cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.11302052
title: MOESM1 of Statistical modeling of STR capillary electrophoresis signal

description: Additional file 1 Provides the optimum locus parameters values for four peak model components and 6 datasets.
DOI: 10.6084/m9.figshare.11302055.v1
title: MOESM2 of Intestinal microbiota domination under extreme selective pressures characterized by metagenomic read cloud sequencing and assembly

description: Additional file 2. Draft genomes generated by read cloud assembly. Binning results and assembly metrics for Athena draft genomes generated for all stool samples sequenced using read clouds (separate tabs for time points A, C, D, and E).
DOI: 10.6084/m9.figshare.11302061.v1
title: MOESM2 of Statistical modeling of STR capillary electrophoresis signal

description: Additional file 2 Provides the optimum locus parameters values for four drop-out model components and 6 datasets.
DOI: 10.6084/m9.figshare.11302052.v1
title: MOESM1 of Statistical modeling of STR capillary electrophoresis signal

description: Additional file 1 Provides the optimum locus parameters values for four peak model components and 6 datasets.
DOI: 10.6084/m9.figshare.11302019
title: MOESM1 of Data mining polycystic ovary morphology in electronic medical record ultrasound reports

description: Additional file 1: Table S1. Confounder - These are phrases that indicate the presence of a volume confounder (words that indicate large volume is due to something other than PCOS). Table S2. PCOS words - These are phrases that indicate presence of polycystic morphology. Figure S3. Pseudocode - This is the pseudocode for the rules-based classifier routine. It implements the Rotterdam Consensus Criteria. Figure S4. Importance plot - The variables (word stems) determined to be most important for classification using the gradient boosted tree classifier. The top two variables, eval_right and eval_left are variables about the left and right ovarian volume extracted from the text.
DOI: 10.6084/m9.figshare.11302019.v1
title: MOESM1 of Data mining polycystic ovary morphology in electronic medical record ultrasound reports

description: Additional file 1: Table S1. Confounder - These are phrases that indicate the presence of a volume confounder (words that indicate large volume is due to something other than PCOS). Table S2. PCOS words - These are phrases that indicate presence of polycystic morphology. Figure S3. Pseudocode - This is the pseudocode for the rules-based classifier routine. It implements the Rotterdam Consensus Criteria. Figure S4. Importance plot - The variables (word stems) determined to be most important for classification using the gradient boosted tree classifier. The top two variables, eval_right and eval_left are variables about the left and right ovarian volume extracted from the text.
DOI: 10.6084/m9.figshare.11301998
title: Study of prevalence biofilm forming bacteria and broad-spectrum antibiofilm activity as anti-quorum sensing agent of streptomyces secondary metabolites on urinary catheter infections

description: Catheter-associated urinary tract infection is caused primarily by the multidrug-resistant biofilm forming bacteria of uropathogens. Theprevention and successful treatment of infections is difficult when catheters are colonized by bacterial biofilm growth. There have beenseveral efforts utilizing biocides in catheterizations, in attempts to inhibit all possible bacterial access to catheterized urethra. Nevertheless, itis clearly the case that such approaches are usually unsuccessful in the long run. Quorum quenching represents a new approach for control ofinfection for highly resistant microorganism. Therefore, there is a pressing need to find out natural Quorum Sensing inhibition compounds toovercome the resistance of the uropathogens. This unique concept of the antimicrobial control depends on reducing the burden of virulenceand suppress the development of antibiotic resistance rather than killing the bacteria. In the present investigation, the target microorganismswere isolated from attached surface of urinary catheters which evaluated quantitatively and qualitatively using standard techniques.Altogether 100 isolates were collected. Antibiotic susceptibility assays results showed that most of the isolates were resistant to a wide rangeof beta lactam and non-beta lactam antibiotics. Results of biofilm forming on microtiter plate and sterile foley catheter reveled that theseisolates possess a high capacity for biofilm formation on the both surface at the different degrees (strong, moderate and weak formingbiofilm) and required the only a short contact time for attachment. An anti-biofilm secondary metabolites from streptomyces strain sdLiexpressed broad-spectrum antibiofilm activity possibly by interfering with their QS systems against six different species producing strongbiofilm and multi-drug resistance. The anti-biofilm of secondary metabolites strain sdLi against six species was examined on sterilized coverglasses and urinary catheter sections using light microscope and scanning electron microscopy respectively. Evidence is presented that sdListreptomyces secondary metabolites showed broad-spectrum activity to attenuate the Quorum Sensing - dependent biofilm. Theinvestigation of quorum-sensing control may lead to the development of novel antipathogenic drugs.
DOI: 10.17028/rd.lboro.12841115.v1
title: Axisymmetric Body Aerodynamic Dataset

description: This is the dataset for the Axisymmetric body (0.16m diameter, elliptical nose cone, l/d = 5), collated by Max Varney (https://www.linkedin.com/in/max-varney/) on 2020/06/15. More information for the geometry and analysis using dataset #1 can be found in the paper "Three dimensional structure of the unsteady wake of an axisymmetric body", Physics of Fluids 31, 025113 (2019); https://doi.org/10.1063/1.5078379. The paper is also available on the Loughbrough University research repository at: https://hdl.handle.net/2134/37058<br>Data was collected in the Large Wind Tunnel at Loughborough University, a 2.5m^2, closed working section, fixed ground open return tunnel. Details of the tunnel can be found in: https://hdl.handle.net/2134/6674<br>The CAD geometry for the wind tunnel and the axisymmetric body with its mounting are included in the dataset as ASCII .stl files, with the units in meters. Note: at the start of every experiment the yaw and pitch of the model was incrementally adjusted to produce a symmetric base pressure.<br>All data is presented in SI units and all measurements are from the origin of the model (on the base of the model, at the centre of the base) with x positive downstream and z positive up, using a right hand rule to find positive y.<br>There are two datasets. DATASET #1 - 30m/s (Re_d=3.2x10^5)Contains: Tomographic Particle Image Velocimetry and Base Pressures<br>DATASET #2 - 40m/s (Re_d=4.3x10^5)Contains: Planar (Y=0m, Z=0m and Z=0.04m) and Stereo (X=0.06m, X=0.12m, X=0.18m and X=0.24m) Particle Image Velocimetry, Base Pressures and Forces
DOI: 10.17028/rd.lboro.12841115
title: Axisymmetric Body Aerodynamic Dataset

description: This is the dataset for the Axisymmetric body (0.16m diameter, elliptical nose cone, l/d = 5), collated by Max Varney (https://www.linkedin.com/in/max-varney/) on 2020/06/15. More information for the geometry and analysis using dataset #1 can be found in the paper "Three dimensional structure of the unsteady wake of an axisymmetric body", Physics of Fluids 31, 025113 (2019); https://doi.org/10.1063/1.5078379. The paper is also available on the Loughbrough University research repository at: https://hdl.handle.net/2134/37058<br>Data was collected in the Large Wind Tunnel at Loughborough University, a 2.5m^2, closed working section, fixed ground open return tunnel. Details of the tunnel can be found in: https://hdl.handle.net/2134/6674<br>The CAD geometry for the wind tunnel and the axisymmetric body with its mounting are included in the dataset as ASCII .stl files, with the units in meters. Note: at the start of every experiment the yaw and pitch of the model was incrementally adjusted to produce a symmetric base pressure.<br>All data is presented in SI units and all measurements are from the origin of the model (on the base of the model, at the centre of the base) with x positive downstream and z positive up, using a right hand rule to find positive y.<br>There are two datasets. DATASET #1 - 30m/s (Re_d=3.2x10^5)Contains: Tomographic Particle Image Velocimetry and Base Pressures<br>DATASET #2 - 40m/s (Re_d=4.3x10^5)Contains: Planar (Y=0m, Z=0m and Z=0.04m) and Stereo (X=0.06m, X=0.12m, X=0.18m and X=0.24m) Particle Image Velocimetry, Base Pressures and Forces
DOI: 10.6084/m9.figshare.11301977
title: Po Valley and Central Valley Daily Climate and Pollution Data

description: Daily averages for precipitation, dew point, dew point depression (DPD), sea level pressure (SLP), average temperature, max temperature (tmax), minimum temperature (tmin), visibility, wind speed, and NOx/SO2/PM concentration. Data looks at fog season averages for variables, eg November-March. <br>Daily averages were derived from hourly measurements. An daily measurements for all continuous variables were created by averaging measurements taken every 3 hours (0:00, 3:00, 6:00, 9:00, 12:00, 15:00, 18:00, 21:00). 7 of 8 hourly observations were required to calculate daily average.<br><br>Time series length varies by location, but near complete data is located for all sites from approximately 1980-2010.
DOI: 10.6084/m9.figshare.11301950
title: RNA-seq transcriptome of Pseudomonas aeruginosa PAO1 in Brain Heart Infusion broth

description: <i>Pseudomonas aeruginosa</i> PAO1 is a Gram-negative opportunistic pathogen. Armed with a relatively large genome encoding a versatile metabolism, <i>P. aeruginosa</i> is also an environmental microbe capable of living and thriving in varied environs. This helps account for the adaptability of this bacterium in different environmental and nutritional conditions. But, chief amongst these is the human pathogenicity of <i>P. aeruginosa</i> PAO1, where knowledge of its transcriptome would aid in deciphering the molecular mechanisms underpinning the relative success of this human pathogen in infecting immunocompromised patients. This thus forms the motivation for this work in processing the RNA-seq data of <i>P. aeruginosa</i> PAO1 wild-type cells cultivated in Brain Heart Infusion broth at 37 <sup>o</sup>C with 200 rpm shaking (ArrayExpress accession number: E-MTAB-6247). Using an in-house MATLAB RNA-seq analysis software, a total of 1.2 million reads were processed yielding a transcriptional map of this opportunistic pathogen. Specifically, the highly expressed genes were those encoding outer membrane lipoprotein OprI, outer membrane porin F OprF, chaperone protein ClpB, arginine/ornithine antiporter ArcD, molecular chaperone GroEL, lipid A 3-O-deacylase PagL, pyruvate carboxylase subunit B, and type 4 fimbrial protein PilA. Altogether, the transcriptome analysis paints a picture of a bacterium devoting significant metabolic resources to the maintenance of the cell membrane as well as mediating the correct folding of proteins. This could form the basis on which drug discovery work aiming to disrupt the outer membrane of <i>P. aeruginosa</i> PAO1 could be centred. Even interfering with protein folding could be a druggable target for drug discovery efforts. In all, the RNA-seq transcriptome of <i>P. aeruginosa</i> PAO1 presented in this work could inform future work aiming to both understand the basic biology of the bacterium’s growth in media reminiscent of the human environs as well as serving as important information for drug discovery efforts.<br><br>
DOI: 10.6084/m9.figshare.11301977.v1
title: Po Valley and Central Valley Daily Climate and Pollution Data

description: Daily averages for precipitation, dew point, dew point depression (DPD), sea level pressure (SLP), average temperature, max temperature (tmax), minimum temperature (tmin), visibility, wind speed, and NOx/SO2/PM concentration. Data looks at fog season averages for variables, eg November-March. <br>Daily averages were derived from hourly measurements. An daily measurements for all continuous variables were created by averaging measurements taken every 3 hours (0:00, 3:00, 6:00, 9:00, 12:00, 15:00, 18:00, 21:00). 7 of 8 hourly observations were required to calculate daily average.<br><br>Time series length varies by location, but near complete data is located for all sites from approximately 1980-2010.
DOI: 10.6084/m9.figshare.11301935
title: Language Divide in Investment - Dataset.xlsx

description: This dataset is compiled from various public sources. The economic variables are compiled from OECD Statistics, Penn World Table 9.1, CEPII Gravity and Distance database, ILO Statistics and World Bank database. The language variable are compiled from Japan Foundation and Educational Testing Services.
DOI: 10.6084/m9.figshare.11301950.v1
title: RNA-seq transcriptome of Pseudomonas aeruginosa PAO1 in Brain Heart Infusion broth

description: <i>Pseudomonas aeruginosa</i> PAO1 is a Gram-negative opportunistic pathogen. Armed with a relatively large genome encoding a versatile metabolism, <i>P. aeruginosa</i> is also an environmental microbe capable of living and thriving in varied environs. This helps account for the adaptability of this bacterium in different environmental and nutritional conditions. But, chief amongst these is the human pathogenicity of <i>P. aeruginosa</i> PAO1, where knowledge of its transcriptome would aid in deciphering the molecular mechanisms underpinning the relative success of this human pathogen in infecting immunocompromised patients. This thus forms the motivation for this work in processing the RNA-seq data of <i>P. aeruginosa</i> PAO1 wild-type cells cultivated in Brain Heart Infusion broth at 37 <sup>o</sup>C with 200 rpm shaking (ArrayExpress accession number: E-MTAB-6247). Using an in-house MATLAB RNA-seq analysis software, a total of 1.2 million reads were processed yielding a transcriptional map of this opportunistic pathogen. Specifically, the highly expressed genes were those encoding outer membrane lipoprotein OprI, outer membrane porin F OprF, chaperone protein ClpB, arginine/ornithine antiporter ArcD, molecular chaperone GroEL, lipid A 3-O-deacylase PagL, pyruvate carboxylase subunit B, and type 4 fimbrial protein PilA. Altogether, the transcriptome analysis paints a picture of a bacterium devoting significant metabolic resources to the maintenance of the cell membrane as well as mediating the correct folding of proteins. This could form the basis on which drug discovery work aiming to disrupt the outer membrane of <i>P. aeruginosa</i> PAO1 could be centred. Even interfering with protein folding could be a druggable target for drug discovery efforts. In all, the RNA-seq transcriptome of <i>P. aeruginosa</i> PAO1 presented in this work could inform future work aiming to both understand the basic biology of the bacterium’s growth in media reminiscent of the human environs as well as serving as important information for drug discovery efforts.<br><br>
DOI: 10.6084/m9.figshare.11301929
title: Marine macroalgae DNA barcode specimen data

description: Specimen data for 109 Arctic species of marine macroalgae and accompanying GenBank accessions for various genetic markers.
DOI: 10.6084/m9.figshare.11301935.v1
title: Language Divide in Investment - Dataset.xlsx

description: This dataset is compiled from various public sources. The economic variables are compiled from OECD Statistics, Penn World Table 9.1, CEPII Gravity and Distance database, ILO Statistics and World Bank database. The language variable are compiled from Japan Foundation and Educational Testing Services.
DOI: 10.6084/m9.figshare.11301884
title: 1

description: To classify RR sequences, we explored linear discriminant analysis (LDA) commonly used for detection of stress (Melillo et al., 2011; Kaur et al., 2014). We used a log-transformation to meet assumptions of homogeneity of variance and normally-distributed residuals. The homogeneity of variances was tested by means of Cochran C statistic. In order to evaluate the overall performance of the classifiers, results of the rest-stress comparisons were quantified by the criteria of sensitivity, specificity, precision, and accuracy. The highest estimates of the total classification accuracy and sensitivity and was achieved by α1 and by recurrent plot measures (Supplement 2).
DOI: 10.6084/m9.figshare.11301929.v1
title: Marine macroalgae DNA barcode specimen data

description: Specimen data for 109 Arctic species of marine macroalgae and accompanying GenBank accessions for various genetic markers.
DOI: 10.6084/m9.figshare.11301929.v3
title: Marine macroalgae DNA barcode specimen data

description: Specimen data for 109 Arctic species of marine macroalgae and accompanying GenBank accessions for various genetic markers.
DOI: 10.6084/m9.figshare.11301884.v1
title: Supplement 2

description: To classify RR sequences, we explored linear discriminant analysis (LDA) commonly used for detection of stress (Melillo et al., 2011; Kaur et al., 2014). We used a log-transformation to meet assumptions of homogeneity of variance and normally-distributed residuals. The homogeneity of variances was tested by means of Cochran C statistic. In order to evaluate the overall performance of the classifiers, results of the rest-stress comparisons were quantified by the criteria of sensitivity, specificity, precision, and accuracy. The highest estimates of the total classification accuracy and sensitivity and was achieved by α1 and by recurrent plot measures (Supplement 2).
DOI: 10.6084/m9.figshare.11301884.v3
title: Supplement

description: To classify RR sequences, we explored linear discriminant analysis (LDA) commonly used for detection of stress (Melillo et al., 2011; Kaur et al., 2014). We used a log-transformation to meet assumptions of homogeneity of variance and normally-distributed residuals. The homogeneity of variances was tested by means of Cochran C statistic. In order to evaluate the overall performance of the classifiers, results of the rest-stress comparisons were quantified by the criteria of sensitivity, specificity, precision, and accuracy. The highest estimates of the total classification accuracy and sensitivity and was achieved by α1 and by recurrent plot measures (Supplement 2).
DOI: 10.6084/m9.figshare.11301929.v2
title: Marine macroalgae DNA barcode specimen data

description: Specimen data for 109 Arctic species of marine macroalgae and accompanying GenBank accessions for various genetic markers.
DOI: 10.6084/m9.figshare.11301884.v2
title: Supplement 2

description: To classify RR sequences, we explored linear discriminant analysis (LDA) commonly used for detection of stress (Melillo et al., 2011; Kaur et al., 2014). We used a log-transformation to meet assumptions of homogeneity of variance and normally-distributed residuals. The homogeneity of variances was tested by means of Cochran C statistic. In order to evaluate the overall performance of the classifiers, results of the rest-stress comparisons were quantified by the criteria of sensitivity, specificity, precision, and accuracy. The highest estimates of the total classification accuracy and sensitivity and was achieved by α1 and by recurrent plot measures (Supplement 2).
DOI: 10.6084/m9.figshare.11301839
title: DisorderedEating_Hutchison

description: This data was collected by Erica N. Hutchison, Ph.D. as part of a dissertation study entitled Externalized Self and Emotion Dysregulation as Predictors of Disordered Eating. <br>Participants were an ethnically diverse sample of 178 men and women.
DOI: 10.6084/m9.figshare.11301839.v1
title: DisorderedEating_Hutchison

description: This data was collected by Erica N. Hutchison, Ph.D. as part of a dissertation study entitled Externalized Self and Emotion Dysregulation as Predictors of Disordered Eating. <br>Participants were an ethnically diverse sample of 178 men and women.
DOI: 10.6084/m9.figshare.11301773
title: Bi-iliac breadth for anthropoids

description: Bi-iliac breadth data used in Figure 2 of Hammond et al. manuscript. Authors request that users cite the original works when using this dataset. The non-human primate data originally appeared in Ward et al. (2017, Am J Phys Anthropol). The human data are from the Goldman dataset used by Auerbach and Ruff (2004, Am J Phys Anthropol). Additional data regarding the Goldman dataset are published online (as of 11/2019) at https://web.utk.edu/~auerbach/GOLD.htm<br>Institutional abbreviations are defined in the original publications. Methods for bi-iliac breadth data collection are defined in the original publications. <br><br>
DOI: 10.6084/m9.figshare.11301773.v2
title: Bi-iliac breadth for anthropoids

description: Bi-iliac breadth data used in Figure 2 of Hammond et al. manuscript. Authors request that users cite the original works when using this dataset. The non-human primate data originally appeared in Ward et al. (2017, Am J Phys Anthropol). The human data are from the Goldman dataset used by Auerbach and Ruff (2004, Am J Phys Anthropol). Additional data regarding the Goldman dataset are published online (as of 11/2019) at https://web.utk.edu/~auerbach/GOLD.htm<br>Institutional abbreviations are defined in the original publications. Methods for bi-iliac breadth data collection are defined in the original publications. <br><br>
DOI: 10.6084/m9.figshare.11301773.v1
title: bi-iliac breadth for anthropoids

description: Bi-iliac breadth data used in Figure 2 of Hammond et al. manuscript. Authors request that users cite the original works when using this dataset. The non-human primate data originally appeared in Ward et al. (2017, Am J Phys Anthropol). The human data are from the Goldman dataset used by Auerbach and Ruff (2004, Am J Phys Anthropol). Additional data regarding the Goldman dataset are published online (as of 11/2019) at https://web.utk.edu/~auerbach/GOLD.htm<br>Institutional abbreviations are defined in the original publications. Methods for bi-iliac breadth data collection are defined in the original publications. <br><br>
DOI: 10.6084/m9.figshare.11301752
title: Data from Gómez-Catasús et al. (2019) Scientific Reports

description: We present here the data employed in the article:<br>Gómez-Catasús, J., Garza, V., Morales, M.B. and Traba, J. Hierarchical habitat-use by an endangered steppe bird in fragmented landscapes is associated with large connected patches and high food availability. Scientific Reports.<br>We upload the following files. For any further queries please contact julia.gomez@uam.es<br>1) "Gomez-Catasus_et_al.2019_Scientific_Reports.csv"This is the dataset.<br>2) "2019-12-01_Description_dataset.rtf"An explanation of the variables included in the file "Gomez-Catasus_et_al.2019_Scientific_Reports.csv"
DOI: 10.6084/m9.figshare.11301722
title: Reducing uncertainty in species management - expert estimates for 60 aquatic invasive species in the Laurentian Great Lakes.

description: Demographic parameter values elicited from 24 North American experts for 60 aquatic invasive species present in the Laurentian Great Lakes. Values represent the minimum, mean, and maximum for a 95% probability of establishment for two parameters: alpha, the per propagule probability of establishment, and X0, the time it would take to reach half maximum population size. These values were subsequently incorporated in an existing mechanistic model (Drake et al., 2015), to examine the potential for secondary spread within the Great Lakes (Chenery et al., 2019).