Matúś Kalaš 1, Hervé Ménager 2, Alban Gaignard 3, Veit Schwämmle 4, Jon Ison 5, and the EDAM contributors and advisors
1. University of Bergen, Norway
2. Institut Pasteur, Paris, France
3. Univerity of Nantes, France
4. University of Southern Denmark, Ødense, Denmark
5. French Institute of Bioinformatics (ELIXIR France)
Project website: https://edamontology.org
Source code: https://github.com/edamontology/edamontology
License: CC BY-SA 4.0
EDAM is an ontology of well-established, familiar concepts that are prevalent within bioinformatics, and bioscientific data analysis in general [1,2]. The scope of EDAM includes types of data and data identifiers, data formats, operations, and topics. EDAM has a relatively simple structure, and comprises a set of concepts with terms, synonyms, definitions, relations, links, persistent identifiers, and some additional information (especially for data formats).
EDAM is developed in a participatory and transparent fashion, within a growing international community of contributors. The development of EDAM is coordinated with the development and curation of tools registries (e.g. bio.tools and BIII.eu); registries of training materials (e.g. TeSS); with packaging of open-source bioinformatics software (especially Debian Med [3]); the Common Workflow Language [4]; and other related communities and initiatives. These include the developers’ community of Galaxy [5], and collaborations with specialised networks of experts, such as within the development of EDAM-bioimaging [6]. EDAM-bioimaging is an extension of EDAM towards bioimage informatics and machine learning, where a broad group of experts in bioimaging, image analysis, and deep learning has been contributing to the common effort. The comprehensive but concise inclusion of machine learning topics is one of the new additions in 2020.The latest release of EDAM at the time of publication was version 1.24 [7], and EDAM-bioimaging version alpha06 [8].
In summary, EDAM functions as common controlled vocabulary when publishing, sharing, and integrating information about bioinformatics tools, workflows, training materials, and other resources. In addition, EDAM is also useful when choosing terminology, for data provenance, and in text mining (e.g. EDAMmap).
Poster published in F1000Research on 6 Jun 2020. https://doi.org/10.7490/f1000research.1117983.1
Video presentation: https://youtu.be/Jq16bnq8kbk
