Breast cancer bone metastasis (BCBM) affects women’s health globally, calling for the development of effective diagnosis and prognosis solutions. While deep learning has exhibited impressive capacities across various healthcare domains, its applicability in BCBM diseases is consistently hindered by the lack of an open, large-scale, deep learning-ready dataset. As such, we introduce the Bone Metastasis (BoneMet) dataset, the first large-scale, publicly available, high-resolution medical resource, which is derived from a well-accepted murine BCBM model. The unique advantage of BoneMet over existing human datasets is repeated sequential scans per subject over the entire disease development phases. The dataset consists of over 67 terabytes of multi-modal medical data, including 2D X-ray images, 3D CT scans, and detailed biological data (e.g., medical records and bone quantitative analysis), collected from more than five hundreds mice spanning from 2019 to 2024. Our BoneMet dataset is well-organized into six components, i.e., Rotation X-Ray, Recon-CT, Seg-CT, Regist-CT, RoI-CT, and MiceMediRec. We further show that BoneMet can be readily adopted to build versatile, large-scale AI models for managing BCBM diseases in terms of diagnosis using 2D or 3D images, prognosis of bone deterioration, and sparse-angle 3D reconstruction for safe long-term disease monitoring. Our preliminary results demonstrate that BoneMet has the potentials to jump-start the development and fine-tuning of AI-driven solutions prior to their applications to human patients. To facilitate its easy access and wide dissemination, we have created the BoneMet package, providing three APIs that enable researchers to (i) flexibly process and download the BoneMet data filtered by specific time frames; and (ii) develop and train large-scale AI models for precise BCBM diagnosis and prognosis. The BoneMet dataset is officially available on Hugging Face Datasets at https://huggingface.co/datasets/BoneMet/BoneMet. The BoneMet package is available on the Python Package Index (PyPI) at https://pypi.org/project/BoneMet. Code and tutorials are available at https://github.com/Tiankuo528/BoneMet.

Programming tutorials are a pervasive, versatile medium for teaching programming. In this paper, we report on the content and structure of programming tutorials, the pain points authors experience in writing them, and a design for a tool to help improve this process. An interview study with 12 experienced tutorial authors found that they construct documents by interleaving code snippets with text and illustrative outputs. It also revealed that authors must often keep related artifacts of source programs, snippets, and outputs consistent as a program evolves. A content analysis of 200 frequently-referenced tutorials on the web also found that most tutorials contain related artifacts—duplicate code and outputs generated from snippets—that an author would need to keep consistent with each other. To address these needs, we designed a tool called Torii with novel authoring capabilities. An in-lab study showed that tutorial authors can successfully use the tool for the unique affordances identified, and provides guidance for designing future tools for tutorial authoring.