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To make a computational mathematics result reproducible, preserve the exact inputs, code, parameters, software versions, and relevant computing environment used to produce it—and give readers a clear way to rerun or inspect the work. Connect the paper’s claims to the specific files that support them, and explain what level of numerical agreement is meaningful. A methods description alone may not be enough for another researcher to repeat a complex computation.
Start by defining what the computation is meant to establish
Before packaging files, make the role of the computation in the paper explicit. Name the mathematical problem, the assumptions made, and the numerical experiment or calculation being performed. Then explain how its result supports the paper’s claim. A computation can provide evidence about a result, test a conjecture, or illustrate a theorem; it is not automatically a proof. Where the paper relies on a formal proof, distinguish that proof from computational evidence so readers can assess each on its own terms.
This framing also helps determine what another researcher needs to reproduce. A figure generated by one parameter sweep may depend on a different subset of data and code than a table summarizing a full experiment. Identify the relevant claim, figure, or table and trace it to the computation and output files behind it.
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Preserve the complete computational recipe
The National Academies’ 2019 report, Reproducibility and Replicability in Science, recommends clear, specific, and complete information about the computational methods and data products that support published results, subject to restrictions on nonpublic data. For computational mathematics, that means documenting more than the name of an algorithm or a short methods narrative.
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Inputs, methods, and parameters
- Include the input data used, or explain how to obtain or generate it. Identify relevant transformations and preprocessing steps.
- Describe the algorithms and computational steps in enough detail for someone else to implement or run them. Record parameter values, configuration, and other choices that affect the calculation.
- Preserve scripts or other executable code used to produce the reported outputs, along with intermediate or output data when a step is nondeterministic and cannot in principle be reproduced.
- Map each important conclusion, figure, and table to the result files that support it. State which inputs and steps produced those files.
A file collection is useful only if its parts have clear roles. Explain which files are inputs, which are generated, and which are used to create a particular reported result. If an input is generated procedurally rather than supplied as a data file, preserve the generation procedure and its settings.
Software and computing environment
Record software names and versions, libraries and dependencies, operating system, hardware architecture, relevant hardware details, and system software. For parallel computations, document the relevant parallel setup. Pin dependencies where feasible, or otherwise describe them precisely enough that a reader can reconstruct the environment. A code repository without dependency information may leave a rerun blocked by software changes; an environment description is part of the method, not an optional convenience.
Report numerical choices and evidence of reliability
In a numerical paper, the output depends not only on code but also on numerical choices. Report the precision used, grid or discretization resolution, stopping criteria, and other choices relevant to the computation. State enough about the algorithm and test environment for readers to judge whether a numerical difference indicates a problem, an expected sensitivity, or a materially different result.
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Describe verification, validation, and sensitivity
Explain the verification and validation tests that bear on the result, and report uncertainty or sensitivity where it affects the claim. The appropriate checks depend on the computation; authoritative guidance does not prescribe one tolerance that fits every numerical method. Instead of declaring that results “match” without qualification, state the comparison standard used and why it is appropriate to the paper’s claim.
Bit-for-bit identity is not a universal requirement. Hardware, libraries, parallel execution, and numerical behavior can affect whether identical output is reasonable to expect. Do not promise exact identity unless the method and computing setup justify it. If exact equality is not appropriate, characterize the relevant computational uncertainty and explain what consistency is sufficient to support the conclusion.
Deposit a citable version of the materials
Put the code, required data, scripts or workflows, configuration, and outputs needed to inspect the published results in a repository suited to the work. A persistent identifier, such as a DOI, helps readers identify the particular artifacts used for the paper. Cite that version in the article rather than pointing only to a project page whose contents may change.
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Choose a repository based on whether it can preserve and identify the exact version; whether it can hold the needed code, data, dependencies, and workflow together; its persistence and identifier support; its licensing and access controls; and its fit with the discipline and journal’s requirements. Also account for the resources required to rerun the computation. The guidance does not establish one repository as best for every field or project.
State the license and any access restrictions. If data are too large to deposit in full, explain the constraint and provide a carefully justified subset sufficient to assess the published figures and tables, where possible. Do not imply that restricted inputs are publicly available. Explain what readers can access and what parts of the result they can still inspect.
Give readers a usable rerun path
A repository should answer practical questions a reader would otherwise have to guess: what prerequisites are needed, where the required inputs are, which workflow or script to run, and what outputs to expect. Include these details in a README or equivalent instructions and make the sequence of steps unambiguous.
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- Identify the preserved release. Point to the versioned repository deposit cited by the paper, not just a mutable development location.
- List prerequisites. Name the operating system or environment assumptions, software versions, dependencies, and any relevant hardware or parallel-computing requirements.
- Locate inputs. Give the reader the deposited data or the documented procedure for obtaining or generating it. Identify configuration and parameter files.
- Describe execution. Provide the commands or workflow steps used to run the computation, in order. State where each step should be run and which files it reads or creates.
- Specify expected results. Identify the outputs that correspond to the paper’s claims, figures, and tables, along with the numerical comparison standard readers should use.
A computational notebook can combine explanation and execution, but it does not replace the supporting files or environment details. Include the data files required to run it, make dependencies explicit, and preserve the notebook in a repository appropriate for long-term access. Consider providing a rendered HTML or PDF version as well, so readers can inspect the narrative and results without running the notebook.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Check the receiving journal’s policy
Requirements differ by journal and by the role computation plays in a paper. The following policies are examples, not universal rules; check the target journal’s current author instructions before submission.
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| Venue and guidance | What the cited policy says | Practical implication |
|---|---|---|
| AAS Journals, notebook policy dated February 2024 | The journals do not accept notebooks themselves as part of an article. The policy asks authors to place notebooks in a public repository that can provide a DOI, include required data in the DOI deposit, make dependencies explicit, and cite the DOI in the paper. It also suggests considering rendered HTML or PDF. | Deposit the notebook and the data needed to run it, then cite the persistent deposit; do not treat the article submission itself as the notebook archive. |
| AGU, “Data and Software Sharing Guidance for Authors Submitting to AGU Journals” | The guidance calls for data and software availability statements and citations, and recommends preserving model versions, configuration, parameters, workflows, and relevant outputs. It recommends persistent identifiers and trusted repositories. For purely theoretical work with no data, models, or analysis software, the statements can say these do not apply. | Describe the materials and versions used, cite them, and make the availability statement fit the actual work. |
| INFORMS Journal on Computing, “Software Policy” | For papers whose primary contribution is computational experiments, software release to the research community is a condition of final acceptance, subject to the policy’s scope and exceptions. The policy may not apply when computation only illustrates a theorem-focused paper. | Determine whether computational experiments are the paper’s primary contribution and review the policy’s scope and exceptions. |
These examples show why a generic claim that “the journal requires open code” can be misleading: a policy may distinguish notebooks from articles, require release only for a defined class of papers, or allow an availability statement to reflect work with no data or analysis software.
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Disclose limits without overstating reproducibility
If data are restricted by a nonpublic-data policy or a dependency is proprietary, say so directly and explain the boundary: what cannot be shared, why access is limited, and what code, parameters, outputs, or other evidence remain available for inspection. Do not present a computation as fully rerunnable if a required input or dependency is unavailable to readers.
A theoretical paper with no data or analysis software can say that those materials do not apply in its availability statement. For other projects, distinguish among the parts that can be rerun, the parts that can only be inspected, and the parts that cannot be shared. This gives readers an accurate account of the evidence without implying that every paper has the same reproducibility requirements.
Use a final reproducibility checklist
- Does the paper state what mathematical claim the computation supports and how the computation bears on it?
- Can a reader find the inputs or reproduce their generation, and identify the algorithms, steps, and parameters?
- Are code, software versions, dependencies, operating system, relevant hardware, and parallel setup documented?
- Are precision, resolution, stopping criteria, verification or validation, and relevant uncertainty described?
- Do the deposited files let a reader connect outputs to the claims, figures, and tables in the paper?
- Is the cited artifact a preserved, identifiable version, with a persistent identifier where available?
- Do the instructions specify prerequisites, execution steps, and expected outputs?
- Are licenses, access restrictions, and the meaningful standard for numerical agreement stated?
- Does the article meet the current policy of its journal and any applicable funder requirements?
The National Academies’ 2019 recommendation captures the central aim: convey enough specific information about computational methods and data products for others to repeat the analysis, except where nonpublic-data policies restrict that information. For computational mathematics, doing so also means making the numerical choices and limits visible, so readers can assess not just whether they can run the work, but what agreement with the published result would mean.
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