The output of research is not only journal articles but also data sets, model codes, samples, etc. Only the entire network of interconnected information can guarantee integrity, transparency, reuse, and reproducibility of scientific findings. Moreover, all of these resources provide great additional value in their own right. Hence, it is particularly important that data and other information underpinning the research findings are "findable, accessible, interoperable, and reusable" (FAIR) not only for humans but also for machines.
Therefore, Copernicus Publications requests depositing data that correspond to journal articles in reliable (public) data repositories, assigning digital object identifiers, and properly citing data sets as individual contributions. Please find your appropriate data repository in the registry for research data repositories: re3data.org. A data citation in a publication resembles a bibliographic citation and needs to be included in the publication's reference list. To foster the accessibility as well as the proper citation of data, Copernicus Publications requires all authors to provide a statement on the availability of underlying data as the last paragraph of each article (see section data availability). In addition, data sets, model code, video supplements, video abstracts, and other digital assets should be linked to the article through DOIs in the assets tab.
Best practice following the Joint Declaration of Data Citation Principles initiated by FORCE 11:
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.1. 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.2. 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.3. Evidence
In scholarly literature, whenever and wherever a claim relies upon data, the corresponding data should be cited.4. Unique identification
A data citation should include a persistent method for identification that is machine actionable, globally unique, and widely used by a community.5. Access
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.6. Persistence
Unique identifiers, and metadata describing the data, and its disposition, should persist – even beyond the lifespan of the data they describe.7. 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 timeslice, version and/or granular portion of data retrieved subsequently is the same as was originally cited.8. 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.
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. Please see the manuscript preparation guidelines for authors for the correct sequence. If the data are not publicly accessible, a clear explanation of why this is the case is required. The best way to provide access to data is by depositing them (as well as related metadata) in FAIR-aligned 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).
Other underlying material
Data do not comprise the only information which is important in the context of reproducibility. Therefore, Copernicus Publications encourages authors to also deposit software, algorithms, model codes, video supplements, video abstracts, and other underlying material on suitable FAIR-aligned repositories/archives whenever possible. These materials should be referenced in the article and cited via a persistent identifier such as a DOI.
With regard to software citation, please refer to the FORCE11 Software Citation Principles.
Guidance on supporting information
All measures should be taken by the authors to ensure that their work can be independently reproduced. This includes providing access to electronic data such as pulse sequences and software where appropriate.
- Supporting information shall contain only complementary information but no scientific interpretations or findings that go beyond the contents of the manuscript.
- Where the electronic data accompanying the manuscript is <50 MB, this should be uploaded with the manuscript as supplemental data.
- For raw data (e.g. NMR spectra) and where the size of the electronic data is >50 MB, authors are asked to deposit their data in a separate repository and to insert a persistent identifier, ideally a DOI, in the manuscript. Options include:
- Standard and citable data repositories appropriate to the field (e.g. BMRB for NMR data).
- Institutional repositories (e.g. library resources where these provide a DOI).
- General data repositories, such as figshare, Dryad, Open Science Framework or Zenodo.
- Software code should be provided via a version-controlled platform such as GitHub.
- Although this is not a requirement for publication, for software we additionally encourage the authors to provide either a containerised version of their software (using data repositories outlined above) or that they implement their software in a publicly available and version controlled VM. The latter can be done using the NMRbox solution, where the authors may seek additional assistance from the NMRbox administrators for software implementation.