Evidence & Conclusion Ontology development site: Use ISSUES to request terms. See WIKI for term request how to. See README for how to cite ECO (we'd be grateful) and other useful information.
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README.md

ABOUT ECO

The Evidence & Conclusion Ontology (ECO) describes types of scientific evidence within the biological research domain that arise from laboratory experiments, computational methods, literature curation, or other means (please cite ECO - see below). Researchers use evidence to support conclusions that arise out of scientific research. Documenting evidence during scientific research is essential, because evidence gives us a sense of why we believe what we think we know. Conclusions are asserted as statements about things that are believed to be true, for example that a protein has a particular function (i.e. a protein functional annotation) or that a disease is associated with a particular gene variant (i.e. a phenotype-gene association). A systematic and structured (i.e. ontological) classification of evidence allows us to store, retreive, share, and compare data associated with that evidence using computers, which are essential to navigate the massive amount of scientific data in existence.

ECO is an ontology comprising two high-level classes, 'evidence' and 'assertion method', where 'evidence' is defined as "a type of information that is used to support an assertion" and 'assertion method' is defined as "a means by which a statement is made about an entity." Together 'evidence' and 'assertion method' can be combined to describe both the supporting evidence for an assertion and the agent who made the assertion, i.e. a human being or a computer. However, ECO can not be used to make an assertion itself; for that, one would use some other means, such as another ontology, controlled vocabulary, or a free text description.

ECO was originally created around the year 2000 to support Gene Ontology (GO) gene product annotations, and GO uses ECO in AmiGO2, Noctua, and other applications. Today many groups use ECO to document evidence in scientific research, including protein & gene resources, model organism databases, software applications, and phenotype projects, among others. ECO collaborates with the Ontology for Biomedical Investigations Consortium in order to achieve harmonious interactions. ECO is committed to the principles established by the Open Biological and Biomedical Ontologies Foundry (OBO Foundry).


DEVELOPMENT INFORMATION

For advice on requesting new terms, please see the Evidence & Conclusion Ontology wiki.

For information about editing, please see the GitHub editors subdirectory.

For further information including history, detailed discussion of evidence, and a complete bibliography, please visit the Evidence & Conclusion Ontology website.


PLEASE CITE ECO

ECO is free for all, but we certainly appreciate attribution and collaboration! If you use ECO in your work, please cite the following paper:

Chibucos MC, Mungall CJ, Balakrishnan R, Christie KR, Huntley RP, White O, Blake JA, Lewis SE, and Giglio M. (2014) Standardized description of scientific evidence using the Evidence Ontology (ECO). Database. Vol. 2014: article ID bau066.

...AND COLLABORATE WITH US!

If you want to help grow ECO for your own project please contact us via the GitHub Issue tracker or email Dr. Marcus Chibucos of the University of Maryland School of Medicine: mchibucos@som.umaryland.edu.

When you contribute your knowledge to ECO, everyone can benefit!


LICENSING

ECO is released into the public domain under CC0 1.0 Universal (CC0 1.0). Anyone is free to copy, modify, or distribute the work, even for commercial purposes, without asking permission. Please see the Public Domain Dedication for an easy-to-read description of CC0 1.0 or the full legal code for more detailed information. To get a sense of why ECO is CC0 as opposed to licensed under CC-BY, please read this thoughtful discussion on the OBO Foundry GitHub site.


FUNDING ACKNOWLEDGEMENT

This material (the ontology & related resources) is based upon work supported by the National Science Foundation Division of Biological Infrastructure under Award Number 1458400 to Dr. Marcus Chibucos, Principal Investigator.

Prior development was supported in part by National Institutes of Health/National Institute of General Medical Sciences under Grant Number 2R01GM089636 and by Dr. Owen White.