• AMS Community
  • Donate
  • Join
  • Log in

Data Archiving and Citation Guidelines

(updated 30 December 2019)

Please refer to Dataset References for guidelines and examples of how to reference and cite data according to AMS style.

Background

The American Meteorological Society (AMS) is committed to promoting full, open, and timely access to the environmental data, associated metadata, and derived data products that underlie scientific findings (see the updated 2019 AMS policy statement). These data and metadata must be properly cited and readily available to the scientific community and the general public.

AMS, as a member of the Coalition on Publishing Data in the Earth and Space Sciences (COPDESS), is working toward becoming a signatory of the Enabling FAIR Data initiative (Stall et al. 2018), which is committed to aligning publishers, repositories, and other organizations in the Earth, space, and environmental sciences to enable scientific data to be FAIR: Findable, Accessible, Interoperable, and Reusable (Wilkinson et al. 2016). As part of the FAIR initiative, COPDESS provides a tool to help researchers identify possible repositories for their data and associated research output and recommends best practices around data and identifiers. These best practices are the basis for the AMS guidelines.

For authors, this means that at initial submission of the manuscript, they must confirm that their data are archived and cited/referenced properly. Likewise, peer review editors are asked to ensure that this AMS expectation is being met. As laid out in the AMS policy statement, the spectrum of what constitutes “data” is diverse and includes in situ and remotely sensed observations, environmental predictions generated by numerical models, and data products derived from integrations of observational and model-generated sources. Associated software should also be archived if possible. A companion policy specifically addressing software and algorithms is being developed.

Archiving Data

Authors are expected to direct all core research outputs (data, software, appropriate samples and sample descriptions) to FAIR-aligned repositories, following the FAIR principles. This means that article supplemental material should no longer be used as the primary archive for data. AMS also strongly discourages the archiving of data on personal servers and websites because of their lack of permanence. FAIR-aligned repositories provide additional quality checks around domain data and data services, and facilitate discovery and reuse of data and other research outputs. Authors who are unsure about appropriate repositories are encouraged to use the Repository Finder tool, developed by DataCite for the Enabling FAIR Data project. This tool uses the content of re3data.org, a registry of repositories, to allow authors to search by topic and lists repositories that currently are accepting data to support publication, including those that are certified and support the FAIR principles.

If data repositories are not available or appropriate for particular datasets, authors should investigate other archiving options, including a general repository such as Dryad or figshare, or their local institutional library. If none of these options are available or appropriate, authors must provide a transparent process to make the data available to anyone upon request.

AMS recognizes that sharing data may not be feasible in some instances, such as in studies that collect sensitive data about human subjects. Authors must comply with applicable institutional review board and funding agency policies and regulations when collecting human subject data. Any other limitations or restrictions on sharing data, such as proprietary or other legal restrictions, must be reported to the journal editor at initial submission and included in a Data Availability Statement section of the manuscript immediately preceding the Acknowledgements section.

More information about the Enabling FAIR Data guidelines is available at the project FAQ page.

Citing Data

Authors should cite and link to the data in the article, following the Joint Declaration of Data Citation Principles and ESIP Guidelines, using the unique, resolvable, and persistent identifiers provided by the repository in which the data are archived. In particular, citations should appear in the body of the article with a corresponding reference in the reference list. Citations should include persistent identifiers in well-formed references to data and software, so they can be accurately tracked. Also, citations should include software used in the research following the FORCE11 Software Citation Principles, which recommends a similar depositing of the software in an archival repository, and citation/references that include the persistent identifiers provided by the repository.

Refer to the AMS Dataset References for guidelines and examples of how to reference and cite data according to AMS style. Citations should include as much of the following information as possible: Dataset or software authors/producers, release date; title; version; archive/distributor, and the locator/identifier (persistent identifier such as DOI preferred), and year.

Authors are expected to include a separate Data Availability Statement section in their manuscript immediately preceding the Acknowledgments section describing how the data underlying the findings of their article can be accessed and reused. In special cases, where data access is restricted, authors are required to mention these restrictions in the data availability statement. In short, authors should provide unrestricted access to all data and materials underlying reported findings for which ethical or legal constraints do not apply, to the greatest extent possible. Datasets that are not curated or cannot be reliably made available should not be cited in AMS publications but should be noted through an in-text reference to unpublished data, as noted in AMS Dataset References.