cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.91441.v1
title: FMM_flow_diagram.pdf

description: Diagram illustrating the algorithmic flow of the fast multipole method, FMM, showing all the operations (kernels) required: P2M is the transformation of points to multipole expansions (point-to-multipole); M2M is the translation of multipole expansions from a deep level in the tree to parent levels (multipole-to-multipole); M2L is the transformation of multipole expansions (valid far from an evaluation point) to local expansions (valid nearby an evaluation point); L2L is the translation of local expansions down the tree; L2P is the evaluation of local expansions at evaluation points. This figure appeared in the following paper, under (c) 2011 The Authors. “A tuned and scalable fast multipole method as a preeminent algorithm for exascale systems”, Rio Yokota, L A Barba. Int. J. High-perf. Comput. (2011) Preprint arXiv:1106.2176 - doi:10.1177/1094342011429952 The figure is here shared under CC-BY.
DOI: 10.6084/m9.figshare.91439.v1
title: TreeSweeps.pdf

description: Illustration of the components in a fast multipole method (FMM), with the upward sweep depicted on the left side of the tree, and the downward sweep depicted on the right side of the tree. In the FMM, multipole expansions are created at the leaf level of the tree (P2M operation), they are then translated upwards to the center of the parent cells in the multipole-to-multipole (M2M) translation, then transformed to a local expansion in the multipole-to-local (M2L) operation for the siblings at all levels deeper than level 1. The local expansions are translated downward to children cells in the local-to-local (L2L) operation and finally, the local expansions are added at the leaf level and evaluated in the local-to-particle (L2P) operation. This figure appeared in the following paper, under (c) 2011 The Authors. “A tuned and scalable fast multipole method as a preeminent algorithm for exascale systems”, Rio Yokota, L A Barba. Int. J. High-perf. Comput. (2011) Preprint arXiv:1106.2176 - doi:10.1177/1094342011429952 The figure is here shared under CC-BY.
DOI: 10.6084/m9.figshare.91440.v1
title: TreeDomain.pdf

description: The multi-resolution nature of the hierarchical space division, with its associated tree, in the fast multipole method, FMM. The diagram shows a three-level hierarchical decomposition of a 2D domain. Tree nodes corresponding to domain boxes are shown in corresponding color. This figure appeared in the following paper, under (c) 2011 The Authors. “A tuned and scalable fast multipole method as a preeminent algorithm for exascale systems”, Rio Yokota, L A Barba. Int. J. High-perf. Comput. (2011) Preprint arXiv:1106.2176 - doi:10.1177/1094342011429952 The figure is here shared under CC-BY.
DOI: 10.6084/m9.figshare.91458.v3
title: Publication Process with OA

description: Updated 4-26-2012. Please leave a comment if you have any recommendations! (CC-BY)
DOI: 10.6084/m9.figshare.91441.v2
title: Illustration of the flow of an FMM calculation (fast multipole method)

description: Illustration of the components in a fast multipole method (FMM), with the upward sweep depicted on the left side of the tree, and the downward sweep depicted on the right side of the tree. In the FMM, multipole expansions are created at the leaf level of the tree (P2M operation), they are then translated upwards to the center of the parent cells in the multipole-to-multipole (M2M) translation, then transformed to a local expansion in the multipole-to-local (M2L) operation for the siblings at all levels deeper than level 1. The local expansions are translated downward to children cells in the local-to-local (L2L) operation and finally, the local expansions are added at the leaf level and evaluated in the local-to-particle (L2P) operation. This figure appeared in the following paper, under (c) 2011 The Authors. “A tuned and scalable fast multipole method as a preeminent algorithm for exascale systems”, Rio Yokota, L A Barba. Int. J. High-perf. Comput. (2011) Preprint arXiv:1106.2176 - doi:10.1177/1094342011429952 The figure is here shared under CC-BY.
DOI: 10.6084/m9.figshare.91437
title: PantonFellowshipApplicationVideo.ogv

description: This is my application video for stage 2 of the Panton Fellowship application process (http://pantonprinciples.org/panton-fellowships/) As befits this fellowship, I have created this video using free open source software and free open formats, including: * Ubuntu 'main' - operating system http://www.ubuntu.com/project/about-ubuntu/licensing (mostly GPL) * GUCViewer - webcam video & audio capture (GPL) * PiTiVi - video editing (LGPL 2.1) * Theora - video format, Vorbis - audio format, Ogg - container file format of which the libraries and software used to create it are GPL or BSD-like http://www.xiph.org/licenses/bsd/ Metadata is also important, thus I have embedded key details into the metatags of the file e.g. re-use licence, opting for a Creative Commons Attribution 3.0 license (http://creativecommons.org/licenses/by/3.0/).
DOI: 10.6084/m9.figshare.91440.v2
title: Hierarchical subdivision of space in FMM (fast multipole method)

description: Illustration of the components in a fast multipole method (FMM), with the upward sweep depicted on the left side of the tree, and the downward sweep depicted on the right side of the tree. In the FMM, multipole expansions are created at the leaf level of the tree (P2M operation), they are then translated upwards to the center of the parent cells in the multipole-to-multipole (M2M) translation, then transformed to a local expansion in the multipole-to-local (M2L) operation for the siblings at all levels deeper than level 1. The local expansions are translated downward to children cells in the local-to-local (L2L) operation and finally, the local expansions are added at the leaf level and evaluated in the local-to-particle (L2P) operation. This figure appeared in the following paper, under (c) 2011 The Authors. “A tuned and scalable fast multipole method as a preeminent algorithm for exascale systems”, Rio Yokota, L A Barba. Int. J. High-perf. Comput. (2011) Preprint arXiv:1106.2176 - doi:10.1177/1094342011429952 The figure is here shared under CC-BY.
DOI: 10.6084/m9.figshare.91437.v1
title: PantonFellowshipApplicationVideo.ogv

description: This is my application video for stage 2 of the Panton Fellowship application process (http://pantonprinciples.org/panton-fellowships/) As befits this fellowship, I have created this video using free open source software and free open formats, including: * Ubuntu 'main' - operating system http://www.ubuntu.com/project/about-ubuntu/licensing (mostly GPL) * GUCViewer - webcam video & audio capture (GPL) * PiTiVi - video editing (LGPL 2.1) * Theora - video format, Vorbis - audio format, Ogg - container file format of which the libraries and software used to create it are GPL or BSD-like http://www.xiph.org/licenses/bsd/ Metadata is also important, thus I have embedded key details into the metatags of the file e.g. re-use licence, opting for a Creative Commons Attribution 3.0 license (http://creativecommons.org/licenses/by/3.0/).
DOI: 10.6084/m9.figshare.91439.v2
title: TreeSweeps.pdf

description: Illustration of the components in a fast multipole method (FMM), with the upward sweep depicted on the left side of the tree, and the downward sweep depicted on the right side of the tree. In the FMM, multipole expansions are created at the leaf level of the tree (P2M operation), they are then translated upwards to the center of the parent cells in the multipole-to-multipole (M2M) translation, then transformed to a local expansion in the multipole-to-local (M2L) operation for the siblings at all levels deeper than level 1. The local expansions are translated downward to children cells in the local-to-local (L2L) operation and finally, the local expansions are added at the leaf level and evaluated in the local-to-particle (L2P) operation. This figure appeared in the following paper, under (c) 2011 The Authors. “A tuned and scalable fast multipole method as a preeminent algorithm for exascale systems”, Rio Yokota, L A Barba. Int. J. High-perf. Comput. (2011) Preprint arXiv:1106.2176 - doi:10.1177/1094342011429952 The figure is here shared under CC-BY.
DOI: 10.6084/m9.figshare.91429
title: MMVR/NextMed 2012 Oral Presentation: Augmented Reality Visualization for Guidance in Neurovascular Surgery

description: Arteriovenous Malformations (AVMs) are abnormal collections of blood vessels, fed by one or more feeding arteries (feeders) and drained by draining veins (drainers). Vessels may be unusually winding or large, and have weakened walls, which may result in intracranial hemorrhage. Treatment via surgery is recommended to protect against rupture and bleeding. In AVM surgery (and other types of neurovascular surgery), an operating microscope enables a magnified view of the surgical scene. However there is no:<br>– Information as to the vessel anatomy below the surface.<br>– Indication of the location of feeding and draining vessels<br>The surgical task is made more difficult with the frequent repositioning of the microscope during the surgery. This often difficult mapping task may be facilitated by using mixed reality visualizations such as augmented reality<br><br>
DOI: 10.6084/m9.figshare.91436
title: Green Solvents (ACS GCI)

description: List of green solvents selected by the American Chemical Society Green Chemistry Institute. Format: XML DataSheet, used by SketchEl, Mobile Molecular DataSheet.
DOI: 10.6084/m9.figshare.91430.v1
title: MICCAI 2010 : An Anthropomorphic Polyvinyl Alcohol Triple-Modality Brain Phantom based on Colin27

description: We propose a method for the creation of an anatomically and mechanically realistic brain phantom from polyvinyl alcohol cryogel (PVA-C) for validation of image processing methods for segmentation, reconstruction, registration, and denoising. PVA-C is material widely used in medical imaging phantoms for its mechanical similarities to soft tissues. The phantom was cast in a mold designed using the left hemiphere of the Colin27 brain dataset [Holmes el al. 1998] and contains deep sulci, a complete insular region, and an anatomically accurate left ventricle. Marker spheres and inflatable catheters were also implanted to enable good registration and simulate tissue deformation, respectively. The phantom was designed for triple modality imaging, giving good contrast images in computed tomography, ultrasound, and magnetic resonance imaging. Multimodal data acquired from this phantom are made freely available to the image processing community (http://pvabrain.inria.fr) and will aid in the validation and further development of medical image processing techniques.
DOI: 10.6084/m9.figshare.91429.v1
title: MMVR 2012 : Augmented Reality Visualization for Guidance in Neurovascular Surgery

description: In AVM surgery (and other type of neurovascular surgery), an operating microscope enables a magnified view of the surgical scene. However there is no:<br>– Information as to the vessel anatomy below the surface.<br>– Indication of the location of feeding and draining vessels The surgical task is made more difficult with the frequent repositioning of the microscope during the surgery. This often difficult mapping task may be facilitated by using mixed reality visualizations such as augmented reality.
DOI: 10.6084/m9.figshare.91428.v1
title: 20120205SPIEPoster.pdf

description: Cerebral arteriovenous malformations (AVMs) are a type of vascular anomaly consisting of large intertwined vascular growth (the nidus) that are prone to serious hemorrhaging and can result in patient death if left untreated. Intervention through surgical clipping of feeding and draining vessels to the nidus is a common treatment. However, identification of which vessels to clip is challenging even to experienced surgeons aided by conventional image guidance systems. In this work, we describe our methods for processing static preoperative angiographic images in order to effectively visualize the feeding and draining vessels of an AVM nidus. This is one by processing the vessel images with level-set front propagation and using the resulting maps to label the vessels by colour and decluttering the image by vessel topological distances. In order to aid the surgeon in the vessel clipping decision-making process during surgery, the results are displayed to the surgeon using augmented virtuality.
DOI: 10.6084/m9.figshare.91429.v3
title: MMVR/NextMed 2012 Oral Presentation: Augmented Reality Visualization for Guidance in Neurovascular Surgery

description: Arteriovenous Malformations (AVMs) are abnormal collections of blood vessels, fed by one or more feeding arteries (feeders) and drained by draining veins (drainers). Vessels may be unusually winding or large, and have weakened walls, which may result in intracranial hemorrhage. Treatment via surgery is recommended to protect against rupture and bleeding. In AVM surgery (and other types of neurovascular surgery), an operating microscope enables a magnified view of the surgical scene. However there is no:<br>– Information as to the vessel anatomy below the surface.<br>– Indication of the location of feeding and draining vessels<br>The surgical task is made more difficult with the frequent repositioning of the microscope during the surgery. This often difficult mapping task may be facilitated by using mixed reality visualizations such as augmented reality<br><br>
DOI: 10.6084/m9.figshare.91429.v2
title: MMVR/NextMed 2012 : Augmented Reality Visualization for Guidance in Neurovascular Surgery

description: Cerebral arteriovenous malformations (AVMs) are a type of vascular anomaly consisting of large intertwined vascular growth (the nidus) that are prone to serious hemorrhaging and can result in patient death if left untreated. Intervention through surgical clipping of feeding and draining vessels to the nidus is a common treatment. However, identification of which vessels to clip is challenging even to experienced surgeons aided by conventional image guidance systems. In this work, we describe our methods for processing static preoperative angiographic images in order to effectively visualize the feeding and draining vessels of an AVM nidus. This is one by processing the vessel images with level-set front propagation and using the resulting maps to label the vessels by colour and decluttering the image by vessel topological distances. In order to aid the surgeon in the vessel clipping decision-making process during surgery, the results are displayed to the surgeon using augmented virtuality.
DOI: 10.6084/m9.figshare.91428.v2
title: SPIE Medical Imaging 2012 : Visualizing the path of blood flow in static vessel images for Image Guided Surgery of Cerebral Arteriovenous Malformations

description: Cerebral arteriovenous malformations (AVMs) are a type of vascular anomaly consisting of large intertwined vascular growth (the nidus) that are prone to serious hemorrhaging and can result in patient death if left untreated. Intervention through surgical clipping of feeding and draining vessels to the nidus is a common treatment. However, identification of which vessels to clip is challenging even to experienced surgeons aided by conventional image guidance systems. In this work, we describe our methods for processing static preoperative angiographic images in order to effectively visualize the feeding and draining vessels of an AVM nidus. This is one by processing the vessel images with level-set front propagation and using the resulting maps to label the vessels by colour and decluttering the image by vessel topological distances. In order to aid the surgeon in the vessel clipping decision-making process during surgery, the results are displayed to the surgeon using augmented virtuality.
DOI: 10.6084/m9.figshare.91420.v1
title: Frequency Distribution of Unique Intron Size (25bp mean)

description: A frequency distribution plot of unique intron size (with the within intron repeat elements removed) in five teleost fish genomes, not included in the linked paper, but produced during the data analyses. Points on the plot are the mean of 25bp windows in intron size, to reduce noise.
DOI: 10.6084/m9.figshare.91401
title: The PEEL0 ("Partial Equilibrium Economic Land use model, time zero") initialization dataset.

description: We present a new land cover dataset for the conterminous USA called “PEEL<sub>0</sub>.” It is designed for climate change impact analysis based on land use change at 5 arc-minute resolution. The procedure described herein is adaptable for generating similar datasets for other regions and requirements. PEEL0 is derived from existing data products – the MODIS Land Cover Type (MLCT) and National Land Cover Database (NLCD) – but its design overcomes certain limitations that hinder their use for climate change impact analysis. First, while other products that focus on agriculture neglect non-agricultural land use/land cover (LULC) categories, PEELo contains eight distinct LULC classes in addition to a “crop” class. PEEL0 features subcell area fractions for each class, increasing its depth of information over traditional single-category LULC maps. Second, PEEL0 offers improved accuracy in characterizing cultivated lands, important for quantifying agriculturalactivity. PEEL0 provides a more accurate spatial distribution of cultivated lands over MLCT as compared to reference datasets and improved totals for cultivated land relative to USDA Major Land Uses census data. We present here landcover data for 2001 plus PEEL0's synthesis methodology, which combines information from multiple sources by establishing a common classification scheme at lower spatial resolution. PEEL0 was developed as an initialization dataset for a partial-equilibrium economic land use model (PEEL) that simulates land use/land cover change in response to exogenous agricultural prices and climate change scenarios. We anticipate that similar landcover data products will be of use to other modeling efforts worldwide. References: Best, N. (2011), "Synthesis of a complete land use/land cover data set for the conterminous United States emphasizing accuracy in area and distribution of agricultural activity", Master’s thesis, Northeastern Illinois University. Best, Neil, Elliott, Joshua and Foster, Ian, Synthesis of a Complete Land Use/Land Cover Dataset for the Conterminous United States (May 4, 2012). RDCEP Working Paper No. 12-08. Available at SSRN: http://ssrn.com/abstract=2051158 or http://dx.doi.org/10.2139/ssrn.2051158
DOI: 10.6084/m9.figshare.91408.v1
title: Teleost Fish Introns Frequency Distribution (25bp rolling mean)

description: A frequency distribution plot of intron size in five teleost fish genomes, not included in the linked paper, but produced during the data analyses.
DOI: 10.6084/m9.figshare.91403.v1
title: The transfer of dPNA:GFP from the Bolwig's Organ to Laterna Neurons and two other candidate neuronal clusters

description: <strong>S1. </strong>Confocal optical sections of a Pdf-RFP1 expressing Lateral Neuron (LN) in the 2n instar larval brain that colocalized dPNA:GFP label (S1', arrowheads). S1" is a merged image of S1 and S1' channels. <strong>S2. </strong>Yet another example of dPNA:GFP (arrowheads) co-localization in Pdf-RFP1 expressing LNs. RFP expression often times inside the LNs appear to be aggregated. <strong>S3. </strong>Confocal image of the left hemisphere of a larval brain expressing dPNA:GFP in Bolwig's Organ terminals (BOt). Arrowhead indicates dPNA:GFP puncta in one LN that colocalizes with the corresponding Pdf-RFP1 expressing LN in S3". The encircled cluster of GFP-label farther away from the BOt and the highly localized dPNA:GFP relatively closer (arrows) represent candidate neurons that are not LNs. This supports the notion that other interneurons, besides the LNs, also contact the BOt. The background RFP label in the brain excluding the Pdf-RFP1 is due to the activity of 3xP3-RFP that is a part of the chromosomal attP landing site. I used the PhiC31 method (Bischoff et al. 2007) to integrate the UAS-dPNA:GFP-attB transgene into the 3rd chromosome. Removal of the 3xP3-RFP transgene from the landing site is currently underway.
DOI: 10.6084/m9.figshare.91420.v2
title: Teleost Fish Unique Intron Size Frequency Distribution (25bp mean)

description: A frequency distribution plot of unique intron size (with the within intron repeat elements removed) in five teleost fish genomes, not included in the linked paper, but produced during the data analyses. Points on the plot are the mean of 25bp windows in intron size, to reduce noise.
DOI: 10.6084/m9.figshare.91384
title: BSc Poster. An Investigation Into the Culture Media Influence Within Plant Biotechnology

description: A poster for my BSc work and my first upload on Figshare. Unfortunatly, I did not receive the grade I was looking for though!
DOI: 10.6084/m9.figshare.91408.v2
title: Teleost Fish Intron Size Frequency Distribution (25bp rolling mean)

description: A frequency distribution plot of intron size in five teleost fish genomes, not included in the linked paper, but produced during the data analyses.
DOI: 10.6084/m9.figshare.91401.v1
title: peel.nc

description: The PEEL_0 ("Partial Equilibrium Economic Land use model, time zero") initialization dataset.
DOI: 10.6084/m9.figshare.91403.v2
title: The transfer of dPNA:GFP from the Bolwig's organ to Pdf expressing Lateral Neurons and two other candidate neuronal clusters

description: <strong>S1. </strong>Confocal optical sections of a Pdf-RFP1 expressing Lateral Neuron (LN) in the 2n instar larval brain that colocalized dPNA:GFP label (S1', arrowheads). S1" is a merged image of S1 and S1' channels. <strong>S2. </strong>Yet another example of dPNA:GFP (arrowheads) co-localization in Pdf-RFP1 expressing LNs. RFP expression often times inside the LNs appear to be aggregated. <strong>S3. </strong>Confocal image of the left hemisphere of a larval brain expressing dPNA:GFP in Bolwig's Organ terminals (BOt). Arrowhead indicates dPNA:GFP puncta in one LN that colocalizes with the corresponding Pdf-RFP1 expressing LN in S3". The encircled cluster of GFP-label farther away from the BOt and the highly localized dPNA:GFP relatively closer (arrows) represent candidate neurons that are not LNs. This supports the notion that other interneurons, besides the LNs, also contact the BOt. The background RFP label in the brain excluding the Pdf-RFP1 is due to the activity of 3xP3-RFP that is a part of the chromosomal attP landing site. I used the PhiC31 method (Bischoff et al. 2007) to integrate the UAS-dPNA:GFP-attB transgene into the 3rd chromosome. Removal of the 3xP3-RFP transgene from the landing site is currently underway.
DOI: 10.6084/m9.figshare.91384.v1
title: James Hutchinson

description: A poster for my BSc work and my first upload on Figshare.
DOI: 10.6084/m9.figshare.91408.v3
title: Teleost Fish Intron Size Frequency Distribution (25bp mean)

description: A frequency distribution plot of intron size in five teleost fish genomes, not included in the linked paper, but produced during the data analyses. Points on the plot are the mean of 25bp windows in intron size, to reduce noise.
DOI: 10.6084/m9.figshare.91401.v2
title: peel.nc

description: The PEEL_0 ("Partial Equilibrium Economic Land use model, time zero") initialization dataset.