cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25829/idiv.1875-8-3136
title: Occurrence probability estimates of plant taxa in Germany

description: This data set contains occurrence probability (OP) estimates (incl. standard deviation) of 2136 plant species. The data comprise OP estimates for three time-steps between 1960 and 2017. OPs were estimated using the FRESCALO algorithm (Hill, 2012). Plant taxa in this data set have been harmonised to species level (or aggregate, where necessary) according to an accepted reference list (GermanSL, ver. 1.4; Jansen & Dengler, 2008). OPs are estimated on a grid cell basis (MTB_Q: MessTischBlattQuadrant) and are a continuous numerical variable between 0 and 1.
DOI: 10.6084/m9.figshare.12782576.v1
title: Susceptibility to gingipains and transcriptomic response to P. gingivalis highlights the ribosome, hypothalamus, and cholinergic neurons

description: Porphyromonas gingivalis, a keystone species in the development of periodontal disease, is a suspected cause of Alzheimer’s disease. This bacterium is reliant on gingipain proteases, which cleave host proteins after arginine and lysine residues. To characterize gingipain susceptibility, we performed enrichment analyses of arginine and lysine proportion proteome-wide. Proteins in the SRP-dependent cotranslational protein targeting to membrane pathway were enriched for these residues and previously associated with periodontal and Alzheimer’s disease. These ribosomal genes are up-regulated in prefrontal cortex samples with detected P. gingivalis sequences. Other differentially expressed genes have been previously associated with dementia (ITM2B, MAPT, ZNF267, and DHX37). For an anatomical perspective, we characterized the expression of the P. gingivalis associated genes in the mouse and human brain. This analysis highlighted the hypothalamus, cholinergic neurons, and the basal forebrain. Our results suggest markers of neural P. gingivalis infection and link the gingipain and cholinergic hypotheses of Alzheimer’s disease.
DOI: 10.21227/f7j4-e691
title: 6.66 kVA Solar

description: This dataset is in support of my original Research Paper " 6.66kVA Solar " submitted to "IEEE Transactions on Energy Conversion". (https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=60).The results of this experiment are useful as the proposed model is different from the existing approach and the author hadThis experiment is different from the exisiting approaches in the following ways(a)(b)(c)(d)(e)Abstract of the above submitted paper
DOI: 10.21227/1pm5-9g36
title: wind -Part 1

description: This dataset is in support of my original Research Paper " Grid-Connected Wind " submitted to "IEEE Transactions on Energy Conversion". (https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=60).This experiment is different from the exisiting approaches in the following ways(a)(b)(c)(d)(e)Abstract of the above submitted paper
DOI: 10.21227/83hm-g211
title: wind 3

description: This dataset is in support of my original Research Paper " Grid-Connected Wind " submitted to "IEEE Transactions on Energy Conversion". (https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=60)usaAbstract of the above submitted paper
DOI: 10.21227/h0kb-y832
title: 6.66 kVA Solar Inverter

description: This dataset is in support of my original Research Paper " 6.66 kVA Solar Inverter " submitted to "IEEE Transactions on Energy Conversion".(https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=60)This experiment is different from the exisiting approaches in the following ways(a)(b)(c)(d)(e)Abstract of the above submitted paper
DOI: 10.21227/w5zp-3423
title: wind-Part 2

description: This dataset is in support of my original Research Paper " Grid-Connected Wind " submitted to "IEEE Transactions on Energy Conversion". (https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=60)This experiment is different from the exisiting approaches in the following ways(a)(b)(c)(d)(e)Abstract of the above submitted paper
DOI: 10.25773/7gbc-6739
title: The importance of organic content to fractal floc properties in estuarine surface waters, insights from video, LISST, and pump sampling: Supporting data
DOI: 10.5281/zenodo.4270253
title: Trajectories of RNA adsorption on a flat surface

description: SPQR trajectories of a RNA fragment (40 nts). More to come...
description: Any further information please contact horacio.v.g@gmail.com
DOI: 10.5281/zenodo.4269740
title: Dataset related to article "Computed tomography (CT)-derived radiomic features differentiate prevascular mediastinum masses as thymic neoplasms versus lymphomas"

description: This record contains raw data related to article "Computed tomography (CT)-derived radiomic features differentiate prevascular mediastinum masses as thymic neoplasms versus lymphomas" <strong>Objectives: </strong>We aimed to assess the ability of radiomics, applied to not-enhanced computed tomography (CT), to differentiate mediastinal masses as thymic neoplasms vs lymphomas. <strong>Methods: </strong>The present study was an observational retrospective trial. Inclusion criteria were pathology-proven thymic neoplasia or lymphoma with mediastinal localization, availability of CT. Exclusion criteria were age &lt; 16 years and mediastinal lymphoma lesion &lt; 4 cm. We selected 108 patients (M:F = 47:61, median age 48 years, range 17-79) and divided them into a training and a validation group. Radiomic features were used as predictors in linear discriminant analysis. We built different radiomic models considering segmentation software and resampling setting. Clinical variables were used as predictors to build a clinical model. Scoring metrics included sensitivity, specificity, accuracy and area under the curve (AUC). Wilcoxon paired test was used to compare the AUCs. <strong>Results: </strong>Fifty-five patients were affected by thymic neoplasia and 53 by lymphoma. In the validation analysis, the best radiomics model sensitivity, specificity, accuracy and AUC resulted 76.2 ± 7.0, 77.8 ± 5.5, 76.9 ± 6.0 and 0.84 ± 0.06, respectively. In the validation analysis of the clinical model, the same metrics resulted 95.2 ± 7.0, 88.9 ± 8.9, 92.3 ± 8.5 and 0.98 ± 0.07, respectively. The AUCs of the best radiomic and the clinical model not differed. <strong>Conclusions: </strong>We developed and validated a CT-based radiomic model able to differentiate mediastinal masses on non-contrast-enhanced images, as thymic neoplasms or lymphoma. The proposed method was not affected by image postprocessing. Therefore, the present image-derived method has the potential to noninvasively support diagnosis in patients with prevascular mediastinal masses with major impact on management of asymptomatic cases.
DOI: 10.5281/zenodo.4269739
title: Dataset related to article "Computed tomography (CT)-derived radiomic features differentiate prevascular mediastinum masses as thymic neoplasms versus lymphomas"

description: This record contains raw data related to article "Computed tomography (CT)-derived radiomic features differentiate prevascular mediastinum masses as thymic neoplasms versus lymphomas" <strong>Objectives: </strong>We aimed to assess the ability of radiomics, applied to not-enhanced computed tomography (CT), to differentiate mediastinal masses as thymic neoplasms vs lymphomas. <strong>Methods: </strong>The present study was an observational retrospective trial. Inclusion criteria were pathology-proven thymic neoplasia or lymphoma with mediastinal localization, availability of CT. Exclusion criteria were age &lt; 16 years and mediastinal lymphoma lesion &lt; 4 cm. We selected 108 patients (M:F = 47:61, median age 48 years, range 17-79) and divided them into a training and a validation group. Radiomic features were used as predictors in linear discriminant analysis. We built different radiomic models considering segmentation software and resampling setting. Clinical variables were used as predictors to build a clinical model. Scoring metrics included sensitivity, specificity, accuracy and area under the curve (AUC). Wilcoxon paired test was used to compare the AUCs. <strong>Results: </strong>Fifty-five patients were affected by thymic neoplasia and 53 by lymphoma. In the validation analysis, the best radiomics model sensitivity, specificity, accuracy and AUC resulted 76.2 ± 7.0, 77.8 ± 5.5, 76.9 ± 6.0 and 0.84 ± 0.06, respectively. In the validation analysis of the clinical model, the same metrics resulted 95.2 ± 7.0, 88.9 ± 8.9, 92.3 ± 8.5 and 0.98 ± 0.07, respectively. The AUCs of the best radiomic and the clinical model not differed. <strong>Conclusions: </strong>We developed and validated a CT-based radiomic model able to differentiate mediastinal masses on non-contrast-enhanced images, as thymic neoplasms or lymphoma. The proposed method was not affected by image postprocessing. Therefore, the present image-derived method has the potential to noninvasively support diagnosis in patients with prevascular mediastinal masses with major impact on management of asymptomatic cases.
DOI: 10.5281/zenodo.4263370
title: AREPO for DIRAC benchmarking

description: These input files are needed for some of the AREPO benchmarks for DIRAC benchmarking in the UK.
DOI: 10.5281/zenodo.4263369
title: AREPO for DIRAC benchmarking

description: These input files are needed for some of the AREPO benchmarks for DIRAC benchmarking in the UK.
DOI: 10.5281/zenodo.4270235
title: Supplementary dataset to "Draft genome assembly of the biofuel grass crop Miscanthus sacchariflorus"

description: <em>Miscanthus sacchariflorus</em> (Maxim.) Hack. is a C4 perennial rhizomatous biofuel grass crop. <em>M. sacchariflorus</em> is among the most widely distributed species within the genus, particularly at cold northern latitudes, and one of the progenitor species of the main biomass commercial crop <em>M. × giganteus</em>. We generated a 2.54 Gbps whole-genome assembly of the diploid <em>M. sacchariflorus</em> “Robustus 297” genotype, which represented ~59% of the expected genome size. We later anchored this assembly in the chromosomal-scale <em>M. sinensis</em> genome to improve its contiguity. We annotated 86,767 and 69,049 protein-coding genes in the unanchored and anchored, respectively. We estimated our assemblies include ~85% of the <em>M. sacchariflorus</em> genes based on homology, core markers and RNA-seq alignments stats. Raw data and further metadata are available under Bioproject PRJNA435476. Msac_v2.fasta: Unanchored whole-genome assembly (WGA) of M. sacchariflorus in FASTA format. Msac_v3.fasta: The previous WGA re-scaffolded with the M. sinensis public reference. Msac_v3.agp: Chromosomal position in the M. sinensis reference of the previous scaffolds in Msac_v3.fasta Msac_v2.gff3: Gene annotation of the unanchored WGA in GFF3 format, which contains 86,767 coding genes Msac_v3.gff3: Gene annotation of the anchored WGA in GFF3 format, which contains 69,049 coding genes Msac_v2.func_annot.tsv: Text table containing the functional annotation of the 86,767 coding genes in Msac_v2.gff3 Msac_v2.repeats_annotation.gff3: Repeats annotation (Repeatmasker) of the unanchored reference. Msac_v2.masked.fasta.gz: Repeats-masked version (Repeatmasker) of Msac_v2.fasta all.satsuma.blocks_Msac_v2-vs-Msin.gz: Every alignment from scaffolds in Msac_v3.fasta into M. sinensis reference Msac_v2.orthology_Msin.tsv: Ortologous between Msac_v2 and M. sinensis Msac_v3-vs-Msin.tsv: Ortologous between Msac_v3 and M. sinensis
DOI: 10.5281/zenodo.4270234
title: Supplementary dataset to "Draft genome assembly of the biofuel grass crop Miscanthus sacchariflorus"

description: <em>Miscanthus sacchariflorus</em> (Maxim.) Hack. is a C4 perennial rhizomatous biofuel grass crop. <em>M. sacchariflorus</em> is among the most widely distributed species within the genus, particularly at cold northern latitudes, and one of the progenitor species of the main biomass commercial crop <em>M. × giganteus</em>. We generated a 2.54 Gbps whole-genome assembly of the diploid <em>M. sacchariflorus</em> “Robustus 297” genotype, which represented ~59% of the expected genome size. We later anchored this assembly in the chromosomal-scale <em>M. sinensis</em> genome to improve its contiguity. We annotated 86,767 and 69,049 protein-coding genes in the unanchored and anchored, respectively. We estimated our assemblies include ~85% of the <em>M. sacchariflorus</em> genes based on homology, core markers and RNA-seq alignments stats. Raw data and further metadata are available under Bioproject PRJNA435476. Msac_v2.fasta: Unanchored whole-genome assembly (WGA) of M. sacchariflorus in FASTA format. Msac_v3.fasta: The previous WGA re-scaffolded with the M. sinensis public reference. Msac_v3.agp: Chromosomal position in the M. sinensis reference of the previous scaffolds in Msac_v3.fasta Msac_v2.gff3: Gene annotation of the unanchored WGA in GFF3 format, which contains 86,767 coding genes Msac_v3.gff3: Gene annotation of the anchored WGA in GFF3 format, which contains 69,049 coding genes Msac_v2.func_annot.tsv: Text table containing the functional annotation of the 86,767 coding genes in Msac_v2.gff3 Msac_v2.repeats_annotation.gff3: Repeats annotation (Repeatmasker) of the unanchored reference. Msac_v2.masked.fasta.gz: Repeats-masked version (Repeatmasker) of Msac_v2.fasta all.satsuma.blocks_Msac_v2-vs-Msin.gz: Every alignment from scaffolds in Msac_v3.fasta into M. sinensis reference Msac_v2.orthology_Msin.tsv: Ortologous between Msac_v2 and M. sinensis Msac_v3-vs-Msin.tsv: Ortologous between Msac_v3 and M. sinensis
DOI: 10.6084/m9.figshare.13229141
title: MM Data Base.pdf

description: miRNA-196a2
DOI: 10.6084/m9.figshare.13229141.v1
title: MM Data Base.pdf

description: miRNA-196a2
DOI: 10.5281/zenodo.3940692
title: Wikipedia Citations: A comprehensive dataset of citations with identifiers extracted from English Wikipedia

description: The dataset is composed of <strong>3 parts</strong>: 1. The dataset of 29.276 million citations from 35 different citation templates, out of which 3.92 million citations already contained identifiers, and approximately 260,752 citations were equipped with identifiers from Crossref. This is under the filename: <strong>citations_from_wikipedia.zip</strong> 2. A minimal dataset containing a few of the columns from the citations from Wikipedia dataset. These columns are as follows: 'type_of_citation', 'page_title', 'Title', 'ID_list', metadata_file', 'updated_identifier'. This is under the filename: <strong>minimal_dataset.zip. </strong>The 'metadata_file' column can be used to refer to the metadata collected from CrossRef and page title, the title of the citation can be used to refer to the 'citations_from_wikipedia.zip' dataset and get more information for a particular citation (such as author, periodical, chapter). 3. Citations classified as a journal and their corresponding metadata/identifier extracted from Crossref to make the dataset more complete. This is under the filename: <strong>lookup_data.zip</strong>. This zip file contains a CSV file: <strong>lookup_table.gzip</strong> (a parquet file containing all citations classified as a journal) and a folder:<strong> metadata_extracted </strong>(a folder containing the metadata from CrossRef for all the citations mentioned in the table) <br> The data was parsed from the Wikipedia XML content dumps published in May 2020. The source code to extract and getting used to the pipeline can be found here: <strong>https://github.com/Harshdeep1996/cite-classifications-wiki</strong> The taxonomy of the dataset in (1) can be found here: <strong>https://github.com/Harshdeep1996/cite-classifications-wiki/wiki/Taxonomy-of-the-parent-dataset</strong>
DOI: 10.5281/zenodo.3606914
title: Wikipedia Citations: A comprehensive dataset of citations with identifiers extracted from English Wikipedia

description: The dataset is composed of <strong>3 parts</strong>: 1. The dataset of 29.276 million citations from 35 different citation templates, out of which 3.92 million citations already contained identifiers, and approximately 260,752 citations were equipped with identifiers from Crossref. This is under the filename: <strong>citations_from_wikipedia.zip</strong> 2. A minimal dataset containing a few of the columns from the citations from Wikipedia dataset. These columns are as follows: 'type_of_citation', 'page_title', 'Title', 'ID_list', metadata_file', 'updated_identifier'. This is under the filename: <strong>minimal_dataset.zip. </strong>The 'metadata_file' column can be used to refer to the metadata collected from CrossRef and page title, the title of the citation can be used to refer to the 'citations_from_wikipedia.zip' dataset and get more information for a particular citation (such as author, periodical, chapter). 3. Citations classified as a journal and their corresponding metadata/identifier extracted from Crossref to make the dataset more complete. This is under the filename: <strong>lookup_data.zip</strong>. This zip file contains a CSV file: <strong>lookup_table.gzip</strong> (a parquet file containing all citations classified as a journal) and a folder:<strong> metadata_extracted </strong>(a folder containing the metadata from CrossRef for all the citations mentioned in the table) <br> The data was parsed from the Wikipedia XML content dumps published in May 2020. The source code to extract and getting used to the pipeline can be found here: <strong>https://github.com/Harshdeep1996/cite-classifications-wiki</strong> The taxonomy of the dataset in (1) can be found here: <strong>https://github.com/Harshdeep1996/cite-classifications-wiki/wiki/Taxonomy-of-the-parent-dataset</strong>
DOI: 10.5281/zenodo.4268359
title: Figures, plotting scripts, and data for "Resolving non-uniform temperature distributions with single-beam absorption spectroscopy. Part II: Implementation from broadband spectra""

description: This dataset contains the necessary* materials to recreate the figures in "Resolving nonuniform temperature distributions with single-beam absorption spectroscopy: Part II: Implementation from broadband spectra." It also contains the time-averaged dual-frequency-comb transmission spectra in the tube furnace experiment of Section 3, and the fits to those data using the NTfit software package. * The Python scripts also require the NTfit package developed for this paper, available at https://gitlab.com/nam5312/ntfit The code included in this dataset is released under the BSD 3-Clause License. The figures are shared under the Creative Commons Attribution 4.0 International License (CC BY 4.0, https://creativecommons.org/licenses/by/4.0/).
description: This work was funded by the NSF under grant CBET 1454496 with additional funding under AFOSR grant FA9550-17-1-0224.
DOI: 10.6084/m9.figshare.13227449
title: S1 Table.xlsx

description: First flowering date of Miscanthus and sugarcane in 2014-2015 greenhouse experiment.
DOI: 10.6084/m9.figshare.13227449.v1
title: S1 Table.xlsx

description: First flowering date of Miscanthus and sugarcane in 2014-2015 greenhouse experiment.
DOI: 10.5517/ccdc.csd.cc25hsbt
title: CCDC 2011166: Experimental Crystal Structure Determination

description: Related Article: Kaan Menekse, Pengzhong Chen, Bernhard Mahlmeister, Olga Anhalt, Astrid Kudzus, Matthias Stolte, Frank Würthner|2020|J.Mater.Chem.C|8|15303|doi:10.1039/D0TC02988B
DOI: 10.5517/ccdc.csd.cc25hscv
title: CCDC 2011167: Experimental Crystal Structure Determination

description: Related Article: Kaan Menekse, Pengzhong Chen, Bernhard Mahlmeister, Olga Anhalt, Astrid Kudzus, Matthias Stolte, Frank Würthner|2020|J.Mater.Chem.C|8|15303|doi:10.1039/D0TC02988B
DOI: 10.5517/ccdc.csd.cc25hsdw
title: CCDC 2011168: Experimental Crystal Structure Determination

description: Related Article: Kaan Menekse, Pengzhong Chen, Bernhard Mahlmeister, Olga Anhalt, Astrid Kudzus, Matthias Stolte, Frank Würthner|2020|J.Mater.Chem.C|8|15303|doi:10.1039/D0TC02988B
DOI: 10.5517/ccdc.csd.cc25hs9s
title: CCDC 2011165: Experimental Crystal Structure Determination

description: Related Article: Kaan Menekse, Pengzhong Chen, Bernhard Mahlmeister, Olga Anhalt, Astrid Kudzus, Matthias Stolte, Frank Würthner|2020|J.Mater.Chem.C|8|15303|doi:10.1039/D0TC02988B
DOI: 10.21227/xwcj-9322
title: Method B

description: Method based on a modified CNN LSTM
DOI: 10.21227/9e2k-h051
title: General Bi-tri Logic SPWM for Current Source Converter with Optimized Zero-state Replacement

description: This paper proposed an optimized zero-state replacement (ZSR) method for general Bi-tri logic SPWM (BTSPWM) to deal with the common-mode voltage (CMV) issue. The BTSPWM can take advantage of the well-developed modulation strategies adopted in voltage source converters (VSCs) and transfer them into a current source converter (CSC) system through logic translation, which shares more general features compared with other modulations developed for CSC. However, all-off gating signals can be generated through the logic translation, which should be replaced with the redundant zero-states to avoid open-circuit. Most of the ZSR strategies are focused on switching time minimization while not considering the CMV excited by the replaced zero-states. To address the CMV issue, an optimized ZSR method for general BTSPWM was proposed to suppress the CMV while not influence the pulse width modulation (PWM) features. Not only the CMV peak value but also the third-order component can be effectively reduced. Moreover, the proposed method can be easily applied to a parallel CSC system due to the inherent modularity. The validity of the proposed methods was verified on both single and parallel CSC systems.
DOI: 10.21227/v6f9-et22
title: General Bi-tri Logic SPWM for Current Source Converter with Optimized Zero-state Replacement

description: This paper proposed an optimized zero-state replacement (ZSR) method for general Bi-tri logic SPWM (BTSPWM) to deal with the common-mode voltage (CMV) issue. The BTSPWM can take advantage of the well-developed modulation strategies adopted in voltage source converters (VSCs) and transfer them into a current source converter (CSC) system through logic translation, which shares more general features compared with other modulations developed for CSC. However, all-off gating signals can be generated through the logic translation, which should be replaced with the redundant zero-states to avoid open-circuit. Most of the ZSR strategies are focused on switching time minimization while not considering the CMV excited by the replaced zero-states. To address the CMV issue, an optimized ZSR method for general BTSPWM was proposed to suppress the CMV while not influence the pulse width modulation (PWM) features. Not only the CMV peak value but also the third-order component can be effectively reduced. Moreover, the proposed method can be easily applied to a parallel CSC system due to the inherent modularity. The validity of the proposed methods was verified on both single and parallel CSC systems.
DOI: 10.25345/c5849s
title: MassIVE MSV000086446 - KiRNet: Integrated, kinase-centered network model for investigating kinase-driven phenotypes
title: The ever-increasing size and scale of biological information have created a need for systems-level tools that synthesize large quantities of dispersed and distinct data and inform decision- and hypothesis-making processes in basic and translational sciences. To address this need, we have created KiRNet, an kinase-centered method designed to integrate results of functional screens with interactome or other protein-protein interaction data, as well as additional molecular data to generate functional network-level models. These network models can be further optimized and refined to identify small, differentially regulated subnetworks, even in the absence of large-scale datasets. As a proof of concept, we applied KiRNet to liver cancer cells driven by Fzd2, a gene known to cause epithelial-mesenchymal transition and cancer metastasis and identified a subnetwork of 166 proteins that regulate this cell state. The KiRNet derived information can be used to formulate high-value predictions for future testing and thus accelerate basic and translational discoveries.