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1,548 results for “Trajectories”
Datasets for fitting trajectories of elementary particles using deep learning
<p>Training and testing datasets of simulated elementary particles, used for fitting the trajectories of elementary particles in dense materials immersed in a magnetic field using deep learning. Once decompressed, the directory structure is the following:<br><br><em>datasets.zip:</em></p> <ul> <li><strong>training</strong> (1,762,327 particles in total): <ul> <li><strong>proton</strong>: 414,824 particles.</li> <li><strong>pion</strong>: 432,855 particles.</li> <li><strong>muon</strong>: 446,858 particles (muons and antimuons).</li> <li><strong>electron</strong>: 467,790 particles (electrons and positrons).</li> </ul> </li> <li><strong>testing</strong> (1,759,491): <ul> <li><strong>proton</strong>: 412,092 particles.</li> <li><strong>pion</strong>: 432,807 particles.</li> <li><strong>muon</strong>: 447,003 particles (muons and antimuons).</li> <li><strong>electron</strong>: 467,589 particles (electrons and positrons).</li> </ul> </li> </ul> <p><em>checkpoints.zip:</em></p> <ul> <li> <p><strong>checkpoint_rnn.pth</strong>: PyTorch weights of the RNN trained model.</p> </li> <li> <p><strong>checkpoint_transformer_encoder.pth</strong>: PyTorch weights of the RNN trained model.</p> </li> </ul>
Molecular Dynamics Trajectories for GPR6 Basal Activity (Volume 02)
<p>Molecular Dynamics Data for 10.1126/scisignal.ado8741 for publication at Science Signalling</p> <p>Barekatain M., Johansson L.C., Lam J.H. et al Structural Insights into the High Basal Activity and Inverse Agonism of the Orphan Receptor GPR6 Implicated in Parkinson's Disease, Sci Signal. 2024 Dec 3;17(865):eado8741. doi: 10.1126/scisignal.ado8741. Epub 2024 Dec 3.</p> <p><strong>Instruction</strong></p> <p>The compressed folder contains the PDB format file ("Topology") and the XTC format file (Trajectories). The timestep in this strided trajectory is 0.1 ns per frame.The compression was created with, for example,</p> <p>`tar czpvf - ./Nolig_Respawn00/ | split -d -b 5000M - Nolig_Respawn00a.tar.`</p> <p>They can be decompressed with </p> <p>`cat Nolig_Respawn00a.tar.* | tar xzpvf -`</p> <p> </p> <p> </p> <p><strong>Remarks</strong></p> <ul> <li>Trajectories in Respawn00 are only used as an initial sampling round. Trajectories in Respawn01 are the final sampling round for building Markov state model.</li> <li>There are in total 4 Volumes of Respawn01 data. Currently, the union of these volumes must be downloaded before decompression. See https://zenodo.org/communities/mdtrajectorygpr6/records?q=&l=list&p=1&s=10&sort=newest</li> <li>For trajectories <em>without</em> keyword 'fitted', the Periodic boundary condition (PBC) can be restored using VMD's standard pbc commands. For<em>`*</em>fitted<em>*</em>.xtc`, the protein has already been centered and the PBC is destroyed for the sake of visual inspection only. </li> </ul> <p> </p> <p>Please cite us if you find this data useful!</p>
Molecular Dynamics Trajectories for GPR6 Basal Activity (Volume 03)
<p>Molecular Dynamics Data for 10.1126/scisignal.ado8741 for publication at Science Signalling</p> <p>Barekatain M., Johansson L.C., Lam J.H. et al Structural Insights into the High Basal Activity and Inverse Agonism of the Orphan Receptor GPR6 Implicated in Parkinson's Disease, Sci Signal. 2024 Dec 3;17(865):eado8741. doi: 10.1126/scisignal.ado8741. Epub 2024 Dec 3.</p> <p><strong>Instruction</strong></p> <p>The compressed folder contains the PDB format file ("Topology") and the XTC format file (Trajectories). The timestep in this strided trajectory is 0.1 ns per frame.The compression was created with, for example,</p> <p>`tar czpvf - ./Nolig_Respawn00/ | split -d -b 5000M - Nolig_Respawn00a.tar.`</p> <p>They can be decompressed with </p> <p>`cat Nolig_Respawn00a.tar.* | tar xzpvf -`</p> <p> </p> <p><strong>Remarks</strong></p> <ul> <li>Trajectories in Respawn00 are only used as an initial sampling round. Trajectories in Respawn01 are the final sampling round for building Markov state model.</li> <li>There are in total 4 Volumes of Respawn01 data. Currently, the union of these volumes must be downloaded before decompression. See https://zenodo.org/communities/mdtrajectorygpr6/records?q=&l=list&p=1&s=10&sort=newest</li> <li>For trajectories <em>without</em> keyword 'fitted', the Periodic boundary condition (PBC) can be restored using VMD's standard pbc commands. For<em>`*</em>fitted<em>*</em>.xtc`, the protein has already been centered and the PBC is destroyed for the sake of visual inspection only. </li> </ul> <p> </p> <p>Please cite us if you find this data useful!</p>
Molecular Dynamics Trajectories for GPR6 Basal Activity (Volume 01)
<p>Molecular Dynamics Data for 10.1126/scisignal.ado8741 for publication at Science Signalling</p> <p>Barekatain M., Johansson L.C., Lam J.H. et al Structural Insights into the High Basal Activity and Inverse Agonism of the Orphan Receptor GPR6 Implicated in Parkinson's Disease, Sci Signal. 2024 Dec 3;17(865):eado8741. doi: 10.1126/scisignal.ado8741. Epub 2024 Dec 3.</p> <p><strong>Instruction</strong></p> <p>The compressed folder contains the PDB format file ("Topology") and the XTC format file (Trajectories). The timestep in this strided trajectory is 0.1 ns per frame.The compression was created with, for example,</p> <p>`tar czpvf - ./Nolig_Respawn00/ | split -d -b 5000M - Nolig_Respawn00a.tar.`</p> <p>They can be decompressed with </p> <p>`cat Nolig_Respawn00a.tar.* | tar xzpvf -`</p> <p> </p> <p><strong>Remarks</strong></p> <ul> <li>Trajectories in Respawn00 are only used as an initial sampling round. Trajectories in Respawn01 are the final sampling round for building Markov state model.</li> <li>There are in total 4 Volumes of Respawn01 data. Currently, the union of these volumes must be downloaded before decompression. See https://zenodo.org/communities/mdtrajectorygpr6/records?q=&l=list&p=1&s=10&sort=newest</li> <li>For trajectories <em>without</em> keyword 'fitted', the Periodic boundary condition (PBC) can be restored using VMD's standard pbc commands. For<em>`*</em>fitted<em>*</em>.xtc`, the protein has already been centered and the PBC is destroyed for the sake of visual inspection only. </li> </ul> <p>Please cite us if you find this data useful!</p>
Path recovery using parametric trajectories.
<p>This video is part of a PhD thesis and demonstrates the robot's path recovery mechanism for pre-established trajectories. It highlights how the system responds when the robot's position deviates significantly from the predetermined path.</p>
Numerical dataset for quantum trajectory simulations of a dissipative qubit under continuous measurement and feedback
<p>The data consist of numerical results obtained from quantum trajectory simulations of a dissipative qubit under continuous measurement and feedback.</p> <p>These data are used in the preprint titled <em>"Heat current and fluctuations between a dissipative qubit and a monitor under continuous measurement and feedback."</em></p> <p>We performed numerical simulations of the stochastic master equation using the supercomputer at ISSP.</p> <p>The contents are as follows:</p> <ul> <li>Numerical results for the correlation functions (<code>F*.dat</code>).</li> <li>Numerical results for the Fano factor and the power spectrum (<code>fano_factor*.dat</code>).</li> <li>Python code to calculate the Fano factor and the power spectrum from the correlation functions (<code>fano_factor_v6.py</code>).</li> <li>Figures illustrating the Fano factor and the power spectrum (<code>fig*.eps</code>).</li> </ul>
Molcular Dynamics Trajectories for Delta Opioid Receptor bound with C6-Quino
<p>Molecular Dynamics Data for publication at Nature Communications, doi.org/10.1038/s41467-025-57734-5.</p> <p>Balazs R. Varga1#, Sarah M. Bernhard1#, Amal El Daibani1#, Saheem Zaidi2#, Jordy H. Lam2, Jhoan Aguilar1, Kevin Appourchaux1, Antonina Nazarova2, Alexa Kouvelis1, Ryosuke Shinouchi3, Haylee R. Hammond3, Shainnel O. Eans3, Violetta Weinreb4, Elyssa B. Margolis5, Jonathan F. Fay6, Xi-Ping Huang4, Amynah Pradhan1, Vsevolod Katritch2*, Jay P. McLaughlin3*, Susruta Majumdar1* and Tao Che1*</p> <p>Structure-guided design of partial agonists at an opioid receptor</p> <p>This folder contains the PDB format file ("Topology") and the XTC format file (Trajectories). The timestep in this strided trajectory is 0.5 ns per frame. Periodic boundary condition (pbc) can be restored using VMD's standard pbc commands.</p> <p>Please cite us if you find this data useful!</p>
Molcular Dynamics Trajectories for Delta Opioid Receptor bound with C5-Quino
<p>Molecular Dynamics Data for publication at Nature Communications, doi.org/10.1038/s41467-025-57734-5.</p> <p>Balazs R. Varga1#, Sarah M. Bernhard1#, Amal El Daibani1#, Saheem Zaidi2#, Jordy H. Lam2, Jhoan Aguilar1, Kevin Appourchaux1, Antonina Nazarova2, Alexa Kouvelis1, Ryosuke Shinouchi3, Haylee R. Hammond3, Shainnel O. Eans3, Violetta Weinreb4, Elyssa B. Margolis5, Jonathan F. Fay6, Xi-Ping Huang4, Amynah Pradhan1, Vsevolod Katritch2*, Jay P. McLaughlin3*, Susruta Majumdar1* and Tao Che1*</p> <p>Structure-guided design of partial agonists at an opioid receptor</p> <p>This folder contains the PDB format file ("Topology") and the XTC format file (Trajectories). The timestep in this strided trajectory is 0.5 ns per frame. Periodic boundary condition (pbc) can be restored using VMD's standard pbc commands.</p> <p>Please cite us if you find this data useful!</p>
Edaphic heterogeneity and the evolutionary trajectory of Amazonian plant communities
<p>Fern community data.</p>
Fig. 3 in Neotropical Ichthyology: trajectory and bibliometric index (2003 - 2010)
Fig. 3. Co-authorship network among institutions.
Fig. 1 in Neotropical Ichthyology: trajectory and bibliometric index (2003 - 2010)
Fig. 1. Number of authors per article.
Southern Ocean kelp particle trajectories from Kerguelen, Macquarie Island and South Georgia
<p>Trajectory files from particle tracking simulations to model kelp drift pathways from three sub-Antarctic islands. More than 3.8 million virtual particles were released from a 2° latitude by 4° longitude box surrounding each of three sub-Antarctic source locations: Kerguelen Island, Macquarie Island and South Georgia. Particles were released daily throughout 2013 to sample seasonality and storm variability. Virtual particles were advected offline for three years from the time of release using the Connectivity Modelling System (Paris et al. 2013, <a href="https://github.com/beatrixparis/connectivity-modeling-system">https://github.com/beatrixparis/connectivity-modeling-system</a>). Particles were advected with the sum of daily snapshots of two-dimensional surface velocity data from an eddying ocean model (HYCOM; Bleck 2002) and wave-driven Stokes drift velocities from WAVEWATCH III (Rascle and Ardhuin 2013) over the period 2013-2016, as described in further detail in Fraser et al. 2018.</p> <p>Netcdf files contain particle trajectory latitude and longitude, and release date. Output has a temporal frequency of 1 day. Each release site has multiple zip files (e.g. Kerguelen_1.zip) which each contain multiple trajectory files. Each trajectory file contains a subset of the full ~ 4 million particles released at the site.</p> <p> </p> <p>Citation of associated paper: Fraser, C. I, Dutoit, L., Morrison, A. K., Miguel Pardo, L., Smith, S., Pearman, W., Parvizi, E., Waters, J., Macaya Horta, E. (2022). Southern Hemisphere coastal ecosystems are biologically connected by frequent, long-distance rafting events, submitted to <em>Current Biology</em>.</p> <p> </p> <p>References:</p> <p>Bleck, R. (2002). An oceanic general circulation model framed in hybrid isopycnic-Cartesian coordinates. <em>Ocean Modelling,</em> <strong>4</strong>, 55-88.</p> <p>Fraser, C. I. , A. K. Morrison, A. McC Hogg, E. C. Macaya, E. van Sebille, P. G. Ryan, A. Padovan, C. Jack, N. Valdivia, J. M. Waters (2018), Antarctica’s ecological isolation will be broken by storm-driven dispersal and warming, <em>Nat. Clim. Change</em>, <strong>8</strong>, 704-708.</p> <p>Paris, C. B., Helgers, J., van Sebille, E., & Srinivasan, A. (2013). Connectivity Modeling System: A probabilistic modeling tool for the multi-scale tracking of biotic and abiotic variability in the ocean. <em>Environmental Modelling and Software</em>, <strong>42</strong>, 47-54.</p> <p>Rascle, N., and Ardhuin, F. (2013). A global wave parameter database for geophysical applications. Part 2: Model validation with improved source term parameterization. <em>Ocean Modelling,</em> <strong>70</strong>, 174-188.</p>
Lagrangian Sequestration Efficiency Trajectories and Extracted Particle Metrics – 500m Y13 & Y14
<p>A dataset of Lagrangian trajectories used to estimate North Atlantic sequestration efficiency and extracted metrics for the re-entrained and sequestered particles. All variables have long names and units. These files have been used for the analysis in Baker et al. ‘Biological carbon pump sequestration efficiency in the North Atlantic: a leaky or a long-term sink?’ with further information about the methodology available in the paper. Due to the size of the datasets, each DOI only contains two files. This dataset contains the 500m particles releases for the years 2008 (Y13) and 2009 (Y14).</p>
Lagrangian Sequestration Efficiency Trajectories and Extracted Particle Metrics – 500m Y17 & Y18
<p>A dataset of Lagrangian trajectories used to estimate North Atlantic sequestration efficiency and extracted metrics for the re-entrained and sequestered particles. All variables have long names and units. These files have been used for the analysis in Baker et al. ‘Biological carbon pump sequestration efficiency in the North Atlantic: a leaky or a long-term sink?’ with further information about the methodology available in the paper. Due to the size of the datasets, each DOI only contains two files. This dataset contains the 500m particles releases for the years 2012 (Y17) and 2013 (Y18).</p>
Lagrangian Sequestration Efficiency Trajectories and Extracted Particle Metrics – 500m Y7 & Y8
<p>A dataset of Lagrangian trajectories used to estimate North Atlantic sequestration efficiency and extracted metrics for the re-entrained and sequestered particles. All variables have long names and units. These files have been used for the analysis in Baker et al. ‘Biological carbon pump sequestration efficiency in the North Atlantic: a leaky or a long-term sink?’ with further information about the methodology available in the paper. Due to the size of the datasets, each DOI only contains two files. This dataset contains the 500m particles releases for the years 2002 (Y7) and 2003 (Y8).</p>
Lagrangian Sequestration Efficiency Trajectories and Extracted Particle Metrics – 2000m Y17 & Y18
<p>A dataset of Lagrangian trajectories used to estimate North Atlantic sequestration efficiency and extracted metrics for the re-entrained and sequestered particles. All variables have long names and units. These files have been used for the analysis in Baker et al. ‘Biological carbon pump sequestration efficiency in the North Atlantic: a leaky or a long-term sink?’ with further information about the methodology available in the paper. Due to the size of the datasets, each DOI only contains two files. This dataset contains the 2000m particles releases for the years 2012 (Y17) and 2013 (Y18).</p>
Lagrangian Sequestration Efficiency Trajectories and Extracted Particle Metrics – 500m Y3 & Y4
<p>A dataset of Lagrangian trajectories used to estimate North Atlantic sequestration efficiency and extracted metrics for the re-entrained and sequestered particles. All variables have long names and units. These files have been used for the analysis in Baker et al. ‘Biological carbon pump sequestration efficiency in the North Atlantic: a leaky or a long-term sink?’ with further information about the methodology available in the paper. Due to the size of the datasets, each DOI only contains two files. This dataset contains the 500m particles releases for the years 1998 (Y3) and 1999 (Y4).</p>
Lagrangian Sequestration Efficiency Trajectories and Extracted Particle Metrics – 2000m Y15 & Y16
<p>A dataset of Lagrangian trajectories used to estimate North Atlantic sequestration efficiency and extracted metrics for the re-entrained and sequestered particles. All variables have long names and units. These files have been used for the analysis in Baker et al. ‘Biological carbon pump sequestration efficiency in the North Atlantic: a leaky or a long-term sink?’ with further information about the methodology available in the paper. Due to the size of the datasets, each DOI only contains two files. This dataset contains the 2000m particles releases for the years 2010 (Y15) and 2011 (Y16).</p>
Lagrangian Sequestration Efficiency Trajectories and Extracted Particle Metrics – 2000m Y13 & Y14
<p>A dataset of Lagrangian trajectories used to estimate North Atlantic sequestration efficiency and extracted metrics for the re-entrained and sequestered particles. All variables have long names and units. These files have been used for the analysis in Baker et al. ‘Biological carbon pump sequestration efficiency in the North Atlantic: a leaky or a long-term sink?’ with further information about the methodology available in the paper. Due to the size of the datasets, each DOI only contains two files. This dataset contains the 2000m particles releases for the years 2008 (Y13) and 2009 (Y14).</p>
Lagrangian Sequestration Efficiency Trajectories and Extracted Particle Metrics – 500m Y1 & Y2
<p>A dataset of Lagrangian trajectories used to estimate North Atlantic sequestration efficiency and extracted metrics for the re-entrained and sequestered particles. All variables have long names and units. These files have been used for the analysis in Baker et al. ‘Biological carbon pump sequestration efficiency in the North Atlantic: a leaky or a long-term sink?’ with further information about the methodology available in the paper. Due to the size of the datasets, each DOI only contains two files. This dataset contains the 500m particles releases for the years 1996 (Y1) and 1997 (Y2).</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.