Acceptable Research Standards

Scientific researchers can ensure they are meeting acceptable research standards by modeling their ethics practices and behaviors against institutional practices and policies. The data practices and research guidelines listed below can also serve as examples for institutions seeking to incorporate best practices into their own policies.

Data Integrity and Management Practices and Expectations

Earth and space science data should be widely accessible in multiple formats. Long‐term preservation of data is an integral responsibility of scientists and sponsoring institutions. Data should be Findable, Accessible, Interoperable, and Reusable (FAIR). Data should be available publicly at the time of publication and available to reviewers at submission.

The American Geophysical Union's (AGU) Data Policy supports FAIR Data practices and follows the guidelines outlined by COPDESS. To understand how this affects the rigorous peer review for AGU journals, please read the Enabling Fair Data FAQs.

Data Sovereignty and Draft Indigenous Data Governance Principles

Indigenous Data are data, information, and knowledge, in any format, that impacts Indigenous lives at the collective and individual levels. Indigenous data sovereignty refers to the right of Indigenous peoples and nations to govern the collection, ownership, and application of their own data. Read More.

Obligations of Authors and Contributors

Scientific research, and the preparation of results, must be free of any impropriety or undisclosed conflicts of interest. Intentional plagiarism, fabrication, or falsification are serious examples of scientific misconduct and as such are inappropriate actions that will discredit the union and compromise the integrity of science. Authorship should be attributed only to those who have made significant contributions to the work.
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Data Ethics Framework

Data ethics is an emerging branch of applied ethics which describes the value judgments and approaches we make when generating, analyzing, and disseminating data. This includes a sound knowledge of data protection law and other relevant legislation, and the appropriate use of new technologies. It requires a holistic approach incorporating good practice in computing techniques, ethics, and information assurance.