cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.6465512.v1
title: Additional file 1: of Visual and radiographic caries detection: a tailored meta-analysis for two different settings, Egypt and Germany

description: All data used the in the meta-analysis, tabulated. (ZIP 105 kb)
DOI: 10.6084/m9.figshare.6465437
title: Additional file 6: of Identification of common coexpression modules based on quantitative network comparison

description: GO Term Enrichment Analysis Results for Similar HD-aging module pairs. The file contains enriched GO terms of common genes from similar module pairs. (A) First sheet is the result of HD-yellow and Age-red pair. (B) Second sheet is the result of HD-magenta and Age-yellow pair. (XLS 69 kb)
DOI: 10.6084/m9.figshare.6465437.v1
title: Additional file 6: of Identification of common coexpression modules based on quantitative network comparison

description: GO Term Enrichment Analysis Results for Similar HD-aging module pairs. The file contains enriched GO terms of common genes from similar module pairs. (A) First sheet is the result of HD-yellow and Age-red pair. (B) Second sheet is the result of HD-magenta and Age-yellow pair. (XLS 69 kb)
DOI: 10.6084/m9.figshare.6465419
title: Additional file 2: of Adapting medical guidelines to be patient-centered using a patient-driven process for individuals with sickle cell disease and their caregivers

description: List of questions asked at the community forums. These are the questions that were asked of participants at the community forums. (XLSX 12 kb)
DOI: 10.6084/m9.figshare.6465419.v1
title: Additional file 2: of Adapting medical guidelines to be patient-centered using a patient-driven process for individuals with sickle cell disease and their caregivers

description: List of questions asked at the community forums. These are the questions that were asked of participants at the community forums. (XLSX 12 kb)
DOI: 10.6084/m9.figshare.6465416
title: Additional file 1: of Spatial distribution modelling of Culicoides (Diptera: Ceratopogonidae) biting midges, potential vectors of African horse sickness and bluetongue viruses in Senegal

description: Table S1. Culicoides data from Senegal throughout a nation-wide trapping campaign in 2012. (XLSX 15 kb)
DOI: 10.6084/m9.figshare.6465416.v1
title: Additional file 1: of Spatial distribution modelling of Culicoides (Diptera: Ceratopogonidae) biting midges, potential vectors of African horse sickness and bluetongue viruses in Senegal

description: Table S1. Culicoides data from Senegal throughout a nation-wide trapping campaign in 2012. (XLSX 15 kb)
DOI: 10.6084/m9.figshare.6465413
title: Additional file 5: of Identification of common coexpression modules based on quantitative network comparison

description: Correlation Coefficient between Modular Expression and Phenotypes. (A) First sheet contains correlation coefficient between eigenvalue of each module and HD. (B) Second sheet contains correlation coefficient between eigenvalue and age. (XLS 34 kb)
DOI: 10.6084/m9.figshare.6465413.v1
title: Additional file 5: of Identification of common coexpression modules based on quantitative network comparison

description: Correlation Coefficient between Modular Expression and Phenotypes. (A) First sheet contains correlation coefficient between eigenvalue of each module and HD. (B) Second sheet contains correlation coefficient between eigenvalue and age. (XLS 34 kb)
DOI: 10.6084/m9.figshare.6465395
title: Additional file 4: of Identification of common coexpression modules based on quantitative network comparison

description: Similarity Profiles between HD-specific Modules and Aging-related Modules. The file contains similarity profiles between HD-specific modules and aging-related modules. Two sheets are similarity profiles from COEXsim and fuzzy set similarity, respectively. (XLS 28 kb)
DOI: 10.6084/m9.figshare.6465395.v1
title: Additional file 4: of Identification of common coexpression modules based on quantitative network comparison

description: Similarity Profiles between HD-specific Modules and Aging-related Modules. The file contains similarity profiles between HD-specific modules and aging-related modules. Two sheets are similarity profiles from COEXsim and fuzzy set similarity, respectively. (XLS 28 kb)
DOI: 10.6084/m9.figshare.6465392
title: Additional file 1: of Adapting medical guidelines to be patient-centered using a patient-driven process for individuals with sickle cell disease and their caregivers

description: Raw data. (XLSX 8 kb)
DOI: 10.6084/m9.figshare.6465392.v1
title: Additional file 1: of Adapting medical guidelines to be patient-centered using a patient-driven process for individuals with sickle cell disease and their caregivers

description: Raw data. (XLSX 8 kb)
DOI: 10.6084/m9.figshare.6465371
title: Additional file 3: of Identification of common coexpression modules based on quantitative network comparison

description: Similarity Profiles for Threshold Evaluation. The file contains similarity profiles of two network groups used for threshold evaluation. First and second sheets are similarity profiles of innate immunity group from COEXsim and fuzzy set similarity. Third and last sheets are similarity profiles of angiogenesis group from COEXsim and fuzzy set similarity. (XLS 34 kb)
DOI: 10.6084/m9.figshare.6465371.v1
title: Additional file 3: of Identification of common coexpression modules based on quantitative network comparison

description: Similarity Profiles for Threshold Evaluation. The file contains similarity profiles of two network groups used for threshold evaluation. First and second sheets are similarity profiles of innate immunity group from COEXsim and fuzzy set similarity. Third and last sheets are similarity profiles of angiogenesis group from COEXsim and fuzzy set similarity. (XLS 34 kb)
DOI: 10.6084/m9.figshare.6465353
title: MOESM4 of Exhaustion of mitochondrial and autophagic reserve may contribute to the development of LRRK2 G2019S -Parkinson’s disease

description: Additional file 4: Table S2. Raw data of mitochondrial phenotype and autophagic print parameters. The following legend accompanies the table: Glu: glucose; Gal: galactose; NM-LRRK2 G2019S : Non-manifesting carriers of LRRK2 G2019S -mutation; PD- LRRK2 G2019S : patients with LRRK2 G2019S -mutation and clinically manifest PD; mtDNA: Mitochondrial DNA; mtRNA: Mitochondrial RNA; CI: Complex I; O2 consumption stimulated for CI: Oxygen consumption stimulated for Complex I substrates (pyruvate, malate and glutamate); CIV: Complex IV. Pa. P value when comparing NM-LRRK2 G2019S vs. controls; Pb. P value when comparing PD- LRRK2 G2019S vs. controls; Pc. P value when comparing NM-LRRK2 G2019S vs. PD-LRRK2 G2019S .
DOI: 10.6084/m9.figshare.6465353.v1
title: MOESM4 of Exhaustion of mitochondrial and autophagic reserve may contribute to the development of LRRK2 G2019S -Parkinson’s disease

description: Additional file 4: Table S2. Raw data of mitochondrial phenotype and autophagic print parameters. The following legend accompanies the table: Glu: glucose; Gal: galactose; NM-LRRK2 G2019S : Non-manifesting carriers of LRRK2 G2019S -mutation; PD- LRRK2 G2019S : patients with LRRK2 G2019S -mutation and clinically manifest PD; mtDNA: Mitochondrial DNA; mtRNA: Mitochondrial RNA; CI: Complex I; O2 consumption stimulated for CI: Oxygen consumption stimulated for Complex I substrates (pyruvate, malate and glutamate); CIV: Complex IV. Pa. P value when comparing NM-LRRK2 G2019S vs. controls; Pb. P value when comparing PD- LRRK2 G2019S vs. controls; Pc. P value when comparing NM-LRRK2 G2019S vs. PD-LRRK2 G2019S .
DOI: 10.6084/m9.figshare.6465347
title: Additional file 2: of Identification of common coexpression modules based on quantitative network comparison

description: Similarity Profiles among 20 Coexpression Networks for Validation. The file contains similarity profiles among 20 coexpression networks used for validation. Similarity profiles from GO semantic similarity, COEXsim, fuzzy set similarity and GCD are included in each sheet of the file. (XLS 56 kb)
DOI: 10.6084/m9.figshare.6465347.v1
title: Additional file 2: of Identification of common coexpression modules based on quantitative network comparison

description: Similarity Profiles among 20 Coexpression Networks for Validation. The file contains similarity profiles among 20 coexpression networks used for validation. Similarity profiles from GO semantic similarity, COEXsim, fuzzy set similarity and GCD are included in each sheet of the file. (XLS 56 kb)
DOI: 10.6084/m9.figshare.6465344
title: Additional file 1: of Identification of common coexpression modules based on quantitative network comparison

description: Selected GO Terms for Validation and Network Statistics. GO ID, name, number of nodes and number of edges of selected GO terms are included in the file. (XLS 33 kb)
DOI: 10.6084/m9.figshare.6465344.v1
title: Additional file 1: of Identification of common coexpression modules based on quantitative network comparison

description: Selected GO Terms for Validation and Network Statistics. GO ID, name, number of nodes and number of edges of selected GO terms are included in the file. (XLS 33 kb)
DOI: 10.6084/m9.figshare.6465329
title: MOESM3 of Genome-wide association and genomic prediction of resistance to viral nervous necrosis in European sea bass (Dicentrarchus labrax) using RAD sequencing

description: Additional file 3: Table S3. SNP location. Location of SNPs on the sea bass reference genome v1_0.
DOI: 10.6084/m9.figshare.6465329.v1
title: MOESM3 of Genome-wide association and genomic prediction of resistance to viral nervous necrosis in European sea bass (Dicentrarchus labrax) using RAD sequencing

description: Additional file 3: Table S3. SNP location. Location of SNPs on the sea bass reference genome v1_0.
DOI: 10.6084/m9.figshare.6465305
title: MOESM2 of Genome-wide association and genomic prediction of resistance to viral nervous necrosis in European sea bass (Dicentrarchus labrax) using RAD sequencing

description: Additional file 2: Table S2. Identified SNPs. Sequence information of identified SNPs.
DOI: 10.6084/m9.figshare.6465305.v1
title: MOESM2 of Genome-wide association and genomic prediction of resistance to viral nervous necrosis in European sea bass (Dicentrarchus labrax) using RAD sequencing

description: Additional file 2: Table S2. Identified SNPs. Sequence information of identified SNPs.
DOI: 10.6084/m9.figshare.6465284
title: MOESM1 of Genome-wide association and genomic prediction of resistance to viral nervous necrosis in European sea bass (Dicentrarchus labrax) using RAD sequencing

description: Additional file 1: Table S1. VNN challenged sea bass. Phenotypic information of VNN challenged sea bass and pedigree structure.
DOI: 10.6084/m9.figshare.6465284.v1
title: MOESM1 of Genome-wide association and genomic prediction of resistance to viral nervous necrosis in European sea bass (Dicentrarchus labrax) using RAD sequencing

description: Additional file 1: Table S1. VNN challenged sea bass. Phenotypic information of VNN challenged sea bass and pedigree structure.
DOI: 10.6084/m9.figshare.6465215
title: Categorizing Defects in Infrastructure as Code

description: Materials for our paper 'Categorizing Defects in Infrastructure as Code'<br>
DOI: 10.6084/m9.figshare.6465215.v1
title: Categorizing Defects in Infrastructure as Code

description: Materials for our paper 'Categorizing Defects in Infrastructure as Code'<br>
DOI: 10.6084/m9.figshare.6465191
title: Supplemental Table 1

description: Data of analyzed sections.
DOI: 10.6084/m9.figshare.6465179
title: Dataset for: Metabolite assignment of Ultra-Filtered Synovial Fluid extracted from knee joints of Reactive Arthritis patients using High-Resolution NMR spectroscopy

description: Currently, there are no reliable clinical biomarkers available that can aid early differential diagnosis of reactive arthritis (ReA) from other inflammatory joint diseases. Metabolic profiling of synovial fluid (SF) –obtained from joints affected in ReA- holds great promise in this regard and will further aid monitoring treatment and improving our understanding about disease mechanism. As a first step in this direction, we report here the metabolite specific assignment of 1H and 13C resonances detected in the NMR spectra of SF samples extracted from human patients with established ReA. The metabolite characterization has been carried out on both normal as well as on ultra-filtered (deproteinized) SF samples of eight ReA patients (n=8) using high resolution (800 MHz) 1H and 1H-13C NMR spectroscopy methods such as one-dimensional (1D) 1H CPMG and two-dimensional (2D) J-resolved1H NMR and homonuclear 1H-1H TOCSY and heteronuclear1H-13C HSQC correlation spectra. Compared to normal SF samples, several distinctive 1H NMR signals were identified and assigned to metabolites in the 1H NMR spectra of ultra-filtered SF samples. Overall, we assigned 53 metabolites in normal filtered SF and 64 metabolites in filtered pooled SF sample compared to normal (un-filtered) SF samples for which only 48 metabolites (including lipid/membrane metabolites as well) have been identified. The established NMR characterization of SF metabolites will serve to guide future metabolomics studies aiming to identify/evaluate the SF based metabolic biomarkers of diagnostic/prognostic potential or seeking biochemical insights into disease mechanisms in a clinical perspective.
DOI: 10.6084/m9.figshare.6465179.v1
title: Dataset for: Metabolite assignment of Ultra-Filtered Synovial Fluid extracted from knee joints of Reactive Arthritis patients using High-Resolution NMR spectroscopy

description: Currently, there are no reliable clinical biomarkers available that can aid early differential diagnosis of reactive arthritis (ReA) from other inflammatory joint diseases. Metabolic profiling of synovial fluid (SF) –obtained from joints affected in ReA- holds great promise in this regard and will further aid monitoring treatment and improving our understanding about disease mechanism. As a first step in this direction, we report here the metabolite specific assignment of 1H and 13C resonances detected in the NMR spectra of SF samples extracted from human patients with established ReA. The metabolite characterization has been carried out on both normal as well as on ultra-filtered (deproteinized) SF samples of eight ReA patients (n=8) using high resolution (800 MHz) 1H and 1H-13C NMR spectroscopy methods such as one-dimensional (1D) 1H CPMG and two-dimensional (2D) J-resolved1H NMR and homonuclear 1H-1H TOCSY and heteronuclear1H-13C HSQC correlation spectra. Compared to normal SF samples, several distinctive 1H NMR signals were identified and assigned to metabolites in the 1H NMR spectra of ultra-filtered SF samples. Overall, we assigned 53 metabolites in normal filtered SF and 64 metabolites in filtered pooled SF sample compared to normal (un-filtered) SF samples for which only 48 metabolites (including lipid/membrane metabolites as well) have been identified. The established NMR characterization of SF metabolites will serve to guide future metabolomics studies aiming to identify/evaluate the SF based metabolic biomarkers of diagnostic/prognostic potential or seeking biochemical insights into disease mechanisms in a clinical perspective.
DOI: 10.6084/m9.figshare.6465173
title: CmTn_mutants_1.tsv

description: Table of engineered Chlamydia muridarum mutants with transposon insertions.
DOI: 10.6084/m9.figshare.6465173.v1
title: CmTn_mutants_1.tsv

description: Table of engineered Chlamydia muridarum mutants with transposon insertions.
DOI: 10.6084/m9.figshare.6465125
title: (Un)Conditional Sample Generation Based on Distribution Element Trees

description: Recently, distribution element trees (DETs) were introduced as an accurate and computationally efficient method for density estimation. In this work, we demonstrate that the DET formulation promotes an easy and inexpensive way to generate random samples similar to a smooth bootstrap. These samples can be generated unconditionally, but also, without further complications, conditionally using available information about certain probability-space components. This article is accompanied by the R codes that were used to produce all simulation results. Supplementary material for this article is available online.
DOI: 10.6084/m9.figshare.6465125.v1
title: (Un)Conditional Sample Generation Based on Distribution Element Trees

description: Recently, distribution element trees (DETs) were introduced as an accurate and computationally efficient method for density estimation. In this work, we demonstrate that the DET formulation promotes an easy and inexpensive way to generate random samples similar to a smooth bootstrap. These samples can be generated unconditionally, but also, without further complications, conditionally using available information about certain probability-space components. This article is accompanied by the R codes that were used to produce all simulation results. Supplementary material for this article is available online.
DOI: 10.6084/m9.figshare.6465080
title: Optimal Noncollapsing Space-Filling Designs for Irregular Experimental Regions

description: Space-filling and noncollapsing are two important properties in designing computer experiments. We study how the noncollapsing, space-filling designs for irregular experimental regions can be generated efficiently by the proposed metaheuristic methods. We solve this optimal design problem using variants of the discrete particle swarm optimization (DPSO) approaches. Numerical results, including an application in data center thermal management, are used to illustrate the performances of the proposed algorithms. Based on these numerical results, we assert that the most efficient approach is to reformulate the target optimal design problem as a constrained optimization problem and then use a modified DPSO to solve the constrained optimization problem.
DOI: 10.6084/m9.figshare.6465080.v2
title: Optimal Noncollapsing Space-Filling Designs for Irregular Experimental Regions

description: Space-filling and noncollapsing are two important properties in designing computer experiments. We study how the noncollapsing, space-filling designs for irregular experimental regions can be generated efficiently by the proposed metaheuristic methods. We solve this optimal design problem using variants of the discrete particle swarm optimization (DPSO) approaches. Numerical results, including an application in data center thermal management, are used to illustrate the performances of the proposed algorithms. Based on these numerical results, we assert that the most efficient approach is to reformulate the target optimal design problem as a constrained optimization problem and then use a modified DPSO to solve the constrained optimization problem.
DOI: 10.6084/m9.figshare.6465080.v1
title: Optimal Non-collapsing Space-filling Designs for Irregular Experimental Regions

description: Space-filling and non-collapsing are two important properties in designing computer experiments. We study how the non-collapsing, space-filling designs for irregular experimental regions can be generated efficiently by the proposed metaheuristic methods. We solve this optimal design problem using variants of the discrete particle swarm optimization (DPSO) approaches. Numerical results, including an application in data center thermal management, are used to illustrate the performances of the proposed algorithms. Based on these numerical results, we assert that the most efficient approach is to reformulate the target optimal design problem as a constrained optimization problem and then use a modified DPSO to solve the constrained optimization problem.
DOI: 10.6084/m9.figshare.6464981
title: 3-Aminopyrazolo[4,3-<i>c</i>]pyridine-4,6-dione as a precursor for novel pyrazolo[4,5,1-ij][1,6]naphthyridines and pyrido[4’,3’:3,4]pyrazolo[1,5-<i>a</i>]pyrimidines

description: The versatile, 3-aminopyrazolo[4,3-c]pyridine-4,6-dione (<b>2</b>) was synthesized and allowed to react with aldehydes, aryldiazonium chlorides, chalcones and enaminones to afford regioselectively the novel pyrazolo[4,3-c]pyridine derivatives <b>4a-c</b>, pyrazolo[4,5,1-ij][1,6] naphthyridines <b>9a-d</b> and pyrido[4’,3':3,4]pyrazlo[1,5-a]pyrimidines <b>19a-i</b>. The structure of all the newly synthesized compounds was assigned from elemental analysis and spectral data. Also, the mechanistic aspects for the formation of the newly synthesized compounds is discussed.
DOI: 10.6084/m9.figshare.6464981.v1
title: 3-Aminopyrazolo[4,3-<i>c</i>]pyridine-4,6-dione as a precursor for novel pyrazolo[4,5,1-ij][1,6]naphthyridines and pyrido[4’,3’:3,4]pyrazolo[1,5-<i>a</i>]pyrimidines

description: The versatile, 3-aminopyrazolo[4,3-c]pyridine-4,6-dione (<b>2</b>) was synthesized and allowed to react with aldehydes, aryldiazonium chlorides, chalcones and enaminones to afford regioselectively the novel pyrazolo[4,3-c]pyridine derivatives <b>4a-c</b>, pyrazolo[4,5,1-ij][1,6] naphthyridines <b>9a-d</b> and pyrido[4’,3':3,4]pyrazlo[1,5-a]pyrimidines <b>19a-i</b>. The structure of all the newly synthesized compounds was assigned from elemental analysis and spectral data. Also, the mechanistic aspects for the formation of the newly synthesized compounds is discussed.
DOI: 10.6084/m9.figshare.6464948
title: Popper Test

description: Test archive for popper archiving zenodo
DOI: 10.6084/m9.figshare.6464948.v1
title: Popper Test

description: Test archive for popper archiving zenodo
DOI: 10.6084/m9.figshare.6464948.v3
title: Popper Test

description: Test archive for popper archiving zenodo
DOI: 10.6084/m9.figshare.6464948.v2
title: Popper Test

description: Test archive for popper archiving zenodo
DOI: 10.6084/m9.figshare.6464840
title: DATA.xlsx

description: The SCOPUS database was searched (October 2017) for publications from the University of Waterloo to which citations in the database exhibit a pattern of delayed recognition. Five such "Sleeping Beauty" articles were identified.<br>This dataset lists these five articles and the number of times each has been cited in the years since their publication. In addition, a comparator publication (the classic Einstein, Podolsky, and Rosen paper of 1935) is listed along with its citation history.<br>
DOI: 10.6084/m9.figshare.6464840.v1
title: DATA.xlsx

description: The SCOPUS database was searched (October 2017) for publications from the University of Waterloo to which citations in the database exhibit a pattern of delayed recognition. Five such "Sleeping Beauty" articles were identified.<br>This dataset lists these five articles and the number of times each has been cited in the years since their publication. In addition, a comparator publication (the classic Einstein, Podolsky, and Rosen paper of 1935) is listed along with its citation history.<br>
DOI: 10.6084/m9.figshare.6464819
title: My paper

description: A short description of the article~
DOI: 10.6084/m9.figshare.6464819.v1
title: My paper

description: A short description of the article~
DOI: 10.6084/m9.figshare.6464816
title: My paper

description: A short description of the article~
DOI: 10.6084/m9.figshare.6464816.v1
title: My paper

description: A short description of the article~
DOI: 10.6084/m9.figshare.6464816.v2
title: My paper

description: A short description of the article~
DOI: 10.6084/m9.figshare.6464816.v4
title: My paper

description: A short description of the article~
DOI: 10.6084/m9.figshare.6464816.v3
title: My paper

description: A short description of the article~
DOI: 10.6084/m9.figshare.6463430
title: My paper

description: A short description of the article
DOI: 10.6084/m9.figshare.6463430.v2
title: My paper

description: A short description of the article
DOI: 10.6084/m9.figshare.6463427
title: My paper

description: A short description of the article
DOI: 10.6084/m9.figshare.6463430.v1
title: My paper

description: A short description of the article~
DOI: 10.6084/m9.figshare.6463427.v2
title: My paper

description: A short description of the article
DOI: 10.6084/m9.figshare.6463427.v1
title: My paper

description: A short description of the article~