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52 results for “surface states”
Three-channel surface electrogastrogram (EGG) dataset recorded during fasting and post-prandial states in 20 healthy individuals
<p>This repository contains Electrogastrography signals termed Electrogastrograms (<a href="https://en.wikipedia.org/wiki/Electrogastrogram">EGG</a>) recorded with surface Ag/AgCl electrodes placed over stomach and pre-processed in 20 healthy individuals (8 Females and 12 Males). The method for EGG recording and pre-processing together with subjects' data can be found in <a href="http://doi.org/10.1515/bmt-2017-0218">Popović et al. 2019</a>.</p> <p>For each subject, EGG was recorded from three locations before (fasting state) and after (postprandial state) a commercial oat meal (274 kcal). Two 20 minutes recordings (files) are obtained for each subject - fasting and postprandial.</p> <p>Naming convention for files: <strong>subjects ID _ type of recording (fasting / postprandial)</strong>.</p> <p>Sample rate was set at 2 Hz and <a href="https://en.wikipedia.org/wiki/Analog-to-digital_converter">A/D card</a> had 16 bits resolution. Gain of the amplifier was set at 1000. Overall, file size is 7200 samples (2400 samples for each channel). All signals were filtered with 3<sup>rd</sup> order band-pass <a href="https://en.wikipedia.org/wiki/Butterworth_filter">Butterworth filter</a> with cut-off frequencies of 0.03 Hz and 0.25 Hz. In order to avoid phase distortion, zero-phase digital filtering was performed in <a href="https://www.mathworks.com/products/matlab.html">Matlab</a> R2013a by <a href="https://www.mathworks.com/help/signal/ref/filtfilt.html">filtfilt()</a> function. <a href="https://www.gnu.org/software/octave/">GNU Octave</a> code for analysis of EGG signals with statistical calculations presented in <a href="http://doi.org/10.1515/bmt-2017-0218">Popović et al. 2019</a> is also provided (<a href="https://zenodo.org/record/3878435/files/eggAnalysis.m?download=1">eggAnalysis.m</a>).</p> <p>For convenient test download and appropriate preview, we provided all signals in <a href="https://en.wikipedia.org/wiki/Zip_(file_format)">.zip</a> and sample signal for ID1 in <a href="https://en.wikipedia.org/wiki/Text_file">.txt</a> form.</p> <p><strong>Dataset contents</strong></p> <ol> <li><a href="https://zenodo.org/api/files/7ef74d7b-694f-4047-b9e5-ef530e8b245d/EGG-database.zip?versionId=84315b6b-58da-4655-83f4-8f1d43c3b02c">EGG-database.zip</a>, data files, text format</li> <li><a href="https://zenodo.org/record/3878435/files/eggAnalysis.m?download=1">eggAnalysis.m</a>, GNU Octave code</li> <li><a href="https://zenodo.org/api/files/7ef74d7b-694f-4047-b9e5-ef530e8b245d/README.txt">README.txt</a>, metadata for data files, text format</li> <li><a href="https://zenodo.org/api/files/7ef74d7b-694f-4047-b9e5-ef530e8b245d/ID1_fasting.txt?versionId=47d0bd09-1a87-42f2-a3e5-ef0c4b4a18e2">ID1_fasting.txt</a> and <a href="https://zenodo.org/api/files/7ef74d7b-694f-4047-b9e5-ef530e8b245d/ID1_postprandial.txt?versionId=c8936a32-2896-44d6-bf3d-2ee37887766c">ID1_postprandial.txt</a>, sample data files for subject ID1, text format</li> </ol> <p><strong>Data files contain numerical values with decimal point according to the following structure</strong></p> <ol> <li>column - CH1* (recorded samples from channel 1)</li> <li>column - CH2* (recorded samples from channel 2)</li> <li>column - CH3* (recorded samples from channel 3)</li> </ol> <p>* For exact anatomical locations for EGG channels CH1, CH2, and CH3, please refer to <a href="http://doi.org/10.1515/bmt-2017-0218">Popović et al. 2019</a>.</p> <p>If you find these signals useful for your own research or teaching class, please cite both relevant paper and dataset as:</p> <ol> <li> <p>Popović, N.B., Miljković, N. and Popović, M.B., 2019. Simple gastric motility assessment method with a single-channel electrogastrogram. <em>Biomedical Engineering/Biomedizinische Technik</em>, <em>64</em>(2), pp.177-185, doi: <a href="https://doi.org/10.1515/bmt-2017-0218">10.1515/bmt-2017-0218</a>.</p> </li> <li> <p>Popović, N.B., Miljković, N. and Popović, M.B., 2020. Three-channel surface electrogastrogram (EGG) dataset recorded during fasting and post-prandial states in 20 healthy individuals [Data set]. <em>Zenodo</em>, doi: <a href="https://doi.org/10.5281/zenodo.3730617">10.5281/zenodo.3730617</a>.</p> </li> </ol> <p>DISCLAIMER: The GNU Octave code is provided without any guarantee and it is not intended for medical purposes.</p>
Uncovering the Triplet Ground State of Triangular Graphene Nanoflakes Engineered with Atomic Precision on a Metal Surface
<p>OPEN DATA related to the research publication:</p> <p>J. Li, S. Sanz, J. Castro-Esteban, M. Vilas-Varela, N. Friedrich, T. Frederiksen, D. Peña, and J. I. Pascual, <em>Uncovering the triplet ground state of triangular graphene nanoflakes engineered with atomic precision on a metal surface</em>, Phys. Rev. Lett. <strong>124</strong>, 177201 (2020) [arXiv:1912.08298]</p> <p>Abstract: Graphene can develop large magnetic moments in custom-crafted open-shell nanostructures such as triangulene, a triangular piece of graphene with zigzag edges. Current methods of engineering graphene nanosystems on surfaces succeeded in producing atomically precise open-shell structures, but demonstration of their net spin remains elusive to date. Here, we fabricate triangulenelike graphene systems and demonstrate that they possess a spin S=1 ground state. Scanning tunneling spectroscopy identifies the fingerprint of an underscreened S=1 Kondo state on these flakes at low temperatures, signaling the dominant ferromagnetic interactions between two spins. Combined with simulations based on the meanfield Hubbard model, we show that this S=1 π paramagnetism is robust and can be turned into an S=1/2 state by additional H atoms attached to the radical sites. Our results demonstrate that π paramagnetism of high-spin graphene flakes can survive on surfaces, opening the door to study the quantum behavior of interacting π spins in graphene systems.</p>
Quantitative Content Analysis Data for Hand Labeling Road Surface Conditions in New York State Department of Transportation Camera Images
<p><strong>Foundational Codebook and Data: </strong></p> <p>Traffic camera images from the New York State Department of Transportation (511ny.org) are used to create a hand-labeled dataset of images classified into to one of six road surface conditions: 1) severe snow, 2) snow, 3) wet, 4) dry, 5) poor visibility, or 6) obstructed. Six labelers (authors Sutter, Wirz, Przybylo, Cains, Radford, and Evans) went through a series of four labeling trials where reliability across all six labelers were assessed using the Krippendorff’s alpha (KA) metric (Krippendorff, 2007). The online tool by Dr. Freelon (Freelon, 2013; Freelon, 2010) was used to calculate reliability metrics after each trial, and the group achieved inter-coder reliability with KA of 0.888 on the 4th trial. This process is known as quantitative content analysis, and three pieces of data used in this process are shared, including: 1) a PDF of the codebook which serves as a set of rules for labeling images, 2) images from each of the four labeling trials, including the use of New York State Mesonet weather observation data (Brotzge et al., 2020), and 3) an Excel spreadsheet including the calculated inter-coder reliability (ICR) metrics and other summaries used to asses reliability after each trial. The data are included in NYSDOT_quantitative_content_analysis.zip.</p> <p>The broader purpose of this work is that the six human labelers, after achieving inter-coder reliability, can then label large sets of images independently, each contributing to the creation of larger labeled dataset used for training supervised machine learning models to predict road surface conditions from camera images. The xCITE lab (xCITE, 2023) is used to store camera images from 511ny.org, and the lab provides computing resources for training machine learning models.</p> <p><strong>Obstructed Class Variation: </strong></p> <p>There are many applications for labeling roadside camera images, and as a variation of the foundational codebook, an addendum codebook provides another version of labeling the obstructed class. Specifically, this variation prioritizes labeling an image as “obstructed” only in extreme circumstances where there is a camera- or image- specific problem that prevents the assessment of any road surfaces. For labelers who want to use this version of the obstructed class (in this document) and also the other five weather-related classes (in the foundational codebook), the guidance is to use both documents in tandem, making sure to use the obstructed rules/definitions in this document while disregarding the obstructed rules/definitions in the foundational codebook. Alternatively, this codebook may be used alone in applications where the goal is to solely classify obstructed vs not obstructed. To ensure reliability and quality of this variation, quantitative content analysis was conducted on this addendum codebook, just as it was for the foundational codebook. Two labelers were tested with a sample of 30 images and achieved inter-coder reliability with Krippendorff's Alpha of 0.934 after one trial. The data, including the addendum codebook and labeling trial data (images and results) are included in ObstructedVariation_quantitative_content_analysis.zip.</p> <p>This material is based upon work supported by the U.S. National Science Foundation under Grant No. RISE-2019758.</p>
Topological surface states in epitaxial (SnBi2Te4 )n (Bi2Te3)m natural van der Waals superlattices (data)
<p>This dataset contains the raw data files connected to the figures included in the paper "T<em>opological surface states in epitaxial (SnBi<sub>2</sub>Te<sub>4</sub> )<sub>n</sub> (Bi<sub>2</sub>Te<sub>3</sub>)<sub>m</sub> natural van der Waals superlattices</em>" by S. Fragkos et al., Phys. Rev. Materials <strong>5</strong>, 014203 (2021) <a href="https://doi.org/10.1103/PhysRevMaterials.5.014203">https://doi.org/10.1103/PhysRevMaterials.5.014203</a></p> <p>An Open Access version of the paper can be found here: <a href="https://zenodo.org/record/4562057#.YaDC4NBBxPY">https://zenodo.org/record/4563899#.YaDQ5NBBxPY</a></p>
The thermal state of Volgo–Uralia from Bayesian inversion of surface heat flow and temperature [data set]
<p>This collection contains the dataset and the code which were used to find the thermal parameters’ lateral variations of the Volgo–Uralian subcraton through the Bayesian Markov Chain Monte Carlo (MCMC) statistical approach. The code originally was given in the analogous study of Antarctica's geothermal structure by Lösing et al. (2020) and it can be found in https://github.com/MareenLoesing/GHF-Antarctica-Bayesian. The main changes to the code of Lösing et al. (2020) are listed in the section 2 of the readme file.</p> <p>For an official use of the Bayesian inversion code please also cite: Lösing, M., Ebbing, J. & Szwillus, W. (2020) Geothermal Heat Flux in Antarctica: Assessing Models and Observations by Bayesian Inversion. Front. Earth Sci., 8, 105. doi:10.3389/feart.2020.00105</p> <p>The lateral variations of the thermal parameters for the single-layer and multi-layer crust are saved in “GHF_Volgo-Uralia_Single-layer.csv” and “GHF_Volgo-Uralia_Multi-layer.csv” respectively.</p>
Source data for "Discovery of topological Weyl fermion lines and drumhead surface states in a room temperature magnet"
<p>Source data for "Discovery of topological Weyl fermion lines and drumhead surface states in a room temperature magnet" by I. Belopolski et al., Science 365, 1278-1281 (2019).</p>
Defect tolerance of lead-halide perovskite (100) surface relative to bulk: band bending, surface states, and characteristics of vacancies (dataset)
<p>This repository contains an input data set as well as the output data that were used in a study of surface and bulk defects in cubic CsPbI<sub>3</sub>. using a Vienna ab initio simulation package (VASP) and PyDEF 2 package for processing of defect calculations. More details about organization of the dataset can be found within REDME.txt files</p>
Data for simultaneous inference of sea ice state and surface emissivity model using machine learning and data assimilation
<h2>Overview</h2> <p>This dataset supports the draft manuscript "Simultaneous inference of sea ice state and surface emissivity model using machine learning and data assimilation" which describes a way to infer the daily maps of the sea ice concentration and empirical properties of the sea ice (relating to its snow cover and its physical properties, such as air inclusions) along with the creation of a new empirical model for the sea ice surface emissivity. This is done using knowledge of the atmosphere state, skin temperature and ocean water emissivity from the European Centre for Medium-range Weather Forecasts (ECMWF) weather forecasting model and the observed radiances at microwave frequencies from the Advanced Microwave Scanning Radiometer 2 (AMSR2). The inverse modelling and state estimation is achieved by combining empirical machine learning elements in a Bayesian-inspired network along with a number of physical components. The work also introduces the idea of an "empirical state", in this case describing the aspects of the sea ice physical state which affect the observations, and which is defined by the inputs to the new empirical model component (in machine learning terms, it is defined by the latent input state of a neural network). This dataset includes the data used in training the model and inferring the sea ice parameters, as well as the outputs from that training process. The software used to perform the training is in Python and uses the Keras and Tensorflow software. See the draft manuscript for full details of this data.</p> <p>The code used in the draft manuscript is archived at <a href="https://doi.org/10.5281/zenodo.10013542">https://doi.org/10.5281/zenodo.10013542</a></p> <p>The data used in the draft manuscript is archived at <a href="https://doi.org/10.5281/zenodo.10033377">https://doi.org/10.5281/zenodo.10033377</a></p> <h2>Training data </h2> <h3>Observation space training and ancillary data</h3> <p>Training is done at the location of AMSR2 superobservations (superobs) over ocean with less than 1% land contamination and polewards of 45 degrees latitude, between 1st July 2020 and 30th June 2021. There are 64,184,021 superobs used. A superob is the average of all raw JAXA level 1B observations from one orbit falling into a grid box on an approximately constant area (reduced Gaussian) grid at approximately 40 km by 40 km resolution (noting that polar regions can thus have up to around 7 superobs per day). The superobs have been computed using the field of view central locations for each channel as derived from the JAXA level 1B data. A subset of 10 of the AMSR2 channels is used, from 10 GHz, V polarised, to 89 GHz, H polarised.</p> <p>At each superob location, the relevant fields from the ECMWF 12 hour 'background' forecast are interpolated to the observation time and location. The atmosphere is represented indirectly by the relevant radiative transfer terms from a scattering radiative transfer model. The sea ice concentration from the ECMWF OCEAN5 analysis is included as a validation reference but is not used in the training itself, except to provide a monthly mean first guess to speed up the training. Each field is provided in a separate netCDF file:</p> <ul> <li>field_v2_JULIAN_DAY.nc - superob time in days since 12 UTC on Nov 24th 4714 BC on the proleptic Gregorian calendar</li> <li>field_v2_LAT.nc - superob central latitude in degrees</li> <li>field_v2_LON.nc - superob central longitude in degrees</li> <li>field_v2_IGRID.nc - corresponding grid number on the map grid used in this work (see below)</li> <li>field_v2_OBSVALUE.nc - observed superob brightness temperature at each of 10 AMSR2 channels.</li> <li>field_v2_TSFC.nc - skin temperature computed by the ECMWF forecast model</li> <li>field_v2_WINDSPEED10M.nc - 10m wind speed computed by the ECMWF forecast model</li> <li>field_v2_EMIS_WATER.nc - Ocean water surface emissivity at 10 AMSR2 channels, simulated from the ECMWF forecast fields using the FASTEM-6 model</li> <li>field_v2_CLOUD_FRACTION.nc - Effective cloud fraction used in the atmospheric radiative transfer model at each of 10 AMSR2 channels</li> <li>field_v2_TAUSFC_CLD.nc - Surface to space transmittance in the cloudy column at each of 10 AMSR2 channels</li> <li>field_v2_TUP_CLD.nc - Upwelling brightness temperature from the atmosphere in the cloudy column at each of 10 AMSR2 channels</li> <li>field_v2_TDOWN_CLD.nc - Downwelling brightness temperature from the atmosphere in the cloudy column at each of 10 AMSR2 channels</li> <li>field_v2_TAUSFC.nc - Equivalently for the clear column</li> <li>field_v2_TUP.nc - Equivalently for the clear column</li> <li>field_v2_TDOWN.nc - Equivalently for the clear column</li> <li>field_v2_SEAICE.nc - Sea ice concentration from the ECMWF OCEAN5 analysis, for validation only (not used in training)</li> </ul> <h3>Grid space data: initial data for training; validation sea ice data</h3> <p>A number of properties are provided to the hybrid physical-empirical model that is being trained, on a special map grid defined in this project, including all 62,499 of the reduced Gaussian 40km grid points that have at least one superob at some point during the year of training data. These are:</p> <ul> <li>ifs_seaice_initials_year.nc - sea ice concentration from OCEAN5, monthly averaged on the grid, and then provided on all days of the relevant month as initial conditions (technically, first guess) for the training. This includes an additional day before the beginning of the training, used for time-lagging (see draft paper).</li> <li>ifs_tsfc_year_dailyx.nc - skin temperature from ECMWF forecast fields at observation locations, averaged onto the daily grid, to help provide constraints on the likelihood of sea ice as part of a sea ice loss function.</li> </ul> <p>For diagnostic and validation purposes, the ECMWF OCEAN5 analysis is also provided on the grid:</p> <ul> <li>ifs_seaice_year.nc - sea ice concentration from OCEAN5 at observation locations, averaged onto the daily grid</li> </ul> <p>All these fields are provided on the following dimensions:</p> <ul> <li>LON - the longitude of the grid point in degrees</li> <li>DAY - the day through the training year (0-364, 1st July 2020 to 30th June 2021) or through the training year extended forward by one day (30th June 2020) for the sea ice (0-365). In practice the days are offset by 3 hours from the UTC day to match the ECMWF data assimilation windows, which start at 21 UTC the day before.</li> </ul> <p>The latitude is also provided</p> <ul> <li>LAT - the latitude of the grid point in degrees</li> </ul> <p>Note that the observation location IGRID is on the custom grid of the ML model that is defined implicitly in these gridded files. The LON and LAT vectors in these files are the longitude and latitude points of the grid and are of 62499 in length. The IGRID number for an observation is the index into these arrays from 0-62498.</p> <h2>Outputs from training</h2> <p>The following files are the output and diagnostics from the year-long training. The python code and the draft paper are the primary documentation for these:</p> <ul> <li>models_year.nc - settings of the model are recorded here, along with the trained values of the smaller empirical components/layers within the hybrid model. For example, the layer weights of the wind speed bias correction, the observation space bias correction, and the empirical surface emissivity model are recorded here. The values of the loss function at each epoch are also recorded here.</li> <li>properties_year.nc - trained values of each of 3 empirical properties of sea ice on the map grid (3 properties by 62499 locations by 365 days from 1st July 2020)</li> <li>seaice_year.nc - inferred values of sea ice fraction on the map grid (62499 locations by 365 days from 1st July 2020, discarding the additional day at the start)</li> <li>tbsim_year.nc - simulated AMSR2 brightness temperatures from the trained network</li> <li>tbsim_initial_year.nc - simulated AMSR2 brightness temperatures using the untrained network</li> </ul> <p>The longitude and latitude of the map grid is found in any of the initial data files described in the previous section. The days are 0-364 corresponding to 1st July 2020 to 30th June 2021.</p> <h3>Sea ice surface emissivity at grid locations</h3> <p>A packaged version of the sea ice surface emissivity is provided at grid locations, alongside the surface emissivity model, the sea ice concentration and the four inputs to the model, i.e. the normalised skin temperature and the three empirical variables:</p> <ul> <li>emissivity_grid_year.nc</li> </ul> <p>Note that in the training, the surface emissivity is computed at observation locations and has not been stored due to memory limitations. For easier comparison to other datasets, the surface emissivity has been recomputed on grid locations in this package, using the year-long trained emissivity model and its trained inputs. The sea ice surface emissivity is only physically meaningful for sea ice concentrations above around 0.25. Also be aware of the "hole at the pole" which is the small region of the Arctic ocean that is sometimes not covered by an AMSR2 overpass, and which is found from 88 degrees N. On days where the hole or part of the hole exists, the sea ice emissivity on the grid is not valid at these locations. These locations can be identified by having all values of the empirical properties zero (because the empirical properties were never constrained by any observations on that day, and remain at their initial values before training).</p> <h2>Sensitivity tests</h2> <p>Extensive sensitivity tests were carried out, as described in the appendices of the draft paper and as documented in the Python code, using the month of August 2020 as an example. These required equivalent month-long training and initial data similar to those described above, but all observation space fields are contained within the same file in this case. Output files follow similar principles to those described above. The full package is provided as a tar file:</p> <ul> <li>sensitivity.tar</li> </ul> <p>This contains the training and initial files:</p> <ul> <li>amsr2_v2_202008.nc</li> <li>ifs_tsfc_dailyx_202008.nc</li> <li>ifs_seaice_202008.nc</li> </ul> <p>as well as directories containing the trained model outputs and diagnostics at each of the sensitivity tests, using the same formats as described for the yearly training, with these names:</p> <ul> <li>nprop - number of empirical properties</li> <li>epoch - number of epochs</li> <li>deep - configuration of the empirical sea ice emissivity model, including multiple layers of nonlinear dense neural network</li> <li>bseaice - background error for the sea ice physical bounds background error (loss) term</li> <li>bemis - background error for the sea ice emissivity background error (loss) term</li> <li>bbias - background error for the bias correction background error (loss) term</li> <li>batchsize - batch size used in training</li> <li>bbatchsize - extended epochs testing of batch size used in training</li> </ul> <h2>Licensing</h2> <p>This data product is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). To view a copy of this licence, visit <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a></p> <p>You are free to:</p> <ul> <li>Share — copy and redistribute the material in any medium or format</li> <li>Adapt — remix, transform, and build upon the material for any purpose, even commercially.</li> </ul> <p>Under the following terms:</p> <ul> <li>You must give appropriate credit (attribution) to ECMWF as outlined below, provide a link to the licence, and indicate if changes were made.</li> <li>No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the licence permits.</li> </ul> <p>The following wording shall be attached to the use of this ECMWF data product: </p> <ol> <li>Copyright statement: Copyright "© 2023 European Centre for Medium-Range Weather Forecasts (ECMWF)".</li> <li>Source <a href="http://www.ecmwf.int/">www.ecmwf.int </a>and <a href="https://doi.org/10.5281/zenodo.10009497">https://doi.org/10.5281/zenodo.10009497</a></li> <li>Licence Statement: This data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a></li> <li>Disclaimer: ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.</li> <li>Where applicable, an indication if the material has been modified and an indication of previous modifications.</li> <li>DOI: 10.5281/zenodo.10009498</li> </ol> <p>Original data for this value-added product was provided by Japan Aerospace Exploitation Agency (JAXA). Specifically, this dataset builds on the Advanced Microwave Scanning Radiometer 2 (AMSR2) level 1B data available from the JAXA G-Portal, https://gportal.jaxa.jp/gpr/, which has the following attribution and licensing:</p> <ol> <li>Give credit for the original data to JAXA, i.e. "Original data for this value added data product was provided by Japan Aerospace Exploration Agency"</li> <li>DOI for original JAXA data is L1B-Brightness temperature (TB) GCOM-W/AMSR2 L1B Brightness Temperature: <a href="https://doi.org/10.57746/EO.01gs73ans548qghaknzdjyxd2h">https://doi.org/10.57746/EO.01gs73ans548qghaknzdjyxd2h</a></li> <li>Original terms of data service from JAXA, with highlighted extracts: <ul> <li><a href="https://gportal.jaxa.jp/gpr/index/eula?lang=en">https://gportal.jaxa.jp/gpr/index/eula</a> <ol> <li>The user is entitled to use G-Portal data free of charge without any restrictions (including commercial use) except for the condition about acknowledgement of data credit as stipulated in Article 7.(2). (see above)</li> <li>JAXA is collecting results (papers, theses, reports, etc.) using G-Portal data. If you have any results using G-Portal data, please mail/e-mail a copy of the result to G-Portal Support Desk (Contact Information written at the end of the Terms of Use). We appreciate your cooperation very much.</li> </ol> </li> </ul> </li> </ol>
Data for: Hidden non-collinear spin-order induced topological surface states
<p>Rare-earth monopnictides are a family of materials simultaneously displaying complex magnetism, strong electronic correlation, and topological band structure. The recently discovered emergent arc-like surface states in these materials have been attributed to the multi-wave-vector antiferromagnetic order, yet the direct experimental evidence has been elusive. Here we report the observation of non-collinear antiferromagnetic order with multiple modulations using spin-polarized scanning tunneling microscopy. Moreover, we discover a hidden spin-rotation transition of single-to-multiple modulations 2 K below the Neel temperature. The hidden transition coincides with the onset of the surface state splitting observed by our angle-resolved photoemission spectroscopy measurements. Single modulation gives rise to a band inversion with induced topological surface states in a local momentum region while the full Brillouin zone carries trivial topological indices, and multiple modulation further splits the surface bands via non-collinear spin tilting, as revealed by our calculations. The direct evidence of the non-collinear spin order in NdSb not only clarifies the mechanism of the emergent topological surface states but also opens up a new paradigm of control and manipulation of band topology with magnetism.</p>
Pair Wavefunction Symmetry in UTe2 from Zero-Energy Surface State Visualization
<p>This dataset contains all the data in "Pair Wavefunction Symmetry in UTe2 from Zero-Energy Surface State Visualization"</p>
Supporting data for: Post-fire early successional vegetation buffers surface microclimate and increases survival of planted conifer seedlings in the southwestern United States
<p>Climate change and fire-exclusion have increased the flammability of western US forests, leading to forest cover loss when wildfires occur under severe weather conditions. Increasingly large high-severity burn patches are a limitation to natural regeneration because of dispersal distance, increasing the chance that these areas are converted to non-forest. Post-fire planting can overcome dispersal limitations, yet warmer and drier post-fire conditions can still limit survival. Early successional vegetation can alter surface microclimate; however, it is unclear whether this is enough to increase planted seedling survival in southwestern US forests. Here we examined how two shrub species of different canopy density would affect survival rates of planted tree seedlings following a high-severity fire in northern New Mexico. We expected that shrubs with a higher density canopy (Gambel oak) would have a greater effect on buffering below-shrub climate than shrubs with a lower density canopy (New Mexico locust) and seedlings planted under Gambel oak would have higher survival rates. We found that seedlings planted under Gambel oak had survival rates approximately 10% to 35% greater than those planted under New Mexico locust. The higher light availability beneath New Mexico locust corresponded to higher temperatures, lower humidity, and higher VPD, which impacted the mortality of planted tree seedlings. These results suggest that by waiting for post-fire shrub establishment, shrubs can be leveraged to buffer microclimate and increase post-fire planting success in the southwestern US.</p>
Modification of electronic surface states by graphene islands on Cu(111)
<p>Data set of raw scanning tunneling microscope images and spectroscopy of monolayer graphene grown on Cu(111).</p> <p>Related papers: </p> <p><a href="https://journals.aps.org/prb/abstract/10.1103/PhysRevB.91.195425">Phys. Rev. B 91, 195425 (2015) - Modification of electronic surface states by graphene islands on Cu(111) (aps.org)</a></p> <p><a href="https://iopscience.iop.org/article/10.1088/0953-8984/28/3/034003/meta">Native defects in ultra-high vacuum grown graphene islands on Cu(1 1 1) - IOPscience</a></p>
Surface state evolution induced by magnetic order in axion insulator candidate EuIn2As2 - sample characterization
<p>Crystal-chemical characterization data of single crystals used in the work</p> <p>M.Gong, D.Sar, J.Friedman, D.Kaczorowski, S.Abdel Razek, W.-C.Lee, and P.Aynajian, "Electronic surface states across the magnetic transition in the axion insulator candidate EuIn<sub>2</sub>As<sub>2</sub>", <em>Phys. Rev. B</em> 106 (2022) 125156 </p> <p>DOI: 10.1103/PhysRevB.106.125156</p>
Data for: Hidden non-collinear spin-order induced topological surface states
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Quantum interference observed in state-resolved molecule-surface scattering
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Supporting data for: Post-fire early successional vegetation buffers surface microclimate and increases survival of planted conifer seedlings in the southwestern United States
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Estimates of surface layer net community production based on underway Lagrangian measurements of the dissolved O2/Ar ratio using Equilibrator Inlet Mass Spectrometry (EIMS), based on both steady-state and non-steady-state assumptions of the mixed-layer biological oxygen budget. Also included are estimates of the potential contribution of vertical fluxes: advection, eddy diffusion, and entrainment.
The ratio of dissolved oxygen to argon in surface seawater is frequently employed to estimate rates of net community production (NCP) in the oceanic mixed layer. The in situ O2/Ar-based method accounts for many physical factors that influence oxygen concentrations in the surface ocean, permitting isolation of the biological oxygen signal produced by the balance of photosynthesis and respiration. However, this technique traditionally relies upon several assumptions when calculating the mixed layer O2/Ar budget, most notably the absence of vertical fluxes of O2/Ar and the existence of a steady-state balance between net productivity and the air-sea gas exchange of biological oxygen. Employing a Lagrangian study design and leveraging data outputs from a regional physical oceanographic model, we conducted in situ measurements of O2/Ar in the California Current Ecosystem in spring 2016 and summer 2017 to evaluate these assumptions within a ‘worst-case’ field environment. Quantifying the magnitude of vertical fluxes and comparing NCP estimates obtained using steady-state versus non-steady-state assumptions, we find the importance of the non-steady-state term to be considerable, also observing significant potential effects from vertical flux terms, particularly advection. Additionally, we observe strong diel variability in O2/Ar and calculated NCP rates at multiple stations. Our results reemphasize the importance of accounting for vertical fluxes when interpreting O2/Ar-derived NCP data as well as the potentially large effect of non-steady-state conditions, including diel cycles in surface O2/Ar that can bias interpretation of NCP data based on local productivity and the time of day at which measurements were made.
ONERA copper TEEY measurements with various surface state and incidence angle
<p>These files are Total Electron Emission Yield (TEEY) measurements of two copper samples with various surface treatments. Energies range from 10 eV to 1 keV and incidence angle from 0° to 60°.</p> <p>Measurements were performed at the ONERA (Toulouse, France) in a Ultra-High Vacuum facility (3*10<sup>9</sup> millibar).</p>
Land surface phenology based crop maps for Continental United States 2000-2018
<p>This data collection contains annual maps of crop types for Continental United States for the period 2000-2018 at spatial resolution of ~231m derived using <a href="https://doi.org/10.3334/ORNLDAAC/1299">MODIS Land Surface Phenology</a>. This data collection is a companion to the paper <strong><em>Konduri, V., Kumar, J., Hargrove, W. W., Hoffman, F. M., Ganguly, A. R. (2020) <a href="https://doi.org/10.1016/j.rse.2020.112048">Mapping Crops Within the Growing Season Across the United States. Remote Sensing of Environment</a>, Vol 251, 2020 </em></strong><a href="https://doi.org/10.1016/j.rse.2020.112048">https://doi.org/10.1016/j.rse.2020.112048</a>, which describes the methodology for development of these datasets, validation metrics and analysis.</p> <p> </p> <p><strong><strong>Files in collection (28): </strong></strong></p> <ul> <li><strong><em>crop_map_predicted_[YEAR].nc</em>: </strong>Annual crop type maps for YEAR = 2000-2018</li> <li><strong><em>Crop_map_legend.csv</em>: </strong>Legend for crop type categories in the maps (Category numbers and legends for crop types are consistent with those used by <a href="https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php">USDA Crop Data Layer</a>)</li> <li>Maps of earliest date (Day of Year) of classification for eight dominant crop types for year 2015: <ul> <li><strong>earliest_classification_date_corn_CONUS.nc</strong> : Earliest date of classification for corn</li> <li><strong>earliest_classification_date_soybeans_CONUS.nc</strong> : Earliest date of classification for soybeans</li> <li><strong>earliest_classification_date_winter_wheat_CONUS.nc</strong> : Earliest date of classification for winter wheat</li> <li><strong>earliest_classification_date_fallow_CONUS.nc</strong> : Earliest date of classification for fallow</li> <li><strong>earliest_classification_date_other_hay_non_alfalfa_CONUS.nc</strong> : Earliest date of classification for other hay/non-alfalfa</li> <li><strong>earliest_classification_date_alfalfa_CONUS.nc</strong> : Earliest date of classification for alfalfa</li> <li><strong>earliest_classification_date_sorghum_CONUS.nc</strong> : Earliest date of classification for sorghum</li> <li><strong>earliest_classification_date_rice_CONUS.nc</strong> : Earliest date of classification for rice</li> </ul> </li> </ul> <p> </p> <p><strong><strong>Data formats:</strong></strong></p> <ul> <li>All map products are in gridded <a href="https://www.unidata.ucar.edu/software/netcdf/">NetCDF</a> format.</li> <li>Annual crop type maps use category types described in Crop_map_legend.csv. </li> <li>Earliest date of classification maps are encoded as Day of the Year.</li> </ul> <p> </p> <p><strong><strong>Projection for geospatial data</strong> (in <a href="https://live.osgeo.org/en/overview/proj4_overview.html">PROJ4 format</a>):</strong></p> <pre><code class="language-bash">PROJCS["US_National_Atlas_Equal_Area", GEOGCS["sphere", DATUM["unknown", SPHEROID["Spherical_Earth",6370997,"inf"]], PRIMEM["Greenwich",0], UNIT["degree",0.0174532925199433]], PROJECTION["Lambert_Azimuthal_Equal_Area"], PARAMETER["latitude_of_center",45], PARAMETER["longitude_of_center",-100], PARAMETER["false_easting",0], PARAMETER["false_northing",0], UNIT["Meter",1]]</code></pre> <p><strong>Paper Citation:</strong></p> <blockquote> <p><strong><em>Konduri, V., Kumar, J., Hargrove, W. W., Hoffman, F. M., Ganguly, A. R. (2020) Mapping Crops Within the Growing Season Across the United States. Remote Sensing of Environment (in revision)</em></strong></p> </blockquote>
Pernambuco State, Brazil (MZSP 30975). Fig. 1: ventral, lateral & dorsal views; Fig. 2: apical view, V = varix, L = edge of outer lip. Figs. 3–4: paratype from the type lot (MZSP 122078). Fig. 3: ventral view; Fig. 4: apical view (varix not developed in this immature specimen). Figs. 5–6: shell surface SEM images of holotype (MZSP 30975). Fig. 5: image taken at 500x showing pattern of paired grooves separated by wider region; the thin line is parallel to the axis of shell. Fig. 6: a closer look showing pattern of punctae (1,000x). SEM images by Yolanda Villacampa and courtesy of the Smithsonian. in Taxonomic review of tropical western Atlantic shallow water Drilliidae (Mollusca: Gastropoda: Conoidea) including descriptions of 100 new species
Pernambuco State, Brazil (MZSP 30975). Fig. 1: ventral, lateral & dorsal views; Fig. 2: apical view, V = varix, L = edge of outer lip. Figs. 3–4: paratype from the type lot (MZSP 122078). Fig. 3: ventral view; Fig. 4: apical view (varix not developed in this immature specimen). Figs. 5–6: shell surface SEM images of holotype (MZSP 30975). Fig. 5: image taken at 500x showing pattern of paired grooves separated by wider region; the thin line is parallel to the axis of shell. Fig. 6: a closer look showing pattern of punctae (1,000x). SEM images by Yolanda Villacampa and courtesy of the Smithsonian.
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.