cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5506936
title: Japanese Visual Media Graph - Visual Novel Database Ontology

description: This group of files represents the RDF ontology used in the Japanese Visual Media Graph for the Visual Novel Database. Included in the upload are an explanatory PDF, the ontology in the Turtle serialization, and an HTML visualization of the ontology.
description: This work has been funded by Deutsche Forschungsgemeinschaft (German Research Foundation) with a grant from the funding program "e-Research Technologies".
DOI: 10.5281/zenodo.5036039
title: Japanese Visual Media Graph - Visual Novel Database Ontology

description: This group of files represents the RDF ontology used in the Japanese Visual Media Graph for the Visual Novel Database. Included in the upload are an explanatory PDF, the ontology in the Turtle serialization, and an HTML visualization of the ontology.
description: This work has been funded by Deutsche Forschungsgemeinschaft (German Research Foundation) with a grant from the funding program "e-Research Technologies".
DOI: 10.25790/bml0cm.88
title: In vivo symmetric multi-contrast MRI brain templates and atlas for spontaneously hypertensive rats
DOI: 10.5281/zenodo.5497508
title: Dataset related to article: "Functional recovery in hip fracture patients: the role of pharmacotherapy"

description: We provide the raw data used for the following article: Gialanella B, Santoro R, Prometti P, Giordano A, Monguzzi V, Comini L, Olivares A, Grioni G.<br> <em>Functional recovery in hip fracture patients: the role of pharmacotherapy</em>.<br> "Aging Clin Exp Res." 2020 Jan;32(1):49-57.<br> doi: 10.1007/s40520-019-01175-w. Epub 2019 Mar 21. PMID: 30900212. <strong>Background and aim: </strong>The aim of this prospective observational cohort study was to verify the relationship between number of drugs used and functional outcome in hip fracture patients undergoing rehabilitation. <strong>Methods: </strong>This study was conducted on 139 patients with hip fracture who underwent a rehabilitation program. Efficiency rate in the Functional Independence Measure (FIM) and Berg Balance Scale (BBS), and length of stay (LOS) were the outcome measures. <strong>Results: </strong>At the end of rehabilitation, 66.1% of patients showed an increase in number of drugs used, while 33.9% used the same or lower number of drugs than at admission. At the end of rehabilitation patients with increased pharmacotherapy took a higher total number of drug classes (p = 0.001), had longer LOS (p = 0.009) and lower Berg efficiency (p = 0.048) than patients with the same or lower pharmacotherapy. The number of drugs used at discharge was an independent determinant of LOS (beta = 0.19, p = 0.022) and FIM efficiency (beta = - 0.20, p = 0.025). Age was a determinant of LOS (beta = 0.17, p = 0.044) and BBS efficiency (beta = - 0.23, p = 0.009), while CIRS severity was a determinant of BBS efficiency only (beta = - 0.22, p = 0.016). <strong>Discussion: </strong>Findings of study indicate that in hip fracture patients, the number of drugs prescribed at discharge is an important indicator of LOS and rehabilitation efficiency. <strong>Conclusions: </strong>These findings can help the physician to better plan the rehabilitation of hip fracture patients who require polypharmacy.
DOI: 10.5281/zenodo.5497507
title: Dataset related to article: "Functional recovery in hip fracture patients: the role of pharmacotherapy"

description: We provide the raw data used for the following article: Gialanella B, Santoro R, Prometti P, Giordano A, Monguzzi V, Comini L, Olivares A, Grioni G.<br> <em>Functional recovery in hip fracture patients: the role of pharmacotherapy</em>.<br> "Aging Clin Exp Res." 2020 Jan;32(1):49-57.<br> doi: 10.1007/s40520-019-01175-w. Epub 2019 Mar 21. PMID: 30900212. <strong>Background and aim: </strong>The aim of this prospective observational cohort study was to verify the relationship between number of drugs used and functional outcome in hip fracture patients undergoing rehabilitation. <strong>Methods: </strong>This study was conducted on 139 patients with hip fracture who underwent a rehabilitation program. Efficiency rate in the Functional Independence Measure (FIM) and Berg Balance Scale (BBS), and length of stay (LOS) were the outcome measures. <strong>Results: </strong>At the end of rehabilitation, 66.1% of patients showed an increase in number of drugs used, while 33.9% used the same or lower number of drugs than at admission. At the end of rehabilitation patients with increased pharmacotherapy took a higher total number of drug classes (p = 0.001), had longer LOS (p = 0.009) and lower Berg efficiency (p = 0.048) than patients with the same or lower pharmacotherapy. The number of drugs used at discharge was an independent determinant of LOS (beta = 0.19, p = 0.022) and FIM efficiency (beta = - 0.20, p = 0.025). Age was a determinant of LOS (beta = 0.17, p = 0.044) and BBS efficiency (beta = - 0.23, p = 0.009), while CIRS severity was a determinant of BBS efficiency only (beta = - 0.22, p = 0.016). <strong>Discussion: </strong>Findings of study indicate that in hip fracture patients, the number of drugs prescribed at discharge is an important indicator of LOS and rehabilitation efficiency. <strong>Conclusions: </strong>These findings can help the physician to better plan the rehabilitation of hip fracture patients who require polypharmacy.
DOI: 10.5880/sfb806.71
title: Dataset of Upper Palaeolithic site probability in Lower Austria
DOI: 10.6084/m9.figshare.16616632.v1
title: Eutrophication leads to the formation of a sulfide-rich deep-water layer in Lake Sevan, Armenia

description: Lake Sevan is a meso-eutrophic water body, which was severely impacted by anthropogenic level decrease, pollution and eutrophication during the last century. Starting in the 1970s, these processes resulted in the formation of an oxygen-depleted hypolimnion during summer–autumn stratification of the lake. In this work, we demonstrate for the first time that eutrophication of the lake leads not only to the full depletion of oxygen and nitrate in the hypolimnion but as well to the presence of sulfate-reducing microorganisms and toxic hydrogen sulfide. Concentrations of hydrogen sulfide in the hypolimnion of Major and Minor Sevan in October were as high as 9 and 39 μM, respectively. In October 2019, 66 % of lake's bottom was covered by sulfidic waters, while the fraction of sulfidic water volume reached 19 %. Values of δ<sup>34</sup>S for hypolimnetic sulfide are lower by only 7–12 ‰ compared to epilimnetic sulfate, while δ<sup>33</sup>S values of sulfide are similar to the δ<sup>33</sup>S values of sulfate. These isotopic fingerprints are not consistent with microbial sulfate reduction as the sole source of hydrogen sulfide in the hypolimnion. We attribute the formation of a sulfidic deep-water layer to a combination of microbial sulfate reduction in the water column and diffusion of hydrogen sulfide from the sediments.
DOI: 10.6084/m9.figshare.16616632
title: Eutrophication leads to the formation of a sulfide-rich deep-water layer in Lake Sevan, Armenia

description: Lake Sevan is a meso-eutrophic water body, which was severely impacted by anthropogenic level decrease, pollution and eutrophication during the last century. Starting in the 1970s, these processes resulted in the formation of an oxygen-depleted hypolimnion during summer–autumn stratification of the lake. In this work, we demonstrate for the first time that eutrophication of the lake leads not only to the full depletion of oxygen and nitrate in the hypolimnion but as well to the presence of sulfate-reducing microorganisms and toxic hydrogen sulfide. Concentrations of hydrogen sulfide in the hypolimnion of Major and Minor Sevan in October were as high as 9 and 39 μM, respectively. In October 2019, 66 % of lake's bottom was covered by sulfidic waters, while the fraction of sulfidic water volume reached 19 %. Values of δ<sup>34</sup>S for hypolimnetic sulfide are lower by only 7–12 ‰ compared to epilimnetic sulfate, while δ<sup>33</sup>S values of sulfide are similar to the δ<sup>33</sup>S values of sulfate. These isotopic fingerprints are not consistent with microbial sulfate reduction as the sole source of hydrogen sulfide in the hypolimnion. We attribute the formation of a sulfidic deep-water layer to a combination of microbial sulfate reduction in the water column and diffusion of hydrogen sulfide from the sediments.
DOI: 10.6084/m9.figshare.13280372
title: Additional files referenced in paper related to the 'Metagenome-assembled genomes of phytoplankton microbiomes from Arctic and Atlantic Oceans repository'

description: Additional for article 'Metagenome-assembled genomes of phytoplaknton microbiomes from Arctic and Atlantic Oceans'1. Summary of data and binning2. Average nucleotide identity results & plots for MAGs and references from select groups3. Average Amino acid Identity results & plots for MAGs and references4. Tables summarising function of predicted genes, prior to binning. Counts of Pfam domains and GO terms are provided, along with a PCA based on Pfams.<br>5. Tables of taxonomic classification of predicted genes from IMG pipeline<br>6. Coverage of MAGs across samples7. Phylogenomic trees constructed from MAGs and references8. Cross domain associations between eukaryotic and prokaryotic MAGs for polar regions<br>9. Coverage table for each MAG in each sample<br>10. Plots of intersections between enriched terms in control sets of MAGs, and the associated pair investigated in the paper<br><br>
DOI: 10.6084/m9.figshare.13280372.v2
title: Additional files referenced in paper related to the 'Metagenome-assembled genomes of phytoplankton microbiomes from Arctic and Atlantic Oceans repository'

description: Additional for article 'Metagenome-assembled genomes of phytoplaknton microbiomes from Arctic and Atlantic Oceans'1. Summary of data and binning2. Average nucleotide identity results & plots for MAGs and references from select groups3. Average Amino acid Identity results & plots for MAGs and references4. Tables summarising function of predicted genes, prior to binning. Counts of Pfam domains and GO terms are provided, along with a PCA based on Pfams.<br>5. Tables of taxonomic classification of predicted genes from IMG pipeline<br>6. Coverage of MAGs across samples7. Phylogenomic trees constructed from MAGs and references8. Cross domain associations between eukaryotic and prokaryotic MAGs for polar regions<br>9. Coverage table for each MAG in each sample<br>10. Plots of intersections between enriched terms in control sets of MAGs, and the associated pair investigated in the paper<br><br>
DOI: 10.5281/zenodo.4899539
title: Plasmonic Spin-Hall Effect of propagating Surface Plasmon Polaritons in Ni80Fe20 microstructures

description: Dataset providing the data used for the Paper "Plasmonic Spin-Hall Effect of propagating Surface Plasmon Polaritons in Ni<sub>80</sub>Fe<sub>20</sub> microstructures" already published as preprint in arxiv and to be published in NJP. The metadata.txt file serves as a README to guide through the dataset. The corresponding article can be found here: https://iopscience.iop.org/article/10.1088/1367-2630/ac1c83 The preprint is still available here: https://arxiv.org/abs/2105.12501
description: This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) within the Collaborative Research Center/Transregio 227 "Ultrafast SpinDynamics" (projects A06 and B01).
DOI: 10.5281/zenodo.4899538
title: Plasmonic Spin-Hall Effect of propagating Surface Plasmon Polaritons in Ni80Fe20 microstructures

description: Dataset providing the data used for the Paper "Plasmonic Spin-Hall Effect of propagating Surface Plasmon Polaritons in Ni<sub>80</sub>Fe<sub>20</sub> microstructures" already published as preprint in arxiv and to be published in NJP. The metadata.txt file serves as a README to guide through the dataset. The corresponding article can be found here: https://iopscience.iop.org/article/10.1088/1367-2630/ac1c83 The preprint is still available here: https://arxiv.org/abs/2105.12501
description: This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) within the Collaborative Research Center/Transregio 227 "Ultrafast SpinDynamics" (projects A06 and B01).
DOI: 10.5522/04/16592669
title: Cyclohexadiene and Butatriene MCTDH/vMCG/TSH

description: The datasets contain the results from quantum dynamics simulations on cyclohexadiene and butatriene using both analytical potentials and direct dynamics. They were produced using the Quantics package and different methods : MCTDH, vMCG and TSH.<br>
DOI: 10.5522/04/16592669.v1
title: Cyclohexadiene and Butatriene MCTDH/vMCG/TSH

description: The datasets contain the results from quantum dynamics simulations on cyclohexadiene and butatriene using both analytical potentials and direct dynamics. They were produced using the Quantics package and different methods : MCTDH, vMCG and TSH.<br>
DOI: 10.5281/zenodo.5493599
title: Dataset related to article: "Machine learning to predict mortality after rehabilitation among patients with severe stroke"

description: We provide the raw data used for the following article: Scrutinio D, Ricciardi C, Donisi L, Losavio E, Battista P, Guida P, Cesarelli M, Pagano G, D'Addio G.<br> <em>Machine learning to predict mortality after rehabilitation among patients with severe stroke.</em> "Sci Rep." 2020 Nov 18;10(1):20127.<br> doi: 10.1038/s41598-020-77243-3. PMID: 33208913; PMCID: PMC7674405. <strong>Abstract:</strong> Stroke is among the leading causes of death and disability worldwide. Approximately 20–25% of stroke survivors present severe disability, which is associated with increased mortality risk. Prognostication is inherent in the process of clinical decision-making. Machine learning (ML) methods have gained increasing popularity in the setting of biomedical research. The aim of this study was twofold: assessing the performance of ML tree-based algorithms for predicting three-year mortality model in 1207 stroke patients with severe disability who completed rehabilitation and comparing the performance of ML algorithms to that of a standard logistic regression. The logistic regression model achieved an area under the Receiver Operating Characteristics curve (AUC) of 0.745 and was well calibrated. At the optimal risk threshold, the model had an accuracy of 75.7%, a positive predictive value (PPV) of 33.9%, and a negative predictive value (NPV) of 91.0%. The ML algorithm outperformed the logistic regression model through the implementation of synthetic minority oversampling technique and the Random Forests, achieving an AUC of 0.928 and an accuracy of 86.3%. The PPV was 84.6% and the NPV 87.5%. This study introduced a step forward in the creation of standardisable tools for predicting health outcomes in individuals affected by stroke.
DOI: 10.6084/m9.figshare.16616467.v1
title: PRET1608S01F02_megahit_assembly

description: PRET1608S01F02_megahit_assembly
DOI: 10.5281/zenodo.5493598
title: Dataset related to article: "Machine learning to predict mortality after rehabilitation among patients with severe stroke"

description: We provide the raw data used for the following article: Scrutinio D, Ricciardi C, Donisi L, Losavio E, Battista P, Guida P, Cesarelli M, Pagano G, D'Addio G.<br> <em>Machine learning to predict mortality after rehabilitation among patients with severe stroke.</em> "Sci Rep." 2020 Nov 18;10(1):20127.<br> doi: 10.1038/s41598-020-77243-3. PMID: 33208913; PMCID: PMC7674405. <strong>Abstract:</strong> Stroke is among the leading causes of death and disability worldwide. Approximately 20–25% of stroke survivors present severe disability, which is associated with increased mortality risk. Prognostication is inherent in the process of clinical decision-making. Machine learning (ML) methods have gained increasing popularity in the setting of biomedical research. The aim of this study was twofold: assessing the performance of ML tree-based algorithms for predicting three-year mortality model in 1207 stroke patients with severe disability who completed rehabilitation and comparing the performance of ML algorithms to that of a standard logistic regression. The logistic regression model achieved an area under the Receiver Operating Characteristics curve (AUC) of 0.745 and was well calibrated. At the optimal risk threshold, the model had an accuracy of 75.7%, a positive predictive value (PPV) of 33.9%, and a negative predictive value (NPV) of 91.0%. The ML algorithm outperformed the logistic regression model through the implementation of synthetic minority oversampling technique and the Random Forests, achieving an AUC of 0.928 and an accuracy of 86.3%. The PPV was 84.6% and the NPV 87.5%. This study introduced a step forward in the creation of standardisable tools for predicting health outcomes in individuals affected by stroke.
DOI: 10.5281/zenodo.5493735
title: Dataset related to article: "Association Between Malnutrition and Outcomes in Patients With Severe Ischemic Stroke Undergoing Rehabilitation"

description: We provide the raw data used for the following article: Scrutinio D, Lanzillo B, Guida P, Passantino A, Spaccavento S, Battista P.<br> <em>Association Between Malnutrition and Outcomes in Patients With Severe Ischemic Stroke Undergoing Rehabilitation</em><br> "Arch Phys Med Rehabil." 2020 May;101(5):852-860.<br> doi: 10.1016/j.apmr.2019.11.012. Epub 2019 Dec 28. PMID: 31891712. <strong>Abstract</strong><br> <strong>Objective: </strong>To investigate the incremental prognostic significance of malnutrition in patients with severe poststroke disability.<br> <strong>Design: </strong>Retrospective cohort study. The patients were recruited from 3 specialized inpatient rehabilitation facilities. Nutritional status was assessed using the Prognostic Nutritional Index (PNI), which is calculated from serum albumin and total lymphocyte count. Scores &gt;38 points reflect normal nutrition status, scores of 35-38 indicate moderate malnutrition, and scores &lt;35 indicate severe malnutrition. The association of PNI categories with outcomes was assessed using multivariable regression analyses.<br> <strong>Setting: </strong>Inpatient rehabilitation facility.<br> <strong>Participants:</strong> Patients (NZ668) with ischemic stroke admitted to inpatient rehabilitation within 90 days from stroke occurrence and classified as Case-Mix Groups 0108, 0109, and 0110 of the current Medicare case-mix classification system.<br> <strong>Interventions: </strong>Not applicable.<br> <strong>Main Outcome Measures:</strong> Three outcomes were examined: (1) the combined outcome of transfer to acute care and death within 90 days from admission to rehabilitation; (2) 2-year mortality; and (3) FIM motor effectiveness, calculated as (FIM motor change/maximum FIM motoradmission FIM motor score)100.<br> <strong>Results: </strong>Overall, the median time to rehabilitation admission was 18 days (range, 12-26 days). The prevalence of moderate and severe malnutrition was 12.7% and 11.5%, respectively. Ninety-one patients (13.6%) experienced the combined outcome. After adjusting for independent predictors including sex, atrial fibrillation, dysphagia, FIM cognitive score, and hemoglobin levels, neither moderate (PZ.280) nor severe malnutrition (PZ.482) were associated with the combined outcome. Similar results were observed when looking at 2-year mortality. Overall, FIM motor effectiveness was 30%24%. After adjusting for independent predictors, severe malnutrition (b coefficient 0.4580.216; PZ.034) was associated with FIM motor effectiveness.<br> <strong>Conclusions:</strong> Approximately 1 in every 9 patients presented severe malnutrition. On top of the independent predictors, severe malnutrition did not provide additional prognostic information concerning risk of the combined outcome or 2-year mortality. Conversely, severe malnutrition was associated with poorer functional outcome as expressed by FIM motor effectiveness.
DOI: 10.5281/zenodo.5493734
title: Dataset related to article: "Association Between Malnutrition and Outcomes in Patients With Severe Ischemic Stroke Undergoing Rehabilitation"

description: We provide the raw data used for the following article: Scrutinio D, Lanzillo B, Guida P, Passantino A, Spaccavento S, Battista P.<br> <em>Association Between Malnutrition and Outcomes in Patients With Severe Ischemic Stroke Undergoing Rehabilitation</em><br> "Arch Phys Med Rehabil." 2020 May;101(5):852-860.<br> doi: 10.1016/j.apmr.2019.11.012. Epub 2019 Dec 28. PMID: 31891712. <strong>Abstract</strong><br> <strong>Objective: </strong>To investigate the incremental prognostic significance of malnutrition in patients with severe poststroke disability.<br> <strong>Design: </strong>Retrospective cohort study. The patients were recruited from 3 specialized inpatient rehabilitation facilities. Nutritional status was assessed using the Prognostic Nutritional Index (PNI), which is calculated from serum albumin and total lymphocyte count. Scores &gt;38 points reflect normal nutrition status, scores of 35-38 indicate moderate malnutrition, and scores &lt;35 indicate severe malnutrition. The association of PNI categories with outcomes was assessed using multivariable regression analyses.<br> <strong>Setting: </strong>Inpatient rehabilitation facility.<br> <strong>Participants:</strong> Patients (NZ668) with ischemic stroke admitted to inpatient rehabilitation within 90 days from stroke occurrence and classified as Case-Mix Groups 0108, 0109, and 0110 of the current Medicare case-mix classification system.<br> <strong>Interventions: </strong>Not applicable.<br> <strong>Main Outcome Measures:</strong> Three outcomes were examined: (1) the combined outcome of transfer to acute care and death within 90 days from admission to rehabilitation; (2) 2-year mortality; and (3) FIM motor effectiveness, calculated as (FIM motor change/maximum FIM motoradmission FIM motor score)100.<br> <strong>Results: </strong>Overall, the median time to rehabilitation admission was 18 days (range, 12-26 days). The prevalence of moderate and severe malnutrition was 12.7% and 11.5%, respectively. Ninety-one patients (13.6%) experienced the combined outcome. After adjusting for independent predictors including sex, atrial fibrillation, dysphagia, FIM cognitive score, and hemoglobin levels, neither moderate (PZ.280) nor severe malnutrition (PZ.482) were associated with the combined outcome. Similar results were observed when looking at 2-year mortality. Overall, FIM motor effectiveness was 30%24%. After adjusting for independent predictors, severe malnutrition (b coefficient 0.4580.216; PZ.034) was associated with FIM motor effectiveness.<br> <strong>Conclusions:</strong> Approximately 1 in every 9 patients presented severe malnutrition. On top of the independent predictors, severe malnutrition did not provide additional prognostic information concerning risk of the combined outcome or 2-year mortality. Conversely, severe malnutrition was associated with poorer functional outcome as expressed by FIM motor effectiveness.
DOI: 10.6084/m9.figshare.16616653
title: PRET1608S01F00_idba-ud_final.fna.gz

description: PRET1608S01F00_idba-ud_final.fna.gz
DOI: 10.6084/m9.figshare.16616653.v1
title: PRET1608S01F00_idba-ud_final.fna.gz

description: PRET1608S01F00_idba-ud_final.fna.gz
DOI: 10.6084/m9.figshare.16616650
title: Kuipers_et_al_2021_One_Earth

description: This repository contains the supplemental data and code of the publication of Kuipers et al. (2021) in One Earth. It contains Data S1-2 and Code S1-3 an ad README.md file describing the key elements of these data and code objects.