{ "workflow": { "id": 5221, "name": "Automate research paper collection with Bright Data & n8n", "views": 5946, "recentViews": 1, "totalViews": 5946, "createdAt": "2025-06-24T15:55:40.455Z", "description": "#\n\n## Description\n\nThis workflow automatically collects and organizes research papers from academic databases and journals into Google Sheets. It helps researchers and students save time by eliminating manual searches across multiple academic sources and centralizing research materials.\n\n## Overview\n\nThis workflow automatically scrapes research papers from academic databases and journals, then organizes them in Google Sheets. It uses Bright Data to access academic sources and extracts key information like titles, authors, abstracts, and citations.\n\n### Tools Used\n\n* **n8n:** The automation platform that orchestrates the workflow.\n* **Bright Data:** For scraping academic websites and research databases without getting blocked.\n* **Google Sheets:** For organizing and storing research paper data.\n\n## How to Install\n\n1. **Import the Workflow:** Download the `.json` file and import it into your n8n instance.\n2. **Configure Bright Data:** Add your Bright Data credentials to the Bright Data node.\n3. **Connect Google Sheets:** Authenticate your Google account.\n4. **Customize:** Specify research topics, journals, or authors to track.\n\n## Use Cases\n\n* **Academic Researchers:** Stay updated on new papers in your field.\n* **Students:** Collect research for literature reviews and dissertations.\n* **Research Teams:** Collaborate on literature databases.\n\n---\n\n## Connect with Me\n\n* **Website:** [https://www.nofluff.online](https://www.nofluff.online)\n* **YouTube:** [https://www.youtube.com/@YaronBeen/videos](https://www.youtube.com/@YaronBeen/videos)\n* **LinkedIn:** [https://www.linkedin.com/in/yaronbeen/](https://www.linkedin.com/in/yaronbeen/)\n* **Get Bright Data:** [https://get.brightdata.com/1tndi4600b25](https://get.brightdata.com/1tndi4600b25) (Using this link supports my free workflows with a small commission)\n\n#n8n #automation #research #academicpapers #brightdata #googlesheets #researchpapers #academicresearch #literaturesearch #scholarlyarticles #n8nworkflow #workflow #nocode #researchautomation #academicscraping #researchtools #papertracking #academicjournals #researchdatabase #literaturereview #academicwriting #datascraping #researchorganization #scholarlyresearch #citationmanagement #academicproductivity\n", "workflow": { "id": "giq3zqaP4QbY6LgC", "meta": { "instanceId": "60046904b104f0f72b2629a9d88fe9f676be4035769f1f08dad1dd38a76b9480" }, "name": "Research_Paper_Scraper_to_Google_Sheets", "tags": [], "nodes": [ { "id": "7d81edf3-6f00-4634-b79f-dbda3f9958e5", "name": "Start Scraping (Manual Trigger)", "type": "n8n-nodes-base.manualTrigger", "position": [ -1080, 580 ], "parameters": {}, "typeVersion": 1 }, { "id": "6e172db5-7483-4079-bf8a-785602526bdc", "name": "Set Research topic", "type": "n8n-nodes-base.set", "position": [ -860, 580 ], "parameters": { "options": {}, "assignments": { "assignments": [ { "id": "b530a847-0bb2-4039-9ad0-cbc9cc4d909e", "name": "Topic", "type": "string", "value": "machine+learning" } ] } }, "typeVersion": 3.4 }, { "id": "e65d092a-6854-478c-b33e-2fc309f71ae8", "name": "Send Request to Bright Data API", "type": "n8n-nodes-base.httpRequest", "position": [ -600, 580 ], "parameters": { "url": "https://api.brightdata.com/request", "method": "POST", "options": {}, "sendBody": true, "sendHeaders": true, "bodyParameters": { "parameters": [ { "name": "zone", "value": "n8n_unblocker" }, { "name": "url", "value": "=https://scholar.google.com/scholar?q={{ $json.Topic }}" }, { "name": "country", "value": "us" }, { "name": "format", "value": "raw" } ] }, "headerParameters": { "parameters": [ { "name": "Authorization", "value": "Bearer YOUR_TOKEN_HERE" } ] } }, "typeVersion": 4.2 }, { "id": "211bae33-32c5-44e8-b306-a5e0d520a4a0", "name": "Extract Data from HTML (Title, Author, etc.)", "type": "n8n-nodes-base.html", "position": [ -400, 580 ], "parameters": { "options": {}, "operation": "extractHtmlContent", "extractionValues": { "values": [ { "key": "Title", "cssSelector": "h3.gs_rt, a.gs_rt", "returnArray": true }, { "key": "Author", "cssSelector": "div.gs_a", "returnArray": true }, { "key": "Abstract", "cssSelector": "div.gs_rs", "returnArray": true }, { "key": "PDF Link\t", "cssSelector": "a[href*='pdf']", "returnArray": true, "returnValue": "attribute" } ] } }, "typeVersion": 1.2 }, { "id": "9ab7ba20-8614-46c5-b57a-3749d6ae04c4", "name": "Clean & Structure Extracted Data", "type": "n8n-nodes-base.code", "position": [ -200, 580 ], "parameters": { "jsCode": "const titles = items[0].json.Title || [];\nconst authors = items[0].json.Author || [];\nconst abstracts = items[0].json.Abstract || [];\nconst pdfLinks = items[0].json[\"PDF Link\\t\"] || [];\n\nconst output = [];\n\nfor (let i = 0; i < titles.length; i++) {\n // Clean title (remove tags like [PDF][B])\n let title = titles[i].replace(/\\[.*?\\]/g, '').trim();\n\n // Clean author (remove any trailing dashes or HTML leftovers)\n let author = authors[i] ? authors[i].replace(/\\s*-\\s*.*/, '').trim() : '';\n\n // Abstract fallback\n let abstract = abstracts[i] || '';\n\n // Get PDF link β€” from either a single object or array of duplicates\n let linkObj = pdfLinks[i];\n let pdfLink = '';\n\n if (Array.isArray(linkObj)) {\n // If multiple objects per item\n pdfLink = linkObj.find(obj => obj.href)?.href || '';\n } else if (linkObj?.href) {\n pdfLink = linkObj.href;\n }\n\n // Push cleaned object\n output.push({\n json: {\n title,\n author,\n abstract,\n pdfLink\n }\n });\n}\n\nreturn output;\n" }, "typeVersion": 2 }, { "id": "a246f20c-2bb9-4319-8812-e296c87a7df0", "name": "Save Results to Google Sheet", "type": "n8n-nodes-base.googleSheets", "position": [ 120, 580 ], "parameters": { "columns": { "value": { "Topic": "={{ $('Set Research topic').item.json.Topic }}", "title": "={{ $json.title }}", "author": "={{ $json.author }}", "abstract": "={{ $json.abstract }}", "pdf link": "={{ $json.pdfLink }}" }, "schema": [ { "id": "Topic", "type": "string", "display": true, "required": false, "displayName": "Topic", "defaultMatch": false, "canBeUsedToMatch": true }, { "id": "title", "type": "string", "display": true, "required": false, "displayName": "title", "defaultMatch": false, "canBeUsedToMatch": true }, { "id": "author", "type": "string", "display": true, "required": false, "displayName": "author", "defaultMatch": false, "canBeUsedToMatch": true }, { "id": "abstract", "type": "string", "display": true, "required": false, "displayName": "abstract", "defaultMatch": false, "canBeUsedToMatch": true }, { "id": "pdf link", "type": "string", "display": true, "required": false, "displayName": "pdf link", "defaultMatch": false, "canBeUsedToMatch": true } ], "mappingMode": "defineBelow", "matchingColumns": [], "attemptToConvertTypes": false, "convertFieldsToString": false }, "options": {}, "operation": "append", "sheetName": { "__rl": true, "mode": "list", "value": "gid=0", "cachedResultUrl": "https://docs.google.com/spreadsheets/d/1sOfCFsvHS9-BeE_PQ6J_jtQofCRcOv02XS7hrmFmpxQ/edit#gid=0", "cachedResultName": "Sheet1" }, "documentId": { "__rl": true, "mode": "list", "value": "1sOfCFsvHS9-BeE_PQ6J_jtQofCRcOv02XS7hrmFmpxQ", "cachedResultUrl": "https://docs.google.com/spreadsheets/d/1sOfCFsvHS9-BeE_PQ6J_jtQofCRcOv02XS7hrmFmpxQ/edit?usp=drivesdk", "cachedResultName": "Research papers from Google Scholar" } }, "credentials": { "googleSheetsOAuth2Api": { "id": "credential-id", "name": "googleSheetsOAuth2Api Credential" } }, "typeVersion": 4.6 }, { "id": "1b4a1504-4a4a-4a0d-892b-d0c3e205ed85", "name": "Sticky Note", "type": "n8n-nodes-base.stickyNote", "position": [ -1140, 60 ], "parameters": { "color": 5, "width": 420, "height": 720, "content": "## πŸ”Ή **Section 1: User Input & Trigger**\n\n**🧩 Nodes: Start Scraping | Set Topic**\nπŸ“ **Purpose:** Let users easily input the topic they want to scrape β€” no need to deal with complex URLs.\n\n| 🧱 Node | βœ… New Name | πŸ’‘ Description |\n| --------- | ---------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| ⚑ Trigger | **Start Scraping (Manual)** | This node starts the workflow when you click β€œExecute Workflow.” It's the entry point. |\n| ✏️ Set | **Set Topic (Manual Input)** | Instead of requiring a URL, the user will enter a topic (like \"machine learning\" or \"digital marketing\"). This topic will be used to automatically generate the URL behind the scenes. |\n\n### 🧠 How it helps:\n\n* Great for beginners: Just type the topic, hit run.\n* Keeps the interface clean and user-friendly.\n* Avoids confusion around URLs and formats.\n\n---\n\n" }, "typeVersion": 1 }, { "id": "bc56f528-6d18-4e05-942f-c06bb6e10b27", "name": "Sticky Note1", "type": "n8n-nodes-base.stickyNote", "position": [ -660, 80 ], "parameters": { "color": 6, "width": 600, "height": 700, "content": "## πŸ”Έ **Section 2: Scrape & Parse Website**\n\n**🧩 Nodes: Send Request | Extract HTML | Clean Data**\nπŸ“ **Purpose:** Uses the Bright Data proxy to access the webpage, extract raw HTML content, and clean it up into a readable format (title, author, abstract, etc.).\n\n| 🧱 Node | βœ… New Name | πŸ’‘ Description |\n| --------------- | ------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| 🌐 HTTP Request | **Send Topic Request to Bright Data** | This sends a request to the Bright Data API using the topic you set earlier. It uses Bright Data’s network to safely load the actual website and return HTML content. |\n| 🧱 HTML Extract | **Extract Data from Webpage** | Parses the returned HTML to find relevant data like titles, authors, abstracts, and links. |\n| πŸ”£ Code | **Clean and Format Scraped Data** | A custom code block that organizes the messy data into neat records. For example: title β†’ column A, abstract β†’ column B, etc. |\n\n### 🧠 How it helps:\n\n* Makes web scraping safe and reliable by using proxies.\n* Converts unreadable HTML into structured information.\n* Beginner-friendly: No need to write a parser yourself.\n\n---\n\n" }, "typeVersion": 1 }, { "id": "2c54e5e6-011a-4562-98ac-9cc9834bc284", "name": "Sticky Note2", "type": "n8n-nodes-base.stickyNote", "position": [ 0, 0 ], "parameters": { "color": 3, "width": 340, "height": 780, "content": "## 🟒 **Section 3: Save to Google Sheets**\n\n**🧩 Node: Append to Google Sheets**\nπŸ“ **Purpose:** Automatically sends the clean data into a Google Sheet for easy access, filtering, or sharing.\n\n| 🧱 Node | βœ… New Name | πŸ’‘ Description |\n| ---------------- | ------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------- |\n| πŸ“„ Google Sheets | **Store Scraped Data in Spreadsheet** | Takes the structured output and appends it to the connected Google Sheet. Each result gets a row with title, author, abstract, etc. |\n\n### 🧠 How it helps:\n\n* No manual copy-pasting ever again!\n* Shareable and searchable format.\n* Updates automatically as you scrape more topics.\n\n---\n\n" }, "typeVersion": 1 }, { "id": "4ce90703-961e-4070-9356-c9dffc23a6c5", "name": "Sticky Note9", "type": "n8n-nodes-base.stickyNote", "position": [ -2980, 80 ], "parameters": { "color": 4, "width": 1300, "height": 320, "content": "=======================================\n WORKFLOW ASSISTANCE\n=======================================\nFor any questions or support, please contact:\n Yaron@nofluff.online\n\nExplore more tips and tutorials here:\n - YouTube: https://www.youtube.com/@YaronBeen/videos\n - LinkedIn: https://www.linkedin.com/in/yaronbeen/\n=======================================\n" }, "typeVersion": 1 }, { "id": "069ddb89-f7a1-4c4b-b65d-212be3252750", "name": "Sticky Note4", "type": "n8n-nodes-base.stickyNote", "position": [ -2980, 420 ], "parameters": { "color": 4, "width": 1289, "height": 1878, "content": "## 🌟 Research Paper Scraper to Google Sheets\n\n**Automate extraction of data from any website based on a topic β€” no coding needed!**\n\n---\n\n## πŸ”Ή **Section 1: User Input & Trigger**\n\n**🧩 Nodes: Start Scraping | Set Topic**\nπŸ“ **Purpose:** Let users easily input the topic they want to scrape β€” no need to deal with complex URLs.\n\n| 🧱 Node | βœ… New Name | πŸ’‘ Description |\n| --------- | ---------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| ⚑ Trigger | **Start Scraping (Manual)** | This node starts the workflow when you click β€œExecute Workflow.” It's the entry point. |\n| ✏️ Set | **Set Topic (Manual Input)** | Instead of requiring a URL, the user will enter a topic (like \"machine learning\" or \"digital marketing\"). This topic will be used to automatically generate the URL behind the scenes. |\n\n### 🧠 How it helps:\n\n* Great for beginners: Just type the topic, hit run.\n* Keeps the interface clean and user-friendly.\n* Avoids confusion around URLs and formats.\n\n---\n\n## πŸ”Έ **Section 2: Scrape & Parse Website**\n\n**🧩 Nodes: Send Request | Extract HTML | Clean Data**\nπŸ“ **Purpose:** Uses the Bright Data proxy to access the webpage, extract raw HTML content, and clean it up into a readable format (title, author, abstract, etc.).\n\n| 🧱 Node | βœ… New Name | πŸ’‘ Description |\n| --------------- | ------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| 🌐 HTTP Request | **Send Topic Request to Bright Data** | This sends a request to the Bright Data API using the topic you set earlier. It uses Bright Data’s network to safely load the actual website and return HTML content. |\n| 🧱 HTML Extract | **Extract Data from Webpage** | Parses the returned HTML to find relevant data like titles, authors, abstracts, and links. |\n| πŸ”£ Code | **Clean and Format Scraped Data** | A custom code block that organizes the messy data into neat records. For example: title β†’ column A, abstract β†’ column B, etc. |\n\n### 🧠 How it helps:\n\n* Makes web scraping safe and reliable by using proxies.\n* Converts unreadable HTML into structured information.\n* Beginner-friendly: No need to write a parser yourself.\n\n---\n\n## 🟒 **Section 3: Save to Google Sheets**\n\n**🧩 Node: Append to Google Sheets**\nπŸ“ **Purpose:** Automatically sends the clean data into a Google Sheet for easy access, filtering, or sharing.\n\n| 🧱 Node | βœ… New Name | πŸ’‘ Description |\n| ---------------- | ------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------- |\n| πŸ“„ Google Sheets | **Store Scraped Data in Spreadsheet** | Takes the structured output and appends it to the connected Google Sheet. Each result gets a row with title, author, abstract, etc. |\n\n### 🧠 How it helps:\n\n* No manual copy-pasting ever again!\n* Shareable and searchable format.\n* Updates automatically as you scrape more topics.\n\n---\n\n## βœ… What a Beginner Gains from This Workflow\n\n| πŸ’‘ Feature | πŸš€ Benefit |\n| --------------------------- | --------------------------------------------------------------------------------- |\n| Topic-based input | You don’t need to find or understand complex URLs. Just type β€œAI” or β€œmarketing.” |\n| Fully automated scraping | You don’t need to open browsers or inspect elements. |\n| Ready-to-use Google Sheet | The final data is clean and saved into a sheet you can use anywhere. |\n| Beautiful, modular workflow | Each step is visual, editable, and reusable without coding skills. |\n\n---\n\n## 🎯 Final Result:\n\nYou type a **topic** β†’ Bright Data scrapes the web β†’ It extracts content β†’ Cleans it β†’ Saves it into **Google Sheets**.\nEverything happens automatically. **No code. No hassle. Just data.**\n\n---\n\n" }, "typeVersion": 1 }, { "id": "a1a5e609-756a-4757-a026-1349cf388e61", "name": "Sticky Note5", "type": "n8n-nodes-base.stickyNote", "position": [ 400, 0 ], "parameters": { "color": 7, "width": 380, "height": 240, "content": "## I’ll receive a tiny commission if you join Bright Data through this linkβ€”thanks for fueling more free content!\n\n### https://get.brightdata.com/1tndi4600b25" }, "typeVersion": 1 } ], "active": false, "pinData": {}, "settings": { "executionOrder": "v1" }, "versionId": "f931202a-3c22-495d-b775-71665bdf6c27", "connections": { "Set Research topic": { "main": [ [ { "node": "Send Request to Bright Data API", "type": "main", "index": 0 } ] ] }, "Send Request to Bright Data API": { "main": [ [ { "node": "Extract Data from HTML (Title, Author, etc.)", "type": "main", "index": 0 } ] ] }, "Start Scraping (Manual Trigger)": { "main": [ [ { "node": "Set Research topic", "type": "main", "index": 0 } ] ] }, "Clean & Structure Extracted Data": { "main": [ [ { "node": "Save Results to Google Sheet", "type": "main", "index": 0 } ] ] }, "Extract Data from HTML (Title, Author, etc.)": { "main": [ [ { "node": "Clean & Structure Extracted Data", "type": "main", "index": 0 } ] ] } } }, "lastUpdatedBy": 29, "workflowInfo": { "nodeCount": 12, "nodeTypes": { "n8n-nodes-base.set": { "count": 1 }, "n8n-nodes-base.code": { "count": 1 }, "n8n-nodes-base.html": { "count": 1 }, "n8n-nodes-base.stickyNote": { "count": 6 }, "n8n-nodes-base.httpRequest": { "count": 1 }, "n8n-nodes-base.googleSheets": { "count": 1 }, "n8n-nodes-base.manualTrigger": { "count": 1 } } }, "status": "published", "user": { "name": "Yaron Been", "username": "yaron-nofluff", "bio": "Building AI Agents and Automations | Growth Marketer | Entrepreneur | Book Author & Podcast Host\n\nIf you need any help with Automations, feel free to reach out via linkedin:\nhttps://www.linkedin.com/in/yaronbeen/\n\nAnd check out my Youtube channel:\nhttps://www.youtube.com/@YaronBeen/videos", "verified": true, "links": [ "https://www.nofluff.online/automation-services/" ], "avatar": "https://gravatar.com/avatar/a4e4dcaa1f76ff5266bbf80e8df86d22efda890474c68f7796e72fd82e3f2375?r=pg&d=retro&size=200" }, "nodes": [ { "id": 18, "icon": "file:googleSheets.svg", "name": "n8n-nodes-base.googleSheets", "codex": { "data": { "alias": [ "CSV", "Sheet", "Spreadsheet", "GS" ], "resources": { "generic": [ { "url": "https://n8n.io/blog/love-at-first-sight-ricardos-n8n-journey/", "icon": "❀️", "label": "Love at first sight: Ricardo’s n8n journey" }, { "url": "https://n8n.io/blog/why-business-process-automation-with-n8n-can-change-your-daily-life/", "icon": "🧬", "label": "Why business process automation with n8n can change your daily life" }, { "url": "https://n8n.io/blog/automatically-adding-expense-receipts-to-google-sheets-with-telegram-mindee-twilio-and-n8n/", "icon": "🧾", "label": "Automatically Adding Expense Receipts to Google Sheets with Telegram, Mindee, Twilio, and n8n" }, { "url": "https://n8n.io/blog/supercharging-your-conference-registration-process-with-n8n/", "icon": "🎫", "label": "Supercharging your conference registration process with n8n" }, { "url": "https://n8n.io/blog/creating-triggers-for-n8n-workflows-using-polling/", "icon": "⏲", "label": "Creating triggers for n8n workflows using polling" }, { "url": "https://n8n.io/blog/no-code-ecommerce-workflow-automations/", "icon": "store", "label": "6 e-commerce workflows to power up your Shopify s" }, { "url": "https://n8n.io/blog/migrating-community-metrics-to-orbit-using-n8n/", "icon": "πŸ“ˆ", "label": "Migrating Community Metrics to Orbit using n8n" }, { "url": "https://n8n.io/blog/automate-google-apps-for-productivity/", "icon": "πŸ’‘", "label": "15 Google apps you can combine and automate to increase productivity" }, { "url": "https://n8n.io/blog/your-business-doesnt-need-you-to-operate/", "icon": " πŸ–₯️", "label": "Hey founders! 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