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384 results for “Data Transfers”
Data for Universal prediction of cell cycle position using transfer learning
<p>The repo contains the data for Universal prediction of cell cycle position using transfer learning (https://www.biorxiv.org/content/10.1101/2021.04.06.438463v2).</p> <p>The scripts to analyze and generate all figures could be found at https://github.com/hansenlab/tricycle_paper_figs</p> <p>v1.1 update: add neurosphere_scvelo.qs - an R SingleCellExperiment object saved as qs file that has spliced counts, unspliced counts, and all outputs from scvelo.</p>
Data from: Evaluating temporal and spatial transferability of a tidal inundation model for foraging waterbirds
<p>For ecosystem models to be applicable outside their context of development, temporal and spatial transferability must be demonstrated. This presents a challenge for modeling intertidal ecosystems where spatiotemporal variation arises at multiple scales. Models specializing in tidal dynamics are generally inhibited from having wider ecological applications by coarse spatiotemporal resolution or high user competency. The Tidal Inundation Model of Shallow-water Availability (TiMSA) uniquely simulates tides to empirically derive a time-integrated measure of availability for a shallow water depth range defined by the user. To evaluate temporal and spatiotemporal transferability, we employed TiMSA at the development site in the Florida Keys and at novel sub-sites in the Florida Bay (application site) under a different time period (application period). We used foraging Little Blue Herons (<em>Egretta caerulea</em>) as the ecological unit with which to constrain the model's 'water depth window', i.e., range of water depths to estimate shallow-water availability. At the development site, temporally consistent water depth windows contrasted with interannual variation in shallow-water availability which revealed short-term changes in Little Blue Heron foraging habitat. At the application site, water depth accuracy varied by sub-site and was correlated with spatial error in bathymetric elevation. Although TiMSA parameters were sensitive to environmental temporal variation and uncertainty in spatial data, a spatially-explicit water depth window generated reliable estimates of shallow-water conditions over space and time at the development and application sites. By exploring the contributing factors to model error, we provide solutions to reduce uncertainty of TiMSA parameters at potential application sites and recommendations for addressing bathymetric inaccuracy in digital elevation models. Accurately quantifying spatiotemporal changes of shallow-water has implications for monitoring habitat conditions for tidally-influenced species and projecting future changes to coastal ecosystems in response to anthropogenic stressors and natural disturbances such as sea level rise.</p>
Data generated by the model presented in the research article entitled "Simulation of mass and heat transfer in an evaporatively cooled PEM fuel cell"
<p>This repository provides all the data and scripts necessary to reproduce the line plots shown in the manuscript entitled "Simulation of mass and heat transfer in an evaporatively cooled PEM fuel cell".</p>
Data from: Operando Proton Transfer Reaction-Time of Flight-Mass Spectrometry of Carbon Dioxide Reduction Electrocatalysis
<p>Seven top-level folders</p> <p>GC-PTR-TOF-MS<br> - Raw data and Jupyter Notebook used for analysis of GC-PTR-TOF-MS data</p> <p>LSV-PTR-TOF-MS<br> - Raw data and Jupyter Notebook used for analysis of PTR-TOF-MS data under linear sweep voltammetry</p> <p>MSCP-PTR-TOF-MS<br> - Raw data and Jupyter Notebook used for analysis of PTR-TOF-MS data under multi-step chronopotentiometry</p> <p>PTR-TOF-MS-Calibration<br> - Raw data and Jupyter Notebook used for analysis of PTR-TOF-MS calibration data</p> <p>SEM<br> - Raw images from scanning electron microscope</p> <p>Stability<br> - Raw data of electrochemical stability</p> <p>TEM<br> - Raw images from transmission electron microscopy</p>
Excitation energy transfer and vibronic coherence in intact phycobilisomes — multidimensional electronic spectroscopy data set and MATLAB and Julia analysis code
<p>Data sets used in the article "Excitation energy transfer and vibronic coherence in intact phycobilisomes" by Sil et al. The phycobilisomes were isolated from the short-filament mutant (SF33) of <em>Fremyella diplosiphon</em> UTEX 481 (also known as <em>Tolypothrix</em> sp. PCC 7601). Multidimensional electronic spectroscopy was performed with 6.7 fs mid-visible pulses (520–700 nm) using a pump–probe optical configuration using adaptive pulse shaping techniques. In addition to the full set of two-dimensional spectra and analysis files generated using global and target modeling and analysis of coherences (3DES oscillation maps), we provide here a linear absorption spectrum with phycobiliprotein component analysis as well as a set of 2D excitation–emission fluorescence spectra of intact and broken phycobilisome preparations. </p> <p>Sil, S.; Tilluck, R. W.; Mohan TM, N.; Leslie, C. H.; Rose, J. B.; Domínguez-Martín, M. A.; Lou, W.; Kerfeld, C. A.; Beck, W. F. Excitation energy transfer and vibronic coherence in intact phycobilisomes. Nat. Chem. (2022), DOI: 10.1038/s41557-022-01026-8.</p> <p><a href="https://urldefense.com/v3/__https://www.nature.com/articles/s41557-022-01026-8__;!!HXCxUKc!yaVwTZFk8T-j3ROhygpOGW5Xy_E2wQvf-QgNGr9FZZbp4oNpfp_ZmhkdWYLdg2mKSDP8yYrNAZs$">https://www.nature.com/articles/s41557-022-01026-8</a></p> <p> </p> <p> </p>
Data for Novel Approach to Quantify Sediment Transfer and Storage in Rivers with Feldspar Single-Grain pIRIR
<p>Raw data of feldspar post infra-red infra-red (pIRIR) stimulated luminescence signal from modern sand from New Zealand rivers (Rakaia, six samples; Waimakariri, height samples) used in the paper Guyez et al. (2022) submitted to JGR: Earth Surface. The samples were collected in February 2020 by S. Bonnet and A. Guyez. They were measured in the Netherlands Center for Luminescence dating (Wageningen University and Research). </p>
Level 3 data in support of Modulation of Bubble Mediated Gas Transfer due to Wave-Current Interactions
<p>WaveWatchIII data output from solutions of a nested configuration off the coast of California. These data are a subset of the solutions reported by Romero et al. 2020.</p> <p>These data are Level 3 of the nested configuration with a horizontal resolution of 120 m.</p> <p>Data are in Netcdf format including the metadata.</p> <p>The simulation period December 2006</p> <p>List of files:</p> <p>L2_Dec2006.nc -- December control solution only forced by winds</p> <p>L2_cew_Dec2006.nc -- December solution including forcing by both winds and currents</p> <p>Grid files can be found at: https://doi.org/10.5281/zenodo.7140916</p>
Level 2 Spring data in support of Modulation of Bubble Mediated Gas Transfer due to Wave-Current Interactions
<p>WaveWatchIII data output from solutions of a nested configuration off the coast of California. These data are a subset of the solutions reported by Romero et al. 2020.</p> <p>These data are Level 2 of the nested configuration with a horizontal resolution of 270 m.</p> <p>Data are in Netcdf format including the metadata.</p> <p>The two simulation periods are December 2006 and Spring 2007.</p> <p>List of files:</p> <p>L2_Mar2007.nc -- Spring control solution only forced by winds</p> <p>L2_cew_Mar2007.nc --- Spring solution including forcing by both winds and currents.</p> <p>Grid files can be found at: https://doi.org/10.5281/zenodo.7140916</p> <p> </p> <p> </p> <p> </p>
Raw data accompanying the manuscript "Cost-effective high-speed, three-dimensional live-cell imaging of HIV-1 transfer at the T cell virological synapse"
<p>These are the raw datasets used to generate the figures for the manuscript entitled "Cost-effective high-speed, three-dimensional live-cell imaging of HIV-1 transfer at the T cell virological synapse". The data files are 3D image stacks of a custom-built wide field deconvolution fluorescence microscope (.tif) and super-resolution structured illumination microscopy data (.dv) of Jurkat T cells transferring HIV-1 virus particles to previously uninfected primary T cells.</p>
Data & Codes used in: Quantum Mechanical Derived (VdW-DFT) Transferable Lennard-Jones and Morse Potentials to Model Cysteine and Alkanethiol Adsorption on Au(111)
<p>Here we provide the data, codes, and outline the procedure to reproduce the results presented in the paper "Quantum Mechanical Derived (VdW-DFT) Transferable Lennard-Jones and Morse Potentials to Model Cysteine and Alkanethiol Adsorption on Au(111)" by E. Ventura-Macias, P. M. Martinez, Rubén Pérez, and J. G. Vilhena.</p> <p>The following sections contain a detailed description of the data and codes. At the end of this README, you will find instructions on how to generate the classical force-field parameters (Morse and Lennard-Jones) from the potential energy surfaces (PES) computed at the DFT level.</p> <p>The procedure is general and applies to any given pair of molecule and surface. The provided codes will allow you to swiftly generate the PES at the DFT level, fit the Lennard-Jones and Morse potentials, and test them in a LAMMPS MD simulation.</p> <p>For a thorough explanation of the procedure and the relevance of these results, please refer to the original publication (ARTICLE_DOI).</p> <p>If you find this helpful, please consider citing the article (ARTICLE_DOI).</p> <h2>Data structure</h2> <p>The data is organized in the following way:</p> <ol> <li> <p>DFT</p> <ul> <li>The equilibrium adsorption geometry of methanethiol (MTH), propanethiol (PTH), and cysteine (CYS) for the Au-mol configuration with PBE+DFT-D3.</li> <li>Potential Energy Surface (PES) computed at the DFT level (Figure 3 of the main manuscript).</li> <li>The scripts used to generate the PES.</li> </ul> </li> <li> <p>MD</p> <ul> <li>Fitting code and general instructions on how to use it.</li> <li>General input scripts used to generate MD data within LAMMPS.</li> </ul> </li> </ol> <h3>DFT Data</h3> <p>The DFT data in <code>DFT.zip</code> is organized in the following way:</p> <ul> <li> <p><code>DFT_PES/</code></p> <p>This folder contains the DFT potential energy surfaces (PES) for the interaction of the sulfur atom of methanethiolate (<code>mth</code>), propanethiolate (<code>pth</code>), and cysteine (<code>cys</code>) with the Au(111) surface and the necessary scripts to reproduce it.</p> <ul> <li> <p><code>results/</code></p> <p>The PES are given in one csv file per molecule named as <em><code>mol</code></em> + <code>_PES_S-Au111.csv</code>, where <em><code>mol</code></em> is the molecule name. Columns are as follows:</p> <table> <tbody> <tr> <td><strong>Label</strong></td> <td><strong>Definition</strong></td> </tr> <tr> <td><em>i</em></td> <td>calculation number</td> </tr> <tr> <td><em>site</em></td> <td>adsorption site</td> </tr> <tr> <td><em>z</em></td> <td>distance of the S atom to the surface</td> </tr> <tr> <td><em>deltaz</em></td> <td>distance difference from the minimum energy position of the S atom</td> </tr> <tr> <td><em>pbed3</em></td> <td>PBE+D3 binding energy</td> </tr> <tr> <td><em>pbe</em></td> <td>PBE component of the binding energy</td> </tr> <tr> <td><em>d3</em></td> <td>DFT-D3 component of the binding energy</td> </tr> </tbody> </table> </li> </ul> </li> </ul> <ul> <li> <ul> <li> <p><code>mth/</code> | <code>pth/</code> | <code>cys/</code></p> <p>Each folder contains the CONTCAR (VASP) file for the optimized geometry of the molecule adsorbed on the Au(111) surface with PBE+D3.</p> </li> <li> <p><code>setup_grid.py</code></p> <p>Python script to set up the POSCAR files for the PES calculations.</p> </li> <li> <p><code>read_results.py</code></p> <p>Python script to read the results of the PES calculations.</p> </li> <li> <p><code>sub_array.sh</code></p> <p>Bash script to submit the PES calculations to an SLURM-based cluster.</p> </li> <li> <p><code>INCAR</code> | <code>KPOINTS</code> | <code>surf.CONTCAR</code></p> <p>VASP input files for the PES calculations.</p> </li> </ul> </li> </ul> <h3>MD fitting</h3> <p>The MD data in MD.zip is are organized as:</p> <ul> <li> <p><code>Fitting/</code></p> <ul> <li><code>optimize_Morse.py</code></li> </ul> <p>Contains the Python script used for the fitting procedure of Morse potential.</p> <ul> <li><code>optimize_LJ.py</code></li> </ul> <p>Contains the Python script used for the fitting procedure of LJ potential.</p> </li> <li> <p><code>Histogram/</code></p> <ul> <li><code>in.test</code></li> </ul> <p>LAMMPS input script to extract an XY file for the position of the S atom in an NVT simulation.</p> <ul> <li><code>coord.data</code></li> </ul> <p>Au surface for LAMMPS simulations.</p> <ul> <li><code>SCH3.data</code></li> </ul> <p>SCH3 molecule for LAMMPS simulations.</p> <ul> <li><code>sheng.eam</code></li> </ul> <p>EAM potential file in case Au dynamics are wished to be included.</p> </li> <li> <p><code>Single_point_scan/</code></p> <ul> <li><code>in.scan</code></li> </ul> <p>LAMMPS input script to perform single-point energy scan of a given molecule.</p> <ul> <li><code>coord.data</code></li> </ul> <p>Au surface for LAMMPS simulations.</p> <ul> <li><code>SCH3.data</code></li> </ul> <p>SCH3 molecule for LAMMPS simulations.</p> <ul> <li><code>launch.sh</code></li> </ul> <p>Launches the single point scan.</p> <ul> <li><code>plot_scan.py</code></li> </ul> </li> <li> <p><code>Molecules</code></p> <p>LAMMPS sample geometries for the 3 molecules.</p> </li> </ul> <h2>Steps to reproduce the results</h2> <p>The PES calculations were performed using the VASP code and two Python scripts for setup and results parsing.</p> <h3>Requirements</h3> <ul> <li>VASP (tested with version 5.4.4) <ul> <li><code>PAW_PBE</code> pseudopotentials set version 5.4</li> </ul> </li> <li>Python3 with packages: <ul> <li>ASE (Atomic Simulation Environment)</li> <li>Numpy</li> <li>Pandas</li> <li>matplotlib (optional)</li> </ul> </li> </ul> <h3>Steps</h3> <p>Each molecule has its own directory with the necessary files to reproduce the results. The following steps are for the methanethiolate molecule (<code>mth</code>).</p> <ol> <li>Set up the grid of points for the PES calculations by running the <code>setup_grid.py</code> script. It will create a subfolder <code>run/</code> inside the molecule's directory with the POSCAR files for each point in the grid.</li> </ol> <blockquote> <p>python setup_grid.py mth</p> </blockquote> <ol> <li> <p>Create the corresponding <code>POTCAR</code> file and place it in the molecule's directory.</p> </li> <li> <p>Change the <code>sub_array.sh</code> script to match the number of calculations in the array numbers and the MOL variable.</p> </li> </ol> <pre><code>#SBATCH --array=1-number of calculations MOL=mth</code></pre> <ol> <li>Submit the calculations to a SLURM-based cluster by running the <code>sub_array.sh</code> script from the <code>DFT_PES</code> directory.</li> </ol> <blockquote> <p>sbatch sub_array.sh</p> </blockquote> <ol> <li>After the calculations are finished, run the <code>read_results.py</code> script to parse the results and generate the PES csv files. It takes the arguments <code>--surf</code> and <code>--mol</code> to specify the surface and molecule PBE+D3 and D3 total energies.</li> </ol> <blockquote> <p>python read_results.py mth --surf -128.5228 -17.7147 --mol -22.6026 -0.0083</p> </blockquote> <h3>Reference values for the PBE+D3 and D3 surface and molecule energies</h3> <table> <tbody> <tr> <td> </td> <td><strong>Surface</strong></td> <td> </td> <td><strong>Molecule</strong></td> <td> </td> </tr> <tr> <td><strong>Molecule</strong></td> <td><strong>PBE+D3<br></strong></td> <td><strong>D3</strong></td> <td><strong>PBE+D3</strong></td> <td><strong>D3</strong></td> </tr> <tr> <td>mth</td> <td>-128.5228</td> <td>-17.7147</td> <td>-22.6026</td> <td>-0.0083</td> </tr> <tr> <td>pth</td> <td>-128.5228</td> <td>-17.7147</td> <td>-55.8615</td> <td>-0.0086</td> </tr> <tr> <td>cys</td> <td>-128.5228</td> <td>-17.7147</td> <td>-74.0338</td> <td>-0.1611</td> </tr> </tbody> </table> <h3>Fitting procedure</h3> <p>Both the .xyz and .csv files should be located at the same folder as the script. Then, simply run the code (<code>mth</code> is used as an example):</p> <blockquote> <p>python optimize_Morse.py mth</p> </blockquote> <p>or</p> <blockquote> <p>python optimize_LJ.py mth</p> </blockquote> <p>The script will print the optimized parameters: [De, re, α] or [ϵ, σ] for Morse or LJ respectively. It will also plot a fitting plot and a birdview of the resulting PES.</p> <h3>Using the potential. Histogram example</h3> <p>The code will run for the optimized <code>mth</code> Morse parameters and extract a <code>occ.lammpstrj</code> containing the (x,y) positions of the S atom throughout the NVT simulation. Note that only Au-S interaction is included. Start as:</p> <blockquote> <p>lmp -in in.test</p> </blockquote> <p>This is easily adaptable to other routines or molecules and is thought to be a generic LAMMPS starting input.</p> <h3>Single-point energy scan</h3> <p>Go to the 2C) folder and launch the scan with:</p> <blockquote> <p>./launch.sh</p> </blockquote> <p>This will create a folder named <code>fine_scan</code> containing 128 folders. Each folder is assigned to an (x,y) position. Then, inside each folder, a single-point energy evaluation is performed at various Z heights around the absolute minima.</p> <p>The <code>in.scan</code> file should be modified accordingly with the appropriate potentials. It is set to perform the scan with the optimized Morse potential by default.</p> <p>The output is gathered in the <code>E_readout.dat</code> folder with the following structure (all energies in eV):</p> <table> <tbody> <tr> <th>Total Energy</th> <th>Intramolecular energy</th> <th>Au-mol vdW interaction energy</th> <th>Au-S interaction energy</th> </tr> </tbody> <tbody> <tr> <td>-133.62</td> <td>0.123112</td> <td>-0.2403</td> <td>-1.31765</td> </tr> <tr> <td>-133.721</td> <td>0.123112</td> <td>-0.28-401</td> <td>-1.37853</td> </tr> </tbody> </table> <p>Therefore, the total adsorption energy will be the sum of the last two columns.</p> <p>Energies are ordered in increasing Z for the same (x,y) point. That is, the first 12 lines correspond to 12 heights of the starting (x,y) coordinate, the next 12 lines to heights at the second (x,y) configuration and so on.</p> <p>The python script <code>plot_scan.py</code> may be used to plot the results. The <code>E_readout.dat</code> file and <code>.csv</code> must be in the same folder.</p> <blockquote> <p>python plot_scan.py mth</p> </blockquote>
Data from: Forecasting animal distribution through individual habitat selection: Insights for population inference and transferable predictions
<p>Habitat selection models frequently use data collected from a small geographic area over a short window of time to extrapolate patterns of relative abundance to unobserved areas or periods of time. However, these types of models often poorly predict how animals will use habitat beyond the place and time of data collection because space-use behaviors vary between individuals and are context-dependent. Here, we present a modelling workflow to advance predictive distribution performance by explicitly accounting for individual variability in habitat selection behavior and dependence on environmental context. Using global positioning system (GPS) data collected from 238 individual pronghorn, (<em>Antilocapra americana</em>), across 3 years in Utah, we combine individual-year-season-specific exponential habitat-selection models with weighted mixed-effects regressions to both draw inference about the drivers of habitat selection and predict space-use in areas/times where/when pronghorn were not monitored. We found a tremendous amount of variation in both the magnitude and direction of habitat selection behavior across seasons, but also across individuals, geographic regions, and years. We were able to attribute portions of this variation to season, movement strategy, sex, and regional variability in resources, conditions, and risks. We were also able to partition residual variation into inter- and intra-individual components. We then used the results to predict population-level, spatially and temporally dynamic, habitat-selection coefficients across Utah, resulting in a temporally dynamic map of pronghorn distribution at a 30x30m resolution but an extent of 220,000km2. We believe our transferable workflow can provide managers and researchers alike a way to turn limitations of traditional habitat selection models - variability in habitat selection - into a tool to understand and predict species-habitat associations across space and time.</p>
Supplementary data to the paper: Toward a Novel Set of Pinna Anthropometric Features for Individualizing Head-Related Transfer Functions
<p>Supplementary research data to the <a href="https://doi.org/10.5281/zenodo.14338958" target="_blank" rel="noopener">paper</a>:</p> <blockquote> <p>Davide Fantini, Stavros Ntalampiras, Giorgio Presti, and Federico Avanzini. Toward a novel set of pinna anthropometric features for individualizing<br>head-related transfer functions. In <em>Proceedings of the 21th Sound and Music Computing Conference</em>, Porto, Portugal, July 2024.</p> </blockquote> <p>The repository includes the research data generated in the abovementioned paper. In particular, the repository includes:</p> <ul> <li><a href="../api/records/10805885/draft/files/README.md/content" target="_blank" rel="noopener noreferrer">README.md</a>: instructions for the data</li> <li><a href="../api/records/10805885/draft/files/pinna_images.mat/content" target="_blank" rel="noopener noreferrer">pinna_images.mat</a>: pinna depth images extracted from the 3D head meshes of the <a href="https://depositonce.tu-berlin.de/items/dc2a3076-a291-417e-97f0-7697e332c960">HUTUBS dataset</a></li> <li><a href="../api/records/10805885/draft/files/landmarks.mat/content" target="_blank" rel="noopener noreferrer">landmarks.mat</a>: coordinates of the landmarks manually annotated on pinna depth images</li> <li><a href="../api/records/10805885/draft/files/anthropometry.mat/content" target="_blank" rel="noopener noreferrer">anthropometry.mat</a>: anthropometric parameters automatically extracted from manually annotated landmarks</li> <li><a href="../api/records/10805885/draft/files/anthropometry_documentation.pdf/content" target="_blank" rel="noopener">anthropometry_documentation.pdf</a>: documentation of the pinna anthropometric parameters</li> <li><a href="../records/12698286/files/poster.pdf?download=1">poster.pdf</a>: poster presented at the SMC conference 2024</li> </ul> <p>The data are provided in the Matlab file format MAT. Nevertheless, the MAT files can be read with other programming languages, such as Python (<a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html">scipy.io.loadmat</a>).</p> <p>A GitHub repository to automatically extract the pinna landmarks and features as described in the paper is available <a href="https://github.com/DavideFantini/pinna-anthropometry-extraction" target="_blank" rel="noopener">here</a>.</p>
Data from the manuscript: "Few-femtosecond electron transfer dynamics in photoionized donor-pi-acceptor molecules"
Open the record for dataset details and reuse information.
Supporting Data for "Proton transfer in nonpolar solvents: an approach to generate electrolytes in aprotic media" (Phys. Chem. Chem. Phys., doi:10.1039/c8cp02349b)
<p>Conductivity data for all cation-anion pairs [units given in the header for each column].</p> <p>Small-angle neutron scattering (SANS) data (Q [1/Å], I(Q) [SAXS - arbitrary, SANS - 1/cm], error I(Q) [same units]) of PLMA48 as a 2 wt % solution in n-dodecande-d26.</p>
Trained neural network data for synchrotron radiative transfer in the Stokes basis, power law model, computed by rimphony, for consumption by neurosynchro
<p>This archive contains data representing a trained-up neural network suitable for use with the <a href="https://github.com/pkgw/neurosynchro/">neurosynchro</a> package. The network generates coefficients that can be used for numerical radiative transfer of synchrotron emission in the Stokes basis with a package such as <a href="https://github.com/jadexter/grtrans/">grtrans</a>.</p> <p>In this particular dataset, networks were trained on a training set of coefficients generated by <a href="https://github.com/pkgw/rimphony/">rimphony</a> that is available as <a href="https://doi.org/10.5281/zenodo.1341154">DOI:10.5281/zenodo.1341154</a>. The data were generated using a model of a power law electron distribution isotropic in pitch angle. The input parameters, which were sampled randomly in a three-dimensional space, were:</p> <ul> <li><em>s</em>, the harmonic number, dimensionless, sampled logarithmically between 5 and 50,000,000.</li> <li><em>theta</em>, the angle between the ray path and the local magnetic field, measured in radians, sampled linearly between 0.001 and π/2 (namely, 1.5707963267948966).</li> <li><em>p</em>, the power-law index of the energetic electrons, dimensionless, sampled linearly between 1.5 and 7.</li> </ul> <p>The training set was computed on Harvard’s Odyssey cluster using Git commit <a href="https://github.com/pkgw/rimphony/commit/772161ebda0217b8c1ccb8ce3801ad9dc3701a4f">772161</a> of rimphony. A total of about 5,000 CPU hours were used, with 500 processes running for about 10 hours each, yielding about 22 million numbers. Training the networks took about 3 hours on an 8-core laptop.</p> <p>For the purposes of <em>neurosynchro</em>, the formats of the files in this package should be regarded as internal implementation details. The <a href="https://pypi.org/project/neurosynchro/">neurosynchro</a> Python package will load up the files in this archive and use them to predict synchrotron coefficients. For specifics, see <a href="https://neurosynchro.readthedocs.io/en/stable/">the neurosynchro documentation</a>.</p>
NMR, HPLC and HRMS data - General Cyclopropane Assembly via Enantioselective Transfer of a Redox-Active Carbene to Aliphatic Olefins
<p>Raw data for the article with the same title.</p> <p>ChemRxiv pre-print (<a href="https://doi.org/10.26434/chemrxiv.7436795">https://doi.org/10.26434/chemrxiv.7436795</a>)</p>
Data for "Orthogonal phase transfer of oppositely charged FeII4L6 cages"
<p>The data deposited herein supports the manuscript entitled "Orthogonal phase transfer of oppositely charged FeII4L6 cages". Data includes raw UV-Vis, and raw and processed NMR. Data is saved under experiment codes which are explained in the "Data Summary" file.</p>
Electrochemical data plotted in A. Fasano, C. Guendon, A. Jacq-Bailly, A. Kpebe, J. Wozniak, C. Baffert, M. del Barrio, V. Fourmond, M. Brugna, C. Léger , « A chimeric NiFe hydrogenase heterodimer to assess the role of the electron transfer chain in tuning the enzyme's catalytic bias and oxygen tolerance », J. Am. Chem. Soc. 145, 36, 20021–20030 (2023). doi: 10.1021/jacs.3c06895
<p>Text file of all the electrochemical data shown in the following paper: A. Fasano, C. Guendon, A. Jacq-Bailly, A. Kpebe, J. Wozniak, C. Baffert, M. del Barrio, V. Fourmond, M. Brugna, C. Léger , « A chimeric NiFe hydrogenase heterodimer to assess the role of the electron transfer chain in tuning the enzyme's catalytic bias and oxygen tolerance », J. Am. Chem. Soc. 145, 36, 20021–20030 (2023). <a href="dx.doi.org/10.1021/jacs.3c06895" target="_blank" rel="noopener">doi: 10.1021/jacs.3c06895</a></p>
Data set: Modeling of Electron-Transfer Kinetics in Magnesium Electrolytes: Influence of the Solvent on the Battery Performance
<p>Dataset of the continuum simulations generated and used within the paper "<span>Modeling of Electron-Transfer Kinetics in Magnesium Electrolytes: Influence of the Solvent on the Battery Performance</span>", published in ChemSusChem (<span>2021</span><span>, </span><span>14 (21)</span><span>, 4820-4835, DOI: <span>10.1002/cssc.202101498</span></span>).</p> <p><span>The performance of rechargeable magnesium batteries is strongly dependent on the choice of electrolyte. The desolvation of multivalent cations usually goes along with high energy barriers, which can have a crucial impact on the plating reaction. This can lead to significantly higher overpotentials for magnesium deposition compared to magnesium dissolution. In this work we combine experimental measurements with DFT calculations and continuum modeling to analyze magnesium deposition in various solvents. Jointly, these methods provide a better understanding of the electrode reactions and especially the magnesium deposition mechanism. Thereby, a kinetic model for electrochemical reactions at metal electrodes is developed, which explicitly couples desolvation to electron transfer and, furthermore, qualitatively takes into account effects of the electrochemical double layer. The influence of different solvents on the battery performance is studied for<br>the state-of-the-art magnesium tetrakis(hexafluoroisopropyloxy)borate electrolyte salt. It becomes apparent that not necessarily a whole solvent molecule must be stripped from the</span> <span>solvated magnesium cation before the first reduction step can take place. For magnesium reduction it seems to be sufficient to have one coordination site available, so that the magnesium cation is able to get closer to the electrode surface. Thereby, the initial desolvation of the magnesium cation determines the deposition reaction for mono-, tri- and tetraglyme, whereas the influence of the desolvation on the plating reaction is minor for diglyme and<br>tetrahydrofuran. Overall, we can give a clear recommendation for diglyme to be applied as solvent in magnesium electrolytes</span>.<br><br></p>
BIO4AFRICA_Co-definition of technologies to be transferred with local farmers and communities - Technology Validation Data (Findings)_D1.4_021122_1
<p>Validation Data (Findings) for Task 1.4: Co-definition of technologies to be transferred with local farmers and communities.</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.