cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.1348803.v8
title: Grid convergence of PyGBe with protein G B1 D4

description: This file bundle contains data, running script and final plot of grid-convergence study for protein G B1 D4' near a charged surface. The running script invokes the bioelectrostatics solver (PyGBe) with the given input data, computes the extrapolated value of solvation plus surface energy (with Richardson extrapolation), and plots the final result of convergence. This result is part of an upcoming paper. Publication information will be updated at the time of publication with the final citation. The pre-print is: —<strong>"Probing protein orientation near charged surfaces with an implicit-solvent model and the PyGBe code,"</strong> Christopher D. Cooper, Lorena A. Barba; arXiv: 1503.08150 (submitted). PyGBe solves biomolecular electrostatics problems using an implicit-solvent model (Poisson-Boltzmann) and it uses GPU hardware for fast execution. It is written in Python, PyCUDA and CUDA. More information about the PyGBe code in: <em>—"Validation of the PyGBe code for Poisson-Boltzmann equation with boundary element methods,"</em> Christopher Cooper, Lorena A. Barba. figshare.<br>http://dx.doi.org/10.6084/m9.figshare.154331 —"A biomolecular electrostatics solver using Python, GPUs and boundary elements that can handle solvent-filled cavities and Stern layers," Christopher D. Cooper, Jaydeep P. Bardhan, L. A. Barba. <em>Comput. Phys. Comm.</em>,<strong> 185</strong>(3):720–729 (March 2014). 10.1016/j.cpc.2013.10.028 // Preprint arXiv:1309.4018 <strong>Acknowledgement:</strong><br>This research is made possible by support from the Office of Naval Research, Applied Computational Analysis Program, N00014-11-1-0356. LAB also acknowledges support from NSF CAREER award OCI-1149784.
DOI: 10.6084/m9.figshare.1348804.v5
title: Protein orientation near a charged surface using PyGBe and protein G B1 D4

description: This file bundle contains data, running script and final plot of the protein-orientation study for protein G B1 D4' near a charged surface. The running script invokes the bioelectrostatics solver (PyGBe) with the given input data, computes the orientation probability distribution and plots the final result. File set includes: —1 mesh, 2 bodies<br>—PQR file<br>—mesh-generation script for sensor surface<br>—input parameters and running script for mesher<br>—input file for PyGBe and running script<br>—CSV file of output (tilt, rotation, energy)<br>—Figure 11 of the paper and plotting script This result is part of an upcoming paper. Publication information will be updated at the time of publication with the final citation. The pre-print is: —<strong>"Probing protein orientation near charged surfaces with an implicit-solvent model and the PyGBe code,"</strong> Christopher D. Cooper, Lorena A. Barba; arXiv: 1503.08150 (submitted). PyGBe solves biomolecular electrostatics problems using an implicit-solvent model (Poisson-Boltzmann) and it uses GPU hardware for fast execution. It is written in Python, PyCUDA and CUDA. More information about the PyGBe code in: —<em>"Validation of the PyGBe code for Poisson-Boltzmann equation with boundary element methods."</em> Christopher Cooper, Lorena A. Barba. figshare. http://dx.doi.org/10.6084/m9.figshare.154331 —"A biomolecular electrostatics solver using Python, GPUs and boundary elements that can handle solvent-filled cavities and Stern layers", Christopher D. Cooper, Jaydeep P. Bardhan, L. A. Barba. <em>Comput. Phys. Comm.</em>, <strong>185</strong>(3):720–729 (March 2014). 10.1016/j.cpc.2013.10.028 // Preprint arXiv:1309.4018 <strong>Acknowledgement:</strong> This research is made possible by support from the Office of Naval Research, Applied Computational Analysis Program, N00014-11-1-0356. LAB also acknowledges support from NSF CAREER award OCI-1149784.
DOI: 10.6084/m9.figshare.1348797
title: Composition and variation of the skin microbiota in sympatric species of European newts (Salamandridae)

description: The mucous skin of amphibians provides a habitat for microorganisms which may interact with their hosts and thereby affect their condition and health. Cultivation-independent analyses of the bacterial communities based on the detection of PCR-amplified bacterial 16S rRNA genes provides a direct approach to characterize their diversity. In the present pilot study we utilized this approach in combination with a high-throughput DNA sequencing technology (454 pyrosequencing), to characterize the bacterial community structure of the skin of three newt species (<em>Lissotriton vulgaris, Ichthyosaura alpestris, Triturus cristatus</em>), collected near Braunschweig, Germany. 16S rDNA sequences were obtained from 19 unique samples. On average, 6113 amplicon sequences were obtained per sample and these could phylogenetically be assigned to a total of 1615 different operational taxonomic units (OTUs). Altogether, most samples were rather similar in their dominant bacterial taxa. Most represented were Betaproteobacteria (46%; mostly <em>Janthinobacterium</em>), Gammaproteobacteria (28%; mostly <em>Pseudomonas</em>), Flavobacteria (phylum Bacteroidetes: 19%, mostly <em>Flavobacterium</em>), and Sphingobacteria (Bacteroidetes: 5%, mostly <em>Pedobacter</em>). We found no significant differences between the three newt species, or between hemi-nested vs. non-nested PCR, but a strong difference among sampling dates (15 and 17 April 2013) which might be explained by the ongoing transition of the newts from their terrestrial to aquatic phase which coincided with this period, or by differences between sexes as these were unevenly sampled on the two dates. 16S rRNA gene sequences retrieved in this study in several cases were identical or very similar to those previously found on the skin of North American salamanders.
DOI: 10.6084/m9.figshare.1348798
title: Sexual dimorphism and geographic variation in the morphology of a small southern African tortoise Psammobates oculifer

description: We studied morphological traits of <em>Psammobates oculifer</em> over its range to evaluate patterns in sexual size dimorphism (SSD) and geographic variation. Females were larger than males for 40 of the 44 characters measured and the growth trajectories of female parameters, scaled to body size (ANCOVA and MANCOVA on carapace length), most often followed juvenile patterns. For males, either the slopes or elevations of morphometric parameters were lower than in females. These divergent growth trajectories resulted in shape differences with female shells being higher and wider than the shells of males. Males matured at a smaller age and size, and had wider hind feet and larger shell openings relative to females; the latter being due to a shorter and narrower plastron, a shorter bridge length, and a bigger anal gap and cranial space. These male characteristics possibly enhance mobility for mate searching and combat, as well as courtship and mating behaviour. Small sample sizes for females in two of the three regions restricted geographic evaluations to males, for which shell shape, front foot width and hind leg length of the eastern group differed from the others. We interpreted these variations as the consequence of differences in the substratum and vegetation openness over the range of the species.
DOI: 10.6084/m9.figshare.1348802.v3
title: Protein orientation near charged surface using PyGBe with immunoglobulin G

description: This file bundle contains data, running script and final plot of the protein-orientation study for immunoglobulin G near a charged surface. The running script invokes the bioelectrostatics solver (PyGBe) with the given input data, computes the orientation probability distribution and plots the final result. File set includes: —1 mesh, 2 bodies —PQR file —mesh-generation script for sensor surface —input parameters and running script for mesher —input file for PyGBe and running script —CSV file of output (tilt, rotation, energy) —Figure 11 of the paper and plotting script This result is part of an upcoming paper. Publication information will be updated at the time of submission with the arXiv pre-print number, and at the time of publication with the final citation. PyGBe solves biomolecular electrostatics problems using an implicit-solvent model (Poisson-Boltzmann) and it uses GPU hardware for fast execution. It is written in Python, PyCUDA and CUDA. More information about the PyGBe code in: —<em>"Validation of the PyGBe code for Poisson-Boltzmann equation with boundary element methods."</em> Christopher Cooper, Lorena A. Barba. figshare. http://dx.doi.org/10.6084/m9.figshare.154331 —"A biomolecular electrostatics solver using Python, GPUs and boundary elements that can handle solvent-filled cavities and Stern layers", Christopher D. Cooper, Jaydeep P. Bardhan, L. A. Barba. <em>Comput. Phys. Comm.</em>, <strong>185</strong>(3):720–729 (March 2014). 10.1016/j.cpc.2013.10.028 // Preprint arXiv:1309.4018 <strong>Acknowledgement:</strong> This research is made possible by support from the Office of Naval Research, Applied Computational Analysis Progr
DOI: 10.6084/m9.figshare.1348797.v1
title: Composition and variation of the skin microbiota in sympatric species of European newts (Salamandridae)

description: The mucous skin of amphibians provides a habitat for microorganisms which may interact with their hosts and thereby affect their condition and health. Cultivation-independent analyses of the bacterial communities based on the detection of PCR-amplified bacterial 16S rRNA genes provides a direct approach to characterize their diversity. In the present pilot study we utilized this approach in combination with a high-throughput DNA sequencing technology (454 pyrosequencing), to characterize the bacterial community structure of the skin of three newt species (<em>Lissotriton vulgaris, Ichthyosaura alpestris, Triturus cristatus</em>), collected near Braunschweig, Germany. 16S rDNA sequences were obtained from 19 unique samples. On average, 6113 amplicon sequences were obtained per sample and these could phylogenetically be assigned to a total of 1615 different operational taxonomic units (OTUs). Altogether, most samples were rather similar in their dominant bacterial taxa. Most represented were Betaproteobacteria (46%; mostly <em>Janthinobacterium</em>), Gammaproteobacteria (28%; mostly <em>Pseudomonas</em>), Flavobacteria (phylum Bacteroidetes: 19%, mostly <em>Flavobacterium</em>), and Sphingobacteria (Bacteroidetes: 5%, mostly <em>Pedobacter</em>). We found no significant differences between the three newt species, or between hemi-nested vs. non-nested PCR, but a strong difference among sampling dates (15 and 17 April 2013) which might be explained by the ongoing transition of the newts from their terrestrial to aquatic phase which coincided with this period, or by differences between sexes as these were unevenly sampled on the two dates. 16S rRNA gene sequences retrieved in this study in several cases were identical or very similar to those previously found on the skin of North American salamanders.
DOI: 10.6084/m9.figshare.1348798.v1
title: Sexual dimorphism and geographic variation in the morphology of a small southern African tortoise Psammobates oculifer

description: We studied morphological traits of <em>Psammobates oculifer</em> over its range to evaluate patterns in sexual size dimorphism (SSD) and geographic variation. Females were larger than males for 40 of the 44 characters measured and the growth trajectories of female parameters, scaled to body size (ANCOVA and MANCOVA on carapace length), most often followed juvenile patterns. For males, either the slopes or elevations of morphometric parameters were lower than in females. These divergent growth trajectories resulted in shape differences with female shells being higher and wider than the shells of males. Males matured at a smaller age and size, and had wider hind feet and larger shell openings relative to females; the latter being due to a shorter and narrower plastron, a shorter bridge length, and a bigger anal gap and cranial space. These male characteristics possibly enhance mobility for mate searching and combat, as well as courtship and mating behaviour. Small sample sizes for females in two of the three regions restricted geographic evaluations to males, for which shell shape, front foot width and hind leg length of the eastern group differed from the others. We interpreted these variations as the consequence of differences in the substratum and vegetation openness over the range of the species.
DOI: 10.6084/m9.figshare.1348801.v8
title: Grid convergence of PyGBe with immunoglobulin G near a charged surface

description: Reproducibility package containing data, running script, plotting script and final plot of grid-convergence study for immunoglobulin G near a charged surface.The running script invokes the open-source bioelectrostatics solver PyGBe with the given input data, computes the extrapolated value of solvation plus surface energy (with Richardson extrapolation), and plots the final result of convergence. This result is part of the publication:—<strong>"Probing protein orientation near charged nanosurfaces for simulation-assisted biosensor design,"</strong> Christopher D. Cooper, Natalia C. Clementi, Lorena A. Barba; <i>J. Chem. Phys</i>., Vol. 143: 124709 (2015) PyGBe solves biomolecular electrostatics problems using an implicit-solvent model (Poisson-Boltzmann) and it uses GPU hardware for fast execution. It is written in Python, PyCUDA and CUDA. More information about the PyGBe code in: <em>—"Validation of the PyGBe code for Poisson-Boltzmann equation with boundary element methods,"</em> Christopher Cooper, Lorena A. Barba. figshare.<br>http://dx.doi.org/10.6084/m9.figshare.154331 — —"A biomolecular electrostatics solver using Python, GPUs and boundary elements that can handle solvent-filled cavities and Stern layers", Christopher D. Cooper, Jaydeep P. Bardhan, L. A. Barba. <em>Comput. Phys. Comm.</em>, <strong>185(</strong>3):720–729 (March 2014). 10.1016/j.cpc.2013.10.028 // Preprint arXiv:1309.4018 <strong>Acknowledgement:</strong><br>This research is made possible by support from the Office of Naval Research, Applied Computational Analysis Program, N00014-11-1-0356. LAB also acknowledges support from NSF CAREER award OCI-1149784.
DOI: 10.6084/m9.figshare.1348803.v9
title: Grid convergence of PyGBe with protein G B1 D4'

description: This file bundle contains data, running script and final plot of grid-convergence study for protein G B1 D4' near a charged surface. The running script invokes the bioelectrostatics solver (PyGBe) with the given input data, computes the extrapolated value of solvation plus surface energy (with Richardson extrapolation), and plots the final result of convergence. This result is part of an upcoming paper. Publication information will be updated at the time of publication with the final citation. The pre-print is: —<strong>"Probing protein orientation near charged surfaces with an implicit-solvent model and the PyGBe code,"</strong> Christopher D. Cooper, Lorena A. Barba; arXiv: 1503.08150 (submitted). PyGBe solves biomolecular electrostatics problems using an implicit-solvent model (Poisson-Boltzmann) and it uses GPU hardware for fast execution. It is written in Python, PyCUDA and CUDA. More information about the PyGBe code in: <em>—"Validation of the PyGBe code for Poisson-Boltzmann equation with boundary element methods,"</em> Christopher Cooper, Lorena A. Barba. figshare.<br>http://dx.doi.org/10.6084/m9.figshare.154331 —"A biomolecular electrostatics solver using Python, GPUs and boundary elements that can handle solvent-filled cavities and Stern layers," Christopher D. Cooper, Jaydeep P. Bardhan, L. A. Barba. <em>Comput. Phys. Comm.</em>,<strong> 185</strong>(3):720–729 (March 2014). 10.1016/j.cpc.2013.10.028 // Preprint arXiv:1309.4018 <strong>Acknowledgement:</strong><br>This research is made possible by support from the Office of Naval Research, Applied Computational Analysis Program, N00014-11-1-0356. LAB also acknowledges support from NSF CAREER award OCI-1149784.
DOI: 10.6084/m9.figshare.1348793
title: Nature Communications - Constructing Minimal Models for Complex Network Dynamics

description: <strong>Supplementary Software for Nature Communications - Constructing minimal models for complex system dynamics.</strong> <strong>Supplementary Software 1</strong> - reverse engineering sub-cellular dynamics: reproduces all data of Fig. 3 and related supplementary figures. <strong>Supplementary Software 2</strong> - reverse engineering human dynamics: reproduces all data of Fig. 4 and related supplementary figures.
DOI: 10.6084/m9.figshare.1348692
title: BRATS 2013 Leaderboard and Test Datasets

description: These files contain the BRATS2013 Brain tumour data and belong to the International BRATS 2013 Challenge in Image Segmentation from the MICCAI Conference of 2013.<br>Public data can be found in https://www.virtualskeleton.ch/BRATS/Start2013<br>More information can be found in https://wiki.cancerimagingarchive.net/display/Public/NCI-MICCAI+2013+Grand+Challenges+in+Image+Segmentation<br>The BRATS2013_CHALLENGE.zip file contains the 10 test cases released for the evaluation of the methods that participated in the Challenge.<br>The BRATS_Leaderboard.zip file conforms the Learderboard set used to perform an initial ranking of the best methods before the final test set evaluation.
DOI: 10.6084/m9.figshare.1348706
title: A review of the dinosaur track record from Jurassic and Cretaceous shallow marine carbonate depositional environments - Appendix

description: Appendix data for "A review of the dinosaur track record from Jurassic and Cretaceous shallow marine carbonate depositional environments" Chapter 20, Dinosaur Tracks: Next Steps. Files include both Excel data, and List of references
DOI: 10.6084/m9.figshare.1348801.v7
title: Grid convergence of PyGBe with immunoglobulin G near a charged surface

description: This file bundgle contains data, running script and final plot of grid-convergence study for immunoglobulin G near a charged surface. The running script invokes the bioelectrostatics solver (PyGBe) with the given input data, computes the extrapolated value of solvation plus surface energy (with Richardson extrapolation), and plots the final result of convergence. This result is part of an upcoming paper. Publication information will be updated at the time of submission with the arXiv pre-print number, and at the time of publication with the final citation. PyGBe solves biomolecular electrostatics problems using an implicit-solvent model (Poisson-Boltzmann) and it uses GPU hardware for fast execution. It is written in Python, PyCUDA and CUDA. More information about the PyGBe code in: <em>—"Validation of the PyGBe code for Poisson-Boltzmann equation with boundary element methods,"</em> Christopher Cooper, Lorena A. Barba. figshare.<br>http://dx.doi.org/10.6084/m9.figshare.154331 — —"A biomolecular electrostatics solver using Python, GPUs and boundary elements that can handle solvent-filled cavities and Stern layers", Christopher D. Cooper, Jaydeep P. Bardhan, L. A. Barba. <em>Comput. Phys. Comm.</em>, <strong>185(</strong>3):720–729 (March 2014). 10.1016/j.cpc.2013.10.028 // Preprint arXiv:1309.4018 <strong>Acknowledgement:</strong><br>This research is made possible by support from the Office of Naval Research, Applied Computational Analysis Program, N00014-11-1-0356. LAB also acknowledges support from NSF CAREER award OCI-1149784.
DOI: 10.6084/m9.figshare.1348802.v4
title: Protein orientation near charged surface using PyGBe with immunoglobulin G

description: This file bundle contains data, running script and final plot of the protein-orientation study for immunoglobulin G near a charged surface. The running script invokes the bioelectrostatics solver (PyGBe) with the given input data, computes the orientation probability distribution and plots the final result. File set includes: —1 mesh, 2 bodies —PQR file —mesh-generation script for sensor surface —input parameters and running script for mesher —input file for PyGBe and running script —CSV file of output (tilt, rotation, energy) —Figure 11 of the paper and plotting script This result is part of an upcoming paper. Publication information will be updated at the time of publication with the final citation. The pre-print is: —<strong>"Probing protein orientation near charged surfaces with an implicit-solvent model and the PyGBe code,"</strong> Christopher D. Cooper, Lorena A. Barba; arXiv: 1503.08150 (submitted). PyGBe solves biomolecular electrostatics problems using an implicit-solvent model (Poisson-Boltzmann) and it uses GPU hardware for fast execution. It is written in Python, PyCUDA and CUDA. More information about the PyGBe code in: —<em>"Validation of the PyGBe code for Poisson-Boltzmann equation with boundary element methods."</em> Christopher Cooper, Lorena A. Barba. figshare. http://dx.doi.org/10.6084/m9.figshare.154331 —"A biomolecular electrostatics solver using Python, GPUs and boundary elements that can handle solvent-filled cavities and Stern layers", Christopher D. Cooper, Jaydeep P. Bardhan, L. A. Barba. <em>Comput. Phys. Comm.</em>, <strong>185</strong>(3):720–729 (March 2014). 10.1016/j.cpc.2013.10.028 // Preprint arXiv:1309.4018 <strong>Acknowledgement:</strong> This research is made possible by support from the Office of Naval Research, Applied Computational Analysis Progr
DOI: 10.6084/m9.figshare.1348632
title: Barometro CIS 2015

description: See https://sevillarusers.wordpress.com/
DOI: 10.6084/m9.figshare.1348575
title: Predicting the Oligomeric States of Fluorescent Proteins

description: Dataset and R source code from the article "Predicting the Oligomeric States of Fluorescent Proteins".
DOI: 10.6084/m9.figshare.1348508
title: Two DNA barcodes and morphology for multi-method species delimitation in <i>Bonnetina</i> tarantulas (Araneae: Theraphosidae)

description: Supplementary data of the paper in the title
DOI: 10.6084/m9.figshare.1348632.v1
title: Barometro CIS 2015

description: See https://sevillarusers.wordpress.com/
DOI: 10.6084/m9.figshare.1348802.v5
title: Protein orientation near charged surface using PyGBe with immunoglobulin G

description: This file bundle contains data, running script and final plot of the protein-orientation study for immunoglobulin G near a charged surface. The running script invokes the bioelectrostatics solver (PyGBe) with the given input data, computes the orientation probability distribution and plots the final result. File set includes: —1 mesh, 2 bodies —PQR file —mesh-generation script for sensor surface —input parameters and running script for mesher —input file for PyGBe and running script —CSV file of output (tilt, rotation, energy) —Figure 11 of the paper and plotting script This result is part of an upcoming paper. Publication information will be updated at the time of publication with the final citation. The pre-print is: —<strong>"Probing protein orientation near charged surfaces with an implicit-solvent model and the PyGBe code,"</strong> Christopher D. Cooper, Lorena A. Barba; arXiv: 1503.08150 (submitted). PyGBe solves biomolecular electrostatics problems using an implicit-solvent model (Poisson-Boltzmann) and it uses GPU hardware for fast execution. It is written in Python, PyCUDA and CUDA. More information about the PyGBe code in: —<em>"Validation of the PyGBe code for Poisson-Boltzmann equation with boundary element methods."</em> Christopher Cooper, Lorena A. Barba. figshare. http://dx.doi.org/10.6084/m9.figshare.154331 —"A biomolecular electrostatics solver using Python, GPUs and boundary elements that can handle solvent-filled cavities and Stern layers", Christopher D. Cooper, Jaydeep P. Bardhan, L. A. Barba. <em>Comput. Phys. Comm.</em>, <strong>185</strong>(3):720–729 (March 2014). 10.1016/j.cpc.2013.10.028 // Preprint arXiv:1309.4018 <strong>Acknowledgement:</strong> This research is made possible by support from the Office of Naval Research, Applied Computational Analysis Progr