Biological databases and tools for neurological disorders.

Social media analytics has emerged as a critical tool for understanding consumer behavior in today’s digital landscape. This study explores the integration of social media analytics with Power BI to gain actionable insights into consumer preferences, behaviors, and trends at Cryptex Technologies, Nagpur. Leveraging the capabilities of Power BI, the research examines how data visualization and analytics tools can translate vast amounts of social media data into meaningful insights that drive decision-making. The study employs a mixed-methods approach, combining qualitative and quantitative data analysis, to evaluate the effectiveness of Power BI in analysing social media metrics such as engagement rates, sentiment analysis, and demographic segmentation. Results indicate that social media analytics, when effectively integrated with advanced visualization tools, significantly enhances the understanding of consumer behavior, enabling businesses to make data-driven marketing decisions. The findings further reveal that the use of interactive dashboards and real-time data processing capabilities of Power BI facilitates a deeper comprehension of market trends, helping organizations remain competitive in a rapidly evolving business environment. This research contributes to the growing body of knowledge on the strategic application of social media analytics in consumer behavior studies and offers practical insights for businesses aiming to optimize their social media strategies using data visualization tools.

Development of public toxicogenomics software for microarray data management and analysis

Wireless Sensor Networks (WSN) have been useful in a variety of domains. These types of networks have an intimate interaction, via sensors, with the physical environment they operate within. The part of the world with which an application is concerned is defined as that application's domain. This paper advocates that the application domain can serve as a supplement to sense data analysis, interpretation, and visualisation methods and tools. To achieve this, we propose a Multi-Dimensional Application Domain-driven (M-DAD) information extraction and visualisation framework that uses the application domain to accurately visualise multimodal sense data. M-DAD harnesses the inherent redundancies and relationships among the collected sense data as information about a specific event of interest in a WSN is usually captured in multiple sensed modalities. The proposed mapping framework utilises these correlations, defined in the application domain, to visualise a sense modality, e.g. soil nitrate levels, using other related but independent sense modalities, e.g. temperature and pH, which results in higher accuracy visualisations than visualising from a single sense modality. The primary experimental results demonstrate that the proposed framework performs as well or better than methods without its extended capabilities.

This paper proposes a dynamic analytical processing (DAP) visualization tool based on the Bubble-Wall Plot. It can be handily used to develop visual warning systems for visualizing the dynamic analytical processes of hazard data. Comparative analysis and case study methods are used in this research. Based on a literature review of Q1 publications since 2017, 23 types of data visualization approaches/tools are identified, including seven anomaly data visualization tools. This study presents three significant findings by comparing existing data visualization approaches. The primary finding is that no single visualization tool can fully satisfy industry requirements. This finding motivates academics to develop new DAP visualization tools. The second finding is that there are different views of Line Charts and various perspectives on Scatter Plots. The other one is that different researchers may perceive an existing data visualization tool differently, such as arguments between Scatter Plots and Line Charts and diverse opinions about Parallel Coordinate Plots and Scatter Plots. Users' awareness rises when they choose data visualization tools that satisfy their requirements. By conducting a comparative analysis based on five categories (Style, Value, Change, Correlation, and Others) with 26 subcategories of metric features, results show that this new tool can effectively solve the limitations of existing visualization tools as it appears to have three remarkable characteristics: the simplest cartographic tool, the most straightforward visual result, and the most intuitive tool. Furthermore, this paper illustrates how the Bubble-Wall Plot can be effectively applied to develop a warning system for presenting dynamic analytical processes of hazard data in the coal mine. Lastly, this paper provides two recommendations, one implication, six research limitations, and eleven further study topics.

BackgroundAs high-throughput sequencing applications continue to evolve, the rapid growth in quantity and variety of sequence-based data calls for the development of new software libraries and tools for data analysis and visualization. Often, effective use of these tools requires computational skills beyond those of many researchers. To ease this computational barrier, we have created a dynamic web-based platform, NASQAR (Nucleic Acid SeQuence Analysis Resource).ResultsNASQAR offers a collection of custom and publicly available open-source web applications that make extensive use of a variety of R packages to provide interactive data analysis and visualization. The platform is publicly accessible at http://nasqar.abudhabi.nyu.edu/. Open-source code is on GitHub at https://github.com/nasqar/NASQAR, and the system is also available as a Docker image at https://hub.docker.com/r/aymanm/nasqarall. NASQAR is a collaboration between the core bioinformatics teams of the NYU Abu Dhabi and NYU New York Centers for Genomics and Systems Biology.ConclusionsNASQAR empowers non-programming experts with a versatile and intuitive toolbox to easily and efficiently explore, analyze, and visualize their Transcriptomics data interactively. Popular tools for a variety of applications are currently available, including Transcriptome Data Preprocessing, RNA-seq Analysis (including Single-cell RNA-seq), Metagenomics, and Gene Enrichment.

Optical biosensors: a decade in review

Glacial geomorphology and geographic information systems

Mood disorders, including Major Depression (MD) and Bipolar Disorder (BP), are among the most common psychiatric disorders1. Although genetic factors play an important role in their etiology2, 3, the heritability of these disorders remains largely unexplained. Due to advances in high-throughput genomic technologies, it is becoming increasingly feasible to investigate the genetic architecture of these disorders at an unprecedented resolution. The challenge is how to make sense of the enormous flood of data generated by the diverse genomic studies that use these new technologies. It is essential to integrate the findings from such studies in order to develop a more comprehensive understanding of the genetic contribution to these disorders that can be readily translated into new and more rational therapeutic interventions. Towards this end, we have developed a bioinformatics resource called Metamoodics (www.metamoodics.org), which integrate results from various genomic studies of mood disorders and enhance their interpretation by placing them within an enriched genomic context. Metamoodics was created as a resource for the research community to explore what is currently known about the genetic contribution to mood disorders. Metamoodics synthesizes data from existing genome-wide linkage, expression and association studies for both MD and BP. It brings together results from up-to-date meta- or mega-analyses of these studies, and presents them in both tabular and graphical modes. The genome-wide linkage data comes from among the largest and most comprehensive of such studies with MD and BP. Data for BP comes from a mega-analysis carried out with 972 informative pedigrees of European ancestry pooled from eight research groups in North America and the United Kingdom3. Linkage statistics for twelve different phenotype-by-genotype models were calculated for a genome-wide panel of 6090 SNPs. For MD, data from the Genetics of Recurrent Early-Onset Major Depression (GenRED) collaborative are provided4. This included data on 418 microsatellite markers genotyped in 656 families consisting of 1,494 all possible informative affective relative pairs. Data for genome-wide association studies (GWAS) were obtained from the psychiatric GWAS consortium (PGC). For BP, the sample included 7,481 cases and 9,250 controls, all of European ancestry, with data on ~2.5M genotyped and/or imputed SNPs5. Data on MD consisted of more than 1.2 million SNPs in 9,240 cases and 9,519 controls of recent European ancestry6. Gene expression data was obtained through a systematic review of studies on BP (n=13) and MD (n=7) using post-mortem brain tissue. Mega-analyses of the most comprehensive and rigorous of these studies were carried out using samples from all regions of the brain, the pre-frontal cortex, and the hippocampus, where sufficient data was available7. Finally, Metamoodics includes a comprehensive meta-analysis of published candidate gene association studies of BP. Data on 33 polymorphisms in 18 genes reported on by three or more case-control studies were included in the meta-analysis8. A similar meta-analysis for MD is forthcoming. Metamoodics includes a variety of online applications and tools for visualization and further analysis of the genomics data. Data visualization makes use of NCBI SeqView API and a local mirror of the UCSC genome browser. The results can be queried by gene or chromosomal position to obtain either a gene level view via chromosomal or regional plots, or a more global view via the genome browser to investigate the genomic context of the findings. User-defined data can be uploaded to the genome browser using the custom track option to visualize alongside the Metamoodics tracks. Analysis tools include SVAw9 for surrogate variable analysis, METAw for meta-analysis and a gene expression package for individual and mega analysis of expression data. Metamoodics presents the latest results from genomic studies of mood disorders in a user-friendly web interface that enables users to rapidly compare findings from different experimental platforms across the genome, as well as drill down to specific findings to visualize how these relate to underlying genomic structure and function. Metamoodics provides the entire set of results of a given study rather than just significant ones, thus providing a complete and unbiased report of the findings. We will continue to update Metamoodics with findings from the latest genomic experiments of mood disorders, such as whole exome or genome sequencing and epigenetic studies. Metamoodics is a vital bioinformatics tool for investigating the genetic etiology of mood disorders that will only grow more powerful as new data are added.

ERPWAVELAB: A toolbox for multi-channel analysis of time–frequency transformed event related potentials

In the modern business landscape, data visualization has become an indispensable element of business intelligence (BI), particularly within the context of US companies. The aim of this research paper is to examine how data visualization technologies might improve business intelligence and decision making in American businesses by examining adaptation, advantages and disadvantages. This research paper explores the transformative contribution of data visualization tools to improving business intelligence capabilities and their significant influence on the processes of decision-making. The study begins by examining the adoption of various data visualization tools and platforms, such as Tableau, Power BI, and Elkview within US companies. It delves into how these tools facilitate the conversion of complex data sets into visually intuitive formats, allowing stakeholders at all levels to grasp trends, patterns, and anomalies with greater ease. By integrating these visualization tools into their BI systems, companies can achieve a more nuanced understanding of their operational landscapes and market dynamics. The paper further evaluates the impact of data visualization on decision-making processes. Through an analysis of case studies and empirical data, it highlights several key benefits, including improved accuracy in data interpretation, enhanced ability to identify actionable insights, and accelerated decision-making timelines. The paper discusses strategies to overcome these challenges, such as ensuring data integrity and investing in user training to maximize the efficacy of visualization tools. Overall, the findings underscore that while data visualization is not a panacea, it significantly augments the decision-making capabilities of US companies by making data more accessible and actionable. The integration of advanced visualization techniques into BI practices is increasingly seen as a strategic advantage, driving both operational efficiencies and competitive differentiation. As data continues to proliferate, the role of data visualization in shaping business strategies and outcomes will likely become even more pronounced, making it a critical area of focus for future research and development in the field of business intelligence.

A web based MDSplus data analysis and visualization system for EAST

Visualization can be defined as a technique that allows us to obtain the perception of an object/event in a clear and consistent way. The use of visualization in education is a key factor to explain complex information in a clear way. Therefore, it is essential to have tools capable of visualizing various types of data. An example of a data type is the weather forecast data, which includes various atmospheric data for a given place, and allows the simulation of the atmospheric evolution. It is used for decision making in many areas, such as, agriculture, fishing, tourism, etc. Thus, it is beneficial to demonstrate the usefulness of this type of visualization to better understand the meteorological phenomena, as well as to teach scientific visualization techniques in order to enable access to information that otherwise can only be interpreted by qualified people. In this article it will be discussed the scientific visualization and its benefits to the area of meteorology, and it will be presented a case study of data visualization using the ParaView tools for meteorological data visualization and analysis. ParaView is a multiplatform tool based on the Visualization Toolkit (VTK) that provides features to process, analyze, and visualize various types of data. This study aims to present a tool for scientific visualization and to demonstrate its applications and usefulness for education.

High Impact Data Visualization withPower View, Power Map, and Power BIhelps youtake business intelligence delivery to a new level that is interactive, engaging, even fun, all while driving commercial success through sound decision-making. Learn to harness the power of Microsofts flagship, self-service business intelligence suite to deliver compelling and interactive insight with remarkable ease. Learn the essential techniques needed to enhance the look and feel of reports and dashboards so that you can seize your audiences attention and provide them with clear and accurate information. Also learn to integrate data from a variety of sources and create coherent data models displaying clear metrics and attributes. Power View is Microsoft's ground-breaking tool for ad-hoc data visualization and analysis. It's designed to produce elegant and visually arresting output. It's also built to enhance user experience through polished interactivity. Power Map is a similarly powerful mechanism for analyzing data across geographic and political units. Power Query lets you load, shape and streamline data from multiple sources. PowerPivot can extend and develop data into a dynamic model. Power BI allows you to share your findings with colleagues, and present your insights to clients. High Impact Data Visualization withPower View, Power Map, and Power BIhelps you master this suite of powerful tools from Microsoft. You'll learn to identify data sources, and to save time by preparing your underlying data correctly. You'll also learn to deliver your powerful visualizations and analyses through the cloud to PCs, tablets and smartphones. Simple techniques take raw data and convert it into information. Slicing and dicing metrics delivers interactive insight. Visually arresting output grabs and focuses attention on key indicators. What youll learnProduce designer output that will astound your bosses and peers. Drive business intelligence from Excel using BI in the Cloud. Gather source data from corporate and public sources. Integrate charts, maps, and tables to deliver visually stunning information. Discover new insights as you chop and tweak your data as never before. Adapt delivery to mobile devices. Outshine competing products and enhance existing skills. Who this book is for High Impact Data Visualization withPower View, Power Map, and Power BIis written for any Excel or SharePoint user. Business Intelligence developers, power users, IT managers, finance experts, and more can use this book to outshine the competition by producing high-impact business intelligence reporting on a variety of devices from a variety of sources.

Development and case study of a science-based software platform to support policy making on air quality

This chapter examines information overload and its impact on organizational performance and productivity, the level of use of data analytics tools by organizations to address the information overload problem, and reports on the results from some of the data collected from an online survey about the use of visual analytics tools in organizations. The survey was aimed at gathering users' experiences in dealing with information overload and their level of exposure to data analytics tools. The results from the survey show that email is still the most time-consuming application, with a reported increase in remote access via handheld devices. A relevant percentage of respondents (65%) confirmed having knowledge and experience using some sort of data analytics tools, while 69.23% stated that the exposure to large amounts of information at work causes stress and anxiety.