cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8300807
title: EXTREMA: Random autonomous interplanetary mission scenarios for EXTREMA Simulation Hub (ESH) hardware-in-the-loop simulations

description: EXTREMA (short for Engineering Extremely Rare Events in Astrodynamics for Deep-Space Missions in Autonomy) enables self-driving spacecraft, challenging the current paradigm under which spacecraft are piloted in the interplanetary space [1]. Deep-space guidance, navigation, and control applied in a complex scenario is the subject of EXTREMA, which wants to engineer ballistic capture in a totally autonomous fashion. EXTREMA is erected on three pillars. Pillar 1 is on autonomous navigation. Pillar 2 involves autonomous guidance and control. Pillar 3 deals with autonomous ballistic capture. The outcomes from each one of the three pillars is validated with a tailored experiment featuring the model of the associated CubeSat subsystems. Integrated experiments involving all the components of a CubeSat GNC system are carried on in the EXTREMA Simulation Hub, an integrated facility simulating an interplanetary transfer with a hardware-in-the-loop setup. The project has been awarded a European Research Council (ERC) Consolidator Grant in 2019. The data set is made of 1000 (even more in some releases) random interplanetary mission scenarios used for hardware-in-the-loop simulations performed in the EXTREMA simulation Hub (ESH). Each scenario comes with the following information: generation seed, initial epoch, initial state (Keplerian elements and Cartesian coordinates), initial attitude (quaternion and direct cosine matrix), initial mass, number of revolution, time of flight, target final epoch, target final state (Keplerian elements and Cartesian Coordinates), and other supplementary data. Scenarios are organized in directories each containing at least the following files: 'esh_ic.json': random scenario information saved in .json format; 'esh_ic.mat': random scenario information saved in .mat format. Some releases provide only a limited set of data in the 'esh_ic.json' file. For those, the full set of information is found in the 'esh_ic_full.json' file. For additional information about the EXTREMA project visit the page extrema.polimi.it. <strong>References</strong><br> [1] Di Domenico G., et al. "The ERC-Funded EXTREMA Project: Achieving Self-Driving Interplanetary CubeSats." <em>Modeling and Optimization in Space Engineering: New Concepts and Approaches</em>. Cham: Springer International Publishing, 2022. 167-199. DOI: 10.1007/978-3-031-24812-2_6.
DOI: 10.5281/zenodo.8300632
title: EXTREMA: Random autonomous interplanetary mission scenarios for EXTREMA Simulation Hub (ESH) hardware-in-the-loop simulations

description: EXTREMA (short for Engineering Extremely Rare Events in Astrodynamics for Deep-Space Missions in Autonomy) enables self-driving spacecraft, challenging the current paradigm under which spacecraft are piloted in the interplanetary space [1]. Deep-space guidance, navigation, and control applied in a complex scenario is the subject of EXTREMA, which wants to engineer ballistic capture in a totally autonomous fashion. EXTREMA is erected on three pillars. Pillar 1 is on autonomous navigation. Pillar 2 involves autonomous guidance and control. Pillar 3 deals with autonomous ballistic capture. The outcomes from each one of the three pillars is validated with a tailored experiment featuring the model of the associated CubeSat subsystems. Integrated experiments involving all the components of a CubeSat GNC system are carried on in the EXTREMA Simulation Hub, an integrated facility simulating an interplanetary transfer with a hardware-in-the-loop setup. The project has been awarded a European Research Council (ERC) Consolidator Grant in 2019. The data set is made of 1000 (even more in some releases) random interplanetary mission scenarios used for hardware-in-the-loop simulations performed in the EXTREMA simulation Hub (ESH). Each scenario comes with the following information: generation seed, initial epoch, initial state (Keplerian elements and Cartesian coordinates), initial attitude (quaternion and direct cosine matrix), initial mass, number of revolution, time of flight, target final epoch, target final state (Keplerian elements and Cartesian Coordinates), and other supplementary data. Scenarios are organized in directories each containing at least the following files: 'esh_ic.json': random scenario information saved in .json format; 'esh_ic.mat': random scenario information saved in .mat format. Some releases provide only a limited set of data in the 'esh_ic.json' file. For those, the full set of information is found in the 'esh_ic_full.json' file. For additional information about the EXTREMA project visit the page extrema.polimi.it. <strong>References</strong><br> [1] Di Domenico G., et al. "The ERC-Funded EXTREMA Project: Achieving Self-Driving Interplanetary CubeSats." <em>Modeling and Optimization in Space Engineering: New Concepts and Approaches</em>. Cham: Springer International Publishing, 2022. 167-199. DOI: 10.1007/978-3-031-24812-2_6.
DOI: 10.5281/zenodo.8300616
title: Legacy files for the Gaia selection function

description: This upload gathers several precomputed files related to the selection function of the Gaia DR2 and DR3 catalogues. <strong>cog_dr2_scanning_law_v2.csv</strong> - Corrected Gaia scanning law for DR2 (Boubert & Everall 2020MNRAS.497.1826B) <strong>cog_ii_dr2.h5</strong> - Precomputed completeness for DR2 (Boubert & Everall 2020MNRAS.497.4246B) <strong>n_field_dr3.h5</strong> - Precomputed completeness for EDR3 (Everall & Boubert 2022MNRAS.509.6205E) <strong>dr3-rvs-nk.h5</strong> - Table containing counts of stars with and without radial velocity, binned by G, G_RP and healpix (level 5) (Castro-Ginard et al. 2023) <strong>rvs_cogv.h5</strong> - Precomputed selection function for the RVS subsample (Everall & Boubert 2022MNRAS.509.6205E)
DOI: 10.5281/zenodo.8300615
title: Legacy files for the Gaia selection function

description: This upload gathers several precomputed files related to the selection function of the Gaia DR2 and DR3 catalogues. <strong>cog_dr2_scanning_law_v2.csv</strong> - Corrected Gaia scanning law for DR2 (Boubert & Everall 2020MNRAS.497.1826B) <strong>cog_ii_dr2.h5</strong> - Precomputed completeness for DR2 (Boubert & Everall 2020MNRAS.497.4246B) <strong>n_field_dr3.h5</strong> - Precomputed completeness for EDR3 (Everall & Boubert 2022MNRAS.509.6205E) <strong>dr3-rvs-nk.h5</strong> - Table containing counts of stars with and without radial velocity, binned by G, G_RP and healpix (level 5) (Castro-Ginard et al. 2023) <strong>rvs_cogv.h5</strong> - Precomputed selection function for the RVS subsample (Everall & Boubert 2022MNRAS.509.6205E)
DOI: 10.5281/zenodo.8295507
title: Assessing Sustainability Potential of Spent Lithium-ion Battery Mining using Deep Eutectic Solvents

description: This dataset provides technical advantages/disadvantages, economic inputs/outputs, and life cycle environmental impacts of solvometallurgy (deep eutectic solvents), pyrometallurgy, and hydrometallurgy systems for critical metals recycling from spent lithium-ion battery.
DOI: 10.5281/zenodo.8295508
title: Assessing Sustainability Potential of Spent Lithium-ion Battery Mining using Deep Eutectic Solvents

description: This dataset provides technical advantages/disadvantages, economic inputs/outputs, and life cycle environmental impacts of solvometallurgy (deep eutectic solvents), pyrometallurgy, and hydrometallurgy systems for critical metals recycling from spent lithium-ion battery.
DOI: 10.58032/agh/5wwr7q
title: Data for "Self-assembled epitaxial BiFeO3 nanostructures as a tailored platform for vertically aligned nanocomposites development"

description: XRD measurement data of thin film nanostructures of bismuth ferrite on strontium-titanium oxide substrates (&lt;001&gt;,&lt;110&gt;,&lt;111&gt;).
DOI: 10.11583/dtu.22178171
title: Input-Target database, machine learning modeling

description: The dataset contains input and target variables for Waste-to-DME plant scenarios that were used to develop artificial neural networks. Applied waste types: (1) the organic fraction of municipal solid waste (OFMSW), (2) sewage sludge (SS).
DOI: 10.11583/dtu.22178171.v1
title: Input-Target database, machine learning modeling

description: The dataset contains input and target variables for Waste-to-DME plant scenarios that were used to develop artificial neural networks. Applied waste types: (1) the organic fraction of municipal solid waste (OFMSW), (2) sewage sludge (SS).
DOI: 10.5281/zenodo.8297966
title: agroBRIDGES EIP-AGRI Practice Abstracts - Regional insights and business models for SFSC development

description: The present dataset completed the collection of the 25 regional practice abstracts developed in the framework of the agroBRIDGES project, using the EIP-AGRI common format. The aim is to share project outputs so other actors of the agri-food sector can leverage on them and to see the main added value and benefits met by the end-users if the generated knowledge is implemented. The Practice Abstracts are also published on the EIP-AGRI website and available at this <strong>link. </strong> The current (second) version of the dataset builds upon the previous set of Practice Abstracts generated in each of the 12 European focal countries of the project to share the project's knowledge and insights gained from co-creation, data collection, communication, toolbox validation and event organisation activities. These activities aim at promoting the development of Short Food Supply Chains, while motivating the local network of agri-food actors in the process (multi-actor approach). At least one Practice Abstract has been generated per Beacon Region (i.e. focal region of the agroBRIDGES project). The Practice Abstracts aim to support proactitioners in replicating our approaches and methodologies. 2 Practice Abstracts have been elaborated per country (23 in total): Ireland Denmark Italy (Lazio & Piemonte regions) Greece (Central Macedonia region) Spain (Andalusia region) France (Pays de Loire, Auvergne and Bretagne regions) Poland Latvia Lithuania Turkey Finland Netherlands Additionally, 1 Practice Abstract was developed to present 5 business model categories of SFSC analysed in the context of agroBRIDGES.
DOI: 10.5281/zenodo.7386478
title: agroBRIDGES EIP-AGRI Practice Abstracts - Regional insights and business models for SFSC development

description: The present dataset completed the collection of the 25 regional practice abstracts developed in the framework of the agroBRIDGES project, using the EIP-AGRI common format. The aim is to share project outputs so other actors of the agri-food sector can leverage on them and to see the main added value and benefits met by the end-users if the generated knowledge is implemented. The Practice Abstracts are also published on the EIP-AGRI website and available at this <strong>link. </strong> The current (second) version of the dataset builds upon the previous set of Practice Abstracts generated in each of the 12 European focal countries of the project to share the project's knowledge and insights gained from co-creation, data collection, communication, toolbox validation and event organisation activities. These activities aim at promoting the development of Short Food Supply Chains, while motivating the local network of agri-food actors in the process (multi-actor approach). At least one Practice Abstract has been generated per Beacon Region (i.e. focal region of the agroBRIDGES project). The Practice Abstracts aim to support proactitioners in replicating our approaches and methodologies. 2 Practice Abstracts have been elaborated per country (23 in total): Ireland Denmark Italy (Lazio & Piemonte regions) Greece (Central Macedonia region) Spain (Andalusia region) France (Pays de Loire, Auvergne and Bretagne regions) Poland Latvia Lithuania Turkey Finland Netherlands Additionally, 1 Practice Abstract was developed to present 5 business model categories of SFSC analysed in the context of agroBRIDGES.
DOI: 10.34740/kaggle/dsv/6388402
title: 100 Largest companies
DOI: 10.6084/m9.figshare.24055902.v1
title: The Awe-Habitual Model: Exploring Tourist’s Pro-Environmental Behaviors in Religious Settings

description: <b>The Awe-Habitual Model: Exploring Tourist’s Pro-Environmental Behaviors in Religious Settings</b>
DOI: 10.5281/zenodo.8299644
title: Demand-Side Constraints in Development: The Role of Market Size, Trade, and (In)Equality

description: Data and replication code package for the paper "Demand-Side Constraints in Development: The Role of Market Size, Trade, and (In)Equality"
DOI: 10.5281/zenodo.8299643
title: Demand-Side Constraints in Development: The Role of Market Size, Trade, and (In)Equality

description: Data and replication code package for the paper "Demand-Side Constraints in Development: The Role of Market Size, Trade, and (In)Equality"
DOI: 10.5281/zenodo.8298887
title: Simulated reads for benchmarking SARS-CoV-2 lineage abundance estimation

description: To evaluate the accuracy of lineage abundance estimates from amplicon-based and whole genome-based sequencing, we simulated paired-end reads from amplicons determined by AmpliDiff, and reads spanning full genomes. Abundances of lineages are based on the relative abundance of a lineage within the dataset. The data consists of the following 8 independent datasets: 200 bp reads from the Netherlands based on AmpliDiff amplicons (1, 2, 5 or 10 amplicons) at 1000x coverage, 400 bp reads from the Netherlands based on AmpliDiff amplicons (1, 2, 5 or 10 amplicons) at 1000x coverage, 200 bp reads from the Netherlands based on whole genome sequencing at 100x coverage, 400 bp reads from the Netherlands based on whole genome sequencing at 100x coverage, 200 bp reads from Texas based on AmpliDiff amplicons (1, 2, 5 or 10 amplicons) at 1000x coverage, 400 bp reads from Texas based on AmpliDiff amplicons (1, 2, 5 or 10 amplicons) at 1000x coverage, 200 bp reads from Texas based on whole genome sequencing at 100x coverage, 400 bp reads from Texas based on whole genome sequencing at 100x coverage. Every independent dataset contains 20 sets of reads (generated with different random seeds). The genomes used for the Netherlands-based simulations can be obtained via GISAID through accession id EPI_SET_230825fe, and the genomes used for the Texas-based simulations can be obtained via GISAID through accession id EPI_SET_230825pe.
description: {"references": ["Khare, S., et al (2021) GISAID's Role in Pandemic Response. China CDC Weekly, 3(49): 1049-1051. doi: 10.46234/ccdcw2021.255 PMCID: 8668406"]}
DOI: 10.5281/zenodo.8298886
title: Simulated reads for benchmarking SARS-CoV-2 lineage abundance estimation

description: To evaluate the accuracy of lineage abundance estimates from amplicon-based and whole genome-based sequencing, we simulated paired-end reads from amplicons determined by AmpliDiff, and reads spanning full genomes. Abundances of lineages are based on the relative abundance of a lineage within the dataset. The data consists of the following 8 independent datasets: 200 bp reads from the Netherlands based on AmpliDiff amplicons (1, 2, 5 or 10 amplicons) at 1000x coverage, 400 bp reads from the Netherlands based on AmpliDiff amplicons (1, 2, 5 or 10 amplicons) at 1000x coverage, 200 bp reads from the Netherlands based on whole genome sequencing at 100x coverage, 400 bp reads from the Netherlands based on whole genome sequencing at 100x coverage, 200 bp reads from Texas based on AmpliDiff amplicons (1, 2, 5 or 10 amplicons) at 1000x coverage, 400 bp reads from Texas based on AmpliDiff amplicons (1, 2, 5 or 10 amplicons) at 1000x coverage, 200 bp reads from Texas based on whole genome sequencing at 100x coverage, 400 bp reads from Texas based on whole genome sequencing at 100x coverage. Every independent dataset contains 20 sets of reads (generated with different random seeds). The genomes used for the Netherlands-based simulations can be obtained via GISAID through accession id EPI_SET_230825fe, and the genomes used for the Texas-based simulations can be obtained via GISAID through accession id EPI_SET_230825pe.
description: {"references": ["Khare, S., et al (2021) GISAID's Role in Pandemic Response. China CDC Weekly, 3(49): 1049-1051. doi: 10.46234/ccdcw2021.255 PMCID: 8668406"]}
DOI: 10.5281/zenodo.8300527
title: Italian TikTok users online behaviour patterns and social attitudes (survey)

description: Survey of 500 young TikTok users (18-35) in Italy covering online behaviour patterns and social attitudes
DOI: 10.5281/zenodo.8300528
title: Italian TikTok users online behaviour patterns and social attitudes (survey)

description: Survey of 500 young TikTok users (18-35) in Italy covering online behaviour patterns and social attitudes
DOI: 10.6084/m9.figshare.24057162
title: Spore_SEMs_figshare.zip

description: Dataset for "Comparing Preparation Techniques to Preserve Bacterial Spore Structure and Surface Features for Scanning Electron Microscopy Evaluation"(non-watermarked dataset avalaible on request).
DOI: 10.5281/zenodo.8233483
title: CyCAR Dataset

description: CyCAR is collected through real UAV flights above the city of Nicosia in Cyprus. Overall the dataset features 27 min of high-resolution video, captured by 3 different UAVs with their camera pose being vertical to the ground. Vehicles on a subset of selected frames have been annotated with tight axis-aligned bounding boxes by human experts. The annotated frames span from heavily congested to clear traffic situations. Furthermore, CyCAR videos comprise a wide variety of scenarios including: (i) static UAV above an urban area-of-interest, (ii) moving UAV on a prespecified path and (iii) UAV following a moving "target" vehicle on the ground, in order to facilitate a comprehensive set of traffic monitoring scenarios such as persistent monitoring of an area (e.g. for traffic regulation purposes), periodical data collection (e.g. for extracting statistics) and live traffic density estimation (e.g. for emergency response). A key differentiating feature of CyCAR compared to other Aerial Detection datasets, is the broad range of UAV flying altitudes during its collection process. With a minimum level of 20m above ground, highly detailed close-distance images of vehicles and their surrounding environments are captured. Simultaneously, the maximum flight-level of 500m above ground provides enormous and challenging ground-area coverage embracing a large number of vehicles and diverse cluttered background in a single frame. Most importantly, CyCAR frames are altitude-stamped with respect to its take-off point based on discretised flying height estimation acquired by the UAV's on-board sensors. Please cite the following paper if you find this dataset useful for your work: A. Kouris, C. Kyrkou and C.S. Bouganis, Informed Region Selection for Efficient UAV-based Object Detectors: Altitude-aware Vehicle Detection with CyCAR Dataset, i<em>n 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019</em> Publication: https://ieeexplore.ieee.org/document/8967722
description: A. Kouris, C. Kyrkou and C. -S. Bouganis, "Informed Region Selection for Efficient UAV-based Object Detectors: Altitude-aware Vehicle Detection with CyCAR Dataset," 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China, 2019, pp. 51-58, doi: 10.1109/IROS40897.2019.8967722.
DOI: 10.5281/zenodo.8233484
title: CyCAR Dataset

description: CyCAR is collected through real UAV flights above the city of Nicosia in Cyprus. Overall the dataset features 27 min of high-resolution video, captured by 3 different UAVs with their camera pose being vertical to the ground. Vehicles on a subset of selected frames have been annotated with tight axis-aligned bounding boxes by human experts. The annotated frames span from heavily congested to clear traffic situations. Furthermore, CyCAR videos comprise a wide variety of scenarios including: (i) static UAV above an urban area-of-interest, (ii) moving UAV on a prespecified path and (iii) UAV following a moving "target" vehicle on the ground, in order to facilitate a comprehensive set of traffic monitoring scenarios such as persistent monitoring of an area (e.g. for traffic regulation purposes), periodical data collection (e.g. for extracting statistics) and live traffic density estimation (e.g. for emergency response). A key differentiating feature of CyCAR compared to other Aerial Detection datasets, is the broad range of UAV flying altitudes during its collection process. With a minimum level of 20m above ground, highly detailed close-distance images of vehicles and their surrounding environments are captured. Simultaneously, the maximum flight-level of 500m above ground provides enormous and challenging ground-area coverage embracing a large number of vehicles and diverse cluttered background in a single frame. Most importantly, CyCAR frames are altitude-stamped with respect to its take-off point based on discretised flying height estimation acquired by the UAV's on-board sensors. Please cite the following paper if you find this dataset useful for your work: A. Kouris, C. Kyrkou and C.S. Bouganis, Informed Region Selection for Efficient UAV-based Object Detectors: Altitude-aware Vehicle Detection with CyCAR Dataset, i<em>n 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019</em> Publication: https://ieeexplore.ieee.org/document/8967722
description: A. Kouris, C. Kyrkou and C. -S. Bouganis, "Informed Region Selection for Efficient UAV-based Object Detectors: Altitude-aware Vehicle Detection with CyCAR Dataset," 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China, 2019, pp. 51-58, doi: 10.1109/IROS40897.2019.8967722.
DOI: 10.4121/4b8d0613-5ee7-4971-b695-835f287336e8
title: Supplementary material for Hydrodynamics in a Randomly Packed Bed with Spherical Particles: A Comparison between PR-CFD Simulations and MRI Experiments

description: The data and scripts used in the experiments and simulations can be found in ‘SupplementaryMaterial.zip’. Inside the folder ‘/Figures’ the data to plot the figures of the article ‘Hydrodynamics in a Randomly Packed Bed with Spherical Particles: A Comparison between PR-CFD Simulations and MRI Experiments’ are provided. <br>Inside the folder ‘/Reconstructed packing’ the positions and radii of the particles are provided, which allows to reconstruct the spherical particle packing used in the flow simulations. The raw image data in DICOM format from which these sphere positions and radii are obtained is provided inside the subfolder ‘/Raw data’. Inside the folder ‘/Matlab script for MRI’ the velocity data obtained from the MRI experiments is provided. The MATLAB scripts to calculate the velocity profiles in the article are provided as well. The raw spectrometer files in MRD format from which the velocity data is obtained are provided inside the subfolder ‘/Raw data’. The data in the error analysis section of the paper can be found in the “Error Analysis” folder.<br>Inside the folder ‘/Matlab script for PR-CFD’ the scripts for calculating the radial velocity and cumulative flow profiles which are reported in the article can be found. Figure 4 and 5 can be generated with the python script inside the folder ‘/Python script for PR-CFD’ from the vtk files which can be provided upon requests.<br>