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12,393 results for “materiality”
Supplementary Material Accompaning the Paper "A Revised Proposal of Proto-Tukanoan Consonants and Tukanoan Family Classification"
<p>This is the dataset that was used in Chacon (2014) publication.</p> <p>The data has been aligned using Edictor (List 2017).</p> <p>References</p> <p>Chacon, Thiago. 2014. ‘A Revised Proposal of Proto-Tukanoan Consonants and Tukanoan Family Classification <sup>1</sup>’. <em>International Journal of American Linguistics</em> 80 (3): 275–322. doi:10.1086/676393.</p> <p>List, Johann-Mattis. forthcoming. ‘A Web-Based Interactive Tool for Creating, Inspecting, Editing, and Publishing Etymological Datasets’. In <em>Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics. System Demonstrations</em>.</p>
Supplementary material 1 from: Baum S, Weih M, Bolte A (2012) Stand age characteristics and soil properties affect species composition of vascular plants in short rotation coppice plantations. BioRisk 7: 51-71. https://doi.org/10.3897/biorisk.7.2699
Number of plots containing the respective species is stated.
Supplementary material 1 from: Venere P, Valente G, Vitorino C, Cabral-de-Mello D, Oliveira C, Souza I, Martins C (2012) Comparative cytogenetics of ten species of cichlid fishes (Teleostei, Cichlidae) from the Araguaia River system, Brazil, by conventional cytogenetic methods. Comparative Cytogenetics 6(2): 163-181. https://doi.org/10.3897/compcytogen.v6i2.1739
Table with synthesis of karyotypic traits of cichlids.
Supplementary material 1 from: Humair F, Edwards P, Siegrist M, Kueffer C (2014) Understanding misunderstandings in invasion science: why experts don't agree on common concepts and risk assessments. NeoBiota 20: 1-30. https://doi.org/10.3897/neobiota.20.6043
Interview guideline: perception / valuation of ecosystem change related non-native, invasive plants
Supplementary material 1 from: Steinberg E, Nieves M, Mudry M (2014) Multiple sex chromosome systems in howler monkeys (Platyrrhini, Alouatta). Comparative Cytogenetics 8(1): 43-69. https://doi.org/10.3897/compcytogen.v8i1.6716
Data matrix. (doi: 10.3897/CompCytogen.v8i1.6716.app1) File format: Microsoft Word file (doc).:
Supplementary material 1 from: Scacchetti P, Pansonato-Alves J, Utsunomia R, Oliveira C, Foresti F (2011) Karyotypic diversity in four species of the genus Gymnotus Linnaeus, 1758 (Teleostei, Gymnotiformes, Gymnotidae): physical mapping of ribosomal genes and telomeric sequences. Comparative Cytogenetics 5(3): 223-235. https://doi.org/10.3897/compcytogen.v5i3.1375
Nexus file of aligned COI and COII nucleotide sequences.
Supplementary material 1 from: Haemig P (2014) Aztec introduction of the great-tailed grackle in ancient Mesoamerica: Formal defense of the Sahaguntine historical account. NeoBiota 22: 59-75. https://doi.org/10.3897/neobiota.22.6791
This file show in bold type the novel parts of my submitted manuscript
Supplementary material 1 from: Virkkala R, Rajasärkkä A (2012) Preserving species populations in the boreal zone in a changing climate: contrasting trends of bird species groups in a protected area network. Nature Conservation 3: 1-20. https://doi.org/10.3897/natureconservation.3.3635
Mean densities and number of observations of species in 1981–1999 and in 2000–2009
Additonal material for the dissertation "An Accelerated Solution Method for Two-Stage Stochastic Models in Disaster Management": Data, MATLAB codes and results
<p>File "DataImport" contains a "ReadMe" file, raw data for all case studies in Excel and the MATLAB code "ImportData.m" importing Excel data into MATLAB</p> <p>File "LShaped" contains a "ReadMe" file, all data in the form of matrices and the MATLAB code "LShaped_MultiCut.m" solving all case studies via the standard or accelerated L-shaped method using a multi-cut approach</p> <p>File "Results" contains a "ReadMe" file, results of all case studies and computation time required by Gurobi, der standard L-shaped method and accelerated L-shaped method</p>
Training data for 'Mapping-by-sequencing' tutorial (Galaxy Training Material)
<p>The data provided here are part of a Galaxy Training Network tutorial that demonstrates mapping-by-sequencing analysis and represent a subsample of the data used in Sun & Schneeberger, 2015 (DOI:10.1007/978-1-4939-2444-8_19).</p>
Supplementary Material for Nested Segmentation of Web Search Queries
<p>1. Query test of SGCL12 [SGCL12QueryTestSet.txt]</p> <p>2. Outputs of 16 nesting algorithms on the query test set of SGCL12 [Input flat segmention: Saha Roy et al., SIGIR 2012] [Nested Segmentation Outputs SGCL12.zip]</p> <p>3. Query set of TREC-WT [TREC-WTQueryTestSet.txt]</p> <p>4. Outputs of 16 nesting algorithms on the query test set of TREC-WT [Input flat segmention: Saha Roy et al., SIGIR 2012] [Nested Segmentation Outputs TREC-WT.zip]</p> <p>5. Code and executables for generating the nested segmentations for a set of queries [Code and execs for generating nested segmentations.zip]</p> <p>6. Code and executables for IR-based evaluation of a nested segmentation [Code and execs for IR evaluation of a nested segmentation.zip]</p> <p>7. Readme.txt</p>
Supplementary material: Mammal diversity responses to anthropic, environmental, and seasonal changes within Caatinga seasonal dry forest landscapes
<p>Caatinga's conservation and biodiversity are threatened due to the intensification of anthropic activities and climate change. The mammals have different responses to seasonal and anthropic changes, however particularly in Caatinga, these effects are still poorly understood. We assessed the influence of anthropic (distance from urban areas and wind farms), environmental (distance from water), and seasonal (Normalized Difference Vegetation Index – NDVI and land surface temperature - LST) variables on the number of records and richness of medium and large-sized mammals in Brazilian Caatinga. We used camera traps in 2016/2017 and 2018/2019, estimated the variation (cv) of NDVI and LST, and generated Euclidean distance maps to anthropic and environmental variables at 250, 500, and 1000 m spatial scales. We performed Generalized Linear Models, used the Akaike information criterion, and calculated model averaging to assess the strength and direction of effect and the uncertainties of the winner models, respectively. The distance from wind farms and maximum LST had a noticeable effect on the number of records and total richness. The distance from wind farms had a negative effect on the records of <em>Dicotyles tajacu</em> and a positive effect on the records of <em>Leopardus pardalis</em> and richness. The maximum LST had a negative effect on the records of<em> Leopardus pardalis </em>and a positive<em> </em>effect on the records of <em>Puma concolor </em>and<em> Cerdocyon thous</em>. Our results emphasize that an unsustainable expansion of wind farms is likely to compromise mammal diversity. We found an opposite pattern for some species regarding LST. However, it is important to highlight that the conservation of vegetation areas on the top of mountains and springs, and the installation of artificial water sources are important strategies to mitigate the impacts of high temperatures on mammals' biodiversity in Caatinga.</p>
Supplementary Material for "Exposure Assessment & Risks associated to wearing silver nanoparticle-coated textiles"
<p>Calculation of release rate constants and parameters for the calculations of dermal exposure. Source code for Matlab.</p>
Supplementary material from: Modeling biodiesel properties by preference learning: case study of cetane number
<p><span>Data obtained from the available bibliography between the year 2002 and 2022. This document contains the FAME distribution and cetane number of 543 biodiesels.</span></p> <p><span> </span><span>The dataset is divided into three sections within the worksheet.</span></p> <ul> <li><span>The first section contains the definition of the data's feedstock and its source reference. The reference includes the year, DOI (if available, as some are collected from books), publication journal, article title, and authors.</span></li> <li><span>The second section describes the FAME distribution, starting from C4:0 up to C24:0, including a column of unidentified FAMEs.</span></li> <li><span>The third and final section describes the measured property Cetane Number.</span></li> </ul>
Supplementary materials for "Phonetic differences between affirmative and feedback head nods in German Sign Language (DGS): A pose estimation study"
<div> <pre>This is the supplementary data for the article "Phonetic differences between affirmative and feedback head nods in German Sign Language (DGS): A pose estimation study" by Anastasia Bauer, Anna Kuder, Marc Schulder and Job Schepens.<br><br>The supplementary data consists of three components, stored in separate directories:<br>- <code>annotations/</code>: The manual annotations of head nod categories, produced by Anna Kuder and Anastasia Bauer.<br>- <code>pose_analysis/</code>: Code and input/output files for the pose-based automatic analysis of phonetic attributes head nods, produced by Marc Schulder.<br>- <code>statistical_analysis/</code>: Code for the statistical analysis of the other two components and for the creation of related figures, produced by Job Schepens.<br><br>For further details, see the README files of the respective directories.</pre> </div>
3. MC T0TEM output-UJV-material JRQ
<div>Fractesus project. Fracture test mini-CT. Master curve output JRQ. UJV. </div> <div> <div> <p> </p> </div> </div>
2. MC_T0TEM input-UJV-material JRQ
<div>Fractesus project. Fracture test mini-CT. Master curve input JRQ. UJV. </div> <div> <div> <p> </p> </div> </div>
Supplementary material for: Sparse Arrays for Four-Dimensional Scanning Transmission Electron Microscopy
<p>Supplementary material for: Sparse Arrays for Four-Dimensional Scanning Transmission Electron Microscopy</p> <p>The link to the main publication with detailed information will be added later.</p> <p>The three videos show liv eand offline processing with the CheeTah T3, ASI Serval, ASI Accos, LiberTEM and CEOS Panta Rhei. They are also available at https://www.youtube.com/playlist?list=PLZCH_qD2RkB5oFdBc12xcYUcbmWZpJQHh</p> <p>benchmark.ipynb is a Jupyter notebook that was used to test the performance, as reported in the paper.</p> <p>raw_csr.zip is a test dataset recorded on a gold grid in the CSR format that can be opened by LiberTEM</p> <p>benchmark.ipynb is a Jupyter notebook that shows how the test dataset can be opened and processed with common Python packages and with LiberTEM.</p>
Randomised material parameter piezoelectric impedance dataset with structured electrodes (RaPIDstruc)
<h1>Description of the dataset</h1> <p>This dataset contains the results of 652517 finite-element simulations of the complex, frequency-dependent electrical impedance of a piezoelectric disc with structured electrodes and randomised material parameters. Each impedance consists of 1500 samples in the frequency domain up to 9.37 MHz. The electrode structure is the result of a topology optimisation process with the aim to enhance the sensitivity of the impedance with respect to all material parameters (DOI: <a href="https://jsss.copernicus.org/articles/12/163/2023/">10.5194/jsss-12-163-2023</a>). The piezoelectric disc has a radius of 5 mm and a thickness of 1 mm. The transverse isotropic material parameters are sampled from independent uniform distributions with ranges that are intended to represent the behaviour of hard as well as soft piezoceramic materials. The parameters of the Rayleigh damping model (alpha_M and alpha_K) are sampled from a logarithmic distribution to account for the larger parameter range.</p> <table> <tbody> <tr> <td><strong>Parameter</strong></td> <td><strong>Min</strong></td> <td><strong>Max</strong></td> <td><strong>Unit</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>c11</td> <td>115</td> <td>150</td> <td>GPa</td> <td>Elastic stiffness </td> </tr> <tr> <td>c12</td> <td>70</td> <td>110</td> <td>GPa</td> <td>Elastic stiffness </td> </tr> <tr> <td>c13</td> <td>65</td> <td>95</td> <td>GPa</td> <td>Elastic stiffness </td> </tr> <tr> <td>c33</td> <td>95</td> <td>125</td> <td>GPa</td> <td>Elastic stiffness </td> </tr> <tr> <td>c44</td> <td>18</td> <td>25</td> <td>GPa</td> <td>Elastic stiffness </td> </tr> <tr> <td>eps11</td> <td>5</td> <td>18</td> <td>nF/m</td> <td>Dielectric permittivity</td> </tr> <tr> <td>eps33</td> <td>6</td> <td>12</td> <td>nF/m</td> <td>Dielectric permittivity</td> </tr> <tr> <td>e15</td> <td>7</td> <td>19</td> <td>C/m^2</td> <td>Piezoelectric coupling</td> </tr> <tr> <td>e31</td> <td>1</td> <td>7.5</td> <td>C/m^2</td> <td>Piezoelectric coupling</td> </tr> <tr> <td>e33</td> <td>13</td> <td>23</td> <td>C/m^2</td> <td>Piezoelectric coupling</td> </tr> <tr> <td>alpha_M</td> <td>2</td> <td>150</td> <td>1/ms</td> <td>Mass-proportional damping</td> </tr> <tr> <td>alpha_K</td> <td>0.1</td> <td>0.65</td> <td>ns</td> <td>Stiffness-proportional damping</td> </tr> <tr> <td>density</td> <td>7600</td> <td>7850</td> <td>kg/m^3</td> <td>Density</td> </tr> </tbody> </table> <h1>Files</h1> <p>The dataset contains the following files:</p> <ul> <li><strong>dataset.hdf5</strong>: The main dataset file in HDF5 format. Refer to the next section on how to load the dataset.</li> <li><strong>simulation.xml</strong>: The file describing the parameters of the finite element simulation. This file can be used with <a href="https://www.opencfs.org/">openCFS</a> along with the mesh and material file to simulate the behaviour of the piezoelectric ceramic in the frequency domain. This simulation file was used with randomised material parameters to generate the dataset.</li> <li><strong>material.xml</strong>: The material file used for the simulation with exemplary material parameters values.</li> <li><strong>piezo.geo</strong>: The gmsh geometry file with the axisymmetric representation of the piezoelectric disc and the electrode structure.</li> <li><strong>piezo.msh</strong>: The mesh file generated with gmsh using the piezo.geo file.</li> </ul> <h1>Loading the dataset</h1> <p>The dataset is stored as a HDF5 file, which can be opened with all libraries that support that format, e.g. in Python using the h5py library:</p> <pre><code>import h5py # Open the dataset in read mode. file = h5py.File("dataset.hdf5", "r") # Impedances as a 652517 x 1500 array of complex numbers. impedances = file["impedances"] # Material parameter values as a 652517 x 13 array of real numbers. parameters = file["parameters"] # Frequency vector of the impedance with length 1500. frequencies = file["meta"]["frequencies"] # 13 strings with the identifiers of the material parameters. parameter_labels = file["meta"]["parameter_labels"]</code></pre> <h1>Simulating impedances</h1> <p>To generate a result for the electrical impedance using the supplied simulation files, download and install openCFS and call the executable with the simulation.xml, but omit the file extension, e.g.:</p> <pre><code>cfsbin.exe simulation</code></pre> <p>The path to the executable of openCFS will depend on your operating system and installation. Running the simulation will result in the creation of several files and folders. Among those files will be the result for the electric charge on one of the electrodes of the sample, which will be placed in the 'history' subfolder. We can determine the current by taking the time derivative of the charge and already know the voltage because we excited the piezoceramic with an electric potential of 1 V in the simulation. Because the simulation is conducted in the frequency regime, all we have to do is to divide voltage by current to get the frequency dependent electrical impedance. The loading of the result and calculation of the impedance is implemented in the following Python script as an example:</p> <pre><code>import numpy as np # Load result file for electric charge result_path = 'history/simulation-elecCharge-surfRegion-ground.hist' data = np.loadtxt(result_path) frequency = data[:, 0] # Convert polar representation from file to complex numbers. charge = data[:, 1] * np.exp(1j * 2 * np. pi / 360 * data[:, 2]) # Excitation potential is 1 V in simulation. potential = 1 # Determine impedance by applying Z = V / I = V / (j omega Q). impedance = potential / (1j * 2 * np.pi * frequency * charge)</code></pre>
Supplementary materials: "Synthesizing Particle-in-Cell Simulations Through Learning and GPU Computing for Hybrid Particle Accelerator Beamlines"
<p>Data archive for stage 3 of manuscript for PASC24. See Readme stage 3.txt for more details.</p> <p> </p> <p>This work was supported by the Laboratory Directed Research and Development Program of Lawrence Berkeley National Laboratory under U.S. Department of Energy Contract No. DE-AC02-05CH11231 and by LLNL under Contract DE-AC52-07NA27344. This material is based upon work supported by the U.S. Department of Energy, OFfice of Science, Office of High Energy Physics, General Accelerator R&D (GARD), under contract number DE-AC02-05CH11231. This material is based upon work supported by the CAMPA collaboration, a project of the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research and Office of High Energy Physics, Scientific Discovery through Advanced Computing (SciDAC) program. This research was supported by the Exascale Computing Project (17-SC-20-SC), a joint project of the U.S. Department of Energy's Office of Science and National Nuclear Security Administration, responsible for delivering a capable exascale ecosystem, including software, applications, and hardware technology, to support the nation's exascale computing imperative.This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 using NERSC award HEP-ERCAP0023719.</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.