cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.27154209
title: A clinically applicable AI system for detection and diagnosis of bone metastases using CT scans

description: the development and validation of the bone lesion detection system
DOI: 10.6084/m9.figshare.27154209.v1
title: A clinically applicable AI system for detection and diagnosis of bone metastases using CT scans

description: the development and validation of the bone lesion detection system
DOI: 10.6084/m9.figshare.27058924
title: A haplotype-resolved genome assembly and gene expression map of Cushion willow

description: The resources of genome and transcriptome map of Salix brachista
DOI: 10.6084/m9.figshare.27058924.v1
title: A haplotype-resolved genome assembly and gene expression map of Cushion willow

description: The resources of genome and transcriptome map of Salix brachista
DOI: 10.15468/dl.8rnqkj
title: Occurrence Download

description: A dataset containing 10214 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Spodoptera frugiperda J.E.Smith, 1797"
 ]
}

 The dataset includes 10214 records from 66 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029700-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4121/12777259-c2f6-4b44-b71f-eec5557824d1.v1
title: Scripts and data for PRADS publication that varies the acquisition function of the Adaptive Screening method

description: This repository can be used to re-generate the results in the paper referenced below. Abstract of the paper: "The Adaptive Screening extreme value prediction method defined in \cite{VS2025} was applied to three cases, predicting most probable maximum values in a given duration. We used either Gaussian Process Regression (GPR) or its multi-fidelity form (MF-GPR) for the regression, and compared seven acquisition functions for the adaptive sampling. The results were judged based on several performance metrics, of which the required number of HF samples for convergence is the most important one. The results of this exercise show that the best option is to use Adaptive Screening with MF-GPR and an acquisition function that balances obtaining new samples around the probability of interest and the tail of the distribution. If the use of MF-GPR is not feasible for any reason, it should be replaced with GPR (or Generalised Pareto fitting) combined with an acquisition function that selects new samples based on the largest probability gap from the existing samples."Van Essen, S.M. and Seyffert, H.C. (2025). Designing ships for extreme non-linear responses - the role of the acquisition function in the Adaptive Screening method. 16th Practical Design of Ships and other Floating Structures (PRADS) Conference, 19-23 Oct, Ann Arbor, USA.
DOI: 10.15468/dl.785dyd
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Kenya",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Kalanchoe pinnata (Lam.) Pers."
 ]
}

 The dataset includes 11 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029704-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.27620397.v1
title: Distributed Acoustic Sensing of Sounds in Audible Spectrum in Realistic Optical Cable Infrastructure

description: The dataset is available through figshare. The dataset contains separate files from measurements of individual sounds (background noise (background), continuous-frequency sweep (cont_freq), stepped-frequency sweep (stepped_freq) and STIPA) in the hierarchical data format version 5 (HDF5). These measurements are divided into two folders das_sp1 and das_p9. Both two folders contain the measured signals for the relaxed, normal and raised levels described in the acoustic part chapter. The individual files store both the measurement data and the DAS parameter settings (in attributes of HDF5 files).

In addition, all source signals in waveform audio file format (WAV) format are attached to the dataset in folder audio. The dataset also includes recordings from the reference microphone for the background, continuous frequency and STIPA signals and the final report produced by the APx analyzer (folder APx525).
DOI: 10.6084/m9.figshare.27620397
title: Distributed Acoustic Sensing of Sounds in Audible Spectrum in Realistic Optical Cable Infrastructure

description: The dataset is available through figshare. The dataset contains separate files from measurements of individual sounds (background noise (background), continuous-frequency sweep (cont_freq), stepped-frequency sweep (stepped_freq) and STIPA) in the hierarchical data format version 5 (HDF5). These measurements are divided into two folders das_sp1 and das_p9. Both two folders contain the measured signals for the relaxed, normal and raised levels described in the acoustic part chapter. The individual files store both the measurement data and the DAS parameter settings (in attributes of HDF5 files).

In addition, all source signals in waveform audio file format (WAV) format are attached to the dataset in folder audio. The dataset also includes recordings from the reference microphone for the background, continuous frequency and STIPA signals and the final report produced by the APx analyzer (folder APx525).
DOI: 10.15468/dl.78gd6n
title: Occurrence Download

description: A dataset containing 199718 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((31.46302 26.96058,36.61601 26.96058,36.61601 29.02178,31.46302 29.02178,31.46302 26.96058))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Year 1922-2025"
 ]
}

 The dataset includes 199718 records from 204 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029666-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.29062691
title: Open research data

description: It contains GNSS scintillation (BHAO & JEJU) data and DMSP data for research.
DOI: 10.6084/m9.figshare.26170351.v1
title: Source Data

description: Source Data containing Figures 2-4 and Supplementary Figures.
DOI: 10.6084/m9.figshare.26170351
title: Source Data

description: Source Data containing Figures 2-4 and Supplementary Figures.
DOI: 10.6084/m9.figshare.28587416
title: The Classical-to-Quantum Crossover in strain-induced ferroelectric transition in SrTiO<sub>3</sub> membranes

description: Data for The Classical-to-Quantum Crossover in strain-induced ferroelectric transition in SrTiO3 membranes https://doi.org/10.48550/arXiv.2502.02586
DOI: 10.6084/m9.figshare.28587416.v1
title: The Classical-to-Quantum Crossover in strain-induced ferroelectric transition in SrTiO<sub>3</sub> membranes

description: Data for The Classical-to-Quantum Crossover in strain-induced ferroelectric transition in SrTiO3 membranes https://doi.org/10.48550/arXiv.2502.02586
DOI: 10.4121/f1348609-c912-4d06-82b8-197c01f3437b.v1
title: Scripts and data for application of the Adaptive Screening method to a second-order wave case study

description: This set of scripts and data files can be used to re-generate the Adaptive Screening method and its application to a case study. Both are described in the paper "Designing for dangerous waves – a new ‘Adaptive Screening’ method to predict extreme values for non-linear responses of marine structures to waves", which is presently under review.<br>Predicting extreme values of strongly non-linear ship responses (such as wave impact loads) is crucial for ensuring safety and performance of maritime structures. However, this is challenging due to the complexity and rarity of the responses. Existing methods are limited, as they are either suitable for only weakly non-linear responses or are otherwise very computationally expensive. The paper above in combination with this dataset introduces a new event-based multi-fidelity method called ‘Adaptive Screening’ to efficiently predict extreme values of strongly non-linear waves and wave-induced responses. It combines elements of screening, multi-fidelity Gaussian Process Regression, and adaptive sampling. A case study with second-order wave data validates the effectiveness of the new method in the paper. The input necessary to reproduce the case study is also included in the dataset. The paper showed that Adaptive Screening outperforms the conventional brute force method (which is also included in the dataset) by providing more accurate extreme values for this problem, while also significantly reducing high-fidelity simulation time. Adaptive Screening depends on a response-dependent low-fidelity indicator variable. We also show that the quality of the used screening indicator does not have a significant influence on the accuracy of the predicted results, but the high-fidelity simulation time required to obtain converged results increases with decreasing indicator quality. Still, the method is foreseen to be more efficient than the conventional method by a factor of five. The weakly non-linear test case shows that Adaptive Screening is very promising for the strongly non-linear responses it was designed for. Using the attached dataset, all results in the paper can be reproduced.
DOI: 10.4121/f1348609-c912-4d06-82b8-197c01f3437b.v2
title: Scripts and data for application of the Adaptive Screening method to a second-order wave case study

description: This set of scripts and data files can be used to re-generate the Adaptive Screening method and its application to a case study. Both are described in the paper "Designing for dangerous waves – a new ‘Adaptive Screening’ method to predict extreme values for non-linear responses of marine structures to waves", which is presently under review.<br>Predicting extreme values of strongly non-linear ship responses (such as wave impact loads) is crucial for ensuring safety and performance of maritime structures. However, this is challenging due to the complexity and rarity of the responses. Existing methods are limited, as they are either suitable for only weakly non-linear responses or are otherwise very computationally expensive. The paper above in combination with this dataset introduces a new event-based multi-fidelity method called ‘Adaptive Screening’ to efficiently predict extreme values of strongly non-linear waves and wave-induced responses. It combines elements of screening, multi-fidelity Gaussian Process Regression, and adaptive sampling. A case study with second-order wave data validates the effectiveness of the new method in the paper. The input necessary to reproduce the case study is also included in the dataset. The paper showed that Adaptive Screening outperforms the conventional brute force method (which is also included in the dataset) by providing more accurate extreme values for this problem, while also significantly reducing high-fidelity simulation time. Adaptive Screening depends on a response-dependent low-fidelity indicator variable. We also show that the quality of the used screening indicator does not have a significant influence on the accuracy of the predicted results, but the high-fidelity simulation time required to obtain converged results increases with decreasing indicator quality. Still, the method is foreseen to be more efficient than the conventional method by a factor of five. The weakly non-linear test case shows that Adaptive Screening is very promising for the strongly non-linear responses it was designed for. Using the attached dataset, all results in the paper can be reproduced.
DOI: 10.4121/f1348609-c912-4d06-82b8-197c01f3437b.v3
title: Scripts and data for application of the Adaptive Screening method to three applications

description: This set of scripts and data files can be used to re-generate the Adaptive Screening method and the three applications described in the paper "Designing for dangerous waves – definition and application of a new ‘Adaptive Screening’ method to predict extreme values of non-linear ship responses to waves".<br>Predicting extreme values of strongly non-linear ship responses (such as wave impact loads) is crucial for ensuring safety and performance of maritime structures. However, this is challenging due to the complexity and rarity of the responses. Existing methods are limited, as they are either suitable for weakly non-linear responses only, or are very computationally intensive. The paper above in combination with this dataset introduces a new event-based multi-fidelity method called ‘Adaptive Screening’ to efficiently predict extreme values of strongly non-linear wave-induced responses. It combines elements of screening, multi-fidelity Gaussian Process Regression, and adaptive sampling. Three applications validate the effectiveness of the new method in the paper: one weakly non-linear case where we predict extreme values of second-order waves, one intermediate case where we predict extreme values of vertical bending moments, and one strongly non-linear case where we predict extreme values of green water impact loads. The input necessary to reproduce applications 1 and 2 is also included in the dataset (application 3 relies on proprietary data, which are not part of this repository).<br>The paper demonstrates that Adaptive Screening outperforms conventional brute-force methods, achieving comparable accuracy in predicting extreme values while significantly reducing high-fidelity simulation times (especially for the most non-linear cases). Like many alternative methods, Adaptive Screening relies on a response-dependent low-fidelity indicator variable. We also show that the method performs well with realistic indicators for a range of applications. The test cases indicate that Adaptive Screening is very promising for the strongly non-linear responses it was designed for.
DOI: 10.4121/f1348609-c912-4d06-82b8-197c01f3437b.v4
title: Scripts and data underlying the publication that defines and applies the new Adaptive Screening method, for extreme value prediction of non-linear wave-induced responses

description: This set of scripts and data files can be used to re-generate the Adaptive Screening method and the three applications described in the paper "Designing for dangerous waves – a new ‘Adaptive Screening’ method to predict extreme values of non-linear marine and coastal structure responses to waves".<br>Predicting extreme values of strongly non-linear hydrodynamic responses (such as wave impact loads) is crucial for ensuring the safety and reliability of marine and coastal structures. However, this task is challenging due to the complexity and rarity of these responses. Existing methods are often limited to weakly non-linear responses or are very computationally expensive. This paper presents a new multi-fidelity method called ‘Adaptive Screening’, designed to efficiently predict extreme values of strongly non-linear wave-induced responses. Predicting these values is a critical element of structural design and reliability analysis. Adaptive Screening combines elements of screening, multi-fidelity Gaussian Process Regression, and adaptive sampling. We validate its effectiveness through three applications: predicting the most probable maxima of second-orderwave crests, vertical bending moments on a ferry, and greenwater impact loads on a containership. Our results demonstrate that Adaptive Screening outperforms conventional brute force methods, achieving comparable accuracy in predicting extreme values while significantlyreducing high-fidelity simulation times (especially for the most non-linear cases). Like many alternative methods, Adaptive Screening relies on a response-dependent low-fidelity indicator variable. We also show that the method performs well with realistic indicators for a range of applications. The test cases indicate that Adaptive Screening is very promising for the strongly non-linear responses it was designed for.
DOI: 10.15468/dl.4zcy86
title: Occurrence Download

description: A dataset containing 774455 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Zambia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 774455 records from 393 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029686-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.27452835
title: Coinciding Spring and Autumn Frosts Have a Limited Impact on Carbon Fluxes in a Grassland Ecosystem

description: The data and R code were used for this manuscript.
DOI: 10.6084/m9.figshare.27452835.v1
title: Coinciding Spring and Autumn Frosts Have a Limited Impact on Carbon Fluxes in a Grassland Ecosystem

description: The data and R code were used for this manuscript.
DOI: 10.6084/m9.figshare.28582001.v1
title: Exciton-phonon Coupling and Phonon-assisted Exciton Relaxation Dynamics in In1-xGaxP Quantum Dots

description: Raw two-dimensional electronic spectra of In1-xGaxP Quantum Dots, Raman spectra of In1-xGaxP Quantum Dots, and absorption spectra of of In1-xGaxP Quantum Dots; videos of 2DES data; Theoretical calculation and simulation results.
DOI: 10.6084/m9.figshare.28582001
title: Exciton-phonon Coupling and Phonon-assisted Exciton Relaxation Dynamics in In1-xGaxP Quantum Dots

description: Raw two-dimensional electronic spectra of In1-xGaxP Quantum Dots, Raman spectra of In1-xGaxP Quantum Dots, and absorption spectra of of In1-xGaxP Quantum Dots; videos of 2DES data; Theoretical calculation and simulation results.
DOI: 10.15468/dl.6zrstv
title: Occurrence Download

description: A dataset containing 25 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Ethiopia, Kenya)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Kalanchoe prolifera (Bowie ex Hook.) Raym.-Hamet"
 ]
}

 The dataset includes 25 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029705-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vyhrrs
title: Occurrence Download

description: A dataset containing 845 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Symplocarpus renifolius Schott ex Tzvelev"
 ]
}

 The dataset includes 845 records from 80 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029706-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jce529
title: Occurrence Download

description: A dataset containing 243637 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is Bombycilla garrulus (Linnaeus, 1758)",
 "Year is greater than or equal to 1950",
 {
 "or" : [
 "GadmGid is USA",
 "GadmGid is CAN",
 "GadmGid is MEX"
 ]
 }
 ]
}

 The dataset includes 243637 records from 57 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029687-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.brmsq9
title: Occurrence Download

description: A dataset containing 72 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is Philippines",
 "TaxonKey is Mene maculata (Bloch & Schneider, 1801)"
 ]
}

 The dataset includes 72 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029707-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.29062409
title: REQUEST TO BECOME A RESPONDENT.docx

description: Below I include the research protocol, data analysis test results and so on.
DOI: 10.6084/m9.figshare.29062409.v1
title: REQUEST TO BECOME A RESPONDENT.docx

description: Below I include the research protocol, data analysis test results and so on.
DOI: 10.15468/dl.dsk52s
title: Occurrence Download

description: A dataset containing 302 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Locality is one of (Gau-Algerheimer Kopf, D, Gau- Algesheim, Gau-Algesheim (Rheinhessen)., Gau-Algesheim Stadtgebiet und angrenzende Feldflur, Gau-Algesheim [Nassau], Gau-Algesheim,, Gau-Algesheim bei Bingen, Gau-Algesheim bei Bingen /Rhein, Ingelheim, Gau-Algesheim, D, Gau-Algesheim, Gaualgesheimer Berg, Gau-Algesheim, Landkreis Mainz-Bingen, In der Rhei, Gau-Algesheim, Rheinhessen, Gau-Algesheim, a/Rhein, Gau-Algesheim: Westhang des 'Auf dem Berg', Gau-Algesheimer Berg, Gau-Algesheimer Kopf, Gau-Algesheimer Kopf (NSG), Gau-Algesheimer Kopf (Rheinhessen)., Gau-Algesheimer Kopf (bei Mainz), Gau-Algesheimer Kopf b. Mainz, Gau-Algesheimer Kopf, Rheinhessen, Gau-Algesheimer Kopf bei Mainz, Gau-algesheim)",
 "TaxonKey is Tracheophyta"
 ]
}

 The dataset includes 302 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029709-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8qkak3
title: Occurrence Download

description: A dataset containing 25357 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Cyanthillium Blume"
 ]
}

 The dataset includes 25357 records from 364 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029710-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.h9ds64
title: Occurrence Download

description: A dataset containing 25 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Ethiopia, Kenya)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Bryophyllum proliferum Bowie ex Hook., Kalanchoe prolifera (Bowie ex Hook.) Raym.-Hamet)"
 ]
}

 The dataset includes 25 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029712-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.29062706
title: Process table raw data--<b>The influence of methanol mixture formation mode on diesel engine combustion process</b>

description: When methanol is applied in diesel engines, two modes can be selected: methanol intake injection (MII) and methanol mixing direct injection (MMDI). Study the influence of the formation mode of methanol mixture in the cylinder on the combustion process for MMDI and MII methanol injection methods.
DOI: 10.6084/m9.figshare.29062706.v1
title: Process table raw data--<b>The influence of methanol mixture formation mode on diesel engine combustion process</b>

description: When methanol is applied in diesel engines, two modes can be selected: methanol intake injection (MII) and methanol mixing direct injection (MMDI). Study the influence of the formation mode of methanol mixture in the cylinder on the combustion process for MMDI and MII methanol injection methods.