Research Data Management (RDM)
If I have seen further
it is by standing on ye shoulders of giants. Sir Isaac Newton, 16761)
Scientific knowledge is always rooted in the prior work of others. Essential aspects of any science are therefore that its findings should be independent from the observer and its results comprehensible. Accordingly, the handling of research data (“research data management”, RDM) should be characterised by the idea of comprehensibility and reusability by other people - including my future self (“future-me”).
Research data management therefore encompasses (almost) all sub-aspects of the research process, from the initial idea and concrete planning, through the actual data collection and evaluation, to the publication and subsequent (re)use of the findings and the underlying data (see the research data life cycle). Ultimately, research data management is nothing else than good scientific work. As such, it is the necessary, but not sufficient, condition for gaining scientific knowledge.
Most aspects of research data management can only be implemented by the researchers themselves. Since any non-trivial research is a complex undertaking, this requires clear structures that are developed, or at least brought to life, by the researchers themselves out of an understanding of the processes and contexts. In the researchers' own interest, the focus here is on the comprehensibility and usability of the results and findings of their (own) research.
These pages focus on a research-oriented research data management with direct relevance for the individual researcher. The emphasis is on the personal handling of a multitude of different research data (“little science” instead of “big science”) and the accompanying high complexity, according to the author's background and experience with spectroscopy. The concepts, tools and best practices presented address the individual researcher, institutional aspects are mainly only touched upon.