cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28252215
title: Additional file 3 of Patient and public involvement in the co-design and assessment of unobtrusive sensing technologies for care at home: a user-centric design approach

description: Supplementary Material 3
DOI: 10.6084/m9.figshare.28252215.v1
title: Additional file 3 of Patient and public involvement in the co-design and assessment of unobtrusive sensing technologies for care at home: a user-centric design approach

description: Supplementary Material 3
DOI: 10.6084/m9.figshare.28252236
title: Additional file 9 of Patient and public involvement in the co-design and assessment of unobtrusive sensing technologies for care at home: a user-centric design approach

description: Supplementary Material 9
DOI: 10.6084/m9.figshare.28252236.v1
title: Additional file 9 of Patient and public involvement in the co-design and assessment of unobtrusive sensing technologies for care at home: a user-centric design approach

description: Supplementary Material 9
DOI: 10.6084/m9.figshare.28252254
title: Additional file 2 of Diagnostic delay in cerebral creatine deficiency disorders: lessons learned from a cross-sectional single center study, and guanidinoacetate and creatine measurements in Switzerland between 2015 and 2023

description: Supplementary Material 2
DOI: 10.6084/m9.figshare.28252254.v1
title: Additional file 2 of Diagnostic delay in cerebral creatine deficiency disorders: lessons learned from a cross-sectional single center study, and guanidinoacetate and creatine measurements in Switzerland between 2015 and 2023

description: Supplementary Material 2
DOI: 10.6084/m9.figshare.28252293
title: Additional file 1 of Unveiling cell-type-specific microRNA networks through alternative polyadenylation in glioblastoma

description: Additional file 1: Table S1. Marker genes used for cell annotation. Table S2. Adjusted p-values for microRNA biding site loss for each microRNA family. Table S3. SFFL networks. Table S4. MFFL networks. Table S5. Differential expression results for pseudotime trajectory from neoplastic cells to OPCs. Table S6. GO enrichment results (BP). Table S7. GO enrichment results (MF).
DOI: 10.6084/m9.figshare.28252293.v1
title: Additional file 1 of Unveiling cell-type-specific microRNA networks through alternative polyadenylation in glioblastoma

description: Additional file 1: Table S1. Marker genes used for cell annotation. Table S2. Adjusted p-values for microRNA biding site loss for each microRNA family. Table S3. SFFL networks. Table S4. MFFL networks. Table S5. Differential expression results for pseudotime trajectory from neoplastic cells to OPCs. Table S6. GO enrichment results (BP). Table S7. GO enrichment results (MF).
DOI: 10.6084/m9.figshare.28252296
title: Additional file 2 of Unveiling cell-type-specific microRNA networks through alternative polyadenylation in glioblastoma

description: Additional file 2: Differential alternative polyadenylation results computed with SCAPE for the cluster of neoplastic cells (Table S1), OPCs (Table S2), oligodendrocytes (Table S3), macrophages (Table S4) and endothelial cells (Table S5). Table S6. Differential APA events for genes regulating 3’ end processing of mRNAs.
DOI: 10.6084/m9.figshare.28252296.v1
title: Additional file 2 of Unveiling cell-type-specific microRNA networks through alternative polyadenylation in glioblastoma

description: Additional file 2: Differential alternative polyadenylation results computed with SCAPE for the cluster of neoplastic cells (Table S1), OPCs (Table S2), oligodendrocytes (Table S3), macrophages (Table S4) and endothelial cells (Table S5). Table S6. Differential APA events for genes regulating 3’ end processing of mRNAs.
DOI: 10.6084/m9.figshare.28252307
title: Additional file 1 of A graph neural network approach for hierarchical mapping of breast cancer protein communities

description: Additional file 1.
DOI: 10.6084/m9.figshare.28252307.v1
title: Additional file 1 of A graph neural network approach for hierarchical mapping of breast cancer protein communities

description: Additional file 1.
DOI: 10.6084/m9.figshare.28252313
title: Additional file 3 of A graph neural network approach for hierarchical mapping of breast cancer protein communities

description: Additional file 3.
DOI: 10.6084/m9.figshare.28252313.v1
title: Additional file 3 of A graph neural network approach for hierarchical mapping of breast cancer protein communities

description: Additional file 3.
DOI: 10.6084/m9.figshare.28252316.v1
title: Additional file 4 of A graph neural network approach for hierarchical mapping of breast cancer protein communities

description: Additional file 4.
DOI: 10.6084/m9.figshare.28252316
title: Additional file 4 of A graph neural network approach for hierarchical mapping of breast cancer protein communities

description: Additional file 4.
DOI: 10.6084/m9.figshare.28252319
title: Additional file 5 of A graph neural network approach for hierarchical mapping of breast cancer protein communities

description: Additional file 5.
DOI: 10.6084/m9.figshare.28252319.v1
title: Additional file 5 of A graph neural network approach for hierarchical mapping of breast cancer protein communities

description: Additional file 5.
DOI: 10.6084/m9.figshare.28252322
title: Additional file 6 of A graph neural network approach for hierarchical mapping of breast cancer protein communities

description: Additional file 6.
DOI: 10.6084/m9.figshare.28252322.v1
title: Additional file 6 of A graph neural network approach for hierarchical mapping of breast cancer protein communities

description: Additional file 6.
DOI: 10.6084/m9.figshare.28252325
title: Additional file 7 of A graph neural network approach for hierarchical mapping of breast cancer protein communities

description: Additional file 7.
DOI: 10.6084/m9.figshare.28252325.v1
title: Additional file 7 of A graph neural network approach for hierarchical mapping of breast cancer protein communities

description: Additional file 7.
DOI: 10.6084/m9.figshare.28252326
title: Additional file 5 of ClearFinder: a Python GUI for annotating cells in cleared mouse brain

description: Supplementary material 5 Embedded ontolo gy files for #1, #2, #3 analyzed with ClearMap and CellFinder
DOI: 10.6084/m9.figshare.28252329
title: Additional file 6 of ClearFinder: a Python GUI for annotating cells in cleared mouse brain

description: Supplementary material 6 Parameters of cell detection used for analysis of data
DOI: 10.6084/m9.figshare.28252326.v1
title: Additional file 5 of ClearFinder: a Python GUI for annotating cells in cleared mouse brain

description: Supplementary material 5 Embedded ontolo gy files for #1, #2, #3 analyzed with ClearMap and CellFinder
DOI: 10.6084/m9.figshare.28252329.v1
title: Additional file 6 of ClearFinder: a Python GUI for annotating cells in cleared mouse brain

description: Supplementary material 6 Parameters of cell detection used for analysis of data
DOI: 10.6084/m9.figshare.28252347
title: Additional file 2 of Identification of robust RT-qPCR reference genes for studying changes in gene expression in response to hypoxia in breast cancer cell lines

description: Supplementary Material 2
DOI: 10.6084/m9.figshare.28252347.v1
title: Additional file 2 of Identification of robust RT-qPCR reference genes for studying changes in gene expression in response to hypoxia in breast cancer cell lines

description: Supplementary Material 2
DOI: 10.6084/m9.figshare.28252350
title: Additional file 3 of Identification of robust RT-qPCR reference genes for studying changes in gene expression in response to hypoxia in breast cancer cell lines

description: Supplementary Material 3
DOI: 10.6084/m9.figshare.28252350.v1
title: Additional file 3 of Identification of robust RT-qPCR reference genes for studying changes in gene expression in response to hypoxia in breast cancer cell lines

description: Supplementary Material 3
DOI: 10.6084/m9.figshare.28252353
title: Additional file 4 of Identification of robust RT-qPCR reference genes for studying changes in gene expression in response to hypoxia in breast cancer cell lines

description: Supplementary Material 4
DOI: 10.6084/m9.figshare.28252353.v1
title: Additional file 4 of Identification of robust RT-qPCR reference genes for studying changes in gene expression in response to hypoxia in breast cancer cell lines

description: Supplementary Material 4
DOI: 10.6084/m9.figshare.28252356
title: Additional file 5 of Identification of robust RT-qPCR reference genes for studying changes in gene expression in response to hypoxia in breast cancer cell lines

description: Supplementary Material 5
DOI: 10.6084/m9.figshare.28252356.v1
title: Additional file 5 of Identification of robust RT-qPCR reference genes for studying changes in gene expression in response to hypoxia in breast cancer cell lines

description: Supplementary Material 5
DOI: 10.6084/m9.figshare.28252361
title: Additional file 2 of Computational analysis of congenital heart disease associated SNPs: unveiling their impact on the gene regulatory system

description: Supplementary Material 2.
DOI: 10.6084/m9.figshare.28252361.v1
title: Additional file 2 of Computational analysis of congenital heart disease associated SNPs: unveiling their impact on the gene regulatory system

description: Supplementary Material 2.
DOI: 10.6084/m9.figshare.28252364
title: Additional file 3 of Computational analysis of congenital heart disease associated SNPs: unveiling their impact on the gene regulatory system

description: Supplementary Material 3.
DOI: 10.6084/m9.figshare.28252364.v1
title: Additional file 3 of Computational analysis of congenital heart disease associated SNPs: unveiling their impact on the gene regulatory system

description: Supplementary Material 3.
DOI: 10.6084/m9.figshare.28252367
title: Additional file 4 of Computational analysis of congenital heart disease associated SNPs: unveiling their impact on the gene regulatory system

description: Supplementary Material 4.
DOI: 10.6084/m9.figshare.28252367.v1
title: Additional file 4 of Computational analysis of congenital heart disease associated SNPs: unveiling their impact on the gene regulatory system

description: Supplementary Material 4.
DOI: 10.6084/m9.figshare.28252370.v1
title: Additional file 5 of Computational analysis of congenital heart disease associated SNPs: unveiling their impact on the gene regulatory system

description: Supplementary Material 5.
DOI: 10.6084/m9.figshare.28252370
title: Additional file 5 of Computational analysis of congenital heart disease associated SNPs: unveiling their impact on the gene regulatory system

description: Supplementary Material 5.
DOI: 10.6084/m9.figshare.28252373
title: Additional file 6 of Computational analysis of congenital heart disease associated SNPs: unveiling their impact on the gene regulatory system

description: Supplementary Material 6.
DOI: 10.6084/m9.figshare.28252373.v1
title: Additional file 6 of Computational analysis of congenital heart disease associated SNPs: unveiling their impact on the gene regulatory system

description: Supplementary Material 6.
DOI: 10.6084/m9.figshare.28252376
title: Additional file 7 of Computational analysis of congenital heart disease associated SNPs: unveiling their impact on the gene regulatory system

description: Supplementary Material 7.
DOI: 10.6084/m9.figshare.28252376.v1
title: Additional file 7 of Computational analysis of congenital heart disease associated SNPs: unveiling their impact on the gene regulatory system

description: Supplementary Material 7.
DOI: 10.6084/m9.figshare.28252379
title: Additional file 8 of Computational analysis of congenital heart disease associated SNPs: unveiling their impact on the gene regulatory system

description: Supplementary Material 8.
DOI: 10.6084/m9.figshare.28252379.v1
title: Additional file 8 of Computational analysis of congenital heart disease associated SNPs: unveiling their impact on the gene regulatory system

description: Supplementary Material 8.
DOI: 10.6084/m9.figshare.28252382
title: Additional file 9 of Computational analysis of congenital heart disease associated SNPs: unveiling their impact on the gene regulatory system

description: Supplementary Material 9.
DOI: 10.6084/m9.figshare.28252382.v1
title: Additional file 9 of Computational analysis of congenital heart disease associated SNPs: unveiling their impact on the gene regulatory system

description: Supplementary Material 9.
DOI: 10.6084/m9.figshare.28252385
title: Additional file 10 of Computational analysis of congenital heart disease associated SNPs: unveiling their impact on the gene regulatory system

description: Supplementary Material 10.
DOI: 10.6084/m9.figshare.28252385.v1
title: Additional file 10 of Computational analysis of congenital heart disease associated SNPs: unveiling their impact on the gene regulatory system

description: Supplementary Material 10.
DOI: 10.6084/m9.figshare.28252388
title: Additional file 11 of Computational analysis of congenital heart disease associated SNPs: unveiling their impact on the gene regulatory system

description: Supplementary Material 11.
DOI: 10.6084/m9.figshare.28252388.v1
title: Additional file 11 of Computational analysis of congenital heart disease associated SNPs: unveiling their impact on the gene regulatory system

description: Supplementary Material 11.
DOI: 10.6084/m9.figshare.28252391
title: Additional file 12 of Computational analysis of congenital heart disease associated SNPs: unveiling their impact on the gene regulatory system

description: Supplementary Material 12.
DOI: 10.6084/m9.figshare.28252391.v1
title: Additional file 12 of Computational analysis of congenital heart disease associated SNPs: unveiling their impact on the gene regulatory system

description: Supplementary Material 12.
DOI: 10.6084/m9.figshare.28252394
title: Additional file 13 of Computational analysis of congenital heart disease associated SNPs: unveiling their impact on the gene regulatory system

description: Supplementary Material 13.
DOI: 10.6084/m9.figshare.28252394.v1
title: Additional file 13 of Computational analysis of congenital heart disease associated SNPs: unveiling their impact on the gene regulatory system

description: Supplementary Material 13.
DOI: 10.5281/zenodo.10035626
title: Detectability of very low frequency earthquakes in Nankai subduction zone

description: How to cite this data



data DOI of this repository

method and conceptionShunsuke Takemura, Satoru Baba, Suguru Yabe, Yusuke Yamashita, Katsuhiko Shiomi, Takanori Matsuzawa, Detectability analysis of very low frequency earthquakes: methods and application in Nankai using F-net and DONET broad-band seismometers, Geophysical Journal International, Volume 237, Issue 1, April 2024, Pages 49–63, https://doi.org/10.1093/gji/ggae033


source_meca.dat

Assumed focal mechanisms. Longitude, Latitude, depth, strike, dip, and rake are listed. Focal mechanisms at each source grid were assumed to be low-angle thrust faulting mechanisms from the plate geometry of Koketsu et al. (2012) and the convergence direction of NUVEL-1A (DeMets et al. 2010).

VLFEdetectability_Fnet.dat

The detectable limits of very low frequency earthquakes (VLFEs) in Nankai using F-net only. Longitude, Latitude, depth, and detectable limit (the lowest moment rate of detectable VLFEs) at each source grid are listed. The file with "Z" is the detectability of VLFEs using only vertical components. 

VLFEdetectability_FnetDONET.dat

The detectable limits of VLFEs in Nankai using F-net and DONET. Longitude, Latitude, depth, and detectable limit (the lowest moment rate of detectable VLFEs) at each source grid are listed. The file with "Z" is the detectability of VLFEs using only vertical components.
DOI: 10.5281/zenodo.7272617
title: Detectability of very low frequency earthquakes in Nankai subduction zone

description: How to cite this data



data DOI of this repository

method and conceptionShunsuke Takemura, Satoru Baba, Suguru Yabe, Yusuke Yamashita, Katsuhiko Shiomi, Takanori Matsuzawa, Detectability analysis of very low frequency earthquakes: methods and application in Nankai using F-net and DONET broad-band seismometers, Geophysical Journal International, Volume 237, Issue 1, April 2024, Pages 49–63, https://doi.org/10.1093/gji/ggae033


source_meca.dat

Assumed focal mechanisms. Longitude, Latitude, depth, strike, dip, and rake are listed. Focal mechanisms at each source grid were assumed to be low-angle thrust faulting mechanisms from the plate geometry of Koketsu et al. (2012) and the convergence direction of NUVEL-1A (DeMets et al. 2010).

VLFEdetectability_Fnet.dat

The detectable limits of very low frequency earthquakes (VLFEs) in Nankai using F-net only. Longitude, Latitude, depth, and detectable limit (the lowest moment rate of detectable VLFEs) at each source grid are listed. The file with "Z" is the detectability of VLFEs using only vertical components. 

VLFEdetectability_FnetDONET.dat

The detectable limits of VLFEs in Nankai using F-net and DONET. Longitude, Latitude, depth, and detectable limit (the lowest moment rate of detectable VLFEs) at each source grid are listed. The file with "Z" is the detectability of VLFEs using only vertical components.
DOI: 10.5281/zenodo.7272618
title: Detectability of very low frequency earthquakes in Nankai subduction zone

description: <strong>How to cite this data</strong> data DOI of this repository Preprint https://doi.org/10.21203/rs.3.rs-2351814/v1 <strong>source_meca.dat</strong> Assumed focal mechanisms. Longitude, Latitude, depth, strike, dip, and rake are listed. Focal mechanisms at each source grid were assumed to be low-angle thrust faulting mechanisms from the plate geometry of Koketsu et al. (2012) and the convergence direction of NUVEL-1A (DeMets et al. 2010). <strong>VLFEdetectability_Fnet.dat</strong> The detectable limits of very low frequency earthquakes (VLFEs) in Nankai using F-net only. Longitude, Latitude, depth, and detectable limit (the lowest moment rate of detectable VLFEs) at each source grid are listed. <strong>VLFEdetectability_FnetDONET.dat</strong> The detectable limits of VLFEs in Nankai using F-net and DONET. Longitude, Latitude, depth, and detectable limit (the lowest moment rate of detectable VLFEs) at each source grid are listed.
DOI: 10.15468/dl.yerjr2
title: Occurrence Download

description: A dataset containing 400 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pinus herrerae Martínez"
 ]
}

 The dataset includes 400 records from 50 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000794-250121130708018/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14715811
title: Global Management Consulting Market 2025 To 2034

description: Management Consulting Market Size, Trends and Insights By Type (HR Consulting, Strategy Consulting, Operations Consulting, Other Types), By Organization Size (Large Enterprises, Small & Medium-Sized Enterprises), By End-user Industry (IT and Telecommunication, Healthcare, Hotel and Hospitality, Media and Entertainment, Real Estate, Other End-user Industries (BFSI, Government, Energy)), and By Region - Global Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2025–2034.

Reports Description

Global Management Consulting Market size was valued at USD 5,10,645.87 Million in 2025 and is expected to reach at USD 8,97,442.21 Million by 2034 at a CAGR of 6.56% during the forecast period 2025 – 2034.

Management Consulting services are advisory & operational services offered to improve an organization’s business strategy. These services are used in industries such as banking, healthcare, and technology to tackle complex problems such as strategic planning, operational efficiency, and human resource management.

Management Consulting helps companies to develop and implement competitive strategies and gain an edge over competitors. Operations consulting focused on process and supply chain optimization and IT consulting that aligns technology solutions with business goals.

For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=61144
DOI: 10.5281/zenodo.14715812
title: Global Management Consulting Market 2025 To 2034

description: Management Consulting Market Size, Trends and Insights By Type (HR Consulting, Strategy Consulting, Operations Consulting, Other Types), By Organization Size (Large Enterprises, Small & Medium-Sized Enterprises), By End-user Industry (IT and Telecommunication, Healthcare, Hotel and Hospitality, Media and Entertainment, Real Estate, Other End-user Industries (BFSI, Government, Energy)), and By Region - Global Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2025–2034.

Reports Description

Global Management Consulting Market size was valued at USD 5,10,645.87 Million in 2025 and is expected to reach at USD 8,97,442.21 Million by 2034 at a CAGR of 6.56% during the forecast period 2025 – 2034.

Management Consulting services are advisory & operational services offered to improve an organization’s business strategy. These services are used in industries such as banking, healthcare, and technology to tackle complex problems such as strategic planning, operational efficiency, and human resource management.

Management Consulting helps companies to develop and implement competitive strategies and gain an edge over competitors. Operations consulting focused on process and supply chain optimization and IT consulting that aligns technology solutions with business goals.

For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=61144
DOI: 10.14457/tu.the.2024.44
title: Flexural strength and dynamic mechanical properties of polypropylene/natural rubber blends and the effect of autoclave sterilization: a preliminary study for customized rubber dam clamp

description: Introduction: The aim of this study was to identify the optimum ratios ofpolypropylene and natural rubber (PP/NR) for a customized rubber dam clamp indentistry. This study was focused on the flexural strength and dynamic mechanicalproperties of PP/NR in various ratios. Moreover, the impact of autoclave sterilizationwas also considered.Materials and methods: Six proportions of PP/NR blends (100/0, 90/10, 80/20, 70/30,60/40 and 50/50) were prepared and assessed for flexural strength using a three-pointbending test and dynamic mechanical properties were measured through an automaticdynamic mechanical analyzer. After these tests, the PP/NR with 100/0, 90/10, 80/20ratios were selected and underwent autoclave sterilization for 1, 5 and 10 cycles. Then,the flexural strength testing was repeated and investigated.Results: The flexural strength of PP/NR blended materials was statistically significantdifference in every group of this study. The autoclave sterilization test revealed that theflexural strength of PP/NR 90/10 and 80/20 ratios were significantly increased aftersterilization for 1, 5 and 10 cycles. In addition, the PP/NR 90/10 ratio was alsocomparable to the 100/0 ratio (pure PP). The dynamic mechanical properties testshowed all PP/NR blends remained stable during autoclave sterilization condition (121°C, 30 minutes). Furthermore, the PP/NR 90/10 and 80/20 ratios displayed dynamicmechanical properties similar to pure PP at room temperature (25 °C), mimickingclinical conditions.Conclusion: The lower NR content in PP/NR blends results in significantly higherflexural strength, and autoclave sterilization has an effect on this property. The dynamicmechanical properties of all PP/NR blends were also influenced by NR addition, withlower NR content suggesting favorable elastic characteristics. The findings suggest thatthe PP/NR blend with 90/10 ratio might be considered as an alternative material fordeveloping rubber dam clamp