cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.29396378.v1
title: A construction method of a multidimensional and multilingual association network for earth surface system science data

description: The use of association networks to calculate metadata similarity for data association and recommendation is a well-established approach. However, challenges persist in the field of open scientific data association within the Earth surface system. Existing methodologies are primarily designed for single data sources, limited dimensions, and specific linguistic domains, limiting their applicability and effectiveness. Enhancing the efficiency and adaptability of association algorithms is crucial for further advancements in this field. In this paper, we propose a multidimensional and multilingual association method for Earth surface system scientific data to support precise data retrieval and intelligent recommendation. The method quantitatively computes one-dimensional and multidimensional similarities using five primary indicators of multilingual data, enabling semantic interconnection and facilitating knowledge discovery within open scientific data related to the Earth surface system. We obtained metadata from several authoritative sources, including NESSDC, NASA, and Data.gov, and performed association calculations, constructed multidimensional association networks, and evaluated them for optimization. The key strengths of this study include its ability to construct one-, two-, and multidimensional association networks effectively, support cross-lingual and cross-standard data interconnections, and assess network node importance. Finally, we conducted a comparative analysis with classical methods, demonstrating that our approach achieves higher accuracy and better scalability.
DOI: 10.6084/m9.figshare.29979535.v1
title: table 4: Crosstabulation of ASA Physical Status classification: questionnaire versus medical assessment

description: This table shows the crosstabulation of ASA Physical Status (PS) classifications derived from the pediatric preanesthesia questionnaire versus those obtained through the medical assessment. It includes cell counts and accuracy percentages for each ASA category
DOI: 10.6084/m9.figshare.29290638
title: Efficient processing of side-scan sonar images and fast detection of sparse targets in large-scale images

description: Poor feature representation, confusing background topography, and excessive data volume render detecting sparse targets in large-size acoustic imagery challenging. Especially when conducting real-time processing tasks, accuracy and speed are required to be optimized with limited computational resources. Therefore, this paper proposes an efficient method for real-time side-scan sonar (SSS) image processing and detection of sparse targets in large-scale images. Primarily, an intelligent real-time processing method is proposed for the raw SSS data to acquire high-quality SSS images. Aiming at the characteristics of large-size SSS images and sparse targets, we propose an innovative two-stage inference method: The SSS image slices are pre-classified based on the MobileViTv3-XXS model, and then the optimized detection model of RepVGG+YOLOv5m is employed for target detection of image slices containing targets. Experiments show that real-time preprocessing yields SSS images with an average PSNR of 27.112 and SSIM of 0.816, comparable to the post-processing methods. Meanwhile, it maintains high efficiency and achieves 88.2% mAP, significantly outperforming the slice-only method in detection accuracy and efficiency.
DOI: 10.6084/m9.figshare.29290638.v1
title: Efficient processing of side-scan sonar images and fast detection of sparse targets in large-scale images

description: Poor feature representation, confusing background topography, and excessive data volume render detecting sparse targets in large-size acoustic imagery challenging. Especially when conducting real-time processing tasks, accuracy and speed are required to be optimized with limited computational resources. Therefore, this paper proposes an efficient method for real-time side-scan sonar (SSS) image processing and detection of sparse targets in large-scale images. Primarily, an intelligent real-time processing method is proposed for the raw SSS data to acquire high-quality SSS images. Aiming at the characteristics of large-size SSS images and sparse targets, we propose an innovative two-stage inference method: The SSS image slices are pre-classified based on the MobileViTv3-XXS model, and then the optimized detection model of RepVGG+YOLOv5m is employed for target detection of image slices containing targets. Experiments show that real-time preprocessing yields SSS images with an average PSNR of 27.112 and SSIM of 0.816, comparable to the post-processing methods. Meanwhile, it maintains high efficiency and achieves 88.2% mAP, significantly outperforming the slice-only method in detection accuracy and efficiency.
DOI: 10.6084/m9.figshare.29979535
title: table 4: Crosstabulation of ASA Physical Status classification: questionnaire versus medical assessment

description: This table shows the crosstabulation of ASA Physical Status (PS) classifications derived from the pediatric preanesthesia questionnaire versus those obtained through the medical assessment. It includes cell counts and accuracy percentages for each ASA category
DOI: 10.6084/m9.figshare.29251598
title: A method for assessing urban flood impacts by combining hydrodynamic models with entity semantic evolution

description: Urban flood impact assessment is crucial for effective disaster management. These impacts, driven by changes in entity semantics (function, status), are essential for understanding the true vulnerabilities of urban systems, while current assessment models suffer from low intelligence and poor accuracy in this regard. To address this challenge, this research proposes a novel framework integrating hydrodynamic simulation, knowledge graphs, and semantic reasoning with Large Language Models (LLMs). The framework simulates flood physical processes, combines urban entity knowledge graphs, and utilizes LLMs to automatically reason the dynamic evolution of functional attributes and cross-domain impact chains. Key results demonstrate that this method effectively identifies both known and unknown cascade impact pathways, with LLM reasoning showing high logical consistency (LC = 0.91) in cross-model validation, and automatically inferred impact chains rated as highly reasonable by experts (average score 3.99/5). By introducing dynamic analysis at the semantic level, this research significantly enhances the automation and depth of flood impact assessment, providing a powerful tool for revealing potential functional vulnerabilities in urban systems, which has important implications for improving disaster resilience.
DOI: 10.6084/m9.figshare.29251598.v1
title: A method for assessing urban flood impacts by combining hydrodynamic models with entity semantic evolution

description: Urban flood impact assessment is crucial for effective disaster management. These impacts, driven by changes in entity semantics (function, status), are essential for understanding the true vulnerabilities of urban systems, while current assessment models suffer from low intelligence and poor accuracy in this regard. To address this challenge, this research proposes a novel framework integrating hydrodynamic simulation, knowledge graphs, and semantic reasoning with Large Language Models (LLMs). The framework simulates flood physical processes, combines urban entity knowledge graphs, and utilizes LLMs to automatically reason the dynamic evolution of functional attributes and cross-domain impact chains. Key results demonstrate that this method effectively identifies both known and unknown cascade impact pathways, with LLM reasoning showing high logical consistency (LC = 0.91) in cross-model validation, and automatically inferred impact chains rated as highly reasonable by experts (average score 3.99/5). By introducing dynamic analysis at the semantic level, this research significantly enhances the automation and depth of flood impact assessment, providing a powerful tool for revealing potential functional vulnerabilities in urban systems, which has important implications for improving disaster resilience.
DOI: 10.6084/m9.figshare.29453457
title: Detection of weekly buried meltwater in Greenland Ice Sheet with Sentinel-1 imagery

description: Recent studies show that buried meltwater (BM) beneath the Greenland Ice Sheet (GrIS) can persist through winter, influencing the englacial hydrological system and thermal regime. However, previous research has primarily focused on annual distributions, lacking insight into its seasonal evolution. This study introduces a deep learning network, UNet-BM, to obtain weekly BM distributions across the GrIS from 2016 to 2020. However, little is known about the weekly BM distributions and its dynamic variations. Validation results demonstrate its effectiveness, achieving an overall accuracy of 99.87%, precision of 0.83, and a kappa score of 0.77, demonstrating superior performance compared to previous studies. The results reveal an average of 713 BM bodies per year, covering 523.94 km<sup>2</sup>, predominantly located in marginal areas at elevations of 800–1700 m. Over 80% of BM bodies are concentrated in the Center-West, North-West, and South-West basins. The reduction rates of BM range from 4.8–15.5 km<sup>2</sup> per day. Approximately 27.22% of BM bodies disappear due to freezing or drainage, with higher disappearance rates in the northern GrIS (52.33% in the North and 37.2% in the Northeast). These findings enhance our understanding of BM dynamics and demonstrate the value of deep learning for monitoring englacial hydrology.
DOI: 10.6084/m9.figshare.29453457.v1
title: Detection of weekly buried meltwater in Greenland Ice Sheet with Sentinel-1 imagery

description: Recent studies show that buried meltwater (BM) beneath the Greenland Ice Sheet (GrIS) can persist through winter, influencing the englacial hydrological system and thermal regime. However, previous research has primarily focused on annual distributions, lacking insight into its seasonal evolution. This study introduces a deep learning network, UNet-BM, to obtain weekly BM distributions across the GrIS from 2016 to 2020. However, little is known about the weekly BM distributions and its dynamic variations. Validation results demonstrate its effectiveness, achieving an overall accuracy of 99.87%, precision of 0.83, and a kappa score of 0.77, demonstrating superior performance compared to previous studies. The results reveal an average of 713 BM bodies per year, covering 523.94 km<sup>2</sup>, predominantly located in marginal areas at elevations of 800–1700 m. Over 80% of BM bodies are concentrated in the Center-West, North-West, and South-West basins. The reduction rates of BM range from 4.8–15.5 km<sup>2</sup> per day. Approximately 27.22% of BM bodies disappear due to freezing or drainage, with higher disappearance rates in the northern GrIS (52.33% in the North and 37.2% in the Northeast). These findings enhance our understanding of BM dynamics and demonstrate the value of deep learning for monitoring englacial hydrology.
DOI: 10.6084/m9.figshare.28148111.v1
title: Geo-computation techniques for identifying spatio-temporal patterns of reported oil spills along crude oil pipeline networks

description: The Niger Delta region of Nigeria is a major oil-producing area which experiences frequent oil spills that severely impacts the local environment and communities. Effective environmental monitoring and management remain inadequate in this area due to negligence, slow response times following oil spills, and difficulties regarding access and safety. This study investigates the spatiotemporal patterns of oil spills along the pipeline network from 2013 to 2021 using geo-computation techniques. Utilising the spNetwork package in R, Network Kernel Density Estimates (NKDE) and its temporal extension, Temporal Network Kernel Density Estimates (TNKDE) were carried out. Pipeline data were transformed into 500-metre lixels (linear pixels) to compute network distances and generate density estimates. NKDE identified oil spill hotspots, while TNKDE illustrated the temporal transitions of spills. These methods surpass traditional approaches (e.g. KDE, cluster analysis, point pattern analysis) by incorporating network constraints and uncovering critical spatial–temporal patterns. The findings offer valuable insights for targeted interventions to reduce future spills and mitigate past impacts.
DOI: 10.15468/dl.tnwr8f
title: Occurrence Download

description: A dataset listing the 1 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is South Africa",
 "OccurrenceStatus is Present",
 "TaxonKey is Panthera pardus pardus"
 ]
}

 The dataset's 1 records were derived from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042247-250811113504898/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.28466580
title: Enhancing flexibility and efficiency for urban waterlogging response scenarios simulation: an open-ended approach involving user participation

description: The current urban waterlogging response simulations prioritise the professional developers’ perspective, with limited user participation, focusing mainly on the mapping and transforming the physical and information space of waterlogging. Such simulation results are fixed in content and lack the necessary response information, which cannot satisfy multiple users’ risk perception and response requirements. This study proposes an open-ended method for scenario simulation involving user participation to address these shortcomings. We first established an information transmission model to guide waterlogging response scenario template designs. We then designed scenario templates based on the coupling between waterlogging and the response information (inundated points, evacuation routes, etc) to constrain the scenario expansion. Subsequently, a scenario editor was generated using Unity Annotation Programming, providing an interface for users to participate in simulations. Finally, we designed two controlled experiments for response tasks in developer-provided and end-users-generated scenarios, with participants divided according to their experience of using 3D software and experiencing waterlogging, respectively, to evaluate the proposed simulation method. According to the experimental results, we inferred that the method proposed in this study increases the flexibility and efficiency of scenario simulation and enhances end-users understanding of urban waterlogging risk information and response methods.
DOI: 10.6084/m9.figshare.28466580.v1
title: Enhancing flexibility and efficiency for urban waterlogging response scenarios simulation: an open-ended approach involving user participation

description: The current urban waterlogging response simulations prioritise the professional developers’ perspective, with limited user participation, focusing mainly on the mapping and transforming the physical and information space of waterlogging. Such simulation results are fixed in content and lack the necessary response information, which cannot satisfy multiple users’ risk perception and response requirements. This study proposes an open-ended method for scenario simulation involving user participation to address these shortcomings. We first established an information transmission model to guide waterlogging response scenario template designs. We then designed scenario templates based on the coupling between waterlogging and the response information (inundated points, evacuation routes, etc) to constrain the scenario expansion. Subsequently, a scenario editor was generated using Unity Annotation Programming, providing an interface for users to participate in simulations. Finally, we designed two controlled experiments for response tasks in developer-provided and end-users-generated scenarios, with participants divided according to their experience of using 3D software and experiencing waterlogging, respectively, to evaluate the proposed simulation method. According to the experimental results, we inferred that the method proposed in this study increases the flexibility and efficiency of scenario simulation and enhances end-users understanding of urban waterlogging risk information and response methods.
DOI: 10.6084/m9.figshare.28123321
title: Interactive visualization of ocean unsteady flow data based on dynamic adaptive pathline

description: The visualization of flow fields is crucial for comprehending and explaining spatio-temporal geographic processes that encompass diverse fluid phenomena in the ocean. This study presents an interactive visualization algorithm designed for the spatio-temporal correlated ocean unsteady flow field utilizing dynamic adaptive pathlines. The algorithm based on the pathline space–time continuum framework, provides enhanced space–time continuity and computational efficiency, and it modify the pathline length interactively by adjusting the correlation coefficient. Furthermore, an interactive transfer function has been created to extract different characteristics of the 2D/3D flow field from the ambient current. Using the high-resolution 2D/3D ocean current datasets GLORYS12V1 and OMEGA3D to apply and test, the experimental results illustrate that the pathline-based visualization algorithm, as opposed to the streamline-based approach, can effectively represent a wide array of marine phenomena in real-time with improved clarity. Moreover, it has the capability to dynamically capture intricate features within complex flow fields.
DOI: 10.6084/m9.figshare.28123321.v1
title: Interactive visualization of ocean unsteady flow data based on dynamic adaptive pathline

description: The visualization of flow fields is crucial for comprehending and explaining spatio-temporal geographic processes that encompass diverse fluid phenomena in the ocean. This study presents an interactive visualization algorithm designed for the spatio-temporal correlated ocean unsteady flow field utilizing dynamic adaptive pathlines. The algorithm based on the pathline space–time continuum framework, provides enhanced space–time continuity and computational efficiency, and it modify the pathline length interactively by adjusting the correlation coefficient. Furthermore, an interactive transfer function has been created to extract different characteristics of the 2D/3D flow field from the ambient current. Using the high-resolution 2D/3D ocean current datasets GLORYS12V1 and OMEGA3D to apply and test, the experimental results illustrate that the pathline-based visualization algorithm, as opposed to the streamline-based approach, can effectively represent a wide array of marine phenomena in real-time with improved clarity. Moreover, it has the capability to dynamically capture intricate features within complex flow fields.
DOI: 10.15468/dl.unshr7
title: Occurrence Download

description: A dataset containing 36515 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Taxon occurrence data for the FungalRoot database"
 ]
}

 The dataset includes 36515 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042260-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16941475
title: Labeled and Unlabeled Picosirius Red Images

description: 256x256 picosirius red microscopy images of heart tissues - tiled
DOI: 10.5281/zenodo.16941492
title: Labeled and Unlabeled Picosirius Red Images

description: 256x256 picosirius red microscopy images of heart tissues - tiled
DOI: 10.15468/dl.2hmyr4
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Nothobranchius luekei)",
 "BasisOfRecord is one of (Specimen, Literature Occurrence, Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1991",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042265-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n9fgtb
title: Occurrence Download

description: A dataset containing 43 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lotus pedunculatus",
 "Country is Japan",
 "HasCoordinate is true"
 ]
}

 The dataset includes 43 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042264-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8vvqfb
title: Occurrence Download

description: A dataset containing 31 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lotus pedunculatus",
 "Country is Estonia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 31 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042262-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gnktbu
title: Occurrence Download

description: A dataset containing 151 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lotus pedunculatus",
 "Country is Chile",
 "HasCoordinate is true"
 ]
}

 The dataset includes 151 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042261-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16941330
title: CompactObject RMF EOS Database: Relativistic Mean Field Models for Neutron Star Equation of State

description: CompactObject RMF Database

Title: CompactObject RMF EOS Database: Relativistic Mean Field Models for Neutron Star Equation of State

Description

This repository contains the comprehensive database of relativistic mean field (RMF) models for neutron star equations of state (EOS) developed as part of the CompactObject framework. The database includes posterior samples constrained by nuclear matter properties, astrophysical observations (NICER, GW170817), and theoretical constraints from χEFT and pQCD calculations.

The dataset represents approximately 170,000 samples across multiple RMF models (RMF-NL, DDH, DDHy, DDB, DDBy, GDFM, GDFMX), providing a robust foundation for studying neutron star properties and dense matter physics.

Repository Contents

This Zenodo repository contains:

1. CompactObject_RMF_EOS_Database_v1.h5 (1.71 GB): The main HDF5 database file containing all posterior samples and their properties.

2. CompactObject_RMF_sample.py: A Python script for extracting and filtering data from the database.

3. README.md: This overview documentation.
DOI: 10.5284/1098990
title: Images from Archaeological Investigations at Cobthorn Way, Congresbury, North Somerset 2018

description: This collection comprises images from archaeological investigations at Cobthorn Way, Congresbury. This work was carried out in advance of residential development in July and August 2018 by AC Archaeology on behalf of Strongvox Homes.