Benefits and Challenges: Data Management Plans in Two Collaborative Projects

COLLECTION:

DATA MANAGEMENT

PLANNING ACROSS

DISCIPLINES AND

INFRASTRUCTURES

PRACTICE PAPER

CORRESPONDING AUTHOR:

Denise Jäckel

Humboldt-Universität zu

Berlin, DE

[email protected]

KEYWORDS:

data management plan;

research data management;

collaboration

TO CITE THIS ARTICLE:

Jäckel, D and Lehmann, A.

2023. Beneﬁts and Challenges:

Data Management Plans in

Two Collaborative Projects.

Data Science Journal, 22:

25, pp. 1–7. DOI: https://doi.

org/10.5334/dsj-2023-025

Beneﬁts and Challenges:

Data Management Plans in

Two Collaborative Projects

DENISE JÄCKEL

ANNA LEHMANN

ABSTRACT

The data-driven shift in the science research leads to a wider range of research data. To

manage this data in a sustainable and adequate way, data management plans (DMPs)

were established as a method. However, some researchers still do not create DMPs

due to lack of time, resources and understanding of the needs. Furthermore, most of

the existing templates and tools are largely unknown. In this article, we investigated

the beneﬁts and challenges of DMPs in two joint research projects of several academic

institutions. For this, we described the process during the DMP creation, potential

challenges and beneﬁts experienced. We showed that a DMP with completely uniform

content among the partner institutions was not possible due to individual and subject

differences (e.g., in storage and policies). Instead, individual texts had to be formulated

in some cases to overcome the diversity. This complexity could not be handled with

the existing tools. Therefore, both projects created an own adapted template with

some generic contents. Existing guidelines and internal project policies helped during

the generation. We experienced that fewer people work more efﬁciently on a DMP than

many and that all researchers within the project can proﬁt from every individual DMP.

Although we were not required to produce one, we recognised the associated beneﬁts

as a guide during the research process in joint projects.

*Author afﬁliations can be found in the back matter of this article

2Jäckel and Lehmann

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DOI: 10.5334/dsj-2023-

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INTRODUCTION

Science depends on data. To address research questions, data need to be collected, interpreted

and analysed. As their collection is time and resource consuming, funding organisations

increasingly demand their sustainability and free accessibility. While scientiﬁc results are often

publicly available, the underlying research data mostly remain private by the researchers or

their organisation (Trippel & Zinn 2022). This prevents good scientiﬁc practices, where results

can be replicated and new research can be built on existing data for similar scientiﬁc questions,

contrastive studies or meta-analysis (DFG 2015) in the sense of FAIR data principles (Michener

2015). FAIR data are findable, accessible, interoperable and reusable (Wilkinson et al. 2016)

and a valuable resource to support information equity, accelerate science and enhance

research impact (Vision 2010). Thus, secure and efﬁcient data sharing is essential to support

and advance science; it allows researchers to save (funding) money and effort for redundant

data production if comparable data already exist (Gonzales, Carson & Holmes 2022). Therefore,

adequate research data management (RDM) became a prerequisite for research funding when

applying for research grants (Patterton, Bothma & Van Deventer 2018). RDM is a task that

includes planning, collection, storage, analysis, documentation, archiving and publication of

research data (Higgins 2018; Neuroth, Putnings & Neumann 2021).

Increasingly, more funders request researchers to specify how their generated data will be

managed. This task can be addressed in different ways, such as research data policies, data

managements plans (DMPs) or even within a cooperation agreement (Schmiederer & Kuberek

2022). A policy contains a framework for action and orientation to create transparency and

clarity in the handling of research data. Policies address ethical-legal and organisational-

technical principles and framework conditions in terms of RDM (Hiemenz & Kuberek 2019). DMPs

are text-based documents that orientate on policy guidelines. A DMP describes the handling of

research data, how they are collected, processed, documented, analysed, stored and archived

throughout as well as after the research project (Kitchener et al. 2017). The structure of a DMP

thus includes project planning and data management, handling of existing and new research

data, metadata to document the data and the context of its creation and their organisation,

long-term archiving and access (Hobohm & Müller 2011).

In the past, DMPs were not mandatory. In recent years, several funding agencies have required

a detailed DMP to be submitted in grant applications to support good data practices and to

promote data sharing as well as reuse (Holdren 2013). The European Framework Programme

for Research and Innovation, Horizon Europe,

has made their creation obligatory for funding.

The National Institutes of Health

is in the process of establishing a data management and

sharing (DMS) policy. The speciﬁc requirements vary between funding organisations in length,

detail and extent of review (Whitmire et al. 2015). This has led to an increasing need for

support, guidance and appropriate tools for researchers for DMP preparation (Mannheimer

2018). Therefore, most service-providing departments in German research institutions offer

various tools on how to handle research data (Dreyer, Lehmann & Odebrecht 2022).

Thus, a DMP should not be a burden but an easy-to-follow road map or guide with the

opportunity for it to become an integral part of research processes and good scientiﬁc practices.

This impacts and beneﬁts everyone from researchers and publishers to funders which makes

it worth the effort (Gonzales, Carson & Holmes 2022). Persistent identiﬁers, standardisation

(metadata, vocabularies) and security (legal issues, archiving) make the research process easier

and science FAIR (Blumesberger 2020). DMPs are also not ﬁxed but evolving, living documents

for all project phases, which should be started early and reviewed and revised regularly to

reﬂect the status quo of the project and to react on needs or changes (Trippel & Zinn 2022).

They likewise enable continuity in the event of staff changes, prevent double work, promote

collaboration and increase the visibility and impact of research (Jones 2011).

However, there are beneﬁts and challenges in every project, which can increase when several

institutions are involved. In the following, we will describe the experiences of two projects with

four to six project partners based on the process to a ﬁnal DMP, with focus on the complexity,

potential challenges and advantages that occurred (Table 1).

1 https://www.horizont-europa.de/.

2 https://sharing.nih.gov/.

PROJECT-SPECIFIC EXPERIENCE FROM FDNEXT FUNDING CODE

429828830

In the FDNext

research project funded by the German Research Foundation (DFG), six

universities from Berlin and Brandenburg are working together to evolve tools and services

for a sustainable institutional RDM. In the three-year funding phase, various tools and

concepts for departments, trainings for speciﬁc target groups, legal advice, policies and service

management will be compiled and ﬁnally evaluated with stakeholders from the nationwide

RDM community (FDNext 2020). To address those questions in a suitable manner, different

methods to generate research data are used: for example, expert interviews, questionnaires,

surveys and data analysis. In order to handle these data even beyond the funding phase, the

FDNext project members decided to develop a project-wide DMP with a research-speciﬁc focus,

although there was no formal need from the funder.

Due to the project structure, meaning different researchers from different institutions each

working on small pieces of the puzzle to address the overall research questions, we decided

to give everyone in the project the maximum freedom on how to handle their own research

data (in the limits of FAIR and the funding directives). This means every researcher had the

ability to write their own DMP. In order to still gain a project-wide narrative, a template was

formulated. To meet all the requirements from the funder (DFG), we based our template on the

‘code for good scientiﬁc practice’ (DFG 2015). In addition, we oriented our template to a model

plan on DMP (Helbig 2016) as well as a DMP template especially created for students of the

Institute for Library and Information Science of the Humboldt-Universität zu Berlin (IBI 2022).

As a result, our template contains the main metadata regarding FDNext, such as project name,

ID, short description and research focus within the project, including names and contacts of the

scientists working on this task and also the main questions regarding new or reused data. Since

FDNext is a very diverse joint project, every associated researcher had a slightly different vision

on how to work with the (generated or existing) research data. Luckily, the questions regarding

handling data could be categorised in four different sections: data strategy, data design, data

transition and data storage.

3 https://www.forschungsdaten.org/index.php/FDNext.

Table 1 Differences between

DMPs in general (left), in the

FDNext project (middle) and

BUA-FDM (right) with regard to

seven aspects (vertical).

DMPS GENERAL FDNext BUA-FDM

Goal Creating FAIR research data for a joint

research project

Creating FAIR research data for a joint

research project

Creating FAIR research data for a joint

research project

Project partners Few to many 6 4

Guidelines • Code for good scientiﬁc practice • Code for good scientiﬁc practice

• Project policy

• Code for good scientiﬁc practice

• Institutional research data policy

Collaborative work Sustainability and free accessibility of

data, challenging lack of time, resources

and understanding of the needs

Creating, reviewing, commenting

and discussing the project-wide DMP

template

Collaborative text work using Overleaf,

commenting and discussing the

different aspects of the DMP

Content • Collecting

• Processing

• Documenting

• Analysing

• Storing and archiving data including

persistent identiﬁers

• Standardizations

• Security

• Metadata of the project

• Data strategy

• Data design

• Data transition

• Data storage

• Administrative information

• Data description

• Documentation and data quality

• Storage and technical backup

• Legal obligations

• Data exchange and accessibility

• Responsibilities

Document Living document based on policy

guidelines and a text-based document

Living document based on a self-

created questionnaire, not published

Living document based on a

personalized questionnaire, published

Advantages Enable continuity in the event of staff

changes, prevent double work, promote

collaborations and increase visibility

and impact of research

Continuity even though the joint project

faced staff changes and clarify the

structure of the project ﬁndings

Identify and clarify open questions

regarding data handling, prevented

redundant work, promoted cooperation

and the visibility of the project results

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025

The [1] data strategy on how to handle research data within FDNext is regulated in the

project policy (Schmiederer et al. 2022). If necessary, there are subject-speciﬁc concepts and

measures for quality assurance, which can be described separately in the ﬁrst sections of the

FDNext DMP template. The [2] data design deals with the form of research data used in the

project. This includes a description of the ﬁle formats and ﬁle types as well as ﬁle naming.

Third-party rights can also be described in this section if the handling exceeds the provisions

set out in the project policy. As long as there are no legal restrictions (e.g., third-party rights)

on the [3] data transition and publication of research data, they should be published as quickly

as possible. It is important that the data are made available in a form (e.g., ﬁle type) that is

useful for subsequent users. If research data is released by a publisher, it must be determined

how access to the data is nevertheless maintained for scientists from other ﬁelds as well as

an interested public. The rules of good scientiﬁc practice regarding [4] data storage stipulate

that research data must be archived for at least 10 years. This must be guaranteed in relevant,

supra-regional infrastructures which will be described in the fourth and last section of the

FDNext DMP template.

Once the template was reviewed, commented and revised by all project members, it was

shared as a plain document in a collaborative cloud. This way every associated researcher

could elaborate their own DMP regarding the special needs of their research focus within the

project. Furthermore, there was a deadline for every researcher to ﬁnish their sketch of the DMP.

From the day of this deadline on, we once more reviewed, commented and revised all DMPs

and also seized the opportunity to gain a wider understanding of how our colleagues address

our overall research questions. Due to the fact that a DMP is a living document and as thus it

wont be ﬁnished before the project ends we decided to not publish our texts. In conclusion, the

process is still ongoing, supporting the idea of a living document. Nevertheless, the discussion

about the project-wide DMP template as well as the exchange concerning the individual DMPs

helped to reach a common understanding not just of how research is to be done in FDNext but

also on how we want to successfully answer our research questions. In that way, the additional

work of creating, reviewing, commenting and discussing our DMPs was perfectly worth it.

PROJECT-SPECIFIC EXPERIENCE FROM BUA-FDM FUNDING CODE

501_CRDMS

The Concept Development for Collaborative Research Data Management Services (short BUA-

FDM

) project, funded by the Berlin University Alliance (BUA), aims to establish and strengthen

sustainable RDM services and infrastructures. In order to closely align support, training,

communication and services based on researchers’ requirements, these were determined in

the course of a survey. This enquiry also captured the researchers’ needs for DMPs (Ariza de

Schellenberger et al. 2022a) regarding support (e.g., in the form of tools) or reasons against

their production (Jäckel, Helbig & Odebrecht 2022a; Jäckel, Helbig & Odebrecht 2022b).

Furthermore, to handle the project data, a DMP was generated, although it was not requested

by the funder.

As everyone had been working on the same datasets in the project, we chose a coordinated

approach for a uniform DMP. Various suitable tools were available, such as Research Data

Management Organiser (RDMO

), DMPTool,

DMPonline

or TUP-DMP,

with varying advantages

and disadvantages. We decided for a freely available template called RDMOkurz.

RDMO is an

open-source software and web application developed by a DFG project and was mentioned in

our survey as a potential solution for missing technical tools regarding DMPs. It is already very

well established in Germany, used or offered by various scientiﬁc institutions and within the

National Research Data Infrastructure (NFDI

). The RDMO template was easily implemented

4 https://www.berlin-university-alliance.de/commitments/sharing-resources/fdm/index.html.

5 https://rdmorganiser.github.io/.

6 https://dmptool.org/.

7 https://dmponline.dcc.ac.uk/.

8 https://dmp.tu-berlin.de/.

9 https://github.com/rdmorganiser/rdmo-catalog/tree/master/rdmorganiser/questions.

10 https://www.nfdi.de/.

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into the German information RDM portal Forschungsdaten.info

for a collaborative work.

Filling out the questionnaire was intuitively feasible in a short time. However, it turned out

that the questions were not suitable for us, as some only allowed yes or no answers, but the

complexity of our project required a detailed description. Subsequently, the templates from

Freie Universität Berlin

and Humboldt-Universität zu Berlin

were compared. The BUA-FDM

team chose the ﬁrst one and combined it with the one from RDMO. Not all questions were

used, and a selection was made with regard to relevant issues, leading to an individual project

template that summarised information in a continuous text and from one question group,

rather than many individual answers.

Our template contained [1] administrative information on the project name and description,

funding code and agency, principle investigators, participating institutions and relevant policies.

In the [2] data description, we stated that we did not reuse any data but collected them ourselves

through a self-evaluation with RISE-DE (Hartmann, Jacob & Weiß 2019) and the mentioned

survey. We described the software as well as tools used for data collection and evaluation, the

resulting datasets with their (open) formats and access rights. The [3] documentation and data

quality section described the publication of the data, additional helpful information (code book,

read-me ﬁle), selected metadata schema, DOI assignment and ﬁle naming. The [4] storage

and technical backup during the course of the project differed depending on the institution

and was presented individually. The [5] legal obligations and framework conditions included

information on cross-institutional data storage and information security. [6] Data exchange

and permanent accessibility described where (the open repository Zenodo

) and how (open

access) the data will be published. [7] Responsibilities and resources were divided according to

the project leaders and the project staff.

For an easier collaborative work with all project members, we transferred our created template

to the software Overleaf.

Since the project had been ongoing for a while, most questions

could easily be answered directly without any problems. Others (e.g., legal uncertainty) needed

to be discussed. Uniform information from all institutions was combined and standardised,

and differences were clearly indicated. In addition, we implemented a preliminary description

with information about the institution-speciﬁc requirements (e.g., for storage or their policies).

During this process, the document was kept up to date and revised as necessary. The ﬁnal

version was published in December 2022 (Ariza de Schellenberger et al. 2022b) and can be

continuously updated as new versions in the future if required in the sense of a living DMP.

Since the project members of BUA-FDM worked constantly on and with the DMP throughout the

project, its preparation helped to identify and clarify open questions. The early creation of the

DMP prevented us from doing redundant work and promoted cooperation; it will promote the

visibility of the project results in the future.

GENERAL RECOMMENDATIONS FOR IMPROVEMENTS

The reasons against DMPs (e.g., lack of time, resources, necessity) mentioned by the researchers

in the BUA-FDM survey were only partly evident in our projects. Both projects lacked suitable

tools and templates and therefore created a questionnaire themselves. We understand why

researchers suggested RDMO as a suitable tool for DMPs, as it is very simple, intuitive and fast

to use, although it was unfortunately not sufﬁcient enough for the BUA-FDM project. To capture

the complexity of the collaboration of different institutions, more detailed DMPs are needed

than the current existing templates allow. It should be clear that institutions differ in their work

with (generated) research data, which means that not all contents of the DMPs can be written

in a uniform way. Therefore, it was a big help in the FDNext project to categorise all questions

regarding handling of research data circulating in the RDM community. In this way, we have

been able to point out our research focus while still including all aspects on modern RDM.

Since the processing of the consistent answers took a lot of time in the BUA-FDM project, we

11 https://forschungsdaten.info/.

12 https://www.fu-berlin.de/sites/forschungsdatenmanagement/_dokumente/dmp-muster_v01.pdf.

13 https://www.cms.hu-berlin.de/de/dl/dataman/arbeiten/dmp_erstellen/dmp-info.

14 https://zenodo.org/communities/bua-fdm/.

15 https://de.overleaf.com/.

6Jäckel and Lehmann

Data Science Journal

DOI: 10.5334/dsj-2023-

025

made the whole DMP with its generic preliminary information about the respective institutions

and their speciﬁcs (e.g., storage, policies) available for future projects. This can be used for

subsequent DMPs, if required, to save time and resources.

In order to save personnel resources, tasks and responsibilities for the DMP should be precisely

deﬁned and delegated. Here, less is more. The great advantage of the project FDNext is and

was the deﬁned role of a coordinator. Thus, only one or two people were working on the

plain template, and therefore double work could be prevented. Through the opportunity of

internal reviews, everybody within the project was still able to adjust the DMP template for their

needs and in the meaning of subject-speciﬁc requirements. In contrast, the BUA-FDM project

experienced long processing during the development (e.g., through legal uncertainties and the

long consultations with all project members). This ﬁrst aspect should be better supported in

the future to adequately assist researchers. For example, guidelines such as the DFG’s code for

Safeguarding Good Scientiﬁc Practice about data accessibility should be considered as a help

during the DMP generation. Similar, the FDNext project policy (Schmiederer et al. 2022) worked

as a (also legal) framework that enabled us to freely describe our way of handling data.

DMPs have existed for years but have only recently become increasingly obligatory for research

funding. Even though DMPs are not mandatory by all funding agencies, they should be

prepared, as they are a road map during the research process and facilitate the work. A DMP

should be generated at an early stage of a research project and be constantly updated as a

living document. In addition, it should be reused as much as possible for subsequent projects.

Thus, we were not able to conﬁrm an asserted lack of relevance or beneﬁt, as stated by several

researchers from the BUA-FDM survey. Since we constantly worked on our DMPs throughout the

projects, its preparation helped us to identify and clarify open questions. Thus, the elaboration

of DMPs, even if not required from the funders, was a welcome support and helpful guide for

our projects.

COMPETING INTERESTS

The authors have no competing interests to declare.

AUTHOR AFFILIATIONS

Denise Jäckel orcid.org/0000-0002-8720-6559

Humboldt-Universität zu Berlin, DE

Anna Lehmann

orcid.org/0000-0002-5739-4472

Humboldt-Universität zu Berlin, DE

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TO CITE THIS ARTICLE:

Jäckel, D and Lehmann, A.

2023. Beneﬁts and Challenges:

Data Management Plans in

Two Collaborative Projects.

Data Science Journal, 22:

25, pp. 1–7. DOI: https://doi.

org/10.5334/dsj-2023-025

Submitted: 14 December 2022

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