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1,899 results for “Running”
Folded data for first three observing runs of Advanced LIGO and Advanced Virgo
<p>This release contains folded datasets from the first three observing runs of Advanced LIGO and Advanced Virgo detectors, which were recently used to perform the broadband directional search (https://dcc.ligo.org/LIGO-P2000500/public) and the all-sky-all-frequency directional search (https://dcc.ligo.org/LIGO-P2100292/public) for a persistent anisotropic gravitational wave background. The DataContent_Readme.md file provides information on the content of the files and how to read these datasets.</p>
Inputs for lake morphology in ISIMIP3 runs of the global lake sector
<p>Empirical evidence demonstrates that lakes and reservoirs are warming across the globe. Consequently, there is an increased need to project future changes in lake thermal structure and resulting changes in lake biogeochemistry in order to plan for the likely impacts. Previous studies of the impacts of climate change on lakes have often relied on a single model forced with limited scenario-driven projections of future climate for a relatively small number of lakes. As a result, our understanding of the effects of climate change on lakes is fragmentary, based on scattered studies using different data sources and modelling protocols, and mainly focused on individual lakes or lake regions. This has precluded identification of the main impacts of climate change on lakes at global and regional scales and has likely contributed to the lack of lake water quality considerations in policy-relevant documents, such as the Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC). The Lake Sector of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) was founded for simulating climate change impacts on lakes using an ensemble of lake models and climate change scenarios. The protocol prescribes lake simulations driven by climate forcing from gridded observations and different Earth system models under various Representative Greenhouse Gas Concentration Pathways, all consistently bias-corrected on a 0.5° × 0.5° global grid. The ISIMIP Lake Sector is the largest international effort to project future water temperature, thermal structure, and ice phenology of lakes at local and global scales and paves the way for future simulations of the impacts of climate change on water quality and biogeochemistry in lakes.</p> <p>For a comprehensive description of ISIMIP, sectors, and protocols please see https://www.isimip.org/</p> <p>In order to simulate the impacts of climate change on lakes worldwide, the ISIMIP3 Lake Sector protocol has defined a set of lakes to be modelled by all participating lake models, as well as the basic morphometry (i.e., hypsographic curves) information for each lake. This repository includes all calculations performed to obtain the final set of lake location and mosphometry inpput information for ISIMIP3 runs. For this, available datasets on global lake extension and morphometry were used first for selecting a set of representative lakes on Earth (~40000 lakes, one for each 0.5º pixel of the normalized input/putput grid for ISIMIP across sectors), and then morphological characteristics were assigned to each representative lake using a database on global lake morphology. The final set of files constitute the input data for lake morphology and location for ISIMIP3 Lake Sector runs, which are produced in netCDF format.<br> </p>
Reviewed Articles from the study "Running away to sea: a theoretical model of anthropogenic factors contribution for microplastics transportation from rivers to the ocean".
<p>Table containing the papers reviewed from the study "Running away to sea: a theoretical model of anthropogenic factors contribution for microplastics transportation from rivers to the ocean". </p>
Amyloid-beta 16-22 peptide monomer simulation (150 mM NaCl) with the CHARMM36m force field and Gromacs (Run 3)
<p>MD simulations of the Amyloid-beta 16-22 monomer at 150 mM NaCl concentration with CHARMM36m force field and Gromacs. This repository contains the third out of three independent runs. </p> <p>Files belong to the publication "<a href="https://doi.org/10.1021/acs.jcim.0c01063">https://doi.org/10.1021/acs.jcim.0c01063</a>"</p> <p>All the simulation parameters and force field files are uploaded into this repository. Simulations are done with Gromacs 2018.3</p> <p>Total simulation time is 500 ns. Frames are saved with 100 ps frequency. </p>
Model run output for Warren & Kite 202x
<p>Model run output for oxygen loss modeling and 40Ar degassing modeling for Warren & Kite 202x (in preparation). Full timeseries output for individual runs in zipped file. Summary data in individual csvs. File names with "nomelt" in the title refer to baseline model runs. File names containing "melt" are model runs including runaway greenhouse surface melting. Code for running full model and generating figures from publication available at: https://github.com/aowarren/Venus_O2</p>
Amyloid-beta 16-22 peptide dimer simulation (without salt) with the CHARMM-Drude force field and OpenMM (Run 2)
<p>MD simulations of the Amyloid-beta 16-22 dimer at 0 mM NaCl concentration with CHARMM-Drude force field and OpenMM. Initial structure is obtained from CHARMM-GUI. In the initial configuration, two amyloid-beta 16-22 monomers are not interacting. This repository contains the second out of three independent runs.</p> <p>All the simulation parameters and force field files are uploaded into this repository. Simulations are done with OpenMM v. 7.5.1.</p>
Amyloid-beta 16-22 peptide dimer simulation (without salt) with the CHARMM-Drude force field and OpenMM (Run 3)
<p>MD simulations of the Amyloid-beta 16-22 dimer at 0 mM NaCl concentration with CHARMM-Drude force field and OpenMM. Initial structure is obtained from CHARMM-GUI. In the initial configuration, two amyloid-beta 16-22 monomers are not interacting. This repository contains the third out of three independent runs.</p> <p>All the simulation parameters and force field files are uploaded into this repository. Simulations are done with OpenMM v. 7.5.1.</p>
Amyloid-beta 16-22 peptide dimer simulation (without salt) with the CHARMM-Drude force field and OpenMM (Run 1)
<p>MD simulations of the Amyloid-beta 16-22 dimer at 0 mM NaCl concentration with CHARMM-Drude force field and OpenMM. Initial structure is obtained from CHARMM-GUI. In the initial configuration, two amyloid-beta 16-22 monomers are not interacting. This repository contains the first out of three independent runs.</p> <p>All the simulation parameters and force field files are uploaded into this repository. Simulations are done with OpenMM v. 7.5.1.</p>
TrainRuns.jl: an Open-Source Tool for Running Time Estimation - Supplement Data
<p>This additional data contains the initial data and the calculated results for comparing FBS and TrainRuns.jl.</p> <p><strong>File description</strong></p> <ul> <li><em>local.yaml</em>: input parameters for the local train</li> <li><em>freight.yaml</em>: input parameters for the freight train</li> <li><em>running_path.yaml</em>: input parameters for the path</li> <li><em>freight_FBS.csv</em>: export of calculation from FBS for the freight train</li> <li><em>freight_TrainRuns.csv</em>: export of calculation from TrainRun.jl converted in FBS units for the freight train</li> <li><em>freight_diff.csv</em>: the calculated difference between FBS.csv and TrainRuns.csv for the freight train</li> <li><em>local_FBS.csv</em>: export of calculation from FBS for the local train</li> <li><em>local_TrainRuns.csv</em>: export of calculation from TrainRun.jl converted in FBS units for the local train</li> <li><em>local_diff.csv</em>: the calculated difference between FBS.csv and TrainRuns.csv for the local train</li> <li><em>running_path.csv</em>: converted running_path.yaml for displaying</li> <li><em>comparison.tex</em>: LaTeX code for the graph in comparison.pdf</li> </ul> <p><strong>Sources</strong></p> <p>The calculations in FBS were done with the file 'Ostsachsen_V220.railml'. FBS needs a commercial license, which can be purchased. License for 'Ostsachsen_V220.railml' is Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0).<br> The file 'Ostsachsen_V220.railml' can be found at:<br> https://www.railml.org/en/user/exampledata.html (last accessed 2022-06-06 with login) -> "Real world railway examples from professional tools" -> "East Saxony railway network by FBS" -> "Ostsachsen_V220.railml"</p> <p>Other sources are mentioned in the files.</p>
SESMG scenario-files of the study "Model-based run-time and memory reduction for a mixed-use multi-energy system model with high spatial resolution"
<p>This dataset contains model scenario-files belonging to the publication "Model-based run-time and memory reduction for a mixed-use multi-energy system model with high spatial resolution".</p> <p>The individual scenarios can be executed and evaluated with the "Spreadsheet Energy System Model Generator" (<a href="https://github.com/chrklemm/SESMG">SESMG</a>) <a href="https://github.com/chrklemm/SESMG/tree/v0.4.0rc1">v0.4.0rc1</a></p> <p>The respective file names indicate to which model run mentioned in the main study the scenario-files belong. For model runs for which no sepparate scenario file exists, the scenario "reference.xlsx" with adjusted SESMG settings was used.</p> <p> </p>
Data set of 1D model runs, CTRL and ICE runs, associated with "Underestimation of oceanic carbon uptake in the Arctic Ocean: Ice melt as predictor of the sea ice carbon pump"
<p>Dataset of one-dimensional runs for investigation on the sea ice carbon pump. Associated with Sect. 3.1 and 4.1 of manuscript "Underestimation of oceanic carbon uptake in the Arctic Ocean: Ice melt as predictor of the sea ice carbon pump".</p>
The nanoCUT&RUN technique visualizes telomeric chromatin in Drosophila
<p>Advances in genomic technology lead to a more focused pattern for the distribution of chromosomal proteins and a better understanding of their functions. The recent development of the CUT&RUN technique marks one of the important such advances. Here we develop a modified CUT&RUN technique that we termed nanoCUT&RUN, in which a high affinity nanobody to GFP is used to bring micrococcal nuclease to the binding sites of GFP-tagged chromatin proteins. Subsequent activation of the nuclease cleaves the chromatin, and sequencing of released DNA identifies binding sites. We show that nanoCUT&RUN efficiently produces high quality data for the TRL transcription factor in Drosophila embryos, and distinguishes binding sites specific between two TRL isoforms. We further show that nanoCUT&RUN dissects the distributions of the HipHop and HOAP telomere capping proteins, and uncovers unexpected binding of telomeric proteins at centromeres. nanoCUT&RUN can be readily applied to any system in which a chromatin protein of interest, or its isoforms, carries the GFP tag. </p>
Companion data of Multi-Phase Task-Based HPC Applications: Quickly Learning how to Run Fast
<p>This is the companion data repository for the paper entitled <strong>Multi-Phase Task-Based HPC Applications: Quickly Learning how to Run Fast</strong> by Lucas Leandro Nesi, Lucas Mello Schnorr, and Arnaud Legrand. The manuscript has been accepted for publication in the <a href="https://www.ipdps.org/ipdps2022/2022-organization.html">IPDPS 2022</a>.</p>
A Thirty Minute Nap Enhances Performance in Running Based Anaerobic Sprint Test During and After Ramadan Observance
<table> <tbody> <tr> <td> <p>Age</p> </td> <td> <p>height</p> </td> <td> <p> weight</p> </td> <td> <p>BMI</p> </td> <td> <p>MAX POWER</p> <p>(Watt)-DURING RAMADAN NO NAP</p> </td> <td> <p>MİNİMUM POWER</p> <p>(Watt)-DURING RAMADAN NO NAP</p> </td> <td> <p>AVERAGE POWER</p> <p>(Watt)-DURING RAMADAN NO NAP</p> </td> <td> <p>FATİGUE İNDEX</p> <p>(%)-DURING RAMADAN NO NAP</p> </td> <td> <p>MAX POWER</p> <p>(Watt)- AFTER RAMADAN NO NAP</p> </td> <td> <p>MİNİMUM POWER</p> <p>(Watt)- AFTER RAMADAN NO NAP</p> </td> <td> <p>AVERAGE POWER</p> <p>(Watt)- AFTER RAMADAN NO NAP</p> </td> <td> <p>FATİGUE İNDEX</p> <p>(%)- AFTER RAMADAN NO NAP</p> </td> <td> <p>MAX POWER</p> <p>(Watt)- DURING RAMADAN 30M NAP</p> </td> <td> <p>MİNİMUM POWER</p> <p>(Watt)- DURING RAMADAN 30M NAP</p> </td> <td> <p>AVERAGE POWER</p> <p>(Watt)- DURING RAMADAN 30M NAP</p> </td> <td> <p>FATİGUE İNDEX</p> <p>(%)- DURING RAMADAN 30M NAP</p> </td> <td> <p>MAX POWER</p> <p>(Watt)- AFTER RAMADAN 30M NAP</p> </td> <td> <p>MİNİMUM POWER</p> <p>(Watt)- AFTER RAMADAN 30M NAP</p> </td> <td> <p>AVERAGE POWER</p> <p>(Watt)- AFTER RAMADAN 30M NAP</p> </td> <td> <p>FATİGUE İNDEX</p> <p>(%)- AFTER RAMADAN 30M NAP</p> </td> </tr> <tr> <td> <p>24.00</p> </td> <td> <p>177.00</p> </td> <td> <p>89.00</p> </td> <td> <p>28.40</p> </td> <td> <p>388</p> </td> <td> <p>303</p> </td> <td> <p>334</p> </td> <td> <p>2.05</p> </td> <td> <p>432</p> </td> <td> <p>312</p> </td> <td> <p>365</p> </td> <td> <p>2.97</p> </td> <td> <p>453</p> </td> <td> <p>326</p> </td> <td> <p>392</p> </td> <td> <p>3.23</p> </td> <td> <p>466</p> </td> <td> <p>350</p> </td> <td> <p>411</p> </td> <td> <p>3.02</p> </td> </tr> <tr> <td> <p>23.00</p> </td> <td> <p>178.00</p> </td> <td> <p>70.00</p> </td> <td> <p>22.10</p> </td> <td> <p>513</p> </td> <td> <p>399</p> </td> <td> <p>469</p> </td> <td> <p>3.33</p> </td> <td> <p>539</p> </td> <td> <p>428</p> </td> <td> <p>485</p> </td> <td> <p>3.27</p> </td> <td> <p>560</p> </td> <td> <p>451</p> </td> <td> <p>513</p> </td> <td> <p>3.29</p> </td> <td> <p>606</p> </td> <td> <p>468</p> </td> <td> <p>535</p> </td> <td> <p>4.24</p> </td> </tr> <tr> <td> <p>21.00</p> </td> <td> <p>170.00</p> </td> <td> <p>76.00</p> </td> <td> <p>26.30</p> </td> <td> <p>563</p> </td> <td> <p>444</p> </td> <td> <p>506</p> </td> <td> <p>3.47</p> </td> <td> <p>585</p> </td> <td> <p>456</p> </td> <td> <p>516</p> </td> <td> <p>3.80</p> </td> <td> <p>605</p> </td> <td> <p>482</p> </td> <td> <p>533</p> </td> <td> <p>4.82</p> </td> <td> <p>640</p> </td> <td> <p>475</p> </td> <td> <p>538</p> </td> <td> <p>4.92</p> </td> </tr> <tr> <td> <p>22.00</p> </td> <td> <p>174.00</p> </td> <td> <p>67.00</p> </td> <td> <p>22.10</p> </td> <td> <p>711</p> </td> <td> <p>376</p> </td> <td> <p>507</p> </td> <td> <p>10.12</p> </td> <td> <p>733</p> </td> <td> <p>402</p> </td> <td> <p>538</p> </td> <td> <p>10.24</p> </td> <td> <p>791</p> </td> <td> <p>432</p> </td> <td> <p>575</p> </td> <td> <p>11.34</p> </td> <td> <p>866</p> </td> <td> <p>448</p> </td> <td> <p>613</p> </td> <td> <p>13.48</p> </td> </tr> <tr> <td> <p>20.00</p> </td> <td> <p>170.00</p> </td> <td> <p>72.00</p> </td> <td> <p>24.90</p> </td> <td> <p>673</p> </td> <td> <p>367</p> </td> <td> <p>503</p> </td> <td> <p>9.02</p> </td> <td> <p>697</p> </td> <td> <p>356</p> </td> <td> <p>534</p> </td> <td> <p>10.24</p> </td> <td> <p>741</p> </td> <td> <p>381</p> </td> <td> <p>570</p> </td> <td> <p>11.03</p> </td> <td> <p>808</p> </td> <td> <p>412</p> </td> <td> <p>608</p> </td> <td> <p>12.38</p> </td> </tr> <tr> <td> <p>19.00</p> </td> <td> <p>172.00</p> </td> <td> <p>62.00</p> </td> <td> <p>21.00</p> </td> <td> <p>566</p> </td> <td> <p>290</p> </td> <td> <p>434</p> </td> <td> <p>8.13</p> </td> <td> <p>553</p> </td> <td> <p>305</p> </td> <td> <p>445</p> </td> <td> <p>7.35</p> </td> <td> <p>579</p> </td> <td> <p>311</p> </td> <td> <p>468</p> </td> <td> <p>8.11</p> </td> <td> <p>604</p> </td> <td> <p>346</p> </td> <td> <p>502</p> </td> <td> <p>7.99</p> </td> </tr> <tr> <td> <p>21.00</p> </td> <td> <p>171.00</p> </td> <td> <p>73.00</p> </td> <td> <p>25.00</p> </td> <td> <p>769</p> </td> <td> <p>295</p> </td> <td> <p>513</p> </td> <td> <p>13.84</p> </td> <td> <p>789</p> </td> <td> <p>304</p> </td> <td> <p>527</p> </td> <td> <p>14.26</p> </td> <td> <p>824</p> </td> <td> <p>329</p> </td> <td> <p>563</p> </td> <td> <p>14.91</p> </td> <td> <p>878</p> </td> <td> <p>390</p> </td> <td> <p>610</p> </td> <td> <p>15.17</p> </td> </tr> <tr> <td> <p>19.00</p> </td> <td> <p>179.00</p> </td> <td> <p>76.00</p> </td> <td> <p>23.70</p> </td> <td> <p>908</p> </td> <td> <p>305</p> </td> <td> <p>551</p> </td> <td> <p>17.56</p> </td> <td> <p>944</p> </td> <td> <p>330</p> </td> <td> <p>574</p> </td> <td> <p>18.18</p> </td> <td> <p>982</p> </td> <td> <p>362</p> </td> <td> <p>600</p> </td> <td> <p>18.67</p> </td> <td> <p>1029</p> </td> <td> <p>394</p> </td> <td> <p>647</p> </td> <td> <p>19.57</p> </td> </tr> <tr> <td> <p>22.00</p> </td> <td> <p>174.00</p> </td> <td> <p>71.00</p> </td> <td> <p>23.50</p> </td> <td> <p>717</p> </td> <td> <p>358</p> </td> <td> <p>517</p> </td> <td> <p>10.72</p> </td> <td> <p>735</p> </td> <td> <p>385</p> </td> <td> <p>543</p> </td> <td> <p>10.63</p> </td> <td> <p>753</p> </td> <td> <p>399</p> </td> <td> <p>565</p> </td> <td> <p>10.93</p> </td> <td> <p>806</p> </td> <td> <p>434</p> </td> <td> <p>606</p> </td> <td> <p>11.74</p> </td> </tr> <tr> <td> <p>21.00</p> </td> <td> <p>183.00</p> </td> <td> <p>77.00</p> </td> <td> <p>23.00</p> </td> <td> <p>1001</p> </td> <td> <p>499</p> </td> <td> <p>675</p> </td> <td> <p>15.91</p> </td> <td> <p>1028</p> </td> <td> <p>540</p> </td> <td> <p>711</p> </td> <td> <p>15.78</p> </td> <td> <p>1035</p> </td> <td> <p>592</p> </td> <td> <p>760</p> </td> <td> <p>14.67</p> </td> <td> <p>1100</p> </td> <td> <p>606</p> </td> <td> <p>804</p> </td> <td> <p>16.66</p> </td> </tr> </tbody> </table> <p> </p>
CWL run of Somatic Variant Calling Workflow (CWLProv 0.5.0 Research Object)
<p>The somatic variant calling workflow included in this case study is designed by <a href="http://bcb.io/">Blue Collar Bioinformatics (bcbio)</a>, a community-driven initiative to develop best-practice pipelines for variant calling, RNA-seq and small RNA analysis workflows. According to the documentation, the goal of this project is to facilitate the automated analysis of high throughput data by making the resources quantifiable, analyzable, scalable, accessible and reproducible.</p> <p>All the underlying tools are containerized, facilitating software use in the workflow. The somatic variant calling workflow defined in CWL is available on GitHub and equipped with a well defined test dataset.</p> <p>This dataset folder is a CWLProv Research Object that captures the Common Workflow Language execution provenance, see <a href="https://w3id.org/cwl/prov/0.5.0">https://w3id.org/cwl/prov/0.5.0</a> or use <a href="https://pypi.org/project/cwlprov/">https://pypi.org/project/cwlprov/</a> to explore</p> <p><strong>Steps to reproduce</strong></p> <p>To build the research object again, use Python 3 on macOS. Built on:</p> <ul> <li>Processor 2.8GHz Intel Core i7</li> <li>Memory: 16GB</li> <li>OS: macOS High Sierra, Version 10.13.3</li> <li>Storage: 250GB</li> </ul> <p>To run the workflow:<br> </p> <pre><code class="language-bash">pip3 install cwltool==1.0.20180912090223 git clone https://github.com/FarahZKhan/bcbio_test_cwlprov cd bcbio_test_cwlprov/somatic/somatic-workflow/ cwltool --provenance somaticwf_0.5.0_mac main-somatic.cwl main-somatic-samples.json</code></pre> <p>To package the research object:<br> </p> <pre><code class="language-bash">zip -r somaticwf_0.5.0_mac.zip somaticwf_0.5.0_mac/ sha256sum somaticwf_0.5.0_mac.zip > somaticwf_0.5.0_mac.zip.sha256</code></pre> <p>The <a href="https://github.com/FarahZKhan/bcbio_test_cwlprov">cloned git repository</a> is a fork of <a href="https://github.com/bcbio/test_bcbio_cwl">https://github.com/bcbio/test_bcbio_cwl</a>. It was obtained using:</p> <pre><code class="language-bash">wget -O test_bcbio_cwl.tar.gz https://github.com/bcbio/test_bcbio_cwl/archive/master.tar.gz</code></pre> <p>The content is from an archived version from the documentation here: <a href="https://bcbio-nextgen.readthedocs.io/en/latest/contents/cwl.html#install-bcbio-vm-with-containers">https://bcbio-nextgen.readthedocs.io/en/latest/contents/cwl.html#install-bcbio-vm-with-containers</a></p>
Stables Testing run
shot with phantom4 pro + manula flight path to avoid wires. rendered with Pix4D with some point cleanup applied Source: Objaverse 1.0 / Sketchfab
OBR running reference data and software
<p>These files contain the data/software that was obtained/used in a research project using an<br> Optical Backscatter Reflectometer (OBR) to obtain strain data of curing epoxy.</p> <p>ATTENTION: The archive that contains the raw-measurement files is several GB big. It may take a while to open it.</p> <p>The results are to be published and the journal/article-title will be added as soon as possible.</p> <p>Please, see the README-Files for additional information.</p> <p>The software is published under the GNU General Public License, version 3.<br> For the data the stated license is valid.</p>
Data from: Influences of ski-runs, meadow management and climate on the occupancy of reptiles and amphibians in a high-altitude environment of Italy
<p>Alpine ecosystems harbour a rich and highly-specialized biodiversity which is particularly susceptible to anthropogenic disturbances such as habitat loss and fragmentation as well as to climate change. Combined with other forms of land-use conversion, construction and maintenance of ski resorts can have severe consequences on alpine biodiversity. In this study, we show how one amphibian and two reptile species, namely <em>Rana </em><em>temporaria</em>, <em>Zootoca</em><em> vivipara</em> and <em>Vipera</em><em> </em><em>berus</em>, respond to such impacts by means of a multi-season occupancy analysis. We found all three species both in and outside ski-runs, showing that these habitats do not necessarily preclude their occurrence. Contrarily, this is influenced more by microhabitat availability, such as ground vegetation, humid areas, and rock cover, rather than by macro-characteristics like elevation or habitat type. Moreover, we found a climatic influence on the year-to-year occupancy change of the species, with activity-months conditions being more relevant than overwintering ones. Our results demonstrate how, in the specific case of reptiles and amphibians, ski resorts do not necessarily limit species' occurrence and that a mild series of management actions might secure the species' persistence in the area.</p>
Molecular dynamics trajectories and portable binary run files for the self-assembly of heparin and amyloid-β(16-22) with the ProMPT forcefield
<h1>About this repository</h1> <p>Self-assembly simulations of heparin and amyloid-β(16-22) were performed at various numbers of peptides (<em>N_pep</em>), number of heparin molecules (<em>N_hep</em>), degrees of polymerization of heparin (<em>hep_dp</em>) and rigidity factors (<em>rig_f</em>) for heparin. This repository contains one folder per system, characterized by a combination of four system variables: <em>N_pep</em>, <em>N_hep</em>, <em>hep_dp</em> and <em>rig_f. </em></p> <p>The simulations were performed on the GROMACS 2019.4 molecular dynamics engine, with the ProMPT forcefield for coarse-grained molecular dynamics. Four independent trials: <em>trial_A</em>, <em>trial_B</em>, <em>trial_C</em> and <em>trial_D</em> were performed per system, each with different starting velocities. Each trial was run for 3000 ns.</p> <h1>Contents</h1> <h2><code>> heparin_abeta_trajectories.zip</code></h2> <p>The repository contains the zip file <code>heparin_abeta_trajectories.zip</code> with 13 folders named according to the convention,<code> "{<em>N_pep</em>}pep_{<em>N_hep</em>}hep_dp{<em>hep_dp</em>}_{<em>rig_f</em>}xRigid"</code>. For example, data for the system consisting of 16 peptides (<em>N_pep</em>), 1 heparin (<em>N_hep</em>), 18 monosaccharides in length (<em>hep_dp</em>) with a rigidity factor of 100 (<em>rig_f</em>) would be stored in the directory <code>16pep_1hep_dp18_100xRigid/</code>.</p> <p>If the system did not contain heparin, <em>N_hep, hep_dp</em> and <em>rig_f</em> were set to 0 by default. For example, data for the system consisting of 16 peptides (<em>N_pep</em>) and no heparin would be stored in the directory <code>16pep_0hep_dp0_0xRigid/</code>.</p> <p>A directory such as <code>16pep_1hep_dp18_100xRigid/</code> will have the following contents:</p> <ul> <li><code><strong>solute_only.ndx</strong></code><br>Contains an index group for solute molecules (peptides and/or heparin) only.</li> <li><code><strong>trial_A/</strong></code> <ul> <li><code><strong>md.tpr</strong></code> <br>Portable run file with which the current trajectory was generated. This file may be used to reproduce the trajectory as well.</li> <li><code><strong>solute_only.cluster_center.xtc </strong></code><br>A gromacs trajectory containing only the solute molecules (peptides and/or heparin), centered with the gromacs tool <em>gmx trjconv</em></li> <li><code><strong>solute_only.tpr </strong></code><br>A gromacs portable binary input file containing data for the solute molecules (peptides and/or heparin) only, generated with the <em>gmx convert-tpr</em> tool.<br>This file may be used during analysis of the solute_only.cluster_center.xtc trajectory.</li> </ul> </li> <li><code><strong>trial_B/</strong></code><br>contents same as <code><strong>trial_A/</strong></code></li> <li><strong><code>trial_C/</code><br></strong>contents same as <code><strong>trial_A/</strong></code></li> <li><strong><code>trial_D/</code><br></strong>contents same as <code><strong>trial_A/</strong></code></li> </ul> <h2><code>> psf_files_for_VMD_visualization.zip</code></h2> <p>This zip file contains thirteen .psf files, one per system, that can be used in accord with <code>solute_only.cluster_center.xtc </code>files to visualize trajectories on the Visual Molecular Dynamics (VMD) software.</p> <p><strong>Please seek out the associated publication for essential context on these trajectories. </strong></p> <p><strong>To access the source files with which these simulations were set-up, and a brief tutorial, see: </strong><a href="https://github.com/suhasgotla/heparin_amyloid_self-assembly">https://github.com/suhasgotla/heparin_amyloid_self-assembly</a></p>
Lower-body Inertial Sensor and Optical Motion Capture Recordings of Walking and Running
<pre>This dataset contains lower-body inertial sensor (IMU) data and optical motion capture (OMC) data from ten participants walking and running overground at different speeds. <br><br><br>The data recording is described in this publication: Dorschky, E., Nitschke, M., Seifer, A. K., van den Bogert, A. J., & Eskofier, B. M. (2019). Estimation of gait kinematics and kinetics from inertial sensor data using optimal control of musculoskeletal models. Journal of biomechanics, 95, 109278. (https://doi.org/10.1016/j.jbiomech.2019.07.022)<br><br>Please look at the README.txt file for further information.</pre>
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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)
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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.