From experiment results to a research paper
A useful manuscript makes it possible to trace a conclusion back to the work that supports it. Before drafting, organize what was actually run, what was measured and what remains uncertain. This guide provides a practical evidence table and a final review checklist; it contains no example performance claims.
Prepare the evidence before drafting
- List the completed experiments separately from planned, failed and unfinished work.
- Record each result with its method, dataset version, evaluation split, metric definition and run configuration.
- Keep baseline comparisons and ablations attached to the same evaluation protocol, noting exceptions.
- Identify the source values for every table and figure; keep units and aggregation rules visible.
- Locate the original publications behind citations and check that each cited source supports the nearby statement.
- Write down limitations, missing comparisons and conditions where the method was not evaluated.
Build an evidence map before the narrative
Start with the research question and the observations that address it. A claim should name the setting in which it holds: the tested datasets, evaluation procedure and comparison. Keep broader explanations separate from measured findings. If a statement depends on an experiment that has not finished, leave it unresolved instead of writing the anticipated result.
Turn recorded values into readable figures
Choose a figure that answers one question. Check the plotted values against their source, state the metric and units, and explain what points, lines and error bars represent. When uncertainty is shown, identify how it was computed and how many runs or observations it summarizes. A schematic describes a method; it should not be presented as measured evidence.
Draft methods and results together
The methods section should explain the protocol needed to understand the reported results. Check that the described preprocessing, splits, models and evaluation settings match the experiments actually used. Explain changes between experimental versions. Distinguish an observed association from an established mechanism, and report inconclusive or unfavorable findings when relevant.
A claim-to-evidence table to reuse
Copy these fields into the project notes and create one entry for each important claim. Replace every prompt with a real reference or an explicit unresolved status. An empty cell is a missing check, not permission to invent evidence.
| Field | What to record | Review question |
|---|---|---|
| Claim and scope | The exact proposed sentence and the conditions it describes | Does the sentence extend beyond the datasets, settings or comparisons evaluated? |
| Result source | The actual result file or record, run configuration and metric definition | Can the reported value be located and interpreted without guessing? |
| Table or figure | The manuscript label, source values and plotting settings | Do the visual, caption, units and numerical text agree? |
| Comparison | Baseline or ablation results and the shared protocol | Is the comparison fair, and are material differences disclosed? |
| Citation | Original publication, identifier or URL, and the supporting passage | Does the source support this specific statement rather than only a related topic? |
| Status and limitation | Checked, unresolved or unsupported, with the remaining issue | Should the claim be narrowed, withheld or supported by additional work? |
Review the complete manuscript before sharing
Check that the abstract, results, captions and conclusion describe the same evidence. Verify cross-references and bibliography entries, distinguish completed work from future work, and ensure that limitations are not lost during revision. If a result changes, review every place that depends on it. Scientific review and any venue-specific requirements remain the researcher’s responsibility.
How AutoResearch fits this process
AutoResearch connects experiment outputs with figure preparation, manuscript drafting, review and revision. Its Chinese and English workflows support organizing the research story around inspectable artifacts. Researchers still need to verify sources, numerical claims and interpretation; using the platform does not guarantee publication acceptance.