cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.k8hwug
title: Occurrence Download

description: A dataset containing 2098 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Camelina sativa)",
 "Year is greater than or equal to 2000",
 "Year is less than or equal to 2025",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 2098 records from 196 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0074153-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17283632
title: Multisource Meteorological Data during Rainstorm in Beijing

description: Datasets includes the following items during Aug 5 to Aug 10 in 2024:



absolute humidity/cloud base height dataset;

automatic weather station/eddy covariance measurement datasets on the Beijing 325m tower;

data process code

representativeness data 

bias evaluation data for plotting figures

Reanalysis datasets including ERA5 and CMA-RA v1.5 interpolated to the observation site

Radiosonde data
DOI: 10.21227/qywa-3949
title: "PEM-43 Dataset on Meditation"

description: "This Dataset consists of Wavelet Components created from the original PEM-43 Dataset.experimental set up:::  I recorded EEG in three states resting eyes closed, while listening to mantra and after listening to mantra . all the three states were recorded in eyes closed state and all are of duration 2min.....data is stored here D:\\thesis24\\EEG_ANTENATAL_OPD\\mat_eeg_final\\bp_eeg\\aftr_amp and one thing you must note is that the data is not labelled...I have labelled the file name as per event eg. Sub43_RS_trial2_processed.mat Sub43_RS_trial1_processed.mat, Sub43_M_trial2_processed.mat,Sub43_M_trial1_processed.mat, Sub43_AM_trial2_processed.mat,Sub43_AM_trial1_processed.mat till Sub01_AM_trial1_processed.mat ....there are 43 subjects that recorded EEG in three states RS(Resting State),M(Listening to Mantra),AM(After listening to Mantra) and two trial eachand data variable name is data=32x15470 double where 32 is the number of channels and 128hz is the sampling frequency."
DOI: 10.15468/dl.egszga
title: Occurrence Download

description: A dataset containing 257 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "GadmGid is CHN.13.13.6_1",
 "OccurrenceStatus is Present",
 "Year 2000-2024"
 ]
}

 The dataset includes 257 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0074154-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5963hj
title: Occurrence Download

description: A dataset containing 1500 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "GadmGid is CHN.13.13_1",
 "OccurrenceStatus is Present",
 "Year 2000-2024"
 ]
}

 The dataset includes 1500 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0074155-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nf5hzg
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Living Specimen, Material sample)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Pethia gelius",
 "Year 2000-2026"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0074156-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30996127
title: <b>EGID: A Comprehensive Multi-label Endoscopic Image Dataset for Gastritis Classification——2</b>

description: Chronic atrophic gastritis is a well-recognized precancerous condition, emphasizing the need for accurate endoscopic classification. However, existing endoscopic datasets lack comprehensive multi-dimensional annotations for systematic gastritis classification. We present the Endoscopic Gastritis Image Dataset (EGID), comprising 5,883 high-quality white-light endoscopic images from 229 patients retrospectively collected at Renji Hospital, Shanghai, using the Olympus CV-290 system between March and November 2024. Each patient’s images are systematically annotated across four independent clinical dimensions: H. pylori infection status, presence of gastric atrophy, distribution of atrophy, and gastritis type. Annotations were validated through independent assessment by two experienced endoscopists, with consensus review by a senior gastroenterologist for discordant cases, achieving excellent inter-rater agreement (Cohen’s Kappa: 0.947-1.000). EGID provides the first publicly available multi-label endoscopic gastritis dataset, enabling development of artificial intelligence (AI)-based classification systems and serving as an educational resource for clinical training in gastritis diagnosis and cancer risk stratification.
DOI: 10.6084/m9.figshare.30996127.v2
title: <b>EGID: A Comprehensive Multi-label Endoscopic Image Dataset for Gastritis Classification——2</b>

description: Chronic atrophic gastritis is a well-recognized precancerous condition, emphasizing the need for accurate endoscopic classification. However, existing endoscopic datasets lack comprehensive multi-dimensional annotations for systematic gastritis classification. We present the Endoscopic Gastritis Image Dataset (EGID), comprising 5,883 high-quality white-light endoscopic images from 229 patients retrospectively collected at Renji Hospital, Shanghai, using the Olympus CV-290 system between March and November 2024. Each patient’s images are systematically annotated across four independent clinical dimensions: H. pylori infection status, presence of gastric atrophy, distribution of atrophy, and gastritis type. Annotations were validated through independent assessment by two experienced endoscopists, with consensus review by a senior gastroenterologist for discordant cases, achieving excellent inter-rater agreement (Cohen’s Kappa: 0.947-1.000). EGID provides the first publicly available multi-label endoscopic gastritis dataset, enabling development of artificial intelligence (AI)-based classification systems and serving as an educational resource for clinical training in gastritis diagnosis and cancer risk stratification.
DOI: 10.6084/m9.figshare.30996127.v1
title: <b>EGID: A Comprehensive Multi-label Endoscopic Image Dataset for Gastritis Classification——2</b>

description: Chronic atrophic gastritis is a well-recognized precancerous condition, emphasizing the need for accurate endoscopic classification. However, existing endoscopic datasets lack comprehensive multi-dimensional annotations for systematic gastritis classification. We present the Endoscopic Gastritis Image Dataset (EGID), comprising 5,883 high-quality white-light endoscopic images from 229 patients retrospectively collected at Renji Hospital, Shanghai, using the Olympus CV-290 system between March and November 2024. Each patient’s images are systematically annotated across four independent clinical dimensions: H. pylori infection status, presence of gastric atrophy, distribution of atrophy, and gastritis type. Annotations were validated through independent assessment by two experienced endoscopists, with consensus review by a senior gastroenterologist for discordant cases, achieving excellent inter-rater agreement (Cohen’s Kappa: 0.947-1.000). EGID provides the first publicly available multi-label endoscopic gastritis dataset, enabling development of artificial intelligence (AI)-based classification systems and serving as an educational resource for clinical training in gastritis diagnosis and cancer risk stratification.
DOI: 10.6084/m9.figshare.30996985
title: Processed gut microbiota data from 16S rRNA sequencing

description: This dataset contains processed outputs from 16S rRNA gene sequencing for gut microbiota community analysis across control, COPD, and intervention groups.The files include an OTU count table (<code>otu_table.absolute.csv</code>), relative abundance tables at the phylum, family, genus, and species levels, and sample grouping metadata (<code>Group.csv</code>). These data support analyses of microbial community composition and structure, alpha diversity, beta diversity, and differential abundance (e.g., LEfSe) reported in the associated study.The dataset is provided to facilitate transparency and reproducibility of downstream statistical analyses and figure generation.
DOI: 10.6084/m9.figshare.30996985.v1
title: Processed gut microbiota data from 16S rRNA sequencing

description: This dataset contains processed outputs from 16S rRNA gene sequencing for gut microbiota community analysis across control, COPD, and intervention groups.The files include an OTU count table (<code>otu_table.absolute.csv</code>), relative abundance tables at the phylum, family, genus, and species levels, and sample grouping metadata (<code>Group.csv</code>). These data support analyses of microbial community composition and structure, alpha diversity, beta diversity, and differential abundance (e.g., LEfSe) reported in the associated study.The dataset is provided to facilitate transparency and reproducibility of downstream statistical analyses and figure generation.
DOI: 10.15468/dl.364grk
title: Occurrence Download

description: A dataset containing 962 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ambystoma mexicanum"
 ]
}

 The dataset includes 962 records from 69 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0074158-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sawum9
title: Occurrence Download

description: A dataset containing 31 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen, Occurrence evidence)",
 "Country is China",
 "GadmGid is CHN.25_1",
 "Geometry POLYGON((110 34,113 34,113 36,110 36,110 34))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Issue is The taxon concept ID was not found in the checklist",
 "OccurrenceStatus is Present",
 "TaxonKey is Spilopelia chinensis",
 "Year 2015-2026"
 ]
}

 The dataset includes 31 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0074157-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vccpc4
title: Occurrence Download

description: A dataset containing 458 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Tamarix gracilis"
 ]
}

 The dataset includes 458 records from 48 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0074159-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17283631
title: Multisource Meteorological Data during Rainstorm in Beijing

description: Datasets includes the following items during from Jun 6 to Jun 9 and from Aug 5 to Aug 10 in 2024:



absolute humidity/relative humidity/cloud base height dataset;

automatic weather station/eddy covariance measurement datasets at 80/140/200/280m on the Beijing 325m tower;

bias evaluation data for plotting figures

Reanalysis datasets including ERA5 and CMA-RA v1.5 interpolated to the observation site

Radiosonde data

FY-4B satellite data
DOI: 10.5281/zenodo.18149515
title: Multisource Meteorological Data during Rainstorm in Beijing

description: Datasets includes the following items during from Jun 6 to Jun 9 and from Aug 5 to Aug 10 in 2024:



absolute humidity/relative humidity/cloud base height dataset;

automatic weather station/eddy covariance measurement datasets at 80/140/200/280m on the Beijing 325m tower;

bias evaluation data for plotting figures

Reanalysis datasets including ERA5 and CMA-RA v1.5 interpolated to the observation site

Radiosonde data

FY-4B satellite data
DOI: 10.15468/dl.erat4p
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Living Specimen, Material sample)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Pethia gelius",
 "Year 2000-2026"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0074162-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b3kezt
title: Occurrence Download

description: A dataset containing 52374 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Bunias orientalis)",
 "Year is greater than or equal to 2000",
 "Year is less than or equal to 2025",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 52374 records from 393 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0074152-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wmsgxj
title: Occurrence Download

description: A dataset containing 54806 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Brassica rapa)",
 "Year is greater than or equal to 2000",
 "Year is less than or equal to 2025",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 54806 records from 507 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0074151-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18149852
title: Data set for "Slip behavior and earthquake nucleation depth in granitic faults modulated by effective normal stress"

description: After decompressing the zip file, you will find raw data from 18 hydrothermal granite friction experiments organized into 18 folders, each named according to the run number. Inside each folder, the experimental data are presented as follows

“xxx_time.txt”, Time (s)

“xxx_V_loadpoint.txt”, Slip Velocity (m/s)

“xxx_Displ.txt”, Displacement (m)

"xxx_f.txt", Friction Coefficient

"xxx_a_d.txt", Axial Displacement (mm)

"xxx_n_s_eff.txt", Effective Normal Stress (MPa)

"xxx_r_a1.txt", Revolution Angle 1 (°)

"xxx_r_a2.txt ", Revolution Angle 2 (°)

"xxx_T_C1.txt", Temperature near sample (°C)
DOI: 10.5281/zenodo.15055554
title: Data set for "Slip behavior and earthquake nucleation depth in granitic faults modulated by effective normal stress"

description: After decompressing the zip file, you will find raw data from 18 hydrothermal granite friction experiments organized into 18 folders, each named according to the run number. Inside each folder, the experimental data are presented as follows

“xxx_time.txt”, Time (s)

“xxx_V_loadpoint.txt”, Slip Velocity (m/s)

“xxx_Displ.txt”, Displacement (m)

"xxx_f.txt", Friction Coefficient

"xxx_a_d.txt", Axial Displacement (mm)

"xxx_n_s_eff.txt", Effective Normal Stress (MPa)

"xxx_r_a1.txt", Revolution Angle 1 (°)

"xxx_r_a2.txt ", Revolution Angle 2 (°)

"xxx_T_C1.txt", Temperature near sample (°C)
DOI: 10.15468/dl.hunzm6
title: Occurrence Download

description: A dataset containing 24 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Dontostemon micranthus",
 "Year 1970-2000"
 ]
}

 The dataset includes 24 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0074163-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.a5ffv3
title: Occurrence Download

description: A dataset containing 131080 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Burhinus capensis",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Geometry POLYGON((-180 -90, 180 -90, 180 90, -180 90, -180 -90))"
 ]
}

 The dataset includes 131080 records from 55 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0074147-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xhxefa
title: Occurrence Download

description: A dataset containing 2098 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Camelina sativa)",
 "Year is greater than or equal to 2000",
 "Year is less than or equal to 2025",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 2098 records from 196 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0074164-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bhs63u
title: Occurrence Download

description: A dataset containing 193569 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Viscum album L.",
 "HasCoordinate is true"
 ]
}

 The dataset includes 193569 records from 1156 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018275-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10461702
title: UFLUX European ensemble 0.25deg daily carbon, water, and energy fluxes from 2000 - 2020

description: UFLUX Ensemble Europe025ddaily (European 0.25° Daily)

OverviewThe UFLUX ensemble dataset offers European daily fluxes at 0.25° spatial resolution, generated using Deep Forest machine learning models. It integrates satellite-based vegetation proxies — including MODIS NIRv, GOME-2 SIF, and OCO-2 SIF — with ERA5 climate reanalysis, and is trained against ICOS eddy covariance observations. The dataset includes five core flux components:





Gross Primary Production (GPP)




Ecosystem Respiration (RECO)




Net Ecosystem Exchange (NEE)




Sensible Heat Flux (H)




Latent Energy Flux (LE)



Background and MethodologyThe Unified FLUXes (UFLUX) initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change.

Key innovations of UFLUX include:





Consistent Upscaling Framework: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data.




Hybrid Explainable ML: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2).




Uncertainty Quantification: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner.




Multisource Integration: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches.




Superior Gap-Filling: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods.




High Performance: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop.




Community Adoption: Already used by other global upscaling projects, highlighting its reliability and impact.



ApplicationsUFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements.

Resources



UFLUX Website: https://sites.google.com/view/uflux



Code Repository: https://github.com/soonyenju/uflux




Technical & Descriptive Publication: https://doi.org/10.1080/01431161.2024.2312266
DOI: 10.5281/zenodo.10461703
title: UFLUX European ensemble 0.25deg daily carbon, water, and energy fluxes from 2000 - 2020

description: UFLUX Ensemble Europe025ddaily (European 0.25° Daily)

OverviewThe UFLUX ensemble dataset offers European daily fluxes at 0.25° spatial resolution, generated using Deep Forest machine learning models. It integrates satellite-based vegetation proxies — including MODIS NIRv, GOME-2 SIF, and OCO-2 SIF — with ERA5 climate reanalysis, and is trained against ICOS eddy covariance observations. The dataset includes five core flux components:





Gross Primary Production (GPP)




Ecosystem Respiration (RECO)




Net Ecosystem Exchange (NEE)




Sensible Heat Flux (H)




Latent Energy Flux (LE)



Background and MethodologyThe Unified FLUXes (UFLUX) initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change.

Key innovations of UFLUX include:





Consistent Upscaling Framework: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data.




Hybrid Explainable ML: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2).




Uncertainty Quantification: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner.




Multisource Integration: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches.




Superior Gap-Filling: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods.




High Performance: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop.




Community Adoption: Already used by other global upscaling projects, highlighting its reliability and impact.



ApplicationsUFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements.

Resources



UFLUX Website: https://sites.google.com/view/uflux



Code Repository: https://github.com/soonyenju/uflux




Technical & Descriptive Publication: https://doi.org/10.1080/01431161.2024.2312266
DOI: 10.6084/m9.figshare.29414282
title: CRISPR-Cas.spacers

description: This file includes the CRISPR-Cas spacers we predicted from the de novo assembled gut metagenomic datasets (125 samples in total), which we have used to predict the bacterial hosts of Mac phages.