cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/cc5psf1
title: CCDC 169893: Experimental Crystal Structure Determination

description: Related Article: V.G.Albano, M.D.Serio, M.Monari, I.Orabona, A.Panunzi, F.Ruffo|2002|Inorg.Chem.|41|2672|doi:10.1021/ic011059p
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cc50n4y
title: CCDC 149579: Experimental Crystal Structure Determination

description: Related Article: S.Bhattacharyya, S.Mukhopadhyay, S.Samanta, T.J.R.Weakley, M.Chaudhury|2002|Inorg.Chem.|41|2433|doi:10.1021/ic0108639
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cc5psg2
title: CCDC 169894: Experimental Crystal Structure Determination

description: Related Article: V.G.Albano, M.D.Serio, M.Monari, I.Orabona, A.Panunzi, F.Ruffo|2002|Inorg.Chem.|41|2672|doi:10.1021/ic011059p
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cc50n5z
title: CCDC 149580: Experimental Crystal Structure Determination

description: Related Article: S.Bhattacharyya, S.Mukhopadhyay, S.Samanta, T.J.R.Weakley, M.Chaudhury|2002|Inorg.Chem.|41|2433|doi:10.1021/ic0108639
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cc50n60
title: CCDC 149581: Experimental Crystal Structure Determination

description: Related Article: S.Bhattacharyya, S.Mukhopadhyay, S.Samanta, T.J.R.Weakley, M.Chaudhury|2002|Inorg.Chem.|41|2433|doi:10.1021/ic0108639
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cc5wmqc
title: CCDC 175513: Experimental Crystal Structure Determination

description: Related Article: P.Leoni, F.Marchetti, M.Pasquali, L.Marchetti, A.Albinati|2002|Organometallics|21|2176|doi:10.1021/om020027m
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cc5wmrd
title: CCDC 175514: Experimental Crystal Structure Determination

description: Related Article: P.Leoni, F.Marchetti, M.Pasquali, L.Marchetti, A.Albinati|2002|Organometallics|21|2176|doi:10.1021/om020027m
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cc5xnpd
title: CCDC 176504: Experimental Crystal Structure Determination

description: Related Article: B.Walfort, T.Auth, B.Degel, H.Helten, D.Stalke|2002|Organometallics|21|2208|doi:10.1021/om020032q
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.17632/gkphzkmdc7.1
title: Abdominal mechanics experiments and twin model data

description: Research Hypothesis: This study identified 18 key acupoints on the human abdomen that aid in relieving constipation, including Zhongwan (CV12), Tianshu (ST25), Daxiong (ST21), and Zusanli (ST36). In the experiment, a mechanical data acquisition platform was used to conduct quasi-static, vertical pressure tests on these acupoints. Experimental data were collected from four subjects, whose abdominal soft tissue layer thicknesses were measured via CT imaging.
Pressure measurements were performed using an SBT-674 sensor attached to the contact end of a push-rod motor. This sensor featured a radius of 7.5 mm, a measurement range of 0–20 N, and a sensitivity of 2.0 ± 10% mV/V. During the experiment, subjects maintained a supine position on an empty stomach while the motor applied vertical downward pressure at 5 mm/s, simultaneously recording pressure data.
Based on the abdominal CT-derived soft tissue 3D model, a finite element model of the abdomen was established. This finite element model comprised 147,851 tetrahedral elements. A cylindrical object applied vertical pressure at a constant speed of 5 mm/s, with a circular contact area of 7.5 mm radius.
Finally, based on the validated finite element model, finite element calculations were performed on 18 key action points and their surrounding auxiliary points. This yielded corresponding massage force values and intra-abdominal wall deformation data at different pressing depths.
The data presented: The first column is the subject number, the second column is the number of the massage points, the third and fourth columns represent the thickness of the skin layer and the muscle layer respectively, the fifth column is the massage depth, the sixth column is the massage force corresponding to the massage depth in the mechanical experiment, and the seventh column is the medial variable of the abdominal wall corresponding to the massage depth calculated by the finite element model of the abdomen.
Data interpretation and application: This study generated a multi-modal dataset on the biomechanical characteristics of key acupoints in the human abdomen through systematic experimental measurements and finite element simulations. The core value of this data lies in the first time converting the empirical operation of traditional massage into quantifiable mechanical parameters, establishing an accurate mapping relationship between "massage depth" and "tissue mechanical response". Specifically, the dataset comprehensively records the massage force required and the medial abdominal wall deformation triggered by different subjects and acupoints at a specific pressing depth.
DOI: 10.17632/2gkbpjsdwt
title: Raw data for paper（2025）
DOI: 10.17632/2gkbpjsdwt.1
title: Raw data for paper（2025）
DOI: 10.17632/fmy4kyhh3z
title: Exploring Reasonable Extension of Secukinumab Dosing Intervals in Real-World Settings

description: Supplemental material for "Exploring Reasonable Extension of Secukinumab Dosing Intervals in Real-World Settings".
DOI: 10.17632/fmy4kyhh3z.2
title: Exploring Reasonable Extension of Secukinumab Dosing Intervals in Real-World Settings

description: Supplemental material for "Exploring Reasonable Extension of Secukinumab Dosing Intervals in Real-World Settings".
DOI: 10.17632/ds6smymmx2
title: Uncropped Western Blots for Obesity-induced Pyroptotic Adipocyte Death Leads to TREM2-Dependent Macrophage Dysfunction and Adipose Tissue Inflammation

description: Original Western Blot data for manuscript entitle "Obesity-induced Pyroptotic Adipocyte Death Leads to TREM2-Dependent Macrophage Dysfunction and Adipose Tissue Inflammation".
DOI: 10.17632/ds6smymmx2.1
title: Uncropped Western Blots for Obesity-induced Pyroptotic Adipocyte Death Leads to TREM2-Dependent Macrophage Dysfunction and Adipose Tissue Inflammation

description: Original Western Blot data for manuscript entitle "Obesity-induced Pyroptotic Adipocyte Death Leads to TREM2-Dependent Macrophage Dysfunction and Adipose Tissue Inflammation".
DOI: 10.17632/bfjxsnj5h8
title: Early-life glucocorticoids accelerate lymphocyte count senescence in roe deer

description: The current dataset has been obtained from a longitudinal survey as part of a Capture-Mark-Recapture program taking place in two french wild roe deer (<i>Capreolus capreolus</i>) populations since almost 50 years. 

We used these longitudinal data to test whether higher baseline GC levels measured within the first year of life were associated with a more pronounced immunosenescence and parasite susceptibility. We first assessed immunosenescence trajectories in these populations facing contrasting environmental conditions. Then, we found that juvenile GC levels can modulate lymphocyte trajectory. Lymphocyte depletion was accelerated late in life when FGMs were elevated early in life. Although the exact mechanism remains to be elucidated it could involve a role of GCs on thymic characteristics. In addition, elevated GC levels in juveniles were associated with a higher abundance of lung parasites during adulthood for individuals born during bad years, suggesting short-term negative effects of GCs on juvenile immunity, having in turn long-lasting consequences on adult parasite load, depending on juvenile environmental conditions. These findings offer promising research directions in assessing the carry-over consequences of GCs on life-history traits in the wild.
DOI: 10.5519/qd.y6kxvwta
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (393 records)
DOI: 10.17632/bfjxsnj5h8.2
title: Early-life glucocorticoids accelerate lymphocyte count senescence in roe deer

description: The current dataset has been obtained from a longitudinal survey as part of a Capture-Mark-Recapture program taking place in two french wild roe deer (<i>Capreolus capreolus</i>) populations since almost 50 years. 

We used these longitudinal data to test whether higher baseline GC levels measured within the first year of life were associated with a more pronounced immunosenescence and parasite susceptibility. We first assessed immunosenescence trajectories in these populations facing contrasting environmental conditions. Then, we found that juvenile GC levels can modulate lymphocyte trajectory. Lymphocyte depletion was accelerated late in life when FGMs were elevated early in life. Although the exact mechanism remains to be elucidated it could involve a role of GCs on thymic characteristics. In addition, elevated GC levels in juveniles were associated with a higher abundance of lung parasites during adulthood for individuals born during bad years, suggesting short-term negative effects of GCs on juvenile immunity, having in turn long-lasting consequences on adult parasite load, depending on juvenile environmental conditions. These findings offer promising research directions in assessing the carry-over consequences of GCs on life-history traits in the wild.
DOI: 10.13140/rg.2.2.12840.64001
title: Colegio de Sanclemente de Pasantes-Fuente de Sanclemente-Calle Sanclemente-Iglesia y Convento de las Huérfanas-Casa de los Ejercitantes-puerta lateral-Claustro de la Catedral- entrada al Museo-Santiago de Compostela-Reseña gráfica
DOI: 10.17632/n8szyggzm7.1
title: Doctoral Research Dataset

description: Details about our qualitative research performed in my doctoral research. The study conducted 18 semi-structured interviews with project managers, software engineers, designers, and testers to perform deep interpretive analyses aiming to understand their experiences and challenges in software development contracting stage at CESAR, a leading Brazilian Institute of Science and Technology focused on software-driven innovation projects. Due to the sensitive nature of the questions asked in this study, semi-structured interviews respondents were assured raw data would remain confidential and would not be shared.
DOI: 10.17632/sdksm4fb66
title: Validity and transferability of MAS-AI - Open Source Data

description: The raw data behind the analysis in the article: Validity and transferability of Model for ASsessing the value of Artificial
Intelligence (MAS-AI). Details about the collected data can be seen in the method section of this article.
DOI: 10.17632/sdksm4fb66.1
title: Validity and transferability of MAS-AI - Open Source Data

description: The raw data behind the analysis in the article: Validity and transferability of Model for ASsessing the value of Artificial
Intelligence (MAS-AI). Details about the collected data can be seen in the method section of this article.
DOI: 10.17632/md6rryc3hy
title: Deep Learning Pipeline and Dataset for 3D ECG-Based Ischemia Detection

description: 0_Preprocessing_and_IDs: This folder contains a single document (3DECG_preprocessing_and_recording_IDs.pdf) that includes the anonymous patient ECG recording identifiers (Precath and Postcath) extracted from the PTB Diagnostic ECG Database, the amplitude thresholds used for R-peak detection, and the corresponding preprocessing details required for data standardization and reproducibility.
1_Metrics_Computation: This folder contains the Python pipeline, explanatory documentation, and output files for the analysis of three-dimensional electrocardiogram (3D ECG) trajectories in patients with LAD ischemia before and after coronary revascularization. It includes the computation of geometric metrics (perimeter, curvature, almost-curvature, and torsion) along with statistical analyses.
2_DeepLearning_Classification: This folder includes the deep learning pipeline developed to extend the 3D ECG framework toward predictive modeling. It enables automated discrimination between ischemic and post-revascularization states based on 3D ECG-derived geometric descriptors, integrating residual MLP architectures, isotonic calibration, and patient-wise cross-validation for classification.
DOI: 10.15468/dl.gbbn9w
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "TaxonKey is Isachne borii"
 ]
}

 The dataset includes 4 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017557-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/md6rryc3hy.3
title: Deep Learning Pipeline and Dataset for 3D ECG-Based Ischemia Detection

description: 0_Preprocessing_and_IDs: This folder contains a single document (3DECG_preprocessing_and_recording_IDs.pdf) that includes the anonymous patient ECG recording identifiers (Precath and Postcath) extracted from the PTB Diagnostic ECG Database, the amplitude thresholds used for R-peak detection, and the corresponding preprocessing details required for data standardization and reproducibility.
1_Metrics_Computation: This folder contains the Python pipeline, explanatory documentation, and output files for the analysis of three-dimensional electrocardiogram (3D ECG) trajectories in patients with LAD ischemia before and after coronary revascularization. It includes the computation of geometric metrics (perimeter, curvature, almost-curvature, and torsion) along with statistical analyses.
2_DeepLearning_Classification: This folder includes the deep learning pipeline developed to extend the 3D ECG framework toward predictive modeling. It enables automated discrimination between ischemic and post-revascularization states based on 3D ECG-derived geometric descriptors, integrating residual MLP architectures, isotonic calibration, and patient-wise cross-validation for classification.
DOI: 10.17632/nwdx7x6jts
title: the source data

description: Land use data from the China Land Cover Dataset (CLCD) to calculate carbon absorption based on the sequestration coefficients of different land types (forest, grassland, cropland, water, built-up land).NPP-VIIRS-like Nighttime Light (NTL) dataset (2000–2022) was employed to estimate county-level carbon emissions, serving as a proxy for human activity intensity and energy consumption. This dataset provides inter-calibrated and harmonized NTL data combining DMSP/OLS and NPP/VIIRS to ensure temporal consistency and high spatial resolution.
DOI: 10.17632/nwdx7x6jts.1
title: the source data

description: Land use data from the China Land Cover Dataset (CLCD) to calculate carbon absorption based on the sequestration coefficients of different land types (forest, grassland, cropland, water, built-up land).NPP-VIIRS-like Nighttime Light (NTL) dataset (2000–2022) was employed to estimate county-level carbon emissions, serving as a proxy for human activity intensity and energy consumption. This dataset provides inter-calibrated and harmonized NTL data combining DMSP/OLS and NPP/VIIRS to ensure temporal consistency and high spatial resolution.
DOI: 10.17632/kwkwt8c8br
title: Water In Thick-Walled Pipes

description: Water In Thick-Walled Pipes
The thick of wall is 10mm, and the diameter is 80mm
DOI: 10.17632/kwkwt8c8br.1
title: Water In Thick-Walled Pipes

description: Water In Thick-Walled Pipes
The thick of wall is 10mm, and the diameter is 80mm
DOI: 10.15468/dl.ynqdrd
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cerebratulus macroren",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017558-251025141854904/datasets/export for details.
DOI: 10.17632/zn7t5rrcwk
title: Culture as Social Infrastructure for Innovation

description: README — Replication Package
Paper Title
Culture as Social Infrastructure for Innovation
Authors
Aaron Hui Lin (Shanghai Lixin University of Accounting and Finance)
Teng Zhong (University of International Business and Economics)
Qianhui Yan (Fudan University)
Maohua Wu (Nanjing University of Finance & Economics)
1. Overview
This replication package provides all the publicly available data, code, and instructions require to reproduce the main empirical results of the above-mentioned research paper. The package is designed to facilitate transparency and enable independent verification of all statistical findings reported in the study.
DOI: 10.17632/zn7t5rrcwk.2
title: Culture as Social Infrastructure for Innovation

description: README — Replication Package
Paper Title
Culture as Social Infrastructure for Innovation
Authors
Aaron Hui Lin (Shanghai Lixin University of Accounting and Finance)
Teng Zhong (University of International Business and Economics)
Qianhui Yan (Fudan University)
Maohua Wu (Nanjing University of Finance & Economics)
1. Overview
This replication package provides all the publicly available data, code, and instructions require to reproduce the main empirical results of the above-mentioned research paper. The package is designed to facilitate transparency and enable independent verification of all statistical findings reported in the study.
DOI: 10.17632/527w9ccw2r
title: Consumption Markets and Culture

description: Consumption Markets and Culture
DOI: 10.17632/527w9ccw2r.1
title: Consumption Markets and Culture

description: Consumption Markets and Culture
DOI: 10.17632/ncg7kk3gwx
title: Medicinal Plant Leaf Disease Dataset

description: This dataset contains 2,547 high-resolution leaf images (4000 × 3000 px, 4:3 aspect ratio) of three medicinal plant species, Kalanchoe pinnata (PatharKuchi), Azadirachta indica (Neem), and Ocimum tenuiflorum (Tulsi), collected from Senbag Upazila, Noakhali, Bangladesh, during August 2025. Each species includes one healthy and three disease or symptom classes, forming twelve balanced categories verified by agricultural experts.

All images were captured in natural daylight using a 50 MP Honor 200 smartphone camera, ensuring clear color and texture representation. Images were taken from multiple angles and distances to preserve natural variations in illumination and background. Leaf samples were collected directly from local fields and home-grown medicinal gardens.

Class distribution:

Kalanchoe pinnata (PatharKuchi): Healthy (209), Web Blight (213), Yellow (204), Yellow Blight (213) → Total 839

Azadirachta indica (Neem): Healthy (244), Leaf Spot (211), Web Blight (205), Yellow (225) → Total 885

Ocimum tenuiflorum (Tulsi): Healthy (213), Downy Mildew (200), Web Blight (205), Yellow Spot (205) → Total 823
Grand Total: 2,547 images across 12 classes.

Each image is stored in .jpg format (4000 × 3000 px) and named following a class-based convention (e.g., Neem_Healthy, Neem_LeafSpot, Tulsi_Healthy, Patharkuchi_YellowBlight). All files are placed in a single folder, with class names embedded in filenames for convenient parsing and automatic label extraction during model training.

The dataset is designed for AI-based plant disease detection, particularly hybrid CNN/Transformer models and Explainable AI (XAI) research. It enables studies in agricultural image classification, medicinal plant health monitoring, and digital pathology applications.

Verification and labeling were conducted under the supervision of the Upazila Agriculture Officer, Senbag, on 19 October 2025, ensuring correct disease identification and class validity.
DOI: 10.17632/ncg7kk3gwx.1
title: Medicinal Plant Leaf Disease Dataset

description: This dataset contains 2,547 high-resolution leaf images (4000 × 3000 px, 4:3 aspect ratio) of three medicinal plant species, Kalanchoe pinnata (PatharKuchi), Azadirachta indica (Neem), and Ocimum tenuiflorum (Tulsi), collected from Senbag Upazila, Noakhali, Bangladesh, during August 2025. Each species includes one healthy and three disease or symptom classes, forming twelve balanced categories verified by agricultural experts.

All images were captured in natural daylight using a 50 MP Honor 200 smartphone camera, ensuring clear color and texture representation. Images were taken from multiple angles and distances to preserve natural variations in illumination and background. Leaf samples were collected directly from local fields and home-grown medicinal gardens.

Class distribution:

Kalanchoe pinnata (PatharKuchi): Healthy (209), Web Blight (213), Yellow (204), Yellow Blight (213) → Total 839

Azadirachta indica (Neem): Healthy (244), Leaf Spot (211), Web Blight (205), Yellow (225) → Total 885

Ocimum tenuiflorum (Tulsi): Healthy (213), Downy Mildew (200), Web Blight (205), Yellow Spot (205) → Total 823
Grand Total: 2,547 images across 12 classes.

Each image is stored in .jpg format (4000 × 3000 px) and named following a class-based convention (e.g., Neem_Healthy, Neem_LeafSpot, Tulsi_Healthy, Patharkuchi_YellowBlight). All files are placed in a single folder, with class names embedded in filenames for convenient parsing and automatic label extraction during model training.

The dataset is designed for AI-based plant disease detection, particularly hybrid CNN/Transformer models and Explainable AI (XAI) research. It enables studies in agricultural image classification, medicinal plant health monitoring, and digital pathology applications.

Verification and labeling were conducted under the supervision of the Upazila Agriculture Officer, Senbag, on 19 October 2025, ensuring correct disease identification and class validity.