cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.16767058.v1
title: Dataset for the adjustment of a wave forecasting system for the deep waters of the South Atlantic Ocean and for the southern coast of Brazil (Output files in GrADS format).

description: This dataset corresponds to the outputs of the test domains generated by the simulations of the coupled GFS (Global Forecast System) and WAVEWATCH III models in the waters of the South Atlantic Ocean and in waters of the Brazilian Southeastern during the passage of a cold front and the presence of strong pressure gradient between a low-pressure system and a high-pressure system.<br> <b>ww3-2.grads</b>: Output file in GrADS format of wave data for WW3-2 domain. <b>ww3-2.ctl</b>: Descriptor of the wave GrADS output file for WW3-2 domain. <b>ww3-3.grads</b>: Output file in GrADS format of wave data for WW3-3 domain. <b>ww3-3.ctl</b>: Descriptor of the wave GrADS output file for WW3-3 domain.<br> The <b>ww3-2_tabs.tar.gz</b> and <b>ww3-3_tabs.tar.gz</b> files contain the output of the wave parameters at specific nodes from the output grids of the WAVEWATCH III simulations that were matched to the locations of the data logging devices in the study area.<br>
DOI: 10.25345/c50v9j
title: MassIVE MSV000088203 - Chasing the elusive viscacha in Precolumbian textiles
title: Proteomics identification of viscacha in Precolumbian textiles
DOI: 10.34944/dspace/6942
title: Immunotherapy for Treatment of Head and Neck Cancer Systematic Review Search Strategy

description: To identify studies to include or consider for this systematic review, the review team worked with a librarian to develop detailed search strategies for each database. The search was developed for PubMed (NLM) and was translated to Embase (Elsevier), Web of Science (Clarivate Analytics), CINAHL (EBSCOHost) and Cochrane Central (Wiley) using a combination of keywords and subject headings. A grey literature search included ClinicalTrials.gov and the WHO International Clinical Trials Registry Platform search portal. The search was restricted to human clinical trials since 2000. The final search was completed on July 20, 2021. The full search details are provided. PubMed (NLM) from inception to 7/20/21 (2,807 Results) Embase (Elsevier) from inception to 7/20/21 (6,240 Results) Web of Science (Clarivate Analytics) from inception to 7/20/21 (711 Results) CINAHL (EBSCOHost) from inception to 7/20/21 (462 Results) Cochrane Central (Wiley) from inception to 7/20/21 (651 Results) Clinicaltrials.gov from inception to 7/20/21 (126 Results) WHO ICTRP from inception to 7/20/21 (38 Results) The search resulted in 11,035 studies (164 from grey literature sources). 2,488 duplicate studies were found and omitted using Endnote 20 for the deduplication of records and 8.547 references were eligible to screen. Studies were screened by title and abstract by two blinded and independent reviewers. If a tiebreaker was needed, a third reviewer was called in. This process was repeated for full text article screening and article selection.
description: To identify studies to include or consider for this systematic review, the review team worked with a librarian to develop detailed search strategies for each database. The search was developed for PubMed (NLM) and was translated to Embase (Elsevier), Web of Science (Clarivate Analytics), CINAHL (EBSCOHost) and Cochrane Central (Wiley) using a combination of keywords and subject headings. A grey literature search included ClinicalTrials.gov and the WHO International Clinical Trials Registry Platform search portal. The search was restricted to human clinical trials since 2000. The final search was completed on July 20, 2021. The full search details are provided.
DOI: 10.5281/zenodo.5555478
title: Comprehensive observation data in Xiangyang for the period January 3 to January 9, 2019

description: This dataset presents the comprehensive observation data used in the analysis of a heavy pollution event in Xiangyang for the period January 3 to January 9, 2019. The data of meteorological elements, turbulence variables, temperature profile, PBL height, inversion-layer, relatively humidity profile, vertical distribution of PM<sub>2.5</sub>, wind speed and direction was available in each sheet of the Excel file.
DOI: 10.5281/zenodo.5555477
title: Comprehensive observation data in Xiangyang for the period January 3 to January 9, 2019

description: This dataset presents the comprehensive observation data used in the analysis of a heavy pollution event in Xiangyang for the period January 3 to January 9, 2019. The data of meteorological elements, turbulence variables, temperature profile, PBL height, inversion-layer, relatively humidity profile, vertical distribution of PM<sub>2.5</sub>, wind speed and direction was available in each sheet of the Excel file.
DOI: 10.6084/m9.figshare.16727506
title: Supplementary Materials: High-frequency turbulent fluctuations promote elastin fatigue degradation and aortic lumen dilatation in swine

description: Supplementary Materials: High-frequency turbulent fluctuations promote elastin fatigue degradation and aortic lumen dilatation in swine
DOI: 10.6084/m9.figshare.14910399.v1
title: Global_syndromes_induced_by_changes_in_solutes_of_the_world’s_large_rivers_600stations&info.zip

description: Nature Communications, 2021, DOI: <b>10.1038/s41467-021-26231-w: </b>Data on solutes concentrations (<i>C</i>), annual runoff (<i>Q</i>), co-varying trend in <i>Q</i> and <i>C</i>, and the corresponding solute-induced river syndrome at 600 stations in 149 large rivers
DOI: 10.6084/m9.figshare.16755214
title: Figure1_ephys_data.mat

description: Dataset for figure 1 ephys
DOI: 10.6084/m9.figshare.16755247.v1
title: Figure3_ephys_data.mat

description: ephys dataset for figure 3
DOI: 10.6084/m9.figshare.16755247
title: Figure3_ephys_data.mat

description: ephys dataset for figure 3
DOI: 10.6084/m9.figshare.16755286
title: SuppFigure1_ephys_data.mat

description: ephys dataset for supp figure 1
DOI: 10.6084/m9.figshare.16755310
title: SuppFigure3_ephys_data.mat

description: Dataset for ephys in supp figure 3
DOI: 10.6084/m9.figshare.16755310.v1
title: SuppFigure3_ephys_data.mat

description: Dataset for ephys in supp figure 3
DOI: 10.6084/m9.figshare.16755478.v1
title: SuppFigure5_ephys_data.mat

description: ephys dataset for supp figure 5
DOI: 10.6084/m9.figshare.16755478
title: SuppFigure5_ephys_data.mat

description: ephys dataset for supp figure 5
DOI: 10.6084/m9.figshare.16764577
title: Figure5_ephys_data.mat

description: ephys data shown in figure 5
DOI: 10.5281/zenodo.5555496
title: Data for "Clinical and virological impact of single and dual infections with influenza A (H1N1) and SARS-CoV-2 in adult inpatients"

description: The baseline enrolled 505 patients admitted to Guangzhou Eighth People's Hospital (Guangzhou, Guangdong) with a diagnosis of COVID-19 or H1N1. All the patients were tested by both viruses at admission. Demographic, clinical, treatment, and laboratory data were extracted from electronic medical records and compared among adults (≥18 years) hospitalized for H1N1 infection (<em>n</em> = 220), SARS-CoV-2 infection (<em>n</em> = 249) and co-infection with both viruses (<em>n</em> = 36). The prevalence rate of H1N1 co-infection was 12.6% (36/285) among patients hospitalized with COVID-19. Co-infection affected a predominantly older age group and was associated with poorer clinical outcome. We also described the viral load trajectory in patients with diverse types of infection. Lower initial Ct values (higher viral loads in nasopharyngeal swabs) of co-infected patients was found to be associated with a higher number of adverse events and clinical symptoms. Considering the COVID-19 pandemic and a simultaneous epidemic of seasonal influenza, the data in China may critically inform future therapeutic or prophylactic strategies, especially for other developing countries.
DOI: 10.5281/zenodo.5555495
title: Data for "Clinical and virological impact of single and dual infections with influenza A (H1N1) and SARS-CoV-2 in adult inpatients"

description: The baseline enrolled 505 patients admitted to Guangzhou Eighth People's Hospital (Guangzhou, Guangdong) with a diagnosis of COVID-19 or H1N1. All the patients were tested by both viruses at admission. Demographic, clinical, treatment, and laboratory data were extracted from electronic medical records and compared among adults (≥18 years) hospitalized for H1N1 infection (<em>n</em> = 220), SARS-CoV-2 infection (<em>n</em> = 249) and co-infection with both viruses (<em>n</em> = 36). The prevalence rate of H1N1 co-infection was 12.6% (36/285) among patients hospitalized with COVID-19. Co-infection affected a predominantly older age group and was associated with poorer clinical outcome. We also described the viral load trajectory in patients with diverse types of infection. Lower initial Ct values (higher viral loads in nasopharyngeal swabs) of co-infected patients was found to be associated with a higher number of adverse events and clinical symptoms. Considering the COVID-19 pandemic and a simultaneous epidemic of seasonal influenza, the data in China may critically inform future therapeutic or prophylactic strategies, especially for other developing countries.
DOI: 10.6084/m9.figshare.16766701
title: Figure7_ephys_data.mat

description: ephys dataset shown in figure 7
DOI: 10.6084/m9.figshare.16766701.v1
title: Figure7_ephys_data.mat

description: ephys dataset shown in figure 7
DOI: 10.6084/m9.figshare.16441944
title: Data Files

description: Data Files
DOI: 10.6084/m9.figshare.16441944.v3
title: Data Files

description: Data Files
DOI: 10.6084/m9.figshare.16441944.v2
title: Supplemental Files

description: Supplemental Files
DOI: 10.6084/m9.figshare.16767289.v1
title: Additional file 2 of Global trend of clinical biomarkers of health and disease during the period (1913–2021): systematic review and bibliometric analysis

description: Additional file 2. Enzyme AChE reference ranges from the literature.
DOI: 10.6084/m9.figshare.16767289
title: Additional file 2 of Global trend of clinical biomarkers of health and disease during the period (1913–2021): systematic review and bibliometric analysis

description: Additional file 2. Enzyme AChE reference ranges from the literature.
DOI: 10.6084/m9.figshare.16767298
title: Additional file 2 of TGF-β gene polimorphisms as risk factors for asthma control among clinic patients

description: Additional file 2.
DOI: 10.6084/m9.figshare.16767298.v1
title: Additional file 2 of TGF-β gene polimorphisms as risk factors for asthma control among clinic patients

description: Additional file 2.
DOI: 10.6084/m9.figshare.16767299
title: Additional file 10 of Pesticides and pesticide-related products in ambient air in Germany

description: Additional file 10: Pesticides and related substances detected in filter mats, including anthraquinone and PBO.
DOI: 10.6084/m9.figshare.16767299.v1
title: Additional file 10 of Pesticides and pesticide-related products in ambient air in Germany

description: Additional file 10: Pesticides and related substances detected in filter mats, including anthraquinone and PBO.
DOI: 10.6084/m9.figshare.16767314
title: Additional file 15 of Pesticides and pesticide-related products in ambient air in Germany

description: Additional file 15: Comparison between PUF, glass fibre filter, and filter mat data from Hallahus, Sweden 2017 and CSP 2019, Germany.
DOI: 10.6084/m9.figshare.16767314.v1
title: Additional file 15 of Pesticides and pesticide-related products in ambient air in Germany

description: Additional file 15: Comparison between PUF, glass fibre filter, and filter mat data from Hallahus, Sweden 2017 and CSP 2019, Germany.
DOI: 10.6084/m9.figshare.16767320
title: Additional file 17 of Pesticides and pesticide-related products in ambient air in Germany

description: Additional file 17: Data sheets for substances detected in Hallahus, Sweden, in 2017.
DOI: 10.6084/m9.figshare.16767320.v1
title: Additional file 17 of Pesticides and pesticide-related products in ambient air in Germany

description: Additional file 17: Data sheets for substances detected in Hallahus, Sweden, in 2017.
DOI: 10.6084/m9.figshare.16767350
title: Additional file 9 of Pesticides and pesticide-related products in ambient air in Germany

description: Additional file 9: Pesticides and related substances detected in passive air samplers, including DCBP-pp, HCB, PBO, and five PCBs
DOI: 10.6084/m9.figshare.16767350.v1
title: Additional file 9 of Pesticides and pesticide-related products in ambient air in Germany

description: Additional file 9: Pesticides and related substances detected in passive air samplers, including DCBP-pp, HCB, PBO, and five PCBs
DOI: 10.6084/m9.figshare.16767371
title: Additional file 1 of The precision of gingival recession measurements is increased by an automated curvature analysis method

description: Additional file 1. Digital models used in the present study in a STL file format.
DOI: 10.6084/m9.figshare.16767371.v1
title: Additional file 1 of The precision of gingival recession measurements is increased by an automated curvature analysis method

description: Additional file 1. Digital models used in the present study in a STL file format.
DOI: 10.6084/m9.figshare.16767377
title: Additional file 2 of The precision of gingival recession measurements is increased by an automated curvature analysis method

description: Additional file 2. Raw measurements and data used in the present study.
DOI: 10.6084/m9.figshare.16767377.v1
title: Additional file 2 of The precision of gingival recession measurements is increased by an automated curvature analysis method

description: Additional file 2. Raw measurements and data used in the present study.
DOI: 10.6084/m9.figshare.16767392.v1
title: Additional file 2 of Mapping and modeling the genomic basis of differential RNA isoform expression at single-cell resolution with LR-Split-seq

description: Additional file 2:. Tables S1-S14.
DOI: 10.6084/m9.figshare.16767392
title: Additional file 2 of Mapping and modeling the genomic basis of differential RNA isoform expression at single-cell resolution with LR-Split-seq

description: Additional file 2:. Tables S1-S14.
DOI: 10.6084/m9.figshare.16767403
title: Additional file 2 of The current situation of Angiostrongylus vasorum in Romania: a national questionnaire-based survey

description: Additional file 2.
DOI: 10.6084/m9.figshare.16767403.v1
title: Additional file 2 of The current situation of Angiostrongylus vasorum in Romania: a national questionnaire-based survey

description: Additional file 2.
DOI: 10.6084/m9.figshare.16767406.v1
title: Additional file 3 of The current situation of Angiostrongylus vasorum in Romania: a national questionnaire-based survey

description: Additional file 3.
DOI: 10.6084/m9.figshare.16767406
title: Additional file 3 of The current situation of Angiostrongylus vasorum in Romania: a national questionnaire-based survey

description: Additional file 3.
DOI: 10.6084/m9.figshare.16767427
title: Additional file 15 of Skin-penetrating nematodes exhibit life-stage-specific interactions with host-associated and environmental bacteria

description: Additional file 15: Dataset S1. Microbiome Sequencing Dataset. This dataset contains the BIOM file generated by QIIME2 summarizing the ASV distribution per sample for Experiment 1 (tab 1), the BIOM file generated by QIIME2 summarizing the ASV distribution per sample for Experiment 2 (tab 2), the relevant sample metadata (tab 3), the ASV taxonomies predicted by the QIIME2 pipeline (tab 4), and the taxonomy summaries at the family level that are depicted in Additional file 1: Fig. S1 (tab 5).
DOI: 10.6084/m9.figshare.16767427.v1
title: Additional file 15 of Skin-penetrating nematodes exhibit life-stage-specific interactions with host-associated and environmental bacteria

description: Additional file 15: Dataset S1. Microbiome Sequencing Dataset. This dataset contains the BIOM file generated by QIIME2 summarizing the ASV distribution per sample for Experiment 1 (tab 1), the BIOM file generated by QIIME2 summarizing the ASV distribution per sample for Experiment 2 (tab 2), the relevant sample metadata (tab 3), the ASV taxonomies predicted by the QIIME2 pipeline (tab 4), and the taxonomy summaries at the family level that are depicted in Additional file 1: Fig. S1 (tab 5).
DOI: 10.6084/m9.figshare.16767473
title: Additional file 1 of Translating the InChI: adapting neural machine translation to predict IUPAC names from a chemical identifier

description: Additional file 1. 100 molecule test set including InChI and IUPAC names from PubChem, ACD /I-Labs, ChemAxon, Mestrelab and the transformer presented in the current paper.
DOI: 10.6084/m9.figshare.16767473.v1
title: Additional file 1 of Translating the InChI: adapting neural machine translation to predict IUPAC names from a chemical identifier

description: Additional file 1. 100 molecule test set including InChI and IUPAC names from PubChem, ACD /I-Labs, ChemAxon, Mestrelab and the transformer presented in the current paper.
DOI: 10.6084/m9.figshare.16767476
title: Additional file 2 of Translating the InChI: adapting neural machine translation to predict IUPAC names from a chemical identifier

description: Additional file 2. Training script for OpenNMT-py.
DOI: 10.6084/m9.figshare.16767479
title: Additional file 3 of Translating the InChI: adapting neural machine translation to predict IUPAC names from a chemical identifier

description: Additional file 3. InChI character vocabulary needed for training the model with OpenNMT.
DOI: 10.6084/m9.figshare.16767479.v1
title: Additional file 3 of Translating the InChI: adapting neural machine translation to predict IUPAC names from a chemical identifier

description: Additional file 3. InChI character vocabulary needed for training the model with OpenNMT.
DOI: 10.6084/m9.figshare.16767482
title: Additional file 4 of Translating the InChI: adapting neural machine translation to predict IUPAC names from a chemical identifier

description: Additional file 4. IUPAC character vocabulary needed for training the model with OpenNMT.
DOI: 10.6084/m9.figshare.16767476.v1
title: Additional file 2 of Translating the InChI: adapting neural machine translation to predict IUPAC names from a chemical identifier

description: Additional file 2. Training script for OpenNMT-py.
DOI: 10.6084/m9.figshare.16767503.v1
title: Additional file 1 of Potentially inappropriate medication use among older adult patients on follow-up at the chronic care clinic of a specialized teaching hospital in Ethiopia. A cross-sectional study

description: Additional file 1: S1 file. PIM data dataset.xlsx.
DOI: 10.6084/m9.figshare.16767503
title: Additional file 1 of Potentially inappropriate medication use among older adult patients on follow-up at the chronic care clinic of a specialized teaching hospital in Ethiopia. A cross-sectional study

description: Additional file 1: S1 file. PIM data dataset.xlsx.
DOI: 10.6084/m9.figshare.16767506
title: Additional file 2 of Potentially inappropriate medication use among older adult patients on follow-up at the chronic care clinic of a specialized teaching hospital in Ethiopia. A cross-sectional study

description: Additional file 2: S2 file. PIM data dataset.xlsx.
DOI: 10.6084/m9.figshare.16767506.v1
title: Additional file 2 of Potentially inappropriate medication use among older adult patients on follow-up at the chronic care clinic of a specialized teaching hospital in Ethiopia. A cross-sectional study

description: Additional file 2: S2 file. PIM data dataset.xlsx.
DOI: 10.6084/m9.figshare.16767530
title: Additional file 1 of Chemical composition and the insecticidal activity of Aeollanthus pubescens leaf essential oil against Anopheles gambiae sensu stricto

description: Additional file 1: Table S1. Larval bioassay data. Table S2. Adult mosquito knockdown time following exposure to the net pieces impregnated with the essential oil. Table S3. Adult mosquito daily mortality data following exposure to the net pieces impregnated with the essential oil.
DOI: 10.6084/m9.figshare.16767508
title: Additional file 1 of Network propagation-based prioritization of long tail genes in 17 cancer types

description: Additional file 1: Table S1. Cohort Size and number of PPI network nodes after expression-based filtering
DOI: 10.6084/m9.figshare.16767511
title: Additional file 2 of Network propagation-based prioritization of long tail genes in 17 cancer types

description: Additional file 2: Table S2. Statistical validation results of post-propagation rankings
DOI: 10.6084/m9.figshare.16767508.v1
title: Additional file 1 of Network propagation-based prioritization of long tail genes in 17 cancer types

description: Additional file 1: Table S1. Cohort Size and number of PPI network nodes after expression-based filtering
DOI: 10.6084/m9.figshare.16767511.v1
title: Additional file 2 of Network propagation-based prioritization of long tail genes in 17 cancer types

description: Additional file 2: Table S2. Statistical validation results of post-propagation rankings
DOI: 10.6084/m9.figshare.16767514
title: Additional file 3 of Network propagation-based prioritization of long tail genes in 17 cancer types

description: Additional file 3: Table S3. Number of UMGs across cancer types and PPI networks
DOI: 10.6084/m9.figshare.16767530.v1
title: Additional file 1 of Chemical composition and the insecticidal activity of Aeollanthus pubescens leaf essential oil against Anopheles gambiae sensu stricto

description: Additional file 1: Table S1. Larval bioassay data. Table S2. Adult mosquito knockdown time following exposure to the net pieces impregnated with the essential oil. Table S3. Adult mosquito daily mortality data following exposure to the net pieces impregnated with the essential oil.
DOI: 10.6084/m9.figshare.16767514.v1
title: Additional file 3 of Network propagation-based prioritization of long tail genes in 17 cancer types

description: Additional file 3: Table S3. Number of UMGs across cancer types and PPI networks
DOI: 10.6084/m9.figshare.16767517
title: Additional file 4 of Network propagation-based prioritization of long tail genes in 17 cancer types

description: Additional file 4: Table S4. Statistical validation results of UMGs' negative impact on in vitro cancer cell line survival
DOI: 10.6084/m9.figshare.16767517.v1
title: Additional file 4 of Network propagation-based prioritization of long tail genes in 17 cancer types

description: Additional file 4: Table S4. Statistical validation results of UMGs' negative impact on in vitro cancer cell line survival
DOI: 10.6084/m9.figshare.16767523.v1
title: Additional file 6 of Network propagation-based prioritization of long tail genes in 17 cancer types

description: Additional file 6: Table S5. Functional validation and text mining results associated with UMGs across cancer types
DOI: 10.6084/m9.figshare.16767523
title: Additional file 6 of Network propagation-based prioritization of long tail genes in 17 cancer types

description: Additional file 6: Table S5. Functional validation and text mining results associated with UMGs across cancer types
DOI: 10.6084/m9.figshare.16767526
title: Additional file 7 of Network propagation-based prioritization of long tail genes in 17 cancer types

description: Additional file 7: Table S6. Results of in vitro experiments
DOI: 10.6084/m9.figshare.16767526.v1
title: Additional file 7 of Network propagation-based prioritization of long tail genes in 17 cancer types

description: Additional file 7: Table S6. Results of in vitro experiments
DOI: 10.6084/m9.figshare.16767529
title: Additional file 8 of Network propagation-based prioritization of long tail genes in 17 cancer types

description: Additional file 8: Table S7. References of functionally validated genes in Table S5
DOI: 10.6084/m9.figshare.16767529.v1
title: Additional file 8 of Network propagation-based prioritization of long tail genes in 17 cancer types

description: Additional file 8: Table S7. References of functionally validated genes in Table S5