Scholar Data

Give your datasets the credit they deserve.

Scholar Data helps you measure, improve, and showcase the impact of what you share-beyond publications. Get credit for the data you share-clearly, fairly, and publicly.

What Scholar Data offers

• S-Index & Dataset Index - A dataset-first impact metric. Each dataset earns a Dataset Index (FAIRness, citations, and mentions), and your S-Index rolls them up into one clear score you can actually explain.
• FAIR Assessment - See how Findable, Accessible, Interoperable, and Reusable your datasets really are, with transparent scores you can improve over time.
• Dataset Discovery - Find datasets by topic, DOI, or keyword. Track citations and attention over time and spot real-world reuse across the community.
• Authors & Organizations - Look up researchers and institutions in seconds. See S-Index scores, claimed datasets, and data-sharing footprint in one place.
• Claim Your Datasets - Connect DOIs and URLs to your profile and start building measurable credit for the data you publish-not just the papers.
• Resolve & Enrich - Turn a DOI or URL into rich dataset metadata: citations, mentions, normalization, and domain context.

Resolver

Paste a DOI or dataset URL to get on-demand dataset metrics, computed in real time. The Dataset Index is calculated at request time so you always see the latest citations, mentions, FAIR score, and normalized results in one place.

Integrations

We’re building integrations with repositories and make it easy to display S-Index scores, and connect your data impact across the ecosystem. If you want to integrate with Scholar Data, let us know by opening an issue or reaching out to us.

Why it matters

Publications aren’t the whole story-datasets drive discovery. Traditional metrics reward papers. The S-Index rewards shared datasets: how findable they are, how often they’re cited or mentioned, and how they’re reused. It’s simple to interpret, field-sensitive, and built on tools researchers already use.

• Dataset-first - Every dataset earns a Dataset Index; your S-Index reflects your full sharing footprint.
• Fair across fields - Context and normalization help comparisons stay meaningful across disciplines.
• Built on reuse + FAIR - FAIRness, citations, and attention combined into one transparent, improvable score.

Getting started

Prerequisites/Dependencies

You will need the following installed on your system:

• Node.js
• Yarn
• Docker
• Volta (optional)

Setup

1. Clone the repository

   git clone https://github.com/data-S-index/web-app

2. Install the dependencies

   yarn install

3. Add your environment variables. An example is provided at .env.example

   cp .env.example .env

4. Start the development server

   yarn dev

5. Open the application in your browser

   open http://localhost:3000

Development

Database, Meilisearch & Redis

The application uses:

• PostgreSQL 18 - primary database (Prisma)
• Meilisearch - search engine for datasets, authors, and organizations
• Redis - caching (resolve, metrics), rate limiting, and job state

Run all services locally with Docker:

docker-compose -f ./dev-docker-compose.yaml up
docker-compose -f ./dev-docker-compose.yaml up -d  # run in background

Service	Host port	Purpose
PostgreSQL	43997	Database
Meilisearch	42341	Search (datasets, au, ao)
Redis	44001	Cache, rate limit, jobs

Add to your .env when using the dev stack:

# Meilisearch (matches dev-docker-compose.yaml)
MEILISEARCH_API_URL=http://localhost:42341
MEILISEARCH_API_KEY=K8xP2mN9vQ5rT7wY3zA6bC1dE4fG8hJ0kL2mN5pQ8sT1vW4xZ7aB0cD3eF6gH9

# Redis (host port 44001 from docker-compose)
REDIS_HOST=localhost
REDIS_PORT=44001

Stop all services:

docker-compose -f ./dev-docker-compose.yaml down

Prisma

The application uses Prisma to interact with the PostgreSQL database.

UI

The application uses Nuxt UI to build the UI components. It also uses Tailwind CSS for styling.