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# Beginner user guide

## What is an evidence graph?

Imagine a wall with cards connected by string. One card is a paper, another is a policy idea, and the string says *why* they may be related. Here the strings can say **supports**, **challenges**, or **context**. Every string should contain the real words or table location you checked.

## First test (fictional data)

1. Open the App tab. You should see **EvidenceGraph Research Intelligence** and the banner **SYNTHETIC DEMO / MIXED SESSION**.
2. Open **01 Evidence network** and click a blue publication node.
3. Scroll to see its details and connected excerpts. The title explicitly says fictional.
4. Try **Trace a research connection** by selecting two cards and pressing the button.
5. Open **05 Hypotheses** and observe supporting, challenging and context link counts.
6. Open **06 Quality audit** to see records that need human attention.
7. Open **07 Export & methods** and download a JSON session, CSV and Markdown report.

## Start a real research project

1. On **01 Evidence network**, click **New empty session**. This removes the fictitious demo *from the current browser tab only*. It never deletes your Hugging Face repository.
2. Use **03 Literature hub** to search Crossref and import bibliography details, or enter a publication manually.
3. Use **04 Indicator hub** to request an official numeric indicator and preserve the observed year.
4. Use **02 Evidence desk** to write an original claim/hypothesis and enter a source excerpt plus a page or table locator.
5. Compare hypotheses and check audit warnings. **Do not use unverified references to claim scientific validity.**
6. Export the complete session as JSON before refreshing or closing the tab.

## Restore a session

Go to **07 Export & methods → Restore a saved session**, select the JSON exported earlier, and confirm replacement. If the imported file is malformed, the existing session remains intact.

## No-cost hosting

Static Hosting does not execute Python or deploy an LLM. The analysis here is a browser-only combination of graph algorithms, metadata fetching, user-authored links, and deterministic documentation diagnostics.

	# Beginner user guide

	## What is an evidence graph?

	Imagine a wall with cards connected by string. One card is a paper, another is a policy idea, and the string says why they may be related. Here the strings can say supports, challenges, or context. Every string should contain the real words or table location you checked.

	## First test (fictional data)

	1. Open the App tab. You should see EvidenceGraph Research Intelligence and the banner SYNTHETIC DEMO / MIXED SESSION.
	2. Open 01 Evidence network and click a blue publication node.
	3. Scroll to see its details and connected excerpts. The title explicitly says fictional.
	4. Try Trace a research connection by selecting two cards and pressing the button.
	5. Open 05 Hypotheses and observe supporting, challenging and context link counts.
	6. Open 06 Quality audit to see records that need human attention.
	7. Open 07 Export & methods and download a JSON session, CSV and Markdown report.

	## Start a real research project

	1. On 01 Evidence network, click New empty session. This removes the fictitious demo from the current browser tab only. It never deletes your Hugging Face repository.
	2. Use 03 Literature hub to search Crossref and import bibliography details, or enter a publication manually.
	3. Use 04 Indicator hub to request an official numeric indicator and preserve the observed year.
	4. Use 02 Evidence desk to write an original claim/hypothesis and enter a source excerpt plus a page or table locator.
	5. Compare hypotheses and check audit warnings. Do not use unverified references to claim scientific validity.
	6. Export the complete session as JSON before refreshing or closing the tab.

	## Restore a session

	Go to 07 Export & methods → Restore a saved session, select the JSON exported earlier, and confirm replacement. If the imported file is malformed, the existing session remains intact.

	## No-cost hosting

	Static Hosting does not execute Python or deploy an LLM. The analysis here is a browser-only combination of graph algorithms, metadata fetching, user-authored links, and deterministic documentation diagnostics.