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982 results for “Interface”
Consequences of non-random tree species loss on litter mass loss, nutrient dynamics, carbon cycling, and decomposer communities across a terrestrial-aquatic interface at Coweeta Hydrologic Lab, Otto, NC
Although litter decomposition is a fundamental ecological process, most of our understanding comes from studies of single-species decay. Recently, litter-mixing studies have tested whether monoculture data can be applied to mixed-litter systems. These studies have mainly attempted to detect non-additive effects of litter mixing, which address potential consequences of random species loss. The focus is not on which species are lost, but the decline in diversity per se. Under global change, species loss is likely to be non-random, with some species more vulnerable to extinction than others. Under such scenarios, the effects of individual species (additivity) as well as of species interactions (non-additivity) on decomposition rates are of interest. To examine potential impacts of non-random species loss on ecosystems, we studied additive and non-additive effects of litter mixing on decomposition. A full-factorial litterbag experiment was conducted using four deciduous leaf species, from which mass loss and nitrogen content were measured. Data were analysed using a statistical approach that first looks for additive identity effects based on the presence or absence of species and then significant species interactions occurring beyond those. It partitions non-additive effects into those caused by richness and or composition.
A dataset recorded during development of a tempo-based brain-computer music interface
Open the record for dataset details and reuse information.
The User Interface and Functionality Charts of Erkki Kurenniemi's Electronic Musical Instruments (EKIS)
<p>This spreadsheet includes data related to user interface and functionality charts of Erkki Kurenniemi's electronic musical instruments. Data covers only musical instruments; not studio equipment. The data set produced as a part of the PhD project "User Stories of Erkki Kurenniemi’s Electronic Musical Instruments" by the author. The data is visualized with a video published in https://vimeo.com/375784663</p> <p>PI and contact information: Mikko Ojanen / https://orcid.org/0000-0002-7833-9659</p> <p>The outlining of charts is based on previous research on DMIs, e.g. by</p> <p>Birnbaum, D., Fiebrink, R., Malloch, J., & Wanderley, M. M. Towards a dimension space for musical devices. <em>Proceedings of the 2005 Conference on New Interfaces for Musical Expression, </em>192-195.</p> <p>Magnusson, T. An Epistemic Dimension Space for Musical Devices. <em>Proceedings of the 2010 Conference on New Interfaces for Musical Expression, </em>43-46.</p> <p>Wanderley, Mortensen M. 2002. Evaluation of input devices for musical expression: Borrowing tools from HCI.<em> Computer Music Journal, </em><em>26</em>(3), 62-76.</p>
InterFlex WP3 data set_ SGAM diagrams_interface data base_service identification
<p>This data set contains the InterFlex demonstration use case descriptions in the form of SGAM diagrams as well as the interface data base which was used for different deliverables and the repective results within work package 3 "Impact and deployment analysis of the innovative solutions". There has also been one publication in this regard ( <a href="https://doi.org/10.1109/INDIN.2018.8472053">10.1109/INDIN.2018.8472053</a>)</p> <p>Furthermore, it includes the service mappings for the InterFlex (under GA 731289) demonstrators as an input for different WP3 3.1 subtasks, deliverbale (D3.2) as well as a scientific publication (ICRERA 2019, ID 239, online ISSN: 2572-6013)</p>
DS_LH_Tullii et al._ACS Appl. Mater. Interfaces_2019_SEM images
<p>Scanning electron microscopy images showing P3HT pillar arrays with and without living cells on top</p>
Scanning electron diffraction tilt series data of an aluminium-steel interface region
<p>This dataset contains scanning electron diffraction (SED) data used in the publication entitled "<strong>Microstructural and mechanical characterisation of a second generation hybrid metal extrusion & bonding aluminium-steel butt joint</strong>". The data denoted “SED_HYB_...” were recorded from an aluminium-steel interface region that includes aluminium and steel grains, an interfacial Al-Fe-Si layer, and dispersoids and some oxide particles located within the aluminium region. The nanoscale interfacial intermetallic phase layer is polycrystalline, and to increase the probability of recording data from intermetallic phase crystals oriented close to zone axes, the data were recorded in a tilt series covering 30 degrees, in steps of 1 degree. The file names give the goniometer x-tilt values in degrees, e.g. " SED_HYB_TX-150.hdf5" denotes an x-tilt of -15.0 degrees. SED data recorded from an Au cross-grating specimen, named "SED_AuX.hdf5", and from a MoO3 specimen, named "SED_MoO3.hdf5", are also included for calibration purposes.</p>
Volcano-Independent Seismic Recognition (VI.VSR): case studies with 'geoStudio' graphical interface
<p>Video-documentation of the <strong><em><a href="https://zenodo.org/record/3594080#.X9JP-XVudQJ">geoStudio</a></em> Volcano-Independent Seismic Recognition (VI.VSR) software</strong>, supported by the <a href="https://cordis.europa.eu/project/id/749249"><strong><em>VULCAN.ears</em></strong></a> EU-funded project (H2020-MSCA-IF-2016 Grant) and referenced in the <em>"Practical Volcano-Independent Recognition of Seismic Events: VULCAN.ears project" - </em>(Cortés et al., Frontiers in Earth Sciences, 2021) article. <em><strong>VI.VSR aim</strong></em> is to automatically detect and classify volcano-seismic events in any volcano 'V' of the world by models built by other volcanoes data. This provides volcano-seismic catalogs of the given volcano 'V', without the fuss of designing a custom recognition system for it, being specially useful in real-time monitoring scenarios.</p> <p>The material includes 2 VDs:</p> <ol> <li><em>"VI.VSR+geoStudio_intro.mp4"</em> -> introducing the main idea and concepts behind the Volcano-Independent Seismic Recognition (VI.VSR) and presenting <em>geoStudio</em> and its role in the whole <em>VULCAN.ears</em> platform.</li> <li><em>"VI.VSR.by.geoStudio_case.studies.mp4"</em> -> running the VI.VSR case studies presented in the <em>(Cortés et al., 2021)</em> manuscript.</li> </ol> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No.[749249] (VULCAN.ears).</p>
Supporting Data for: Information Retrieval Interfaces in Virtual Reality - A Scoping Review Focused on Current Generation Technology
<p>This is the full data set of all reviewed research items obtained from Google Scholar, Web of Science and Scopus for the Scoping Literature Review <em><a href="https://doi.org/10.1371/journal.pone.0246398">Information Retrieval Interfaces in Virtual Reality - A Scoping Review Focused on Current Generation VR technology</a>.</em></p>
Comparative electrochemical study of veterinary drug – danofloxacin – at glassy carbon electrode and electrified liquid-liquid interface
<p>Data set for the paper " Comparative electrochemical study of veterinary drug – danofloxacin – at glassy carbon electrode and electrified liquid-liquid interface"</p>
Effect of Surfactants on 1,2-Dichloroethane-in-Water Droplet Impacts at Electrified Liquid-Liquid Interface
<p>The data set for the submited publication "Effect of Surfactants on 1,2-Dichloroethane-in-Water Droplet Impacts at Electrified Liquid-Liquid Interface". </p>
Coupling charge and topological reconstructions at polar oxide interfaces
<p>Dataset corresponding to the publication 'Coupling charge and topological reconstructions at polar oxide interfaces' (<a href="https://arxiv.org/abs/2107.03359">arXiv:2107.03359</a>) (Phys. Rev. Lett. <strong>127</strong>, 127202) </p>
Data for "Electroferrofluids with Non-Equilibrium Voltage-Controlled Magnetism, Diffuse Interfaces, and Patterns"
<p>This dataset contains the raw data used for the publication "Electroferrofluids with Non-Equilibrium Voltage-Controlled Magnetism, Diffuse Interfaces, and Patterns".</p>
Raw EEG Data for: Learning from Label Proportions in Brain-Computer Interfaces
<p>If you prefer to use the preprocessed and epoched data, please refer to: https://zenodo.org/record/192684</p> <p>Note that this repository ontains only the visual paradigm with the N=13 subjects recorded at 31 EEG channels, as described in the above link. We copied the relevant section of the description below:</p> <blockquote> <p>This data repository contains raw EEG of an EEG experiment utilizing visual event-related potentials (ERPs) with N=13 healthy subjects.</p> <p>The dataset is used and described in the following journal article:</p> <p><em>Hübner, D., Verhoeven, T., Schmid, K., Müller, K. R., Tangermann, M., & Kindermans, P. J. (2017). Learning from label proportions in brain-computer interfaces: online unsupervised learning with guarantees. PloS one, 12(4), e0175856.</em></p> <p><strong>Please cite the above article when using the data.</strong></p> <p>The data set with N=13 subjects is different to ordinary ERP datasets in the sense that the train of stimuli to spell one character (68) is divided into repetitions of two interleaved sequences with length 8 and 18, respectively. We added '#' symbols to the spelling matrix which should never be attended by the subject and hence, are non-targets by definition. The first, shorter sequence, now highlights only ordinary characters, while the second sequence also highlights '#' -- visual blank symbols. By construction, sequence 1 has a higher target ratio than sequence 2. These known, but different target and non-target proportions are then used to reconstruct the target and non-target class means. This approach which does not need explicit class labels is termed Learning from Label Proportions (LLP). It can be used to decode brain signals without prior calibration session. More details can be found in the article.</p> <p>In another study, the above data set was used to simulate a new unsupervised mixture approach which combines the mean estimation of the unsupervised expectation-maximization algorithm by Kindermans et al. (2012, PLoS One) with the means obtained with the LLP approach. This leads to an unsupervised solution for which the performance is as good as in the supervised scenario. Please find more details in the following article:</p> <p><em>Verhoeven, T., Hübner, D., Tangermann, M., Müller, K. R., Dambre, J., & Kindermans, P. J. (2017). Improving zero-training brain-computer interfaces by mixing model estimators. Journal of neural engineering, 14(3), 036021.</em></p> </blockquote> <p>The data was recorded with BrainVision recorder. A new file was recorded for every group of 7 characters. The .eeg file contains the RAW EEG data in the format as described in the .vhdr file. Events / stimuli markers are provided in the .vmrk files. Note that there is a wrapper available to use this data in MOABB here: TODO INSERT LINK</p> <p>The subjects had the task to spell a specific sentence with 63 letters. In the online experiment, this was repeated 3 times and each time the online unsupervised classifier was reset at the start of the sentence.</p>
Intrinsic and apparent slip at gas-enriched liquid-liquid interfaces: a molecular dynamics study
<p>- "sl1.dat" : text file with data slip length vs number of gas atoms for k_gas = 1.0</p> <p>- "sl5.dat" : text file with data slip length vs number of gas atoms for k_gas = 0.5</p> <p>- "sl25.dat" : text file with data slip length vs number of gas atoms for k_gas = 0.25</p> <p>- "sl125.dat" : text file with data slip length vs number of gas atoms for k_gas = 0.125</p> <p>- "plotsl.plt": gnuplot script to plot slip lengths data and obtain figure 5a of the article</p> <p>- "dg.dat": data for solubilities in kbT units from figure 3 of the article</p> <p>- "3600gask0125.xyz": trajectory file in xyz format for the system with k_gas= 0.125 and 3600 gas atoms</p> <p>- "3600.data": starting configuration for k_gas = 0.125 and 3600 gas atoms in restart.data format for lammps</p> <p>- "in.shear": lammps input script to run the shear simulation for the system with k_gas = 0.125 and 3600 gas atoms starting from configuration store in "3600.data" file</p> <p> </p>
Biogenic supported lipid bilayers as a tool to investigate nano-bio interfaces
<p>Colorimentric Nanoplasmonic Assay (CONAN) assay of EVs from TRAMP cells. UV/VIS spectrophotometer analysis of samples of EVs from TRAMP cell line incubated with gold nanoparticles, following the protocol described in Montis et al. <a href="https://doi.org/10.1016/j.jcis.2020.03.014">https://doi.org/10.1016/j.jcis.2020.03.014</a></p>
Data repository for "Interface rotation in Cu/Nb accumulative roll bonded (ARB) nanolaminates"
<p>Data repository for "Interface rotation in Cu/Nb accumulative roll bonded (ARB) nanolaminates"</p> <p>This repository contains raw experimental data for "Interface rotation in Cu/Nb accumulative roll bonded (ARB) nanolaminates" manuscript. Please refer to the manuscript for the data interpretation.</p> <p>The repository structure:</p> <ul> <li><code>CuNbARB-sample-photo.jpg</code> shows a photograph of as-received Cu(63nm)/Nb(63nm) accumulative roll bonded (ARB) nanolaminate sample</li> <li><code>ARB_63nm_DRX</code> contains X-ray diffraction measurements</li> <li><code>RD/TDXFIBmilling</code> folders contain focused ion beam images captured during pillar milling <ul> <li>In <code>RDX/TDX</code>, <code>X</code> refers to pillar number (see Supplementary information.org for the full pillar list). The numbers in the file names inside refer to the corresponding pillars.</li> </ul> </li> <li><code>RD/TDXSEMbefore</code> folders contain scanning electron (SEM) images of the as-fabricated pillars</li> <li><code>RD/TDX-compression</code> folders contain in situ pillar compression data, including some of the SEM images captured before/after the compression, raw load-displacement data (in <code>.hys</code> native Hysitron piconindenter format), load-displacement data exported to raw text (see Supplementary information for examples how to plot load-displacement using the raw text files), SEM videos, SEM videos combined with the load-displacement data, and accelerated videos</li> <li><code>RD/TDX-SEMafter</code> folders contain SEM images of the compressed pillars</li> <li>Supplementary-info folder contains supplementary information</li> </ul> <p>Author: I. Radchenko, W. Zhu, L. Qing, E. Navarro, R. Sahay, P.S. Lee, N. Raghavan, O. Thomas, A.S. Budiman, K. Chen</p>
Data of publication 'Optical spin-state polarization in a binuclear europium complex towards molecule-based coherent light-spin interfaces'
<p>Data of publication 'Optical spin-state polarization in a binuclear europium complex towards molecule-based coherent light-spin interfaces' by Kuppusamy Senthil Kumar et al. The two versions of Fig. 4d datasets correspond to the preprint version (https://zenodo.org/record/4905692#.Ymj9odpBxaQ) and publication version (https://www.nature.com/articles/s41467-021-22383-x), since a new set of data was taken during the review process. </p>
Hybrid Deep Learning Techniques for Securing Bioluminescent Interfaces in Internet of Bio Nano Things
<p>The data-set presents normal and anomalous values of twelve traffic parameters, generated by <strong>Bioluminescent bio-cyber Interfacing </strong>(BBI) in the I<strong>nternet of Bio Nano Things </strong>(IoBNT) based systems.</p> <p>The traffic parameters included in the data-set represent bio-electric and electro-bio transduction unit operation of BBI incorporating normal, as well as abnormal data to train and test machine/deep learning classifiers in discriminating attack scenarios.</p> <p>The parameters considered include the following: <strong>Cumulative concentration of released molecules, Elimination rate, Michaelis-Menten constant, Kinetic constant, Forward rate constant, Catalytic reaction constant, Ligand-receptor binding constant, Concentration of ATP, Concentration of information molecules, Release rate Reverse kinetic constant,</strong> and <strong>Reverse forward rate constant.</strong></p> <p>The data set is divided into training and testing data for simplified analysis, and application.</p>
Replication Data for: Geometric Transformers for Protein Interface Contact Prediction
<p>This dataset contains replication data for the paper titled "Geometric Transformers for Protein Interface Contact Prediction". The dataset consists of pickled Python dictionaries containing pairs of DGLGraphs that can be used to train and validate protein interface contact prediction models. It also contains our best model checkpoints saved as PyTorch LightningModules. Our GitHub repository, DeepInteract, linked in the "Additional notes" metadata section below provides more details on how we use these files as examples for cross-validation.</p>
Intermolecular interactions in G protein-coupled receptor allosteric sites at the membrane interface from molecular dynamics simulations and quantum chemical calculations
<p>Allosteric modulators are called to be promising candidates in G protein-coupled receptor (GPCR) drug development by displaying target selectivity and fewer side effects. Among the allosteric sites known to date, extrahelical cavities represent an uncharacteristic binding location that raises many questions about the ligand interactions and stability; the binding site structure, and how all of these are affected by lipid molecules. In this work, we analyze the dynamics and interactions in the PAR2, C5aR1, and GCGR receptors unbound and bound to allosteric modulators at the receptor-lipid interface using molecular dynamics simulations in three lipid compositions. In addition, we performed quantum chemical calculations to further explore electrostatic interactions and the strength of atom pairwise contacts in the stabilization of the ligand-receptor complexes. We show that besides classical hydrogen bonds weak polar interactions such as O-HC, O-Br, and S-HC contacts and aromatic interactions contribute to the binding of allosteric modulators at the extrahelical sites in the middle of the membrane. The allosteric cavities are open and detectable in various membrane compositions but not always predicted as druggable. The availability of polar atoms for interactions in such cavities can be assessed by water molecules from the simulations. Although ligand-lipid interactions are weak, the lipid tails play a role in sizing and shaping the large part of the allosteric cavity. </p> <p>You will find the following files:</p> <ul> <li>Input files of the equilibration and production protocols of MD simulations (MD_simulations_inputs.zip)</li> <li>Input files and coordinate files of F-SAPT and NCIPLOT calculations (quantum_chemical_coordiates_inputs.zip)</li> </ul>
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.