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56 results for “Cluster structures”
Structure matters – Direct in-situ observation of cluster nucleation at atomic scale in a liquid phase (supplementary data)
<p>This a dataset of scanning transmission electron microscopy data showing Pt clusters nucleating in an ionic liquid. For each of the 4 movies there is the raw data (uncompressed .tif and compressed as .avi) and denoised versions (uncompressed .tif and compressed as .avi).</p> <p>This data is for the article "Structure matters – Direct in-situ observation of cluster nucleation at atomic scale in a liquid phase" published in ChemNanoMat (2020), by Trond R. Henninen, Debora Keller and Rolf Erni. (https://onlinelibrary.wiley.com/doi/full/10.1002/cnma.202000503)</p> <p><strong>Movie 1:</strong> Homogeneous nucleations of two clusters in a suspended thin film of ionic liquid. </p> <p><strong>Movie 2: </strong>Heterogeneous nucleation of a ca 8-9 atom cluster near the edge of a nanodroplet supported on a carbon film.</p> <p><strong>Movie 3: </strong>Heterogeneous nucleation of multiple clusters in a nanodroplet. Shortly after nucleation, they coalesce to form disordered nanoclusters.</p> <p><strong>Movie 4:</strong> Heterogeneous nucleation and dissolution cycles of spherical particles in a nanodroplet.</p>
GFN2-xTB structures of iCOM adsorbed on a cluster model of water molecules derived from a periodic model of crystalline ice
<p>This dataset contains the atomic coordinates in the <a href="http://www.moldraw.unito.it/">.</a>xyz format of the GFN2-xTB optimized structures of 20 iCOMs adsorbed at the surface of a cluster of 84 water molecules mimicking the periodic model of crystalline water icy grain as described by Ferrero, S.; Zamirri, L., Ceccarelli, C.; Witzel, A.; Rimola, A.; Ugliengo, P. ApJ, (2020) 904:11. For all considered structures we also provided a specific file in the Gaussian format with the computed harmonic frequencies. Each file can be easily converted in input for the variety of quantum mechanical programs, like VASP, QE, Gaussian 16 etc.</p> <p> </p> <p> </p>
Data for "Atomic structure of solute clusters in Al-Zn-Mg alloys"
<p>This dataset contains the data used in the publication entitled "<a href="https://www.sciencedirect.com/science/article/abs/pii/S1359645420310119"><strong>Atomic structure of solute clusters in Al-Zn-Mg alloys</strong></a>", published in Acta Materialia 17. December 2020.</p> <p>The data contained herein are:</p> <ul> <li>As-acquired transmission electron microscopy (TEM) images.</li> <li>Atom probe tomography data.</li> <li>All structural models used in density functional theory (DFT) calculations.</li> <li>Structures used for simulating scanning-TEM (STEM) images and nanobeam diffraction (NBD) patterns.</li> </ul> <p> </p> <p>The TEM images includes high angle annular dark field (HAADF) images and selected area diffraction patterns. These are given in .dm3/.dm4 files, and can be opened in e.g. the "<a href="https://www.gatan.com/products/tem-analysis/gatan-microscopy-suite-software">Gatan Microscopy Suite" </a>software. The images are also given as .tif images. The files are names after the "Figx_alloy_condition_xxx". "Figx" refers to the figure in the main article, "alloy" describes the alloy used and "condition" describes from what ageing condition. The uncorrected image series used for Fig. 6c (in the article) is included and requires the <a href="http://lewysjones.com/software/smart-align/">SmartAlign </a>plugin in the Gatan Microscopy Suite to analyse the dataset. SmartAlign allows for correcting rigid and non-rigid distortions in the STEM images in order to reduce effect of specimen drift and scan noise during acquisition. </p> <p>The ATP data is given as a .xlsx file. The data here is the processed data after applying the maximum separation algorithm. The data here is used to produce Figs. 2b and 2c in the paper. <br> <br> The structures used in the DFT calculations are given here as .cif files. These are separated into "Single_clusters" and "Stacked_clusters" and named according to Tabs. 1 and 2 in the Supplementary material of the paper.</p> <p>The two structures used for simulating STEM-HAADF and NBD patterns are given in the folder "TEM_simulations". "Mg32Zn124D_94x94" was used for NBD and "Mg32Zn124D_X_Zn4" was used for HAADF-STEM. The stack used for Supplementary Fig. 7c is labeled "Mg32Zn124D_94x94_slab_1Allayerop.cif".</p> <p> </p> <p> </p> <p> </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>
Assemblies, synapse clustering and network topology interact with plasticity to explain structure-function relationships of the cortical connectome
<p>Dataset linked to the article with the same title</p> <p>The model itself is very similar to its non-plastic counterpart under the following DOI: <a href="../record/7930275">10.5281/zenodo.7930275</a>, i.e. a 1.5 mm diameter cortical tissue comprising 211,712 neurons and their connectivity in the front limb and jaw subregions and the dysgranular zone of the Paxinos & Watson rat brain atlas. It's formatted in the open <a href="https://github.com/AllenInstitute/sonata">SONATA</a> standard and contains neuron locations and their properties (such as morphological types, cortical layer, etc.), their detailed morphologies, and synaptic connectivity (with all their anatomical and physiological parameters). The main difference from the non-plastic version is the addition of plasticity related parameters to <em>O1/S1nonbarrel_neurons__S1nonbarrel_neurons__chemical/edges.h5. </em>Extrinsic synaptic connections from the thalamus are included in this release, but for inputs from neurons in the remainder of non-barrel somatosensory cortex please see the non-plastic version of the circuit.</p> <p><strong>Analyzing the model</strong></p> <p>The model can be analyzed in terms of its anatomy, physiology and connectivity using the packages <a href="https://neurom.readthedocs.io/en/stable/">NeuroM</a>, <a href="https://bluebrainsnap.readthedocs.io/en/stable/">BlueBrain SNAP</a> and <a href="https://github.com/BlueBrain/ConnectomeUtilities">ConnectomeUtilities</a>. (see first Jupyter notebook)</p> <p><strong>Simulating the model</strong></p> <p>To simulate the model we'd recommend using out using our open-source simulator <a href="https://github.com/BlueBrain/neurodamus">Neurodamus</a>. The reference version is the branch <em>nbS1-2023</em>, which is archived under the following DOI: <a href="http://doi.org/10.5281/zenodo.8075202">10.5281/zenodo.8075202</a>. Instructions on how to use the simulator are provided on the GitHub page linked above. Briefly, you'll first have to <a href="https://github.com/BlueBrain/neurodamus#install-neurodamus">install Neurodamus</a>. Next, build a <em>"special"</em> executable that include compiled versions of ion channel and synapse models. To do that, follow <a href="https://github.com/BlueBrain/neurodamus#build-special-with-mod-files">these instructions</a>, where <em>mod-files-from-released-circuit </em>is replaced by the location of <em>O1/mods</em> on your system. Finally, <a href="https://github.com/BlueBrain/neurodamus#examples">run a simulation</a>. The specific simulation conditions and stimuli are specified in simulation configuration files. An exemplary simulation configuration is included in this release (<em>simulation_config.zip</em>).</p> <p><strong>Analyzing simulation results</strong></p> <p>Simulation results can be analyzed with <a href="https://bluebrainsnap.readthedocs.io/en/stable/">BlueBrain SNAP</a>, <a href="https://github.com/BlueBrain/ConnectomeUtilities">ConnectomeUtilities</a>, and <a href="https://github.com/BlueBrain/assemblyfire">assemblyfire</a>. Notebooks 2-5 go though these analysis and recreate some of the panels from our article. In most cases the notebooks can be run with the shared HDF5 files and don't require running any simulations.</p> <p><strong>Version 2</strong></p> <p>Bug fix in simulation_config.json and therefore new version of results (and corresponding notebooks). The underlying circuit model (O1.xz) did not change from v1.</p> <p>--</p> <p><em>The development of this dataset was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL), from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology.</em></p>
The Structure of Sub-nm Platinum Clusters at Elevated Temperatures (Supplementary Information)
<p><strong><em>This dataset consists of raw data and denoised scanning transmission electron microscopy videos of sub-nm sized clusters of Pt on a carbon substrate. The data is used in the article "The Structure of Sub-nm Platinum Clusters at Elevated Temperatures" published in Angewandte Chemie International Edition, 2019, DOI:10.1002/anie.201911068</em></strong><strong><em> </em></strong></p> <p><strong>Video S1.</strong> A typical sub-nm amorphous cluster at room temperature. 0.5 nm scale bar.</p> <p><strong>Video S2.</strong> Two typical crystalline sub-nm clusters at 350°C. 0.5 nm scale bar.</p> <p><strong>Video S3. </strong>In this high-speed recording at 147 fps, the high beam current required for this fast imaging has suppressed the crystallinity of the cluster, despite the temperature of 350°C. 0.5 nm scale bar.</p> <p><strong>Video S4. </strong>The unusually stable 13-atom cluster at the bottom forms an fcc cuboid, and can be seen rotating at three orientations, as shown by the inset model and in Fig. 3a-c. 0.5 nm scale bar.</p> <p><strong>Video S5. </strong>This 15-atom cluster initially forms an fcc cube, then transforms into multiple hcp structures. (Recorded at 2 fps, but animated at 5x real time at 10fps). 0.5 nm scale bar.</p> <p><strong>Video S6. </strong>The cluster in this movie is a 22-atom truncated rectangular cuboid. 0.5 nm scale bar.</p> <p><strong>Video S7. </strong>In the center and bottom, two 6-atom octagons are rotating (shown in Fig. S4) as they add onto their larger neighboring clusters. The 13-atom cluster in the top forms an unusually stable fcc cuboctahedron from frame 219. 0.5 nm scale bar.</p> <p><strong>Video S8. </strong>The cluster on the bottom left forms a fleeting icosahedron-like structure. 0.5 nm scale bar.</p> <p><strong>Video S9. </strong>This cluster shows fcc structures, despite being recorded at 200°C, but with a very low beam dose. (Recorded at 2 fps, but animated at 5x real time at 10fps). 0.5 nm scale bar.</p>
Dataset of structural and energetic descriptors for optimized geometries of the pyrene dimer in the S1 state and Jupyter Notebbok used for the unsupervised clustering, analysis and visualization
<p>Geometrical and energy data extracted from a set of 188 optimized geometries for the pyrene dimer in the first excited singlet state at the TD-CAMB3LYP + D3BJ / 6-31G* / C-CPCM(Cyclohexane) level. </p> <p>Coordinates (in xyz format) of the 188 optimized geometries considered.</p> <p>Jupyter notebook used to perform unsupervised clustering and analysis of the available structures.</p>
Database of Short Large-Amplitude Magnetic Structures (SLAMS) detected by spacecraft 1 of the Cluster mission in the foreshock of Earth
<p>Database of Short Large-Amplitude Magnetic Structures (SLAMS) detected in the foreshock of Earth by spacecraft 1 of the Cluster mission between the years 2002-2012.</p> <p>An automated algorithm has been used for SLAMS identification followed by a manual verification process to remove bow shock oscillations and other false detections. SLAMS have been defined to have an amplitude of at least two times the background magnetic field. </p> <p>More details on the creation of the database are given in the following publication:</p> <p><span><span lang="EN-US">Bergman, S.</span></span><span lang="EN-US">, Karlsson, T., Wong Chan, T. K., & Trollvik, H. (2025). Statistical properties of Short Large</span><span lang="EN-US">‐</span><span lang="EN-US">Amplitude Magnetic Structures (SLAMS) in the foreshock of Earth from Cluster measurements. <em>Journal of Geophysical Research: Space Physics</em>, 130. </span><a href="https://doi.org/10.1029/2024JA033568"><span lang="EN-US">https://doi.org/10.1029/2024JA033568</span></a></p> <p>Contact: S. Bergman, sofiabergmanphd@gmail.com </p>
Dataset for "Structural Linkage Unit-Dependent Catalytic Activity for CO Oxidation over Cerium Oxysulfate Cluster Assemblies"
<p>Dataset relating to the publication "Structural Linkage Unit-Dependent Catalytic Activity for CO Oxidation over Cerium Oxysulfate Cluster Assemblies".</p> <p>Raw input and output data as well as pre- and postprocessing scripts and figures.</p>
Text-fig. 2. SEM images of Miranthus elegans gen. et sp. nov.; Mira locality, Portugal. a, b: Flowers in oblique lateral view showing remains of calyx and slightly semi-inferior ovary with elongated apical style (a); note larger openings in the floral tissue (asterisk) interpreted as schizogenous secretory cavities and the stomata-like secretory structures on the upper portion of the ovary (arrows) that are interpreted as nectariferous (b). c: Detail of ovary surface showing secretory stomata-like structures (arrows). d: Flower in lateral view showing fragmentary calyx and broken slightly semi-inferior ovary with secretory stomata-like structures; note the point of attachment of the central placenta (pl). e: Cluster of seeds removed from the ovary in (d) showing reticulate surface. f: Outer (abaxial) surface of calyx lobe showing the slightly pointed papillae and scattered, fine trichomes (arrows). g: Triaperturate pollen grains from the ovary surface. Specimens, Mira 100-S153146 (a, b), Mira 100-S170155 (c), Mira 100-S101266 (d, e), Mira 105-S100732 (f), Mira 100-S170125 (g). Scale bars = 600 µm (a, b, d), 300 µm (f), 100 µm (c, e), 10 µm (g). in Early Flowers Of Primuloid Ericales From The Late Cretaceous Of Portugal And Their Ecological And Phytogeographic Implications
Text-fig. 2. SEM images of Miranthus elegans gen. et sp. nov.; Mira locality, Portugal. a, b: Flowers in oblique lateral view showing remains of calyx and slightly semi-inferior ovary with elongated apical style (a); note larger openings in the floral tissue (asterisk) interpreted as schizogenous secretory cavities and the stomata-like secretory structures on the upper portion of the ovary (arrows) that are interpreted as nectariferous (b). c: Detail of ovary surface showing secretory stomata-like structures (arrows). d: Flower in lateral view showing fragmentary calyx and broken slightly semi-inferior ovary with secretory stomata-like structures; note the point of attachment of the central placenta (pl). e: Cluster of seeds removed from the ovary in (d) showing reticulate surface. f: Outer (abaxial) surface of calyx lobe showing the slightly pointed papillae and scattered, fine trichomes (arrows). g: Triaperturate pollen grains from the ovary surface. Specimens, Mira 100-S153146 (a, b), Mira 100-S170155 (c), Mira 100-S101266 (d, e), Mira 105-S100732 (f), Mira 100-S170125 (g). Scale bars = 600 µm (a, b, d), 300 µm (f), 100 µm (c, e), 10 µm (g).
BRAIN Journal-Redesigning a Flexible Material Master Data Application with Language Dependency-Figure 7. Dialog structure of the View Cluster
<p>Field dependency has been generated automatically and as a result the following desired structure has been achieved (figure 7). After populating the database tables with data in different languages with the help of the view cluster, the popup has been adapted in order to support the translation of the tabs and areas. Supplementary internal tables have been defined in the function module POPUP_FLEX, in order to copy data from the translation tables. The corresponding SELECT statements have been embedded in TRY-CATCH blocks, in order to prevent short dumps due to faulty selection processes.</p>
Clusters of cause specific neonatal mortality and its association with per capita gross domestic product: a structured spatial analytical approach
<p>Database used for the analysis of the manuscript entitled: Clusters of cause specific neonatal mortality and its association with per capita gross domestic product: a structured spatial analytical approach. The aim of the study was to investigate the cluster areas of asphyxia-associated neonatal mortality and to explore the per capita gross domestic product (GDP) as an associated risk factor in São Paulo State (SP), Brazil.</p>
Text-fig. 45. Scanning electron microscope (SEM) images of "Staminate structure"; Catefica locality, Portugal. a–c) Staminate structure in oblique apical (a), oblique basal (b) and lateral (c) views showing distinct stalk and head with a cluster of about 20 stamens; note bracts at the base of the structure (asterisks) and probable secretory openings in the anther tissues (arrows). Specimen, Catefica 358-S135451 (a–c). Scale bars = 600 Μm (a–c). in The Early Cretaceous Mesofossil Flora Of Catefica, Portugal: Angiosperms
Text-fig. 45. Scanning electron microscope (SEM) images of "Staminate structure"; Catefica locality, Portugal. a–c) Staminate structure in oblique apical (a), oblique basal (b) and lateral (c) views showing distinct stalk and head with a cluster of about 20 stamens; note bracts at the base of the structure (asterisks) and probable secretory openings in the anther tissues (arrows). Specimen, Catefica 358-S135451 (a–c). Scale bars = 600 Μm (a–c).
A Toroidal Zr70 Oxysulfate Cluster and Its Diverse Packing Structures
<p>Supplementary material: Syntheses, XRD, TGA, SEM, PXRD</p>
Data from: Defaunation increases clustering and fine-scale spatial genetic structure in a small-seeded palm despite remaining small-bodied frugivores
Open the record for dataset details and reuse information.
POMFinder: Identifying polyoxometalate cluster structures from pair distribution function data using explainable machine learning
<p>Databases that were used to train the POMFinder ML model.</p>
Integrated Analysis of Seismic Sources and Structures: Understanding Earthquake Clustering during Hydraulic Fracturing
<p>The uploaded files include the 3D velocity model, 2D seismic reflection profiles, and horizontal slice utilized in this study.</p>
Assessing changes in clusters of wildlife road mortalities after the construction wildlife mitigation structures
<p>Collisions with vehicles can be a major threat to wildlife populations, so wildlife mitigation structures, including exclusionary fencing and wildlife crossings, are often constructed. To assess mitigation structure effectiveness, it is useful to compare wildlife road mortalities (WRMs) before, during, and after mitigation structure construction; however, differences in survey methodologies may make comparisons of counts impractical. Location-based cluster analyses provide a means to assess how WRM spatial patterns have changed over time. We collected WRM data between 2015 and 2019 on State Highway 100 in Texas, USA. Five wildlife crossings and exclusionary fencing, were installed in this area between Sep 2016 and May 2018 for the endangered ocelot (Leopardus pardalis) and other similarly sized mammals. Roads intersecting State Highway 100 were mitigated by gates, wildlife guards, and wing walls. However, these structures may have provided wildlife access to the highway. We combined local hot spot analysis and time series analysis to assess how WRM cluster intensity changed after mitigation structure construction at fine spatial and temporal scales and generalized linear regression to assess how gaps in fencing and landcover were related to WRM cluster intensity in the before, during, and after construction periods. Overall, WRMs/survey day decreased after mitigation structure construction and most hot spots occurred where there were more fence gaps, and, while cluster intensity increased in a few locations, these were not at fence gaps. Cluster intensity of WRMs increased when nearer to fence gaps in naturally vegetated areas, especially forested areas, and decreased nearer to fence gaps in areas with less natural vegetation. We recommend that if fence gaps are necessary in forested areas, less permeable mitigation structures, such as gates should be used. Local hot spot analysis, coupled with time series and regression techniques, can effectively assess how WRM clustering changes over time.</p>
Dataset for the Manuscript: Structural and mechanistic insights into the cleavage of clustered O-glycan patches-containing glycoproteins by mucinases of the human gut (in revision)
<p>This dataset provides the classical and QM/MM MD simulation trajectory data to the manuscript:</p> <p><strong>Structural and mechanistic insights into the cleavage of clustered O-glycan patches-containing glycoproteins by mucinases of the human gut</strong></p> <p>The data set contains classical MD simulations of AM0627 with three substrate peptides P1, P2, P9, and BT4244 with glycopeptides, as well as QM/MM metadynamics simulations for our manuscript. PDB files for Figures 4,5 and Figure S5-8,10 are also included.</p>
Semi-empirical error ellipsoid clustering for identifying the second-order structural features from a laboratory AE source location cloud—method, validation, and application to a hydraulic fracturing test [DATA]
<p>Data and metadata for the publication "Semi-empirical error ellipsoid clustering for identifying the second-order structural features from a laboratory AE source location cloud—method, validation, and application to a hydraulic fracturing test", published in Earth and Space Science.</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.