The Multidimensional Data Visualization Jigsaw is designed to enables users to create, organize and synthesis custom designed visualizations. It helps users work together to analyze the data more efficiently and effectively. Jigsaw URL ：http://vis.pku.edu.cn/mddv/jigsaw/sketch

For detail：http://vis.pku.edu.cn/mddv/ For detail of Assemble Factory: http://vis.pku.edu.cn/mddv/?page_id=134， For detail of Scatterplots in Parallel Cooridnates User Mannul：http://vis.pku.edu.cn/mddv/?page_id=195

CB, color-blending; EM, electron microscopic images; FM, fluorescent microscopic images (often laser-scanning-microscopic images); HL, hardware-limited; WM, wide-field light microscopic images.

HTML file corresponding to https://lea-urpa.github.io/PaperSupplement.html . To view the file, download the zip file, unzip, and double click the HTML file to open in any browser with Javascript enabled. (ZIP 2891 kb)

This data includes two example databases from the paper "Hybrid Sankey diagrams: visual analysis of multidimensional data for understanding resource use": the made-up fruit flows, and real global steel flow data from Cullen et al. (2012). It also includes the Sankey Diagram Definitions to reproduce the diagrams in the paper. The code to reproduce the figures is written in Python in the form of Jupyter notebooks. A conda environment file is included to easily set up the necessary Python packages to run the notebooks. All files are included in the "examples.zip" file. The notebook files are also uploaded standalone so they can be linked to nbviewer.

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.

Subsurface Data Visualization Market Outlook

According to our latest research, the global Subsurface Data Visualization market size reached USD 2.84 billion in 2024, and is expected to grow at a robust CAGR of 13.2% from 2025 to 2033. By the end of the forecast period, the market is projected to achieve a value of USD 8.38 billion. This impressive growth is primarily driven by the increasing demand for advanced visualization technologies in sectors such as oil & gas, mining, and environmental sciences, where accurate interpretation of subsurface data is crucial for operational efficiency and risk mitigation. As per our latest research, technological advancements, coupled with the rising adoption of cloud-based solutions and immersive visualization platforms, are further propelling the market forward.

A significant growth driver for the Subsurface Data Visualization market is the escalating complexity of subsurface data generated by modern exploration and monitoring technologies. With the proliferation of sensors and high-resolution imaging tools, industries like oil & gas and mining are now producing vast volumes of multidimensional data that require sophisticated visualization solutions for effective analysis. The ability to transform raw data into actionable insights through intuitive 2D and 3D models has become indispensable, enabling organizations to make informed decisions, optimize resource allocation, and minimize operational risks. This trend is further accentuated by the integration of artificial intelligence and machine learning algorithms, which enhance the analytical capabilities of visualization platforms, making them more adaptive and predictive.

Another key factor fueling the growth of the Subsurface Data Visualization market is the rapid adoption of cloud-based deployment models. Cloud solutions offer unparalleled scalability, flexibility, and cost-efficiency, allowing organizations to access advanced visualization tools without the need for significant upfront investments in hardware or infrastructure. This has democratized access to powerful analytics and visualization capabilities, particularly for small and medium enterprises (SMEs) and research institutions that previously faced budgetary constraints. In addition, the cloud facilitates seamless collaboration among geographically dispersed teams, accelerating project timelines and fostering innovation in subsurface data interpretation.

The emergence of immersive technologies such as virtual reality (VR) and augmented reality (AR) is also reshaping the Subsurface Data Visualization market. These cutting-edge visualization types enable users to interact with subsurface models in a highly intuitive and immersive manner, enhancing understanding and communication among stakeholders. For example, VR and AR solutions are increasingly being used in training, simulation, and remote operations, reducing the need for physical presence in hazardous environments. This not only improves safety but also enhances the efficiency of exploration, drilling, and monitoring activities across various sectors, thereby driving market growth.

Regionally, North America continues to dominate the Subsurface Data Visualization market due to its strong presence of leading technology providers, high investments in research and development, and advanced infrastructure in industries such as oil & gas and environmental science. However, the Asia Pacific region is witnessing the fastest growth, driven by increasing exploration activities, rapid industrialization, and government initiatives aimed at sustainable resource management. Europe also holds a significant share, supported by stringent environmental regulations and the adoption of innovative technologies in mining and construction. The Middle East & Africa and Latin America are emerging as promising markets, fueled by expanding energy and mining sectors and growing awareness of the benefits of advanced data visualization.

Component Analysis

The Subsurface Data Visualization market is segmented by component into software, hardware, and services. The software segment holds the largest share, accounting for over 50% of the total market revenue in 2024. This dominance is attributed to the continuous innovation in visualization algorithms, user interfaces, and integration capabilities with other data management and analytics platforms. Modern software solutions offer comprehensive toolsets for data p

this graph was created in Loocker studio,PowerBi and Tableau :

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This dataset contains four files: (1) Raw data of 13 indicators closely related to society, economy, environment, infrastructure, and innovation at the provincial level in China from 1990 to 2021, including GDP per capita, disposable income per capita, rate of high school graduates and above, density of physicians per 10,000 people, unemployment rate, living space per capita, PM2.5 concentration, carbon emission per capita, urban green space per capita, road density, internet penetration rate, patents granted per capita, and R&D expenditure per capita; (2) Population-weighted coefficient of variation for the 13 indicators; (3) Gini coefficient for the 13 indicators; (4) Moran's I for the 13 indicators.

This dataset centers on the Confucian core classics, "The Four Books" (The Analects, Mencius, The Great Learning, and The Doctrine of the Mean). It integrates English translations from four representative large language models (DeepSeek, ERNIE Bot (Wenxin Yiyan), ChatGPT-5, and Google Translate), with authoritative human translations by James Legge and Wu Guozhen serving as references. The dataset is designed to support research in translation studies, particularly focusing on digital translator style.

The core value of this dataset lies in its provision of a suite of multi-dimensional, quantifiable style assessment metrics, encompassing:

Semantic Level: BERTScore-based semantic similarity data between translations and reference translations. Pragmatic Level: Evaluation of translation consistency and conceptual accuracy for key Confucian terms such as "benevolence (Ren)", "righteousness (Yi)", and "propriety (Li)". Discourse and Syntactic Level: Discourse features including average sentence length, parallelism/antithesis preservation, coherence scores, and detailed syntactic complexity data (e.g., mean length of T-unit, clause ratio) calculated based on Spacy. Utilizing this dataset, researchers can systematically analyze and compare the stable stylistic features exhibited by different digital translators when processing classical texts. It provides an empirical foundation for the theoretical construction of "digital translator style" and offers references for model selection and optimization in classical text translation.

Proximity scaling methods such as multidimensional scaling represent objects in a low-dimensional configuration so that fitted object distances optimally approximate object proximities. Besides finding the optimal configuration, an additional goal may be to make statements about the cluster arrangement of objects. This fails if the configuration lacks appreciable clusteredness. We present cluster optimized proximity scaling (COPS), which attempts to find a configuration that exhibits clusteredness. In COPS, a flexible parameterized scaling loss function that may emphasize differentiation information in the proximities is augmented with an index (OPTICS Cordillera) that penalizes lack of clusteredness of the configuration. We present two variants of this, one for finding a configuration directly and one for hyperparameter selection for parametric stresses. We apply both to a functional magnetic resonance imaging dataset on neural representations of mental states in a social cognition task and show that COPS improves clusteredness of the configuration, enabling visual identification of clusters of mental states. Online supplementary materials are available including an R package and a document with additional details.

Indigenous peoples represent one of the most vulnerable, marginalized, and overlooked sectors in society. Poverty, the age-old social problem, poses significant challenges to overcome. The Agta Tabangnon, our Indigenous community, experiences poverty and various socio-economic deprivations. While poverty studies typically employ generic approaches with large sampling errors for nationwide decision-making, studies focused on Indigenous peoples are qualitative in nature. Therefore, it is crucial to measure poverty for specific tribes through a comprehensive enumeration that considers multiple dimensions, fostering economic development. Unfortunately, there is currently no comprehensive census specifically designed to capture the multidimensional aspects of Indigenous peoples' way of life. However, we have been resourceful in generating valuable multidimensional data through partnerships. Our local community is situated in the poorest district of the poorest province within the poorest region of Luzon, Philippines. Our datasets encompass various indicators of multidimensional poverty and include complementary analytics for data visualization. These resources can serve as a foundation for measuring poverty among Indigenous communities across different regions and countries. By utilizing this data, further empirical analysis, regressions, machine learning, and econometric modeling can be conducted. This information can be freely utilized to target policies and interventions that address the multifaceted poverty experienced by tribal communities, thereby promoting their economic development.

Multidimensional scaling (MDS) is a dimensionality reduction technique for microbial ecology data analysis that represents the multivariate structure while preserving pairwise distances between samples. While its improvements have enhanced the ability to reveal data patterns by sample groups, these MDS-based methods require prior assumptions for inference, limiting their application in general microbiome analysis. In this study, we introduce a new MDS-based ordination, “F-informed MDS,†which configures the data distribution based on the F-statistic, the ratio of dispersion between groups sharing common and different characteristics. Using simulated compositional datasets, we demonstrate that the proposed method is robust to hyperparameter selection while maintaining statistical significance throughout the ordination process. Various quality metrics for evaluating dimensionality reduction confirm that F-informed MDS is comparable to state-of-the-art methods in preserving both local and ..., , # Multidimensional scaling informed by F-statistic: Visualizing grouped microbiome data with inference

• Dataset DOI: 10.5061/dryad.vmcvdnd3x
• Software: https://github.com/soob-kim/FinfoMDS
  (also in prep for Bioconductor submission)
• File or folder names are italicized. Package or variable names are monospaced.Â

File: Data.zip

Description:Â Raw data used in this study. Includes 3 folders and 1 file (see below).

1. Folder Simulated contains pairwise distances and ordination results from three simulated datasets. Includes 7 subfolders and 6 files.
   • Six files are the original dataset and its associated labels set. The names are formatted as "sim_<*x*>-<*type*>.*csv*" where <*x*> is the replicate number and <*type*> indicates whether the file is the design matrix ("data") or response vector ("Y").
   • Seven subfolders are grouped by the ordination method. Likewise, the file ...,

Set Visualization Tools Market Outlook

According to our latest research, the global set visualization tools market size reached USD 3.2 billion in 2024, driven by the increasing demand for advanced data analytics and visual representation across diverse industries. The market is expected to grow at a robust CAGR of 12.8% from 2025 to 2033, reaching a forecasted value of USD 9.1 billion by 2033. This significant growth is primarily attributed to the proliferation of big data, the rising importance of data-driven decision-making, and the expansion of digital transformation initiatives worldwide.

One of the primary growth factors fueling the set visualization tools market is the exponential surge in data generation from numerous sources, including IoT devices, enterprise applications, and digital platforms. Organizations are increasingly seeking efficient ways to interpret complex and voluminous datasets, making advanced visualization tools indispensable for extracting actionable insights. The integration of artificial intelligence (AI) and machine learning (ML) into these tools further enhances their capability to identify patterns, trends, and anomalies, thus supporting more informed strategic decisions. As businesses across sectors recognize the value of data visualization in driving operational efficiency and innovation, the adoption of set visualization tools continues to accelerate.

Another key driver is the growing emphasis on business intelligence (BI) and analytics within enterprises of all sizes. Modern set visualization tools are evolving to offer intuitive interfaces, real-time analytics, and seamless integration with existing IT infrastructure, making them accessible to non-technical users as well. This democratization of data analytics empowers a broader range of stakeholders to participate in data-driven processes, fostering a culture of collaboration and agility. Additionally, the increasing complexity of datasets, especially in sectors like healthcare, finance, and scientific research, necessitates sophisticated visualization solutions capable of handling multidimensional and hierarchical data structures.

The rapid adoption of cloud computing and the shift towards remote and hybrid work environments have also played a pivotal role in the expansion of the set visualization tools market. Cloud-based deployment models offer unparalleled scalability, flexibility, and cost-effectiveness, enabling organizations to access visualization capabilities without significant upfront investments in hardware or infrastructure. Furthermore, the emergence of mobile and web-based visualization platforms ensures that users can interact with data visualizations anytime, anywhere, thereby enhancing productivity and decision-making speed. As digital transformation initiatives gain momentum globally, the demand for advanced, user-friendly, and scalable set visualization tools is expected to remain strong.

From a regional perspective, North America currently dominates the set visualization tools market, accounting for the largest share in 2024, followed closely by Europe and the Asia Pacific. The presence of leading technology companies, a mature IT infrastructure, and high investment in analytics and business intelligence solutions contribute to North America's leadership position. However, the Asia Pacific region is witnessing the fastest growth, propelled by rapid digitalization, expanding enterprise IT budgets, and increasing awareness about the benefits of data visualization. As emerging economies in Latin America and the Middle East & Africa continue to invest in digital transformation, these regions are also expected to offer lucrative growth opportunities for market players over the forecast period.

Component Analysis

The set visualization tools market by component is primarily segmented into software and services, each playing a crucial role in the overall ecosystem. The software segment holds the majority share, driven by the continuous evolution of visualization platforms

Heliophysics Data-Visualization Display Market Outlook

According to our latest research, the global heliophysics data-visualization display market size reached USD 1.26 billion in 2024, reflecting a robust growth trajectory driven by the increasing demand for advanced visualization tools in space science. The market is expected to expand at a CAGR of 9.8% from 2025 to 2033, reaching a projected value of USD 2.93 billion by 2033. This growth is underpinned by the escalating need for sophisticated data analysis, visualization, and interpretation tools that support the complex study of solar, magnetospheric, and heliospheric phenomena. The surge in space research activities, investments in scientific missions, and the proliferation of big data analytics in heliophysics are key factors propelling the market forward.

One of the primary growth drivers for the heliophysics data-visualization display market is the exponential increase in the volume and complexity of space data generated by modern observatories and space missions. With the launch of advanced satellites and ground-based solar observatories, researchers are inundated with multidimensional datasets that require high-performance visualization platforms for effective analysis. The ability to visualize and interpret solar events, magnetospheric interactions, and space weather phenomena in real-time is critical for both scientific discovery and operational decision-making. As a result, there is a substantial push for the integration of artificial intelligence and machine learning algorithms into visualization software, enabling automated pattern recognition, anomaly detection, and predictive modeling. This technological convergence is fundamentally transforming how heliophysics data is processed, visualized, and disseminated across the global research community.

Another significant growth factor is the increasing collaboration between government space agencies, academic institutions, and commercial enterprises. These collaborations are fostering innovation in hardware and software components, leading to the development of more user-friendly, scalable, and interoperable visualization solutions. The growing emphasis on open data policies and the democratization of space science are encouraging the adoption of cloud-based platforms, which facilitate seamless data sharing, remote access, and collaborative research. Furthermore, educational and research institutions are leveraging these advanced visualization displays to enhance STEM curricula, promote public engagement, and inspire the next generation of space scientists. The continuous evolution of visualization technologies, coupled with supportive government initiatives, is expected to sustain the strong momentum in the heliophysics data-visualization display market over the forecast period.

The market is also benefiting from the rising awareness of the societal and economic impacts of space weather events, such as geomagnetic storms and solar flares. Governments and commercial enterprises, particularly those operating in satellite communications, power grids, and aviation, are increasingly investing in real-time space weather monitoring and forecasting systems. These systems rely heavily on sophisticated data-visualization displays to interpret and communicate complex heliophysical phenomena to diverse stakeholders. The integration of augmented reality (AR) and virtual reality (VR) technologies is further enhancing the immersive experience of data visualization, enabling users to interact with solar and magnetospheric data in novel ways. As the importance of space weather resilience grows, the demand for advanced visualization solutions is expected to rise, driving further market expansion.

Regionally, North America continues to dominate the heliophysics data-visualization display market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The United States, with its strong presence of leading space agencies such as NASA and a vibrant ecosystem of research institutions, remains at the forefront of technological innovation and adoption. Europe is witnessing steady growth, supported by collaborative initiatives within the European Space Agency (ESA) and increased funding for space science research. The Asia Pacific region is emerging as a high-growth market, driven by rising investments in space exploration by countries like China, India, and Japan. The Middle East & Africa and Latin America, while currently holding smaller shares, are expected to experience ac

Source code of our visual analysis system for the exploration of the visual quality of multidimensional time series projections. This project contains source code for preprocessing data and the visual analysis system. Additionally, we added precomputed data for immediate use in the visual analysis system. Our project contains the following directories/files of interest: datasets: Data sets for the use with our visual analysis system. The data can also be generated with the data preparation scripts. static, templates, and dimRed: Java script / Python code of our visualization approach. run_windows: Scripts to run our system on windows. run_linux: Scripts to run our system on linux. datasets.txt: List of directories used in preprocessing and for the visualization. Please have a look at the README file for more details.

Supplemental material for our paper "Exploring visual quality of multidimensional time series projections": A video demonstrating the interactive use of our exploration system. A table containing publications using dimensionality reduction on multidimensional time series to project them to 2D for visualization and exploration. A file containing images illustrating the data used in section 5.2. (Simulation Data: Kármán Vortex Street) and section 5.3. (Real Footage: Hurricane Dorian Timelapse). A video illustrating the data used in section 5.2. (Simulation Data: Kármán Vortex Street).

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.

Presentation Date: Friday, March 1, 2019 Location: Visual Communication Symposium, Rice University, Houston, TX Abstract: Astronomy has long been a field reliant on visualization. First, it was literal visualization—looking at the Sky. Today, though, astronomers are faced with the daunting task of understanding gigantic digital images from across the electromagnetic spectrum and contextualizing them with hugely complex physics simulations, in order to make more sense of our Universe. In this talk, I will explain how new approaches to simultaneously exploring and explaining vast data sets allow astronomers—and other scientists—to make sense of what the data have to say, and to communicate what they learn, to each other and to the public. I will focus on the multi-dimensional linked-view data visualization environment known as “glue” (glueviz.org), explaining how it is being used in astronomy, medical imaging, and geographic information sciences. I will discuss its future potential to expand into all fields where diverse but related multi-dimensional data sets can be profitably analyzed together. Toward the aim of bringing the fruits of visualization to a broader audience, I will also introduce the new “10 Questions to Ask When Creating a Visualization” website, 10QViz.org. Full program downloadable from: https://vcs.rice.edu/sites/g/files/bxs2036/f/VCS%202019%20program%20booklet.pdf

These images show simple test system diagrams for three of the test power systems in the Texas A&M "Electric Grid Test Cases" collection (see https://electricgrids.engr.tamu.edu/ and A.B. Birchfield, T. Xu, K.M. Gegner, K.S. Shetye, T.J. Overbye, "Grid Structural Characteristics as Validation Criteria for Synthetic Networks," to appear IEEE Transactions Power Systems)The three networks visualized here are "Illinois 200-Bus System: ACTIVSg200", "SouthCarolina 500-Bus System: ACTIVSg500" and "Texas 2000-Bus System: ACTIVSg2k" These network diagrams were created by applying the techniques described in:P. Cuffe; A. Keane, "Visualizing the Electrical Structure of Power Systems," in IEEE Systems Journal, 2015The "Power Transfer" distance was use to create each diagram (see ibid. Section II. 3) As such, the diagrams are electrically meaningful: loosely speaking, buses drawn in close proximity should be able to transact power with each other with ease.Buses have been labelled as space permits, starting with the highest voltage buses first.

According to Cognitive Market Research, the global Lifescience Data Mining And Visualization market size is USD 5815.2 million in 2023 and will expand at a compound annual growth rate (CAGR) of 9.60% from 2023 to 2030.

North America held the major market of more than 40% of the global revenue with a market size of USD 2326.08 million in 2023 and will grow at a compound annual growth rate (CAGR) of 7.8% from 2023 to 2030
Europe held the major market of more than 40% of the global revenue with a market size of USD 1744.56 million in 2023 and will grow at a compound annual growth rate (CAGR) of 8.1% from 2023 to 2030. 
Asia Pacific held the fastest growing market of more than 23% of the global revenue with a market size of USD 1337.50 million in 2023 and will grow at a compound annual growth rate (CAGR) of 11.6% from 2023 to 2030
Latin America market held of more than 5% of the global revenue with a market size of USD 290.76 million in 2023 and will grow at a compound annual growth rate (CAGR) of 9.0% from 2023 to 2030
Middle East and Africa market held of more than 2.00% of the global revenue with a market size of USD 116.30 million in 2023 and will grow at a compound annual growth rate (CAGR) of 9.3% from 2023 to 2030
The demand for Lifescience Data Mining And Visualizations is rising due to rapid growth in biological data and increasing emphasis on personalized medicine.
Demand for On-Demand remains higher in the Lifescience Data Mining And Visualization market.
The Pharmaceuticals category held the highest Lifescience Data Mining And Visualization market revenue share in 2023.

Market Dynamics of Lifescience Data Mining And Visualization

Key Drivers of Lifescience Data Mining And Visualization

Advancements in Healthcare Informatics to Provide Viable Market Output

The Lifescience Data Mining and Visualization market are driven by continuous advancements in healthcare informatics. As the life sciences industry generates vast volumes of complex data, sophisticated data mining and visualization tools are increasingly crucial. Advancements in healthcare informatics, including electronic health records (EHRs), genomics, and clinical trial data, provide a wealth of information. Data mining and visualization technologies empower researchers and healthcare professionals to extract meaningful insights, aiding in personalized medicine, drug discovery, and treatment optimization.

August 2020: Johnson & Johnson and Regeneron Pharmaceuticals announced a strategic collaboration to develop and commercialize cancer immunotherapies.

(Source:investor.regeneron.com/news-releases/news-release-details/regeneron-and-cytomx-announce-strategic-research-collaboration)

Rising Focus on Precision Medicine Propel Market Growth

A key driver in the Lifescience Data Mining and Visualization market is the growing focus on precision medicine. As healthcare shifts towards personalized treatment strategies, there is an increasing need to analyze diverse datasets, including genetic, clinical, and lifestyle information. Data mining and visualization tools facilitate the identification of patterns and correlations within this multidimensional data, enabling the development of tailored treatment approaches. The emphasis on precision medicine, driven by advancements in genomics and molecular profiling, positions data mining and visualization as essential components in deciphering the intricate relationships between biological factors and individual health, thereby fostering innovation in life science research and healthcare practices.

In June 2022, SAS Institute Inc. (US) entered into an agreement with Gunvatta (US) to expedite clinical trials and FDA reporting through the SAS Life Science Analytics Framework on Azure.

(Source:www.prnewswire.com/news-releases/clinical-research-and-drug-development-accelerated-via-analytics-301571580.html)

Increasing adoption of artificial intelligence (AI) and machine learning (ML) algorithms is propelling the market growth of life science data mining and visualization

These technologies have revolutionized the ability to analyze and interpret vast, complex datasets in fields such as drug discovery and personalized medicine. For instance, companies like Insitro are utilizing AI-driven models to analyze biological and chemical data, dramatically accelerating drug discovery timelines and optimizing the identification of new therape...