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359 results for “packing”
Shear-stabilized jammed packings
<p>Authors are listed in alphabetical order.</p> <p>This data set contains approximately 140,000 shear-stabilized jammed packings, as described in [1].</p> <p>These packings contain <span class="math-tex">\(N = 16 \ldots 4096\)</span> particles with harmonic interactions, under a confining pressure <span class="math-tex">\(p=10^{-7}\ldots10^{-2}\)</span>. Ensemble sizes range from 10 (N=4096) to 5000 (N=16). </p> <p><strong>Particle interactions</strong></p> <p>The simulation code minimizes the enthalpy</p> <p><span class="math-tex">\(H = \sum_{} \frac{k}{2} \delta_{ij}^2 + pL^2\)</span></p> <p>where L² is the simulation box area, p the externally applied pressure, k=1 the spring constant and </p> <p><span class="math-tex">\(\delta_{ij} = \left\{ \begin{array}{ll} R_i + R_j - |\vec{r_{ij}}| & \textrm{if } |\vec{r_{ij}}| < R_i + R_j, \\ 0 & \textrm{otherwise.} \end{array}\right.\)</span></p> <p> </p> <p><strong>Data files</strong></p> <p>The packings are stored in an HDF5 data file, with the following format:</p> <ul> <li>Example name: N1024P3162e-3_tables.h5 <ul> <li>Packings with <span class="math-tex">\(N=1024\)</span> particles</li> <li>Pressure <span class="math-tex">\(p = 3.162\cdot 10^{-3}\)</span></li> </ul> </li> <li>/packing_attr_cache is a data table containing properties of each packing, such as <ul> <li>the lattice vectors L1 and L2, describing the positions of periodic copies,</li> <li>sxx, syy, sxy, the boundary stresses,</li> <li>phi, the packing fraction,</li> <li>N - Ncorrected, the effective number of particles,</li> <li>Z, the contact number, and</li> <li>path, the path in the HDF5 file this packing can be found</li> </ul> </li> <li>Packings are stored in a directory structure, e.g. /N1024/P3.1620e-03/0090 <ul> <li>Each directory has attributes with the same data as in packing_attr_cache </li> <li>Each directory contains a table 'particles' which stores x,y and r. <ul> <li>HDF5 does not support float128 values, so the positions are stored as two float64 values x and x_err. Sum them as float128 to get the full-resolution value.</li> </ul> </li> </ul> </li> </ul> <p> </p> <p>[1] Simon Dagois-Bohy, Brian P. Tighe, Johannes Simon, Silke Henkes, and Martin van Hecke. <em>Soft-Sphere Packings at Finite Pressure but Unstable to Shear. </em>Phys. Rev. Lett. <strong>109</strong>, 095703. http://dx.doi.org/10.1103/PhysRevLett.109.095703</p>
Contact changes in shear-stabilized jammed packings
<p>Authors are listed in alphabetical order.</p> <p>This data set contains the result of small simple shear deformations applied to approximately 140,000 shear-stabilized jammed packings (see [...]), focusing on contact changes, as described in [1,2,3,4].</p> <p>These packings contain <span class="math-tex">\(N = 16 \ldots 4096\)</span> particles with harmonic interactions, under a confining pressure <span class="math-tex">\(p=10^{-7}\ldots10^{-2}\)</span>. Ensemble sizes range from 10 (N=4096) to 5000 (N=16). </p> <p>In addition, a data file summarizing properties of the first contact change for each packing is provided.</p> <p><strong>Particle interactions</strong></p> <p>The simulation code minimizes the enthalpy</p> <p><span class="math-tex">\(H = \sum_{} \frac{k}{2} \delta_{ij}^2 + pL^2\)</span></p> <p>where L² is the simulation box area, p the externally applied pressure, k=1 the spring constant and </p> <p><span class="math-tex">\(\delta_{ij} = \left\{ \begin{array}{ll} R_i + R_j - |\vec{r_{ij}}| & \textrm{if } |\vec{r_{ij}}| < R_i + R_j, \\ 0 & \textrm{otherwise.} \end{array}\right.\)</span></p> <p> </p> <p>During shear, the boundary conditions are changed, and the system is relaxed to the new state. The simulation uses a bisection algorithm to efficiently step towards each subsequent contact change; see [3,4] for details.</p> <p><strong>Data files</strong></p> <p>The packings are stored in HDF5 data files. For each ensemble, we provide two files: one with and one without particle positions:</p> <ul> <li>N1024~P3162e-3_shear_noparticles.h5 includes all simulation data, but omits particle positions (see below for which data is included).</li> <li>For small data sets, N1024~P3162e-3_shear.h5 contains all simulations and all particle positions</li> <li>For large data sets, N1024~P3162e-3_shear_partial.h5 contains <em>a subset</em> of all simulations, but with all particle positions.</li> <li>Full particle positions for all simulations are available upon request to the authors. Please contact Martin van Hecke .</li> </ul> <p>All files follow the same HDF5 layout:</p> <ul> <li>Example name: N1024~P3162e-3_shear.h5 and N1024~P3162e-3_shear_noparticles.h5 <ul> <li>Packings with <span class="math-tex">\(N=1024\)</span> particles</li> <li>Pressure <span class="math-tex">\(p = 3.162\cdot 10^{-3}\)</span></li> </ul> </li> <li>Packings are stored in a directory structure, e.g. /N1024/P3.1620e-03/0090/SR for the packing with id 0090. <ul> <li>This directory contains a table 'data' indicating system parameters for each simulation step: <ul> <li>boundary conditions L1 and L2 (also as L, alpha, delta)</li> <li>pressure P,</li> <li>strain gamma,</li> <li>stresses s_xy (simple shear), s_xx and s_yy, </li> <li>number of contacts Ncontacts, contact number Z and number of rattlers #rattler</li> <li>number of changed contacts for this contact change bisection Nchanges, N+ (created), N- (broken)</li> <li>contact number Z</li> <li>energy U, enthalpy H and their change in the last relaxation step (dU, dH)</li> <li>step runtime t_run (seconds), #CG, #FIRE (number of conjugate gradient and FIRE iterations)</li> <li>path to the packing corresponding to this state (does not always exist for each state for older simulations)</li> </ul> </li> <li>Each state is saved in /N1024/P3.1620e-03/0090/SR/0000 (initial), /N1024/P3.1620e-03/0090/SR/0001, ...etc. <ul> <li>States are not included in the _noparticles.h5 files</li> <li>Some files omit intermediate positions, and only store positions just before and just after a contact change.</li> <li>The format of these directories is the same as in https://dx.doi.org/10.5281/zenodo.59216.</li> </ul> </li> </ul> </li> </ul> <p>Finally, we provide a summary file (shear_summary_cache.h5) which contains one table ('data') with properties of the first contact change of all packings. We provide the following columns:</p> <ul> <li>The variable postfix determines whether the value was calculated in the initial state (_base), just before the first contact change (_min) or just after the first contact change (_plus).</li> </ul> <p> </p> <ul> <li>General/simulation properties <ul> <li>Number of particles 'N'</li> <li>Random seed ['num', 'PackingNumber_base']</li> <li>External pressure 'P0_base'</li> <li>Simulation step ['i_min', 'i_plus']</li> </ul> </li> <li>Relaxation statistics <ul> <li>Last change in enthalpy during relaxation ['dH_base', 'dH_plus', 'dH_min']</li> <li>Last change in energy during relaxation ['dU_base', 'dU_plus', 'dU_min']</li> <li>Maximum gradient ['maxGrad_base', 'gg_min', 'gg_plus']</li> <li>Initial simulation runtime ['runtime (s)_base']</li> </ul> </li> <li>State properties <ul> <li>Number of rattlers 'N - Ncorrected_base'</li> <li>Number of non-rattler particles ['Neff_min', 'Neff_plus']</li> <li>Number of contacts ['Ncontacts_plus', 'Ncontacts_min']</li> <li>Contact number z ['Z_base', 'Z_min', 'Z_plus']</li> <li>Internal pressure ['P', 'P_base', 'P_min', 'P_plus']</li> <li>Mean overlap δ ['mean_delta_base']</li> <li>Packing fraction ['phi_base', 'phi_min', 'phi_plus']</li> <li>Enthalpy ['H_base', 'H_plus', 'H_min']</li> <li>Energy ['Uhelper_base', 'U_min', 'U_plus']</li> <li>Simple shear parameter alpha ['alpha_base', 'alpha_min', 'alpha_plus']</li> <li>Pure shear parameter delta ['delta_base', 'delta_plus', 'delta_min']</li> <li>Square root of area ['L_base', 'L_min', 'L_plus']</li> <li>Stresses on boundaries: <ul> <li>xx ['sxx_base', 's_xx_min', 's_xx_plus',]</li> <li>yy [ 'syy_base', 's_yy_plus', 's_yy_min',]</li> <li>xy ['sxy_base', 's_xy_plus', 's_xy_min']</li> </ul> </li> <li>Elastic moduli: <ul> <li> ['c1_base', 'c1_min', 'c1_plus',</li> <li>'c2_base', 'c2_min', 'c2_plus',</li> <li>'c3_base', 'c3_min', 'c3_plus',</li> <li>'c4_base', 'c4_plus', 'c4_min',</li> <li>'c5_base', 'c5_plus', 'c5_min',</li> <li>'c6_base', 'c6_min', 'c6_plus',</li> <li>'Dac_base', 'Dac_plus', 'Dac_min',</li> </ul> </li> <li>AC component of G(θ) ['Gac_base', 'Gac_min', 'Gac_plus']</li> <li>DC component of G(θ) ['Gdc_base', 'Gdc_min', 'Gdc_plus',]</li> <li>AC component of U(θ) ['Uac_base', 'Uac_plus', 'Uac_min']</li> <li>DC component of U(θ) ['Udc_base', 'Udc_plus', 'Udc_min']</li> <li>Simple shear ['Galpha_base', 'Galpha_plus', 'Galpha_min' ]</li> </ul> </li> <li>Contact change properties <ul> <li>Applied strain gamma ['gamma_plus', 'gamma_min'] <ul> <li><em>gamma_min is used as contact change strain</em></li> </ul> </li> <li>Number of created/broken contacts ['N+_plus', 'N+_min', 'N-_plus', 'N-_min']</li> <li>Number of changed contacts (=N<sup>+</sup> + N<sup>-</sup>) ['Nchanges_plus', 'Nchanges_min']</li> <li>Making & breaking strain from upar and uperp: <ul> <li>simple linear (SL) solution: ['gmk_SL_base', 'gbk_SL_base']</li> <li>full quadratic (FQ) solution: ['gmk_FQ_base' 'gbk_FQ_base']</li> </ul> </li> <li>G up to CC from fit σ=Gγ & error bar ['Glin', 'Glinerr']</li> <li>G up to CC from fit σ=Gγ + λγ² & error bar ['Gquad', 'Gquaderr']</li> <li>λ up to CC from fit σ=Gγ + λγ² & error bar ['lambdaquad', 'lambdaquaderr']</li> </ul> </li> </ul> <p>[1] Simon Dagois-Bohy, Brian P. Tighe, Johannes Simon, Silke Henkes, and Martin van Hecke. <em>Soft-Sphere Packings at Finite Pressure but Unstable to Shear. </em>Phys. Rev. Lett. <strong>109</strong>, 095703. http://dx.doi.org/10.1103/PhysRevLett.109.095703</p> <p>[2] Merlijn S. van Deen, Johannes Simon, Zorana Zeravcic, Simon Dagois-Bohy, Brian P. Tighe, and Martin van Hecke. <em>Contact changes near jamming</em>. Phys. Rev. E <strong>90</strong> 020202(R). http://dx.doi.org/10.1103/PhysRevE.90.020202</p> <p>[3] Merlijn S. van Deen, Brian P. Tighe, and Martin van Hecke. <em>Contact Changes of Sheared Systems: Scaling, Correlations, and Mechanisms</em>. arXiv:1606.04799. https://arxiv.org/abs/1606.04799</p> <p>[4] Merlijn S. van Deen. <em>Mechanical Response of Foams: Elasticity, Plasticity, and Rearrangements</em>. PhD Thesis, Leiden University, 2016. https://openaccess.leidenuniv.nl/handle/1887/40902</p>
Low-entropy Packed Binary Detection using Hardware Performance Counters
<p><span>Malware analysis faces a critical challenge in accurately identifying packed executables, especially those with low entropy. Existing software-based solutions often fail in detecting packers used by malware, resulting in inaccurate classifications. To address this shortcoming, in this study we introduce a novel method using<br>Hardware Performance Counters (HPCs) to facilitate the classification of binary packers due to HPCs’ minimal access overhead and ability to obviate the necessity for source code. We trained classic machine-learning models by selecting relevant hardware attributes associated with the unpacking procedure for detecting<br>packers used by low-entropy binary programs. Extensive experiments shows the substantial role played by Hardware Performance Counters in detecting binary packing characterized by low entropy,<br>offering a promising avenue for further exploration and refinement of techniques in malware analysis<br><br><br></span></p> <p><span>The following zip files are executables that represent low entropy versions of software packers using byte-padding. The name of the files are the names of the packers which are represened, Acprotect, Armadillo, Aspack, Nspack, Pecompact, Petite, UPX, and Zprotect. These can be used to measure the unpacking process using hardware performance counters in order to test & train machine earning classifiers for accurate classification of low entropy packers.</span></p>
Observation of a regular structure formation on the surface of vibrated ball beds started from random lose packing
<p>Near 4,000 2-mm diameter plastic balls were poured 88 times into plexiglass cylinder of internal diameter 26 mm. Then, such initially random loose-packing systems/beds were vibrated vertically with 100 Hz frequency using the vibration table Vibrax (Renfert GmbH, Germany) working in sinusoidal mode until a regular structure was observed on the cylinder surface. The power levels of the vibrations in the recorded ordering of balls were selected to represent all four levels (1, 2, 3 or 4) of vibrations available in the table, where number 1 means the weakest vibration and 4 means the strongest one.</p> <p>Locations of the balls on all sides of a vibrated cylindrical bed were simultaneously recorded on one video frame thanks to the use of two perpendicular mirrors, which enables observation of four images: one of the real cylinder and three of its mirror reflections. View of the table with the attached cylinder containing balls and two mirrors is presented in Fig. 1, while an explanation of the scene, as seen by the recording camera, is given in the scheme in Fig. 2. Video names were given in a standard form explained in the README.txt file.</p>
Social Accounting Matrix for Lithuania, 2017 (with the shift to bioplastics in packing industry)
<p>The dataset is based on doi: 10.5281/zenodo.5077893 but includes bioplastics as the main input for the production of plastic sacks and bags.</p>
New upper bounds for some instances from benchmark for vector packing problem
<p>This dataset is a result of the research: Đorđe Stakić, Miodrag Živković, Ana Anokić, "A Reduced Variable Neighborhood Search Approach to the Heterogeneous Vector Bin Packing Problem", Information Technology and Control, 2021, 50(4), 808-826, <a href="https://doi.org/10.5755/j01.itc.50.4.29009">https://doi.org/10.5755/j01.itc.50.4.29009</a> Files are given by algorithm described in it. </p> <p>This dataset consists of 14 solutions with better bounds for instances described in paper: Heßler, K., Gschwind, T., Irnich, S. Stabilized branch-and-price algorithms for vector packing problems. European Journal of Operational Research, 2018, 271(2), 401-419. <a href="https://doi.org/10.1016/j.ejor.2018.04.047">https://doi.org/10.1016/j.ejor.2018.04.047</a> </p> <p>File structure: </p> <p>Instance name: UB: solution (bins with indices of items)</p> <ul> <li>CL_04_100_06: 627</li> <li>CL_04_100_08: 642</li> <li>CL_04_200_01: 1293</li> <li>CL_05_100_06: 314</li> <li>CL_05_100_08: 321</li> <li>CL_05_100_10: 327</li> <li>CL_05_200_02: 627</li> <li>CL_05_200_03: 633</li> <li>CL_05_200_04: 630</li> <li>CL_05_200_05: 632</li> <li>CL_05_200_06: 627</li> <li>CL_05_200_07: 634</li> <li>CL_05_200_08: 635</li> <li>CL_05_200_10: 632</li> </ul>
Data Archive: Local and Global Order in Dense Packings of Semiflexible Polymers of Hard Spheres
<p>Data archive corresponding to the publication "Local and Global Order in Dense Packings of Semi-flexible 2 Polymers of Hard Spheres" by D. Martinez-Fernandez et al., Polymers 15, 551 (2023); DOI: https://doi.org/10.3390/polym15030551.</p> <p>Please see README.txt for instructions on how to access and read the files from the crystallographic analysis based on the CCE norm descriptor.</p> <p>All snapshots have been generated and successively analyzed by the Simu-D software.</p>
Materials Science Optimization Benchmark Dataset for Multi-Objective, Multi-Fidelity Optimization of Hard-Sphere Packing Simulations
<p>Benchmarks are an essential driver of progress in scientific disciplines. Ideal benchmarks mimic real-world tasks as closely as possible, where insufficient difficulty or applicability can stunt growth in the field. Benchmarks should also have sufficiently low computational overhead to promote accessibility and repeatability. The goal is then to win a “Turing test” of sorts by creating a surrogate model that is indistinguishable from the ground truth observation (at least within the dataset bounds that were explored), necessitating a large amount of data. In the fields of materials science and chemistry, industry-relevant optimization tasks are often hierarchical, noisy, multi-fidelity, multi-objective, high-dimensional, and non-linearly correlated while exhibiting mixed numerical and categorical variables subject to linear and non-linear constraints. To complicate matters, unexpected, failed simulation or experimental regions may be present in the search space. In this study, 494498 random hard-sphere packing simulations representing 206 CPU days worth of computational overhead were performed across nine input parameters with linear constraints and two discrete fidelities each with continuous fidelity parameters and results were logged to a free-tier shared MongoDB Atlas database. Two core tabular datasets resulted from this study: 1. a failure probability dataset containing unique input parameter sets and the estimated probabilities that the simulation will fail at each of the two steps, and 2. a regression dataset mapping input parameter sets (including repeats) to particle packing fractions and computational runtimes for each of the two steps. These two datasets are used to create a surrogate model as close as possible to running the actual simulations by incorporating simulation failure and heteroskedastic noise. For the regression dataset, percentile ranks were computed within each of the groups of identical parameter sets to enable capturing heteroskedastic noise. This is in contrast with a more traditional approach that imposes a-priori assumptions such as Gaussian noise e.g., by providing a mean and standard deviation. A similar approach can be applied to other benchmark datasets to bridge the gap between optimization benchmarks with low computational overhead and realistically complex, real-world optimization scenarios.</p> <p>For usage instructions, see https://matsci-opt-benchmarks.readthedocs.io/.</p>
Packing provenance using CPM RO-Crate profile
<p>This dataset is an <a href="https://www.researchobject.org/ro-crate/">RO-Crate</a> that bundles artifacts of an AI-based computational pipeline execution. It is an example of application of the <a href="https://by-covid.github.io/cpm-ro-crate/0.1/">CPM RO-Crate profile</a>, which integrates the <a href="https://doi.org/10.1038/s41597-022-01537-6">Common Provenance Model</a> (CPM), and the <a href="https://www.researchobject.org/workflow-run-crate/profiles/process_run_crate">Process Run Crate profile</a>.</p> <p>As the CPM is a groundwork for the<em> ISO 23494 Biotechnology — Provenance information model for biological material and data</em> provenance standards series development, the resulting profile and the example is intended to be presented at one of the ISO TC275 WG5 regular meetings, and will become an input for the<em> ISO 23494-5 Biotechnology — Provenance information model for biological material and data — Part 5: Provenance of Data Processing</em> standard development.</p> <p><strong>Description of the AI pipeline</strong></p> <p>The goal of the AI pipeline whose execution is described in the dataset is to train an AI model to detect the presence of carcinoma cells in high resolution human prostate images. The pipeline is implemented as a set of python scripts that work over a filesystem, where the datasets, intermediate results, configurations, logs, and other artifacts are stored. In particular, the AI pipeline consists of the following three general parts:</p> <ul> <li> <p><strong>Image data preprocessing</strong>. Goal of this step is to prepare the input dataset – whole slide images (WSIs) and their annotations – for the AI model. As the model is not able to process the entire high resolution images, the preprocessing step of the pipeline splits the WSIs into groups (training and testing). Furthermore, each WSI is broken down into smaller overlapping parts called patches. The background patches are filtered out and the remaining tissue patches are labeled according to the provided pathologists’ annotations.</p> </li> <li> <p><strong>AI model training</strong>. Goal of this step is to train the AI model using the training dataset generated in the previous step of the pipeline. Result of this step is a trained AI model.</p> </li> <li> <p><strong>AI model evaluation</strong>. Goal of this step is to evaluate the trained model performance on a dataset which was not provided to the model during the training. Results of this step are statistics describing the AI model performance.</p> </li> </ul> <p>In addition to the above, execution of the steps results in generation of log files. The log files contain detailed traces of the AI pipeline execution, such as file paths, model weight parameters, timestamps, etc. As suggested by the CPM, the logfiles and additional metadata present on the filesystem are then used by a provenance generation step that transforms available information into the CPM compliant data structures, and serializes them into files. </p> <p>Finally, all these artifacts are packed together in an RO-Crate.</p> <p>For the purpose of the example, we have included only a small fragment of the input image dataset in the resulting crate, as this has no effect on how the Process Run Crate and CPM RO-Crate profiles are applied to the use case. In real world execution, the input dataset would consist of terabytes of data. In this example, we have selected a representative image for each of the input dataset parts. As a result, the only difference between the real world application and this example would be that the resulting real world crate would contain more input files. </p> <p><strong>Description of the RO-Crate</strong></p> <p><strong>Process Run Crate related aspects</strong></p> <p>The Process Run Crate profile can be used to pack artifacts of a computational workflow of which individual steps are not controlled centrally. Since the pipeline presented in this example consists of steps that are executed individually, and that the pipeline execution is not managed centrally by a workflow engine, the process run crate can be applied. </p> <p>Each of the computational steps is expressed within the crate’s ro-crate-metadata.json file as a pair of elements: 1) SW used to create files; 2) specific execution of that SW. In particular, we use the SoftwareSourceCode type to indicate the executed python scripts and the CreateAction type to indicate actual executions. </p> <p>As a result, the crate consists the seven following “executables”:</p> <ul> <li> <p>Three python scripts, each corresponding to a part of the pipeline: preprocessing, training, and evaluation.</p> </li> <li> <p>Four provenance generation scripts, three of which implement the transformation of the proprietary log files generated by the AI pipeline scripts into CPM compliant provenance files. The fourth one is a meta provenance generation script.</p> </li> </ul> <p>For each of the executables, their execution is expressed in the resulting ro-crate-metadata.json using the CreateAction type. As a result, seven create-actions are present in the resulting crate.</p> <p>Input dataset, intermediate results, configuration files and resulting provenance files are expressed according to the underlying RO Crate specification.</p> <p><strong>CPM RO-Crate related aspects</strong></p> <p>The main purpose of the CPM RO-Crate profile is to enable identification of the CPM compliant provenance files within a crate. To achieve this, the CPM RO-Crate profile specification prescribes specific file types for such files: CPMProvenanceFile, and CPMMetaProvenanceFile.</p> <p>In this case, the RO Crate contains three CPM Compliant files, each documenting a step of the pipeline, and a single meta-provenance file. These files are generated as a result of the three provenance generation scripts that use available log files and additional information to generate the CPM compliant files. In terms of the CPM, the provenance generation scripts are implementing the concept of provenance finalization event. The three provenance generation scripts are assigned SoftwareSourceCode type, and have corresponding executions expressed in the crate using the CreateAction type.</p> <p><strong>Remarks</strong></p> <p>The resulting RO Crate packs artifacts of an execution of the AI pipeline. The scripts that implement individual steps of the pipeline and provenance generation are not included in the crate directly. The implementation scripts are hosted on github and just referenced from the crate’s ro-crate-metadata.json file to their remote location.</p> <p>The input image files included in this RO-Crate are coming from the <a href="http://gigadb.org/dataset/100439">Camelyon16 dataset</a>.</p>
Incidence of SARs-CoV-2 in Gütersloh county, Germany, after the outbreak in the slaughterhouse and meat packing plant Tönnies
<p>Figure </p> <p>Seven day incidence of SARS-CoV-2 per 100,000 people from March 15 to September 3, 2020 in Gütersloh, North Rhine-Westphalia, Germany</p> <p>Table</p> <p>Pandemic control measures in Gütersloh county</p>
Dataset accompanying the article: Analyzing X-Ray tomographies of granular packings
<p>This dataset (and the added analysis software) belong to the article: <em>Analyzing X-Ray tomographies of granular packings</em> in Review of Scientific Instruments.</p> <p>The abstract of the article: Starting from three-dimensional volume data of a granular packing, as e.g. obtained by X-ray Computed Tomography, we discuss methods to first detect the individual particles in the sample and then analyze their properties. This analysis includes the pair correlation function, the volume and shape of the Voronoi cells and the number and type of contacts formed between individual particles. We mainly focus on packings of monodisperse spheres, but we will also comment on other monoschematic particles such as ellipsoids and tetrahedra. This paper is accompanied by a package of free software containing all programs (including source code) and an example three-dimensional dataset which allows the reader to reproduce and modify all examples given.</p> <p> </p>
Underlying dataset for battery pack degradation - Understanding aging in parallel-connected lithium-ion batteries under thermal gradients
<p>This record constitutes the raw data underlying the paper "<i>Battery pack degradation - Understanding aging in parallel-connected lithium-ion batteries under thermal gradients</i>" (<a href="https://www.researchsquare.com/article/rs-2535223/v1">preprint link</a>)</p><p>The dataset contains all raw data, processed data and analysis codes used to generate figures in the publication. Abstract is as follows:</p><blockquote><p>Practical lithium-ion battery systems require parallelisation of tens to hundreds of cells, however understanding of how pack-level thermal gradients influence lifetime performance remains a research gap. Here we present an experimental study of surface cooled parallel-string battery packs (temperature range 20-45 °C), and identify two main operational modes; convergent degradation with homogeneous temperatures, and (the more detrimental) divergent degradation driven by thermal gradients. We attribute the divergent case to the, often overlooked, cathode impedance growth. This was negatively correlated with temperature and can cause positive feedback where the impedance of cells in parallel diverge over time; increasing heterogeneous current and state-of-charge distributions. These conclusions are supported by current distribution measurements, decoupled impedance measurements and degradation mode analysis. From this, mechanistic explanations are proposed, alongside a publicly available aging dataset, which highlights the critical role of capturing cathode degradation in parallel-connected batteries; a key insight for battery pack developers.</p></blockquote>
Matrices for a thermal model of a battery pack
<p>This dataset contains matrices for a numerical thermal model of a battery pack.</p> <p>For more information see the description in the <a href="https://morwiki.mpi-magdeburg.mpg.de/morwiki/index.php/Battery_pack">MOR Wiki</a>.</p>
Das Europäische Parlament in der Wirtschafts- und Währungsunion: Six-Pack und Fiskalpakt
<p><strong>Das Europäische Parlament in der Wirtschafts- und Währungsunion (WWU): Six-Pack und Fiskalpakt</strong></p> <p> </p> <p>Siehe auch:</p> <ul> <li>Walli, Thomas (2016): <a href="https://www.icer.at/app/download/9759439652/ICER+Paper+Vol+2+No+1+2016.pdf?t=1516781694">Stärkere Integration – mangelnde Legitimation? Der Einfluss des Europäischen Parlaments auf die WWU-Governance seit Beginn der Euro-Krise am Beispiel des Six-Packs und Fiskalpakts</a>, in: <em>ICER Papers on European Governance</em> 2 (1).</li> </ul> <p> </p> <p><em>Zitiervorschlag Six-Pack:</em></p> <p>Walli, Thomas (2016): Das Europäische Parlament in der Wirtschafts- und Währungsunion: Six-Pack und Fiskalpakt. Appendix: Daten und Tabellen, DOI: 10.5281/zenodo.5707670.</p>
Simple OMERO project packed with omero-cli-transfer
<p>This is a simple zip file with a "minimal" OMERO project packed using <a href="http://pypi.org/project/omero-cli-transfer">omero-cli-transfer</a>. It contains 4 datasets with examples of "regular" images, single-file-multiple-images (.ndpi), multiple-files-multiple-images (.vsi) and an image without an underlying file (exported as a tiff - obviously, upon reimporting, it WILL have an underlying file!). These have multiple annotations, tags and ROIs of multiple types, to cover most of the existing/common use cases in an OMERO instance.</p> <p>To unpack it into a project in your OMERO server, use "omero transfer unpack simple_project.zip".</p>
Quality control traceability during the packing and release process of Ceratitis capitata for Sterile Insect Technique
<p><strong>Abstract</strong></p> <p>In programs applying the Sterile Insect Technique (SIT), the quality of insects deployed in the field determines the success in preventing, suppressing, containing, or eradicating the pest population. In the fruit fly emergence and release facility (ERF) of the Moscamed Program in Mexico, irradiated pupae of <em>Ceratitis capitata</em> are packed in Mexico-type towers, and key adult quality parameters, such as emergence, fliers, and survival, are determined throughout the packing, handling and release process. However, different methodologies are used to estimate the percentage of fliers in the different stages of the process, raising doubts of whether observed differences are due to the effect of each stage or to the methodology used. With this in mind, we developed an alternative called “Adult Flier device” (= AF-device) to evaluate the adult flier parameter following a critical evaluation path of five steps: 1) upon arrival at ERF, 2) post-packing, 3) post-holding, 4) post-chilling, and 5) post-release, where adult fliers and survival under stress were evaluated. We also compared the current methodologies for the estimation of "absolute fliers" available in different operating manuals. Our results suggest that the AF-device allows reliable traceability of sterile insect quality parameters throughout the packing and release process, since no significant differences were observed with the control treatments. In the chilling stage, the five methodologies tested were equivalent, but the AF-device was less time-consuming and required less manpower and biological material than the other methodological options. Our results demonstrate that the use of the AF-device can be a feasible, versatile, innovative, and efficient alternative to evaluate quality control parameters throughout the process of packing and releasing sterile insects, providing reliable results in a timely manner with less hand labor using minimal biological material.</p>
Electronic appendix to: Spectral Induced Polarization (SIP) of Denitrification-Driven Microbial Activity in Column Experiments Packed with Calcareous Aquifer Sediments
<p>This is the electronic appendix of the publication <br> C. Strobel, S. Abramov, J. A. Huisman, O.A. Cirpka, A. Mellage (2022): Spectral Induced Polarization (SIP) of Denitrification-Driven Microbial Activity in Column Experiments Packed with Calcareous Aquifer Sediments (submitted)</p>
Data for "Sound velocity of hexagonal close-packed iron to the Earth's inner core pressure"
<p>This file is the dataset used in the article "Sound velocity of hexagonal close-packed iron to the Earth's inner core pressure", Nat. Commun. 13, 7211 (2022). https://doi.org/10.1038/s41467-022-34789-2</p>
Data for "Grand canonically optimized grain boundary phases in hexagonal close-packed titanium"
<p>Supporting data of GB structures and scripts for the paper titled "Grand canonically optimized grain boundary phases in hexagonal close-packed titanium."</p>
Data Pack fro VirSorter: mining viral signal from microbial genomic data
<p>This is the data pack for VirSorter, the publication of which by Roux et al. titled "<strong>VirSorter: mining viral signal from microbial genomic data</strong>" appeared in PeerJ on 2015-05-28 (<a href="https://doi.org/10.7717/peerj.985">doi:10.7717/peerj.985</a>).</p> <p>Most up-to-date tutorials and the code for VirSorter can be found at the GitHub repository <a href="https://github.com/simroux/VirSorter">https://github.com/simroux/VirSorter</a>.</p> <p>The original source of this data pack was here (last accessed 2018-02-03): <a href="http://datacommons.cyverse.org/browse/iplant/home/shared/imicrobe/VirSorter/virsorter-data.tar.gz">http://datacommons.cyverse.org/browse/iplant/home/shared/imicrobe/VirSorter/virsorter-data.tar.gz</a></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.