/essdive-model-data-archiving-guidelines

READY TO USE. Guidelines for archiving model data associated with a scientific publication.

Creative Commons Attribution 4.0 InternationalCC-BY-4.0

ESS-DIVE Model Data Archiving Guidelines v1.1.0

These guidelines were informed by input from the U.S. Department of Energy's (DOE) Environmental System Science (ESS) land modeling community and are associated with the manuscript by Simmonds et al. (In Revision). The terrestrial modeling community has unique challenges related to data archiving because the models and simulations they use span scientific domains and address a diversity of research questions. By working with DOE terrestrial modelers we created these guidelines to help users determine which components of model simulations to archive, how to bundle files for data publication, and we discuss data repository tools that can facilitate future archiving of model data. We envision the recommendations being applied to other types of models beyond the terrestrial modeling community. Following the guidelines will help modelers have a clear understanding of what components of their model to archive and also enable model data reuse and integration.

These guidelines are the culmination of the aforementioned efforts, they will evolve over time based on ongoing community engagement and feedback received on the material in this GitHub repository.

Getting started

To begin using the model data archiving guidelines, visit the instructions page. There you will find the step-by-step procedure for making decisions about which files to archive.

Updates in v1.1.0

Updates to v1.1.0 as of November 18th 2021 include:

  • Removing figure of model data archiving guidelines and replacing the figure with a text-based description of the guidelines based on feedback.
  • Updating citation for our In Revision manuscript and adding two co-authors.
  • Updating figure of file-level metadata guidance with new recommendations from v1.0.0 of the file-level metadata reporting format.

How to contribute

If you would like to suggest a change to the model data archiving guidelines, please submit a GitHub issue using one of our issue templates.

If you would prefer to submit feedback over email, or for any other inquiries contact us at ess-dive-support [at] lbl.gov.

Usage license

The content in this repository is free to use under the CC BY 4.0 license, and we ask that you cite the paper below to attribute credit.

How to cite these guidelines

  • Simmonds, M.B., Riley, W.J., Agarwal, D.A., Chen, X., Cholia, S., Crystal-Ornelas, R., Coon, E.T., Dwivedi, D., Hendrix, V.C., Huang, M., Jan, A., Kakalia, Z., Kumar, J., Koven, C.D., Li, L., Melara, M., Ramakrishnan, L., Ricciuto, D.M., Walker, A.P., Zhi, W., Zhu, Q. and Varadharajan, C., 2022. Guidelines for Publicly Archiving Terrestrial Model Data to Enhance Usability, Intercomparison, and Synthesis. Data Science Journal, 21(1), p.3. DOI: http://doi.org/10.5334/dsj-2022-003
  • Simmonds M.B., Crystal-Ornelas, R., Riley, W.J., Agarwal, D.A., Chen, X., Cholia, S., Coon, E.T., Dwivedi, D., Hendrix, V.C., Huang, M., Jan, A., Kakalia, Z., Kumar, J., Li, L., Melara, M., Ramakrishnan, L., Ricciuto, D.M., Walker, A.P., Zhi, W., Zhu, Q., Varadharajan, C. (2021). ESS-DIVE guidelines for archiving terrestrial model data. Environmental Systems Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE), ESS-DIVE repository. https://doi.org/10.15485/1813868

Funding and acknowledgements

ESS-DIVE is funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, Earth and Environmental Sciences Division, Data Management program under contract number DE-AC02-05CH11231. ESS-DIVE uses resources of the National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science User Facility operated under Contract No. DE-AC02-05CH11231. ORNL is managed by UT-Battelle, LLC, for the DOE under contract DE-AC05-1008 00OR22725. We thank two anonymous reviewers whose provided feedback on our associated manuscript, and William Collins (LBNL) for his thoughtful insights on model data archiving.

Related references

Simmonds, M B, W J Riley, M Melara, S Cholia, C Varadharajan (2020, Dec 8). Addressing Model Data Archiving Needs for the Department of Energy’s Environmental Systems Science Community, AGU Fall Meeting, Poster