standards > model/format > DOI:10.25504/FAIRsharing.pg4NHk
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ready Open Metadata Markup Language

Abbreviation: odML

General Information
odML is a format to collect and share metadata in an organized, human- and machine-readable way. The format specifies a hierarchical structure for storing arbitrary metadata as extended key-value pairs. It is inherently extensible and can be adapted flexibly to the specific requirements of any laboratory. Developed within the Neuroinformatics community, it is a generic format intended for all types of data.


Countries that developed this resource Germany

Created in 2011

Taxonomic range

How to cite this record odML; Open Metadata Markup Language; DOI:; Last edited: March 12, 2021, 3:19 p.m.; Last accessed: Oct 27 2021 1:23 a.m.

This record is maintained by twachtler  ORCID

Record added: April 4, 2018, 2:30 p.m.
Record updated: Feb. 24, 2021, 10:23 a.m. by The FAIRsharing Team.

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Additional Information


No XSD schemas defined

Access / Retrieve Data


A Bottom-up Approach to Data Annotation in Neurophysiology.

Grewe J,Wachtler T,Benda J
Front Neuroinform 2011

View Paper (PubMed) View Publication

Related Standards

Reporting Guidelines

No guidelines defined

Terminology Artifacts

No semantic standards defined

Identifier Schemas

No identifier schema standards defined


No metrics standards defined

Related Databases (2)
G-Node Data Infrastructure Services
The German Neuroinformatics Node's data infrastructure (GIN) services provide a platform for comprehensive and reproducible management and sharing of neuroscience data. Building on well established versioning technology, GIN offers the power of a web based repository management service combined with a distributed file storage. The service addresses the range of research data workflows starting from data analysis on the local workstation to remote collaboration and data publication.

EBRAINS is a platform for sharing brain research data ranging in type as well as spatial and temporal scale. The EBRAINS data curation service aims to provide maximum impact, visibility, reusability, and longevity. The user interface of the EBRAINS Knowledge Graph allows you to easily find data of interest. EBRAINS hosts a wide range of data types and models from different species. All data are well described and can be accessed immediately for further analysis.

Implementing Policies

This record is not implemented by any policy.


Record Maintainer



Grant Number(s)

  • 01GQ0801 (Federal Ministry of Education and Research (BMBF), Berlin, Germany)

  • 01GQ0802 (Federal Ministry of Education and Research (BMBF), Berlin, Germany)

  • 01GQ1302 (Federal Ministry of Education and Research (BMBF), Berlin, Germany)