Big science is beautiful, but little science and personal science are sexier.
Science as an exemplar operating system for meaningful work
When a team of scientists hit a decade-long dead end trying to solve the structure of a tricky viral protein, it wasn’t a new algorithm or a bigger research grant that broke the impasse; it was the effort of thousands of ordinary people.
For years, scientists struggled to crack the structure of a stubborn viral protein, the structure of the Mason-Pfizer monkey virus (MPMV), a simian AIDS-causing retrovirus, protease that would turn out to be key to developing treatments for HIV. It was a problem that resisted even the most advanced lab techniques and the resources of major research initiatives. The challenge was so complex it seemed insurmountable, highlighting the limits of both big, collaborative science and focused, hypothesis-driven research.
The Human Genome Project, a quintessential big science endeavour, laid the groundwork for understanding genetic mechanisms underlying diseases like HIV/AIDS38. By mapping the human genome, it created a shared knowledge infrastructure that enabled targeted drug development. For MPMV, resolving the protease structure was critical for designing inhibitors to block viral replication. Despite its importance, the protein’s monomeric crystal structure resisted traditional methods like molecular replacement and NMR spectroscopy.
Biochemists focused on retroviral proteases epitomise little science: hypothesis-driven, technique-oriented research. Their work relies on incremental advances in X-ray crystallography and computational modeling. However, the MPMV protease’s unusual monomeric conformation defied existing algorithms.
The solution came from 57,000 players of an online game, Foldit, that allowed individuals to experiment iteratively and personally with 3D models. Solutions emerged in weeks, a stark contrast to years of stalled academic efforts. This personal science approach generated models accurate enough for molecular replacement, enabling the protease’s structure determination.
It was a 15-year timeline:
2000–2011: Little Science efforts stalled on the MPMV protease10.
Post-2011: The solved structure informed inhibitor designs targeting the dimer interface, advancing antiretroviral drug development.
In this case, Big Science enabled Little Science, and Personal Science provided the solution. As Derek de Solla Price predicted, the transition from Little to Big Science isn’t a replacement but an integration, one now enriched by grassroots participation.
Big science is beautiful, but it is not a swan.
When large flagship initiatives have been announced, such as the Human Genome Project and the War on Cancer, there has been scepticism that such large efforts draw away from more focused and creative individual investigator efforts. Critics criticised the Human Genome Project because it had no hypothesis. Yet now efforts such as the Human Genome Project and the War on Cancer are heralded as the drivers of important breakthroughs. The same was true for the effort to put a man on the moon.
So, looking retrospectively we can clearly see that big coordinated structured efforts can and have produced. They do so mainly by helping to resolve bottlenecks in research and innovation that are exacerbated by competition. Great examples are the lack of standards or sharing of data, both of which limit the extent of data available or data that is comparable enough to answer big questions. However, the question remains if that kind of work is science, or is it something else. Is it a swan masquerading as a duck? That argument can be supported by the initial criticism of the Human Genome Project that it had no hypothesis.
I can myself say from experience that trying to fit a single hypothesis onto a large multi-partner collaboration with multiple objectives is at best highly arbitrary. That is the hint to the answer to our question. A project like the Human Genome Project is a collection of smaller science projects. When you come down to it, forming hypotheses is a way to structure focused scientific efforts. If we consider that you only learn something and advance our knowledge when a hypothesis is proven wrong, making a large overarching hypothesis does not make sense. It should be a number of focused hypotheses that can be tested and rejected or further tested.
What Big Science projects do is allow those individual efforts to test hypotheses to be much more efficient and much more powerful. They also allow for much bigger experiments than would ever be possible. So, it is not Big vs Little science. They are parts of a whole. They are like three Russian dolls, separate but each of the small ones fitting nicely into the largest one.
Each of these types of science has features that are a distinct part of their nature.
1. Big Science
Challenging (wicked) problems
The problems big science works on are difficult, almost defying a solution. Wicked problems are problems that have many stakeholders and many interdependencies. Think about world hunger as an example. Getting to the Moon is another example.
Impact-oriented
A Big Science project, on the other hand, should be framed and linked to an impact. It’s how we make our science useful. This is not to be confused with high-impact publications. No high-impact publication has ever relieved the suffering of an individual patient.
Collaboration
Collaboration is central to a big science project. While collaboration is often essential for Little Science and personal science, the scale of the collaboration in big science is greater. This leads to a natural aversion to Big Science because we know that coordinating multiple different voices is very difficult. However, this is also the strength of Big Science as people with many different perspectives, when herded properly, produce the most powerful form of creativity.
Complex
Big science is a complex solution to complex problems. Big Science is about achieving transformation and making changes. Change only happens as you build an idea from early adopters into a majority. So, to a degree, the more people you can get on board, the more likely you are to achieve the change you seek. In scientific terms, these are paradigms, and paradigms only shift when there is evidence and belief that they need to shift.
Multi-disciplinary
All fields are moving towards a sort of singularity. Medical science, for example, now routinely involves data scientists, computational engineers, economists, statisticians, chemists, and so on. Working in a multi-disciplinary project requires a lot of effort, but it can be worth the investment.
Multi-stakeholder
Creating change requires that you get stakeholders on board. Each stakeholder will have a different perspective and often the ability to resist or even block the change that you are seeking.
Pitfalls of Big Science
The pitfalls of Big Science are that it can lead to a type of groupthink where paradigms are ossified, forming a barrier to paradigm-shifting breakthroughs.
Big Science can also end up being Little Science in Big Science clothing. Scientists are used to working in their small groups, their silos. They often fall back into that pattern when a Big Science project starts. The problem with that is you do not get the full advantage of Big Science, which is the creativity and problem-solving that comes with interacting deeply with a wide group of collaborators. Big Science projects where that happens are just a collection of Little Science projects with a whole lot of extra bureaucratic burden.
2. Little Science
Approachable problems
Little science is about experiments and studies that are feasible and achievable. It is about testable hypotheses. So, the vague and complex nature of wicked problems is to be avoided if you are going to have success with little science.
Curiosity-oriented
Little science is about following curiosity. It is the unexpected finding that reigns supreme. Pursuing unexpected findings is the essence of curiosity. Even before you have findings, little science proceeds from the standpoint that any testable question is of interest because it might lead to an unexpected finding. Our minds are feeble when it comes to anticipating the true nature of things around us.
Knowledge generation-oriented
Little science is highly focused on knowledge generation. It is where something new becomes known. This is its value. The challenge is that knowledge gets stuck as just being knowledge and not translate into something valuable.
Smaller collaborations
The collaborations in little science tend to be smaller. The caveat is that big clinical studies often have multiple investigators; nonetheless, I would still classify it as little science because it is focused on a tractable question and a testable hypothesis.
Simpler
Little science is simpler. If something is too complex and has too many variables to control, it’s not a good topic for little science.
Techniques
The currency of little science is the techniques that people develop. New techniques increase the chance that you will find something new and novel, which is the glory of little science.
Testable hypotheses
Little science is focused on testing hypotheses and, as such, requires that they are testable.
Incremental
Little science is incremental. Even the major breakthroughs are followed by lots of additional work, not only verifying but extending the findings.
The false peak pitfall
False peaks are the pitfall of Little Science. Publications can become sort of a false peak. Instead of combining efforts and creating the amount of data necessary to definitively answer a question, scientists opt for a smaller amount of data or the use of a unique model in order to get their names first on the publication. This is largely why great concepts languish in the translational gap between research and innovation.
3. Personal science
Personal science is both individuals working in a crowd sourcing way and the process of personal improvement. Thinking of that as science is something that only became clear to me by reading and participating in challenge groups by Anne-Laure Le Cunff. It is about using a scientific approach to improve yourself. It begins with curiosity and the design of what Le Cunff calls Tiny Experiments.
For example, in a 30-day challenge as part of Le Cunff's Ness Labs, I wrote at least one permanent note on my Roam Research Knowledge Graph. A permanent note is a note on a topic you are reading about that is meant to stand alone. I had tried multiple times to become consistent with writing permanent notes but failed. By taking it as an experiment and iterating my process, I was able to write 46 permanent notes over 30 days. I now use permanent notes as part of my writing practice, including this article.
Individual curiosity
Personal science is the height of curiosity. Literally, you can follow it anywhere you want to go. Creating tiny experiments provides a framework and a structure for being curious.
Tiny Experiments
Designing and testing Tiny Experiments are two key activities for personal science. The personal aspect makes the concerns and constraints about designing experiments less important. Tiny Experiments are a way to indulge your curiosity and to take action in the face of uncertainty.
Rapid iteration
Like with the Foldit example, personal science is about rapidly trying out different things. It is about not getting hung up waiting to develop a plan. It’s action-oriented. Just try and see what happens, and then try again.
Shifting your own mind-set
Personal science is great for shifting your own mindset. A mindset is a strategy for thinking, and we can be very rigid about our thinking. Giving ourselves permission to experiment with different ways of thinking or different habits helps us gather evidence that a different mindset is better or worse.
Habit formation
Personal science is also a great way to form habits because you have adaptation built into the process.
Journalling
One of the most useful tools for personal science is journaling. You are working with your mind, whether that is to learn something or to test if a different mindset or habit is valuable. Capturing your thoughts as you go is the data capture of personal science. Here is an excerpt from my experience of experimenting with writing permanent notes.
The ‘shiny new experiment’ pitfall
The challenge with personal science is that it can become a means unto itself instead of a way to increase our ability to do more with Little Science and Big Science. This is what happens when we procrastinate by doing more learning.
An operating system for meaningful work
These three types of science are not mutually exclusive. They are, in fact, integral to each other. Together, they form a sort of operating system for meaningful work.
If you are not experimenting and trying to improve your own abilities, you are unlikely to have the agency to do what it takes to do little science and are almost certainly not going to gain the equanimity and the insights needed to operate in a big science environment.
The Little Science is generating the assets, the knowledge, and the techniques that big science seeks to move forward to be applied in creating a "permanently better life."
Is beauty or sexiness more important?
Big Science is beautiful because it is about solving long-standing problems or bottlenecks that enable more research or the translation of science into impact. Little science and personal science are sexier because they are where the stunning breakthroughs are first identified.
So, if we want to do meaningful work, we need all three of these types of science, and they need to be operating at full capacity.
It is a worthwhile exercise to stop and ask yourself if you are optimised on all three levels.
It is very easy to gravitate to one or the other of these three components. We can become addicted to self-improvement, yet if we do not use that improvement to generate new knowledge, it is not going to be that meaningful. Similarly, if we generate new knowledge but no one uses it, the meaning is also drained. If you stay focused on the big science-type projects, its highly social nature and the big vision can be captivating, but that alone is likely to leave you frustrated at the slow pace of progress.
It’s not just for scientists.
Up until this point, I have been referring to this meaningful work operating system in the context of doing what is traditionally thought of as science. However, this operating system is relevant to any line of work.
Ash Maurya advocates for a scientific approach to lean startup development of new enterprises. You have lean experiments to validate ideas, value propositions, and business models. You also have the larger context, the Big Science, of strategy for the company, the mission if you will. The essence of the operating system is that there is a bigger, fuzzier science that you need to push what has been created into a form where it will benefit others; you have a more concrete little science component where you are creating something new, and you have personal science which helps you have the agency to achieve the little and big science.
For myself, I think I use this operating system for the writing I do here on Substack. The personal science is my practice to improve my writing and, more importantly, learn what is the most valued content. The Little Science is the articles themselves. The Big Science is putting this work into the context and the service of getting people to engage and work together more in Big Science projects.
How to put this into practice?
When you think about the three types of science as an integrating operating system for the meaningful, the implication is that all three sciences should be optimised and interacting with each other.
Review each of the features for each type of science.
Jot down what you are doing in relation to each feature or how you perceive the benefits of each feature.
Are you in balance across all three areas?
Does your little science feed into your big science and vice versa?
Do you feel you are as good as you could be at accomplishing things in the little science or big science context?
Read Tiny Experiments and then design your 1-2 experiments to increase the area of your operating system that is most out of balance. Do these over the next two weeks.
Journal the results.
Repeat.
Do more meaningful work
Its not difficult to decide you want to do meaningful work. The real challenge however is continuing to take action towards meaningful work. This post is my first attempt at formulating this science inspired Operating System for Meaningful Work. I would love to have your feedback, even if it is just a thumbs up.
Big Science and big projects are the pieces that are most often missing from our approaches to meaningful work. Start your big project journeying by booking a call to explore how to increase your big project footprint.