scholarly journals Gosling: A Grammar-based Toolkit for Scalable and Interactive Genomics Data Visualization

2021 ◽  
Author(s):  
Sehi L'Yi ◽  
Qianwen Wang ◽  
Fritz Lekschas ◽  
Nils Gehlenborg
Keyword(s):  
Data Visualization ◽  
Data Types ◽  
Interactive Rendering ◽  
Web Based ◽  
Multi Scale ◽  
Data Formats ◽  
Wide Range ◽  
Visualization Techniques ◽  
Diverse Data ◽  
Data Visualizations

The combination of diverse data types and analysis tasks in genomics has resulted in the development of a wide range of visualization techniques and tools. However, most existing tools are tailored to a specific problem or data type and offer limited customization, making it challenging to optimize visualizations for new analysis tasks or datasets. To address this challenge, we designed Gosling—a grammar for interactive and scalable genomics data visualization. Gosling balances expressiveness for comprehensive multi-scale genomics data visualizations with accessibility for domain scientists. Our accompanying JavaScript toolkit called Gosling.js provides scalable and interactive rendering. Gosling.js is built on top of an existing platform for web-based genomics data visualization to further simplify the visualization of common genomics data formats. We demonstrate the expressiveness of the grammar through a variety of real-world examples. Furthermore, we show how Gosling supports the design of novel genomics visualizations. An online editor and examples of Gosling.js and its source code are available at https://gosling.js.org.

3D data visualization: The advantages of volume graphics and big data to support geologic interpretation

2015 ◽  
Vol 3 (3) ◽  
pp. SX29-SX39 ◽  
Author(s):  
Carl Byers ◽  
Andrew Woo
Keyword(s):  
Big Data ◽  
Case Studies ◽  
Data Visualization ◽  
3D Visualization ◽  
Data Types ◽  
Volume Graphics ◽  
3D Data ◽  
Geologic Interpretation ◽  
Diverse Data

The ability to integrate diverse data types from multiple live and simulated sources, manipulate them dynamically, and deploy them in integrated, visual formats and in mobile settings provides significant advantages. We have reviewed some of the benefits of volume graphics and the use of big data in the context of 3D visualization case studies, in which inherent features, such as representation efficiencies, dynamic modifications, cross sectioning, and others, could improve interpretation processes and workflows.

scholarly journals Graph structures for data visualizations

2021 ◽  
Vol 6 (2) ◽  
pp. 24-31
Author(s):  
Stefana Janićijević ◽  
Vojkan Nikolić
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Data Types ◽  
Current State ◽  
Graph Structures ◽  
Big Data Visualization ◽  
Visualization Tools ◽  
Graph System ◽  
Data Visualizations

Networks are all around us. Graph structures are established in the core of every network system therefore it is assumed to be understood as graphs as data visualization objects. Those objects grow from abstract mathematical paradigms up to information insights and connection channels. Essential metrics in graphs were calculated such as degree centrality, closeness centrality, betweenness centrality and page rank centrality and in all of them describe communication inside the graph system. The main goal of this research is to look at the methods of visualization over the existing Big data and to present new approaches and solutions for the current state of Big data visualization. This paper provides a classification of existing data types, analytical methods, techniques and visualization tools, with special emphasis on researching the evolution of visualization methodology in recent years. Based on the obtained results, the shortcomings of the existing visualization methods can be noticed.

scholarly journals What Geoscience Experts And Novices Look At, And What They See, When Viewing Data Visualizations

2016 ◽  
Vol 3 (1) ◽  
pp. 27 ◽  
Author(s):  
Kim A. Kastens ◽  
Thomas F. Shipley ◽  
Alexander P. Boone ◽  
Frances Straccia
Keyword(s):  
High Resolution ◽  
Eye Tracking ◽  
Data Visualization ◽  
Gaze Direction ◽  
Exploration Time ◽  
Wide Range ◽  
Tracking Behavior ◽  
Data Visualizations ◽  
Shaded Relief ◽  
The Continuum

This study examines how geoscience experts and novices make meaning from an iconic type of data visualization: shaded relief images of bathymetry and topography.  Participants examined, described, and interpreted a global image, two high-resolution seafloor images, and 2 high-resolution continental images, while having their gaze direction eye-tracked and their utterances and gestures videoed. In addition, experts were asked about how they would coach an undergraduate intern on how to interpret this data.  Not unexpectedly, all experts were more skillful than any of the novices at describing and explaining what they were seeing.  However, the novices showed a wide range of performance.  Along the continuum from weakest novice to strongest expert, proficiency developed in the following order: making qualitative observations of salient features, making simple interpretations, making quantitative observations.  The eye-tracking analysis examined how the experts and novices invested 20 seconds of unguided exploration, after the image came into view but before the researcher began to ask questions.  On the cartographic elements of the images, experts and novices allocated their exploration time differently:  experts invested proportionately more fixations on the latitude and longitude axes, while students paid more attention to the color bar.  In contrast, within the parts of the image showing the actual geomorphological data, experts and novices on average allocated their attention similarly, attending preferentially to the geologically significant landforms.   Combining their spoken responses with their eye-tracking behavior, we conclude that the experts and novices are looking in the same places but “seeing” different things.

scholarly journals NG-Circos: next-generation Circos for data visualization and interpretation

2020 ◽  
Vol 2 (3) ◽  
Author(s):  
Ya Cui ◽  
Zhe Cui ◽  
Jianfeng Xu ◽  
Dapeng Hao ◽  
Jiejun Shi ◽  
...  
Keyword(s):  
Data Visualization ◽  
Reproducible Research ◽  
Visualization Tool ◽  
Functional Modules ◽  
Next Generation ◽  
Biomedical Sciences ◽  
Data Types ◽  
Genome Visualization ◽  
Limited Support ◽  
Diverse Data

Abstract Circos plots are widely used to display multi-dimensional next-generation genomic data, but existing implementations of Circos are not interactive with limited support of data types. Here, we developed next-generation Circos (NG-Circos), a flexible JavaScript-based circular genome visualization tool for designing highly interactive Circos plots using 21 functional modules with various data types. To our knowledge, NG-Circos is the most powerful software to construct interactive Circos plots. By supporting diverse data types in a dynamic browser interface, NG-Circos will accelerate the next-generation data visualization and interpretation, thus promoting the reproducible research in biomedical sciences and beyond. NG-Circos is available at https://wlcb.oit.uci.edu/NG-Circos and https://github.com/YaCui/NG-Circos.

scholarly journals An Interactive Data Visualization Framework for Exploring Geospatial Environmental Datasets and Model Predictions

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2928
Author(s):  
Jeffrey D. Walker ◽  
Benjamin H. Letcher ◽  
Kirk D. Rodgers ◽  
Clint C. Muhlfeld ◽  
Vincent S. D’Angelo
Keyword(s):  
Decision Making ◽  
Case Studies ◽  
Data Visualization ◽  
Large Scale ◽  
Research Management ◽  
Web Based ◽  
Environmental Models ◽  
Potential Benefits ◽  
Interactive Data ◽  
Data Visualizations

With the rise of large-scale environmental models comes new challenges for how we best utilize this information in research, management and decision making. Interactive data visualizations can make large and complex datasets easier to access and explore, which can lead to knowledge discovery, hypothesis formation and improved understanding. Here, we present a web-based interactive data visualization framework, the Interactive Catchment Explorer (ICE), for exploring environmental datasets and model outputs. Using a client-based architecture, the ICE framework provides a highly interactive user experience for discovering spatial patterns, evaluating relationships between variables and identifying specific locations using multivariate criteria. Through a series of case studies, we demonstrate the application of the ICE framework to datasets and models associated with three separate research projects covering different regions in North America. From these case studies, we provide specific examples of the broader impacts that tools like these can have, including fostering discussion and collaboration among stakeholders and playing a central role in the iterative process of data collection, analysis and decision making. Overall, the ICE framework demonstrates the potential benefits and impacts of using web-based interactive data visualization tools to place environmental datasets and model outputs directly into the hands of stakeholders, managers, decision makers and other researchers.

scholarly journals AUDiaL: A Natural Language Interface to Make Statistical Charts Accessible to Blind Persons

2020 ◽  
pp. 373-384
Author(s):  
Tomas Murillo-Morales ◽  
Klaus Miesenberger
Keyword(s):  
Natural Language ◽  
Data Visualization ◽  
Initial Evaluation ◽  
Natural Manner ◽  
Web Based ◽  
User Training ◽  
Natural Language Interface ◽  
Blind Persons ◽  
Visualization Techniques ◽  
Traditional Approaches

AbstractThis paper discusses the design and evaluation of AUDiaL (Accessible Universal Diagrams through Language). AUDiaL is a web-based, accessible natural language interface (NLI) prototype that allows blind persons to access statistical charts, such as bar and line charts, by means of free-formed analytical and navigational queries expressed in natural language. Initial evaluation shows that NLIs are an innovative, promising approach to accessibility of knowledge representation graphics, since, as opposed to traditional approaches, they do not require of additional software/hardware nor user training while allowing users to carry out most tasks commonly supported by data visualization techniques in an efficient, natural manner.

scholarly journals Trrack: A Library for Provenance Tracking in Web-Based Visualizations

2020 ◽  
Author(s):  
Zachary T Cutler ◽  
Kiran Gadhave ◽  
Alexander Lex
Keyword(s):  
Data Visualization ◽  
Ad Hoc ◽  
Use Cases ◽  
Software Library ◽  
Web Based ◽  
Provenance Data ◽  
Wide Range ◽  
Visualization Systems ◽  
Simple Action ◽  
Easy Integration

Provenance tracking is widely acknowledged as an important component of visualization systems. By tracking provenance data, visualization designers can achieve a wide variety of important functionality, ranging from action recovery (undo/redo), reproducibility, collaboration and sharing, to logging in support of quantitative and longitudinal evaluation. Yet, for web-based visualizations, there are currently no libraries that make provenance tracking easy to implement in visualization systems. The result of this is that visualization designers either develop ad-hoc solutions that are rarely comprehensive, or don't track provenance at all. In this paper, we introduce a web-based software library --- Trrack --- that is designed for easy integration in existing or future visualization systems. Trrack supports a wide range of use cases, from simple action recovery, to capturing intent and reasoning, and can be used to share states with collaborators and store provenance on a server. Trrack also includes an optional provenance visualization component that supports annotation of states and aggregation of events.

New open source tools for MARSIS: providing access to SEG-Y data format for 3D analysis.

2021 ◽  
Author(s):  
Giacomo Nodjoumi ◽  
Luca Guallini ◽  
Roberto Orosei ◽  
Luca Penasa ◽  
Angelo Pio Rossi
Keyword(s):  
Open Source ◽  
Planetary Science ◽  
Seismic Interpretation ◽  
3D Analysis ◽  
The European Union ◽  
Data Types ◽  
Data Formats ◽  
Multiple Data ◽  
Wide Range ◽  
Data Record

<p>The objective of this work is to present a new Free and Open-Source Software (FOSS) to read and convert to multiple data formats data acquired by the Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS) instrument on board Mars Express (MEX) orbiting Mars since 2005.</p><p>MARSIS is an orbital synthetic aperture radar sounder that operates with dual-frequency between 1.3 and 5.5 MHz and wavelengths between 230 and 55 m for subsurface sounding. The Experiment Data Record (EDR) and Reduced Data Record (RDR) datasets are available for download on public access platforms such as the Planetary Science Archive fo ESA and the PDS-NASA Orbital Data Explorer (ODE).</p><p>These datasets have been widely used for different research, focused to study the subsurface of the red planet up to a depth of a few kilometres, and especially for studying ice caps and looking for subsurface ice and water deposits, producing relevant results. (Lauro et al., 2020; Orosei et al., 2020)</p><p>The Python tool presented here is capable of reading common data types used to distribute MARSIS dataset and then converting into multiple data formats. Users can interactively configure data source, destination, pre-processing and type of outputs among:</p><ul><li>Geopackages: for GIS software, is a single self-contained file containing a layer in which are stored all parameters for each file processed.</li> <li>Numpy array dump: for fast reading and analysis of original data for both frequencies.</li> <li>PNG images: for fast inspections, created for each frequency, and saved. Image pre-processing filters, such as image-denoising, standardization and normalization, can be selected by user.</li> <li>SEG-Y: for analysing data with seismic interpretation and processing software, see e.g. OpendTect, consist of a SEG-Y file for each frequency.</li> </ul><p>SEG-Y capability is the most relevant feature, since is not present in any of other FOSS tool and give to researchers the possibility to visualize radargrams in advanced software, specific for seismic interpretation and analysis, making it possible to interpret the data in a fully three-dimensional environment.</p><p>This tool, available on zenodo (Nodjoumi, 2021), has been developed completely in Python 3, relying only on open-source libraries, compatible with principal operating systems and with parallel processing capabilities, granting easy scalability and usability across a wide range of computing machines. It is also highly customizable since it can be expanded adding processing steps before export or new types of output. An additional module to ingest data directly into PostgreSQL/PostGIS and a module to interact directly with ACT-REACT interface of data platforms are under development.</p><p>Acknowledgments:</p><p>This study is within the Europlanet 2024 RI, and it has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871149. </p><p>References:</p><p>Lauro, S. E. et al. (2020) ‘Multiple subglacial water bodies below the south pole of Mars unveiled by new MARSIS data’, doi: 10.1038/s41550-020-1200-6.</p><p>Nodjoumi, G. (2021) 'MARSIS-xDR-READER', doi: 10.5281/zenodo.4436199</p><p>Orosei, R. et al. (2020) ‘The global search for liquid water on mars from orbit: Current and future perspectives’, doi: 10.3390/life10080120.</p>

scholarly journals Griottes: a generalist tool for network generation from segmented tissue images

2022 ◽  
Author(s):  
Gustave Ronteix ◽  
Valentin Bonnet ◽  
Sebastien Sart ◽  
Jeremie Sobel ◽  
Elric Esposito ◽  
...  
Keyword(s):  
Open Source ◽  
Relevant Information ◽  
Data Types ◽  
Biologically Relevant ◽  
Open Source Tool ◽  
Biological Images ◽  
Multi Scale ◽  
Wide Range ◽  
Microscopy Techniques ◽  
2D And 3D

Microscopy techniques and image segmentation algorithms have improved dramatically this decade, leading to an ever increasing amount of biological images and a greater reliance on imaging to investigate biological questions. This has created a need for methods to extract the relevant information on the behaviors of cells and their interactions, while reducing the amount of computing power required to organize this information. This task can be performed by using a network representation in which the cells and their properties are encoded in the nodes, while the neighborhood interactions are encoded by the links. Here we introduce Griottes, an open-source tool to build the "network twin" of 2D and 3D tissues from segmented microscopy images. We show how the library can provide a wide range of biologically relevant metrics on individual cells and their neighborhoods, with the objective of providing multi-scale biological insights. The library's capacities are demonstrated on different image and data types. This library is provided as an open-source tool that can be integrated into common image analysis workflows to increase their capacities.

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