This repository is a Claude Code/Pi skills framework for academic research and writing. It packages markdown skills, agent definitions, and Python tooling that together support the full research pipeline: topic discovery, deep research, paper writing, review, citation verification, and meta-analysis. The dominant character is application/tooling: it has CLI entry points, a Pi wrapper, dozens of lint/check scripts, and a large test suite. It is not a data pipeline, though it includes network-backed citation lookup scripts and research-oriented data-integrity checks.
This repository is a curated awesome list and survey companion for diffusion models applied to time series, spatio-temporal, and tabular data. It contains no executable code; the sole content is a large README.md that organizes hundreds of papers into a taxonomy by task (prediction, generation, imputation, anomaly detection, classification/regression, causal inference, event prediction, foundation models) and by application domain (healthcare, sequential recommendation, weather, energy, finance, AIOps, environment, traffic, manufacturing, audio, biology, communication). Each entry links to the paper and, where available, official code repositories. It is the companion to an ACM Computing Surveys paper (DOI 10.1145/3783986) and includes citation information, a Google Sheets taxonomy, and an overview figure. The repository is actively maintained with recent commits and welcomes pull requests. As a research resource, it is highly reproducible: all content is in the markdown file, with no dependencies, datasets, or environment setup required. Although classified as data_research due to its research orientation, it is not a data pipeline or ML codebase. Its value is as a reference index and literature map for researchers; the README itself is the reusable asset.
This repository is a curated 'awesome list' of research resources on multimodal learning in medical imaging. It contains no application code, tests, or data pipelines; its sole substantive file is a large README.md that catalogs papers, datasets, benchmarks, and code repositories. Topics span medical vision-language models, report generation, visual question answering, retrieval-augmented generation (RAG), and multimodal LLMs. The list is organized by task and includes links to arXiv PDFs, GitHub code, and dataset sources, with badges indicating publication venues.
This repository is a curated, comprehensive list of academic papers on the application of deep learning to protein design. It is not a software application or a data pipeline; rather, it is a living bibliography organized by research area (e.g., inverse folding, diffusion-based design, language models, antibody design). The README.md file contains hundreds of paper entries with links to publications, code repositories, and supplementary materials, updated frequently (last update shown as 2026.08.02). There is no executable code, no datasets, and no computational workflows. The value lies in its role as a reference index for researchers and practitioners tracking the rapidly evolving field of AI-driven protein design.
This repository is a literature survey on recent advances in large language model benchmarks against data contamination, specifically the transition from static to dynamic evaluation. It contains a single README.md file that lists and categorizes numerous research papers, benchmarks, and methods. There is no executable code, data pipelines, notebooks, or any software artifacts. The repository serves as a curated reference list and taxonomy for researchers, but offers no reusable code or data assets. It is not a functional application or a data research project with ETL or model training components.