Complex Scientific Data Research Articles

Emphasizing the Social in Sociotechnical Approaches to the Digital Curation of Visual Information

ABSTRACTThis panel explores the unique challenges and opportunities presented by the expanded sociotechnical practices associated with the preservation, curation, and use of visual information and ongoing perceptions of the affordances and constraints associated with new and emerging visual information objects. The panelists and the respondent work with the curation and preservation of visual information across a variety of research areas and utilize both qualitative and quantitative methods to understand the parallel and divergent sociotechnical challenges of curating visual information across analog, digitized, and born‐digital contexts. Sites of analysis range from format‐specific identity‐representation issues to the cultural practices of media archivists, translational challenges when moving complex scientific data across various digital and analog formats, and the role of accessibility in the design and deployment of discovery systems within cultural heritage institutions. Responding to the unique and intersecting challenges produced by working with visual information across various archival and digital curation contexts, the panelist will reflect on the practical and theoretical outcomes from ongoing research projects and identify persistent and emergent issues within the digital curation of visual information.

Numerical and Symbolic Analysis for Mathematical Problem-Solving with Maple

This study explores the versatile capabilities of Maple, a widely used mathematical software, in addressing a wide range of numerical and symbolic computations essential for scientific and engineering applications. The researchers investigated Maple's diverse suite of tools, including numerical integration, nonlinear equation solving, polynomial interpolation, symbolic integration, and various numerical methods. Through an in-depth literature review, illustrated case studies, and detailed performance evaluations, the paper demonstrates the effectiveness and accuracy of Maple's computational approaches in dealing with complex problems in various areas of applied mathematics. This study's findings underscored Maple's tremendous value as a reliable and comprehensive software package for researchers, scientists, and professionals involved in advanced mathematical analysis and scientific computing. Furthermore, the paper highlighted Maple's versatility in creating high-quality three-dimensional plots, crucial for visualizing and analyzing complex mathematical and scientific data. Using either sets or lists, the ability to display multiple surfaces in a single three-dimensional plot showcases Maple's power in data visualization and communicating complex ideas. By positioning Maple as a powerful platform for solving versatile mathematical problems, this study highlights the software's indispensable role in advancing scientific discoveries and engineering innovations.

Improving Efficiency of Iso-Surface Extraction on Implicit Neural Representations Using Uncertainty Propagation.

Implicit Neural representations (INRs) are widely used for scientific data reduction and visualization by modeling the function that maps a spatial location to a data value. Without any prior knowledge about the spatial distribution of values, we are forced to sample densely from INRs to perform visualization tasks like iso-surface extraction which can be very computationally expensive. Recently, range analysis has shown promising results in improving the efficiency of geometric queries, such as ray casting and hierarchical mesh extraction, on INRs for 3D geometries by using arithmetic rules to bound the output range of the network within a spatial region. However, the analysis bounds are often too conservative for complex scientific data. In this paper, we present an improved technique for range analysis by revisiting the arithmetic rules and analyzing the probability distribution of the network output within a spatial region. We model this distribution efficiently as a Gaussian distribution by applying the central limit theorem. Excluding low probability values, we are able to tighten the output bounds, resulting in a more accurate estimation of the value range, and hence more accurate identification of iso-surface cells and more efficient iso-surface extraction on INRs. Our approach demonstrates superior performance in terms of the iso-surface extraction time on four datasets compared to the original range analysis method and can also be generalized to other geometric query tasks.

Communicating environmental data through art: the role of emotion and memory in evoking environmental action

Innovative approaches to environmental communication are needed to transcend existing scientific knowledge, challenge individual value-action gaps, and engage more people in science. Within a co-created community science project, a case-control study was conducted to determine whether data visualization type could impact participant scientific learning, emotional response, behavioral outcomes, and environmental action. Two novel data sharing types were designed to communicate roof-harvested rainwater data to environmental justice communities: (1) A static booklet and (2) An interactive environmental art installation called Ripple Effect paired with a booklet. Our results indicate that environmental art can not only communicate complex scientific data effectively, but can also overcome barriers associated with traditional science communication by affecting people’s emotion and memory—which increases the likelihood of changing their behavior or taking new action in their environment. These results are consistent with the environmental psychology literature; however, we have successfully captured the role of memory and long-lasting impacts of environmental art on pro-environmental health behavior. This research further paves the way for others to create innovative environmental communication formats to communicate environmental health.

Integrated Systems Biology Pipeline to Compare Co-Expression Networks in Plants and Elucidate Differential Regulators.

To identify sets of genes that exhibit similar expression characteristics, co-expression networks were constructed from transcriptome datasets that were obtained from plant samples at various stages of growth and development or treated with diverse biotic, abiotic, and other environmental stresses. In addition, co-expression network analysis can provide deeper insights into gene regulation when combined with transcriptomics. The coordination and integration of all these complex networks to deduce gene regulation are major challenges for plant biologists. Python and R have emerged as major tools for managing complex scientific data over the past decade. In this study, we describe a reproducible protocol POTFUL (pant co-expression transcription factor regulators), implemented in Python 3, for integrating co-expression and transcription factor target protein networks to infer gene regulation.

MEinVR: Multimodal interaction techniques in immersive exploration

Immersive environments have become increasingly popular for visualizing and exploring large-scale, complex scientific data because of their key features: immersion, engagement, and awareness. Virtual reality offers numerous new interaction possibilities, including tactile and tangible interactions, gestures, and voice commands. However, it is crucial to determine the most effective combination of these techniques for a more natural interaction experience. In this paper, we present MEinVR, a novel multimodal interaction technique for exploring 3D molecular data in virtual reality. MEinVR combines VR controller and voice input to provide a more intuitive way for users to manipulate data in immersive environments. By using the VR controller to select locations and regions of interest and voice commands to perform tasks, users can efficiently perform complex data exploration tasks. Our findings provide suggestions for the design of multimodal interaction techniques in 3D data exploration in virtual reality.

Decoding structure-spectrum relationships with physically organized latent spaces

A new semi-supervised machine learning method for the discovery of structure-spectrum relationships is developed and demonstrated using the specific example of interpreting X-ray absorption near-edge structure (XANES) spectra. This method constructs a one-to-one mapping between individual structure descriptors and spectral trends. Specifically, an adversarial autoencoder is augmented with a novel rank constraint (RankAAE). The RankAAE methodology produces a continuous and interpretable latent space, where each dimension can track an individual structure descriptor. As a part of this process, the model provides a robust and quantitative measure of the structure-spectrum relationship by decoupling intertwined spectral contributions from multiple structural characteristics. This makes it ideal for spectral interpretation and the discovery of new descriptors. The capability of this procedure is showcased by considering five local structure descriptors and a database of over fifty thousand simulated XANES spectra across eight first-row transition metal oxide families. The resulting structure-spectrum relationships not only reproduce known trends in the literature, but also reveal unintuitive ones that are visually indiscernible in large data sets. The results suggest that the RankAAE methodology has great potential to assist researchers to interpret complex scientific data, test physical hypotheses, and reveal new patterns that extend scientific insight.

Visualization and Exploration of Complex Scientific Data

Researchers can acquire insights from complex data sets and better share their findings with others when they employ data visualization and exploration tools. We discuss existing techniques and tools for visualizing and exploring complex scientific data, identify the key concepts and principles of data visualization, and propose a new technique to address some of the limitations of existing approaches to data exploration and visualization. Our suggested method utilizes cloud computing and ML to deliver dynamic, interactive visualizations in real-time, regardless of data size or complexity. Our research provides a unique method for overcoming the limits of existing data visualization and exploration tools in the context of scientific inquiry, in addition to a detailed overview of existing methodologies and tools. We hope our results will stimulate greater investigation into this vital area, leading to improved methods and tools for visualizing and navigating intricate scientific data

A Multi-Branch Decoder Network Approach to Adaptive Temporal Data Selection and Reconstruction for Big Scientific Simulation Data

A key challenge in scientific simulation is that the simulation outputs often require intensive I/O and storage space to store the results for effective post hoc analysis. This article focuses on a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">quality-aware adaptive temporal data selection and reconstruction</i> problem where the goal is to adaptively select simulation data samples at certain key timesteps in situ and reconstruct the discarded samples with quality assurance during post hoc analysis. This problem is motivated by the limitation of current solutions that a significant amount of simulation data samples are either discarded or aggregated during the sampling process, leading to inaccurate modeling of the simulated phenomena. Two unique challenges exist: 1) the sampling decisions have to be made in situ and adapted to the dynamics of the complex scientific simulation data; 2) the reconstruction error must be strictly bounded to meet the application requirement. To address the above challenges, we develop <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DeepSample</i> , an error-controlled convolutional neural network framework, that jointly integrates a set of coherent multi-branch deep decoders to effectively reconstruct the simulation data with rigorous quality assurance. The results on two real-world scientific simulation applications show that DeepSample significantly outperforms other state-of-the-art methods on both sampling efficiency and reconstructed simulation data quality.

Integrating Data Directly into Publications with Augmented Reality and Web-Based Technologies – Schol-AR

Scientific research has become highly intertwined with digital information, however scientific publication remains based on the static text and figures of principal articles. This discrepancy constrains complex scientific data into 2D static figures, hindering our ability to effectively exchange the complex and extensive information that underlies modern research. Here, we demonstrate how the viewing of digital data can be directly integrated into the existing publication system through both web based and augmented reality (AR) technologies. We additionally provide a framework that makes these capabilities available to the scientific community. Ultimately, augmenting articles with data can modernize scientific communication by bridging the gap between the digital basis of present-day research and the natural limitations of printable articles.

Computational Estimation by Scientific Data Mining with Classical Methods to Automate Learning Strategies of Scientists

Experimental results are often plotted as 2-dimensional graphical plots (aka graphs) in scientific domains depicting dependent versus independent variables to aid visual analysis of processes. Repeatedly performing laboratory experiments consumes significant time and resources, motivating the need for computational estimation. The goals are to estimate the graph obtained in an experiment given its input conditions, and to estimate the conditions that would lead to a desired graph. Existing estimation approaches often do not meet accuracy and efficiency needs of targeted applications. We develop a computational estimation approach called AutoDomainMine that integrates clustering and classification over complex scientific data in a framework so as to automate classical learning methods of scientists. Knowledge discovered thereby from a database of existing experiments serves as the basis for estimation. Challenges include preserving domain semantics in clustering, finding matching strategies in classification, striking a good balance between elaboration and conciseness while displaying estimation results based on needs of targeted users, and deriving objective measures to capture subjective user interests. These and other challenges are addressed in this work. The AutoDomainMine approach is used to build a computational estimation system, rigorously evaluated with real data in Materials Science. Our evaluation confirms that AutoDomainMine provides desired accuracy and efficiency in computational estimation. It is extendable to other science and engineering domains as proved by adaptation of its sub-processes within fields such as Bioinformatics and Nanotechnology.

Research on remote data access and visualization for long pulse experiment based on web technologies

Model Drive System plus (MDSplus) is an efficient and convenient data storage and management software designed by MIT specifically for pulse experiments. After years of development, MDSplus has become a data management system that integrates data acquisition, storage and processing of complex scientific data. However, with the development of the experimental pulse, long pulse experiment has now become the main mode of pulse-type experiment. Compared with the short pulse experiment, the data acquisition under the long pulse experiment speed is faster, the experiment time is longer, and the amount of data is larger. In view of the fact that most of the existing visualization systems are based on client/server (C/S) architecture, which can only display local visualization. Therefore, In order to enrich the way for experimenters to obtain data, this paper designs a visual platform based on browser/server (B/S) that using the WAMP (Windows+apache+MySQL+php) architecture to build the server. The bootstrap framework is used to build the front-end. Before the visualization data transmission, the original data is processed by segmentation technology to reduce server load and improve transmission utilization so that experimenters can query and obtain the required data remotely through the platform.

Poor accessibility of water utilities’ consumer confidence reports

Public water systems across the U.S. are required to annually issue a water quality report (more formally, consumer confidence report or CCR) and make it publicly accessible. CCRs are ineffective at communicating risk and safety information to the customers for several reasons: they are hard to find and poorly advertised, present complex scientific data at a high reading level, and are written predominantly in English. In this paper, we analyze a representative sample of 268 CCRs to measure their accessibility along three dimensions: adherence to Web Content Accessibility Guidelines (WCAG) 2.0 standards, their Flesch Reading Ease score, and the availability of translation to non-English languages. Our analysis found that water utilities of all sizes, customer demographics, and geographic locations scored poorly on each measure of accessibility. However, accessibility scores were correlated with utility size, racial composition, and the presence of bilingual speakers. As one of the few mandated non-crisis communication to customers, CCRs present a meaningful opportunity for water systems to share information about water quality, public health, and community concerns. Accessible and easy-to-understand CCRs can achieve all that and build customer trust.

Volcanic hazard map visualisation affects cognition and crisis decision-making

Hazard maps are used to communicate complex scientific data with many audiences during volcanic unrest crises, but it is unclear how common hazard visualisation styles affect cognition, behaviour, and decision-making. Here we use eye-gaze tracking and questionnaires to explore how 81 people near a volcano in New Zealand read and make decisions with hazard maps for a hypothetical eruption. We find that greater mental effort is required to read early-stage hazard maps associated with higher uncertainty, and that showing integrated zones of low to high hazard can result in 1) higher perceived threat, 2) limited visual attention to the maps, and 3) more risk-averse decisions under pressure compared to visualising discrete hazard footprints. People with high prior risk perceptions demonstrate less attention to map content overall. The results show how map visualisation can influence cognition, decision-making, and behaviour, and have implications for crisis communication. For example, in the early stages of a volcanic event, integrated hazard zones may help capture audience attention by facilitating a high-level assessment of risk. As the event evolves, discrete footprints of individual hazard processes may help better inform high-stakes decisions for which detailed hazard knowledge is needed. Additionally, we find that providing simple tasks for the audience to carry out using the maps, and showing safe areas alongside hazardous areas, may encourage map reading. We discuss the implications of these findings, and present four evidence-based points for scientists to consider across a range of fields where hazard maps are used to communicate with non-specialist audiences.

VizSciFlow: A Visually Guided Scripting Framework for Supporting Complex Scientific Data Analysis

Scientific workflow management systems such as Galaxy, Taverna and Workspace, have been developed to automate scientific workflow management and are increasingly being used to accelerate the specification, execution, visualization, and monitoring of data-intensive tasks. For example, the popular bioinformatics platform Galaxy is installed on over 168 servers around the world and the social networking space myExperiment shares almost 4,000 Galaxy scientific workflows among its 10,665 members. Most of these systems offer graphical interfaces for composing workflows. However, while graphical languages are considered easier to use, graphical workflow models are more difficult to comprehend and maintain as they become larger and more complex. Text-based languages are considered harder to use but have the potential to provide a clean and concise expression of workflow even for large and complex workflows. A recent study showed that some scientists prefer script/text-based environments to perform complex scientific analysis with workflows. Unfortunately, such environments are unable to meet the needs of scientists who prefer graphical workflows. In order to address the needs of both types of scientists and at the same time to have script-based workflow models because of their underlying benefits, we propose a visually guided workflow modeling framework that combines interactive graphical user interface elements in an integrated development environment with the power of a domain-specific language to compose independently developed and loosely coupled services into workflows. Our domain-specific language provides scientists with a clean, concise, and abstract view of workflow to better support workflow modeling. As a proof of concept, we developed VizSciFlow, a generalized scientific workflow management system that can be customized for use in a variety of scientific domains. As a first use case, we configured and customized VizSciFlow for the bioinformatics domain. We conducted three user studies to assess its usability, expressiveness, efficiency, and flexibility. Results are promising, and in particular, our user studies show that VizSciFlow is more desirable for users to use than either Python or Galaxy for solving complex scientific problems.

2020 National High School Big Data Challenge: New Climate and Information Realities - From Oceans to Glass of Water

STEM Fellowship’s Big Data Challenge is a unique pedagogical experiment, providing an inquiry and learning experience for high school students that, upon equipping them with top-notch analytical tools, tasks them to find hidden patterns and trends in complex scientific data. This year’s challenge provided a multidisciplinary competitive opportunity; over a period of three months, teams analysed sustainability data through the prism of computational methods. Teams worked to reveal the impact of environmental conditions on human health and well-being, diving into predictive analytics of the global and micro-climate change impacts of water and oceans on communities, energy generation, agriculture, entrepreneurship, and more.

Integrating ABCD and DarwinCore: Toward a better foundation for biodiversity information standards

For the last 15 years, Biodiversity Information Standards (TDWG) has recognized two competing standards for organism occurrence data, ABCD (Access to Biological Collections Data; Holetschek et al. 2012) and DarwinCore (Wieczorek et al. 2012). These two representations emerged from contrasting strategies for mobilizing information about organism occurrences (also commonly called species occurrence data). ABCD was capable of representing details of more kinds of information, but was necessarily more complicated. DarwinCore, on the other hand, was simpler but more limited in its ability to represent data of different kinds and formats. TDWG endorsed both standards because the different projects and communities that generated them remained dedicated to their different strategies and tool sets, and the Global Biodiversity Information Facility (GBIF) developed the ability to integrate data published in either standard. Since their inceptions, DarwinCore and ABCD have become more similar. DarwinCore has gotten more complicated through the addition of terms and has begun to assign terms to classes. ABCD is now expressed in RDF (Resource Description Framework), potentially enabling re-use of terms with alternative structures among classes. At the same time, methodologies for conceptual modeling and representing complex scientific data have continued to evolve. In particular, a suite of modeling and data representation methods related to linked data and the semantic web, i.e., RDF, SKOS (Simple Knowledge Organization System), and OWL (web Ontology Language), promise to make it easier for us to reconcile shared concepts among different representations or schemas. A mapping between ABCD 2.1 and DarwinCore has existed since before 2005.*1 ABCD 3.0 and DarwinCore are both now represented in RDF. In addition, the BioCollections Ontology (BCO) covers many of the shared concepts and is derived from the Basic Formal Ontology (BFO), an upper level ontology that has oriented many other biomedical ontologies. Reconciling ABCD and DarwinCore through alignment with BCO (in the OBO Foundry; Smith et al. 2007) would better connect TDWG standards to other domains in biology. We appreciate that many working scientists and data managers perceive ontologies as overly complicated. To mitigate the steep learning curve associated with ontologies, we expect to create simpler application profiles or schemas to guide and serve narrower communities of practice within the wider biodiversity domain. We also plan to integrate the current work of the Taxonomic Names and Concepts Interest Group and thereby eliminate the redundancy between DarwinCore and Taxonomic Concepts Transfer Schema (TCS; Kennedy et al. 2006). At the time of this writing, we have only agreements from the authors (i.e., conveners of relevant TDWG Interest Groups and other key stakeholders) to collaborate in pursuit of these common goals. In this presentation we will give a more detailed description of our objectives and products, the methods we are using to achieve them, and our progress to date.

Designing for Real-Time Groupware Systems to Support Complex Scientific Data Analysis

Scientific Workflow Management Systems (SWfMSs) have become popular for accelerating the specification, execution, visualization, and monitoring of data-intensive scientific experiments. Unfortunately, to the best of our knowledge no existing SWfMSs directly support collaboration. Data is increasing in complexity, dimensionality, and volume, and the efficient analysis of data often goes beyond the realm of an individual and requires collaboration with multiple researchers from varying domains. In this paper, we propose a groupware system architecture for data analysis that in addition to supporting collaboration, also incorporates features from SWfMSs to support modern data analysis processes. As a proof of concept for the proposed architecture we developed SciWorCS - a groupware system for scientific data analysis. We present two real-world use-cases: collaborative software repository analysis and bioinformatics data analysis. The results of the experiments evaluating the proposed system are promising. Our bioinformatics user study demonstrates that SciWorCS can leverage real-world data analysis tasks by supporting real-time collaboration among users.

Medical Communications: The "Write" Career Path for You?

The pharmaceutical industry spends billions each year on the clinical development of new medicines. Getting those products to the patients who will benefit from them requires an ability to convey the results of extensive clinical research programs to regulatory authorities, physicians, and payers. Employed by medical communications agencies, contract research organizations, and pharmaceutical companies, medical writers distill and translate complex clinical and scientific data to develop documentation spanning the entire pharmaceutical-product life cycle, from clinical development to registration and marketing. Despite being a relatively new career, the market for medical communications professionals has doubled in recent years and future job prospects look promising.

Designing 3D tangible interfaces for understanding complex big data

ABSTRACTUnderstanding big and complex scientific data is still an immature topic. It involves studying visualization methods to faithfully represent data, on the one hand, and designing interfaces that truly assist users with data analysis, on the other hand. In an earlier study, we developed guidelines for choosing display environment for four specific, but common, data analysis tasks: identification and judgment of the size, shape, density, and connectivity of objects in a volume. The results showed that using the fish tank virtual reality (VR) system was significantly more accurate at judging the shape, density, and connectivity of objects and significantly faster than the immersive Head-mounted display VR system. Based on those results, we asked the question whether or not the user performance could be further improved by adding tangible elements into the fish tank VR system. We propose several different interface prototypes of a clipping plane that have been realized with the help of wireless vision-...