arXiv subjects

Nils Gehlenborg

Publications and source records attributed to Nils Gehlenborg.

26 records · Page 2Linked to original sources

arXiv2024

Beyond Generating Code: Evaluating GPT on a Data Visualization Course

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.

cs.HC↗

arXiv2022

MITI Minimum Information guidelines for highly multiplexed tissue images

The imminent release of tissue atlases combining multi-channel microscopy with single cell sequencing and other omics data from normal and diseased specimens creates an urgent need for data and metadata standards that guide data deposition, curation and release. We describe a Minimum Information about highly multiplexed Tissue Imaging (MITI) standard that applies best practices developed for genomics and other microscopy data to highly multiplexed tissue images and traditional histology.

q-bio.OT↗

arXiv2020

A Generic Framework and Library for Exploration of Small Multiples through Interactive Piling

Small multiples are miniature representations of visual information used generically across many domains. Handling large numbers of small multiples imposes challenges on many analytic tasks like inspection, comparison, navigation, or annotation. To address these challenges, we developed a framework and implemented a library called Piling.js for designing interactive piling interfaces. Based on the piling metaphor, such interfaces afford flexible organization, exploration, and comparison of large numbers of small multiples by interactively aggregating visual objects into piles. Based on a systematic analysis of previous work, we present a structured design space to guide the design of visual piling interfaces. To enable designers to efficiently build their own visual piling interfaces, Piling.js provides a declarative interface to avoid having to write low-level code and implements common aspects of the design space. An accompanying GUI additionally supports the dynamic configuration of the piling interface. We demonstrate the expressiveness of Piling.js with examples from machine learning, immunofluorescence microscopy, genomics, and public health.

cs.HC↗

arXiv2019

Guidelines for reporting single-cell RNA-Seq experiments

Single-cell RNA-Sequencing (scRNA-Seq) has undergone major technological advances in recent years, enabling the conception of various organism-level cell atlassing projects. With increasing numbers of datasets being deposited in public archives, there is a need to address the challenges of enabling the reproducibility of such data sets. Here, we describe guidelines for a minimum set of metadata to sufficiently describe scRNA-Seq experiments, ensuring reproducibility of data analyses.

q-bio.GN↗

arXiv2019

Periphery Plots for Contextualizing Heterogeneous Time-Based Charts

Patterns in temporal data can often be found across different scales, such as days, weeks, and months, making effective visualization of time-based data challenging. Here we propose a new approach for providing focus and context in time-based charts to enable interpretation of patterns across time scales. Our approach employs a focus zone with a time and a second axis, that can either represent quantities or categories, as well as a set of adjacent periphery plots that can aggregate data along the time, value, or both dimensions. We present a framework for periphery plots and describe two use cases that demonstrate the utility of our approach.

cs.HC↗

arXiv2019

Mapping the Human Body at Cellular Resolution -- The NIH Common Fund Human BioMolecular Atlas Program

Transformative technologies are enabling the construction of three dimensional (3D) maps of tissues with unprecedented spatial and molecular resolution. Over the next seven years, the NIH Common Fund Human Biomolecular Atlas Program (HuBMAP) intends to develop a widely accessible framework for comprehensively mapping the human body at single-cell resolution by supporting technology development, data acquisition, and detailed spatial mapping. HuBMAP will integrate its efforts with other funding agencies, programs, consortia, and the biomedical research community at large towards the shared vision of a comprehensive, accessible 3D molecular and cellular atlas of the human body, in health and various disease settings.

q-bio.OT↗

arXiv2019

Tasks, Techniques, and Tools for Genomic Data Visualization

Genomic data visualization is essential for interpretation and hypothesis generation as well as a valuable aid in communicating discoveries. Visual tools bridge the gap between algorithmic approaches and the cognitive skills of investigators. Addressing this need has become crucial in genomics, as biomedical research is increasingly data-driven and many studies lack well-defined hypotheses. A key challenge in data-driven research is to discover unexpected patterns and to formulate hypotheses in an unbiased manner in vast amounts of genomic and other associated data. Over the past two decades, this has driven the development of numerous data visualization techniques and tools for visualizing genomic data. Based on a comprehensive literature survey, we propose taxonomies for data, visualization, and tasks involved in genomic data visualization. Furthermore, we provide a comprehensive review of published genomic visualization tools in the context of the proposed taxonomies.

q-bio.GN↗

arXiv2018

Visual Pattern-Driven Exploration of Big Data

Pattern extraction algorithms are enabling insights into the ever-growing amount of today's datasets by translating reoccurring data properties into compact representations. Yet, a practical problem arises: With increasing data volumes and complexity also the number of patterns increases, leaving the analyst with a vast result space. Current algorithmic and especially visualization approaches often fail to answer central overview questions essential for a comprehensive understanding of pattern distributions and support, their quality, and relevance to the analysis task. To address these challenges, we contribute a visual analytics pipeline targeted on the pattern-driven exploration of result spaces in a semi-automatic fashion. Specifically, we combine image feature analysis and unsupervised learning to partition the pattern space into interpretable, coherent chunks, which should be given priority in a subsequent in-depth analysis. In our analysis scenarios, no ground-truth is given. Thus, we employ and evaluate novel quality metrics derived from the distance distributions of our image feature vectors and the derived cluster model to guide the feature selection process. We visualize our results interactively, allowing the user to drill down from overview to detail into the pattern space and demonstrate our techniques in a case study on biomedical genomic data.

cs.IR↗