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2,885 results for “transferability”
VirgoA M87 Heat Transfer
<p>The research continued from the white hole observation experiment with data analysis. The math- ematical method derived before the observation is supplemented with the heat transfer method. The research took an empirical boundary approach on heat flux for the heat transfer between a black hole and white hole, without considering the Big Bang theory. The heat transfer in the system material boundary is seen as a gravitational indicator from asymptotic decay. Albeit the numerical results are instrumentation specific, the conceptualization of the method can be applied to other data imaging apparatus. However, this method is material dependent, and the estimation of the imaginary time di- mension depends on the chemical components of incident light and detection plate. It puts the cosmic microwave background to the account of instrumentation noise, and conceptualized cosmic background radiation for asymptotic safety. The convergence and divergence can be further corrected by different bias factors.</p>
A dataset of global variations in directional solar radiation exposure for ocular research using the libRadtran radiative transfer model
<p>Directional solar photon flux density has particular relevance to eye disease research (keratitis, cataract formation, macula degeneration) because ocular components (cornea, lens, retina) experience different exposures dependent on global location, structural geometry of the eye and human behaviour (Sliney, 1997). The human macula has a field of view of ~17<strong>°</strong>, or 0.06901537 sr (Strasburger, Rentschler & Jüttner, 2011) and its cone of exposure can be modelled at a range of global locations using a radiation transfer model to estimate different directions of irradiation. This dataset provides examples of spectral radiance within the macula field of vision, calculated with the radiative transfer model libRadtran v2.0.3 (Mayer & Kylling, 2005). Three data sets are provided at different latitudes without correction for spectral ocular transmission. Unless otherwise specified, all simulations were parametrized according to local meteorological condition (altitude, pressure, temperature) and atmospheric conditions on the simulated day (aerosol optical density, water column, O<sub>3</sub> and NO<sub>2</sub> concentrations). The model was parametrized for a subject looking northward toward the ground (-15<strong>°</strong> from horizon), at a height of 170 cm above the ground.</p> <p>For each simulation, a separate file is available for each condition (latitude, time, date, see below) that includes radiance at each wavelength. Radiance values are in mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup>.</p> <p>The technique provides future opportunity to model global exposures of different ocular components to spectral solar irradiance using information on ocular transmission, local terrain, albedo and human behaviour in order to explore their relevance in epidemiological studies of age-related eye disease.</p> <p>For each simulation, a separate file is available for each condition (latitude, time, date, see below) that includes radiance at each wavelength. Radiance values are in mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup>.</p> <p>The technique provides future opportunity to model global exposures of different ocular components to spectral solar irradiance using information on ocular transmission, local terrain, albedo and human behaviour in order to explore their relevance in epidemiological studies of age-related eye disease.</p> <p><em>Simulation 1: </em>This data set reports the spectral radiance from 250 - 500 nm at:</p> <ul> <li>3 latitudes (61.0: Southern Finland, 50.1 Northern France, 38.0: Central Spain).</li> <li>4 dates (April 17<sup>th</sup>, July 1<sup>st</sup>, September 1<sup>st</sup>, November 6<sup>th</sup> 2019).</li> <li>24 hours.</li> <li>8 cardinal directions (every 45<strong>° </strong>from North).</li> <li>2 aerosol optical densities (0.1 and 2.5).</li> </ul> <p><em>Simulation 2: </em>This data set reports the spectral radiance from 250 - 2,500 nm at:</p> <ul> <li>3 latitudes (61.0: Southern Finland, 50.1 Northern France, 38.0: Central Spain).</li> <li>4 dates (April 17<sup>th</sup>, July 1<sup>st</sup>, September 1<sup>st</sup>, November 6<sup>th</sup> 2019).</li> <li>24 hours.</li> <li>1 cardinal direction (North).</li> <li>2 aerosol optical densities (0.1 and 2.5).</li> </ul> <p><em>Simulation 3: </em>This data set reports the spectral radiance from 250 - 500 nm at:</p> <ul> <li>1 latitude (61.0: Southern Finland).</li> <li>4 dates (April 17<sup>th</sup>, July 1<sup>st</sup>, September 1<sup>st</sup>, November 6<sup>th</sup> 2019).</li> <li>24 hours.</li> <li>9 cardinal directions (every 40<strong>° </strong>from North).</li> <li>3 bidirectional reflectance distribution functions for the ground (forest, urban, snow).</li> <li>2 tilt angles for the eye direction (0<strong>° </strong> from horizon or -15<strong>°</strong> from horizon, toward the ground).</li> </ul> <p> </p>
Radiative Transfer Edge-on Protoplanetary Disk Images
<p>Dataset used to train a Convolutional Autoencoder model to generate synthetic images of edge-on protoplanetary disks. The work is described in "A machine learning framework to predict images of edge-on protoplanetary disks", Telkamps et al. 2022, submitted to AAS. This image dataset was created using the radiative transfer (RT) modeling code MCFOST (Pinte et al. 2006; Pinte et al. 2009). </p>
Data for "Hot-carrier transfer across a nanoparticle-molecule junction: The importance of orbital hybridization and level alignment"
<p>This upload includes the data presented and analyzed in the article "Hot-carrier transfer across a nanoparticle-molecule junction: The importance of orbital hybridization and level alignment" by Jakub Fojt, Tuomas P. Rossi, Mikael Kuisma, and Paul Erhart.</p> <p>The codes for reproducing the data are provided at <a href="https://doi.org/10.5281/zenodo.7118376">doi:10.5281/zenodo.7118376</a>.</p> <p>See <em>README.md</em> in <em>data.zip</em> for a detailed description.</p>
Idealized wave data in support of Modulation of Bubble Mediated Gas Transfer due to Wave-Current Interactions
<p>WaveWatchIII data output from idealized solutions reported by Romero 2019</p> <p>Data are in Netcdf format and include metadata.</p> <p>Each file corresponds to a duration-limited solution with constant wind speed as indicated in the filename</p> <p>(e.g. 10mps means 10 m/s winds)</p> <p> </p>
Level 2 Winter 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. Also included in this repository are the Level 2 and Level 3 grid files.</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_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>L2_grid.nc -- Level 2 grid</p> <p>L3_grid.nc -- Level 3 grid</p> <p> </p> <p> </p> <p> </p> <p> </p>
Glass_Procedure_punty_transfering_Mingei
Documentation material from the Mingei project
Transferring energy signatures across space and time to assess their viability for rapid urban energy demand estimation
<p>This data archive provides simulated hourly heating and cooling building energy demand for current and future RCP85 climate for 8 representative cities for a single-family and small office building archetype.</p> <p>The data forms part of the following publication:</p> <p><em>Eggimann S.; Fiorentini M. (2024): Transferring energy signatures across space and time to assess their viability for rapid urban energy demand estimation. Energy and Buildings. https://doi.org/10.1016/j.enbuild.2024.114348</em></p> <p><strong>Attributes</strong></p> <ul> <li>ID_origin: City ID of source city</li> <li>ID_destination: City ID of target city</li> <li>Signature_Cooling: Cooling demand determined by the signature approach</li> <li>Model_Cooling: Cooling demand determined by EnergyPlus</li> <li>Absolute_Diff: Absolute difference</li> <li>Percentage_Diff: Relative difference</li> <li>Daily_Tout: Average daily dry-bulb ambient temperature</li> </ul> <p><strong>Instruction</strong></p> <p>To obtain the simulation and energy signature-based results, it is required to filter the dataset and set the source ID to the destination ID. The city IDs are provided in the file city_table_ID.</p> <p><strong>Source</strong></p> <p>The archetypes are provided by the Office of Energy Efficiency & Renewable Energy: https://www.energycodes.gov/prototype-building-models</p>
Data underpinning "Pulse sequence considerations for interleaved chemical exchange saturation transfer acquisition sequences."
<p>=================================================<br> Robert Casper Brand, PhD Candidate<br> Wellcome Centre for Integrative Neuroimaging, FMRIB Division, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.<br> =================================================</p> <p>This folder contains the images and datasets used to generate the figures of the paper named: "Pulse sequence considerations for interleaved chemical exchange saturation transfer acquisition sequences." </p> <p>Each figure of the paper, with its corresponding data, is contained in an opensource TikZ format file. The TikZ files include both information on the axis as well as the supporting data and can be opened with any generic text editor. For more information on TikZ, see:<br> https://www.sharelatex.com/learn/TikZ_package). </p> <p>Where datasets were too large to be run by standard TeX distributions, the data was attached in an alternative format, and a TikZ wrapper included.</p> <p>The figures can be generated through any of the opensource TeX distributions. For more information on LaTeX and TeX, please see: <br> https://www.latex-project.org/get/ and<br> https://www.sharelatex.com/learn/Pgfplots_package.</p> <p>A compilation example of all figures, which also lists any additional packages, is included in the "wrapper. Tex" file. The output of this process was added to this folder as well (wrapper.pdf).</p> <p>The included files were created using directly from Matlab using the matlab2tikz code:<br> https://www.mathworks.com/matlabcentral/fileexchange/22022-matlab2tikz-matlab2tikz</p>
Transfer curve models for synthetic AHL-receiver devices
<p>The input-output function of the AHL-receiver devices was determined by fitting cell fluorescence data with the following four-parameter logistical curve model:</p> <p>GFP = b + ( a-b ) / 1 +10^( ( log(EC50)-log([AHL]) )*h)</p> <p>where <em>a</em> is the maximal GFP output, <em>b</em> is the basal GFP output, <em>[AHL]</em> is the AHL inducer concentration, <em>GFP</em> is the green fluorescence response of the device for that inducer concentration, <em>EC50</em> is the inducer concentration that results in half maximal activation of the device, and <em>h</em> is proportional to the value of the steepest slope along the curve (Hill coefficient) that indicates the responsiveness of the device to the input.</p>
Supplementary phylogenetic data for Manzano-Marín et. al. 2020 "Serial horizontal transfer of vitamin-biosynthetic genes enables the establishment of new nutritional symbionts in aphids' di-symbiotic systems"
<p>Supplementary data for Manzano-Marín et. al. 2019 "Serial horizontal transfer of vitamin-biosynthetic genes enables the establishment of new nutritional symbionts in aphids' di-symbiotic systems".</p> <p>The data set consists of four folders:</p> <p>1) "Buchnera_phylo”: PHYLIP-formatted file used for phylogenetic reconstruction of <em>Buchnera</em> and resulting tree in NEWICK format.</p> <p>2) "Erwinia_phylo”: PHYLIP-formatted file used for phylogenetic reconstruction of <em>Erwinia</em> and resulting tree in NEWICK format.</p> <p>3) "Hamiltonella_phylo”: FASTA-formatted nucleotide alignment files of each gene and NEXUS-formatted files used for Bayesian phylogenetic reconstruction of <em>Hamiltonella</em> symbionts.</p> <p>4) "HGT_genes": FASTA-formatted nucleotide alignment files of each horizontally transferred gene and non-horizontally transferred genes nupC, and <em>gpmA</em>. Also, NEXUS-formatted files used for Bayesian phylogenetic reconstruction and of resulting trees.</p> <p>5) "Tn3_pylo": FASTA-formatted amino acid alignment files of mobile elements related to the Tn3 family resolvase/invertase found in <em>Hamiltonella</em>-associated <em>Erwinia haradaeae</em> symbionts. Also, NEXUS-formatted files used for Bayesian phylogenetic reconstruction and of resulting trees.</p>
The PIRATE: an anthropometric earPlug with exchangeable microphones for Individual Reliable Acquisition of Transfer functions at the Ear canal entrance
<p>We present the open design of the PIRATE, an anthropometric earPlug with exchangable microphones for Individual Reliable Acquisition of Transfer functions at the Ear canal entrance. Its outer shape is available in 5 sizes and provides a deep, tight and reproducible fit in virtually all human ears. The design includes a recess to accommodate a MEMS microphone. Thus, the same microphone can be conveniently used in different earplugs without losing accuracy, and the microphone can be removed for calibration. The PIRATE or previous versions of it have been utilized in several studies with more than 200 subjects</p> <p>From the provided model, the earplugs can be 3D printed, and only minor working steps are necessary before use. These steps are described in the documentation.</p> <p> </p> <p>Reference:</p> <p>Denk F., Brinkmann F., Stirnemann S., Kollmeier B. (2019) "The PIRATE: an anthropometric earPlug with exchangeable microphones for Individual Reliable Acquisition of Transfer functions at the Ear canal entrance," Fortschritte der Akustik - DAGA, Rostock, Germany</p>
Role of energy migration in the efficiency of upconversion-based resonance energy transfer to organic acceptors
<p>Graphs, data set and algorithms (in Matlab) for the article:</p> <div>Kotulska, A. M., Prorok, K., Bezkrovnyi, O., Pilch-Wrobel, A., & Bednarkiewicz, A. (2024). Role of energy migration in the efficiency of upconversion-based resonance energy transfer to organic acceptors. <em>Journal of Luminescence</em>, <em>275</em>, 120823. https://doi.org/10.1016/J.JLUMIN.2024.120823</div> <p>(https://www.sciencedirect.com/science/article/pii/S0022231324003879)<br>Abstract: Lanthanide (Ln)-doped upconverting nanocrystals (LnNPs) exhibit suitable features as energy donors for Förster resonance energy transfer (FRET). The sensitivity of biosensors can be improved by optically active materials with anti-Stokes emission, narrowband absorption and emission spectral lines, and long luminescence lifetimes. In contrast to energy reabsorption, energy transfer between the upconversion nanocrystals (UCNPs) and organic dyes attached to their surface can be observed through donor emission quenching and acceptor emission and decreases in the luminescence lifetimes of donors. Although the emission spectra confirmed that FRET occurred from the Er3+ ions to the Rose Bengal acceptor, the luminescence lifetimes were generally not affected by the presence of the acceptor. The Ln3+ dopant in LnNPs, which typically has 20–100 % Yb3+ sensitizer ions and 0.2–2% activator (Er3+/Tm3+/Ho3+) ions, results in hundreds to thousands of Ln3+ ions in a single UCNP. The interaction between multiple Ln3+ ions results in significant energy migration and storage in the Yb3+ sensitizer network, which is often recharged with the energy of the Er3+ ions when they emit and nonradiatively transfer their energy to acceptor species. However, the energy transfer mechanisms could not be unambiguously determined through spectroscopic data due to the nature the upconversion process. Studies confirmed that the energy migration distance was significantly shortened when the LnNP surface contained acceptors; this affected the energy storage and ‘recharging’ capability of the Yb3+ sensitizer network within the UCNPs. These results provide hints on the future use of LnNP as effective FRET probes, in which the highest possible absorption cross section and possibly lowest dopant concentration should be maintained.<br>Keywords: Nanocrystals; Resonance energy transfer; FRET; Monte Carlo; Lanthanide ions</p>
Simulation data for "Characteristics of Wave-Particle Power Transfer as a Function of Electron Pitch Angle in Nonlinear Frequency Chirping" which will be submitted to Journal of Geophysical Research: Space Physics
<p>Simulation data for "Characteristics of Wave-Particle Power Transfer as a Function of Electron Pitch Angle in Nonlinear Frequency Chirping" which will be submitted to Journal of Geophysical Research: Space Physics.</p> <p>Including the simulation input parameter file and the necessary output data to plot each figure in the article. </p>
On energy transfer of parametric resonance for wave energy conversion
<p>Parametric resonance has been observed, both numerically and experimentally, in various studies of wave energy converters (WECs). A large motion in heave induces a periodic variation in the metacentric height of a WEC body, and, consequently, causes a harmonic variation in pitch/roll restoring coefficients, which can parametrically excite the pitch/roll modes. Current studies have a specific focus to determine the occurrence conditions of parametric resonance, by detecting the boundaries between stable and unstable regions in the parameter space. In the literature, some studies aim to make use of parametric resonance for improving power capture. In contrast, some studies try to suppress the effect of parametric resonance, as it reduces power capture efficiency. However, how energy transfers from one mode to another is not fully understood. This study aims to analyse the energy transfer between heave and pitch/roll modes when parametric resonance occurs. A generic cylindrical point absorber is studied as a WEC floater to considering non-linear wave-structure interaction, including non-linear Froude-Krylov and viscous forces. A heave-pitch-roll three-degree-of-freedom model is derived for numerical study of the energy transfer between different operation modes.</p>
Enhanced 3D radiative transfer ACM-RT calculations and output for Cole et al., 2022
<p>For the Earth Cloud, Aerosol, Radiation Explorer (EarthCARE) satellite mission there are a number of algorithms used to process the observations. One of these algorithms, called ACM-RT, is designed to use retrieved geophysical properties to perform forward radiative transfer calculations using 1D and 3D solar and thermal radiative transfer models. The ACM-RT algorithm is documented in an Atmospheric Measurements and Techniques (AMT) article. </p> <p>To illustrate outputs from ACM-RT, including the benefits of 3D radiative transfer, “enhanced” radiative transfer calculations were performed, relative to calculations that would be performed operationally during the EarthCARE mission. In particular, the 3D Monte Carlo radiative transfer calculations used an increased number of samples to reduce the Monte Carlo uncertainty and calculations were performed for more of the input data.</p> <p>The relevant publication for these calculations is:</p> <p>Cole, J. N. S., H. W. Barker, Z. Qu, N. Villefranque and M. W. Shephard : Broadband Radiative Quantities for the EarthCARE Mission: The ACM-COM and ACM-RT Products. Submitted to AMT, November 2022.</p>
Understanding Electron Transfer Reactions using Constrained Density Functional Theory: Complications due to Surface Interactions
<p>For reproducing the results presented in "<strong>Hashemi, A., Peljo, P., & Laasonen, K. (2022). Understanding Electron Transfer Reactions using Constrained Density Functional Theory: Complications due to Surface Interactions</strong>", this database provides the input files and CDFT-AIMD trajectory information. Please refer to the publication if you wish to use these data.</p> <p>---------------------------------------**************************************************************************-------------------------------------------------</p> <p><em>This study was financed by the Horizon 2020 Framework Programme CompBat with project number 875565. We also thank CSC-IT Center for Science Ltd. and Aalto Science-IT project for generous grants of computer time.</em><br> -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>The content of a directory is shown in a tree-like format:</strong><br> ├── 1DMDQ<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── dmdq-md-pos-1.xyz<br> │ │ ├── md.inp<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a.tar.gz<br> │ │ └── b_to_c.tar.gz<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> ├── 2MeVi<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── md.inp<br> │ │ ├── mevi-md-pos-1.xyz<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ ├── frame.xyz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a<br> │ │ │ ├── framePrint.py<br> │ │ │ ├── input_files<br> │ │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ │ ├── dft-common-params.inc<br> │ │ │ │ ├── energy_cdft.inp<br> │ │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ │ └── subsys.inc<br> │ │ │ └── README<br> │ │ └── b_to_c<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> ├── 3OHVi<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── md.inp<br> │ │ ├── ohvi-md-pos-1.xyz<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ ├── frame.xyz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a<br> │ │ │ ├── framePrint.py<br> │ │ │ ├── input_files<br> │ │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ │ ├── dft-common-params.inc<br> │ │ │ │ ├── energy_cdft.inp<br> │ │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ │ └── subsys.inc<br> │ │ │ └── README<br> │ │ ├── b_to_a.tar.gz<br> │ │ ├── b_to_c<br> │ │ │ ├── framePrint.py<br> │ │ │ ├── input_files<br> │ │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ │ ├── dft-common-params.inc<br> │ │ │ │ ├── energy_cdft.inp<br> │ │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ │ └── subsys.inc<br> │ │ │ └── README<br> │ │ └── b_to_c.tar.gz<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> ├── 4dBR5<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── dmdq-md-pos-1.xyz<br> │ │ ├── md.inp<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ ├── frame.xyz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a.tar.gz<br> │ │ └── b_to_c.tar.gz<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> ├── 52HNQ<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── hnq-md-pos-1.xyz<br> │ │ ├── md.inp<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ ├── frame.xyz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a.tar.gz<br> │ │ └── b_to_c.tar.gz<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> └── 6_n_H2O_effect_mevi<br> ├── 08h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> ├── 10h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> ├── 20h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> ├── 40h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> ├── 97h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> └── fig3.png</p> <p>74 directories, 301 files<br> -------------------------------------------------------<br> There are 6 directories: 1DMDQ, 2MeVi, 3OHVi, 4dBR5, 52HNQ, 6_n_H2O_effect_mevi. Except for "6_n_H2O_effect_mevi", we see 3 subdirectories named 1_md, 2_cdftaimd, and 3_cdft_wH2O_sccs. The input files and AIMD trajectories can be found in 1_md. While 2_cdftaimd contains the CDFT-AIMD input files and trajectories. To reproduce snapshots and input files of 3_cdft_wH2O_sccs, follow the README files in the subdirectories.</p> <p>The directory "6_n_H2O_effect_mevi" contains the number of water effects (Figure 3 of the publication). Users are guided by README files once again. </p>
Radiative transfer calculation results for Arctic cirrus
<p>Data of the figure content from the radiative transfer calculations in the publication Marsing, Meerkötter et al: Investigating the radiative effect of Arctic cirrus measured in situ during the winter 2015/2016</p> <p>Preprint DOI: https://doi.org/10.5194/acp-2022-395</p>
Fast measurement of the gradient system transfer function at 7 T
<p>Measurement data complementing our publication "Fast measurement of the gradient system transfer function at 7 T" (DOI: https://doi.org/10.1002/mrm.29523). The corresponding MATLAB code is available at https://github.com/expRad/Fast_GIRF .</p>
Dataset Single-subject EEG measurement of interhemispheric transfer-time for the in-vivo estimation of axonal morphology
<p>This dataset is a subset of the data presented in the article Single‐subject electroencephalography measurement of interhemispheric transfer time for the in‐vivo estimation of axonal morphology Rita Oliveira, Marzia De Lucia, Antoine Lutti</p> <p><a href="https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.26420">https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.26420</a></p> <p><br> For a complete description of our approach for axonal morphology estimation in-vivo, see: <em>Oliveira, R., Pelentritou, A., Di Domenicantonio, G., De Lucia, M., and Lutti, A. (2022). In vivo Estimation of Axonal Morphology From Magnetic Resonance Imaging and Electroencephalography Data. Front. Neurosci. 16, 1–18. doi: 10.3389/fnins.2022.874023.</em></p> <p>This dataset contains the following Matlab files:</p> <ul> <li>CD_CondNameVisualField_LeftBrainOccipital.mat - Current source densities (pA.m) of each brain vertice, EEG trial, and time point for the left brain occipital cortex [#trials x #vertices x #timepoints]</li> <li>CD_CondNameVisualField_RightBrainOccipital.mat - Current source densities (pA.m) of each brain vertice, EEG trial, and time point for the right brain occipital cortex [#trials x #vertices x #timepoints]</li> <li>Stats_Source_CondNameVF_Occipital_LeftBrainOccipital.mat - Result of the cluster permutation for the left brain cortex for the CondNameVF, CondName being Left or Right visual stimulation (Fieldtrip stat structure)</li> <li>Stats_Source_CondNameVF_Occipital_RightBrainOccipital.mat - Result of the cluster permutation for the right brain cortex for the CondNameVF, CondName being Left or - Right visual stimulation (Fieldtrip stat structure)</li> <li>time_vec.mat - Time vector associated with the timecourses [1 x #timepoints]</li> <li>Occipital_vertices.mat - Structure containing the vertices of the brain mesh of the region of interest. Occipital_vertices.Vertices [1 x #vertices]</li> <li>G_ratio_samples.mat - MRI g-ratio sampled along the occipital transcallosal tract [#samples x 1]</li> <li>Tract_length.mat - Length of the occipital transcallosal tract (double)</li> </ul> <p>The analysis scripts that allow the users to replicate the results of the original publication can be found here: <a href="https://github.com/DNC-EEG-platform/SingleSubjectIHTTEstimation">https://github.com/DNC-EEG-platform/SingleSubjectIHTTEstimation</a><br> <br> Funding: Swiss National Science Foundation (grant no 320030 184784 and 32003B 212981), ROGER DE SPOELBERCH Foundation and Bertarelli Catalyst Foundation.</p> <p> </p> <p>Author: Rita Oliveira<br> PIs: Marzia De Lucia, Antoine Lutti</p> <p>Laboratory for Neuroimaging Research</p> <p>Lausanne University Hospital & University of Lausanne, Lausanne, Switzerland</p> <p>Copyright (C) 2022 Laboratory for Neuroimaging Research</p> <p> </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.