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982 results for “Interface”
North Temperate Lakes LTER Northern Highland Lake District Wildland Urban Interface
The Wildland-Urban Interface (WUI) is the area where houses meet or intermingle with undeveloped wildland vegetation. This makes the WUI a focal area for human-environment conflicts such as wildland fires, habitat fragmentation, invasive species, and biodiversity decline. Using geographic information systems (GIS), we integrated U.S. Census and USGS National Land Cover Data, to map the Federal Register definition of WUI (Federal Register 66:751, 2001). These data are useful within a GIS for mapping and analysis at national, state, and local levels.
North Temperate Lakes LTER Yahara Lakes District Wildland Urban Interface
The Wildland-Urban Interface (WUI) is the area where houses meet or intermingle with undeveloped wildland vegetation. This makes the WUI a focal area for human-environment conflicts such as wildland fires, habitat fragmentation, invasive species, and biodiversity decline. Using geographic information systems (GIS), we integrated U.S. Census and USGS National Land Cover Data, to map the Federal Register definition of WUI (Federal Register 66:751, 2001). These data are useful within a GIS for mapping and analysis at national, state, and local levels.
Organized actors at the biodiversity science-policy-society interface
<p>This database was developed in the context of the Deliverable 2.1 of the BioAgora project 'Developing the Science Service for European Research and Biodiversity Policymaking' (<a href="https://bioagora.eu/)">https://bioagora.eu/)</a>. BioAgora is a collaborative European project funded by the Horizon Europe programme (Horizon Europe research and innovation programme, grant agreement No. 101059438). The project's main outcome is intended to be the development of a Science Service for Biodiversity, the principal EU mechanism to connect research and knowledge on biodiversity to the needs of policy making through a continuous dialogue. The ultimate goal of BioAgora and of the Science Service is to support the implementation of the Biodiversity Strategy for 2030, and more broadly the sustainability transition required by the EU Green Deal. The BioAgora project was launched in July 2022 for a duration of 5 years. It gathers a Consortium of 22 partners, from 13 European countries, led the Finnish Environment Institute (Syke). Partners represent a diversity of actors coming from academia, public authorities, SMEs, and associations. Views and opinions expressed are those of the authors only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the granting authority can be held responsible for them. </p> <p>In order to develop the database, a thorough desk search was conducted to compile an extensive, albeit not exhaustive, list of organizations operating at the science-policy-society interface in the context of biodiversity and sustainability. In collecting the list, we focused on actors operating at EU level, although we also included particularly relevant international, regional or national organized actors. The desk search built upon the work already developed in the context of two pan-European projects, funded by the Seventh framework programme of the European Community: ‘Developing a Knowledge Network for European Expertise on biodiversity and ecosystem services to inform policy making and economic sectors (KNEU, 2010-2014, grant 265299) and ‘Establishing a European Knowledge and Learning Mechanism to Improve the Policy-Science-Society Interface on Biodiversity and Ecosystem Services’ (Eklipse, 2016-2020, grant 690474). The two above-mentioned projects preceded the BioAgora project in that they aimed at understanding and improving the effectiveness of the biodiversity science-policy(-society) interface in Europe. Such projects had thus already compiled extensive databases of relevant organizations in Europe (including national and international actors, in addition to EU level actors), and quantified the relevance of such organizations based on votes cast by project members and based on interviews with key organizations. The database developed through the desk search conducted was further refined with suggestions for relevant organizations provided by BioAgora’s participants and by the representatives of the organizations interviewed during the other steps of the data collection. The data collection processes started in September 2022 and was updated until June 2024. Note that the categories for network types (Columns E-F) are not mutually exclusive. For further details about the development of the database please see Deliverable 2.1 (<a href="https://bioagora.eu/deliverables/">https://bioagora.eu/deliverables/</a>). </p>
A dataset recorded during development of an affective brain-computer music interface: calibration session
Open the record for dataset details and reuse information.
A dataset recorded during development of an affective brain-computer music interface: testing session
Open the record for dataset details and reuse information.
A dataset recorded during development of an affective brain-computer music interface: training sessions
Open the record for dataset details and reuse information.
Fe/Sb2Te3 Interface Reconstruction through Mild Thermal Annealing (data)
<p>This dataset contains the raw data files connected to the figures included in the paper "<em>Fe/Sb<sub>2</sub>Te<sub>3</sub> Interface Reconstruction through Mild Thermal Annealing</em>" by E. Longo et al., Adv. Mat. Interfaces (2020): <a href="https://onlinelibrary.wiley.com/doi/full/10.1002/admi.202000905">https://onlinelibrary.wiley.com/doi/full/10.1002/admi.202000905</a></p> <p>Note for users: </p> <p>CEMS data of Figure 5 can be treated with the open source software Vinda: <a href="https://e-ms.web.cern.ch/content/vinda">https://e-ms.web.cern.ch/content/vinda</a>, H. P. Gunnlaugsson, "<em>Spreadsheet based analysis of Mössbauer spectra</em>", Hyperfine Interact., 237 (1) (2016), p. 79, <a href="https://doi.org/10.1007/s10751-016-1271-z">10.1007/s10751-016-1271-z</a></p>
Videos of fluid flow in contact interfaces
<p>These videos demonstrate the capabilities of the computational framework presented in [1] to solve complex coupled problem of viscous thin fluid flow in contact interfaces while handling the possibility of the fluid to be trapped in pockets surrounded by contact zones.</p> <p>[1] Andrei G. Shvarts, Julien Vignollet, Vladislav A. Yastrebov "Computational framework for monolithic coupling for thin fluid flow in contact interfaces" https://arxiv.org/abs/1912.11292v3</p>
Supporting data "Scaling theory for the statistics of slip at frictional interfaces"
<p>Principle data supporting "Scaling theory for the statistics of slip at frictional interfaces"</p> <p>T. W. J. de Geus and M. Wyart (2022), <em>Phys. Rev. E</em>, 106(6):065001.</p> <ul> <li>See code at <a href="../doi/10.5281/zenodo.10723197">doi: 10.5281/zenodo.10723197</a> (and its documentation) for workflow, detailed information of the data, and further dependencies. </li> <li>The files <code>N=*_Run*.zip</code> contain fully restorable events for event-driven athermal quasi-static shear. Sequentually numbered files contain different parts of a single dataset.</li> <li>The file <code>summary.zip</code> contains an extract of the key variables of these runs, and of triggers at different stresses. Finally, it contains "flow" data acquired by driving at finite rate. </li> <li>The files <code>N=3^6x4_Trigger_EnsemblePack.zip</code> contain fully restorable triggers at different stresses in the largest system. The sequentially numbered files correspond to one dataset split in different<em> </em><code>.h5</code> files.</li> <li>Highly specific (and poorly documentated) plotting functions are available upon request.</li> </ul>
Peridigm User Material Interface Dataset
<p>The dataset includes a Fortran user material routine which is used for a reference finite element and peridynamic model of a dogbone.</p> <p>The user material interface allows the simplified use of already existing material routines in the peridynamic framework Peridigm. The interface is based on the Abaqus UMAT definition and allows the integration of these Fortran routines directly into Peridigm. The integration of UMAT routines based on finite elements in Peridigm eliminates the need for parallel development of existing material models from classical continuum mechanics theory. Thus, all developer of material models can utilize both finite element frameworks and Peridynamics. This opens up new possibilities for analysis, verification and comparison. In order to be able to use the material routine, the UMAT file must be precompiled and copied to a specific folder. With this interface many material routines can be reused and applied to progressive failure analysis.</p>
Vapor Film Lifetime at Magma-Water Interface
<p><strong>Video records of the meta-stable vapor film conditions on a spherical magma sample in contact with water.</strong></p> <p>The dataset is the base the following article:<br> <em>Experimental constraints on the stability and oscillation of water vapor film–a precursor for phreatomagmatic and explosive submarine eruptions. </em>By I. Sonder, and P. Moitra, 2022 in Frontiers in Earth Science, 10, <a href="https://doi.org/10.3389/feart.2022.983112">doi: 10.3389/feart.2022.983112</a> .</p> <p>The dataset consists of observations (videos) of three experiments, and manually drawn polygons outlining the vapor film on the melt sample, or areas of direct magma-water contact.</p> <ul> <li>Video material is stored in two formats: (a) as video file (<code>.mp4</code>) and (b) as zip container that contains each of the video's frames in <code>.jpg</code> format.</li> <li>The polygon markup is stored in JSON format.</li> </ul> <p> </p> <p><strong>Changes</strong></p> <ul> <li><strong><em>Version</em> 0.1:</strong><br> Initial upload of video and polygonal markup material.</li> </ul>
Survey of digitized newspaper interfaces (dataset and notebooks)
<p>This record contains the datasets and jupyter notebooks which support the analysis presented in the paper "Historical Newspaper User Interfaces: A Review". Please refer to the paper or the github repository for more information (see links below), or do not hesitate to contact us!</p>
Datasets and Jupyter notebook for the structural analysis of protein-RNA interface evolution
<p>The present repository contains data and code related to our manuscript "Structural comparison of protein-RNA homologous interfaces reveals widespread overall conservation contrasted with versatility in polar contacts". In the manuscript, we analyze the evolution of protein-RNA interfaces by building a dataset of protein-RNA interologs (homologous interfaces) and exploring how interface contacts are conserved between homologous interfaces, as well as possible explanations for non-conserved contacts.</p> <p>This repository contains the following files:</p> <ul> <li>DataAnalysisNotebook.ipynb is a Jupyter notebook to reproduce contact conservation analysis and all figures from our manuscript, and to explore data</li> <li>env.yaml is an environment file in order to build a Conda/Mamba environment to run the Jupyter notebook </li> <li>2022-02-21-PDB.csv contains data from the PDB about 3D structures of complexes containing interacting protein and RNA chains (PDB structure identifier, chain identifiers, experimental technique and resolution)</li> <li>2022-02-21-PDB_proteinchainscontactingRNAchains.groupbp.tsv contains more detailed information about interacting protein and RNA chains from these complexes (PDB and chain identifiers, protein and RNA size, interface size and number of contacts)</li> <li>2022-02-21-PDB_proteinchainscontactingRNAchains.groupbp.txt.selectXE_2.50_p30_r10_pi5_ri5_rep_bc-100.out_RNAcl_0.99.tsv contains the same detailed information, restricted to the filtered dataset used as a starting point in our interolog search pipeline</li> <li>PDBinterfaceAlign.csv contains information about the structural alignment of pairs of protein-RNA interactions (structural alignment TM-scores, sequence identity and coverage)</li> <li>DataInterologsParam.tsv contains information about a pre-filtered set of 2587 potential interologs (including interface RMSD, sequence identity and coverage and interface size)</li> <li>DataInterologsContactsFixedSASA.tsv contains detailed information about conserved and non-conserved contacts in the final set of 2022 interologs (atomic contacts, apolar contacts, hydrogen bonds, salt bridges and stacking information for aminoacid-nucleotide pairs, as well as information about whether each belongs to the interface, secondary structures, and the aminoacid surface accessibility and evolutionary conservation metrics) - compared to version 1, the calculation of solvent accessibility was fixed for a number of interolog pairs</li> <li>DataCons.csv contains precomputed contact conservation metrics for each of the 2022 interolog pairs, for fast reproduction of manuscript figures</li> <li>DataInterologsContactsResampledMaintainStructSeqId.tsv, DataInterologsContactsShuffled.tsv and DataInterologsShuffled.tsv relate to baselines computed for contact conservation assessment</li> <li>clan.txt, clan_membership.txt, ecod.latest.domains.uniq.txt, rfam_interfaces_977.txt, DataGroupsECOD.tsv, DataGroupesRFAM.tsv, DataGroupsRFAMClan.tsv, DataInterfaceGroupsECOD.tsv and DataInterfaceGroupsRFAM.tsv relate to the ECOD (respectively Rfam) classification of protein domains (respectively RNA) in protein-RNA interfaces from our dataset</li> <li>ListeIntraHbonds.pkl and ListeIntraSaltBridges.pkl are pickle-format data files containing intra-molecular hydrogen bonds and salt bridges (respectively) that are used to analyse scenarii of compensation for non-conserved polar contacts.</li> </ul>
Data underlying the paper titled "Positron unveiling high mobility graphene stack interfaces in Li-ion cathodes"
<p>The folder includes data regarding 4 figures shown in this paper. </p> <p>FIG_1: Simulation structure of 6 layers of graphene bulk and slab (6C_Bulk.vasp, 6C_Slab.vasp), LiCoO2(LCO_336.vasp), ABA Graphite coating LiCoO2(G@LCO.vasp). </p> <p>FIG_2: Raw data of band structure of Graphite coating LCO (Band_G@LCO_EIGENVAL), Density of States (DOS_G@LCO_DOSCAR)</p> <p>FIG_3: Calculated plane-averaged charge density difference of SW-G@LCO perpendicular to (001) plane at the equilibrium distance. G@LCO_CHGCAR, LCO_CHGCAR, C_CHGCAR are the CHGCAR for Graphitte coating LCO, LCO, graphite, respectively. </p> <p>d24_diff.vasp is the charge difference</p> <p>d24_PACD.dat is the plane-averaged charge density difference. </p> <p>FIG_4: posden indicates the positron density, while posvtot means positron potential. Data are named by their structure. </p>
Global wildland-urban interface maps in 2000, 2010, and 2020, based on GlobeLand30
<p>This dataset provides global wildland-urban interface (WUI) maps at a spatial resolution of <strong>30 meters </strong>for the years <strong>2000, 2010, and 2020</strong>. The WUI is defined as areas where the 200-meter buffers of urban areas (characterized by artificial surfaces) intersect with the 400-meter buffers of wildland areas, including forests, shrublands, and grasslands. These maps are produced based on land cover classification results from the GlobeLand30 datasets.</p> <p><strong>Projection Information:</strong><br>The projection information aligns with GlobeLand30 standards:</p> <ul> <li><strong>Projection:</strong> UTM (Universal Transverse Mercator) for latitudes from S85 to N85, using a 6-degree zone system without zone numbers.</li> <li><strong>Polar Azimuthal Projection:</strong> Applicable for latitudes from S85 to N90 and N85 to N90, with the projection surface intersecting at the South and North Poles.</li> </ul> <p><strong>Naming Convention:</strong><br>The file naming convention is as follows:</p> <div> <div> <div> <div><strong>WUI_LHH_VV_YYYYlc030.tif</strong></div> <div> </div> </div> </div> </div> <p>Where:</p> <ul> <li><strong>L</strong> = Latitude code (N for the Northern Hemisphere, S for the Southern Hemisphere)</li> <li><strong>HH</strong> = Number of UTM zone</li> <li><strong>VV</strong> = Starting latitude of the tile (each tile crosses 5° latitude)</li> <li><strong>YYYY</strong> = Year mapped</li> <li><strong>lc</strong> = Land cover abbreviation</li> <li><strong>030</strong> = Spatial resolution of 30 meters</li> </ul> <p><strong>Example File Name:</strong><br>For instance, the file named <strong>WUI_n15_45_2020lc030.tif</strong> can be interpreted as follows:</p> <ul> <li><strong>WUI</strong>: Wildland-Urban Interface dataset</li> <li><strong>n</strong>: Northern latitude</li> <li><strong>15</strong>: UTM zone 15</li> <li><strong>45</strong>: Starting latitude of 45 degrees</li> <li><strong>2020</strong>: Product year of 2020</li> <li><strong>lc</strong>: Land cover classification</li> <li><strong>030</strong>: Spatial resolution of 30 meters</li> </ul>
Graphics for implanted brain-computer interfaces for communication and sensorimotor control applications.
<p>Updated information from Nature Reviews Bioengineering, doi: 10.1038/s44222-024-00239-5. Current as of 27 September 2024. Please reference the original publication if using these graphics. As the field moves into the "Translational Era", the original publication reviews the clinical trials up to December 2023. </p>
Testing whole slide image for OpenPhi - Open Pathology Interface
<p>An anonymous whole slide image in Philips iSyntax format for running software tests on OpenPhi - Open Pathology Interface (https://zenodo.org/record/4680748#.YNnBxDqxXJU). See the repository (https://gitlab.com/BioimageInformaticsGroup/openphi/) for up to date information.</p>
A large EEG database with users' profile information for motor imagery Brain-Computer Interface research
<p><em><strong>Context </strong></em>: <br> We share a large database containing electroencephalographic signals from 87 human participants, with more than 20,800 trials in total representing about 70 hours of recording. It was collected during brain-computer interface (BCI) experiments and organized into 3 datasets (A, B, and C) that were all recorded following the same protocol: right and left hand motor imagery (MI) tasks during one single day session.<br> It includes the performance of the associated BCI users, detailed information about the demographics, personality and cognitive user’s profile, and the experimental instructions and codes (executed in the open-source platform OpenViBE).<br> Such database could prove useful for various studies, including but not limited to: 1) studying the relationships between BCI users' profiles and their BCI performances, 2) studying how EEG signals properties varies for different users' profiles and MI tasks, 3) using the large number of participants to design cross-user BCI machine learning algorithms or 4) incorporating users' profile information into the design of EEG signal classification algorithms.<br> <br> Sixty participants (Dataset A) performed the first experiment, designed in order to investigated the impact of experimenters' and users' gender on MI-BCI user training outcomes, i.e., users performance and experience, (Pillette & al). Twenty one participants (Dataset B) performed the second one, designed to examined the relationship between users' online performance (i.e., classification accuracy) and the characteristics of the chosen user-specific Most Discriminant Frequency Band (MDFB) (Benaroch & al). The only difference between the two experiments lies in the algorithm used to select the MDFB. Dataset C contains 6 additional participants who completed one of the two experiments described above. Physiological signals were measured using a g.USBAmp (g.tec, Austria), sampled at 512 Hz, and processed online using OpenViBE 2.1.0 (Dataset A) & OpenVIBE 2.2.0 (Dataset B). For Dataset C, participants C83 and C85 were collected with OpenViBE 2.1.0 and the remaining 4 participants with OpenViBE 2.2.0. Experiments were recorded at Inria Bordeaux sud-ouest, France.</p> <p><em><strong>Duration</strong> </em>: Each participant's folder is composed of approximately 48 minutes EEG recording. Meaning six 7-minutes runs and a 6-minutes baseline.</p> <p><br> <strong><em>Documents</em></strong><em> </em><br> <em>Instructions</em>: checklist read by experimenters during the experiments.<br> <em>Questionnaires</em>: the Mental Rotation test used, the translation of 4 questionnaires, notably the Demographic and Social information, the Pre and Post-session questionnaires, and the Index of Learning style. English and french version<br> <em>Performance</em>: The online OpenViBE BCI classification performances obtained by each participant are provided for each run, as well as answers to all questionnaires<br> <em>Scenarios/scripts</em> : set of OpenViBE scenarios used to perform each of the steps of the MI-BCI protocol, e.g., acquire training data, calibrate the classifier or run the online MI-BCI</p> <p><strong><em>Database </em></strong>: raw signals<br> Dataset A : N=60 participants<br> Dataset B : N=21 participants<br> Dataset C : N=6 participants<br> <br> The article that expained the database is available here:<br> Dreyer, P., Roc, A., Pillette, L. <em>et al.</em> A large EEG database with users’ profile information for motor imagery brain-computer interface research. <em>Sci Data</em> <strong>10</strong>, 580 (2023).<br> https://doi.org/10.1038/s41597-023-02445-z<br> </p>
Data for: Adaptive P300-Based Brain-Computer Interface for Attention Training
<p>The dataset contains EEG and behavioral data of 47 participants who completed 9 runs (i.e. copy-spelled 9 words) in a P300 speller task, as well as a random dot motion (RDM) task and questionnaires in a single experimental session. Details of the experimental protocol can be found here:</p> <p>Noble SC, Woods E, Ward T, Ringwood JV. “Adaptive P300-Based Brain-Computer Interface for Attention Training: Protocol for a Randomized Controlled Trial.” <em>JMIR Res Protoc</em> 2023, 12:e46135, doi: <a href="https://doi.org/10.2196/46135">10.2196/46135</a></p> <p>A journal article describing the results of the study can be found here:<br><br>Noble SC, Woods E, Ward T, Ringwood JV. “Accelerating P300-Based Neurofeedback Training for Attention Enhancement Using Iterative Learning Control: A Randomised Controlled Trial.” <em>J Neural Eng</em> 2024, 21(2), doi: <a href="https://doi.org/10.1088/1741-2552/ad2c9e" target="_blank" rel="noopener">10.1088/1741-2552/ad2c9e</a></p> <p>Please cite the results paper when using the data.</p> <p>Each participant folder contains:</p> <ul> <li>[xxx]-raw.[xxx] – unprocessed EEG signals (<strong>in</strong> <strong>mV</strong>) from 32 electrodes for all 9 P300 speller runs in Openvibe (.ov) and Matlab (.mat) file formats, see details of the runs below</li> <li>[xxx]-processed.[xxx] – contains 3 xDAWN components extracted by the xDAWN spatial filter according to the weights in “spatial-filter.cfg”</li> <li>classifier.cfg - LDA classifier weights</li> <li>spatial-filter.cfg - xDAWN spatial filter weights</li> <li>log.txt - contains the group assignment, start and end time of the experiment, and performance in the P300 speller and RDM tasks</li> </ul> <p>The file “Subject Information.csv” contains the age and gender of all participants.</p> <p>The file “Questionnaire scores.csv” contains the responses to the questionnaire described in the experimental protocol and the NASA Task Load Index (TLX) for all participants.</p> <p>The .ov and .mat files contain data from the following runs:</p> <table> <tbody> <tr> <th>Filename</th> <th>Word to be copy-spelled</th> <th>Number of flashes per row and column</th> <th>Feedback given to participant</th> </tr> </tbody> <tbody> <tr> <td>calibration-signal1</td> <td>THE</td> <td>12</td> <td>no</td> </tr> <tr> <td>calibration-signal2</td> <td>QUICK</td> <td>12</td> <td>no</td> </tr> <tr> <td>calibration-signals</td> <td>Concatenation of calibration-signal1 and calibration-signal2</td> </tr> <tr> <td>eval</td> <td>DOG</td> <td>12</td> <td>yes</td> </tr> <tr> <td>training-run-1</td> <td>BEAUTIFUL</td> <td>10</td> <td>yes</td> </tr> <tr> <td>training-run-2 to training-run-5</td> <td>BEAUTIFUL</td> <td>varying</td> <td>yes</td> </tr> <tr> <td>post-training-run</td> <td>DANCE</td> <td>12</td> <td>yes</td> </tr> </tbody> </table> <p> </p> <p>This research is supported by the Irish Research Council under project ID GOIPG/2020/692 and Science Foundation Ireland under grant number 12/RC/2289_P2.</p>
FIB-SEM tomograms of the steel-concrete interface of mortar and concrete specimens
<p>This datasets contains tomograms showing the steel-concrete interface of mortar (files starting with NCI) and concrete (files starting with CI) specimens. They were acquired by a FIB-SEM (focused ion beam-scanning electron microscope).</p> <p>There are five datasets:</p> <ol> <li>NCI-1, with a voxel size of 30 nm</li> <li>NCI-2, with a voxel size of 50 nm</li> <li>NCI-3, with a voxel size of 50 nm, consisting of four microscopy sessions (A, B, C, D)</li> <li>NCI-4, with a voxel size of 30 nm, consisting of four microscopy sessions (A, B, C, D)</li> <li>CI, with a voxel size of 30 nm, consisting of six microscopy sessions (A, B, C, D, E, F)</li> </ol> <p>More information can be found here: (TBD)</p> <p> </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.