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search Multiple Types of Research Assets From Jupyter notebook

SOFTWARE-PRACTICE & EXPERIENCE 2025年第5期55卷 946-965页

作者： Li, Na Farshidi, Siamak Zhao, Zhiming Univ Amsterdam Multiscale Networked Syst Amsterdam Netherlands Wageningen Univ & Res WUR Informat Technol Grp Wageningen Netherlands Virtual Lab & Innovat Ctr VLIC LifeWatch ERIC Amsterdam Netherlands

ObjectiveData science and machine learning methodologies are essential to address complex scientific challenges across various domains. These advancements generate numerous research assets such as datasets, software tools, and workflows, which are shared within the open science community. Concurrently, computational notebook environments like Jupyter notebook, along with platforms like Google Colab and Kaggle Kernel, facilitate data science research and machine learning workflows, transforming data analysis, model development, and knowledge sharing processes. The proliferation of computational notebooks has further enriched the pool of valuable research assets. Researchers frequently require efficient access to these assets to advance their work, yet current tools often require navigating multiple websites and portals, leading to inefficiency and information overload. The challenge is compounded when relying on general web search engines that might not adequately highlight niche scientific *** address these issues, we propose the development of an innovative Multiple Research Asset search (MRAS) system designed to index diverse research assets from heterogeneous sources, offering a unified search interface for researchers. Our system aims to significantly improve the discovery of computational notebooks and datasets, facilitating data-driven *** developed a pipeline for data extraction and indexing, reviewed and applied state-of-the-art ranking algorithms, enhanced indexing documents with content analysis, and created a Jupyter extension for asset discovery within the working *** work is structured to detail our approach, literature review, system development, empirical validation, results, and conclusions, illustrating the potential impact of our MRAS system on scientific research efficiency.

关键词： computational notebook search data discovery data science dataset search Jupyter notebook Multiple Research Asset search

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CNSVRE: A Query Reformulated search System with Explainable Summarization for Virtual Research Environment

CNSVRE: A Query Reformulated Search System with Explainable ...

引用

32nd World Wide Web Conference (WWW)

作者： Li, Na Zhang, Yangjun Zhao, Zhiming Univ Amsterdam Informat Inst Amsterdam Netherlands

ISBN: (纸本)9781450394161

computational notebook environments have drawn broad attention in data-centric research applications, e.g., virtual research environment, for exploratory data analysis and algorithm prototyping. Vanilla computational notebook search solutions have been proposed but they do not pay much attention to the information needs of scientific researchers. Previous studies either treat computational notebook search as a code search problem or focus on content-based computational notebook search. The queries being considered are neither research-concerning nor diversified whereas researchers' information needs are highly specialized and complex. Moreover, relevance evaluation for computational notebooks is tricky and unreliable since computational notebooks contain fragments of text and code and are usually poorly organized. To solve the above challenges, we propose a computational notebook search system for virtual research environment (VRE), i.e., CNSVRE, with scientific query reformulation and computational notebook summarization. We conduct a user study to demonstrate the effectiveness, efficiency, and satisfaction with the system.

关键词： computational notebook search virtual research environment scientific query reformulation code summarization

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