Reporting & Data Policy (fcr-reporting-and-data-policy)

SkillMedia

Use when preparing the reporting completeness and research-data materials for a Field Crops Research (FCR) manuscript. FCR requires a data-availability statement at submission, full agronomic reporting (cultivar, soil, weather vs. phenology, management, design), declaration of any generative-AI use, and supports data/methods co-submission to Data in Brief / MethodsX. Prepares the materials; it does not waive requirements.

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the Reporting & Data Policy (fcr-reporting-and-data-policy) skill

What this skill tells your AI

The instructions your AI receives, as published by brycewang-stanford/awesome-journal-skills in Field-Crops-Research-Skills/skills/fcr-reporting-and-data-policy/SKILL.md and read by ahel’s review.

FCR does not just want results — it wants the agronomic context that makes them interpretable and reproducible, plus a statement of data availability at submission. Build these as you go so they do not stall the submission.

When to trigger

  • Assembling the methods section for completeness before submission
  • Writing the data-availability statement required at submission
  • Deciding whether to co-submit a dataset/method to Data in Brief / MethodsX
  • Disclosing any use of generative AI in preparing the manuscript

Reporting completeness (FCR-specific)

A field-crop paper is reproducible only if the methods report all of:

  • Crop & cultivar(s) and, where relevant, maturity group / genotype identity
  • Site(s) & seasons with coordinates; the environments and what they represent
  • Soil properties (type, texture, relevant chemistry) for each site
  • Weather (radiation, temperature, rainfall) shown in relation to crop phenology
  • Management: sowing date/density, fertilisation, irrigation, crop protection, tillage
  • Experimental design: layout, randomization, replication, plot size, guard rows
  • Statistics: model, error structure, software/version (see fcr-data-analysis)
  • Yield data (encouraged) and the biophysical processes linked to it

Data availability (state it at submission)

  1. Declare availability. FCR/Elsevier requires authors to state the availability of any data at submission; the statement is published with the article on ScienceDirect.
  2. Share where possible. Deposit data in a recognised repository (e.g., Mendeley Data, or a domain repository) and cite it with a persistent identifier; FCR datasets are hosted openly under Creative Commons terms on Mendeley Data.
  3. If data cannot be shared. State the reason (e.g., sensitive, confidential, or provider-restricted) in the data-availability statement during submission.
  4. Co-submission. Standalone datasets or methods can be forwarded to Data in Brief / MethodsX alongside the article.

Other declarations

  • Generative AI. Declare any use of generative-AI tools in the manuscript-preparation process at submission, per Elsevier policy.
  • Ethics / authorship / conflicts. Standard Elsevier declarations (CRediT roles, competing interests, funding) where applicable.

Minimum agronomic metadata table (what an FCR methods reviewer audits)

A methods reviewer for a field-crop journal reads for reproducibility line by line. The gaps that draw "cannot evaluate" comments are predictable; carry this table so each item is on the page, not in a lab notebook.

BlockMust reportWhy FCR cares
Genotypecultivar name, maturity group, seed source/yearG×E claims need genotype identity
Environmentsite coordinates, elevation, seasons, what each environment representsdefines the target population of environments
Soilclassification, texture, depth, pH, organic C, available N/P/Kyield response is soil-conditional
Weatherradiation, max/min temperature, rainfall, aligned to phenologylets readers interpret G×E
Managementsowing date/density, N/P/K rate and timing, irrigation, crop protection, tillagethe "M" in G×E×M
Designlayout, randomization, replication, plot size, guard rowserror structure depends on it

Worked data-availability vignette (illustrative)

Illustrative; figures are for demonstration only. A multi-environment trial of two wheat cultivars across 3 seasons × 5 sites (15 site-years) reports grain yields of 4.2–7.8 t ha⁻¹ and a per-plot dataset of N rate, anthesis date, and yield. Drafting the statement: the plot-level table (15 site-years × 4 N rates × 3 reps ≈ 180 rows) is non-sensitive, so deposit it in Mendeley Data under Creative Commons, cite it with the DOI, and state "data are openly available at [DOI]." One site's soil-survey layer is licensed from a provider that forbids redistribution — for that layer only, state the restriction and its reason rather than leaving a blanket "available on request." The published statement must let a reader regenerate every yield mean and SED in the results. Check the repository and statement wording against the journal's author guidelines during the final upload-week pass.

Anti-patterns

  • A methods section missing soil, weather-vs-phenology, or management detail (not reproducible)
  • Leaving the data-availability statement blank or to the last minute
  • "Data available on request" with no reason and no repository where sharing was feasible
  • Forgetting the generative-AI declaration
  • Reported yields that the deposited data or analysis cannot regenerate
  • Depositing summary means only, so the plot-level structure (blocks, sub-plots) cannot be reconstructed

Output format

【Reporting complete?】cultivar + site/season + soil + weather-vs-phenology + management + design + stats? [Y/N]
【Yield data linked to process?】[Y/N]
【Data-availability statement】shared (repo + ID) / restricted (reason) — drafted? [Y/N]
【Co-submission】Data in Brief / MethodsX relevant? [Y/N/NA]
【Generative-AI declaration】[Y/N/NA]
【Next】fcr-writing-style

Supplementary resources

Signals

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Key
fcr-reporting-and-data-policy
Source
github.com/brycewang-stanford/awesome-journal-skills