EDAM: the ontology of bioinformatics operations, topics, data, and formats (update 2020) [version 1; not peer reviewed]

Project website: http://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, 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). Slightly shorter versions of this abstract were reviewed by members of the corresponding committees of the listed conferences. [1] Jon Ison, Matúš Kalaš, Inge Jonassen, et al. (2013). EDAM: an ontology of bioinformatics operations, types of data and identifiers, topics and formats. Bioinformatics, 29(10): 1325-1332. DOI: 10.1093/bioinformatics/btt113 [2] Matúš Kalaš et al. (2017-2020). edamontology/edamontology (All versions). Zenodo. DOI: 10.5281/zenodo.822690 [3] Steffen Möller et al. (2017). Robust Cross-Platform Workflows: How Technical and Scientific Communities Collaborate to Develop, Test and Share Best Practices for Data Analysis. Data Sci. Eng., 2: 232–244. DOI: 10.1007/s41019-017-0050-4 [4] Peter Amstutz at al. (2016). Common Workflow Language, v1.0. Specification, Common Workflow Language working group. https://w3id.org/cwl/v1.0/ DOI: 10.6084/m9.figshare.3115156.v2 [5] Hervé Ménager, Jon Ison, Matúš Kalaš, Veit Schwämmle (2017). The EDAM ontology and its integration into Galaxy [version 1; not peer reviewed]. F1000Research, 6(Galaxy):1032 (Poster). DOI: 10.7490/f1000research.1114336.1 [6] Matúš Kalaš et al. (2020). EDAM-bioimaging: the ontology of bioimage informatics operations, topics, data, and formats (update 2020) [version 1; not peer reviewed]. F1000Research, 9(ELIXIR,NEUBIAS):162 (Poster). DOI: 10.7490/f1000research.1117826.1 [7] Matúš Kalaš et al. (2020). edamontology/edamontology: EDAM 1.24 (Version 1.24). Zenodo. DOI: 10.5281/zenodo.3608238 [8] Joakim Lindblad et al. (2020). edamontology/edam-bioimaging: alpha06 (Version alpha06). Zenodo. DOI: 10.5281/zenodo.3695725