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982 results for “interface”
Data associated with Band theory for heterostructures with interface superlattices
<p>Data used to produce the figures in the paper "Band theory for heterostructures with interface superlattices" to be published in Physical Review B.</p>
Dataset for 'Chaperone Solvent Assisted Assembly of Polymers at the Interface of Two Immiscible Liquids'
<p><span>The authors acknowledge financial support from the Research Grants Council of Hong Kong (Project No.21304421), the National Natural Science Foundation of China (Project No. 22003053), the Natural Science Foundation of Guangdong Province, China (Project No. 2023A1515011457),</span><span> </span><span>the Natural Science Foundation of Sichuan Province, China (Project No. 2023NSFSC0312), CityU Strategic Interdisciplinary Research Grant (Project No. 2020SIRG035), and</span><span> Hong Kong Institute for Advanced Study. The authors also thank OpenAI for editing.</span></p>
Fig. 2 in Generalists at the interface: Nematode transmission between wild and domestic ungulates
Fig. 2. Histogram of liability index, L, for domestic and wild species with degree greater than 10.
IoT Sensor Deployment in the Wildland Urban Interface: Leveraging Fire Risk Analysis
<p>Included here are individual burn maps used for evaluating algorithm results in the paper: IoT Sensor Deployment in the Wildland Urban Interface: Leveraging Fire Risk Analysis. This paper will be presented at the IEEE World Forum on Internet of Things in November 2024 and available on IEEE Xplore after that.</p> <p>Also included are maps of fuel load and elevation (geotifs) and the daily weather (in .csv format) for the region of interest used in the burn probability simulator Burn-P3+ to generate the individual burn maps.</p> <p>This paper investigates various algorithms for distributing Internet of Things sensors within the Wildland-Urban Interface to enhance early wildland fire detection. Utilizing geospatial data analysis and a validated wildland fire growth model burn maps were generated to guide sensor placement strategies across a defined region of interest. The algorithms evaluated include an even grid distribution, random distributions, and genetic algorithm-based methods. Each algorithm was tested against 50,000 selected burn maps to assess detection rates, with sensor counts ranging from 50 to 800 across 500 experimental runs. Results indicate that while the even grid distribution yielded the highest detection rates, the practicality of such a method in real-world applications is limited. Genetic algorithms showed promise, but require further exploration to more accurately simulate random distribution used in field deployment. Surprisingly, weighting sensor placement based on wildland fire growth risk did not significantly impact detection effectiveness, suggesting the need for additional research into the representativeness of selected burn maps.</p> <p>Partial code for the sensor deployment algorithms discussed in the above mentioned paper is <a href="https://github.com/richardjpurcell/sensor-deployment-algorithms">available on GitHub</a>.</p>
Quantifying vertical fluxes near the sediment water interface
<p>Field and laboratory observations used in GRL article "<a href="http://onlinelibrary.wiley.com/doi/10.1002/2017GL076789/abstract?campaign=wolacceptedarticle">Determining near-bottom fluxes of passive tracers in aquatic environments</a>" DOI: 10.1002/2017GL076789.</p>
Beamforming in Noninvasive Brain–Computer Interfaces Dataset
<p>This is the dataset of 10 motor imagery subjects upon which the paper cited here is written. It is saved in the EEGLAB .set format with the digitized electrode positions included. The only issue is that the names for channels 65-128 are missing, and the head model that corresponds to these electrode locations is also poorly specified (see field Headmodel of the EEG struct). When using this data please cite:</p> <p>Grosse-Wentrup, Moritz, et al. "Beamforming in noninvasive brain–computer interfaces." <em>IEEE Transactions on Biomedical Engineering</em> 56.4 (2009): 1209-1219.</p> <p>DOI: <a href="https://doi.org/10.1109/TBME.2008.2009768">10.1109/TBME.2008.2009768</a></p>
Pressure-based algorithm for compressible interfacial flows with acoustically-conservative interface discretisation (Supporting data)
<p>The dataset contains sample numerical results associated with the manuscript under the same title, "Pressure-based algorithm for compressible interfacial flows with acoustically-conservative interface discretisation", published in Journal of Computational Physics (2018), https://doi.org/10.1016/j.jcp.2018.04.028.</p>
Vector shapefiles of the rural-urban interface in Portugal estimated for different periods (1990, 2000, 2006, 2012)
<p>Vector shapefiles of the rural-urban interface (RUI) in Portugal estimated for different periods (1990, 2000, 2006, 2012). RUI was mapped considering the first and the second level hierarchy of CORINE Land Cover (CLC). A specific geospatial approach was designed to extract the area of intersection between a buffer around the artificial surfaces and the area resulting from the sum of the forest and semi natural areas plus the heterogeneous agricultural areas. We adopted a buffer width of 1 km, corresponding to two times the spatial resolution of CLC inventories. </p> <p>The field “code_yy” refers to CLC class level 3. More about the Corine Land Cover (CLC) programme and datasets can be found at <a href="http://www.eea.eu">http://www.eea.eu</a></p> <p>More about the the RUI maps elaboration can be found at: <a href="https://doi.org/10.5194/nhess-18-1-2018">https://doi.org/10.5194/nhess-18-1-2018</a>. Please, cite data and the related paper if you use them. (cite as: Tonini., M., Parente., J., Pereira., M., Global assessment of rural-urban interface in Portugal related to land cover changes; Nat. Hazards Earth Syst. Sci., 18, 1–18, 2018)</p>
Supplementary materials for the paper Adamatzky A. 2018 Towards fungal computer. Interface Focus 8: 20180029. http://dx.doi.org/10.1098/rsfs.2018.0029
<p>Supplementary materials for the paper Adamatzky A. 2018 Towards fungal computer. Interface Focus 8: 20180029. http://dx.doi.org/10.1098/rsfs.2018.0029</p>
Dataset for "Open-Circuit Voltage Deficit in Cu2ZnSnS4 Solar Cells by Interface Band Gap Narrowing"
<p><strong>Files in "ExchangeCorrelation": POSCAR_material_Exc</strong></p> <p><em>material</em></p> <ul> <li>CdS </li> <li>CZTS = Cu2ZnSnS4</li> <li>CZTSe = Cu2ZnSnSe4</li> </ul> <p><em>Exchange-correlation functionals</em></p> <ul> <li>PBE</li> <li>RP = revised PBE</li> <li>PBEsol</li> <li>AM05</li> <li>SCAN</li> <li>HSE06</li> <li>6_6: PBE+U (U= 6 eV for Cu d and Zn d)</li> <li>8_8: PBE+U (U= 8 eV for Cu d and Zn d)</li> </ul> <p><strong>Files in "Interface"</strong></p> <p>POSCAR_S_fixed: CZTS/CdS interface, CdS layers were fully relaxed.<br> POSCAR_S_PBE_U: CZTS/CdS interface, the atomic coordinates were relaxed using PBE+U<br> POSCAR_S_SCAN_U: CZTS/CdS interface, the atomic coordinates were relaxed using SCAN+U<br> POSCAR_Se_fixed: CZTSe/CdS interface, CdS layers were fully relaxed.<br> POSCAR_Se_PBE_U: CZTSe/CdS interface, the atomic coordinates were relaxed using PBE+U<br> POSCAR_Se_SCAN_U: CZTSe/CdS interface, the atomic coordinates were relaxed using SCAN+U</p>
Effects of roughness Reynolds number on scalar transfer mechanisms at the sediment-water interface
<p>The data are used in the paper "Effects of roughness Reynolds number on scalar transfer mechanisms at the sediment-water interface" which was submitted to the journal-Water Resources Research. The paper used particles with different sizes, experimental bed is reproduced and the scalar transfer from water to sediment is modelled. We found that The transfer factor changes to turbulent diffusion at the sediment-water interface as the roughness Reynolds number increases. And the turbulent Schmidt number is higher near the SWI and decrease to 0.5 at the SWI, ranging from 0.5 to 1 within the water depth.</p>
Brain Invaders Adaptive versus Non-Adaptive P300 Brain-Computer Interface dataset
<p><strong>Summary:</strong></p> <p>This dataset contains electroencephalographic (EEG) recordings of 24 subjects doing a visual P300 Brain-Computer Interface experiment on PC. The visual P300 is an event-related potential elicited by visual stimulation, peaking 240-600 ms after stimulus onset. The experiment was designed in order to compare the use of a P300-based brain-computer interface on a PC with and without adaptive calibration using Riemannian geometry. The brain-computer interface is based on electroencephalography (EEG). EEG data were recorded thanks to 16 electrodes. A full description of the experiment is available at <a href="https://hal.archives-ouvertes.fr/hal-02103098">https://hal.archives-ouvertes.fr/hal-02103098</a>. Data were recorded during an experiment taking place in the GIPSA-lab, Grenoble, France, in 2013 (Congedo, 2013). Python code for manipulating the data is available at <a href="https://github.com/plcrodrigues/py.BI.EEG.2013-GIPSA">https://github.com/plcrodrigues/py.BI.EEG.2013-GIPSA</a>. The ID of this dataset is<em> BI.EEG.2013-GIPSA</em>.</p> <p> </p> <p><strong>Full description of the experiment and dataset:</strong> <a href="https://hal.archives-ouvertes.fr/hal-02103098">https://hal.archives-ouvertes.fr/hal-02103098</a></p> <p> </p> <p><strong><em>Principal Investigator</em>:</strong> B.Sc. Erwan Vaineau, Ph.D. Alexandre Barachant</p> <p> </p> <p><strong><em>Technical Supervisors</em>: </strong>Eng. Anton Andreev, Eng. Pedro. L. C. Rodrigues, Eng. Grégoire Cattan</p> <p> </p> <p><strong><em>Scientific Supervisor:</em></strong> Ph.D. Marco Congedo</p> <p> </p> <p><strong>ID of the dataset: </strong><em>BI.EEG.2013-GIPSA</em></p>
Datasets for Communicating Wildland Urban Interface Fire Risk
<p>Wind and Fire Danger daily data for years 2007, 2012 and 2017 with relevant MATLAB codes to extract the data.</p>
Revealing the Reovirus T1L-NgR1 binding interface using single-molecule approaches
<p>Initial topology, parameter and coordinate files of the molecular dynamics (MD) simulation of <em>human and mouse NgR1 in complex with two σ3 subunits</em>. We used AMBER for the equilibration phase and GROMACS (2022.3) for the production as molecular engines (input_gromacs.mdp). For each system we run three independent replicas of 2.5 µs. Water molecules and ion atoms were removed from the original trajectories and topology prior to upload. All replicas were previously aligned to chain A (NgR1):</p> <ul> <li>system 1: human NgR1 in complex with two σ3 subunits</li> <li>system 2: murine NgR1 in complex with two σ3 subunits</li> </ul>
Data and Codes for "Thermal properties of the superconductor-quantum Hall interfaces"
<p>Datasets and processing code for reproduction of "Thermal properties of the superconductor-quantum Hall interfaces"</p>
Magnetic particles (Fe3O4) magnify ion transfer processes at the electrified liquid-liquid interface. Case study: Levamisole detection.
<p>Data set for the publication Magnetic particles (Fe3O4) magnify ion transfer processes at the electrified liquid-liquid interface. Case study: Levamisole detection. </p>
Slip Opacity and Fast Osmotic Transport of Hydrophobes at Aqueous Interfaces with Two-Dimensional Materials, part 4/4
<h2>Supplementary data for paper "Slip Opacity and Fast Osmotic Transport of Hydrophobes at Aqueous Interfaces with Two-Dimensional Materials" </h2> <p>Maria Bilichenko, Marcella Iannuzzi, and Gabriele Tocci, ACS Nano, 2024, DOI: 10.1021/acsnano.4c05118</p> <h3>Contents</h3> <p>Trajectories of positions and forces with corresponding input files of systems with 550 water molecules on 2D materials:</p> <ul> <li>MoS2/water</li> <li>MoS2/MoS2/water</li> <li>graphene/MoS2/water</li> <li>MoS2/graphene/water</li> <li>MoS2/hBN/water</li> <li>hBN/MoS2/water</li> <li>graphene/water</li> <li>hBN/water</li> </ul>
Slip Opacity and Fast Osmotic Transport of Hydrophobes at Aqueous Interfaces with Two-Dimensional Materials, part 3/4
<h2>Supplementary data for paper "Slip Opacity and Fast Osmotic Transport of Hydrophobes at Aqueous Interfaces with Two-Dimensional Materials" </h2> <p>Maria Bilichenko, Marcella Iannuzzi, and Gabriele Tocci, ACS Nano, 2024, DOI: 10.1021/acsnano.4c05118</p> <h3>Contents</h3> <p>Trajectories of positions and forces with corresponding input files of systems with 550 water molecules on 2D materials:</p> <ul> <li>graphene/graphene/water</li> <li>hBN/hBN/water</li> <li>graphene/hBN/water</li> <li>hBN/graphene/water</li> </ul>
Slip Opacity and Fast Osmotic Transport of Hydrophobes at Aqueous Interfaces with Two-Dimensional Materials, part 1/4
<h2>Supplementary data for paper "Slip Opacity and Fast Osmotic Transport of Hydrophobes at Aqueous Interfaces with Two-Dimensional Materials" </h2> <p>Maria Bilichenko, Marcella Iannuzzi, and Gabriele Tocci, ACS Nano, 2024, DOI: 10.1021/acsnano.4c05118</p> <h3>Contents</h3> <p>Trajectories of positions and forces with corresponding input files of systems with 120 water molecules confined between 2D materials:</p> <ul> <li>graphene/water/graphene</li> <li>hBN/water/hBN</li> <li>graphene/graphene/water/graphene/graphene</li> <li>hBN/hBN/water/hBN/hBN</li> <li>graphene/hBN/water/hBN/graphene</li> <li>hBN/graphene/water/graphene/hBN</li> </ul> <p> </p>
Slip Opacity and Fast Osmotic Transport of Hydrophobes at Aqueous Interfaces with Two-Dimensional Materials, part 2/4
<h2>Supplementary data for paper "Slip Opacity and Fast Osmotic Transport of Hydrophobes at Aqueous Interfaces with Two-Dimensional Materials" </h2> <p>Maria Bilichenko, Marcella Iannuzzi, and Gabriele Tocci, ACS Nano, 2024, DOI: 10.1021/acsnano.4c05118</p> <h3>Contents</h3> <p>Trajectories of positions and forces with corresponding input files of systems with 120 water molecules confined between 2D materials:</p> <ul> <li>MoS2/water/MoS2</li> <li>MoS2/MoS2/water/MoS2/MoS2</li> <li>graphene/MoS2/water/MoS2/graphene</li> <li>MoS2/graphene/water/graphene/MoS2</li> </ul>
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.