What is a Data Paper?

A Data Paper is a peer-reviewed, citable scholarly article whose primary purpose is to describe a research dataset and its documentation (metadata), so the data become discoverable, accessible, and reusable by other researchers. Rather than “telling a results story,” a Data Paper provides a rigorous record of what the data are, how they were generated, how they are structured, and how they can be reused responsibly.

How is it different from a traditional research article?

  • Data-centered (not conclusion-centered): A Data Paper prioritizes the dataset description and the conditions of data collection/production, instead of emphasizing hypotheses, statistical results, and conclusions as the main contribution.

  • Reuse and transparency: It is written so other researchers can understand and use the dataset with confidence, including in new contexts and across disciplines.

  • Scholarly credit for data curation: It formally recognizes the work involved in collecting, cleaning, organizing, and documenting data—effort that is often under-credited in conventional publications.

Why publish a Data Paper?

  • Greater visibility and impact: Your dataset can support new analyses and generate new publications within and beyond your field.

  • Higher trust in the data: Detailed documentation enables verification, validation, and responsible reuse.

  • Academic credit: The dataset and its curation become citable through a peer-reviewed article.

  • More collaboration: Data availability increases the likelihood of partnerships with researchers interested in your dataset.

Core requirement at LADS: open, robust datasets

At LADS, every Data Paper must be associated with a robust dataset, properly documented and openly available (as supplementary material within the system and/or deposited in a recognized open repository). This is a fundamental part of the journal’s scope.

What should accompany the manuscript?

To maximize reuse and reproducibility, we recommend that the data package include:

  • Dataset files in appropriate formats, with a clear organization by files/tables/tabs.

  • Data dictionary (codebook) defining variables, units, codes, categories, missing values, and consistency rules.

  • README describing folder/file structure, how to open and interpret the data, versions, limitations, and recommended use.

  • Data availability statement indicating where the data are hosted, how to access them, and the applicable license.

  • Persistent identifier (when available, a DOI or repository ID) and a recommended citation for the dataset.

  • Code/scripts (when applicable) to reproduce cleaning, transformation, and validation steps.

Recommended structure of a Data Paper

Although formats vary by discipline, a strong Data Paper typically includes:

  1. Context and motivation: Why the dataset was produced and what gap it addresses.

  2. Data collection/production: Study design, instruments, sampling, procedures, and relevant protocols.

  3. Dataset description: File structure, variables, formats, organization, and curation decisions.

  4. Validation and quality control: Checks performed, known limitations, and evidence of internal consistency.

  5. Access and licensing: Where the data are deposited, how to access them, and the terms of use.

  6. Reuse potential: Example use cases, research questions the data can support, and reuse recommendations.

What we typically evaluate

  • Dataset robustness and usefulness to the research community (value and reuse potential).

  • Documentation quality (sufficient metadata to “understand and reuse”).

  • Accessibility (data available to reviewers and publicly available upon publication).

  • Ethical and legal compliance (including appropriate de-identification when applicable).

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