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Data visualization aims to explore and analyze data quickly, interactively, and intuitively using visual representations. Faced with the constant growth of data in terms of volume and diversity, visualization techniques must confront the challenge of dealing with increasingly large datasets in terms of representation, interaction, and performance. Therefore, these techniques must be able to effectively convey the characteristics of the information space and inspire discovery. In this article, a web dynamic dashboard for data visualization called VISUEL is presented. VISUEL supports multiple coordinated views, integrating visualization techniques such as scatterplots, parallel coordinates, and box plots, and interactive schematic maps to represent information enriched with spatial references. VISUEL is fully interactive, supporting traditional interactions like filtering, selection, brushing and linking, and zooming, among others. It also allows the user to configure the visual representation of their data, by selecting the color and shape of the representations. The usefulness of this tool is illustrated using real-life dataset related to the wine industry in Argentina. Important aspects of the case study are discovered through the construction and analysis of multiple views., La visualización de datos tiene como objetivo explorar y analizar los datos de forma rápida, interactiva e intuitiva mediante representaciones visuales. Ante el constante crecimiento de los datos en términos de volumen y diversidad, las técnicas de visualización deben afrontar el desafío de lidiar con conjuntos de datos cada vez más grandes en términos de representación, interacción y desempeño. Por lo tanto, estas técnicas deben ser capaces de transmitir de manera efectiva las características del espacio de información e inspirar el descubrimiento. En este artículo, se presenta un tablero web dinámico para la visualización de datos llamado VISUEL. VISUEL admite múltiples vistas coordinadas, integrando técnicas de visualización como diagramas de dispersión, coordenadas paralelas, diagramas de caja, y mapas esquemáticos interactivos para representar información enriquecida con referencias espaciales. VISUEL es totalmente interactivo y admite interacciones tradicionales como filtrado, selección, brushing and linking, y zoom, entre otras. También permite al usuario configurar la representación visual de sus datos, seleccionando el color y la forma de las representaciones. La utilidad de esta herramienta se ilustra utilizando datos reales relacionados con la industria del vino en Argentina. Se descubren aspectos importantes del caso de estudio mediante la construcción y el análisis de múltiples vistas., Facultad de Informática

One of the main challenges when analyzing complex metagenomics data is the fact that large amounts of information need to be presented in a comprehensive and easy-to-navigate way. In the process of analyzing FASTQ sequencing data, visualizing which organisms are present in the data can be useful, especially with metagenomics data or data suspected to be contaminated. Here, we describe the development and application of a command-line tool, Keanu, for visualizing and exploring sample content in metagenomics data. We developed Keanu as an interactive tool to make viewing complex data easier. Keanu, a tool for exploring sequence content, helps a user to understand the presence and abundance of organisms in a sample by analyzing alignments against a database that contains taxonomy data and displaying them in an interactive web page. The content of a sample can be presented either as a collapsible tree, with node size indicating abundance, or as a bilevel partition graph, with arc size indicating abundance. Here, we illustrate how Keanu works by exploring shotgun metagenomics data from a sample collected from a bluff that contained paleosols and a krotovina in an alpine site in Ft. Greely, Alaska. Keanu provides a simple means by which researchers can explore and visualize species present in sequence data generated from complex communities and environments. Keanu is written in Python and is freely available at https://github.com/IGBB/keanu .

A Python software package for data reduction and visualization of continuous angle multiple energy analysis (CAMEA) type detector backend MultiFLEXX is presented. The software concept focuses on unambiguous, automated aggregation of experimental data and preservation of raw data structure in graphical representation, enabling on-the-fly analysis of experimental data from MultiFLEXX with minimal amount of user input, reducing confusion and human error in studies involving multiple parameters. The software also provides a set of interfaces for versatile tweaking of graphing parameters, facilitating generation of production-quality graphs for use in publications. The software enhances the role of MultiFLEXX as a swift mapping option available at the cold-neutron triple-axis spectrometer FLEXX. Keywords: Inelastic neutron scattering, Three-axis spectroscopy, Visualization tool

Spreadsheet applications are widely adopted by millions of end users from several application domains and provide strategic support to many business, scientific, industrial, and organizational processes. These applications are usually developed by rapid application development processes, exploiting host scripting languages allowing the basic spreadsheets to provide complex functionality, business rules, and user interfaces. Several factors complicate the comprehension of these applications because they are usually developed and maintained by end users without specific software engineering skills, grow over time, are not adequately documented, and do not present explicit separation between data, business logic, and user interface layers. This paper presents a reverse engineering tool intended to support the comprehension of Excel spreadsheet applications developed using the Visual Basic for Application programming language. The tool has been implemented as an add-in that extends the Excel working environment by providing analysis and visualization features. It is able to extract information about the elements composing the analyzed Excel spreadsheet application, the functionality it exposes through its user interface, and the dependencies among its cells. This information is provided by means of interactive views. The validity of the tool has been assessed by a qualitative case study performed with professional end users from an automotive industrial domain.

Background The analysis of high-throughput data in biology is aided by integrative approaches such as gene-set analysis. Gene-sets can represent well-defined biological entities (e.g. metabolites) that interact in networks (e.g. metabolic networks), to exert their function within the cell. Data interpretation can benefit from incorporating the underlying network, but there are currently no optimal methods that link gene-set analysis and network structures. Results Here we present Kiwi, a new tool that processes output data from gene-set analysis and integrates them with a network structure such that the inherent connectivity between gene-sets, i.e. not simply the gene overlap, becomes apparent. In two case studies, we demonstrate that standard gene-set analysis points at metabolites regulated in the interrogated condition. Nevertheless, only the integration of the interactions between these metabolites provides an extra layer of information that highlights how they are tightly connected in the metabolic network. Conclusions Kiwi is a tool that enhances interpretability of high-throughput data. It allows the users not only to discover a list of significant entities or processes as in gene-set analysis, but also to visualize whether these entities or processes are isolated or connected by means of their biological interaction. Kiwi is available as a Python package at http://www.sysbio.se/kiwi and an online tool in the BioMet Toolbox at http://www.biomet-toolbox.org. Electronic supplementary material The online version of this article (doi:10.1186/s12859-014-0408-9) contains supplementary material, which is available to authorized users.

Vast numbers of free/open source software (F/OSS) development projects use hosting sites such as Java.net and Source-Forge.net. These sites provide each project with a variety of software repositories (e.g. repositories for source code sharing, bug tracking, discussions, etc.) as a media for communication and collaboration. They tend to focus on supporting rich collaboration among members in each project. However, a majority of hosted projects are relatively small projects consisting of few developers and often need more resources for solving problems. In order to support cross-project knowledge collaboration in F/OSS development, we have been developing tools to collect data of projects and developers at SourceForge, and to visualize the relationship among them using the techniques of collaborative filtering and social networks. The tools help a developer identify who should I ask? and what can I ask? and so on. In this paper, we report a case study of applying the tools to F/OSS projects data collected from SourceForge and how effective the tools can be used for helping cross-project knowledge collaboration., MSR '05 : the 2005 international workshop on Mining software repositories, May 17, 2005, St. Louis, Missouri

Background Large amounts of mammalian protein-protein interaction (PPI) data have been generated and are available for public use. From a systems biology perspective, Proteins/genes interactions encode the key mechanisms distinguishing disease and health, and such mechanisms can be uncovered through network analysis. An effective network analysis tool should integrate different content-specific PPI databases into a comprehensive network format with a user-friendly platform to identify key functional modules/pathways and the underlying mechanisms of disease and toxicity. Results atBioNet integrates seven publicly available PPI databases into a network-specific knowledge base. Knowledge expansion is achieved by expanding a user supplied proteins/genes list with interactions from its integrated PPI network. The statistically significant functional modules are determined by applying a fast network-clustering algorithm (SCAN: a Structural Clustering Algorithm for Networks). The functional modules can be visualized either separately or together in the context of the whole network. Integration of pathway information enables enrichment analysis and assessment of the biological function of modules. Three case studies are presented using publicly available disease gene signatures as a basis to discover new biomarkers for acute leukemia, systemic lupus erythematosus, and breast cancer. The results demonstrated that atBioNet can not only identify functional modules and pathways related to the studied diseases, but this information can also be used to hypothesize novel biomarkers for future analysis. Conclusion atBioNet is a free web-based network analysis tool that provides a systematic insight into proteins/genes interactions through examining significant functional modules. The identified functional modules are useful for determining underlying mechanisms of disease and biomarker discovery. It can be accessed at: http://www.fda.gov/ScienceResearch/BioinformaticsTools/ucm285284.htm.