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What Google Maps can do for biomedical data dissemination: examples and a design study
BACKGROUND: Biologists often need to assess whether unfamiliar datasets warrant the time investment required for more detailed exploration. Basing such assessments on brief descriptions provided by data publishers is unwieldy for large datasets that contain insights dependent on specific scientific questions. Alternatively, using complex software systems for a preliminary analysis may be deemed as too time consuming in itself, especially for unfamiliar data types and formats. This may lead to wasted analysis time and discarding of potentially useful data.
RESULTS: We present an exploration of design opportunities that the Google Maps interface offers to biomedical data visualization. In particular, we focus on synergies between visualization techniques and Google Maps that facilitate the development of biological visualizations which have both low-overhead and sufficient expressivity to support the exploration of data at multiple scales. The methods we explore rely on displaying pre-rendered visualizations of biological data in browsers, with sparse yet powerful interactions, by using the Google Maps API. We structure our discussion around five visualizations: a gene co-regulation visualization, a heatmap viewer, a genome browser, a protein interaction network, and a planar visualization of white matter in the brain. Feedback from collaborative work with domain experts suggests that our Google Maps visualizations offer multiple, scale-dependent perspectives and can be particularly helpful for unfamiliar datasets due to their accessibility. We also find that users, particularly those less experienced with computer use, are attracted by the familiarity of the Google Maps API. Our five implementations introduce design elements that can benefit visualization developers.
CONCLUSIONS: We describe a low-overhead approach that lets biologists access readily analyzed views of unfamiliar scientific datasets. We rely on pre-computed visualizations prepared by data experts, accompanied by sparse and intuitive interactions, and distributed via the familiar Google Maps framework. Our contributions are an evaluation demonstrating the validity and opportunities of this approach, a set of design guidelines benefiting those wanting to create such visualizations, and five concrete example visualizations
AI in Medical Imaging Informatics: Current Challenges and Future Directions
This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice. More specifically, it summarizes advances in medical imaging acquisition technologies for different modalities, highlighting the necessity for efficient medical data management strategies in the context of AI in big healthcare data analytics. It then provides a synopsis of contemporary and emerging algorithmic methods for disease classification and organ/ tissue segmentation, focusing on AI and deep learning architectures that have already become the de facto approach. The clinical benefits of in-silico modelling advances linked with evolving 3D reconstruction and visualization applications are further documented. Concluding, integrative analytics approaches driven by associate research branches highlighted in this study promise to revolutionize imaging informatics as known today across the healthcare continuum for both radiology and digital pathology applications. The latter, is projected to enable informed, more accurate diagnosis, timely prognosis, and effective treatment planning, underpinning precision medicine
A Comparison of Neuroelectrophysiology Databases
As data sharing has become more prevalent, three pillars - archives,
standards, and analysis tools - have emerged as critical components in
facilitating effective data sharing and collaboration. This paper compares four
freely available intracranial neuroelectrophysiology data repositories: Data
Archive for the BRAIN Initiative (DABI), Distributed Archives for
Neurophysiology Data Integration (DANDI), OpenNeuro, and Brain-CODE. These
archives provide researchers with tools to store, share, and reanalyze
neurophysiology data though the means of accomplishing these objectives differ.
The Brain Imaging Data Structure (BIDS) and Neurodata Without Borders (NWB) are
utilized by these archives to make data more accessible to researchers by
implementing a common standard. While many tools are available to reanalyze
data on and off the archives' platforms, this article features Reproducible
Analysis and Visualization of Intracranial EEG (RAVE) toolkit, developed
specifically for the analysis of intracranial signal data and integrated with
the discussed standards and archives. Neuroelectrophysiology data archives
improve how researchers can aggregate, analyze, distribute, and parse these
data, which can lead to more significant findings in neuroscience research.Comment: 25 pages, 8 figures, 1 tabl
A survey of visualisation for live cell imaging
Live cell imaging is an important biomedical research paradigm for studying dynamic cellular behaviour. Although phenotypic data derived from images are difficult to explore and analyse, some researchers have successfully addressed this with visualisation. Nonetheless, visualisation methods for live cell imaging data have been reported in an ad hoc and fragmented fashion. This leads to a knowledge gap where it is difficult for biologists and visualisation developers to evaluate the advantages and disadvantages of different visualisation methods, and for visualisation researchers to gain an overview of existing work to identify research priorities. To address this gap, we survey existing visualisation methods for live cell imaging from a visualisation research perspective for the first time. Based on recent visualisation theory, we perform a structured qualitative analysis of visualisation methods that includes characterising the domain and data, abstracting tasks, and describing visual encoding and interaction design. Based on our survey, we identify and discuss research gaps that future work should address: the broad analytical context of live cell imaging; the importance of behavioural comparisons; links with dynamic data visualisation; the consequences of different data modalities; shortcomings in interactive support; and, in addition to analysis, the value of the presentation of phenotypic data and insights to other stakeholders
Tools of Trade of the Next Blue-Collar Job? Antecedents, Design Features, and Outcomes of Interactive Labeling Systems
Supervised machine learning is becoming increasingly popular - and so is the need for annotated training data. Such data often needs to be manually labeled by human workers, not unlikely to negatively impact the involved workforce. To alleviate this issue, a new information systems class has emerged - interactive labeling systems. However, this young, but rapidly growing field lacks guidance and structure regarding the design of such systems. Against this backdrop, this paper describes antecedents, design features, and outcomes of interactive labeling systems. We perform a systematic literature review, identifying 188 relevant articles. Our results are presented as a morphological box with 14 dimensions, which we evaluate using card sorting. By additionally offering this box as a web-based artifact, we provide actionable guidance for interactive labeling system development for scholars and practitioners. Lastly, we discuss imbalances in the article distribution of our morphological box and suggest future work directions
Dataremix: Aesthetic Experiences of Big Data and Data Abstraction
This PhD by published work expands on the contribution to knowledge in two recent large-scale transdisciplinary artistic research projects: ATLAS in silico and INSTRUMENT | One Antarctic Night and their exhibited and published outputs. The thesis reflects upon this practice-based artistic research that interrogates data abstraction: the digitization, datafication and abstraction of culture and nature, as vast and abstract digital data. The research is situated in digital arts practices that engage a combination of big (scientific) data as artistic material, embodied interaction in virtual environments, and poetic recombination.
A transdisciplinary and collaborative artistic practice, x-resonance, provides a framework for the hybrid processes, outcomes, and contributions to knowledge from the research. These are purposefully and productively situated at the objective | subjective interface, have potential to convey multiple meanings simultaneously to a variety of audiences and resist disciplinary definition. In the course of the research, a novel methodology emerges, dataremix, which is employed and iteratively evolved through artistic practice to address the research questions: 1) How can a visceral and poetic experience of data abstraction be created? and 2) How would one go about generating an artistically-informed (scientific) discovery?
Several interconnected contributions to knowledge arise through the first research question: creation of representational elements for artistic visualization of big (scientific) data that includes four new forms (genomic calligraphy, algorithmic objects as natural specimens, scalable auditory data signatures, and signal objects); an aesthetic of slowness that contributes an extension to the operative forces in Jevbrattâs inverted sublime of looking down and in to also include looking fast and slow; an extension of Corbyâs objective and subjective image consisting of âinformational and aesthetic componentsâ to novel virtual environments created from big 3 (scientific) data that extend Daviesâ poetic virtual spatiality to poetic objective | subjective generative virtual spaces; and an extension of Seamanâs embodied interactive recombinant poetics through embodied interaction in virtual environments as a recapitulation of scientific (objective) and algorithmic processes through aesthetic (subjective) physical gestures. These contributions holistically combine in the artworks ATLAS in silico and INSTRUMENT | One Antarctic Night to create visceral poetic experiences of big data abstraction.
Contributions to knowledge from the first research question develop artworks that are visceral and poetic experiences of data abstraction, and which manifest the objective | subjective through art. Contributions to knowledge from the second research question occur through the process of the artworks functioning as experimental systems in which experiments using analytical tools from the scientific domain are enacted within the process of creation of the artwork. The results are âreturnedâ into the artwork. These contributions are: elucidating differences in DNA helix bending and curvature along regions of gene sequences specified as either introns or exons, revealing nuanced differences in BLAST results in relation to genomics sequence metadata, and cross-correlation of astronomical data to identify putative variable signals from astronomical objects for further scientific evaluation
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