-
Notifications
You must be signed in to change notification settings - Fork 0
/
01_introduction.Rmd
56 lines (41 loc) · 2.84 KB
/
01_introduction.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
# Introduction {#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
`r citet(manual[c("Buettner_2011", "Bertelmann_2014", "Kitzesetal_2018")])`.
This report defines [best-practices](#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](#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](#case-studies) serve as examples to show how [best-practices](#best-practices)
are realized in concrete projects. A [literature review](#literature-review) an
[FAQ](#faq) and a [glossary](#glossary) for commonly used terms complete this report.