cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/jrmmrbxwsw.1
title: Research Data for Article “Modeling the Impact of Tributary Debris Flow on a Confluence: A Case Study in Tianmo Gully, Southeast Tibet Plateau, China”

description: This database contains the raw data for the original research paper “Modeling the Impact of Tributary Debris Flow on a Confluence: A Case Study of Tianmo Gully, Southeastern Tibetan Plateau, China.” The data are fully described in the manuscript submitted to CATENA: Weipeng Hou, Chendi Zhang, Peng Cui, Carlo Gualtieri (2026). Modeling the Impact of Tribitray Debris Flow on a Confluence: A Case Study of Tianmo Gully, Southeastern Tibetan Plateau, China. (submitted).
This database mainly includes sediment transport rates over the fan and the riverbed with the discharge ratio between the tributary and mainstream ranging from 0.17 to 0.83.
DOI: 10.17632/7ppmjyf9d2
title: Data - Waqf-Based Endowment Funds as a Sustainable Financing Model to Enhance University Education Quality in Indonesia
DOI: 10.17632/7ppmjyf9d2.1
title: Data - Waqf-Based Endowment Funds as a Sustainable Financing Model to Enhance University Education Quality in Indonesia
DOI: 10.15468/dl.ew2zrz
title: Occurrence Download

description: A dataset containing 429 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Amaranthus californicus"
 ]
}

 The dataset includes 429 records from 62 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001991-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/bjm47c4s3g
title: U.S. State-level Renewable Energy Production (2020-2023) Econometric Geospatial Dataset

description: This dataset contains raw and analyzed data. The analyzed dataset is derived from a cross-sectional panel of all 50 U.S. states, constructed by averaging annual values from 2020–2023 to smooth short-term volatility. The primary outcome /dependent variable is renewable energy production per capita (Renew_prod_pc, Btu per person), transformed using a log1p function (log_Renew_prod_pc) to address skewness and enable elasticity-based interpretation. Key predictors capture demographic, economic, political, policy, and energy-structure characteristics. State size and economic scale are controlled using logged population (log_Population) and logged gross state product per capita (log_Gdp_pc). Policy context is measured by the count of renewable energy incentives (Policy_Incentives_count) from DSIRE and partisan control of state government (Political_persuasion, binary). Regional and structural factors include Appalachian designation (Appa_st), coal production per capita, renewable energy consumption per capita, and the number of active coal and renewable power plants. Additional controls describe industrial composition (Gdp_oilgas_share, Gdp_mfg_share), labor market conditions (Unemp_rate), human capital (Pop_18_25_lt9_pct), and socioeconomic vulnerability (Poverty_rate). All variables are state-level and sourced from authoritative U.S. agencies (EIA, BEA, Census Bureau, BLS) or established policy databases. The analytical outputs include: (1) Bayesian regression parameter estimates (posterior means, SDs, 95% HDIs, R-hat, ESS, and credibility). (2) Model-level comparison statistics across alternative specifications (Full, Parsimonious, Minimal), including Bayesian R² and LOO-ELPD. (3) Spatial diagnostics, comprising bivariate Moran’s I statistics and Local Indicators of Spatial Association (LISA) cluster summaries and state-level classifications.
DOI: 10.17632/bjm47c4s3g.1
title: U.S. State-level Renewable Energy Production (2020-2023) Econometric Geospatial Dataset

description: This dataset contains raw and analyzed data. The analyzed dataset is derived from a cross-sectional panel of all 50 U.S. states, constructed by averaging annual values from 2020–2023 to smooth short-term volatility. The primary outcome /dependent variable is renewable energy production per capita (Renew_prod_pc, Btu per person), transformed using a log1p function (log_Renew_prod_pc) to address skewness and enable elasticity-based interpretation. Key predictors capture demographic, economic, political, policy, and energy-structure characteristics. State size and economic scale are controlled using logged population (log_Population) and logged gross state product per capita (log_Gdp_pc). Policy context is measured by the count of renewable energy incentives (Policy_Incentives_count) from DSIRE and partisan control of state government (Political_persuasion, binary). Regional and structural factors include Appalachian designation (Appa_st), coal production per capita, renewable energy consumption per capita, and the number of active coal and renewable power plants. Additional controls describe industrial composition (Gdp_oilgas_share, Gdp_mfg_share), labor market conditions (Unemp_rate), human capital (Pop_18_25_lt9_pct), and socioeconomic vulnerability (Poverty_rate). All variables are state-level and sourced from authoritative U.S. agencies (EIA, BEA, Census Bureau, BLS) or established policy databases. The analytical outputs include: (1) Bayesian regression parameter estimates (posterior means, SDs, 95% HDIs, R-hat, ESS, and credibility). (2) Model-level comparison statistics across alternative specifications (Full, Parsimonious, Minimal), including Bayesian R² and LOO-ELPD. (3) Spatial diagnostics, comprising bivariate Moran’s I statistics and Local Indicators of Spatial Association (LISA) cluster summaries and state-level classifications.
DOI: 10.17632/pnfz3gngsv
title: Dataset for language and environment perception

description: This dataset is for the replacation of the paper"
DOI: 10.17632/pnfz3gngsv.1
title: Dataset for language and environment perception

description: This dataset is for the replacation of the paper"
DOI: 10.17632/jr5yps4g6x
title: Integrated Geological, Geochemistry and Geophysical Analysis Dataset of the Tan-Lu Fault Zone (XRD, XRF, Mössbauer Spectroscopy, Magnetic Susceptibility)

description: This dataset is a comprehensive collection of geochemical and geological analysis data, with its core focus on the study of fault gouge from the Tan-Lu Fault Zone. It systematically integrates four major types of analytical data: mineral composition data from X-Ray Diffraction (XRD), geochemical element data from X-Ray Fluorescence (XRF), iron phase and valence state data from Mössbauer Spectroscopy, and rock magnetic property data from Magnetic Susceptibility. These multi-method datasets complement each other, providing detailed mineralogical and geochemical constraints for an in-depth understanding of the tectonic activity and fluid-rock interaction within the Tan-Lu Fault Zone.
DOI: 10.17632/jr5yps4g6x.1
title: Integrated Geological, Geochemistry and Geophysical Analysis Dataset of the Tan-Lu Fault Zone (XRD, XRF, Mössbauer Spectroscopy, Magnetic Susceptibility)

description: This dataset is a comprehensive collection of geochemical and geological analysis data, with its core focus on the study of fault gouge from the Tan-Lu Fault Zone. It systematically integrates four major types of analytical data: mineral composition data from X-Ray Diffraction (XRD), geochemical element data from X-Ray Fluorescence (XRF), iron phase and valence state data from Mössbauer Spectroscopy, and rock magnetic property data from Magnetic Susceptibility. These multi-method datasets complement each other, providing detailed mineralogical and geochemical constraints for an in-depth understanding of the tectonic activity and fluid-rock interaction within the Tan-Lu Fault Zone.
DOI: 10.17632/kyfhrm4rjr
title: taste sensation for preterm infants

description: The hypothesis is that the taste sensation promotes feeding and growth outcomes in preterm infants with gastric tube.This trial data showed higher weight and head circumference z scores at 36 weeks’ postmenstrual age and discharge, less feeding intolerance and better feeding performance in preterm infants with taste sensation while tube feeding.
DOI: 10.17632/kyfhrm4rjr.1
title: taste sensation for preterm infants

description: The hypothesis is that the taste sensation promotes feeding and growth outcomes in preterm infants with gastric tube.This trial data showed higher weight and head circumference z scores at 36 weeks’ postmenstrual age and discharge, less feeding intolerance and better feeding performance in preterm infants with taste sensation while tube feeding.
DOI: 10.17632/yxkfjc3b2h
title: Study notes

description: This is study notes.
DOI: 10.17632/yxkfjc3b2h.1
title: Study notes

description: This is study notes.
DOI: 10.17632/cmzg4pshkx
title: Dataset of Deep Learning from Landsat-9 Satellite Images for Segmentation Burned Areas in South Sumatera

description: This dataset comprises a curated collection of Landsat-9 Level-1 Top of Atmosphere (TOA) satellite imagery, rigorously processed to facilitate the training and benchmarking of deep learning algorithms, specifically semantic segmentation architectures such as U-Net, for burned area delineation. The geographic scope of this study encompasses South Sumatra Province, Indonesia, specifically within the WRS-2 path/rows 124/062 and 125/062. This region represents a critical area of interest due to its high susceptibility to recurrent forest and land fires. The dataset provides a robust baseline for remote sensing research aimed at automating the detection of fire-induced land cover changes using multispectral data.

Each data instance within this repository constitutes a co-registered pair consisting of a multispectral input image and a corresponding binary ground truth mask, standardized to a spatial resolution of 30 meters. The input data is encoded in GeoTIFF raster format and features a five-channel spectral stack designed to maximize the separability between burned and unburned features. These channels include the Normalized Difference Vegetation Index (NDVI), the Normalized Burn Ratio (NBR), Band 4 (Red), Band 5 (Near-Infrared/NIR), and Band 7 (Shortwave Infrared/SWIR-2). Prior to patch generation, the imagery underwent a stringent pre-processing pipeline, which included atmospheric correction via the Dark Object Subtraction (DOS) method and the removal of cloud and shadow artifacts utilizing the Quality Assessment (QA) band to ensure radiometric integrity.

The ground truth annotations were generated via an automated thresholding mechanism applied to the differenced Normalized Burn Ratio (dNBR) index. The dNBR metric was computed as the temporal difference between pre-fire acquisitions (January 1 – July 30, 2023) and post-fire acquisitions (November 1, 2023 – June 30, 2024). A threshold of dNBR &gt; 0.1 was applied to classify pixels as "burned" (class 1) versus "unburned" (class 0), thereby isolating significant spectral alterations indicative of vegetation loss.

The final dataset consists of 1,008 image patches, each with dimensions of 512 x 512 pixels, extracted using a sliding window technique with a 50% spatial overlap. To ensure data quality, an automated filtering protocol was employed to exclude patches containing excessive void pixels resulting from cloud masking or scene boundaries. It is pertinent to note that the dataset provided herein is raw and unsplit to allow for flexible experimental design. However, the foundational research associated with this dataset employed a stratified split ratio of 70:15:15 (yielding 706 training samples, 151 validation samples, and 151 testing samples). Researchers utilizing this dataset are encouraged to replicate this partitioning strategy for comparative analysis or to implement alternative cross-validation schemes as required.
DOI: 10.17632/cmzg4pshkx.1
title: Dataset of Deep Learning from Landsat-9 Satellite Images for Segmentation Burned Areas in South Sumatera

description: This dataset comprises a curated collection of Landsat-9 Level-1 Top of Atmosphere (TOA) satellite imagery, rigorously processed to facilitate the training and benchmarking of deep learning algorithms, specifically semantic segmentation architectures such as U-Net, for burned area delineation. The geographic scope of this study encompasses South Sumatra Province, Indonesia, specifically within the WRS-2 path/rows 124/062 and 125/062. This region represents a critical area of interest due to its high susceptibility to recurrent forest and land fires. The dataset provides a robust baseline for remote sensing research aimed at automating the detection of fire-induced land cover changes using multispectral data.

Each data instance within this repository constitutes a co-registered pair consisting of a multispectral input image and a corresponding binary ground truth mask, standardized to a spatial resolution of 30 meters. The input data is encoded in GeoTIFF raster format and features a five-channel spectral stack designed to maximize the separability between burned and unburned features. These channels include the Normalized Difference Vegetation Index (NDVI), the Normalized Burn Ratio (NBR), Band 4 (Red), Band 5 (Near-Infrared/NIR), and Band 7 (Shortwave Infrared/SWIR-2). Prior to patch generation, the imagery underwent a stringent pre-processing pipeline, which included atmospheric correction via the Dark Object Subtraction (DOS) method and the removal of cloud and shadow artifacts utilizing the Quality Assessment (QA) band to ensure radiometric integrity.

The ground truth annotations were generated via an automated thresholding mechanism applied to the differenced Normalized Burn Ratio (dNBR) index. The dNBR metric was computed as the temporal difference between pre-fire acquisitions (January 1 – July 30, 2023) and post-fire acquisitions (November 1, 2023 – June 30, 2024). A threshold of dNBR &gt; 0.1 was applied to classify pixels as "burned" (class 1) versus "unburned" (class 0), thereby isolating significant spectral alterations indicative of vegetation loss.

The final dataset consists of 1,008 image patches, each with dimensions of 512 x 512 pixels, extracted using a sliding window technique with a 50% spatial overlap. To ensure data quality, an automated filtering protocol was employed to exclude patches containing excessive void pixels resulting from cloud masking or scene boundaries. It is pertinent to note that the dataset provided herein is raw and unsplit to allow for flexible experimental design. However, the foundational research associated with this dataset employed a stratified split ratio of 70:15:15 (yielding 706 training samples, 151 validation samples, and 151 testing samples). Researchers utilizing this dataset are encouraged to replicate this partitioning strategy for comparative analysis or to implement alternative cross-validation schemes as required.
DOI: 10.17632/fkhrttyfgm
title: iCCA datasets

description: Description
This dataset contains supplementary data associated with the study of intrahepatic cholangiocarcinoma (iCCA) and lymph node involvement. It includes four Excel files:

Bulk RNA-seq (expression profiles, derived from NODE Project ID: OEP001105)

WES (somatic mutation calls and annotations, derived from NODE Project ID: OEP001105)

Clinical data (demographic and clinical characteristics of the patient cohort)

scRNA-seq (cell-type annotations and quality metrics, derived from GSA BioProject ID: PRJCA007744)

Together, these processed datasets provide integrated molecular, clinical, and single-cell information supporting multi-omics and imaging-based analyses of lymph node involvement in iCCA.
DOI: 10.17632/fkhrttyfgm.1
title: iCCA datasets

description: Description
This dataset contains supplementary data associated with the study of intrahepatic cholangiocarcinoma (iCCA) and lymph node involvement. It includes four Excel files:

Bulk RNA-seq (expression profiles, derived from NODE Project ID: OEP001105)

WES (somatic mutation calls and annotations, derived from NODE Project ID: OEP001105)

Clinical data (demographic and clinical characteristics of the patient cohort)

scRNA-seq (cell-type annotations and quality metrics, derived from GSA BioProject ID: PRJCA007744)

Together, these processed datasets provide integrated molecular, clinical, and single-cell information supporting multi-omics and imaging-based analyses of lymph node involvement in iCCA.
DOI: 10.15468/dl.gcz6q9
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is New records of Ctenolepisma calvum (Ritter,1910) (Zygentoma, Lepismatidae) from Japan"
 ]
}

 The dataset includes 10 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001994-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/zht334mw4r
title: Bioactive compounds from Pomegranate (Punica granatum) peel extracts possess the antiviral effects against red grouper nervous necrosis virus.

description: Pomegranate peel was ground into coarse powder and extracted separately with 60% ethanol and pure water. The resulting extracts were found to possess activity against RGNNV.
DOI: 10.17632/zht334mw4r.3
title: Bioactive compounds from Pomegranate (Punica granatum) peel extracts possess the antiviral effects against red grouper nervous necrosis virus.

description: Pomegranate peel was ground into coarse powder and extracted separately with 60% ethanol and pure water. The resulting extracts were found to possess activity against RGNNV.
DOI: 10.17632/th3h46py7d
title: An Integrated Spatial-Legal Diagnosis of Governance Barriers to Green Corridor Implementation in China’s Gan River Basin

description: Publicly available data
DOI: 10.17632/th3h46py7d.1
title: An Integrated Spatial-Legal Diagnosis of Governance Barriers to Green Corridor Implementation in China’s Gan River Basin

description: Publicly available data
DOI: 10.17632/cps4vtxpkr
title: BMI and heart failure prognosis

description: The dataset provides de-identified, structured clinical data for heart failure admissions, including demographics, BMI, physical examination data, laboratory indicators, echocardiographic parameters, readmission and all-cause mortality.
DOI: 10.17632/cps4vtxpkr.1
title: BMI and heart failure prognosis

description: The dataset provides de-identified, structured clinical data for heart failure admissions, including demographics, BMI, physical examination data, laboratory indicators, echocardiographic parameters, readmission and all-cause mortality.
DOI: 10.17632/j8j9krt2db
title: EGFR Promotes Internalization of Porcine Epidemic Diarrhea Virus through Interaction with the Spike Protein and the Endo-/Lysosomal Pathway

description: Figure 1. Interaction between the PEDV spike protein and EGFR. 
Figure 2. EGFR is involved in the early stage of PEDV infection. 
Figure 3. EGFR does not affect PEDV attachment to the cell surface. 
Figure 4. EGFR is involved in PEDV internalization. 
Figure 5. EGFR participates in the endolysosomal trafficking of PEDV.
Figure 6. EGFR knockout reduces co‑localization of PEDV with late endosomes.
DOI: 10.17632/j8j9krt2db.1
title: EGFR Promotes Internalization of Porcine Epidemic Diarrhea Virus through Interaction with the Spike Protein and the Endo-/Lysosomal Pathway

description: Figure 1. Interaction between the PEDV spike protein and EGFR. 
Figure 2. EGFR is involved in the early stage of PEDV infection. 
Figure 3. EGFR does not affect PEDV attachment to the cell surface. 
Figure 4. EGFR is involved in PEDV internalization. 
Figure 5. EGFR participates in the endolysosomal trafficking of PEDV.
Figure 6. EGFR knockout reduces co‑localization of PEDV with late endosomes.
DOI: 10.17632/gsjmvbbsps
title: research data

description: Experimental Data.
DOI: 10.17632/gsjmvbbsps.1
title: research data

description: Experimental Data.
DOI: 10.17632/6gb6rpm69w
title: Live-cell single-vRNP imaging identifies viral gene expression signatures that shape influenza infection heterogeneity. Rabouw, Schokolowski, Müller et al.

description: Selection of raw data used in Rabouw, Schokolowski, Müller et al
DOI: 10.17632/6gb6rpm69w.1
title: Live-cell single-vRNP imaging identifies viral gene expression signatures that shape influenza infection heterogeneity. Rabouw, Schokolowski, Müller et al.

description: Selection of raw data used in Rabouw, Schokolowski, Müller et al
DOI: 10.17632/hbbtbykt5p
title: 2014 and 2019 aerosol field observation dataset in Guangzhou, china

description: This dataset contains comprehensive, high-time-resolution atmospheric measurement data obtained from two field campaigns conducted in 2014 and 2019. It includes detailed aerosol physical properties, chemical composition, and meteorological parameters.

2014 Campaign:
Aerosol Size Distribution: Particle Number Size Distributions (PNSD) in the range of 9.8–414.2 nm were measured at a 5-minute resolution using a Scanning Mobility Particle Sizer (SMPS, TSI 3938) comprising a Differential Mobility Analyzer (DMA, TSI 3081L) and a Condensation Particle Counter (CPC, TSI 3772).
Non-Refractory PM1 Chemical Composition: Mass concentrations of organic matter (Org), nitrate (NO₃⁻), sulfate (SO₄²⁻), and ammonium (NH₄⁺) were quantified using a High-Resolution Time-of-Flight Aerosol Mass Spectrometer (HR-ToF-AMS, Aerodyne).

2019 Campaign:
Aerosol Size Distribution: Particle size distributions in the range of 11.3–532.8 nm were obtained using an SMPS system identical to the 2014 setup. The data have been corrected for multiple charging and diffusion losses.
Aerosol Hygroscopicity: Hygroscopic growth factors at 90% relative humidity were measured for particles with selected dry diameters (40, 80, 110, 150, and 200 nm) using a Hygroscopicity Tandem Differential Mobility Analyzer (H-TDMA).
Non-Refractory PM2.5 Chemical Composition: Mass concentrations of Org, NO₃⁻, SO₄²⁻, NH₄⁺, and chloride (Chl) were measured with a Quadrupole Aerosol Chemical Speciation Monitor (Q-ACSM).
Black Carbon Mass Concentration: Continuous monitoring was performed using an aethalometer (AE33, Magee Scientific).
Meteorological Data (Both Campaigns):
Wind Speed and Wind Direction data were recorded synchronously, typically at matching high-time-resolution intervals, providing essential context for atmospheric process analysis.

These two comprehensive field investigations at two adjacent sites during two different time periods. One was from 6th to 24th of November in 2019, at the Guangzhou Meteorological Bureau in Panyu, Guangzhou, China (23.01 ° N, 113.32 ° E), and the other was from November 22nd, 2014 to December 31st in 2014, at the Chinese Meteorological Administration (CMA) Atmospheric Watch Network (CAWNET) station in Panyu, Guangzhou, China (23.00 ° N, 133.21 ° E).