Software tools for conducting bibliometric analysis in science: An up-to-date review

Bibliometrics has become an essential tool for assessing and analyzing the output of scientists, cooperation between universities, the effect of state-owned science funding on national research and development performance and educational efficiency, among other applications. Therefore, professionals and scientists need a range of theoretical and practical tools to measure experimental data. This review aims to provide an up-to-date review of the various tools available for conducting bibliometric and scientometric analyses, including the sources of data acquisition, performance analysis and visualization tools. The included tools were divided into three categories: general bibliometric and performance analysis, science mapping analysis, and libraries; a description of all of them is provided. A comparative analysis of the database sources support, pre-processing capabilities, analysis and visualization options were also provided in order to facilitate its understanding. Although there are numerous bibliometric databases to obtain data for bibliometric and scientometric analysis, they have been developed for a different purpose. The number of exportable records is between 500 and 50,000 and the coverage of the different science fields is unequal in each database. Concerning the analyzed tools, Bibliometrix contains the more extensive set of techniques and suitable for practitioners through Biblioshiny. VOSviewer has a fantastic visualization and is capable of loading and exporting information from many sources. SciMAT is the tool with a powerful pre-processing and export capability. In views of the variability of features, the users need to decide the desired analysis output and chose the option that better fits into their aims

PREDICTION OF 1P/19Q CODELETION STATUS IN DIFFUSE GLIOMA PATIENTS USING PREOPERATIVE MULTIPARAMETRIC MAGNETIC RESONANCE IMAGING

A complete codeletion of chromosome 1p/19q is strongly correlated with better overall survival of diffuse glioma patients, hence determining the codeletion status early in the course of a patient’s disease would be valuable in that patient’s care. The current practice requires a surgical biopsy in order to assess the codeletion status, which exposes patients to risks and is limited in its accuracy by sampling variations. To overcome such limitations, we utilized four conventional magnetic resonance imaging sequences to predict the 1p/19q status. We extracted three sets of image-derived features, namely texture-based, topology-based, and convolutional neural network (CNN)-based, and analyzed each feature’s prediction performance. The topology-based model (AUC = 0.855 +/- 0.079) performed significantly better compared to the texture-based model (AUC = 0.707 +/- 0.118) while comparably against the CNN-based model (0.787 +/- 0.195). However, none of the models performed better than the baseline model that is built with only clinical variables, namely, age, gender, and Karnofsky Performance Score (AUC = 0.703 +/- 0.256). In summary, predicting 1p/19q chromosome codeletion status via MRI scan analysis can be a viable non-invasive assessment tool at an early stage of gliomas and in follow-ups although further investigation is needed to improve the model performance

Mass Spectrometry Based Molecular 3D-Cartography of Plant Metabolites

Plants play an essential part in global carbon fixing through photosynthesis and are the primary food and energy source for humans. Understanding them thoroughly is therefore of highest interest for humanity. Advances in DNA and RNA sequencing and in protein and metabolite analysis allow the systematic description of plant composition at the molecular level. With imaging mass spectrometry, we can now add a spatial level, typically in the micrometer-to-centimeter range, to their compositions, essential for a detailed molecular understanding. Here we present an LC-MS based approach for 3D plant imaging, which is scalable and allows the analysis of entire plants. We applied this approach in a case study to pepper and tomato plants. Together with MS/MS spectra library matching and spectral networking, this non-targeted workflow provides the highest sensitivity and selectivity for the molecular annotations and imaging of plants, laying the foundation for studies of plant metabolism and plant-environment interactions

EXPLORATORY VISUALIZATION OF GRAPHS BASED ON COMMUNITY STRUCTURE

Communities, also called clusters or modules, are groups of nodes which probably share common properties and/or play similar roles within a graph. They widely exist in real networks such as biological, social, and information networks. Allowing users to interactively browse and explore the community structure, which is essential for understanding complex systems, is a challenging yet important research topic. My work has been focused on visualization approaches to exploring the community structure in graphs based on automatic community detection results. In this dissertation, we first report a formal user study that investigated the essen- tial influence factors, benefits, and constraints of a community based graph visual- ization system in a background application of seeking information from text corpora. A general evaluation methodology for exploratory visualization systems has been proposed and practiced. The evaluation methodology integrates detailed cognitive load analysis and users’ prior knowledge evaluation with quantitative and qualitative measures, so that in-depth insights can be gained. The study revealed that visual exploration based on the community structure benefits the understanding of real net- works. A literature review and a set of interviews were then conducted to learn tasks facing such graph exploration and the state-of-the-arts. This work led to commu- nity related graph visualization task taxonomy. Our examination of existing graph visualization systems revealed that a large number of community related graph visualization tasks are poorly supported in existing approaches. To bridge the gap, several novel visualization techniques are proposed. In these approaches, graph topology information is mapped to a multidimensional space where the relationships between the communities and the nodes can be explicitly explored. Several user studies and case studies have been conducted to demonstrate the usefulness of these systems in real-world applications

A Descriptive Framework for Temporal Data Visualizations Based on Generalized Space-Time Cubes

International audienceWe present the generalized space-time cube, a descriptive model for visualizations of temporal data. Visualizations are described as operations on the cube, which transform the cube's 3D shape into readable 2D visualizations. Operations include extracting subparts of the cube, flattening it across space or time or transforming the cubes geometry and content. We introduce a taxonomy of elementary space-time cube operations and explain how these operations can be combined and parameterized. The generalized space-time cube has two properties: (1) it is purely conceptual without the need to be implemented, and (2) it applies to all datasets that can be represented in two dimensions plus time (e.g. geo-spatial, videos, networks, multivariate data). The proper choice of space-time cube operations depends on many factors, for example, density or sparsity of a cube. Hence, we propose a characterization of structures within space-time cubes, which allows us to discuss strengths and limitations of operations. We finally review interactive systems that support multiple operations, allowing a user to customize his view on the data. With this framework, we hope to facilitate the description, criticism and comparison of temporal data visualizations, as well as encourage the exploration of new techniques and systems. This paper is an extension of Bach et al.'s (2014) work

Visualização de padrões temporais cíclicos em estudos de fenologia

Orientadores: Ricardo da Silva Torres, Leonor Patrícia Cerdeira MorellatoTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Em diversas aplicações, grandes volumes de dados multidimensionais têm sido gerados continuamente ao longo do tempo. Uma abordagem adequada para lidar com estas coleções consiste no uso de métodos de visualização de informação, a partir dos quais padrões de interesse podem ser identificados, possibilitando o entendimento de fenômenos temporais complexos. De fato, em diversos domínios, o desenvolvimento de ferramentas adequadas para apoiar análises complexas, por exemplo, aquelas baseadas na identificação de padrões de mudanças ou correlações existentes entre múltiplas variáveis ao longo do tempo é de suma importância. Em estudos de fenologia, por exemplo, especialistas observam as mudanças que ocorrem ao longo da vida de plantas e animais e investigam qual é a relação entre essas mudanças com variáveis ambientais. Neste cenário, especialistas em fenologia cada vez mais precisam de ferramentas para, adequadamente, visualizar séries temporais longas, com muitas variáveis e de diferentes tipos (por exemplo, texto e imagem), assim como identificar padrões temporais cíclicos. Embora diversas abordagens tenham sido propostas para visualizar dados que variam ao longo do tempo, muitas não são apropriadas ou aplicáveis para dados de fenologia, porque não são capazes de: (i) lidar com séries temporais longas, com muitas variáveis de diferentes tipos de dados e de uma ou mais dimensões; e (ii) permitir a identificação de padrões temporais cíclicos e drivers ambientais associados. Este trabalho aborda essas questões a partir da proposta de duas novas abordagens para apoiar a análise e visualização de dados temporais multidimensionais. Nossa primeira proposta combina estruturas visuais radiais com ritmos visuais. As estruturas radiais são usadas para fornecer informação contextual sobre fenômenos cíclicos, enquanto que o ritmo visual é usado para sumarizar séries temporais longas em representações compactas. Nós desenvolvemos, avaliamos e validamos nossa proposta com especialistas em fenologia em tarefas relacionadas à visualização de dados de observação direta da fenologia de plantas em nível tanto de indivíduos quanto de espécies. Nós também validamos a proposta usando dados temporais relacionados a imagens obtidas de sistemas de monitoramento de vegetação próxima à superfície. Nossa segunda abordagem é uma nova representação baseada em imagem, chamada Change Frequency Heatmap (CFH), usada para codificar mudanças temporais de dados numéricos multivariados. O método calcula histogramas de padrões de mudanças observados em sucessivos instantes de tempo. Nós validamos o uso do CFH a partir da criação de uma ferramenta de caracterização de mudanças no ciclo de vida de plantas de múltiplos indivíduos e espécies ao longo do tempo. Nós demonstramos o potencial do CFH para ajudar na identificação visual de padrões de mudanças temporais complexas, especialmente na identificação de variações entre indivíduos em estudos relacionados à fenologia de plantasAbstract: In several applications, large volumes of multidimensional data have been generated continuously over time. One suitable approach for handling those collections in a meaningful way consists in the use of information visualization methods, based on which patterns of interest can be identified, triggering the understanding of complex temporal phenomena. In fact, in several domains, the development of appropriate tools for supporting complex analysis based, for example, on the identification of change patterns in temporal data or existing correlations, over time, among multiple variables, is of paramount importance. In phenology studies, for instance, phenologists observe changes in the development of plants and animals throughout their lives and investigate what is the relationship between these changes with environmental changes. Therefore, phenologists increasingly need tools for visualizing appropriately long-term series with many variables of different data types, as well as for identifying cyclical temporal patterns. Although several approaches have been proposed to visualize data varying over time, most of them are not appropriate or applicable to phenology data, because they are not able: (i) to handle long-term series with many variables of different data types and one or more dimensions and (ii) to support the identification of cyclical temporal patterns and associated environmental drivers. This work addresses these shortcomings by presenting two new approaches to support the analysis and visualization of multidimensional temporal data. Our first proposal to visualize phenological data combines radial visual structures along with visual rhythms. Radial visual structures are used to provide contextual insights regarding cyclical phenomena, while the visual rhythm encoding is used to summarize long-term time series into compact representations. We developed, evaluated, and validated our proposal with phenology experts using plant phenology direct observational data both at individuals and species levels. Also we validated the proposal using image-related temporal data obtained from near-surface vegetation monitoring systems. Our second approach is a novel image-based representation, named Change Frequency Heatmap (CFH), used to encode temporal changes of multivariate numerical data. The method computes histograms of change patterns observed at successive timestamps. We validated the use of CFHs through the creation of a temporal change characterization tool to support complex plant phenology analysis, concerning the characterization of plant life cycle changes of multiple individuals and species over time. We demonstrated the potential of CFH to support visual identification of complex temporal change patterns, especially to decipher interindividual variations in plant phenologyDoutoradoCiência da ComputaçãoDoutora em Ciência da Computação162312/2015-62013/501550-0CNPQCAPESFAPES

Visual Analysis of Variability and Features of Climate Simulation Ensembles

This PhD thesis is concerned with the visual analysis of time-dependent scalar field ensembles as occur in climate simulations. Modern climate projections consist of multiple simulation runs (ensemble members) that vary in parameter settings and/or initial values, which leads to variations in the resulting simulation data. The goal of ensemble simulations is to sample the space of possible futures under the given climate model and provide quantitative information about uncertainty in the results. The analysis of such data is challenging because apart from the spatiotemporal data, also variability has to be analyzed and communicated. This thesis presents novel techniques to analyze climate simulation ensembles visually. A central question is how the data can be aggregated under minimized information loss. To address this question, a key technique applied in several places in this work is clustering. The first part of the thesis addresses the challenge of finding clusters in the ensemble simulation data. Various distance metrics lend themselves for the comparison of scalar fields which are explored theoretically and practically. A visual analytics interface allows the user to interactively explore and compare multiple parameter settings for the clustering and investigate the resulting clusters, i.e. prototypical climate phenomena. A central contribution here is the development of design principles for analyzing variability in decadal climate simulations, which has lead to a visualization system centered around the new Clustering Timeline. This is a variant of a Sankey diagram that utilizes clustering results to communicate climatic states over time coupled with ensemble member agreement. It can reveal several interesting properties of the dataset, such as: into how many inherently similar groups the ensemble can be divided at any given time, whether the ensemble diverges in general, whether there are different phases in the time lapse, maybe periodicity, or outliers. The Clustering Timeline is also used to compare multiple climate simulation models and assess their performance. The Hierarchical Clustering Timeline is an advanced version of the above. It introduces the concept of a cluster hierarchy that may group the whole dataset down to the individual static scalar fields into clusters of various sizes and densities recording the nesting relationship between them. One more contribution of this work in terms of visualization research is, that ways are investigated how to practically utilize a hierarchical clustering of time-dependent scalar fields to analyze the data. To this end, a system of different views is proposed which are linked through various interaction possibilities. The main advantage of the system is that a dataset can now be inspected at an arbitrary level of detail without having to recompute a clustering with different parameters. Interesting branches of the simulation can be expanded to reveal smaller differences in critical clusters or folded to show only a coarse representation of the less interesting parts of the dataset. The last building block of the suit of visual analysis methods developed for this thesis aims at a robust, (largely) automatic detection and tracking of certain features in a scalar field ensemble. Techniques are presented that I found can identify and track super- and sub-levelsets. And I derive “centers of action” from these sets which mark the location of extremal climate phenomena that govern the weather (e.g. Icelandic Low and Azores High). The thesis also presents visual and quantitative techniques to evaluate the temporal change of the positions of these centers; such a displacement would be likely to manifest in changes in weather. In a preliminary analysis with my collaborators, we indeed observed changes in the loci of the centers of action in a simulation with increased greenhouse gas concentration as compared to pre-industrial concentration levels

VIOLA - A multi-purpose and web-based visualization tool for neuronal-network simulation output

Neuronal network models and corresponding computer simulations are invaluable tools to aid the interpretation of the relationship between neuron properties, connectivity and measured activity in cortical tissue. Spatiotemporal patterns of activity propagating across the cortical surface as observed experimentally can for example be described by neuronal network models with layered geometry and distance-dependent connectivity. The interpretation of the resulting stream of multi-modal and multi-dimensional simulation data calls for integrating interactive visualization steps into existing simulation-analysis workflows. Here, we present a set of interactive visualization concepts called views for the visual analysis of activity data in topological network models, and a corresponding reference implementation VIOLA (VIsualization Of Layer Activity). The software is a lightweight, open-source, web-based and platform-independent application combining and adapting modern interactive visualization paradigms, such as coordinated multiple views, for massively parallel neurophysiological data. For a use-case demonstration we consider spiking activity data of a two-population, layered point-neuron network model subject to a spatially confined excitation originating from an external population. With the multiple coordinated views, an explorative and qualitative assessment of the spatiotemporal features of neuronal activity can be performed upfront of a detailed quantitative data analysis of specific aspects of the data. Furthermore, ongoing efforts including the European Human Brain Project aim at providing online user portals for integrated model development, simulation, analysis and provenance tracking, wherein interactive visual analysis tools are one component. Browser-compatible, web-technology based solutions are therefore required. Within this scope, with VIOLA we provide a first prototype.Comment: 38 pages, 10 figures, 3 table