Research Data Management Organiser
A tool to support the planning, implementation and organisation of research data management
Claudia Engelhardt

Harry Enke

Jochen Klar

University of Applied Sciences Potsdam
Faculty of Information Sciences
Kiepenheuerallee 514469 Potsdam
Germanyclaudia.engelhardt@fh-potsdam.de

Leibniz Institute for Astrophysics
Potsdam (AIP)An der Sternwarte 16
14482 Potsdam
Germany
henke@aip.de

Leibniz Institute for Astrophysics
Potsdam (AIP)An der Sternwarte 16
14482 Potsdam
Germany
jklar@aip.de

Jens Ludwig

Heike Neuroth

Staatsbibliothek zu Berlin
Preußischer Kulturbesitz
Potsdamer Straße 33
10102 Berlin
Germany
jens.ludwig@sbb.spk-berlin.de

University of Applied Sciences Potsdam
Faculty of Information Sciences
Kiepenheuerallee 514469 Potsdam
Germany
neuroth@fh-potsdam.de

ABSTRACT

1 INTRODUCTION
1

The project Research Data Management Organiser (RDMO)
develops a tool to support the planning, implementation andorganisation of research data management. The multilingual
open source tool can be installed locally and adapted to
institutional or discipline-specific needs with regards tocontents. It provides interfaces to institutional authentication
procedures. Key features of the first version, released in April
2017, include:

the ability to continuously update and augment the
information in the course of a project
access for different stakeholders, such as researchers,
project coordinators, the IT department, data managers
etc., with customized views
export formats for different purposes including data
management plans (DMPs) according to funderrequirements.
Planned future developments include, among other things,
features to support actual data management, e.g. tasks (withdeadlines and a reminder functionality) that can be linked withdedicated stakeholder responsibilities.

1

http://rdmorganiser.github.io/en/

2 DATA MANAGEMENT PLANNING –THEORY AND PRACTICE
2.1 Data Management Plans

KEYWORDS
research data, data management
management, tool, organiser

The basis for a successful long-term management and provisionof digital research data is a thorough research data management
throughout the whole project lifetime. The goal is to get FAIR
data – data that are findable, accessible, interoperable and reusable [6]. Data management is not solely a responsibility of
researchers, but also of research institutions that have to provide
the necessary technical infrastructure, consulting and support.
The project Research Data Management Organiser (RDMO)
developed RDMO as a tool that supports both researchers andinstitutions in the planning, implementation and organisation ofresearch data management.
RDMO was funded by the German Research Foundation DFG
and, in its first phase, ran from November 2015 to April 2017.
The paper outlines the starting points, summarises theconceptual and development work done so far, describes thetool’s features and fields of application and gives an outlook on
future developments.

planning,

activedata

The foundation for FAIR data is laid very early in the research
process, namely in the planning stage. For this reason, data
management plans have become a crucial element of data
management policies of funding agencies, and subsequently foruniversities and other research institutions. DMPs vary in extent
and detail; typical elements include statements about what data
will be created with which methods, applicable policies, plans forsharing and preservation, data curation measures and respectiveresponsibilities, ownership and access conditions, restrictions(e.g. for legal or ethical reasons) and required resources [2, 3].
In recent years, tools to support the creation of DMPs according
to funder requirements have been developed, the most well2
known and popular of which are DMPonline by the Digital
3
Curation Centre (DCC) and DMPTool by the California Digital
Library (CDL).

2.2 Active Data Management
But creating a plan is just the first step – since a plan does nothelp much if the outlined data management measures are not put
into practice. Data management is an ongoing effort which
includes adjusting the DMP and the associated activities ifnecessary. To emphasize this continuous character, the term
active data management has been introduced.
The growing awareness of the need for such an active data
management shows, for example, in the Research Data Alliance
4(RDA) Interest Group on “Active Data Management Plans” , therequirement for EU Horizon 2020 projects to update their data
management plans in case of significant changes [5] as well as in
the joint activities of DMPonline and DMPTool to “reposition
DMPs as living documents” [11] and “integrat[e] them into thebroader ecosystem of data management infrastructure” [10].
The same motivation inspired the RDMO project.

2.3 Why another tool?
Both DMPonline and DMPTool, at least currently, have a strong
focus on the requirements of research funders. Their main
purpose is to discover, edit and fill in DMP templates of fundingorganisations, mainly from the UK (in the case of DMPonline)
and the US (in the case of DMPTool). In most instances, these
DMPs will be forgotten after the submission. Although active
data management is considered in their future plans, it is notsupported by the tools at the moment.
Both tools are centralised web applications. This is associatedwith the transfer of potentially sensitive information off-site.
Also, it offers institutions only limited possibilities forcustomisation. Furthermore, DMPonline and DMPTool are not
re-deployable without investing considerable effort.
All of these points are addressed in the RDMO concept.

3 CONCEPTUAL DESIGN
The design process of RDMO was guided by the following aims.
The tool should:
support the data management throughout the wholeproject lifetimeenable users to gather and organise all informationnecessary for a sustainable data management5involve all relevant stakeholders

be locally installable and configurable.

As crucial features required to realise these aims we identified:collaborative editing
specific roles, rights and views for different stakeholders

input via a structured interview; skipping of redundantor unnecessary questions based on given information
output of gathered data in various forms, amongst thesetextual data management plans for different funderrequirementspossibility to adapt the contents (questions as well as
answering options)tasks and reminderseasy application and administration in different
contexts (e.g. university, research institute, joint
research project).
The conceptual design was developed mainly on the basis ofthree activities: 1) the further development of previous work todesign a basic, generic questionnaire, 2) desk research, user tests
and interviews on discipline-specific requirements and content,
and 3) user stories to model the requirements and the associatedfunctions for different stakeholders.
During the whole process, we were supported by a number of6
projects and institutions who tested the tool at different stages
and gave us valuable feedback and input.

3.1 Basic questionnaire
The major source for the development of the basic questionnaire
was the WissGrid-Leitfaden zum Forschungsdatenmanagement[9]. This is a research data management manual and checklist
7
produced by the project WissGrid (2009-2012). In Germany, it
has become one of the standard works on research data
8
management. In addition, we also looked at DMPs and similarmaterial of other tools, institutions or funding agencies as well
as checklists that one of the project members had developed forthe use in consultations with researchers while working as adata manager at a Max Planck Institute. The latter alreadyincluded tasks associated with certain questions to stress theactive element in data management.
The basic questionnaire of RDMO is designed to cover all
relevant data management aspects. In particular, it comprises ofthe following areas:general information about the project (including data
management requirements from policies, e.g. of funders
or the home institution)
content classification

technical classification
data usage
data storage and security
collaborative work
quality assurance
62

https://dmponline.dcc.ac.uk/
3
https://dmptool.org/
4
https://www.rd-alliance.org/groups/active-data-management-plans.html
5
See section 3.3 for a description of stakeholder roles and requirement.

See http://rdmorganiser.github.io/en/cooperations/
http://www.wissgrid.de/
The WissGrid checklist again was inspired by previous work, too. In the
introduction, the editors particularly refer to the DCC’s Checklist for a DataManagement Plan, v3.0 (http://dcc.ac.uk/webfm_send/431).
7
8


costs

metadata and referencing
legal and ethical issues
long-term preservation (incl. selection and appraisal).
Users can either use this pre-assembled generic questionnaire –or can build their own. The latter allows the customisation of thetool in terms of discipline, method, institutional context and / orother relevant factors.

3.2 Discipline-specific requirements
To find out more about the discipline-specific requirements ofsuch a tool, the project investigated two disciplines as an
example: the social sciences and astrophysics. In a first step, weundertook desk research on data management practices andrequirements in these two fields [the main sources being 4, 7, 8].
The results were then verified in expert interviews with a data
9
manager of TwinLife , a 12-year longitudinal twin study on thedevelopment of social inequality, and the working group on
10
optical solar physics at Leibniz Institute for Astrophysics
Potsdam (AIP). The interviews took place in May and June 2016
and included testing a draft version of the generic RDMO
questionnaire. Finally, the results were also discussed in a
breakout session of an RDMO workshop held in Potsdam in July
2016, where our previous findings were verified.
The results suggest that – on the mid-range or coarse-grainedlevel of granularity which most DMPs or tools, including ourown, cover – the need for the customisation of questions
according to specifics of certain disciplines is given only forsingle questions or topics. In astrophysics, for example, usually
no personal data or data protected by copyright is gathered or
produced, so that these topic areas can be omitted. For large,
quantitative studies in the social sciences, it is common to
outsource the data collection and parts of the data preparation toexternal survey institutes. Therefore, a DMP tailored to thesocial sciences should address the topic of external survey
institutes and the steps in the research process these areresponsible for. But as mentioned before, in general this applies
only to a few selected areas of the DMP. A discipline-specificcustomisation of larger parts or the whole DMP would only be
useful on a very detailed, fine-grained level. Then, however, thequestions become so specific to a narrowly defined set of use
cases that this does not seem to be a sensible strategy for thevast majority of user groups.
With respect to the answering options as well as help texts andlinks for further information provided by a DMP or RDM tool,
however, our interview partners as well as the workshop
participants saw great potential benefit in customised options,e.g. by preparing sets of options that refer to data types,
methods, tools, standards, vocabularies etc. commonly used in
the discipline in question or suggesting repositories for certain
disciplines or data types where possible. This would make it
9

http://www.twin-life.de/en
http://www.aip.de/en/research/research-area-cmf/cosmic-magnetic-fields/solarphysics/optical-solar-physics?set_language=en10

more comfortable for researchers to answer the questions. In
addition it would help to standardise the answers which thenwould make it easier for an institution or IT department tocollect information across a number of projects to, for example,
assess what infrastructure resources (e.g. storage) need to beprovided. However, the design of questionnaires tailored todifferent disciplines or sub-disciplines is a task that cannot be
accomplished by a project like RDMO. It can best be tackled by
the scholarly communities in question, and RDMO offers themeans to implement this.

3.3 Stakeholders and user stories
To find out more about the requirements of the different data
management stakeholders, we collected user stories. Instead ofjust simply postulating requirements, user stories reflect theperspectives of different actors (in my role as …), describe what
activity they want to use the tool for (I want to …) and thenindicate the purpose (to get the benefit of...) to put the activity
into context [1].
In total, we compiled about 70 user stories for the following
stakeholder roles:
author (most common)
infrastructure provider (second most common)superior (third most common)
data manager

guest

manager
IT administratordeveloperIT supportreviewer
We cannot go into detail at this point, but shortly present thecategories that were aggregated from the user stories, each with
one or two examples.
Collaboration
As author, I want to invite other persons to my DMP as
reader or author, so that they can contribute.
As superior, I want to be able to read and approve
DMPs, to fulfil my controlling duties.
Usability, input assistance and templates
As author, I want to use templates andrecommendations from my institution and fundingorganisation, to know what to focus on to fulfil theirrequirements.

As author, I want to have predefined selections ofuseful, correct and standardised answering options
where possible, to save time because then I do not haveto think of my own ones first.
Versioning
As author, I want to access older versions of my DMP,
to undo incorrect entries.

Adaptability of questions and answering options

As infrastructure provider, I want to be able to define
selections of useful, correct and standardised answering
options, in order to be able to aggregate user inputs foreasier analysis.
Logic of questions
As author, I do not want to answer questions that canbe identified as irrelevant on the basis of my previous
entries, so that I can concentrate on the questions thatare important for my project.

As infrastructure provider, I want to offer questions andanswers in different levels of granularity, so that plans
can be made according to the needs, abilities and mostuseful level of detail for a certain project.
Task administration
As infrastructure provider, I want to connect certain
questions and answers to data management tasks fordifferent actors and roles, so that all stakeholders areaware of their tasks and responsibilities in the process.

As infrastructure provider, I want to be able to view theanswers for relevant questions, in order to plan
accordingly (e.g. how much storage space needs to beprovided).
Review
As reviewer, I want to have access to the information in
RDMO, to evaluate if the planning and implementationof a project’s data management are / were carried out
properly.
APIs and export functions
As author, I wish that information I or others entered inthis or other systems can be migrated, so that I do nothave to enter everything from scratch again.
As author, I want to be able to export my answers in amachine readable form and link them to other systems,
to be able to use the information entered in other
RDMO installations (at other institutions).

4 TECHNICAL DEVELOPMENT AND
11
FUNCTIONSRDMO is implemented as a web application based on the Python
12
13
framework Django and the JavaScript framework AngularJS .
14
It is an open source tool licensed under Apache Version 2.0 and15
is available on GitHub .
The information in RDMO is organised along projects. It is up to
the users to define what project means in their specific context.
In most cases, a project in RDMO will represent a ‘real life’
research project, but it might also relate to a subproject, one

11

For the whole chapter see also http://rdmorganiser.github.io/en/software/
https://www.djangoproject.com/
https://angularjs.org/
14
https://www.apache.org/licenses/LICENSE-2.0
15
https://github.com/rdmorganiser/rdmo
12

13

particular survey or study or a range of studies inside a project,
or others.
The RDMO implementation is multilingual, currently available
languages are German and English. It is designed to support
internationalisation, so that more languages can easily be
implemented in the future.
In the project life span of 18 months we achieved the goals set in
the project proposal and even realised some additional features.
However, not all requirements and desirable features that wereidentified by the user stories and feedback from testers could be
implemented by the end of the project in April 2017.
In the following, we give a brief description of the features of thefirst version, released at the end of April 2017.

4.1 Input
Information can be entered in a web interface and edited bydifferent stakeholders. It is collected via a structured interview
that guides users through all relevant topic areas. Based on theanswers given, redundant or irrelevant questions are skipped.
Depending on the type of question/answer, different widgets areused (e.g. radio buttons, check boxes, drop-down lists, rulers,
free text). Controlled vocabularies or predefined sets of answers
are used when available. However, as the basic questionnaire is
generic, this is extensible in questionnaires tailored to particulardisciplines or fields of study. Snapshots can be made at any time
to freeze and document the state of the information about aproject at a given point in time.

4.2 Output
There are several kinds of output. On the project level, theinformation previously entered can be aggregated into textual
views (e.g. DMPs to be used for project proposals). These views
can be defined as required. There is a to-do list of data
management tasks to be performed and a reminder function that
notifies the responsible parties of upcoming assignments.
On a departmental or institutional level, information can be
aggregated across projects, which is useful for a number of
purposes, e.g. to derive the demand regarding infrastructure
resources or to get an overview of the types and amount of data
produced at the institution.

4.3 Setup and operation
One aim was to design RDMO in a way that makes it easy toinstall and to customise in various contexts (e.g. universities,
research institutes, departments of universities or research
institutes, libraries, larger joint research projects, researchgroups etc.). Accordingly, the tool is flexible in several respects.
In order to allow institutional branding, the user interface can be
freely customised. The content can also be tailored completely tosuit context-specific needs. This applies to the questions andanswering options as well as to help texts, templates for the
output of DMPs and tasks.
Another important point is the ability to integrate it into the
local infrastructure. RDMO can be linked to the institutional

authentication and authorisation infrastructure, namely an
LDAP system or a Shibboleth federation.

5 OUTLOOKIn addition to the demo version provided by the RDMO project(hosted by Leibniz Institute for Astrophysics Potsdam (AIP) andavailable at https://rdmo.aip.de), several institutions have set up
local instances, among them Karlsruhe Institute of Technology,
University of Stuttgart, University of Konstanz, University ofDuisburg-Essen, and the Göttingen eResearch Alliance. Several
more have expressed interest in doing so.
Further development of RDMO is planned in the future, thesecond project phase will start in autumn 2017. Some of theaforementioned institutions will closely collaborate with the
project in this respect.
The range of functionalities will be extended, e.g. by thepossibility to upload relevant documents, such as codebooks,
metadata documentation or applicable guidelines, and acommenting functionality. Existing functions will be refined.
The focus of these projected activities lies on features supporting
the implementation and organisation of the actual data
management throughout the project lifetime, e.g. roles, tasks,
modules for cost estimation and ingest-process. Theinteroperability both between different RDMO instances and16
with external services such as re3data or research information
systems will be enhanced. Set-up, upkeep and integration indifferent institutional environments will be made easier withstandardised installation, integrated maintenance mechanisms
and extended support of authentication and authorization
procedures. Exchange and cooperation with different
stakeholder groups will be continued and intensified with theobjective to build an active user community dedicated to thecontinuous joint distributed development as the basis for asustainable future of RDMO. This includes tutorials andworkshops for different user groups as well as the extension of
our cooperation with existing data management andinfrastructure initiatives

16

http://www.re3data.org/

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