cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12569423.v1
title: Additional file 5 of Epigenome-wide DNA methylation analysis of small cell lung cancer cell lines suggests potential chemotherapy targets

description: Additional file 5: Supplementary Table 5. Gene regions with FDR adjusted p-value pFDR &lt; 0.15 for Spearman correlations of average gene region methylation with log(IC50) of antitumor agents in the epigenome-wide analysis of all antitumor agents.
DOI: 10.6084/m9.figshare.12569372
title: Additional file 12 of Epigenome-wide DNA methylation analysis of small cell lung cancer cell lines suggests potential chemotherapy targets

description: Additional file 12: Supplementary Data 4. Average methylation of TREX1 gene regions.
DOI: 10.6084/m9.figshare.12569366
title: Additional file 11 of Epigenome-wide DNA methylation analysis of small cell lung cancer cell lines suggests potential chemotherapy targets

description: Additional file 11: Supplementary Data 3. Methylation beta-values and Illumina EPIC array annotation of probes which passed QC and filtering and are located within or adjacent to the TREX1 gene.
DOI: 10.6084/m9.figshare.12569372.v1
title: Additional file 12 of Epigenome-wide DNA methylation analysis of small cell lung cancer cell lines suggests potential chemotherapy targets

description: Additional file 12: Supplementary Data 4. Average methylation of TREX1 gene regions.
DOI: 10.6084/m9.figshare.12569366.v1
title: Additional file 11 of Epigenome-wide DNA methylation analysis of small cell lung cancer cell lines suggests potential chemotherapy targets

description: Additional file 11: Supplementary Data 3. Methylation beta-values and Illumina EPIC array annotation of probes which passed QC and filtering and are located within or adjacent to the TREX1 gene.
DOI: 10.6084/m9.figshare.12844286
title: Engineering economy as a vibrant and relevant course in the engineering programs of today and tomorrow

description: Engineering economy has been studied by the majority of engineering students for many years, yet its place in engineering education is often misunderstood. Logic suggests that the engineering economy course would be highly valued since it is the only course many engineering students will take related to financial matters, but instead there is evidence that the subject is being marginalized. While pressures to reduce program credit hours and changes to the Fundamentals of Engineering exam may play a role in this, perhaps engineering economy has not sufficiently evolved to meet the needs of students or the realities of the contemporary workplace. What can be done to ensure that engineering economy fulfills its potential as an important part of engineering education? There may be few clear-cut answers, but we believe that engineering economy should shift toward a future characterized by rigorous coursework grounded in engineering design principles and applications of risk and uncertainty. This has been our goal in teaching engineering economy at Western Michigan University for many years. The purpose of this paper is to communicate the essential elements of a strategy that has helped to make the course a vibrant component of several engineering programs.
DOI: 10.6084/m9.figshare.12844286.v1
title: Engineering economy as a vibrant and relevant course in the engineering programs of today and tomorrow

description: Engineering economy has been studied by the majority of engineering students for many years, yet its place in engineering education is often misunderstood. Logic suggests that the engineering economy course would be highly valued since it is the only course many engineering students will take related to financial matters, but instead there is evidence that the subject is being marginalized. While pressures to reduce program credit hours and changes to the Fundamentals of Engineering exam may play a role in this, perhaps engineering economy has not sufficiently evolved to meet the needs of students or the realities of the contemporary workplace. What can be done to ensure that engineering economy fulfills its potential as an important part of engineering education? There may be few clear-cut answers, but we believe that engineering economy should shift toward a future characterized by rigorous coursework grounded in engineering design principles and applications of risk and uncertainty. This has been our goal in teaching engineering economy at Western Michigan University for many years. The purpose of this paper is to communicate the essential elements of a strategy that has helped to make the course a vibrant component of several engineering programs.
DOI: 10.6084/m9.figshare.12569294
title: Additional file 1 of Genome-wide association mapping of Sclerotinia sclerotiorum resistance in soybean using whole-genome resequencing data

description: Additional file 1: Table S1. Responses of 127 soybean lines to SSR, 7 days after inoculation. (*) Indicates lines used for WGS (Supplementary file “Supplementary table S1”)
DOI: 10.6084/m9.figshare.12569294.v1
title: Additional file 1 of Genome-wide association mapping of Sclerotinia sclerotiorum resistance in soybean using whole-genome resequencing data

description: Additional file 1: Table S1. Responses of 127 soybean lines to SSR, 7 days after inoculation. (*) Indicates lines used for WGS (Supplementary file “Supplementary table S1”)
DOI: 10.6084/m9.figshare.12569291
title: Additional file 2 of Comparative genomics of four strains of the edible brown alga, Cladosiphon okamuranus

description: Additional file 2: Table S1. A summary of DNA and RNA sequence data from three strains of Cladosiphon okamuranus.Table S2. Numbers of GO annotated genes in Cladosiphon okamuranus strain-specific gene groups. Table S3. Gene IDs of randomly selected 200 single-copy orthologous nuclear genes.
DOI: 10.6084/m9.figshare.12569291.v1
title: Additional file 2 of Comparative genomics of four strains of the edible brown alga, Cladosiphon okamuranus

description: Additional file 2: Table S1. A summary of DNA and RNA sequence data from three strains of Cladosiphon okamuranus.Table S2. Numbers of GO annotated genes in Cladosiphon okamuranus strain-specific gene groups. Table S3. Gene IDs of randomly selected 200 single-copy orthologous nuclear genes.
DOI: 10.6084/m9.figshare.12569165
title: Additional file 2 of State-dependent mortality can enhance behavioral unpredictability

description: Additional file 2. R script to produce Figs. 2, 3, 4, 6, and 7.
DOI: 10.6084/m9.figshare.12569159
title: Additional file 1 of State-dependent mortality can enhance behavioral unpredictability

description: Additional file 1. R script to produce Fig. 1.
DOI: 10.6084/m9.figshare.12569165.v1
title: Additional file 2 of State-dependent mortality can enhance behavioral unpredictability

description: Additional file 2. R script to produce Figs. 2, 3, 4, 6, and 7.
DOI: 10.6084/m9.figshare.12569159.v1
title: Additional file 1 of State-dependent mortality can enhance behavioral unpredictability

description: Additional file 1. R script to produce Fig. 1.
DOI: 10.6084/m9.figshare.12569147
title: Additional file 2 of Logistic regression has similar performance to optimised machine learning algorithms in a clinical setting: application to the discrimination between type 1 and type 2 diabetes in young adults

description: Additional file 2. R script.
DOI: 10.6084/m9.figshare.12569144
title: Additional file 1 of Logistic regression has similar performance to optimised machine learning algorithms in a clinical setting: application to the discrimination between type 1 and type 2 diabetes in young adults

description: Additional file 1: Figure S1. Flow diagram of participants through the model development stages. T1D: type 1 diabetes, T2D: type 2 diabetes. Figure S2. ROC AUC plots obtained using external validation dataset for seven prediction models. Legend: Solid lines: black = Support Vector Machine, dark grey = Logistic Regression, light grey = Random Forest. Dotted lines: black = Neural Network, dark grey = K-Nearest Neighbours, light grey = Gradient Boosting Machine. Figure S3. Correlation coefficient matrix and scatter plot of model predictions obtained from external test validation data.
DOI: 10.6084/m9.figshare.12569144.v1
title: Additional file 1 of Logistic regression has similar performance to optimised machine learning algorithms in a clinical setting: application to the discrimination between type 1 and type 2 diabetes in young adults

description: Additional file 1: Figure S1. Flow diagram of participants through the model development stages. T1D: type 1 diabetes, T2D: type 2 diabetes. Figure S2. ROC AUC plots obtained using external validation dataset for seven prediction models. Legend: Solid lines: black = Support Vector Machine, dark grey = Logistic Regression, light grey = Random Forest. Dotted lines: black = Neural Network, dark grey = K-Nearest Neighbours, light grey = Gradient Boosting Machine. Figure S3. Correlation coefficient matrix and scatter plot of model predictions obtained from external test validation data.
DOI: 10.6084/m9.figshare.12569147.v1
title: Additional file 2 of Logistic regression has similar performance to optimised machine learning algorithms in a clinical setting: application to the discrimination between type 1 and type 2 diabetes in young adults

description: Additional file 2. R script.
DOI: 10.6084/m9.figshare.12568973
title: Additional file 4 of A novel method for interpreting survival analysis data: description and test on three major clinical trials on cardiovascular prevention

description: Additional file 4.
DOI: 10.6084/m9.figshare.12568973.v1
title: Additional file 4 of A novel method for interpreting survival analysis data: description and test on three major clinical trials on cardiovascular prevention

description: Additional file 4.
DOI: 10.5281/zenodo.3999424
title: PhD-delay Dataset for Online Stats Training

description: This is a dataset used for the online stats training website (https://www.rensvandeschoot.com/tutorials/) and is based on the data used by Van de Schoot, Yerkes, Mouw and Sonneveld 2013 Among many other questions, the researchers asked the Ph.D. recipients how long it took them to finish their Ph.D. thesis (n=333). It appeared that Ph.D. recipients took an average of 59.8 months (five years and four months) to complete their Ph.D. trajectory. The variable B3_difference_extra measures the difference between planned and actual project time in months (mean=9.97, minimum=-31, maximum=91, sd=14.43). For the the exercises we are interested in the question whether age (M = 31.7, SD = 6.86) of the Ph.D. recipients is related to a delay in their project. The relation between completion time and age is expected to be non-linear. This might be due to that at a certain point in your life (i.e., mid thirties), family life takes up more of your time than when you are in your twenties or when you are older. So, in our model the gapgap (<em>B3_difference_extra</em>) is the dependent variable and ageage (<em>E22_Age</em>) and age2age2(<em>E22_Age_Squared </em>) are the predictors. For more information on the sample, instruments, methodology and research context we refer the interested reader to Van de Schoot, Yerkes, Mouw and Sonneveld 2013.
description: Note that this dataset is for teaching purposes only.
DOI: 10.5281/zenodo.3999423
title: PhD-delay Dataset for Online Stats Training

description: This is a dataset used for the online stats training website (https://www.rensvandeschoot.com/tutorials/) and is based on the data used by Van de Schoot, Yerkes, Mouw and Sonneveld 2013 Among many other questions, the researchers asked the Ph.D. recipients how long it took them to finish their Ph.D. thesis (n=333). It appeared that Ph.D. recipients took an average of 59.8 months (five years and four months) to complete their Ph.D. trajectory. The variable B3_difference_extra measures the difference between planned and actual project time in months (mean=9.97, minimum=-31, maximum=91, sd=14.43). For the the exercises we are interested in the question whether age (M = 31.7, SD = 6.86) of the Ph.D. recipients is related to a delay in their project. The relation between completion time and age is expected to be non-linear. This might be due to that at a certain point in your life (i.e., mid thirties), family life takes up more of your time than when you are in your twenties or when you are older. So, in our model the gapgap (<em>B3_difference_extra</em>) is the dependent variable and ageage (<em>E22_Age</em>) and age2age2(<em>E22_Age_Squared </em>) are the predictors. For more information on the sample, instruments, methodology and research context we refer the interested reader to Van de Schoot, Yerkes, Mouw and Sonneveld 2013.
description: Note that this dataset is for teaching purposes only.
DOI: 10.6084/m9.figshare.12568871
title: Additional file 1 of A spatial-temporal study for the spread of dengue depending on climate factors in Pakistan (2006–2017)

description: Additional file 1.
DOI: 10.6084/m9.figshare.12568877
title: Additional file 2 of A spatial-temporal study for the spread of dengue depending on climate factors in Pakistan (2006–2017)

description: Additional file 2.
DOI: 10.6084/m9.figshare.12568871.v1
title: Additional file 1 of A spatial-temporal study for the spread of dengue depending on climate factors in Pakistan (2006–2017)

description: Additional file 1.
DOI: 10.6084/m9.figshare.12568877.v1
title: Additional file 2 of A spatial-temporal study for the spread of dengue depending on climate factors in Pakistan (2006–2017)

description: Additional file 2.
DOI: 10.6084/m9.figshare.12568814
title: Additional file 1 of Alternative splicing is highly variable among Daphnia pulex lineages in response to acute copper exposure

description: Additional file 1: Table S1. Quality and mapping information for the 21 Daphnia pulex RNA-sequencing libraries against both the JGI V1.0 2011 assembly from Ensembl (as reported in Table S3 in Chain et al. 2019 [53]) and also against the newer Daphnia PA42 3.0 reference assembly from Ye et al. 2017 [42]. Sample names consist of tank number (T), individual ID number, and clone (D, K or S). Samples were run in one of two lanes, and average quality (Q) is presented as PHRED scores. The % mapped reads and number of expressed genes are shown for each of the reference assemblies, as well as the alternative spliced genes against the newer reference. Table S2. Differentially expressed genes in response to acute Cu exposure when comparing all 6 controls with all 15 Cu exposed samples. Table S3. Summary of blast analysis comparing the differentially expressed (DE) genes identified by Chain et al. [53] with the genes from the newer Daphnia PA42 3.0 reference assembly from Ye et al. 2017 [42]. Table S4. Comparison of the DE genes identified by Chain et al. 2019 [53] and the genes from the newer Daphnia PA42 3.0 reference assembly from Ye et al. 2017 [42] using BLASTn. Table S5. Differentially spliced genes in response to acute Cu exposure when comparing all 6 controls with all 15 Cu exposed samples. Δ|Ψ| is the difference in the exon/intron inclusion level between the two conditions. Table S6. Differentially expressed genes in response to acute Cu exposure from clone specific analyses. Table S7. Genes commonly differentially expressed from global analysis and clone specific analyses. Table S8. Differentially spliced genes in response to acute Cu exposure in Clone D. Table S9. Differentially spliced genes in response to acute Cu exposure in Clone S. Table S10. Differentially spliced genes in response to acute Cu exposure in Clone K. Table S11. Gene Ontology enrichment analysis of the DE genes from the global analysis combining all 6 controls with all 15 Cu exposed samples (BP: biological process, MF: molecular function). Table S12. Gene Ontology enrichment analysis of the DE genes from clone specific analyses (BP: biological process, MF: molecular function). Table S13. Gene Ontology enrichment analysis of the DS genes from the global analysis combining all 6 controls with all 15 Cu exposed samples (BP: biological process, MF: molecular function). Table S14. Gene Ontology enrichment analysis of the DS genes from clone specific analyses (BP: biological process, MF: molecular function).
DOI: 10.6084/m9.figshare.12568808
title: Additional file 1 of A narrative review of gaps in the provision of integrated care for noncommunicable diseases in India

description: Additional file 1:. Mapping of reviewed studies across identified gaps. All reviewed studies presented in the result section are mapped for study locations (states and urban-rural location) across identified gaps
DOI: 10.6084/m9.figshare.12568760
title: IMTLBO algorithm for solving HFSP.zip

description: The code is designed for IMTLBO algorithm.
DOI: 10.6084/m9.figshare.12568808.v1
title: Additional file 1 of A narrative review of gaps in the provision of integrated care for noncommunicable diseases in India

description: Additional file 1:. Mapping of reviewed studies across identified gaps. All reviewed studies presented in the result section are mapped for study locations (states and urban-rural location) across identified gaps
DOI: 10.6084/m9.figshare.12568637
title: Gene database of Klebsiella pneumoniae

description: <i>Klebsiella pneumoniae </i>is a Gram-negative, facultative anaerobic, non-motile bacterium whose natural habitat is the soil environment. Also present in human mouth, skin and gastrointestinal tract, <i>K. pneumoniae</i> is a significant pathogen when in the lungs, where it results in distinctive sputum. Implicated in nosocomial infections, the bacterium remains a subject of intense study, where one pertinent research topic is in understanding how immunocompromised patients may be affected by the pathogen. This work sought to provide a resource of the genetic repertoire of <i>K. pneumoniae</i> based on the annotated genome sequence downloaded from Genbank. Comprising gene name, gene function and gene sequence of all genes in the genome of <i>K. pneumoniae</i>, the dataset should find use in guiding genome editing studies in generating loss-of-function mutants for understanding the pathogenesis mechanisms of this important pathogen.
DOI: 10.6084/m9.figshare.12568760.v2
title: IMTLBO algorithm for solving HFSP.zip

description: The code is designed for IMTLBO algorithm.
DOI: 10.6084/m9.figshare.12568760.v1
title: IMTLBO algorithm for solving HFSP.zip

description: The code is designed for IMTLBO algorithm.
DOI: 10.6084/m9.figshare.12568631
title: JVASP-55029 to JVASP-56656

description: JARVIS-DFT input/output files
DOI: 10.6084/m9.figshare.12568634
title: JVASP-56659 to JVASP-57577

description: JARVIS-DFT input/output files
DOI: 10.6084/m9.figshare.12568607
title: Dataset for Inexpensive, Non-invasive Biomarkers Predict Alzheimer Transition using Machine Learning Analysis of the Alzheimer’s Disease Neuroimaging (ADNI) Database

description: Data supplementing the information found in "Inexpensive, Non-invasive Biomarkers Predict Alzheimer Transition using Machine Learning Analysis of the Alzheimer’s Disease Neuroimaging (ADNI) Database" (Beltran et al 2020)
DOI: 10.6084/m9.figshare.12568631.v1
title: JVASP-55029 to JVASP-56656

description: JARVIS-DFT input/output files
DOI: 10.6084/m9.figshare.12568565
title: Study dataset.xlsx

description: Analytical dataset used in the study
DOI: 10.23641/asha.12568475
title: Tinnitus and Mental Distress (Reavis et al., 2020)

description: <b>Purpose:</b> The aim of this study was to examine therelationship between tinnitus and self-reported mental healthdistress, namely, depression symptoms and perceivedanxiety, in adults who participated in the National Healthand Nutrition Examinations Survey between 2009 and2012. A secondary aim was to determine if a history ofserving in the military modified the associations betweentinnitus and mental health distress.<b>Method:</b> This was a cross-sectional study design of anational data set that included 5,550 U.S. community-dwelling adults ages 20 years and older, 12.7% of whomwere military Veterans. Bivariable and multivariable logisticregression was used to estimate the association betweentinnitus and mental health distress. All measures werebased on self-report. Tinnitus and perceived anxiety wereeach assessed using a single question. Depression symptoms were assessed using the Patient Health Questionnaire, a validated questionnaire. Multivariable regression models were adjusted for key demographic and health factors, including self-reported hearing ability.<b>Results:</b> Prevalence of tinnitus was 15%. Compared toadults without tinnitus, adults with tinnitus had a 1.8-foldincrease in depression symptoms and a 1.5-fold increase inperceived anxiety after adjusting for potential confounders.Military Veteran status did not modify these observedassociations.<b>Conclusions:</b> Findings revealed an association betweentinnitus and both depression symptoms and perceivedanxiety, independent of potential confounders, among bothVeterans and non-Veterans. These results suggest, on apopulation level, that individuals with tinnitus have a greaterburden of perceived mental health distress and may benefitfrom interdisciplinary health care, self-help, and community-based interventions.<br>Reavis, K. M., Henry J. A., Marshall, L. M., & Carlson, K. F. (2020). Prevalence of self-reported depressionsymptoms and perceived anxiety among community-dwelling U.S. adults reporting tinnitus. Perspectives of the ASHA Special Interest Groups. Advance online publication. https://doi.org/10.1044/2020_PERSP-19-00178
DOI: 10.6084/m9.figshare.12568502
title: Geospatial data from: Connectivity of Protected Areas: Effect of Human Pressure and Subnational Contributions in the Ecoregions of Tropical Andean Countries

description: The dataset contains four folders with shapefiles (collection of four separate files to be opened in a GIS program) showing the results of connectivity indicators. Please consult the journal article (doi: 10.3390/land9080239) for information about connectivity indicators and scientific context.
DOI: 10.6084/m9.figshare.12858122
title: R0186_20170901a_videos

description: Rat 0186, skilled reaching data collected on 9/1/2017 in Leventhal laboratory by Alexandra Bova