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695 results for “topologies”
Ferromagnetic resonance of Co thin films grown by atomic layer deposition on the Sb2Te3 topological insulator (data)
<p>This dataset contains the raw data files connected with the figures included in the paper "<em>Ferromagnetic resonance of Co thin films grown by atomic layer deposition on the Sb<sub>2</sub>Te<sub>3</sub> topological insulator</em>" by E. Longo et al., JMMM 209, 166885 (2020): <a href="https://linkinghub.elsevier.com/retrieve/pii/S0304885319336029">https://linkinghub.elsevier.com/retrieve/pii/S0304885319336029</a></p>
Topological Data Analysis of Monopoles in U(1) Lattice Gauge Theory — Data Release
<div>This release contains data used to prepare the publication <a href="https://arxiv.org/abs/2403.07739">X. Crean, J. Giansiracusa and B. Lucini, Topological Data Analysis of Monopoles in U(1) Lattice Gauge Theory (2024)</a>. There exists an <a href="https://doi.org/10.5281/zenodo.10806185">accompanying software release</a> that explains in detail how to extract and use the compressed data files on a Linux distribution (or compatible environment).</div>
Large-Area MOVPE Growth of Topological Insulator Bi2Te3 Epitaxial Layers on i-Si(111) (data)
<p>This dataset contains the raw data files connected with the figures included in the paper "<em>Large-Area MOVPE Growth of Topological Insulator Bi<sub>2</sub>Te<sub>3</sub> Epitaxial Layers on i-Si(111)</em>" by <a href="https://pubs.acs.org/doi/10.1021/acs.cgd.1c00328">A. Kumar et al., <em>Cryst. Growth Des.</em> 2021, 21, 7, 4023–4029</a> </p>
Large Spin-to-Charge Conversion at Room Temperature in Extended Epitaxial Sb2Te3 Topological Insulator Chemically Grown on Silicon (data)
<p>This dataset contains the raw data files connected with the figures included in the paper "<em>Large Spin-to-Charge Conversion at Room Temperature in Extended Epitaxial Sb<sub>2</sub>Te<sub>3</sub> Topological Insulator Chemically Grown on Silicon</em>" by <a href="https://doi.org/10.1002/adfm.202109361">E. Longo et al., <em>Adv. Funct. Mater.</em> 2021, 2109361</a></p>
Forward and backward Raman scattering photon counts in a FTTH topology.
<p>This set comprises a simulation tool in Mathematica for the generated Raman noise in a<br> GPON-based FTTH topology. This set takes into consideration various FTTH parameters,<br> such as the number of Optical Network Terminals (ONTs) and the splitting ratios, as well<br> as the drop and feeder fiber lengths, as it is depicted in Figure 1, providing in the output<br> the expected Raman noise counts calculated in counts per second (cps) detected in a<br> Single Photon Detector (SPD). The user of the code can manipulate various setup<br> parameters, such as the filtering passband and loss, as well as the specific SPAD<br> parameters which can be selected to be operating either in gated or free running mode.<br> The code provides as an output the expected noise count rates (cps) associated with the<br> forward and backward Raman scattering effect.<br> </p>
TDA4ContextualEmbeddings - Public - Debug Data for the codebase of the publication "Local Topology Measures of Contextual Language Model Latent Spaces With Applications to Dialogue Term Extraction"
<p>Debug dataset for testing the <a href="https://gitlab.cs.uni-duesseldorf.de/general/dsml/tda4contextualembeddings-public">codebase</a> of the paper <a href="https://doi.org/10.18653/v1/2024.sigdial-1.31">“Local Topology Measures of Contextual Language Model Latent Spaces With Applications to Dialogue Term Extraction”</a> published at the 25th Meeting of the Special Interest Group on Discourse and Dialogue, Kyoto, Japan (SIGDIAL 2024).</p>
GeoDAR-TopoCat: Drainage topology and catchment database (TopoCat) for Georeferenced global Dams And Reservoirs (GeoDAR)
<p><strong>Contact</strong>: Md Safat Sikder (msikder@ksu.edu), Jida Wang (jidawang@ksu.edu; gdbruins@ucla.edu)</p> <p> </p> <p><strong>Data description</strong></p> <p>This data can be considered a supplement to the Georeferenced global Dams And Reservoirs (GeoDAR) dataset (doi:10.5281/zenodo.6163413). </p> <p>Here in GeoDAR-TopoCat, the method of TopoCat (doi:10.5281/zenodo.7420810) has been applied on GeoDAR reservoirs in order to construct the drainage topology and catchments for global reservoirs.</p> <p>To avoid ambiguity, please refer to this version of GeoDAR-TopoCat as “<strong>GeoDAR-TopoCat v1.1-1.0</strong>”, where “1.1” specifies the version of GeoDAR reservoirs, whose drainage topology and catchments are constructed using the method in version “1.0” of TopoCat.</p> <p> </p> <p><strong>Relevant datasets</strong></p> <ul> <li>The original GeoDAR v1.1 dataset without topology can be accessed here: doi:10.5281/zenodo.6163413.</li> <li>The TopoCat v1.0 dataset, originally developed based on HydroLAKES v1.0, can be accessed here: doi:10.5281/zenodo.7420810.</li> </ul> <p> </p> <p><strong>Attribute description</strong></p> <p>Description of the attributes of GeoDAR-TopoCat is the same as those of TopoCat v1.0. The unique ID of each GeoDAR reservoir is specified in “id_v11” (consistent with the GeoDAR dataset). Please refer to the attributes of TopoCat and GeoDAR for more details.</p> <p> </p> <p><strong>Data and code availability</strong></p> <p>All datasets are available under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license (<a href="https://creativecommons.org/licenses/by/4.0">https://creativecommons.org/licenses/by/4.0</a>).</p> <p>Please refer to GeoDAR and TopoCat datasets for other details and disclaimers.</p> <p> </p> <p><strong>Citation</strong></p> <p>We request anyone who uses GeoDAR-TopoCat to cite <strong>both GeoDAR and TopoCat papers</strong>:</p> <p>Wang, J., Walter, B. A., Yao, F., Song, C., Ding, M., Maroof, A. S., Zhu, J., Fan, C., McAlister, J. M., Sikder, M. S., Sheng, Y., Allen, G. H., Crétaux, J.-F., and Wada, Y.: GeoDAR: georeferenced global dams and reservoirs database for bridging attributes and geolocations. Earth System Science Data, 14, 1869-1899, 2022, <a href="https://doi.org/10.5194/essd-14-1869-2022">https://doi.org/10.5194/essd-14-1869-2022</a>.</p> <p>Sikder, M. S., Wang, J., Allen, G. H., Sheng, Y., Yamazaki, D., Song, C., Ding, M., Crétaux, J.-F., and Pavelsky, T. M., 2023. Lake-TopoCat: A global lake drainage topology and catchment dataset. Earth System Science Data Discussion, in review, <a href="https://doi.org/10.5194/essd-2022-433">https://doi.org/10.5194/essd-2022-433</a>.</p>
Experimental data for the article on Topological nodal line in superfluid 3He and the Anderson theorem
<p>This submission contains the minimal dataset required to reproduce the experimental findings related to the article titled <em>Topological nodal line in superfluid <sup>3</sup>He and the Anderson theorem</em> associated with DOI 10.1038/s41467-023-39977-2.</p>
Magnetism of Topological Boundary States Induced by Boron Substitution in Graphene Nanoribbons
<p>OPEN DATA related to the research publication:</p> <p>Niklas Friedrich, Pedro Brandimarte, Jingcheng Li, Shohei Saito, Shigehiro Yamaguchi, Iago Pozo, Diego Peña, Thomas Frederiksen, Aran Garcia-Lekue, Daniel Sánchez-Portal, and José Ignacio Pascual, <em>Magnetism of Topological Boundary States Induced by Boron Substitution in Graphene Nanoribbons</em>, Phys. Rev. Lett. <strong>125</strong>, 146801 (2020) [arXiv:2004.10280]</p> <p>Abstract: Graphene nanoribbons (GNRs), low-dimensional platforms for carbon-based electronics, show the promising perspective to also incorporate spin polarization in their conjugated electron system. However, magnetism in GNRs is generally associated with localized states around zigzag edges, difficult to fabricate and with high reactivity. Here we demonstrate that magnetism can also be induced away from physical GNR zigzag edges through atomically precise engineering topological defects in its interior. A pair of substitutional boron atoms inserted in the carbon backbone breaks the conjugation of their topological bands and builds two spin-polarized boundary states around them. The spin state was detected in electrical transport measurements through boron-substituted GNRs suspended between the tip and the sample of a scanning tunneling microscope. First-principle simulations find that boron pairs induce a spin 1, which is modified by tuning the spacing between pairs. Our results demonstrate a route to embed spin chains in GNRs, turning them into basic elements of spintronic devices.</p>
Research data supporting "Observation of a Topological Edge State Stabilized by Dissipation"
<div> <p>This repository contains the data presented in the manuscript titled "Observation of a Topological Edge State Stabilized by Dissipation" by H. Wetter et al., Phys. Rev. Lett. 131, 083801 (2023). The files contain the final data sets relevant to reproduce all plots shown in the paper. Data types are CSV, TIF, SVG, TXT, PNG. No licensed software is required for opening and reading the files.</p> </div>
Coupling charge and topological reconstructions at polar oxide interfaces
<p>Dataset corresponding to the publication 'Coupling charge and topological reconstructions at polar oxide interfaces' (<a href="https://arxiv.org/abs/2107.03359">arXiv:2107.03359</a>) (Phys. Rev. Lett. <strong>127</strong>, 127202) </p>
Topological surface states in epitaxial (SnBi2Te4 )n (Bi2Te3)m natural van der Waals superlattices (data)
<p>This dataset contains the raw data files connected to the figures included in the paper "T<em>opological surface states in epitaxial (SnBi<sub>2</sub>Te<sub>4</sub> )<sub>n</sub> (Bi<sub>2</sub>Te<sub>3</sub>)<sub>m</sub> natural van der Waals superlattices</em>" by S. Fragkos et al., Phys. Rev. Materials <strong>5</strong>, 014203 (2021) <a href="https://doi.org/10.1103/PhysRevMaterials.5.014203">https://doi.org/10.1103/PhysRevMaterials.5.014203</a></p> <p>An Open Access version of the paper can be found here: <a href="https://zenodo.org/record/4562057#.YaDC4NBBxPY">https://zenodo.org/record/4563899#.YaDQ5NBBxPY</a></p>
Type-III Dirac fermions in HfxZr1-xTe2 topological semimetal candidate (data)
<p>This dataset contains the raw data files connected to the figures included in the paper "<em>Type-III Dirac fermions in Hf<sub>x</sub>Zr<sub>1-x</sub>Te<sub>2</sub> topological semimetal candidate</em>" by S. Fragkos et al., Journal of Applied Physics <strong>129</strong>, 075104 (2021); <a href="https://doi.org/10.1063/5.0038799">https://doi.org/10.1063/5.0038799</a></p> <p>An Open Access version of the paper can be found here: <a href="https://zenodo.org/record/4562057#.YaDC4NBBxPY">https://zenodo.org/record/4562057#.YaDC4NBBxPY</a></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>
Sp(2N) Yang-Mills theories on the lattice: scale setting and topology—data release
<p>This release contains all data and metadata used to prepare the publications <a href="https://arxiv.org/abs/2205.09254">Topological susceptibility in Yang-Mills theories</a> and <a href="https://arxiv.org/abs/2205.09364">Sp(2N) Yang-Mills theories on the lattice: scale setting and topology</a>.</p> <p>Included are:</p> <ul> <li>The raw log output from the Wilson flow computation, as well as metadata describing the ensembles used, in `raw_data.zip`. These include all numbers used in the publication (aside from fit parameters) in plaintext form. The archive contains a separate `README.md` describing the layout of the data.</li> <li>All numbers included in the above logs, restructured into HDF5 format for convenience, in `datapackage.h5`.</li> <li>The data presented in all tables in both papers, in CSV format, as described in more detail below.</li> </ul> <p>Further details are given in the file README.md.</p>
Numerical data for "Nonlocal correlations transmitted between quantum dots via short topological superconductor"
<p>Raw numerical data used to produce figures 2-8 in the article <em>Nonlocal correlations transmitted between quantum dots </em><em>via short topological superconductor</em> (arXive preprint <a href="http://arxiv.org/abs/2405.06630">http://arxiv.org/abs/2405.06630</a>), and other data obtained within the same project. See file headers for details concerning the content.</p>
Global River Topology (GRIT) vector datasets
<p>The Global River Topology (GRIT) is a vector-based, global river network that not only represents the tributary components of the global drainage network but also the distributary ones, including multi-thread rivers, canals and delta distributaries. It is also the first global hydrography (excl. Antarctica) produced at 30m raster resolution. It is created by merging Landsat-based river mask (GRWL) with elevation-generated streams to ensure a homogeneous drainage density outside of the river mask (rivers narrower than approx. 30m). Crucially, it uses a new 30m digital terrain model (FABDEM, based on TanDEM-X) that shows greater accuracy over the traditionally used SRTM derivatives. After vectorisation and pruning, directionality is assigned by a combination of elevation, flow angle, heuristic and continuity approaches (based on RivGraph). The network topology (lines and nodes, upstream/downstream IDs) is available as layers and attribute information in the GeoPackage files (readable by QGIS/ArcMap/GDAL).</p> <p>A map of GRIT segments labelled with OSM river names is available here: <a href="https://michelwortmann.com/research/gritv05-segments-river-names/" target="_blank" rel="noopener">Map with names</a></p> <p><strong>Report bugs and feedback</strong></p> <p>Your feedback and bug reports are welcome here: <a href="https://forms.gle/JrT58QStNKBHPJAH6" target="_blank" rel="noopener">GRIT bug report form</a></p> <p>The feedback may be used to improve and validate GRIT in future versions.</p> <p><strong>Regions</strong></p> <p>Vector files are provided in 7 regions with the following codes:</p> <ul> <li>AF - Africa</li> <li>AS - Asia (excl. Siberia)</li> <li>EU - Europe</li> <li>NA - North America</li> <li>SA - South America</li> <li>SI - Siberia</li> <li>SP - South Pacific/Australia</li> </ul> <p>The domain polygons (GRITv06_domain_GLOBAL.gpkg.zip) provide 60 subcontinental catchment groups that are available as vector attributes. They allow for more fine-grained subsetting of data (e.g. with ogr2ogr --where and the domain attribute).</p> <p>Vector files are provided both in the original equal-area Equal Earth Greenwich projection (EPSG:8857) as well as in geographic WGS84 coordinates (EPSG:4326).</p> <p><strong>Change log</strong></p> <ul> <li>v0.6 - 2024-05-30 <ul> <li>Rivers/streams outside of the GRWL mask forced by all OSM water lines (not only those with waterway=river/canal)</li> <li>Some manual directions in the Irrawaddy delta and fixed erronous sink in the Volga delta</li> </ul> </li> <li>v0.5 - 2024-02-14 <ul> <li>Cyclicity and discontinuities resolved through improved algorithms, bug fixes, more sophisticated cycle solving algorithms and some manually forced directions. Only insignificant cycles (non-sinks, less than 50) were removed.</li> <li>Added segment and reach attributes</li> <li>Computational domain fixes</li> <li>Segments include OSM river names</li> <li>Asia domain split into Siberia and rest of Asia</li> <li>Vector files available in EPSG:8857 and EPSG:4326</li> </ul> </li> <li>v0.4 - 2023-03-11<br> <ul> <li>First globally complete dataset published</li> </ul> </li> </ul> <p><strong>Network segments</strong></p> <p>Lines between inlet, outlet, confluence and bifurcation nodes. Files have lines and nodes layers.</p> <p><em><strong>Attribute description of lines layer</strong></em></p> <table> <tbody> <tr> <th>Name</th> <th>Data type</th> <th>Description</th> </tr> </tbody> <tbody> <tr> <td>cat</td> <td>integer</td> <td>domain internal feature ID</td> </tr> <tr> <td>global_id</td> <td>integer</td> <td>global river segment ID, same as FID</td> </tr> <tr> <td>catchment_id</td> <td>integer</td> <td>global catchment ID</td> </tr> <tr> <td>upstream_node_id</td> <td>integer</td> <td>global segment node ID at upstream end of line</td> </tr> <tr> <td>downstream_node_id</td> <td>integer</td> <td>global segment node ID at downstream end of line</td> </tr> <tr> <td>upstream_line_ids</td> <td>text</td> <td>comma-separated list of global river segment IDs connecting at upstream end of line</td> </tr> <tr> <td>downstream_line_ids</td> <td>text</td> <td>comma-separated list of global river segment IDs connecting at downstream end of line</td> </tr> <tr> <td>direction_algorithm</td> <td>float</td> <td>code of RivGraph method used to set the direction of line</td> </tr> <tr> <td>width_adjusted</td> <td>float</td> <td>median river width in m without accounting for width of segments connecting upstream/downstream</td> </tr> <tr> <td>length_adjusted</td> <td>float</td> <td>segment length in m without accounting for width of segments connecting upstream/downstream in m</td> </tr> <tr> <td>is_mainstem</td> <td>integer</td> <td>1 if widest segment of bifurcated flow or no bifurcation upstream, otherwise 0</td> </tr> <tr> <td>strahler_order</td> <td>integer</td> <td>Strahler order of segment, can be used to route in topological order</td> </tr> <tr> <td>length</td> <td>float</td> <td>segment length in m</td> </tr> <tr> <td>azimuth</td> <td>float</td> <td>direction of line connecting upstream-downstream nodes in degrees from North</td> </tr> <tr> <td>sinuousity</td> <td>float</td> <td>ratio of Euclidean distance between upstream-downstream nodes and line length, i.e. 1 meaning a perfectly straight line</td> </tr> <tr> <td>drainage_area_in</td> <td>float</td> <td>drainage area at beginning of segment, partitioned by width at bifurcations, in km2</td> </tr> <tr> <td>drainage_area_out</td> <td>float</td> <td>drainage area at end of segment, partitioned by width at bifurcations, in km2</td> </tr> <tr> <td>drainage_area_mainstem_in</td> <td>float</td> <td>drainage area at beginning of segment, following the mainstem, in km2</td> </tr> <tr> <td>drainage_area_mainstem_out</td> <td>float</td> <td>drainage area at end of segment, following the mainstem, in km2</td> </tr> <tr> <td>bifurcation_balance_out</td> <td>float</td> <td>(drainage_area_out - drainage_area_mainstem_out) / max(drainage_area_out, drainage_area_mainstem_out), dimensionless ratio</td> </tr> <tr> <td>grwl_overlap</td> <td>float</td> <td>fraction of the segment overlapping with the GRWL river mask</td> </tr> <tr> <td>grwl_value</td> <td>integer</td> <td>dominant GRWL value of segment</td> </tr> <tr> <td>name</td> <td>text</td> <td>river name from Openstreetmap where available, English preferred</td> </tr> <tr> <td>name_local</td> <td>text</td> <td>river name from Openstreetmap where available, local name</td> </tr> <tr> <td>n_bifurcations_upstream</td> <td>integer</td> <td>number of bifurcations upstream of segment</td> </tr> <tr> <td>domain</td> <td>text</td> <td>catchment group ID, see domain index file</td> </tr> </tbody> </table> <p> </p> <p><em><strong>Attribute description of nodes layer</strong></em></p> <table> <tbody> <tr> <th>Name</th> <th>Data type</th> <th>Description</th> </tr> </tbody> <tbody> <tr> <td>cat</td> <td>integer</td> <td>domain internal feature ID</td> </tr> <tr> <td>global_id</td> <td>integer</td> <td>global river node ID, same as FID</td> </tr> <tr> <td>catchment_id</td> <td>integer</td> <td>global catchment ID</td> </tr> <tr> <td>upstream_line_ids</td> <td>text</td> <td>comma-separated list of global river segment IDs flowing into node</td> </tr> <tr> <td>downstream_line_ids</td> <td>text</td> <td>comma-separated list of global river segment IDs flowing out of node</td> </tr> <tr> <td>node_type</td> <td>text</td> <td>description of node, one of bifurcation, confluence, inlet, coastal_outlet, sink_outlet, grwl_change</td> </tr> <tr> <td>grwl_value</td> <td>integer</td> <td>GRWL code at node</td> </tr> <tr> <td>grwl_transition</td> <td>text</td> <td>GRWL codes of change at grwl_change nodes</td> </tr> <tr> <td>cycle</td> <td>integer</td> <td>>0 if segment is part of an unresolved cycle, 0 otherwise</td> </tr> <tr> <td>continuity_violated</td> <td>integer</td> <td>1 if flow continuity is violated, otherwise 0</td> </tr> <tr> <td>drainage_area</td> <td>float</td> <td>drainage area, partitioned by width at bifurcations, in km2</td> </tr> <tr> <td>drainage_area_mainstem</td> <td>float</td> <td>drainage area, following the mainstem, in km2</td> </tr> <tr> <td>n_bifurcations_upstream</td> <td>integer</td> <td>number of bifurcations upstream of node</td> </tr> <tr> <td>domain</td> <td>text</td> <td>catchment group, see domain index file</td> </tr> </tbody> </table> <p> </p> <p><strong>Network reaches</strong></p> <p>Segment lines split to not exceed 1km in length, i.e. these lines will be shorter than 1km and longer than 500m unless the segment is shorter. A simplified version with no vertices between nodes is also provided. Files have lines and nodes layers.</p> <p><em><strong>Attribute description of lines layer</strong></em></p> <table> <tbody> <tr> <th>Name</th> <th>Data type</th> <th>Description</th> </tr> </tbody> <tbody> <tr> <td>cat</td> <td>integer</td> <td>domain internal feature ID</td> </tr> <tr> <td>segment_id</td> <td>integer</td> <td>global segment ID of reach</td> </tr> <tr> <td>global_id</td> <td>integer</td> <td>global river reach ID, same as FID</td> </tr> <tr> <td>catchment_id</td> <td>integer</td> <td>global catchment ID</td> </tr> <tr> <td>upstream_node_id</td> <td>integer</td> <td>global reach node ID at upstream end of line</td> </tr> <tr> <td>downstream_node_id</td> <td>integer</td> <td>global reach node ID at downstream end of line</td> </tr> <tr> <td>upstream_line_ids</td> <td>text</td> <td>comma-separated list of global river reach IDs connecting at upstream end of line</td> </tr> <tr> <td>downstream_line_ids</td> <td>text</td> <td>comma-separated list of global river reach IDs connecting at downstream end of line</td> </tr> <tr> <td>grwl_overlap</td> <td>float</td> <td>fraction of the reach overlapping with the GRWL river mask</td> </tr> <tr> <td>grwl_value</td> <td>integer</td> <td>dominant GRWL value of node</td> </tr> <tr> <td>grwl_width_median</td> <td>float</td> <td>median width of the GRWL river mask, meters</td> </tr> <tr> <td>grwl_width_std</td> <td>float</td> <td>standard deviation of width of the GRWL river mask, meters</td> </tr> <tr> <td>length</td> <td>float</td> <td>length of reach in meters</td> </tr> <tr> <td>sinuousity</td> <td>float</td> <td>ratio of eucledian distance betwen upstream-downstream nodes and line length, i.e. 1 meaning a perfectly straight line</td> </tr> <tr> <td>azimuth</td> <td>float</td> <td>direction of line connecting upstream-downstream nodes in degrees from North</td> </tr> <tr> <td>domain</td> <td>text</td> <td>catchment group, see domain index file</td> </tr> </tbody> </table> <p><em><strong>Attribute description of nodes layer</strong></em></p> <table> <tbody> <tr> <th>Name</th> <th>Data type</th> <th>Description</th> </tr> </tbody> <tbody> <tr> <td>cat</td> <td>integer</td> <td>domain internal feature ID</td> </tr> <tr> <td>segment_node_id</td> <td>integer</td> <td>global ID of segment node at segment intersections, otherwise blank</td> </tr> <tr> <td>n_segments</td> <td>integer</td> <td>number of segments attached to node</td> </tr> <tr> <td>global_id</td> <td>integer</td> <td>global river reach node ID, same as FID</td> </tr> <tr> <td>upstream_line_ids</td> <td>text</td> <td>comma-separated list of global river reach IDs flowing into node</td> </tr> <tr> <td>downstream_line_ids</td> <td>text</td> <td>comma-separated list of global river reach IDs flowing out of node</td> </tr> <tr> <td>domain</td> <td>text</td> <td>catchment group, see domain index file</td> </tr> </tbody> </table> <p> </p> <p><strong>Catchments</strong></p> <p>Catchment outlines for entire river basins (network components, including coastal drainage areas). Catchments for segments (aka. subbasins) and reaches are also available on request.</p> <p><em><strong>Attribute description</strong></em></p> <table> <tbody> <tr> <th>Name</th> <th>Data type</th> <th>Description</th> </tr> </tbody> <tbody> <tr> <td>cat</td> <td>integer</td> <td>domain internal feature ID</td> </tr> <tr> <td>global_id</td> <td>integer</td> <td>global catchment ID, same as global_id of segment/reach ID if is_coastal == 0 for respective catchments or the catchment_id for component_catchments, same as FID</td> </tr> <tr> <td>area</td> <td>float</td> <td>catchment area in km2</td> </tr> <tr> <td>is_coastal</td> <td>integer</td> <td>1 for coastal drainage areas, 0 otherwise</td> </tr> <tr> <td>domain</td> <td>text</td> <td>catchment group, see domain index file</td> </tr> </tbody> </table> <p> </p> <p><strong>Raster </strong></p> <p>Upstream drainage area and other raster-based products are also available upon request.</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>
Topologies collected from 3 Community Networks
<p>This data-set contains graph topologies of several networks that were analysed in two scientific works and used in several more. </p> <p>The data in the 'topologies' folder contains two sets of data: The '2014' folder contains about 5000 snapshots of three community networks, namely Freifunk Wien, Freifunk Graz and ninux Rome. <br> This data-set was collected between 2014 and 2015 and is at the base of the work "A week in the life of three large Wireless Community Networks" (link to the paper below), it describes three large-scale wireless mesh networks running in three cities. <br> The data-set is fully described in the paper, here I report the information needed to use it.<br> - For FFWien and ninux, each snapshot is taken once every 5 minutes, for Graz, one every 10.<br> - one snapshot corresponds to the real state of the network in a specific moment, correlating the database of active nodes with the topology exported by the routing protocol. Some elaboration has been made to merge into one logical nodes some nodes that were running multiple instances of the routing protocol in the same physical location (see the paper for details)<br> - the format is the well known graphml XML format, you can open the files with networkx, gephi and many more tools<br> - the link weight represents the ETX metric (high = bad, see the paper)</p> <p>The network-evolution folder contains the network graphs collected for the two networks of Wien and Graz only, but in a different period of time, and with a much larger time-span between the snapshots. This data-set was used for the paper "On the Technical and Social Structure of Community Networks", and again, represents the physical structure of the network, annotated with link quality from the routing protocol. Format is graphml, metric is ETX.</p> <p>Finally, the mailing_list folder contains the ninux-ml.xml that contains the interactions in the mailing list of the ninux network, as described in the same paper. </p> <p>The second part of the data-set was collected and elaborated during the netCommons (see http://netcommons.eu) research project, while the first was collected before, but contributed to the results of the project too.</p> <p>If you use the data, pleas cite the relevant papers below.</p> <p>If you need more information, feel free to contact me:</p> <p>Leonardo Maccari, Assistant Professor @DISI, University of Trento<br> Tel: +39 0461 285323, www.disi.unitn.it/~maccari, gpg ID: AABE2BD7<br> leonardo.maccari(at)unitn.it.</p> <p>Related Papers:</p> <p>"A week in the life of three large Wireless Community Networks"</p> <p>https://ans.disi.unitn.it/users/maccari/assets/files/bibliography/Maccari2014Week.pdf</p> <p>"On the Technical and Social Structure of Community Networks"</p> <p>https://ans.disi.unitn.it/users/maccari/assets/files/bibliography/Maccari2016Technical.pdf</p>
A Topological Data Analysis Perspective on Non-Covalent Interactions in Relativistic Calculations - supplementary information
<p>This repository contains the supplementary data to the following publication:</p> <p>"A Topological Data Analysis Perspective on Non-Covalent Interactions in Relativistic Calculations", by the same authors.</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.