>The data can be loaded in python using h5py.</p> <p>For the Medipix3 data:</p> <pre><code class="language-python">import h5py f = h5py.File('m004_LSMO_LFO_STO_medipix.hdf5', mode='r') data = f['fpd_expt/fpd_data/data'] data_subset = data[0:16, 0:16, :, :]</code></pre> <p>For the STEM-ADF or STEM-ABF data:</p> <pre><code class="language-python">import h5py f = h5py.File('s007_ADF.hdf5', mode='r') data = f['Experiments/__unnamed__/data']</code></pre> <p>Exploring the Medipix3 dataset lazily, i.e. without loading the whole dataset into memory at the same time. Using pixStem:</p> <pre><code class="language-python">import pixstem.api as ps s = ps.load_ps_signal("003_stripe1.hdf5", lazy=True) s.plot()</code></pre> <p>Loading the STEM-ADF or STEM-ABF data using HyperSpy, which automatically loads the probe scaling:</p> <pre><code class="language-python">import hyperspy.api as hs s = hs.load("s007_ADF.hdf5") s.plot()</code></pre> <p><br> <strong>Processing files</strong></p> <p>All the TEM data has been processed using python scripts, which is named based on the type of processing:</p> <ul> <li>d00N_...: Medipix3 data processing</li> <li>a00N_...: Atomic resolution STEM-ADF and STEM-ABF processing using Atomap</li> </ul> <p>The scripts generate intermediate files, which are saved in folders with the same prefix as the scripts. So the d001_... script makes a folder named d001_... . These intermediate files are included here as zip-files, since Zenodo doesn't support folder structures.</p> <p>The python libraries required to run the scripts are listed in requirements.txt. Newer versions of the libraries will most likely also work.</p> <p>To setup the python environment with the required libraries, and run all the scripts:</p> <pre><code class="language-bash">pip3 install -r requirements.txt python3 run_all_scripts.py</code></pre> <p> </p>
Terrestrial water storage changes across the contiguous United States from GPS and GRACE, 2007–2017
<p>In this dataset, we provide terrestrial water storage anomalies (TWSA) from 2007-2017 at weekly time scales derived using Global Positioning System (GPS) displacements, further constrained by lower-resolution TWSA observations from the Gravity Recovery and Climate Experiment (GRACE).</p> <p>There are six fields in the HDF5 product provided here:</p> <ol> <li>'/cmwe', which provides terrestrial water storage in units of cm. of water equivalent.</li> <li>'/latitude', latitude at the center of each 0.5 degree grid cell</li> <li>/longitude', longitude at the center of each 0.5 degree grid cell</li> <li>'/time', time in days since January 1st, 2007. The resolution of our time series is weekly, and the first day in our record is January 3rd, 2007.</li> <li>'/signal_to_noise_ratio', the variance of the signal divided by variance of noise for each grid cell in the dataset. Please read the supplementary information document in the paper below for more details.</li> <li>'/uncertainty', 95% confidence interval for each grid cell in the dataset. Please read the supplementary information document in the paper below for more details.</li> </ol> <p>As a condition of using these data, we request that you acknowledge the authors of this data set by citing the following peer-reviewed publication. </p> <p>Adusumilli, S., Borsa, A. A., Fish, M. A., McMillan, H. K., & Silverii, F. (2019). A decade of water storage changes across the contiguous United States from GPS and Satellite Gravity. <em>Geophysical Research Letters</em>, 46, 13006-13015. <a href="https://doi.org/10.1029/2019GL085370">https://doi.org/10.1029/2019GL085370</a></p>
AirHeritage Datalake: Multi-site, Multi-season, Multi Unit dataset including Fixed and Mobile Citizen science data from networked Air Quality Low-Cost Multi-Sensors devices and reference stations
<p>This datalake comprises several datasets from <strong>37 networked low cost air quality multisensors</strong> (<strong>30</strong> <strong>mobile</strong> ENEA MONICA(tm) + <strong>7</strong> <strong>fixed</strong>) along with <strong>3</strong> (fixed) + <strong>1</strong> (mobile) <strong>reference stations</strong> operated by Campania Regional Envronmental Protection Agency. The datalake is organized in 3 main directories respectively related to fixed nodes, mobile nodes and nearby reference stations including a mobile laboratory used for colocation campaigns; each subdirectory include its own metadata description file.</p> <p>Data, curated by Energy and Data Science Laboratory of ENEA, include multi-weeks colocation periods when low cost devices have been colocated with reference stations as well as operational periods during which sensors are deployed for fixed or mobile monitoring campaigns. Data have been recorded during 2021 and 2022 in a<strong> pervasive, multi-site, multi-seasonal deployment</strong> in Portici, a densely populated small area city (4km2, 55k + inhabitants) located 7km south of Naples, Italy.</p> <p>The datalake consists in actual sensors and reference intrumentations timeseries along with metadata description files with deployment dates and location data. The dataset files include high sampling frequency raw sensor data of quality-controlled sensor network along with co-located reference stations data sets. Sensor data include electrochemical sensors data (intended target pollutants: NO2, O3, CO), Optical sensor data (PM2.5, PM10, PM1) readings along with meteorological parameters. .</p> <p>Further description of sensors and reference instruments are reported in the accompanying paper (see citation request).</p> <p>The dataset can be used for </p> <ul> <li> <strong>advanced (remote/universal/in field) data driven calibration strategies</strong> test or development including <strong>machine learning </strong>models</li> <li><strong>mobile opportunistic data fusion</strong> methods development</li> <li><strong>geomatics and data assimilation</strong> models studies</li> </ul> <p>as well as low cost sensor characterization performance studies. </p>
Displacements on selected rock glaciers in the United States
<p>Lateral displacements between two years on selected rock glaciers in the United States (Rocky Mountains and westwards). Displacements were measured using image correlation on repeat orthoimages from the United States Geological Survey archive. Correlation software used: CIAS (http://mn.uio.no/icemass).</p> <p><em><strong>The data are the raw data behind the following publication. This publication contains more details about the measurements. </strong></em></p> <p><a href="https://www.nature.com/articles/s41467-024-52093-z">Kääb, A., Røste, J. Rock glaciers across the United States predominantly accelerate coincident with rise in air temperatures. Nat Commun 15, 7581 (2024). https://doi.org/10.1038/s41467-024-52093-z</a></p> <h3>Detailed rock glacier positions [lat°, lon°]:</h3> <p>Star Peak [44.253,-120.417]<br>Galena creek [44.645,-109.791]<br>Sulphur creek [44.617,-109.756]<br>Crater Mtn [44.023,-109.627]<br>Old Hyndman [43.743, -114.106]<br>Ferguson ranch [39.270, -107.194]<br>Thomas lake [39.270,-107.155]<br>Arapaho [40.020,-105.641]<br>Mt Mears [38.018,-107.871]<br>Mt Sneffels [38.010,-107.781]<br>Teakettle Mtn [38.011,-107.768]<br>Twin Sisters [37.762,-107.803]<br>Pine creek [37.078,-118.450]<br>Birch creek [37.065,-118.431]<br>Cardinal Mtn N [37.011,-118.414]<br>Cardinal Mtn S [37.008,-118.409].</p> <h3>Names of individual displacement files:</h3> <p>Short-name-of-rock-glacier_year1_year2_correlation-window-size_search-window-size_xxx.txt/.csv<br>xxx is either 'helm' indicating that the two orthoimages have been coregistered using Helmert transformation, or 'filt' indicating Helmert transformation and filtering of grid-based measurements for outliers. </p> <h3>Data columns of each file:</h3> <p>X: UTM coordinate, Easting of displacement measurement point <br>Y: UTM coordinate, Northing of displacement measurement point<br>dx: displacement component in east<br>dy: displacement component in north<br>length: pythagoras of dx and dy (vector length)<br>direction: azimuth of vector (from North, clock-wise)<br>max_corrcoeff: correlation coefficient of displacement match<br>avg_corrcoeff: background correlation coefficent at a matching poistion. <br><em><strong> </strong></em></p> <p> </p>
Estimated individual methane emission rates for oil and gas facilities from the continental United States in 2021
<p>File containing 500 separate estimates of 673,940 individual facility-level methane emission rates for oil and gas facilities for the year 2021 in the continental United States. Each column contains one full estimate of the individual facility-level emissions, presented in units of kilograms per hour of methane per facility. The facility categories included in these estimates are production well sites, gathering and boosting compressor stations, transmission and storage compressor stations, processing plants, and flares. This data can be used to recreate the 500 emission distributions presented in Figure 3 in the following manuscript (link: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1402) which is currently under review. This dataset may be updated as the review stages progress</p>
National Checklists 2017: United States Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details<p></p>A list of species from the United States collected using effechecka and geonames polygons
National Checklists 2017: United Kingdom Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details<p></p>A list of species from United Kingdom collected using effechecka and geonames polygons
National Checklists 2017: United Arab Emirates Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details<p></p>A list of species from United Arab Emirates collected using effechecka and geonames polygons
National Checklists 2019: United Kingdom Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details.<p></p>A list of species from United Kingdom collected using effechecka and geonames polygons
National Checklists 2019: United States Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details.<p></p>A list of species from the United States collected using effechecka and geonames polygons
National Checklists 2019: United Arab Emirates Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details.<p></p>A list of species from United Arab Emirates collected using effechecka and geonames polygons
Output of global termite CH4 emission estimation (unit corrected: g CH4/m2/yr)
<p>NetCDF file: grid map of annual emissions from 1901 to 2021</p>
Acoustic- and Moth sampling at Etonbury Wood (Bedford) - United Kingdom
<p>Moths sampling by hand of led buckets (https://www.vlinderstichting.nl/wat-wij-doen/meetnetten/meetnet-nachtvlinders/ledemmers/) and acoustic sampling by hand of AudioMoths (https://www.openacousticdevices.info/audiomoth) in a silvoarable system</p>
Polyubiquitin ligand-induced phase transitions are optimized by spacing between ubiquitin units
<p>These are the original data used to make figures for the manuscript titled "Polyubiquitin ligand-induced phase transitions are optimized by spacing between ubiquitin units" by Sarasi Galagedera et al.</p> <p> </p>
Deep Learning Reach-level Estimates of Mean River Depth at the Conterminous United States Spatial Scale
<p>Abstract: Estimates of riverine channel geometry play a vital role in the physical representation of stream networks in models used to predict flood and drought conditions, manage water resources, and increase our knowledge of fluvial conditions under a changing climate. A well established body of literature exists that explains the relationship between channel geometry parameters width, depth, and velocity to instantaneous river discharge using a log-log linear power-law regression. In this study, a state-of-the-art deep learning regression model is presented and compared against the power-law method to evaluate their ability to estimate cross-sectional mean river depth. Results reveal three key findings, the neural network: (1) decreases RMSE by 22% verse a CONUS scale power-law equation, (2) reduces prediction variance across Strahler stream orders, and (3) generally outperforms regional power-law equations with an average decrease in RMSE of 8.7% Lastly, a reach-level CONUS dataset of estimated mean river depth is delivered.</p> <p> </p> <p>The deep learning model was trained using the following features:</p> <ul> <li>AI - Mean aridity index of unit catchment - Trabucco and Zomer, 2019</li> <li>area - Upstream drainage area (km2) - P. Lin et al., 2020</li> <li>CLY - Mean clay content (mass percentage, %) of unit catchment - Hengl et al., 2017</li> <li>DOR - Stream segment degree of dam regulation (Scale 0. – 100.) - Grill et al., 2019</li> <li>Elev - Stream segment mean elevation - P. Lin et al., 2020</li> <li>K - Mean bedrock permeability of unit catchment surrounding stream segment - Huscroft et al., 2018</li> <li>LAI - Mean leaf area index of unit catchment - Zhu et al., 2013</li> <li>order - Strahler-Horton stream order - P. Lin et al., 2020</li> <li>P - Mean bedrock porosity of unit catchment - Huscroft et al., 2018</li> <li>QMEAN - Stream segment mean annual discharge (m3/s) - P. Lin et al., 2019</li> <li>Sin - Stream segment sinuosity - P. Lin et al., 2020</li> <li>Slp - Stream segment mean longitudinal slope - P. Lin et al., 2020</li> <li>SLT - Mean silt content (mass percentage, %) of unit catchment - Hengl et al., 2017</li> <li>SND - Mean sand content (mass percentage, %) of unit catchment - Hengl et al., 2017</li> <li>stream_wdth_va - Measured stream cross-sectional width (m) - Canova et al., 2016</li> <li>Urb - Mean urban fraction of unit catchment - Liu et al., 2018</li> </ul> <p>The deep learning model was trained using the following label:</p> <ul> <li>mean_depth_va - Measured stream mean depth (m) - Canova et al., 2016</li> </ul> <p>Predictions of mean depth were made by replacing stream_wdth_va from Canova et al., (2016) with bankfull width estimates (width_m) from P. Lin et al., (2020). Missing records from the P. Lin et al., (2020) dataset were excluded when making predictions, thus there are missing reaches in the dataset.</p>
LANID-US: Landsat-based Irrigation Dataset for the United States
<p><strong>Annual 30-m resolution irrigation maps, derivative products, and ground reference locations for the United States, 1997-2017</strong>, including: </p> <p>(1) lanidYYYY (binary): Annual irrigation layer for the year YYYY (ranging from 1997 to 2017), e.g., lanid2017 is the irrigation map for the year 2017.</p> <p>(2) irrFreq (1-21): The number of years irrigated during 1997-2017.</p> <p>(3) irrFreqPasture_West: irrFreq of pasture/hay for the western CONUS.</p> <p>(4) maxIrrExt (binary): Croplands that have been irrigated at least once during 1997-2017.</p> <p>(5) irrFreqChange (1-21): The difference of number of years irrigated between 1998-2007 and that of between 2008-2017 (i.e., irrFreq<sub>2008-2017</sub> - irrFreq<sub>1998-2007</sub>).</p> <p>(6) irrAreaGain (-1684-2333): LANID-derived irrigation gain (in hectare) from 1998-2007 to 2008-2017 at the 6km×6km scale.</p> <p>(7) formerIrr (binary): Not irrigated anytime in 2015-2017, but irrigated at least 3 times prior.</p> <p>(8) irrWholePeriod (binary): Irrigated at least once for both 1997-1999 and 2015-2017.</p> <p>(9) intermittentIrr (binary): Irrigated at least once for both 1997-1999 and 2015-2017, and irrigation frequency ≤ 18.</p> <p>(10) irrSamples_eastCONUS: Sample locations of center pivot fields in the eastern CONUS (4976 sample locations).</p> <p>(11) rainfedSamples_eastCONUS: Sample locations of stable rainfed fields in the eastern CONUS (4978 sample locations).</p> <p><strong>For technical details that used to create the dataset, please refer to <em>https://doi.org/10.1016/j.rse.2021.112445</em>; Data description details are available via <em>https://essd.copernicus.org/preprints/essd-2021-207/</em>.</strong></p>
A global inventory of solar photovoltaic generating units - dataset
<p>This is the data repository accompanying Kruitwagen, L., Story, K., Friedrich, J., Byers, L., Skillman, S., & Hepburn, C. (2021) A global inventory of photovoltaic solar generating units, <strong>Nature, </strong><em>forthcoming</em>. This repository contains the training, cross-validation, test, and predicted data set as described in the publication. The contents of this repository are briefly summarised here, see the publication for further details.</p> <p><strong>Repository contents:</strong></p> <p><em>trn_tiles.geojson: </em>18,570 rectangular areas-of-interest used for sampling training patch data.</p> <p><em>trn_polygons.geojson: </em>36,882 polygons obtained from OSM in 2017 used to label training patches.</p> <p><em>cv_tiles.geojson: </em>560 rectangular areas-of-interest used for sampling cross-validation data seeded from <a href="https://www.wri.org/research/global-database-power-plants">WRI GPPDB</a></p> <p><em>cv_polygons.geojson: </em>6,281 polygons corresponding to all PV solar generating units present in cv_tiles.geojson at the end of 2018.</p> <p><em>test_tiles.geojson: </em>122 rectangular regions-of-interest used for building the test set.</p> <p><em>test_polygons.geojson: </em>7,263 polygons corresponding to all utility-scale (>10kW) solar generating units present in test_tiles.geojson at the end of 2018.</p> <p><em>predicted_polygons.geojson: </em>68,661 polygons corresponding to predicted polygons in global deployment, capturing the status of deployed photovoltaic solar energy generating capacity at the end of 2018.</p>
Data and Code from: On-farm land management strategies and production challenges in United States Organic Agricultural Systems.
<p>This repository contains data and code used in:</p> <p>Isaac Mpanga, Russel Trondstad, Jessica Guo, David LeBauer, and John Omololu, 2021. On-farm land management strategies and production challenges in United States Organic Agricultural Systems. Current Research in Environmental Sustainability.</p> <p>It provides USDA Surveys of Agricultural Production from 2008-2019 to investigate state and national trends by state in organic farm area, number, and sales, as well to evaluate national trends in on-farm land-use practices and challenges facing US organic production.</p> <p>It also includes code used to transform, visualize, and analyze the data, and derived data products - notably organic farm area and sales with values imputed to correct for redacted state level measures.</p>
"Hydration of a clay-rich unit on Mars, comparison of orbital data to rover data" supplementary data
<p>MCMC results from individual DAN measurements from sols 1814 to 3069 used in this work with material classifications from Figure 11.</p>
Data set for Water Affordability in the United States (Cardoso and Wichman, 2022)
<p><strong>Author Information</strong><br> - Diego S. Cardoso, Department of Agricultural Economics - Purdue University<br> - Casey J. Wichman, School of Economics - Georgia Institute of Technology and Resources for the Future</p> <p><strong>Corresponding Author:</strong> Casey J. Wichman: wichman@gatech.edu</p> <p><strong>Period of data collection:</strong> 2017--2018</p> <p><strong>License: </strong>This data set can be used under terms of the <a href="https://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International license</a>. </p> <p><strong>Recommended citation for the research paper</strong> <br> Cardoso, D. S., & Wichman, C. J. (2022). Water affordability in the United States. Water Resources Research, 58, e2022WR032206. https://doi.org/10.1029/2022WR032206</p> <p><strong>Recommended citation for the data:</strong> <br> Cardoso, Diego S., & Wichman, Casey J. (2022). Data set for Water Affordability in the United States [Data set]. In Water Resources Research (v0.1, Vol. 58, Numbers e2022WR032206). Zenodo. https://doi.org/10.5281/zenodo.6991563</p> <p> </p> <p> </p> <p>Please see the README.txt file for the data dictionary and additional information.</p>
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