How AIIM Could Reward Data, Code, Proofs, and Replications Separately

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Scientific funding usually treats a research paper as the main unit of achievement. This approach overlooks much of the work that makes science possible: collecting reliable data, developing research software, proving mathematical results, reproducing experiments, and identifying results that do not survive independent testing.

AI Internet-Meritocracy, or AIIM, could adopt a more granular model. Instead of assigning one reward to an entire project, AIIM could identify distinct scientific contributions and compensate each one separately.

The central principle is simple:

A scientific contribution should be rewarded according to the value it adds to the research ecosystem—not merely according to whether it appears inside a conventional journal article.

Under this model, a dataset, software library, proof, replication, correction, or methodological improvement could each become an independently evaluated and funded research object.

Why Paper-Level Funding Misses Important Scientific Work

A typical scientific paper may depend on many contributors:

• researchers who designed the study;
• technicians who collected and cleaned the data;
• programmers who developed the analytical software;
• theoreticians who proved supporting results;
• statisticians who validated the methodology;
• independent teams that later replicated the findings.

However, funding and academic credit are usually concentrated around the paper’s authorship list. Even authorship may not accurately represent the relative value of each contribution.

This creates distorted incentives. Researchers are encouraged to produce complete, novel-looking papers rather than reusable components. A scientist who publishes a carefully documented dataset may receive less recognition than someone who uses that dataset to publish a striking conclusion. A developer who maintains essential research software may be treated as technical support rather than as a scientific contributor.

AIIM could address this problem by treating scientific outputs as a network of separately attributable objects.

A Modular Scientific Reward System

An AIIM evaluation could decompose research into several contribution classes:

Contribution type	Primary value
Data	Provides reliable empirical observations
Code	Makes analysis, simulation, or experimentation possible
Proofs	Establishes logically valid mathematical conclusions
Replications	Tests whether previous findings remain reliable
Methods	Improves how research is conducted
Reviews	Evaluates and explains existing work
Corrections	Removes errors from the scientific record
Documentation	Makes other outputs understandable and reusable

Each object would have its own identifier, contributors, version history, evidence, dependency relationships, and evaluation record.

A research paper could still receive a reward, but it would no longer absorb all the credit generated by its underlying components.

Rewarding Scientific Data

Scientific data can be valuable independently of the conclusions originally drawn from it. A well-designed dataset may support dozens of later studies, including studies that its creators never anticipated.

The widely used FAIR principles state that research data should be findable, accessible, interoperable, and reusable. They also emphasize machine-readable metadata and the ability of computational systems to locate and process research objects. These principles provide a useful foundation for AIIM’s evaluation of datasets.

What AIIM Could Evaluate

AIIM could assess a dataset according to:

• the quality of its collection methodology;
• sample size and representativeness;
• completeness and error rates;
• metadata quality;
• documentation of transformations and exclusions;
• licensing and accessibility;
• privacy and ethical safeguards;
• compatibility with common formats;
• independent validation;
• demonstrated reuse in later research.

The system should distinguish large data from valuable data. A small dataset measuring a rare phenomenon may be more scientifically useful than a massive collection of poorly documented observations.

Initial and Retroactive Data Rewards

AIIM could issue two forms of compensation.

An initial reward would recognize the work required to create, document, and publish the dataset. A retroactive reward would reflect later evidence of utility, such as reuse by independent researchers, successful model training, inclusion in a meta-analysis, or contribution to a verified discovery.

This prevents evaluation from depending entirely on predictions. AIIM would not need to know in advance which dataset will become important. It could increase the reward as actual scientific use becomes visible.

Data rewards would therefore resemble result-based scientific funding: value could be recognized after the contribution demonstrates utility rather than only before the work begins.

Rewarding Research Code

Modern science increasingly depends on software. Code controls simulations, statistical analyses, laboratory equipment, data processing pipelines, visualizations, and machine-learning models.

Nevertheless, research software is often difficult to cite and inadequately rewarded. The FORCE11 Software Citation Principles argue that software should be treated as a legitimate research product and should support attribution, unique identification, persistence, accessibility, and version specificity.

Code Should Not Be Evaluated Only by Popularity

Repository stars, download counts, and citation numbers can provide evidence of adoption, but they are not sufficient measures of scientific value.

AIIM could evaluate research code using several dimensions:

• correctness;
• test coverage;
• reproducibility of published results;
• documentation;
• maintainability;
• computational efficiency;
• interoperability;
• security;
• version stability;
• independent reuse;
• dependency importance.

A small library used deep inside important scientific workflows could deserve a substantial reward even if few people interact with it directly.

Dependency-Aware Rewards

Scientific code frequently forms a dependency graph. A visible application may depend on a numerical solver, which depends on a matrix library, which in turn depends on low-level infrastructure maintained by another group.

AIIM could trace these relationships and distribute part of the value backward through the dependency chain.

For example, suppose an open-source package enables a major improvement in protein analysis. The final application should receive credit, but so should:

1. the algorithmic implementation;
2. the data-processing library;
3. the numerical infrastructure;
4. the testing framework that established reliability;
5. earlier code from which the method was derived.

This model would reward maintenance and foundational infrastructure instead of concentrating all compensation on the most visible interface.

The FAIR4RS principles extend FAIR-style thinking to research software and emphasize transparency, discoverability, reproducibility, and reuse. These standards could help AIIM define machine-readable criteria for software evaluation.

Rewarding Mathematical Proofs

Mathematics requires a different evaluation model from empirical science. A proof does not become correct because it receives many citations, and it does not become incorrect because it is unpopular.

The primary question is whether the argument validly establishes its stated theorem from its assumptions.

Separating Different Mathematical Contributions

AIIM should distinguish among:

• proving a new theorem;
• simplifying an existing proof;
• weakening a theorem’s assumptions;
• strengthening its conclusion;
• formalizing a proof in a proof assistant;
• constructing a counterexample;
• connecting previously separate mathematical theories;
• developing definitions that enable later results;
• correcting a flawed proof;
• independently verifying a difficult argument.

These contributions are not interchangeable. A short counterexample may invalidate years of research. A simplified proof may make an important theorem accessible to an entire discipline. A formal verification may establish a level of confidence that ordinary peer review cannot provide.

Proof Evaluation by Multiple Methods

AIIM could combine:

• automated logical checks;
• proof-assistant verification where available;
• specialist review;
• comparison with known results;
• dependency analysis;
• novelty detection;
• adversarial attempts to identify gaps;
• later mathematical use.

AI evaluation should not be treated as a declaration of absolute mathematical truth. A model can miss hidden assumptions, misunderstand notation, or accept a persuasive but invalid argument. High-value proofs would therefore require escalating levels of verification.

A preliminary reward could recognize a credible contribution. Additional rewards could follow specialist confirmation, formal verification, or successful use in later theorems.

Rewarding Foundational Mathematics

Some mathematical work has little immediate citation activity because it creates concepts, structures, or languages that take years to diffuse. AIIM should therefore avoid evaluating proofs solely through short-term popularity.

A dependency-aware system could reward foundational work retroactively when later proofs, software, physical models, or formal libraries rely on it. This is particularly important for abstract mathematics, where practical consequences may emerge long after the original discovery.

Rewarding Replication Studies

Replication tests whether a scientific result can be obtained again under sufficiently similar or deliberately varied conditions. It is essential to scientific reliability, but conventional publishing systems often provide weak incentives for it.

The Center for Open Science explicitly focuses on openness, integrity, and reproducibility. Its replication initiatives illustrate that reproducing previous work is an organized scientific activity, not merely the mechanical repetition of an experiment.

Successful and Failed Replications Both Have Value

AIIM should not reward a replication only when it confirms the original result.

A replication may:

• reproduce the original effect;
• produce a smaller effect;
• fail to detect the effect;
• identify sensitivity to experimental conditions;
• reveal an analytical error;
• show that the result applies only to a narrower population;
• uncover missing methodological information.

Each outcome can improve the scientific record.

A failed replication does not automatically prove that the original research was fraudulent or worthless. Differences in samples, instruments, protocols, statistical power, or environmental conditions may explain the disagreement. AIIM should therefore evaluate the quality of the replication process separately from its conclusion.

Criteria for Replication Rewards

A replication reward could depend on:

• fidelity to the original protocol;
• justification for deliberate methodological changes;
• statistical power;
• preregistration;
• transparency of data and code;
• independence from the original team;
• quality of uncertainty analysis;
• attempts to explain conflicting results;
• successful reproduction by additional teams.

AIIM could also provide greater rewards for replicating influential, expensive, clinically important, or policy-relevant findings.

Preventing Double Payment

Separating contributions creates a risk that the same work could be rewarded repeatedly under different labels.

For example, researchers might submit one analytical pipeline as code, method, documentation, and reproducibility work. All four descriptions may be partly accurate, but AIIM should not automatically issue four full rewards.

The system would need contribution-overlap analysis. It could determine:

1. which work is genuinely distinct;
2. which outputs share the same labor and intellectual content;
3. whether one contribution adds value beyond another;
4. whether rewards should be divided or combined.

The goal is not to minimize payment. It is to connect each payment to a distinct addition of scientific value.

Contributor-Level Attribution

After AIIM evaluates a research object, it must determine how to allocate its reward among contributors.

Contributor roles could include:

• conceptualization;
• data collection;
• software development;
• mathematical derivation;
• experimental execution;
• validation;
• documentation;
• project coordination;
• replication;
• maintenance.

Researchers could initially report their roles, but self-reports should be supported by evidence such as repository histories, laboratory records, version-control commits, signed attestations, preregistrations, notebooks, and peer confirmation.

AIIM could then propose a distribution while allowing contributors to challenge factual errors.

This would be more precise than assuming that every co-author made the same contribution or that author order has the same meaning across disciplines.

A Scientific Contribution Graph

The most important architectural element would be a scientific contribution graph.

In this graph:

• nodes represent datasets, code, proofs, experiments, papers, reviews, and replications;
• edges represent dependencies, reuse, criticism, confirmation, derivation, and correction;
• contributors are linked to the specific nodes they helped produce;
• rewards can flow through both direct evaluation and demonstrated downstream utility.

Consider a simplified chain:

Dataset → analytical code → published claim → independent replication → corrected method

A conventional system may primarily reward the published claim. AIIM could assign value to every node and update the allocation as the scientific record develops.

The replication might reduce confidence in the original claim while increasing the value of the dataset. The corrected method might increase the usefulness of both the code and the replication. Scientific value would be represented as dynamic rather than fixed at publication.

Risks and Necessary Safeguards

A modular reward system would introduce new failure modes.

Metric Gaming

Researchers may optimize documentation, repository activity, or superficial reuse indicators without producing equivalent scientific value. AIIM must treat metrics as evidence, not as the final objective.

Artificial Fragmentation

Contributors might divide one project into dozens of trivial objects to generate additional evaluations. Minimum significance thresholds and overlap detection would be required.

Popularity Bias

Widely used contributions are not always the most original or rigorous. Usage should be balanced against correctness, difficulty, scarcity, and substitutability.

AI Evaluation Errors

Automated systems may misread specialized notation, underestimate niche contributions, or favor fields with abundant training data. Human review, appeals, uncertainty estimates, and adversarial testing of scientific funding AI would remain necessary.

Confidential and Restricted Data

Not all valuable scientific data can be made public. Medical, ecological, security-sensitive, or personally identifiable information may require controlled access. AIIM should evaluate responsible stewardship rather than equating openness with unrestricted publication.

How AIIM Could Introduce the Model Gradually

AIIM would not need to solve every attribution problem before launching modular rewards.

A practical implementation could proceed in stages:

1. Allow researchers to register datasets, code, proofs, and replications as separate objects.
2. Assign persistent identifiers and standardized metadata.
3. Perform preliminary AI evaluation with explicit confidence levels.
4. Invite specialist review and challenges.
5. issue modest initial rewards.
6. Track reuse, dependencies, corrections, and replications.
7. Distribute additional retroactive rewards when demonstrated utility grows.
8. Publish the reasoning behind each evaluation and allocation.

This approach would make the system auditable and allow its evaluation rules to improve through evidence.

From Paper Funding to Contribution Funding

The paper remains useful as a structured scientific narrative, but it should not remain the only unit that funding systems can recognize.

Data supplies evidence. Code makes analysis executable. Proofs establish logical results. Replications test reliability. Documentation permits reuse. Corrections prevent errors from propagating.

These outputs perform different functions and should be evaluated by different criteria.

AIIM could therefore move scientific funding from paper-level prestige toward contribution-level merit. Instead of asking only who published the final article, the system would ask:

Which identifiable contributions made the result possible, how reliable are they, and how much value did each one add to science?

By answering those questions separately, AIIM could reward the less visible work on which credible, reusable, and cumulative science depends.