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12,393 results for “materiality”
Witchcraft trials in the Czech lands: Data on persons, places, charges, punishments, and material substances
<p>This is the most comprehensive digital dataset of witchcraft trials in the Czech lands since the earliest case in 1491 until as late as 1785. The dataset covers 257 cases. It records the year, the suspects' names, their sex, places of residence (including geographic coordinates), charges, results of the trial, places of interrogation (including geographic coordinates), and the material substances they were charged of using to perform magic. The dataset allows researchers to conduct systematic and quantitative research into early modern withcraft trials, crime and punishment, as well as the imagery of magic and evil-doing. It also allows to include the Czech lands in broader quantitative studies of European witchcraft trials on a large temporal and geographic scale. Five B.A. theses have been written at Masaryk University, Brno, Czech Republic on the basis of this dataset.</p> <p>The dataset does not cover all known trials. Based on data availability, we did our best to cover what was published and reasonably accessible, but we did not perform original archival research. Our very rough estimate is that up to 100 further specific witchcraft trials in the Czech lands could be identified in published material, and archival work could reveal further ca. 250-800 cases.</p>
Seeing Through the "Science Eyes" of the ExoMars Rover - Supplementary Material
<p>Simulated views from ExoMars PanCam instrument to assist operations planning.</p> <p>Described in more detail in the linked journal article.</p>
Supplementary Materials to the publication Wood structure explained by complex spatial source-sink interactions
<p>Model output and visualisation scripts to the publication Wood structure explained by complex spatial source-sink interactions. A readme explains the file origin. model output files and their variables and units are described within the .R analysis code.</p>
Supplementary material for "Surface frustration re-patterning underlies the structural landscape and evolvability of fungal orphan candidate effectors"
<p><strong>Tables</strong></p> <p>Table S1. List of fungal genomes analyzed in this work, associated references and properties.</p> <p>Table S2. List of all secreted proteins less than 300 amino-acids from the 20 fungal genomes. The table includes Signalp4.0 output, mature sequence, Espritz % disorder, pfam domains, AlphaFold top prediction pLDDT and the associated pdb file in Dataset S1.</p> <p>Table S3. Top Hits to pdb database for all OCE structures. 'network_node_name' corresponds to the portein identifier in the OCE structure similarity network provided in Dataset S3. 'Hidef_raw_community' corresponds to groups of structural OCE analogs identified by HiDEF community detection performed on the network provided in Dataset S3.</p> <p>Table S4. Table S4. List of the 62 major OCE folds with associated statistics. Columns I to AB provide the number of occurrences per species. Note that the actual number of members per species might be underestimated due to the stringent pipeline used for OCE identification (excluding proteins larger than 300 amino acids or containing PFAMs for instance).</p> <p> </p> <p>Table S5. Relative surface exposure, conformational flexibility and conservation data mapped on residues of members of the Alt-A1 and BoNT families. RMSD, root mean square deviation for all aligned atoms; Conservation, percentage conservation in multiple structure alignment.</p> <p>Table S6. Assignment of NCBI accessions to MMseqs clusters and assignment of MMseqs clusters to HMM matching-based super-clusters.</p> <p>Table S7. Co-mutation occurrences and associated p-values in two OCE clades from the Alt-A1 and KP6 families.</p> <p>Table S8. Amino acid properties inferred from mutation scans and frustration analyses in Alt-A1 cluster yellow1 and KP6 cluster 43. 'Number of aa variants' corresponds to the number of different amino acids found at each position (deletion counts as 1). 'Alanine scan ∆Z' and 'Deletion scan ∆Z' correspond to the difference between Z-score for the native protein agains itself and Z-score for the native protein against mutant at each position (either Alanine replacement or 5-aa deletion). 'Destabilization factor' is the average of column E and F. 'Stabilization factor' corresponds to the difference between expected structural variation due to destabilization factor and the observed structural variation in multiple mutants. 'netEffect' is difference between column G and H. 'Max co-mutation %' is the highest frequency of co-mutation observed with other residues in natural variants, with 'Min co-mutation p-value (Bonferroni corrected)' the associated p-value.Table S9. Sequence and delta Z of natural variants and mutants from AA1_cl25</p> <p>Table S9. List of natural variants and <em>in silico</em> mutants from the Alt-A1 cluster 25 analyzed in this work, including protein sequence and structure comparison scores (comparison with the reconstructed clade ancestor n0).</p> <p>Table S10. List of natural variants and in silico mutants from the KP6 cluster 43 analyzed in this work, including protein sequence and structure comparison scores (comparison with the reconstructed clade ancestor n0).</p> <p>Table S11. Summary statistics for the phylogenetic trees of 15 OCE clades analyzed for structure and frustration evolution.</p> <p>Table S12. Mapping of structural and frustration data onto phylogenetic trees for 15 OCE clades. The corresponding trees and protein structures are provided in Dataset S7.</p> <p><strong>Datasets</strong></p> <p>Dataset S1. AlphaFold rank1 models for 3 927 OCEs (.pdb format).</p> <p>Dataset S2. Pairwise structure comparison for 3 911 OCE. DALI matrix output containing pairwise Z-scores.</p> <p>Dataset S3. Network file including 2 561 OCEs with 3 or more vertices of Z-score weight 5.2 or more, in .sif and .xgmml formats.</p> <p>Dataset S4. Videos illustrating the mapping of relative surface exposure and structural variability in Alt-A1 and BoNT groups, amino-acids conservation, co-selected mutation patches and residue net stabilization effects on Alt-A1 clade 25 ancestor and KP6 cluster 43 ancestor. Color scales are as in Figure 2 and 3 respectively (.mp4 format).</p> <p>Dataset S5. Phylogenetic trees (.nwk), ancestral (.fasta) and modern variant (.faa) sequences, and AlphaFold best protein models (.pdb) for members of KP6 cluster 43 and Alt-A1 cluster 25. The archive includes 140 Alt-A1 protein structure and 128 KP6 protein structures.</p> <p>Dataset S6. Best predicted structures for 917 natural variants and mutants of AA1_cl25 and 801 natural variants and mutants of KP6_cl43 (.pdb format).</p> <p>Dataset S7. Phylogenetic trees (.nwk) and AlphaFold best protein models (.pdb) for 15 OCE clades. The file includes 2 598 protein structures distributed from clades AA1_s (139), AA1_t (135), AA1_y1 (140), AA1_y2 (90), AA1_y3 (128), BoNT_s (291), CIP_s (167), CIP_t (231), crystallin (233), GNK2 (189), KP6_cl3 (203), KP6_cl26 (111), KP6_cl43 (123), KP6_cl96 (231), KP6_cl242 (187).</p> <p><strong>Text and Figures</strong></p> <p>Text S1. Contains supplementary methods, results and figures S1 to S13.</p>
Suplementary material: Towards uncoding hepatotoxicity of approved drugs through navigation of multiverse and consensus chemical spaces
<p>Supplementary material: "Towards uncoding hepatotoxicity of approved drugs through navigation of multiverse and consensus chemical spaces"</p>
Graphic materials from the Prus Plus Digital Collection
<p>A digital collection of visual materials (drawings) from 19th-century periodicals held in the resources of the <a href="http://ibl.waw.pl/pl/o-instytucie/biblioteka">IBL PAN Library</a>, prepared for the Prus Plus digital monography (in <a href="https://nplp.pl/kolekcja/prus-plus/">Polish </a>and <a href="http://nplp.pl/en/kolekcja/prus-plus/">English</a>) by the New Panorama of the Polish Literature team.</p> <p>Note: Authors in this case means: people processing, remixing, selecting and analyzing the collection of visual materials (New Panorama of Polish Literature) and the institution curating these resources (IBL PAN Library).</p> <p>Kolekcja materiałów wizualnych (rysunków) z XIX-wiecznych czasopism, znajdujących się w zbiorach <a href="https://ibl.waw.pl/pl/o-instytucie/biblioteka">Biblioteki IBL PAN</a>, przygotowana na potrzeby cyfrowej monografii Prus Plus (w <a href="https://nplp.pl/kolekcja/prus-plus/">języku polskim</a> i <a href="http://nplp.pl/en/kolekcja/prus-plus/">angielskim</a>) przez zespół Nowej Panoramy Literatury Polskiej.</p> <p>Uwaga! Autorzy oznacza w tym przypadku: osoby przetwarzające, remiksujące, selekcjonujące i analizujące kolekcję materiałów wizualnych (Nowa Panorama Literatury Polskiej) oraz instytucję przechowującą zasoby (Biblioteka IBL PAN). </p> <p> </p> <p> </p>
Material Property Database of Organic Liquids, Ices, and Hazes on Titan
<p>Titan has a diverse range of materials in its atmosphere and on its surface: the simple organics that reside in various phases (gas, liquid, ice) and the solid complex refractory organics that form Titan's haze layers. These materials all actively participate in various physical processes on Titan, and many material properties are found to be important in shaping these processes. Future in-situ exploration on Titan would likely encounter a range of materials, and a comprehensive database to archive the material properties of all possible material candidates will be needed.</p> <p>Here we archive several important material properties of the organic liquids, ices, and the refractory hazes on Titan that are available in the literature and/or that we have computed. These properties include thermodynamic properties (phase change points, sublimation and vaporization saturation vapor pressure, and latent heat), physical property (density), and surface properties (liquid surface tensions and solid surface energies).</p> <p>We have archived all the data involved in our first paper (https://arxiv.org/abs/2210.01394 for the Arxiv version and https://doi.org/10.3847/1538-4365/acc6cf for the publisher version) here to make them available to the science community. These data can be used as inputs for various theoretical models to interpret current and future remote sensing and in-situ atmospheric and surface measurements on Titan. The material properties of the simple organics may also be applicable to giant planets and icy bodies in the outer solar system, interstellar medium, and protoplanetary disks.</p> <p>The "Summary of Data Tables and Jupyter Notebook Files" summarizes the names of all the data files (.csv) and Jupyter Notebook files (.ipynb) and their corresponding Tables in the paper.</p> <p><strong>Please cite our paper in your use of the data: Yu et al. (2023), https://doi.org/10.3847/1538-4365/acc6cf</strong></p> <p><strong>Yu, X., Yu, Y., Garver, J., Li, J., Hawthorn, A., Sciamma-O’Brien, E., ... & Barth, E. (2023). Material Properties of Organic Liquids, Ices, and Hazes on Titan. The Astrophysical Journal Supplement Series, 266(2), 30.</strong></p>
MuSpinSim data files for Galaxy materials science tutorials
<p>This is a training dataset for use in Galaxy materials science tutorials. These files can be compared to the output of simulations by MuSpinSim for dissipation of muon spins.</p> <p>The files included are:</p> <ul> <li><strong>dissipation_theory.dat:</strong> theoretical values formatted as a MuSpinSim output</li> <li><strong>experiment.dat:</strong> mock experimental values formatted as a MuSpinSim output</li> </ul>
Supplementary material for "Increased sensitivity of marine invertebrates to metal toxicity in the past two decades linked to Climate Change and Ocean Acidification: revelations from a natural population of sea urchins in the Mediterranean Sea." by "Davide Sartori, Guido Scatena, Cristina Vrinceanu, Andrea Gaion".
<p>Satellite observations of environmental factors and effect concentration 50 for copper to sea urchin, from 2003 to 2022.</p>
Dataset for Appearance evaluation of digital materials in material jetting
<p>Dataset for Appearance evaluation of digital materials in material jetting: Including dataset for gloss, haze, scattering, specular BRDF, reflectance, and transmittance</p>
Dataset for the optical properties of tilted surfaces in material jetting
<p>Dataset for the optical properties of tilted surfaces in material jetting:</p> <p>Including dataset for gloss, haze, scattering, specular BRDF, reflectance, transmittance, and statistical analysis</p>
Supplementary material to: Highly resolved topography and illumination at Mercury south pole from MESSENGER MDIS-NAC
<p>We produced a new higher-resolution topographic map of Mercury’s south polar region (75°-90° South, covering ~1.3 million km<sup>2</sup>) by using data collected by the NASA MESSENGER spacecraft’s Mercury Dual Imaging System (MDIS; Hawkins et al, 2007) over the years 2011-2015. This new map enables, <em>e.g.</em>, the first detailed modeling of illumination and thermal conditions in these southern radar-bright locations and the first constraints on the nature and history of volatiles residing there, but it is also intended as a resource for other geophysical analyses and for the preparation of the BepiColombo mission, currently en-route to the planet.</p> <p>For more details, please visit <a href="https://pgda.gsfc.nasa.gov/products/88">https://pgda.gsfc.nasa.gov/products/88</a>.</p> <p><strong>Products:</strong></p> <p>DEM (interpolated), DEM (filled), Slopes, PSR masks</p> <p>All these files (except the PSR masks shapefile) are 250 m/pix GeoTiffs with south polar stereographic X/Y coords in meters.</p> <p><br> <em>If using these products, please cite:</em><br> Bertone, S., E. Mazarico, M.K. Barker, M. Siegler, J. M. Martinez Camacho, C. Hamill, A. Glatzenberg, N. L. Chabot, 2022: <em>Highly resolved topography and illumination at Mercury south pole from MESSENGER MDIS-NAC</em>. The Planetary Science Journal, 02/2023, <a href="http://dx.doi.org/10.3847/PSJ/acaddb">doi:10.3847/PSJ/acaddb</a></p>
Simulated shielding performance of 1024 five-layer permutations of four satellite and radiation shielding materials against trapped particles on GTO
<p>The 5Layer.csv file contains ionizing dose results of simulating the shielding performance of all 1024 five-layer permutations of four satellite and radiation shielding materials against trapped particles on GTO.<br> The materials are the aluminium alloy 7075 (Al_7075), polyethylene (G4_POLYETHYLENE), tungsten (G4_W) and the circuit board composite material FR4 (FR4).<br> The particle spectra used for this simulation are provided in the files AE9500keV.mac and AP910MeV.mac.<br> The simulated geometry is provided in the file 5Layer.gdml.</p> <p>The particle spectra were generated with the AE9/AP9 models on SPENVIS, with electron energies starting at 500 keV and proton energies starting at 10 MeV.<br> The simulation was performed with GRAS / Geant4 with the FTFP_BERT physics model using 1.4e+11 electrons and 3.1e+09 protons directed against slabs of shielding materials of 1.5 g/cm2 total depth with each of the five layers being 0.3 g/cm2 in depth.<br> The ionizing dose is recorded in 0.5mm thick silicon plates behind the shielding plates.<br> <br> Column A: ID of the material combination<br> Column B: Name of the top layer material.<br> Column C: Name of the second layer material.<br> Column D: Name of the third layer material.<br> Column E: Name of the fourth layer material.<br> Column F: Name of the bottom layer material.<br> Column G: Ionizing dose due to electrons in units of kRad per month.<br> Column H: Absolute statistical error in the electron dose in kRad per month.<br> Column I: Ionizing dose due to protons in kRad per month.<br> Column J: Absolute statistical error in the proton dose in kRad per month. <br> Column K: Total ionizing dose from electrons and protons in kRad per month.<br> Column L: Absolute statistical error in the total ionizing dose in kRad per month. <br> Column M: Relative statistical error of the total ionizing dose in per cent.<br> Column N: Rank of the material combination sorted from lowest total ionizing dose to highest.<br> Column O: Rank of the material combination sorted from lowest electron dose to highest.<br> Column P: Rank of the material combination sorted from lowest proton dose to highest.</p> <p>All dose values are rounded according to their uncertainty.</p>
Simulated shielding performance of 1296 four-layer permutations of six common satellite and radiation shielding materials against trapped particles on GTO
<p>The 4Layer.csv file contains ionizing dose results of simulating the shielding performance of all 1296 four-layer permutations of six common satellite and radiation shielding materials against trapped particles on GTO.<br> The materials are the aluminium alloy 7075 (Al_7075), polyethylene (G4_POLYETHYLENE), kevlar (G4_KEVLAR), tungsten (G4_W), stainless steel (G4_STAINLESS_STEEL) and the circuit board composite material FR4 (FR4).<br> The particle spectra used for this simulation are provided in the files AE9500keV.mac and AP910MeV.mac.<br> The simulated geometry is provided in the file 4Layer.gdml.</p> <p>The particle spectra were generated with the AE9/AP9 models on SPENVIS, with electron energies starting at 500 keV and proton energies starting at 10 MeV.<br> The simulation was performed with GRAS / Geant4 with the FTFP_BERT physics model using 6.6e+10 electrons and 3.8e+09 protons directed against slabs of shielding materials of 1.5 g/cm2 total depth with each of the four layers being 0.375 g/cm2 in depth.<br> The ionizing dose is recorded in 0.5mm thick silicon plates behind the shielding plates.<br> <br> Column A: ID of the material combination<br> Column B: Name of the top layer material.<br> Column C: Name of the second layer material.<br> Column D: Name of the third layer material.<br> Column E: Name of the bottom layer material.<br> Column F: Ionizing dose due to electrons in units of kRad per month.<br> Column G: Absolute statistical error in the electron dose in kRad per month.<br> Column H: Ionizing dose due to protons in kRad per month.<br> Column I: Absolute statistical error in the proton dose in kRad per month. <br> Column J: Total ionizing dose from electrons and protons in kRad per month.<br> Column K: Absolute statistical error in the total ionizing dose in kRad per month. <br> Column L: Relative statistical error of the total ionizing dose in per cent.<br> Column M: Rank of the material combination sorted from lowest total ionizing dose to highest.<br> Column N: Rank of the material combination sorted from lowest electron dose to highest.<br> Column O: Rank of the material combination sorted from lowest proton dose to highest.</p> <p>All dose values are rounded according to their uncertainty.</p>
Supplementary material: Burial Analysis on the Middle Bronze Age in the Carpathian Basin (dataset and scripts)
<p>This is the supplementary material of the paper "Wealth Consumption, Sociopolitical Organization, and Change: A Perspective from Burial Analysis on the Middle Bronze Age in the Carpathian Basin" (accessible over doi: https://doi.org/10.1515/opar-2022-0281). Please consult the publication for in depth description of the data, its context and for the method applied on the data, as well as references to primary sources. The data tables comprise the burial data of the Hungarian Middle Bronze Age cemeteries of Dunaújváros-Duna-dűlő, Dömsöd, Adony, Lovasberény, Csanytelek-Palé, Kelebia, Hernádkak, Gelej, Pusztaszikszó and Streda nad Bodrogom. The script "supplementary_material_2_wealth_index_calculation.py" provides the calculation of a wealth index, based on grave goods, for the provided data. The script "supplementary_material_3_population_estimation.py" models the living population of Dunaújváros-Duna-dűlő. Both can be run by double-click. Requirements to be installed to run the scripts: Python 3 (https://www.python.org/) with the packages numpy (https://numpy.org/), pandas (https://pandas.pydata.org/), matplotlib (https://matplotlib.org/), seaborn (https://seaborn.pydata.org/) and scipy (https://scipy.org/); all included in Ancaonda (Python-Distribution, https://www.anaconda.com/).</p>
Additional online material for publication: Frames and Narratives in scientific press releases on ocean climate change and ocean plastic.
<p>This is the additional material for the publication of paper: Frames and Narratives in scientific press releases on ocean climate change and ocean plastic. The paper is currently under submission. </p> <p>Included with the material is a codebook used to code narrative- and frame variables in scientific press releases and a cross-tabulate showing the frame variables that were coded per press release. </p> <p>For questions about the material, or information about how to reference to the material, please contact Aike Vonk (a.n.vonk@uu.nl).</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>
Supplementary Material for 'Leveraging the GIDAS Database for the Criticality Analysis of Automated Driving Systems'
<p>This repository contains the supplementary material for the publication 'Leveraging the GIDAS Database for the Criticality Analysis of Automated Driving Systems'.<br> It consists of four files:</p> <ol> <li>Criticality-Phenomena-Catalog.CSV: The catalog of criticality phenomena (CP)</li> <li>Criticality-Phenomena-Phi-Coefficient.CSV: The calculation of the Phi coefficient between all pairs of CP</li> <li>Criticality-Phenomena-Risk-Calculation.CSV: The case-phenomenon relation matrix, including the calculated values for the risk of each CP for all three severity levels</li> <li>Criticality-Phenomena-Sorted-By-Risk.CSV: A list of the CP from the CP catalog sorted by risk for all three severity classes</li> </ol>
Dataset for BRDF representation in response to the build orientation in 3D-printed digital materials
<p>This dataset folder contains data for the project "BRDF representation in response to the build orientation in 3D-printed digital materials"<br> For more information please contact Ali Payami Golhin (payami.ag@gmail.com)</p> <p>Abbreviations:<br> C:Cyan; M:Magenta; Y:Yellow; K:Black<br> GoG: Glossy on Glossy finish; GoM: Glossy on Matte finish</p> <p>Folder "Color values": presents data for CIEXYZ, CIELab, CIELCh for 328 measurement geometries for each CMYK resins<br> Folder "Spectral data": presents reflctance data for 328 measurement geometries for each CMYK resins. The first column in each file represent wavelength (nm) and the second column contains spectral data<br> File "PCA.xlsx": presents PCA (PC1) scores for CMYK colors</p>
Supporting materials for analysis of global emissions from rivers and streams
<p>These are the raw files to run the analysis detailed here: https://github.com/rocher-ros/RiverMethaneFlux. Further instructions of use can be find in the code repository. </p> <p>The results of this analysis are a global gridded product of methane river concentrations and fluxes, which can be found in: https://doi.org/10.5281/zenodo.8108959.</p> <p>The journal article based on the results of this analysis is currently in press in Nature (https://doi.org/10.1038/s41586-023-06344-6)</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.