In order to make research data more findable and comprehensible, documentation of data is essential. It considerably facilitates the further use of the data and enables reproducibility. Well-documented data will be used and cited more often, which will increase the reputation of the creator. Documentation is also important with regard to the subsequent usability and traceability of the data for your own work. Over time, details might be forgotten, so it is recommended to document the data while working on it. read more
Basic contents of a documentation include:
- description of the research project
- project goals
- detailed information on data collection (methods, units, time periods, locations, technology used)
- measures for data cleansing
- structure of data and their relationships to each other
- explanation of variables, labels and codes
- differences between versions
What is metadata?
Metadata refers to structured data that contains information on other data – “data about data”. They are stored either independently of or in combination with the data they describe. There is a distinction between content-related and technical metadata. They form a specific subset of the documentation data and serve primarily to make the data findable, including in library reference systems. In order to make them machine-readable, for example in Semantic Web applications, they are often stored in XML format.
Standardisation of metadata vocabulary is necessary to improve findability of the data and to provide interoperability. The linking of the metadata will ensure this. Furthermore, standards allow a uniform description of similar data sets in terms of content and structure.
Metadata standards contain a defined selection of information which is necessary to find and identify these data. This does not necessarily guarantee a reusability of the data (compare section on documentation). Among the most common bibliographic interdisciplinary metadata standards are Dublin Core, DataCite Metadata Schema, and MARC21.
Discipline-specific metadata standards
Since each scientific community has its own requirements, different discipline-specific metadata standards are also being developed. For example, in the social and economic sciences the Data Documentation Initiative (DDI) standard is frequently used, while in the natural sciences the ICAT scheme or the Crystallographic Information Framework are used.
An overview on discipline-specific metadata standards is available, for example, on the pages of the British Digital Curation Centre49 and in an overview of the Research Data Alliance.
(Biernacka, K., Bierwirth, M., Buchholz, P., Dolzycka, D., Helbig, K., Neumann, J., Odebrecht, C., Wiljes, C. and Wuttke, U. (2020). “Train-the-Trainer Concept on Research Data Management” Version 3.0. Berlin, DOI: https://doi.org/10.5281/zenodo.4071471, Creative Commons Attribution 4.0 International)
Duration: 14:30 mins
Content: Research Data and their Metadata” is an educational video on research data management. The video briefly explains the concept of metadata and where in the research data lifecycle they become important.
Schmitz, D., Hausen, D., Trautwein-Bruns, U. (2018). “Research data and their metadata” RWTH Aachen University, DOI: 10.18154/RWTH-2019-10060
License: CC BY 4.0
Metadata standards directory:
A comprehensive list of metadata standards with short description and further links.
Metadata and describing data:
A short description on the topic of metadata with examples and further links.
Metadata Guide by Australian Research Data Commons (ARDC):
This Guide is intended to provide a simple generic working-level view of the needs, issues, and processes around metadata collection and creation as it relates to research data.
FAIR Data Austria (2021). “Metadata”. In: Research Data Management Open Educational Resources Collection. (https://fair-office.at/index.php/metadaten/?lang=en).
License: CC BY 4.0 unless otherwise stated.