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12,393 results for “materiality”
Datasets for "Ice Content of Mantling Materials in Deuteronilus Mensae, Mars"
<p>These are supporting datasets for the paper titled, "Ice Content of Mantling Materials in Deuteronilus Mensae, Mars," Baker and Carter, 2023, Journal of Geophysical Research: Planets. Data Set S1 (ds01.txt) is a text file table of SHARAD radargrams analyzed in the paper. Data Sets S2 and S3 (ds02.zip and ds03.zip) are provided as ESRI shapefiles (.shp) and compressed as separate zip files. The shapefiles can be opened in most geographical information systems (GIS) software. Shapefiles consist of three individual files: main file (.shp), index file (.shx), and dBASE table (.dbf). Descriptions of each column or attribute in the text file or shapefile is provided in the readme documentation.</p>
Data for "Detachment of a rigid flat punch from a viscoelastic material"
<p>This dataset contains all the data generated for the publication</p> <p>[1] Papangelo, A., & Ciavarella, M. (2023). Detachment of a rigid flat punch from a viscoelastic material, Tribology Letters, DOI: 10.1007/s11249-023-01720-9<br> </p> <p>The provided data are those which appear in the figures of Ref. [1]. Data are stored using informative named structures in a ".mat" file. The data are easily accessible through the Commercial Software MATLAB (© 1994-2023 The MathWorks, Inc.) or by using the free software GNU Octave. An exemplary code for loading and plotting the data contained in "dataFlatPunch.mat" is the following:</p> <p>%%%%%%%%%%%%%</p> <p>load('dataFlatPunch.mat') % load workspace containing data</p> <p>figure, hold on % open a new figure</p> <p>curve_number = 1; % select the curve to plot</p> <p>plot(Fig1.x{curve_number},Fig1.y{curve_number}) % Plot the curve number "curve_number" of figure "Fig1"</p> <p>%%%%%%%%%%%%%</p> <p>The code can be easily adapted for plotting the curves of all the figures contained in Ref. [1]. </p>
Supplementary materials for "TUJI1 Dataset: Multi-device dataset for indoor localization with high measurement density"
<p>Supplementary materials for "TUJI1 Dataset: Multi-device dataset for indoor localization with high measurement density"</p> <p> </p> <p>For more information please refer to the data descriptor available at: https://www.sciencedirect.com/science/article/pii/S2352340924003251</p> <p>Please cite as:</p> <p>Klus, L., Klus, R., Lohan, E.S., Nurmi, J., Granell, C., Valkama, M., Talvitie, J., Casteleyn, S. and Torres-Sospedra, J., 2024. TUJI1 Dataset: Multi-device dataset for indoor localization with high measurement density. <em>Data in Brief</em>, p.110356.</p> <p> </p>
Supplementary material: Sagart 250-concept list for Caijia
<p>The word list is a supplementary material for the article "Phonological Features of Caijia that are Notable from a Diachronic Perspective" published in the <em>Journal of Historical Linguistics</em>. </p> <p>If you use this word list, please cite the original paper:</p> <p>Lee, Man Hei. 2023. Phonological Features of Caijia that are Notable from a Diachronic Perspective. <em>Journal of Historical Linguistics</em> 13:1. 82-114.</p> <p> </p>
Ionization of sputtered material in high power impulse magnetron sputtering plasmas - comparison of titanium, chromium and aluminum
<p>Experimental data set and modeling results to an upcoming publication titled: "Ionization of sputtered material in high power impulse magnetron sputtering plasmas - comparison of titanium, chromium and aluminum".</p> <p>The dataset contains current and voltage measurements (current-voltage-XX.txt), Langmuir probe measurements (probe-XX.txt) performed 8 mm above the racetrack position (using the method described here https://doi.org/10.1088/1361-6595/ab5e46), model results as explained in the paper and spectroscopic imaging profiles. The spectroscopic imaging profiles are obtained from Abel-inverted images in the radial direction by integrating between z=1mm and z=3mm and in the axial direction between r=12mm and r=15mm.</p>
Genome-scale community modelling reveals key metabolic cross-feedings in epipelagic bacterioplankton communities (Supplementary Materials)
<p>A comprehensive catalog of 19,791 marine prokaryotic isolates (WGS), single-amplified genomes (SAGs) and metagenomic-assembled genomes (MAGs) compiled from MarRef v4.0 (N=943, mostly high-quality WGS), MarDB v4.0 (N=12,963), and the aquatic representative genomes from the ProGenomes database v1.0 (N=566). This collection of well-documented genomes was complemented by 5,319 MAGs assembled from four distinct studies, namely: Parks et al. 2017 (<a href="https://doi.org/10.1038/s41564-017-0012-7">DOI</a>; N=1,765; downloaded from EBI), Tully et al. 2017/2018 (<a href="https://doi.org/10.7717/peerj.3558">DOI</a> and <a href="https://10.1038/sdata.2017.203">DOI</a>; N=2,597; downloaded from EBI), and Delmont et al. 2018 (<a href="https://doi.org/10.1038/s41564-018-0176-9">DOI</a>; N=957; downloaded from FIGSHARE). The Parks et al. study contained genomes reconstructed from non-marine biomes. Thus, a selection of 1,765 genomes was extracted by searching for specific keywords: “tara|marine|sea|ocean|mediterranean” (case insensitive). Note that depending on their study of origin, included MAGs may have been reconstructed using different assembling and binning methods.</p> <p>The archive includes:</p> <ul> <li>a metadata file describing the quality and redundancy of the genomes named `EcoSysMic_metadata.tsv`</li> <li>sequences of the 19,791 (redundant) genomes in `All/WGS`</li> <li>companion files in `All/Data` and `dRep95/Data` (see Methods in the associated paper), including <ul> <li>predicted CDS and EggNOG functional annotations</li> <li>predicted GTDB taxonomy</li> <li>CarveMe reconstructed metabolic models and their MEMOTE quality</li> </ul> </li> </ul> <p>The 7,658 non-redundant species-level genomes (delineated by a 95% ANI threshold over 60% of genome length) that were used in the associated paper are defined by the column `is_drep95` in the metadata file.</p>
Supplementary Material of the Manuscript: "The effects of blank size and knapping strategy on the estimation of core's reduction intensity"
<p>This repository hosts the R-markdown and dataset that allow reproducibility and replicability of the statistical analyses implemented in the paper: Lombao, D., Cueva-Temprana, A., Rabuñal, J.R., Morales, J.I., Mosquera, M. The effects of blank size and knapping strategy on the estimation of core’s reduction intensity. <em>Archaeol Anthropol Sci</em> <strong>11</strong>, 5445–5461 (2019). https://doi.org/10.1007/s12520-019-00879-4<br> For further information, please open first the readme.txt. document.</p>
Galaxy Training Material for Mass spectrometry: GC-MS data processing (with XCMS, RAMClustR, RIAssigner, and matchms)
<p>This dataset contains the training data for the <strong>Mass spectrometry: GC-MS data processing (with XCMS, RAMClustR, RIAssigner, and matchms)</strong> GTN tutorial. It includes 3 GC-[EI+]-HRMS files from seminal plasma samples, the RECETOX Metabolome HR-[EI+]-MS library collected from mostly endogoenous compounds from MetaSci Human Metabolite Library, reference alkanes, sample metadata table, and preprocessed XCMS object.</p>
Supplementary Materials for "UJI Probes: Dataset of Wi-Fi Probe Requests"
<p>This package contains an anonymized packets of 802.11 probe requests captured throughout March of 2023 at Universitat Jaume I. The packet capture file is in the standardized *.pcap binary format and can be opened with any packet analysis tool such as Wireshark or scapy (Python packet analysis and manipulation package).<br> <br> The dataset is usable for analyzis of Wi-Fi probe requests, presence detection, occupancy estimation or signal stability analyzis.</p>
Supplementary Materials to "Subgrouping in a `dialect continuum': A Bayesian phylogenetic analysis of the Mixtecan language family"
<p>SM0: metadata on the languages of the sample</p> <p>SM 1: custom word list</p> <p>SM2: prose explanation of cognate coding and IPA conversion</p> <p>SM3: annotated cognate sets</p> <p>SM4: nexus files of the broad and fine grained cognate coding</p> <p>SM5: NeighborNet visualization with coloring by Josserand (1983)'s groupings and by groupings from our analysis</p> <p>SM6: BEAST2 xml files</p> <p>SM7: MCC trees from BEAST2 analysis</p> <p>SM8: DensiTree visualization and visualization of full MCC tree of best performing model</p>
Bandgap fluctuations and robustness in two-dimensional hyperuniform dielectric materials
<p>These are the data associated with the paper "Bandgap fluctuations and robustness in two-dimensional hyperuniform dielectric materials" published in <a href="http://doi.org/10.1364/OE.484232"><em>Optics Express</em> <strong>31</strong>, 18509 (2023)</a></p> <ul> <li>gapsFrequenciesAndWidths.h5: each line of the table lists the largest and second largest gap widths and frequencies in GHz for all 5000 samples studied in the paper <table align="center"> <thead> <tr> <th scope="col">Column</th> <th scope="col">Quantity</th> </tr> </thead> <tbody> <tr> <td>0</td> <td><span class="math-tex">\(\chi\)</span></td> </tr> <tr> <td>1</td> <td>largest gap frequency (GHz)</td> </tr> <tr> <td>2</td> <td>largest gap width (GHz)</td> </tr> <tr> <td>3</td> <td>second largest gap frequency (GHz)</td> </tr> <tr> <td>4</td> <td>second largest gap width (GHz)</td> </tr> </tbody> </table> </li> <li>DOS.h5: normalized density of states as a function of the frequency (GHz) averaged over all samples for all <span class="math-tex">\(\chi\)</span> values studied in the paper <table align="center"> <thead> <tr> <th scope="col">Column</th> <th scope="col">Quantity</th> </tr> </thead> <tbody> <tr> <td>0</td> <td><span class="math-tex">\(\chi\)</span></td> </tr> <tr> <td>1</td> <td>Frequency (GHz)</td> </tr> <tr> <td>2</td> <td>nDOS</td> </tr> </tbody> </table> <p> </p> </li> </ul>
Do Repeat Yourself: Understanding Sufficient Conditions for Restricted Chase Non-Termination - Evaluation Material
<p>In this archive, we provide the (already normalized and translated) rule sets that we used for the evaluation<br> of RMFA2, DRPC, and RPC in our paper "Do Repeat Yourself: Understanding Sufficient Conditions for Restricted Chase Non-Termination" at KR 2023. We also provide the raw result files and some basic scripts that we used<br> to produce and count the results.<br> <br> Please refer to the provided README.md for more information.</p>
Supplementary material for the article "High-resolution projections of ambient heat for major European cities using different heat metrics"
<p>This dataset contains the data displayed in the figures or the article "High-resolution projections of ambient heat for major European cities using different heat metrics".</p> <p>The different files contain:</p> <ul> <li>Data_Fig1_DeltaTXx_EURO-CORDEX_1981-2010_to_3K-European-warming_RCP85.nc:<br> Change of yearly maximum temperature in Europe between 1981-2010 and 3 °C European warming relative to 1981-2010.</li> <li>Data_Fig2_timeseries-GSAT-ESAT_EURO-CORDEX_CMIP5_CMIP6_1971-2100_RCP85_SSP585.xlsx:<br> Time series of global mean surface air temperature (GSAT) for CMIP5 and CMIP6 models, and for European mean surface air temperature (ESAT) for EURO-CORDEX, CMIP5, and CMIP6 models for the period 1971-2100.</li> <li>Data_Fig3_TX-distribution_distance-from-city-centre_E-OBS_1981-2010.xlsx:<br> Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for E-OBS for all investigated cities. Temperature data are indicated as a function of the distance to the city centre.</li> <li>Data_Fig3_TX-distribution_distance-from-city-centre_ERA5-Land_1981-2010.xlsx:<br> Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for ERA5-Land for all investigated cities. Temperature data are indicated as a function of the distance to the city centre.</li> <li>Data_Fig3_TX-distribution_distance-from-city-centre_EURO-CORDEX_1981-2010.xlsx:<br> Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for the EURO-CORDEX models for all investigated cities. Temperature data are indicated as a function of the distance to the city centre.</li> <li>Data_Fig3_TX-distribution_distance-from-city-centre_weather-stations_1981-2010.xlsx:<br> Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for GSOD and ECA&D stations for all investigated cities. Temperature data are indicated as a function of the distance to the city centre.</li> <li>Data_Fig4_TX-ambient-heat_EURO-CORDEX_3K-European-warming.xlsx:<br> Daytime heat metrics for the investigated cities: HWMId-TX at 3 °C European warming relative to 1981-2010, TX exceedances above 30 °C at 3 °C European warming relative to 1981-2010, and TXx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for EURO-CORDEX models.</li> <li>Data_Fig5_Contribution-of-explanatory-variables-to-total-explained-variance.xlsx:<br> Contribution of different explanatory variables (climate and location factors) to the total explained variance of spatial patterns of heat metrics.</li> <li>Data_Fig6_TN-ambient-heat_EURO-CORDEX_3K-European-warming.xlsx:<br> Nighttime heat metrics for the investigated cities: HWMId-TN at 3 °C European warming relative to 1981-2010, TN exceedances above 20 °C at 3 °C European warming relative to 1981-2010, and TNx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for EURO-CORDEX models.</li> <li>Data_Fig7_TX-ambient-heat_CMIP5_3K-European-warming.xlsx:<br> Daytime heat metrics for the investigated cities: HWMId-TX at 3 °C European warming relative to 1981-2010, TX exceedances above 30 °C at 3 °C European warming relative to 1981-2010, and TXx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for CMIP5 models.</li> <li>Data_Fig7_TX-ambient-heat_CMIP6_3K-European-warming.xlsx:<br> Daytime heat metrics for the investigated cities: HWMId-TX at 3 °C European warming relative to 1981-2010, TX exceedances above 30 °C at 3 °C European warming relative to 1981-2010, and TXx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for CMIP6 models.</li> <li>Data_Fig8_GCM-RCM-matrix_ambient-heat_3K-European-warming.xlsx:<br> GCM-RCM matrices for the three heat metrics.</li> </ul>
Data of Chinese treatment group for the research work "Disentangling material, social, and cognitive determinants of human behavior and belief".
<p>This repository contains data files of Chinese treatment group for the research work "Disentangling material, social, and cognitive determinants of human behavior and belief".</p>
Characterisation of post-scrap material from coupons including melting test data
<p>Remelting tests data including characterisation and melting process parameters of post-scrap material from LBW and FSW coupons.</p> <p>Research pertaining to Task 4.1 (WP4), Deliverable D13 (D4.2)</p>
Supplementary Material no. 2 to the manuscript: Reemission of inorganic pollution from permafrost? – a freshwater hydrochemistry study in the lower Kolyma basin (North-East Siberia)
<p>A dataset on the inorganic chemistry of permafrost-related creeks and ice, thermokarst lakes and the Kolyma river and its tributaries in late July 2021.<br> Companion dataset to the manuscript: "Reemission of inorganic pollution from permafrost? – a freshwater hydrochemistry study in the lower Kolyma basin (North-East Siberia)".<br> Current abstract of the manuscript (prior to peer review):</p> <p>Permafrost regions are under particular pressure from climate change resulting in widespread landscape changes, which impact also freshwater chemistry. We investigated a snapshot of hydrochemistry in various freshwater environments in the lower Kolyma river basin (North-East Siberia, continuous permafrost zone) to explore the mobility of metals, metalloids and non-metals resulting from permafrost thaw. Particular attention was focused on heavy metals as contaminants potentially released from the secondary source in the permafrozen Yedoma complex. Permafrost creeks represented the Mg-Ca-Na-HCO<sub>3</sub>-Cl-SO<sub>4</sub> ionic water type (with mineralisation in the range 600-800 mg/L), while permafrost ice and thermokarst lake waters were the HCO<sub>3</sub>-Ca-Mg type. Multiple heavy metals (As, Cu, Co, Mn and Ni) showed much higher dissolved phase concentrations in permafrost creeks and ice than in Kolyma and its tributaries, and only in the permafrost samples and one Kolyma tributary have we detected dissolved Ti or Hg. In thermokarst lakes, several metal and metalloid dissolved concentrations increased with water depth (Fe, Mn, Ni and Zn - in both lakes; Al, Cu, K, Sb, Sr and Pb in either lake), reaching 1370 µg/L Cu, 4610 µg/L Mn, and 687 µg/L Zn in the bottom water layers. Permafrost-related waters were also enriched in dissolved phosphorus (up to 512 µg/L in Yedoma-fed creeks). The impact of permafrost thaw on river and lake water chemistry is a complex problem which needs to be considered both in the context of legacy permafrost shrinkage and the interference of the deepening active layer with newly deposited antropogenic contaminants.</p>
Supplementary Material: A method for the estimation of a motor unit innervation zone center position evaluated with a computational sEMG model
<p>This repository contains supplementary data for the journal paper:</p> <blockquote> <p>Mechtenberg M and Schneider A (2023) A method for the estimation of a motor unit innervation zone center position evaluated with a computational sEMG model. Front. Neurorobot. 17:1179224. doi: 10.3389/fnbot.2023.1179224</p> </blockquote> <p>It contains the configuration files for the simulator used in that publication [1]. These configuration files are to be found in the archive <strong>EMG_model_configs.zip</strong>.</p> <p><br> The files <strong>IP_tracking_opt_res.json</strong><a href="https://zenodo.org/api/files/d21f2990-1849-40d8-90de-674fb0965938/IP_tracking_opt_res.json"> </a>and <strong>IP_tracking_opt_res.pkl</strong> contain the same information but in different file formats. In these files the results of the optimization described in the corresponding paper are stored.</p> <p>For each optimization condition the optimal parameters for the innervation point tracking algorithm are stored, as well as the error score for all calculated parameter combinations.</p> <p> </p> <p>[1] Mechtenberg, Malte. (2023). UAS-Embedded-Systems-Biomechatronics/EMG-concentrated-current-sources: v0.2.1 (v0.2.1). Zenodo. https://doi.org/10.5281/zenodo.7995152</p>
Data of the characterisation of conventional 87Sr/86Sr isotope ratios in cement, limestone and slate reference materials based on an interlaboratory comparison study
<p>This dataset represents the electronic supplementary material (ESM) of the publication entitled "Characterisation of conventional <sup>87</sup>Sr/<sup>86</sup>Sr isotope ratios in cement, limestone and slate reference materials based on an interlaboratory comparison study", which is published in Geostandards and Geoanalytical Research under the DOI: 10.1111/GGR.12517. It consists of four files. 'ESM_Data.xlsx' contains all reported data of the participants, a description of the applied analytical procedures, basic calculations, the consensus values, and part of the uncertainty assessment. 'ESM_Figure-S1' displays a schematic on how measurements, sequences and replicates are treated for the uncertainty calculation carried out by PTB. 'ESM_Technical-protocol.pdf' is the technical protocol of the interlaboratory comparison, which has been provided to all participants together with the samples and which contains bedside others the definition of the measurand and guidelines for data assessment and calculations. 'ESM_Reporting-template.xlsx' is the Excel template which has been submitted to all participants for reporting their results within the interlaboratory comparison. Excel files with names of the the structure 'GeoReM_Material_Sr8786_Date.xlsx' represent the <em>R</em><sub>con</sub>(<sup>87</sup>Sr/<sup>86</sup>Sr) data for a specific reference material downloaded from GeoReM at the specified date, e.g. 'GeoReM_IAPSO_Sr8786_20221115.xlsx' contains all <em>R</em><sub>con</sub>(<sup>87</sup>Sr/<sup>86</sup>Sr) data for the IAPSO seawater standard listed in GeoReM until 15 November 2022.</p>
Characterisation of post-scrap material from multi-stringer panels
<p>Pre-scrapping material characterisation of multi-stringer panels including raw material characterisation and remelting tests of similar concentration of all raw material present in FSW and LBW demo panels.</p> <p>Research pertaining to Task 4.1 (WP4), Deliverable D16 (D4.3): Analysis of (post) scalable recycled sample.</p> <p>METHODOLOGIES:</p> <p>The chemical composition of all raw material and each remelt sample was analyzed using inductively coupled plasma optical emission spectroscopy (ICP-OES) equipment, Thermo Scientific Icap 7400 radial model. Test conditions: power 1250, exposure time 15 s, nebulizer gas flow 0.65 L/min, and 3 samples per measurement.</p> <p>An analysis sample representative of each demo panel with mass between 27 and 31 g was melted in a LIFUMAT—MET 3,3-Vac remelting unit for sample preparation for spectroscopy (LINN), under an argon protective atmosphere (5 L/min flow rate of high purity 99.999% Ar). In every remelting test, the induction furnace was kept switched on for a few additional seconds after observing the full melting of the sample in the crucible. The molten scrap sample was cast in a metallic mold, and the chemical composition of the obtained specimen was analyzed. The metal yield was calculated as the ratio between the metal extracted in the obtained solid sample di-vided by the initial weight of the sample. That means not considering the dross formed during the remelting test.</p>
Material stock map of CONUS - Great Plains
<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>Great Plains CONUS</strong>, i.e.</p> <ul> <li>KS</li> <li>ND</li> <li>NE</li> <li>OK</li> <li>SD</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. <a href="https://doi.org/10.5281/zenodo.5045337.">Zenodo (2022), 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>
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.