Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
10,561
datasets available to search
ShareScore release 0.9.0
Dataset results
10,561 results for “mechanics”
G-Protein Coupled Receptor-Ligand Dissociation Rates and Mechanisms from tauRAMD Simulations
<p>Data and Python scripts used for generation and analysis of RAMD dissociation trajectories for several GPCR complexes (including example showing generation of the Protein-Ligand Interaction Fingerprints, IFP, for several representative RAMD trajectories),</p> <p>reported in the manuscript</p> <p>"G-Protein Coupled Receptor-Ligand Dissociation Rates and Mechanisms from tRAMD Simulations" "G-Protein Coupled Receptor-Ligand Dissociation Rates and Mechanisms from tauRAMD Simulations"</p> <p>by Daria B. Kokh, Rebecca C. Wade</p> <p>submitted to the Journal of Chemical Theory and Computation</p> <p> </p> <p>1. <strong>README.txt </strong>- instruction for script usage</p> <p>2. <strong>PDBs.zip</strong> - PDB structures of complexes in water box used in the analysis, ligand PDB and mol2 structures</p> <p>3. <strong>tauRAMD_v2.py </strong>- Python sctipt for estimation relative residence times from Gromacs-RAMD output </p> <p>4. <strong>IFP_preprocess_Gromacs.py</strong> and <strong>IFP_SL-B2AR-WB-EX.py - </strong>Python scripts for preprocessing of RAMD trajectories and generation of IFPs</p> <p>5. <strong>Scripts.zip</strong> - additional python functions </p> <p>6. <strong>IXO-CHL.zip, IXO-ALO-CHL.zip, ACh-CHL.zip, b2AR.zip</strong> - Protein-Ligand Interaction Fingerprints (PL IFPs) generated from RAMD trajectories for <em>mAChR M2 with iperoxo</em>, <em>mAChR M2 </em><em> with iperoxo and PAM, mAChR M2 with ACh, and </em>β<em>2AR with alprenolol </em><em>.</em> </p> <p>7. <strong>Topology.zip</strong> - Gromacs topology, index.ndx, and coordinate gro files for all four systems</p> <p>8. <strong>Example_b2AR-alprenolol.zip </strong>- a set of data for a test example showing how IFP can be generated from RAMD trajectories (including several representative trajectories)</p> <p>9. <strong>Example_b2AR-alprenolol.tar </strong>- almost the same set of data as above (compressed in Windows) but for Linux users. The only difference between tar and zip archive: a short equilibration trajectory that is missing in the zip set but is included in the tar archive.</p> <p>10.<strong> Gromacs-IFP-GPCR.ipynb</strong> - Jupyter Notebook for analysis of trajectories using generated IFP data</p> <p>11. <strong>Auxi-Plots-GPCR.ipynb - </strong>Jupyter Notebook for generation additional plots from the paper</p> <p>12. <strong>Waters.zip</strong> - number of water molecules in the binding pocket in dissociation trajectories of the <em> </em>β<em>2AR - alprenolol system</em></p> <p>13. <strong>GPCR.yml</strong> - JN environment file</p> <p> </p>
A compilation of experimental data on the mechanical properties and microstructural features of Ti-alloys
<p>A compilation of mechanical properties of 282 distinct multicomponent Ti-based alloys. The majority of the data was published in high-quality journals after 2010 (≈84%) and concerns alloys produced via an ingot metallurgy route, followed by solubilization and water quench (≈58%), considered a standard condition for β-Ti alloys. The dataset includes the chemical composition (in at.%), phase constituents, Young modulus, hardness, yield strength, ultimate strength, and elongation, among other relevant features. The authors established a blind-review procedure for 1/3 of the dataset to mitigate human error during data extraction.</p> <p>Files:</p> <ul> <li><strong>dax-ti-static.csv</strong>: static version of the dataset; can be easily imported into your preferred data processing software.</li> <li><strong>table1-static.md</strong>: detailed description of properties and additional fields included in the database; requires *markdown extra* syntax;</li> <li><strong>utils.py</strong>: a helper script to load and filter desired entries; dependencies are matplotlib (3.4.3+), numpy (1.21.2+), and pymatgen (2022.0.16+).</li> </ul> <p>For more information, please visit <strong>https://gitlab.com/comari/dax-ti</strong>.</p>
Supplementary codes and datasets for "Modular-topology optimization of structures and mechanisms with free material design and clustering"
<p>This repository supports Tyburec, M., Doškář, M., Zeman, J., & Kružík, M. (2022). Modular-topology optimization of structures and mechanisms with free material design and clustering. <em>Computer Methods in Applied Mechanics and Engineering</em>, <em>395</em>, 114977. <a href="https://doi.org/10.1016/j.cma.2022.114977">https://doi.org/10.1016/j.cma.2022.114977</a> (first published as preprint <a href="http://arxiv.org/abs/2111.10439">2111.10439</a> at arXiv.org).</p> <p>This repository contains:</p> <ol> <li>MATLAB source codes for <em>(modular) free material optimisation</em> and <em>hierarchical stiffness clustering</em> (folder <code>./mFMO/</code>)</li> <li>C++ source codes for <em>modular topology optimization</em> (folder <code>./MTO/</code>)</li> <li>Input/output data of the test suite (folder <code>./data/</code>)</li> </ol> <p><strong>1. Data flow</strong></p> <p>The test suite considered in the manuscript covers 4 problems:</p> <ol> <li>Messerschmitt-Bölkow-Blohm beam (labelled as <code>mbb</code>)</li> <li>Inverter compliant mechanism (labelled as <code>inv</code>)</li> <li>Gripper compliant mechanism (labelled as <code>grip</code>)</li> <li>Reusable design of both compliant mechanisms (labelled as <code>invgrip</code>)</li> </ol> <p>Each problem in the dataset is stored within a separate subfolder named according to the labels mentioned above. The final level of subdirectories <code>{X}color</code> comprises of the results for problems with <code>X</code> denoting the number of edge codes considered for each edge direction during the clustering (<code>0color</code> stands for a non-modular design and <code>1color</code> represents the design based on Periodic Unit Cell).</p> <p>Each of the folders contains outputs of the modular free material optimisation in the following form:</p> <ul> <li><code>{label}{X}.mat</code></li> <li><code>{label}{X}.til</code></li> <li><code>{label}{X}.tset</code></li> <li><code>{label}{X}guess.mat</code></li> </ul> <p>Files <code>*.til</code>, <code>*.tset</code>, and <code>*guess.mat</code> are then converted into a JSON input file for the modular topology optimization code with generator scripts which can be found in <code>./MTO/scripts</code> folder. Note that each of the problems in the test suite has its own generator script <code>generate_modular_problem_{MBB,inverter,gripper,inverterAndGripper}.mat</code>. The generator scripts make a directory named according to the key <code>MTO_{n}_kernelSensitivity</code>, where <code>n</code> denotes the resolution of each module (i.e. the number of nodes along one direction). The directory also contains the outputs of the modular topology optimisation in the form of the initial and the final state of the optimization in <code>VTK</code> files and visualisation of the final state in <code>SVG</code> files. The log file <code>log.txt</code> stores the optimized objective and progress of the value along with stopping criteria quantities during iterations.</p> <p><strong>2. Running codes</strong></p> <p><strong>2.1 Modular free material optimisation</strong></p> <p>MATLAB scripts and functions for (modular) Free Material Optimization (FMO) are contained in the <code>mFMO</code> data folder. The codes have been tested with MATLAB R2019b. To run the codes the user is required to install the <a href="http://www.penopt.com">PENNON optimizer</a>. A free academic license is provided by its authors on request.</p> <p>Input files for individual problems are defined in the <code>mFMO/problems</code> folder and are launched with the <code>runproblem(problemName, numClusters)</code>, where <code>problemName</code> refers to the file in the <code>mFMO/problems</code> folder without the file extension and <code>numClusters</code> denotes the maximum number of color codes in Wang tiling formalism.</p> <p>If successful, the optimization produces output files in <code>mFMO/fmo_fig/{label}/{X}colors/{T}/</code>:</p> <ul> <li><code>{label}{X}.mat</code> (contains clustering and tiling information)</li> <li><code>{label}{X}_tmp.mat</code> (contains results of non-modular FMO)</li> <li><code>{label}{X}.til</code> (the assembly plan)</li> <li><code>{label}{X}.tset</code> (Wang tile set)</li> <li><code>{label}{X}guess.mat</code> (guess for TO)</li> </ul> <p>where <code>T</code> is the optimization time stamp.</p> <p><strong>2.2 Modular topology optimisation</strong></p> <p>All results were obtained with version <code>v1.1.2</code>, which is also provided in the folder <code>MTO</code>, and linked Intel® oneAPI Math Kernel Library and the incorporated PARDISO sparse solver. For the recent development of the code see the open git repository at <a href="https://gitlab.com/MartinDoskar/modular-topology-optimization">https://gitlab.com/MartinDoskar/modular-topology-optimization</a>. The repository also contains a detailed description of input parameters and code design.</p> <p>Modular topology optimisation code uses CMake for the cross-platform build automation. For instance, under Linux, the whole code can be compiled in the standard five steps:</p> <pre><code>cd ./MTO mkdir build cd ./build cmake -DCMAKE_BUILD_TYPE=Release .. make </code></pre> <p>All executables are automatically stored in <code>./MTO/bin/</code> folder. Individual problems can be optimized by parsing the JSON files obtained from the generator scripts as an argument to the MTO.Application binary, e.g.,</p> <pre><code>./MTO/bin/MTO.Application.exe path_to_data/mbb/2color/MTO_100_kernelSensitivity/input_modular_mbb_2colours_100.json </code></pre> <p><strong>Acknowledgement</strong></p> <p>The related research and code development was supported by the <a href="https://gacr.cz/en/">Czech Science Foundation</a>, project No. 19-26143X.</p>
EDGAR v5.0 emissions inventory speciated for the MOZART chemical mechanism
<p>Emission inventories need to be adapted to be used in chemical transport models (CTMs). They usually need ad-hoc preprocessing based on the chemical mechanism used in the CTM, including speciation of non-methane volatile organic compounds (NMVOCs). </p> <p><strong>Here we provide monthly <a href="https://edgar.jrc.ec.europa.eu/index.php/dataset_ap50">EDGAR v5.0 </a> global air pollutant emissions for the year 2015, speciated for the <a href="https://gmd.copernicus.org/articles/3/43/2010/">MOZART</a> chemical mechanism.</strong></p> <p><strong>The dataset is also ready to use in <a href="https://ruc.noaa.gov/wrf/wrf-chem/">WRF-Chem </a>atmospheric model with MOZART-MOSAIC options.</strong></p> <p>Emission files are provided as individual NetCDF files for each pollutant containing anthropogenic sector emissions as individual variables.</p> <p>In the folder you will find:</p> <ul> <li><strong>edgarv5_MOZART_data.tar.gz</strong>: EDGAR v5.0 monthly emissions for the year 2015 (NetCDFformat), speciated for MOZART chemical mechanism. Both total and individual sector emissions are included in each file. </li> <li><strong>edgarv5_MOZART_MOSAIC.inp</strong>: Input file for anthroemiss preprocessing tool for MOZART-MOSAIC options in WRF-Chem.</li> <li><strong>technical_note_EDGARv5_MOZART.pdf </strong>: documentation.</li> </ul> <p>These files are also ready-to be used in <a href="https://www2.acom.ucar.edu/wrf-chem/wrf-chem-tools-community">WRF-Chem anthro-emiss preprocessing tool</a> with the MOZART-MOSAIC options.</p> <p>Accompanying code for preparing the dataset can be found at repository: <a href="https://doi.org/10.5281/zenodo.6145846">https://doi.org/10.5281/zenodo.6145846</a></p> <p>For more detail, please refer to the technical documentation (technical_note_EDGARv5_MOZART.pdf).</p> <p> </p> <p> </p>
Coral calcification mechanisms in a warming ocean and the interactive effects of temperature and light
<p>Ross et al 2022 Supplementary data for coral (<em>Acropora nasuta</em>) temperature and light experiments. </p>
Mechanism of landslide induced by glacier-retreat on the Tungnakvíslarjökull area, Iceland
<p><strong>Introduction</strong></p> <p>This repository contains the data used for the study of the slope instability of Tungnakvíslarjökull, Iceland, described in Lacroix et al. (submitted). Specifically, the repository contains three time series in Tungnakvíslarjökull:</p> <ol> <li> <p>Time series of Digital Elevation Models (DEMs) from ASTER, 2000-2020.</p> </li> <li> <p>Time series of horizontal ground displacements, 1999-2019.</p> </li> <li> <p>Time series of earthquakes, 1995-2019.</p> </li> </ol> <p>Finally, we provide the map of the rate of elevation difference and the map of horizontal ground displacements for the whole period 2000-2019, as shown in Figure 1 of Lacroix et al. (submitted).</p> <p>The data and methods used for the elaboration of this data repository are described in detail in Lacroix et al. (submitted). In this repository we also provide a short summary and overview of the data and methods used.</p> <p><strong>Data</strong></p> <p>A total of 160 ASTER scenes were used to produce the time series of DEMs. A series of images from SPOT1, Landsat-7, ASTER and Landsat-8 was used in order to produce the horizontal ground displacements maps. The South-Iceland Lowlands (SIL) network (Jóndsdóttir et al., 2007) was obtained from Veðurstofan Íslands (www.vedur.is). Table 1 provides an overview of these data.</p> <table> <caption>Table1: Data used for the creation of this repository</caption> <tbody> <tr> <td>Application</td> <td>Platforms</td> <td>Acquisition dates</td> </tr> <tr> <td>DEM</td> <td>ASTER</td> <td>160 scenes from 2000-10-16 to 2020-08-27. <p>Format for the date is YYYYMMDD</p> </td> </tr> <tr> <td>Horizontal ground displacement</td> <td>SPOT1</td> <td>1987-08-05</td> </tr> <tr> <td> </td> <td>Landsat-7</td> <td>1999-07-26, 2000-08-20, 2001-09-24, 2002-07-09</td> </tr> <tr> <td> </td> <td>ASTER</td> <td>2003-08-04, 2004-09-18, 2007-08-15, 2011-08-10, 2013-07-24, 2014-08-18, 2016-08-07</td> </tr> <tr> <td> </td> <td>Landsat-8</td> <td>2014-08-12, 2015-09-16, 2016-08-24, 2017-08-20, 2018-09-14, 2019-08-10</td> </tr> <tr> <td>Seismicity</td> <td>SIL network</td> <td>370491 events recorded between 1995-2019 in the Mýrdalsjökull (S-Iceland) area and surroundings</td> </tr> </tbody> </table> <p><strong>Methods</strong></p> <p>The DEMs were created using the Ames StereoPipeline (ASP, Shean et al., 2016) with the same setup as used in Brun et al., (2017). Each DEM was then co-registered to a lidar DEM acquired in 2010 (Jóhannesson et al., 2013), using the co-registration methods from Berthier et al. (2007), and adding an across-track fifth-degree polynomial correction (Gardelle et al., 2013). The stack of elevations obtained from the DEM time series was linearly fitted in order to produce the map of elevation difference (file name 20000101_20210101_30x30m_UTM27N_DHDT_Lacroixetal2022.tif) of the period 2000-2020.</p> <p>The horizontal ground displacement maps were created using the offset tracking methodology described in Bontemps et al. (2018), consisting of: (1) pairwise image correlation using Mic-Mac (Rupnik et al., 2017), (2) masking of areas with low correlation coefficients (3) correction of co-registration bias by subtracting the mean values of the NS and EW displacement fields and (4) pixelwise fit of the horizontal ground displacements by least squares, using the time interval between measurements as weights and obtaining the full horizontal ground displacement for the analyzed period (file name 19990726_20200101_15x15m_UTM27N_HGD_Lacroixetal2022.tif)</p> <p>The time series of earthquakes obtained from the SIL network was filtered, and 2089 earthquakes with depth <5 km and magnitude <1.7 were used in this study and data repository (file name 19950814_20181118_SILvedur_time_lon_lat_dep_mag.txt).</p> <p><strong>Acknowledgements</strong></p> <p>We thank Bryndís Brandsdóttir for providing the seismic data used in this repository. E.B. and P.L. acknowledge the support from the French Space Agency (CNES) through the TOSCA, PNTS, SWH and ISIS programs.</p> <p><strong>Dataset attribution</strong></p> <p>This dataset is licensed under a Creative Commons CC BY 4.0 International License.</p> <p><strong>Dataset Citation</strong></p> <p>Lacroix, P., Belart, J.M.C., Berthier, E., Sæmundsson, Þ., Jónsdóttir, K.: Data Repository: Mechanism of landslide induced by glacier-retreat on the Tungnakvíslarjökull area, Iceland. Dataset distributed on Zenodo: 10.5281/zenodo.6388069</p>
Data for: Wind-induced hypolimnetic upwelling between the multi-depth basins of Lake Geneva during winter: An overlooked deepwater renewal mechanism?
<p>Combining field observations, 3D hydrodynamic modeling and particle tracking, we investigated wind-driven interbasin exchange, and in particular hypolimnetic upwelling, between the deep <em>Grand Lac</em> (max. depth 309 m) and shallow <em>Petit Lac</em> (max. depth 75 m) basins of Lake Geneva (Switzerland/France) during the weakly stratified fall/winter period 2018-2019.</p> <p><br> The data include measurements from moored Acoustic Doppler Current Profilers (ADCPs) and vertical thermistor lines along with the corresponding 3D modeling and particle tracking results.</p> <p><br> The three-dimensional model used in this study is based on the MIT General Circulation Model (MITgcm, http://mitgcm.org/, https://doi.org/10.1029/96JC02775).</p> <p><br> The particle tracking code is based on ctracker (https://doi.org/10.5281/zenodo.1034118)</p>
A chromosome-level genome resource for studying virulence mechanisms and evolution of the coffee rust pathogen Hemileia vastatrix
<p>Recurrent epidemics of coffee leaf rust, caused by the fungal pathogen <em>Hemileia vastatrix,</em> have constrained the sustainable production of Arabica coffee for over 150 years. The ability of <em>H. vastatrix </em>to overcome resistance in coffee cultivars and evolve new races is inexplicable for a pathogen that supposedly only utilizes clonal reproduction. Understanding the evolutionary complexity between <em>H. vastatrix</em> and its only known host, including determining how the pathogen evolves virulence so rapidly is crucial for disease management. Achieving such goals relies on the availability of a comprehensive and high-quality genome reference assembly. To date, two reference genomes have been assembled and published for <em>H. vastatrix</em> that, while useful, remain fragmented and do not represent chromosomal scaffolds. Here, we present a complete scaffolded pseudochromosome-level genome resource for <em>H. vastatrix </em>strain 178a (Hv178a). Our initial assembly revealed an unusually high degree of gene duplication (over 50% BUSCO basidiomycota_odb10 genes). Upon inspection, this was predominantly due to a single scaffold that itself showed 91.9% BUSCO Completeness. Taxonomic analysis of predicted BUSCO genes placed this scaffold in Exobasidiomycetes and suggests it is a distinct genome, which we have named Hv178a associated fungal genome (Hv178a AFG). The high depth of coverage and close association with Hv178a raises the prospect of symbiosis, although we cannot completely rule out contamination at this time. The main Ca. 546 Mbp Hv178a genome was primarily (97.7%) localised to 11 pseudochromosomes (51.5 Mb N50), building the foundation for future advanced studies of genome structure and organization. Citation: https://doi.org/10.1101/2022.07.29.502101</p>
Testing a biological mechanism of the insurancehypothesis in experimental aquatic communities - Data Deposit
<p>1.The insurance hypothesis predicts a stabilizing effect of increasing species richness on commu-nity and ecosystem properties. Difference among species’ responses to environmental fluctuationsprovides a general mechanism for the hypothesis. Previous experimental investigations of theinsurance hypothesis have not examined this mechanism directly.</p> <p>2.First, responses to temperature of four protist species were measured in laboratory microcosms.For each species, we measured the response of intrinsic rate of increase (r) and carrying capacity(K) to temperature.</p> <p>3.Next, communities containing pairs of species were exposed to temperature fluctuations. Com-munity biomass varied less when correlation inKbetween species (but notr) was more negative,and this resulted from more negative covariances in population sizes, as predicted. Results werecontingent on species identity, with findings differing between analyses including or not includingcommunities containing one particular species.</p> <p>4.These findings provide the clearest support to date for this mechanism of the insurance hypo-thesis. Biodiversity, in terms of differences in species’ responses to environmental fluctuations (i.e.functional response diversity) stabilizes community dynamics.</p>
Mechanical Properties and Fracture Characterization of Additive Manufacturing Polyamide 12 After Accelerated Weathering
<p>A dataset for the publication: T. Puttonen, M. Salmi, J. Partanen, Mechanical Properties and Fracture Characterization of Additive Manufacturing Polyamide 12 After Accelerated Weathering, 2021.</p> <p>The paper studies the mechanical properties and fracture mechanics of Additive Manufacturing (AM) polyamide 12 (PA12) in two build orientations exposed to a 1500-hour accelerated weathering cycle (ISO-4982-3) followed by tensile testing (ISO-527). Fracture surfaces of X and Z build orientation AM PA12 and X build orientation AM glass-filled PA12 were studied with scanning electron microscopy. The tested AM materials were PA12, glass-filled PA12, and carbon-reinforced PA12. The reference materials cut from sheet included glass-filled and molybdenum disulfide-filled PA66, PMMA, ABS, PC, and cast PA12.</p> <p>The dataset contains:</p> <p>- Full tensile test results in PDF format, and individual CSV files</p> <p>- A python script for tensile CSV data plotting</p> <p>- Overall pictures of all samples after tensile tests</p> <p>- 3D models and drawings for tensile samples, manufacturing files for a custom QUV holder assembly</p> <p>- SEM images of fracture surfaces for AM polyamide 12 (SLS), X and Z build orientation, and glass-filled polyamide 12 (SLS) in the X build orientation</p> <p> </p> <p>Version history:</p> <p>1.0.1: A partially corrupted version of the tensile test results PDF file replaced (Tensile_test_results.pdf)</p>
Infection increases activity via Toll dependent and independent mechanisms in Drosophila melanogaster - ethoscope dataset
<p>Ethoscope dataset for Vicent et al 2022, PLoS Pathogens</p> <p>Original preprint available at: https://www.biorxiv.org/content/10.1101/2021.08.24.457493v1</p> <p> </p>
Dynamic full-field imaging of rupture radiation: Material contrast governs source mechanism
<p>Datasets related to the research article 'Dynamic full-field imaging of rupture radiation: Material contrast governs source mechanism'. <br> A readme with the necessary Matlab code to load the data is included.<br> Tested on Matlab2020b</p> <p>For the analytic rupture radiation simulation code please check the linked github repository.</p>
Dataset of "PEMFC performance decay during real-world automotive operation: evincing degradation mechanisms and heterogeneity of ageing"
<p>This is the underlying dataset of "PEMFC performance decay during real-world automotive operation: evincing degradation mechanisms and heterogeneity of ageing"</p>
A Ligand Field Molecular Mechanics Study of CO2 Induced Breathing in the metal-organic framework DUT-8(Ni)
<p>Raw Data, scripts and processed data for the publication "A Ligand Field Molecular Mechanics Study of CO2 Induced Breathing in metal-organic framework DUT-8(Ni)"</p>
Data from: "Non-metabolizable" glucose analogue shines new light on priming mechanisms: Triggering of microbial metabolism
<p>Raw data for the article:</p> <p>Kyle Mason-Jones*, Yakov Kuzyakov (2017)<br> “Non-metabolizable” glucose analogue shines new light on priming mechanisms: Triggering of microbial metabolism</p> <p>Soil Biology and Biochemistry 107, 68-76</p> <p>https://doi.org/10.1016/j.soilbio.2016.12.015</p> <p>* corresponding author: kmasonj@gwdg.de</p>
SFB1451 - key mechanisms of motor control in health and disease
<p>Note: this dataset has been deprecated in favour of <a href="https://github.com/sfb1451/all-datasets" target="_blank" rel="noopener">sfb1451/all-datasets</a>; see <a href="https://doi.org/10.5281/zenodo.11119424">10.5281/zenodo.11119424</a><br><br>This dataset indexes projects of the Collaborative Research Centre 1451 (CRC1451; dt: Sonderforschungsbereich, SFB1451). This collaborative research centre brings together neuroscientists investigating genetic factors, cellular, and synaptic as well as systems/neural network processes underlying motor control in animals and humans, in both health and neuropsychiatric diseases. All investigators are committed to the CRC's multi-faceted, iterative, and integrative agenda with the long-term goal of identifying the essential mechanisms underlying normal and pathological motor control.</p>
SFB1451 - key mechanisms of motor control in health and disease
This is a collection of datasets created within the Collaborative Research Center 1451. The "superdataset" contains information about datasets stored in various locations. This information can be used to obtain programmatic, fine-grained data access to the level of individual files, depending on the level of detail provided by dataset authors, storage location, and access restrictions. The Collaborative Research Center 1451 (CRC1451; dt: Sonderforschungsbereich, SFB1451) brings together neuroscientists investigating genetic factors, cellular, and synaptic as well as systems/neural network processes underlying motor control in animals and humans, in both health and neuropsychiatric diseases. All investigators are committed to the CRC's multi-faceted, iterative, and integrative agenda with the long-term goal of identifying the essential mechanisms underlying normal and pathological motor control.
Data for the article "Mechanically induced correlated errors on superconducting qubits with relaxation times exceeding 0.4 milliseconds"
<p>Here you will find all the raw data and data processing scripts for the plots presented in the article "Mechanically induced correlated errors on superconducting qubits with relaxation times exceeding 0.4 milliseconds."</p>
Online Appendix for PhD Thesis Titled "Dissecting Causal Relationships and Molecular Mechanisms in Disease using Genetic Risk Profiles"
<p>This repository contains 23 tables and two figures, which are too big to be included in the Appendix section of my thesis document.</p> <p>The second version includes additional summary statistics of metabolite-PGS associations which can be found at http://mrcieu.mrsoftware.org/metabolites_PRS_atlas/.</p>
Computational Design of Multimodal Combinatorial Mechanical Metamaterials
<p>This dataset contains the data used to design multimodal mechanical metamaterials as described in the paper 'Prospecting for Pluripotency in Metamaterial Design', as published in Phys. Rev. Research 7(2), 023299.</p> <p>In this paper, the data is used to design 5×5 unit cells with desired deformation (zero) modes. The dataset contains the data used to train neural networks (CNN_data.zip), the designs generated by genetic algorithm (step_i.zip) and their mode structures (step_ii.zip), and the designs obtained through our design approach as described in the paper (step_ii.zip). Additionally, there is data comparing the efficiency of using a genetic algorithm or a hill climbing method to generate designs with a large number of intensive modes (step_i.zip).</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.