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We have accepted the importance of data sharing

No one is denying the importance of data sharing. It is well accepted that data sharing is essential for ensuring transparency, enabling reproducibility, and increasing the pace of discoveries. Its impact has been demonstrated again and again. Consider COVID-19: the rapid global sharing of viral genomic sequences allowed researchers to track emerging variants and develop vaccines on a timeline that would have been impossible otherwise.

There is an accepted framework for doing it

Recognizing that data sharing is more than simply making files available, the research community rallied around the FAIR principles. These principles provide general recommendations for making data Findable, Accessible, Interoperable, and Reusable, giving scientific research a shared framework for what "good" data sharing practices actually look like. A decade after their publication, the FAIR principles have now been broadly adopted, and in many cases formally imposed, by funders, institutions, and communities across the world and across disciplines.

Then why isn’t it the norm?

Despite the proven benefit and an accepted framework for doing it, data sharing still isn’t a standard practice of scientific research. Ask researchers to share their data, and a common question typically surfaces: “What do I gain?” It’s a fair question that deserves to be treated seriously rather than dismissed as a sign of bad faith. 

I asked myself this exact question when, as a postdoc back in 2017, I was put in charge of curating and sharing data collected from one of our grants funded by the National Institutes of Health (NIH). I was aware that academic careers are typically built on a set of well-accepted currencies: publications and their citations. Metrics derived from these currencies, like the h-index, are what funding panels or tenure committees look at, and that shapes who gets hired, promoted, and funded. So all I wanted to do back then was focus on the science and write papers. Data sharing felt like a burden.

And the burden was real. Making a dataset genuinely FAIR requires cleaning it, documenting it, structuring its metadata, depositing it in the right repository, and much more. This takes real time and expertise, with nothing on the other side of the ledger to show for it. I believe my reluctance towards data sharing was a rational response to a scientific reward system that hadn't caught up with its own stated values.

To address this, I shifted my focus towards building tools, standards, and guidelines that make it easier for researchers to share FAIR and AI-ready data. I even started a division within my institute called the FAIR Data Innovations Hub, entirely focused on that effort. But this still felt like a band-aid on a broken system, one where a growing number of mandates from funders and journals required FAIR data sharing without offering any real incentive to do it well. It still didn't answer the question that had started it all: What do I gain?

An NIH Challenge built to close this gap

The NIH recognized this gap and, in 2024, launched the Data Sharing Index (S-index) Challenge: a $1 million prize competition to develop a standardized metric, inspired by the h-index, that would recognize and reward researchers for high-quality data sharing. Our team entered the Challenge with a proposal titled “A Simple and Field-Aware S-index for Promoting and Rewarding Data Sharing”, which NIH announced as the first-place winner in July 2026, during the NIH S-index Innovation Event.

At its core, our proposed S-index answers a simple question: 

How impactful is a researcher's data sharing?

Our S-index framework starts at the level of a single dataset. Every dataset is assigned a D-index (Dataset Index), a score that combines three things: how FAIR the dataset is, how often it has been cited in the scholarly literature, and how often it is mentioned in other meaningful but not traditional forms of reuse, such as code, machine learning models, and patents. The three components (FAIRness, number of citations, and number of mentions) are field- and time-normalized. A researcher's S-index is then simply the sum of the D-indices across every dataset they've shared.

While there could be other ways to formulate an index that measures data sharing impact, we agreed on this formulation for several reasons. We wanted the sharing effort to be rewarded even before reuse happens. This is why FAIRness is integrated directly. It also ensures the S-index of researchers increases the moment they share a FAIR dataset, therefore encouraging rapid data sharing, which is the need of the moment. We also wanted the S-index to prevent field bias, which is a major criticism of the h-index, so every signal is normalized against comparable datasets in the same discipline and time period, rather than favoring fields that already cite heavily or follow advanced FAIR practices. And we wanted the S-index to stay simple. Despite blending several signals together, it is easy to interpret, capturing both a researcher's data sharing productivity and the impact of their individual datasets in one number.

LEARN ABOUT THE S-INDEX VALIDATION

From metric to culture 

The S-index was designed to make the effort of curating and sharing a dataset show up on a CV, in a funding application or in a tenure case, in the same way a published manuscript counts today, rather than to produce yet another number. A metric can only do that if researchers can track it and others can see it, which is why we built Scholar Data, a free and open-source platform that works like Google Scholar for datasets. Researchers can sign up and claim their datasets from Scholar Data's index of more than 70 million, which continues to grow. Researchers get their public profiles with their own URL showing the S-index, dataset count, list of datasets and related metrics, and they can include this link in CVs, grant proposals and elsewhere as a record of their data sharing impact.

Publication metrics such as the h-index didn't change academic behavior on their own; they mattered because institutions and funders adopted them as something they actually looked at when making decisions. The S-index needs to follow the same path, and the one major step still needed is for institutions and funders to start using it in hiring, promotion cases, and grant review.

When that happens, the answer to "What do I gain?" stops being “nothing” or “I don’t know” and becomes concrete: recognition that counts toward career advancement. Researchers will then stop asking "What do I gain?" and start asking "How do I make my data count?" That's the culture shift the S-index is designed to support.

CHECK YOUR S-INDEX ON THE SCHOLAR DATA PLATFORM

Disclosures: Bhavesh Patel is the lead of the team that developed the S-index and Scholar Data platform described in this article. He is a Research Professor at the California Medical Innovations Institute (San Diego, CA). Participation in the NIH Data Sharing Index Challenge was conducted as a personal project, independent of the Institute. Scholar Data is open-source and free to use, and the author has no financial interest in the platform.