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IODP Expedition 367 Ion chromatography
Cation and anion concentration in interstitial water samples was measured by ion chromatography using a conductivity detector. Cation analytes include calcium, magnesium, potassium, and sodium. Anion analytes include chloride, sulfate, and bromide.
Tool for the environmental and economic impact assessment of industrial recycling routes for lithium-ion traction batteries
<p>This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).</p> <p> </p> <p>Please send your inquiries regarding the tool to s.bloemeke@tu-braunschweig.de.</p>
Datasets: Performance Characterization of Lithium-Ion Battery Cells Within Restricted Operating Range Using an Extended Ragone Plot
<h1>Documentation</h1> <p>This repository contains the measurement data presented in <strong>"Performance Characterization of Lithium-Ion Battery Cells Within Restricted Operating Range Using an Extended Ragone Plot."</strong></p> <p>The performance characterization was conducted on three lithium-ion battery cell types, each with two samples. In the publication only datasets from the following cells are included: #1: SCiB-23Ah-01, #2: SLPB8644143-353, #3: M1B-1223-01. The measurements were performed using a Scienlab SL60/300/18BT2C battery test system in combination with a BINDER type MK 720 temperature chamber. Further details about the battery cells, the experimental setup and procedures are available in the publication. An uncertainty analysis for these measurements is included in the supplementary material of the publication.</p> <blockquote> <p><strong>Note:</strong> Please cite the referenced publication when using these datasets in your work.</p> </blockquote> <h2>Datasets Overview</h2> <p><strong>Toshiba SCiB™ 23 Ah (prismatic)<br></strong></p> <ul> <li>SCiB-23Ah-01<br> <ul> <li>OCVTest#1</li> <li>PowerTemperatureTest_dis-Umax#1<br> <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 1 (2.7 V); 2 (2.55 V); 3 (2.4 V); 4 (2.25 V); 5 (2.1 V).</em></li> </ul> </li> </ul> </li> <li>SCiB-23Ah-02 <ul> <li>OCVTest#1</li> <li>PowerTemperatureTest_dis-Umax#1 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 1 (2.7 V); 2 (2.55 V); 3 (2.4 V); 4 (2.25 V); 5 (2.1 V).</em></li> </ul> </li> </ul> </li> </ul> <p><strong>Shenzen Melasta Battery SLPB8644143 (pouch)<br></strong></p> <ul> <li>SLPB8644143-353<br> <ul> <li>OCVTest#1</li> <li>PowerTemperatureTest_dis-Umax#1 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 1 (4.2 V).</em></li> </ul> </li> <li>PowerTemperatureTest_dis-Umax#2 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 2 (4.1 V); 3 (4.0 V); 4 (3.9 V); 5 (3.8 V).</em></li> </ul> </li> </ul> </li> <li>SLPB8644143-45 <ul> <li>OCVTest#1</li> <li>PowerTemperatureTest_dis-Umax#1 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 1 (4.2 V).</em></li> </ul> </li> <li>PowerTemperatureTest_dis-Umax#2 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 2 (4.1 V); 3 (4.0 V); 4 (3.9 V); 5 (3.8 V).</em></li> </ul> </li> </ul> </li> </ul> <p><strong>LithiumWerks (A123) ANR26650m1B (cylindrical)<br></strong></p> <ul> <li>M1B-1223-01<br> <ul> <li>OCVTest#1</li> <li>PowerTemperatureTest_dis-Umax#1 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 1 (3.6 V); 2 (3.45 V); 5 (3.3 V).</em></li> </ul> </li> <li>PowerTemperatureTest_dis-Umax#2<br> <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 3 (3.4 V); 4 (3.35 V).</em></li> </ul> </li> </ul> </li> <li> M1B-1223-02 <ul> <li>PowerTemperatureTest_dis-Umax#2 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 3 (3.4 V); 4 (3.35 V).</em></li> </ul> </li> </ul> </li> </ul> <h2>Usage instructions</h2> <ol> <li>Raw test reports are stored in the <code>test reports</code> directory as <code>*.csv</code> and <code>*.info.txt</code> files. These files contain the following measurement signals: <ul> <li>Time, Test step, ind_T, ind_U, ind_xP, E [J], Eneg [J], Epos [J], I [A], P [W], Q [As], Qneg [As], Qpos [As], T_1 [°C], T_2 [°C], T_3 [°C], T_Clima [°C], U [V]</li> </ul> </li> <li>The test reports are structured and consolidated into an HDF5 file (<code>Scienlab.h5</code>) for efficient storage and analysis. This HDF5 file can be processed using the <strong>HDF5 Data Analysis and Visualization Toolkit</strong>, provided in this repository. <ul> <li>The Python script <code>hdf5_main.py</code> is included for processing and visualizing the datasets.</li> <li>PowerTemperatureTest_dis-Umax datasets contain cyclization data from multiple constant power (CP) discharges and standardized constant current constant voltage (CCCV) charges, with varying end-of-charge voltages.</li> <li>OCVTest datasets contain low-current galvanostatic data required for performance characterization via reconstruction-based approaches. The test protocol is extensively described in the publication.</li> </ul> </li> </ol>
Major ions in Trambau ice core, Nepal Himalaya
<p>An 81.2-m-long ice core was drilled in November 2019 at 5862m a.s.l. of Trambau Glacier, Rolwaling region, Nepal Himalaya (27.919° N, 86.545° E). This data set contains the concentrations of major ions and tritium concentrations in the ice core.</p> <p>The data set contains Depth in snow/ice (m), Depth in water equivalent (m w.e.), Date (digit year), Na (ppb), Cl (ppb), NH4 (ppb), K (ppb), Mg (ppb), Ca (ppb), NO3 (ppb), SO4 (ppb), T_rough (TU), T_fine (TU)</p>
Basis sets for the 1s2 ground states of two-electron rare gas ions
<p>This file contains the exponents of Gaussian basis functions optimized in the Dirac-Coulomb approximation for the two-electron ions of the rare gases, He, Ne, Ar, Kr, Rn, and Og. The basis sets are of double-zeta (2z) to septuple-zeta (7z) quality, and include correlating sets:1p (2z), 2p1d (3z), 3p2d1f (4z), 4p3d2f1g (5g), 5p4d3f2g1h (6z), and 6p5d4f3g2h1i (7z). The basis sets are included with the DIRAC program.</p>
Measurement of energies and intensities of multiple ionization satellite (MIS) excited in light elements by helium ion beams
<p>TNA project number: <strong>19001708-ST</strong></p> <p><strong>Measurement of energies and intensities of multiple ionization satellite (MIS) excited in light elements by helium ion beams</strong></p> <p><em>Scientific background:</em></p> <p>Over 1600 X-ray spectra have been collected from the alpha particle X-ray spectrometers on Mars rovers including the present Curiosity rover. The spectra are excited by the radionuclide <sup>244</sup>Cm, which emits 5 MeV He ions for PIXE and Pu L X-rays for XRF. These spectra provide elemental analysis of rocks, soils and dust as part of the quest to identify formerly habitable (water-bearing) environments. Applicant is a member of the Curiosity APXS team within NASA’s Mars Science Laboratory. Excitation of K X-rays by 5 MeV He ions produces also energy-shifted satellites due to 1, 2 or 3 L-shell spectator vacancies. These cause significant distortion of the diagram lines and worsen the quality of spectrum fits by the GUPIX(Mars) code. An MIS database is needed to support a correction procedure that is already devised. This work will increase analytical accuracy and will also support more accurate terrestrial PIXE analysis using alpha beams, especially when partnered with RBS; this could lead to increased use of these two IBA methods in a complementary manner.</p> <p> </p> <p><em>Measurements performed within TNA project:</em></p> <p>The wavelength-dispersive in-vacuum x-ray spectrometer of J. Stefan Institute (Ljubljana, Slovenia) [1] have been used to record high energy resolution KaL<sup>N</sup> X-ray spectra of Ca and Cr induced in collisions with MeV alpha particles. The targets used were metallic Cr, Cr<sub>2</sub>O<sub>3</sub>, and CaF<sub>2</sub>. The KaL<sup>N</sup> X-ray spectra of Cr and Cr<sub>2</sub>O<sub>3</sub> were measured using three different energies of He ions, namely 3 MeV, 4 MeV and 5 MeV. For CaF<sub>2</sub> we have collected only spectra induced with 5 MeV He beam. The main purpose of the experiment was to record KaL<sup>N</sup> spectra with good enough statistics to determine precisely energy/intensity of the corresponding satellite lines. The results for the energy shifts and relative intensities of the groups will be incorporated in the MIS database providing an empirical means for inclusion of one peak per satellite group when modelling energy-dispersive spectra (GUPIX(Mars) code).</p> <p>[1] M. Kavčič, M. Budnar, A. Mühleisen, F. Gasser, M. Žitnik, K. Bučar, R. Bohinc, <em>Design and performance of a versatile curved-crystal spectrometer for high-resolution spectroscopy in the tender x-ray range</em>, Rev. Sci. Instr. 83, 033113 (2012). <a href="http://dx.doi.org/10.1063/1.3697862">http://dx.doi.org/10.1063/1.3697862</a></p> <p> </p> <p><em>Data files:</em></p> <p>We are sharing the detector files (a series of single exposure 2D raw images) collected by the Andor DX438-BV CCD camera (770 × 1152 pixels with pixel size 22.5×22.5 <em>μ</em>m<sup>2</sup>) after the diffraction on the crystal analyzer. The horizontal axis of the detector corresponds to the dispersion axis and diffracted photons are detected at different horizontal positions according to their wavelength, the vertical axis of the detector serves mainly to accumulate more statistics. The final emission spectra are obtained from the corresponding detector files using the home-written data processing software.</p> <p> </p> <p> </p>
Dataset for the publication: First-principles studies on the atomistic properties of metallic magnesium as anode material in magnesium-ion batteries
<p>This dataset contains the input and output files from the calculation of the atomistic properties of metallic magnesium, such as bulk, surface, adsorption, and diffusion properties.</p> <p>The discussion of the results were published in the ChemSusChem article: 'First-principles studies on the atomistic properties of metallic magnesium as anode material in magnesium-ion batteries' (<a href="https://doi.org/10.1002/cssc.202200414">https://doi.org/10.1002/cssc.202200414</a>). A preprint of the publication is further available under: <a href="http://doi.org/10.26434/chemrxiv-2022-qz055">https://doi.org/10.26434/chemrxiv-2022-qz055</a>.</p> <p>All calculations were performed using the density function theory code Vienna <em>ab initio</em> simulation package (VASP).</p> <p>The dataset contains all raw data for the performed convergence studies and calculated bulk-, surface-, adsorption-, and diffusion properties. An overview of the folder structure of the Zip archive, more precisely in which folders the data for the respective figures or tables of the underlying publication (<a href="https://doi.org/10.1002/cssc.202200414">https://doi.org/10.1002/cssc.202200414</a>) are stored, is provided in the following table:</p> <table> <tbody> <tr> <td>Convergence_study</td> <td>Figure S1</td> </tr> <tr> <td>Bulk_properties</td> <td>Table S3</td> </tr> <tr> <td>Surface_properties</td> <td>Table 1, Table 2, Figure 1, Table S5</td> </tr> <tr> <td>Adsorption_properties</td> <td>Monomer: Table S6; Dimer: Table 4, Table 5, Table 6; Islands: Figure S5, Table S9</td> </tr> <tr> <td>Diffusion_properties</td> <td>Table 3, Table 7, Table 8, Table 9, Table 10, Table 11, Table 12, Figure 13, Figure 14, Table S7, Table S8, Table S10 Table S11, Figure S4, Figure S7, Figure S9</td> </tr> </tbody> </table> <p> </p>
Nonlinear spectral analysis of ion acoustic solitons arising from a streaming charged object using the numerical inverse scattering transform data
<p>Data files used in the publication: "Nonlinear spectral analysis of ion acoustic solitons arising from a streaming charged object using the numerical inverse scattering transform", submitted to Physics of Plasma August 2022. To be used in conjunction with analysis software KVIST.</p> <p>KVIST can be found at:</p> <ul> <li>https://doi.org/10.5281/zenodo.7017043</li> <li>https://github.com/Planetary-Surfaces-and-Spacecraft-Lab/KVIST</li> </ul> <p>Data files generated with:</p> <p>Truitt, A. (2020). Simulation of Forced Korteweg De Vries Equation as Applied to Small Orbital Debris. Digital Repository at the University of Maryland. https://doi.org/10.13016/FOR0-XJYD</p>
Dataset for the publication: Development of a Mg/O ReaxFF Potential to describe the Passivation Processes in Magnesium-Ion Batteries
<p>This dataset contains all input and output files of the performed calculations, which results were published in the ChemSusChem article: 'Development of a Mg/O ReaxFF Potential to describe the Passivation Processes in Magnesium-Ion Batteries' (<a href="https://doi.org/10.1002/cssc.202201821">https://doi.org/10.1002/cssc.202201821</a>). A preprint of the publication is further available under: <a href="http://doi.org/10.26434/chemrxiv-2022-3chph">https://doi.org/10.26434/chemrxiv-2022-3chph</a>.</p>
Molecular dynamics simulation trajectory of an anionic lipid bilayer: 100 mol% DOPS with Na+ counterions using ff99 Ions
<p><strong>System: </strong>Symmetric bilayer of anionic DOPS (1,2-Dioleoyl-<em>sn</em>-glycero-3-phosphoserine 100 mol-%) lipids with sodium (Na<sup>+</sup>) counter ions.</p> <p><strong>Number of DOPS:</strong> 128.<br> <strong>Number of Na<sup>+</sup>-ions:</strong> 128.<br> <strong>Number of waters:</strong> 4480.</p> <p><strong>Lipid model:</strong> Amber Lipid 17 [IR Gould, AA Skjevik, CJ Dickson, BD Madej, RC Walker: "Lipid17: A Comprehensive AMBER Force Field for the Simulation of Zwitterionic and Anionic Lipids" in prep. (2018)].</p> <p><strong>Ion models: </strong> Amber ff99 [J Åqvist <em>J. Phys. Chem.</em> <strong>94</strong> 8021 (1990)].</p> <p><strong>Water model:</strong> TIP3P [WL Jorgensen, J Chandrasekhar, JD Madura, RW Impey, ML Klein <em>J. Chem. Phys.</em> <strong>79</strong> 926 (1983)].</p> <p><strong>Simulation engine:</strong> Amber16 [DA Case et al. <em>AMBER 2017</em> UCSF (2017)].</p> <p><strong>Number of independent repeats per setup: </strong>2.<br> <strong>Trajectory lengths per repeat:</strong> 400 ns + 100 ns.<br> <strong>Previously equilibrated for:</strong> 100 ns.<br> <strong>Sampling rate:</strong> every 10 ps.</p> <p><strong>Time integration step:</strong> 2 fs.</p> <p><strong>Thermodynamic ensemble:</strong> NpT. <br> <strong>Temperature coupling:</strong> 'Langevin' at T = 303 K.<br> <strong>Pressure coupling: '</strong>Berendsen' [<em>J. Chem. Phys.</em> <strong>81</strong> 3684 (1984); <em>J. Chem. Phys.</em> <strong>103</strong> 10252 (1995)] with xy and z coupled separately at p = 1.0 bar with no surface tension.</p> <p><strong>Electrostatics: </strong>PME [<em>J. Chem. Phys.</em> <strong>98</strong> 10089 (1993);<em> J. Chem. Theory Comput.</em> <strong>9</strong> 3878 (2013)].<br> <strong>Van der Waals:</strong> Turned off between 1.0 nm and 1.5 nm.</p> <p><strong>Constraints: </strong>Lengths of covalent bonds involving Hydrogens in lipids using SHAKE [<em>J. Comput. Phys.</em> <strong>23</strong> 327 (1977)], in water using SETTLE [<em>J. Comput. Chem. </em><strong>13</strong> 952 (1992)].</p> <p><strong>Used in publications: </strong>OHS Ollila et al. "NMRlipids IV: Headgroup & glycerol backbone structures, and cation binding in bilayers with PS lipids" in prep (2018).</p>
Molecular dynamics simulation trajectory of an anionic lipid bilayer: 100 mol% POPS with Na+ counterions using Joung-Cheatham Ions
<p><strong>System:</strong> Symmetric bilayer of anionic POPS (palmitoyl-oleoyl-phosphatidylserine 100 mol-%) lipids with sodium (Na<sup>+</sup>) counter ions.</p> <p><strong>Number of POPS:</strong> 128.<br> <strong>Number of Na<sup>+</sup>-ions:</strong> 128.<br> <strong>Number of waters:</strong> 4480.</p> <p><strong>Lipid model:</strong> Amber Lipid 17 [IR Gould, AA Skjevik, CJ Dickson, BD Madej, RC Walker: "Lipid17: A Comprehensive AMBER Force Field for the Simulation of Zwitterionic and Anionic Lipids" in prep. (2018)].</p> <p><strong>Ion model:</strong> Joung–Cheatham [IS Joung, TE Cheatham III <em>J. Phys. Chem. B</em> <strong>112</strong> 9020 (2008)].</p> <p><strong>Water model:</strong> TIP3P [WL Jorgensen, J Chandrasekhar, JD Madura, RW Impey, ML Klein <em>J. Chem. Phys.</em> <strong>79</strong> 926 (1983)].</p> <p><strong>Simulation engine:</strong> Amber16 [DA Case et al. <em>AMBER 2017</em> UCSF (2017)].</p> <p><strong>Number of independent repeats per setup:</strong> 2.<br> <strong>Trajectory lengths per repeat:</strong> 400 ns + 100 ns.<br> <strong>Previously equilibrated for:</strong> 100 ns.<br> <strong>Sampling rate:</strong> every 10 ps.</p> <p><strong>Time integration step:</strong> 2 fs.</p> <p><strong>Thermodynamic ensemble:</strong> NpT. <br> <strong>Temperature coupling:</strong> 'Langevin' at T = 298 K.<br> <strong>Pressure coupling:</strong> 'Berendsen' [<em>J. Chem. Phys.</em> <strong>81</strong> 3684 (1984); <em>J. Chem. Phys</em>. <strong>103</strong> 10252 (1995)] with <em>xy</em> and <em>z</em> coupled separately at p = 1.0 bar with no surface tension.</p> <p><strong>Electrostatics:</strong> PME [<em>J. Chem. Phys.</em> <strong>98</strong> 10089 (1993); <em>J. Chem. Theory Comput. </em><strong>9</strong> 3878 (2013)].<br> <strong>Van der Waals:</strong> Turned off between 1.0 nm and 1.5 nm.</p> <p><strong>Constraints:</strong> Lengths of covalent bonds involving Hydrogens in lipids using SHAKE [<em>J. Comput. Phys.</em> <strong>23</strong> 327 (1977)], in water using SETTLE [<em>J. Comput. Chem.</em> <strong>13</strong> 952 (1992)].</p> <p><strong>Used in publications:</strong> OHS Ollila et al. "NMRlipids IV: Headgroup & glycerol backbone structures, and cation binding in bilayers with PS lipids" in prep (2018).</p>
Apples to Apples: Shift from Mass Ratio to Additive Molecules per Electrode Area to Optimize Li-Ion Batteries
<p>Electrolyte additives in liquid electrolyte batteries can trigger the formation of a protective interphase (SEI) atthe electrodes that aims to suppress side reactions at the electrodes. Studies of varying amounts of additives have been done over the last years, providing a comprehensive understanding of the impact of the electrolyte formulation on the lifetime of the cells. However, these studies mostly focus on the variation of the mass fraction of additive in the electrolyte while disregarding the ratio (radd) of the additive's amount of substance (nadd) to the electrode area (Aelectrode). Herein we utilize our extremely accurate automatic battery assembly system (AUTOBASS) to vary electrode area and amount of substance of the additive. The data provides strong evidence that reporting the mass ratios of electrolyte components is insufficient and the mol of additive relative to the electrodes' area should be reported. Herein, the two most utilized additives, namely fluoroethylene carbonate (FEC) and vinylene carbonate (VC) were studied. Each additive was varied from 0.1 wt.-% - 3.0 wt.-% for VC, and 5 wt.-% - 15 wt.-% for FEC for two mass loadings of 1 mAh/cm2 and 3 mAh/cm2. To engage the community to find better descriptors, such as the proposed radd, we publish the dataset alongside this manuscript.</p> <p>Codes and mechanical parts of the project:</p> <p>AutoBASS 2.0: <a href="https://github.com/Helge-Stein-Group/AutoBASS/tree/AutoBASS_2.0">GitHub - Helge-Stein-Group/AutoBASS at AutoBASS_2.0</a></p>
Ion Implantation Sensor and Process Target Data for Predicting Ion Beam Tuning in Semiconductor Manufacturing
<h2><strong>Dataset Description:</strong></h2> <p>This dataset is designed to predict ion beam tuning setup processes in semiconductor manufacturing, in terms of tuning success or failure, and tuning duration. It is split into <strong><code>X</code></strong> and <code><strong>y</strong></code> to allow for supervised learning approaches.</p> <ul> <li><code><strong>X</strong></code> represents the current equipment condition and the process targets of the currently processed and the upcoming lot, as defined within recipes.</li> <li><code><strong>y</strong></code> represents the ion beam tuning setup report, which informs about the tuning success ratio and tuning duration. These setups are necessary, when switching between recipes to prepare the equipment for processing the next lot. <strong><code>y</code></strong> contains three labels, enabling classification of (1) tuning success or fail, and (2) prolonged tuning, as well as (3) estimation of tuning duration as a regression task.</li> </ul> <p>About <strong><code>X</code></strong>:</p> <p>Each lot is processed with a specific recipe to achieve the process target. The tuning takes place before the first wafer of the to-be-tuned recipe is processed. Each row in <strong><code>X</code></strong> includes logistical information such as the equipment used for processing and parsed recipe / process target information for the current and upcoming lot. The majority of data consists out of aggregated metrics of equipment-internally tracked sensor traces, recording physical parameters such as gas flows, temperatures, voltages and currents. When analyzed in conjunction with the processed recipe, these sensors provide insights into the current equipment condition. </p> <p>About <code><strong>y</strong></code>:</p> <p>The <code>setup_result</code> column indicates the success or failure of tuning - with <code>setup_result=0</code> indicating tuning success, while <code>setup_result=1</code> signals tuning failure. If the first tuning attempt fails, there may be follow-up attempts, but these are not included in this dataset. The <code>duration</code> column represents the tuning duration in seconds, as used for regression analysis. The <code>duration_interval</code> column is a binary label for prolonged tunings, i.e. <code>duration_interval=1</code> for instances, which take more than 6 minutes to tune.</p> <p>For reproducibility of the corresponding paper's results:</p> <ol> <li>The dataset contains the same carefully curated subset of features.</li> <li>The train_test_split() has already been performed, thus we provide <code>x_train</code> and <code>x_valid</code> separately.</li> <li>To reduce the effect of outliers in the data, the sensor data has already been scaled, as derived from <code>x_train</code>.</li> </ol> <p>In summary, these datasets (<code><strong>X</strong></code>, <code><strong>y</strong></code>) provide comprehensive information for predicting ion beam tuning in semiconductor manufacturing, making it a valuable resource for researchers and practitioners in the field.</p> <h2><strong>Python Code for Reproducibility:</strong></h2> <p>Furthermore, we share a jupyter notebook <code>ionbeamtuning.ipynb</code> with Python code to train the best performing model on the provided data, as described in the paper. To execute the code, you may need to install any missing packages specified in the <code>requirements.txt</code>, as indicated within the notebook.</p>
IODP Expedition 379 Ion chromatography
Cation and anion concentration in interstitial water samples was measured by ion chromatography using a conductivity detector. Cation analytes include calcium, magnesium, potassium, and sodium. Anion analytes include chloride, sulfate, and bromide.
IODP Expedition 371 Ion chromatography
Cation and anion concentration in interstitial water samples was measured by ion chromatography using a conductivity detector. Cation analytes include calcium, magnesium, potassium, and sodium. Anion analytes include chloride, sulfate, and bromide.
IODP Expedition 397 Ion chromatography
Cation and anion concentration in interstitial water samples was measured by ion chromatography using a conductivity detector. Cation analytes include calcium, magnesium, potassium, and sodium. Anion analytes include chloride, sulfate, and bromide.
Molecular dynamics simulation trajectory of an anionic lipid bilayer: 100 mol% POPS with Na+ counterions using ff99 ions
<p><strong>System: </strong>Symmetric bilayer of anionic POPS (palmitoyl-oleoyl-phosphatidylserine 100 mol-%) lipids with sodium (Na<sup>+</sup>) counter ions.</p> <p><strong>Number of POPS:</strong> 128.<br> <strong>Number of Na<sup>+</sup>-ions:</strong> 128.<br> <strong>Number of waters:</strong> 4480.</p> <p><strong>Lipid model:</strong> Amber Lipid 17 [IR Gould, AA Skjevik, CJ Dickson, BD Madej, RC Walker: "Lipid17: A Comprehensive AMBER Force Field for the Simulation of Zwitterionic and Anionic Lipids" in prep. (2018)].</p> <p><strong>Ion model:</strong> Amber ff99 [J Åqvist <em>J. Phys. Chem.</em> <strong>94</strong> 8021 (1990)].</p> <p><strong>Water model:</strong> TIP3P [WL Jorgensen, J Chandrasekhar, JD Madura, RW Impey, ML Klein <em>J. Chem. Phys.</em> <strong>79</strong> 926 (1983)].</p> <p><strong>Simulation engine:</strong> Amber16 [DA Case et al. <em>AMBER 2017</em> UCSF (2017)].</p> <p><strong>Number of independent repeats per setup: </strong>2.<br> <strong>Trajectory lengths per repeat:</strong> 400 ns + 100 ns.<br> <strong>Previously equilibrated for:</strong> 100 ns.<br> <strong>Sampling rate:</strong> every 10 ps.</p> <p><strong>Time integration step:</strong> 2 fs.</p> <p><strong>Thermodynamic ensemble:</strong> NpT. <br> <strong>Temperature coupling:</strong> 'Langevin' at T = 298 K.<br> <strong>Pressure coupling: '</strong>Berendsen' [<em>J. Chem. Phys.</em> <strong>81</strong> 3684 (1984); <em>J. Chem. Phys.</em> <strong>103</strong> 10252 (1995)] with xy and z coupled separately at p = 1.0 bar with no surface tension.</p> <p><strong>Electrostatics: </strong>PME [<em>J. Chem. Phys.</em> <strong>98</strong> 10089 (1993);<em> J. Chem. Theory Comput.</em> <strong>9</strong> 3878 (2013)].<br> <strong>Van der Waals:</strong> Turned off between 1.0 nm and 1.5 nm.</p> <p><strong>Constraints: </strong>Lengths of covalent bonds involving Hydrogens in lipids using SHAKE [<em>J. Comput. Phys.</em> <strong>23</strong> 327 (1977)], in water using SETTLE [<em>J. Comput. Chem. </em><strong>13</strong> 952 (1992)].</p> <p><strong>Used in publications: </strong>OHS Ollila et al. "NMRlipids IV: Headgroup & glycerol backbone structures, and cation binding in bilayers with PS lipids" in prep (2018).</p>
Molecular dynamics simulation trajectory of an anionic lipid bilayer: 100 mol% DOPS with Na+ counterions using Joung-Cheetham Ions
<p><strong>System: </strong>Symmetric bilayer of anionic DOPS (1,2-Dioleoyl-<em>sn</em>-glycero-3-phosphoserine 100 mol-%) lipids with sodium (Na<sup>+</sup>) counter ions.</p> <p><strong>Number of DOPS:</strong> 128.<br> <strong>Number of Na<sup>+</sup>-ions:</strong> 128.<br> <strong>Number of waters:</strong> 4480.</p> <p><strong>Lipid model:</strong> Amber Lipid 17 [IR Gould, AA Skjevik, CJ Dickson, BD Madej, RC Walker: "Lipid17: A Comprehensive AMBER Force Field for the Simulation of Zwitterionic and Anionic Lipids" in prep. (2018)].</p> <p><strong>Ion models: </strong>Joung–Cheatham [IS Joung, TE Cheatham III <em>J. Phys. Chem. B </em><strong>112</strong> 9020 (2008)].</p> <p><strong>Water model:</strong> TIP3P [WL Jorgensen, J Chandrasekhar, JD Madura, RW Impey, ML Klein <em>J. Chem. Phys.</em> <strong>79</strong> 926 (1983)].</p> <p><strong>Simulation engine:</strong> Amber16 [DA Case et al. <em>AMBER 2017</em> UCSF (2017)].</p> <p><strong>Number of independent repeats per setup: </strong>2.<br> <strong>Trajectory lengths per repeat:</strong> 400 ns + 100 ns.<br> <strong>Previously equilibrated for:</strong> 100 ns.<br> <strong>Sampling rate:</strong> every 10 ps.</p> <p><strong>Time integration step:</strong> 2 fs.</p> <p><strong>Thermodynamic ensemble:</strong> NpT. <br> <strong>Temperature coupling:</strong> 'Langevin' at T = 303 K.<br> <strong>Pressure coupling: '</strong>Berendsen' [<em>J. Chem. Phys.</em> <strong>81</strong> 3684 (1984); <em>J. Chem. Phys.</em> <strong>103</strong> 10252 (1995)] with xy and z coupled separately at p = 1.0 bar with no surface tension.</p> <p><strong>Electrostatics: </strong>PME [<em>J. Chem. Phys.</em> <strong>98</strong> 10089 (1993);<em> J. Chem. Theory Comput.</em> <strong>9</strong> 3878 (2013)].<br> <strong>Van der Waals:</strong> Turned off between 1.0 nm and 1.5 nm.</p> <p><strong>Constraints: </strong>Lengths of covalent bonds involving Hydrogens in lipids using SHAKE [<em>J. Comput. Phys.</em> <strong>23</strong> 327 (1977)], in water using SETTLE [<em>J. Comput. Chem. </em><strong>13</strong> 952 (1992)].</p> <p><strong>Used in publications: </strong>OHS Ollila et al. "NMRlipids IV: Headgroup & glycerol backbone structures, and cation binding in bilayers with PS lipids" in prep (2018).</p>
Dataset of molecular docking data of neuropeptides to acid-sensing ion channels
<p>The *.dock4 files are result files of molecular docking with the software Autodock Vina to the human ASIC1a closed state model, of the peptides FRRFa and KNFLRFa (FRRF.dock4, KNFLRF.dock4) that can be visualized with structure viewing programs such as UCSF Chimera on the closed ASIC1a model file (closed_ASIC_pH7.4.pdb). The file “FRRF_KNFLRF_complexes.pdb” provides the structures of selected poses of FRRFa and KNFLRFa peptides docked to the closed conformation of the human ASIC1a model.</p>
Unstable Ion Composition Boundary Layer Crossings
<p>A list of unstable ion composition boundary layer crossings by the MAVEN spacecraft, separated into three categories. "Snowplow" events have discrete parcels of low energy escaping heavy ion plasma interspersed with more energetic magnetosheath plasma. "Plume" events have higher energy escaping heavy ions with a beam-like distribution, interspersed with magnetosheath plasma. "Unclassified" events do not neatly fit in either of these categories. </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.