agsy-reproducibility-and-data-policy

Reproducibility & Data Policy (agsy-reproducibility-and-data-policy)

AgSy is a modelling journal, so reproducibility is not just about data — it is about whether someone

else could re-run or re-implement your model. Elsevier's research-data policy explicitly counts

software, code, models, algorithms, protocols, and methods as research data. Build the package as

you go so submission and revision do not stall. The 2026-06-20 Guide for Authors refresh says

Agricultural Systems applies Option C: deposit research data, cite and link it, or state why

sharing is not possible.

When to trigger

• Building the data + code + model materials for submission
• Writing the data-availability statement
• Data, code, or the model cannot be fully shared (licence, privacy, proprietary model) and you need

the exemption path

• Preparing model documentation so reviewers can assess reproducibility

What AgSy / Elsevier expects

1. Deposit research data in a repository. Use a recognized repository (Mendeley Data, Zenodo, OSF,

or a domain repository), cite and link the dataset in the article. Not a personal website.

1. Code and models count as data. Deposit run scripts, parameter files, and — where licensing

allows — the model code or a pointer to the exact model version. A black-box model with no access

path weakens the paper.

1. Data-availability statement. State the availability of data at submission. If data, code, or the

model cannot be shared, explain why (third-party licence, privacy, proprietary model) and how

others could obtain equivalent access.

1. Model description. Document model version, structure/equations, parameter sources, calibration

vs. evaluation data, and driving inputs. For agent-based models, follow the ODD protocol so the

model can be re-implemented.

When data/code/model cannot be shared (exemption path)

• Explain why (proprietary model, licensed input data, privacy/legal restrictions).
• Give a README describing exactly how others can obtain access (provider, licence, version).
• Where possible, share synthetic inputs or a reduced example so the workflow can be exercised.

Build-as-you-go checklist

• [ ] One master workflow regenerates every table/figure from inputs + model runs
• [ ] Data + code + model/run scripts deposited in a repository with a DOI/permanent link
• [ ] README documents data provenance, model version, calibration/evaluation split, and how to reproduce each exhibit
• [ ] Seeds set and reported for every stochastic step (Monte Carlo, ABM, weather/price generators)
• [ ] Software/model versions pinned (renv.lock / requirements.txt / environment file)
• [ ] Exhibit numbers in the manuscript match the package output exactly
• [ ] Restricted materials: exemption note + access instructions + synthetic example where feasible
• [ ] Data-availability statement drafted for the manuscript

Anti-patterns

• Treating the package as a post-acceptance afterthought
• Depositing data but not the code or model (Elsevier counts them as research data)
• A black-box model with no version, parameters, or access path
• A personal URL instead of a citable repository with a permanent identifier
• Undocumented, un-seeded, unpinned runs that "work on my machine"

Worked micro-example (illustrative)

An agent-based crop–livestock model with a licensed weather input is packaged. Elsevier treats more than

tabular data as "research data," so each artifact maps to a sharing path:

• Model code is open → deposited on Zenodo with a tagged version and DOI; the commit is cited.
• Weather data is licensed → cannot be redeposited. The README names the provider, license, and

version, and ships a synthetic series so a reader can exercise the workflow end-to-end.

• ABM documentation follows the ODD protocol so the model can be re-implemented, not just re-run;

seeds are fixed and renv.lock pins the toolchain.

Outcome: a reviewer can reproduce every exhibit except the licensed input, for which a documented,

exercisable substitute exists.

Referee pushback → the AgSy-specific fix

• "The model is a black box." → Deposit code (or pin the version) and document structure, parameters,

and the calibration/evaluation split; add the ODD protocol for an ABM.

• "Data are on a personal website." → Move to a citable repository with a permanent identifier.
• "Only the data is shared." → Add run scripts, parameter files, and the model/version — Elsevier

counts them as research data.

Calibration anchors

• Agricultural Systems currently uses Elsevier Option C research-data instructions: deposit, cite,

and link research data, or explain why sharing is not possible.

• The ODD protocol is a community standard for agent-based models, not a journal format.

Output format

【Repository】data + code + model deposited with DOI/link? [Y/N]
【Reproduces tables/figures?】master workflow verified locally? [Y/N]
【Model documented】version + parameters + calibration/eval split (+ODD if ABM)? [Y/N]
【Restricted?】exemption note + access path + synthetic example?
【Data-availability statement】drafted? [Y/N]
【Next】agsy-review-process

Supplementary resources

• [../../resources/external_tools.md](../../resources/external_tools.md) — repositories, version-pinning, and model-description standards (ODD)
• [../../resources/official-source-map.md](../../resources/official-source-map.md) — Elsevier research-data policy (data, code, models)