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<h1 class="title-article" id="articleContentId">MinerU 团队新产品 | Sciverse 科学数据基座，把可信的科学知识接入你的 AI 工作流</h1>
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<div class="up-time">最新推荐文章于 2026-09-21 18:18:29 发布</div>
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<article class="baidu_pl">
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<p>Auto-Research 正在成为科研 AI 里热门方向之一。</p>
<p>它把科研 AI 从“单次问答”推向“自动化工作流”：让 Agent 围绕一个研究问题，连续完成文献检索、论文筛选、原文读取、证据整理、方法对比和综述生成，以此帮助研究者更快找到关键证据，更快推进科学发现。</p>
<p>但当科研 AI 进入这样的工作方式，底层数据能力就变得很关键。没有可信、完整、可追溯的科研数据基座，AI 很容易生成“看起来像科研”的内容，根本无法保证结论可靠、实验可复现、创新可验证。</p>
<p>现在，<a class="link-info" href="https://sciverse.space" title="Sciverse " rel="nofollow">Sciverse</a>来了！</p>
<p><a class="link-info" href="https://sciverse.space" title="Sciverse " rel="nofollow">Sciverse</a>是 MinerU 团队重磅打造的科研数据基座。</p>
<p>它将海量公开学术文献，处理为原生 Agent 友好的 AI-Ready 科学数据，提供元数据检索、语义证据片段、原文上下文和图表资源，让科研 AI 不再停留于模型记忆和零散搜索结果，而是能够读取论文、理解上下文、调用关键证据，并追溯每一个结论的来源，通过 API、Skills 灵活调用方式，进入科研产品、RAG 系统、Agent 工作流，以及 Claude、Cursor、Codex 等 AI 工具，让 Agent 能够基于真实、可验证、可追溯的科学数据完成研究。</p>
<p></p>
<p class="img-center"><img alt="图片" height="688" src="https://i-blog.csdnimg.cn/img_convert/f6440d7ad364934941a711f42bfd8e22.png" width="1080" /></p>
<p></p>
<p>快来试试：</p>
<p>⭐ Sciverse 科学数据基座官网：<a class="link-info" href="https://sciverse.space" title="https://sciverse.space" rel="nofollow">https://sciverse.space</a></p>
<p>🧾 Sciverse API/Skills 文档：<a class="link-info" href="https://sciverse.space/docs" title="https://sciverse.space/docs" rel="nofollow">https://sciverse.space/docs</a></p>
<p>📚 Cookbook：<a class="link-info" href="https://sciverse.space/docs#cookbook" title="https://sciverse.space/docs#cookbook" rel="nofollow">https://sciverse.space/docs#cookbook</a></p>
<p></p>
<p class="img-center"><img alt="图片" height="105" src="https://i-blog.csdnimg.cn/img_convert/567632152b2e625c04c953549817cd6a.png" width="900" /></p>
<h2><strong>Sciverse 提供什么？</strong></h2>
<p><a class="link-info" href="https://sciverse.space" title="Sciverse" rel="nofollow">Sciverse</a> 把论文、图书、专利、期刊、会议等公开的科学知识资产，处理成 Agent 可以直接调用的科学 AI-Ready 数据。其已覆盖：</p>
<p>● 4.66 亿条学术元数据</p>
<p>● 1.06 亿条图书记录</p>
<p>● 7000 万条专利记录</p>
<p>● 2,800 万余篇 AI-Ready 开放获取全文</p>
<p></p>
<p class="img-center"><img alt="图片" height="695" src="https://i-blog.csdnimg.cn/img_convert/828fa76fdf7ffb8c7a006004d04647e2.png" width="1080" /></p>
<p style="text-align:center">Sciverse 覆盖论文、图书、专利的知识总览</p>
<p></p>
<p>这些跨学科、跨语种、多来源科学知识数据，已经被清洗、解析、结构化和索引，并通过 T+1 增量更新持续进入可调用状态。</p>
<p>与传统数据库不同，<a class="link-info" href="https://sciverse.space" title="Sciverse " rel="nofollow">Sciverse</a>不只返回“文献结果列表”，而是提供一套可直接进入科研工作流的科学证据能力。基于 <a class="link-info" href="https://sciverse.space" title="Sciverse" rel="nofollow">Sciverse</a>，科研 Agent 可以调用结构化元数据、全文片段、章节、图表、引用上下文和证据位置，完成更多细粒度研究任务，包括但不限于：</p>
<p>● 跨文档方法对比；</p>
<p>● 实验条件和数据集定位；</p>
<p>● 图表证据查找；</p>
<p>● 指标和结论抽取；</p>
<p>● 技术路线梳理；</p>
<p>● 论文与专利技术表达对照；</p>
<p>● 带来源的综述草稿生成；</p>
<p>● 研究方向持续追踪。</p>
<p></p>
<p class="img-center"><img alt="图片" height="105" src="https://i-blog.csdnimg.cn/img_convert/ef5cd3fa0a8c514c77fcbf05e9415a79.png" width="900" /></p>
<h2><strong>六个核心 API：从检索到证据再到图表资源</strong></h2>
<p>在 Auto-Research 工作流中，科研 Agent 需要的不只是“搜到论文”，而是稳定完成证据发现、论文筛选、字段理解、原文核验和图表调用。</p>
<p><a class="link-info" href="https://sciverse.space" title="Sciverse " rel="nofollow">Sciverse</a>用六个核心 API 串起这条科研证据链路，为科研 Agent 工作流提供六类能力：从语义找证据，到结构化筛论文，再到论文关系扩展、原文校验和图表调用。</p>
<h3>1. agentic-search：语义证据检索</h3>
<p>当用户提出一个自然语言研究问题时，Agent 可以调用 <code>agentic-search</code> 获取相关文献中的可引用证据片段。</p>
<p><strong>适合场景：</strong>Scientific RAG；文献综述 Agent；观点证据检索；Citation Grounding；Evidence Pack 构建。</p>
<p><strong>典型链路：</strong><code>agentic-search -> content -> 带来源证据输出</code></p>
<h3>2. meta-search：结构化元数据检索</h3>
<p>当用户需要按年份、期刊、作者、学科、语言、引用数等条件筛选论文时，应使用 <code>meta-search</code>。</p>
<p><strong>适合场景：</strong>高级论文筛选；论文清单生成；系统综述初筛；研究方向趋势扫描；学术产品中的筛选器和排序功能。</p>
<p><strong>典型链路：</strong><code>meta-catalog -> meta-search -> 论文清单 / 统计分析</code></p>
<h3>3. meta-catalog：字段能力发现</h3>
<p><code>meta-catalog</code> 用于查看 <a class="link-info" href="https://sciverse.space" title="Sciverse" rel="nofollow">Sciverse</a> 当前支持哪些元数据字段、字段类型和字段能力。Agent 或前端应用不应硬编码未知字段，而应该先通过 <code>meta-catalog</code> 获取可用字段，再构造 <code>meta-search</code> 查询。</p>
<p><strong>适合场景：</strong>动态生成筛选器；检查字段是否可 filter / sort；避免 Agent 编造不存在的字段；构建稳定的高级检索体验。</p>
<p><strong>典型链路：</strong><code>meta-catalog -> meta-search</code></p>
<h3>4. meta-paper-relations：论文关系网络检索</h3>
<p>meta-paper-relations 用于基于一篇论文的 unique_id，分页获取它的引用关系、参考文献关系和相关工作关系。 当 Agent 已经通过 meta-search 找到目标论文后，可以继续调用 meta-paper-relations 扩展这篇论文的 citation network，获取 citations、references 或 related_works。</p>
<p>适合场景：引用网络分析；Related Work 扩展；系统综述滚雪球检索；从种子论文发现上游参考文献和下游引用论文；研究脉络追踪。</p>
<p>典型链路：meta-search -> meta-paper-relations -> 论文关系扩展 / 引用网络分析</p>
<h3>5. content：读取原文上下文</h3>
<p><code>content</code> 用于按 <code>doc_id + offset</code> 读取论文原文片段。当 <code>agentic-search</code> 返回一个 evidence chunk 后，Agent 可以继续调用 <code>content</code> 读取该片段前后的上下文，确认原文是否真正支持某个论点。</p>
<p><strong>适合场景：</strong>原文核验；论文阅读助手；综述证据扩展；Evidence Pack 构建；按段读取全文。</p>
<p><strong>典型链路：</strong><code>agentic-search -> content</code></p>
<h3>6. resource：获取 Figure / Table 资源</h3>
<p><code>resource</code> 用于获取论文中的 Figure / Table 等图表资源。Agent 可以先通过 <code>content</code> 读取全文 Markdown，从中提取图表路径，再调用 <code>resource</code> 获取图片或表格资源。</p>
<p><strong>适合场景：</strong>图表证据检索；多模态 RAG；实验结果图分析；论文图表问答；科研报告和演示材料生成。</p>
<p><strong>典型链路：</strong><code>agentic-search -> content -> resource</code></p>
<h3></h3>
<p class="img-center"><img alt="图片" height="105" src="https://i-blog.csdnimg.cn/img_convert/7b7f620662533d9d90562738ba797282.png" width="900" /></p>
<h2><strong>三条最常见的科研 AI 工作流</strong></h2>
<p>下面是三条最常见的工作流。</p>
<h3>工作流一：文献综述 Agent</h3>
<p>用户输入一个研究问题，例如：</p>
<pre><code>请帮我综述 2020-2024 年 Transformer 在蛋白质结构预测领域的应用进展。</code></pre>
<p>Agent 可以这样调用 Sciverse：</p>
<pre><code class="hljs">1. 调用 agentic-search，检索相关文献证据片段；
2. 对高相关片段调用 content，读取原文上下文；
3. 整理方法、实验条件、数据集、指标和结论；
4. 输出带` doc_id`、`offset`、`title、year` 等来源信息的综述草稿；
5. 用户或研究者再进行人工审阅和修改。</code></pre>
<p>* 需要注意的是，Sciverse 提供证据和上下文，不替代最终科研判断。最终综述仍应由研究者审阅。</p>
<h3>工作流二：Scientific RAG</h3>
<p>很多 RAG 系统最大的问题是：召回内容不够可信，或者回答中缺少可核查来源。</p>
<p>使用 Sciverse 后，Scientific RAG 可以这样工作：</p>
<pre><code class="hljs">1. 用户提出科学问题；
2. 系统调用 agentic-search 获取相关 evidence chunks；
3. 根据 score 和来源信息过滤证据；
4. 必要时调用 content 补充上下文；
5. LLM 基于证据生成回答；
6. 回答中保留引用编号、doc_id、offset 和论文标题。</code></pre>
<p>这样，LLM 的回答不再只是“看起来合理”，而是可以回到原文片段进行核查。</p>
<h3>工作流三：图表证据与多模态分析</h3>
<p>很多科研结论并不只存在于正文段落中，也存在于 Figure、Table、实验流程图和结果图中。</p>
<p>Sciverse 支持从文本证据进一步走向图表证据：</p>
<pre><code class="hljs">1. 调用 agentic-search 找到相关论文；
2. 调用 content 读取全文 Markdown；
3. 从 Markdown 中提取 Figure / Table 路径；
4. 调用 resource 获取图表资源；
5. 将图表交给多模态模型或科研应用分析；
6. 输出图表来源、论文标题、doc_id 和 file_name。</code></pre>
<p></p>
<p class="img-center"><img alt="图片" height="105" src="https://i-blog.csdnimg.cn/img_convert/cde61b5b37b2261e3872bf0b3d76663f.png" width="900" /></p>
<p><strong>Sciverse 能接入哪些场景？</strong></p>
<p><a class="link-info" href="https://sciverse.space" title="Sciverse" rel="nofollow">Sciverse</a> 可接入科研 Agent、RAG 系统、数据管线和学术产品，支撑从文献综述、知识库建设到技术情报、数据集构建和产品集成的科研 AI 工作流</p>
<h4>1. 文献综述 Agent</h4>
<p>围绕一个研究问题，Agent 调用 Sciverse 检索相关论文，读取全文片段，提取方法、实验条件、数据集、指标和结论，生成带来源证据的综述草稿。适用于研究方向入门、课题背景梳理、近期进展总结和论文写作前的资料准备。</p>
<p></p>
<h4>2. 科研知识库与 RAG 系统</h4>
<p>课题组或产品团队可以将 Sciverse 接入 RAG pipeline，为内部知识库、科研问答系统和论文阅读工具提供结构化科学数据源。系统不仅可以召回论文记录，也可以调用正文片段、证据位置和来源信息，提升回答的可核查性。</p>
<p></p>
<h4>3. 专利与技术情报分析</h4>
<p>Sciverse 覆盖论文与专利数据，可用于技术路线梳理、专利 claims 对照、竞争情报分析和前沿方向监测。适用于企业研发、技术转移、产业研究、专利分析和科研管理场景。</p>
<p></p>
<h4>4. 垂直领域数据集构建</h4>
<p>开发者可以基于 Sciverse 构建特定学科或任务的数据集，用于模型评测、知识抽取、领域微调、论文计量分析和学术产品开发。相比从零开始做论文获取、PDF 解析、元数据清洗和全文索引，Sciverse 提供了更完整的数据底座和可追溯来源。</p>
<p></p>
<h4>5. 学术产品与数据管线集成</h4>
<p>Sciverse 提供 API 与 Skill 接入能力，可集成到 AI 学术搜索、自动综述、论文阅读、专利分析、技术情报和 Research Agent 产品中。</p>
<p></p>
<p class="img-center"><img alt="图片" height="105" src="https://i-blog.csdnimg.cn/img_convert/48c21444ceb62d4842a51d11565e58d9.png" width="900" /></p>
<h2><strong>立即体验</strong></h2>
<p>Sciverse 已开放使用。快来试试~</p>
<p>⭐ Sciverse 科学数据基座官网：<em><strong><a class="link-info" href="https://sciverse.space/" title="https://sciverse.space/" rel="nofollow">https://sciverse.space/</a></strong></em></p>
<p>🧾 API/Skills 文档：<strong><a class="link-info" href="https://sciverse.space/docs" title="https://sciverse.space/docs" rel="nofollow">https://sciverse.space/docs</a></strong></p>
<p>📚 Cookbook：<strong><a class="link-info" href="https://sciverse.space/docs#cookbook" title="https://sciverse.space/docs#cookbook" rel="nofollow">https://sciverse.space/docs#cookbook</a></strong></p>
<p></p>
<p class="img-center"><img alt="图片" height="105" src="https://i-blog.csdnimg.cn/img_convert/1719ec83b0a4879afe797e36dbea0b45.png" width="900" /></p>
<p><strong>写在最后——Sciverse 的边界</strong></p>
<p>为了让科研 AI 工作流更可信，Sciverse 会坚持几个清晰边界：</p>
<p>● 提供科学数据、元数据、证据片段、全文上下文和图表资源；</p>
<p>● 不替代研究者做最终科学判断；</p>
<p>● 不保证每篇文献都有可读取全文或图表资源；</p>
<p>● 不鼓励 Agent 编造字段、引用或结论；</p>
<p>● 需要训练、批量处理或商业化集成时，应遵守数据来源、授权范围和平台账号权限。</p>
<p>Sciverse 的核心价值不是替用户“自动写完论文”，而是让科研 Agent 能够基于真实、可追溯、可调用的科学证据工作。</p>
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// 优先使用 HTML-CSS 而非 NativeMML
"HTML-CSS": {
preferredFont: "STIX", // 使用 STIX 字体
availableFonts: ["STIX", "TeX"],
webFont: "STIX-Web",
scale: 100, // 放大 20%
minScaleAdjust: 100,
linebreaks: {
automatic: true,
width: "90% container"
},
matchFontHeight: true,
styles: {
".MathJax": {
"font-weight": "500 !important" // 加粗
}
}
},
// 禁用 NativeMML（浏览器原生渲染质量不好）
NativeMML: {
scale: 100
},
// 设置优先级：HTML-CSS > SVG > NativeMML
menuSettings: {
renderer: "HTML-CSS"
},
messageStyle: "none"
});
MathJax.Hub.Queue(["Typeset", MathJax.Hub]);
</script>
<script src="https://g.csdnimg.cn/static/mathjax-2.7.9/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script>
<script type="text/javascript" crossorigin src="https://g.csdnimg.cn/common/csdn-login-box/csdn-login-box.js"></script></html>
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