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Chapter 1 Introduction

Research data management (RDM) comprises all parts of the “life cycle” of data, from its creation and initial storage over its active usage and preservation to the time when it may become obsolete and is deleted. RDM aims to make the whole research process more efficient for your own institute and to meet the ever growing requirements of partner organizations, research funders and legislation Büttner, Hobohm, and Müller (2011), Bertelmann et al. (2014), Kitzes, Turek, and Deniz (2018).

This report defines best-practices for research data management especially designed for small research institutes. Small institutes with less than somewhat 50 employees are in particular addressed in this report because of the following characteristics:

  • usually no own IT department and lack of employees that are solely dedicated to data management related issues

  • data management guidelines, if existent, are often not implemented in daily routine

  • loss of knowledge may be disproportionately serious in case an employee leaves the company

  • simple organisational structure and flexibility allows for fast adaptations of innovations

Data management is often not centrally organised but is left to the project leaders and researchers. Employees are expected to be their own data experts. Depending on the individual skills and knowledge, different ways and levels of data handling are practiced.

In small institutes, work is organised in terms of projects. There may not be an overall strategy or target that is followed by the institute. Targets are depending on research programs and requirements may set by funding organisations that may differ from project to project.

The small number of employees allows for an easy and straight-forward exchange of information and fast decisions, but may lacks of sufficient documentation because decisions are made informally. In case of strong staff fluctuation this can lead to a disproportionately serious loss of knowledge.

In small institutes with a simple organisational structure and a flat hierarchy innovations such as the usage of new methods or tools very often start as initiatives of individual employees applied in few projects. Practices that have been proven to be beneficial may then be used in future projects or even be set as a standard for the whole institute (bottom-up).

This report shows how to implement best-practices RDM tools and guidelines under the consideration of specific characteristics of small institutes, trying to find a good balance between flexibility and formality.

Test projects serve as examples to show how best-practices
are realized in concrete projects. A literature review an FAQ and a glossary for commonly used terms complete this report.

References

Bertelmann, Roland, Petra Gebauer, Tim Hasler, Ingo Kirchner, Wolfgang Peters-Kottig, Matthias Razum, Astrid Recker, D. Ulbricht, and Stephan van Gasselt. 2014. “Einstieg Ins Forschungsdatenmanagement in Den Geowissenschaften.” Booklet. GFZ Potsdam. https://doi.org/10.2312/lis.14.01.

Büttner, Stephan, Hans-Christoph Hobohm, and Lars Müller, eds. 2011. Handbuch Forschungsdatenmanagement. Bad Honnef: Bock u. Herchen. https://doi.org/10.34678/opus4-208.

Kitzes, Justin, Daniel Turek, and Fatma Deniz, eds. 2018. The Practice of Reproducible Research: Case Studies and Lessons from the Data-Intensive Sciences. Oakland, CA: University of California Press. https://www.practicereproducibleresearch.org/.