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887 results for “tunnels”
Data for paper "Automated Structure Discovery for Scanning Tunneling Microscopy"
<p>Contents of the dataset:</p> <ul> <li>band.h5 -- keys are molecule indices, each molecule has the following keys:<br> <ul> <li>eigs: KS eigenvalues for each state</li> <li>coefs: KS eigenvectors for each basis set</li> <li>xyz: atomic positions</li> <li>Z: atomic species</li> <li>qs: mulliken point charges</li> </ul> </li> <li>rotations_210611.pickle -- keys train/val/test <ul> <li>Each set is a dict containing id -- rotation pairs</li> <li>rotations are 3x3 numpy arrays</li> </ul> </li> <li>disks.pt -- a pretrained model for Atomic Disks predictions</li> </ul>
Reinforcing Tunnel Network Exploration in Proteins using Gaussian Accelerated Molecular Dynamics (parameters, trajectories)
<ul> <li>00_LinB-Wt.tar.gz - LinB-Wt simulation files:</li> </ul> <p> 1.cMD(Classical MD simulation):<br> <br> 1. Stripped parameter file *.parm7.<br> 2. Simulation file after removing ions and water and merging last 5us of production run in amber *.nc format.<br> <br> <br> 2.GaMD(Gaussian Accelerated MD simulation): <br> <br> 1. Stripped parameter file *.parm7.<br> 2. Simulation file after removing ions and water and merging last 5us of production run in amber *.nc format.</p> <ul> <li>01_LinB-Open.tar.gz - LinB Open mutant simulation files:</li> </ul> <p> 1.cMD(Classical MD simulation):<br> <br> 1. Stripped parameter file *.parm7. <br> 2. Simulation file after removing ions and water and merging last 5us of production run in amber *.nc format.<br> <br> <br> 2.GaMD(Gaussian Accelerated MD simulation): <br> 1. Stripped parameter file *.parm7.<br> 2. Simulation file after removing ions and water and merging last 5us of production run in amber *.nc format.</p> <ul> <li>02_LinB-Closed.tar.gz - LinB Closed mutant simulation files:</li> </ul> <p> 1.cMD(Classical MD simulation):<br> 1. Stripped parameter file *.parm7.<br> 2. Simulation file after removing ions and water and merging last 5us of production run in amber *.nc format.<br> <br> <br> 2.GaMD(Gaussian Accelerated MD simulation): <br> 1. Stripped parameter file *.parm7.<br> 2. Simulation file after removing ions and water and merging last 5us of production run in amber *.nc format.</p>
High-power in-phase and anti-phase mode emission from linear arrays of resonant-tunneling-diode oscillators in the 0.4-to-0.8-THz frequency range - data
<p>Experimental and simulation data from the paper "High-power in-phase and anti-phase mode emission from linear arrays of resonant-tunneling-diode oscillators in the 0.4-to-0.8-THz frequency range".</p>
Wind tunnel experiments on wind turbine wakes in yaw
<p>This data set contains Laser Doppler Anemometer measurements in the wake behind two different model wind turbines, recorded in a wind tunnel campain at the NTNU in Trondheim. Full plane wake data were recorded, with a focus on the effect of yaw misalignment and inflow turbulence. Please refer to the documentation document for more information.</p>
Skeletomuscular adaptations of head and legs of Melissotarsus ants for tunnelling through living wood
<p>Micro-CT raw datasets (in DICOM format) used in "Skeletomuscular adaptations of head and legs of Melissotarsus ants for tunnelling through living wood"</p> <p> </p> <p><strong>Abstract:</strong></p> <p>Background: While thousands of ant species are arboreal, very few are able to chew and tunnel through living wood. Ants of the genus <em>Melissotarsus</em> (subfamily Myrmicinae) inhabit tunnel systems excavated under the bark of living trees, where they keep large numbers of symbiotic armoured scale insects (family Diaspididae). Construction of these tunnels by chewing through healthy wood requires tremendous power, but the adaptations that give <em>Melissotarsus</em> these abilities are unclear. Here, we investigate the morphology of the musculoskeletal system of <em>Melissotarsus</em> using histology, scanning electron microscopy, X-ray spectrometry, X-ray microcomputed tomography (micro-CT), and 3D modelling.</p> <p>Results: Both the head and legs of <em>Melissotarsus</em> workers contain novel skeletomuscular adaptations to increase their ability to tunnel through living wood. The head is greatly enlarged dorsoventrally, with large mandibular closer muscles occupying most of the dorsal half of the head cavity, while ventrally-located opener muscles are also exceptionally large. This differs from the strong closing: opening asymmetry typical of most mandibulated animals, where closing the mandibles requires more force than opening. Furthermore, the mandibles are short and cone-shaped with a wide articulatory base that concentrates the force generated by the muscles towards the tips. The increased distance between the axis of mandibular rotation and the points of muscle insertion provides a mechanical advantage that amplifies the force from the closer and opener muscles. We suggest that the uncommonly strong opening action is required to move away crushed plant tissues during tunnelling and allow a steady forward motion. X-ray spectrometry showed that the tip of the mandibles is reinforced with zinc. Workers in this genus have aberrant legs, including mid- and hindlegs with hypertrophied coxae and stout basitarsi equipped with peg-like setae, and midleg femura pointed upward and close to the body. This unusual design famously prevents them from standing and walking on a normal two-dimensional surface. We reinterpret these unique traits as modifications to brace the body during tunnelling rather than locomotion per se.</p> <p>Conclusions: <em>Melissotarsus</em> represents an extraordinary case study of how the adaptation to – and indeed engineering of – a novel ecological niche can lead to the evolutionary redesign of core biomechanical systems.</p>
Magneto-Seebeck Tunneling on the Atomic Scale
<p>Data files for the figures in the publication "Magneto-Seebeck Tunneling on the Atomic Scale".</p>
Supplemental Material to Journal Article "Tunneling Crack Initiation in Trailing-Edge Bond Lines of Wind-Turbine Blades"
<p>This set supplements the figure data to the article "Tunneling Crack Initiation in Trailing-Edge Bond Lines of Wind-Turbine Blades", DOI: <a href="http://doi.org/10.2514/1.J058179">10.2514/1.J058179</a>.</p>
Dataset for Wenz et al. Phys. Rev. B 99, 201409(R) (2019), "Quantum dot state initialization by control of tunneling rates"
<p>Collection of the datasets used to generate the figures in the following journal paper:</p> <p>"Quantum dot state initialization by control of tunneling rates"<br> Tobias Wenz, Jevgeny Klochan, Frank Hohls, Thomas Gerster, Vyacheslavs Kashcheyevs, and Hans W. Schumacher<br> PHYSICAL REVIEW B 99, 201409(R) (2019)</p> <p>DOI: 10.1103/PhysRevB.99.201409</p>
Figure 3 in Do disturbed environments affect density of the tunnel-web spider Acanthogonatus centralis (Mygalomorphae: Nemesiidae) from native grasslands in Argentina?
Figure 3. Relationship between the density of potential shelters and the density of spiders.
Additional information for Acidification from microbial oxidation of N, S, Fe and Mn as potential key mechanism for sprayed concrete deterioration in a subsea road tunnel
<p>Here we include additional files for Karacic <em>et al</em>. <strong>Acidification from microbial oxidation of N, S, Fe and Mn as potential key mechanism for sprayed concrete deterioration in a subsea road tunnel</strong></p> <p>Metagenome reads and MAGs are avaliable in the NCBI BioProject PRJNA755678</p> <p>The following files are included here</p> <ul> <li><strong>MAGs_summary_Oslofjord.tsv</strong>: Information about the Oslofjord MAGs (GTDB R207 taxonomy, relative abundance, Completness, Contamination, NCBI accessions)</li> <li><strong>gtdbtk.ar53.summary.</strong> Output from GTDB-Tk v2.1.0 with GTDB R207_v2. Archaeal MAGs</li> <li><strong>gtdbtk.bac120.summary.tsv</strong>. Output from GTDB-Tk v2.1.0 with GTDB R207_v2. Bacterial MAGs</li> <li><strong>FeGenie-geneSummary.csv</strong>: Output from FeGenie</li> <li><strong>Annotations_1.tsv</strong> : Output from DRAM (215 MAGs)</li> <li><strong>Annotations_2.tsv</strong>: Output from DRAM (186 MAGs)</li> <li><strong>OFTMs_DRAM_genes.DiSCo.filtered.txt</strong>. Output from DiSCo</li> <li><strong>code_R.zip</strong>: R-code and additional files used for making MAGs figures</li> </ul> <p>This is version 2 of the dataset and it replaces version 1 (https://doi.org/10.5281/zenodo.5292093) </p>
Figure 43. Cycloid teleost scale, hypotype UCMP 270032 in Miocene marine macropaleontology of the fourth bore Caldecott Tunnel excavation, Berkeley Hills, Oakland, California, USA
Figure 43. Cycloid teleost scale, hypotype UCMP 270032.
Figure 42 in Miocene marine macropaleontology of the fourth bore Caldecott Tunnel excavation, Berkeley Hills, Oakland, California, USA
Figure 42. Thunnus vertebra, hypotype, UCMP 218506.
Figure 41. Carcharhinus obscurus tooth, hypotype UCMP 218505 in Miocene marine macropaleontology of the fourth bore Caldecott Tunnel excavation, Berkeley Hills, Oakland, California, USA
Figure 41. Carcharhinus obscurus tooth, hypotype UCMP 218505.
Figure 40 in Miocene marine macropaleontology of the fourth bore Caldecott Tunnel excavation, Berkeley Hills, Oakland, California, USA
Figure 40. Indeterminate barnacle. Hypotype from UCMP locality IP13002, UCMP 218825.
Figure 36 in Miocene marine macropaleontology of the fourth bore Caldecott Tunnel excavation, Berkeley Hills, Oakland, California, USA
Figure 36. Odontocete premaxilla, lateral view of hypotype, UCMP 269020.
Figure 33. Indeterminate Actinopterygii fragments. Hypotype, UCMP 218647 in Miocene marine macropaleontology of the fourth bore Caldecott Tunnel excavation, Berkeley Hills, Oakland, California, USA
Figure 33. Indeterminate Actinopterygii fragments. Hypotype, UCMP 218647.
Figure 34 in Miocene marine macropaleontology of the fourth bore Caldecott Tunnel excavation, Berkeley Hills, Oakland, California, USA
Figure 34. Mammalian rib fragment, hypotype, UCMP 270043.
Figure 32 in Miocene marine macropaleontology of the fourth bore Caldecott Tunnel excavation, Berkeley Hills, Oakland, California, USA
Figure 32. Balanidae indeterminate. Hypotype from UCMP locality IP13001, UCMP 218826.
Figure 31 in Miocene marine macropaleontology of the fourth bore Caldecott Tunnel excavation, Berkeley Hills, Oakland, California, USA
Figure 31. Indeterminate crinoid stem. Hypotype from UCMP locality IP13001, UCMP 412360.
Impact of water models on structure and dynamics of enzyme tunnels
<ul> <li>1-initial_topologies_coordinates.tar.gz <ul> <li>primary input coordinates and parameter-topology files of all initial systems (LinBwt, LinB32, and Linb86 variants of haloalkane dehalogenase) in OPC and TIP3P water models</li> <li>prepared with the tleap module of AMBER18 package</li> <li>parm7 and crd formatted</li> </ul> </li> <li>2-cap_domain_gate_distances.tar.gz <ul> <li>datasets with minimum distance calculation between Asp146 and Leu176</li> <li>calculated by CPPTRAJ module of AMBER 18 for each performed simulation</li> <li>plain text formatted</li> </ul> </li> <li>3-protein_trajectories-linbwt.tar.gz <ul> <li>three replicated 400 ns (20,000 frames, i.e., every second frame) dry production phase trajectories of LinBwt in OPC and Tip3P water models with corresponding parameter-topology files</li> <li>produced by pmemd.cuda module of AMBER 18</li> <li>netcdf and parm7 formatted</li> </ul> </li> <li>3-protein_trajectories-linb32-closed.tar.gz <ul> <li>two replicated 400 ns (20,000 frames, i.e., every second frame) dry production phase trajectories of closed state LinB32 in OPC and Tip3P water models with corresponding parameter-topology files</li> <li>produced by pmemd.cuda module of AMBER 18</li> <li>netcdf and parm7 formatted</li> </ul> </li> <li>3-protein_trajectories-linb32-open.tar.gz <ul> <li>two replicated 400 ns (20,000 frames, i.e., every second frame) dry production phase trajectories of open state LinB32 in OPC and Tip3P water models with corresponding parameter-topology files</li> <li>produced by pmemd.cuda module of AMBER 18</li> <li>netcdf and parm7 formatted</li> </ul> </li> <li>3-protein_trajectories-linb86-closed.tar.gz <ul> <li> two replicated 400 ns (20,000 frames, i.e., every second frame) dry production phase trajectories of closed state LinB86 in OPC and Tip3P water models with corresponding parameter-topology files</li> <li>produced by pmemd.cuda module of AMBER 18</li> <li>netcdf and parm7 formatted</li> </ul> </li> <li>3-protein_trajectories-linb86-open.tar.gz <ul> <li>two replicated 400 ns (20,000 frames, i.e., every second frame) dry production phase trajectories of open state LinB86 in OPC and Tip3P water models with corresponding parameter-topology files</li> <li>produced by pmemd.cuda module of AMBER 18</li> <li>netcdf and parm7 formatted</li> </ul> </li> <li>4-basic_analyses.tar.gz <ul> <li>datasets on RMSF, RMSD, RoG, and RDF from the CPPTRAJ module of AMBER 18</li> <li>for selected replicas 3x LinBwt, 2x LinB32-closed, 2x LinB32-open, 2x LinB86-closed and 2x LinB86-open</li> <li>plain text and PDB formatted</li> </ul> </li> <li>5-caver_analyses.tar.gz <ul> <li>results of tunnel analyses for two replicas of open & closed state each for LinB32 & LinB86 in OPC and TIP3P, and three replicas of LinBWT in OPC and TIP3P</li> <li>generated by CAVER 3.0 using "Divide-and-conquer approach" (MethodsX, 10, 2023, 101968)</li> <li>comprising csv and pdb formatted: tunnel_profiles.csv and bottlenecks.csv, stripped_system.10001.pdb, v_origins.pdb</li> <li>For this and following analyses, the names of the trajectories were modified as follows: <ul> <li>linbwt_opc1_2 = md1_opc_linbwt; linbwt_opc2_2 = md2_opc_linbwt; linbwt_opc3_2 = md3_opc_linbwt;</li> <li>linbwt_tip3p1_2 = md1_tip3p_linbwt; linbwt_tip3p2_2 = md2_tip3p_linbwt; linbwt_tip3p3_2 = md3_tip3p_linbwt;</li> <li>linb32-closed_opc1_2 = md1_closed_opc_linb32; linb32-closed_opc2_2 = md2_closed_opc_linb32;</li> <li>linb32-open_opc1_2 = md1_open_opc_linb32; linb32-open_opc2_2 = md2_open_opc_linb32;</li> <li>linb32-closed_tip3p1_2 = md1_closed_tip3p_linb32; linb32-closed_tip3p2_2 = md2_closed_tip3p_linb32;</li> <li>linb32-open_tip3p1_2 = md1_open_tip3p_linb32; linb32-open_tip3p2_2 = md2_open_tip3p_linb32;</li> <li>linb86-closed_opc1_2 = md1_closed_opc_linb86; linb86-closed_opc2_2 = md2_closed_opc_linb86;</li> <li>linb86-open_opc1_2 = md1_open_opc_linb86; linb86-open_opc2_2 = md2_open_opc_linb86;</li> <li>linb86-closed_tip3p1_2 = md1_closed_tip3p_linb86; linb86-closed_tip3p2_2 = md2_closed_tip3p_linb86;</li> <li>linb86-open_tip3p1_2 = md1_open_tip3p_linb86; linb86-open_tip3p2_2 = md2_open_tip3p_linb86.</li> </ul> </li> </ul> </li> <li>6-transport_tools_analyses.tar.gz <ul> <li>results of comparative analyses for all simulations generated in 5-caver_analyses.tar.gz</li> <li>generated by TransportTools 0.9.3</li> <li>comprising csv, pdb, plain text and py formatted: configuration file (config_TT.ini), tunnel_profiles (data folder) for all filtered tunnels and bottlenecks (data folder) for all filtered tunnels, statistics (statistics folder) and visualization (visualization folder)<br> </li> </ul> </li> </ul> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
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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.