Platform in Beta - Launching Soon! 🚀

Give your datasets
the credit they deserve.

Scholar Data helps you measure, improve, and showcase the impact of your datasets.

About

As part of an NIH-organized Challenge, we are developing a novel metric called S-index (or Sharing Index) that measures the data sharing impact of a researcher-similarly to how the h-index measures publication impact.

Scholar Data is intended to provide a Google Scholar-like platform for researchers and organizations to track their S-index.

This site is currently in beta, released for testing and demonstrating the potential impact and value of our S-index as part of the NIH S‑index Challenge.

There are several features you can already try out:

Get your S-index

Create a profile, add your datasets, and track your S-index, citations to your datasets, FAIR scores, and other metrics.

Browse profiles

We auto-generated 1M+ author profiles for demo purpose based on our preliminary large scale testing of the S-index with 49M datasets. You can find and view an author's profile along with their S-index and other data impact metrics.

Browse datasets

You can also browse the impact pages of the 49M datasets we already processed. Find a dataset and checkout its Dataset Index and other impact metrics.

Evaluate datasets

Are you not able to find a dataset? No worries! Just drop the DOI or URL of the dataset and we will generate its impact page live.

Integrate with ScholarData

Are you a repository maintainer who wants to display the impact of their datasets? We provide simple ways to showcase the Dataset Index and other impact metrics of a dataset directly in your repository.

View platform metrics

Curious about how many datasets we have tracked, how many citations and mentions we have found, how many dataset FAIR scores we have computed? There is a page just for that!

Privacy & transparency

Usage is fully anonymous-we do not track you. The source code is open in our GitHub repository.

What is the S-index?

Despite growing adoption of data sharing, there is still no standardized, transparent, and equitable way to measure and incentivize it. To address this gap, we propose the S-index, a metric that evaluates the data sharing impact of a researcher based on dataset-level signals of FAIRness, citations, and alternative mentions.

To support its calculation, we are continuously identifying datasets from DataCite and data repositories, calculating their FAIR scores using tools like F-UJI, identifying citations from sources like Make Data Count corpus, OpenAlex, and DataCite, and searching for alternative mentions in code (Software Heritage, Hugging Face), patents, and policies.

We refer to the resources GitHub repository for more details about the formulation and calculation of the S-index.

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.

Give your datasets the credit they deserve.