cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/3grbh5s2zh.1
title: Structure of the MIWI endoribonuclease bound to pachytene piRNAs from mouse testes

description: Gels, small RNA denaturing gels, agarose gels, histological microscopy images of testis sections. The figure names where these data are used are indicated as Folder names.
DOI: 10.17632/tk3cn3rhb8.2
title: Raw western blot data-Loss of p53 exacerbates autoimmunity by reprogramming propionyl-CoA metabolism and epigenetics in Treg cells

description: Metabolic regulation is central to the tumor suppressor function of p53. Here we report that propionyl-CoA metabolic remodeling and epigenetic changes underpin p53-mediated restraint of autoimmunity through regulatory T (Treg) cells. By analyzing the human patients with autoimmune diseases, we found p53 expression was significantly reduced in Treg cells negatively correlating with abnormally elevated BCL-6 levels. p53 loss causes dysregulated immune homeostasis and dampens Tregs function in vitro and in vivo. Mechanistically, p53 transcriptionally activates ALDH6A1 expression and propionyl-CoA anabolism to upregulate functional Treg gene expression via histone propionylation. Treg-specific knockout of ALDH6A1 phenocopies the autoimmune responses of p53 deficiency, and propionyl-CoA restoration largely recovers Treg cell function in mice lacking p53 or ALDH6A1. Clinically, impaired p53-ALDH6A1-histone propionylation signaling is observed in AS and SLE patients and correlates with poor efficacy of first-line therapies in autoimmune patients. Together, these findings reveal a directly connection between propionyl-CoA metabolism and epigenetic changes, which is governed by p53 and is crucial for Treg cell function and immune tolerance suppression.
DOI: 10.17632/nbbkvs3k3s
title: Reliability and Deformation Analysis of Semi-Cover-Plate Foundations Considering the Width Effect

description: Using the Monte Carlo method and Coupla function, calculate the reliability data of anti-overturning, anti-sliding, and anti-uplift stability of foundation pits under different width conditions
DOI: 10.17632/nbbkvs3k3s.1
title: Reliability and Deformation Analysis of Semi-Cover-Plate Foundations Considering the Width Effect

description: Using the Monte Carlo method and Coupla function, calculate the reliability data of anti-overturning, anti-sliding, and anti-uplift stability of foundation pits under different width conditions
DOI: 10.17632/9jv542fd34
title: Volakas marble data and MLP-RFF Python code

description: This repository contains the code, example data, and supporting materials used in the study “Anisotropic Marble Quality Estimation Using Orientation-Aware Neural Networks”. The purpose of the repository is to ensure full computational reproducibility of the modelling workflow and to provide a transparent reference implementation for orientation-aware machine-learning approaches applied to ornamental stone resource evaluation.
The codebase implements a set of supervised neural-network models for estimating marble product proportions from drillhole and block-model data. Two principal modelling approaches are provided: (i) a baseline multilayer perceptron (MLP) using spatial coordinates only (X, Y, Z), and (ii) an orientation-aware MLP that additionally incorporates structural orientation information through azimuth and dip encodings, with optional Random Fourier Features (RFFs) to capture multi-scale spatial variability. All models predict compositional outputs (marble product proportions) that are constrained to be non-negative and to sum to unity, and are trained using Kullback–Leibler divergence as the primary loss function.
The repository includes Python scripts for model training, prediction, uncertainty quantification via Monte Carlo Dropout, neural-network architecture search, and permutation feature importance (PFI) analysis. The architecture search explores combinations of network depth, width, dropout rate, L2 regularisation, and learning rate, and selects optimal configurations using a KL-aware validation criterion. The PFI implementation quantifies the contribution of grouped input features (spatial coordinates, orientation encodings, and RFF frequency bands) to predictive performance.
Example input data are provided as comma-separated value (CSV) files. The samples.csv file represents drillhole composite samples and includes spatial coordinates, orientation measurements, and target marble product proportions. The blocks.csv file represents block-model locations used for prediction.
All scripts are written in Python (tested with Python 3.11) and rely on widely used scientific and machine-learning libraries, including TensorFlow/Keras, NumPy, Pandas, and scikit-learn. Fixed random seeds, saved feature scalers, and metadata files are used to promote reproducibility. 
The repository is intended to support reproducibility of the published results and methodological reuse in mineral-resource and geoscience applications. It is provided for research and educational purposes and is not intended as a production-ready system without further validation.
DOI: 10.17632/9jv542fd34.1
title: Volakas marble data and MLP-RFF Python code

description: This repository contains the code, example data, and supporting materials used in the study “Anisotropic Marble Quality Estimation Using Orientation-Aware Neural Networks”. The purpose of the repository is to ensure full computational reproducibility of the modelling workflow and to provide a transparent reference implementation for orientation-aware machine-learning approaches applied to ornamental stone resource evaluation.
The codebase implements a set of supervised neural-network models for estimating marble product proportions from drillhole and block-model data. Two principal modelling approaches are provided: (i) a baseline multilayer perceptron (MLP) using spatial coordinates only (X, Y, Z), and (ii) an orientation-aware MLP that additionally incorporates structural orientation information through azimuth and dip encodings, with optional Random Fourier Features (RFFs) to capture multi-scale spatial variability. All models predict compositional outputs (marble product proportions) that are constrained to be non-negative and to sum to unity, and are trained using Kullback–Leibler divergence as the primary loss function.
The repository includes Python scripts for model training, prediction, uncertainty quantification via Monte Carlo Dropout, neural-network architecture search, and permutation feature importance (PFI) analysis. The architecture search explores combinations of network depth, width, dropout rate, L2 regularisation, and learning rate, and selects optimal configurations using a KL-aware validation criterion. The PFI implementation quantifies the contribution of grouped input features (spatial coordinates, orientation encodings, and RFF frequency bands) to predictive performance.
Example input data are provided as comma-separated value (CSV) files. The samples.csv file represents drillhole composite samples and includes spatial coordinates, orientation measurements, and target marble product proportions. The blocks.csv file represents block-model locations used for prediction.
All scripts are written in Python (tested with Python 3.11) and rely on widely used scientific and machine-learning libraries, including TensorFlow/Keras, NumPy, Pandas, and scikit-learn. Fixed random seeds, saved feature scalers, and metadata files are used to promote reproducibility. 
The repository is intended to support reproducibility of the published results and methodological reuse in mineral-resource and geoscience applications. It is provided for research and educational purposes and is not intended as a production-ready system without further validation.
DOI: 10.17632/fdk624szg4.2
title: Files_for_Replication_of_Proposed_Models_py

description: Files_for_Replication_of_Proposed_Models_py.zip contains the source codes, synthetic data, a requirements.txt file, and a Readme.pdf file with detailed instructions for executing the code associated with the paper Bitcoin Volatility Modeling with Realized Measures and Jump Dynamics. The source codes are provided in .py format, the requirements.txt file specifies the required Python dependencies, and the Readme.pdf file describes the execution procedure.
DOI: 10.17632/8x4h7jt393
title: APT data

description: APT data for H
DOI: 10.17632/8x4h7jt393.1
title: APT data

description: APT data for H
DOI: 10.5066/p97qpy0e
title: Data from a capture-mark-recapture survey of native treefrogs in Bayou Teche National Wildlife Refuge, Louisiana from 2017 to 2023

description: This dataset contains data from a capture-mark-recapture survey of four species of native treefrogs across four sites in Bayou Teche National Wildlife Refuge, Louisiana.
DOI: 10.17632/gpmp7zkd82
title: Geothermal energy developments in Indonesia: Examining social acceptance and environmental polarization through green energy narratives and grassroots opposition movements

description: This dataset contains summary data and methodological explanations for a community-based participatory research study on 14 geothermal energy developments in Indonesia in 2024-5. 

The study contributes to research on the social acceptance of renewable energy transitions by analyzing polarization around environmental decision-making between proponents and opponents of geothermal energy developments in Indonesia. 

The dataset includes two files:
 
Appendix A: Methods and data classification
This document provides further details about the methods and data classification used to conduct the study and to classify descriptive data. 

Appendix B. Data summary
This Excel spreadsheets contains summary descriptive data used in this study including data about each location, status of geothermal developments, strategies and participation of opposition movements, socio-cultural factors, and local livelihoods.
DOI: 10.17632/gpmp7zkd82.1
title: Geothermal energy developments in Indonesia: Examining social acceptance and environmental polarization through green energy narratives and grassroots opposition movements

description: This dataset contains summary data and methodological explanations for a community-based participatory research study on 14 geothermal energy developments in Indonesia in 2024-5. 

The study contributes to research on the social acceptance of renewable energy transitions by analyzing polarization around environmental decision-making between proponents and opponents of geothermal energy developments in Indonesia. 

The dataset includes two files:
 
Appendix A: Methods and data classification
This document provides further details about the methods and data classification used to conduct the study and to classify descriptive data. 

Appendix B. Data summary
This Excel spreadsheets contains summary descriptive data used in this study including data about each location, status of geothermal developments, strategies and participation of opposition movements, socio-cultural factors, and local livelihoods.
DOI: 10.17632/wrw2mvwrfv
title: Unveiling diversity: Does diversity of uncertainty influence corporate cash holdings? (Treepongkaruna et al. - Economic Modelling,2026)

description: This package contains the Stata replication code and supporting documentation for the empirical analysis in the paper: "Unveiling Diversity: Does Diversity of Uncertainty Influence Corporate Cash Holdings?" published in Economic Modelling by Treepongkaruna, S., Jiraporn, P., Kyaw, K., and Padungsaksawasdi, C.
DOI: 10.17632/wrw2mvwrfv.1
title: Unveiling diversity: Does diversity of uncertainty influence corporate cash holdings? (Treepongkaruna et al. - Economic Modelling,2026)

description: This package contains the Stata replication code and supporting documentation for the empirical analysis in the paper: "Unveiling Diversity: Does Diversity of Uncertainty Influence Corporate Cash Holdings?" published in Economic Modelling by Treepongkaruna, S., Jiraporn, P., Kyaw, K., and Padungsaksawasdi, C.
DOI: 10.17632/xxfjj3zr6b
title: BYD_Iron_data

description: This dataset “BYD_Iron_data” contains the original data of all the figures in the submitted article “Iron isotope compositions of sediment with depth-dependent iron reduction in a temperate eutrophic freshwater lake” .
DOI: 10.17632/xxfjj3zr6b.1
title: BYD_Iron_data

description: This dataset “BYD_Iron_data” contains the original data of all the figures in the submitted article “Iron isotope compositions of sediment with depth-dependent iron reduction in a temperate eutrophic freshwater lake” .
DOI: 10.15468/dl.tr9636
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"GADM_LEVEL_2_GID","value":"IND.28.3_1","matchCase":false},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009507-260108223611665/datasets/export for details.
DOI: 10.17632/zpbnbn9pr8
title: Low-Quality Phase Image Dataset for Cascade Detection-Inpainting Network

description: Low-Quality Phase Image Dataset for Cascade Detection-Inpainting Network
DOI: 10.17632/zpbnbn9pr8.1
title: Low-Quality Phase Image Dataset for Cascade Detection-Inpainting Network

description: Low-Quality Phase Image Dataset for Cascade Detection-Inpainting Network
DOI: 10.17632/423kys3w8c
title: Landscape Connectivity Dataset

description: Describe the connectivity dataset of the Maowusu Desert landscape, including data on land use change, climate change, nighttime lighting, etc., which can be used to reproduce the charts in the paper.
DOI: 10.17632/423kys3w8c.1
title: Landscape Connectivity Dataset

description: Describe the connectivity dataset of the Maowusu Desert landscape, including data on land use change, climate change, nighttime lighting, etc., which can be used to reproduce the charts in the paper.
DOI: 10.34740/kaggle/dsv/14499192
title: Biology Benchmark Dataset
DOI: 10.15468/dl.xph2xy
title: Occurrence Download

description: A dataset containing 6428 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Melampsoridium betulinum"
 ]
}

 The dataset includes 6428 records from 177 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009506-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/gny3jctv25
title: Study on mechanical behavior and microstructure of reactive powder concrete with hybrid short-cut steel fibers

description: Fig.6. The effects of the aspect ratio and steel fiber content on compressive strength of SRPC
Fig.7 Comparison of compressive strength of MSRPC specimens.
Fig.8 Non-dimensional compressive strength of specimens with fiber dosage of 2%.
Fig.9 The effects of steel fiber aspect ratio and dosage on the splitting tensile strength of SRPCs
Fig.10 Comparison of splitting tensile strength of MSRPCs
Fig.11 Non-dimensional splitting tensile strength of specimens with fiber dosage of 2%
Fig.13 Effects of steel fiber dosage and aspect ratio on tension-compression ratio of SRPC specimens
Fig.14 Effects of 4mm steel fiber percentage in hybrid fiber on tension compression ratio of MSRPC specimens
DOI: 10.17632/gny3jctv25.1
title: Study on mechanical behavior and microstructure of reactive powder concrete with hybrid short-cut steel fibers

description: Fig.6. The effects of the aspect ratio and steel fiber content on compressive strength of SRPC
Fig.7 Comparison of compressive strength of MSRPC specimens.
Fig.8 Non-dimensional compressive strength of specimens with fiber dosage of 2%.
Fig.9 The effects of steel fiber aspect ratio and dosage on the splitting tensile strength of SRPCs
Fig.10 Comparison of splitting tensile strength of MSRPCs
Fig.11 Non-dimensional splitting tensile strength of specimens with fiber dosage of 2%
Fig.13 Effects of steel fiber dosage and aspect ratio on tension-compression ratio of SRPC specimens
Fig.14 Effects of 4mm steel fiber percentage in hybrid fiber on tension compression ratio of MSRPC specimens
DOI: 10.17632/xrr3m9y6cf
title: The Impact of Immersion on Consumers' Purchase Intention: A Moderated Mediation Model Mediated by Trust and Moderated by Shopping Involvement

description: A Moderated Mediation Model Mediated by Trust and Moderated by Shopping Involvement
DOI: 10.17632/xrr3m9y6cf.1
title: The Impact of Immersion on Consumers' Purchase Intention: A Moderated Mediation Model Mediated by Trust and Moderated by Shopping Involvement

description: A Moderated Mediation Model Mediated by Trust and Moderated by Shopping Involvement
DOI: 10.17632/ys52wn39y5
title: Event-Based Insect Classification

description: This dataset comprises event-based recordings of individual insect flight trajectories collected under controlled outdoor conditions using a catch-and-release protocol. All insects were captured on the grounds of the University of Hohenheim (Stuttgart, Germany) in 2024 and 2025 using butterfly nets and released unharmed after recording.

Recordings were performed using a stereo event-camera setup positioned inside an open-sided structure at the State Institute for Bee Research Hohenheim. A uniform wall served as background to reduce non-insect events and shadows. The camera was mounted at approximately 65 cm height and 1.40 m distance to the wall; additional recordings at 1.80 m distance were included to increase variability. Each insect was recorded individually and released just outside the camera’s field of view to capture natural take-off trajectories. A barrier was introduced behind the release point to increase successful flight captures.

Raw event streams were manually segmented to extract individual insect flight trajectories. Temporal scaling was standardized, and background events were removed using 3D spatiotemporal cropping. The resulting foreground-only trajectories were restored to the original temporal resolution and stored as separate CSV files containing spatial coordinates (x, y), polarity (p), timestamp (t), and event index. Trajectories are named according to pollinator group, individual ID, stereo camera (left/right), and segment index.

The dataset includes 783 trajectories comprising approximately 82 million events with a total duration of 497 s. It covers six pollinator groups: honey bees, bumble bees, wild bees, hoverflies, butterflies, and other insects. Data are split into training (564), validation (112), and test (107) subsets with approximately balanced class distributions and no trajectory overlap between splits.

All files are organized into training/, validation/, and test/ directories and are free of background activity.
DOI: 10.17632/ys52wn39y5.1
title: Event-Based Insect Classification

description: This dataset comprises event-based recordings of individual insect flight trajectories collected under controlled outdoor conditions using a catch-and-release protocol. All insects were captured on the grounds of the University of Hohenheim (Stuttgart, Germany) in 2024 and 2025 using butterfly nets and released unharmed after recording.

Recordings were performed using a stereo event-camera setup positioned inside an open-sided structure at the State Institute for Bee Research Hohenheim. A uniform wall served as background to reduce non-insect events and shadows. The camera was mounted at approximately 65 cm height and 1.40 m distance to the wall; additional recordings at 1.80 m distance were included to increase variability. Each insect was recorded individually and released just outside the camera’s field of view to capture natural take-off trajectories. A barrier was introduced behind the release point to increase successful flight captures.

Raw event streams were manually segmented to extract individual insect flight trajectories. Temporal scaling was standardized, and background events were removed using 3D spatiotemporal cropping. The resulting foreground-only trajectories were restored to the original temporal resolution and stored as separate CSV files containing spatial coordinates (x, y), polarity (p), timestamp (t), and event index. Trajectories are named according to pollinator group, individual ID, stereo camera (left/right), and segment index.

The dataset includes 783 trajectories comprising approximately 82 million events with a total duration of 497 s. It covers six pollinator groups: honey bees, bumble bees, wild bees, hoverflies, butterflies, and other insects. Data are split into training (564), validation (112), and test (107) subsets with approximately balanced class distributions and no trajectory overlap between splits.

All files are organized into training/, validation/, and test/ directories and are free of background activity.
DOI: 10.3886/e244147
title: National Health and Nutrition Examination Survey (NHANES) – Vision and Eye Health Surveillance

description: 1999-2008; 2005-2008. This dataset is a de-identified summary table of vision and eye health data indicators from NHANES, stratified by all available combinations of age group, race/ethnicity, sex, and risk factor. NHANES is a program of studies conducted by the National Center for Health Statistics at CDC designed to assess the health and nutritional status of adults and children in the U.S, and combines interviews and physical examinations. NHANES stopped collecting vision and eye health data in 2008. Approximate sample size is 5,000 persons per year. NHANES data for VEHSS includes questions and examinations related to Visual Function, Vision Exam Measures, Eye Health Conditions, Service Utilization, and Examination Measures. Data were suppressed for cell sizes less than 30 persons, or where the relative standard error more than 30% of the mean. Detailed information on VEHSS NHANES analyses can be found on the VEHSS NHANES webpage (https://www.cdc.gov/visionhealth/vehss/data/national-surveys/national-health-and-nutrition-examination-survey.html). Additional information about NHANES can be found on the NHANES website (https://www.cdc.gov/nchs/nhanes/index.htm). The VEHSS NHANES dataset was last updated in June 2018.
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description: (Downloaded 1/14/2026)
DOI: 10.3886/e244147v1
title: National Health and Nutrition Examination Survey (NHANES) – Vision and Eye Health Surveillance

description: 1999-2008; 2005-2008. This dataset is a de-identified summary table of vision and eye health data indicators from NHANES, stratified by all available combinations of age group, race/ethnicity, sex, and risk factor. NHANES is a program of studies conducted by the National Center for Health Statistics at CDC designed to assess the health and nutritional status of adults and children in the U.S, and combines interviews and physical examinations. NHANES stopped collecting vision and eye health data in 2008. Approximate sample size is 5,000 persons per year. NHANES data for VEHSS includes questions and examinations related to Visual Function, Vision Exam Measures, Eye Health Conditions, Service Utilization, and Examination Measures. Data were suppressed for cell sizes less than 30 persons, or where the relative standard error more than 30% of the mean. Detailed information on VEHSS NHANES analyses can be found on the VEHSS NHANES webpage (https://www.cdc.gov/visionhealth/vehss/data/national-surveys/national-health-and-nutrition-examination-survey.html). Additional information about NHANES can be found on the NHANES website (https://www.cdc.gov/nchs/nhanes/index.htm). The VEHSS NHANES dataset was last updated in June 2018.
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description: (Downloaded 1/14/2026)