cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.8294333
title: Additional file 3: of Comparative genomics of Mycobacterium mucogenicum and Mycobacterium neoaurum clade members emphasizing tRNA and non-coding RNA

description: Introduction. Table and Figure legends. Table S5. Compilation of predicted tRNA genes in the "32 tRNA gene cluster". Table S6a. Compilation of predicted aminoacyl-tRNA synthetases (AARS) paralogs. Table S6b. Compilation of predicted genes encoding GatCAB enzymes. Table S6c. Compilation of regular and extra gene copy aminoacyl-tRNA synthetase genes. Supplementary text information. Prediction of genes encoding aminoacyl-tRNA synthetase paralogs and cyclodipeptide synthetase genes in Mmuc- and Mneo-clade members. Figure S4a-e. Analysis of tRNA genes [90]. Figure S5. tRNA sequence alignment for all tRNA genes. Figure S6a-f. Analysis of isoleucyl-tRNA synthetase and selected AARS genes. Figure S7a, b. Cyclodipeptide synthase (CDPS) â PF16715 [106, 107]. (ZIP 166 kb)
DOI: 10.6084/m9.figshare.8294339.v1
title: Additional file 4: of Comparative genomics of Mycobacterium mucogenicum and Mycobacterium neoaurum clade members emphasizing tRNA and non-coding RNA

description: Introduction. Table and Figure legends. Table S7. Rfam non-coding RNA annotations. Figure S8a, b. ncRNA genes. Figure S9a-i. RNase P RNA (rnpB), tmRNA and 4.5S RNA. Figure S10. Northern blot analysis of selected tRNAs and ncRNAs with size markers. Figure S11a, b. Analysis of GOLLD RNA. Figure S12a-d. Comparison of Ms1 RNA and 6C RNA genes in mycobacteria. Figure S13a, b. Comparison of a intron Group II gene cluster. (ZIP 7530 kb)
DOI: 10.6084/m9.figshare.8294327
title: Additional file 2: of Comparative genomics of Mycobacterium mucogenicum and Mycobacterium neoaurum clade members emphasizing tRNA and non-coding RNA

description: Introduction. Figure legends. Figure S2a, b. Functional classification of genes. Figure S3a, b. Phylogenetic analysis. (ZIP 151 kb)
DOI: 10.6084/m9.figshare.8294333.v1
title: Additional file 3: of Comparative genomics of Mycobacterium mucogenicum and Mycobacterium neoaurum clade members emphasizing tRNA and non-coding RNA

description: Introduction. Table and Figure legends. Table S5. Compilation of predicted tRNA genes in the "32 tRNA gene cluster". Table S6a. Compilation of predicted aminoacyl-tRNA synthetases (AARS) paralogs. Table S6b. Compilation of predicted genes encoding GatCAB enzymes. Table S6c. Compilation of regular and extra gene copy aminoacyl-tRNA synthetase genes. Supplementary text information. Prediction of genes encoding aminoacyl-tRNA synthetase paralogs and cyclodipeptide synthetase genes in Mmuc- and Mneo-clade members. Figure S4a-e. Analysis of tRNA genes [90]. Figure S5. tRNA sequence alignment for all tRNA genes. Figure S6a-f. Analysis of isoleucyl-tRNA synthetase and selected AARS genes. Figure S7a, b. Cyclodipeptide synthase (CDPS) â PF16715 [106, 107]. (ZIP 166 kb)
DOI: 10.6084/m9.figshare.8294321
title: Additional file 1: of Comparative genomics of Mycobacterium mucogenicum and Mycobacterium neoaurum clade members emphasizing tRNA and non-coding RNA

description: Introduction. Table and Figure legends. Table S1. Compilation of oligonucleotide probes used in the present study. Table S2. Summary of genome assembly. Table S3. Phage analysis of Mmuc- and Mneo-clade members. Table S4a. Annotation of IS elements in MmucT. Table S4b. Annotation of IS elements in Mmuc- and Mneo-clade members. Figure S1. Genome alignments. (ZIP 1120 kb)
DOI: 10.6084/m9.figshare.8294327.v1
title: Additional file 2: of Comparative genomics of Mycobacterium mucogenicum and Mycobacterium neoaurum clade members emphasizing tRNA and non-coding RNA

description: Introduction. Figure legends. Figure S2a, b. Functional classification of genes. Figure S3a, b. Phylogenetic analysis. (ZIP 151 kb)
DOI: 10.6084/m9.figshare.8294321.v1
title: Additional file 1: of Comparative genomics of Mycobacterium mucogenicum and Mycobacterium neoaurum clade members emphasizing tRNA and non-coding RNA

description: Introduction. Table and Figure legends. Table S1. Compilation of oligonucleotide probes used in the present study. Table S2. Summary of genome assembly. Table S3. Phage analysis of Mmuc- and Mneo-clade members. Table S4a. Annotation of IS elements in MmucT. Table S4b. Annotation of IS elements in Mmuc- and Mneo-clade members. Figure S1. Genome alignments. (ZIP 1120 kb)
DOI: 10.6084/m9.figshare.8294270
title: Additional file 2: of Distribution differences in prognostic copy number alteration profiles in IDH-wild-type glioblastoma cause survival discrepancies across cohorts

description: Table S1. Dataset of patients with glioblastoma from Kansai Molecular Diagnosis Network for CNS tumors (n = 234). Table S2. Dataset of patients with glioblastoma from The Cancer Genome Atlas (n = 577). Table S3. Log odds ratio in pair association analysis for genetic aberrations. Table S4. P-value from two-tailed Fisher’s exact test in pair association analysis for genetic aberrations. Table S5. Univariate analysis for each molecular variable using Cox proportional hazards models in Step2. Table S6. Sequences of primers used for amplification and Sanger sequencing. Table S7. Sequences of primers used for quantitative methylation specific PCR. (ZIP 256 kb)
DOI: 10.6084/m9.figshare.8294270.v1
title: Additional file 2: of Distribution differences in prognostic copy number alteration profiles in IDH-wild-type glioblastoma cause survival discrepancies across cohorts

description: Table S1. Dataset of patients with glioblastoma from Kansai Molecular Diagnosis Network for CNS tumors (n = 234). Table S2. Dataset of patients with glioblastoma from The Cancer Genome Atlas (n = 577). Table S3. Log odds ratio in pair association analysis for genetic aberrations. Table S4. P-value from two-tailed Fisher’s exact test in pair association analysis for genetic aberrations. Table S5. Univariate analysis for each molecular variable using Cox proportional hazards models in Step2. Table S6. Sequences of primers used for amplification and Sanger sequencing. Table S7. Sequences of primers used for quantitative methylation specific PCR. (ZIP 256 kb)
DOI: 10.6084/m9.figshare.8294249
title: Additional file 2: of Analysing public sector institutional capacity for health workforce governance in the South-East Asia region of WHO

description: HRH survey SEARO data collection. (XLSX 659 kb)
DOI: 10.6084/m9.figshare.8294249.v1
title: Additional file 2: of Analysing public sector institutional capacity for health workforce governance in the South-East Asia region of WHO

description: HRH survey SEARO data collection. (XLSX 659 kb)
DOI: 10.6084/m9.figshare.8294162
title: Additional file 1: of Enterobius vermicularis infection: prevalence and risk factors among preschool children in kindergarten in the capital area, Republic of the Marshall Islands

description: Anonymous original data for statistical analysis. (XLSX 83 kb)
DOI: 10.6084/m9.figshare.8294162.v1
title: Additional file 1: of Enterobius vermicularis infection: prevalence and risk factors among preschool children in kindergarten in the capital area, Republic of the Marshall Islands

description: Anonymous original data for statistical analysis. (XLSX 83 kb)
DOI: 10.6084/m9.figshare.8294078
title: Analysis of the skill demand for testers

description: This data file shows the structure of skills we have created, as taxonomy for the study of skill demand for testers. It lists the way we categorised each skill, the activities we included under each skill, and the additional data analysis method for each activity included in the skill sub-category.
DOI: 10.6084/m9.figshare.8294078.v2
title: Analysis of the skill demand for testers

description: This data file shows the structure of skills we have created, as taxonomy for the study of skill demand for testers. It lists the way we categorised each skill, the activities we included under each skill, and the additional data analysis method for each activity included in the skill sub-category.
DOI: 10.6084/m9.figshare.8294078.v1
title: The protocol for the analysis of skill demand for testers

description: This data file shows the structure of skills we have created, as taxonomy for the study of skill demand for testers. It lists the way we categorised each skill, the activities we included under each skill, and the additional data analysis method for each activity included in the skill sub-category.
DOI: 10.6084/m9.figshare.8294054
title: Processed data from the job ads for testers.xlsx

description: This is the file with the data that we processed from the 400 job ads for software testers. It includes the copied, pasted and translated bits from the job advertisements. All the content of the job ads was copied and pasted to this file, in various places where it belonged. The rest of the file is our coding, contraction and aggregation of data, together with additional calculations that we performed.
DOI: 10.6084/m9.figshare.8294048
title: GAD67 & Iba1 abutments measurements 8224 GP 1

description: GAD67 & Iba1 abutments measurements 8224 GP 1 2019 paper
DOI: 10.6084/m9.figshare.8294054.v1
title: Processed_data_jobads_testers.xlsx

description: This is the file with the data that we processed from the 400 job ads for software testers. It includes the copied, pasted and translated bits from the job advertisements. All the content of the job ads was copied and pasted to this file, in various places where it belonged. The rest of the file is our coding, contraction and aggregation of data, together with additional calculations that we performed.
DOI: 10.6084/m9.figshare.8294048.v1
title: GAD67 & Iba1 abutments measurements 8224 GP 1

description: GAD67 & Iba1 abutments measurements 8224 GP 1 2019 paper
DOI: 10.6084/m9.figshare.8294027
title: Alcohol and other drug (AOD) staffing and their workplace: examining the relationship between clinician and organisational workforce characteristics and treatment outcomes in the AOD field

description: While there is a long-standing and commonly held belief that the characteristics of the alcohol and other drug (AOD) workforce and workplace can impact client treatment outcomes, the available literature has not been systematically reviewed to date. Knowing which characteristics may impact treatment outcomes can help maximise workforce development in AOD services. A systematic review was undertaken, to identify studies of five clinician and organisational workforce characteristics: (1) years of clinical experience; (2) level of education/qualifications; (3) staff turnover; (4) staff-to-client ratio; and (5) professional development, and their relationship to client treatment outcome. Each study was assessed for quality using the Cochrane risk of bias tool. The search identified 1317 records; only 12 studies directly examined the relationship between clinician and organisational workforce characteristics and AOD treatment outcomes. Our analysis revealed a limited number of studies, a lack of high-quality research, and highly variable evidence regarding the relationship between clinician and organisational characteristics, and treatment outcomes. At present, there is an absence of evidence to support a strong association in any direction. Importantly, these findings illustrate the need for higher quality and larger scale research that focuses on clinician and organisational characteristics, taking into account multiple intervening and mediating factors.
DOI: 10.6084/m9.figshare.8294027.v1
title: Alcohol and other drug (AOD) staffing and their workplace: examining the relationship between clinician and organisational workforce characteristics and treatment outcomes in the AOD field

description: While there is a long-standing and commonly held belief that the characteristics of the alcohol and other drug (AOD) workforce and workplace can impact client treatment outcomes, the available literature has not been systematically reviewed to date. Knowing which characteristics may impact treatment outcomes can help maximise workforce development in AOD services. A systematic review was undertaken, to identify studies of five clinician and organisational workforce characteristics: (1) years of clinical experience; (2) level of education/qualifications; (3) staff turnover; (4) staff-to-client ratio; and (5) professional development, and their relationship to client treatment outcome. Each study was assessed for quality using the Cochrane risk of bias tool. The search identified 1317 records; only 12 studies directly examined the relationship between clinician and organisational workforce characteristics and AOD treatment outcomes. Our analysis revealed a limited number of studies, a lack of high-quality research, and highly variable evidence regarding the relationship between clinician and organisational characteristics, and treatment outcomes. At present, there is an absence of evidence to support a strong association in any direction. Importantly, these findings illustrate the need for higher quality and larger scale research that focuses on clinician and organisational characteristics, taking into account multiple intervening and mediating factors.
DOI: 10.21942/uva.8293946
title: FOC NGS RAW DATA

description: FOC NGS RAW DATA
DOI: 10.21942/uva.8293946.v1
title: FOC NGS RAW DATA

description: FOC NGS RAW DATA
DOI: 10.6084/m9.figshare.8293895
title: Supplementary Material for: A Clinical Trial to Verify the Efficiency of the LC-1000 Exfoliative Cell Analyzer as a New Method of Cervical Cancer Screening

description: <b><i>Objective:</i></b> The exfoliative cell analyzer, LC-1000 (Sysmex Corporation, Japan), is a medical device that presents the cell proliferation index and 23 research parameters as indicators of cellular proliferative potential. The objective was to evaluate the clinical usability of qualitative assessment by LC-1000 compared with cytology, the human papillomavirus (HPV) test, and histology as gold standard. <b><i>Study Design:</i></b> Women that visited 3 sites between July 2015 and March 2017 were registered. The primary endpoint in this study was the comparison between LC-1000 measurement and HPV test for sensitivity and specificity for cervical intraepithelial neoplasia 2+ (CIN2+). A tree model algorithm was newly constructed by a statistical method and its relationship with histological results was evaluated. <b><i>Results:</i></b> The sensitivity and specificity of LC-1000 were 78.3 and 74.1%, while those of the HPV test were 94.7 and 85.4%, respectively. A tree model comprising five categories was constructed. The proportion of advanced lesions was higher with the change in the rank classification results from 1 to 5. The positive predictive values of CIN2+ in the categories 4 and 5 were high. Despite the small number of subjects, cancer was undetected in categories 1 and 2. In addition, the comparison with follow-up results in 19 women assessed as CIN1 showed that the rate of progression in the categories 3–5 was 50% (7/14); progression in the categories 1 and 2 was 0% (0/5). <b><i>Conclusions:</i></b> LC-1000 may be useful for cervical cancer screening as an index to qualitatively evaluate CIN and cancer based on the changes in characteristics of cells.
DOI: 10.6084/m9.figshare.8293895.v1
title: Supplementary Material for: A Clinical Trial to Verify the Efficiency of the LC-1000 Exfoliative Cell Analyzer as a New Method of Cervical Cancer Screening

description: <b><i>Objective:</i></b> The exfoliative cell analyzer, LC-1000 (Sysmex Corporation, Japan), is a medical device that presents the cell proliferation index and 23 research parameters as indicators of cellular proliferative potential. The objective was to evaluate the clinical usability of qualitative assessment by LC-1000 compared with cytology, the human papillomavirus (HPV) test, and histology as gold standard. <b><i>Study Design:</i></b> Women that visited 3 sites between July 2015 and March 2017 were registered. The primary endpoint in this study was the comparison between LC-1000 measurement and HPV test for sensitivity and specificity for cervical intraepithelial neoplasia 2+ (CIN2+). A tree model algorithm was newly constructed by a statistical method and its relationship with histological results was evaluated. <b><i>Results:</i></b> The sensitivity and specificity of LC-1000 were 78.3 and 74.1%, while those of the HPV test were 94.7 and 85.4%, respectively. A tree model comprising five categories was constructed. The proportion of advanced lesions was higher with the change in the rank classification results from 1 to 5. The positive predictive values of CIN2+ in the categories 4 and 5 were high. Despite the small number of subjects, cancer was undetected in categories 1 and 2. In addition, the comparison with follow-up results in 19 women assessed as CIN1 showed that the rate of progression in the categories 3–5 was 50% (7/14); progression in the categories 1 and 2 was 0% (0/5). <b><i>Conclusions:</i></b> LC-1000 may be useful for cervical cancer screening as an index to qualitatively evaluate CIN and cancer based on the changes in characteristics of cells.
DOI: 10.6084/m9.figshare.8293883
title: Identification and Validation of Metabolomic Biomarkers in Cervicovaginal Fluid for Detecting Endometrial Cancer Through Nuclear Magnetic Resonance Spectroscopym

description: IntroductionEndometrial cancer (EC) is one of the most common gynecologic neoplasms in developed countries but lacks screening biomarkers.Objectives We aim to identify and validate metabolomic biomarkers in cervicovaginal fluid (CVF) for detecting EC through nuclear magnetic resonance (NMR) spectroscopy.MethodsWe screened 200 women with suspicion of EC and benign gynecological conditions, and randomized them into the training and independent testingdatasets using a 5:1 study design. CVF samples were analyzed using a 600-MHz NMR spectrometer equipped with a cryoprobe. Four machine learning algorithms—support vector machine (SVM), partial least squares discriminant analysis (PLS-DA), random forest (RF), and logistic regression (LR), were applied to develop the model for identifying metabolomic biomarkers in cervicovaginal fluid for EC detection. ResultsAtotal of 54 women were eligible for the final analysis, with 21 EC and 33 non-EC. From 29 identified metabolites in cervicovaginal fluid samples, we selected phosphocholine, asparagine, and malate to build the prediction model. The SVM, PLS-DA, RF, and LR methods all yielded area under the curve values between 0.88 and 0.92 in the training dataset. In the testing dataset, the SVM and RF methods yielded the highest accuracy of 0.78 and the specificity of 0.75 and 0.80, respectively.ConclusionPhosphocholine, asparagine, and malate from cervicovaginal fluid, which were identified and independently validated through models built using machine learning algorithms, are promising metabolomic biomarkers for the detection of EC using NMR spectroscopy.
DOI: 10.6084/m9.figshare.8293883.v1
title: Identification and Validation of Metabolomic Biomarkers in Cervicovaginal Fluid for Detecting Endometrial Cancer Through Nuclear Magnetic Resonance Spectroscopym

description: IntroductionEndometrial cancer (EC) is one of the most common gynecologic neoplasms in developed countries but lacks screening biomarkers.Objectives We aim to identify and validate metabolomic biomarkers in cervicovaginal fluid (CVF) for detecting EC through nuclear magnetic resonance (NMR) spectroscopy.MethodsWe screened 200 women with suspicion of EC and benign gynecological conditions, and randomized them into the training and independent testingdatasets using a 5:1 study design. CVF samples were analyzed using a 600-MHz NMR spectrometer equipped with a cryoprobe. Four machine learning algorithms—support vector machine (SVM), partial least squares discriminant analysis (PLS-DA), random forest (RF), and logistic regression (LR), were applied to develop the model for identifying metabolomic biomarkers in cervicovaginal fluid for EC detection. ResultsAtotal of 54 women were eligible for the final analysis, with 21 EC and 33 non-EC. From 29 identified metabolites in cervicovaginal fluid samples, we selected phosphocholine, asparagine, and malate to build the prediction model. The SVM, PLS-DA, RF, and LR methods all yielded area under the curve values between 0.88 and 0.92 in the training dataset. In the testing dataset, the SVM and RF methods yielded the highest accuracy of 0.78 and the specificity of 0.75 and 0.80, respectively.ConclusionPhosphocholine, asparagine, and malate from cervicovaginal fluid, which were identified and independently validated through models built using machine learning algorithms, are promising metabolomic biomarkers for the detection of EC using NMR spectroscopy.
DOI: 10.6084/m9.figshare.8293838
title: Bad News Dataset2

description: Dataset for polarization badge Roozenbeek and van der Linden (2019) Fake news game confers psychological resistance against online misinformation.<br>
DOI: 10.6084/m9.figshare.8293832
title: R example for model simuations from Behaviour, life history and persistence in novel environments

description: Example of how to use the R-package to run the simulations
DOI: 10.6084/m9.figshare.8293832.v1
title: R example for model simuations from Behaviour, life history and persistence in novel environments

description: Example of how to use the R-package to run the simulations
DOI: 10.6084/m9.figshare.8293826
title: LHR R-package from Behaviour, life history and persistence in novel environments

description: R-package that implements the model
DOI: 10.6084/m9.figshare.8293802
title: GAD67 & Iba1 abutments measurements 8212 GP 2

description: GAD67 & Iba1 abutments measurements 8212 GP 2 2019 paper
DOI: 10.6084/m9.figshare.8293823
title: Patient Data and Shear Wave Speed Estimates from Quantitative assessment of cervical softening during pregnancy with shear wave elasticity imaging: an in vivo longitudinal study

description: A tab delimited file containing measurements for each patient ID at each exam reporting the gestational age, parity, canal angle and SWS estimates within the sub-ROI for each location away from the internal os/bladder reflection (0.25, 0.75, 1.25. and 1.75 cm). (N = Nulliparous, M = Multiparous, CA = Cervical Canal Angle, P = 0.25 cm, MP1 = 0.75 cm, MP2 = 1.25 cm, M = 1.75 cm)
DOI: 10.6084/m9.figshare.8293826.v1
title: LHR R-package from Behaviour, life history and persistence in novel environments

description: R-package that implements the model