cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/rnbhb65hnf
title: Supply Chain Efficiency in the Arab Gulf: A Comparative Review Against Global Leaders (2023-2025)

description: This dataset includes Logistics Performance Index (LPI) scores and Container Port Performance Index (CPPI) rankings for GCC countries, extracted from global benchmarks. It supports the paper “Supply Chain Efficiency in the Arab Gulf”.
DOI: 10.17632/rnbhb65hnf.1
title: Supply Chain Efficiency in the Arab Gulf: A Comparative Review Against Global Leaders (2023-2025)

description: This dataset includes Logistics Performance Index (LPI) scores and Container Port Performance Index (CPPI) rankings for GCC countries, extracted from global benchmarks. It supports the paper “Supply Chain Efficiency in the Arab Gulf”.
DOI: 10.3886/e238161
title: 2000 Yearbook of Immigration Statistics

description: The 2000 Yearbook of Immigration Statistics is a collection of tables about immigration for the fiscal year. The yearbook tables include data about:Foreign nationals who came to the United States during a fiscal year. This includes lawful permanent residents, temporary visitors (nonimmigrants), refugees and asylees, and naturalizations.Immigration enforcement actions, including alien apprehensions, removals, and returns.
DOI: 10.15468/dl.gm9jcn
title: Occurrence Download

description: A dataset containing 22 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 one of (Pteris calcarea)"
 ]
}

 The dataset includes 22 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009833-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/e238161v1
title: 2000 Yearbook of Immigration Statistics

description: The 2000 Yearbook of Immigration Statistics is a collection of tables about immigration for the fiscal year. The yearbook tables include data about:Foreign nationals who came to the United States during a fiscal year. This includes lawful permanent residents, temporary visitors (nonimmigrants), refugees and asylees, and naturalizations.Immigration enforcement actions, including alien apprehensions, removals, and returns.
DOI: 10.17632/rtpv55sjjt
title: Understanding Social Anxiety: A Study of Symptoms And Coping Mechanisms (2024-2025)

description: Social anxiety is characterized by a persistent fear of social or performance situations in which individuals fear embarrassment or negative evaluation. Affecting people across all demographics, social anxiety manifests through both physical symptoms, such as shivering and sweating, and psychological symptoms, including avoidance behaviors and heightened sensitivity to criticism. This study comprehensively examines the symptoms, prevalence, and far-reaching impact of social anxiety, emphasizing its detrimental effects on social relationships, academic achievement, and professional performance. These challenges often result in social withdrawal and an increased vulnerability to secondary mental health issues. 
The study also explores how various social contexts, including crowd size and online interactions, can intensify or alleviate social anxiety. It evaluates a range of coping strategies, from cognitive-behavioral techniques like journaling and exposure therapy to emotional regulation methods such as deep breathing and mindfulness. Survey findings presented in this study reveal common patterns in avoidance behaviors, symptom severity, and their impact on functional outcomes. The critical importance of coping mechanisms and social support networks in managing anxiety symptoms is also highlighted. This direct input reveals the specific needs, challenges, and preferences of target populations, ensuring that programs are designed and adjusted based on actual, not assumed, community priorities. Ultimately, the study advocates for greater awareness and targeted approaches to improve the quality of life for those affected by social anxiety.
DOI: 10.17632/rtpv55sjjt.1
title: Understanding Social Anxiety: A Study of Symptoms And Coping Mechanisms (2024-2025)

description: Social anxiety is characterized by a persistent fear of social or performance situations in which individuals fear embarrassment or negative evaluation. Affecting people across all demographics, social anxiety manifests through both physical symptoms, such as shivering and sweating, and psychological symptoms, including avoidance behaviors and heightened sensitivity to criticism. This study comprehensively examines the symptoms, prevalence, and far-reaching impact of social anxiety, emphasizing its detrimental effects on social relationships, academic achievement, and professional performance. These challenges often result in social withdrawal and an increased vulnerability to secondary mental health issues. 
The study also explores how various social contexts, including crowd size and online interactions, can intensify or alleviate social anxiety. It evaluates a range of coping strategies, from cognitive-behavioral techniques like journaling and exposure therapy to emotional regulation methods such as deep breathing and mindfulness. Survey findings presented in this study reveal common patterns in avoidance behaviors, symptom severity, and their impact on functional outcomes. The critical importance of coping mechanisms and social support networks in managing anxiety symptoms is also highlighted. This direct input reveals the specific needs, challenges, and preferences of target populations, ensuring that programs are designed and adjusted based on actual, not assumed, community priorities. Ultimately, the study advocates for greater awareness and targeted approaches to improve the quality of life for those affected by social anxiety.
DOI: 10.17632/5w44xsc4g3
title: Digital twin-enabled BIM-blockchain integration for automated and transparent construction progress payments

description: ACC_File_Tool.py: Utility script for managing file structures and interactions with Autodesk Construction Cloud (ACC) folders, including automated directory creation and file registration within BIM project environments called by Auto_Manual_Upload.py.
Auto_Manual_Upload.py: Hybrid upload handler for both automated and manual document uploads to ACC and IPFS, enabling structured data ingestion from construction progress submissions.
Contract_OCR_Parser.py: Implements OCR-based parsing for scanned PDF construction contracts, extracting structured metadata such as parties, dates, and payment clauses using AI-assisted NLP.
DT_PAYMENT.py: Main entry script for initializing the BIM-Digital Twin-Blockchain integrated progress payment system. Orchestrates file parsing, progress validation, IPFS upload, and smart contract invocation.
ethereum_client.py: Web3.py-based Ethereum client wrapper for handling blockchain connectivity, transaction broadcasting, and interaction with deployed smart contracts as a shared utility module used by both Smart_Contract.py and Gnosis_Triggering.py.
Forge_Viewer.py: Provides interface functionalities to Autodesk Forge Viewer, supporting real-time visualization of BIM model progress and associating URNs with payment-triggered records called by DT_PAYMENT.py.
Gnosis_Triggering.py: Implements interaction with Gnosis Safe for multi-signature payment authorization using ethereum_client.py. Monitors contract events and triggers Gnosis transactions upon validated progress milestones. Listens to progress update events triggered by DT_PAYMENT.py and Smart_Contract.py.
Smart_Contract.py: Contains the complete Solidity smart contract and the Python service class (ForgeBlockchainService) for compilation, deployment, and interaction with Ethereum-based milestone payment contracts called by DT_PAYMENT.py, and interfaces with Gnosis_Triggering.py for multi-signature transactions;
Upload_Tester.py: Simple testing utility to simulate local PDF upload to the backend API for debugging the full file ingestion and smart contract execution pipeline.
DOI: 10.17632/5w44xsc4g3.2
title: Digital twin-enabled BIM-blockchain integration for automated and transparent construction progress payments

description: ACC_File_Tool.py: Utility script for managing file structures and interactions with Autodesk Construction Cloud (ACC) folders, including automated directory creation and file registration within BIM project environments called by Auto_Manual_Upload.py.
Auto_Manual_Upload.py: Hybrid upload handler for both automated and manual document uploads to ACC and IPFS, enabling structured data ingestion from construction progress submissions.
Contract_OCR_Parser.py: Implements OCR-based parsing for scanned PDF construction contracts, extracting structured metadata such as parties, dates, and payment clauses using AI-assisted NLP.
DT_PAYMENT.py: Main entry script for initializing the BIM-Digital Twin-Blockchain integrated progress payment system. Orchestrates file parsing, progress validation, IPFS upload, and smart contract invocation.
ethereum_client.py: Web3.py-based Ethereum client wrapper for handling blockchain connectivity, transaction broadcasting, and interaction with deployed smart contracts as a shared utility module used by both Smart_Contract.py and Gnosis_Triggering.py.
Forge_Viewer.py: Provides interface functionalities to Autodesk Forge Viewer, supporting real-time visualization of BIM model progress and associating URNs with payment-triggered records called by DT_PAYMENT.py.
Gnosis_Triggering.py: Implements interaction with Gnosis Safe for multi-signature payment authorization using ethereum_client.py. Monitors contract events and triggers Gnosis transactions upon validated progress milestones. Listens to progress update events triggered by DT_PAYMENT.py and Smart_Contract.py.
Smart_Contract.py: Contains the complete Solidity smart contract and the Python service class (ForgeBlockchainService) for compilation, deployment, and interaction with Ethereum-based milestone payment contracts called by DT_PAYMENT.py, and interfaces with Gnosis_Triggering.py for multi-signature transactions;
Upload_Tester.py: Simple testing utility to simulate local PDF upload to the backend API for debugging the full file ingestion and smart contract execution pipeline.
DOI: 10.17632/hgcbnmxvr8.4
title: UAV-derived BRDF datasets for the wheat and peach trees

description: This dataset consists of multi-angle remote sensing data of wheat and peach trees collected in Pinggu District, Beijing, China, using a DJI Matrice 350 RTK UAV. The dataset spans key growth stages of the two crops, offering valuable insights into their canopy reflectance dynamics across different phenological periods and different remote sensing geometry.
The dataset includes the following components:
1、Raw Data:
Digital Number (DN) values directly exported from the onboard sensors of the UAV. These data were acquired at multiple sun-view geometry.
2、Processed Data:
Multi-angle reflectance data derived through radiometric calibration of the raw DN values, along with associated geometric parameters such as view and solar angles. These processed data are suitable for further remote sensing analysis, including vegetation index computation and BRDF characterization.
DOI: 10.17632/hccf5bs246
title: Disasters Weaken the Synergy Effects among the Global Sustainable Development Goals

description: This research dataset aims to systematically analyze the impact of natural disasters on the synergy effects among the Sustainable Development Goals (SDGs), providing scientific evidence for understanding how disaster shocks disrupt the global sustainable development process. The dataset is constructed from panel data covering 137 countries with relatively complete cross-national data over the period 2010–2022, encompassing multiple dimensions including sustainable development performance, exposure to natural disasters, socioeconomic characteristics, and governance capacity.

The data show that scores for 13 SDGs (SDG1, 2, 3, 5, 7, 8, 9, 11, 12, 13, 14, 15, and 17), calculated using the entropy method, are used to construct two types of synergy indicators: first, the composite synergy index among 13 SDGs (CSSDGs), which measures the overall coupling coordination degree of the 13 goals and reflects the systemic synergy level of national sustainable development; and second, the synergy index between pair of SDGs (PSSDGs), comprising 78 pairwise combinations, used to identify interactive relationships between specific goal pairs. The SDG indicators data are sourced from the World Bank database and the Global SDG Indicators Database, while the mediating and control variables are primarily obtained from the World Bank database. Natural disaster data are drawn from the EM-DAT International Disaster Database, focusing on nine severe disaster types: floods, droughts, earthquakes, storms, extreme temperatures, wildfires, epidemics, landslides, and volcanic activity. The core explanatory variable is the logarithm of disaster duration (LnDuration), supplemented by the logarithm of death tolls (LnDeath) and the logarithm of affected population (LnAffected) for robustness checks.

Key findings from the study include: natural disasters significantly weaken the synergy effects among SDGs, and this negative impact persists for up to two years, with droughts and extreme temperatures having particularly pronounced effects. Mechanism analysis reveals that human capital and government governance mitigate these negative impacts, whereas trade openness exacerbates them. Heterogeneity analysis shows that lower-middle-income countries, as well as countries in Europe and Central Asia, South Asia, East Asia and the Pacific, and Sub-Saharan Africa, are more vulnerable to the disruptive effects of disasters on SDG synergies. Furthermore, disasters particularly weaken the synergies between SDG1 (No Poverty) and other goals, and between SDG3 (Good Health and Well-being) and other goals in low-income countries.
DOI: 10.17632/hccf5bs246.1
title: Disasters Weaken the Synergy Effects among the Global Sustainable Development Goals

description: This research dataset aims to systematically analyze the impact of natural disasters on the synergy effects among the Sustainable Development Goals (SDGs), providing scientific evidence for understanding how disaster shocks disrupt the global sustainable development process. The dataset is constructed from panel data covering 137 countries with relatively complete cross-national data over the period 2010–2022, encompassing multiple dimensions including sustainable development performance, exposure to natural disasters, socioeconomic characteristics, and governance capacity.

The data show that scores for 13 SDGs (SDG1, 2, 3, 5, 7, 8, 9, 11, 12, 13, 14, 15, and 17), calculated using the entropy method, are used to construct two types of synergy indicators: first, the composite synergy index among 13 SDGs (CSSDGs), which measures the overall coupling coordination degree of the 13 goals and reflects the systemic synergy level of national sustainable development; and second, the synergy index between pair of SDGs (PSSDGs), comprising 78 pairwise combinations, used to identify interactive relationships between specific goal pairs. The SDG indicators data are sourced from the World Bank database and the Global SDG Indicators Database, while the mediating and control variables are primarily obtained from the World Bank database. Natural disaster data are drawn from the EM-DAT International Disaster Database, focusing on nine severe disaster types: floods, droughts, earthquakes, storms, extreme temperatures, wildfires, epidemics, landslides, and volcanic activity. The core explanatory variable is the logarithm of disaster duration (LnDuration), supplemented by the logarithm of death tolls (LnDeath) and the logarithm of affected population (LnAffected) for robustness checks.

Key findings from the study include: natural disasters significantly weaken the synergy effects among SDGs, and this negative impact persists for up to two years, with droughts and extreme temperatures having particularly pronounced effects. Mechanism analysis reveals that human capital and government governance mitigate these negative impacts, whereas trade openness exacerbates them. Heterogeneity analysis shows that lower-middle-income countries, as well as countries in Europe and Central Asia, South Asia, East Asia and the Pacific, and Sub-Saharan Africa, are more vulnerable to the disruptive effects of disasters on SDG synergies. Furthermore, disasters particularly weaken the synergies between SDG1 (No Poverty) and other goals, and between SDG3 (Good Health and Well-being) and other goals in low-income countries.
DOI: 10.17632/xj8mhjpvck
title: Result-X101SC25013530_Z02_J001_B1_43

description: This dataset contains quantitative proteomic profiles of bovine cumulus cells collected at key stages of in vitro oocyte maturation. The purpose of this study was to investigate the dynamic protein expression changes that occur in cumulus cells during the transition from the germinal vesicle (GV) stage to the metaphase II (MII) stage, providing insight into the molecular mechanisms that support oocyte maturation.

Cumulus–oocyte complexes (COCs) were aspirated from bovine ovaries and cultured under standard in vitro maturation (IVM) conditions. Cumulus cells were isolated at GV (0 h) and MII (24 h) time points. Proteomic analysis was performed using high-resolution liquid chromatography–tandem mass spectrometry (LC–MS/MS). Protein identification and quantification were achieved through database-dependent searches, and label-free quantification was applied to determine relative protein abundance across biological replicates.

The dataset includes raw mass spectrometry files, processed protein identification and quantification tables, and experimental metadata. These data provide a resource for understanding the proteomic remodeling of cumulus cells during oocyte maturation and may serve as a reference for studies on mammalian follicular development, oocyte competence, and assisted reproductive technologies.
DOI: 10.17632/xj8mhjpvck.1
title: Result-X101SC25013530_Z02_J001_B1_43

description: This dataset contains quantitative proteomic profiles of bovine cumulus cells collected at key stages of in vitro oocyte maturation. The purpose of this study was to investigate the dynamic protein expression changes that occur in cumulus cells during the transition from the germinal vesicle (GV) stage to the metaphase II (MII) stage, providing insight into the molecular mechanisms that support oocyte maturation.

Cumulus–oocyte complexes (COCs) were aspirated from bovine ovaries and cultured under standard in vitro maturation (IVM) conditions. Cumulus cells were isolated at GV (0 h) and MII (24 h) time points. Proteomic analysis was performed using high-resolution liquid chromatography–tandem mass spectrometry (LC–MS/MS). Protein identification and quantification were achieved through database-dependent searches, and label-free quantification was applied to determine relative protein abundance across biological replicates.

The dataset includes raw mass spectrometry files, processed protein identification and quantification tables, and experimental metadata. These data provide a resource for understanding the proteomic remodeling of cumulus cells during oocyte maturation and may serve as a reference for studies on mammalian follicular development, oocyte competence, and assisted reproductive technologies.
DOI: 10.17632/rv58kttr8k
title: Time-Resolved Simultaneous Imaging of Mitochondrial Reactive Oxygen Species and Lysosomal Permeabilization to Determine Organelle-Centred Cell Death

description: Supplementary video files
DOI: 10.17632/rv58kttr8k.1
title: Time-Resolved Simultaneous Imaging of Mitochondrial Reactive Oxygen Species and Lysosomal Permeabilization to Determine Organelle-Centred Cell Death

description: Supplementary video files
DOI: 10.15468/dl.cex364
title: Occurrence Download

description: A dataset containing 43022 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is South America",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Oryctolagus cuniculus",
 "Year 2004-2025"
 ]
}

 The dataset includes 43022 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009834-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/576dvrrsdx
title: Permeability Prediction in 2D: Dataset and Trained Convolutional Neural Networks

description: The dataset was generated for the purpose of training convolutional neural network (CNN) models for permeability prediction of 2D structures. The whole dataset is part of a study on predicting permeability using CNNs, while addressing discussions that are largely absent from the current literature, such as the effect of data diversity in the accuracy, input pre-processing, error estimation, architecture comparisons, and sources of error. A link to the publication, which includes a lot more detail about the dataset and CNN models, will be added once it is published. 

The data included in this dataset is split into three different folders. The data under the "Training Data" folder includes 4,500 images, divided in 15 sub-folders. Each sub-folder contains 901 files, which are 300 images, a pressure-velocity map for each of the 300 structures, convergence data for each individual structure, and one comma-separated file (csv) summarizing all simulation results in the folder. The pressure and velocity maps together with the convergence information are direct results of the CFD algorithm used, but the important information for training the CNN models are the images and the permeability data in the csv files.

The "Trained CNNs" folder contains all of the trained CNN models as described in the linked publication for predicting permeability. That includes the ensemble of VGG19 networks. The "External Test Set" includes the same type of data as the "Training Data" folder, but this section of data was only used to test the CNN models. In other words, the trained CNN models never saw any of this data in training, only in testing. The "External Test Set" folder also includes data for phase-size distributions and surface area for all data in this repository. For more details on those, refer to the publication.

The CFD code and the image generation code can be found in the following GitHub, along with more extensive documentation: 
https://github.com/adama-wzr/PixelBasedPermeability/