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1,491 results for “Ione”
Meta-analysis of multiple ion uptake kinetics in crop roots
<p>This dataset collates measures of Michaelis-Menten root uptake parameters for several nutrients based on reported literature. The analysis appears in a review article by Marcus Griffiths (mdgriffiths at noble.org) and Larry York (lmyork at noble.org).</p> <p>Please cite this repository and the review article if the data is reused.</p> <p><strong>Citation:</strong></p> <p>Griffiths, M. G., York, L. M. (In Press) Targeting root ion uptake kinetics to increase plant productivity and nutrient use efficiency. Plant Physiology. <a href="https://doi.org/10.1104/pp.19.01496">https://doi.org/10.1104/pp.19.01496</a></p> <p><strong>File descriptions and use</strong></p> <p>The R script assumes that all data files are in the working directory and that the "tidyverse" meta-package is installed.</p> <p><strong>Kinetics_metaanalysis_all_files_v2.zip</strong> - Contains all the files.<br> <br> <strong>Kinetics_metaanalysis_column_definitions.txt </strong>- Definitions of column names used in data files (not used by R script)<br> <br> <strong>Kinetics_metaanalysis_csv.csv </strong>- Complete dataset in CSV format<strong> </strong>(used by R script)<br> <br> <strong>Kinetics_metaanalysis_excel.xlsx</strong> - Complete dataset in Excel format (not used by R script)<br> <br> <strong>Kinetics_metaanalysis_figure_subset.csv </strong>- Subset of total data used for figure generation (used by R script)<br> <br> <strong>Kinetics_metaanalysis_figures_code.R </strong>- Required R script for statistical analysis </p> <p>Version 2 of this repository includes minor updates to include new papers and data.</p> <p> </p>
Dataset: Highly sensitive single molecule detection of macromolecule ion beams
<p>Figure 6 & Figure 7.zip: Figure 6 Superconducting nanowire detector (SSPD) beam profile of a mass-selected Insulin (Ins5+) ion beam at 1000 eV of impact energy.; Figure 7 Area-normalized molecule detection quantum yield. - raw data and Origin 2022 files</p><p>Voltage Shifts.zip: Calibration of deflection electrodes with linear stage - raw data and Origin 2022 files</p><p>ESI source stability.opju: 1 Hour measurement of the source stability with a TOF-MS - Origin 2022 file</p><p>Figure 2.opju: Impact energy in ESI-QMS-SSPD vs. ESI-QMS-Phosphorscreen mass spectra. - Origin 2022 file</p><p>Figure 3.opju: Signal background and complexity in ESI-QMS-SSPD vs. ESI-TOF-MCP spectra. - Origin 2022 file</p><p>Figure 4.opju: Influence of Energy/Mass/Momentum/structure on the detection mechanism. - Origin 2022 file</p><p>Figure 5.opju: Confirmation of the hot-spot model for SSPD detector D2 - Origin 2022 file</p><p>Figure S2.opju: Identification of protein mass spectra. - Origin 2022 file</p><p>Figure S3.opju: ESI-QMS-SSPD spectrum measured with SSPD. - Origin 2022 file</p><p>Figure S5.opju: Beam profile at different deflection voltages. - Origin 2022 file</p><p>Figure S9.opju: Photo-activated ESI-QMS-SSPD spectrum of Ru-complex modified Insulin. - Origin 2022 file</p><p>Linear stage position readback.txt: Position readback when lifting the Linear Stage recorded with a laser triangulometer. - raw data</p>
Statistical Analysis of Overlapping Double Ion Energy Dispersion Events in the Northern Cusp (Paper Data and Code)
<p>This data and code accompanies the paper <i>Statistical Analysis of Overlapping Double Ion Energy Dispersion Events in the Northern Cusp</i>, published in Frontiers in Astronomy and Space Sciences in 2023. This upload includes a CSV list of the selected events, a human-readable table of the selected events (see below), plots of each selected event, and the code used to select the events.</p><p>The code in this repository is a fork of <a href="https://github.com/ddasilva/dmsp-dispersion-detection">https://github.com/ddasilva/dmsp-dispersion-detection</a> at the time of publication. Future updates may exist on GitHub.</p><p><br> </p>
Quantification of phosphorylated metabolites, organic acids, and intermediates of the TCA cycle using capillary ion chromatography tandem mass spectrometry (capIC-MS/MS) following treatment of Escherichia coli with ciprofloxacin
<p>Capillary ion chromatography tandem mass spectrometry (capIC-MS/MS) was used to quantify phosphorylated metabolites, organic acids, and intermediates of the TCA cycle of Escherichia coli treated with ciprofloxacin, BTP-001 (a novel antimicrobial peptide), and a combination of the two . Metabolite extracts were analyzed with a Xevo TQ-XS triple quadrupole mass spectrometer (Waters, USA).</p><p>Samples were gathered from E. coli cultures grown in batch cultivations using 1 liter bioreactors. Briefly, intracellular metabolites were extracted by cycling samples between −20 °C EtOH and N2 (<i>l</i>) in three consecutive freeze–thaw cycles, with vortexing every 10 min during the thawing phase. Filters were removed and the cell debris was pelleted (4500 rcf, 10 min, -9 °C). The supernatants were transferred to a new tube, snap frozen in N2 (<i>l</i>), and lyophilized. Lyophilized extracts were reconstituted in 500 µL cold Milli-Q H2O and cleared by spin-filtration with a 10 kDa molecular cutoff (20817 rcf, 10 min, 0 °C). A mix of 80 µL centrifuged sample and 20 µL 13C-labeled ISTD extract from yeast was sent to analysis. </p><p>Data processing and absolute quantification was performed as earlier described using the TargetLynx application manager of MassLynx v 4.1 (Waters) to interpolate calibration curves made with appropriate dilutions of analytical grade standards (Sigma-Aldrich). The response factor of the corresponding U13C-isotopologues were used to correct the standard and sample extract response factors. Extract concentrations were normalized to the CDW, which was calculated from interpolation of the OD600 vs. CDW (g/L) curve. </p><p>Further statistical analysis in MetaboAnalyst v 5.0 replaced missing values with 1/5 of the minimum value of the respective metabolite. An unpaired T-test with unequal variance determined differential enriched metabolites with a false discovery rate (FDR) < 0.05 which are presented as log2 fold-change compared to control. </p>
Dataset for the paper "The magnetopause deformation indicated by fast cold ion motion"
<p>This dataset is used for the paper "The magnetopause deformation indicated by fast cold ion motion" and contains pubilicly available data of MMS dayside magnetopause crossing events with fast cold ion motion from 2015 to 2021, which includes crossing time and position, solar wind parameters (dynamic pressure, IMF, solar wind speed) and data source, the normals from the model (Shue et al., 1997 and Liu et al., 2015) and observations (Timing and MVA methods), the normal speed of magnetopause (VMP) from Timing method, the component speeds of cold ion along normal and tangential directions, the deflection angle between the observed magnetopause normal and the prediction, and the magnetopause deformation amplitude calculated by integrating cold ion speed (Positive or negative values indicate whether the magnetopause is moving outward or inward) of all 30 crossings in geocentric solar magnetospheric (GSM) coordinates.</p>
GC raw data of sodium ion electrolytes before and after battery storage
<p>Raw data of the GC measurements before and after battery storage. Additionally, the pure electrolyte as well as the pure solvent (DCM, dichloromethane) is mentioned. </p>
Model Output for: "MESSENGER observations of Mercury's planetary ion escape rates and their dependence on true anomaly angle"
<p>In the manuscript titled ‘MESSENGER Observations of Mercury’s Planetary Ion Escape Rates and Their Dependence on True Anomaly Angle’, submitted to Geophysical Research Letters, we provide an analysis of test sodium ion (Na+) particles. These test particles, integral to our research, are illustrated in Figure 4. This dataset contains an ASCII file with detailed information and descriptions of these test particles.</p>
The response of silicon carbide composites to He ion implantation and ramifications for use as a fusion reactor structural material
<p>Raw datasets for the publication "The response of silicon carbide composites to He ion implantation and ramifications for use as a fusion reactor structural material".</p>
Data from: Blood pressure pulsations modulate central neuronal activity via mechanosensitive ion channels
<p><span>The transmission of heartbeat through the cerebral vascular system is known to cause intracranial pressure pulsations. Here we report that arterial pressure pulsations can directly modulate central neuronal activity. </span><span>In a semi-intact rat brain preparation, vascular pressure pulsations elicit correlated local field oscillations in the olfactory bulb (OB) mitral cell layer. These oscillations do not require synaptic transmission, but reflect baroreceptive transduction in mitral cells. This transduction is mediated by an excitatory mechanosensitive ion channel and modulates neuronal spiking activity. Indeed, in awake animals, the heartbeat entrains the activity of a subset of OB neurons within ~20 ms. Thus, we propose that this fast intrinsic interoceptive mechanism can modulate perception, e.g. during arousal, within the OB and also possibly across various brain areas.</span></p>
ION IRIMESCU - TORS
Sculptura de ipsos masurand aprox. 45 cm. Redat în stil clasicizant, nudul feminin, simbol al frumuseţii, poate fi o metaforă a destăinuirii, a revelaţiei impresive a frumosului și esteticului – o alegorie perfectă pentru sensul artei. Source: Objaverse 1.0 / Sketchfab
Ion Torrent data for the genome assembly and phylogenomic placement of mitochondrial genomes with a focus on houndsharks (Chondrichthyes: Triakidae)
<p>Here, we present the Ion Torrent® next-generation sequencing (NGS) data for five houndsharks (Chondrichthyes: Triakidae), which include <em>Galeorhinus galeus</em> (17,487 bp; GenBank accession number ON652874), <em>Mustelus asterias</em> (16,708; ON652873), <em>Mustelus mosis</em> (16,755; ON075077), <em>Mustelus palumbes</em> (16,708; ON075076), and <em>Triakis megalopterus</em> (16,746 bp; ON075075). All assembled mitogenomes encode 13 protein-coding genes (PCGs), two ribosomal (r)RNA genes, and 22 transfer (t)RNA genes (<em>tRNA<sup>Leu</sup></em><sup> </sup>and <em>tRNA<sup>Ser</sup> </em>are duplicated), except for <em>G</em>. <em>galeus</em> which contains 23 tRNA genes where <em>tRNA<sup>Thr</sup> </em>is duplicated. The data presented in this paper can assist other researchers in further elucidating the diversification of triakid species and the phylogenetic relationships within Carcharhiniformes (groundsharks) as mitogenomes accumulate in public repositories.</p>
Dataset and R-scripts to the manuscript: Beyond salinity: plants show divergent responses to soil ion composition
<p>Dataset and R-scripts to the article: Pätsch R., Midolo G., Dítě Z., Dítě, D., Wagner, V., Pavonič, M., Danihelka, J., Preislerová Z., Ćuk M., Stroh H.G., Tóth T., Chytrá H., Chytrý M. (2024) Beyond salinity: plants show divergent responses to soil ion composition. <em>Global Evology and Biogeography, </em>00, e13821. <a href="https://doi.org/10.1111/geb.13821%E2%80%8B" target="_blank" rel="noopener">https://doi.org/10.1111/geb.13821</a></p>
Touch sensation requires the mechanically-gated ion channel Elkin1
<p>The extraordinary speed of touch perception is enabled by mechanically-activated ion channels, the opening of which excites cutaneous sensory endings to initiate sensation. We identify Elkin1(1) as an ion channel likely gated by mechanical force necessary for normal behavioral touch sensitivity in mice. Touch insensitivity in Elkin1-/- mice was caused by a loss of mechanically-activated currents (MA-currents) in around half of all sensory neurons that are activated by light touch (low threshold mechanoreceptors, LTMRs). Reintroduction of Elkin1 into sensory neurons from Elkin1-/- mice acutely restored MA-currents. Additionally, siRNA mediated knockdown of Elkin1 from induced human sensory neurons substantially reduced indentation-induced MA-currents supporting a conserved role for Elkin1 in human touch. Our data identify Elkin1 as a novel core component of touch transduction in mammals.</p>
Data set related to the manuscript "Dynamics and energetics of ion adsorption at the interface between a pure ionic liquid and carbon electrodes"
<p>Text files with data for plotting figures in the quoted manuscript. Example input files for simulations.</p>
Structural modeling of ion channels using AlphaFold2, RoseTTAFold2, and ESMFold
<p>Ion channels play key roles in human physiology and are important targets in drug discovery. The atomic-scale structures of ion channels provide invaluable insights into a fundamental understanding of the molecular mechanisms of channel gating and modulation. Recent breakthroughs in deep learning-based computational methods, such as AlphaFold, RoseTTAFold, and ESMFold have transformed research in protein structure prediction and design. We review the application of AlphaFold, RoseTTAFold, and ESMFold to structural modeling of ion channels using representative voltage-gated ion channels, including human voltage-gated sodium (Na<sub>V</sub>) channel - Na<sub>V</sub>1.8, human voltage-gated calcium (Ca<sub>V</sub>) channel – Ca<sub>V</sub>1.1, and human voltage-gated potassium (K<sub>V</sub>) channel – K<sub>V</sub>1.3. We compared AlphaFold, RoseTTAFold, and ESMFold structural models of Na<sub>V</sub>1.8, Ca<sub>V</sub>1.1, and K<sub>V</sub>1.3 with corresponding cryo-EM structures to assess details of their similarities and differences. Our findings shed light on the strengths and limitations of the current state-of-the-art deep learning-based computational methods for modeling ion channel structures, offering valuable insights to guide their future applications for ion channel research.</p>
Data from: "Lithium-ion battery degradation: comprehensive cycle ageing data and analysis for commercial 21700 cells"
<h1><strong>Intro</strong></h1> <p>Dataset from the publication "Lithium-ion battery degradation: comprehensive cycle ageing data and analysis for commercial 21700 cells", DOI: https://doi.org/10.1016/j.jpowsour.2024.234185</p> <p>Full details of the study can be found in the publication, including thorough descriptions of the experimental methods and structure. A basic desciption of the experimental procedure and data structure is included here for ease of use.</p> <p>Commercial 21700 cylindrical cells (LG M50T, LG GBM50T2170) were cycle aged under 3 different temperatures [10, 25, 40] °C and 4 different SoC ranges [0-30, 70-85, 85-100, 0-100]%, as well as a further [0-100]% SoC range experiment which utilised a drive-cycle discharge instead of constant-current. The same C-rates (0.3C / 1 C, for charge / discharge) were used in all tests; multiple cells were tested under each condition. These are listed in the table below.</p> <table> <tbody> <tr> <td> <div> <p><strong>Experiment</strong></p> </div> </td> <td> <div> <p><strong>SOC Window</strong></p> </div> </td> <td> <div> <p><strong>Cycles per ageing set</strong></p> </div> </td> <td> <div> <p><strong>Current</strong></p> </div> </td> <td> <div> <p><strong>Temperature</strong></p> </div> </td> <td> <div> <p><strong>Number of Cells</strong></p> </div> </td> </tr> <tr> <td> <div> <p>1</p> </div> </td> <td> <div> <p>0-30%</p> </div> </td> <td> <div> <p>257</p> </div> </td> <td> <div> <p>0.3C / 1D</p> </div> </td> <td> <div> <p>10°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> <div> <p> </p> </div> </td> <td> <div> <p> </p> </div> </td> <td> <div> <p> </p> </div> </td> <td> <div> <p> </p> </div> </td> <td> <div> <p>25°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>40°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> <div> <p>2,2</p> </div> </td> <td> <div> <p>70-85%</p> </div> </td> <td> <div> <p>515</p> </div> </td> <td> <div> <p>0.3C / 1D</p> </div> </td> <td> <div> <p>10°C</p> </div> </td> <td> <div> <p>2</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>25°C</p> </div> </td> <td> <div> <p>2</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>40°C</p> </div> </td> <td> <div> <p>2</p> </div> </td> </tr> <tr> <td> <div> <p>3</p> </div> </td> <td> <div> <p>85-100%</p> </div> </td> <td> <div> <p>515</p> </div> </td> <td> <div> <p>0.3C / 1D</p> </div> </td> <td> <div> <p>10°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>25°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>40°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> <div> <p>4</p> </div> </td> <td> <div> <p>0-100% (drive-cycle)</p> </div> </td> <td> <div> <p>78</p> </div> </td> <td> <div> <p>0.3C / noisy D</p> </div> </td> <td> <div> <p>10°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>25°C</p> </div> </td> <td> <div> <p>2</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>40°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> <div> <p>5</p> </div> </td> <td> <div> <p>0-100%</p> </div> </td> <td> <div> <p>78</p> </div> </td> <td> <div> <p>0.3C / 1D</p> </div> </td> <td> <div> <p>10°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>25°C</p> </div> </td> <td> <div> <p>2</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>40°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> </tbody> </table> <p>Cells were base-cooled at set temperatures using bespoke test rigs (see our linked publications for details; the supporting information file contains detailed descriptions and photographs). Cells were subject to break-in cycles prior to beginning of life (BoL) performance tests using the ‘Reference Performance Test’ (RPT) procedures. They were then alternately subject to ageing sets and RPTs until the end of testing. Full details of each of these procedures are described in the linked publication.</p> <p>The data contained in this repository is then described in the Data section below. This includes a description of the folder structure and naming conventions, file formats, and data analysis methods used for the ‘Processed Data’ which has been calculated from the raw data.</p> <p>An 'experimental_metadata' .xlsx file is included to aid parsing of data. A jupyter notebook has also been included to demonstate how to access some of the data.</p> <h1>Data</h1> <p>Data are organised according to their parent ‘Experiment’, as defined above, with a folder for each. Within each Experiment folder, there are 3 subfolders: ‘Summary Data’, ‘Processed Timeseries Data’, and ‘Raw Data’.</p> <h2>Summary Data</h2> <p>This folder contains data which has been extracted by processing the raw data in the ‘Degradation Cycling’ and ‘Performance Checks’ folders. In most cases, the data you are looking for will be stored here.</p> <p>It contains: </p> <h3>Performance Summary</h3> <p>A summary file for each cell which details key ageing metrics such as number of ageing cycles, charge throughput, cell capacity, resistance, and degradation mode analysis results. Each row of data corresponds to a different SoH.</p> <p>Degradation Mode Analysis (DMA) was also performed on the C/10 discharge data at each RPT. This analysis uses an optimisation function to determine the capacities and offset of the positive and negative electrodes by calculating a full cell voltage vs capacity curve using 1/2 cell data and comparing against the experimentally measured voltage vs capacity data from the C/10 discharge. See our <a href="https://doi.org/10.1021/acsaem.2c02047">ACS publication</a> for more details.</p> <p>Data includes:</p> <p>· Ageing Set: numbered 0 (BoL) to x, where x is the number of ageing sets the cell has been subject to.</p> <p>· Ageing Cycles: number of ageing cycles the cell has been subject to. *this is not equivalent full cycles.</p> <p>· Ageing Set Start Date/ End date: The date that each ageing set began/ ended.</p> <p>· Days of degradation: Number of days between the date of the first ageing set beginning and the current ageing set ending.</p> <p>· Age set average temperature: average recorded surface temperature of the cell during cycle ageing. Temperature was recorded approximately 1/2 way up the length of the cell (i.e. between positive and negative caps).</p> <p>· Charge throughput: total accumulated charge recorded during all cycles during ageing (i.e. sum of charge and discharge). This is the cumulative total since BoL (not including RPTs, and not including break-in cycles).</p> <p>· Energy throughput: as with "charge throughput", but for energy.</p> <p>· C/10 Capacity: the capacity recorded during the C/10 discharge test of each RPT.</p> <p>· C/2 Capacity: the capacity recorded during the C/2 discharge test of each even-numbered RPT.</p> <p>· 0.1s Resistance: The resistance calculated from the 25-pulse GITT test of each even-numbered RPT. This value is taken from the 12th pulse of the procedure (which corresponds to ~52% SoC at BoL). The resistance is calculated by dividing the voltage drop by the current at a timecale of 0.1 seconds after the current pulse is applied (the fastest timescale possible under the 10 Hz recording condition).</p> <p>· Fitting parameters: output from the DMA optimisation function; 5 parameters which detail the upper/lower SoCs of each electrode, and the capacity fraction of graphite in the negative electrode.</p> <p>· Capacity and offset data: calculated based on the fitting parameters above alongside the measured C/10 discharge capacity.</p> <p>· DM data: Quantities of LLI, LAM-PE, LAM-NE, LAM-NE-Gr, and LAM-NE-Si calculated from the change in capacities/offset of each electrode since BoL.</p> <p>· RMSE data: the root mean squared error of the optimisation function calculated from the residual between the measured and simulated voltage vs capacity profiles.</p> <h3>Ageing Sets Summary</h3> <p>Data from the ageing cycles, summarised on an average per cycle and an average per ageing set basis. Metrics include mean/ max/ min temperatures, voltages etc.</p> <h2>Processed Timeseries data</h2> <p>Timeseries data (voltage, current, temperature, etc.) from each subtest (pOCV, GITT, etc.) of the RPTs, all grouped by subtest-type and by cell ID.</p> <p>Contains the same data as in the ‘Performance Checks’ subfolder of the 'Raw Data' folder, but has been processed to slice into relevant subtests from the RPT procedure and includes only limited variables (time, voltage, current, charge, temperature). These are all saved as .csv files. In general this data will be easier to access than the raw data, but perhaps not as rich.</p> <h2>Raw Data</h2> <p>These are the raw data from the performance checks and from the degradation cycles themselves. The data from here has already been processed by me to get values of ‘energy throughput’, ‘charge throughput’, ‘average ageing temperature’, etc., which are all saved in the ‘Summary Data’ folder as described in the relevant section above.</p> <p>The data in the ‘Degradation Cycling’ folder are organised by ageing set (where an ageing set is a defined number of ageing cycles, as described in the paper). In theory, each cell should have one datafile in each ageing set subfolder. However, due to experimental issues, tests can sometimes be interrupted midway though, requiring the test to be subsequently resumed. In this case, there may be multiple datafiles for each cell in a given ageing set; during analysis, these should be concatenated according to the descriptor in the filename (e.g., ‘cycling7’ + ‘cycling7 (part 2)').</p> <p>Similarly, the unprocessed raw data from the performance checks (i.e. RPTs) is stored in the 'Performance Checks' folder, and structured in the same way.</p> <p>The raw data are saved in the .mpr format produced by the Biologic battery cycler. This is a binary format which is storage-efficient but can be more difficult to process for analysis purposes. We have therefore also exported the data into .txt files (called .mpt) for the performance checks (RPTs) which make analysis easier. However, the exported .mpt files could not be included for the degradation cycling files due to their larger size. If you require access these degradation cycle data, the .mpr binary file can be parsed using the <a href="https://github.com/echemdata/galvani">Galvani</a> package in python, or you can use Biologic’s (proprietary) BT-Lab software to export the data into .txt files.</p> <h3>File Naming Convention</h3> <p>The raw datafiles are named with a standard format. This is:</p> <p> <em>NDK - LG M50 deg - exp 1 - rig 1 - 10degC - cell A - RPT1_01_MB_CB1</em></p> <p> {NDK - LG M50 deg} - {exp 1} – {rig 1} – {10degC} – {cell A} – {RPT1}_{01}_{MB}_{CB1}</p> <p>{Standard prefix} – {experiment number} – {ID of test rig} – {control temperature} – {Cell ID} – {RPT number <em>or</em> aging cycle number}_{step number for the characterisation procedure (see above)}_{experimental technique name (will always be “MB”)}_{battery cycler channel ID used (always the same for a particular cell/experiment)}</p> <p> </p>
Dataset for publication "Decoding Niobium Carbide MXene Dual Functional Photoactive Cathode in Photoenhanced Hybrid Zinc-Ion Capacitor"
<p>This is a dataset for publication "Decoding Niobium Carbide MXene Dual-Functional Photoactive Cathode in Photoenhanced Hybrid Zinc-Ion Capacitor". It contains all relevant data from the aforementioned publication. The details about how to read these data is contained within the readme file.</p>
Dataset for "Speciation of Lanthanide Metal Ion Dopants in Microcrystalline All-Inorganic Halide Perovskite CsPbCl3"
<p>Raw solid-state NMR and XRD data for "Speciation of Lanthanide Metal Ion Dopants in Microcrystalline All-Inorganic Halide Perovskite CsPbCl3"</p>
Supporting data for "Electrochemical Removal of HF from Carbonate-based LiPF6-containing Li-ion Battery Electrolytes"
<p>This is the dataset of electrochemical experiments and BET analysis for our publication "Electrochemical Removal of HF from Carbonate-based LiPF6-containing Li-ion Battery Electrolytes" (DOI: <span><a href="https://doi.org/10.1149/1945-7111/ad30d3"><span>https://doi.org/10.1149/1945-7111/ad30d3</span></a></span>). This archive contains the raw data and Python Jupyter Notebook computer code to process, analyze, and generate the plots in this manuscript and its Supporting Information.</p> <p> </p> <p><span>Abstract for the manuscript:</span></p> <p> </p> <p><span>Due to the hydrolytic instability of LiPF6 in carbonate-based solvents, HF is a typical impurity in Li-ion battery electrolytes. HF significantly influences the performance of Li-ion batteries, for example by impacting the formation of the solid electrolyte interphase at the anode and by affecting transition metal dissolution at the cathode. Additionally, HF complicates studying fundamental interfacial electrochemistry of Li-ion battery electrolytes, such as direct anion reduction, because it is electrocatalytically relatively unstable, resulting in a LiF passivation layer. Methods to selectively remove ppm levels of HF from LiPF6-containing carbonate-based electrolytes are limited. We introduce and benchmark a simple yet efficient electrochemical method to selectively remove ppm amounts of HF from LiPF6-containing carbonate-based electrolytes. The basic idea is the application of a suitable potential to a high surface-area metallic electrode upon which only HF reacts (electrocatalytically) while all other electrolyte components are unaffected under the respective conditions.</span></p> <p> </p>
Multimode ion-photon entanglement over 101 kilometers
<p>Data and analysis code for the paper entitled "Multimode ion-photon entanglement over 101 kilometers" which is to be published in PRX QUantum journal. </p> <p>Description of the content is provided in the README.txt. Analysis of the data was executed in matlab 2023b.<br><br>Abstract of the paper that results from this dataset:<br>"A three-qubit quantum network node based on trapped atomic ions is presented. The ability to<br>establish entanglement between each individual qubit in the node and a separate photon that has<br>travelled over a 101 km-long optical fiber is demonstrated. By sending those photons through the<br>fiber in close succession, a remote entanglement rate is achieved that is greater than when using<br>only a single qubit in the node. Once extended to more qubits, this multimode approach can be a<br>useful technique to boost entanglement distribution rates in future long-distance quantum networks<br>of light and matter."</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.