cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.2072890
title: Transcriptomic Analysis of Staphylococcal sRNAs: Insights ino Species Specific Adaption and the Evolution of Pathogeness

description: Next-generation sequencing technologies have dramatically increased the rate at which new genomes are sequenced. Accordingly, automated-annotation programs have become adept at identifying and annotating protein coding regions, as well as common and conserved RNAs. Additionally, RNAseq techniques have advanced our ability to identify and annotate regulatory RNAs (sRNAs), which remain significantly understudied. Recently, our group catalogued and annotated all previously known and newly identified sRNAs in several <i>Staphylococcus aureus </i>strains. These complete annotation files now serve as tools to compare the sRNA content of <i>S. aureus to</i> other bacterial strains to investigate the conservation of their sRNomes. Accordingly, in this study we performed RNAseq on two staphylococcal species, <i>S. epidermidis </i>and <i>S. carnosus,</i> identifying 118 and 89 sRNAs in these organisms, respectively. The sRNA content of all three species were then compared to elucidate their common and species-specific sRNA content, identifying a core set between 53 and 36 sRNAs encoded in each organism. In addition, we determined that <i>S. aureus has</i> the largest set of unique sRNAs (137) while <i>S. epidermidis has</i> the fewest (25). Finally, we identify a highly conserved sequence and structural motif differentially represented within, yet common to, both <i>S. aureus </i>and <i>S. epidermidis. </i>Collectively, in this study, we uncover the sRNome common to three staphylococcal species, shedding light on sRNAs that are likely to be involved in basic physiological processes common to the genus. More significantly, we have identified species-specific sRNAs that are likely to influence the individual lifestyle and behavior of these diverse staphylococcal strains.
DOI: 10.6084/m9.figshare.2951263.v486
title: UNH-RVAT 3-D Spalart--Allmaras RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions with the Spalart--Allmaras RANS model in OpenFOAM 2.3.x (590d57f32fed). See link below for case files (note: use the `SpalartAllmaras` branch), which include a script for downloading and extracting, as well as scripts for post-processing.
DOI: 10.6084/m9.figshare.2072929.v1
title: Data for preprint: Viral coinfection is shaped by bacterial ecology and virus-virus interactions across diverse microbial taxa and environments

description: Data sets (previously published) on virus-host interactions, reanalyzed here. Code for stats and graphs in article. See README file for data sources and more information.ABSTRACT Viral coinfection is a common across taxa and environments. Coinfection can enable genetic exchange, alter the dynamics of infections, and change the course of viral evolution. Despite the importance of coinfection to viral ecology and evolution, the factors that influence the frequency and extent of viral coinfection remain largely unexplored. Here I employ an extensive data set of virus-host interactions representing 6,564 microbial hosts and 13,103 viruses, to test the importance of bacterial traits and virus-virus interactions in shaping coinfection dynamics across a wide variety of taxa and environments. Using data from phage-host infection matrices, I found that bacterial ecology was the most important factor explaining variation (&gt;28%) in the potential for coinfection. Realized (actual) coinfection was affected by bacterial defense mechanisms at the single-cell level. In a natural environment, the presence of CRISPR spacers in marine bacteria limited coinfections with active viruses by ~50%, despite the absence of spacer matches in any active infection. Analysis of viral infections mined from published bacterial and archaeal sequence data (n= 5,492 hosts), showed prophages limited coinfection of host cultures by other prophages, but not extrachromosomal viruses. At the single-cell level, prophages virtually eliminated coinfection. Virus-virus interactions also enhanced coinfection with culture coinfection by ssDNA and dsDNA viruses twice as likely to occur than ssDNA-only coinfections. Collectively, these results suggest bacterial ecology and virus-virus interactions are strong drivers of coinfection across different taxa and environments. These findings highlight that virus-virus interactions constitute an important selective pressure on viruses that is often underappreciated.
DOI: 10.6084/m9.figshare.2951263.v487
title: UNH-RVAT 3-D Spalart--Allmaras RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions with the Spalart--Allmaras RANS model in OpenFOAM 2.3.x (590d57f32fed). See link below for case files (note: use the `SpalartAllmaras` branch), which include a script for downloading and extracting, as well as scripts for post-processing.
DOI: 10.6084/m9.figshare.2072320
title: London School of Hygiene and Tropical Medicine APCs 2015.xlsx

description: This spreadsheet contains details of article processing charges (APCs) paid by the London School of Hygiene & Tropical Medicine during 2015. The data is being collected as part of Jisc's APC data collection project to address the Total Cost of Ownership of scholarly communication (https://www.jisc-collections.ac.uk/Jisc-Monitor/APC-data-collection/).
DOI: 10.6084/m9.figshare.2072320.v1
title: London School of Hygiene and Tropical Medicine APCs 2015.xlsx

description: This spreadsheet contains details of article processing charges (APCs) paid by the London School of Hygiene & Tropical Medicine during 2015. The data is being collected as part of Jisc's APC data collection project to address the Total Cost of Ownership of scholarly communication (https://www.jisc-collections.ac.uk/Jisc-Monitor/APC-data-collection/).
DOI: 10.6084/m9.figshare.2072929.v3
title: Data for preprint: Viral coinfection is shaped by host ecology and virus-virus interactions across diverse microbial taxa and environments (Version 2)

description: Infection of more than one virus in a host, coinfection, is common across taxa and environments. Viral coinfection can enable genetic exchange, alter the dynamics of infections, and change the course of viral evolution. Yet, the factors influencing the frequency and extent of viral coinfection remain largely unexplored. Here, employing three microbial data sets of virus-host interactions covering cross-infectivity, culture coinfection, and single-cell coinfection (total: 6,564 microbial hosts, 13,103 viruses), I found evidence that ecology and virus-virus interactions are recurrent factors shaping coinfection patterns. Host ecology was a consistent and strong predictor of coinfection across all three datasets: potential, culture, and single-cell coinfection. Host phylogeny or taxonomy was a less consistent predictor, being weak or absent in potential and single-cell coinfection models, yet it was the strongest predictor in the culture coinfection model. Virus-virus interactions strongly affected coinfection. In the largest test of superinfection exclusion to date, prophage infection reduced culture coinfection by other prophages, with a weaker effect on extrachromosomal virus coinfection. At the single-cell level, prophages eliminated coinfection. Virus-virus interactions also <i>increased</i> culture coinfection with ssDNA-dsDNA coinfections &gt;2x more likely than ssDNA-only coinfections. Bacterial defense limited single-cell coinfection in marine bacteria CRISPR spacers reduced coinfections by ~50%, despite the absence of spacer matches in any active infection. Collectively, these results suggest the environment bacteria inhabit and the interactions among surrounding viruses are two factors consistently shaping viral coinfection patterns. These findings highlight the role of virus-virus interactions in coinfection with implications for phage therapy, microbiome dynamics, and viral infection treatments.
DOI: 10.6084/m9.figshare.2072176
title: teosinte_reinforcement

description: Pre proposal to NSF on incompatibility loci and reinforcement in maize and teosinte.
DOI: 10.6084/m9.figshare.2072305
title: table 1 references JDS.docx

description: Table 1. reviews that evaluated publication bias with less than 10 primary studies<br>
DOI: 10.6084/m9.figshare.2951263.v488
title: UNH-RVAT 3-D Spalart--Allmaras RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions with the Spalart--Allmaras RANS model in OpenFOAM 2.3.x (590d57f32fed). See link below for case files (note: use the `SpalartAllmaras` branch), which include a script for downloading and extracting, as well as scripts for post-processing.
DOI: 10.6084/m9.figshare.2072929.v4
title: Data for preprint: Viral coinfection is shaped by host ecology and virus-virus interactions across diverse microbial taxa and environments (Version 3)

description: Infection of more than one virus in a host, coinfection, is common across taxa and environments. Viral coinfection can enable genetic exchange, alter the dynamics of infections, and change the course of viral evolution. Yet, a systematic test of the factors explaining variation in viral coinfection across different taxa and environments awaits completion. Here I employ three microbial data sets of virus-host interactions covering cross-infectivity, culture coinfection, and single-cell coinfection (total: 6,564 microbial hosts, 13,103 viruses) to provide a broad, comprehensive picture of the ecological and biological factors shaping viral coinfection. I found evidence that ecology and virus-virus interactions are recurrent factors shaping coinfection patterns. Host ecology was a consistent and strong predictor of coinfection across all three datasets: cross-infectivity, culture coinfection, and single-cell coinfection. Host phylogeny or taxonomy was a less consistent predictor, being weak or absent in the cross-infectivity and single-cell coinfection models, yet it was the strongest predictor in the culture coinfection model. Virus-virus interactions strongly affected coinfection. In the largest test of superinfection exclusion to date, prophage sequences reduced culture coinfection by other prophages, with a weaker effect on extrachromosomal virus coinfection. At the single-cell level, prophage sequences eliminated coinfection. Virus-virus interactions also increased culture coinfection with ssDNA-dsDNA coinfections &gt;2x more likely than ssDNA-only coinfections. The presence of CRISPR spacers was associated with a ~50% reduction in single-cell coinfection in a marine bacteria, despite the absence of exact spacer matches in any active infection. Collectively, these results suggest the environment bacteria inhabit and the interactions among surrounding viruses are two factors consistently shaping viral coinfection patterns. These findings highlight the role of virus-virus interactions in coinfection with implications for phage therapy, microbiome dynamics, and viral infection treatments.<br>
DOI: 10.6084/m9.figshare.2951263.v489
title: UNH-RVAT 3-D Spalart--Allmaras RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions with the Spalart--Allmaras RANS model in OpenFOAM 2.3.x (590d57f32fed). See link below for case files (note: use the `SpalartAllmaras` branch), which include a script for downloading and extracting, as well as scripts for post-processing.
DOI: 10.6084/m9.figshare.2071048
title: Database: Image quality as a function of system gamma and dynamic range.

description: This dataset is from the soon-to-be-published: <br><br>System gamma as a function of image- and monitor- dynamic range. Journal of Vision (under review) by David Kane and Marcelo Bertalmio. <br><br>There are four files: Two data files containing the psychophysical and image data respectively, one example Matlab script (FigShareDemo) which loads the data and presents an analysis and one auxiliary file. The code is designed to be self-contained. <br>
DOI: 10.6084/m9.figshare.2072305.v1
title: table 1 references JDS.docx

description: Table 1. reviews that evaluated publication bias with less than 10 primary studies<br>
DOI: 10.6084/m9.figshare.2072176.v2
title: teosinte_reinforcement

description: Pre proposal to NSF on incompatibility loci and reinforcement in maize and teosinte.
DOI: 10.6084/m9.figshare.2072176.v1
title: FastLane.Print.pdf

description: Pre proposal to NSF on incompatibility loci and reinforcement in maize and teosinte.
DOI: 10.6084/m9.figshare.2951263.v490
title: UNH-RVAT 3-D Spalart--Allmaras RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions with the Spalart--Allmaras RANS model in OpenFOAM 2.3.x (590d57f32fed). See link below for case files (note: use the `SpalartAllmaras` branch), which include a script for downloading and extracting, as well as scripts for post-processing.
DOI: 10.6084/m9.figshare.2071048.v1
title: Database: Image quality as a function of system gamma and dynamic range.

description: This dataset is from the soon-to-be-published: <br><br>System gamma as a function of image- and monitor- dynamic range. Journal of Vision (under review) by David Kane and Marcelo Bertalmio. <br><br>There are four files: Two data files containing the psychophysical and image data respectively, one example Matlab script (FigShareDemo) which loads the data and presents an analysis and one auxiliary file. The code is designed to be self-contained. <br>
DOI: 10.6084/m9.figshare.2070496
title: Open Drug Discovery for the Zika Virus

description: latest version of preprint - with changes accepted in text.. supplemental included too<br>
DOI: 10.6084/m9.figshare.2070496.v1
title: Open Drug Discovery for the Zika Virus

description: latest version of preprint - with changes accepted in text.. supplemental included too<br>
DOI: 10.6084/m9.figshare.2070145
title: OpenGeoSys simulation of seawater intrusion

description: This is a modeling study of seawater intrusion problem simulation by OpenGeoSys. The results is prepared from science valentine. <br>
DOI: 10.6084/m9.figshare.2951263.v491
title: UNH-RVAT 3-D Spalart--Allmaras RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions with the Spalart--Allmaras RANS model in OpenFOAM 2.3.x (590d57f32fed). See link below for case files (note: use the `SpalartAllmaras` branch), which include a script for downloading and extracting, as well as scripts for post-processing.
DOI: 10.6084/m9.figshare.2070952.v1
title: 29 March 2015 Field Notes

description: Field notes including image and/or video records.
DOI: 10.6084/m9.figshare.2070124
title: Comparing Alzheimer's and Parkinson's Diseases Networks Using Graph Communities Structure

description: Datasets supporting the conclusions of the article "Comparing Alzheimer's and Parkinson's Diseases Networks Using Graph Communities Structure"<br>
DOI: 10.6084/m9.figshare.2070145.v1
title: OpenGeoSys simulation of seawater intrusion

description: This is a modeling study of seawater intrusion problem simulation by OpenGeoSys. The results is prepared from science valentine. <br>
DOI: 10.6084/m9.figshare.2951263.v492
title: UNH-RVAT 3-D Spalart--Allmaras RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions with the Spalart--Allmaras RANS model in OpenFOAM 2.3.x (590d57f32fed). See link below for case files (note: use the `SpalartAllmaras` branch), which include a script for downloading and extracting, as well as scripts for post-processing.
DOI: 10.6084/m9.figshare.2070124.v2
title: Comparing Alzheimer's and Parkinson's Diseases Networks Using Graph Communities Structure

description: Datasets supporting the conclusions of the article "Comparing Alzheimer's and Parkinson's Diseases Networks Using Graph Communities Structure"<br>
DOI: 10.6084/m9.figshare.2951263.v493
title: UNH-RVAT 3-D Spalart--Allmaras RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions with the Spalart--Allmaras RANS model in OpenFOAM 2.3.x (590d57f32fed). See link below for case files (note: use the `SpalartAllmaras` branch), which include a script for downloading and extracting, as well as scripts for post-processing.
DOI: 10.6084/m9.figshare.2070124.v1
title: Additional File 1.zip

description: Datasets supporting the conclusions of the article "Comparing Alzheimer's and Parkinson's Diseases Networks Using Graph Communities Structure"<br>
DOI: 10.6084/m9.figshare.2070166.v2
title: Acta of Bioengineering and Biomechanics Supplementary materials.wmv

description: Streamlines in the a-v fistula. An increase of blood velocity and vortices in the cephalic vein are visible
DOI: 10.6084/m9.figshare.2070097
title: Data from "Enigmatic diversity of the maxillary sinus in macaques and its possible role as a spatial compromise in craniofacial modifications"

description: Data from "Ito T., Nishimura T. D. (in press). Enigmatic diversity of the maxillary sinus in macaques and its possible role as a spatial compromise in craniofacial modifications. Evolutionary Biology. doi: 10.1007/s11692-016-9369-4."
DOI: 10.6084/m9.figshare.2070109
title: BioMediTech RPE dataset

description: <b>--BioMediTech RPE dataset--</b><br><i>(Further detailed explanation provided inside the archive)</i><br>RPE dataset for the article:- L. Nanni, M. Paci, F. L. C. Santos, H. Skottman, K. Juuti-Uusitalo, J. Hyttinen, Texture descriptors ensembles enable image-based classification of maturation of human stem cell-derived retinal pigmented epithelium, Plos One 2016<br><br><b>Image acquisition</b><br>Each RPE cell culture was imaged with the same settings. The microscope used was a Nikon Eclipse TE200S phase-contrast microscope (Nikon Instruments Europe B.V., Amstelveen, Netherlands) with the 20x objective and Ph1 phase contrast. <br>Cell imaging parameters are the following:- Quality 2560 x 1920 pixels- Mode Manual exposure- Exposure 25 – 125 ms- AE compensation 0- Gain 1.2 x- Contrast Dynamic<br><b>Subwindows dividing process</b><br>The dataset is composed of 195 images that were divided into 16 subwindows and each subwindow labeled into 4 classes by two trained operators. Every subwindow that presented clutters, out of focus elements or just background was discarded and is not part of this dataset.<br><b>Subwindows coding</b><br>Each subwindow was renamed after manual classification using a specific code. This code was assembled by five elements: (initial image classification)_( image number)_(subwindow number)_(subwindow classification)_(inclusion bit).tif<b><br></b><b>Classification criteria</b><b><br></b>The full dataset is composed of 1862 subwindows. The subwindows classification used the following parameters:<br><i>Fusiform </i>(216 images)-Fuse shaped cell contours and nucleus-Separated cells <br><i>Epithelioid</i> (547 images)-Globular shaped cell contours and nucleus-More packed<br><i>Cobblestone</i> (949 images)-Well defined cell contours and cell wall-Hexagonal shape-Homogeneous cytoplasm-Tightly packed <br><i>Mixed</i> (150 images)-Two or more of afore mentioned classes<br><b>Dataset file structure</b><b><br></b>RPE_dataset.zip├ Description.pdf Description of the acquisition, creation and structure of the dataset.├ Images Initial images (RPE cell culture)├ Subwindows Subwindows from the division of the initial images. Coded as mentioned in Subwindows coding├ license.txt The Creative Common Attribution 4.0. license
DOI: 10.17028/rd.lboro.2070091
title: REFIT Smart Home dataset

description: This dataset is maintained by Steven Firth (s.k.firth@lboro.ac.uk), Building Energy Research Group (BERG), School of Civil and Building Engineering, Loughborough University. The REFIT project (www.refitsmarthomes.org) carried out a study from 2013 to 2015 in which 20 UK homes were upgraded to Smart Homes through the installation of devices including Smart Meters, programmable thermostats, programmable radiator valves, motion sensors, door sensors and window sensors.Data was collected using building surveys, sensor placements and household interviews.The REFIT Smart Home dataset is one of the datasets made publically available by the project. This dataset includes: - Building survey data for the 20 homes. - Sensor measurements made before the Smart Home equipment was installed. - Sensor measurements made after the Smart Home equipment was installed. - Climate data recorded at a nearby weather station.---<br> This work has been carried out as part of the REFIT project (‘Personalised Retrofit Decision Support Tools for UK Homes using Smart Home Technology’, Grant Reference EP/K002457/1). REFIT is a consortium of three universities - Loughborough, Strathclyde and East Anglia - and ten industry stakeholders funded by the Engineering and Physical Sciences Research Council (EPSRC) under the Transforming Energy Demand in Buildings through Digital Innovation (BuildTEDDI) funding programme. For more information see: www.epsrc.ac.uk and www.refitsmarthomes.org---<br>The references below provide links to the REFIT project website, the TEDDINET website, a journal article which uses the dataset, and three additional datasets collected as part of the REFIT project by the University of Strathclyde and the University of East Anglia.
DOI: 10.6084/m9.figshare.2951263.v494
title: UNH-RVAT 3-D Spalart--Allmaras RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions with the Spalart--Allmaras RANS model in OpenFOAM 2.3.x (590d57f32fed). See link below for case files (note: use the `SpalartAllmaras` branch), which include a script for downloading and extracting, as well as scripts for post-processing.
DOI: 10.6084/m9.figshare.2070097.v1
title: Data from "Enigmatic diversity of the maxillary sinus in macaques and its possible role as a spatial compromise in craniofacial modifications"

description: Data from "Ito T., Nishimura T. D. (in press). Enigmatic diversity of the maxillary sinus in macaques and its possible role as a spatial compromise in craniofacial modifications. Evolutionary Biology. doi: 10.1007/s11692-016-9369-4."
DOI: 10.6084/m9.figshare.2951263.v495
title: UNH-RVAT 3-D Spalart--Allmaras RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions with the Spalart--Allmaras RANS model in OpenFOAM 2.3.x (590d57f32fed). See link below for case files (note: use the `SpalartAllmaras` branch), which include a script for downloading and extracting, as well as scripts for post-processing.
DOI: 10.6084/m9.figshare.2070061
title: S1 Data. Fluorescence data of No.3 specimen.csv

description: Fluorescence data of one sinus fungus ball specimen
DOI: 10.6084/m9.figshare.2070070
title: PAL_summary.txt

description: Information on short tandem repeats (SSR) and primer sequences spanning SSR identified in whole genome shotgun sequences (MiSeq) of single Chelonibia testudinaria individual. This is the output of the PALFINDER perl script (described in Castoe et al. 2012), which identifies SSR in unassembled next-generation genomic data, as well as primer sequences spanning the SSR. Tab-delimited file.
DOI: 10.6084/m9.figshare.2070070.v1
title: PAL_summary.txt

description: Information on short tandem repeats (SSR) and primer sequences spanning SSR identified in whole genome shotgun sequences (MiSeq) of single Chelonibia testudinaria individual. This is the output of the PALFINDER perl script (described in Castoe et al. 2012), which identifies SSR in unassembled next-generation genomic data, as well as primer sequences spanning the SSR. Tab-delimited file.
DOI: 10.6084/m9.figshare.2951263.v496
title: UNH-RVAT 3-D Spalart--Allmaras RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions with the Spalart--Allmaras RANS model in OpenFOAM 2.3.x (590d57f32fed). See link below for case files (note: use the `SpalartAllmaras` branch), which include a script for downloading and extracting, as well as scripts for post-processing.