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414 results for “Lithium”
Modulating the Surface Properties of Lithium Niobate Nanoparticles by Multifunctional Coatings Using Water-in-Oil Microemulsions
<p>Raw data associated to the study entitled <em>Modulating the Surface Properties of Lithium Niobate Nanoparticles by Multifunctional Coatings Using Water-in-Oil Microemulsions.</em></p> <p>Metadata file</p> <p>DLS measurements</p> <p>FTIR characterization</p> <p>Labbook</p> <p>NMR characterization</p> <p>TEM and EDX analysis</p> <p>XRD measurements</p>
Lithium Pollution of White Dwarfs and Other Secrets of MORDOR (CSV File of MORDOR Survey Objects)
<p>CSV file containing the Gaia DR2 data for the MORDOR Survey from Benjamin C. Kaiser's Ph.D. Dissertation. It also contains the spectral types and SED types that were identified.</p> <p>If you use this data please cite my dissertation, which should be accessible via the UNC Chapel Hill Library in some way. You should probably also cite Gaia DR2 if you use anything other than my spectral types pretty much because all the rest of the data is from Gaia DR2.</p>
Cellulose nanofiber-reinforced solid polymer electrolytes with high ionic conductivity for lithium batteries
<p>The data contained herein support both the results described in the research article entitled "Cellulose nanofiber-reinforced solid polymer electrolytes with high ionic conductivity for lithium batteries" with the following DOI: <a href="https://doi.org/10.1039/D3TA00380A">10.1039/D3TA00380A</a>, and the corresponding supporting information.</p>
Dataset and Model for "Understanding the limits to short-range order suppression in many-component disordered rock salt lithium-ion cathode materials"
<p>Data and Model For "Understanding the limits to Short-range order Suppression in Many-Component Disordered Rock Salt Lithium-ion Cathode Materials"</p> <p>Paper DOI: 10.1039/D3TA02088F</p> <p>Raw data (VASP calculations) and production model for the associated paper.</p> <p>- `cluster_expansions` contains the final model used for the analysis in the<br> paper. The model can be loaded using the `icet` cluster expansion package<br> using </p> <p> ```<br> from icet import ClusterExpansion # requires the icet python package</p> <p> ce = ClusterExpansion.read("cluster_expansion.ce")<br> ```<br> <br> see the icet docs for how to manipulate this object: https://icet.materialsmodeling.org/</p> <p>- `raw_data` contains the data used to fit the model. The calculation data<br> contains the raw vasp calculations in tar.gz files, and each training<br> generation has an associated `.json` files. The files contain Pymatgen<br> ComputedStructureEntry objects. Probably the easiest way to load these <br> into a python script is </p> <p> ```<br> from monty serialization import loadfn # requires the monty package<br> <br> training_data = loadfn("calculation_data.json")<br> ```</p> <p>- element references contains VASP calculations (stored as above) for <br> the elemental reference calculations used to determine the formation<br> energies of the training structures</p>
Dataset for Interfacial Chemistry Effects in the Electrochemical Performance of Silicon Electrodes under Lithium-ion Battery Conditions
<p>This dataset provides the raw data to the manuscript</p> <p>"<strong>Interfacial Chemistry Effects in the Electrochemical Performance of Silicon Electrodes under Lithium-ion Battery Conditions"</strong> published in Small (<a href="https://doi.org/10.1002/smll.202303442">https://doi.org/10.1002/smll.202303442</a>)</p> <p>Specifically, the following measurements are provided:</p> <ul> <li>Scanning electrochemical cell microscopy (SECCM) data</li> <li>Scanning electron microscopy (SEM) images</li> <li>Secondary ion mass spectrometry (SIMS) data</li> <li>Transmission electron microscopy (TEM) images</li> <li>X-ray photoelectron spectroscopy (XPS) data</li> </ul>
Lithium recovery from brines by lithium membrane flow capacitive deionization (Li-MFCDI) – A proof of concept
<p>Dataset to H.M. Saif et al., <a href="https://www.sciencedirect.com/journal/journal-of-membrane-science-letters">Journal of Membrane Science Letters</a>, <a href="https://www.sciencedirect.com/journal/journal-of-membrane-science-letters/vol/3/issue/2">Volume 3, Issue 2</a>, November 2023, 100059</p> <p>The demand of lithium for electric vehicles and energy storage devices is increasing rapidly, thus new sources of<br> lithium (such as seawater and natural or industrial brines), as well as sustainable methods for its recovery, will<br> need to be explored/developed soon. This work presents a novel electromembrane process, called Lithium<br> Membrane Flow Capacitive Deionization (Li-MFCDI), which was tested to recover lithium from a synthetic<br> geothermal brine containing a much higher mass concentration of sodium than lithium (more than 650 times).<br> Specifically, a ceramic lithium-selective membrane was integrated into a flow capacitive deionization (FCDI)<br> cell, which was specifically designed, and 3D printed, to allow simultaneous charging and regeneration of the<br> employed flow electrodes. Despite the extremely high Na+/Li+ mass ratio in the feed stream, 99.98% of the<br> sodium was rejected and the process selectivity for lithium over other monovalent cations was 141 ± 5.85 for<br> Li+/Na+ and 46 ± 1.46 for Li+/K+. The Li-MFCDI process exhibited a stable behaviour over a 7-day test period,<br> and the estimated energy consumption was 16.70 ± 1.63 kWh/kg of Li+ recovered in the draw solution. These<br> results demonstrate promising potential of the Li-MFCDI for the sustainable lithium recovery from saline streams.</p>
Simulation data of Schmidt et al., A three-dimensional finite element formulation coupling electrochemistry and solid mechanics on resolved microstructures of all-solid-state lithium-ion batteries, DOI: https://doi.org/10.1016/j.cma.2023.116468
<p>This data set includes the simulation results of the relevant simulations published in the paper: "Schmidt et al., A three-dimensional finite element formulation coupling electrochemistry and solid mechanics on resolved microstructures of all-solid-state lithium-ion batteries, DOI: https://doi.org/10.1016/j.cma.2023.116468".</p> <p>Please refer to the paper for the details of the model as well as the parameterization of the model for the respective simulations.</p> <p>The provided lzip archive is structured into separate folders, one per simulation. Each folder contains the output data and a short README.txt with further hints. For information on the compression algorithm and how to uncompress it lzip please refer to https://en.wikipedia.org/wiki/Lzip.</p>
Observation of an isotope effect in state-selective mutual neutralization of lithium with hydrogen
<p>The data files found here contain the data as obtained and displayed in : "Observation of an isotope effect in state-selective mutual neutralization of lithium with hydrogen" published in Physical Review A (2023). Each file contains an explanatory header. Header lines start with #.</p> <p> </p>
Lithium for Suicidal Behavior in Mood Disorders
ClinicalTrials.gov study NCT01928446. IPD Sharing: YES. Countries: 1. Publications: 1.
Circularity of lithium-ion battery materials in electric vehicles
Open the record for dataset details and reuse information.
Experimental data for "Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models"
<p>This dataset is for the validation data in Chen et al. (2020). It contains data for three different LG M50 cells undergoing an experiment in which the cells are charged in a constant-current/constant-voltage fashion and discharge at a constant current for different C-rates (C/10, C/2, 1C and 1.5C). Apart from the current and voltage, the temperatures of the cell surface and the thermal chamber in which they are cycled is recorded too.</p> <p><strong>References:</strong></p> <p>Chang-Hui Chen <em>et al</em> 2020 <em>J. Electrochem. Soc.</em> <strong>167</strong> 080534 (<a href="https://doi.org/10.1149/1945-7111/ab9050">https://doi.org/10.1149/1945-7111/ab9050</a>)</p>
Data for Lithium Isotope Fractionation during Intensive Felsic Magmatic Differentiation
<p>Data for original research of Lithium isotopes of granites in South China</p> <p>Table 1. Major elemental compositions (in wt.%) for granites from the Xihuashan and Yaogang Xian</p> <p>Table 2. Li isotopic and selected trace elemental compositions (ppm) for granites from the Xihuashan and Yaogangxian plutons</p> <p>Table 3. Li concentration and Li isotopic compositions of mineral separated from granites and greisen of the Xihuashan pluton</p> <p>Table S1. Chemical compositions of mica from granites in the Xihuashan pluton</p> <p>Table S2. Major and trace element compositions of K-feldspar from granites in the Xihuashan pluton.</p> <p>Table S3. Trace element compositions of zircon from granites in the Xihuashan pluton</p> <p>Table S4. Parameters used for the Rayleigh crystal fractionation modelling</p>
Data from: Hybrid Integration of Silicon Photonic Devices on Lithium Niobate for Optomechanical Wavelength Conversion
<p>Source data for Figures.</p>
Wavefunction for the lithium electride ROGDAS in a continuum solvent water
<p>Wavefunction for the lithium electride ROGDAS in a continuum solvent water at the wB97XD/6-31+G9d)/SCRF=water level</p>
Raw data for "Lithium carbonate-promoted mixed rare earth oxides as a generalized strategy for oxidative coupling of methane with exceptional yields"
<p>Raw data for "Lithium carbonate-promoted mixed rare earth oxides as a generalized strategy for oxidative coupling of methane with exceptional yields"</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 Reaction Mechanism and Performance of Innovative 2D Germanane-Silicane Alloys SixGe1−xH Electrodes in Lithium-Ion Batteries
<p>A dataset for publication Datase for publication Reaction Mechanism and Performance of Innovative 2D Germanane-Silicane Alloys SixGe1−xH Electrodes in Lithium-Ion Batteries including all relevant data used in the manuscript. Information on how to use the dataset are included in the readme file.</p>
Project - Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis
<p>Here are the datasets for our publication entitled "<a href="https://www.nature.com/articles/s41467-024-48779-z">Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis</a>" published in Nature Communications. </p> <p>The object of this experiment is the 18650 nickel-cobalt-manganese (NCM) lithium-ion battery manufactured by "LISHEN". The chemical composition is LiNi<sub>0.5</sub>Co<sub>0.2</sub>Mn<sub>0.3</sub>O<sub>2</sub>. The nominal capacity of the battery is 2000 mAh, and the nominal voltage is 3.6 V. The charging cut-off voltage and discharging cut-off voltage are 4.2 V and 2.5 V, respectively. The whole experiment was conducted at room temperature. A total of 55 batteries were included in this experiment, conducted under 6 different charging and discharging strategies. The charging and discharging platform is ACTS-5V10A-GGS-D, and the sampling frequency for all data is 1Hz.</p> <p>Other details can be found in "Data Introduction.pdf" file.</p> <p>The <strong>Python Code</strong> for reading and preprocessing this dataset is available at: <a href="https://github.com/wang-fujin/Battery-dataset-preprocessing-code-library">https://github.com/wang-fujin/Battery-dataset-preprocessing-code-library</a></p> <p>Summary of articles using the this dataset: <a href="https://github.com/wang-fujin/XJTU-Battery-Dataset-Papers-Summary">https://github.com/wang-fujin/XJTU-Battery-Dataset-Papers-Summary</a></p> <p> </p> <p>If you find this data helpful, please consider citing our paper:</p> <p>Wang, F., Zhai, Z., Zhao, Z. <em>et al.</em> Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis. <em>Nat Commun</em> <strong>15</strong>, 4332 (2024). https://doi.org/10.1038/s41467-024-48779-z</p>
Comprehensive battery aging dataset: capacity and impedance fade measurements of a lithium-ion NMC/C-SiO cell [dataset – version 2: result data]
<p>Battery degradation is critical to the cost-effectiveness and usability of battery-powered products. Aging studies can help to better understand and model degradation and to optimize the operation strategy. Nevertheless, there are only a few comprehensive and freely available aging datasets for these applications.<br>To our knowledge, the dataset presented in the following is one of the largest published to date. It contains data from 228 commercial NMC/C+SiO lithium-ion cells aged for almost 600 days under a wide range of operating conditions. We investigate calendar and cyclic aging and also apply different driving cycles to some of the cells.<br>This dataset is an update to the dataset previously published under the DOI 10.35097/1947 and described in the publication with the DOI 10.1038/s41597-024-03831-x. This dataset only includes result data (capacity, impedance, and pulse resistance measurements). The log data is published under the DOI 10.35097/kww7jv8ajuvchcah.</p>
Comprehensive battery aging dataset: capacity and impedance fade measurements of a lithium-ion NMC/C-SiO cell [dataset – version 2: log data]
<p>Battery degradation is critical to the cost-effectiveness and usability of battery-powered products. Aging studies can help to better understand and model degradation and to optimize the operation strategy. Nevertheless, there are only a few comprehensive and freely available aging datasets for these applications.<br>To our knowledge, the dataset presented in the following is one of the largest published to date. It contains over 3 billion data points from 228 commercial NMC/C+SiO lithium-ion cells aged for almost 600 days under a wide range of operating conditions. We investigate calendar and cyclic aging and also apply different driving cycles to some of the cells.<br>This dataset is an update to the dataset previously published under the DOI 10.35097/1947 and described in the publication with the DOI 10.1038/s41597-024-03831-x. This dataset only includes log data. The result data is published under the DOI 10.35097/1969.</p>
ScienceDex guides
Understand access before you commit
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.