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This paper presents an empirical evaluation of the performance of the Generative Pre-trained Transformer (GPT) model in Harvard's CS171 data visualization course. While previous studies have focused on GPT's ability to generate code for visualizations, this study goes beyond code generation to evaluate GPT's abilities in various visualization tasks, such as data interpretation, visualization design, visual data exploration, and insight communication. The evaluation utilized GPT-3.5 and GPT-4 to complete assignments of CS171, and included a quantitative assessment based on the established course rubrics, a qualitative analysis informed by the feedback of three experienced graders, and an exploratory study of GPT's capabilities in completing border visualization tasks. Findings show that GPT-4 scored 80% on quizzes and homework, and TFs could distinguish between GPT- and human-generated homework with 70% accuracy. The study also demonstrates GPT's potential in completing various visualization tasks, such as data cleanup, interaction with visualizations, and insight communication. The paper concludes by discussing the strengths and limitations of GPT in data visualization, potential avenues for incorporating GPT in broader visualization tasks, and the need to redesign visualization education., Comment: vis short papge

Neueste hochauflösende elektronenmikroskopische Aufnahmen ermöglichen Neurowissenschaftlern, nicht nur Zellen, sondern auch einzelne Zellstrukturen aus Mäusegehirnen zu rekonstruieren. Auf Basis dieser Daten erhoffen sie sich, ein besseres Verständnis der Gehirnfunktion und -entwicklung zu gewinnen, indem sie lokale Nachbarschaften von Zellstrukturen detailliert analysieren. Genaue Analysen erfordern jedoch einen effizienten und skalierbaren Vergleich unterschiedlicher Zellstrukturen, die von zwei bis zu Hunderten von lokalen Nachbarschaften reichen. Wissenschaftler müssen in der Lage sein, die 3D-Morphologie der Zellstrukturen, ihre räumlichen Verteilungen und Abstände sowie deren Korrelationen zu analysieren. Die zentrale Prämisse dieser Arbeit ist, die Schwierigkeit eine einheitliche Lösung für die vergleichende Visualisierung bereitzustellen, um die gegebene breite Palette von Aufgabenzu unterstützen. Deshalb haben wir NeuroKit als leicht konfigurierbaren Werkzeugkasten entwickelt, der es Wissenschaftlern ermöglicht, den Arbeitsablauf, die Visualisierungen und die unterstützten Benutzerinteraktionen an ihre spezifischen Aufgaben und Fragestellungen anzupassen. Darüber hinaus bietet NeuroKit einen skalierbaren, vergleichenden Visualisierungsansatz für die räumliche Nachbarschaftsanalyse von Gehirnstrukturen. NeuroKit unterstützt interaktive 3D-Renderings sowie abstrakte quantitative Visualisierungen und ordnet diese automatisch in der Benutzeroberfläche an. Um neue domänen spezifische Analysen zu unterstützen, erlauben wir die Definition von individualisiertenVisualisierungen und deren Parametern. Diese Konfigurierbarkeit ist mit einem neuartigen skalierbaren visuellen Vergleichsansatz verknüpft, der die Visualisierungen automatisch an die Anzahl der zu vergleichenden Strukturen anpasst. Wir demonstrieren einen detaillierten Anwendungsfall für die Analyse von Mitochondrien und analysieren die Nützlichkeit von NeuroKit in einer qualitativen Studie mit Neurowissenschaftlern., Recent high-resolution electron microscopy imaging allows neuroscientists to reconstruct not just entire cells but individual cell substructures (i.e., cell organelles) as well. Based on these data, scientists hope to get a better understanding of brain function and development through detailed analysis of local organelle neighborhoods. However, in-depth analyses require efficient and scalable comparison of a varying number of cell organelles, ranging from two to hundreds of local spatial neighborhoods. Scientists need to be able to analyze the 3D morphologies of organelles, their spatial distributions and distances, and their spatial correlations. This thesis's central premise is that it is hard to provide a one-size-fits-all comparative visualization solution to support the given broad range of tasks and scales. To address this challenge, we have designed NeuroKit as an easily configurable toolkit that allows scientists to customize the tool's workflow, visualizations, and supported user interactions to their specific tasks and domain questions. Furthermore, NeuroKit provides a scalable comparative visualization approach for spatial neighborhood analysis of nanoscale brain structures. NeuroKit supports small multiples of spatial 3D renderings as well as abstract quantitative visualizations, and arranges them in linked and juxtaposed views. To adapt to new domain-specific analysis scenarios, we allow the definition of individualized visualizations and their parameters for each analysis session. This configurability is tied in with a novel scalable visual comparison approach that automatically adjusts visualizations based on the number of structures that are being compared. We demonstrate an in-depth use case for mitochondria analysis in neuronal tissue and analyze the usefulness of NeuroKit in a qualitative user study with neuroscientists.

Advances in Electron Microscopy, image segmentation and computational infrastructure have given rise to large-scale and richly annotated connectomic datasets which are increasingly shared across communities. To enable collaboration, users need to be able to concurrently create new annotations and correct errors in the automated segmentation by proofreading. In large datasets, every proofreading edit relabels cell identities of millions of voxels and thousands of annotations like synapses. For analysis, users require immediate and reproducible access to this constantly changing and expanding data landscape. Here, we present the Connectome Annotation Versioning Engine (CAVE), a computational infrastructure for immediate and reproducible connectome analysis in up-to petascale datasets (~1mm 3 ) while proofreading and annotating is ongoing. For segmentation, CAVE provides a distributed proofreading infrastructure for continuous versioning of large reconstructions. Annotations in CAVE are defined by locations such that they can be quickly assigned to the underlying segment which enables fast analysis queries of CAVE's data for arbitrary time points. CAVE supports schematized, extensible annotations, so that researchers can readily design novel annotation types. CAVE is already used for many connectomics datasets, including the largest datasets available to date., Competing Interests: Competing interests T. Macrina, K. Lee, S. Popovych, D. Ih, N. Kemnitz, and H. S. Seung declare financial interests in Zetta AI.

The National Cancer Institute (NCI) supports many research programs and consortia, many of which use imaging as a major modality for characterizing cancerous tissue. A trans-consortia Image Analysis Working Group (IAWG) was established in 2019 with a mission to disseminate imaging-related work and foster collaborations. In 2022, the IAWG held a virtual hackathon focused on addressing challenges of analyzing high dimensional datasets from fixed cancerous tissues. Standard image processing techniques have automated feature extraction, but the next generation of imaging data requires more advanced methods to fully utilize the available information. In this perspective, we discuss current limitations of the automated analysis of multiplexed tissue images, the first steps toward deeper understanding of these limitations, what possible solutions have been developed, any new or refined approaches that were developed during the Image Analysis Hackathon 2022, and where further effort is required. The outstanding problems addressed in the hackathon fell into three main themes: 1) challenges to cell type classification and assessment, 2) translation and visual representation of spatial aspects of high dimensional data, and 3) scaling digital image analyses to large (multi-TB) datasets. We describe the rationale for each specific challenge and the progress made toward addressing it during the hackathon. We also suggest areas that would benefit from more focus and offer insight into broader challenges that the community will need to address as new technologies are developed and integrated into the broad range of image-based modalities and analytical resources already in use within the cancer research community.