Open Science

A cheap generic drug reduces a common complication by 40% in a mid-size clinical trial. The results are solid. The methodology is clean. The drug costs pennies per dose. Nobody funds the follow-up study. Not because the science is weak, but because there is no patent to extend, no exclusive license to sell, no billion-dollar market to capture. The discovery sits in a journal archive, cited six times in twelve years. This is the graveyard of solutions that work: research that produces genuine value but lacks a commercial constituency. The pattern repeats across fields. An agricultural technique that reduces water use by 30% but relies on freely available seeds. A diagnostic method that costs one-tenth of the standard test but uses off-patent reagents. A soil remediation approach that works better than commercial alternatives but cannot be trademarked. The market does not fund what it cannot own. And institutional science increasingly follows market logic. Grant committees ask about commercialization potential. Universities measure technology transfer revenue. Researchers learn to frame discoveries in terms of market opportunity rather than public benefit. The result: effective solutions without profit margins join the file drawer, while expensive marginal improvements get funded repeatedly. The question is not whether commercialization matters. It does. The question is whether a system that only values what can be sold is capable of producing the full range of knowledge humanity needs. Some of the most important research of the next decade will produce solutions that save money rather than make it. Methods that reduce costs, simplify processes, or use freely available materials. These contributions are economically valuable to society but economically invisible to the current funding apparatus. A model built around science crowdfunding offers one path forward — funding that comes from people who benefit from solutions rather than from entities that need to own them. When the beneficiaries of research are ordinary people, ordinary people can be the ones who sustain it. Discussion question: What is the most impactful piece of research you know of that was abandoned not because it failed, but because it succeeded too cheaply?

A pharmaceutical researcher tests forty-seven compounds against a cancer cell line. Forty-four show no effect. Three show partial inhibition. Only the three positive results get published. The forty-four failures vanish into a file drawer. Six months later, a lab in Germany tests the same compounds. Same null results. Same file drawer. A year after that, a team in Japan repeats the experiment. Still unpublished. This is publication bias in action, and it wastes billions of dollars annually. A 2018 meta-analysis estimated that roughly half of all clinical trials with negative results never appear in the literature. In preclinical research, the ratio is worse. Journals want novelty. Reviewers want positive findings. Career advancement requires publications. So researchers learn quickly: null results are career poison. The consequences compound. When negative data stays hidden, other researchers waste time and money chasing dead ends. Meta-analyses become skewed toward false positives. Entire fields build on foundations that would collapse if all the data were visible. The solution is not just "publish everything." That creates noise. The real fix is evaluating research based on methodological rigor and data quality rather than on whether the hypothesis happened to be correct. A system built on research impact funding would reward transparent reporting regardless of outcome. Negative results that prevent others from wasting resources are valuable contributions — they just are not currently valued. Question: Have you ever sat on negative results that could have saved someone else months of work? What would it take to make publishing those results career-safe?

A 2024 survey of early-career researchers found that 43% spend more time on administrative compliance than on actual research. Grant reports, ethics paperwork, procurement forms, progress updates, budget justifications — the list never ends. Nobody disputes that accountability matters. But somewhere along the way, the system forgot that accountability should serve the work, not become the work itself. Consider a typical early-career trajectory. A postdoc publishes two strong papers, gets noticed, applies for a faculty position. But the job requires not just research output — it requires demonstrated grant-writing ability. So they spend their postdoc years learning to write proposals instead of running experiments. By the time they land a tenure-track role, they have become excellent fundraisers and mediocre scientists. The irony cuts deep. We select for grant-writing skill, reward it with institutional resources, and then wonder why so much funded research feels incremental and safe. A senior researcher I spoke with recently put it bluntly: "I spend four months a year writing proposals. Two months managing grants I already have. That leaves six months for science. And I am one of the lucky ones with a stable position." For independent researchers outside institutions, the barrier is worse. They cannot access most funding streams at all — not because their ideas lack merit, but because they lack the institutional scaffolding to navigate the bureaucracy. The solution is not less accountability. It is accountability that does not require researchers to become administrators. Imagine a system where funding flows based on demonstrated contribution rather than projected promises. Where researchers submit work, not proposals. Where the community evaluates results, not credentials. This is what research impact funding tries to achieve — shifting the evaluation point from before the research happens to after it produces value. Less paperwork upfront. More recognition for what actually works. What is the most absurd bureaucratic hurdle you have faced as a researcher? And what would you do with those hours if you got them back?

Consider this: a 2020 study found that 87% of published research papers in computational biology depend on fewer than ten open-source tools. Tools maintained by small teams, often volunteers, frequently working nights and weekends. When those tools break, thousands of research pipelines fail. When those maintainers burn out, entire fields slow down. Yet the funding system treats this infrastructure as invisible. Here is the problem in concrete terms: The dependency gap: A bioinformatics researcher uses samtools, BWA, and GATK daily. Each tool represents person-decades of work. Each receives a fraction of the funding allocated to any single wet-lab project that depends on them. The maintenance trap: Grant agencies fund new tools, not maintenance. So researchers build v1.0, publish, get tenure points, then move on. Users are left with unmaintained software that breaks with every OS update. The invisibility tax: When a critical library like NumPy or scikit-learn works flawlessly, nobody notices. When it fails, everyone notices. Funding follows visibility, so foundational work remains chronically underfunded. The bus factor problem: Many critical scientific tools have a bus factor of one or two. If those maintainers leave, entire research workflows become fragile. The open-source software movement solved this problem in tech. Companies pay for support contracts, sponsor maintainers, fund core infrastructure through organizations like the Python Software Foundation or Apache. Science has not caught up. A Grants for Free Software approach would treat open-source scientific tools as public goods worthy of sustained funding—not one-time development grants, but ongoing maintenance support tied to actual usage metrics and community impact. The question: Should grant applications be required to disclose their software dependencies? And if a project depends on unfunded open-source tools, should a percentage of the grant automatically flow to those maintainers?

When a donor wants to give to science, they usually fund what they can see: a new wing on a hospital, a named professorship, a flashy clinical trial for a disease with strong advocacy. But the research that actually changes everything? The methodological improvements that make drug trials more reliable. The statistical frameworks that prevent false positives. The database infrastructure that lets researchers find patterns across decades of work. The open-source tools that thousands of labs depend on but nobody funds. That work is invisible. It does not save a specific life you can point to. It does not produce a headline. It does not make for a good gala speech. So it starves. Here is the uncomfortable truth: a $100,000 donation to improve how clinical trials are designed could prevent millions of dollars in wasted research and accelerate effective treatments across dozens of diseases. A $50,000 grant to maintain a critical open-source bioinformatics tool could enable thousands of discoveries that would never happen otherwise. But donors want stories. They want before-and-after photos. They want to see their name on something. The result is a funding landscape where: Incremental research in popular fields gets funded repeatedly Foundational infrastructure work goes unfunded or relies on volunteer labor Methodological improvements that would benefit entire fields are treated as "not exciting enough" Researchers who build tools others depend on struggle to justify their work to grant committees This is not about bad donors. This is about a mismatch between what looks impactful and what actually is. The people building science research donation platforms are trying to solve this by making impact measurable after the fact. Instead of asking "does this sound important?" they ask "did this work actually get used, cited, and built upon?" That shift could unlock a massive wave of philanthropic capital for the unsexy work that makes everything else possible. What do you think: should donors care more about measurable downstream impact than visible upfront results? And if so, how do we make that cultural shift happen?

Every year, billions of dollars in research funding vanish into what statisticians call the file drawer problem. A team runs an experiment, finds no significant effect, and files it away. Unpublished. Invisible. As if it never happened. A 2018 meta-analysis estimated that roughly half of all clinical trials go unpublished. In some fields, the number is worse. The result? Other researchers spend years and millions repeating the same dead-end experiments because nobody told them the path was already explored and abandoned. This is not a minor inefficiency. It is a structural distortion of scientific knowledge itself. When only positive results see the light, entire fields bend toward false positives. Meta-analyses become unreliable. Policy decisions rest on incomplete evidence. The incentive structure is brutal: journals want novelty, funding bodies want impact, and negative results offer neither. A researcher who publishes "we tried this and it did not work" gets less credit than one who publishes a shaky positive finding. Open science advocates have pushed for pre-registration and results-blind review, but adoption remains patchy. The deeper fix is cultural and infrastructural: we need systems where all rigorous research — positive, negative, or inconclusive — contributes to collective knowledge and earns recognition. This is precisely why Open Science Funding models matter. When evaluation is based on methodological rigor rather than headline results, the file drawer empties. When contributions are tracked transparently, negative findings become as citable as breakthroughs. Discussion question: What is the most wasteful duplication of research effort you have witnessed in your field because negative results were never shared?

Here's a pattern I keep seeing: someone without a PhD develops a genuinely novel approach to a scientific problem, but when they seek funding or collaboration, they hit an invisible wall. Not because their methodology is flawed, but because the system uses credentials as a proxy for competence. The traditional funding pipeline assumes that institutional affiliation equals rigor. Peer review is supposed to be the great equalizer, but reviewers often see the author list before the methodology. Double-blind review helps, but it's not universal, and even when implemented, the discussion phase reveals identities. What if we flipped the model? Instead of funding people based on where they've been, fund ideas based on what they propose and how they plan to validate them. Merit-based evaluation would look at: The specificity and testability of hypotheses The methodological soundness of proposed experiments The track record of actually completing work (not just publishing it) Transparent peer review where anyone can contribute feedback This isn't about lowering standards. It's about recognizing that brilliance doesn't always wear a lab coat or have a university email address. Some of the most innovative thinking happens outside traditional institutions because outsiders aren't constrained by the paradigm their field is currently optimizing for. The counterargument I usually hear: "But how do we filter out crackpots?" My response: the same way we always do - through rigorous methodology review, not credential checking. A well-designed experiment from an amateur is worth more than a sloppy one from a tenured professor. Discussion question: Have you ever had a research idea dismissed because of your background rather than its merits? What would a truly merit-based funding system look like to you? More on building alternative paths for independent researchers: Science DAO's meritocracy model