Microbiology
Found 3 schemes.
MIxS is a superset of metadata elements that can be used to compile minimum information checklists for reporting sequencing data. It was developed by the Genomic Standards Consortium (GSC) as an overarching framework that could act as a single entry point for all their minimum information checklists (as reported in Nature Biotechnology).
MIxS includes the technology-specific checklists from the previous MIGS and MIMS standards (for genomes and metagenomes respectively), provides a way of introducing additional checklists such as MIMARKS (for marker sequences), and also allows annotation of sample data using environmental packages.
Recommended Metadata for Biological Images (REMBI) provides guidelines for metadata for biological images to enable the FAIR sharing of scientific data. REMBI is the result of the bioimaging community coming together to develop metadata standards that describe the imaging data itself, together with supporting metadata such as those describing the biological study and sample.
Our goal with the wildlife disease data standard is to describe interactions between hosts and parasites with sufficient detail and structure as to be useful, but not burdensome on the end user. In this case a parasite can be anything from a micro-parasite like a virus to a macro-parasite like a tick. We use this broad term because systems are complicated and a researcher may want to monitor interactions at multiple levels.
The standard has two components disease data and project metadata. The disease data component describes the contents and structure of data related to the detection (or not) of a parasite in/on a given host and is focused on data exchange. The project metadata component describes the contents and structure of data related to the creation of the disease data component and is focused on data discovery.
The disease data component allows us to create a collection of datasets that can be re-used, aggregated, and shared, while the project metadata component provides context for the data, makes it easier to find the dataset, and gives clear information about attribution and use