cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.u3fmxx
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Squalus suckleyi (Girard, 1855)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 10 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024577-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tpys7b
title: Occurrence Download

description: A dataset containing 8686 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Gamochaeta pensylvanica (Willd.) Cabrera"
 ]
}

 The dataset includes 8686 records from 371 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024576-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14854182
title: YAN&ZHAO-Digital shale
DOI: 10.5281/zenodo.14854183
title: YAN&ZHAO-Digital shale
DOI: 10.5281/zenodo.14854354
title: Unified Resilience Model Using Deep Learning for Assessing Power System Performance

description: Abstract: Energy resilience in renewable energy sources dissemination components such as batteries and inverters is crucial for achieving high operational fidelity. Resilience factors play a vital role in determining the performance of power systems, regardless of their operating environment and interruptions. This article introduces a Unified Resilience Model (URM) using Deep Learning (DL) to enhance power system performance. The proposed model analyzes environmental factors impacting the resilience of batteries and energy storage devices. This deep learning approach trains performance-impacting factors using previously known low resilience drain data. The learning output is utilized to augment various strengthening factors, thereby improving resilience. Drain mitigation and performance improvements are combined for direct impact verification. This process validates the model's fidelity in enhancing power system performance, with a specific focus on the impact of weather factors.
DOI: 10.5281/zenodo.14854355
title: Unified Resilience Model Using Deep Learning for Assessing Power System Performance

description: Abstract: Energy resilience in renewable energy sources dissemination components such as batteries and inverters is crucial for achieving high operational fidelity. Resilience factors play a vital role in determining the performance of power systems, regardless of their operating environment and interruptions. This article introduces a Unified Resilience Model (URM) using Deep Learning (DL) to enhance power system performance. The proposed model analyzes environmental factors impacting the resilience of batteries and energy storage devices. This deep learning approach trains performance-impacting factors using previously known low resilience drain data. The learning output is utilized to augment various strengthening factors, thereby improving resilience. Drain mitigation and performance improvements are combined for direct impact verification. This process validates the model's fidelity in enhancing power system performance, with a specific focus on the impact of weather factors.
DOI: 10.15468/dl.64r4hr
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Squalus megalops (Macleay, 1881)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 6 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024579-250127130748423/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.28396510
title: Additional file 1 of Association between isolated abnormal 1-hour glucose challenge test and adverse pregnancy outcomes: a retrospective review from an urban tertiary care center in the United States

description: Supplementary Material 1
DOI: 10.6084/m9.figshare.28396510.v1
title: Additional file 1 of Association between isolated abnormal 1-hour glucose challenge test and adverse pregnancy outcomes: a retrospective review from an urban tertiary care center in the United States

description: Supplementary Material 1
DOI: 10.5281/zenodo.14854376
title: Europe Small Wind Power Market 2025 To 2034

description: Europe Small Wind Power Market Size, Trends and Insights By Application (Off-Grid Applications, On-Grid Applications), By Rotor Type (Horizontal Axis Wind Turbines (HAWT), Vertical Axis Wind Turbines (VAWT)), By Installation Type (Roof-mounted, Ground-mounted), By Power Rating (Less than 1 kW, 1 kW to 10 kW, Above 10 kW), By End-User (Residential, Commercial & Industrial, Agricultural, Others), and By Region - Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2024–2033.

Reports Description

As per the current market research conducted by the CMI Team, the Europe Small Wind Power Market is expected to record a CAGR of 12.6% from 2024 to 2033. In 2024, the market size is projected to reach a valuation of USD 3,920.2 Million. By 2033, the valuation is anticipated to reach USD 11,406.4 Million.

The Europe Small Wind Power Market encompasses the production, distribution, and installation of small-scale wind turbines primarily designed for residential, commercial, and agricultural applications. These turbines, typically ranging from a few kilowatts to 100 kilowatts in capacity, harness wind energy to generate electricity, offering a sustainable and decentralized energy solution.

The market is characterized by technological innovation, government incentives, and increasing environmental awareness driving demand. Small wind power systems contribute to reducing carbon emissions, enhancing energy security, and providing electricity access in remote areas, aligning with Europe’s renewable energy goals and commitment to combating climate change.

For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=53630
DOI: 10.5281/zenodo.14854377
title: Europe Small Wind Power Market 2025 To 2034

description: Europe Small Wind Power Market Size, Trends and Insights By Application (Off-Grid Applications, On-Grid Applications), By Rotor Type (Horizontal Axis Wind Turbines (HAWT), Vertical Axis Wind Turbines (VAWT)), By Installation Type (Roof-mounted, Ground-mounted), By Power Rating (Less than 1 kW, 1 kW to 10 kW, Above 10 kW), By End-User (Residential, Commercial & Industrial, Agricultural, Others), and By Region - Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2024–2033.

Reports Description

As per the current market research conducted by the CMI Team, the Europe Small Wind Power Market is expected to record a CAGR of 12.6% from 2024 to 2033. In 2024, the market size is projected to reach a valuation of USD 3,920.2 Million. By 2033, the valuation is anticipated to reach USD 11,406.4 Million.

The Europe Small Wind Power Market encompasses the production, distribution, and installation of small-scale wind turbines primarily designed for residential, commercial, and agricultural applications. These turbines, typically ranging from a few kilowatts to 100 kilowatts in capacity, harness wind energy to generate electricity, offering a sustainable and decentralized energy solution.

The market is characterized by technological innovation, government incentives, and increasing environmental awareness driving demand. Small wind power systems contribute to reducing carbon emissions, enhancing energy security, and providing electricity access in remote areas, aligning with Europe’s renewable energy goals and commitment to combating climate change.

For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=53630
DOI: 10.6084/m9.figshare.28396573.v1
title: Additional file 1 of Prevalence and recurrence rates of spontaneous pneumothorax in patients with diffuse cystic lung diseases in China

description: Supplementary Material 1
DOI: 10.6084/m9.figshare.28396573
title: Additional file 1 of Prevalence and recurrence rates of spontaneous pneumothorax in patients with diffuse cystic lung diseases in China

description: Supplementary Material 1
DOI: 10.15468/dl.a9bdst
title: Occurrence Download

description: A dataset containing 25 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Erilepis zonifer (Lockington, 1880)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 25 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024581-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ag6rmt
title: Occurrence Download

description: A dataset containing 28428 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is one of (Ardisia aberrans (E.Walker) C.Y.Wu & C.Chen, Ardisia affinis Hemsl., Ardisia alutacea C.Y.Wu & C.Chen, Ardisia alyxiifolia Tsiang ex C.Chen, Ardisia argenticaulis Y.P.Yang, Ardisia balansana Yuen P.Yang, Ardisia baotingensis C.M.Hu, Ardisia brevicaulis Diels, Ardisia brunnescens E.Walker, Ardisia bullata G.H.Huang & G.Hao, Ardisia carnosicaulis C.Chen & D.Fang, Ardisia caudata Hemsl., Ardisia chinensis Benth., Ardisia conspersa E.Walker, Ardisia cornudentata Mez, Ardisia corymbifera Mez, Ardisia crassinervosa E.Walker, Ardisia crenata Sims, Ardisia crispa (Thunb.) A.DC., Ardisia curvula C.Y.Wu & C.Chen, Ardisia dasyrhizomatica C.Y.Wu & C.Chen, Ardisia densilepidotula Merr., Ardisia elliptica Bedd., Ardisia ensifolia E.Walker, Ardisia faberi Hemsl., Ardisia filiformis E.Walker, Ardisia fordii Hemsl., Ardisia garrettii H.R.Fletcher, Ardisia gigantifolia Stapf, Ardisia hanceana Mez, Ardisia hokouensis Yuen P.Yang, Ardisia humilis Blume, Ardisia hypargyrea C.Y.Wu & C.Chen, Ardisia japonica (Thunb.) Blume, Ardisia kusukusensis Hayata, Ardisia lindleyana D.Dietr., Ardisia maclurei Merr., Ardisia maculosa Mez, Ardisia malipoensis C.M.Hu, Ardisia mamillata Hance, Ardisia medogensis C.M.Hu & G.Hao, Ardisia merrillii E.Walker, Ardisia nigropilosa Pit., Ardisia nutantiflora S.Z.Mao & C.M.Hu, Ardisia obtusa Mez, Ardisia olivacea E.Walker, Ardisia omissa C.M.Hu, Ardisia ordinata E.Walker, Ardisia pedalis E.Walker, Ardisia pingbienensis Yuen P.Yang, Ardisia polysticta Miq., Ardisia porifera E.Walker, Ardisia primulifolia Gardner & Champ., Ardisia pseudocrispa Pit., Ardisia pubivenula E.Walker, Ardisia purpureovillosa C.Y.Wu & C.Chen ex C.M.Hu, Ardisia pusilla A.DC., Ardisia quinquegona Blume, Ardisia ramondiiformis Pit., Ardisia replicata E.Walker, Ardisia retroflexa E.Walker, Ardisia scalarinervis E.Walker, Ardisia shweliensis W.W.Sm., Ardisia sieboldii Miq., Ardisia solanacea Roxb., Ardisia thyrsiflora D.Don, Ardisia verbascifolia Mez, Ardisia villosa Roxb., Ardisia violacea (T.Suzuki) W.Z.Fang & K.Yao, Ardisia waitakii C.M.Hu)",
 "Geometry POLYGON((73.475 54.5, 146.0083 54.5, 146.0083 18, 73.475 18, 73.475 54.5))",
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 10000",
 "CoordinateUncertaintyInMeters is null",
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 }
 ]
}

 The dataset includes 28428 records from 140 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024580-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.p58kgu
title: Occurrence Download

description: A dataset containing 10269 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-79.94379 -4.62073,-66.392 -4.62073,-66.392 12.79175,-79.94379 12.79175,-79.94379 -4.62073))",
 "TaxonKey is Hydrochoerus Brisson, 1762"
 ]
}

 The dataset includes 10269 records from 162 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024582-250127130748423/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.28396629
title: Additional file 1 of A hybrid machine learning framework for functional annotation of mitochondrial glutathione transport and metabolism proteins in cancers

description: Additional file 1.
DOI: 10.6084/m9.figshare.28396629.v1
title: Additional file 1 of A hybrid machine learning framework for functional annotation of mitochondrial glutathione transport and metabolism proteins in cancers

description: Additional file 1.
DOI: 10.15468/dl.4adtdw
title: Occurrence Download

description: A dataset containing 180007 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is one of (COL.2.107_2, COL.2.91_2)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 180007 records from 187 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024573-250127130748423/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.28396659
title: Additional file 2 of A scoping review of factors influencing the implementation of liquid biopsy for cancer care

description: Supplementary Material 2. Appendix 2. Coding Structure and Definitions for keyword analysis.
DOI: 10.6084/m9.figshare.28396659.v1
title: Additional file 2 of A scoping review of factors influencing the implementation of liquid biopsy for cancer care

description: Supplementary Material 2. Appendix 2. Coding Structure and Definitions for keyword analysis.
DOI: 10.6084/m9.figshare.28396662
title: Additional file 3 of A scoping review of factors influencing the implementation of liquid biopsy for cancer care

description: Supplementary Material 3. Appendix 3. Articles included in the study (n = 75).
DOI: 10.6084/m9.figshare.28396662.v1
title: Additional file 3 of A scoping review of factors influencing the implementation of liquid biopsy for cancer care

description: Supplementary Material 3. Appendix 3. Articles included in the study (n = 75).
DOI: 10.6084/m9.figshare.28396675
title: Additional file 2 of Sex-specific role of epigenetic modification of a leptin upstream enhancer in adipose tissue

description: Additional file 2
DOI: 10.6084/m9.figshare.28396675.v1
title: Additional file 2 of Sex-specific role of epigenetic modification of a leptin upstream enhancer in adipose tissue

description: Additional file 2
DOI: 10.15468/dl.n6qcad
title: Occurrence Download

description: A dataset containing 20009 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Euthamia graminifolia (L.) Nutt."
 ]
}

 The dataset includes 20009 records from 320 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024583-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ne3cah
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Myriolepis zonifer Lockington, 1880",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024584-250127130748423/datasets/export for details.
DOI: 10.6084/m9.figshare.28396686
title: Additional file 2 of Identifying differentiation markers between dermal fibroblasts and adipose-derived mesenchymal stromal cells (AD-MSCs) in human visceral and subcutaneous tissues using single-cell transcriptomics

description: Supplementary Material 2
DOI: 10.6084/m9.figshare.28396686.v1
title: Additional file 2 of Identifying differentiation markers between dermal fibroblasts and adipose-derived mesenchymal stromal cells (AD-MSCs) in human visceral and subcutaneous tissues using single-cell transcriptomics

description: Supplementary Material 2
DOI: 10.6084/m9.figshare.28396689
title: Additional file 3 of Identifying differentiation markers between dermal fibroblasts and adipose-derived mesenchymal stromal cells (AD-MSCs) in human visceral and subcutaneous tissues using single-cell transcriptomics

description: Supplementary Material 3
DOI: 10.6084/m9.figshare.28396689.v1
title: Additional file 3 of Identifying differentiation markers between dermal fibroblasts and adipose-derived mesenchymal stromal cells (AD-MSCs) in human visceral and subcutaneous tissues using single-cell transcriptomics

description: Supplementary Material 3
DOI: 10.6084/m9.figshare.28396692
title: Additional file 4 of Identifying differentiation markers between dermal fibroblasts and adipose-derived mesenchymal stromal cells (AD-MSCs) in human visceral and subcutaneous tissues using single-cell transcriptomics

description: Supplementary Material 4
DOI: 10.6084/m9.figshare.28396690
title: Additional file 1 of Metabolomics- and proteomics-based multi-omics integration reveals early metabolite alterations in sepsis-associated acute kidney injury

description: Additional file 1. Detailed metabolomics, proteomics sequencing methods and data quality control methods.
DOI: 10.6084/m9.figshare.28396690.v1
title: Additional file 1 of Metabolomics- and proteomics-based multi-omics integration reveals early metabolite alterations in sepsis-associated acute kidney injury

description: Additional file 1. Detailed metabolomics, proteomics sequencing methods and data quality control methods.
DOI: 10.6084/m9.figshare.28396692.v1
title: Additional file 4 of Identifying differentiation markers between dermal fibroblasts and adipose-derived mesenchymal stromal cells (AD-MSCs) in human visceral and subcutaneous tissues using single-cell transcriptomics

description: Supplementary Material 4
DOI: 10.6084/m9.figshare.28396693
title: Additional file 2 of Metabolomics- and proteomics-based multi-omics integration reveals early metabolite alterations in sepsis-associated acute kidney injury

description: Additional file 2. Biochemical index and renal metabolomics data in mice. Table S1. Biochemical index between SA-AKI mice and sepsis mice. Table S2. Renal metabolomics data. Table S3. Renal metabolomics annotation.
DOI: 10.6084/m9.figshare.28396693.v1
title: Additional file 2 of Metabolomics- and proteomics-based multi-omics integration reveals early metabolite alterations in sepsis-associated acute kidney injury

description: Additional file 2. Biochemical index and renal metabolomics data in mice. Table S1. Biochemical index between SA-AKI mice and sepsis mice. Table S2. Renal metabolomics data. Table S3. Renal metabolomics annotation.
DOI: 10.6084/m9.figshare.28396695
title: Additional file 5 of Identifying differentiation markers between dermal fibroblasts and adipose-derived mesenchymal stromal cells (AD-MSCs) in human visceral and subcutaneous tissues using single-cell transcriptomics

description: Supplementary Material 5
DOI: 10.6084/m9.figshare.28396695.v1
title: Additional file 5 of Identifying differentiation markers between dermal fibroblasts and adipose-derived mesenchymal stromal cells (AD-MSCs) in human visceral and subcutaneous tissues using single-cell transcriptomics

description: Supplementary Material 5
DOI: 10.6084/m9.figshare.28396698
title: Additional file 6 of Identifying differentiation markers between dermal fibroblasts and adipose-derived mesenchymal stromal cells (AD-MSCs) in human visceral and subcutaneous tissues using single-cell transcriptomics

description: Supplementary Material 6
DOI: 10.6084/m9.figshare.28396696
title: Additional file 3 of Metabolomics- and proteomics-based multi-omics integration reveals early metabolite alterations in sepsis-associated acute kidney injury

description: Additional file 3. Renal proteomics analysis. Table S1. Differential renal proteins between SA-AKI and sepsis in mice. Figure S1. Mice renal proteins and enrichment analysis. a) Abundance distributions of mice renal metabolomics and proteomics. b) Heatmap showing the correlation between renal proteins and the three biochemical indicators. Orange indicates a positive correlation and blue indicates a negative correlation. c-e) Gene Ontology Enrichment Analysis for renal differential proteins, including biological process (BP) (c); cell composition (CC) (d); molecular functional (MF) (e).
DOI: 10.6084/m9.figshare.28396696.v1
title: Additional file 3 of Metabolomics- and proteomics-based multi-omics integration reveals early metabolite alterations in sepsis-associated acute kidney injury

description: Additional file 3. Renal proteomics analysis. Table S1. Differential renal proteins between SA-AKI and sepsis in mice. Figure S1. Mice renal proteins and enrichment analysis. a) Abundance distributions of mice renal metabolomics and proteomics. b) Heatmap showing the correlation between renal proteins and the three biochemical indicators. Orange indicates a positive correlation and blue indicates a negative correlation. c-e) Gene Ontology Enrichment Analysis for renal differential proteins, including biological process (BP) (c); cell composition (CC) (d); molecular functional (MF) (e).
DOI: 10.6084/m9.figshare.28396698.v1
title: Additional file 6 of Identifying differentiation markers between dermal fibroblasts and adipose-derived mesenchymal stromal cells (AD-MSCs) in human visceral and subcutaneous tissues using single-cell transcriptomics

description: Supplementary Material 6
DOI: 10.6084/m9.figshare.28396701
title: Additional file 7 of Identifying differentiation markers between dermal fibroblasts and adipose-derived mesenchymal stromal cells (AD-MSCs) in human visceral and subcutaneous tissues using single-cell transcriptomics

description: Supplementary Material 7
DOI: 10.6084/m9.figshare.28396701.v1
title: Additional file 7 of Identifying differentiation markers between dermal fibroblasts and adipose-derived mesenchymal stromal cells (AD-MSCs) in human visceral and subcutaneous tissues using single-cell transcriptomics

description: Supplementary Material 7
DOI: 10.6084/m9.figshare.28396702
title: Additional file 4 of Metabolomics- and proteomics-based multi-omics integration reveals early metabolite alterations in sepsis-associated acute kidney injury

description: Additional file 4. The expression of five core metabolites in serum metabolomics. Table S1. Serum metabolomics in SA-AKI mice. Table S2. Quantitative serum metabolomics in patients with SA-AKI or sepsis. Figure S1. Distribution of 3-Hydroxybutyric acid, inosine and creatine in male and female patients. a-b) Scatter plots illustrating the correlation between inosine, myristic acid and BUN. c) Heatmap of 3-hydroxybutyric acid, creatine, and inosine between sepsis and SA-AKI after grouping by sex. d-f) Box plot of 3-hydroxybutyric acid, inosine and creatine between sepsis and SA-AKI in male group. g-i) Box plot of 3-hydroxybutyric acid, inosine and creatine between sepsis and SA-AKI in female group. Green represents sepsis and purple represents SA-AKI. *, ** and *** indicate p &lt; 0.05, p &lt; 0.01, and p &lt; 0.001, respectively. Renal proteomics data has been deposited in the ProteomeXchange Consortium via the PRIDE (Proteomics Identifications) partner repository with the dataset identifier PXD057050.