Below are some useful resources for researchers. Please use the sidebar to navigate to your content of interest.

How to cite EarthChem Systems

EarthChem Library Data Citation

Cite the dataset with its dataset DOI in the References section of your publication.

The citation should be formatted as follows:

  • Creator(s) (Publication Year): Title. Publisher. Identifier. Data Accessed.

For example:

  • Kurz, M.D.; Curtice, J. (2018): Whole Rock Helium Data from ODP Site U1256D. Interdisciplinary Earth Data Alliance (IEDA). http://dx.doi.org/10.1594/IEDA/100734. Accessed 29 March 2018.

Synthesis Databases Data Citation

To cite a synthesis database or portal, please reference its URL in text and in figure captions:

Please cite the specific download by giving the date and, if possible, parameters of the download.

 

PetDB Example:

“The data were downloaded from the PetDB Database (www.earthchem.org/petdb) on 31 November, 2017, using the following parameters: feature name = Gakkel Ridge and rock classification= basalt.”

 

You should also cite the original scientists who contributed to the downloaded dataset.

We strongly encourage that you create a secondary bibliography for work that uses large datasets. You can easily download all of the references that contributed to a dataset for this secondary bibliography.

Many journals will accept a secondary bibliography as a supplementary material file. This type of citation helps ensure that the hard work performed by members of our community is acknowledged properly.

 

For PetDB, you may also add this publication in the reference list:

Lehnert, K., Su, Y., Langmuir, C., Sarbas, B., & Nohl, U. (2000). A global geochemical database structure for rocks. Geochem. Geophys. Geosyst. 1, doi:10.1029/1999GC000026

Data Management


In the digital era, documenting and sharing our scientific data is growing increasingly important as an integral part of the scientific process. Data Management not only makes our data resources available for others to build upon, but it also enables data syntheses and new analyses that hold the potential for significant scientific advancement. Effective data management begins during the planning stages of a project and continues throughout the research process from field and/or laboratory work, through analysis, and culminating with scientific literature and data publication. By planning ahead, and following some best practices along the way, the process of data management can be simple and relatively low-effort, enabling rapid contribution and publication of data in the appropriate data systems at the conclusion of a project.

Data Management Planning Tools


ezDMP Tool for NSF Grant Applications Data Management Plans

A web-based tool that makes it easy for investigators to fulfill their Data Management Plan (DMP) obligations to NSF or data repositories.

With ezDMP, investigators can perform the following actions to structured, machine-readable DMPs:

  • generate
  • store
  • modify
  • reuse

The resulting DMPs describe how investigators will share and preserve the anticipated products of their project, including data, samples, software, workflows, and curriculum materials.

ESIP Data Management Training (DMT)

The Data Management Training (DMT) Clearinghouse is a registry managed by the Earth Science Information Partners (ESIP) for online learning resources focusing on research data management.