cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.2udn7a
title: Occurrence Download

description: A dataset containing 34080 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Sphagneticola trilobata"
 ]
}

 The dataset includes 34080 records from 430 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023092-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17214254
title: test final

description: test final plugin zenodo ckan
DOI: 10.15468/dl.ve5z4j
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Damburneya patens",
 "Country is Brazil",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023093-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pzb8qk
title: Occurrence Download

description: A dataset containing 233 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Commelina paludosa",
 "Geometry POLYGON((119.300000 20.500000, 122.100000 20.500000, 122.100000 25.400000, 119.300000 25.400000, 119.300000 20.500000))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 233 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023094-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17214264
title: Intensivkapazitäten und COVID-19-Intensivbettenbelegung in Deutschland

description: Der Datensatz "Intensivkapazitäten und COVID-19-Intensivbettenbelegung in Deutschland" des Robert Koch-Instituts dokumentiert die tägliche intensivmedizinische Versorgungslage seit der COVID-19-Pandemie. Basierend auf Meldungen aller intensivbettenführenden Krankenhäuser in Deutschland erfasst das DIVI-Intensivregister Echtzeitdaten zu belegten und freien Intensivbetten. Die Erhebung differenziert nach Altersgruppen, Regionen und Versorgungsstufen. COVID-19-Fälle auf Intensivstationen werden gesondert ausgewiesen. Die Daten stehen aggregiert auf Bundes-, Landes- und Kreisebene zur Verfügung. Damit bildet der Datensatz eine Grundlage für die Überwachung von Kapazitäten, die Koordination von Behandlungskapazitäten und politische Entscheidungsprozesse während der Pandemie und darüber hinaus.
DOI: 10.5281/zenodo.17214265
title: test final

description: test final plugin zenodo ckan
DOI: 10.15468/dl.p7e28h
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Nectandra citrifolia",
 "Country is Brazil",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023095-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15201876
title: Plants, spiders, ground beetles from glacial and periglacial habitats in European Alps
title: Global threats, local opportunities: ice-related landforms provide refugia for high-elevation plants and arthropods

description: Original dataset with presence absence data of vascular plants, spiders and ground beetles from glacial and periglacial habitats on European Alps. For further details, see the original paper
DOI: 10.6084/m9.figshare.28669688.v2
title: Data Set and ReadMe

description: This dataset corresponds to the paper "EMG-Less Muscle Force Estimation in Robot-Assisted Therapy through Inverse Musculoskeletal Modeling and Clinical Validation." It contains the experimental data collected from 36 trials conducted during the study as well as a ReadMe file.
DOI: 10.15468/dl.m69tvr
title: Occurrence Download

description: A dataset containing 3262 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Africa",
 "OccurrenceStatus is Present",
 "TaxonKey is Tecoma stans (L.) Juss. ex Kunth"
 ]
}

 The dataset includes 3262 records from 84 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019275-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14832499
title: Dataset of "In Depth Battery Aging Analysis: A Key to Sustainable Energy Storage in Microgrids"

description:  This work presents the results of an experimental study focused on battery aging,considering micro-cycles that emulate real-world conditions forbatteries in microgrid storage systems. It demonstrates that thedegradation in storage systems occurs differently compared todeep charge and discharge cycles. It provides a detailed analysis ofthe degradation processes occurring in microgrid batteries usingEIS diagnostics and post-mortem analysis and demonstrates theapplicability of the findings for renewable energy systems.
DOI: 10.15468/dl.ajb9ek
title: Occurrence Download

description: A dataset containing 646958 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON((14.45999 45.41263,16.76062 45.48558,16.54178 46.90523,14.51611 46.61905,14.45999 45.41263))"
 ]
}

 The dataset includes 646958 records from 368 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019490-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.7835244
title: Scale dependent diversity of bryozoan assemblages in the reefs of the Vasalemma Formation (Late Ordovician), Estonia: data

description: This upload contains the code and data for the paper Kröger, B. et al., 2023. "Scale dependent diversity of bryozoan assemblages in the reefs of the Vasalemma Formation (Late Ordovician), Estonia", Lethaia,  DOI:
DOI: 10.5281/zenodo.7835245
title: Scale dependent diversity of bryozoan assemblages in the reefs of the Vasalemma Formation (Late Ordovician), Estonia: data

description: This upload contains the code and data for the paper Kröger, B. et al., 2023. "Scale dependent diversity of bryozoan assemblages in the reefs of the Vasalemma Formation (Late Ordovician), Estonia", Lethaia,  DOI:
DOI: 10.6084/m9.figshare.28210337
title: Supplementary Material for: UCP variants are linked to hypercholesterolemia and abdominal obesity in metabolically unhealthy women

description: Introduction. It has been reported that even with the same body mass index (BMI), there are subjects with metabolically healthy or unhealthy phenotype. The main determinants of the unhealthy phenotype are the type and distribution of fat, ectopic fat accumulation, genetics, and lifestyle factors. Uncoupling proteins (UCPs) disengage mitochondrial respiration from ATP synthesis and result in heat production, which in turn is related to energy expenditure and, thus, to fat mass accumulation. The association of the UCP1 -3826A/G (rs1800592), UCP2 Ala55Val (rs660339), and UCP3 -55C/T (rs1800849) variants with metabolic variables was evaluated according to metabolic phenotype in Mexican women. Methods. Women aged 18 to 65 years classified as normal weight (NW) or excessive weight (EW) according to their BMI (from 18.5 to &lt;25 kg/m2 for NW, and from 25 to &lt;40 kg/m2 for EW), were included. Participants were classified into two metabolic phenotypes: metabolically healthy or metabolically unhealthy (MH or MUH, respectively) based on ATP-III criteria and the homeostasis model assessment of insulin resistance (HOMA-IR). The genetic variants were determined by allelic discrimination using TaqMan® probes. Results. In participants with the UCP1 -3826A/G variant, an increased risk of hypercholesterolemia was observed in those with the NW-MUH phenotype (OR=5.09, CI=1.03-25.12, p=0.017). The UCP2 Ala55Val variant in EW-MUH subjects was associated with higher abdominal obesity risk (OR=3.23, CI=1.21-8.60, p=0.019), while no associations were found with the UCP3 -55C/T variant. Conclusion. UCP1 and UCP2 variants are related with hypercholesterolemia and visceral fat accumulation in women with MUH phenotype.
DOI: 10.6084/m9.figshare.28210337.v2
title: Supplementary Material for: UCP variants are linked to hypercholesterolemia and abdominal obesity in metabolically unhealthy women

description: Introduction. It has been reported that even with the same body mass index (BMI), there are subjects with metabolically healthy or unhealthy phenotype. The main determinants of the unhealthy phenotype are the type and distribution of fat, ectopic fat accumulation, genetics, and lifestyle factors. Uncoupling proteins (UCPs) disengage mitochondrial respiration from ATP synthesis and result in heat production, which in turn is related to energy expenditure and, thus, to fat mass accumulation. The association of the UCP1 -3826A/G (rs1800592), UCP2 Ala55Val (rs660339), and UCP3 -55C/T (rs1800849) variants with metabolic variables was evaluated according to metabolic phenotype in Mexican women. Methods. Women aged 18 to 65 years classified as normal weight (NW) or excessive weight (EW) according to their BMI (from 18.5 to &lt;25 kg/m2 for NW, and from 25 to &lt;40 kg/m2 for EW), were included. Participants were classified into two metabolic phenotypes: metabolically healthy or metabolically unhealthy (MH or MUH, respectively) based on ATP-III criteria and the homeostasis model assessment of insulin resistance (HOMA-IR). The genetic variants were determined by allelic discrimination using TaqMan® probes. Results. In participants with the UCP1 -3826A/G variant, an increased risk of hypercholesterolemia was observed in those with the NW-MUH phenotype (OR=5.09, CI=1.03-25.12, p=0.017). The UCP2 Ala55Val variant in EW-MUH subjects was associated with higher abdominal obesity risk (OR=3.23, CI=1.21-8.60, p=0.019), while no associations were found with the UCP3 -55C/T variant. Conclusion. UCP1 and UCP2 variants are related with hypercholesterolemia and visceral fat accumulation in women with MUH phenotype.
DOI: 10.6084/m9.figshare.28210337.v1
title: Supplementary Material for: UCP variants are linked to hypercholesterolemia and abdominal obesity in metabolically unhealthy women

description: Introduction. It has been reported that even with the same body mass index (BMI), there are subjects with metabolically healthy or unhealthy phenotype. The main determinants of the unhealthy phenotype are the type and distribution of fat, ectopic fat accumulation, genetics, and lifestyle factors. Uncoupling proteins (UCPs) disengage mitochondrial respiration from ATP synthesis and result in heat production, which in turn is related to energy expenditure and, thus, to fat mass accumulation. The association of the UCP1 -3826A/G (rs1800592), UCP2 Ala55Val (rs660339), and UCP3 -55C/T (rs1800849) variants with metabolic variables was evaluated according to metabolic phenotype in Mexican women. Methods. Women aged 18 to 65 years classified as normal weight (NW) or excessive weight (EW) according to their BMI (from 18.5 to &lt;25 kg/m2 for NW, and from 25 to &lt;40 kg/m2 for EW), were included. Participants were classified into two metabolic phenotypes: metabolically healthy or metabolically unhealthy (MH or MUH, respectively) based on ATP-III criteria and the homeostasis model assessment of insulin resistance (HOMA-IR). The genetic variants were determined by allelic discrimination using TaqMan® probes. Results. In participants with the UCP1 -3826A/G variant, an increased risk of hypercholesterolemia was observed in those with the NW-MUH phenotype (OR=5.09, CI=1.03-25.12, p=0.017). The UCP2 Ala55Val variant in EW-MUH subjects was associated with higher abdominal obesity risk (OR=3.23, CI=1.21-8.60, p=0.019), while no associations were found with the UCP3 -55C/T variant. Conclusion. UCP1 and UCP2 variants are related with hypercholesterolemia and visceral fat accumulation in women with MUH phenotype.
DOI: 10.15468/dl.bbpga2
title: Occurrence Download

description: A dataset containing 10474 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pennatulacea"
 ]
}

 The dataset includes 10474 records from 138 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019502-250127130748423/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.27368823.v1
title: <b>Symbiosis type in nitrogen fixing trees determines greenhouse gas emissions: a global meta-analysis</b>

description: N-fixing trees is widely spread for planting forest and agroforestry ecosystems due to their advantages for improving soil quality and carbon sequestration. However, the impact of N-fixing trees on N<sub>2</sub>O, CH<sub>4</sub>, and CO<sub>2 </sub>fluxes relative to non-fixing trees remains uncertain. We use meta-analysis to discover this important knowledge gap. Specific description for this study and related dataset and code were attached.
DOI: 10.6084/m9.figshare.28233590
title: Supplementary Material for: GlomCon Hawaii: The First International Hybrid Glomerular Diseases Conference

description: Medicine
DOI: 10.6084/m9.figshare.28233590.v1
title: Supplementary Material for: GlomCon Hawaii: The First International Hybrid Glomerular Diseases Conference

description: Medicine
DOI: 10.6084/m9.figshare.28233590.v2
title: Supplementary Material for: GlomCon Hawaii: The First International Hybrid Glomerular Diseases Conference

description: Medicine
DOI: 10.15468/dl.dm2z3f
title: Occurrence Download

description: A dataset containing 879789 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Brazil",
 "Geometry POLYGON((-47.13593 -23.51211,-45.97217 -23.51211,-45.97217 -22.4407,-47.13593 -22.4407,-47.13593 -23.51211))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Year 1000-2025"
 ]
}

 The dataset includes 879789 records from 385 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019497-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.86pevn
title: Occurrence Download

description: A dataset containing 9688 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is one of (Herbario de la Universidad de Sevilla, Herbario de Universidad de Murcia: MUB, Universidad de Jaén - Herbario de la Universidad de Jaén (JAEN), CeDoc de Biodiversitat Vegetal: BCN-Cormophyta, Colección de plantas vasculares del herbario de la Universitat de València (VAL), Herbario de la Universidad de Granada: Colección GDAC-Fanerogamia, Herbario de la Universidad de Granada: Colección GDA-Fanerogamia, CSIC-Real Jardín Botánico-Colección de Plantas Vasculares (MA), Institut Botanic de Barcelona (IBB, CSIC-Ajuntament de Barcelona), BC-Plantae)",
 "TaxonKey is Campanula L."
 ]
}

 The dataset includes 9688 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019504-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.v3e6b8
title: Occurrence Download

description: A dataset containing 90933 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Antipatharia"
 ]
}

 The dataset includes 90933 records from 194 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019501-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xrh8v3
title: Occurrence Download

description: A dataset containing 84575 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Primnoidae"
 ]
}

 The dataset includes 84575 records from 188 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019503-250127130748423/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.28254233.v1
title: Supplementary Material for: Identifying Neuro-inflammatory Biomarkers of Generalized Anxiety Disorder from Lymphocyte Subsets based on machine learning approaches

description: Introduction: Activation of the inflammatory response system is involved in the pathogenesis of generalized anxiety disorder (GAD). The purpose of this study was to identify and characterize inflammatory biomarkers in the diagnosis of GAD based on machine learning algorithms. 
Methods: The evaluation of peripheral immune parameters and lymphocyte subsets was performed on patients with GAD. Multivariable linear regression was used to explore the association between lymphocyte subsets and the of severity GAD. Receiver operator characteristic (ROC) analysis was used to determine the predictive value of these immunological parameters for GAD. Machine learning technology was applied to classify the collected data from patients in the GAD and healthy control groups.
Results: Of the 340 patients enrolled, 171 were GAD patients and 169 were non-GAD patients as healthy control. The levels of neutrophil (NEU), monocytes (MON) and systemic immune-inflammation index (SII) were significantly elevated in GAD patients (P&lt;0.01), and the count and proportion of immune cells, including CD3+CD4+ T cells, CD3+CD8+ T cells, CD19+ B cells and CD3-CD16+CD56+ NK cells (P&lt;0.001) have undergone large changes. The classification analysis conducted by machine learning using a weighted ensemble-L2 algorithm demonstrated an accuracy of 95.00±2.04% in assessing the predictive value of these lymphocyte subsets in GAD. In addition, the feature importance analysis score is 0.255, which was much more predictive of GAD severity than for other lymphocyte subsets.
Conclusion: In the presented work, we show the level of lymphocyte subsets altered in GAD. Lymphocyte subsets, specifically CD3+CD4+ T %, can serve as neuroinflammatory biomarkers for GAD diagnostics. Furthermore, the application of machine learning offers a highly efficient approach for investigating neuroinflammatory biomarkers and predicting GAD. Our research has provided novel insights into the involvement of cellular immunity in GAD and highlighted the potential predictive value and therapeutic targets of lymphocyte subsets in this disorder.
DOI: 10.21227/y4m0-ka14
title: Dataset for Cross-Device and Cross-Subject Consistency Evaluation in Visual Fixation Prediction

description: This dataset accompanies the paper “Evaluating Cross-Device and Cross-Subject Consistency in Visual Fixation Prediction”. We collected eye gaze data using a 30Hz eye tracker embedded in the Aria Glasses (Meta Platforms, Inc., Menlo Park, CA, USA) on 300 images from the MIT1003 dataset, with each image viewed for 3 seconds by 9 subjects (age range 23-39 years), resulting in a total of 243,000 eye fixations. Besides, we also release the average saliency maps from the subjects' visual fixations. The fixation data is structured to allow for cross-device and cross-subject comparisons, enabling researchers to analyze variations in visual attention across different hardware setups and individual observers.
DOI: 10.6084/m9.figshare.28254233.v2
title: Supplementary Material for: Identifying Neuro-inflammatory Biomarkers of Generalized Anxiety Disorder from Lymphocyte Subsets based on machine learning approaches

description: Introduction: Activation of the inflammatory response system is involved in the pathogenesis of generalized anxiety disorder (GAD). The purpose of this study was to identify and characterize inflammatory biomarkers in the diagnosis of GAD based on machine learning algorithms. 
Methods: The evaluation of peripheral immune parameters and lymphocyte subsets was performed on patients with GAD. Multivariable linear regression was used to explore the association between lymphocyte subsets and the of severity GAD. Receiver operator characteristic (ROC) analysis was used to determine the predictive value of these immunological parameters for GAD. Machine learning technology was applied to classify the collected data from patients in the GAD and healthy control groups.
Results: Of the 340 patients enrolled, 171 were GAD patients and 169 were non-GAD patients as healthy control. The levels of neutrophil (NEU), monocytes (MON) and systemic immune-inflammation index (SII) were significantly elevated in GAD patients (P&lt;0.01), and the count and proportion of immune cells, including CD3+CD4+ T cells, CD3+CD8+ T cells, CD19+ B cells and CD3-CD16+CD56+ NK cells (P&lt;0.001) have undergone large changes. The classification analysis conducted by machine learning using a weighted ensemble-L2 algorithm demonstrated an accuracy of 95.00±2.04% in assessing the predictive value of these lymphocyte subsets in GAD. In addition, the feature importance analysis score is 0.255, which was much more predictive of GAD severity than for other lymphocyte subsets.
Conclusion: In the presented work, we show the level of lymphocyte subsets altered in GAD. Lymphocyte subsets, specifically CD3+CD4+ T %, can serve as neuroinflammatory biomarkers for GAD diagnostics. Furthermore, the application of machine learning offers a highly efficient approach for investigating neuroinflammatory biomarkers and predicting GAD. Our research has provided novel insights into the involvement of cellular immunity in GAD and highlighted the potential predictive value and therapeutic targets of lymphocyte subsets in this disorder.