Visual Workflow Research Articles

Constructing temporal regulatory cascades in the context of development and cell differentiation

Cell differentiation is a complex process orchestrated by sets of regulators precisely appearing at certain time points, resulting in regulatory cascades that affect the expression of broader sets of genes, ending up in the formation of different tissues and organ parts. The identification of stage-specific master regulators and the mechanism by which they activate each other is a key to understanding and controlling differentiation, particularly in the fields of tissue regeneration and organoid engineering. Here we present a workflow that combines a comprehensive general regulatory network based on binding site predictions with user-provided temporal gene expression data, to generate a a temporally connected series of stage-specific regulatory networks, which we call a temporal regulatory cascade (TRC). A TRC identifies those regulators that are unique for each time point, resulting in a cascade that shows the emergence of these regulators and regulatory interactions across time. The model was implemented in the form of a user-friendly, visual web-tool, that requires no expert knowledge in programming or statistics, making it directly usable for life scientists. In addition to generating TRCs the tool links multiple interactive visual workflows, in which a user can track and investigate further different regulators, target genes, and interactions, directing the tool along the way into biologically sensible results based on the given dataset. We applied the TRC model on two different expression datasets, one based on experiments conducted on human induced pluripotent stem cells (hiPSCs) undergoing differentiation into mature cardiomyocytes and the other based on the differentiation of H1-derived human neuronal precursor cells. The model was successful in identifying previously known and new potential key regulators, in addition to the particular time points with which these regulators are associated, in cardiac and neural development.

INTEGRATED PLATFORMS FOR MANAGING A PRE-OWNED PROJECT

For Agile methodology, the use of a workflow visualization tool to reflect the status of project tasks is practiced. For joint projects between Ukrainian scientific institutions and Yancheni Polytechnic Institute, the Odoo complex is used.As an example, a task board for the implementation of a common scientific topic "Development of a gas purifier for industrial premises ventilation systems", which is commissioned by a Chinese enterprise, is presented in the article. The main performers are two sides – Chinese and Ukrainian. The customer is directly involved in the implementation of the project as a party to which each result of the sprint is agreed – backlog. The use of an integrated project management platform minimizes the time for backlog reconciliation. The work on the project teams in different parts of the world is carried out without delay, which makes the implementation of such projects competitive in the international market of research services.

An Ontology-based approach to Knowledge-assisted Integration and Visualization of Urban Mobility Data

This paper proposes an ontology-based framework to support integration and visualization of data from Intelligent Transportation Systems. These activities may be technically demanding for transportation stakeholders, due to technical and human factors, and may hinder the use of visualization tools in practice. The existing ontologies do not provide the necessary semantics for integration of spatio-temporal data from such systems. Moreover, a formal representation of the components of visualization techniques and expert knowledge can leverage the development of visualization tools that facilitate data analysis. The proposed Visualization-oriented Urban Mobility Ontology (VUMO) provides a semantic foundation to knowledge-assisted visualization tools (KVTs). VUMO contains three facets that interrelate the characteristics of spatio-temporal mobility data, visualization techniques and expert knowledge. A built-in rule set leverages semantic technologies standards to infer which visualization techniques are compatible with analytical tasks, and to discover implicit relationships within integrated data. The annotation of expert knowledge encodes qualitative and quantitative feedback from domain experts that can be exploited by recommendation methods to automate part of the visualization workflow. Data from the city of Porto, Portugal were used to demonstrate practical applications of the ontology for each facet. As a foundational domain ontology, VUMO can be extended to meet the distinctiveness of a KVT.

Migrating from partial least squares discriminant analysis to artificial neural networks: a comparison of functionally equivalent visualisation and feature contribution tools using jupyter notebooks

IntroductionMetabolomics data is commonly modelled multivariately using partial least squares discriminant analysis (PLS-DA). Its success is primarily due to ease of interpretation, through projection to latent structures, and transparent assessment of feature importance using regression coefficients and Variable Importance in Projection scores. In recent years several non-linear machine learning (ML) methods have grown in popularity but with limited uptake essentially due to convoluted optimisation and interpretation. Artificial neural networks (ANNs) are a non-linear projection-based ML method that share a structural equivalence with PLS, and as such should be amenable to equivalent optimisation and interpretation methods.ObjectivesWe hypothesise that standardised optimisation, visualisation, evaluation and statistical inference techniques commonly used by metabolomics researchers for PLS-DA can be migrated to a non-linear, single hidden layer, ANN.MethodsWe compared a standardised optimisation, visualisation, evaluation and statistical inference techniques workflow for PLS with the proposed ANN workflow. Both workflows were implemented in the Python programming language. All code and results have been made publicly available as Jupyter notebooks on GitHub.ResultsThe migration of the PLS workflow to a non-linear, single hidden layer, ANN was successful. There was a similarity in significant metabolites determined using PLS model coefficients and ANN Connection Weight Approach.ConclusionWe have shown that it is possible to migrate the standardised PLS-DA workflow to simple non-linear ANNs. This result opens the door for more widespread use and to the investigation of transparent interpretation of more complex ANN architectures.

Visual programming environment based on data visualization grammar specification

Abstract This paper presents a web-based platform for construction and execution of data visualization workflow. The environment enables construction of data visualization workflow with the visual programming interface and provides the ability of previewing the visualisation workflow result in real-time. The use of visual programming makes the workflow composition faster and it’s also lowers entry threshold for the process of data visualization. In this paper we discuss the architecture and main building blocks of the data visualization workflow.

Validating trustworthy service composition through VIPLE and pi-calculus

The current formal verification practice focuses on functionality and does not consider verification of the non-functional attributes. In this study, we propose a method in which non-functional attributes are incorporated into the logic rules of inference in terms of composition of the linear logic and pi-calculus. Giving the credibility to non-functional attributes is important, especially in the cloud computing platform and IoT environments, where trust and security are ultra-important. Such studies have not been paid much attention by researchers and practitioners. In our approach, the evolvement of the non-functional attributes is included in the process of formal verification of the service composition scheme. In addition to theoretical analysis, we applied a tool named Visual IoT/robotics Programming Language Environment (VIPLE) to execute and verify the validity of the service composition model's function. We translate the proving process of the linear logic into the corresponding pi-calculus expressions. VIPLE can translate visual work flow into pi-calculus and can verify the correctness of pi-calculus expressions.

Enabling Domain Expertise in Scientific Visualization With CinemaScience.

Scientific users present unique challenges to visualization researchers. Their high-level tasks require them to apply domain-specific expertise. We introduce a broader audience to the CinemaScience project and demonstrate how CinemaScience enables efficient visualization workflows that can bring in scientist expertise and drive scientific insight.

Developing an All-Digital Workflow for Dental Skills Assessment: Part II, Surface Analysis, Benchmarking, and Grading.

In Part I of this study, evidence was presented that visual inspection (VI) of dental student waxups exhibited low precision (ICC=0.332) and accuracy, resulting in questionable grade validity. VI inappropriately assesses morphology in part due to oversimplified grading rubrics and inappropriate application of ratio measurement to ordinal data. The aim of Part II of this study was to develop, apply, and compare with VI a digital assessment workflow and report the outcomes. After inclusion criteria were met, 67 (83%) student waxups were scanned with an intraoral scanner and analyzed using open source surface comparison software. Each was digitally compared to the homologous standard typodont tooth. Percentage contour variation within a stipulated tolerance was computed. The acceptable tolerance was derived from clinical literature reporting how contour deviations impact the biological and biomechanical stability of teeth and periodontium. Surface roughness of each waxup was also computed and compared with the reference teeth. A formula for digital assessment was developed and applied to each waxup. On average, digital grades exceeded the VI assessments, while correcting individual errors for greater accuracy and precision (ICC=0.866). Students were able to easily apply the digital workflow for visualization and accurate self-evaluation. In this study, a digital grading workflow was developed that used portable data formats and freely available open source analysis software, so the method could be introduced at any dental school where intraoral scanning is available.

VIVA (VIsualization of VAriants): A VCF File Visualization Tool

High-throughput sequencing produces an extraordinary amount of genomic data that is organized into a number of high-dimension datasets. Accordingly, visualization of genomic data has become essential for quality control, exploration, and data interpretation. The Variant Call Format (VCF) is a text file format generated during the variant calling process that contains genomic information and locations of variants in a group of sequenced samples. The current workflow for visualization of genomic variant data from VCF files requires use of a combination of existing tools. Here, we describe VIVA (VIsualization of VAriants), a command line utility and Jupyter Notebook based tool for evaluating and sharing genomic data for variant analysis and quality control of sequencing experiments from VCF files. VIVA combines the functionality of existing tools into a single command to interactively evaluate and share genomic data, as well as create publication quality graphics.

Scalable Brain Atlas Composer: web-based API for brain atlasing and visualization workflows

Scalable Brain Atlas Composer: web-based API for brain atlasing and visualization workflows

Data Civilizer 2.0

Data scientists spend over 80% of their time (1) parameter-tuning machine learning models and (2) iterating between data cleaning and machine learning model execution. While there are existing efforts to support the first requirement, there is currently no integrated workflow system that couples data cleaning and machine learning development. The previous version of Data Civilizer was geared towards data cleaning and discovery using a set of pre-defined tools. In this paper, we introduce Data Civilizer 2.0, an end-to-end workflow system satisfying both requirements. In addition, this system also supports a sophisticated data debugger and a workflow visualization system. In this demo, we will show how we used Data Civilizer 2.0 to help scientists at the Massachusetts General Hospital build their cleaning and machine learning pipeline on their 30TB brain activity dataset.

Enabling immersive engagement in energy system models with deep learning

AbstractComplex ensembles of energy simulation models have become significant components of renewable energy research in recent years. Often the significant computational cost, high‐dimensional structure, and other complexities hinder researchers from fully utilizing these data sources for knowledge building. Researchers at National Renewable Energy Laboratory have developed an immersive visualization workflow to dramatically improve user engagement and analysis capability through a combination of low‐dimensional structure analysis, deep learning, and custom visualization methods. We present case studies for two energy simulation platforms.

Computational modeling for climate change: Simulating and visualizing a resilient landscape architecture design approach

Coastlines are changing, wildfires are raging, cities are getting hotter, and spatial designers are charged with the task of designing to mitigate these unknowns. This research examines computational digital workflows to understand and alleviate the impacts of climate change on urban landscapes. The methodology includes two separate simulation and visualization workflows. The first workflow uses an animated particle fluid simulator in combination with geographic information systems data, Photoshop software, and three-dimensional modeling and animation software to simulate erosion and sedimentation patterns, coastal inundation, and sea level rise. The second workflow integrates building information modeling data, computational fluid dynamics simulators, and parameters from EnergyPlus and Landsat to produce typologies and strategies for mitigating urban heat island effects. The effectiveness of these workflows is demonstrated by inserting design prototypes into modeled environments to visualize their success or failure. The result of these efforts is a suite of workflows which have the potential to vastly improve the efficacy with which architects and landscape architects use existing data to address the urgency of climate change.

IVikodak-A Platform and Standard Workflow for Inferring, Analyzing, Comparing, and Visualizing the Functional Potential of Microbial Communities.

Background: The objectives of any metagenomic study typically include identification of resident microbes and their relative proportions (taxonomic analysis), profiling functional diversity (functional analysis), and comparing the identified microbes and functions with available metadata (comparative metagenomics). Given the advantage of cost-effectiveness and convenient data-size, amplicon-based sequencing has remained the technology of choice for exploring phylogenetic diversity of an environment. A recent school of thought, employing the existing genome annotation information for inferring functional capacity of an identified microbiome community, has given a promising alternative to Whole Genome Shotgun sequencing for functional analysis. Although a handful of tools are currently available for function inference, their scope, functionality and utility has essentially remained limited. Need for a comprehensive framework that expands upon the existing scope and enables a standardized workflow for function inference, analysis, and visualization, is therefore felt.Methods: We present iVikodak, a multi-modular web-platform that hosts a logically inter-connected repertoire of functional inference and analysis tools, coupled with a comprehensive visualization interface. iVikodak is equipped with microbial co-inhabitance pattern driven published algorithms along with multiple updated databases of various curated microbe-function maps. It also features an advanced task management and result sharing system through introduction of personalized and portable dashboards.Results: In addition to inferring functions from 16S rRNA gene data, iVikodak enables (a) an in-depth analysis of specific functions of interest (b) identification of microbes contributing to various functions (c) microbial interaction patterns through function-driven correlation networks, and (d) simultaneous functional comparison between multiple microbial communities. We have bench-marked iVikodak through multiple case studies and comparisons with existing state of art. We also introduce the concept of a public repository which provides a first of its kind community-driven framework for scientific data analytics, collaboration and sharing in this area of microbiome research.Conclusion: Developed using modern design and task management practices, iVikodak provides a multi-modular, yet inter-operable, one-stop framework, that intends to simplify the entire approach toward inferred function analysis. It is anticipated to serve as a significant value addition to the existing space of functional metagenomics.iVikodak web-server may be freely accessed at https://web.rniapps.net/iVikodak/.

Exploring Data Visualization and Analysis with Matplotlib

This research paper seeks to offer a whole knowledge of Matplotlib through exploring its structure and format principles. An in-depth examination of its scripting, artist, and backend layers lays the inspiration for the next discussions on simple and advanced plotting techniques. The paper emphasizes practical insights into customization options, empowering customers to tailor visualizations for maximum impact. Furthermore, a critical element of this exploration is the mixing of Matplotlib with vital facts science libraries like Pandas and NumPy. Through realistic examples, the paper demonstrates how seamless integration complements statistics manipulation and visualization workflows, showcasing Matplotlib's synergy with those foundational tools. Real-global packages function a highlight, illustrating Matplotlib's versatility across domains. Whether visualizing economic traits, organic phenomena, or social dynamics, Matplotlib proves instrumental in distilling complicated facts into meaningful visible narratives. The paper delves into demanding situations encountered in those applications, offering valuable insights and ability answers. In the ever-increasing realm of information technological know-how, powerful visualization and evaluation are paramount for extracting meaningful insights from complicated datasets. This research paper, titled "Exploring Data Visualization and Analysis with Matplotlib," delves into the flexible competencies of Matplotlib, a distinguished information visualization library in Python. With its comprehensive suite of gear, Matplotlib empowers researchers, analysts, and developers to create visually compelling representations of information. This summary presents a succinct overview of the paper, emphasizing its recognition on Matplotlib's functionalities, packages, and satisfactory practices. The subsequent exploration covers essential elements, together with the library's architecture and simple plotting techniques, before advancing to more complex topics like interactive visualization, overall performance optimization, and real-world programs. Through case research and examples, the paper showcases Matplotlib's versatility throughout various domain names, shedding light on its position in enhancing data analysis workflows. As Matplotlib continues to evolve, the paper additionally examines destiny traits and community contributions, positioning itself as a treasured useful resource for both beginners and seasoned practitioners seeking to harness the strength of Matplotlib of their statistics visualization endeavors.

WHAM!: a web-based visualization suite for user-defined analysis of metagenomic shotgun sequencing data

BackgroundExploration of large data sets, such as shotgun metagenomic sequence or expression data, by biomedical experts and medical professionals remains as a major bottleneck in the scientific discovery process. Although tools for this purpose exist for 16S ribosomal RNA sequencing analysis, there is a growing but still insufficient number of user-friendly interactive visualization workflows for easy data exploration and figure generation. The development of such platforms for this purpose is necessary to accelerate and streamline microbiome laboratory research.ResultsWe developed the Workflow Hub for Automated Metagenomic Exploration (WHAM!) as a web-based interactive tool capable of user-directed data visualization and statistical analysis of annotated shotgun metagenomic and metatranscriptomic data sets. WHAM! includes exploratory and hypothesis-based gene and taxa search modules for visualizing differences in microbial taxa and gene family expression across experimental groups, and for creating publication quality figures without the need for command line interface or in-house bioinformatics.ConclusionsWHAM! is an interactive and customizable tool for downstream metagenomic and metatranscriptomic analysis providing a user-friendly interface allowing for easy data exploration by microbiome and ecological experts to facilitate discovery in multi-dimensional and large-scale data sets.

Introduction to the Special Issue on E-Infrastructures for Excellent Science: Advances in Life Sciences, Digital Cultural Heritage and Climatology

Introduction to the Special Issue on E-Infrastructures for Excellent Science: Advances in Life Sciences, Digital Cultural Heritage and Climatology

An Integrated Web-based Interactive Data Platform for Molecular Dynamics Simulations

The article aims to introduce an integrated web-based interactive data platform for molecular dynamic simulations using the datasets generated by different life science communities from Armenia. The suggested platform, consisting of data repository and workflow management services, is vital for current and future scientific discoveries in the life science domain. We focus on interactive data visualization workflow service as a key to perform more in-depth analyzes of research data outputs, helping to understand the problems efficiently and to consolidate the data into one collective illustration platform. The functionalities of the integrated data platform is presented as an advanced integrated environment to capture, analyze, process and visualize the scientific data.

A Workflow Management System for Scalable Data Mining on Clouds

The extraction of useful information from data is often a complex process that can be conveniently modeled as a data analysis workflow. When very large data sets must be analyzed and/or complex data mining algorithms must be executed, data analysis workflows may take very long times to complete their execution. Therefore, efficient systems are required for the scalable execution of data analysis workflows, by exploiting the computing services of the Cloud platforms where data is increasingly being stored. The objective of the paper is to demonstrate how Cloud software technologies can be integrated to implement an effective environment for designing and executing scalable data analysis workflows. We describe the design and implementation of the Data Mining Cloud Framework (DMCF), a data analysis system that integrates a visual workflow language and a parallel runtime with the Software-as-a-Service (SaaS) model. DMCF was designed taking into account the needs of real data mining applications, with the goal of simplifying the development of data mining applications compared to generic workflow management systems that are not specifically designed for this domain. The result is a high-level environment that, through an integrated visual workflow language, minimizes the programming effort, making easier to domain experts the use of common patterns specifically designed for the development and the parallel execution of data mining applications. The DMCF's visual workflow language, system architecture and runtime mechanisms are presented. We also discuss several data mining workflows developed with DMCF and the scalability obtained executing such workflows on a public Cloud.

An Uncertainty-aware Workflow for Keyhole Surgery Planning using Hierarchical Image Semantics

Keyhole surgeries become increasingly important in clinical daily routine as they help minimizing the damage of a patient’s healthy tissue. The planning of keyhole surgeries is based on medical imaging and an important factor that influences the surgeries’ success. Due to the image reconstruction process, medical image data contains uncertainty that exacerbates the planning of a keyhole surgery. In this paper we present a visual workflow that helps clinicians to examine and compare different surgery paths as well as visualizing the patients’ affected tissue. The analysis is based on the concept of hierarchical image semantics, that segment the underlying image data with respect to the input images’ uncertainty and the users understanding of tissue composition. Users can define arbitrary surgery paths that they need to investigate further. The defined paths can be queried by a rating function to identify paths that fulfill user-defined properties. The workflow allows a visual inspection of the affected tissues and its substructures. Therefore, the workflow includes a linked view system indicating the three-dimensional location of selected surgery paths as well as how these paths affect the patients tissue. To show the effectiveness of the presented approach, we applied it to the planning of a keyhole surgery of a brain tumor removal and a kneecap surgery.