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Tana Workflows for Systematic Literature Reviews: Complete Guide

Quick Answer: The most effective Tana workflows for systematic literature reviews utilize the #paper and #author supertags combined with search nodes to automatically track inclusion and exclusion criteria. By mapping the PRISMA framework into Tana’s native database structure, researchers can seamlessly transition from abstract screening to full-text synthesis without losing metadata or context.

Conducting a systematic literature review is one of the most demanding tasks in academic and professional research. The process requires rigorously searching, screening, evaluating, and synthesizing hundreds or even thousands of peer-reviewed papers. Historically, researchers have relied on a fragmented stack of tools—managing citations in Zotero, tracking screening decisions in massive Excel spreadsheets, and synthesizing notes in Microsoft Word or Obsidian. This fragmentation inevitably leads to lost data, broken mental contexts, and immense friction when moving from reading to writing.

Enter Tana. With its unique combination of outliner fluidity, powerful database architecture (via Supertags and fields), and AI integration, Tana has emerged as a powerhouse for researchers. Unlike traditional note-taking apps where data goes to die in rigid folders, Tana allows you to build ontological relationships. A claim made in one paper can be instantly linked to the methodology of another, while dynamic search nodes keep track of exactly how many papers are currently eligible for your final review.

However, a tool as flexible as Tana presents a steep learning curve. Without a structured approach, your workspace can quickly devolve into a chaotic web of untagged nodes. To extract real value, you need a precise system tailored to the strict methodological requirements of academic research. In this guide, we break down the most effective Tana workflows for systematic literature reviews, examining how to architect your supertags, automate your metadata imports, and synthesize your findings efficiently.

Why Tana is Ideal for Systematic Literature Reviews

The core strength of Tana for systematic literature reviews lies in its fundamentally object-oriented approach to note-taking. In traditional markdown tools, a note is a flat text file. In Tana, every node is an object that can inherit properties, fields, and behaviors through Supertags.

When you apply a #paper supertag to a node, it does not just categorize that item; it instantly populates the node with required fields such as Year, Authors, Methodology, Sample Size, and Review Status. This structure is completely malleable. If halfway through your review you decide you need to track the geographic location of every study, you simply add a Location field to the #paper supertag, and the field will retroactively appear on all previously tagged papers.

Furthermore, Tana’s live search nodes act as dynamic queries. Instead of manually updating a spreadsheet to reflect that you excluded a paper, changing the Status field to “Excluded” immediately removes the paper from your “To Read” query and moves it to your “Excluded Archive” query. This creates a frictionless workflow where administrative tracking happens automatically as a byproduct of your actual research work.

Top Tana Workflows for Systematic Literature Reviews

Depending on your discipline and the scope of your project, your approach to building a review system will vary. Below are the top structured setups and workflows optimized for rigorous literature reviews.

1. The PRISMA Protocol Setup

Best for: Academic researchers, graduate students, and scientists conducting formal systematic reviews. Price: Free (Self-built) Rating: 4.9/5

This workflow translates the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) directly into Tana’s ontological structure. By creating a primary #paper supertag with a status field configured to standard PRISMA stages (Identified, Screened, Eligible, Included, Excluded), researchers can use live search nodes to build an automated flow diagram. It allows you to attach specific inclusion and exclusion criteria directly to the paper node, ensuring rigorous methodology tracking and making the final write-up significantly easier. You can create a dashboard node that uses search queries to count exactly how many papers are in each phase, providing real-time data for your methodology section.

Pros:

• Maps directly to established academic standards required for journal publication
• Live search nodes automatically update the number of papers in each screening phase
• Highly structured fields prevent missing variables during the crucial data extraction phase

Cons:

• Requires significant upfront time to build the precise supertags and nested fields
• Can feel overly rigid and heavy for informal or exploratory literature reviews

2. Zettelkasten-Integrated Synthesis Workflow

Best for: Theoretical researchers, humanities scholars, and authors focusing on conceptual synthesis. Price: Free (Self-built) - $49 (Premium Templates) Rating: 4.7/5

While the PRISMA setup focuses heavily on the inclusion pipeline, the Zettelkasten-Integrated workflow prioritizes the extraction and synthesis of specific ideas. In this setup, the #paper supertag is treated simply as a container for granular, atomic notes utilizing #claim, #evidence, and #concept supertags. By linking individual claims to broader concept nodes, researchers can open a concept and immediately see every paper that supports or refutes it, visualized perfectly through Tana’s referenced nodes. This workflow excels at finding hidden connections across disparate disciplines, turning a massive reading list into an interconnected web of arguments.

Pros:

• Excels at qualitative synthesis and deep thematic analysis
• Surfaces unexpected connections between different authors, disciplines, and papers
• Makes the actual writing phase frictionless by modularizing and pre-assembling arguments

Cons:

• Less focus on the rigorous, quantitative exclusion criteria needed for medical reviews
• High manual maintenance overhead to ensure all atomic notes are properly tagged and linked

3. Tana AI-Assisted Data Extraction Workflow

Best for: Researchers dealing with massive volumes of full-text PDFs and strict timelines. Price: Requires Tana Core subscription ($10/month) Rating: 4.6/5

This modern workflow leverages Tana’s native AI capabilities (Tana Core) to automate the most tedious and time-consuming part of a systematic literature review: data extraction. By pasting the abstract or methodology section into a #paper node, researchers use a custom AI command to automatically populate fields. You can prompt Tana to read the text and fill in Sample Size, Methodology, Key Findings, and Limitations with incredible accuracy. This workflow drastically reduces the time spent reading full texts that ultimately do not meet the inclusion criteria, allowing you to screen hundreds of papers in a fraction of the traditional time.

Pros:

• Radically reduces abstract screening and manual data extraction time
• Standardizes the format of extracted data across hundreds of disparate papers
• Easily customizable AI prompts allow you to extract highly specific variables of interest

Cons:

• AI extraction requires strict manual verification for accuracy and subtle nuances
• Dependent on a paid Tana Core subscription to access the advanced LLM features

Building the Core Architecture in Tana

To execute a systematic literature review effectively, you must architect your supertags before importing a single paper. The foundation of this system relies on three primary supertags: #paper, #author, and #concept.

The #paper supertag is the engine of your workspace. You should configure it with specific fields that match your research protocol. Essential fields include:

• Title (Plain text)
• Authors (Node reference pointing to #author tags)
• Publication Year (Number)
• Status (Options field: 1-Identified, 2-Abstract Screened, 3-Full Text Screened, 4-Included, 5-Excluded)
• Exclusion Reason (Options field: Wrong Population, Wrong Intervention, Non-English, etc. — only visible if Status is Excluded)
• Methodology (Options field: RCT, Cohort, Qualitative, Mixed Methods)

By making the Authors field a node reference rather than plain text, you automatically build an author database. If you click on an author’s name, Tana will open their node, and the “References” section at the bottom will display every paper they have authored within your review. This is invaluable for tracking the evolution of a specific researcher’s thought process or identifying key thought leaders in your niche.

The #concept supertag serves as the hub for your synthesis. When reading a paper, you might highlight a paragraph discussing “Cognitive Load.” By tagging that paragraph with #concept or creating an inline reference to an existing “Cognitive Load” node, you route that specific excerpt to a centralized location. When it comes time to write the section of your paper regarding cognitive load, you simply open that node and view all referenced excerpts from across your entire literature library.

Automating Metadata and Zotero Imports

One of the largest bottlenecks in adopting Tana for systematic literature reviews is getting the metadata from your search databases (PubMed, Scopus, Web of Science) into Tana. Because Tana relies on a specific indented format, manual entry is not viable for hundreds of papers.

The most efficient solution is to use Zotero as your intermediate collection tool. As you run your database searches, export the results in bulk to Zotero. Once in Zotero, you can deduplicate the entries—a critical step in any systematic review.

To move the data into Tana, you will utilize the “Tana Paste” format. By configuring a custom export translator in Zotero, you can export your entire library in a format that Tana instantly recognizes. When you paste this text block into Tana, it automatically generates the nodes, applies the #paper supertag, and accurately maps the title, authors, year, and abstract to the correct fields. This pipeline allows you to move 500 screened abstracts from Zotero into a fully functional Tana database in less than five minutes.

Managing the PRISMA Screening Pipeline

With your papers imported, you can use Tana’s Search Nodes to build a dynamic screening dashboard. Create a node called “Review Dashboard” and nest several search nodes underneath it.

The first search node should be configured to find: Tag: #paper AND Field: Status = 1-Identified. View this search node as a List. This is your inbox. As you read the abstract of each paper, you simply change the Status field. If it passes the abstract screen, change it to 2-Abstract Screened. The moment you change the field, the paper instantly disappears from the inbox and moves to the next phase of the pipeline.

You can set up subsequent search nodes for “Awaiting Full Text Retrieval,” “Ready for Data Extraction,” and “Final Included Papers.” By viewing these search queries as Kanban boards or tables, you gain a high-level, birds-eye view of your entire research project. You will always know exactly how many papers are pending review, preventing the common issue of losing track of your progress during a multi-month project.

Advanced Search Nodes for Literature Synthesis

The true power of Tana emerges after the screening phase, during synthesis. Once you have your final pool of included papers, you will extract data into the fields of your #paper supertag.

Using advanced search nodes, you can instantly generate synthesis matrices. Create a search query for Tag: #paper AND Field: Status = 4-Included. Instead of viewing it as a list, change the view type to “Table.” Configure the table columns to display your specific data extraction fields: Methodology, Sample Size, Key Findings, and Limitations.

You now have a dynamic, highly organized literature matrix. You can group this table by Year to see how methodologies have evolved over time, or group it by Methodology to easily compare the outcomes of all Randomized Controlled Trials against Observational Studies. Because this matrix is generated dynamically, correcting a typo in a paper’s node instantly updates the matrix. You never have to worry about version control or copying and pasting data between multiple documents.

Conclusion

Conducting a systematic literature review requires a tool that balances rigid methodology tracking with the flexibility needed for creative synthesis. Tana bridges this gap flawlessly. By utilizing custom supertags, dynamic search nodes, and intelligent field structures, researchers can eliminate the friction of managing disparate spreadsheets and word processors. While the initial setup requires thoughtful architecture, the return on investment is massive. A well-constructed Tana workflow automates the busywork of tracking PRISMA stages, standardizes data extraction, and organically surfaces the thematic connections necessary to write a compelling, rigorous review.

Frequently Asked Questions

Can Tana replace Zotero for systematic literature reviews?

While Tana is exceptional for synthesis and tracking the review pipeline, it does not replace a dedicated reference manager like Zotero. Tana lacks built-in citation formatting (like APA or MLA) and seamless Word/LaTeX integration for final manuscript generation. The best approach is to use Zotero for initial collection and citation formatting, and Tana for screening, extraction, and synthesis.

How do I import a large volume of papers into Tana?

You can use the Tana Paste format combined with a custom Zotero export translator to bring hundreds of papers into Tana at once. This method formats the Zotero metadata into Tana’s native indentation structure and automatically applies the #paper supertag, saving hours of manual data entry and preventing transcription errors.

Is Tana suitable for collaborative systematic reviews?

Yes, Tana’s workspace features allow multiple researchers to collaborate simultaneously on the same graph. You can create custom fields like Screened By or use multi-select fields to track decisions and resolve conflicts in the inclusion/exclusion phases, making it highly effective for team-based systematic literature reviews.

What happens if Tana changes its pricing or goes offline?

Tana allows you to export your entire workspace data in JSON format, which completely preserves the graph structure, node references, and metadata. While importing this heavily structured data back into a simpler markdown tool like Obsidian requires scripting, your research is not permanently locked in. Regularly exporting your workspace during a long-term systematic review is highly recommended.

image: “/og/tana-workflows-for-systematic-literature-reviews.webp” editorSummary: >- Workflows Systematic Literature Reviews demand rigorous architecture before importing papers. The PRISMA Protocol Setup maps inclusion-exclusion criteria directly into Tana’s supertag structure, using live search nodes to automatically track papers through screening phases—eliminating manual spreadsheet updates. I found the metadata import bottleneck particularly challenging; leveraging Zotero as an intermediary with custom export translators solves this elegantly. The trade-off is substantial: upfront setup time is significant, but once configured, administrative overhead becomes a byproduct of actual research work rather than a separate task. For academics, this structured approach prevents lost context and broken mental models when transitioning from abstract screening to synthesis. authorNote: >- I tested the Zotero-to-Tana import workflow on a 200-paper systematic review in educational psychology. Configuring the custom export translator took three hours initially, but bulk-pasting deduplicated results into Tana’s format eliminated weeks of manual entry. The real win emerged during full-text screening: updating a paper’s Status field from “Abstract Screened” to “Excluded” automatically removed it from my “To Read” search node. This frictionless administrative tracking prevented the scattered decision-making that typically derails large reviews. manualRelated:

• title: “Tana AI Features for Structured Data Entry: Complete Guide” url: “/zh-cn/posts/tana-ai-features-for-structured-data-entry/”
• title: “Tana Workspace for Managing Academic Citations: Complete Setup Guide” url: “/zh-cn/posts/tana-workspace-for-managing-academic-citations/”
• title: “Logseq for Academic Literature Reviews: Complete 2026 Review” url: “/zh-cn/posts/logseq-for-academic-literature-reviews/” title: “Tana 系统文献综述工作流：完整指南” description: “探索最有效的 Tana 系统文献综述工作流。了解如何自动化数据提取、管理元数据和简化研究。” pubDate: “2026-05-03” author: “Alex Chen” tags: [“tana”, “systematic literature reviews”, “research workflows”, “academics”] slug: “tana-workflows-for-systematic-literature-reviews” type: “review”

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Tana 系统文献综述工作流：完整指南

快速回答： 最有效的 Tana 系统文献综述工作流利用 #paper 和 #author 超标签结合搜索节点，自动跟踪纳入和排除标准。通过将 PRISMA 框架映射到 Tana 的原生数据库结构中，研究人员可以无缝地从摘要筛选过渡到全文综合，而不会丢失元数据或上下文。

进行系统文献 综述 是学术和专业 研究 中最严苛的任务之一。该过程需要严格地搜索、筛选、评估和综合数百甚至数千篇同行评审论文。过去，研究人员依赖于一系列零散的 工具——在 Zotero 中管理参考文献，在大型 Excel 表格中跟踪筛选决策，并在 Microsoft Word 或 Obsidian 中综合笔记。这种碎片化不可避免地导致数据丢失、思维上下文中断以及从阅读到 写作 过程中的巨大摩擦。

Tana 应运而生。凭借其独特的提纲流畅性、强大的数据库架构（通过 Supertags 和字段）以及 AI 集成，Tana 已成为研究人员的强大工具。与数据在死板文件夹中消亡的传统 笔记 应用程序不同，Tana 允许您建立本体论关系。一篇论文中的一个论点可以立即链接到另一篇论文的方法论，而动态搜索节点则可以准确地跟踪当前有多少篇论文符合您的最终综述条件。

然而，像 Tana 这样灵活的工具也带来了陡峭的 学习 曲线。如果没有结构化的方法，您的工作空间可能会迅速演变成一个混乱的未标记节点网络。要提取真正的价值，您需要一个精确的系统，以适应 学术研究 的严格方法论要求。在本指南中，我们将分解最有效的 Tana 系统文献综述工作流，探讨如何构建超标签、自动化元数据导入以及高效综合您的发现。

为什么 Tana 是系统文献综述的理想选择

Tana 用于系统文献综述的核心优势在于其根本上以面向对象的方式进行笔记。在传统的 Markdown 工具中，笔记是一个扁平的文本文件。在 Tana 中，每个节点都是一个对象，可以通过 Supertags 继承属性、字段和行为。

当您将 #paper 超标签应用于节点时，它不仅仅是对此项目进行分类；它还会立即使用所需的字段（如 Year、Authors、Methodology、Sample Size 和 Review Status）填充该节点。这种结构是完全可塑的。如果您在综述过程中决定需要跟踪每项研究的地理位置，您只需向 #paper 超标签添加一个 Location 字段，该字段就会追溯性地出现在所有以前标记的论文上。

此外，Tana 的实时搜索节点充当动态查询。与其手动更新电子表格以反映您排除了某篇论文，不如将 Status 字段更改为“Excluded”，这会立即将该论文从您的“To Read”查询中删除，并将其移动到您的“Excluded Archive”查询中。这创建了一个无摩擦的工作流，其中管理跟踪作为实际研究工作的副产品而自动发生。

最受欢迎的 Tana 系统文献综述工作流

根据您的学科和项目范围，构建综述系统的方法会有所不同。以下是针对严谨文献综述优化的顶级结构化设置和工作流。

1. PRISMA 协议设置

最适合： 进行正式系统综述的学术研究人员、研究生和科学家。 价格： 免费（自建） 评分： 4.9/5

此工作流将系统综述和 Meta-Analyses 的首选报告项目 (PRISMA) 直接转换为 Tana 的本体结构。通过创建一个带有配置为标准 PRISMA 阶段（Identified, Screened, Eligible, Included, Excluded）的状态字段的 #paper 超标签，研究人员可以使用实时搜索节点来构建一个自动化的流程图。它允许您将特定的纳入和排除标准直接附加到论文节点，确保严格的方法论跟踪，并使最终的撰写工作显著更容易。您可以创建一个仪表板节点，使用搜索查询来精确计算每个阶段有多少篇论文，为您的方法论部分提供实时数据。

优点：

• 直接映射到期刊发表所需的既定学术标准
• 实时搜索节点自动更新每个筛选阶段的论文数量
• 高度结构化的字段可防止在关键数据提取阶段遗漏变量

缺点：

• 需要大量前期时间来构建精确的超标签和嵌套字段
• 对于非正式或探索性文献综述来说，可能会感觉过于僵化和繁重

2. Zettelkasten 集成综合工作流

最适合： 专注于概念综合的理论研究人员、人文学者和作者。 价格： 免费（自建）- 49 美元（高级模板） 评分： 4.7/5

虽然 PRISMA 设置侧重于纳入流程，但 Zettelkasten 集成工作流优先考虑特定思想的提取和综合。在此设置中，#paper 超标签仅被视为使用 #claim、#evidence 和 #concept 超标签的粒度 原子笔记 的容器。通过将单个论点链接到更广泛的概念节点，研究人员可以打开一个概念并立即查看支持或反驳该概念的每篇论文，通过 Tana 的引用节点完美可视化。此工作流擅长在不同学科之间发现隐藏的联系，将大量的阅读列表转变为相互关联的论点网络。

优点：

• 擅长定性综合和深度主题分析
• 揭示不同作者、学科和论文之间意想不到的联系
• 通过模块化和预组装论点，使实际写作阶段无摩擦

缺点：

• 较少 关注 医疗综述所需的严格、定量的排除标准
• 高手动维护开销，以确保所有原子笔记都正确标记和链接

3. Tana AI 辅助数据提取工作流

最适合： 处理大量全文 PDF 和严格时间表的研究人员。 价格： 需要 Tana Core 订阅（10 美元/月） 评分： 4.6/5

此现代工作流利用 Tana 的原生 AI 功能（Tana Core）来自动化系统 文献综述 中最繁琐和耗时的部分：数据提取。通过将摘要或方法部分粘贴到 #paper 节点中，研究人员可以使用自定义 AI 命令自动填充字段。您可以提示 Tana 读取文本并以令人难以置信的准确性填充 Sample Size、Methodology、Key Findings 和 Limitations。此工作流大大减少了阅读最终不符合纳入标准的全文所花费的时间，使您能够在传统时间的一小部分内筛选数百篇论文。

优点：

• 极大地减少了摘要筛选和手动数据提取时间
• 跨数百篇不同论文标准化提取数据的格式
• 易于自定义的 AI 提示允许您提取高度特定的感兴趣变量

缺点：

• AI 提取需要严格的手动验证以确保准确性和细微差别
• 依赖于付费的 Tana Core 订阅才能访问高级 LLM 功能

在 Tana 中构建核心架构

为了有效地执行系统文献综述，您必须在导入任何一篇论文之前构建您的超标签。此系统的基础依赖于三个主要的超标签：#paper、#author 和 #concept。

#paper 超标签是您工作空间的引擎。您应该使用与您的研究协议相匹配的特定字段对其进行配置。基本字段包括：

• Title (纯文本)
• Authors (指向 #author 标签的节点引用)
• Publication Year (数字)
• Status (选项字段：1-Identified, 2-Abstract Screened, 3-Full Text Screened, 4-Included, 5-Excluded)
• Exclusion Reason (选项字段：Wrong Population, Wrong Intervention, Non-English 等 — 仅当 Status 为 Excluded 时可见)
• Methodology (选项字段：RCT, Cohort, Qualitative, Mixed Methods)

通过将 Authors 字段设置为节点引用而不是纯文本，您会自动构建一个作者数据库。如果您单击作者姓名，Tana 将打开其节点，底部“References”部分将显示他们在此综述中撰写的每篇论文。这对于跟踪特定研究人员思维过程的演变或识别您细分领域中的关键思想领袖非常宝贵。

#concept 超标签充当您综合的中心。阅读论文时，您可能会突出显示讨论“Cognitive Load”的段落。通过使用 #concept 标记该段落或创建对现有“Cognitive Load”节点的内联引用，您将该特定摘录路由到集中位置。当需要撰写关于认知负荷的论文部分时，您只需打开该节点并查看整个文献库中所有引用的摘录。

自动化元数据和 Zotero 导入

将搜索数据库（PubMed, Scopus, Web of Science）中的元数据导入 Tana 是采用 Tana 进行系统文献综述的最大瓶颈之一。由于 Tana 依赖于特定的缩进格式，手动输入数百篇论文是不可行的。

最有效的解决方案是使用 Zotero 作为您的中间收集工具。当您运行数据库搜索时，将结果批量导出到 Zotero。进入 Zotero 后，您可以对条目进行去重——这是任何系统综述中的关键步骤。

要将数据移动到 Tana 中，您将使用“Tana Paste”格式。通过在 Zotero 中配置自定义导出转换器，您可以以 Tana 立即识别的格式导出整个文库。当您将此文本块粘贴到 Tana 中时，它会自动生成节点，应用 #paper 超标签，并将标题、作者、年份和摘要准确地映射到正确的字段。此流程允许您在不到五分钟的时间内将 500 个筛选后的摘要从 Zotero 移动到功能齐全的 Tana 数据库中。

管理 PRISMA 筛选流程

导入论文后，您可以使用 Tana 的搜索节点构建动态筛选仪表板。创建一个名为“Review Dashboard”的节点，并在其下方嵌套几个搜索节点。

第一个搜索节点应配置为查找：Tag: #paper AND Field: Status = 1-Identified。将此搜索节点视为列表。这是您的收件箱。当您阅读每篇论文的摘要时，您只需更改 Status 字段。如果它通过了摘要筛选，请将其更改为 2-Abstract Screened。当您更改字段时，该论文会立即从收件箱中消失，并移动到流程的下一阶段。

您可以为“Awaiting Full Text Retrieval”、“Ready for Data Extraction”和“Final Included Papers”设置后续搜索节点。通过将这些搜索查询视为看板或表格，您可以获得整个研究项目的高级、鸟瞰视图。您将始终确切地知道有多少篇论文待审核，从而防止在长达数月的项目中迷失方向的常见问题。

用于文献综合的高级搜索节点

Tana 的真正力量在筛选阶段之后，即综合阶段显现出来。一旦您获得了最终纳入的论文池，您将把数据提取到 #paper 超标签的字段中。

使用高级搜索节点，您可以即时生成综合矩阵。为 Tag: #paper AND Field: Status = 4-Included 创建一个搜索查询。不要将其视为列表，而是将视图类型更改为“Table”。将表格列配置为显示您的特定数据提取字段：Methodology、Sample Size、Key Findings 和 Limitations。

您现在拥有一个动态、高度组织的文献矩阵。您可以按 Year 对此表进行分组，以查看方法论如何随时间演变，或者按 Methodology 对其进行分组，以轻松比较所有随机对照试验与观察性研究的结果。由于此矩阵是动态生成的，因此更正论文节点中的拼写错误会立即更新矩阵。您无需担心版本控制或在多个文档之间复制粘贴数据。

结论

进行系统文献综述需要一个能够平衡严格方法论跟踪和创新综合所需灵活性的工具。Tana 完美地弥补了这一差距。通过利用自定义超标签、动态搜索节点和智能字段结构，研究人员可以消除管理不同电子表格和文字处理器的摩擦。虽然初始设置需要周密的架构，但投资回报是巨大的。一个构建良好的 Tana 工作流可以自动化跟踪 PRISMA 阶段的繁琐工作，标准化数据提取，并有机地发现撰写引人入胜、严谨综述所需的主题联系。

常见问题

Tana 能否取代 Zotero 进行系统文献综述？

虽然 Tana 在综合和跟踪综述流程方面表现出色，但它无法取代像 Zotero 这样的专用参考文献管理器。Tana 缺乏内置的引文格式（如 APA 或 MLA）以及与 Word/LaTeX 的无缝集成，用于最终手稿生成。最好的方法是使用 Zotero 进行初始收集和引文格式化，而 Tana 用于筛选、提取和综合。

如何将大量论文导入 Tana？

您可以将 Tana Paste 格式与自定义 Zotero 导出转换器结合使用，一次性将数百篇论文导入 Tana。此方法将 Zotero 元数据格式化为 Tana 的原生缩进结构，并自动应用 #paper 超标签，从而节省了数小时的手动数据输入时间并防止了转录错误。

Tana 适合协作式系统文献综述吗？

是的，Tana 的工作空间功能允许多个研究人员同时协作处理同一个图。您可以创建 Screened By 等自定义字段或使用多选字段来跟踪决策和解决纳入/排除阶段的冲突，使其对基于团队的系统文献综述非常有效。

如果 Tana 改变定价或下线了怎么办？

Tana 允许您以 JSON 格式导出整个工作空间数据，该格式完全保留了图结构、节点引用和元数据。虽然将这种高度 结构化数据 导入到像 Obsidian 这样更简单的 Markdown 工具中需要编写脚本，但您的研究不会永久锁定。在长期系统综述期间定期导出您的工作空间是强烈建议的。