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The Global Ocean Data Assimilation Experiment (GODAE) is an endeavor that will likely change the path of oceanography for many years. This attempt to assimilate, organize and provide massive quantities of widely varied oceanographic and meteorological data to the world could be a catalyst for new and innovative research opportunities. One of the data sources important to GODAE and of great possible value, the Array for Real-time Geostrophic Oceanography (ARGO), is another innovation that may lead to significant improvements in oceanographic modeling and research. The concept of thousands of autonomous floats, reporting ocean conditions to a database that can assimilate and provide this data in real or near-real time, affords countless opportunities for new methods of ocean prediction. The true test of GODAE is to assess the utility of the data available in a real world setting, and ascertain the relative usefulness as it relates to research opportunities and operational data needs. This thesis will assess the utility of the USGODAE data server by retrieving, processing, visualizing and employing the data to observe conditions in and near the Kuroshio Current. By attempting to use the data server in a method similar to future research and operational use, an understanding of its true potential may be reached.

The Global Ocean Data Assimilation Experiment (GODAE) is an endeavor that will likely change the path of oceanography for many years. This attempt to assimilate, organize and provide massive quantities of widely varied oceanographic and meteorological data to the world could be a catalyst for new and innovative research opportunities. One of the data sources important to GODAE and of great possible value, the Array for Real-time Geostrophic Oceanography (ARGO), is another innovation that may lead to significant improvements in oceanographic modeling and research. The concept of thousands of autonomous floats, reporting ocean conditions to a database that can assimilate and provide this data in real or near-real time, affords countless opportunities for new methods of ocean prediction. The true test of GODAE is to assess the utility of the data available in a real world setting, and ascertain the relative usefulness as it relates to research opportunities and operational data needs. This thesis will assess the utility of the USGODAE data server by retrieving, processing, visualizing and employing the data to observe conditions in and near the Kuroshio Current. By attempting to use the data server in a method similar to future research and operational use, an understanding of its true potential may be reached.

We propose a new method for the construction and visualization of boxplot-type displays for functional data. We use a recent functional data analysis framework, based on a representation of functions called square-root slope functions, to decompose observed variation in functional data into three main components: amplitude, phase, and vertical translation. We then construct separate displays for each component, using the geometry and metric of each representation space, based on a novel definition of the median, the two quartiles, and extreme observations. The outlyingness of functional data is a very complex concept. Thus, we propose to identify outliers based on any of the three main components after decomposition. We provide a variety of visualization tools for the proposed boxplot-type displays including surface plots. We evaluate the proposed method using extensive simulations and then focus our attention on three real data applications including exploratory data analysis of sea surface temperature functions, electrocardiogram functions and growth curves.

We propose a new method for the construction and visualization of boxplot-type displays for functional data. We use a recent functional data analysis framework, based on a representation of functions called square-root slope functions, to decompose observed variation in functional data into three main components: amplitude, phase, and vertical translation. We then construct separate displays for each component, using the geometry and metric of each representation space, based on a novel definition of the median, the two quartiles, and extreme observations. The outlyingness of functional data is a very complex concept. Thus, we propose to identify outliers based on any of the three main components after decomposition. We provide a variety of visualization tools for the proposed boxplot-type displays including surface plots. We evaluate the proposed method using extensive simulations and then focus our attention on three real data applications including exploratory data analysis of sea surface temperature functions, electrocardiogram functions and growth curves.

This article is from Frontiers in Pharmacology , volume 5 . Abstract In this review, some of the challenges and opportunities encountered during protein comparability assessments are summarized with an emphasis on developing new analytical approaches to better monitor higher-order protein structures. Several case studies are presented using high throughput biophysical methods to collect protein physical stability data as function of temperature, agitation, ionic strength and/or solution pH. These large data sets were then used to construct empirical phase diagrams (EPDs), radar charts, and comparative signature diagrams (CSDs) for data visualization and structural comparisons between the different proteins. Protein samples with different sizes, post-translational modifications, and inherent stability are presented: acidic fibroblast growth factor (FGF-1) mutants, different glycoforms of an IgG1 mAb prepared by deglycosylation, as well as comparisons of different formulations of an IgG1 mAb and granulocyte colony stimulating factor (GCSF). Using this approach, differences in structural integrity and conformational stability profiles were detected under stress conditions that could not be resolved by using the same techniques under ambient conditions (i.e., no stress). Thus, an evaluation of conformational stability differences may serve as an effective surrogate to monitor differences in higher-order structure between protein samples. These case studies are discussed in the context of potential utility in protein comparability studies.

This article is from Frontiers in Pharmacology , volume 5 . Abstract In this review, some of the challenges and opportunities encountered during protein comparability assessments are summarized with an emphasis on developing new analytical approaches to better monitor higher-order protein structures. Several case studies are presented using high throughput biophysical methods to collect protein physical stability data as function of temperature, agitation, ionic strength and/or solution pH. These large data sets were then used to construct empirical phase diagrams (EPDs), radar charts, and comparative signature diagrams (CSDs) for data visualization and structural comparisons between the different proteins. Protein samples with different sizes, post-translational modifications, and inherent stability are presented: acidic fibroblast growth factor (FGF-1) mutants, different glycoforms of an IgG1 mAb prepared by deglycosylation, as well as comparisons of different formulations of an IgG1 mAb and granulocyte colony stimulating factor (GCSF). Using this approach, differences in structural integrity and conformational stability profiles were detected under stress conditions that could not be resolved by using the same techniques under ambient conditions (i.e., no stress). Thus, an evaluation of conformational stability differences may serve as an effective surrogate to monitor differences in higher-order structure between protein samples. These case studies are discussed in the context of potential utility in protein comparability studies.

Pixualization: Glitch art and Data visualization by Ernesto Peña, Teresa M. Dobson, Omar Juárez InfoDesign Revista Brasileira de Design da Informação / Brazilian Journal of Information Design São Paulo | v. 17 | n. 1 [2017], p. 89 – 105 | ISSN 1808-5377

This report documents the algorithms of visualization of vertical and horizontal cross-sections of wind field, and the construction of wind direction's 3D flag for wind direction profile display. In addition, this report describes user-friendly Google Earth graphical user interface (GUI) menus designed to offer the user flexibility, and simple options in order to generate 3D displays in Google Earth and to save them in a Keyhole Markup Language (KML) file.

A study in fiscal year (FY) 2014 led by Dr Robert Erbacher, described in Evaluation of the Presentation of Network Data via Visualization Tools for Network Analysts, resulted in a large body of data from trial results. This report presents tools that I have developed to codify, display, and analyze the data. In the current visualization study, test subjects looked at a set of network intrusion alerts and decided which of those alerts represented true threats. The correct answers and the selections by each subject were recorded as fixed-format text files. My tools parse this text, insert the data into tables in a relational database, and create views to facilitate reading selected data from the tables. Additional tables were built, which contained sums of the performance statistics for each trial. I wrote Python programs to analyze the summary data and applied statistical tests to some of the means to determine whether they were statistically different. A plot capability was added as well.

Software was developed with NASA support to aid in the analysis and display of the massive amounts of data generated from satellites, observational field programs, and from model simulations. This software was developed in the context of the PATHFINDER (Probing ATmospHeric Flows in an Interactive and Distributed EnviRonment) Project. The overall aim of this project is to create a flexible, modular, and distributed environment for data handling, modeling simulations, data analysis, and visualization of atmospheric and fluid flows. Software completed with NASA support includes GEMPAK analysis, data handling, and display modules for which collaborators at NASA had primary responsibility, and prototype software modules for three-dimensional interactive and distributed control and display as well as data handling, for which NSCA was responsible. Overall process control was handled through a scientific and visualization application builder from Silicon Graphics known as the Iris Explorer. In addition, the GEMPAK related work (GEMVIS) was also ported to the Advanced Visualization System (AVS) application builder. Many modules were developed to enhance those already available in Iris Explorer including HDF file support, improved visualization and display, simple lattice math, and the handling of metadata through development of a new grid datatype. Complete source and runtime binaries along with on-line documentation is available via the World Wide Web at: http://redrock.ncsa.uiuc.edu/ PATHFINDER/pathre12/top/top.html.

The Lunar Prospector was co-developed by NASA Ames Research Center and Lockheed Martin, and was launched on January 6th, 1998. Its mission is to search for water ice and various elements in the Moon's surface, map its magnetic and gravity fields, and detect volcanic activity. For the first time, the World Wide Web is being used to graphically display near-real-time data from a planetary exploration mission to the global public. Science data from the craft's instruments, as well as engineering data for the spacecraft subsystems, are continuously displayed in time-varying XY plots. The craft's current location is displayed relative to the whole Moon, and as an off-craft observer would see in the reference frame of the craft, with the lunar terrain scrolling underneath. These features are implemented as Java applets. Analyzed data (element and mass distribution) is presented as 3D lunar maps using VRML and Javascript. During the development phase, implementations of the Java Virtual Machine were just beginning to mature enough to adequately accommodate our target featureset; incomplete and varying implementations were the biggest bottleneck to our ideal of ubiquitous browser access. Bottlenecks notwithstanding, the reaction from the Internet community was overwhelmingly enthusiastic.

Perl 2012 Circos Data Visualization How To EN

In the last ten years, significant developments have occurred in the separate fields of three-dimensional visualization and air quality. The primary goal of this research is to find means and methods of coupling air quality data to three-dimensional visualization in order to enhance ozone process analysis. Process analysis data refers to the individual rates associated with the different processes (advection, diffusion, deposition, and chemical reaction) within the mass balance and integrated reaction rate calculations of air quality models. Generally discarded by models after computations are completed, a process analysis method retains these rates for analysis into which factors are most significantly impacting the accumulation of ozone in the troposphere.

416 pages : 24 cm

The intersection of data science and visualization continues to create powerful opportunities for organizations. Power BI, with its robust capabilities, allows data scientists to transform complex datasets into insightful visual narratives. 

A flow visualization test was conducted with the simple straked wing in August, 1996 (at the National Aerospace Laboratory NLR, The Netherlands), for the purpose of obtaining flow visualization data to complement the pressure and force data base generated in earlier tests of the same configuration. This test was conducted in two parts to examine the flow field characteristics: (1) at high alpha conditions that involve vortices, shocks, and separated flows, and (2) at low alpha conditions typical of transonic LCO flows with and without tip stores. Laser light sheet/water vapor techniques were used to illuminate the flows, and video recording was used to obtain the data. Both low and high speed video cameras were used to examine spanwise and streamwise laser sheet positions. In addition, under NLR funding, some preliminary particle image velocimetry (PIV) data were obtained at M=0.225 and 0.6, as well as some pulsed laser flow visualization (9 nano-sec pulse) at M=0.9. Correlation was performed between the flow visualization data from this test and the pressure/force data obtained in 1992 on the same configuration. This report contains the flow visualization results for the stationary model.

Data visualization is a key component to business and data analytics, allowing analysts in businesses to create tools such as dashboards for business executives. Various software packages allow businesses to create these tools in order to manipulate data for making informed business decisions. The focus is to examine what skills employers are looking for in potential job candidates, and compare with the ability to include those technological skills in a business school curriculum. The researchers explored a variety of software tools, and reported their initial results and conclusions.

The increasing complexity of today's information environment is reflected in the increasing complexity of military operations. A brief look at the evening news reveals organizational and technological complexity that was unheard-of even a generation ago: Improved explosive devices detonated via cell phones; unmanned vehicles both ground and air; handheld communication devices on virtually every combatant; ubiquitous and near-instantaneous media coverage of even the smallest military actions. All of these factors, and more, can critically impact the success of military operations. Understanding this new environment to facilitate mission success is truly a challenge. Just as with operations in the real-world tactical environment, our ability to test military equipment depends critically on our ability to rapidly understand causes and effects among numerous variables represented by extremely high-volume data of several types (such as numerical, visual, boolean, and textual). Traditional analysis methods, which often center on very efficient mining of single data sources, are increasingly inadequate to meet this challenge, and advanced means of visualization and understanding are required. While our initial focus was on analysis -- understanding data produced by test events -- we have quickly come to the realization that operational test and evaluation will use visualization technologies not only for analysis, but for planning and control of test events. This control is not only of live operations, but increasingly of sophisticated federations of models and simulations that support the conduct of our tests. The ability to develop, integrate, and operate these federations directly reflects the complexity of actual military operations, and depends on our ability to understand the relationships among multiple software applications, each of which is extremely complex in its own right.

David McCandless turns complex data sets (like worldwide military spending, media buzz, Facebook status updates) into beautiful, simple diagrams that tease out unseen patterns and connections. Good design, he suggests, is the best way to navigate information glut -- and it may just change the way we see the world.

In the 21st century, the age of information and technology, there is an increasing importance to statistical literacy for everyday life. In addition, education innovation and globalisation in the past decade in Europe has resulted in a new perceived complexity of reality that affected the curriculum and statistics education, with a shift from content knowledge to competences. Moreover, the amount of data that can today be accessed on the Internet suggests that students can choose nearly any topic of interest to them for their work on, which can increase their motivation. Furthermore, new location-based technologies actively promote the power of digital spatial representation and geo-communication opportunities. So, the aim of this paper is to present an application of 3D visualization of statistical data in conjunction with regional spatial data from Greece, using the www services and applications in real time. This has been accomplished by modifying the code of an open source of educational software: the online platform iUse, in particular. Nowadays, there has been a clear shift towards Free Software/Open Source Software (FOSS) from users worldwide. FOSS has begun to play an important and decisive role in Greek education, as well. The examples which are taken from Greece are related to statistical data from various sectors of modern life. The spatial background used is the 13 Administrative regions of Greece. All functions and modifications implemented were made with FOSS.

Cluster Analysis; Dimensionality Reduction; Swarm Intelligence; Visualization; Unsupervised Machine Learning; Data Science; Knowledge Discovery; 3D Printing; Self-Organization; Emergence; Game Theory; Advanced Analytics; High-Dimensional Data; Multivariate Data; Analysis of Structured Data

Struggling to align teams or get buy-in from stakeholders? This voice segment by PiLog Group highlights how effective data visualization can transform communication and collaboration across all levels of an organization. Discover how visuals cut through complexity, make reports more intuitive, and drive better decisions. If you're managing projects, reporting to executives, or leading transformation initiatives—this is your guide to making data speak clearly and convincingly.

This report discusses the development of tools to visualize arena data from tests that were set up to characterize munitions. The test data were collected in flash panels and bundles that only capture a small amount of data. The visualization tools allowed the analyst to recreate the test from the raw data and display key elements. Wound ballistics data were visualized and displayed in various Three-Dimensional (3-D) environments. Several innovative Python-based environments have been created to display Vulnerability/Lethality (V/L) in innovative ways. The environments utilized Python and open-source scenegraphs to assist in the understanding of the V/L data and analysis.

Akshat

Six degree of freedom studies were utilized to extract a band of yawing and rolling moment coefficients from the F/A-18 aircraft flight records. These were compared with 0.06 scale model data obtained in a 16T wind tunnel facility. The results, indicate the flight test yawing moment data exhibit an improvement over the wind tunnel data to near neutral stability and a significant reduction in lateral stability (again to anear neutral level). These data are consistent with the flight test results since the motion was characterized by a relatively slo departure. Flight tests repeated the slow yaw departure at M 0.3. Only 0.16 scale model wind tunnel data showed levels of lateral stability similar to the flight test results. Accordingly, geometric modifications were investigated on the 0.16 scale model in the 30x60 foot wind tunnel to improve high angle of attack lateral stability.

Data visualization is an important tool to analyze complex Spatio Temporal data. The spatio-temporal data can be visualized using 2D, 3D or any other type of maps. Cartography is the major technique used in mapping. The data can also be visualized by placing different layers of maps one on other, which is done by using GIS. Many data visualization techniques are in trend but the usage of the techniques must be decided by considering the application requirements.

Data visualization is an important tool to analyze complex Spatio Temporal data. The spatio-temporal data can be visualized using 2D, 3D or any other type of maps. Cartography is the major technique used in mapping. The data can also be visualized by placing different layers of maps one on other, which is done by using GIS. Many data visualization techniques are in trend but the usage of the techniques must be decided by considering the application requirements.

Data visualization is an important tool to analyze complex Spatio Temporal data. The spatio-temporal data can be visualized using 2D, 3D or any other type of maps. Cartography is the major technique used in mapping. The data can also be visualized by placing different layers of maps one on other, which is done by using GIS. Many data visualization techniques are in trend but the usage of the techniques must be decided by considering the application requirements.

We describe Giovanni, the NASA Goddard developed online visualization and analysis tool that allows users explore various phenomena without learning remote sensing data formats and downloading voluminous data. Using MODIS aerosol data as an example, we formulate an approach to the data fusion for Giovanni to further enrich online multi-sensor remote sensing data comparison and analysis.

A wide variety of software tools has been successfully used to visualize, analyze and present computational and experimental data at Langley Research Center. These tools can be categorized according to five primary uses: (1) 2-D image analysis, (2) conventional 3-D visualization, (3) volume visualization, (4) photo-realistic rendering, or (5) special purpose applications. Software is accessible in each of these categories for Langley personnel, and training or consultation can be arranged with the Data Visualization and Animation laboratory staff.

Perma.cc archive of https://public.tableau.com/app/profile/cartesian/viz/GlobalMobilePhonePenetration/GlobalMobilePhonePenetration created on 2022-08-29 20:34:55.149974+00:00.

Steven C. Howell http://pyohio.org/schedule/presentation/288/ Bokeh is a powerful library for creating interactive data visualizations in the style of D3.js without writing JavaScript. In this tutorial, you will learn to use Bokeh to - create simple interactive plots, both from scripts and Jupyter notebooks - link interactive visualizations to a running python instance - plot streamed data - interactively view large datasets with Datashader PyOhio is a free (thanks sponsors!) annual conference for Python programmers in and around Ohio and the entire Midwest. http://pyohio.org/

This is the first part of a presentation of a new method for volume visualization of 3D biomedical data produced by CT, MRI and other biomedical scanner devices.

This is the first part of a presentation of a new method for volume visualization of 3D biomedical data produced by CT, MRI and other biomedical scanner devices.

This article is from Bioinformatics , volume 30 . Abstract Motivation: High-throughput single-cell quantitative real-time polymerase chain reaction (qPCR) is a promising technique allowing for new insights in complex cellular processes. However, the PCR reaction can be detected only up to a certain detection limit, whereas failed reactions could be due to low or absent expression, and the true expression level is unknown. Because this censoring can occur for high proportions of the data, it is one of the main challenges when dealing with single-cell qPCR data. Principal component analysis (PCA) is an important tool for visualizing the structure of high-dimensional data as well as for identifying subpopulations of cells. However, to date it is not clear how to perform a PCA of censored data. We present a probabilistic approach that accounts for the censoring and evaluate it for two typical datasets containing single-cell qPCR data.Results: We use the Gaussian process latent variable model framework to account for censoring by introducing an appropriate noise model and allowing a different kernel for each dimension. We evaluate this new approach for two typical qPCR datasets (of mouse embryonic stem cells and blood stem/progenitor cells, respectively) by performing linear and non-linear probabilistic PCA. Taking the censoring into account results in a 2D representation of the data, which better reflects its known structure: in both datasets, our new approach results in a better separation of known cell types and is able to reveal subpopulations in one dataset that could not be resolved using standard PCA.Availability and implementation: The implementation was based on the existing Gaussian process latent variable model toolbox (https://github.com/SheffieldML/GPmat); extensions for noise models and kernels accounting for censoring are available at http://icb.helmholtz-muenchen.de/censgplvm.Contact:[email protected] mentary information: Supplementary data are available at Bioinformatics online.

This article is from Bioinformatics , volume 30 . Abstract Motivation: High-throughput single-cell quantitative real-time polymerase chain reaction (qPCR) is a promising technique allowing for new insights in complex cellular processes. However, the PCR reaction can be detected only up to a certain detection limit, whereas failed reactions could be due to low or absent expression, and the true expression level is unknown. Because this censoring can occur for high proportions of the data, it is one of the main challenges when dealing with single-cell qPCR data. Principal component analysis (PCA) is an important tool for visualizing the structure of high-dimensional data as well as for identifying subpopulations of cells. However, to date it is not clear how to perform a PCA of censored data. We present a probabilistic approach that accounts for the censoring and evaluate it for two typical datasets containing single-cell qPCR data.Results: We use the Gaussian process latent variable model framework to account for censoring by introducing an appropriate noise model and allowing a different kernel for each dimension. We evaluate this new approach for two typical qPCR datasets (of mouse embryonic stem cells and blood stem/progenitor cells, respectively) by performing linear and non-linear probabilistic PCA. Taking the censoring into account results in a 2D representation of the data, which better reflects its known structure: in both datasets, our new approach results in a better separation of known cell types and is able to reveal subpopulations in one dataset that could not be resolved using standard PCA.Availability and implementation: The implementation was based on the existing Gaussian process latent variable model toolbox (https://github.com/SheffieldML/GPmat); extensions for noise models and kernels accounting for censoring are available at http://icb.helmholtz-muenchen.de/censgplvm.Contact:[email protected] mentary information: Supplementary data are available at Bioinformatics online.

This article is from Bioinformatics , volume 30 . Abstract Motivation: High-throughput single-cell quantitative real-time polymerase chain reaction (qPCR) is a promising technique allowing for new insights in complex cellular processes. However, the PCR reaction can be detected only up to a certain detection limit, whereas failed reactions could be due to low or absent expression, and the true expression level is unknown. Because this censoring can occur for high proportions of the data, it is one of the main challenges when dealing with single-cell qPCR data. Principal component analysis (PCA) is an important tool for visualizing the structure of high-dimensional data as well as for identifying subpopulations of cells. However, to date it is not clear how to perform a PCA of censored data. We present a probabilistic approach that accounts for the censoring and evaluate it for two typical datasets containing single-cell qPCR data.Results: We use the Gaussian process latent variable model framework to account for censoring by introducing an appropriate noise model and allowing a different kernel for each dimension. We evaluate this new approach for two typical qPCR datasets (of mouse embryonic stem cells and blood stem/progenitor cells, respectively) by performing linear and non-linear probabilistic PCA. Taking the censoring into account results in a 2D representation of the data, which better reflects its known structure: in both datasets, our new approach results in a better separation of known cell types and is able to reveal subpopulations in one dataset that could not be resolved using standard PCA.Availability and implementation: The implementation was based on the existing Gaussian process latent variable model toolbox (https://github.com/SheffieldML/GPmat); extensions for noise models and kernels accounting for censoring are available at http://icb.helmholtz-muenchen.de/censgplvm.Contact:[email protected] mentary information: Supplementary data are available at Bioinformatics online.

This article is from Bioinformatics , volume 30 . Abstract Motivation: High-throughput single-cell quantitative real-time polymerase chain reaction (qPCR) is a promising technique allowing for new insights in complex cellular processes. However, the PCR reaction can be detected only up to a certain detection limit, whereas failed reactions could be due to low or absent expression, and the true expression level is unknown. Because this censoring can occur for high proportions of the data, it is one of the main challenges when dealing with single-cell qPCR data. Principal component analysis (PCA) is an important tool for visualizing the structure of high-dimensional data as well as for identifying subpopulations of cells. However, to date it is not clear how to perform a PCA of censored data. We present a probabilistic approach that accounts for the censoring and evaluate it for two typical datasets containing single-cell qPCR data.Results: We use the Gaussian process latent variable model framework to account for censoring by introducing an appropriate noise model and allowing a different kernel for each dimension. We evaluate this new approach for two typical qPCR datasets (of mouse embryonic stem cells and blood stem/progenitor cells, respectively) by performing linear and non-linear probabilistic PCA. Taking the censoring into account results in a 2D representation of the data, which better reflects its known structure: in both datasets, our new approach results in a better separation of known cell types and is able to reveal subpopulations in one dataset that could not be resolved using standard PCA.Availability and implementation: The implementation was based on the existing Gaussian process latent variable model toolbox (https://github.com/SheffieldML/GPmat); extensions for noise models and kernels accounting for censoring are available at http://icb.helmholtz-muenchen.de/censgplvm.Contact:[email protected] mentary information: Supplementary data are available at Bioinformatics online.

This article is from Bioinformatics , volume 30 . Abstract Motivation: High-throughput single-cell quantitative real-time polymerase chain reaction (qPCR) is a promising technique allowing for new insights in complex cellular processes. However, the PCR reaction can be detected only up to a certain detection limit, whereas failed reactions could be due to low or absent expression, and the true expression level is unknown. Because this censoring can occur for high proportions of the data, it is one of the main challenges when dealing with single-cell qPCR data. Principal component analysis (PCA) is an important tool for visualizing the structure of high-dimensional data as well as for identifying subpopulations of cells. However, to date it is not clear how to perform a PCA of censored data. We present a probabilistic approach that accounts for the censoring and evaluate it for two typical datasets containing single-cell qPCR data.Results: We use the Gaussian process latent variable model framework to account for censoring by introducing an appropriate noise model and allowing a different kernel for each dimension. We evaluate this new approach for two typical qPCR datasets (of mouse embryonic stem cells and blood stem/progenitor cells, respectively) by performing linear and non-linear probabilistic PCA. Taking the censoring into account results in a 2D representation of the data, which better reflects its known structure: in both datasets, our new approach results in a better separation of known cell types and is able to reveal subpopulations in one dataset that could not be resolved using standard PCA.Availability and implementation: The implementation was based on the existing Gaussian process latent variable model toolbox (https://github.com/SheffieldML/GPmat); extensions for noise models and kernels accounting for censoring are available at http://icb.helmholtz-muenchen.de/censgplvm.Contact:[email protected] mentary information: Supplementary data are available at Bioinformatics online.

Data Visualization Color 05 ( Blue)

This article is from BMC Bioinformatics , volume 15 . Abstract A common class of biomedical analysis is to explore expression data from high throughput experiments for the purpose of uncovering functional relationships that can lead to a hypothesis about mechanisms of a disease. We call this analysis expression driven, -omics hypothesizing. In it, scientists use interactive data visualizations and read deeply in the research literature. Little is known, however, about the actual flow of reasoning and behaviors (sense making) that scientists enact in this analysis, end-to-end. Understanding this flow is important because if bioinformatics tools are to be truly useful they must support it. Sense making models of visual analytics in other domains have been developed and used to inform the design of useful and usable tools. We believe they would be helpful in bioinformatics. To characterize the sense making involved in expression-driven, -omics hypothesizing, we conducted an in-depth observational study of one scientist as she engaged in this analysis over six months. From findings, we abstracted a preliminary sense making model. Here we describe its stages and suggest guidelines for developing visualization tools that we derived from this case. A single case cannot be generalized. But we offer our findings, sense making model and case-based tool guidelines as a first step toward increasing interest and further research in the bioinformatics field on scientists’ analytical workflows and their implications for tool design.

Twelve scholarly papers and eighteen abstracts comprise the content of the nineteenth annual Brick & Click Libraries Conference, held annually at Northwest Missouri State University in Maryville, Missouri. The proceedings, authored by academic librarians and presented at the conference, portray the contemporary and future face of librarianship. The 2019 paper and abstract titles include: (1) Universally Accessible: Practical Methods for Digital Accessibility for Ordinary Librarians (Rebeca Peacock and Sean McCann); (2) Developing Student Agency through Personalized Learning (Elizabeth Stephan and Shevell Thibou); (3) Students of the Stacks: The Fellowship of Experience (Christina Prucha and Sabrina Davis); (4) Library Assessment: How High the Mountain! (Cheryl L. Blevens, Shelley Arvin, and Valentine K. Muyumba); (5) The Tool We Never Teach: The Good, the Bad and the End (Point) of Google's Usefulness in First-Year Experience Assignments (Lindsay Brownfield); (6) Mentoring and You: Providing Meaningful Experiences for Student Employees (Ruth Harries); (7) Spending Miss Alice's Money: Practical Applications for Evolving Libraries (Phillip Jones); (8) Using Data Visualization to Analyze Topic Development by Business Communication Students in a One-Shot Setting (Heidi Blackburn and Jason Heppler); (9) Collaborate for Student Success: Long Night against Procrastination (Carolyn Johnson); (10) Using Acquisitions: A Library's Steps for Ordering Books to Tracking Funds (Natasha Young); (11) Smart Spending -- Affordable Programming (Amber Carr and Kayla Reed); (12) Be a Leader, Not a Boss: Creating a Unified and Fulfilled Student Employment Team (Courtney Gard and Adrianna Bennett); (13) Revitalization of the Liaison Program (Amber Carr, Kayla Reed, and Nancy Crabtree); (14) Maintaining Training (Kayla Reed); (15) Understanding the Basics of Serials...and Beyond (Carol Doms and Stephanie Spratt); (16) Off the Web and Into the Fishing Hole (Carmen Orth-Alfie and Natalie Mahan); (17) Open Educational Resources: Build a Lesson Using Free Government Resources (Stephanie Hallam, Pat Willingham, and Kris Baranovic); (18) Old Acquaintance -- New Friend...Library and IT Partnerships (Martha Allen); (19) Display and Exhibit Design: Maximizing Existing Spaces for Outreach and Stakeholder Engagement (Alyssa Denneler); (20) Cross-Training Student Workers in a Learning Commons Environment (Meredith Knoff); (21) Deciding, Documenting, and Disseminating Library Policies and Practices: A Case Study from a Newly-Created Library Unit (Alea Henle and Rob Withers); (22) Reference Remodeling: Adapting and Promoting Reference Services (Cyndi Landis); (23) The Reference Interview Beyond Reference: Putting it to the E-Resource Troubleshooting Test (Angela Rathmel and Greta Valentine); (24) Town-Town-Gown Relations: Managing a County-Wide Collaborative Digital Community Archive (Christopher Jones, Allison Haack, Monique Shore, and Julia Bauder); (25) "The Library is Home for a Lot of Us Engineers:" How Engineering Majors Experience Library Space (Noël Kopriva); (26) Creating Library FAQ Guidelines Using Query Spy (Danielle M. Drummond); (27) A Reprieve for Sisyphus: How a Rotating ILL Task Schedule Provides Full Task Coverage and Variety for Staff (Gail Williams); (28) Hosting Comic Book Club in an Academic Setting (Mara Inge); (29) We Need Diverse Digital Resources (Jo Monahan); and (30) Accessible & Active eLearning Game Plan (Monica Maher). [For the 2018 proceedings, see ED590389.]

An experimental investigation of the effect of leading-edge radius, camber, Reynolds number, and boundary-layer state on the incipient separation of a delta wing at supersonic speeds was conducted at the Langley Unitary Plan Wind Tunnel at Mach number of 1.60 over a free-stream Reynolds number range of 1 x 106 to 5 x 106 ft-1. The three delta wing models examined had a 65 deg swept leading edge and varied in cross-sectional shape: a sharp wedge, a 20:1 ellipse, and a 20:1 ellipse with a -9.750 circular camber imposed across the span. The wings were tested with and without transition grit applied. Surface-pressure coefficient data and flow-visualization data indicated that by rounding the wing leading edge or cambering the wing in the spanwise direction, the onset of leading-edge separation on a delta wing can be raised to a higher angle of attack than that observed on a sharp-edged delta wing. The data also showed that the onset of leading-edge separation can be raised to a higher angle of attack by forcing boundary-layer transition to occur closer to the wing leading edge by the application of grit or the increase in free-stream Reynolds number.

Presentation by Jeff Pettiross, Tableau Software, to BayCHI More information: baychi.org/calendar/20150811 Seeing and understanding data is not just about creating a collection of queries, dashboards, and workbooks—it's about amplifying human potential. Jeff discusses how visual and cognitive science explain what makes good data visualization so deeply satisfying, and why many of these lessons apply to software design in general. Why does a collection of bars, lines, colors, and boxes become surprisingly powerful and meaningful? How does fluid interaction multiply our intelligence? Thirty years of research into the beautiful science of data viz explain why "big data" may be a faddish catchphrase, but interacting with data will be an integral part of computer-human interaction for decades to come.

Final files for assignment 3

Learn how to Visualize Data with MATLAB in 2D, 3D, 4D, 5D and create animated plots with tens of projects and examples! What you'll learn Create 2D plots with MATLAB Create 3D plots with MATLAB Create Volumetric plots with MATLAB Express yourself by MATLAB plots Export figures from MATLAB for use in documents Have basic understanding of Quality Code in MATLAB Requirements Have MATLAB Have basic coding skills, preferably with MATLAB Have something to express by MATLAB Description Why Visualize Data? As the volume and complexity of data and results continues to grow with the increasing complexity of data sources and algorithms, the need for intuitive representations of that data and results becomes increasingly critical. We want to create the representations in such a way that the human mind can, after all, better understanding our universe and the processes taking place within – representation of a real-world object, an abstract mathematical expression, specific values of some measurable quantities, etc. Since 80 percent of the sensory information the brain receives comes from our eyes, the visual presentation of data is the natural choice.

Advancements in the capabilities of JavaScript frameworks and web browsing technology make online visualization of large geospatial datasets viable. Commonly this is done using static image overlays, prerendered animations, or cumbersome geoservers. These methods can limit interactivity andor place a large burden on server-side post-processing and storage of data. Geospatial data, and satellite data specifically, benefit from being visualized both on and above a three-dimensional surface. The open-source JavaScript framework CesiumJS, developed by Analytical Graphics, Inc., leverages the WebGL protocol to do just that. It has entered the void left by the abandonment of the Google Earth Web API, and it serves as a capable and well-maintained platform upon which data can be displayed. This paper will describe the technology behind the two primary products developed as part of the NASA Precipitation Processing System STORM website: GPM Near Real Time Viewer (GPMNRTView) and STORM Virtual Globe (STORM VG). GPMNRTView reads small post-processed CZML files derived from various Level 1 through 3 near real-time products. For swath-based products, several brightness temperature channels or precipitation-related variables are available for animating in virtual real-time as the satellite-observed them on and above the Earths surface. With grid-based products, only precipitation rates are available, but the grid points are visualized in such a way that they can be interactively examined to explore raw values. STORM VG reads values directly off the HDF5 files, converting the information into JSON on the fly. All data points both on and above the surface can be examined here as well. Both the raw values and, if relevant, elevations are displayed. Surface and above-ground precipitation rates from select Level 2 and 3 products are shown. Examples from both products will be shown, including visuals from high impact events observed by GPM constellation satellites.

The small and medium enterprise (SME) companies optimize performance using different automated systems to highlight the operations concerns. However, lack of efficient visualization in reporting results in slow feedbacks, difficulties in extracting root cause, and minimal corrective actions. To complicate matters, the data heterogeneity has intensely increased, and it is produced in a fast manner making it unmanageable if the traditional methods of analytics are applied. Hence, we propose the use of a dashboard that can summarize the operational events using real-time data based on the data visualization approach. This proposed solution summarizes the raw data, which allows the user to make informed decisions that can give a positive impact on business performance. An interactive intelligent dashboard for SME (iid-SME) is developed to tackle issues such as measurement of cases completed, the duration of time needed to solve a case, the individual performance of handling cases and other tasks as a proof of concept. From the result, the implementation of the iid-SME approach simplifies the conveyance of the message and helps the SME personnel to make decisions. With the positive feedback obtained, it is envisaged that such a solution can be further employed for SME improvement for better profit and decision making.

Google Data Studio: Uses, limitations and example / How and when to use Google DataStudio (Green, SDCOE)