---
title: ScholarFlow Research Intelligence Studio
emoji: 📚
colorFrom: blue
colorTo: green
sdk: static
app_file: index.html
pinned: false
license: mit
tags:
  - research
  - literature-review
  - evidence-synthesis
  - multilingual
  - reproducibility
  - static
---

# ScholarFlow Research Intelligence Studio | Hugging Face Project #19

A **privacy-conscious, local-first research evidence dashboard** that turns an unstructured list of studies into an inspectable library, human-reviewed screening decisions, an evidence matrix, descriptive research-coverage diagnostics, and reproducible exports.

**Owner / portfolio:** [Hugdrigo](https://huggingface.co/Hugdrigo)  
**Project:** 19 of the Hugging Face portfolio series  
**Release:** 1.0.0  
**Hosting:** Hugging Face Static Space, free tier, no server dependency

## Run

Open `index.html` in a current browser, or upload all files at the repository root of a Hugging Face Space configured as **Static**. No Python backend, paid inference, API keys, or Node packages are required.

## Features

1. Overview with summary metrics and method/decision distributions.
2. Keyword-searchable and filterable study library.
3. Add sources manually, with methodological metadata and traceable origin fields.
4. Manual screening: include, maybe, exclude, and unscreened, with review notes.
5. Evidence matrix focused on included studies.
6. Descriptive distribution and threshold-based coverage insights.
7. JSON, CSV, and Markdown exports plus JSON import.
8. Browser-local persistence with explicit replacement and deletion confirmation.
9. Responsive layout for desktop and mobile, with no remote data collection.

## Crucial integrity note

**The included demonstration dataset is entirely synthetic.** Records marked `SYN-*`, their authors, samples, findings, and decisions are illustrative and **must not be cited as authentic research**. User-entered records are not independently verified by the app. The tool does not query scholarly databases or execute generative-AI models. The term *research intelligence* refers to structured, rule-based decision support, not autonomous research or external LLM inference.

The completeness field counts populated metadata categories (source, methods, findings, limitations, participants), **not scientific quality**. Gap insights identify patterns **in the user's included records** only, not established gaps in the world's research literature.

## Files

- `index.html`: Space entry point and semantic UI.
- `styles.css`: accessible responsive design.
- `seed.js`: demonstration data loaded without remote requests.
- `sample-data.json`: portable, clearly marked synthetic sample dataset.
- `app.js`: library, screening, visualization and import/export logic.
- `METHODOLOGY.md`: interpretation, screening process, reproducibility safeguards.
- `LICENSE`: MIT license.

## Use with authentic literature

1. Back up your current state with **Export full JSON**.
2. In **Export & methods**, select **Clear all records** or import your own JSON.
3. Collect authentic sources from databases or publisher websites outside the app.
4. Add records and verify each bibliographic reference yourself.
5. Establish an a priori inclusion/exclusion protocol; record reasons in the screening desk.
6. Analyze only relevant, verifiable, included studies; export the resulting audit log.

## JSON shape

```json
{
  "records": [
    {
      "id": "REAL-001",
      "title": "Verified paper title",
      "authors": "Author Names",
      "year": 2025,
      "method": "Qualitative",
      "region": "Latin America",
      "language": "Spanish",
      "participants": 25,
      "topic": "academic writing",
      "findings": "Source-verified summary",
      "limitations": "Limitations reported by authors",
      "source": "doi:10.xxxx/example",
      "synthetic": false,
      "status": "unscreened",
      "notes": ""
    }
  ]
}
```

Use an actual, verified DOI rather than the placeholder above. Importing JSON **replaces** the existing dataset after confirmation. The app has no account sync: localStorage is device/browser-specific, so export frequently.

## Next possible release

Optional integrations for Crossref/OpenAlex metadata, DOI verification, a documented review protocol, RAG retrieval, and rubric-based critical appraisal could be built as separately disclosed additions. These capabilities are **not implemented** in v1.0.

## Attribution

Created as Project #19 for the public Hugdrigo Hugging Face professional portfolio. MIT licensed; see LICENSE.