Why Funding Scientific Infrastructure May Outperform Funding One Project

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Funding one scientific project can produce an important paper, experiment, or discovery. Funding scientific infrastructure can make hundreds of such projects possible.

Research infrastructure includes not only laboratories, telescopes, research vessels, and expensive instruments, but also databases, software libraries, computing platforms, biobanks, repositories, standards, communication networks, and systems for evaluating scientific work. When these resources are shared, one investment can increase the productivity of an entire research community.

This does not mean infrastructure should always replace direct project funding. Infrastructure without active researchers becomes an expensive unused asset. But when a resource solves a recurring bottleneck for many scientists, funding it may produce a larger and more durable scientific return than funding a single isolated project.

What Is Scientific Infrastructure?

Scientific infrastructure is the shared foundation researchers use to produce, verify, preserve, and communicate knowledge.

It can include:

• physical facilities such as laboratories, observatories, particle accelerators, and research ships;
• scientific instruments such as microscopes, sequencers, sensors, and spectrometers;
• computational resources, cloud services, and high-performance computing;
• research software, mathematical libraries, and standardized workflows;
• datasets, specimen collections, registries, and repositories;
• publication, peer-review, replication, and research-evaluation systems;
• identifiers, metadata standards, and networks connecting research outputs;
• trained technical staff who maintain equipment, software, and data.

The U.S. National Science Foundation describes research infrastructure as encompassing facilities, instrumentation, computational resources, software, services, open data, and workforce-development programs used by researchers across institutions.

In other words, scientific infrastructure is not merely the building in which research occurs. It is the reusable capacity that allows research to occur repeatedly.

The Central Advantage: One Resource Can Support Many Projects

A conventional grant usually supports a defined research team pursuing a defined objective. Its benefits may extend beyond that objective, but its immediate scope remains limited.

Shared infrastructure has a different economic structure. Its fixed cost can be distributed across many users and many research questions.

Consider three possible donations:

1. A grant pays one team to analyze a particular dataset.
2. A grant creates an openly accessible, well-documented dataset.
3. A grant creates a repository that preserves and distributes many datasets.

The first may produce one valuable result. The second can support several studies. The third can support an evolving research ecosystem.

The same logic applies to laboratory equipment, software, mathematical libraries, and replication services. Once the common resource exists, each additional research project may become cheaper or more feasible.

This creates a scientific multiplier effect: infrastructure increases the output not only of its original creators, but also of researchers who use it later.

Infrastructure Reduces Repeated Costs

Research communities frequently pay for the same basic work multiple times.

Separate teams may independently:

• clean similar datasets;
• write equivalent software tools;
• negotiate access to the same materials;
• build incompatible databases;
• maintain duplicate computing systems;
• develop slightly different measurement standards;
• reproduce common technical procedures.

Some duplication is useful because independent approaches can reveal errors. But avoidable duplication consumes resources without producing corresponding intellectual diversity.

A shared and properly maintained resource can reduce this waste. Instead of paying ten research groups to build ten fragile versions of the same tool, a funder may support one robust, documented, interoperable platform that all ten groups can use.

This is one reason the National Institutes of Health encourages researchers to use established data repositories. NIH states that quality repositories can improve whether data are findable, accessible, interoperable, and reusable—the principles commonly summarized as FAIR.

Infrastructure Preserves Value After a Project Ends

Project grants are commonly temporary. Researchers are funded for a defined period, after which the team may disperse, the software may stop receiving updates, and project websites may disappear.

Infrastructure can preserve the useful outputs of many temporary projects.

A maintained repository can keep datasets accessible. A software library can incorporate methods developed by multiple teams. A biobank can preserve samples for questions that were not anticipated when they were collected. A system of persistent identifiers can continue connecting researchers, grants, publications, software, and datasets.

This preservation function matters because scientific results are not valuable only when first produced. They may become more valuable years later when:

• a new method allows old data to be reanalyzed;
• an unexpected connection appears between fields;
• an experiment needs to be replicated;
• a new theory requires historical observations;
• artificial intelligence systems need structured research records;
• a public emergency creates a new use for existing knowledge.

Funding production without funding preservation can therefore create a large amount of nominal research output but a much smaller body of usable scientific knowledge.

Shared Infrastructure Expands Access

Many researchers cannot afford advanced equipment, proprietary datasets, large computing clusters, or specialized technical teams. Access may depend more on institutional wealth than on scientific merit.

Shared infrastructure can partially correct this imbalance.

An independent scientist, small university, researcher in a lower-income country, or early-career team may be unable to build a sequencing facility or supercomputer. They may nevertheless be able to use a shared facility, open dataset, or cloud-based analytical platform.

The European Union treats research infrastructures as resources that should be open and accessible to researchers across Europe and beyond. Its research-infrastructure programs are intended to support scientific advances, innovation, open science, and international cooperation.

This makes infrastructure funding not only a productivity strategy but also an access strategy. It changes the question from “Which institution owns the resource?” to “Which researchers can use it productively?”

For funders concerned with global inequality in science, open infrastructure may be more scalable than repeatedly selecting individual beneficiaries.

Infrastructure Can Connect Otherwise Separate Fields

A project is usually organized around a research question. Infrastructure is often organized around a capability.

That difference matters.

A microscope can serve materials science, biology, medicine, and chemistry. A large language corpus may support linguistics, history, sociology, and artificial intelligence. A mathematical library may be used in pure mathematics, formal verification, physics, and engineering. A climate database may be relevant to ecology, agriculture, economics, public health, and disaster planning.

Because shared tools are not always confined to the intentions of their creators, they can generate cross-disciplinary spillovers.

The National Science Foundation notes that shared facilities and computational resources enable work across major scientific problems, while the European Commission describes research infrastructures as a backbone connecting fundamental research with applications and innovation.

This makes infrastructure particularly valuable when funders cannot reliably predict which future project will be most important.

Infrastructure Supports Reproducibility

Reproducibility depends on more than researcher honesty or methodological competence. It requires durable systems.

Researchers need access to:

• original or appropriately protected data;
• analysis code;
• software versions;
• experimental protocols;
• metadata;
• calibration records;
• computational environments;
• negative and replication results.

Without repositories, standards, documentation tools, and long-term maintenance, reproducibility becomes difficult even when researchers intend to cooperate.

NIH explicitly connects data sharing with reuse, transparency, and reproducibility. UNESCO similarly treats open infrastructures as a core component of open science, alongside open publications, data, software, hardware, and public participation.

Funding infrastructure can therefore improve the reliability of many studies simultaneously. Funding one replication tests one result; funding a replication platform, shared protocol repository, or reusable verification tool can improve an entire field.

Infrastructure Can Support Unexpected Discoveries

Traditional project funding usually asks researchers to predict what they will do and why it will succeed. This structure can work well when objectives and methods are already reasonably clear.

It works less well when important discoveries are unpredictable.

Infrastructure does not need to predict every future use. A telescope can observe phenomena its designers did not anticipate. A specimen collection can answer questions that did not exist when the samples were gathered. General-purpose software can enable methods developed years after the original grant.

This gives infrastructure a form of option value. It preserves multiple possible research paths instead of betting exclusively on one predefined outcome.

The argument is closely related to prediction-free research funding: scientific systems should not require every valuable contribution to be forecast accurately before work begins.

Why Funders Still Prefer Individual Projects

Despite these advantages, infrastructure is often harder to fund than a project.

A project provides a clear narrative:

• a named investigator;
• a defined research question;
• a budget;
• a schedule;
• expected publications;
• a visible success or failure.

Infrastructure produces more distributed outcomes. Hundreds of users may benefit, but no single publication captures its total impact. Maintenance is less glamorous than discovery. Preventing a database from disappearing is important, yet it does not generate the same public attention as announcing a breakthrough.

Infrastructure also creates attribution problems. Who deserves credit for a discovery: the researchers who published it, the engineers who maintained the instrument, the developers who wrote the software, or the curators who prepared the data?

Conventional citation and grant-evaluation systems tend to reward visible final outputs more easily than enabling work. This is why research software, datasets, technical maintenance, peer review, and other supporting contributions can remain underfunded even when many scientists depend on them.

A more complete system of merit-based research funding would recognize both direct discoveries and the infrastructure that made those discoveries possible.

When Infrastructure Funding Is Most Likely to Outperform

Funding infrastructure is especially promising when five conditions are present.

Many researchers face the same bottleneck

If a problem affects only one team, a project grant may be sufficient. If hundreds of teams lack the same dataset, instrument, standard, or software component, a shared solution becomes more attractive.

The resource can be reused

The strongest infrastructure investments support repeated use across projects, institutions, or disciplines. A highly specialized asset with only one plausible user may effectively be a project cost rather than shared infrastructure.

Access can be made genuinely open or broadly available

A resource should not be called shared infrastructure merely because one institution owns it. Funders should examine eligibility rules, fees, technical barriers, licensing, geographic restrictions, and allocation procedures.

Maintenance is financially credible

Building infrastructure without maintaining it can be worse than not building it. Users may become dependent on a resource that later fails.

A credible infrastructure proposal should include:

• operating costs;
• technical staffing;
• security and backup procedures;
• software maintenance;
• equipment replacement;
• data migration;
• documentation;
• governance;
• long-term funding plans.

Its impact can be measured

Infrastructure should not receive unlimited funding simply because it is labeled foundational. Its use and performance should be evaluated.

Relevant indicators may include:

• number and diversity of active users;
• external projects enabled;
• data or software reuse;
• publications and replications supported;
• uptime and service quality;
• cost per user or completed analysis;
• accessibility across institutions and countries;
• training outcomes;
• downstream tools built on the infrastructure;
• evidence that the resource solves a real bottleneck.

The Risks of Infrastructure Funding

Infrastructure is not automatically efficient. Large facilities can become politically protected, administratively expensive, or technologically obsolete.

Major risks include:

Underuse

Funders may build a resource because it sounds strategically important without verifying demand.

Institutional capture

A nominally shared facility may primarily benefit its host institution or an established network of insiders.

Permanent operating costs

A project ends. Infrastructure often creates recurring obligations. Maintenance, staffing, energy, cybersecurity, and upgrades may eventually cost more than construction.

Technological lock-in

Centralizing a field around one platform or standard can discourage competing approaches. Infrastructure should support interoperability and, where feasible, allow users to export their data and workflows.

Concentration of scientific power

Whoever controls access to essential infrastructure may indirectly control which research can be performed. Transparent access rules and pluralistic governance are therefore important.

Misclassification

Not every organization, conference, website, or administrative office is scientific infrastructure. The term should refer to capabilities that researchers demonstrably use—not merely to institutions claiming to support science.

These limitations explain why the correct conclusion is not “always fund infrastructure.” It is “compare the total portfolio-level value of infrastructure with the value of an additional individual project.”

Infrastructure and Project Funding Are Complements

The choice is rarely absolute.

Infrastructure without research projects has no purpose. Projects without infrastructure may become expensive, inaccessible, difficult to reproduce, or impossible to preserve.

A balanced scientific funding portfolio can support:

• individual investigations;
• high-risk exploratory work;
• shared instruments and laboratories;
• open datasets and repositories;
• software and mathematical libraries;
• replication and verification;
• technical maintenance;
• training and access programs.

The central policy question is therefore not whether to fund projects or infrastructure. It is whether the current funding system has the correct ratio between them.

In many fields, the marginal value of another narrowly defined project may be lower than the marginal value of repairing a shared bottleneck that affects hundreds of projects.

How AIIM Could Evaluate Scientific Infrastructure

The AI Internet Meritocracy, or AIIM, proposed by World Science DAO, offers a possible framework for evaluating infrastructure according to demonstrated scientific use rather than institutional prestige alone.

Under a dependency-aware funding model, a later research result could generate recognition not only for its immediate authors but also for earlier contributors whose data, software, proofs, instruments, reviews, or organizational work enabled it.

This matters for infrastructure because its value is distributed through dependency networks.

For example:

1. A funder supports an open scientific software library.
2. Multiple research teams use the library.
3. Those teams publish useful results.
4. The library’s maintainers receive continuing evidence of impact through those downstream uses.

Such a model could make infrastructure funding more responsive. Instead of committing all resources permanently in advance, a system could combine initial construction funding with continuing rewards based on verified adoption, reliability, reuse, and downstream scientific contribution.

AIIM would not eliminate the need for expert judgment. Funders would still need to assess safety, feasibility, governance, and capital costs. But dependency-aware evaluation could reveal forms of contribution that publication counts and conventional project reports often miss.

Readers can explore the broader model through World Science DAO’s discussion of innovative scientific financing and decentralized governance for science.

How Donors Should Evaluate an Infrastructure Proposal

Before funding scientific infrastructure, donors should ask:

1. What recurring problem does it solve?
   The proposal should identify a concrete bottleneck, not merely describe a desirable facility.
2. How many credible users need it?
   Look for evidence of demand from researchers beyond the host organization.
3. Who will be allowed to use it?
   Access policies should be explicit.
4. What does it replace or improve?
   The proposal should explain whether it reduces duplication, lowers costs, improves quality, or enables previously impossible work.
5. Who will maintain it?
   Infrastructure requires responsible technical and organizational ownership.
6. Can its outputs be reused elsewhere?
   Open standards, exportable data, documented interfaces, and appropriate licensing increase long-term value.
7. How will performance be measured?
   Usage statistics alone are insufficient, but complete absence of measurable indicators is a warning sign.
8. What happens if funding ends?
   A responsible proposal should include continuity, migration, archival, or shutdown plans.
9. Could a smaller pilot test the idea?
   Some infrastructure should begin as a limited service before receiving large capital investment.
10. Does it complement existing resources?
    New infrastructure should not duplicate an adequate system merely to create another institution.

These questions also help distinguish genuine research infrastructure from general organizational overhead.

Conclusion

Funding scientific infrastructure may outperform funding one project because infrastructure can support many researchers, reduce duplicated costs, preserve scientific outputs, widen access, improve reproducibility, and enable discoveries that could not be predicted in advance.

Its advantage is not certainty. Infrastructure can be underused, captured, poorly maintained, or made obsolete. Its advantage is leverage.

A single project attempts to produce one valuable result. Well-designed infrastructure increases the probability that many valuable results can be produced.

For governments, foundations, DAOs, and individual donors, the most important comparison is therefore not between a visible project and an abstract support cost. It is between two possible scientific portfolios:

• one containing another isolated research effort;
• another containing a shared capability that improves many present and future efforts.

When the shared capability solves a genuine recurring bottleneck, remains accessible, and has credible governance and maintenance, it may be one of the highest-leverage investments available in science.