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12,393 results for “materiality”
Supplementary material 1 from: Grabowska J, Kvach Yu, Rewicz T, Pupins M, Kutsokon I, Dykyy I, Antal L, Zięba G, Rakauskas V, Trichkova T, Čeirāns A, Grabowski M (2020) First insights into the molecular population structure and origins of the invasive Chinese sleeper, Perccottus glenii, in Europe. NeoBiota 57: 87-107. https://doi.org/10.3897/neobiota.57.48958
Table S1. Table presents Chinese sleeper haplotypes frequency (belonging to three distinguished groups) found in studies sites.
Supplementary material 7 from: Wiemers M, Chazot N, Wheat CW, Schweiger O, Wahlberg N (2020) A complete time-calibrated multi-gene phylogeny of the European butterflies. ZooKeys 938: 97-124. https://doi.org/10.3897/zookeys.938.50878
Figure S5. Time-calibrated tree of European butterflies Section IV: Nymphalidae Part II: Subfamilies Libytheinae, Danainae & Satyrinae
Supplementary material 4 from: Wiemers M, Chazot N, Wheat CW, Schweiger O, Wahlberg N (2020) A complete time-calibrated multi-gene phylogeny of the European butterflies. ZooKeys 938: 97-124. https://doi.org/10.3897/zookeys.938.50878
Figure S2. Time-calibrated tree of European butterflies Section I: Papilionidae, Hesperiidae & Pieridae
Supplementary material 6 from: Wiemers M, Chazot N, Wheat CW, Schweiger O, Wahlberg N (2020) A complete time-calibrated multi-gene phylogeny of the European butterflies. ZooKeys 938: 97-124. https://doi.org/10.3897/zookeys.938.50878
Figure S4. Time-calibrated tree of European butterflies Section III: Nymphalidae Part I: Subfamilies Limenitidinae, Heliconiinae, Apaturinae & Nymphalinae
Supplementary material 1 from: Wiemers M, Chazot N, Wheat CW, Schweiger O, Wahlberg N (2020) A complete time-calibrated multi-gene phylogeny of the European butterflies. ZooKeys 938: 97-124. https://doi.org/10.3897/zookeys.938.50878
Table listing of European butterfly species with higher taxonomy, voucher codes and accession numbers for the sequences used to build the phylogeny.
Supplementary Material for the paper "Efficient Strategies for CEGAR-based Model Checking"
<p>Supplementary material for the paper "Efficient Strategies for CEGAR-based Model Checking" by Akos Hajdu and Zoltan Micskei. The material includes a detailed report (report.html) and all artifacts to replicate our measurements and analysis (artifact.tar.gz).</p> <p><em>Version 1.2.0 corresponds to the final, published paper. This version (1.2.1) is a minor update that added two new plots.</em></p>
Supplementary material 3 from: Petersen M, Pramann B, Toepfer R, Neumann J, Enke H, Hoffmann J, Mauer R (2020) Research Data Management - Current status and future challenges for German non-university research institutions. Research Ideas and Outcomes 6: e55141. https://doi.org/10.3897/rio.6.e55141
Praxisbericht: Entwicklung eines Maßnahmenkatalogs zur Verbesserung des Forschungsdatenmanagements am Herder-Institut für historische Ostmitteleuropaforschung
Spam: Software for Practical Analysis of Materials - datasets for tutorials
<p>Datasets for <a href="https://ttk.gricad-pages.univ-grenoble-alpes.fr/spam/intro.html">spam tutorials</a>:</p> <ul> <li><strong>VEC4</strong> dataset from E.M. Charalampidou, E. Tudisco, S.A. Hall <ul> <li><a href="https://theses.fr/2011GRENI090">Experimental details available here: Charalampidou, E.M. (2011), "Experimental study of localised deformation in porous sandstones", <em>PhD Thesis</em></a></li> <li>This dataset is two x-ray tomography scans of a notched sandstone sample scanned before and after deformation in an external rig.<br> There is a rigid body displacement between the two scans that can be taken into account nicely with a registration.<br> Scans from Laboratoire 3SR, Grenoble.<br> </li> </ul> </li> <li><strong>M2EA05</strong> dataset from E. Andò, G. Viggiani and J. Andrade.<br> This data already used in the following papers for comparison to numerical simulations: <ul> <li><a href="https://doi.org/10.1016/j.jmps.2016.02.021">Kawamoto, R., Andò, E., Viggiani, G., & Andrade, J. E. (2016). Level set discrete element method for three-dimensional computations with triaxial case study. <em>Journal of the Mechanics and Physics of Solids</em>, <em>91</em>, 1-13</a></li> <li><a href="https://doi.org/10.1680/jgeot.18.T.030">Nadimi, S., Fonseca, J., Andò, E., & Viggiani, G. (2019). A micro finite-element model for soil behaviour: experimental evaluation for sand under triaxial compression. <em>Géotechnique</em>, 1-6</a></li> <li>This dataset is a series of x-ray tomographies acquired during the triaxial compression of a small sample of a "Martian Simulant" soil, performed in Laboratoire 3SR, Grenoble.<br> </li> </ul> </li> <li> <p><strong>c01_F10_b4</strong> dataset from O. Stamati, E. Roubin, E. Andò and Y. Malecot</p> <ul> <li> <p><em>This dataset belongs to a paper which is under review</em></p> </li> <li> <p>This is a 4-binned x-ray tomography of a small cylindrical sample of <em>micro-concrete</em><br> Scans from Laboratoire 3SR, Grenoble.<br> </p> </li> </ul> </li> <li> <p><strong>SandNX</strong> dataset from M. Milatz</p> <ul> <li> <p><em>The dataset is currently being analysed further</em></p> </li> <li> <p>See this publication for the experimental setup: <a href="https://doi.org/10.1007/s11440-020-00922-y">Milatz, M. (2020). An automated testing device for continuous measurement of the hysteretic water retention curve of granular media. <em>Acta Geotechnica</em>, 1-19</a><br> Data acquired on NeXT-Grenoble, a simultaneous Neutron and X-ray scanner at the ILL in Grenoble (<a href="https://dx.doi.org/10.5291/ILL-DATA.UGA-73">Experiment UGA-73</a>).<br> Neutron tomography has had a x0.7 downscale to bring it approximately in line with the pixel size for the x-ray tomography.<br> </p> </li> </ul> </li> <li> <p><strong>YehyaConcreteNX</strong> dataset from M. Yehya, A. Tengattini, E. Andò, F. Dufour</p> <ul> <li> <p><a href="https://doi.org/10.1016/j.nima.2018.02.039">Yehya, M., Ando, E., Dufour, F., & Tengattini, A. (2018). Fluid-flow measurements in low permeability media with high pressure gradients using neutron imaging: Application to concrete. <em>Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment</em>, <em>890</em>, 35-42</a></p> </li> <li> <p>Data acquired on NeXT-Grenoble, a simultaneous Neutron and X-ray scanner at the ILL in Grenoble (<a href="https://dx.doi.org/10.5291/ILL-DATA.UGA-25">Experiment UGA-25</a>).</p> </li> </ul> </li> </ul>
Data of A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths
<pre>Data related to the publication (we would be grateful if you could cite the paper in the case in which you are using the data) title = "A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths", journal = "Computer Methods in Applied Mechanics and Engineering", pages = " 113234", year = "2020", issn = "0045-7825", doi = "https://doi.org/10.1016/j.cma.2020.113234", author = "Wu, Ling and Nguyen, Van Dung and Kilingar, Nanda Gopala and Noels, Ludovic"</pre>
Supplementary material 1 from: Iturrieta-González I, Gené J, Wiederhold N, García D (2020) Three new Curvularia species from clinical and environmental sources. MycoKeys 68: 1-21. https://doi.org/10.3897/mycokeys.68.51667
Figure S1. Phylogenetic tree of the genus Curvularia based on Maximum Likelihood analysis obtained by RAxML, using the combined analysis of ITS, gapdh and tef1 and rooted with Bipolaris maydisCBS 136.29 and Bipolaris saccharicolaCBS 155.26
Supplementary material 3 from: Bieberich J, Feldhaar H, Lauerer M (2020) Micro-habitat and season dependent impact of the invasive Impatiens glandulifera on native vegetation. NeoBiota 57: 109-131. https://doi.org/10.3897/neobiota.57.51331
Figure S1. Initial model of the piecewise structural equation modeling (SEM) for summer (A) and spring (B)
Supplementary material 4 from: Bieberich J, Feldhaar H, Lauerer M (2020) Micro-habitat and season dependent impact of the invasive Impatiens glandulifera on native vegetation. NeoBiota 57: 109-131. https://doi.org/10.3897/neobiota.57.51331
Table S1. Result of the automated model selection approach identifying environmental variables that affected the cover of Impatiens glandulifera in summer 2016 and spring 2017
Supplementary material 7 from: Bieberich J, Feldhaar H, Lauerer M (2020) Micro-habitat and season dependent impact of the invasive Impatiens glandulifera on native vegetation. NeoBiota 57: 109-131. https://doi.org/10.3897/neobiota.57.51331
Figure S3. Micro-habitat specific impact of I. glandulifera on the resident plant species composition
Numerical Fire Spread Simulation Based on Material Pyrolysis - An Application to the CHRISTIFIRE Phase 1 Horizontal Cable Tray Tests - Data Set
<p>This data set is a supplementary resource for the article "<a href="http://www.mdpi.com/2571-6255/3/3/33">Numerical Fire Spread Simulation Based on Material Pyrolysis - An Application to the CHRISTIFIRE Phase 1 Horizontal Cable Tray Tests</a>", published by the peer-reviewed open access journal <a href="https://www.mdpi.com/journal/fire">Fire</a>. It is part of the "<a href="https://www.researchgate.net/project/Fire-Propagation-in-Cable-Tray-Installations">Fire Propagation in Cable Tray Installations</a>" project. The provided data is only a summary of the full data produced for the article, due to its size.</p> <p>This article was previously submitted to the Fire Safety Journal and got eventually rejected.</p> <p>The information is structured into multiple *.rar archieves, which mimic the sub-directory structure created for the work. To be able to run the analysis scripts without much tweaking, extract all archieves into the same directory, with each archieve being a sub-directory in it.</p> <p>The data set is comprised of:</p> <ul> <li>The PROPTI and FDS input files used for the inverse modelling process (IMP) -- the 13* archieves.</li> <li>Full FDS simulation data of the mirco-combustion calorimeter (MCC) simulations of the best parameter sets per generation of the IMP runs, for jacket and insulator materials.</li> <li>Full FDS simulation data of the Cone Calorimeter simulations of the best parameter sets per generation of the IMP runs, for all three (25 kW/m², 50 kW/m², 75 kW/m²) incident heat flux conditions.</li> <li>FDS input files for the MT3 simulations, but full data only for the best parameter sets per IMP run (see below).</li> <li>Jupyter notebooks used for the analysis of the simulation responses including the scripts and plots generated for, and used in, the paper (RunReports).</li> <li>A general information directory, containing the FDS input file templates, experimental data used as target and Python scripts containing helper functions.</li> <li>Videos of a qualitative comparison of the SmokeView animation of the best parameter set in a cable tray simulation against a video from the experiment and an animation of the GAUGE_HEAT_FLUX development for the same simulation over the course of the simulation.</li> </ul> <p>Due to the size of the MT3 simulation data, only the FDS input files for the best parameter sets per generation are uploaded. Complete FDS simulation data is only provieded for the best perameter set of each IMP run, these are :</p> <p>IMP run, best rep.<br> --------------------------------<br> imp_13b, 110751<br> imp_13c, 121106<br> imp_13d, 137738<br> imp_13e, 97493<br> imp_13f, 91988<br> imp_13g, 86988<br> imp_13h, 17480<br> imp_13b_1, 19033<br> imp_13b_2, 25043<br> imp_13b_3b, 24149<br> imp_13b_4, 29666<br> imp_13h_1, 10685<br> imp_13h_2, 10024<br> imp_13h_3, 10109<br> imp_13i, 13253</p> <p> </p> <p>Note: The individual runs of the IMP are named differently as compared to the labeling used in the paper, as described below:</p> <p>IMP run, label in paper<br> --------------------------------</p> <p>imp_13b, T<sub>b</sub><br> imp_13c, T<sub>a</sub><br> imp_13d, T<sub>c</sub><br> imp_13e, T<sub>a,b,c</sub><br> imp_13f, T<sub>b,c</sub><br> imp_13g, T<sub>a,c</sub><br> imp_13h, T<sub>a,b,c</sub>L<sub>A,L1,HC</sub><br> imp_13b_1, T<sub>b</sub>L<sub>1</sub><br> imp_13b_2, T<sub>b</sub>P<sub>L1</sub><br> imp_13b_3b, T<sub>b</sub>P<sub>L1</sub>L<sub>1</sub><br> imp_13b_4, T<sub>b</sub>P<sub>L2,HT</sub><br> imp_13h_1, T<sub>a,b,c</sub>P<sub>A,L1,HC</sub>L<sub>1</sub><br> imp_13h_2, T<sub>a,b,c</sub>P<sub>A,L1,HC</sub>L<sub>2</sub><br> imp_13h_3, T<sub>a,b,c</sub>P<sub>A,L1,HC</sub>L<sub>3</sub><br> imp_13i, T<sub>b</sub>P<sub>A,L1,HC</sub></p> <p> </p> <p>Version 2 changes:</p> <p>Added pre-print.</p> <p> </p> <p>Version 3 changes:</p> <p>Added new files that where produced during the revision process, after the original manuscript got rejected by the Fire Safety Journal. This revision corresponds to the intitial manuscript that was submitted to the journal <a href="https://www.mdpi.com/journal/fire">Fire</a>. The respective Zip archieves are labeled with a "_Revision01".</p>
RIBuild: Material properties for historic building materials and insulation materials
<p>This dataset consists of</p> <p>1) Excel sheets with material properties for a) a collection of historic building materials and b) insulation materials suited for internal insulation. Material properties were collected from all RIBuild partner countries. The main purpose was to locate data set not included in the DELPHIN database that contained the needed data for DELPHIN simulations.</p> <p>2) Excel sheets with material properties for specific historic building materials used in Italy and Switzerland. As these materials were supposed to be included in hygrothermal simulations with DELPHIN performed in RIBuild, a full material characterization was required as described in RIBuild deliverable D2.1 and in the DELPHIN specifications. Material characterization was performed at RTU.</p> <p>Requirements concerning input need for DELPHIN simulations are described in RIBuild deliverable D2.1.</p> <p>Overview of data files to be found in 'RIBuild data WP2 Mat Prop' as part of this dataset.</p> <p> </p>
RIBuild: Laboratory based investigations of the materials' water activity and pH relative to mould growth
<p>This dataset contains measurements of relative humidity and temperature in small test walls, from mycometer test and from measurements of moisture content in materials in the test walls.</p> <p>The objective of the study is to find the causes and conditions that lead to mould growth between a existing wall and installed insulation systems, and to suggest solutions to prevent mould growth.The study examines whether the alkaline environment (pH> 9), which may occur in the adhesive glue joint between the existing wall and the installed insulation is sufficient to prevent mould growth,even if the moisture (water activity (aw)) at the interface exceeds the levels (aw> 0.75) commonly considered critical for mould growth.</p> <p>The dataset also contains extensive photo documentation of preparations and carrying out the experiments.</p> <p>Further details to be found in appendix 5 of RIBuild deliverable 2.2 and in<br> Jensen, N.F., Bjarløv, S.P., Rode, C., Andersen, B., Møller, E.B. (submitted 2020). Laboratory based investigation of the materials’ water activity and pH relative to fungal growth in internally insulated solid masonry walls. Indoor Air. Wiley.</p> <p>Overview of data files to be found in 'RIBuild data WP2_DTU Small walls' as part of this dataset.</p>
Supporting material for "Reappraisal of the effective radiative forcing of ozone-depleting substances"
<p>This is a companion repository, containing data and scripts needed to reproduce the figures and table entries in Morgenstern et al, Reappraisal of the effective radiative forcing of ozone-depleting substances, Geophysical Research Letters, 2020. For more information, please read the README.TXT file which is part of this repository.</p>
From smart apes to human brain boxes. A uniquely derived brain shape in late hominins clade: supplementary material
<p>DATA.rda: an RData file containing the phylogenetic tree, the landmark and semilandmark sets used in the analyses of the endocast shape (123 specimens), the semilandmark sets used in the asymmetry analyses (120 specimens), the vector of the corresponding ECV values and a mesh containing the triangulated semilandmark configuration.</p> <p>script.R: the annotated script used to replicate the analyses reported in this study.</p>
Experts views on information evaluation and verification: source reliability, content credibility and audiovisual material checking
<p>Experts can be asked about nformation evaluation and verification: source reliability, content credibility and audiovisual material checking.</p> <p>OER available at: <a href="https://multimedia.ciberimaginario.es/genially/2020/CRESCEnt/4.2.1/">https://multimedia.ciberimaginario.es/genially/2020/CRESCEnt/4.2.1/</a> </p>
Supplementary Material to "Comparison of equilibrium climate sensitivity estimates from slab ocean, 150-year, and longer simulations"
<p>This is supplementary data for Dunne, J. P., et al. (2020). Comparison of equilibrium climate sensitivity estimates from slab ocean, 150-year, and longer simulations. Geophysical Research Letters. 2020GL088852R. Data in this repository can be unpacked and the files are in the form- NetCDF and matlab files, along with a README. </p> <p>Model data for global average values of net top of the atmosphere radiative balance (W m-2) and 2m air temperature (degrees celsius) for preindustrial control and 4x abrupt CO2 simulations for a variety of models used in LongRunMIP, CMIP5, and CMIP6, as well as scripts used to calculate equilibrium climate sensitivity from these simulations. </p> <p> </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.