Crossref Works Search Scraper is a BrowserAct Bot template for collecting structured scholarly work records from public Crossref works search results. Configure a keyword and result count, then run the Bot to return ranked DOI-based metadata for research discovery, citation enrichment, academic monitoring, publishing intelligence, or automation workflows.
What Does BrowserAct Crossref Works Search Scraper Do?
This template opens Crossref works search for your keyword, reads public scholarly work result rows, follows pagination when needed, and returns one structured record per work.
Key Features
- Extract public Crossref works search results without requiring a login.
- Capture rank, title, work URL, DOI, authors, publication date, container title, work type, and keyword.
- Configure base URL, keyword, and count.
- Continue through Crossref pagination until the requested count is reached.
- Keep DOI URLs ready for downstream enrichment, citation workflows, and research databases.
What Data Can I Extract from Crossref?
| Field | Description |
|---|---|
rank |
Search result position. |
title |
Scholarly work title. |
work_url |
DOI URL or public work link from Crossref search. |
doi |
DOI parsed from the result URL. |
authors |
Author names when visible. |
published |
Publication date shown in the result. |
container_title |
Journal, book, proceedings, or container title when visible. |
type |
Work type, such as journal article, book chapter, monograph, or other. |
keyword |
Search keyword used for the run. |
How to Use Crossref Works Search Scraper
- Click Run task.
- Enter the Crossref works search base URL.
- Enter a research keyword, topic, title, author, or DOI-related query.
- Set how many work records to collect.
- Run the Bot and review the structured scholarly work results.
- Reuse the Bot with another query or connect the output to your workflow.
Input Parameters
| Parameter | Required | Default | Description |
|---|---|---|---|
base_url |
Yes | https://search.crossref.org/search/works |
Crossref works search URL. |
keyword |
Yes | machine learning |
Search term sent to Crossref works search. |
count |
Yes | 20 |
Maximum number of work records to return. |
Example Output
The table below uses records preserved from the validated Bot run. Up to the first 5 records are shown, and long values are shortened in this documentation preview.
| rank | title | work_url | doi | authors | published | container_title | type | keyword |
|---|---|---|---|---|---|---|---|---|
| 1 | Machine learning with sklearn | https://doi.org/10.1093/oso/9780198828044.003.0003 | 10.1093/oso/9780198828044.003.0003 | Thomas P. Trappenberg | 28 November 2019 | Fundamentals of Machine Learning | BOOK CHAPTER | machine learning |
How Does it Work?
BrowserAct constructs the working public Crossref search URL from base_url and keyword, adding the result-rendering parameter used by Crossref's search experience. It then reads each scholarly work row and maps visible metadata into a consistent record with title, DOI URL, DOI, authors, publication date, container title, work type, rank, and keyword.
When count is higher than the first page, BrowserAct follows Crossref pagination with the public page parameter. Crossref search results show 20 records per page. The published Bot stops when the requested count is reached or when there are no more public result pages.
How to Build a New Scraper Bot with Agent Built
Step 1: Describe the Data You Need
Open BrowserAct and start Agent Built from the Home prompt box or from Create -> Build with Agent. Paste a clear request that includes the website, the records to collect, the filters or search conditions, the fields to return, the result limit, and the inputs you want to reuse later.
Step 2: Let BrowserAct Build and Test the Bot
BrowserAct explores the live website, works out the navigation and extraction path, and validates the result. If BrowserAct asks for clarification, reply in the same build conversation with the missing URL, condition, field, example, or expected output.
Step 3: Run the Bot and Review the Result
When the build is complete, run the Bot with the default inputs first. Review the structured records, source URLs, missing fields, and row count before reusing the Bot with new inputs or connecting it to another workflow.
Why Scrape Crossref Works Search Results?
Crossref search data helps teams discover scholarly works, enrich DOI records, collect publication metadata, monitor research topics, map journal coverage, and route academic metadata into databases, dashboards, spreadsheets, or agents.
Who Can Use This Template?
- Researchers collecting publication references.
- Academic publishers monitoring topic coverage.
- Data teams enriching DOI and metadata records.
- Library and knowledge-base teams building research indexes.
- Automation builders routing scholarly metadata into spreadsheets, databases, alerts, or agents.
How Many Results Can You Scrape?
Use count to control the number of Crossref work records returned. Crossref search results typically return 20 records per page, so higher counts require more pagination and may take longer.
Automation and Export
BrowserAct returns structured Crossref work records first. You can then use the output through API JSON responses, n8n workflows, Make scenarios, Zapier automations, MCP-based agent workflows configured from the Bot's Integrations tab, or manual CSV review.
Need Help?
Contact us at
Discord: Discord Community
E-mail: service@browseract.com
