Interactive Data Exploration Research Articles

Towards maximizing geospatial data usage in north eastern India using open-source scalable user-centric applications

Abstract. The north eastern region of India is characterized by limited physical connectivity and challenging terrain, that requires innovative approaches for effective developmental planning and monitoring using geospatial data and user-centric applications. This paper focuses on leveraging space-based data and applications to address these challenges and enhance decision-making processes in governance. A unified framework for interoperable spatial data sharing has been developed, ensuring that data is easily discoverable, searchable, and consumable through metadata standards and OGC interoperable data services. A dedicated spatial data repository has been established, offering real-time access to over 1400 geospatial datasets and facilitating better decision-making and geo-analytics among diverse user groups. The repository operates on an open-source platform with integrated map viewers and geo-analytics tools, allowing for interactive exploration and visualization of geospatial data. Additionally, 35 user-centric geospatial applications have been operationalized, all developed using open-source geospatial tools and standards. Capacity building programs have been conducted to increase awareness and maximize the utilization of geospatial data services and applications across the region. The platform has boosted utilization of geospatial data services and applications in the region for overall planning and developmental activities for smart governance and societal benefits.

Sainsc: A Computational Tool for Segmentation-Free Analysis of In Situ Capture Data.

Spatially resolved transcriptomics (SRT) has become the method of choice for characterising the complexity of biomedical tissue samples. Until recently, scientists were restricted to SRT methods that can profile a limited set of target genes at high spatial resolution or transcriptome-wide but at a low spatial resolution. Through recent developments, there are now methods that offer both subcellular spatial resolution and full transcriptome coverage. However, utilising these new methods' high spatial resolution and gene resolution remains elusive due to several factors, including low detection efficiency and high computational costs. Here, we present Sainsc (Segmentation-free analysis of in situ capture data), which combines a cell-segmentation-free approach with efficient data processing of transcriptome-wide nanometre-resolution spatial data. Sainsc can generate cell-type maps with accurate cell-type assignment at the nanometre scale, together with corresponding maps of the assignment scores that facilitate interpretation of the local confidence of cell-type assignment. We demonstrate its utility and accuracy for different tissues and technologies. Compared to other methods, Sainsc requires lower computational resources and has scalable performance, enabling interactive data exploration. Sainsc is compatible with common data analysis frameworks and is available as open-source software in multiple programming languages.

Learning-Based Sample Tuning for Approximate Query Processing in Interactive Data Exploration

Learning-Based Sample Tuning for Approximate Query Processing in Interactive Data Exploration

DeepMIP-Eocene-p1: multi-model dataset and interactive web application for Eocene climate research

Paleoclimate model simulations provide reference data to help interpret the geological record and offer a unique opportunity to evaluate the performance of current models under diverse boundary conditions. Here, we present a dataset of 35 climate model simulations of the warm early Eocene Climatic Optimum (EECO; ~ 50 million years ago) and corresponding preindustrial reference experiments. To streamline the use of the data, we apply standardised naming conventions and quality checks across eight modelling groups that have carried out coordinated simulations as part of the Deep-Time Model Intercomparison Project (DeepMIP). Gridded model fields can be downloaded from an online repository or accessed through a new web application that provides interactive data exploration. Local model data can be extracted in CSV format or visualised online for streamlined model-data comparisons. Additionally, processing and visualisation code templates may serve as a starting point for advanced analysis. The dataset and online platform aim to simplify accessing and handling complex data, prevent common processing issues, and facilitate the sharing of climate model data across disciplines.

A roadmap to the molecular human linking multiomics with population traits and diabetes subtypes

In-depth multiomic phenotyping provides molecular insights into complex physiological processes and their pathologies. Here, we report on integrating 18 diverse deep molecular phenotyping (omics-) technologies applied to urine, blood, and saliva samples from 391 participants of the multiethnic diabetes Qatar Metabolomics Study of Diabetes (QMDiab). Using 6,304 quantitative molecular traits with 1,221,345 genetic variants, methylation at 470,837 DNA CpG sites, and gene expression of 57,000 transcripts, we determine (1) within-platform partial correlations, (2) between-platform mutual best correlations, and (3) genome-, epigenome-, transcriptome-, and phenome-wide associations. Combined into a molecular network of > 34,000 statistically significant trait-trait links in biofluids, our study portrays “The Molecular Human”. We describe the variances explained by each omics in the phenotypes (age, sex, BMI, and diabetes state), platform complementarity, and the inherent correlation structures of multiomics data. Further, we construct multi-molecular network of diabetes subtypes. Finally, we generated an open-access web interface to “The Molecular Human” (http://comics.metabolomix.com), providing interactive data exploration and hypotheses generation possibilities.

Distinct granzyme k expression in immune cells: a single-cell rna-seq meta-analysis.

Granzymes are essential serine proteases in cytotoxic T cells and natural killer (NK) cells, with GZMK's expression being less understood. This study aims to uncover GZMK expression profiles across various immune cell types using single-cell RNA sequencing meta-analysis. This study aims to uncover GZMK expression profiles across various immune cell types using single-cell RNA sequencing meta-analysis. We conducted a meta-analysis using cellxgene, an interactive data exploration platform developed by the Chan Zuckerberg Initiative. We focused on mature T cells, NK cells, B cells, and NKT cells. We also checked transcription factor binding sites at the granzyme gene promoter regions using JASPAR. Comparative analysis was also done using mouse single-cell RNA sequencing data. GZMK was the most lowly expressed in NK cells and mature NKT cells in most tissues except for colon and lymph nodes. In mature T cells, GZMK is similarly or more highly expressed than other granzymes. HBCA data revealed weak expression of GZMK in NK cells but strong expression in effector memory CD8-positive, alpha-beta T cells. Combined data shows no significant difference in GZMK expression between cell types. Subtype analysis shows that GZMK expression was higher in CD16-negative, CD56-bright NK cells when compared to CD16-positive, CD56-dim NK cells. We also identified unique transcription factor binding sites for GZMK. While this pattern in mouse data with low Gzmk expression in NK cells and higher T cells was repeated. GZMK expression is distinctively regulated among immune cells and tissues, with unique promoter regions and transcription factor binding sites contributing to this differential expression. These insights into GZMK's role in immune function and regulation offer potential therapeutic targets.

Interactive data exploration websites for large-scale electrophysiology.

Methodological advances in neuroscience have enabled the collection of massive datasets which demand innovative approaches for scientific communication. Existing platforms for data storage lack intuitive tools for data exploration, limiting our ability to interact effectively with these brain-wide datasets. We introduce two public websites: (Data and Atlas) developed for the International Brain Laboratory which provide access to millions of behavioral trials and hundreds of thousands of individual neurons. These interfaces allow users to discover both the raw and processed brain-wide data released by the IBL at the scale of the whole brain, individual sessions, trials, and neurons. By hosting these data interfaces as websites they are available cross-platform with no installation. By releasing each site's code as a modular open-source framework, other researchers can easily develop their own web interfaces and explore their own data. As neuroscience datasets continue to expand, customizable web interfaces offer a glimpse into a future of streamlined data exploration and act as blueprints for future tools.

Rational gaze: Presenting the open-source HYPEtools R package for analysis, visualization, and interpretation of hydrological models and datasets

The open-source HYPEtools R package aims to address current challenges in hydrological modeling and data analysis by providing a suite of tools for data management, visualization, interpretation, and exploration. Originally developed as a supporting toolbox for the HYPE hydrological model, HYPEtools has since evolved into a standalone package with features ranging from diverse aggregation and evaluation routines to interactive apps and mapping. Specifically, functions are included for data manipulation and conversion, data summarization, plotting and mapping, and interactive data exploration. Two case studies are provided to highlight how HYPEtools can simplify hydrological workflows and analyses both independently of and within HYPE modeling contexts. First, HYPEtools is used for data exploration and mapping of water transfers in South Africa. Then, a case study for the Baltic Sea region of a pan-European HYPE model setup illustrates how HYPEtools can assist model calibration and validation.

Leveraging Self-Supervised Vision Transformers for Segmentation-based Transfer Function Design.

In volume rendering, transfer functions are used to classify structures of interest, and to assign optical properties such as color and opacity. They are commonly defined as 1D or 2D functions that map simple features to these optical properties. As the process of designing a transfer function is typically tedious and unintuitive, several approaches have been proposed for their interactive specification. In this paper, we present a novel method to define transfer functions for volume rendering by leveraging the feature extraction capabilities of self-supervised pre-trained vision transformers. To design a transfer function, users simply select the structures of interest in a slice viewer, and our method automatically selects similar structures based on the high-level features extracted by the neural network. Contrary to previous learning-based transfer function approaches, our method does not require training of models and allows for quick inference, enabling an interactive exploration of the volume data. Our approach reduces the amount of necessary annotations by interactively informing the user about the current classification, so they can focus on annotating the structures of interest that still require annotation. In practice, this allows users to design transfer functions within seconds, instead of minutes. We compare our method to existing learning-based approaches in terms of annotation and compute time, as well as with respect to segmentation accuracy. Our accompanying video showcases the interactivity and effectiveness of our method.

Single Cell Atlas: a single-cell multi-omics human cell encyclopedia.

Single-cell sequencing datasets are key in biology and medicine for unraveling insights into heterogeneous cell populations with unprecedented resolution. Here, we construct a single-cell multi-omics map of human tissues through in-depth characterizations of datasets from five single-cell omics, spatial transcriptomics, and two bulk omics across 125 healthy adult and fetal tissues. We construct its complement web-based platform, the Single Cell Atlas (SCA, www.singlecellatlas.org ), to enable vast interactive data exploration of deep multi-omics signatures across human fetal and adult tissues. The atlas resources and database queries aspire to serve as a one-stop, comprehensive, and time-effective resource for various omics studies.

MBARq: a versatile and user-friendly framework for the analysis of DNA barcodes from transposon insertion libraries, knockout mutants, and isogenic strain populations.

DNA barcoding has become a powerful tool for assessing the fitness of strains in a variety of studies, including random transposon mutagenesis screens, attenuation of site-directed mutants, and population dynamics of isogenic strain pools. However, the statistical analysis, visualization, and contextualization of the data resulting from such experiments can be complex and require bioinformatic skills. Here, we developed mBARq, a user-friendly tool designed to simplify these steps for diverse experimental setups. The tool is seamlessly integrated with an intuitive web app for interactive data exploration via the STRING and KEGG databases to accelerate scientific discovery. The tool is implemented in Python. The source code is freely available (https://github.com/MicrobiologyETHZ/mbarq) and the web app can be accessed at: https://microbiomics.io/tools/mbarq-app.

The Omics Dashboard for Interactive Exploration of Metabolomics and Multi-Omics Data.

The Omics Dashboard is a software tool for interactive exploration and analysis of metabolomics, transcriptomics, proteomics, and multi-omics datasets. Organized as a hierarchy of cellular systems, the Dashboard at its highest level contains graphical panels for the full range of cellular systems, including biosynthesis, energy metabolism, and response to stimulus. Thus, the Dashboard top level surveys the state of the cell across a broad range of key systems in a single screen. Each Dashboard panel contains a series of X-Y plots depicting the aggregated omics data values relevant to different subsystems of that panel, e.g., subsystems within the biosynthesis panel include amino acid biosynthesis, carbohydrate biosynthesis and cofactor biosynthesis. Users can interactively drill down to focus in on successively lower-level subsystems of interest. In this article, we present for the first time the metabolomics analysis capabilities of the Omics Dashboard, along with significant new extensions to better accommodate metabolomics datasets, enable analysis and visualization of multi-omics datasets, and provide new data-filtering options.

Web-based visualization and rendering of aerial LiDAR point cloud for urban flood simulation

Purpose In an era overshadowed by the alarming consequences of climate change and the escalating peril of recurring floods for communities worldwide, the significance of proficient disaster risk management has reached unprecedented levels. The successful implementation of disaster risk management necessitates the ability to make informed decisions. To this end, the utilization of three-dimensional (3D) visualization and Web-based rendering offers decision-makers the opportunity to engage with interactive data representations. This study aims to focus on Thiruvananthapuram, India, where the analysis of flooding caused by the Karamana River aims to furnish valuable insights for facilitating well-informed decision-making in the realm of disaster management. Design/methodology/approach This work introduces a systematic procedure for evaluating the influence of flooding on 3D building models through the utilization of Web-based visualization and rendering techniques. To ensure precision, aerial light detection and ranging (LiDAR) data is used to generate accurate 3D building models in CityGML format, adhering to the standards set by the Open Geospatial Consortium. By using one-meter digital elevation models derived from LiDAR data, flood simulations are conducted to analyze flow patterns at different discharge levels. The integration of 3D building maps with geographic information system (GIS)-based vector maps and a flood risk map enables the assessment of the extent of inundation. To facilitate visualization and querying tasks, a Web-based graphical user interface (GUI) is developed. Findings The efficiency of comprehensive 3D building maps in evaluating flood consequences in Thiruvananthapuram has been established by the research. By merging with GIS-based vector maps and a flood risk map, it becomes possible to scrutinize the extent of inundation and the affected structures. Furthermore, the Web-based GUI facilitates interactive data exploration, visualization and querying, thereby assisting in decision-making. Originality/value The study introduces an innovative approach that merges LiDAR data, 3D building mapping, flood simulation and Web-based visualization, which can be advantageous for decision-makers in disaster risk management and may have practical use in various regions and urban areas.

When Refreshable Tactile Displays Meet Conversational Agents: Investigating Accessible Data Presentation and Analysis with Touch and Speech.

Despite the recent surge of research efforts to make data visualizations accessible to people who are blind or have low vision (BLV), how to support BLV people's data analysis remains an important and challenging question. As refreshable tactile displays (RTDs) become cheaper and conversational agents continue to improve, their combination provides a promising approach to support BLV people's interactive data exploration and analysis. To understand how BLV people would use and react to a system combining an RTD with a conversational agent, we conducted a Wizard-of-Oz study with 11 BLV participants, where they interacted with line charts, bar charts, and isarithmic maps. Our analysis of participants' interactions led to the identifcation of nine distinct patterns. We also learned that the choice of modalities depended on the type of task and prior experience with tactile graphics, and that participants strongly preferred the combination of RTD and speech to a single modality. In addition, participants with more tactile experience described how tactile images facilitated a deeper engagement with the data and supported independent interpretation. Our fndings will inform the design of interfaces for such interactive mixed-modality systems.

TCCIA: a comprehensive resource for exploring CircRNA in cancer immunotherapy

BackgroundImmunotherapies targeting immune checkpoints have gained increasing attention in cancer treatment, emphasizing the need for predictive biomarkers. Circular RNAs (circRNAs) have emerged as critical regulators of tumor immunity, particularly in...

Interoperable Access and Interactive Exploration of HF Radar Data

Interoperable Access and Interactive Exploration of HF Radar Data

Head-mounted display-based augmented reality for water quality visualisation

Water covers most of the Earth’s surface and is nowhere near a good ecological or recreational state in many areas of the world. Moreover, only a small fraction of the water is potable. As climate change-induced extreme weather events become ever more prevalent, more and more issues arise, such as worsening water quality problems. Therefore, protecting invaluable and useable drinking water is critical. Environmental agencies must continuously check water sources to determine whether they are in a good or healthy state regarding pollutant levels and ecological status. The currently available tools are better suited for stationary laboratory use, and domain specialists lack suitable tools for on-site visualisation and interactive exploration of environmental data. Meanwhile, data collection for laboratory analysis requires substantial time and significant effort. We, therefore, developed an augmented reality system with a Microsoft HoloLens 2 device to explore the visualisation of water quality and status in situ. The developed prototype visualises geo-referenced sensor measurements incorporated into the perspective of the surroundings. Any users interested in water bodies’ conditions can quickly examine and retrieve an overview of water body status using augmented reality and then take necessary steps to address the current situation.

Adverse Drug Events Presented in Health Institutions in Monteria, Colombia, 2018-2021.

To analyze the adverse events reported in the pharmacovigilance of the health service provider institutions of the municipality of Monteria in the period 2018-2021. Descriptive, cross-sectional, retrospective, and quantitative approach study, the information was analyzed by statistical analysis by multiple correspondence using Orange Data Mining software; the analysis consisted of visual programming to perform interactive data exploration, to identify and differentiate associations or oppositions between different categories in space. The most frequently reported adverse events were allergic reactions, with 28.5%. Female sex and adult age are the groups most prone to present these events; antibiotics were the pharmacological group that produced the most adverse events with 18.3%; the main errors that produce these events are related to prescription. Age and sex increase the risk of adverse drug events; most of these events are the product of erroneous prescriptions. The findings presented in this article are useful for pharmacovigilance programs in health institutions.

A GPU-accelerated viewer for HEALPix maps

HEALPix by Górskiet al. (2005) is a de-facto standard for Cosmic Microwave Background (CMB) data storage and analysis, and is widely used in current and upcoming CMB experiments. Almost all the datasets in Legacy Archive for Microwave Background Data Analysis (LAMBDA) use HEALPix as a format of choice. Visualizing the data plays important role in research, and several toolsets were developed to do that for HEALPix maps, most notably original Fortran facilities and Python integration with healpy. With the current state of GPU performance, it is now possible to visualize extremely large maps in real time on a laptop or a tablet. HEALPix Viewer described here is developed for macOS, and takes full advantage of GPU acceleration to handle extremely large datasets in real time. It compiles natively on Intel and Arm64 architectures, and uses Metal framework for high-performance GPU computations. The aim of this project is to reduce the effort required for interactive data exploration, as well as time overhead for producing publication-quality maps. Drag and drop integration with Keynote and Powerpoint makes creating presentations easy. The main codebase is written in Swift, a modern and efficient compiled language, with high-performance computing parts delegated entirely to GPU, and a few inserts in C interfacing to cfitsio library for I/O. Graphical user interface is written in SwiftUI, a new declarative UI framework based on Swift. Most common spherical projections and colormaps are supported out of the box, and the available source code makes it easy to customize the application and to add new features if desired. On a M1 Max laptop, an nside=8192 maps are processed in real time, with geometry effects rendered at 60fps in full resolution with no appreciable load to the machine. Main user-facing delays are limited to CPU-bound cfitsio load times, and sorting needed to construct Cumulative Distribution Function (CDF) estimators for statistical analysis (hidden in background queue). Overall performance improves on the current Python software stack by a factor of 3–180x depending on the task at hand.

Visual Data Exploration as a Statistical Testing Procedure: Within-View and Between-View Multiple Comparisons.

A fundamental problem in visual data exploration concerns whether observed patterns are true or merely random noise. This problem is especially pertinent in visual analytics, where the user is presented with a barrage of patterns, without any guarantees of their statistical validity. Recently this problem has been formulated in terms of statistical testing and the multiple comparisons problem. In this paper, we identify two levels of multiple comparisons problems in visualization: the within-view and the between-view problem. We develop a statistical testing procedure for interactive data exploration that controls the family-wise error rate on both levels. The procedure enables the user to determine the compatibility of their assumptions about the data with visually observed patterns. We present use-cases where we visualize and evaluate patterns in real-world data.