#  Introduction and aim

The main objective of the PREFER project is to strengthen patient-centric
decision making throughout the life cycle of medical products (a term which,
in the context of this project, includes medicinal projects(1) and medical
devices(2)) by developing evidence-based recommendations to guide industry,
Regulatory Authorities, Health technology assessment (HTA) bodies and
reimbursement agencies on how and when patient-preference studies could be
performed, and how the results can be used to support and inform decision
making.

The PREFER Consortium Agreement indicates that a specific data management plan
(DMP) will be created. More specifically, the Consortium agreement indicated
in section 7.5.4 the following:

_‘As an exception to Clause 29.3 of the Grant Agreement, as provided for in
its last paragraph, certain Beneficiaries have indicated that their main
objective in the Action would be jeopardized by making all or specific parts
of the research data of the Action openly accessible. Beneficiaries have
therefore agreed to a data management plan, which describes how data will be
handled instead of open access, and which plan details the reasons for not
giving open access. Such data management plan is a deliverable of Work Package
1 and shall be added as Appendix 7 to this Consortium Agreement.’ (3)_

The DMP is an evolving document with the final DMP forming the Appendix 7 of
the Consortium Agreement, describing all aspects of how the data generated
within PREFER were managed.

The Description of Action (DoA) of PREFER (p.19-20) provides the general
framework regarding data management, data protection, data sharing, data
ownership, accessibility, and sustainability requirements.(4)

In this initial DMP the management of generated and collected individual-level
data is described, not the management of analyses and reports containing
aggregated data. These issues will be covered in the final DMP.

Overall, the DMP provides a description of the data management that will be
applied in the PREFER project including:

  * a description of the data repositories, who is able to access the data, and who owns the data. 

  

  * the main DMP elements for each of the research projects (interviews, literature review, case study, etc.) contributing to PREFER, to be defined and provided to PREFER (Chapter 5). 

  

  * the time period for which data must be stored. 

  

  * the standards for data collection and evaluation. 

  

  * the possibilities of and conditions for sharing data. 

  

  * the implementation of data protection requirements. 

  

As the DMP is an evolving document, some of the aspects may be described in a
later version of the DMP.

In summary, the PREFER DMP gives guidance and provides an oversight of general
data management, while each research project needs to provide specific data
management information including, but not limited to, data capture systems,
data analysis systems, data protection and data privacy measures, including
description of de-identification of data sets and access rules. And in cases
where the research results are not open access a justification needs to be
provided.

#  General principles

This is the Initial DMP for PREFER. The DMP is a working document, that will
evolve during the PREFER project, and will be updated to reflect project
progress. Table 1 lists the deliverable version updates of the DMP for PREFER.
Additional updates will be done whenever important changes occur e.g. due to
the creation of new data sets.

Processes relating to the different data management plan aspects will be
worked out between M6 and M18 and explained further in the next version of the
DMP due in M18.

**Table 1** PREFER Data Management Plan (DMP) deliverables  
  
<table>  
<tr>  
<th>

Del. no.*

</th>  
<th>

Deliverable name

</th>  
<th>

WP no.

</th>  
<th>

Short name of lead participant

</th>  
<th>

Type

</th>  
<th>

Dissemination level

</th>  
<th>

Delivery Date  **  
</th> </tr>  
<tr>  
<td>

1.3

</td>  
<td>

Initial DMP

</td>  
<td>

1

</td>  
<td>

Actelion

</td>  
<td>

R

</td>  
<td>

PU

</td>  
<td>

6 (March 2017)

</td> </tr>  
<tr>  
<td>

1.6

</td>  
<td>

Update DMP  
</td>  
<td>

1

</td>  
<td>

Actelion  
</td>  
<td>

R

</td>  
<td>

CO  
</td>  
<td>

18 (March 2018)

</td> </tr>  
<tr>  
<td>

1.9

</td>  
<td>

Final DMP

</td>  
<td>

1

</td>  
<td>

Actelion

</td>  
<td>

R

</td>  
<td>

PU

</td>  
<td>

60 (September 2020)

</td> </tr> </table>

DMP= Data Management Plan; WP= Work packages; R = Document, report (excluding
the periodic and final reports); DEC = Websites, patents filing, press & media
actions, videos, etc.; PU = Public, fully open, e.g. web; CO = Confidential,
restricted under conditions set out in Model Grant Agreement

*  According to the Table 3.1c: List of deliverables of the PREFER Description of Action(4) ** Measured in months from the project start date (October 2016, Month 1) 

The DMP provides practical instructions with respect to any requirements for
local exceptions to data management.

The DMP follows the principles that research data are findable, accessible,
interoperable and reusable (FAIR)(5) as well as being attributable, legible,
contemporaneous, original and accurate (ALCOA)(6).

The terminology used in this DMP is explained in the glossary (Chapter 11 of
this DMP).

The general principles on access rules are defined in the consortium agreement
(section 8) (3).

For research data generated as part of an ongoing medicinal product
development program within industry, there may be proprietary and privacy
concerns that will be acknowledged and agreements made with the respective
partners on data accessibility and data storage. To acknowledge potential
differences for industry or academic case studies the DMP will refer to “data
generated in industry-led studies” and “data generated in academic-led
studies”.

#  Overview of data managers, data repositories and access rules

Three repositories / platforms are used in the PREFER project. The responsible
contacts are listed in table 2.

  * The platform “Projectplace” is used as an interaction platform for PREFER members to _**store and exchange** _ _**reports and anonymous data** _ . 

  

  * The data repository at KU Leuven (Digital Vault for Private Data) is used to _**store and exchange** _ _**sensitive personal data** _ in a secure and protected environment during the conduct of PREFER. 

  

  * The data repository at Uppsala University (ALLVIS) will be used for _**long-term storage** _ of reports and anonymized data particularly after the end of the PREFER project. 

  

The use of the KU Leuven repository is preferred for the storage of interviews
and academic patient preference studies. Local national laws and requirements
need to be applied and can results in deviations. For example in the UK, the
UK Sponsor and the Research Ethics committee will determine where it is
allowed to store data.

Data sets containing personal data can also be stored by the data owners in
their own repository for a fixed period of time, as defined in the applicable
laws or regulations, but this should be a secure repository. Copies of
datasets containing personal data in the possession of partners other than the
research data owner (see 5.2) must be destroyed at the end of the PREFER
project. Other non-public and public datasets not containing personal data
will be stored for at least 10 years from the end of the PREFER project in the
Uppsala data repository, to ensure their long-term availability to future
researchers.

**Table 2** Main contacts for data management aspects  
  
<table>  
<tr>  
<th>

Responsibilities

</th>  
<th>

Name

</th>  
<th>

E-mail address

</th> </tr>  
<tr>  
<td>

Data Management compliance contact

</td>  
<td>

Monika Brand

</td>  
<td>

_monika.brand@actelion.com_

</td> </tr>  
<tr>  
<td>

Deputy Data Management Compliance contact Eline van Overbeeke

</td>  
<td>

eline.vanoverbeeke@kuleuven.be

</td> </tr>  
<tr>  
<td>

ProjectPlace contact

</td>  
<td>

Carl Steinbeisser

</td>  
<td>

_carl@steinbeisser-management.com_

</td> </tr>  
<tr>  
<td>

KU Leuven repository contact

</td>  
<td>

Isabelle Huys

</td>  
<td>

_Isabelle.huys@kuleuven.be_

</td> </tr>  
<tr>  
<td>

KU Leuven deputy repository contact

</td>  
<td>

Eline van Overbeeke

</td>  
<td>

eline.vanoverbeeke@kuleuven.be

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Uppsala repository contact

</td>  
<td>

Mats Hansson

</td>  
<td>

_mats.hansson@crb.uu.se_

</td> </tr>  
<tr>  
<td>

Uppsala deputy repository contact

</td>  
<td>

Head of the Department of

Public Health and Caring Sciences

</td>  
<td>

</td> </tr> </table>

The WP1 data management team has the responsibility to update the names
related to the responsibilities, as people might change position.

All questions related to data management such as rules for uploading data
sets, request for access rights should be sent to the Data Management
Compliance contact and the Deputy. The processes and the role description of
the Data management compliance contact and its deputy will be worked out in
the next period between M6 and M18 and explained further in the next version
of the DMP due in M18.

WP leads are responsible for informing the Data Management Compliance contacts
about all generated data sets, in their research projects.

##  Projectplace

Projectplace is the platform used by PREFER to facilitate collaboration
between PREFER members, to plan deliverables, to track progress of all tasks,
and to store meeting minutes and task reports. All PREFER members have an
account so they can access Projectplace.

##  The KU Leuven (KUL) repository for personal data

A secured repository to store and to exchange sensitive personal data will be
provided by KUL and is known as a “digital vault for private data”. Within
this digital vault, researchers can keep personal data safe and apply strict
rules for data access. In addition, they can also anonymise information and
process it outside the digital vault without causing any data privacy risk.
The digital vault is a highly secure environment within a secure network.
Several vaults can be set up within this secure network, each for a different
project. Each vault consists of a protected server (Windows or Linux) and can
only be accessed by a well-defined user group.

The KUL repository will function as the virtual workplace to share and assess
the individual-level data as needed to fulfil the PREFER objectives. Processes
relating to the use of the KU Leuven repository will be worked out after M6
and explained further in the next version of the DMP due in M18.

###  Specifications and costs of the KUL repository

The repository consists of:

  * A **secure server and operating system** in the special, secure environment for private data: 

    * A virtual Windows server (1 CPU and 4 GB RAM) or a virtual Linux server (1 CPU and 2 GB RAM). 

  

    * An IP address, DNS entry and name for the virtual server. o An ICTS-guaranteed licence for the operating system (Windows server or Linux CentOS). o Installation of the Windows or Linux operating system (including latest upgrades and security patches) on the virtual server. o Monthly maintenance of the Windows or Linux operating system, i.e. regular application of upgrades and security patches. 

  

    * Access to the virtual server via an RDP client (remote desktop protocol) for Windows or an SSH client for Linux. Preliminary VPN connection is required. 

  

  

  * **Application software on the server** : 

    * Installation of SAS and SPSS on the virtual server. 

  

    * An ICTS-guaranteed SAS and SPSS software license. 

  

  

  * **Storage capacity for data** : 

    * 50 GB storage space for data (server back-end storage, type 1, with mirror). 

  

  

  * **Cost of the repository:** € 1.291,79 per year 

  

###  Procedures/tools for data accessibility / security

Details of all users of the digital vault must be registered with KUL.
External users must have minimal details registered. The user/requestor is
responsible for ensuring their registration. Access to identifiable personal
data on the secure ICTS server is restricted to a minimum number of people,
i.e. people whose task it is to decrypt or anonymise information. Anonymised
information is sufficient for the majority of researchers involved in a
project. These data can be processed outside the digital vault; therefore,
access to the digital vault is not necessary or even desirable for these
researchers. One person (the data owner, see chapter 5) per task will get
access to the data repository. If additional people require access to the
digital vault after its initial set up, this access must be requested by the
person responsible for the digital vault. This can be done by e-mailing
_servicebeheer@icts.kuleuven.be_ . For PREFER the KUL repository manager is
listed in table 2. Access to the digital vault is only possible through a Luna
account (KU Leuven user ID and -password). The digital vault is only
accessible through the KU Leuven VPN solution. The user must authenticate when
setting up the VPN connection. A vault-specific VPN profile ensures that
access is possible only to the corresponding vault in the secure network.
Access to the secure network environment that houses all the vaults is
strictly protected. The secure network environment is protected from the
outside by a firewall, which only allows traffic:

  * from the VPN solution (through a specific profile) to the servers and information in the corresponding vault; 

  

  * from the KUL ICTS management network (for system administration) from a central system console. 

  

The server in the vault is managed by KUL ICTS and only KUL ICTS personnel
have administrator/root rights. KUL ICTS personnel are bound by the KUL ICT
code of conduct for staff.

###  Duration of accessibility

Users with access to the digital vault only have user rights for access to the
data in their own vault. A service agreement for a “Digital vault for private
data” has a duration of 1 year, after which it tacitly renews each year unless
the IT manager responsible gives notice on the agreement by e-mail to
_servicebeheer@icts.kuleuven.be_ , at the latest 3 months before the end of
the agreement. If notice is given on the digital vault agreement after the
project ends, the information will be irrevocably deleted and will become
irrecoverable. An agreement will be set up with KUL to guarantee access for 5
years, namely during the duration of the IMI PREFER project. Long term storage
after the end of the PREFER project are described in section 3.3.

###  Data transfer

Data transfer files to be generated and uploaded to the digital vault can
directly be uploaded by the data owner in the secure environment. For this the
data owner needs to have access to the digital vault (see chapter 5). If the
data is not directly available to the data owner, the data can be transferred
to the data owner through a secure FTP (SFTP) or can be delivered to the data
owner via a physical medium (DVD/CD/USB).

###  Back-up process

Stored data is backed up using “snapshot” technology, where all incremental
changes in respect of the previous version are kept online on a different
server at the KU Leuven. As standard, 10% of the requested storage is reserved
for backups using the following backup regime:

  * An hourly backup (at 8 a.m., 12 p.m., 4 p.m. and 8 p.m.), the last 6 of which are kept. 

  

  * A daily backup (every day) at midnight, the last 6 of which are kept. 

  

  * A weekly backup (every week) at midnight between Saturday and Sunday, the last 2 of which are kept. 

  

###  Disaster Recovery

The repository has 50 GB storage space for data (server back-end storage, type
1), and a mirror storage system at a different building of the KU Leuven in
another part of the city is provided to enable disaster recovery.

##  The Uppsala repository for long-term storage

The data repository ALLVIS at Uppsala university will be used to archive the
PREFER anonymized data used for publications as well as the PREFER
recommendation documents and all content form ProjectPlace. Mats G. Hansson is
the owner and responsible for the ALLVIS repository, listed in table 2. He is
deputized by the Head of the Department of Public Health and Caring Sciences,
if applicable.

###  Specifications of ALLVIS

ALLVIS is a storage platform and the respective research data owner is
responsible for transferring anonymized data to ALLVIS for storage. The
process and timing for such storage will be further worked out after M6 and
detailed in the next version of the DMP due in M18. If stored data need to be
transferred to platform for processing again the research data owner is
responsible for the data transfer. ALLVIS will not release any data without
the agreement between the repository owner and the research data owner (see
section 5.2 for definition). However, the research data owner has to comply
with the principle of public access to official records.

The Principle of Public Access (Offentlighetsprincipen) in Sweden means that
activities of public authorities are open to the public and research
activities are no exception. Universities in Sweden are legally considered as
public authorities. Records of data and research results created in the
research process are subject to implementation of the Principle of Public
Access, regardless of the kind of research or source of funding.

Public access can either be 1) public without restrictions, 2) public but with
restricted access regulated by Secrecy Law. However, there might be working
documents that do not fall under the public access rules.

###  Archiving

Administrative records (e.g. Ethics approval) are stored by public authorities
with reference to Archive Law. During the course of the PREFER project,
administrative records and documents are stored in ProjectPlace. These
documents and records will be archived in the ALLVIS repository at Uppsala
University for 10 years after the end of the project. Once archived, records
are subject to the principle of public access. Uppsala University shall draw
up a description of this archive and a systematic archival inventory.

###  Procedures/tools for data accessibility/security

Access to the file repository is granted via a Windows file share using SMB
v3. Outside Uppsala University, access is granted only through a secure VPN-
connection. Authentication against the VPN and authentication against the file
share is granted using a personal/identifiable user account from Uppsala
University. Authentication at Uppsala University is handled by a central user
database and is used by the VPN and file share. Access to the project area is
limited to the research data owner (e.g. Principal Investigator (PI)) and
users granted access. Data is stored by an enterprise-grade NAS-system, which
has been installed and configured in accordance with the supplier’s guidelines
and is hosted in an on-campus server hall.

###  Back-up process

Backups are incrementally saved every night using an enterprise-grade backup
system at a Universityaffiliated off-campus site.

###  Disaster Recovery

Disaster recovery is in place and is possible. Disaster recovery is handled on
a per-case base. Requests can

be made either by phone or e-mail, contacting Uppsala University´s
Servicedesk. The Servicedesk can be contacted weekday´s 08:00-21:00 and on
weekends 14:00-17:30. E-mail:  _http://uadm.uu.se/it/om/servicedesk._

#  Overview of data types generated and collected in PREFER

The data generated and collected during the PREFER project can be divided into
two categories of decreasing confidentiality:

  1. datasets containing personal data 

  

  2. datasets containing non-personal data 

  

  

The data generated within the PREFER project are (a) primary data (original
research) produced by different stakeholder e.g. interviews and case studies,
and (b) secondary data (reuse of existing data) such as database studies and
literature reviews. Primary data are data sets more likely to contain personal
data, while secondary data sets are more likely to containing non-personal
data.

Patient data will be generated and processed during the activities planned in
WP 2, WP 3 and WP 4 (table 3).

  * **WP 2** will generate datasets containing literature reviews, recorded interviews, transcriptions of interviews, and review of reports in preference research 

  

  * **WP 3** will create datasets containing both aggregated and patient-level identified or de-identified data. These data can be created from historical case studies, prospective industry-led and academic-led case studies, surveys, as well as from simulation studies 

  

  * **WP 4** will generate datasets containing literature reviews and data resulting from expert panel discussions and consultation rounds. 

  

Appropriate strategies have to be put in place by the individual research
project owners, to ensure (personal) data protection/privacy, and individual
studies are asked to provide a small DMP as described in this DMP (Chapter 5).
The processes will be worked out and implemented between M6 and M18, in
collaboration with task WP 3 task 3.1 to align the templates and to use
synergies for research project descriptions.

The WP1 Data Management Team will generate a meta-data repository of all
research projects in a format as outlined in table 3 and with the support from
the WP leads, or research project owners, respectively. This meta-data
repository will be updated regularly (at least on a quarterly basis) and is
the master file for more detailed information of each research project as
described in table 3.

The WP1 Data Management Compliance Contacts (table 2) together with the WP
leads will establish a process to ensure that all generated data sets, or
research projects, respectively, will be gathered as described in this DMP.

The data are expected to be useful for the PREFER project, especially for the
specific tasks that generate or collect or re-use the data, and the analyses
and reports will be useful to all stakeholders.

Table 3 will be updated with the unique identification numbers as described in
Chapter 5.

**Table 3** Summary of the PREFER-generated data  
  
<table>  
<tr>  
<th>

Task* Objective Design

</th>  
<th>

Type

</th>  
<th>

Format

</th>  
<th>

Re-  Origin Size Ca***

use**

</th> </tr>  
<tr>  
<td>

2.1

</td>  
<td>

Identifying desires, expectations, concerns and requirements of stakeholders
about methodologies for PP elicitation and their use in decision making

</td>  
<td>

Literature review

</td>  
<td>

Born digital, reference

</td>  
<td>

Textual

</td>  
<td>

2.3

</td>  
<td>

Secondary TBA 2

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Interviews

</td>  
<td>

Born digital, observational

</td>  
<td>

Multimedia + textual

</td>  
<td>

2.3

</td>  
<td>

Primary TBA 1

</td> </tr>  
<tr>  
<td>

2.2 Determine processes, conditions, contextual factors that influence the

</td>  
<td>

Literature review

</td>  
<td>

Born digital, reference

</td>  
<td>

Textual

</td>  
<td>

2.3

</td>  
<td>

Secondary TBA 2

</td> </tr>  
<tr>  
<td>

utility and role of PP studies

</td>  
<td>

Interviews

</td>  
<td>

Born digital, observational

</td>  
<td>

Multimedia + textual

</td>  
<td>

2.3

</td>  
<td>

Primary TBA 1

</td> </tr>  
<tr>  
<td>

2.3

</td>  
<td>

Identification of assessment criteria

used at decision points throughout the DLC

</td>  
<td>

Literature review

</td>  
<td>

Born digital, reference

</td>  
<td>

Textual

</td>  
<td>

/

</td>  
<td>

Secondary TBA 2

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Interviews

</td>  
<td>

Born digital, observational

</td>  
<td>

Multimedia + textual

</td>  
<td>

/

</td>  
<td>

Primary TBA 1

</td> </tr>  
<tr>  
<td>

2.4 Identification of preference elicitation  Literature  review

</td>  
<td>

Born digital, reference

</td>  
<td>

Textual

</td>  
<td>

2.6

</td>  
<td>

Secondary TBA 2

</td> </tr>  
<tr>  
<td>

methods

Interviews

</td>  
<td>

Born digital, observational

</td>  
<td>

Multimedia + textual

</td>  
<td>

2.6

</td>  
<td>

Primary TBA 1

</td> </tr>  
<tr>  
<td>

1\. Identification of Literature

2.5

educational/gamified tools review

</td>  
<td>

Born digital, reference

</td>  
<td>

Textual

</td>  
<td>

/

</td>  
<td>

Secondary TBA 2

</td> </tr>  
<tr>  
<td>

Literature

2\. Identification of psychological tools review

</td>  
<td>

Born digital, reference

</td>  
<td>

Textual

</td>  
<td>

/

</td>  
<td>

Secondary TBA 2

</td> </tr>  
<tr>  
<td>

3\. Presentation of risks  Literature  review

</td>  
<td>

Born digital, reference

</td>  
<td>

Textual

</td>  
<td>

/

</td>  
<td>

Secondary TBA 2

</td> </tr>  
<tr>  
<td>

Identification of candidate

2.7 methodologies and criteria to assess Interviews empirical case and
simulation studies

</td>  
<td>

Born digital, observational

</td>  
<td>

Multimedia + textual

</td>  
<td>

/

</td>  
<td>

Primary TBA 1

</td> </tr>  
<tr>  
<td>

Review of

Identifying and assessing historical

3.3 historical

case studies from industry partners

case studies

</td>  
<td>

Born digital, reference

</td>  
<td>

Textual

</td>  
<td>

/

</td>  
<td>

Secondary TBA 2

</td> </tr>  
<tr>  
<td>

Lessons learned survey of PREFER

3.3 members with preference research Survey experience.

</td>  
<td>

Born digital, reference

</td>  
<td>

Textual

</td>  
<td>

/

</td>  
<td>

Primary

</td>  
<td>

TBA 1

</td> </tr>  
<tr>  
<td>

3.4 Identifying and supporting prospective PP case case studies from industry
partners study

</td>  
<td>

Origin TBD, observational

</td>  
<td>

Textual, numerical, multimedia

</td>  
<td>

/

</td>  
<td>

Primary

</td>  
<td>

TBA 1

</td> </tr>  
<tr>  
<td>

3.5- Empirical case studies and simulation PP case

3.7 studies study

</td>  
<td>

Origin TBD, observational + simulation

</td>  
<td>

Textual, numerical, multimedia, models

</td>  
<td>

/

</td>  
<td>

Primary

</td>  
<td>

TBA 1

</td> </tr>  
<tr>  
<td>

3.8 Additional case studies  PP case  study

</td>  
<td>

Origin TBD, observational

</td>  
<td>

Textual, numerical, multimedia

</td>  
<td>

/

</td>  
<td>

Primary

</td>  
<td>

TBA 1

</td> </tr>  
<tr>  
<td>

4.3 Expert panels on recommendations Interviews

</td>  
<td>

Born digital, observational

</td>  
<td>

Multimedia + textual

</td>  
<td>

/

</td>  
<td>

Primary

</td>  
<td>

TBA 1

</td> </tr>  
<tr>  
<td>

Consultation rounds on

4.4 recommendations Interviews

</td>  
<td>

Born digital, observational

</td>  
<td>

Multimedia + textual

</td>  
<td>

/

</td>  
<td>

Primary

</td>  
<td>

TBA 1

</td> </tr> </table>

PP= Patient preferences; DLC=Drug Life Cycle; Ca= Category; TBA= To be
announced

* According to the description of the tasks and different work packages in the PREFER DoA document of 16/07/17. 

** Displays in which other tasks of WP2 and WP3 the data are used.

*** The data produced and used during the PREFER project can be divided into
two categories (Ca):

  1. datasets containing (sensitive) personal data 

  

  2. datasets containing non-personal data 

  

  

#  Operational data management requirements for PREFER research projects

Each research project (interviews, literature review, surveys, case studies,
etc.) needs to provide a short dataset specific DMP, including but not limited
to data capture systems, data analysis systems, data protection and data
privacy measures, including description of de-identification of data sets and
access rules. If the research results cannot be open access a justification
needs to be provided.

##  Requirements for the short dataset specific DMP

All data owners need to fill in **Table 4** (available on ProjectPlace as a
template) containing the meta data and describing the data management of data
sets. Metadata are specifications for data that provide the contextual
information required to understand those data. Such specifications describe
the structure, data elements, interrelationships and other characteristics of
data, the data repository used, and need to be securely stored with the
database.

These tables will be reviewed by the WP1 data management team for
completeness, compliance with the DMP and compliance with the Consortium
Agreement. The text in _blue and Italic_ gives guidance on what information
should be provided and should be replaced.

As part of the DMP an evolving data governance document of the different study
types will be maintained (WP 1, Deliverables 1.3, 1.6 and 1.9, M6, M18, M60).
This data governance document (based on table 4) will be kept and maintained
in Projectplace and attached to the DMP at the given deliverables times.

Table 4 Metadata requested per dataset (adapted from the Data Management
General Guidance of the DMP

Tool)(7)

_This table will be made available on Projectplace as a template to fill in
for every dataset, research project by the data owner. The text in blue and
Italic gives guidance on what information should be provided and should be
replaced._  
  
<table>  
<tr>  
<th>

General Overview

</th>  
<th>

</th> </tr>  
<tr>  
<td>

**Title**

</td>  
<td>

_Name of the dataset_

</td> </tr>  
<tr>  
<td>

**PREFER task**

</td>  
<td>

_Mention to which (sub)task in PREFER this dataset belongs_

</td> </tr>  
<tr>  
<td>

**Identifier**

</td>  
<td>

_An identifier will be given to all datasets. Format: PREFER_#.#_L/I/P_yyyy-
mm-dd. (L, I, or P is chosen according to the design of the study: L=
literature review, I= interviews, P= Patient Preference study (whatever design
it takes). Example for the interviews of task 2.2:_

_PREFER_2.2_I_2016-12-10)_

</td> </tr>  
<tr>  
<td>

**Research Data owner**

</td>  
<td>

_Names and addresses of the responsible person and deputy of the organizations
who created the data; preferred format for personal names is surname first
(Format: Organization; Surname, First name)._

</td> </tr>  
<tr>  
<td>

**E-mail address of the data owner**

</td>  
<td>

_Please provide the e-mail address of the data owner_

</td> </tr>  
<tr>  
<td>

**Start and end date**

</td>  
<td>

_Project start and end date. Format: yyyy.mm.dd-yyyy.mm.dd._

</td> </tr>  
<tr>  
<td>

**Method**

</td>  
<td>

_How the data were generated, listing equipment and software used (including
model and version numbers), formulae, algorithms, experimental protocols, and
other things one might include in a lab notebook_

</td> </tr>  
<tr>  
<td>

**Standards**

</td>  
<td>

_Reference to existing suitable standards of the discipline can be made. If
these do not exist, an outline on how and what metadata will be created.
Depending of type of data, different standards for collection exist, including
but not limited to:_

_a. Systematic literature review: Cochrane and Joana Bridge institute
standards b. Interviews: QUAGOL_

  3. _Focus group discussion: AMEE 91 guide_

  

  4. _Patient preference studies: depending on the type of method, e.g. ISPOR guide for DCE_

  

  

</td> </tr>  
<tr>  
<td>

**Type of data**

</td>  
<td>

  * _datasets containing personal data_

  

  * _datasets containing non-personal data_

  

</td> </tr>  
<tr>  
<td>

**Processing**

</td>  
<td>

_How the data have been altered or processed_

</td> </tr>  
<tr>  
<td>

General Overview

</td> </tr>  
<tr>  
<td>

**Source** _Citations to data derived from other sources, including details of
where the source data is held and how it was accessed_

</td> </tr>  
<tr>  
<td>

**Funded by** _Provide information regarding financial support such as
research grants, or indicate that the data owner funds the study_

</td> </tr>  
<tr>  
<td>

Content Description

</td> </tr>  
<tr>  
<td>

**Data description** _Keywords or phrases describing the dataset or content of
the data. Indicate version number if applicable. Describe the nature and
origin of the data._

</td> </tr>  
<tr>  
<td>

**Language** _All languages used in the dataset_

</td> </tr>  
<tr>  
<td>

**Variable list** _Description with variable name, length, tape, etc. and code
lists. Example: SEX, length of field (1 or more characters), values: F for
female; M for male. DOB (Date of birth), length of field (1 or more
characters), values: yyyy.mm.dd_

</td> </tr>  
<tr>  
<td>

**Data quality** _Data quality: This section should include description of
data quality standards, procedures to assure data quality_

</td> </tr>  
<tr>  
<td>

**Code list** _Explanation of codes or abbreviations used in either the file
names or the variables in the data files (e.g. '999 indicates a missing value
in the data')_

</td> </tr>  
<tr>  
<td>

Technical Description

</td> </tr>  
<tr>  
<td>

**Repository** _Mention where the data is stored_

</td> </tr>  
<tr>  
<td>

**File inventory** _All files associated with the project, including
extensions (e.g. 'NWPalaceTR.WRL', 'stone.mov')_

</td> </tr>  
<tr>  
<td>

**File Formats** _Formats of the data, e.g., FITS, SPSS, HTML, JPEG, etc. No
data standards are used in general in PREFER to enable interoperability of
data, but the PREFER consortium is striving to use file formats that are
interoperable, such as .txt, .csv, or .rtf files._

</td> </tr>  
<tr>  
<td>

**File structure** _Organization of the data file(s) and layout of the
variables, where applicable_

</td> </tr>  
<tr>  
<td>

**Checksum** _A digest value computed for each file that can be used to detect
changes; if a recomputed digest differs from the stored digest, the file must
have changed_

</td> </tr>  
<tr>  
<td>

**Necessary** _Names of any special-purpose software packages required to
create, view, analyse, or_ **software** _otherwise use the data_

</td> </tr>  
<tr>  
<td>

Access

</td> </tr>  
<tr>  
<td>

**Rights** _The data owner should indicate which access rights are applicable.
Any known intellectual property rights, statutory rights, licenses, or
restrictions on use of the data_

</td> </tr>  
<tr>  
<td>

**Access** _Where and how your data can be accessed by other researchers_
**information**

</td> </tr>  
<tr>  
<td>

**Sharing** _Description of how data will be shared, including access
procedures, embargo periods (if any), outlines of technical mechanisms for
dissemination and necessary software and other tools for enabling re-use, and
definition of whether access will be widely open or restricted to specific
groups. Identification of the repository where data will be stored, if already
existing and identified, indicating in particular the type of repository
(institutional, standard repository for the discipline, etc.). In case the
dataset cannot be shared or made open access, the reasons for this should be
mentioned (e.g. ethical, rules of personal data, intellectual property,
commercial, privacy-related, security-related)._

</td> </tr>  
<tr>  
<td>

**Archiving and** _Archiving and preservation (including storage and backup):
Description of the procedures that_ **preservation** _will be put in place for
long-term preservation of the data. Indication of how long the data should_
**(including** _be preserved, what is its approximated end volume, what the
associated costs are and how these_ **storage and** _are planned to be
covered. This information should include the archiving procedure of the_
**backup)** _research project at the data owner's site and also if the data
can be archived at the UU repository ALLVIS - for a detailed description see
chapter 6 of the DMP._

</td> </tr> </table>

##  Responsibilities of the data owner

Data owners per task will be identified and described in table 5, which will
be maintained. The data owner of the respective research projects must ensure
and is responsible to comply with all legal and ethical requirements for data
collection, handling, protection and storage. This includes adherence to
regulations, guidelines such as (but not limited to) the EU clinical trial
directive 2001/20/EC, Good clinical practice (GCP), Good Pharmacoepidemiology
Practice (GPP), as applicable. Only the research data owner will be granted
access to the secure data repository of KU Leuven. The process of granting
access to deputies will be worked out between M6 and M18.

All data protection rules described in chapter 7 of the DMP apply to the
archiving of the results underlying

PREFER publications and recommendation documents. Data generated in academic-
led studies which cannot be fully anonymized, e.g. interviews and personal
data, may only be stored at the KUL repository described in chapter 3.

**Table 5** Overview of data owners and data repository used per task

This table will be further employed after M6 to update the research data owner
including additions of research owner deputies, as people might change
position. The updated table will be displayed in the next version of the DMP,
due in M18.  
  
<table>  
<tr>  
<th>

**Data owners**

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th> </tr>  
<tr>  
<td>

Task Design

</td>  
<td>

Data repository

</td>  
<td>

Research Data Owner

</td>  
<td>

E-mail address

</td> </tr>  
<tr>  
<td>

2.1

</td>  
<td>

Literature review

</td>  
<td>

KU Leuven

KU Leuven

</td>  
<td>

Rosanne Janssens

</td>  
<td>

_Rosanne.janssens@kuleuven.be_

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Interviews

</td>  
<td>

Rosanne Janssens

</td>  
<td>

Rosanne.janssens@kuleuven.be

</td> </tr>  
<tr>  
<td>

2.2

</td>  
<td>

Literature review

</td>  
<td>

KU Leuven

</td>  
<td>

Eline van Overbeeke

</td>  
<td>

Eline.vanoverbeeke@kuleuven.be

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Interviews

</td>  
<td>

KU Leuven

</td>  
<td>

Eline van Overbeeke

</td>  
<td>

_Eline.vanoverbeeke@kuleuven.be_

</td> </tr>  
<tr>  
<td>

2.3

</td>  
<td>

Literature review

</td>  
<td>

EUR

KU Leuven

</td>  
<td>

Chiara Whichello

</td>  
<td>

whichello@bmg.eur.nl

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Interviews

</td>  
<td>

Chiara Whichello

</td>  
<td>

_whichello@bmg.eur.nl_

</td> </tr>  
<tr>  
<td>

2.4

</td>  
<td>

Literature review

</td>  
<td>

EUR

</td>  
<td>

Vikas Soekhai

</td>  
<td>

v.soekhai@erasmusmc.nl

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Interviews

</td>  
<td>

KU Leuven

</td>  
<td>

Vikas Soekhai

</td>  
<td>

_v.soekhai@erasmusmc.nl_

</td> </tr>  
<tr>  
<td>

2.5

</td>  
<td>

Literature review 1

</td>  
<td>

TBD

TBD

TBD

</td>  
<td>

Sarah Verschueren

</td>  
<td>

_Sarah.verschueren@mindbytes.be_

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Literature review 2

Literature review 3

</td>  
<td>

Selena Russo

Elisabeth Furberg

</td>  
<td>

Selena.Russo@ieo.it

</td> </tr>  
<tr>  
<td>

Elisabeth.furberg@crb.uu.se

</td> </tr>  
<tr>  
<td>

2.7

</td>  
<td>

Interviews

</td>  
<td>

KU Leuven

</td>  
<td>

TBD

</td>  
<td>

</td> </tr>  
<tr>  
<td>

3.3

</td>  
<td>

Review of historical case studies

</td>  
<td>

KU Leuven

</td>  
<td>

Leo Russo

</td>  
<td>

_leo.russo@pfizer.com_

</td> </tr>  
<tr>  
<td>

3.3

</td>  
<td>

Lessons Learned Survey

</td>  
<td>

Rachel

DiSantosstefano or Jorien Veldwijk

</td>  
<td>

</td> </tr>  
<tr>  
<td>

3.4

</td>  
<td>

PP case study

</td>  
<td>

Industry*

</td>  
<td>

TBD

</td>  
<td>

</td> </tr>  
<tr>  
<td>

3.5

</td>  
<td>

PP case study

</td>  
<td>

KU Leuven

</td>  
<td>

TBD

</td>  
<td>

</td> </tr>  
<tr>  
<td>

3.6

</td>  
<td>

PP case study

</td>  
<td>

KU Leuven

</td>  
<td>

TBD

</td>  
<td>

</td> </tr>  
<tr>  
<td>

3.7

</td>  
<td>

PP case study

</td>  
<td>

KU Leuven

</td>  
<td>

TBD

</td>  
<td>

</td> </tr>  
<tr>  
<td>

3.8

</td>  
<td>

PP case study

</td>  
<td>

KU Leuven

</td>  
<td>

TBD

</td>  
<td>

</td> </tr>  
<tr>  
<td>

4.3

</td>  
<td>

Interviews

</td>  
<td>

KU Leuven

</td>  
<td>

TBD

</td>  
<td>

</td> </tr>  
<tr>  
<td>

4.4

</td>  
<td>

Interviews

</td>  
<td>

KU Leuven

</td>  
<td>

TBD

</td>  
<td>

</td> </tr> </table>

TBD= To be discussed; EUR= Erasmus University Rotterdam

* The datasets containing survey data and/or recorded and transcribed interviews generated by the industry-led case studies are by definition to be regarded as personal data and require safe storage and handling in accordance with national and European regulatory frameworks. The industry partner responsible for conducting the case study will be responsible for the secure storage of the personal data. 

#  Sharing and secondary use of PREFER generated or collected data

##  Procedures for making data findable

With the unique identifier of the individual dataset of PREFER and the
overview of data owners and data repository used per task (table 5) available
on Projectplace, the data owner can be identified and contacted.

##  Re-use within the PREFER consortium

To achieve the objectives of PREFER, it is imperative to follow the
collaborative approach the partners agreed on when signing the consortium
agreement. This includes the necessity to share data from the individual
research projects while respecting data protection and intellectual property
of the partners’ work.

For those individual research projects within PREFER that need to use data
generated in another PREFER task, table 5 contains the data owner contact
details to whom a requester can reach out if they need to access the results.

##  Re-use of PREFER results by third parties

Scientific organizations all over the world are promoting a principle of open
science and sharing of research data. By making data public, duplicate
research can be prevented and there is a possibility to combine data. Also,
money and time can be saved. The PREFER-generated data will be a valuable
asset for further research.

For those external individual research projects wanting to use PREFER
generated or collected data during the course of PREFER, the Data Management
Compliance contact should be contacted (table 2). For those external
individual research projects wanting to use PREFER generated or collected data
when PREFER is completed, the Uppsala repository manager should be contacted
(table 2). Giving access to external parties will be considered by the
Steering Committee on a case by case basis. Access rules for the time after
PREFER termination will be worked out and described in the final DMP.

Only when participants of e.g. patient preference studies or PREFER surveys
agreed via informed consent that their study results may be used for secondary
research and the data are anonymous, the data can be shared. To obtain the
agreement of participants to use their data for secondary, research the
following lines can be included in the consent form:

  * _I understand the information collected about me will be stored in secure database, which will be used for future research._

  

  * _I authorise the research to use my anonymised study data for additional medical and/or scientific research projects._

  

#  Protection of personal data

The collection of personal data will be conducted under the applicable
international, IMI, and national laws and regulations and requires previous
written informed consent by the individual, i.e., with public and commercial
entities and if applicable outside the EU in countries with lower data
protection standards. To obtain the agreement of participants of e.g. patient
preference studies or PREFER surveys to use their data for secondary, research
the following lines can be included in the consent form:

  * _I understand the information collected about me will be stored in secure database, which will be used for future research._

  

  * _I authorise the research to use my anonymised study data for additional medical and/or scientific research projects._

  

PREFER researchers commit to the highest standards of data security and
protection in order to preserve the personal rights and interests of study
participants. They will adhere to the provisions set out in the:

  * General data protection regulation (GDPR), foreseen coming into effect in 2018(8) 

  

  * Directive 2006/24/EC of 15 March 2006 on the retention of data generated or processed in connection with the provision of publicly available electronic communication services or of public communications networks(9) 

  

  * Directive 2002/58/EC of the European Parliament and of the Council of 12 July 2002 concerning the processing of personal data and the protection of privacy in the electronic communications sector (Directive on privacy and electronic communications)(10) 

  

  * Directive 95/46/EC of the European Parliament and of the Council of 24 October 1995 on the protection of individuals with regard to the processing of personal data and on the free movement of such data(11) 

  

Prior to collecting, storing, and processing sensitive personal data, the
consortium will seek approval of the applicable local and/ or national data
protection authorities and work within the processes recommended in the
e-Health Task Force Report “Redesigning Health in Europe for 2020.”

Consent forms will contain information on how personal data will be managed.

To secure the confidentiality, accuracy, and security of data and data
management, the following measures will be taken:

  * All personal data obtained within the academic-led case studies will be transmitted to partners within the consortium only after anonymization or pseudonymization. Keys to identification numbers will be held confidentially within the respective research units. In situations were re-identification of study participants becomes necessary, for example the collection of additional data, this will only be possible through the research unit and in cases where informed consent for such cases has been given. 

  

  * Personal data are entered to secure websites. Data are processed only for the purposes outlined in the patient information and informed consent forms of the respective case studies. Use for other purposes will require explicit patient approval. Also, data are not transferred to any places outside the consortium without patient consent. 

  

  * Access to experimental data will be granted to partners in non-EU countries for restricted use within the PREFER project. Data handling in non-EU countries will be fully conforming to national laws and regulations and the European Directive 95/46/EC. In cases of contradiction, the tighter regulation shall prevail. The necessary and legally adequate measures will be taken to ensure that the data protection standards of the EU shall be complied with (see below). Transfer and subsequent use of PREFER data by partners in US will be governed in accordance with federal and state laws. 

  

  * None of the personal data will be used for commercial purposes, but the knowledge derived from the research using the personal data may be brought forward to such use as appropriate, and this process will be regulated by the Grant Agreement and the Consortium Agreement, in accordance with any generally valid legislation and regulations. 

  

The following points to consider will guide the protection of data within the
PREFER project: (i) The entity providing personal data to the project shall
verify that:

  * the initial collection of these data has been compliant with the requirements of the original purpose 

  

  * the collection and the provision of the data to the project meets all legal requirements to which the entity is subject 

  

  * further storage and processing of the data after completion of the research project is in compliance with applicable law 

  

(ii) The entity which provides personal data to the project shall document any
restriction of use or obligation applicable to these data (e.g., the limited
scope of purpose imposed by the consent form)

The entity which uses personal data in the project shall be responsible to
ensure that it has the right under the applicable data protection and other
laws to perform the activities contemplated in the project.

Personal data shall always be collected, stored, and exchanged in a secure
manner, through secure channels.

#  Ethical aspects

##  General ethical aspects

The participants of PREFER are requested to adhering to all relevant
international, IMI, and national legislation and guidelines relating to the
conduct of prospective case studies as detailed below.

All research activities within PREFER requiring approval on ethical and legal
grounds through responsible local or national Ethics Committees and Regulatory
Authorities will be conducted only after obtaining such approval. All ethics
approvals will be submitted to IMI before commencement of any prospective case
study. A report by the Ethics Advisory Board will be submitted to IMI within
the periodic reports.

The proposed research will comply with the highest ethical standards,
including those outlined in the Grant Agreement (Article 34 of the Model Grant
Agreement) and the European Code of Conduct for Research integrity. The
balance between the research objectives and the means used to achieve them
will be given special attention. To ensure this, PREFER is supported by its
Ethical Advisory Board. The Ethical Advisory Board will consist of four
experts on ethics, law, and drug development representing the key areas of the
project, including a patient representative. The Ethical Advisory Board will
monitor the progress of the project and ensure a high standard of research by
taking part in the annual General Assembly meetings. In addition, it will:

  * provide expert support to the consortium in all relevant ethical questions 

  

  * ensure compliance with legislation and guidelines 

  

  * conduct regular project reviews 

  

  * issue recommendations to the consortium when appropriate 

  

Researchers are requested to have appropriate training regarding Good
Scientific, Good Clinical, Good Pharmacoepidemiology Practice Guidelines and
the legal and regulatory framework described in the following sections.

##  Interviews and patient preference studies

The methodologies for eliciting patient preferences will be tested in
prospective case studies. At this stage, it is not yet fully decided which
patient populations will be involved in the case studies, but we foresee the
possibility of approaching vulnerable patient populations, children, parents,
care givers, and healthy volunteers. Each patient preference study requires
approval from the relevant ethical review boards with adherence to
requirements related to informed consent and protection of privacy.

Our foremost principles for the conduct of any research involving human
participants within PREFER are:

  * respect for the rights, integrity, and privacy of patients 

  

  * protection of vulnerable patients 

  

  * continuous monitoring of patients’ safety 

  

  * generation of meaningful, high-quality data 

  

  * timely publication of case study results 

  

All research in PREFER involving human participants will be conducted under
the applicable international, IMI, and national laws and regulations and only
after obtaining approval by the applicable local or national Ethics Committees
and Regulatory Authorities. In particular, the consortium is committed to:

  * the Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects 

  

(Adopted by the 18th World Medical Association (WMA) General Assembly,
Helsinki, Finland, June

1964, and last amended by the 64th WMA General Assembly, Fortaleza, Brazil,
October 2013)(12)

  * the standards of the International Conference on Harmonisation on Good Clinical Practice(13) 

  

  * the Convention for the Protection of Human Rights and Dignity of the Human Being with regard to the Application of Biology and Medicine: Convention on Human Rights and Biomedicine, ETS No. 164, 

  

Oviedo, 4 April 1997; and the Additional Protocol on Biomedical Research (CETS
No. 195), 2005(14)

  * the UNESCO: Universal Declaration on Bioethics and Human Rights (2005)(15) 

  

  * Case studies have not yet been defined in detail so at this stage it is unclear which countries will be involved. Research with human participants will be conducted in the applicable countries in accordance with national and international regulations. Preference studies regarding future risks or investigating how to balance benefits and risks may cause psychological distress e.g., for vulnerable patient groups. This implies that all studies conducted within the PREFER project will have to take actions in order to be able to support/counsel patients appropriately. This will be one of the requirements assigned to each leader of a clinical case study. 

  

  * As mentioned above PREFER will seek to include a broad selection of patient populations, including vulnerable patients if necessary. For ethical reasons iIt is important that perspectives from these patient groups are also included, and that patients who may experience certain difficulties to get their voice heard and their preferences taken into account. Vulnerable patient populations may be identified in the field of Neuromuscular disorders where many of the diagnosed diseases are rare and the patients are not adults. This is also why the PREFER project has included a patient organization within this disease area, i.e. Muscular Dystrophy UK. They, as well as the other patient organisations, will be asked to give extra attention to the situation of vulnerable patients and the how they are included in the case studies. 

  

Patient Information and informed consent procedures will be approved by the
relevant national or local ethics boards. Data collectors collecting personal
data for a prospective collaborative research project will inform the study
participants about the project in an appropriate manner, including:

  * the identity of the data controller 

  

  * the voluntariness of the collection of data 

  

  * the purposes of the processing 

  

  * the nature of the processed data, including its type (identifiable, coded, anonymised) 

  

  * the handling of the data 

  

  * the existence of the right of access to, and the right to rectify the data concerning themselves 

  

  * if the research project reasonably anticipates the sharing of data across research groups (including academic and commercial entities) and national borders (including information about potentially lower data protection standards outside EU) 

  

  * if the project involves collaboration with both academic and commercial partners 

  

  * that consent may be withdrawn and how this is done 

  

The research conducted in PREFER does not have the potential for
malevolent/criminal/terrorist abuse. There are no other ethics issues
currently identified beyond those discussed above. Any potential issues that
arise during the project duration will be presented to the Ethics Advisory
Board who will ensure they are addressed by taking the appropriate
organisational, legal, and regulatory steps.