stored in a distributed environment

WIDEa is R-based software aiming to provide users with a range of functionalities to explore, manage, clean and analyse "big" environmental and (in/ex situ) experimental data. These functionalities are the following, 1. Loading/reading different data types: basic (called normal), temporal, infrared spectra of mid/near region (called IR) with frequency (wavenumber) used as unit (in cm-1); 2. Interactive data visualization from a multitude of graph representations: 2D/3D scatter-plot, box-plot, hist-plot, bar-plot, correlation matrix; 3. Manipulation of variables: concatenation of qualitative variables, transformation of quantitative variables by generic functions in R; 4. Application of mathematical/statistical methods; 5. Creation/management of data (named flag data) considered as atypical; 6. Study of normal distribution model results for different strategies: calibration (checking assumptions on residuals), validation (comparison between measured and fitted values). The model form can be more or less complex: mixed effects, main/interaction effects, weighted residuals.

Modern research projects incorporate data from several sources, and new insights are increasingly driven by the ability to interpret data in the context of other data. Glue is an interactive environment built on top of the standard Python science stack to visualize relationships within and between datasets. With Glue, users can load and visualize multiple related datasets simultaneously. Users specify the logical connections that exist between data, and Glue transparently uses this information as needed to enable visualization across files. This functionality makes it trivial, for example, to interactively overplot catalogs on top of images. The central philosophy behind Glue is that the structure of research data is highly customized and problem-specific. Glue aims to accommodate this and simplify the "data munging" process, so that researchers can more naturally explore what their data have to say. The result is a cleaner scientific workflow, faster interaction with data, and an easier avenue to insight.

📌**Context**

The Healthcare Workforce Mental Health Dataset is designed to explore workplace mental health challenges in the healthcare industry, an environment known for high stress and burnout rates.

This dataset enables users to analyze key trends related to:

💠 Workplace Stressors: Examining the impact of heavy workloads, poor work environments, and emotional demands.

💠 Mental Health Outcomes: Understanding how stress and burnout influence job satisfaction, absenteeism, and turnover intention.

💠 Educational & Analytical Applications: A valuable resource for data analysts, students, and career changers looking to practice skills in data exploration and data visualization.

To help users gain deeper insights, this dataset is fully compatible with a Power BI Dashboard, available as part of a complete analytics bundle for enhanced visualization and reporting.

📌**Source**

This dataset was synthetically generated using the following methods:

💠 Python & Data Science Techniques: Probabilistic modeling to simulate realistic data distributions. Industry-informed variable relationships based on healthcare workforce studies.

💠 Guidance & Validation Using AI (ChatGPT): Assisted in refining dataset realism and logical mappings.

💠 Industry Research & Reports: Based on insights from WHO, CDC, OSHA, and academic studies on workplace stress and mental health in healthcare settings.

📌**Inspiration**

This dataset was inspired by ongoing discussions in healthcare regarding burnout, mental health, and staff retention. The goal is to bridge the gap between raw data and actionable insights by providing a structured, analyst-friendly dataset.

For those who want a ready-to-use reporting solution, a Power BI Dashboard Template is available, designed for interactive data exploration, workforce insights, and stress factor analysis.

📌**Important Note** This dataset is synthetic and intended for educational purposes only. It is not real-world employee data and should not be used for actual decision-making or policy implementation.

The explosion in the volume of biological imaging data challenges the available technologies for data interrogation and its intersection with related published bioinformatics data sets. Moreover, intersection of highly rich and complex datasets from different sources provided as flat csv files requires advanced informatics skills, which is time consuming and not accessible to all. Here, we provide a “user manual” to our new paradigm for systematically filtering and analysing a dataset with more than 1300 microscopy data figures using Multi-Dimensional Viewer (MDV) -link, a solution for interactive multimodal data visualisation and exploration. The primary data we use are derived from our published systematic analysis of 200 YFP traps reveals common discordance between mRNA and protein across the nervous system (eprint link). This manual provides the raw image data together with the expert annotations of the mRNA and protein distribution as well as associated bioinformatics data. We provide an explanation, with specific examples, of how to use MDV to make the multiple data types interoperable and explore them together. We also provide the open-source python code (github link) used to annotate the figures, which could be adapted to any other kind of data annotation task.

Click on any of the images below to explore an interactive data visualization:

Example use of voila - a JupyterLab extension that creates interactive web pages from an iPython Notebook. The example goes through data exploration and analysis of effects of stimulation on colon motility, based on a SPARC dataset.

The NSF Office of Advanced Cyberinfrastructure has recognized the emerging and evolving need for platforms that fully integrate data and computing workflows, and is calling for research to deliver systems that provide a full spectrum of data services and also offer a coherent coupling with computing software. The Digital Environment to Enable Data-driven Science (DEEDS) project has created a cross-domain, self-serve platform for data and computing that supports the entire end-to-end research investigation process. DEEDS offers interactive interfaces to 1) collect, manage, and explore data, 2) define and launch tools, 3) track computational workflows, and 4) access toolkits for ad hoc analytics. All interfaces are available from a single dashboard so that the workflow between data and tools is smooth and intuitive. In this paper, we describe DEEDS innovations for handling data and computational workflows, and we present the use cases from four science domains that defined features, services, and usability requirements for DEEDS.

The explosion in biological data generation challenges the available technologies and methodologies for data interrogation. Moreover, highly rich and complex datasets together with diverse linked data are difficult to explore when provided in flat files. Here we provide a way to filter and analyse in a systematic way a dataset with more than 18 thousand data points using Zegami, a solution for interactive data visualisation and exploration. The primary data we use are derived from a systematic analysis of 200 YFP gene traps reveals common discordance between mRNA and protein across the nervous system which is submitted elsewhere. This manual provides the raw image data together with annotations and associated data and explains how to use Zegami for exploring all these data types together by providing specific examples. We also provide the open source python code used to annotate the figures.

The explosion in the volume of biological imaging data challenges the available technologies for data interrogation and its intersection with related published bioinformatics data sets. Moreover, intersection of highly rich and complex datasets from different sources provided as flat csv files requires advanced informatics skills, which is time consuming and not accessible to all. Here, we provide a “user manual” to our new paradigm for systematically filtering and analysing a dataset with more than 1300 microscopy data figures using Multi-Dimensional Viewer (MDV: https://mdv.molbiol.ox.ac.uk), a solution for interactive multimodal data visualisation and exploration. The primary data we use are derived from our published systematic analysis of 200 YFP gene traps reveals common discordance between mRNA and protein across the nervous system (https://doi.org/10.1083/jcb.202205129). This manual provides the raw image data together with the expert annotations of the mRNA and protein distribution as well as associated bioinformatics data. We provide an explanation, with specific examples, of how to use MDV to make the multiple data types interoperable and explore them together. We also provide the open-source python code (github link) used to annotate the figures, which could be adapted to any other kind of data annotation task.

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Data Visualization Tools Market Size 2025-2029

The data visualization tools market size is forecast to increase by USD 7.95 billion at a CAGR of 11.2% between 2024 and 2029.

The market is experiencing significant growth due to the increasing demand for business intelligence and AI-powered insights. Companies are recognizing the value of transforming complex data into easily digestible visual representations to inform strategic decision-making. However, this market faces challenges as data complexity and massive data volumes continue to escalate. Organizations must invest in advanced data visualization tools to effectively manage and analyze their data to gain a competitive edge. The ability to automate data visualization processes and integrate AI capabilities will be crucial for companies to overcome the challenges posed by data complexity and volume. By doing so, they can streamline their business operations, enhance data-driven insights, and ultimately drive growth in their respective industries.

What will be the Size of the Data Visualization Tools Market during the forecast period?

Request Free SampleIn today's data-driven business landscape, the market continues to evolve, integrating advanced capabilities to support various sectors in making informed decisions. Data storytelling and preparation are crucial elements, enabling organizations to effectively communicate complex data insights. Real-time data visualization ensures agility, while data security safeguards sensitive information. Data dashboards facilitate data exploration and discovery, offering data-driven finance, strategy, and customer experience. Big data visualization tackles complex datasets, enabling data-driven decision making and innovation. Data blending and filtering streamline data integration and analysis. Data visualization software supports data transformation, cleaning, and aggregation, enhancing data-driven operations and healthcare. On-premises and cloud-based solutions cater to diverse business needs. Data governance, ethics, and literacy are integral components, ensuring data-driven product development, government, and education adhere to best practices. Natural language processing, machine learning, and visual analytics further enrich data-driven insights, enabling interactive charts and data reporting. Data connectivity and data-driven sales fuel business intelligence and marketing, while data discovery and data wrangling simplify data exploration and preparation. The market's continuous dynamism underscores the importance of data culture, data-driven innovation, and data-driven HR, as organizations strive to leverage data to gain a competitive edge.

How is this Data Visualization Tools Industry segmented?

The data visualization tools industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. DeploymentOn-premisesCloudCustomer TypeLarge enterprisesSMEsComponentSoftwareServicesApplicationHuman resourcesFinanceOthersEnd-userBFSIIT and telecommunicationHealthcareRetailOthersGeographyNorth AmericaUSMexicoEuropeFranceGermanyUKMiddle East and AfricaUAEAPACAustraliaChinaIndiaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.The market has experienced notable expansion as businesses across diverse sectors acknowledge the significance of data analysis and representation to uncover valuable insights and inform strategic decisions. Data visualization plays a pivotal role in this domain. On-premises deployment, which involves implementing data visualization tools within an organization's physical infrastructure or dedicated data centers, is a popular choice. This approach offers organizations greater control over their data, ensuring data security, privacy, and adherence to data governance policies. It caters to industries dealing with sensitive data, subject to regulatory requirements, or having stringent security protocols that prohibit cloud-based solutions. Data storytelling, data preparation, data-driven product development, data-driven government, real-time data visualization, data security, data dashboards, data-driven finance, data-driven strategy, big data visualization, data-driven decision making, data blending, data filtering, data visualization software, data exploration, data-driven insights, data-driven customer experience, data mapping, data culture, data cleaning, data-driven operations, data aggregation, data transformation, data-driven healthcare, on-premises data visualization, data governance, data ethics, data discovery, natural language processing, data reporting, data visualization platforms, data-driven innovation, data wrangling, data-driven s

This data set contains Exploration and Development Plans

The Cloud Business Intelligence (BI) Tools market is experiencing robust growth, driven by the increasing adoption of cloud computing, the need for real-time data analytics, and the rising demand for data-driven decision-making across various industries. The market, estimated at $25 billion in 2025, is projected to expand significantly over the forecast period (2025-2033), fueled by a Compound Annual Growth Rate (CAGR) of 15%. This growth is largely attributable to the increasing accessibility and affordability of cloud-based BI solutions, which eliminate the need for expensive on-premise infrastructure and specialized IT expertise. SMEs are rapidly adopting these tools to gain a competitive edge, while large enterprises leverage them to streamline operations, improve efficiency, and enhance strategic planning. The subscription model dominates the market due to its flexibility and cost-effectiveness, while the perpetual license model retains a significant presence among organizations with specific licensing requirements. Key market players such as Microsoft Power BI, Tableau, and SAP are driving innovation through continuous product enhancements and strategic partnerships, while newer entrants are focusing on niche market segments and specialized functionalities. Geographic growth is widespread, with North America currently leading the market, followed by Europe and Asia Pacific. However, the Asia Pacific region is expected to witness the highest growth rate due to increasing digitalization and a burgeoning tech-savvy workforce. Several factors are shaping the future trajectory of the Cloud Business Intelligence market. The increasing integration of Artificial Intelligence (AI) and Machine Learning (ML) into BI platforms is enabling advanced analytics capabilities, such as predictive modeling and automated insights generation. The growing emphasis on data security and compliance is influencing the development of secure and robust cloud-based BI solutions, addressing concerns around data privacy and protection. Furthermore, the expanding ecosystem of cloud-based data integration and visualization tools facilitates seamless data consolidation and interactive data exploration. However, challenges such as data integration complexity, concerns around vendor lock-in, and the need for skilled professionals to effectively utilize these tools could potentially restrain market growth. Despite these challenges, the overall outlook remains highly positive, with continued innovation and broader adoption expected to drive substantial market expansion in the coming years.

This web app launches the MultiSpec Online tool on MyGeoHub (https://mygeohub.org) for geospatial data exploration, analysis and visualization.

Students use GapMinder, an interactive data exploration and visualization tool, to assess human impact on Earth systems. Using the impact = population x affluence x technology (IPAT) framework students examine sustainability metrics by country over time.

Immersive Analytics Market size was valued at USD 1.2 Billion in 2024 and is projected to reach USD 34.6 Billion by 2032, growing at a CAGR of 52.01% from 2025 to 2032.

The global Immersive Analytics market is experiencing significant growth, driven by several key factors. The increasing complexity and volume of data generated across industries necessitate advanced visualization tools that can provide meaningful insights, leading to a rising demand for immersive analytics solutions. Technological advancements in virtual reality (VR), augmented reality (AR), and mixed reality (MR) have enabled more intuitive and interactive data exploration, enhancing user experience and decision-making processes. The growing integration of immersive analytics with Internet of Things (IoT) devices allows for real-time data analysis, further fueling market expansion. Additionally, sectors such as healthcare, media and entertainment, automotive, and government defense are increasingly adopting immersive analytics for various applications, contributing to the market's robust growth trajectory.

This map service shows the locations of the properties under geothermal exploration development (exploration, testing, construction) in Nevada as of mid-2011. The map service contains 20 separate data coverages, individually documented elsewhere by category: http://www.nbmg.unr.edu/Geothermal/Data.html . For more info on this resource or to view the interactive map, please see the links provided.

Both desktop and web-based solutions are included.This table comprises a list of potential software solutions for typical genomic data analysis tasks in molecular ecology (e.g. alignment, phylogenetics, data exploration, etc.).

The data visualization market is experiencing robust growth, driven by the increasing need for businesses to derive actionable insights from complex datasets. The market's expansion is fueled by several key factors, including the proliferation of big data, the rising adoption of cloud-based analytics platforms, and the growing demand for self-service business intelligence (BI) tools. Businesses across various sectors, from finance and healthcare to manufacturing and retail, are increasingly relying on data visualization to improve decision-making, enhance operational efficiency, and gain a competitive edge. The market is witnessing a shift towards interactive and dynamic dashboards, enabling users to explore data more effectively and gain a deeper understanding of key performance indicators (KPIs). Furthermore, the integration of artificial intelligence (AI) and machine learning (ML) capabilities into data visualization tools is enhancing their analytical power, leading to the automation of insights generation and predictive analytics. We estimate the market size in 2025 to be $15 billion, considering the average CAGR of similar markets in the technology sector. The competitive landscape is highly fragmented, with a mix of established players like SAP, SAS Institute, and Qlik, alongside emerging niche players offering specialized solutions. While established vendors hold a significant market share due to their brand recognition and comprehensive product portfolios, smaller companies are gaining traction by focusing on specific industry verticals or offering innovative features like advanced AI/ML integration. The market is characterized by continuous innovation, with new visualization techniques and technologies emerging regularly. The increasing adoption of mobile BI and the growing demand for embedded analytics solutions within applications are further driving market expansion. Geographic growth is expected to be significant across regions, particularly in Asia-Pacific and Latin America, fueled by increased digitalization and technological advancements. However, factors like the high cost of implementation and the need for skilled personnel to effectively utilize these tools can act as restraints on market growth.

Despite exploratory data analysis (EDA) is a powerful approach for uncovering insights from unfamiliar datasets, existing EDA tools face challenges in assisting users to assess the progress of exploration and synthesize coherent insights from isolated findings. To address these challenges, we present FactExplorer, a novel fact-based EDA system that shifts the analysis focus from raw data to data facts. FactExplorer employs a hybrid logical-visual representation, providing users with a comprehensive overview of all potential facts at the outset of their exploration. Moreover, FactExplorer introduces fact-mining techniques, including topic-based drill-down and transition path search capabilities. These features facilitate in-depth analysis of facts and enhance the understanding of interconnections between specific facts. Finally, we present a usage scenario and conduct a user study to assess the effectiveness of FactExplorer. The results indicate that FactExplorer facilitates the understanding of isolated findings and enables users to steer a thorough and effective EDA.