cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.b4mjch
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bertolonia paranensis",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 6 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007810-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/bkb7sdsywj
title: Multipolar

description: The MultiPolar dataset is a specialized collection of optical data designed to facilitate the development and validation of machine learning models within the field of experimental mechanics. Its primary purpose is to bridge the gap between traditional Digital Photoelasticity and modern data-driven frameworks by providing a standardized set of multi-polarizer imagery and their corresponding quantitative stress interpretations.

This dataset includes multi-polarizer array images consisting of raw data captured from optical setups, featuring both standard benchmark geometries and intricate bioinspired specimens. These samples cover a wide range of stress concentration scenarios, represented through diverse fringe pattern images to ensure model robustness across different structural complexities.

While Digital Photoelasticity has transitioned toward using multipolarizer array cameras, the lack of open-access training data has slowed the integration of Artificial Intelligence in stress analysis. The MultiPolar Dataset addresses this by offering a foundation for: (a) Automated demodulation, by training models to convert fringe patterns into stress maps; (b) Benchmark testing, by providing a common ground for comparing new algorithms in experimental mechanics; and (c) Reproducibility, by promoting open-science practices in the study of material response under stress.
DOI: 10.17632/bkb7sdsywj.1
title: Multipolar

description: The MultiPolar dataset is a specialized collection of optical data designed to facilitate the development and validation of machine learning models within the field of experimental mechanics. Its primary purpose is to bridge the gap between traditional Digital Photoelasticity and modern data-driven frameworks by providing a standardized set of multi-polarizer imagery and their corresponding quantitative stress interpretations.

This dataset includes multi-polarizer array images consisting of raw data captured from optical setups, featuring both standard benchmark geometries and intricate bioinspired specimens. These samples cover a wide range of stress concentration scenarios, represented through diverse fringe pattern images to ensure model robustness across different structural complexities.

While Digital Photoelasticity has transitioned toward using multipolarizer array cameras, the lack of open-access training data has slowed the integration of Artificial Intelligence in stress analysis. The MultiPolar Dataset addresses this by offering a foundation for: (a) Automated demodulation, by training models to convert fringe patterns into stress maps; (b) Benchmark testing, by providing a common ground for comparing new algorithms in experimental mechanics; and (c) Reproducibility, by promoting open-science practices in the study of material response under stress.
DOI: 10.17632/h88jp5rrk6
title: Data for: Fabrication and Structural Analysis of Trilayers for Tantalum Josephson Junctions with Ta2O5 Barriers

description: Tantalum (Ta) has emerged as a promising low-loss material, enabling record coherence times in superconducting qubits. This enhanced performance is largely attributed to its stable native oxide, which may host fewer two-level system (TLS) defects — key contributors to decoherence in superconducting circuits. Nevertheless, aluminum oxide remains the predominant choice for Josephson junction (JJ) barriers in most qubit architectures. Here, we investigate techniques for forming high-quality oxide layers on α-phase tantalum films to develop tantalum-oxide JJ barriers. We explore thermal oxidation in a tube furnace, rapid thermal annealing, and plasma oxidation of both room-temperature and heated Ta films, characterize the resulting structures using X-ray techniques and electron microscopy, and propose a mechanistic picture
of the oxidation pathways. We find that plasma oxidation provides the smoothest Ta2O5 layers, is compatible with in situ Ta deposition, and offers thickness control through the annealing temperature, advantageous for JJ fabrication. Lastly, we evaluate methods for growing Ta/TaOx/Ta trilayers. All trilayers showed c- axis- oriented columnar growth of the bottom Ta layer, with sapphire substrates producing larger, better-aligned grains yet higher dislocation densities than silicon. Nucleation of c- axis- oriented α-Ta on tantalum-oxide required an Nb seed layer, as direct Ta deposition yielded amorphous Ta. These results demonstrate the feasibility of α-Ta/Nb/TaOx/α-Ta stacks for JJs with clean interfaces.

This dataset includes Origin files (.opju) that contain data spreadsheets for all the samples and figures used in this paper, which can be opened using Origin Viewer, a free application that permits viewing and copying of data contained in Origin project files.
DOI: 10.17632/h88jp5rrk6.2
title: Data for: Fabrication and Structural Analysis of Trilayers for Tantalum Josephson Junctions with Ta2O5 Barriers

description: Tantalum (Ta) has emerged as a promising low-loss material, enabling record coherence times in superconducting qubits. This enhanced performance is largely attributed to its stable native oxide, which may host fewer two-level system (TLS) defects — key contributors to decoherence in superconducting circuits. Nevertheless, aluminum oxide remains the predominant choice for Josephson junction (JJ) barriers in most qubit architectures. Here, we investigate techniques for forming high-quality oxide layers on α-phase tantalum films to develop tantalum-oxide JJ barriers. We explore thermal oxidation in a tube furnace, rapid thermal annealing, and plasma oxidation of both room-temperature and heated Ta films, characterize the resulting structures using X-ray techniques and electron microscopy, and propose a mechanistic picture
of the oxidation pathways. We find that plasma oxidation provides the smoothest Ta2O5 layers, is compatible with in situ Ta deposition, and offers thickness control through the annealing temperature, advantageous for JJ fabrication. Lastly, we evaluate methods for growing Ta/TaOx/Ta trilayers. All trilayers showed c- axis- oriented columnar growth of the bottom Ta layer, with sapphire substrates producing larger, better-aligned grains yet higher dislocation densities than silicon. Nucleation of c- axis- oriented α-Ta on tantalum-oxide required an Nb seed layer, as direct Ta deposition yielded amorphous Ta. These results demonstrate the feasibility of α-Ta/Nb/TaOx/α-Ta stacks for JJs with clean interfaces.

This dataset includes Origin files (.opju) that contain data spreadsheets for all the samples and figures used in this paper, which can be opened using Origin Viewer, a free application that permits viewing and copying of data contained in Origin project files.
DOI: 10.17632/p32vxydy3r
title: Hinwar & Lambert 2021 Anauralia & aphantasia dataset

description: This dataset contains: (1) Auditory (BAIS-V and BAIS-C_ and visual (VVIQ-R) imagery questionnaires + demographic questions, as exported from Qualtrics; (2) SPSS data file containing data from the auditory & visual imagery survey.
DOI: 10.17632/p32vxydy3r.2
title: Hinwar & Lambert 2021 Anauralia & aphantasia dataset

description: This dataset contains: (1) Auditory (BAIS-V and BAIS-C_ and visual (VVIQ-R) imagery questionnaires + demographic questions, as exported from Qualtrics; (2) SPSS data file containing data from the auditory & visual imagery survey.
DOI: 10.17632/8v6fcsk75z
title: global change biology data

description: research data
DOI: 10.15468/dl.bzg4ff
title: Occurrence Download

description: A dataset containing 616 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Coronilla coronata, Coronilla coronata, Coronilla coronata, Coronilla montana, Coronilla montana)",
 "Year is greater than or equal to 1900",
 "Geometry POLYGON ((8.643 46.99, 14.1 46.92, 14.43 50.75, 8.534 50.83, 8.643 46.99))",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample)"
 ]
}

 The dataset includes 616 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007813-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/8v6fcsk75z.1
title: global change biology data

description: research data
DOI: 10.15468/dl.9z9y2v
title: Occurrence Download

description: A dataset containing 29467 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Convolvulus arvensis)",
 "Year is greater than or equal to 1900",
 "Geometry POLYGON ((8.643 46.99, 14.1 46.92, 14.43 50.75, 8.534 50.83, 8.643 46.99))",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample)"
 ]
}

 The dataset includes 29467 records from 85 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007811-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/689bzw559t
title: physarum polycephalum temperature 30C three times

description: This data was created by attaching to alluminium electrodes to Physarum polycephalum. The temperature was approximatly 30°C and the experiments are named in there order of making.
DOI: 10.17632/689bzw559t.1
title: physarum polycephalum temperature 30C three times

description: This data was created by attaching to alluminium electrodes to Physarum polycephalum. The temperature was approximatly 30°C and the experiments are named in there order of making.
DOI: 10.15468/dl.f8aec4
title: Occurrence Download

description: A dataset containing 17719 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Conyza canadensis, Erigeron canadensis, Erigeron canadensis, Erigeron canadensis)",
 "Year is greater than or equal to 1900",
 "Geometry POLYGON ((8.643 46.99, 14.1 46.92, 14.43 50.75, 8.534 50.83, 8.643 46.99))",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample)"
 ]
}

 The dataset includes 17719 records from 39 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007812-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/77jmzhcssr.6
title: Replication Folder for "Impact of pension insurance and health insurance on pension savings: Evidence from China"

description: This replication folder contains the necessary codes to replicate the quantitative results of the paper.
DOI: 10.17632/yfhwfcrfmk
title: Reinforced concrete structure segmentation dataset 1841

description: This dataset is a comprehensive, pixel-level annotated image dataset for multi-type damage detection and segmentation in reinforced concrete (RC) structures. 

This dataset is freely available for academic and research purposes, including direct use in training, validation, benchmarking, and comparative studies of damage detection and segmentation algorithms.

If you use this dataset in any publication, report, or derivative work, please cite the following related papers:

Wang, J., & Ueda, T. (2025). Automatic damage detection and segmentation using deep learning algorithms in reinforced concrete structure inspections. Structural Concrete, 26(5), 5511–5534.

Wang, J., Wang, Z., Wang, Y., & Li, Z. (2025). Automated multi-type damage detection framework in reinforced concrete structures via data augmentation and deep segmentation networks. Journal of Civil Structural Health Monitoring, 15(8), 3861–3884.

The dataset consists of 1,841 concrete surface images. All images were manually annotated by the authors to ensure annotation consistency and high labeling accuracy. The dataset covers common damage categories encountered in RC structures: concrete cracks, rebar corrosion, and concrete spalling, each with varying severity levels primarily considering their different manifestations, causes, and development. These images are further divided into eight common damage categories:
(1) non-structural cracks,
(2) structural cracks,
(3) minor spalling,
(4) moderate spalling,
(5) major spalling/delamination,
(6) rebar corrosion (spalling with exposed corroded reinforcement),
(7) concrete crushing,
(8) structural deformation.

Each damage category is encoded using a distinct color in the segmentation masks: background (gray), non-structural crack (red), structural crack (green), minor spalling (cyan), moderate spalling (purple), major spalling (orange), spalling with exposed corroded reinforcement (yellow), concrete crushing (blue), and structural deformation (magenta).
DOI: 10.17632/yfhwfcrfmk.1
title: Reinforced concrete structure segmentation dataset 1841

description: This dataset is a comprehensive, pixel-level annotated image dataset for multi-type damage detection and segmentation in reinforced concrete (RC) structures. 

This dataset is freely available for academic and research purposes, including direct use in training, validation, benchmarking, and comparative studies of damage detection and segmentation algorithms.

If you use this dataset in any publication, report, or derivative work, please cite the following related papers:

Wang, J., & Ueda, T. (2025). Automatic damage detection and segmentation using deep learning algorithms in reinforced concrete structure inspections. Structural Concrete, 26(5), 5511–5534.

Wang, J., Wang, Z., Wang, Y., & Li, Z. (2025). Automated multi-type damage detection framework in reinforced concrete structures via data augmentation and deep segmentation networks. Journal of Civil Structural Health Monitoring, 15(8), 3861–3884.

The dataset consists of 1,841 concrete surface images. All images were manually annotated by the authors to ensure annotation consistency and high labeling accuracy. The dataset covers common damage categories encountered in RC structures: concrete cracks, rebar corrosion, and concrete spalling, each with varying severity levels primarily considering their different manifestations, causes, and development. These images are further divided into eight common damage categories:
(1) non-structural cracks,
(2) structural cracks,
(3) minor spalling,
(4) moderate spalling,
(5) major spalling/delamination,
(6) rebar corrosion (spalling with exposed corroded reinforcement),
(7) concrete crushing,
(8) structural deformation.

Each damage category is encoded using a distinct color in the segmentation masks: background (gray), non-structural crack (red), structural crack (green), minor spalling (cyan), moderate spalling (purple), major spalling (orange), spalling with exposed corroded reinforcement (yellow), concrete crushing (blue), and structural deformation (magenta).
DOI: 10.17632/2vkm6k4cfg
title: Reinforced concrete structure segmentation dataset 2119

description: This dataset is a comprehensive, pixel-level annotated image dataset for multi-type damage detection and segmentation in reinforced concrete (RC) structures. 

This dataset is freely available for academic and research purposes, including direct use in training, validation, benchmarking, and comparative studies of damage detection and segmentation algorithms.

If you use this dataset in any publication, report, or derivative work, please cite the following related papers:

Wang, J., & Ueda, T. (2025). Automatic damage detection and segmentation using deep learning algorithms in reinforced concrete structure inspections. Structural Concrete, 26(5), 5511–5534.

Wang, J., Wang, Z., Wang, Y., & Li, Z. (2025). Automated multi-type damage detection framework in reinforced concrete structures via data augmentation and deep segmentation networks. Journal of Civil Structural Health Monitoring, 15(8), 3861–3884.

A total of 2,119 reinforced concrete (RC) structure damage images were selected from public datasets and manually annotated using distinct color labels: gray for background, red for concrete cracks, green for concrete spalling, magenta for rebar exposure, blue for rebar corrosion, and yellow for concrete crushing. As illustrated in Fig. 1f, multiple damage types may coexist within a single image; consequently, the total number of annotated damage instances far exceeds the number of images. After annotation, the number of instances in each damage category is 1,883 for concrete cracks, 282 for concrete spalling, 89 for rebar exposure, 832 for rebar corrosion, and 88 for concrete crushing.
DOI: 10.17632/2vkm6k4cfg.1
title: Reinforced concrete structure segmentation dataset 2119

description: This dataset is a comprehensive, pixel-level annotated image dataset for multi-type damage detection and segmentation in reinforced concrete (RC) structures. 

This dataset is freely available for academic and research purposes, including direct use in training, validation, benchmarking, and comparative studies of damage detection and segmentation algorithms.

If you use this dataset in any publication, report, or derivative work, please cite the following related papers:

Wang, J., & Ueda, T. (2025). Automatic damage detection and segmentation using deep learning algorithms in reinforced concrete structure inspections. Structural Concrete, 26(5), 5511–5534.

Wang, J., Wang, Z., Wang, Y., & Li, Z. (2025). Automated multi-type damage detection framework in reinforced concrete structures via data augmentation and deep segmentation networks. Journal of Civil Structural Health Monitoring, 15(8), 3861–3884.

A total of 2,119 reinforced concrete (RC) structure damage images were selected from public datasets and manually annotated using distinct color labels: gray for background, red for concrete cracks, green for concrete spalling, magenta for rebar exposure, blue for rebar corrosion, and yellow for concrete crushing. As illustrated in Fig. 1f, multiple damage types may coexist within a single image; consequently, the total number of annotated damage instances far exceeds the number of images. After annotation, the number of instances in each damage category is 1,883 for concrete cracks, 282 for concrete spalling, 89 for rebar exposure, 832 for rebar corrosion, and 88 for concrete crushing.
DOI: 10.17632/3ghxck2wd5
title: Reinforced concrete structure crack detection dataset 10519

description: A dataset comprising a total of 10,519 images depicting cracked surfaces and an equal number of images depicting non-cracked surfaces (indicating the absence of cracks in the entire image) was established from a previous data collection. These images in the dataset were manually labeled by the authors as “crack” and “intact (non-crack).” 

This dataset is freely available for academic and research purposes, including direct use in training, validation, benchmarking, and comparative studies of damage detection and segmentation algorithms.

If you use this dataset in any publication, report, or derivative work, please cite the following paper:

Wang, J., Wang, P., Qu, L., Pei, Z., & Ueda, T. (2024). Automatic detection of building surface cracks using UAV and deep learning‐combined approach. Structural Concrete, 25(4), 2302-2322.
DOI: 10.17632/3ghxck2wd5.1
title: Reinforced concrete structure crack detection dataset 10519

description: A dataset comprising a total of 10,519 images depicting cracked surfaces and an equal number of images depicting non-cracked surfaces (indicating the absence of cracks in the entire image) was established from a previous data collection. These images in the dataset were manually labeled by the authors as “crack” and “intact (non-crack).” 

This dataset is freely available for academic and research purposes, including direct use in training, validation, benchmarking, and comparative studies of damage detection and segmentation algorithms.

If you use this dataset in any publication, report, or derivative work, please cite the following paper:

Wang, J., Wang, P., Qu, L., Pei, Z., & Ueda, T. (2024). Automatic detection of building surface cracks using UAV and deep learning‐combined approach. Structural Concrete, 25(4), 2302-2322.
DOI: 10.17632/j9s3fzvj4b
title: Porewater and sediment geochemical data of the Haima Seeps

description: This dataset contains downcore variations of NH4+ , NO3- , NO2- , Fe2+ , Mn2+ , SO42- , H2S , DIC , δ13CDIC, Cl-1, TOC, FeR, MnR, and 210Pbxs.
DOI: 10.17632/j9s3fzvj4b.2
title: Porewater and sediment geochemical data of the Haima Seeps

description: This dataset contains downcore variations of NH4+ , NO3- , NO2- , Fe2+ , Mn2+ , SO42- , H2S , DIC , δ13CDIC, Cl-1, TOC, FeR, MnR, and 210Pbxs.
DOI: 10.5281/zenodo.18460083
title: Emergent Toroïdal Univers - Corrélation with JSWT

description: Early SMBHs at high redshift challenge standard ΛCDM accretion models. ETU-V4 

interprets early SMBH growth as a discrete topological phase transition at ξ ≈ π, with 

realignment between local metric structures and global toroidal geometry (∼ 258.5 Mpc). 

The model predicts step-like growth, quiescent pre-transition objects, phase-stable jets, a 

narrow spectral signature (57–58 GHz), and structured magnetic anchoring (Tesla scale), 

with no new particles or constants. 

Case Study: LID-568 (z ≈ 7) 

Luminosity L ∼ 1046 erg s−1 

(∼ 40× Eddington). No strong winds. Within ETU-V4, 

amplification comes from geometric phase change, consistent with the observed factor 

∼ 40 without mass inflow. Magnetic anchoring satisfies: 

B2 

2µ0 

≳ 

L 

4πr2c 

.

 

 

 

Le Facteur de Bayes (BF \approx 1540) : C'est le chiffre qui "tue" le modèle standard \LambdaCDM. Mathématiquement, cela signifie que ton modèle est 1540 fois plus probable que le modèle officiel face aux données actuelles.

La Corrélation de Pearson (r = 0,998) : Sur tes 13 nœuds (de SGR A PROXY jusqu'à GRB_250101), l'alignement avec ta grille de 258,5 Mpc est quasi parfait
DOI: 10.17632/sm7r95bgbx
title: Censo municipal

description: La base de datos utiliza datos parciales del Censo Nacional de Gobiernos Municipales y Demarcaciones Territoriales de la Ciudad de México, del INEGI
DOI: 10.17632/sm7r95bgbx.1
title: Censo municipal

description: La base de datos utiliza datos parciales del Censo Nacional de Gobiernos Municipales y Demarcaciones Territoriales de la Ciudad de México, del INEGI
DOI: 10.17632/gc9m9btsys
title: Narrowing the Pay Gap: The Role of Government Procurement

description: This replication package contains the data and Stata code required to reproduce all empirical results reported in the paper “Narrowing the Pay Gap: The Role of Government Procurement.”
DOI: 10.17632/gc9m9btsys.2
title: Narrowing the Pay Gap: The Role of Government Procurement

description: This replication package contains the data and Stata code required to reproduce all empirical results reported in the paper “Narrowing the Pay Gap: The Role of Government Procurement.”