One very important aspect of metadata already mentioned at the beginning is its readability for humans and machines. The large number of different metadata needed to describe research data can become a problem in view of the additional large number of different scientific communities, each with their own needs. On the one hand, there is metadata that is necessary across scientific fields (e.g., name of author, title, date of creation, etc.), but on the other hand there is also subject-specific metadata that depends on the research area or even the research subject.

Imagine that research group 1 has created a lot of research data over several experiments of the same kind with different room temperatures. Research group 2 has conducted the same experiment with the same substances at the same room temperature and different levels of oxygen in the air and has also created research data. Research group 1 refers to the parameter “room temperature” as “rtemp” in their metadata, but research group 2 only refers to it as “temp”. How do the researchers of research group 1 and how does a computer system know that the value “temp” of research group 2 is the value “rtemp” of research group 1? It’s just not easily possible and thus reduces the usefulness of the data.

So how can it be ensured that both research groups use the same vocabulary (= terminology) when describing their metadata, so that in the end it is not only readable but also interpretable? For such cases, metadata standards have been and are being developed by various research communities to ensure that all researchers in a scientific discipline use the same descriptive vocabulary. This ensures interoperability between research data, which plays a crucial role in expanding knowledge when working with data.

Metadata standards thus enable a uniform design of metadata. They are a formal definition, based on the conventions of a research community, about how metadata should be collected and recorded. Despite this claim, metadata standards do not represent a static collection of rules for collecting metadata. They are dynamic and adaptable to individual needs. This is particularly necessary because research data in projects with new research methods can be very project-specific and therefore the demands on their metadata are just as strongly project-specific.

There are many different metadata standards, some are more generic (e.g. Dublin Core), others are for specific disciplines (e.g. EML, ABCD, MIxS or Darwin Core for biological data) or data types (e.g. MIABIS for biosamples, MIAPPE for plant phenotyping experiments, MIAME for microarrays). To decide which standard to use, you can have a look at the repository where you want to deposit your data, to see if they have guidelines, checklists or best practices. Some repositories (e.g. GenBank). If you are unsure which repository you are going to use, you can check the following websites for more information about the right metadata [1]: