cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.nrc7il
title: Occurrence Download

description: A dataset containing 324 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "Country is France",
 "Country is Spain"
 ]
 },
 "Year 1899-2017",
 {
 "or" : [
 "TaxonKey is Erebia gorgone Boisduval, 1833",
 "TaxonKey is Erebia pronoe Esper, 1780",
 "TaxonKey is Erebia lefebvrei Duponchel",
 "TaxonKey is Agriades pyrenaica (Boisduval, 1840)"
 ]
 }
 ]
}

 The dataset includes 324 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006875-170714134226665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8p65ah
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Geissorhiza mathewsii L.Bolus"
 ]
}

 The dataset includes 11 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018938-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xv8jcq
title: Occurrence Download

description: A dataset containing 12706 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Deroceras laeve (O.F.Müller, 1774)"
 ]
}

 The dataset includes 12706 records from 246 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031164-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.84u6w3
title: Occurrence Download

description: A dataset containing 126499 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Opisthograptis luteolata (Linnaeus, 1758)"
 ]
}

 The dataset includes 126499 records from 140 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006875-170826194755519/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5st6nu
title: Occurrence Download

description: A dataset containing 26 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is one of (CHN.15_1, CHN.31_1, CHN.1_1, CHN.24_1)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Syrmaticus reevesii (J.E.Gray, 1829)"
 ]
}

 The dataset includes 26 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019354-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.r77ebv
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ophisaurus incomptus Mcconkey, 1955"
 ]
}

 The dataset includes 11 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018903-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.0376yr
title: Occurrence Download

description: A dataset containing 314 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Invertebrate collection of the Seto Marine Biological Laboratory, Kyoto University"
 ]
}

 The dataset includes 314 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006876-160118175350007/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fy4buv
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Geometry POLYGON((122.01713 7.15502,122.02886 6.98032,122.16969 6.98614,122.15796 7.16666,122.01713 7.15502))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Ducula poliocephala (G.R.Gray, 1844)"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007314-230918134249559/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.0t85xn
title: Occurrence Download

description: A dataset containing 323778 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cicadellidae"
 ]
}

 The dataset includes 323778 records from 254 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006876-160311141623029/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vnp4xu
title: Occurrence Download

description: A dataset containing 786 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Geometry POLYGON((122.22543 8.98918,124.18709 8.98918,124.18709 11.09719,122.22543 11.09719,122.22543 8.98918))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Oriolus chinensis Linnaeus, 1766"
 ]
}

 The dataset includes 786 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007339-230918134249559/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3rsf4t
title: Occurrence Download

description: A dataset containing 5410 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Serratia Bizio, 1823",
 "Year 1921-2021"
 ]
}

 The dataset includes 5410 records from 184 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014472-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2yp23c
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Guizotia abyssinica",
 "Country is Czechia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 9 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017767-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.njm9gp
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Guizotia abyssinica",
 "Country is Congo, Democratic Republic of the",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017768-250827131500795/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.30026632
title: Research on Rice Identification Methods in Mountainous Regions

description: Rice is a vital staple crop in China, and accurate identification of rice planting areas is essential for agricultural management and food security. However, mountainous regions pose significant challenges due to cloud contamination, limited use of multi-dimensional features, and low classification accuracy. This study proposed a novel rice identification method based on Graph Convolutional Networks (GCN), integrating multi-source remote sensing data tailored for complex terrains. A coarse-to-fine cloud removal strategy was developed by fusing SAR imagery with temporally adjacent optical images, achieving high accuracy (RMSE=0.0391, SAM=0.0729, mSSIM=0.9221, CC=0.9537), and providing reliable cloud-free data. A comprehensive feature library, including spectral, texture, polarization, and terrain attributes, was constructed and optimized, yielding 19 key features that enhanced classification performance. The GCN model trained with this feature set achieved 98.3% overall accuracy in Huoshan County, Dabie Mountains, with 96.8% agreement with statistical yearbook data. Ablation experiments showed terrain features significantly improved performance in complex topography. Compared with SVM, RF, and U-Net models, the proposed method outperformed in accuracy, spatial adaptability, sample efficiency, and computational cost, demonstrating its effectiveness and potential for high-precision rice mapping in mountainous environments.
DOI: 10.6084/m9.figshare.30026632.v1
title: Research on Rice Identification Methods in Mountainous Regions

description: Rice is a vital staple crop in China, and accurate identification of rice planting areas is essential for agricultural management and food security. However, mountainous regions pose significant challenges due to cloud contamination, limited use of multi-dimensional features, and low classification accuracy. This study proposed a novel rice identification method based on Graph Convolutional Networks (GCN), integrating multi-source remote sensing data tailored for complex terrains. A coarse-to-fine cloud removal strategy was developed by fusing SAR imagery with temporally adjacent optical images, achieving high accuracy (RMSE=0.0391, SAM=0.0729, mSSIM=0.9221, CC=0.9537), and providing reliable cloud-free data. A comprehensive feature library, including spectral, texture, polarization, and terrain attributes, was constructed and optimized, yielding 19 key features that enhanced classification performance. The GCN model trained with this feature set achieved 98.3% overall accuracy in Huoshan County, Dabie Mountains, with 96.8% agreement with statistical yearbook data. Ablation experiments showed terrain features significantly improved performance in complex topography. Compared with SVM, RF, and U-Net models, the proposed method outperformed in accuracy, spatial adaptability, sample efficiency, and computational cost, demonstrating its effectiveness and potential for high-precision rice mapping in mountainous environments.
DOI: 10.15468/dl.9yejtq
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Acheilognathus barbatus)",
 "BasisOfRecord is one of (Specimen, Literature Occurrence, Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1991",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017769-250827131500795/datasets/export for details.
DOI: 10.6084/m9.figshare.28636421
title: <b>Impact of the Rhizosphere on Soil and Microbial Stoichiometry across Global Agroecosystems</b>

description: The stoichiometry of the rhizosphere, specifically regarding carbon (C), nitrogen (N), and phosphorus (P), indicates the balance between nutrient mineralization and retention during organic matter decomposition. However, the extent and underlying mechanisms of rhizospheric effects on soil and microbial stoichiometry remain inadequately quantified on a global scale across various agroecosystems. 882 individual cases from 113 targeted literature sources were integrated and analyzed in a meta-analysis to assess the rhizosphere on soil and microbial stoichiometry. Additional information gathered encompassed latitude, longitude, climate, plant properties, soil properties, microbial properties, and experimental conditions.The rhizosphere significantly increases soil C:N, C:P, and N:P ratios while decreasing e microbial C:N, C:P, and N:P ratios compared to bulk soil. Notably, the rhizospheric effects on soil C:N increase in humid regions and decrease in arid zones, whereas the impact on microbial C:N shows an increasing trend with higher soil organic C and ammonium N levels. Sensitive crops such as maize and vegetables enhance the rhizospheric soil C:N ratio by 5.68% and 8.91% respectively, and reduce the microbial C:N ratio by 11.00% and 19.44%. Soil organic C and ammonium N are identified as critical drivers of the rhizospheric soil and microbial C:N ratios, with relative influences of 37.9% and 30.3%, respectively.<b> </b>The studies establish a coupling relationship between rhizospheric soil and microbial stoichiometry and provide valuable insights into rhizospheric soil and microbial nutrient cycling, contributing to soil health and efficient nutrient utilization through targeted regulatory measures.
DOI: 10.6084/m9.figshare.28636421.v1
title: <b>Impact of the Rhizosphere on Soil and Microbial Stoichiometry across Global Agroecosystems</b>

description: The stoichiometry of the rhizosphere, specifically regarding carbon (C), nitrogen (N), and phosphorus (P), indicates the balance between nutrient mineralization and retention during organic matter decomposition. However, the extent and underlying mechanisms of rhizospheric effects on soil and microbial stoichiometry remain inadequately quantified on a global scale across various agroecosystems. 882 individual cases from 113 targeted literature sources were integrated and analyzed in a meta-analysis to assess the rhizosphere on soil and microbial stoichiometry. Additional information gathered encompassed latitude, longitude, climate, plant properties, soil properties, microbial properties, and experimental conditions.The rhizosphere significantly increases soil C:N, C:P, and N:P ratios while decreasing e microbial C:N, C:P, and N:P ratios compared to bulk soil. Notably, the rhizospheric effects on soil C:N increase in humid regions and decrease in arid zones, whereas the impact on microbial C:N shows an increasing trend with higher soil organic C and ammonium N levels. Sensitive crops such as maize and vegetables enhance the rhizospheric soil C:N ratio by 5.68% and 8.91% respectively, and reduce the microbial C:N ratio by 11.00% and 19.44%. Soil organic C and ammonium N are identified as critical drivers of the rhizospheric soil and microbial C:N ratios, with relative influences of 37.9% and 30.3%, respectively.<b> </b>The studies establish a coupling relationship between rhizospheric soil and microbial stoichiometry and provide valuable insights into rhizospheric soil and microbial nutrient cycling, contributing to soil health and efficient nutrient utilization through targeted regulatory measures.
DOI: 10.6084/m9.figshare.28636421.v2
title: <b>Impact of the Rhizosphere on Soil and Microbial Stoichiometry across Global Agroecosystems</b>

description: The stoichiometry of the rhizosphere, specifically regarding carbon (C), nitrogen (N), and phosphorus (P), indicates the balance between nutrient mineralization and retention during organic matter decomposition. However, the extent and underlying mechanisms of rhizospheric effects on soil and microbial stoichiometry remain inadequately quantified on a global scale across various agroecosystems. 882 individual cases from 113 targeted literature sources were integrated and analyzed in a meta-analysis to assess the rhizosphere on soil and microbial stoichiometry. Additional information gathered encompassed latitude, longitude, climate, plant properties, soil properties, microbial properties, and experimental conditions.The rhizosphere significantly increases soil C:N, C:P, and N:P ratios while decreasing e microbial C:N, C:P, and N:P ratios compared to bulk soil. Notably, the rhizospheric effects on soil C:N increase in humid regions and decrease in arid zones, whereas the impact on microbial C:N shows an increasing trend with higher soil organic C and ammonium N levels. Sensitive crops such as maize and vegetables enhance the rhizospheric soil C:N ratio by 5.68% and 8.91% respectively, and reduce the microbial C:N ratio by 11.00% and 19.44%. Soil organic C and ammonium N are identified as critical drivers of the rhizospheric soil and microbial C:N ratios, with relative influences of 37.9% and 30.3%, respectively.<b> </b>The studies establish a coupling relationship between rhizospheric soil and microbial stoichiometry and provide valuable insights into rhizospheric soil and microbial nutrient cycling, contributing to soil health and efficient nutrient utilization through targeted regulatory measures.
DOI: 10.15468/dl.u5s54b
title: Occurrence Download

description: A dataset containing 13226 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Epifagus virginiana"
 ]
}

 The dataset includes 13226 records from 141 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017770-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.p5qvy3
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Calendula arvensis"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017771-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17033424
title: PI1M_Tg_MAC Developed Dataset and Pseudocode for Closed-Loop Generative Design of Radiation-Resistant Polymers

description: This record contains the supporting dataset and pseudocode for the manuscript"A Closed-Loop Deep Generative Model for the Inverse Design of Radiation-Resistant Polymers."

Contents:- PI1M_Tg_MAC.csv — Augmented PI1M dataset with standardized SMILES, 17 RDKit descriptors, and glass transition temperature (Tg) and mass attenuation coefficient (MAC) values (experimental where available; surrogate-predicted otherwise).- PolymerDesign_Pseudocode.pdf — High-level pseudocode describing the closed-loop workflow used in this study, including dataset preparation, surrogate modeling, candidate generation, scoring, and selection.

The pseudocode is non-executable, provided for transparency and reproducibility. The dataset is sufficient for replicating the surrogate modeling pipeline described in the paper.
DOI: 10.5281/zenodo.17033425
title: PI1M_Tg_MAC Developed Dataset and Pseudocode for Closed-Loop Generative Design of Radiation-Resistant Polymers

description: This record contains the supporting dataset and pseudocode for the manuscript"A Closed-Loop Deep Generative Model for the Inverse Design of Radiation-Resistant Polymers."

Contents:- PI1M_Tg_MAC.csv — Augmented PI1M dataset with standardized SMILES, 17 RDKit descriptors, and glass transition temperature (Tg) and mass attenuation coefficient (MAC) values (experimental where available; surrogate-predicted otherwise).- PolymerDesign_Pseudocode.pdf — High-level pseudocode describing the closed-loop workflow used in this study, including dataset preparation, surrogate modeling, candidate generation, scoring, and selection.

The pseudocode is non-executable, provided for transparency and reproducibility. The dataset is sufficient for replicating the surrogate modeling pipeline described in the paper.
DOI: 10.15468/dl.t844um
title: Occurrence Download

description: A dataset containing 31 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Guizotia abyssinica",
 "Country is Denmark",
 "HasCoordinate is true"
 ]
}

 The dataset includes 31 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017772-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kvkzx8
title: Occurrence Download

description: A dataset containing 474 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Stellaria chinensis"
 ]
}

 The dataset includes 474 records from 33 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017773-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s2xm6g
title: Occurrence Download

description: A dataset containing 27 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Guizotia abyssinica",
 "Country is Italy",
 "HasCoordinate is true"
 ]
}

 The dataset includes 27 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017775-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zdv33w
title: Occurrence Download

description: A dataset containing 30 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Guizotia abyssinica",
 "Country is India",
 "HasCoordinate is true"
 ]
}

 The dataset includes 30 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017774-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.89uc5b
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Aronia melanocarpa"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017777-250827131500795/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.30019228
title: NHANES_Demographics_Cognition_NPS

description: This file contains a single worksheet that documents information on demographic characteristics, lifestyle factors, disease status, and cognitive function of the participants. Demographic variables include age, sex, race, education level, poverty income ratio (PIR), and marital status. Lifestyle-related indicators cover smoking, drinking, and body mass index (BMI). Disease status is represented by diabetes and hypertension. In addition, the file records several cognitive assessment measures, such as delay response (delay), reading test (cread), animal fluency test (aft), and digit symbol substitution test (dsst). For complex survey design, the file provides sequence number (SEQN), sample weight (weight), primary sampling unit (SDMVPSU), and strata (SDMVSTRA). Finally, the dataset also includes the Naples Prognostic Score (NPS) and its functional component (NPSF), which can be used to further investigate the relationships between nutrition, inflammation, metabolic status, and cognitive function.
DOI: 10.6084/m9.figshare.30019228.v2
title: NHANES_Demographics_Cognition_NPS

description: This file contains a single worksheet that documents information on demographic characteristics, lifestyle factors, disease status, and cognitive function of the participants. Demographic variables include age, sex, race, education level, poverty income ratio (PIR), and marital status. Lifestyle-related indicators cover smoking, drinking, and body mass index (BMI). Disease status is represented by diabetes and hypertension. In addition, the file records several cognitive assessment measures, such as delay response (delay), reading test (cread), animal fluency test (aft), and digit symbol substitution test (dsst). For complex survey design, the file provides sequence number (SEQN), sample weight (weight), primary sampling unit (SDMVPSU), and strata (SDMVSTRA). Finally, the dataset also includes the Naples Prognostic Score (NPS) and its functional component (NPSF), which can be used to further investigate the relationships between nutrition, inflammation, metabolic status, and cognitive function.