Earth System Science Data

Earth System Science Data (earth-system-science-data)

Journal positioning

Earth System Science Data (ESSD) is the Copernicus / European Geosciences Union venue for data papers: peer-reviewed articles whose primary product is an original, high-quality, openly deposited dataset of lasting value to earth-system science. The defining expectation is not a new scientific conclusion but a reusable dataset: the manuscript documents how the data were produced, calibrated, quality-controlled, and how others should reuse them, while the dataset itself lives in a FAIR public repository with its own persistent identifier (DOI). An analysis or interpretation paper that mines data toward a hypothesis, or a paper whose data are not openly and permanently available, is a fundamental misfit here. This skill is a fit / venue-selection / re-framing tool. It does not replace the journal's current author guidance. Before submitting, re-check the live ESSD / Copernicus author instructions and data policy.

When to trigger

• The author has produced an original dataset (observations, reanalysis, compilation, model output) and wants to publish it as a citable data paper rather than a research article.
• A measurement campaign or long-term record needs a venue that rewards the data product itself, separate from any later analysis paper.
• The author is choosing between ESSD as a data paper and a domain journal (e.g. water-resources-research, global-biogeochemical-cycles) for an analysis paper.
• The author needs ESSD's open-deposition-with-DOI requirement and quality/reuse documentation bar.

Scope & topic fit

• Original observational datasets across the earth system: atmosphere, ocean, land surface, cryosphere, biosphere, solid earth, and human–environment interfaces.
• Long-term monitoring records, field/campaign measurements, and instrument or station networks with documented provenance.
• Harmonized compilations and syntheses that merge heterogeneous sources into a single consistent, value-added product.
• Gridded products, reanalyses, and model output of broad reuse value, with versioning and uncertainty information.
• Remote-sensing and retrieval datasets with algorithm description, validation, and quality flags.
• Methodologically novel data-processing or quality-control workflows when the resulting dataset is the deliverable.

Method & evidence bar

• The dataset must be original and reusable, not a re-packaging of an already-published product; novelty lies in the data, not in an interpretation.
• The data must be openly deposited in a FAIR repository with a persistent DOI before acceptance; a "data available on request" statement does not satisfy the journal.
• Production must be fully documented: instruments, sampling, calibration, processing chain, versioning, and the exact contents and format of the deposited files.
• Quality must be demonstrated with quantitative quality control, validation against independent references where possible, and explicit, traceable uncertainty.
• Reuse must be enabled: clear metadata, variable definitions, units, flags, file structure, and guidance on appropriate and inappropriate uses.
• The dataset's value and the user community it serves should be argued concretely, not asserted.

Structure & house style

• Copernicus data-paper format; the manuscript and the deposited dataset are reviewed together — re-check current article types and structure on the live guide.
• The text must describe data generation and documentation, not advance a scientific hypothesis; interpretation belongs in a companion analysis paper elsewhere.
• A prominent data availability statement must give the repository, DOI, version, and license; the DOI must resolve to the actual data.
• Figures and tables should characterize the dataset (coverage maps, time–space sampling, validation scatter, uncertainty, flag distributions), not argue a result.
• Open-access, open-review, and open-data are the norm; the deposited files must match what the paper describes exactly.