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12,393 results for “materiality”
Characterisation of moisture sorption of materials used in 3D printing
<p>Investigation of environmental effects on properties of polymers and 3D printed structures was performed. The research methodology of moisture sorption and swelling processes was developed and implemented. Full-scale experiments were performed on 12 materials. Degradation of filaments’ mechanical properties under moisture effect was evaluated. A complex analysis of the results was performed, and sorption characteristics of filaments were defined.</p>
Characterisation of electro- and thermophysical properties of materials used in 3D printing
<p>A complex of materials’ properties was measured: electrical conductivity, thermal expansion, thermal conductivity, the density of the materials, and the technical density of construction with porosity.</p>
Supplementary material for "A database for the comparison of measured datasets of human voice directivity"
<p>[1] C. Pörschmann. “A database for the comparison of measured datasets of human voice directivity," in Proceedings of the Forum Acusticum, Torino, Italy, 2023.</p> <p> </p> <p>This study presents a database that allows direct comparison and visualization of datasets from 19 different studies. The data is collected from tables, plots, and datasets from the supplemental material of the respective studies. Some studies present directivity patterns averaged over a whole sentence, while others report phoneme-dependent data. Furthermore, these datasets vary in their sampling grids, with many measured in the horizontal plane and just a few measured spherically. Most datasets included in this work present frequency-band averaged values, for example, in one-third octave bands, while a few newer studies provide the raw data in the form of transfer functions.</p> <p>Furthermore, the supplementary material contains voice directivity datasets determined over a complete sentence determined for a phonetically balanced German sentence (measured twice for 13 subjects).</p> <p> </p> <p>The .pdf file contains</p> <ul> <li>information on the database that allows comparing the results of 19 publications on voice directivities</li> <li>general information on the voice directivity files in the SOFA format</li> <li>information on the indices and names of the SOFA-files</li> <li>additional plots</li> </ul> <p> </p> <p>The Database.zip</p> <ul> <li>Excel-file containing datasets from the publications given in frequency-bands</li> <li>SOFA-Files (sampled on sparse grid) of all own datasets from previous studies</li> <li>Matlab scripts for importing, upsampling and visualizing all voice directivity datasets considered in the database</li> </ul> <p> </p> <p>The VoiceDirectivitySentence.zip files contain voice directivities patterns averaged over one phonetically balanced sentence in the SOFA format</p> <ul> <li>sampled on the sparse measuring grid</li> <li>upsampled to a dense grid</li> </ul>
Data and data analysis for "Human Preferences for the Visual Appearance of Desks: Examining the Role of Wooden Materials and Desk Designs"
<p>Data and data analysis for the article "Human Preferences for the Visual Appearance of Desks: Examining the Role of Wooden Materials and Desk Designs".</p>
Data of "A micromechanical Mean-Field Homogenization surrogate for the stochastic multiscale analysis of composite materials failure"
<p><strong>Id</strong><br>title = "A micromechanical Mean-Field Homogenization surrogate for the stochastic multiscale analysis of composite materials failure"<br>journal = International Journal for Numerical Methods in Engineering<br>year = 2023<br>volume = 124<br>pages = 5200-5262<br>doi = 10.1002/nme.7344<br>authors = "Calleja, Juan Manuel and Wu Ling, and Nguyen, Van-Dung and Noels, Ludovic"</p> <p>If you use these data or model, we would be grateful if you could cite this above paper</p> <p><strong>Software</strong><br>Requires GMSH and Python 3 with packages numpy, matplotlib, sklearn (scikit-learn), os, pickle, scipy, pandas, cvs, math, seaborn.<br>Each folder contains readme that will help the user to navigate through the data.</p> <p>To run the model you need the open source code <a href="http://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries </a>but you need to request access to cm3MFH as well</p> <p><strong>Directories</strong></p> <ol> <li>Main: Contains fast and easy access to the plots presented in the paper. The readme contained in this plot specifies the plots that are run with each code.</li> <li>1_SVE_Generator:Contains the files needed for the generation of the SVE, the statistical properties of the microstructure, and PLY samples for the full-field simulations, as well as the used samples</li> <li>2_Full_Field: contains the extracted data from the FF composite realizations, as well as the used random SVE geometries.</li> <li>3_Identification: Contains the identification code to find the effective parameters for each SVE realization as well as the obtained identification results.</li> <li>4_Generator: Contains the generated set of parameters for the 25 and 45 micrometer squared SVEs as well as the codes for the new data generation, the file with the generated data and the plots related with the MF-ROM random parameters and their cross-relations shown in Sections 2.5.2, 3.2.3 and 4.</li> <li>5_Tests: Contains all the information concerning the tests used for the verification of the MF-ROM and the ply and experimental compression results.</li> <li>MFH_vs_FF: Allows to easily test the inverse identification process through the use of random SVEs and verify the performance of the identified MFH parameters against its full-field counterpart.</li> </ol> <p><strong>Plot of figures</strong></p> <p>Figure 9 : Run "python plot_Gc.py" which can be found in folder Main/Full_Field_Energy<br>Figure 10: Run "python3 PDF_HIST_Gc.py", which can be found in folder Main/Histograms<br>Figure 23: Run "python3 plot.py" which can be found in folder Main/MFH_FF_Comparison<br>Figure 24: Run "python3 plot.py" which can be found in folder Main/MFH_FF_Comparison<br>Figure 27: Run "python3 Correlation_Graphs_25.py contained in folder Main/Distributions_25_Micrometer_SVE<br>Figure 29: Run "python3 PDF_HIST.py" which can be found in folder Main/Histograms<br>Figure 30: To obtain the data used in this figure, run "python3 DistanceCorrelation_25.py" which can be found in folder /4_Generator<br>Figure 31: To obtain the data used in this figure, run "python3 DistanceCorrelation_45.py" which can be found in folder /4_Generator<br>Figure 32: Run "python3 Correlation_Graphs_25.py" which can be found in folder Main/Distributions_25_Micrometer_SVE<br>Figure 33: Run "python3 Correlation_Graphs_25.py" which can be found in folder Main/Distributions_25_Micrometer_SVE<br>Figure 34: Run "python3 Correlation_Graphs_25.py" which can be found in folder Main/Distributions_25_Micrometer_SVE<br>Figure 36: Run "python3 plot_New.py" which can be found in folder Main/PlyTests<br>Figure 46: Run "python3 plot_Test.py" which can be found in folder Main/CompressionExperiment<br>Figure B3: Run "python3 MicroStrAna.py" which can be found in folder Main/MicroStructStatistics<br>Figure B4: Run "python3 MicroStrAna.py" which can be found in folder Main/MicroStructStatistics<br>Figure D5: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D6: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D7: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D8: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D9: Run "python3 PDF_HIST_B.py" which can be found n folder Main/Histograms<br>Figure D10: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D11: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D12: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D13: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D14: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure E15: Run "python3 Correlation_Graphs_45.py" which can be found in folder Main/Distributions_45_Micrometer_SVE<br>Figure E16: Run "python3 Correlation_Graphs_45.py" which can be found in folder Main/Distributions_45_Micrometer_SVE<br>Figure E17: Run "python3 Correlation_Graphs_45.py" which can be found in folder Main/Distributions_45_Micrometer_SVE<br>Figure F18: Run "python3 plot_Convergence_25.py" which can be found in folder Main/Convergence<br>Figure F19: Run "python3 plot_Convergence_45.py" which can be found in folder Main/Convergence<br> </p> <p> </p> <p> </p> <p> </p>
Supporting materials and data from: Hypotheses concerning global magnetospheric convection, magnetosphere-Ionosphere coupling, and auroral activity at Uranus
<p>Summary: <br>The data and figures contained in this dataset are supporting materials for the paper referenced in the title and abstract below. This study involved an analytical and numerical assessment of the Uranian magnetosphere and its interaction with the solar wind and interplanetary magnetic field (IMF). The data and figures contained in this dataset include supplementary results for a greater number of IMF orientations and Uranian seasons than the examples presented in the corresponding paper.</p> <p><br>Abstract from corresponding paper: doi:10.1029/2023JA031791 with Journal of Geophysical Research Space Physics:<br>We investigate the unique magnetosphere of Uranus and its interaction with the solar wind. Following previous work, we developed and validated a simple yet valuable and illustrative model of Uranus' offset, tilted, and rapidly-spinning magnetic field and magnetopause (nominal and fit to the Voyager-2 inbound crossing point) in three-dimensional space. With this model, we investigated details of the seasonal and interplanetary magnetic field (IMF) orientation dependencies of dayside and flank reconnection along the Uranian magnetopause. We found that anti-parallel (magnetic field shear angle greater than 170-degrees) reconnection occurs nearly continuously along the Uranian dayside and/or flank magnetopause under all seasons of the 84 (Earth) year Uranian orbit and the most likely IMF orientations. Such active and continuous driving of the Uranian magnetosphere should result in constant loading and unloading of the Uranian magnetotail, which may be further complicated and destabilized by sudden changes in the IMF orientation and solar wind conditions plus the reconfigurations from the rotation of Uranus itself. We demonstrate that unlike the other magnetospheric systems that are Dungey-cycle driven (i.e., Mercury and Earth) or rotationally driven (Jupiter and Saturn), global magnetospheric convection of plasma, magnetic flux, and energy flow may occur via three distinct cycles, two of which are unique to Uranus (and possibly also Neptune). Our simple model is also used to map signatures of dayside and flank reconnection down to the Uranian ionosphere, as a function of planetary latitude and longitude. Such mapping demonstrates that "spot"-like auroral features should be very common on the Uranian dayside, consistent with observations from Hubble Space Telescope. We further detail how the combination of Uranus' rapid rotation and unique and very active global magnetospheric convection should be consistent with fueling of the surprisingly intense trapped radiation environment observed by Voyager-2 during its single flyby. Summarizing, Uranus is a very special magnetosphere that offers new insights on the nature, complexity, and diversity of planetary magnetospheric systems and the acceleration of particles in space plasmas, which might have important analogs to exoplanetary magnetospheric systems. Our hypotheses can be tested with further work involving more advanced models, new auroral observations, and unprecedented missions to explore the in situ environment from orbit around Uranus, which should include a complement of magnetospheric instruments in the payload. </p>
The carbon dioxide storage potential of building materials: a systematic literature review - Supplementary material
<p>Supplementary Material of the Study "The carbon dioxide storage potential of building materials: a systematic literature review". The Paper will be published in the conference proceedings of CISBAT 2023, Lausanne, Switzerland from the 13-15 September 2023.</p>
Online course materials in predictive microbiology applied to growth - the EFSA ListeriaPredict Project
<p>Online course materials in predictive microbiology applied to growth</p> <p>A deliverable from the EFSA funded ListeriaPredict Project</p>
Datesets and images of the publication "Probing crystallinity and grain structure of 2D materials and 2D-like van der Waals heterostructures by low-voltage electron diffraction" - DOI: 10.1002/pssa.202300148
<p>Datasets and images of the publication "Probing crystallinity and grain structure of 2D materials and 2D-like van der Waals heterostructures by low-voltage electron diffraction" - DOI: <a href="https://www.doi.org/10.1002/pssa.202300148">10.1002/pssa.202300148</a></p> <p>The Jupyter Notebooks for analyzing the datasets and generating all the figures are available at <a href="https://gitlab.com/JohMu/tds_hios_manuscript">https://gitlab.com/JohMu/tds_hios_manuscript</a>.</p> <p><strong>MoS<sub>2</sub> 4D-STEM dataset:</strong></p> <ul> <li>192x192 scan pixels</li> <li>200x200 camera pixels</li> <li>Acceleration voltage: 20kV</li> <li>Camera length: 10.56 mm</li> <li>Camera pixel size: 4x5.86 µm = 23.44 µm (original dataset with 4x4 binning)</li> <li>File location: Figure 2_3_S1.zip -> 230101205338_20kV_hexz0_camz-10_posi_003_good\scan_data_bin2_centered_crop-imgNx200.h5</li> <li>The original raw dataset (23 GB, 192x192 scan pixels, 800x800 camera pixels, camera pixel size: 5.86 µm), the scan reference dataset and the Jupyter Notebook for the shift-compensation is available from the author. The dataset uploaded here is binned by a factor of 4 and shift-compensated.</li> </ul> <p><strong>C60/MoS<sub>2</sub> 4D-STEM dataset:</strong></p> <ul> <li>113x113 scan pixels</li> <li>512x512 camera pixels</li> <li>Acceleration voltage: 20kV</li> <li>Camera length: 20.56 mm</li> <li>Camera pixel size: 5.86 µm</li> <li>File location: Figure 4.zip -> scan_data_scan113x113_gzip.h5</li> </ul> <p> </p>
Supplementary Material
<p>This dataset provides extended data supporting claims of a systematic review (PRISMA 2020 guidelines) about the process of integration of Media and Information Literacy (MIL) into the curriculum: a) research process; b) studies included; c) data.</p>
Supplementary Materials for: Lost Loops: 19th century thermosiphon ventilation and its potential for heat recovery in buildings today
<p>We investigate ventilation heat recovery with buoyancy flow, arranging interior spaces in an open thermal loop with heat exchange through partition walls. We draw inspiration from a Victorian-era thermosiphon scheme after finding it in Montreal’s former Royal Victoria Hospital and tracing the concept back to the original Centre Block of Canada’s Parliament Hill. We corroborate the archival evidence with analog models to visualize how the original hospital’s ventilation system worked. We use saline baths to verify how the siphon functioned and digital Schlieren to confirm how the full flow cycle worked with heat recovery. Then, to generalize these observations, we develop a mathematical model showing how heat recovery with buoyancy ventilation can work in simple room arrangements. We define a criterion for stable, unidirectional flow and a heat recovery efficiency limit of 50%. Finally, we validate the mathematical model experimentally, demonstrating stable, one-way flow close to the heat recovery limit.</p>
A novel preclinical secondary pharmacology resource illuminates target-adverse drug reaction associations of marketed drugs - Supplementary Material
<p>All Supplementary materials and source data for the manuscript.</p> <p>The reported analyses and results can be reproduced via the described IPython notebooks which can be found under www.github.com/Novartis/SPD</p>
Supplementary material for "Spin-Polarized Tunable Photocurrents"
<p>Supplementary material for "Spin-Polarized Tunable Photocurrents", <em>Nano Lett.</em> 2021, 21, 7, 3177–3183 (2021), https://pubs.acs.org/doi/full/10.1021/acs.nanolett.1c00420</p> <p> </p>
Data for: Genome material costs and functional tradeoffs in the autopolyploid Solidago gigantea (Giant Goldenrod) series
<p><strong>Premise of study</strong>: Increased genomic "material costs" of nitrogen (N) and phosphorus (P) atoms inherent to organisms with larger genome sizes (GS) has been proposed to limit growth under nutrient scarcities and promote growth under nutrient enrichments. Such responsiveness may reflect a nutrient-dependent diploid versus polyploid advantage that could have vast ecological and evolutionary implications, but direct evidence that material costs increase with ploidy-level and/or influence cytotype-dependent growth, metabolic, and/or resource-use tradeoffs is limited.</p> <p><strong>Methods</strong>: We grew diploid, auto-tetraploid, and auto-hexaploid <em>Solidago gigantea</em> plants under one of four ambient and enriched N:P treatments and measured traits related to material costs, primary and secondary metabolism, and resource-use.</p> <p><strong>Key</strong> <strong>results</strong>: Relative to diploids, polyploids invested more N and P into cells and tetraploids grew more following N-enrichments, suggesting that material costs increase with ploidy-level. Polyploids also generally exhibited strategies that could minimize material-cost-constraints over both long (reduced monoploid GS) and short (more extreme transcriptome downsizing, reduced photosynthesis rates and terpene concentrations, enhanced N-use efficiencies) evolutionary time periods. Furthermore, polyploids had lower transpiration rates but higher water-use-efficiencies than diploids, both of which were more pronounced under nutrient-limiting conditions.</p> <p><strong>Conclusions</strong>: Collectively we found that NP material costs increase with ploidy-level but that material-cost-constraints might be lessened by organismal resource allocation/investment mechanisms that can also alter ecological dynamics and selection. Our results enhance mechanistic understanding of how global increases in nutrients might provide a release from material-cost-constraints in polyploids that could impact ploidy (or GS)-specific performances, cytogeographic patterning, and multispecies community structuring.</p>
Supplementary material of the study "Help! I need somebody. A Mapping Study about Expert Identification in Software Development"
<p><strong>Supplementary Material</strong></p> <p><em><strong>Context</strong></em>: Software development is a knowledge-intensive activity, and its success in an organization relies deeply on knowledge sharing. Knowledge management challenges are often increased in agile environments, which involve a lot of tacit knowledge, commonly acquired through experiences and hard to be made explicit. Therefore, knowledge sharing among practitioners is crucial. However, identifying suitable experts to share specific knowledge is not trivial. It involves not only discovering the individuals with the desired knowledge but also considering other factors that may improve the expert responsiveness, such as social connections and availability. <em><strong>Objective</strong></em>: Considering the important role experts play in knowledge sharing, we decided to investigate approaches that help identify experts that can share knowledge in software development. Our goal is to provide a panorama of the existing approaches and shine a light on research opportunities. <em><strong>Method</strong></em>: We carried out a systematic literature mapping and analyzed 17 publications. <em><strong>Results</strong></em>: The results show that most approaches have relied on code repositories as a source of evidence for identifying experts and, consequently, focus on supporting developers and aiding in the codification activity. Additionally, expert identification has been mostly automated, and factors beyond possessing the desired knowledge have often been disregarded. <em><strong>Conclusion</strong></em>: Although there are several expert identification approaches, there has been a lack of concern with factors that influence reaching the most suitable expert for a specific situation (e.g., considering the characteristics of the person seeking knowledge). Moreover, there is a need for deeper reflection on how to better explore different artifacts as sources of expert evidence and how to combine them to improve expert identification.</p> <p>This package contains supplementary material of the study performed to investigate approaches that help identify experts that can share knowledge in software development. It contains:</p> <ul> <li>A spreadsheet containing raw data (research protocol, considered and selected publications, and research questions answers).</li> </ul>
Steam explosion of eucalypt sawdust for ethanol production within a biorefinery approach - Supplementary Material
<p>Supplementary Material to the manuscript entitled "Steam explosion of eucalypt sawdust for ethanol production within a biorefinery approach", which has been revised according to the reviewers' comments and submitted for publication in <em>Processes</em> MDPI (July 18, 2023).</p>
Material stock map of CONUS
<p>Humanity's role in changing the face of the earth is a long-standing concern, as is the human domination of ecosystems. Geologists are debating the introduction of a new geological epoch, the 'anthropocene', as humans are 'overwhelming the great forces of nature'. In this context, the accumulation of artefacts, i.e., human-made physical objects, is a pervasive phenomenon. Variously dubbed 'manufactured capital', 'technomass', 'human-made mass', 'in-use stocks' or 'socioeconomic material stocks', they have become a major focus of sustainability sciences in the last decade. Globally, the mass of socioeconomic material stocks now exceeds 10e14 kg, which is roughly equal to the dry-matter equivalent of all biomass on earth. It is doubling roughly every 20 years, almost perfectly in line with 'real' (i.e. inflation-adjusted) GDP. In terms of mass, buildings and infrastructures (here collectively called 'built structures') represent the overwhelming majority of all socioeconomic material stocks.</p><p>This dataset features a detailed map of material stocks in the CONUS on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), crowd-sourced geodata (OSM) and material intensity factors.</p><p><strong>Spatial extent</strong><br>This dataset covers the whole CONUS. Due to upload constraints, detailed data were split into 7 regions and were uploaded into sub-repositories - see <i>related identifiers</i>. (<strong>This repository</strong> holds aggregated values for the whole CONUS)</p><ul><li>Great Plains</li><li>Mid West</li><li>North East</li><li>Rocky Mountains</li><li>South</li><li>South West</li><li>West Coast</li></ul><p><strong>Temporal extent</strong><br>The map is representative for ca. 2018.</p><p><strong>Data format</strong><br>The data are organized by states. Within each state, data are split into 100km x 100km tiles (EQUI7 grid), and mosaics are provided.</p><p>Within each tile, images for area, volume, and mass at 10m spatial resolution are provided. Units are m², m³, and t, respectively. Each metric is split into buildings, other, rail and street (note: In the paper, other, rail, and street stocks are subsumed to mobility infrastructure). Each category is further split into subcategories (e.g. building types).</p><p>Additionally, a grand total of all stocks is provided at multiple spatial resolutions and units, i.e.</p><ul><li>t at 10m x 10m</li><li>kt at 100m x 100m</li><li>Mt at 1km x 1km</li><li>Gt at 10km x 10km</li></ul><p>For each state, mosaics of all above-described data are provided in GDAL VRT format, which can readily be opened in most Geographic Information Systems. File paths are relative, i.e. DO NOT change the file structure or file naming. </p><p>Additionally, the grand total mass per state is tabulated for each county in <i>mass_grand_total_t_10m2.tif.csv</i>. County FIPS code and the ID in this table can be related via <i>FIPS-dictionary_ENLOCALE.csv</i>.</p><p><strong>Material layers</strong><br>Note that material-specific layers are not included in this repository because of upload limits. Only the totals are provided (i.e. the sum over all materials). However, these can easily be derived by re-applying the material intensity factors from (see <i>related identifiers</i>):</p><p>A. Baumgart, D. Virág, D. Frantz, F. Schug, D. Wiedenhofer, Material intensity factors for buildings, roads and rail-based infrastructure in the United States. Zenodo (2022), <a href="https://doi.org/10.5281/zenodo.5045337">doi:10.5281/zenodo.5045337.</a></p><p><strong>Further information</strong><br>For further information, please see the publication.<br>A web-visualization of this dataset is available <a href="https://ows.geo.hu-berlin.de/webviewer/us-stocks/">here</a>.<br>Visit our <a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">website</a> to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.</p><p><strong>Publication</strong><br>D. Frantz, F. Schug, D. Wiedenhofer, A. Baumgart, D. Virág, S. Cooper, C. Gómez-Medina, F. Lehmann, T. Udelhoven, S. van der Linden, P. Hostert, and H. Haberl (2023): Unveiling patterns in human dominated landscapes through mapping the mass of US built structures. <i>Nature Communications</i> <strong>14</strong>, 8014. <a href="https://doi.org/10.1038/s41467-023-43755-5">https://doi.org/10.1038/s41467-023-43755-5</a></p><p><strong>Funding</strong><br>This research was primarly funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). Workflow development was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Project-ID 414984028-SFB 1404.</p><p><strong>Acknowledgments</strong><br>We thank the European Space Agency and the European Commission for freely and openly sharing Sentinel imagery; USGS for the National Land Cover Database; Microsoft for Building Footprints; Geofabrik and all contributors for OpenStreetMap.This dataset was partly produced on <a href="https://eodc.eu/">EODC</a> - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC, and Wolfgang Wagner for granting access to preprocessed Sentinel-1 data.</p>
Material stock map of CONUS - North East
<p>Humanity’s role in changing the face of the earth is a long-standing concern, as is the human domination of ecosystems. Geologists are debating the introduction of a new geological epoch, the ‘anthropocene’, as humans are ‘overwhelming the great forces of nature’. In this context, the accumulation of artefacts, i.e., human-made physical objects, is a pervasive phenomenon. Variously dubbed ‘manufactured capital’, ‘technomass’, ‘human-made mass’, ‘in-use stocks’ or ‘socioeconomic material stocks’, they have become a major focus of sustainability sciences in the last decade. Globally, the mass of socioeconomic material stocks now exceeds 10e14 kg, which is roughly equal to the dry-matter equivalent of all biomass on earth. It is doubling roughly every 20 years, almost perfectly in line with ‘real’ (i.e. inflation-adjusted) GDP. In terms of mass, buildings and infrastructures (here collectively called ‘built structures’) represent the overwhelming majority of all socioeconomic material stocks.</p> <p>This dataset features a detailed map of material stocks in the CONUS on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), crowd-sourced geodata (OSM) and material intensity factors.</p> <p><strong>Spatial extent</strong><br> This subdataset covers the <strong>North East CONUS</strong>, i.e.</p> <ul> <li>CT</li> <li>DC</li> <li>DE</li> <li>MA</li> <li>MD</li> <li>ME</li> <li>NH</li> <li>NJ</li> <li>NY</li> <li>PA</li> <li>RI</li> <li>VA</li> </ul> <p>For the remaining CONUS, see the <em>related identifiers</em>.</p> <p><strong>Temporal extent</strong><br> The map is representative for ca. 2018.</p> <p><strong>Data format</strong><br> The data are organized by states. Within each state, data are split into 100km x 100km tiles (EQUI7 grid), and mosaics are provided.</p> <p>Within each tile, images for area, volume, and mass at 10m spatial resolution are provided. Units are m², m³, and t, respectively. Each metric is split into buildings, other, rail and street (note: In the paper, other, rail, and street stocks are subsumed to mobility infrastructure). Each category is further split into subcategories (e.g. building types).</p> <p>Additionally, a grand total of all stocks is provided at multiple spatial resolutions and units, i.e.</p> <ul> <li>t at 10m x 10m</li> <li>kt at 100m x 100m</li> <li>Mt at 1km x 1km</li> <li>Gt at 10km x 10km</li> </ul> <p>For each state, mosaics of all above-described data are provided in GDAL VRT format, which can readily be opened in most Geographic Information Systems. File paths are relative, i.e. DO NOT change the file structure or file naming. </p> <p>Additionally, the grand total mass per state is tabulated for each county in <em>mass_grand_total_t_10m2.tif.csv</em>. County FIPS code and the ID in this table can be related via <em>FIPS-dictionary_ENLOCALE.csv</em>.</p> <p><strong>Material layers</strong><br> Note that material-specific layers are not included in this repository because of upload limits. Only the totals are provided (i.e. the sum over all materials). However, these can easily be derived by re-applying the material intensity factors from (see <em>related identifiers</em>):</p> <p>A. Baumgart, D. Virág, D. Frantz, F. Schug, D. Wiedenhofer, Material intensity factors for buildings, roads and rail-based infrastructure in the United States. Zenodo (2022), <a href="https://doi.org/10.5281/zenodo.5045337.">doi:10.5281/zenodo.5045337.</a></p> <p><strong>Further information</strong><br> For further information, please see the publication.<br> A web-visualization of this dataset is available here.<br> Visit our <a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">website</a> to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.</p> <p><strong>Publication</strong><br> D. Frantz, F. Schug, D. Wiedenhofer, A. Baumgart, D. Virág, S. Cooper, C. Gomez-Medina, F. Lehmann, T. Udelhoven, S. van der Linden, P. Hostert, H. Haberl. Weighing the US Economy: Map of Built Structures Unveils Patterns in Human-Dominated Landscapes. <em>In prep</em></p> <p><strong>Funding</strong><br> This research was primarly funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). Workflow development was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Project-ID 414984028-SFB 1404.</p> <p><strong>Acknowledgments</strong><br> We thank the European Space Agency and the European Commission for freely and openly sharing Sentinel imagery; USGS for the National Land Cover Database; Microsoft for Building Footprints; Geofabrik and all contributors for OpenStreetMap.This dataset was partly produced on <a href="https://eodc.eu/">EODC</a> - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC.</p>
Supplementary material 1 for Thesis Chapter 2 - An obligate aerobe hybridises hydrogen fermentation and carbon storage to adapt to hypoxia
<p>Supplementary material for paired comparative metabolomics and proteomics on <em>Mycobacterium smegmatis </em>mc<sup>2</sup>155 during hypoxia, as part of chapter 2 for the thesis "Biochemistry and physiology of mycobacterial adaptations to energy starvation".</p> <p>Description below is identical to that provided in 'Summary.docx'. </p> <p>Proteomics_analysis.xlsx</p> <p>Includes raw and annotated data for comparative proteomics experiments for chapter 2.</p> <p>The tab ‘Annotated comparisons’ contains fold change and p values for the comparisons for each protein from <em>Mycobacterium smegmatis </em>mc<sup>2</sup>155 derived from LFQ-Analyst. Functional annotations are derived from KEGG pathways and modules, which utilise the spreadsheets in ‘MSMEG gene annotation’ ‘Protein ids to KEGG pathway’ and ‘KEGG Pathway and Modules’ to link KEGG annotations to MSMEG_XXXX gene identifiers and MSMEG_XXXX to Uniprot ID. Output from LFQ-Analyst is provided in the ‘Full_dataset’, ‘Imputed_matrix’ and ‘Original_matrix’ tabs.</p> <p>Data provided by the Monash Proteomics and Metabolomics Facility for upload into LFQ-analyst are provided as the ‘combined_protein.tsv’ and ‘LFQ-Analyst_experimental_design.txt’.</p> <p> </p> <p>Metabolism_analysis.xlsx</p> <p>Includes annotated data for comparative metabolomics experiments for chapter 2. Within the spreadsheet, TR refers to transition, ST refers to stationary phase and EXP refers to exponential phase. The tabs ‘TRvsEXP’, ‘STvsTR’ and ‘STvsEXP’ contain fold change and p values for each metabolite detected for each comparison. The remaining tabs categorise the metabolites based on KEGG database and IDEOM annotations. For broader categories (‘Lipid metabolism’,’ Carbohydrate metabolism’, ‘Cofactor metabolism’, ‘Nucleotide metabolism’, ‘Amino acid metabolism’ and ‘Peptides’ tabs), annotations were derived directly from filtering the ‘Map’ column of ‘Comparisons’ tab of the IDEOM worksheet (IDEOM_analysis.xlsb). Screenshots are pasted into each tab to show the filtering settings. The remaining tabs comprise narrower categories which were manually annotated with reference to KEGG pathways and maps, and also include rows corresponding to the proteomics data for these categories, so the proteomics and metabolomics data can be interpreted together. The ‘Proteomics’ tab contains the proteomics data referenced by these tabs, which is a copy of the ‘Annotated comparisons’ tab from the ‘Proteomics_analysis.xlsx’ file. A value of ‘N’ indicates the metabolite or protein (at least according to the name in the same row) was not found in these datasets.</p> <p>The IDEOM worksheet (IDEOM_analysis.xlsb) was provided by the Monash Proteomics and Metabolomics Facility and was used for further analysis and for annotations. ‘Data_for_MA_no_normalization.csv’ was also provided by the Monash Proteomics and Metabolomics Facility for upload into Metaboanalyst (https://www.metaboanalyst.ca/).</p>
Supplementary Material to "Identifying major research themes in sport sciences" (Dataset)
<p>The Excel file is organized into the following sheets:</p> <ul> <li><strong>SM_Sheet1</strong> - global bibliometric indicators in the sport sciences</li> <li><strong>SM_Sheet2</strong> - main indicators at country for two different period</li> <li><strong>SM_Sheet3</strong> - list of 4159 research topics for world science research</li> <li><strong>SM_Sheet4</strong> - a detailed description of main research topics in sport sciences</li> <li><strong>SM_Sheet5</strong> - the number of journals included in the Journal Citation Reports (JCR) in the sport science category and the topic distribution according to the Gini Index are as follows</li> </ul>
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.