Editorial Policies

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Sound, reproducible scholarship rests upon a foundation of robust, accessible data. For this to be so in practice as well as theory, data must be accorded due importance in the practice of scholarship and in the enduring scholarly record. In other words, data should be considered legitimate, citable products of research. Data citation, like the citation of other evidence and sources, is good research practice and is part of the scholarly ecosystem supporting data reuse. In support of this assertion, and to encourage good practice, we offer a set of guiding principles for data within scholarly literature, another dataset, or any other research object.


The Data Citation Principles cover purpose, function and attributes of citations. These principles recognize the dual necessity of creating citation practices that are both human understandable and machine-actionable. These citation principles are not comprehensive recommendations for data stewardship. And, as practices vary across communities and technologies will evolve over time, we do not include recommendations for specific implementations, but encourage communities to develop practices and tools that embody these principles. The principles are grouped so as to facilitate understanding, rather than according to any perceived criteria of importance.


Data should be considered legitimate, citable products of research. Data citations should be accorded the same importance in the scholarly record as citations of other research objects, such as publications.

Credit and attribution

Data citations should facilitate giving scholarly credit and normative and legal attribution to all contributors to the data, recognizing that a single style or mechanism of attribution may not be applicable to all data.


In scholarly literature, whenever and wherever a claim relies upon data, the corresponding data should be cited.

Unique identification

A data citation should include a persistent method for identification that is machine actionable, globally unique, and widely used by a community.


Data citations should facilitate access to the data themselves and to such associated metadata, documentation, code, and other materials, as are necessary for both humans and machines to make informed use of the referenced data.


Unique identifiers, and metadata describing the data, and its disposition, should persist – even beyond the lifespan of the data they describe.

Specificity and verifiability

Data citations should facilitate identification of, access to, and verification of the specific data that support a claim. Citations or citation metadata should include information about provenance and fixity sufficient to facilitate verifying that the specific time slice, version and/or granular portion of data retrieved subsequently is the same as was originally cited.

Interoperability and flexibility

Data citation methods should be sufficiently flexible to accommodate the variant practices among communities, but should not differ so much that they compromise interoperability of data citation practices across communities

Statement on the availability of underlying data

Authors are required to provide a statement on how their underlying research data can be accessed. This must be placed as the section "Data availability" at the end of the manuscript before the acknowledgements. The best way to provide access to data is by depositing them (as well as related metadata) in reliable public data repositories, assigning digital object identifiers, and properly citing data sets as individual contributions. If different data sets are deposited in different repositories, this needs to be indicated in the data availability section. If data from a third party were used, this needs to be explained (including a reference to these data). Data Cite recommends the following elements for a data citation: Creators: Title, Publisher/Repository, Identifier, Publication Year (e.g.: Loew, A., Bennartz, R., Fell, F., Lattanzio, A., Doutriaux-Boucher, M., and Schulz, J.: Surface Albedo Validation Sites, EUMETSAT, http://dx.doi.org/10.15770/EUM_SEC_CLM_1001, 2015). BBM Publishers also accepts supplements containing smaller amounts of data. However, please note that this is not the preferred way of making data available.

Other underlying material

Data do not comprise the only information which is important in the context of reproducibility. Therefore, BBM Publishers encourages authors to also deposit software, algorithms, model code, and other underlying material on suitable repositories/archives whenever possible. These materials should be referenced in the article and preferably cited via a persistent identifier as a DOI.