The Problem
Research Software is Failing
The digital infrastructure that powers modern science is dangerously fragile. Critical research tools operate without sustainable funding, relying on volunteers and short-term grants. This isn’t just inconvenient—it’s a systemic threat to scientific progress.
The Scale of the Crisis
🔬 Dependency Crisis
Essential tools across every research domain—from EEGLAB in neuroscience to NumPy in data science—operate without guaranteed funding.
⚠️ Critical Fragility
The loss of just 1-2 key maintainers could collapse entire research ecosystems that millions depend on.
💔 Maintainer Burnout
Volunteer maintainers burn out from unglamorous but essential work that receives no institutional recognition.
Why Traditional Funding Fails
🎯 The Innovation Bias
Funding agencies reward shiny new projects, not boring maintenance. But keeping software running is just as critical as building it.
⏰ Grant Cycle Mismatch
Research grants last 2-5 years. Software maintenance is forever. When grants end, maintenance stops—and software breaks.
🏢 Career Disincentives
Universities reward faculty for publications, not software upkeep. Research software engineers have no clear career path for maintenance work.
Real Consequences
🚨 Research Disruption
When critical tools fail, entire research programs grind to a halt while researchers scramble for alternatives.
💸 Wasted Resources
Researchers spend months troubleshooting broken software instead of doing science.
🔄 Reproducibility Crisis
Broken or abandoned software makes reproducing scientific results impossible.
🚧 Barriers to Entry
Poor documentation and unstable tools exclude newcomers and underrepresented groups.
The Network Effect Problem
Research software doesn’t exist in isolation. Tools depend on dozens of other packages. When one component fails, it can cascade through the entire ecosystem. A single point of failure can break thousands of research workflows.
Example: The NumPy Near-Miss
NumPy—fundamental to scientific computing in Python—operated for years without dedicated funding despite supporting millions of researchers. Only heroic volunteer efforts and recent foundation support have stabilized it. NumPy is the exception. Most research software lacks such visibility and support.
Why This Threatens Science Itself
Science builds on previous work. When research software fails, it doesn’t just inconvenience current researchers—it breaks the chain of knowledge transfer that enables scientific progress.
The sustainability crisis in research software is a threat to the continuity of scientific knowledge.
There Is a Solution
The math is simple: just 0.1% of grant funding could solve this crisis completely. We know how to fix this—we just need to act.