cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7373254
title: ITACA agent-based simulation model results

description: This files downloaded present the raw results and derived indicators per scenario considered obtained with the agent-based model developed for policy assessment in the scope of the ITACA project (https://www.itaca-h2020.eu/). The files are structured in three different case studies: CS01 - Past unsuccessful and delayed solutions CS02 - Past successful solutions CS03 - Future solutions In each case study there are included a set of scenarios, each of them testing one or several policy measures. Each scenario file includes the short name of the policy under study. For example, "Results_visualisation_CS01_Mandates.xlsm" includes the results when testing mandates as policy measure in the CS01 case study. The corresponding raw outputs of the simulator are included in the folder "yyyymmdd_hhmmss_CS01_Mandates". Each excel file contains the following tabs: Files - Files loaded to create the file. The loading interactive buttons have been disabled to avoid errors. H0 - Tab containing the visualisation of different raw outputs for the adopters agents. The user can select all agents or one specific agent to look at the results by selecting its ID in the slicers on top of each agent type (enable editing in excel to do so). H0_KPI_tech - Shows the number of adoptions per technology and agent type H0_KPI_eco - Shows the surpluses per agent type and total social welfare H0_KPI_eco_metrics - Same results, disaggregated in a tabular format. H0_KPI_op - Shows the total fuel burnt, throughput and Determined Unitary Cost (DUC) for enroute and terminal ANS. H1 - Configuration file used as input for the simulation. H2 - Raw results for airlines. H3 - Raw results for airports. H4 - Raw results for enroute ANSPs. H5 - Raw results for terminal ANSPs. H6 - Raw results per route. H7 - Raw results for regulators. The raw results folders include, apart from the data shown in the visualisation excel files, the input data per stakeholder, policy parameters, technology parameters, exogenous variables that affect the agents and the charging zones defined in the European airspace. The complete description of the case studies and scenarios tested is included in ITACA's deliverable D5.1 Impact Assessment of Policies and Regulations to Boost ATM Technology Adoption. Thank you for downloading the dataset! It would be very helpful if you share your view on the data show with us. We have created the following short questionnaire to facilitate the task.
DOI: 10.5281/zenodo.7373253
title: ITACA agent-based simulation model results

description: This files downloaded present the raw results and derived indicators per scenario considered obtained with the agent-based model developed for policy assessment in the scope of the ITACA project (https://www.itaca-h2020.eu/). The files are structured in three different case studies: CS01 - Past unsuccessful and delayed solutions CS02 - Past successful solutions CS03 - Future solutions In each case study there are included a set of scenarios, each of them testing one or several policy measures. Each scenario file includes the short name of the policy under study. For example, "Results_visualisation_CS01_Mandates.xlsm" includes the results when testing mandates as policy measure in the CS01 case study. The corresponding raw outputs of the simulator are included in the folder "yyyymmdd_hhmmss_CS01_Mandates". Each excel file contains the following tabs: Files - Files loaded to create the file. The loading interactive buttons have been disabled to avoid errors. H0 - Tab containing the visualisation of different raw outputs for the adopters agents. The user can select all agents or one specific agent to look at the results by selecting its ID in the slicers on top of each agent type (enable editing in excel to do so). H0_KPI_tech - Shows the number of adoptions per technology and agent type H0_KPI_eco - Shows the surpluses per agent type and total social welfare H0_KPI_eco_metrics - Same results, disaggregated in a tabular format. H0_KPI_op - Shows the total fuel burnt, throughput and Determined Unitary Cost (DUC) for enroute and terminal ANS. H1 - Configuration file used as input for the simulation. H2 - Raw results for airlines. H3 - Raw results for airports. H4 - Raw results for enroute ANSPs. H5 - Raw results for terminal ANSPs. H6 - Raw results per route. H7 - Raw results for regulators. The raw results folders include, apart from the data shown in the visualisation excel files, the input data per stakeholder, policy parameters, technology parameters, exogenous variables that affect the agents and the charging zones defined in the European airspace. The complete description of the case studies and scenarios tested is included in ITACA's deliverable D5.1 Impact Assessment of Policies and Regulations to Boost ATM Technology Adoption. Thank you for downloading the dataset! It would be very helpful if you share your view on the data show with us. We have created the following short questionnaire to facilitate the task.
DOI: 10.5281/zenodo.7352565
title: WUSA surface wave amplification measurements (Schardong et al. 2019)

description: #Western U.S. surface wave amplification measurements If using this data please cite: <br> Schardong, L., Ferreira, A.M., Berbellini, A. and Sturgeon, W., 2019. The anatomy of uppermost mantle shear-wave speed anomalies in the western US from surface-wave amplification. Earth and Planetary Science Letters, 528, p.115822. Directory format:<br> USA.RAV... = vertical-component Rayleigh wave amplification measurements<br> USA.RAH... = horizontal-component Rayleigh wave amplification measurements<br> USA.TOR... = Love wave amplification measurements File format:<br> motion type index -- mode order (n) -- angular order (l) -- period -- relative local amplification w.r.t. PREM -- estimated error on local amplification The number of data points varies depending on the available data in Hendrik van Heijst's database, and the various selection steps.
description: {"references": ["Schardong, L., Ferreira, A.M., Berbellini, A. and Sturgeon, W., 2019. The anatomy of uppermost mantle shear-wave speed anomalies in the western US from surface-wave amplification. Earth and Planetary Science Letters, 528, p.115822."]}
DOI: 10.6084/m9.figshare.19623768
title: SkyTruth - Surface Mining in Central Appalachia - Site Recovery Data

description: This data is published in Restoration Ecology, in the article Mines to Forests? Analyzing long-term recovery trends for surface coal mines in Central Appalachia.<br> These data contain detail the recovery of post-mining landscapes in Central Appalachia. The data is provided in various formats: processedData_lastMinedBefore_2015_gte10px_gompertzData.csv - this contains analyzed data for NBR, NDMI, and NDVI, the annual band date per site is not available in this file, see other data files provided. processedData_analysisBands_PES_lastMinedBefore_2015_gte10px_gompertzData.csv/.geojson - This is the analysis ready dataset, and it contains the annual data only for those indices used in our analysis (NBR, NDMI, NDVI). processedData_allBands_PES_lastMinedBefore_2015_gte10px_gompertzData.csv/.geojson - This is the full dataset, it contains all band and index data, including those not used over the course of our analysis. For all data, only sites last mined before 2015, and greater than or equal to 10 pixels in size are included.
DOI: 10.6084/m9.figshare.19623768.v1
title: SkyTruth - Surface Mining in Central Appalachia - Site Recovery Data

description: THIS IS A DRAFT DATASET PENDING REVIEW. <br> These data contain detail the recovery of post-mining landscapes in Central Appalachia. The data is provided in various formats: processedData_lastMinedBefore_2015_gte10px_gompertzData.csv - this contains analyzed data for NBR, NDMI, and NDVI, the annual band date per site is not available in this file, see other data files provided. processedData_analysisBands_PES_lastMinedBefore_2015_gte10px_gompertzData.csv/.geojson - This is the analysis ready dataset, and it contains the annual data only for those indices used in our analysis (NBR, NDMI, NDVI). processedData_allBands_PES_lastMinedBefore_2015_gte10px_gompertzData.csv/.geojson - This is the full dataset, it contains all band and index data, including those not used over the course of our analysis. For all data, only sites last mined before 2015, and greater than or equal to 10 pixels in size are included.
DOI: 10.5281/zenodo.7373397
title: High-resolution maps of material stock, population and employment in Austria from 1985 to 2018 - Supplementary Material

description: Global societal material stocks such as buildings and infrastructure accumulated rapidly within recent decades, along with population growth. Material stocks constitute the physical basis of most socio-economic activities and services, such as mobility, housing, health, or education. The dynamics of stock growth, and its relation to the population that demands those services, is an essential indicator for long-term societal resource use and patterns of emissions. The creation of societal material stock creates path dependencies for future resource use, with an important impact on how the transformation towards sustainable societies can succeed. This dataset is a supplement to previously generated detailed maps of the distribution of material stocks, population and employment across Austria from 1985 to 2018 (10.5281/zenodo.7195101). The data are aggregated tabular data used to create illustrations in an accompanying data article. <strong>Data format and units</strong> This dataset features: Tabular aggregated data of material stocks, population and employment on a municipality level from 1985 to 2018 (in administrative borders of 2018. Note: Only layers used to create illustrations in the accompanying data article are presented as tabular data! Municipalities of Austria as as shape file (from https://www.data.gv.at/katalog/dataset/stat_gliederung-osterreichs-in-gemeinden14f53#resources) Annual population and employment numbers on a federal states level, extracted and aggregated from Statistik Austria (see readme.txt) <strong>Further information</strong> For further information, please see the publication or contact Franz Schug (fschug@wisc.edu). Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society. <strong>Funding</strong> This research was funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).
DOI: 10.5281/zenodo.7373398
title: High-resolution maps of material stock, population and employment in Austria from 1985 to 2018 - Supplementary Material

description: Global societal material stocks such as buildings and infrastructure accumulated rapidly within recent decades, along with population growth. Material stocks constitute the physical basis of most socio-economic activities and services, such as mobility, housing, health, or education. The dynamics of stock growth, and its relation to the population that demands those services, is an essential indicator for long-term societal resource use and patterns of emissions. The creation of societal material stock creates path dependencies for future resource use, with an important impact on how the transformation towards sustainable societies can succeed. This dataset is a supplement to previously generated detailed maps of the distribution of material stocks, population and employment across Austria from 1985 to 2018 (10.5281/zenodo.7195101). The data are aggregated tabular data used to create illustrations in an accompanying data article. <strong>Data format and units</strong> This dataset features: Tabular aggregated data of material stocks, population and employment on a municipality level from 1985 to 2018 (in administrative borders of 2018. Note: Only layers used to create illustrations in the accompanying data article are presented as tabular data! Municipalities of Austria as as shape file (from https://www.data.gv.at/katalog/dataset/stat_gliederung-osterreichs-in-gemeinden14f53#resources) Annual population and employment numbers on a federal states level, extracted and aggregated from Statistik Austria (see readme.txt) <strong>Further information</strong> For further information, please see the publication or contact Franz Schug (fschug@wisc.edu). Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society. <strong>Funding</strong> This research was funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).
DOI: 10.5281/zenodo.7195100
title: High-resolution maps of material stock, population and employment in Austria from 1985 to 2018

description: Global societal material stocks such as buildings and infrastructure accumulated rapidly within recent decades, along with population growth. Material stocks constitute the physical basis of most socio-economic activities and services, such as mobility, housing, health, or education. The dynamics of stock growth, and its relation to the population that demands those services, is an essential indicator for long-term societal resource use and patterns of emissions. The creation of societal material stock creates path dependencies for future resource use, with an important impact on how the transformation towards sustainable societies can succeed. This dataset features detailed maps of material stock and population, as well as the distribution of jobs, for Austria on a 30m grid. The data is based on recent maps of material stock and building volume (compare to Haberl et al. 2021, doi: 10.1021/acs.est.0c05642, data: https://zenodo.org/record/4522892), recent and historic census data, and a time series of Landsat TM, ETM+, and OLI Earth Observation data. <strong>Temporal extent</strong> The data contains annual maps from 1985 to 2018. <strong>Data format and units</strong> Per Austrian federal state, the data come in tiles of 30x30km. The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. Please consider the generation of image pyramids before using *.vrt files. All image data has 34 bands, where band 1 is data for 1985, and band 34 is data for 2018. The dataset features population (Scaled by 100 to reduce data storage size. Divide by 100 to get people per cell) jobs (Scaled by 100 to reduce data storage size. Divide by 100 to get jobs per cell) mass (in tons) of … total material stock material stock in buildings in commercial and industrial buildings in multi-family residential buildings in high-rise buildings in single-family residential buildings in lightweight buildings material stock in road infrastructure material stock in rail infrastructure material stock in other infrastructure <strong>Further information</strong> For further information, please see the publication or contact Franz Schug (fschug@wisc.edu). Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society. <strong>Funding</strong> This research was funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).
DOI: 10.5281/zenodo.7195101
title: High-resolution maps of material stock, population and employment in Austria from 1985 to 2018

description: Global societal material stocks such as buildings and infrastructure accumulated rapidly within recent decades, along with population growth. Material stocks constitute the physical basis of most socio-economic activities and services, such as mobility, housing, health, or education. The dynamics of stock growth, and its relation to the population that demands those services, is an essential indicator for long-term societal resource use and patterns of emissions. The creation of societal material stock creates path dependencies for future resource use, with an important impact on how the transformation towards sustainable societies can succeed. This dataset features detailed maps of material stock and population, as well as the distribution of jobs, for Austria on a 30m grid. The data is based on recent maps of material stock and building volume (compare to Haberl et al. 2021, doi: 10.1021/acs.est.0c05642, data: https://zenodo.org/record/4522892), recent and historic census data, and a time series of Landsat TM, ETM+, and OLI Earth Observation data. <strong>Temporal extent</strong> The data contains annual maps from 1985 to 2018. <strong>Data format and units</strong> Per Austrian federal state, the data come in tiles of 30x30km. The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. Please consider the generation of image pyramids before using *.vrt files. All image data has 34 bands, where band 1 is data for 1985, and band 34 is data for 2018. The dataset features population (Scaled by 100 to reduce data storage size. Divide by 100 to get people per cell) jobs (Scaled by 100 to reduce data storage size. Divide by 100 to get jobs per cell) mass (in tons) of … total material stock material stock in buildings in commercial and industrial buildings in multi-family residential buildings in high-rise buildings in single-family residential buildings in lightweight buildings material stock in road infrastructure material stock in rail infrastructure material stock in other infrastructure <strong>Further information</strong> For further information, please see the publication or contact Franz Schug (fschug@wisc.edu). Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society. <strong>Funding</strong> This research was funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).
DOI: 10.6073/pasta/3854ab172596a73a7b4b85e688e7deda
title: Sediment elevation measurements for 10 GCE-LTER sampling sites from December 2001 to December 2020

description: Soil surface elevation was measured in tidal marshes at 10 GCE-LTER sampling sites from December 2001 to December 2020 using a sediment elevation table method. The sites represent three estuaries of the Georgia coast (USA) that vary in delivery of freshwater and sediment. The study was conducted to evaluate the effects of freshwater input on marsh accretion and subsidence. In 2013, rod SETs were added at site 11 and installed to replace broken SETs at sites 7 and 9 (which broke in 2012).
DOI: 10.18112/openneuro.ds004346.v1.0.0
title: FLUX: A pipeline for MEG analysis
DOI: 10.5287/bodleian:mp40zr7yx
title: Performance of Transpiration Cooled Heat Shields for Re-entry Vehicles - Dataset
DOI: 10.5287/bodleian:xmpqpka58
title: Analytical Solution of Flows in Porous Media for Transpiration Cooling Applications - Dataset
DOI: 10.5258/soton/d2106
title: Nanoparticle induced property changes in nematic liquid crystals - Supporting information

description: Supporting information related to the paper "Nanoparticle induced property changes in nematic liquid crystals", Nanomaterials (2022), 12, 341. (DOI: 10.3390/nano12030341) The dataset contain a .pdf named "Supporting information" that display Figure S.1 and Figure S.2, and a .txt named "readme" that explain how the dataset is organised and provide information related to the publication. For any futher information, please contact me at nb1f20@soton.ac.uk.
DOI: 10.5287/bodleian:xkw4ba4xj
title: CONDITION DEVELOPMENT AND COMMISSIONING OF THE OXFORD T6 STALKER TUNNEL IN REFLECTED SHOCK TUNNEL MODE - Dataset
DOI: 10.18112/openneuro.ds004347.v1.0.0
title: Symmetry perception and affective responses: a combined EEG/EMG study
DOI: 10.5281/zenodo.7292961
title: X-ray Fluorescence Mapping dataset for use in Heritage Science

description: The following data sets were collected to support the potential uses of opensource data in the context of digital humanities and heritage sciences. This proposed experiment is conducted by the UCL Institute for Sustainable Heritage in collaboration with the Centre for Digital Humanities. Imaging methods including Photography, Multispectral Imaging, Hyperspectral Imaging and Xray Fluorescence Mapping have been collected along with the complete readout metadata of the instrumentation. We hope that you find the data helpful, and we welcome you to use the data in any way you wish, for all and any analysis development purposes. For us to build upon this research, we ask that in return you would be willing to share in some regard your experiences in using open-source data, using our data, successes and issues. If you would be willing to engage with us in this endeavor, please feel free to contact us so that we may be able to follow up with you. Other Data sets available Here E: molly.fort.21@ucl.ac.uk Object Paradata; <strong>Postcard – c. Early 1900's </strong> <strong>Language – Eng. </strong> <strong>Materials – colour print on card, metallic leafing. </strong> <strong>Front transcription - </strong> <strong> ‘Greetings’ </strong> <strong> ‘May your Birthday bring you Peace & perfect Happiness, Golden hopes & Love of Friends, And every Happiness this world can send.’ </strong> <strong>Object Dimensions – 138mm X 88mm</strong> The postcard is an item of ephemera donated to the UCLDH Digitisation Suite by Prof Melissa Terras, for teaching and training purposes in 2015. This folder contains: X-ray fluorescence imaging map collected from a Bruker M4+ Tornado Micro-XRF System Postcardmap1.bcf - Bruker composite file containing full fluorescence spectral and mapping data with some other information. Can be read by Bruker software or using freely dowloadable Hyperspy. EDX.hdf5 - full fluorescence spectral and mapping data in open format, created and readable via. Hyperspy. Postcard_data.txt - metadata saved in ASCII format by Bruker system. postcardmap1_*.png - individual png images of mapped elements, listed in filename.