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982 results for “interface”
Electrocatalysis at the Polarised Interface between Two Immiscible Electrolyte Solutions (dataset)
<p>This is a dataset used to prepare Figure 4 in the review "<strong>Electrocatalysis at the Polarised Interface between Two Immiscible Electrolyte Solutions</strong>", prepared for the journal Current Opinion in Electrochemistry.</p>
Cation-coordinated inner-sphere CO2 electroreduction at Au-water interfaces-related dataset
<p>ab initio molecular dynamics and slow-growth sampling dataset for CO<sub>2</sub>RR at Au-water-cations interfaces</p>
Dataset for the manuscript: Trap-and-Track for Characterizing Surfactants at Interfaces
<p>This repository includes datasets supporting our manuscript that will be submitted to Molecules – Special issue "Surfactants with Specific Molecular Architecture as Building Blocks for Nanocarriers."</p> <ul> <li><strong>'rawdata.zip'</strong>: Recordings of trapped particle motions, estimated trajectories, and calculated mean squared displacements (MSD). Each recording has a identification number (e.g., 1, 2, 3, etc.); the corresponding trajectories and MSDs have file names '(ID#)_traj.csv' and '(ID#)_msd.csv', respectively. </li> <li><strong>'results_summary+figures.opju'</strong>: This is a Origin file summarizing the raw data and containing data figures in the manuscript.</li> </ul> <p>Typical particle recording has about 30 s duration. The MSDs are calculated for τ up to 30 seconds. For data analysis, MSDs up to τ = 10 s were used in order to avoid errors occurring at marginal τ.</p> <p>The data with cetyltrimethylammonium chloride (<em>CTAC</em>) can be found in a separate repository: </p> <p>Kim, Jeonghyeon, & Martin, Olivier J. F. (2021). Dataset for the Manuscript: Surfactants Control Optical Trapping Near a Glass Wall [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5557074</p>
(new version data) Probing the atomically diffuse interfaces in core-shell nanoparticles in three dimensions
<p><strong>Deciphering the three-dimensional atomic structure of solid-solid interfaces in core-shell nanomaterials is the key to understand their remarkable catalytical, optical and electronic properties. Here, we probe the three-dimensional atomic structures of palladium-platinum core-shell nanoparticles at the single-atom level using atomic resolution electron tomography. We successfully quantify the rich structural variety of core-shell nanoparticles with heteroepitaxy in 3D at atomic resolution. Instead of forming an atomically-sharp boundary, the core-shell interface is atomically diffuse with an average thickness of 4.2 Å, irrespective of the particle's morphology or crystallographic texture. We observed dissolved free Pd and Pt single atoms and sub-nanometer clusters using cryogenic electron microscopy. The high concentration of Pd in the diffusive interface is highly related to the free Pd atoms dissolved from the Pd seeds. These results advance our understanding of core-shell structures at the fundamental level, providing potential strategies into precise nanomaterial manipulation and chemical property regulation.</strong></p> <p> </p> <p>The data and source codes for the paper "Probing the atomically diffuse interfaces in core-shell nanoparticles in three dimensions" are posted below.</p> <p><strong># Repositary Contents</strong></p> <p><strong>### 1. Experiment Data</strong></p> <p>Folder: [Measured_data](./1_Measured_data)</p> <p>This folder contains denoised and aligned ADF-STEM projections and corresponding finalized tilt angles for three Pd@Pt core-shell nanoparticles. Three particles are named PB (pentagonal bipyramid shaped), EPB (elongated pentagonal bipyramid shaped) and TO (truncated octahedron shaped), respectively.</p> <p><strong>### 2. Reconstructed 3D Volume</strong></p> <p>Folder: [Final_reconstruction_volume](./2_Final_reconstruction_volume)</p> <p>This folder contains 3D tomographic reconstruction volumes of three particles. For the source code of RESIRE algorithm used in these reconstructions, please see the [source code](https://github.com/AET-MetallicGlass/Supplementary-Data-Codes/tree/master/2_RESIRE_package) of Yao Yang's paper on github.</p> <p><strong>### 3. Atom Tracing and Classification</strong></p> <p>Folder: [Tracing_and_classification](./3_Tracing_and_classification)</p> <p>This folder contains the source code to trace and classify atoms in the 3D volume.</p> <p><strong>### 4. Experimental Atomic Models</strong></p> <p>Folder: [Final_coordinates](./4_Final_coordinates)</p> <p>This folder contains the final coordinates of three nanoparticles.</p> <p><strong>### 5. Analysis of core-shell interface and others</strong></p> <p>Folder: [Analysis_of_interface](./5_Analysis_of_interface)</p> <p>This folder contains the codes to analyse the pair distribution function (PDF), the core-shell interface, the local coordination structure (PTM), the displacement and strain map of three nanoparticles.</p>
Data associated to the manuscript "Direct Imaging of Micrometer Thick Interfaces in Salt-Salt Aqueous Biphasic Systems"
<p>Supporting data for the article:</p> <p>Direct Imaging of Micrometer Thick Interfaces in Salt-Salt Aqueous Biphasic Systems</p> <p>PNAS 2023, doi: 0.1073/pnas.2220662120</p> <p>The folder contains binodal curves and surface tension measurements, FTIR and NMR raw data, and Raman imaging data.</p> <p> </p>
First-principles design of ferromagnetic monolayer MnO2 at the complex interface
<p>The crystal structure (POSCAR format) of the heterostructre studied in the manuscript entitled "First-principles design of ferromagnetic monolayer MnO$_2$ at the complex interface" that is currently under review at Physica Scripta. These structures have been relaxed with VASP code. </p>
Resistive switching and role of interfaces in memristive devices based on amorphous NbOx grown by anodic oxidation - Dataset
<p>This is the dataset of "Resistive switching and role of interfaces in memristive devices based on amorphous NbOx grown by anodic oxidation"</p>
Map of the global wildland-urban interface
<p>The wildland-urban interface (WUI) is where buildings and wildland vegetation meet or intermingle. It is where human-environmental conflicts and risks are concentrated, including the loss of houses and lives to wildfire, habitat loss and fragmentation, and the spread of zoonotic diseases. However, a global analysis of the WUI has been lacking.</p> <p>This dataset features a global, 10 m resolution map of the wildland-urban interface that was developed in a recent study by the authors of this dataset (see corresponding publication).</p> <p><strong>Temporal extent</strong></p> <p>The data contains data representative for ca. 2020.</p> <p><strong>Data format and units</strong></p> <p>The data are organized in tiles of 100 km x 100 km and follow the EQUI7 tiling grid and projection system. The images are compressed GeoTiff files (*.tif). There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. Please consider the generation of image pyramids before using *.vrt files.</p> <p>The raster dataset contains Wildland-urban interface (WUI) data (one layer), 10 m spatial resolution, 8 discrete classes:</p> <p>1 - Forest/Shrubland/Wetland-dominated Intermix WU</p> <p>2 - Forest/Shrubland/Wetland-dominated Interface WUI</p> <p>3 - Grassland-dominated Intermix WUI</p> <p>4 - Grassland -dominated Interface WUI</p> <p>5 - Non-WUI: Forest/Shrub/Wetland-dominated</p> <p>6 - Non-WUI: Grassland-dominated</p> <p>7 - Non-WUI: Urban</p> <p>8 - Non-WUI: Other</p> <p>In addition, the data contain tabular data on WUI area, population and biomass in the WUI, as well as wildfire area and people affected by wildfire in the WUI per world region, country, subnational administrative unit and biome.</p> <p>The data also contain the key algorithm for WUI mapping (also accessible here: https://github.com/franzschug/global_wildland_urban_interface).</p> <p><strong>Further information</strong></p> <p>For further information, please see the publication or contact Franz Schug (fschug@wisc.edu). Visit the website of SILVIS lab, University of Wisconsin-Madison (http://silvis.forest.wisc.edu/globalwui) to learn more about the Wildland-Urban Interface.</p> <p>The data can be interactively visualizes in a web viewer <a href="https://geoserver.silvis.forest.wisc.edu/geodata/fast/globalwui/">here.</a></p> <p><strong>Corresponding publication</strong></p> <p>Schug, Franz<sup>*</sup>; Bar-Massada, Avi; Carlson, Amanda R.; Cox, Heather; Hawbaker, Todd J.; Helmers, David; Hostert, Patrick; Kaim, Dominik; Kasraee, Neda K.; Martinuzzi, Sebastián; Mockrin, Miranda H.; Pfoch, Kira A.; Radeloff, Volker C. The global wildland-urban interface, DOI: 10.1038/s41586-023-06320-0</p> <p><strong>Funding</strong></p> <p>This research was funded by the NASA Land Cover and Land Use Change Program under agreement 80NSSC21K0310.</p>
PIsToN: Evaluating Protein Binding Interfaces with Transformer Networks (dataset)
<p>Computational protein-binding studies are widely used to investigate fundamental biological processes and facilitate the development of modern drugs, vaccines, and therapeutics. Scoring functions aim to assess and rank the binding strength of the predicted protein complex. Accurate scoring of protein binding interfaces remains a challenge. PIsToN (evaluating Protein binding Interfaces with Transformer Networks) represents a novel approach to distinguish native-like protein complexes from incorrect conformations. Protein interfaces are transformed into a collection of 2D images (interface maps), each corresponding to a geometric or biochemical property. Pixel intensities represent the feature values. A neural network was adapted from a popular vision transformer (ViT) with several enhancements: a hybrid component to accept empirical-based energy terms, a multi-attention module to highlight essential features and binding sites, and the use of contrastive learning for better ranking performance. The resulting PIsToN model significantly outperforms state-of-the-art scoring functions on well-known datasets.</p> <p>This repository contains proteins and pre-computed interface maps for the PIsToN work.</p>
Dataset for "Brain-machine interface learning is facilitated by specific patterning of distributed cortical feedback"
<p><strong>Dataset for the manuscript entitled: Brain-machine interface learning is facilitated by specific patterning of distributed cortical feedback.</strong></p> <p><strong><em>DOI of the manuscript:</em> <a href="https://doi.org/10.1126/sciadv.adh1328">10.1126/sciadv.adh1328</a></strong></p> <p><em><strong>Abstract of the manuscript:</strong></em></p> <p>Neuroprosthetics offer great hope for motor-impaired patients. One obstacle is that fine motor control requires near-instantaneous, rich somatosensory feedback. Such distributed feedback may be recreated in a brain-machine interface using distributed artificial stimulation across the cortical surface. Here, we hypothesized that neuronal stimulation must be contiguous in its spatiotemporal dynamics in order to be efficiently integrated by sensorimotor circuits. Using a closed-loop brain-machine interface, we trained head-fixed mice to control a virtual cursor by modulating the activity of motor cortex neurons. We provided artificial feedback in real time with distributed optogenetic stimulation patterns in the primary somatosensory cortex. Mice developed a specific motor strategy and succeeded to learn the task only when the optogenetic feedback pattern was spatially and temporally contiguous while it moved across the topography of the somatosensory cortex. These results reveal new properties of sensorimotor cortical integration and set new constraints on the design of neuroprosthetics.</p> <p><strong><em>Description of the variables in the data storage dictionary:</em></strong></p> <ol> <li>cursor_positions: sequence of the virtual cursor position over a session</li> <li>range_of_rewardable_cursor_position: range of cursor positions that can be rewarded. Upper threshold excluded. Lower threshold included. </li> <li>cursor_times: timing of the cursor positions provided by the cursor_positions data, in the same clock as spike and lick times. </li> <li>lick_times: timing of all recorded licks. </li> <li>reward_times:timing of the opening of the valve that releases the water reward. </li> <li>master_spike_times: time of the spikes of the master neurons.</li> <li>master_spike_shape: spike shape of each spike stored in master_spike_times. The shipe shapes are shown for 3s (30kHz sampling rate), for each 4 electrode of the corresponding tetrode.</li> <li>neighbor_spike_times': same as master_spike_times for neighbor neurons.</li> <li>neighbor_spike_shape': same as master_spike_shape for neighbor neurons. </li> </ol> <p><em><strong>General structure of the data set:</strong></em></p> <p>Each variable is a hierarchical tree of lists: [<em>Protocol</em>][<em>Mouse</em>][<em>Session</em>]. </p> <p><em>Protocol</em> takes one of the following values: 0: Bar feedback | 1: Full shuffle | 2: No Feedback | 5: Playback Structured Feedback | 7: Spontaneous activity | 8: Barrel Shuffle | 9: Frame shuffle.</p> <p><em>Mouse </em>and <em>Session</em> iterate through respectively the mice that were involved in the protocole, and the sessions, generally 5 in total.</p> <p><em><strong>Python code to load the hdf5 File</strong></em></p> <p>The code below relies on libraries available on a standard, mac anaconda install of the jupyter notebook system on the 25/03/2024. It provides with several nested lists of Numpy arrays.</p> <p>For instance, to access the series of cursor positions of Mouse M during session number S of Protocole P, index as follows: </p> <p>CP = cursor_positions_DATA[P][M][S]</p> <p># 1 - Load the libraries</p> <p> import h5py<br> import numpy as np<br> import pylab as pl</p> <p> filename = "./data.h5"<br> f = h5py.File(filename, "r")</p> <p># 2 - Extraction of the cursor position, time, lick and reward time, as well as the activity of the master and neighbor neurons. </p> <p> cursor_positions = f['cursor_positions']</p> <p> cursor_positions_DATA = []<br> for Protocole in range(9):<br> DATA_protocole = []<br> P = f[cursor_positions[Protocole][0]]<br> for Mouse in range(16): <br> # Loop through the mice, and collect <br> M = f[P[Mouse][0]]<br> if not(list(M) == [0,1]) and (len(M) == 5):<br> DATA_mouse = []<br> for Session in range(5):<br> S = f[M[Session][0]]<br> bfr = np.array(S)<br> DATA_mouse.append(bfr) <br> DATA_protocole.append(DATA_mouse)<br> cursor_positions_DATA.append(DATA_protocole)</p> <p> </p> <p> cursor_times = f['cursor_times']</p> <p> cursor_times_DATA = []<br> for Protocole in range(9):<br> DATA_protocole = []<br> P = f[cursor_times[Protocole][0]]<br> for Mouse in range(16): <br> # Loop through the mice, and collect <br> M = f[P[Mouse][0]]<br> if not(list(M) == [0,1]) and (len(M) == 5):<br> DATA_mouse = []<br> for Session in range(5):<br> S = f[M[Session][0]]<br> bfr = np.array(S)<br> DATA_mouse.append(bfr) <br> DATA_protocole.append(DATA_mouse)<br> cursor_times_DATA.append(DATA_protocole)</p> <p> </p> <p> lick_times = f['lick_times']</p> <p> lick_times_DATA = []<br> for Protocole in range(9):<br> DATA_protocole = []<br> P = f[lick_times[Protocole][0]]<br> for Mouse in range(16): <br> # Loop through the mice, and collect <br> M = f[P[Mouse][0]]<br> if not(list(M) == [0,1]) and (len(M) == 5):<br> DATA_mouse = []<br> for Session in range(5):<br> S = f[M[Session][0]]<br> bfr = np.array(S)<br> DATA_mouse.append(bfr) <br> DATA_protocole.append(DATA_mouse)<br> lick_times_DATA.append(DATA_protocole)</p> <p> </p> <p> reward_times = f['reward_times']</p> <p> reward_times_DATA = []<br> for Protocole in range(9):<br> DATA_protocole = []<br> P = f[reward_times[Protocole][0]]<br> for Mouse in range(16): <br> # Loop through the mice, and collect <br> M = f[P[Mouse][0]]<br> if not(list(M) == [0,1]) and (len(M) == 5):<br> DATA_mouse = []<br> for Session in range(5):<br> S = f[M[Session][0]]<br> bfr = np.array(S)<br> DATA_mouse.append(bfr) <br> DATA_protocole.append(DATA_mouse)<br> reward_times_DATA.append(DATA_protocole)</p> <p> </p> <p> master_spike_times = f['master_spike_times']</p> <p> master_spike_times_DATA = []<br> for Protocole in range(9):<br> DATA_protocole = []<br> P = f[master_spike_times[Protocole][0]]<br> for Mouse in range(16): <br> # Loop through the mice, and collect <br> M = f[P[Mouse][0]]<br> if not(list(M) == [0,1]) and (len(M) == 5):<br> DATA_mouse = []<br> for Session in range(5):<br> S = f[M[Session][0]]<br> DATA_unit = []<br> for Unit in range(len(S)):<br> if not(list(S) == [0,1]):<br> N = f[S[Unit][0]]<br> bfr = np.array(N)<br> DATA_unit.append(bfr) <br> DATA_mouse.append(DATA_unit)<br> DATA_protocole.append(DATA_mouse)<br> master_spike_times_DATA.append(DATA_protocole)</p> <p> </p> <p> neighbor_spike_times = f['neighbor_spike_times']</p> <p> neighbor_spike_times_DATA = []<br> for Protocole in range(9):<br> DATA_protocole = []<br> P = f[neighbor_spike_times[Protocole][0]]<br> for Mouse in range(16): <br> # Loop through the mice, and collect <br> M = f[P[Mouse][0]]<br> if not(list(M) == [0,1]) and (len(M) == 5):<br> DATA_mouse = []<br> for Session in range(5):<br> S = f[M[Session][0]]<br> DATA_unit = []<br> for Unit in range(len(S)):<br> if not(list(S) == [0,1]):<br> N = f[S[Unit][0]]<br> bfr = np.array(N)<br> DATA_unit.append(bfr) <br> DATA_mouse.append(DATA_unit)<br> DATA_protocole.append(DATA_mouse)<br> neighbor_spike_times_DATA.append(DATA_protocole)</p> <p> </p>
UEyes: Understanding Visual Saliency across User Interface Types
<p>UEyes is a large eye-tracking-based dataset including 62 participants and 1,980 UI screenshots, covering four major UI types: webpage, desktop UI, mobile UI, and poster. </p> <p>Please cite the following paper:</p> <p>UEyes: Understanding Visual Saliency across User Interface Types</p> <p>https://dl.acm.org/doi/10.1145/3544548.3581096</p> <p>Yue Jiang, Luis A. Leiva, Hamed Rezazadegan Tavakoli, Paul R. B. Houssel, Julia Kylmälä, and Antti Oulasvirta. 2023. UEyes: Understanding Visual Saliency across User Interface Types. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI '23). Association for Computing Machinery, New York, NY, USA, Article 285, 1–21. https://doi.org/10.1145/3544548.3581096</p>
Data from: The impact of task context on predicting finger movements in a brain-machine interface
<p>A key factor in the clinical translation of brain-machine interfaces (BMIs) for restoring hand motor function will be their robustness to changes in a task. With functional electrical stimulation (FES) for example, the patient's own hand will be used to produce a wide range of forces in otherwise similar movements. To investigate the impact of task changes on BMI performance, we trained two rhesus macaques to control a virtual hand with their physical hand while we added springs to each finger group (index or middle-ring-small) or altered their wrist posture. Using simultaneously recorded intracortical neural activity, finger positions, and electromyography, we found that predicting finger kinematics and finger-related muscle activations across contexts led to significant increases in prediction error, especially for muscle activations. However, with respect to online BMI control of the virtual hand, changing either training task context or the hand's physical context during online control had little effect on online performance. We explain this dichotomy by showing that the structure of neural population activity remained similar in new contexts, which could allow for fast adjustment online. Additionally, we found that neural activity shifted trajectories proportional to the required muscle activation in new contexts, possibly explaining biased kinematic predictions and suggesting a feature that could help predict different magnitude muscle activations while producing similar kinematics.</p>
Data from: Using adversarial networks to extend brain computer interface decoding accuracy over time
<p>Existing intracortical brain computer interfaces (iBCIs) transform neural activity into control signals capable of restoring movement to persons with paralysis. However, the accuracy of the "decoder" at the heart of the iBCI typically degrades over time due to turnover of recorded neurons. To compensate, decoders can be recalibrated, but this requires the user to spend extra time and effort to provide the necessary data, then learn the new dynamics. As the recorded neurons change, one can think of the underlying movement intent signal being expressed in changing coordinates. If a mapping can be computed between the different coordinate systems, it may be possible to stabilize the original decoder's mapping from brain to behavior without recalibration. We previously proposed a method based on Generalized Adversarial Networks (GANs), called "Adversarial Domain Adaptation Network" (ADAN), which aligns the distributions of latent signals within underlying low-dimensional neural manifolds. However, we tested ADAN on only a very limited dataset. Here we propose a method based on Cycle-Consistent Adversarial Networks (Cycle-GAN), which aligns the distributions of the full-dimensional neural recordings. We tested both Cycle-GAN and ADAN on data from multiple monkeys and behaviors and compared them to a third, quite different method based on Procrustes alignment of axes provided by factor analysis. All three methods are unsupervised and require little data, making them practical in real life. Overall, Cycle-GAN had the best performance and was easier to train and more robust than ADAN, making it ideal for stabilizing iBCI systems over time.</p>
Volitional activation of remote place representations with a hippocampal brain‐machine interface
<p><strong>Overview</strong></p> <p>This repository is associated with the following paper: <strong>Lai C, Tanaka S, Harris TD, Lee AK. Volitional activation of remote place representations with a hippocampal brain‐machine interface. Science, 2023 (in press).</strong></p> <p>This dataset demonstrates the ability of animals to activate remote place representations within the hippocampus when they aren't physically present at those locations. Such remote activations serve as a fundamental capability underpinning memory recall, mental simulation/planning, imagination, and reasoning. By employing a hippocampal map-based brain-machine interface (BMI), we designed two specific tasks to test whether animals can intentionally control their hippocampal activity in a flexible, goal-directed, and model-based manner. Our results show that animals can perform both tasks in real-time and in single trials. This dataset provides the neural and behavior data of these two tasks. The details of the tasks and results are described in the paper.</p> <p> </p> <p><strong>Dataset, pre-trained model and code access:</strong></p> <ul> <li> <p>Unzip the <code>data.7z</code> to get a <code>data</code> folder. The <code>data</code> folder contains three subfolders:</p> <ul> <li><strong>1. Running</strong>: This folder has two subfolders: <ul> <li><strong>run_before_jumper</strong>: Contains data files for the Running task performed before the Jumper task.</li> <li><strong>run_before_jedi</strong>: Contains data files for the Running task performed before the Jedi task.</li> </ul> </li> <li><strong>2. Jumper</strong>: Contains data files for the Jumper task.</li> <li><strong>2. Jedi</strong>: Contains data files for the Jedi task.</li> </ul> </li> <li> <p>Unzip the <code>model.7z</code> to get a <code>pretrained_model</code> folder, which contains all 6 pretrained models (<code>pth</code> files) trained using the data from the <code>Running</code> tasks, 3 used in <code>Jumper</code> tasks and 3 used in the <code>Jedi</code> tasks.</p> </li> <li> <p>Unzip the <code>code.7z</code></p> </li> </ul>
Preferential growth of intermetallics under temperature gradient at Cu–Sn interface during transient liquid phase bonding: insights from phase field simulation
<p>The data of (i) heats of transport values and (ii) coefficients for expressions of free energy density of phases used to generate the results in the paper titled "Preferential growth of intermetallics under temperature gradient at Cu–Sn interface during transient liquid phase bonding: insights from phase field simulation" are provided in this dataset.</p> <p><br> <strong>(i )</strong> The heats of transport (Q*) values of Cu and Sn species in LIQUID (Sn-rich), IMC (CU6SN5) and FCC (Cu-rich) phases at T=523.15 K (<strong>constant cold side temperature</strong>) are available in <em>heat_of_transport.csv</em> file. The numerical quantities in the Q* column of the file are expressed in the unit of kJ/mol. In this work, these Q* values have been independently validated to work for applied thermal gradients (<span class="math-tex">\(\nabla T\)</span><sub>a</sub>) of <strong>1.5E+5 K/m </strong>and <strong>1.5E+6 K/m </strong>Thus, the following meanings hold true for the column names of this data file:</p> <p><strong>phase</strong> - it is the name of a phase studies (e.g. Cu-rich FCC phase, Sn-rich LIQUID phase and Cu<sub>6</sub>Sn<sub>5</sub> IMC phase. the data type is string, and has no unit. </p> <p><strong>species</strong> - the element Cu and Sn of the binary Cu-Sn system. the data type is a string, and has no unit. </p> <p><strong>T (K)</strong> - it is the <strong>constant temperature (T = T<sub>cold</sub> = 523.15 K) at the bottom cold edge </strong> of a rectangular computational domain of width = 498 nm and height = 747 nm. the data is a float value, and has a unit of K. <strong> The information about cold edge temperature T<sub>cold</sub> being the constant reference temperature, and the hot edge temperature being T<sub>hot</sub>= T<sub>cold </sub>+ <span class="math-tex">\(\nabla T\)</span><sub>a</sub> , is a novelty of this work. </strong>While most of the other works are based upon hot edge being maintained at constant temperature by a thermal heater, this work presents the data with the temperature of cold edge maintained constant by a thermal cooler. </p> <p><strong>Q* (kJ/mol)</strong> - The data of heat of transport values expressed in terms of unit of kJ/mol can be either negative or positive. In this work, the values presented in the table have been validated for applied vertical thermal gradients of (<span class="math-tex">\(\nabla T\)</span><sub>a</sub>) of <strong>1.5E+5 </strong>and <strong>1.5E+6 K/m .</strong></p> <p> </p> <p><strong>(ii)</strong> The chemical free energy density of a phase i (i = LIQUID, IMC or FCC ) at 523.15 K has been expressed with the function f<sub>i </sub>= 0.5 * A<sub>i</sub> * (c<sub>i </sub>- c<sub>eq,i</sub>)<sup>2</sup> + B<sub>i</sub> * (c<sub>i </sub>- c<sub>eq,i</sub>) + C<sub>i</sub>; where c<sub>i</sub> is the mole-fraction (composition) of Sn in a phase. The data consisting of the numerical values of coefficients A<sub>i</sub>, B<sub>i</sub> and C<sub>i</sub> on the units of J/m<sup>3</sup> are provided in the file free_energy_density.csv. Besides these coefficients, the file also consists the quantified values of the equilibrium composition c<sub>eq,i </sub>at each phase. It is to be noted that c<sub>eq,i</sub> has no units.</p> <p> </p> <p> </p> <p> </p>
Edge effects and vertical stratification of aerial insectivorous bats across the interface of primary-secondary Amazonian rainforest
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Data from: A high-performance brain-computer interface for finger decoding and quadcopter game control in an individual with paralysis
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Miniaturized spectral sensing with a tunable optoelectronic interface
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Data from: A geometric VOF method for interface flow simulations
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Asymmetric fluctuations and self-folding of active interfaces
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ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.