READMEs shape first impressions of software projects, yet what constitutes a good README varies across audiences and contexts. Research software needs reproducibility details, while open-source libraries might prioritize quick-start guides. Through a design probe, LintMe, we explore how linting can be used to improve READMEs given these diverse contexts, aiding style and content issues while preserving authorial agency. Users create context-specific checks using a lightweight DSL that uses a novel combination of programmatic operations (e.g., for broken links) with LLM-based content evaluation (e.g., for detecting jargon), yielding checks that would be challenging for prior linters. Through a user study (N=11), comparison with naive LLM usage, and an extensibility case study, we find that our design is approachable, flexible, and well matched with the needs of this domain. This work opens the door for linting more complex documentation and other culturally mediated text-based documents.

To study complex real-world phenomena using computer simulations, scientists often rely on ensemble datasets generated from multiple simulation runs with varying parameter configurations. This process can produce ensemble datasets with many members, making traditional data analysis pipelines impractical due to limited I/O bandwidth and disk capacity. Distribution-based data representations have been proposed as a promising solution. Processing data in situ to generate compact distribution-based representations not only alleviates the challenges of limited I/O bandwidth and disk capacity but also enables uncertainty quantification, thus mitigating the risk of misinterpretation. Nevertheless, distribution-based methods inherently sacrifice spatial information of data samples within the distribution, potentially reducing precision in the data analysis pipeline. To address this issue, we introduce a deep learning model to reconstruct data volume from the distribution representation. Instead of using a model that predicts a data block directly from its distribution representation, we propose a deep learning model based on the Sinkhorn operator and Gumbel trick that learns to map samples drawn from a distribution to spatial locations within the block. The deep learning model can support high-quality downstream data analysis and visualization, provide point-wise uncertainty quantification, and guarantee the distribution of the reconstructed data block follows the block's distribution representation.

Subgroup studies can help identify risk factors that influence a detailed subpopulation of people and help provide insights into precision medicine. However, the complexity between patients of a specific disease and potential risk factors makes it hard for epidemiologists to find meaningful patterns. It is challenging for epidemiologists to go through all the possible different risk factors and analyze the relationships between risk factors. Therefore we developed an interactive visualization system called SG-RAD (SubGroup Risk factors Analysis and Discovery) to assist users in identifying subgroups, exploring said subgroup’s risk factors, and further investigating the relationship between them. Specifically, the system allows users to define the contrasting subgroup as well as the targeted variables, find notable patterns of risk factors and further investigate the risk factors’ influence within and outside the subgroup. We conduct a case study, to identify lifestyle habits that are considered as risk factors of gout in a subgroup within the same high-risk genetic group for gout in Taiwan Biobank’s population. Our system, SG-RAD, can assist our domain experts in finding patterns in a detailed group of people that can be considered as risk factors for disease and further investigate the risk factors influence within the subgroup.