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62 results for “Liquid water”
Imaging Temperature and Thickness of Thin Planar Liquid Water Jets in Vacuum - data
<p>Data set pertaining to the article "Imaging Temperature and Thickness of Thin Planar Liquid Water Jets in Vacuum", published in <em>Struct. Dyn.</em> 10, 034901 (2023), <a href="https://doi.org/10.1063/4.0000188" target="_blank" rel="noopener">https://doi.org/10.1063/4.0000188 </a>.</p> <p>The following data are provided:</p> <table> <tbody> <tr> <td>(zip-)file/Folder</td> <td>Description</td> <td>Format</td> <td>Extension</td> </tr> <tr> <td>IR_images/calibration_data/vacuum</td> <td> <p>Snapshots from a thermographic movie of our flat jet running in vacuum, at thirty different background temperature. (A snapshot shown in Fig. 3a, rhs.)</p> </td> <td> <p>temperature values per camera pixel (°C), 640 row * 480 columns, semicolon-separated ascii data</p> </td> <td>.asc</td> </tr> <tr> <td>IR_images/calibration_data/1atm</td> <td>As above, for our flat jet running in atmosphere. (Three snapshots shown in Fig. 2a.)</td> <td>as above</td> <td>.asc</td> </tr> <tr> <td>IR_images/calibration_data/chipnozzle</td> <td>As above, for a flat jet produced from a chip nozzle, and running in atmosphere.</td> <td>as above</td> <td>.asc</td> </tr> <tr> <td>IR_images/raw_data</td> <td>As above, for various conditions of the flat jet environment as detailed in table exp_settings.csv.</td> <td>as above</td> <td>.asc</td> </tr> <tr> <td>IR_video</td> <td>Two thermographic movies recorded of our flat jet at varied conditions of the jet environment detailed in table chamber_pressure.pdf.</td> <td>Radiographic image stream, suitable for opening with free software Optris Pix Connect.</td> <td>.ravi</td> </tr> <tr> <td>FJ_cooling_2D.mph</td> <td>Input file for 2D finite element simulation of our flat jet.</td> <td>Input file suitable for Comsol software, proprietary format.</td> <td>.mph</td> </tr> <tr> <td>Y_Z_Temp_Comsol.txt</td> <td>Ascii representation of our simulated temperature profile (Fig. S7 (SI)).</td> <td>List of (y,z,T) tupels, with (y,z) in m and T in °C.</td> <td>.txt</td> </tr> </tbody> </table> <p> </p> <p>In case you have any questions regarding this data set please contact: Uwe Hergenhahn, uhe@fhi.mpg.de .</p>
Quantitative electronic structure and work-function changes of liquid water induced by solute - data
<p>Data set pertaining to the article "Quantitative electronic structure and work-function changes of liquid water induced by solute" | Physical Chemistry Chemical Physics, 24, 1310 (2022).</p> <p>Files with extension .h5 are hdf5-files structured according to the NeXus standard v2022.07 using the NXmpes user contributed format suggested by the Fairmat consortium, see<br> https://www.nexusformat.org/<br> https://fairmat-experimental.github.io/nexus-fairmat-proposal/50433d9039b3f33299bab338998acb5335cd8951/mpes-structure.html<br> A few extensions specific to liquid jet-experiments were added to the standard, and are explained in the notes-group on the top level of each file.<br> NeXus data files can be opened with any software capable of opening hdf5-structured files. The following viewers are adapted to the specifics of the NeXus data format:<br> * nexpy (distributed with python)<br> * https://h5web.panosc.eu/h5wasm (web-based NeXus viewer maintained by the European Photon and Neutron Open Science Cloud-consortium)</p> <p>In each NeXus file-entry, two types of spectra are shown:<br> 1. Sweep-averaged spectra, integrated over the non-dispersive coordinate of our detector ('data').<br> 2. As-measured data ('raw').</p> <p>Files with extension .txt are comma-separated ascii-files.<br> The following files are provided:</p> <p>Photoemission data pertaining to solute measurements using the cut-off as energy reference:<br> NaI_data.h5<br> tbai_data.h5</p> <p>Biased spectra were typically recorded in the following order:<br> [cut-off (fine), cut-off (coarse), (valence band)*(N repeats)]*(M repeats)<br> To avoid the saving of overly complex hdf5-files, these data were saved in a different order, namely:<br> [cut-off (fine)*(M repeats), cut-off (coarse)*(M repeats), (valence band)*(N*M repeats)].</p> <p>Numeric representations of the traces shown in the article's figures:<br> Figure_1a-data.txt<br> Figure_1b-data.txt<br> Figure_2a-data.txt<br> Figure_2b-data.txt<br> Figure_2c-data.txt<br> Figure_3-data.txt<br> Figure_4-data.txt<br> Figure_5a-data.txt<br> Figure_5b-data.txt<br> Figure_6a-data.txt<br> Figure_6b-data.txt<br> Figure_6c-data.txt<br> Figure_7_diff_spectra-data.txt<br> Figure_8-data.txt</p> <p>Traces shown in several figures are included only in the data file pertaining to the figure in which they occur first.</p> <p> </p> <p>Contact: Uwe Hergenhahn, uhe@fhi.mpg.de .</p>
Photosynthetic pigments of water column samples analyzed using High Performance Liquid Chromatography (HPLC), sampled during the Palmer LTER field seasons at Palmer Station, Antarctica, 1991 – 2023.
Phytoplankton pigment sampling was led by Prezelin from the 1991-1992 season through the 1993-1994 season, and then by Vernet from the 1994-1995 season through the 2006-2007 season. Schofield is the third, and current lead, beginning in the 2008-2009 season. Methods have been kept consistent as much as possible over the full time series and different Principal Investigators. Phytoplankton have a suite of accessory pigments in addition to Chlorophyll a, including other Chlorophyll’s (e.g. Chlorophyll b), Xanthophylls, and Carotenes. These accessory pigments can be used as chemotaxonomic markers to assess the composition and distribution of the phytoplankton community. For example, Fucoxanthin is a marker pigment of Diatoms, whereas Alloxanthin is a marker pigment of Cryptophytes. Accessory pigments also assist in photoacclimation and photoprotective processes. Water samples are collected throughout the water column at stations within the Palmer LTER region (primarily B and E, to 50m and 65m respectively). Water samples are filtered onto GF/F filters, and filters kept frozen at -80C until analysis. HPLC analysis is completed following Wright et al (1991). Following the guidelines set by NASA SeaHARRE, we use an internal standard and replicate injects on the HPLC to track recovery and replicability of the pigment extraction methods. Data is unavailable for the Palmer 2009-2010 season due to instrumentation problems and for the Palmer 2011-2012 season due to a freezer failure which resulted in the loss of samples. There is a temporary data gap for the Palmer 2015-2016, Palmer 2016-2017, Palmer 2019-2020, Palmer 2020-2021, and Palmer 2023-2024 seasons because those samples have not been analyzed yet.
Photosynthetic pigments of water column samples and analyzed with High Performance Liquid Chromatography (HPLC), collected aboard Palmer LTER annual cruises off the coast of the Western Antarctica Peninsula, 1991-2024.
Phytoplankton pigment sampling was led by Prezelin from 1991-1994, and then by Vernet from 1995-2008. Schofield is the third, and current lead, beginning in 2009. Methods have been kept consistent as much as possible over the full time series and different Principal Investigators. Phytoplankton have a suite of accessory pigments in addition to Chlorophyll a, including other Chlorophyll's (e.g. Chlorophyll b), Xanthophylls, and Carotenes. These accessory pigments can be used as chemotaxonomic markers to assess the composition and distribution of the phytoplankton community. For example, Fucoxanthin is a marker pigment of Diatoms, whereas Alloxanthin is a marker pigment of Cryptophytes. Accessory pigments also assist in photoacclimation and photoprotective processes. Water samples are collected throughout the water column along the Western Antarctic Peninsula at regular LTER grid stations where CTD casts are preformed and in surface waters at underway stations, where CTD casts are not done, using the ship's flow-through seawater system. Water samples are filtered onto GF/F filters, and filters kept frozen at -80C until analysis. HPLC analysis is completed following Wright et al (1991). Following the guidelines set by NASA SeaHARRE, we use an internal standard and replicate injects on the HPLC to track recovery and replicability of the pigment extraction methods and the HPLC. Data is unavailable for the LMG10-01 cruise due to instrumentation problems and for the LMG12-01 cruise due to a freezer failure which resulted in the loss of samples. There is no data for 2021 because there was no LTER cruise.
Effect of Surfactants on 1,2-Dichloroethane-in-Water Droplet Impacts at Electrified Liquid-Liquid Interface
<p>The data set for the submited publication "Effect of Surfactants on 1,2-Dichloroethane-in-Water Droplet Impacts at Electrified Liquid-Liquid Interface". </p>
Corrections for Geostationary Cloud Liquid Water Path Using Microwave Imagery
<p>Netcdf files containing a set of correctional factors for GOES-16 and GOES-17 cloud liquid-water path (LWP). The correctional factors for both satellites are fractional corrections of microwave imager LWP divided by GOES-16/17 LWP at a given solar zenith, GOES sensor zenith, relative azimuth (solar - sensor zenith), and low-cloud fraction.</p> <p>Uncorrected GOES-16/17 LWP is derived from GOES retrieved cloud-optical thickness and cloud-top effective radius, and it is multiplied by the corresponding correctional factor (i.e. the bins which the uncorrected values are in).</p>
Molecular dynamics trajectories for "Structure and chemistry of graphene oxide in liquid water from first principles"
<p>This dataset contains molecular dynamics (MD) trajectories from the paper <a href="https://doi.org/10.1038/s41467-020-15381-y">“Structure and chemistry of graphene oxide in liquid water from first principles”, F. Mouhat, F.-X. Coudert and M.-L. Bocquet, <em>Nature Commun.</em>, <strong>2020</strong>, <em>11</em>, 1566, 10.1038/s41467-020-15381-y</a></p> <p> </p>
Accurate Vertical Ionization Energy and Work Function Determinations of Liquid Water and Aqueous Solutions
<p>Dataset underlying report about a protocol to determine absolute binding energies from photoionization of liquid microjet samples, published as <a href="https://doi.org/10.1039/D1SC01908B">Accurate vertical ionization energy and work function determinations of liquid water and aqueous solutions</a>.</p>
Appendix A. Supplementary material for: Water-like thermal conductivity of ionanofluids containing high aspect ratio multi-walled carbon nanotubes and 1-ethyl-3-methylimidazolium-based ionic liquids with cyano-functionalized anions
<p><span>Experimental data in numerical form for INFs composed of CNTs and [Emim]-based ILs with cyano-functionalized anions: density (Table S1), viscosity (Tables S2–S5), thermal conductivity (Tables S6, S7), and ANOVA analysis (Table S8).</span></p>
Lattice Boltzmann simulation of liquid water transport in gas diffusion layers of proton exchange membrane fuel cells: Impact of gas diffusion layer and microporous layer degradation on effective transport properties
<p><span>Underlying data to publication Sarkezi-Selsky et al., <em>J. Pow. Sour.</em> 556 (2023) 232415,<span> https://doi.org/10.1016/j.jpowsour.2022.232415</span> <br><br>Polymer Electrolyte Membrane Fuel Cells (PEMFCs) represent a promising technology for clean drivetrain solutions, in particular for heavy-duty applications. However, lifetime requirements demand high durability of each cell component.<br></span><span>In this work, transport of liquid water through pristine and degraded gas diffusion layers (GDL) was simulated with a 3D Color-Gradient Lattice Boltzmann model. The GDL microstructure was reconstructed </span><span>from high-resolution X-ray micro-computed tomography (</span><span>μ</span><span>-CT) of an impregnated Freudenberg H14. The </span><span>effect of a microporous layer (MPL) was considered by reconstruction of an impregnated and MPL-coated H14. Aged microstructures were generated artificially, assuming loss of polytetrafluoroethylene (PTFE) within the GDL and increase of MPL macroporosity as main degradation mechanisms. Liquid water transport within aged microstructures was simulated by imposing a liquid phase flow rate until breakthrough was reached. Subsequently, the GDL microstructures were analyzed for their breakthrough characteristics by means of saturation and effective gas transport properties. When the MPL was pristine, no distinct GDL degradation effect was observable, this was attributed to the MPL dominating capillary transport. MPL aging, however, led to increased saturations and thus to a deterioration of the effective gas transport. With a partially degraded MPL, aging of the GDL then appeared to affect the breakthrough characteristics.</span></p>
Molecular trajectories and general MD files: Molecular insights on confined water in the nanochannels of self-assembled ionic liquid crystal
<p>This repository includes the MD simulation dataset of self-assembled ionic liquid crystal reported in the article of <a href="https://doi.org/10.1126/sciadv.abf0669"><em>Sci. Adv.</em> <strong>7</strong>, eabf0669 (2021) [DOI: 10.1126/sciadv.abf0669]</a>. The chemical structure of ionic liquid crystal is described in <a href="https://advances.sciencemag.org/content/advances/7/31/eabf0669/F1.large.jpg">Fig. 1A</a>. The cation involves an ionic moiety of <em>N</em>-methyl-<em>N</em>,<em>N</em>,<em>N</em>-triethylammonium group, and the terminals of the two alkyl chains are conjugated dienes. The anion is tetrafluoroborate BF<sub>4</sub>. The molecular and atomic-group charges of cation and anion are shown in the topology files named "ilc-oplsdft.itp" and "bf4-oplsdft.itp", respectively. The TIP3P and TIP4P/2005 models are employed for water molecules. After the careful equilibration process, the production MD was performed for 50 ns under the <em>NPT</em> condition at each composition. This dataset provides the configuration, topology, and general MD input files of Gromacs for all the states and models of bicontinuous and columnar structures obtained in the article. The 3D view of bicontinuous and columnar structures can be available in <a href="https://advances.sciencemag.org/content/advances/7/31/eabf0669/F2.large.jpg">Fig. 2</a>. The attached edr and log files are energy and general outputs of the MD simulation generated in our environment, and the xtc file is a trajectory output generated with a time interval of 20 ps.</p>
Retrieval and Validation of Total Seasonal Liquid Water Amounts in the Percolation Zone of Greenland Ice Sheet Using L-band Radiometry
<p>This repository contains the dataset associated with the analyses presented in the following study:</p> <p>Hossan, A., Colliander, A., Vandecrux, B., Schlegel, N.-J., Harper, J., Marshall, S., and Miller, J. Z.: <em>Retrieval and validation of total seasonal liquid water amounts in the percolation zone of the Greenland Ice Sheet using L-band radiometry</em>, <strong>The Cryosphere</strong>, 19, 4237–4258, <a href="https://doi.org/10.5194/tc-19-4237-2025" target="_new">https://doi.org/10.5194/tc-19-4237-2025</a>, 2025.</p> <p>In this study, we demonstrated the capability of NASA's Soil Moisture Active Passive (SMAP) L-band radiometer to estimate surface and subsurface liquid water amounts (LWA) in the percolation zone of the Greenland Ice Sheet. The article presents our initial retrieval algorithm, validation results, and highlights the potential for developing a Greenland-wide LWA data product.</p> <p><strong>Contents of this Repository</strong></p> <p>This repository includes:</p> <ul> <li><strong>SMAP-retrieved daily, vertically integrated LWA gridded initial data products</strong> (2015–2023), derived from enhanced-resolution SMAP TB observations. These data include spatial coordinates, acquisition dates, and a melt flag indicator.</li> <ul> <li>SMAP_LWA_time_series_AWS contains daily time series at a AWS location (point observation)</li> <li>Samimi_EBM_LWA_time_series_AWS contains corresponding time series of LWA estimated by Samimi model forced by PROMICE AWS.</li> <li>GEMB_LWA_time_series_AWS contains corresponding time series of LWA estimated by GEMB model forced by PROMICE AWS</li> <li>The locations and name ID of the AWS are given in AWS.txt/xls file</li> <li>L_band_LWA_yyyy.nc files contain daily LWA and TB data over the entire percolation zone</li> </ul> <li><strong>Corresponding vertically polarized brightness temperature (TBV) data</strong>, including their winter mean and standard deviation.</li> <li><strong>Model-based LWA estimates used for validation</strong>, including outputs from:</li> <ul> <li>The locally calibrated <strong>Energy and Mass Balance (EMB)</strong> model.</li> <li>The <strong>Glacier Energy and Mass Balance (GEMB)</strong> model within NASA’s <strong>Ice-sheet and Sea-level System Model (ISSM)</strong>.</li> </ul> </ul> <p><strong>Retrieval and Validation Codebase</strong></p> <p>The MATLAB scripts and tools used for the microwave retrieval algorithm, radiative transfer modeling, inversion process, and comparative validation with in situ AWS-driven model outputs are available at the following GitHub repository:</p> <p>🔗 <a href="https://github.com/HossanAlamgir/SMAP_MWA_Retrieval_and_Validation_GrIS" target="_new">https://github.com/HossanAlamgir/SMAP_MWA_Retrieval_and_Validation_GrIS</a><br><em>(Last accessed: 17 September 2025)</em></p> <p>The codebase includes:</p> <ul> <li>Preprocessing routines for SMAP TB data.</li> <li>Implementation of the radiative transfer forward model.</li> <li>Inversion and threshold-based detection algorithms.</li> <li>Validation scripts for comparison against AWS-forced EMB and GEMB model outputs.</li> </ul> <p><strong>Relevance</strong></p> <p>These data and methods support ongoing efforts to improve surface mass balance (SMB) estimates and enhance projections of Greenland’s contribution to global sea level rise.</p> <p> </p>
Regression Datasets: Strength as a Function of Liquid Water Content in Different Snow Types
<p>This repository contains two datasets used for determining the shear strength of different snow types as a function of liquid water content. It also contains one dataset with additional information pertaining to our experiment, including site and condition details. <strong>Please read the 'Read me.txt' file before using these datasets.</strong></p>
Attosecond impulsive stimulated x-ray raman scattering in liquid water
Open the record for dataset details and reuse information.
Radiationless Decay Spectrum of O 1s Double Core Holes in Liquid Water - data
<p>Data set pertaining to the publication "Radiationless Decay Spectrum of O 1s Double Core Holes in Liquid Water", published in <a href="https://doi.org/10.1063/5.0205994"><em>J. Chem. Phys.</em> 160, 194503 (2024)</a>.</p> <p>The publication describes results on the Auger emission of double core hole states created by single photon photo-double-ionization, explored experimentally by liquid jet electron spectra and via simulations. Here we provide the underlying experimental data including metadata, ascii representations of the traces shown in the figures, and the coordinates used in the simulations.</p> <p>Files with extension .h5 are hdf5-files structured according to the NeXus standard v2022.07, see<br>https://www.nexusformat.org/<br>https://fairmat-experimental.github.io/nexus-fairmat-proposal/50433d9039b3f33299bab338998acb5335cd8951/mpes-structure.html<br>NeXus data files can be opened with any software capable of opening hdf5-structured files. The following viewers are adapted to the specifics of the NeXus data format:<br>* nexpy (distributed with python)<br>* https://h5web.panosc.eu/h5wasm (web-based NeXus viewer maintained by the European Photon and Neutron Open Science Cloud-consortium)<br>Additionally, some properties of our liquid jet sample environment are described by extensions to standard NeXus explained in a notes-section in each file.</p> <p>In each NeXus file-entry, two types of spectra are shown:<br>1. Sweep-averaged spectra, integrated over the non-dispersive coordinate of our detector ('data').<br>2. As-measured data ('raw').</p> <p>Files with extension .txt or with extension .xyz are tab-separated or space-separated ascii-files. Files with extension .asc are comma-separated ascii-files. Files with extension .zip are compressed multi-file archives, MIME-type application/zip.</p> <p>The following files are provided:</p> <p> </p> <table> <tbody> <tr> <td>Filename</td> <td>Content</td> </tr> <tr> <td> <div><a href="../api/records/10523681/draft/files/DataFig2_3_4.zip/content" target="_blank" rel="noopener noreferrer">DataFig2_3_4.zip</a></div> </td> <td>Ascii representation of the experimental data traces shown in Fig.s 3, 4 and 5</td> </tr> <tr> <td> <div><a href="../api/records/10523681/draft/files/dataFig5_6_7.zip/content" target="_blank" rel="noopener noreferrer">dataFig5_6_7.zip</a></div> </td> <td>Ascii representation of the simulated traces shown in Fig.s 6, 7 and 8</td> </tr> <tr> <td> <div><a href="../api/records/10523681/draft/files/DataSFig1-4.zip/content" target="_blank" rel="noopener noreferrer">DataSFig1-4.zip</a></div> </td> <td>Ascii representation of the experimental data traces shown in Supplementary Fig.s 1,3 and 4</td> </tr> <tr> <td> <div><a href="../api/records/10523681/draft/files/DataSFig5.zip/content" target="_blank" rel="noopener noreferrer">DataSFig5.zip</a></div> </td> <td>Ascii representation of the experimental data traces shown in Supplementary Fig. 5. Data <br>are shown before normalization to unity.</td> </tr> <tr> <td> <div><a href="../api/records/10523681/draft/files/DataSFig6-8.zip/content" target="_blank" rel="noopener noreferrer">DataSFig6-8.zip</a></div> </td> <td>Ascii representation of the experimental data traces shown in Supplementary Fig.s 6,7 and 8</td> </tr> <tr> <td><a href="../api/records/10523681/draft/files/dch-1812.h5/content" target="_blank" rel="noopener noreferrer">dch-1812.h5</a></td> <td>Experimental data, december 2018 campaign.</td> </tr> <tr> <td><a href="../api/records/10523681/draft/files/dch-1904s1.h5/content" target="_blank" rel="noopener noreferrer">dch-1904s1.h5</a></td> <td>Experimental data, april 2019 campaign, data set 1.</td> </tr> <tr> <td><a href="../api/records/10523681/draft/files/dch-1904s2.h5/content" target="_blank" rel="noopener noreferrer">dch-1904s2.h5</a></td> <td>Experimental data, april 2019 campaign, data set 2.</td> </tr> <tr> <td><a href="../api/records/10523681/draft/files/dch-1909h2o.h5/content" target="_blank" rel="noopener noreferrer">dch-1909h2o.h5</a></td> <td>Experimental data, september 2019 campaign.</td> </tr> <tr> <td><a href="../api/records/10523681/draft/files/dch-1909d2o.h5/content" target="_blank" rel="noopener noreferrer">dch-1909d2o.h5</a></td> <td>Experimental data, september 2019 campaign, deuterated water.</td> </tr> <tr> <td><a href="../api/records/10523681/draft/files/dch-2009h2o.h5/content" target="_blank" rel="noopener noreferrer">dch-2009h2o.h5</a></td> <td>Experimental data, september 2020 campaign.</td> </tr> <tr> <td><a href="../api/records/10523681/draft/files/dch-2009d2o.h5/content" target="_blank" rel="noopener noreferrer">dch-2009d2o.h5</a></td> <td>Experimental data, september 2020 campaign, deuterated water.</td> </tr> <tr> <td><a href="../api/records/10523681/draft/files/optimized_pentamer.xyz/content" target="_blank" rel="noopener noreferrer">optimized_pentamer.xyz</a></td> <td>Cartesian coordinates of the water pentamer used for the simulations, in Å.</td> </tr> </tbody> </table> <p> </p> <p>In case you have any questions regarding this data set please contact: Uwe Hergenhahn, uhe@fhi.mpg.de .</p>
Data and geometries for "Understanding X-ray absorption in liquid water using triple excitations in multilevel coupled cluster theory"
<p>Geometries and raw and processed data for the paper "Understanding X-ray absorption in liquid water using<br>triple excitations in multilevel coupled cluster theory"</p> <p>This work has received funding from the European Research Council (ERC)<br>under the European Union’s Horizon 2020 Research and Innovation Program<br>(grant agreement no. 101020016 and 860553), the Research Council of Norway through FRINATEK (project no. 275506), the Swedish Research Council (grant agreement no. 2021-04521), the Independent Research Fund Denmark--Natural Sciences, DFF-RP2 (grant no. 7014-00258B)<br>Computing resources from UNINETT Sigma2—the National Infrastructure for High Performance Computing<br>and Data Storage in Norway (project no. NN2962k),<br>from DeIC—Danish Infrastructure Cooperation (grant no. DeiC-DTU-N3-2023027), and from the Swiss National Supercomputing Centre (project ID uzh1).</p>
Predicting Properties of Periodic Systems from Cluster Data: A Case Study of Liquid Water
<ul> <li> Description</li> </ul> <p>The 1520 water clusters were extracted from Ref. 1. The respective energies and atomic forces were recomputed at the revPBE-D3/def2-TZVP [2-6], B3LYP-D3/def2-TZVP [4-6, 7, 8], and BLYP-D3/def2-TZVP [4-6, 7, 9] level.</p> <ul> <li> Format</li> </ul> <p>The data is stored in python compressed array format (.npz) with the atomization energy in kcal/mol and atomic forces in kcal/mol/Ang. The data set contains five np.ndarray</p> <pre><code>import numpy as np data = np.load('revpbe.npz') data['R'] # Cartesian coordinates of nuclei in Ang. data['E'] # Total energy in kcal/mol data['F'] # Atomic forces in kcal/mol/Ang. data['N'] # Number of atoms in each structure data['Z'] # Nuclear charges</code></pre> <p>References</p> <ul> </ul> <p>[1] Molpeceres G., Zaverkin V., and Kästner J., “Neural-network assisted study of nitrogen atom dynamics on amorphous solid water – I. adsorption and desorption,” Mon. Not. R. Astron. Soc. 499, 1373 (2020).</p> <p>[2] P. E. Blöchl, “Projector augmented-wave method,” Phys. Rev. B 50, 17953 (1994).</p> <p>[3] Y. Zhang and W. Yang, “Comment on “generalized gradient approximation made simple”,” Phys. Rev. Lett. 80, 890 (1998).</p> <p>[4] S. Grimme, J. Antony, S. Ehrlich, and H. Krieg, “A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu,” J. Chem. Phys. 132, 154104 (2010).</p> <p>[5] F. Weigend and R. Ahlrichs, “Balanced basis sets of split valence, triple zeta valence and quadruple zeta valence quality for H to Rn: Design and assessment of accuracy,” Phys. Chem. Chem. Phys. 7, 3297 (2005).</p> <p>[6] F. Weigend, “Accurate Coulomb-fitting basis sets for H to Rn,” Phys. Chem. Chem. Phys. 8, 1057 (2006).</p> <p>[7] A. D. Becke, “Density-functional thermochemistry. iii. the role of exact exchange,” J. Chem. Phys. 98, 5648 (1993).</p> <p>[8] P. J. Stephens, F. J. Devlin, C. F. Chabalowski, and M. J. Frisch, “Ab initio calculation of vibrational absorption and circular dichroism spectra using density functional force fields,” J. Phys. Chem. 98, 11623 (1994).</p> <p>[9] C. Lee, W. Yang, and R. G. Parr, “Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density,” Phys. Rev. B 37, 785 (1988).</p>
Research data supporting "Ephemeral Ice-Like Local Environments in Classical Rigid Models of Liquid Water"
<p>This repository contains the set of data shown in the paper <strong>"</strong><em>Ephemeral Ice-Like Local Environments in Classical Rigid Models of Liquid Water</em>", published on the Journal of Chemical Physics (DOI:10.1063/5.0088599).</p>
Structural changes across thermodynamic maxima in supercooled liquid tellurium: a water-like scenario
<p>This dataset contains all numerical data used to produce the figures in the manuscript, arXiv:2201.06838 by P. Sun et al. (2022).</p> <p>Please see the README file for more information.</p>
Ab initio molecular dynamics trajectories of liquid water at the interface with hBN sheets
<p>Ab initio molecular dynamics trajectories of water confined between hBN sheets for different confinement widths and system sizes.</p> <p>This repository contains data supporting the findings of the paper: </p> <p>G. Tocci, M. Bilichenko, L. Joly, M. Iannuzzi, <em>Ab initio</em> nanofluidics: disentangling the role of the energy landscape and of density correlations on liquid/solid friction, Nanoscale, 12, 10994-11000 (2020). DOI: 10.1039/D0NR02511A.</p>
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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.