Find research datasets worth reusing

Material stock map of CONUS - Mid West

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. 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. Spatial extent This subdataset covers the Mid West CONUS, i.e. <ul> <li>IA</li> <li>IL</li> <li>IN</li> <li>MI</li> <li>MN</li> <li>MO</li> <li>OH</li> <li>WI</li> </ul> For the remaining CONUS, see the related identifiers. Temporal extent The map is representative for ca. 2018. Data format The data are organized by states. Within each state, data are split into 100km x 100km tiles (EQUI7 grid), and mosaics are provided. 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). Additionally, a grand total of all stocks is provided at multiple spatial resolutions and units, i.e. <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> 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.  Additionally, the grand total mass per state is tabulated for each county in mass_grand_total_t_10m2.tif.csv. County FIPS code and the ID in this table can be related via FIPS-dictionary_ENLOCALE.csv. Material layers 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 related identifiers): 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> Further information For further information, please see the publication. A web-visualization of this dataset is available here. 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. Publication 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. In prep Funding 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. Acknowledgments 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.

Material stock map of CONUS - South

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. 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. Spatial extent This subdataset covers the South CONUS, i.e. <ul> <li>AL</li> <li>AR</li> <li>FL</li> <li>GA</li> <li>KY</li> <li>LA</li> <li>MS</li> <li>NC</li> <li>SC</li> <li>TN</li> <li>VA</li> <li>WV</li> </ul> For the remaining CONUS, see the related identifiers. Temporal extent The map is representative for ca. 2018. Data format The data are organized by states. Within each state, data are split into 100km x 100km tiles (EQUI7 grid), and mosaics are provided. 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). Additionally, a grand total of all stocks is provided at multiple spatial resolutions and units, i.e. <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> 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.  Additionally, the grand total mass per state is tabulated for each county in mass_grand_total_t_10m2.tif.csv. County FIPS code and the ID in this table can be related via FIPS-dictionary_ENLOCALE.csv. Material layers 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 related identifiers): 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/doi:10.5281/zenodo.5045337.">doi:10.5281/zenodo.5045337.</a> Further information For further information, please see the publication. A web-visualization of this dataset is available here. 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. Publication 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. In prep Funding 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. Acknowledgments 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.

Material stock map of CONUS - West Coast

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. 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. Spatial extent This subdataset covers the West Coast CONUS, i.e. <ul> <li>CA</li> <li>OR</li> <li>WA</li> </ul> For the remaining CONUS, see the related identifiers. Temporal extent The map is representative for ca. 2018. Data format The data are organized by states. Within each state, data are split into 100km x 100km tiles (EQUI7 grid), and mosaics are provided. 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). Additionally, a grand total of all stocks is provided at multiple spatial resolutions and units, i.e. <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> 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.  Additionally, the grand total mass per state is tabulated for each county in mass_grand_total_t_10m2.tif.csv. County FIPS code and the ID in this table can be related via FIPS-dictionary_ENLOCALE.csv. Material layers 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 related identifiers): 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> Further information For further information, please see the publication. A web-visualization of this dataset is available here. 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. Publication 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. In prep Funding 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. Acknowledgments 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.

Material stock map of CONUS - South West

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. 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. Spatial extent This subdataset covers the South West CONUS, i.e. <ul> <li>AZ</li> <li>NM</li> <li>NV</li> <li>TX</li> </ul> For the remaining CONUS, see the related identifiers. Temporal extent The map is representative for ca. 2018. Data format The data are organized by states. Within each state, data are split into 100km x 100km tiles (EQUI7 grid), and mosaics are provided. 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). Additionally, a grand total of all stocks is provided at multiple spatial resolutions and units, i.e. <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> 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.  Additionally, the grand total mass per state is tabulated for each county in mass_grand_total_t_10m2.tif.csv. County FIPS code and the ID in this table can be related via FIPS-dictionary_ENLOCALE.csv. Material layers 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 related identifiers): 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> Further information For further information, please see the publication. A web-visualization of this dataset is available here. 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. Publication 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. In prep Funding 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. Acknowledgments 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.

Material stock map of CONUS - Rocky Mountains

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. 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. Spatial extent This subdataset covers the Rocky Mountains CONUS, i.e. <ul> <li>CO</li> <li>ID</li> <li>MT</li> <li>UT</li> <li>WY</li> </ul> For the remaining CONUS, see the related identifiers. Temporal extent The map is representative for ca. 2018. Data format The data are organized by states. Within each state, data are split into 100km x 100km tiles (EQUI7 grid), and mosaics are provided. 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). Additionally, a grand total of all stocks is provided at multiple spatial resolutions and units, i.e. <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> 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.  Additionally, the grand total mass per state is tabulated for each county in mass_grand_total_t_10m2.tif.csv. County FIPS code and the ID in this table can be related via FIPS-dictionary_ENLOCALE.csv. Material layers 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 related identifiers): 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> Further information For further information, please see the publication. A web-visualization of this dataset is available here. 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. Publication 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. In prep Funding 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. Acknowledgments 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.

opencc-by-4.0Jul 2023View details →

Self-supervised learning of seismological data reveals undocumented eruptive sequences at the Mayotte submarine volcano - Supplementary Materials

The following files are shared:                 - The scripts used to train the model and generate the figures of the article                 - The input images used to train the model as well as the final outputs (embedding matrix and the associated filenames matrix)                 - The clusters organization with their associated images

opencc-by-4.0Jul 2023View details →

A Thematic Synthesis on Empathy in Software Engineering based on the Practitioners' Perspective - Supplementary Material

This repository contains the supplementary material of the paper "A Thematic Synthesis on Empathy in Software Engineering based on the Practitioners' Perspective"  (DOI https://doi.org/10.1145/3613372.3613407) accepted at the Research Track of the  XXXVII Brazilian Symposium on Software Engineering (SBES 2023). The artifacts are a result of a thematic synthesis of grey literature  performed to investigate the meaning, importance, practices, and effects of empathy  from the perspective of software practitioners.  The analysis was based on web articles from DEV, an online community used by software developers.  The data were collected and stored in the repository to preserve the evidence and ensure the study’s replicability.   The repository contains the following material: 1- <all codes.ods> and <all codes.xlsx> All codes generated in the data extraction process, considering research questions RQ1-RQ5: The two files have the same content in different formats - ODS and XLSX. 2 - <dataset.csv>  The list of web articles collected from the DEV in CSV format with all inclusion and  exclusion information, plus demographic data. 3 - <empathy-framework.jpg> Figure 3 of the paper: A conceptual map of the meaning (boxes in orange) and  the value (boxes in blue) of empathy according to the software practitioners 4 - <empathy-model.jpg>  Figure 4 of the paper: A conceptual framework for communication and collaboration (A),  management and leadership (B), coding (C), and code review (D). 5 - <extraction.ods> and <extraction.xlsx>. The data extracted from the web articles,  including codes and quotes for each research question.  The two files have the same content in different formats - ODS and XLSX.

Dataset for 'Organic Electrochemical Transistors Printed from Degradable Materials as Disposable Biochemical Sensors'

This data set contains the data collected during the FNS project Green Piezo (Grant no. 179064) in association with the publication entitled “Organic Electrochemical Transistors Printed from Degradable Materials as Disposable Biochemical Sensors”. This work aims to study the fabrication of organic electrochemical transistors using more environmentally-friendly materials, in particular carbon contacts and polylactic acid (PLA) as substrate. Organic electrochemical transistors (or OECTs) offer applications in biosensing, for example for point-of-care devices. We use a combination of additive manufacturing methods (screen printing and inkjet printing) to manufacture these transistors and solve the issues with fabricating them on a low-temperature substrate such as PLA. We also assess these transistors as disposable sensors for the detection of various ion concentrations as well as glucose. The data that was collected in the frame of this work is present in this repository. More information about the contents of the dataset is present in the included README files.

The (in)sensitivity of granular creep to materials and boundaries

Experimental data reported in the publication "The (in)sensitivity of granular creep to materials and boundaries". jupyter notebook codes are included for data analysis and generating figures in the paper. datasets for Kaolinite material and for smooth and rough boundaries

opencc-by-4.0Apr 2023View details →

Data and Supplementary Material for "Insights into Lightning K-Leader Initiation and Development from Three Dimensional Broadband Interferometric Observations" by Jensen et al.

Data and Supplementary Material for the manuscript “Insights into Lightning K-Leader Initiation and Development from Three Dimensional Broadband Interferometric Observations” by Jensen et al., submitted to Journal of Geophysical Research: Atmospheres. Further description in included PDF (01file_description.pdf)

Data Extraction table summarizing studies in the scoping review on co-creation of patient education materials

Data extraction table for scoping review on best practices for co-creating patient-facing educational materials

Hyperspectral 2D fan-beam X-ray CT dataset of 5 materials

Hyperspectral X-ray CT dataset acquired at the DTU 3D imaging center. The phantom consists of 5 materials: Aluminium (10 mm) and PVC (7.8 mm) in solid blocks. Sugar, H2O2, and H2O in circular glass containers. 3D array with dimension: 128 x 370 x 258 < channel, angle, horizontal >   Detector parameters: Number of detector pixels: 258 (concatenated from 2 detector modules with 128 pixels each and 2 pixel interpolated across a gap between detectors) Pixel size: 0.077 cm Sep=0.153  Pixels' gap length (cm) det_space=(ndet)*pixel_size+Sep # physical width of detector in cm (pixels*pixel_size), including the gap   Acquisition Parameters 360 # Angular span of projections in degrees 370 # Number of projections. note: last projection taken is not a duplicate of the first projection. At angle: 360/370 degrees from first projection. 115.0 # Source-Detector distance in cm 0 # Vertical source shift from perfect placement 0 # Vertical detector shift from perfect placement 57.5 # Source-AxisOfRotation distance in cm   rot_axis_x = 0 # x-position offset of AxisOfRotation rot_axis_y = 0 # y-position offset of AxisOfRotation

opencc-by-4.0Sep 2023View details →

Data for Water deficit and potassium affect carbon isotope composition in cassava bulk leaf material and extracted carbohydrates

This repository contains data and scripts to reproduce results that are presented in the manuscript: Van Laere, J., Merckx, R., Hood-Nowotny, R., Dercon, G. (2023) Water deficit and potassium affect carbon isotope composition in cassava bulk leaf material and extracted carbohydrates. Front. Plant Sci. 14:1222558 doi: 10.3389/fpls.2023.1222558

opencc-by-4.0Jan 2023View details →

Data describing the life cycle and material flows of neodymium contained in products

Assumptions used to calculate flows of neodymium (Nd) in Europe. For various products (consumer products and industrial goods), the dataset describes the following properties: <ul> <li>lifespan,</li> <li>product weight,</li> <li>neodymium content,</li> <li>end-of-life (EoL) fate,</li> <li>component weight,</li> <li>market share of Nd-containing components</li> </ul> The classification of products is based on UNU Keys.

opencc-by-4.0Jul 2023View details →

All-inorganic micrometric CsPbBr3:Yb3+ powder as a multifunctional material for photovoltaics and optical thermometry: structural and optical characterization

Halide perovskites have been studied very intensively by researchers during the last decade. Development of these materials has improved their unique optoelectrical properties reaching even higher standards making them promising candidates for photovoltaic applications. It should be noted that most inorganic halide perovskites obtained to date are synthesized using organic solvents in the form of nanosized colloids. Here, a low-temperature synthesis protocol for the preparation of microcrystalline CsPbBr3 perovskite powder doped with Yb3+ ions is proposed. The structural and photoluminescence features of the studied material have been thoroughly investigated and described. It turned out that the excitation of the CsPbBr3:Yb3+ perovskite with a 375 nm wavelength leads to spontaneous luminescence of excitons and Yb3+ ions. Hence, the use of CsPbBr3:Yb3+ as a luminescent thermometer or an additional absorbing layer on a solar cell surface is possible. The latter application may result in an increase in the conversion efficiency of the cell. In order to verify this, such a layer was prepared and installed on a commercial silicon solar cell. Its photovoltaic properties have been investigated by the measurements of current-voltage characteristics with 1-sun illumination and spectral characteristics of external quantum efficiency.

opencc-by-4.0Sep 2023View details →

Material of the experiment "More is Less" from the MapMuxing project

This dataset contains the material of a user survey presented in the paper " More is Less - Adding Zoom Levels in Multi-Scale Maps to Reduce the Need for Zooming Interactions". There are several files in the dataset: <ul> <li>the Tiles.zip contained all the tiles of the 5 multi-scale maps used in the experiments.</li> <li>The route.shp shapefiles contains the routes the participants were asked to follow during each trial</li> <li>instructions.doc is the reproduction of the instructions given to each participant prior to the survey. The document is in French.</li> <li>FormulaireConsentement.docx is the consent form the participant had to sign before the survey. It is also in French.</li> <li>script.r contains the R scripts run to analyse the results.</li> <li>qualitative_results.xlsx contains the qualitative information collected during the survey. Most of the information stored in the file is in French.</li> <li>quantitative_results.xlsx contains the quantitative results (completion time, nb of errors, etc.) for each participant and each trial.</li> </ul>

opencc-by-4.0Sep 2023View details →

Datasets and Supporting Materials for the IPIN 2023 Competition Track 3 (Smartphone-based, off-site)

This package contains the datasets and supplementary materials used in the IPIN 2023 Competition.Contents<ul><li>Track-3_TA-2023.pdf: Technical annexe describing the competition (Version 2)</li><li>01 Logfiles: This folder contains a subfolder with the 54 training trials, a subfolder with the 4 testing trials (validation), and a subfolder with the 2 blind scoring trials (test) as provided to competitors.</li><li>02 Supplementary_Materials: This folder contains the Matlab/octave parser, the raster maps, the files for the Matlab tools and the trajectory visualization.</li><li>03 Evaluation: This folder contains the scripts we used to calculate the competition metric, the 75th percentile on the 69 evaluation points. It requires the Matlab Mapping Toolbox. We also provide the ground truth as 2 CSV files. It contains samples of reported estimations and the corresponding results.</li></ul>We provide additional information on the competition at: https://evaal.aaloa.org/2023/call-for-competitionCitation Policy Please cite the following works when using the datasets included in this package:Torres-Sospedra, J.; et al. Datasets and Supporting Materials for the IPIN 2023 Competition Track 3 (Smartphone-based, off-site), Zenodo 2023 http://dx.doi.org/10.5281/zenodo.8362205Check the updated citation policy at: http://dx.doi.org/10.5281/zenodo.8362205ContactFor any further questions about the database and this competition track, please contact: Joaquín Torres-Sospedra  Centro ALGORITMI, Universidade do Minho, Portugal info@jtorr.es - jtorres@algoritmi.uminho.pt   Antonio R. Jiménez  Centre of Automation and Robotics (CAR)-CSIC/UPM, Spain  antonio.jimenez@csic.esAntoni Pérez-Navarro Faculty of Computer Sciences, Multimedia and Telecommunication, Universitat Oberta de Catalunya, Barcelona, Spain aperezn@uoc.eduAcknowledgementsWe thank Maximilian Stahlke and Christopher Mutschler at Fraunhofer ISS, as well as Miguel Ortiz and Ziyou Li at Université Gustave Eiffel, for their invaluable support in collecting the datasets. And last but certainly not least, Antonino Crivello and Francesco Potortì for their huge effort in georeferencing the competition venue and evaluation points.We extend our appreciation to the staff at the Museum for Industrial Culture (Museum Industriekultur) for their unwavering patience and invaluable support throughout our collection days.We are also grateful to Francesco Potortì, the ISTI-CNR team (Paolo, Michele & Filippo), and the Fraunhofer IIS team (Chris, Tobi, Max, ...) for their invaluable commitment to organizing and promoting the IPIN competition.This work and competition belong to the IPIN 2023 Conference in Nuremberg (Germany). Parts of this work received the financial support received from projects and grants: <ul><li>ORIENTATE (H2020-MSCA-IF-2020, Grant Agreement 101023072)</li><li>GeoLibero (from CYTED)</li><li>INDRI (MICINN, ref. PID2021-122642OB-C42, PID2021-122642OB-C43, PID2021-122642OB-C44, MCIU/AEI/FEDER UE)</li><li>MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE)</li><li>TARSIUS (TIN2015-71564-C4-2-R, MINECO/FEDER)</li><li>SmartLoc(CSIC-PIE Ref.201450E011)</li><li>LORIS (TIN2012-38080-C04-04)</li></ul>

opencc-by-4.0Dec 2022View details →

Supplementary material for "Inverse Modeling of the Initial Stage of the 1991 Pinatubo Volcanic Cloud Accounting for Radiative Feedback of Volcanic ash" paper

Supplementary material for "Inverse Modeling of the Initial Stage of the 1991 Pinatubo Volcanic Cloud Accounting for Radiative Feedback of Volcanic ash" by A. Ukhov,  G. Stenchikov, S.Osipov, N. Krotkov, N. Gorkavyi, C. Li, O. Dubovik, and A. Lopatin. Corresponding author: Alexander Ukhov, alexander.ukhov@kaust.edu.sa Contents 0. This 'README' file 1. Emission profiles for ash and SO2    1.1 In pickle and txt format, when radiative feedback is accounted for:       1.1.1 Files 'ash_2d_emission_profiles_rad_on' [Mt/sec] and 'ash_2d_emission_profiles.txt' [Mt/(m sec)]       1.1.2 Files 'so2_2d_emission_profiles_rad_on' [Mt/sec] and 'so2_2d_emission_profiles.txt' [Mt/(m sec)]    1.2 In pickle format, when radiative feedback is not accounted for:       1.2.1 Files 'ash_2d_emission_profiles_rad_off' [Mt/sec]       1.2.2 Files 'so2_2d_emission_profiles_rad_off' [Mt/sec] 2. python script 'draw_supplementary_profiles.py' plots inverted emission profiles     (in pickle format) and their time integrated variants. 3. WRF-Chem output file 'wrfout_d01_1991-06-16_00:00:00' in netcdf format contains     3-D fields of ash, sulfate, and SO2 concentrations at 0000 UTC on 16 of June.     Instructions on how to process WRF-Chem output are available at the Appendix of [1]. 4. WRF-Chem domain grid description in the file 'wrf_small_grid.txt'. This file can be    used for conservative interpolation of 3-D fields to another grid, for example     using 'cdo remapcon'. There are two options:  1. Use inverted ash and SO2 emission profiles (see p.1 and p.2) 2. Use ash, sulfate, and SO2 concentrations from WRF-Chem output file (see p.3 and p.4)    as initial conditions for another run. References: 1. Ukhov, A., Ahmadov, R., Grell, G., and Stenchikov, G.: Improving dust simulations    in WRF-Chem v4.1.3 coupled with the GOCART aerosol module,     Geosci. Model Dev., 14, 473–493, https://doi.org/10.5194/gmd-14-473-2021, 2021. 2. Ukhov et. al, Enhancing Volcanic Eruption Simulations with the WRF-Chem v4.7.x