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Data in DMP

“You can have data without information, but you cannot have information without data.” Daniel Keys Moran.

What is data in DMP

DMP is about data, but what does data mean in research data management (RDM)?

Giving a definition of research data is extremely difficult because it is strictly discipline- and context-dependent. However, multiple definitions of research data have been formulated.

  • The UK Concordat on Open Research Data (2016) defines research data as “the evidence that underpins the answer to the research question, and can be used to validate findings regardless of its form (e.g. print, digital, or physical)”.
  • A similar definition, adopted by UGent, defines research data as “any information collected or generated for the purpose of analysis, in order to generate or validate scientific claims”.

It’s clear that the definition of data in RDM is more related to the role of the data in the research project, rather than to the nature of the data itself. The focus of RDM is more on data collected and used as foundations of your scientific claims, and not on tools or resources used just as “means to an end”.

Data can be:

  • Quantitative or qualitative measurements of a variable (numerical, textual, audiovisual, multimedia etc).
  • Software, scripts, algorithm, models, new genome assembly, molecular structures, platforms, collection, databases etc.

Data documentation in DMP

In addition to the data, a big part of RDM is about managing the documentation that is needed to find, understand, reuse and reproduce the data. If data documentation is highly structured, it is called metadata. Describing how the data documentation will be managed in a DMP is as important as describing how the data itself will be managed. Moreover, very often the difference between what is data and what is documentation (or metadata) can be very slim or dependent on the context.

Documentation tools (such as laboratory notebook, electronic lab note or ELN, certificates, documents, reports, inventories, etc) are not usually considered data, unless they are your research goal; however, they contain (meta)data. The same way of thinking applies to reagents, substances, research materials (such as specimens, biopsies, samples and biomolecules) and living or death organisms (people, animals, plants, bacteria, cells etc); they are not usually considered data, only their description and measurements are (meta)data.

Other research outputs

According to the Horizon Europe DMP Template, researchers are encouraged to give infomation about the management of other research outputs, such as digital outputs (e.g. software, workflows, protocols, models, etc.) and physical materials (e.g. new materials, antibodies, reagents, samples, etc.), if questions pertaining to FAIR data are relevant for other research outputs as well.


Biological research materials generated or used during the project (such as new plasmids, primers, cell lines, strains of organisms, specimens, etc) should be shared through public repositories when one exists or biobanks and provide accession numbers in the data documentation. For instance, Jackson Laboratory, the European Mouse Mutant Archive (EMMA), the European Conditional Mouse Mutagenesis Program (EUCOMM), Addgene, American Type Culture Collection (ATCC). Centralized repositories are equipped to handle the widespread sharing of materials. Such repositories stand to accelerate science by helping the community to make the most of useful reagents and promote scientific reproducibility. Information about how you will manage and share research materials CAN be specified in the DMP.

Step-by-step experimental protocols

It is good practice to share step-by-step experimental protocols on sharing platforms and provide an identifier or other citation details in the data documentation. Information about how you will manage and share experimental protocol CAN be specified in the DMP.

Sources and further reading