cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7513019
title: Materials Science Optimization Benchmark Dataset for Multi-fidelity Hard-sphere Packing Simulations

description: Benchmarks are an essential driver of progress in scientific disciplines. Ideal benchmarks mimic real-world tasks as closely as possible, where insufficient difficulty or applicability can stunt growth in the field. Benchmarks should also have sufficiently low computational overhead to promote accessibility and repeatability. The goal is then to win a "Turing test" of sorts by creating a surrogate model that is indistinguishable from the ground truth observation (at least within the dataset bounds that were explored), necessitating a large amount of data. In the fields of materials science and chemistry, industry-relevant optimization tasks are often hierarchical, noisy, multi-fidelity, multi-objective, high-dimensional, and non-linearly correlated while exhibiting mixed numerical and categorical variables subject to linear and non-linear constraints. To complicate matters, unexpected, failed simulation or experimental regions may be present in the search space. In this study, 438371 random hard-sphere packing simulations representing 279 CPU days worth of computational overhead were performed across nine input parameters with linear constraints and two discrete fidelities each with continuous fidelity parameters and results were logged to a free-tier shared MongoDB Atlas database. Two core tabular datasets resulted from this study: 1. a failure probability dataset containing unique input parameter sets and the estimated probabilities that the simulation will fail at each of the two steps, and 2. a regression dataset mapping input parameter sets (including repeats) to particle packing fractions and computational runtimes for each of the two steps. These two datasets can be used to create a surrogate model as close as possible to running the actual simulations by incorporating simulation failure and heteroskedastic noise. For the regression dataset, percentile ranks were computed within each of the groups of identical parameter sets to enable capturing heteroskedastic noise. This is in contrast with a more traditional approach that imposes a-priori assumptions such as Gaussian noise e.g., by providing a mean and standard deviation. A similar approach can be applied to other benchmark datasets to bridge the gap between optimization benchmarks with low computational overhead and realistically complex, real-world optimization scenarios.
DOI: 10.5281/zenodo.7653759
title: A Multi-Objective, Multi-Fidelity Materials Science Optimization Benchmark Dataset for Hard-Sphere Packing Simulations

description: Benchmarks are an essential driver of progress in scientific disciplines. Ideal benchmarks mimic real-world tasks as closely as possible, where insufficient difficulty or applicability can stunt growth in the field. Benchmarks should also have sufficiently low computational overhead to promote accessibility and repeatability. The goal is then to win a “Turing test” of sorts by creating a surrogate model that is indistinguishable from the ground truth observation (at least within the dataset bounds that were explored), necessitating a large amount of data. In the fields of materials science and chemistry, industry-relevant optimization tasks are often hierarchical, noisy, multi-fidelity, multi-objective, high-dimensional, and non-linearly correlated while exhibiting mixed numerical and categorical variables subject to linear and non-linear constraints. To complicate matters, unexpected, failed simulation or experimental regions may be present in the search space. In this study, 438371 random hard-sphere packing simulations representing 279 CPU days worth of computational overhead were performed across nine input parameters with linear constraints and two discrete fidelities each with continuous fidelity parameters and results were logged to a free-tier shared MongoDB Atlas database. Two core tabular datasets resulted from this study: 1. a failure probability dataset containing unique input parameter sets and the estimated probabilities that the simulation will fail at each of the two steps, and 2. a regression dataset mapping input parameter sets (including repeats) to particle packing fractions and computational runtimes for each of the two steps. These two datasets are used to create a surrogate model as close as possible to running the actual simulations by incorporating simulation failure and heteroskedastic noise. For the regression dataset, percentile ranks were computed within each of the groups of identical parameter sets to enable capturing heteroskedastic noise. This is in contrast with a more traditional approach that imposes a-priori assumptions such as Gaussian noise e.g., by providing a mean and standard deviation. A similar approach can be applied to other benchmark datasets to bridge the gap between optimization benchmarks with low computational overhead and realistically complex, real-world optimization scenarios. For usage instructions, see https://matsci-opt-benchmarks.readthedocs.io/.
description: This work was supported by the National Science Foundation Division of Materials Research [Grant no. DMR-1651668].
DOI: 10.5281/zenodo.7332571
title: Comparison of high-resolution global canopy height maps and their applicability to biodiversity modelling - dataset

description: This repository was created to provide datasets related with an article comparing high-resolution global canopy height maps and exploring their applicability to biodiversity modeling in temperate biomes. EBR stands for Entlebuch Biosphere Reserve, MRF stands for Mount Richmond Forest and TAW stands for Trinity Alps Wilderness. The original airborne laser scanning point clouds used for the generation of the canopy height models were sourced from the LINZ Data Service and OpenTopography, and licensed for reuse under the CC BY 4.0 licence (https://doi.org/10.5069/G97D2SB0); Federal Office of Topography swisstopo (https://www.swisstopo.admin.ch/en/geodata/height/surface3d.html); and U.S. Geological Survey (https://apps.nationalmap.gov/downloader/). The Global Forest Canopy Height Map - GFCH (Potapov et al. 2021; https://glad.umd.edu/dataset/gedi) and the high-resolution canopy height model of the Earth - HRCH (Lang et al. 2022, https://langnico.github.io/globalcanopyheight/) are provided free of charge, without restriction of use under Creative Commons Attribution 4.0 International License. Publications, models, and data products that make use of these datasets must include proper acknowledgement. <em>P. Potapov, X. Li, A. Hernandez-Serna, A. Tyukavina, M.C. Hansen, A. Kommareddy, A. Pickens, S. Turubanova, H. Tang, C.E. Silva, J. Armston, R. Dubayah, J. B. Blair, M. Hofton (2021) Mapping and monitoring global forest canopy height through integration of GEDI and Landsat data. Remote Sensing of Environment, 112165. https://doi.org/10.1016/j.rse.2020.112165</em> <em>Lang, N., Jetz, W., Schindler, K., & Wegner, J. D. (2022). A high-resolution canopy height model of the Earth. arXiv preprint arXiv:2204.08322.</em> R scripts related with this datasets are available at Github (https://github.com/lukasgabor/Comparison-of-high-resolution-global-canopy-height-maps-and-their-applicability; DOI: 10.5281/zenodo.7332716) In the previous version (1.0) the average was calculated for the canopy height. In this version (1.1), the maximum height is calculated for the canopy height.
description: {"references": ["P. Potapov, X. Li, A. Hernandez-Serna, A. Tyukavina, M.C. Hansen, A. Kommareddy, A. Pickens, S. Turubanova, H. Tang, C.E. Silva, J. Armston, R. Dubayah, J. B. Blair, M. Hofton (2021) Mapping and monitoring global forest canopy height through integration of GEDI and Landsat data. Remote Sensing of Environment, 112165.\u00a0https://doi.org/10.1016/j.rse.2020.112165", "Lang, N., Jetz, W., Schindler, K., & Wegner, J. D. (2022). A high-resolution canopy height model of the Earth. arXiv preprint arXiv:2204.08322."]}
DOI: 10.5281/zenodo.7332572
title: Comparison of high-resolution global canopy height maps and their applicability to biodiversity modeling in temperate biomes - dataset

description: This repository was created to provide datasets realted with an article comparing high-resolution global canopy height maps and exploring their applicability to biodiversity modeling in temperate biomes. EBR stands for Entlebuch Biosphere Reserve, MRF stands for Mount Richmond Forest and TAW stands for Trinity Alps Wilderness. The original airborne laser scanning point clouds used for the generation of the canopy height models were sourced from the LINZ Data Service and OpenTopography, and licensed for reuse under the CC BY 4.0 licence (https://doi.org/10.5069/G97D2SB0); Federal Office of Topography swisstopo (https://www.swisstopo.admin.ch/en/geodata/height/surface3d.html); and U.S. Geological Survey (https://apps.nationalmap.gov/downloader/). The Global Forest Canopy Height Map - GFCH (Potapov et al. 2021; https://glad.umd.edu/dataset/gedi) and the high-resolution canopy height model of the Earth - HRCH (Lang et al. 2022, https://langnico.github.io/globalcanopyheight/) are provided free of charge, without restriction of use under Creative Commons Attribution 4.0 International License. Publications, models, and data products that make use of these datasets must include proper acknowledgement. <em>P. Potapov, X. Li, A. Hernandez-Serna, A. Tyukavina, M.C. Hansen, A. Kommareddy, A. Pickens, S. Turubanova, H. Tang, C.E. Silva, J. Armston, R. Dubayah, J. B. Blair, M. Hofton (2021) Mapping and monitoring global forest canopy height through integration of GEDI and Landsat data. Remote Sensing of Environment, 112165. https://doi.org/10.1016/j.rse.2020.112165</em> <em>Lang, N., Jetz, W., Schindler, K., & Wegner, J. D. (2022). A high-resolution canopy height model of the Earth. arXiv preprint arXiv:2204.08322.</em> R scripts related with this datasets are available at Github (https://github.com/lukasgabor/Comparison-of-high-resolution-global-canopy-height-maps-and-their-applicability; DOI: 10.5281/zenodo.7332716)
description: {"references": ["P. Potapov, X. Li, A. Hernandez-Serna, A. Tyukavina, M.C. Hansen, A. Kommareddy, A. Pickens, S. Turubanova, H. Tang, C.E. Silva, J. Armston, R. Dubayah, J. B. Blair, M. Hofton (2021) Mapping and monitoring global forest canopy height through integration of GEDI and Landsat data. Remote Sensing of Environment, 112165.\u00a0https://doi.org/10.1016/j.rse.2020.112165", "Lang, N., Jetz, W., Schindler, K., & Wegner, J. D. (2022). A high-resolution canopy height model of the Earth. arXiv preprint arXiv:2204.08322."]}
DOI: 10.25921/zaea-1s39
title: DCLDE 2013 NOAA NEFSC North Atlantic Right Whale Annotations
DOI: 10.6084/m9.figshare.3761562.v2514
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.5281/zenodo.7696165
title: Materials Science Optimization Benchmark Dataset for Multi-Objective, Multi-Fidelity Optimization of Hard-Sphere Packing Simulations

description: Benchmarks are an essential driver of progress in scientific disciplines. Ideal benchmarks mimic real-world tasks as closely as possible, where insufficient difficulty or applicability can stunt growth in the field. Benchmarks should also have sufficiently low computational overhead to promote accessibility and repeatability. The goal is then to win a “Turing test” of sorts by creating a surrogate model that is indistinguishable from the ground truth observation (at least within the dataset bounds that were explored), necessitating a large amount of data. In the fields of materials science and chemistry, industry-relevant optimization tasks are often hierarchical, noisy, multi-fidelity, multi-objective, high-dimensional, and non-linearly correlated while exhibiting mixed numerical and categorical variables subject to linear and non-linear constraints. To complicate matters, unexpected, failed simulation or experimental regions may be present in the search space. In this study, 494498 random hard-sphere packing simulations representing 206 CPU days worth of computational overhead were performed across nine input parameters with linear constraints and two discrete fidelities each with continuous fidelity parameters and results were logged to a free-tier shared MongoDB Atlas database. Two core tabular datasets resulted from this study: 1. a failure probability dataset containing unique input parameter sets and the estimated probabilities that the simulation will fail at each of the two steps, and 2. a regression dataset mapping input parameter sets (including repeats) to particle packing fractions and computational runtimes for each of the two steps. These two datasets are used to create a surrogate model as close as possible to running the actual simulations by incorporating simulation failure and heteroskedastic noise. For the regression dataset, percentile ranks were computed within each of the groups of identical parameter sets to enable capturing heteroskedastic noise. This is in contrast with a more traditional approach that imposes a-priori assumptions such as Gaussian noise e.g., by providing a mean and standard deviation. A similar approach can be applied to other benchmark datasets to bridge the gap between optimization benchmarks with low computational overhead and realistically complex, real-world optimization scenarios. For usage instructions, see https://matsci-opt-benchmarks.readthedocs.io/.
description: This work was supported by the National Science Foundation Division of Materials Research [Grant no. DMR-1651668].
DOI: 10.5281/zenodo.7513018
title: Materials Science Optimization Benchmark Dataset for Multi-Objective, Multi-Fidelity Optimization of Hard-Sphere Packing Simulations

description: Benchmarks are an essential driver of progress in scientific disciplines. Ideal benchmarks mimic real-world tasks as closely as possible, where insufficient difficulty or applicability can stunt growth in the field. Benchmarks should also have sufficiently low computational overhead to promote accessibility and repeatability. The goal is then to win a “Turing test” of sorts by creating a surrogate model that is indistinguishable from the ground truth observation (at least within the dataset bounds that were explored), necessitating a large amount of data. In the fields of materials science and chemistry, industry-relevant optimization tasks are often hierarchical, noisy, multi-fidelity, multi-objective, high-dimensional, and non-linearly correlated while exhibiting mixed numerical and categorical variables subject to linear and non-linear constraints. To complicate matters, unexpected, failed simulation or experimental regions may be present in the search space. In this study, 494498 random hard-sphere packing simulations representing 206 CPU days worth of computational overhead were performed across nine input parameters with linear constraints and two discrete fidelities each with continuous fidelity parameters and results were logged to a free-tier shared MongoDB Atlas database. Two core tabular datasets resulted from this study: 1. a failure probability dataset containing unique input parameter sets and the estimated probabilities that the simulation will fail at each of the two steps, and 2. a regression dataset mapping input parameter sets (including repeats) to particle packing fractions and computational runtimes for each of the two steps. These two datasets are used to create a surrogate model as close as possible to running the actual simulations by incorporating simulation failure and heteroskedastic noise. For the regression dataset, percentile ranks were computed within each of the groups of identical parameter sets to enable capturing heteroskedastic noise. This is in contrast with a more traditional approach that imposes a-priori assumptions such as Gaussian noise e.g., by providing a mean and standard deviation. A similar approach can be applied to other benchmark datasets to bridge the gap between optimization benchmarks with low computational overhead and realistically complex, real-world optimization scenarios. For usage instructions, see https://matsci-opt-benchmarks.readthedocs.io/.
description: This work was supported by the National Science Foundation Division of Materials Research [Grant no. DMR-1651668].
DOI: 10.26093/cds/vizier.22270026
title: ATCA obs. of ground-state OH masers

description: VizieR online Data Catalogue associated with article published in journal Astronomical Journal (AAS) with title 'Accurate OH maser positions from the SPLASH pilot region.' (bibcode: 2016ApJS..227...26Q)
DOI: 10.15785/sbgrid/975
title: X-Ray Diffraction data from LRP6 E1E2 domains bound to YW210.09 Fab and engineered XWnt8 peptide, source of 8FFE structure

description: Native dataset collected from two crystals
description: Data were processed with XDS. 1447_Pin1_0_?????.cbf used frames 1-360. 1447_Pin2_0_?????.cbf used frames 1-720
DOI: 10.25921/2c09-ng58
title: NOAA NEFSC North Atlantic Right Whale Annotations
DOI: 10.5281/zenodo.7697275
title: CASP decoy datasets

description: It contains all the 3D structure files of all the decoys of CASP 9 through 14
DOI: 10.5281/zenodo.7697274
title: CASP decoy datasets

description: It contains all the 3D structure files of all the decoys of CASP 9 through 14
DOI: 10.5281/zenodo.7697355
title: Datasets analyzed with SIMBA

description: Source datasets to fully reproduce the SIMBA analyses.
DOI: 10.5281/zenodo.7697354
title: Datasets analyzed with SIMBA

description: Source datasets to fully reproduce the SIMBA analyses.
DOI: 10.3886/e169061v1-133302
title: Determinants of the COVID-19 Vaccine Hesitancy Spectrum

description: This study conducted an online survey (N=3,013) using the Social Science Research Solution (SSRS) Opinion Panel web panelists, representative of U.S. adults age 18 and older who use the internet, with an oversample of rural-dwelling and minority populations between April 8 and April 22, 2021- as vaccine eligibility opened to the country. We examined the relationship between COVID-19 exposure and socio-demographics with vaccine intentions [eager-to-take, wait-and-see, undecided, refuse] among the unvaccinated using multinomial logistic regressions [ref: fully/partially vaccinated]. Results showed vaccine intentions varied by demographic characteristics and COVID-19 experience during the period that eligibility for the vaccine was extended to all adults. At the time of the survey approximately 40% of respondents were unvaccinated; 41% knew someone who had died of COVID-19, and 38% had experienced financial hardship as a result of the pandemic. The vaccinated were more likely to be highly educated, older adults, consistent with the United States initial eligibility criteria. Political affiliation and financial hardship experienced during the pandemic were the two most salient factors associated with being undecided or unwilling to take the vaccine.
DOI: 10.17632/hjrfgys29n.1
title: Compressive Strength of Steel Fiber Reinforced Concrete Structures at Elevated Temperatures

description: Experimental studies using a substantial number of datasets can be avoided by employing efficient methods to predict the mechanical properties of construction materials. The correlation between the mechanical attributes and structural performance of these structures can be determined using an efficient mathematical model.
DOI: 10.17632/ctks2b3ymc.1
title: Sewage sludge treatment plant (generated by ASPEN)

description: Data-rich framework including 76 sets is generated via ASPEN PLUS software in order
to adopt prediction models for the fluidized bed temperature, steam heat transfer rate, and dryer residence
time in the sewage sludge (SS) incineration system in a wastewater treatment plant.
DOI: 10.5281/zenodo.7697395
title: Benzodiazepines

description: Data set for the publication: "<strong>Nitrazepam and 7-aminonitrazepam studied at the macroscopic and microscopic electrified liquid-liquid interface. </strong>"
DOI: 10.5281/zenodo.7697394
title: Benzodiazepines

description: Data set for the publication: "<strong>Nitrazepam and 7-aminonitrazepam studied at the macroscopic and microscopic electrified liquid-liquid interface. </strong>"
DOI: 10.3886/e185222
title: ECIN Replication Package for "Aggregate and Individual Effects of Information in a Coordination (Traffic) Game"

description: Using an existing coordination (traffic) experiment, we investigate information’seffect on traffic congestion when subjects already have a history of past play. Incontrast to previous studies, our interventions neither alter aggregate nor individualpayoffs. A second study isolates individual-subject response to information using afixed distribution of past subjects. We find information alters subject play: subjectsswitch roads more often and receive higher payoffs conditional on switching roads.Because switching reduces payoffs unconditionally, information does not generallyimprove payoffs overall. Only subjects that receive information upon starting thegame appear to increase their payoffs due to the information treatment.
description: Two populations are represented in this data. The first population is Texas A&M undergraduatestudents that have signed up to be part of Economics experiments. The second is a generalpopulation of commuters in the Dallas area that use toll roads regularly.
DOI: 10.3886/e185222v1
title: ECIN Replication Package for "Aggregate and Individual Effects of Information in a Coordination (Traffic) Game"

description: Using an existing coordination (traffic) experiment, we investigate information’seffect on traffic congestion when subjects already have a history of past play. Incontrast to previous studies, our interventions neither alter aggregate nor individualpayoffs. A second study isolates individual-subject response to information using afixed distribution of past subjects. We find information alters subject play: subjectsswitch roads more often and receive higher payoffs conditional on switching roads.Because switching reduces payoffs unconditionally, information does not generallyimprove payoffs overall. Only subjects that receive information upon starting thegame appear to increase their payoffs due to the information treatment.
description: Two populations are represented in this data. The first population is Texas A&M undergraduatestudents that have signed up to be part of Economics experiments. The second is a generalpopulation of commuters in the Dallas area that use toll roads regularly.
DOI: 10.3886/e185222v2
title: ECIN Replication Package for "Aggregate and Individual Effects of Information in a Coordination (Traffic) Game"

description: Using an existing coordination (traffic) experiment, we investigate information’seffect on traffic congestion when subjects already have a history of past play. Incontrast to previous studies, our interventions neither alter aggregate nor individualpayoffs. A second study isolates individual-subject response to information using afixed distribution of past subjects. We find information alters subject play: subjectsswitch roads more often and receive higher payoffs conditional on switching roads.Because switching reduces payoffs unconditionally, information does not generallyimprove payoffs overall. Only subjects that receive information upon starting thegame appear to increase their payoffs due to the information treatment.
description: Two populations are represented in this data. The first population is Texas A&M undergraduatestudents that have signed up to be part of Economics experiments. The second is a generalpopulation of commuters in the Dallas area that use toll roads regularly.
DOI: 10.5281/zenodo.6811054
title: Mutation in glutamate transporter homologue GltTk provides insights into pathologic mechanism of episodic ataxia 6

description: Dataset to reproduce MD simulations described in "New insights into a mutation leading to episodic ataxia 6"