DeepConnect：用于搭建跨学科研究合作的可视分析系统
DeepConnect: A Visual Analytics System for Bridging Interdisciplinary Research Collaborations
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中文总结 AI 辅助
DeepConnect是一种LLM增强的可视分析系统，可解决跨学科合作启动难的问题，通过转化合作目标、检索论文、可视化比较研究者等功能，经评估可用于互补团队组建等场景。
中文摘要 AI 辅助
跨学科研究合作对科学创新至关重要，但在实践中仍难以启动。现有的合作者发现方法往往受限于学科边界和静态研究者档案，无法反映新合作目标的具体情境。因此，研究者难以将开放式合作目标转化为特定领域任务、评估候选研究者的适配性与互补性，以及在初次接触前建立共同基础。为应对这些挑战，我们提出DeepConnect，一种大语言模型（LLM）增强的跨学科合作者发现可视分析系统。DeepConnect将合作想法转化为特定领域任务，检索相关论文以支撑跨领域探索，并提供协调的可视化工具以探索和比较候选研究者。它还揭示跨领域的术语差距与重叠，支持基于论文的对话演练，帮助用户为 outreach（ outreach 在此语境指学术合作拓展）做准备。我们通过两个案例研究、一项用户研究和一项组件级评估对DeepConnect进行评估，结果表明其在互补团队组建、想法细化和初次接触准备方面具有价值。DeepConnect网站可通过此https URL访问。
英文摘要
Interdisciplinary research collaboration is crucial for scientific innovation, but it remains difficult to initiate in practice. Existing collaborator discovery approaches are often constrained by disciplinary boundaries and static researcher profiles that do not reflect the specific context of a new collaboration goal. As a result, researchers struggle to translate open-ended collaboration goals into domain-specific tasks, evaluate candidate researchers' fit and complementarity, and establish common ground before initial contact. To address these challenges, we present DeepConnect, an LLM-augmented visual analytics system for interdisciplinary collaborator discovery. DeepConnect translates collaboration ideas into domain-specific tasks, retrieves relevant papers to ground cross-domain exploration, and provides coordinated visualizations for exploring and comparing candidate researchers. It further reveals terminology gaps and overlaps across domains and supports publication-grounded conversation rehearsal to help users prepare for outreach. We evaluate DeepConnect through two case studies, a user study, and a component-level evaluation, showing its value for complementary team formation, idea refinement, and pre-contact preparation. The DeepConnect website is available at https://deepconnect.sg.
