How to measure brilliance?

The metrics we now lean on

Modern science runs on metrics. The most commonly used ones include:

• Citation count: How often a paper is referenced by others
• H-index: A blended measure of productivity and citation impact
• Journal impact factor: The perceived prestige of where work is published

These tools are appealing because they’re simple, quantitative, and scalable. They allow hiring committees, funding agencies, and institutions to compare researchers quickly. But here’s the problem: they measure attention, not necessarily importance. A paper might be cited frequently because it’s controversial, flawed, or simply trendy. Meanwhile, truly groundbreaking work can go unnoticed for years.

The problem of time

Scientific brilliance often reveals itself slowly. Some ideas are ahead of their time. They may be ignored, misunderstood, or dismissed, until technology or popular thought catches up. When they finally gain recognition, their influence can be massive. This raises a key issue: short-term metrics systematically undervalue long-term impact.

And what about depth vs. breadth? Not all contributions are equal in kind. Some scientists produce many incremental advances, where others produce few but transformative insights. Both are valuable, but traditional metrics tend to favor volume over depth. A single idea that reshapes an entire field may matter more than hundreds of smaller contributions. Yet, quantitatively, the latter often looks more ‘productive’.

Invisible contributions

Scientific brilliance isn’t limited to published papers. Consider contributions like:

• Developing foundational tools or datasets
• Mentoring future leaders
• Shaping entire research directions
• Asking the right questions

These forms of impact are hard to measure, but deeply important. And then we have the social dimension: research is not done in isolation. Networks, institutions, and timing matter. Two equally brilliant scientists may have vastly different impacts depending on:

• Access to resources
• Institutional prestige
• Collaboration opportunities
• Biases within the system

This means that what we measure as ‘impact’ is often entangled with social context, not just intellectual merit.

It’s precisely these limitations that initiatives like the San Francisco Declaration on Research Assessment (DORA) aim to address. By encouraging institutions to move beyond journal-based metrics and adopt more holistic ways of evaluating research, efforts like DORA highlight a growing recognition: we need to rethink not just how we measure impact, but what we choose to value in the first place.

Toward better measures

If current metrics fall short, what could improve them? Some possibilities include:

• Narrative evaluation: instead of relying purely on numbers, assess what a scientist has contributed and why it matters.
• Field-normalized metrics: compare researchers within the context of their discipline, where citation practices vary widely.
• Long-term tracking: evaluate influence over decades, not just a few years.
• Diverse impact indicators, like software usage, policy influence, public engagement, and educational contributions.
• Peer judgment (carefully used): expert evaluation can capture nuance, but must be structured to reduce bias.

But would these measures work? Can we measure brilliance? My honest opinion? Not perfectly. Brilliance is partly about originality, insight, and the ability to see what others cannot. These qualities resist neat quantification, and recognition too, since originality, insight and the ability to see what other cannot, are easier to dismiss as irrelevance than things that are on trend. At best, we approximate. We triangulate using imperfect signals, like influence on others, longevity of ideas, and transformational effects. Ultimately, scientific brilliance is something we often recognize more clearly in hindsight than in real time. And it’s not just about measuring impact: it’s about valuing the right kinds of impact.

If we rely too heavily on easy metrics, we risk rewarding what is visible, fast, and popular, rather than what is deep, difficult, and enduring. And that, ironically, may cause us to overlook the very brilliance we seek to identify. As Head of Communications, this tension is something I navigate every day. I rely on metrics to guide strategy, demonstrate value, and communicate impact. But I’m also constantly aware of their limits. My role sits at the intersection of storytelling and measurement: translating complex, often long-term scientific contributions into narratives that resonate now, without reducing them to what is easiest to count. Is that balance, between data and judgment, visibility and depth, where the real work lies?