Overview
Open-Science is an open source AI scientific research workbench launched by AIPOCH. It is aimed at researchers who need to continuously check information, process data and organize results around the topic. It undertakes research tasks on a project basis, keeps the background of the problem relevant to subsequent work, and reduces repeated switching between chat tools, computing environments, and folders. The software is available in macOS, Windows and Linux desktop versions and can connect to different model services or compatible local models. Project data is stored on the local machine by default, and data may be sent out when calling external models, scientific research services, or remote computing power. It emphasizes that the research process can be checked, and the analysis conclusions still need to be verified by the researcher. Core function: Scientific research data retrieval: Connect literature and scientific research databases, allowing research tasks to call data sources in corresponding fields, and collect reference information for reviews and topic analysis. Code analysis and calculations: Run Python or R in the workspace, retain analysis status, and combine data processing, calculations, and charting in the same research task. Division of special tasks: Let the main agent hand over a limited range of work to a dedicated agent, and call tools according to established permissions to assist in advancing multi-step research tasks. Research product tracing: View the input, code and execution records associated with reports, tables or graphics, and use the review function to locate missing evidence and content inconsistencies. Research project packaging: Export selected records into a.science package for review of results, task handover, or continuing existing research on another computer.
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Open-Science 是 AIPOCH 推出的开源 AI 科研工作台，面向需要围绕课题持续查资料、处理数据并整理成果的研究人员。它以项目为单位承接研究任务，让问题背景与后续工作保持关联，减少在聊天工具、计算环境和文件夹之间反复切换。软件提供 macOS、Windows 和 Linux 桌面版本，可连接不同模型服务或兼容的本地模型。项目资料默认存储在本机，调用外部模型、科研服务或远程算力时可能向外发送数据。它强调研究过程可检查，分析结论仍需研究者验证。核心功能科研资料检索：连接文献与科研数据库，让研究任务调用对应领域的资料来源，为综述和课题分析收集参考信息。代码分析与计算：在工作区运行 Python 或 R，保留分析状态，将数据处理、计算和图表制作接在同一研究任务中。专项任务分工：让主智能体将限定范围的工作交给专用智能体，并按既定权限调用工具，协助推进多步骤研究任务。研究产物追溯：查看报告、表格或图形关联的输入、代码与执行记录，结合审查功能定位缺失证据和内容不一致。研究项目打包：将选定记录导出为 .science 包，用于成果审阅、任务交接，或在另一台电脑上继续已有研究。