#  Introduction

The COMPAR-EU project is part of the HORIZON 2020 Open Research Data Pilot,
the Pilot project of the European Commission (EC), which aims to improve and
maximise access to and reuse of research data generated by projects.

COMPAR-EU is a multimethod, inter-disciplinary project that will contribute to
bridging the gap between current knowledge and practice of self-management
interventions. It aims to identify, compare, and rank the most effective and
cost-effective self-management interventions (SMIs) for adults in Europe
living with one of the four high-priority chronic conditions: type 2 diabetes,
obesity, chronic obstructive pulmonary disease (COPD), and heart failure. It
will provide support for policymakers, guideline developers and professionals
to make informed decisions on the adoption of the most suitable self-
management interventions through an IT platform, featuring decision-making
tools adapted to the needs of a wide range of end users (including
researchers, patients, and industry).

Although COMPAR-EU will largely be based on secondary data extracted from
published randomised-controlled trials, it will also draw on potentially
sensitive health and socioeconomic information from stakeholder consultation
processes including interviews and surveys during the pilot phase.

The Data Management Plan (DMP) of COMPAR-EU describes how the data will be
handled during and after the project, the types of research data that will be
generated or collected during the project, the standards that will be used,
how the research data will be preserved and what parts of the datasets will be
shared for verification or reuse.

Research data linked to exploitable results will be deposited in an open
access repository except they compromise its commercialisation prospects or
have inadequate protection. Related questions will be subject to our internal
IP assessment to ensure background and foreground IP is protected, while
supporting exploitation of research findings.

The DMP is intended to be a living document that will evolve during the
lifespan of the project. It will be updated annually during project lifetime
with the Project Periodic Reports (month 12/24/36/48/60).

#  Data summary

The expected types of research data that will be collected or generated along
the project are categorised into eight groups:

_**Table 1. Datasets** _  
  
<table>  
<tr>  
<th>

**#**

</th>  
<th>

**Dataset**

</th>  
<th>

**Lead partner**

</th>  
<th>

**Related WP(s)**

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

**1**

</td>  
<td>

Taxonomy of SMIs

</td>  
<td>

FAD

</td>  
<td>

WP2

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

**2**

</td>  
<td>

Core Outcome Sets (COS)

</td>  
<td>

EPF

</td>  
<td>

WP2, WP3

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

**3**

</td>  
<td>

Summary of evidence of SMIs

</td>  
<td>

NIVEL

</td>  
<td>

WP2, WP3, WP4

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

**4**

</td>  
<td>

Effectiveness of SMIs

</td>  
<td>

UOI

</td>  
<td>

WP4, WP5

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

**5**

</td>  
<td>

Cost-effectiveness of SMIs

</td>  
<td>

iMTA

</td>  
<td>

WP5, WP6

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

**6**

</td>  
<td>

Contextual implementation factors

</td>  
<td>

NIVEL

</td>  
<td>

WP5, WP7

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

**7**

</td>  
<td>

Decision-making tools

</td>  
<td>

IR-HCSP

</td>  
<td>

WP5, WP6, WP7, WP8

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

**8**

</td>  
<td>

Dissemination and exploitation

</td>  
<td>

OptiMedis

</td>  
<td>

WP9

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

We briefly describe the type of data to be collected or generated within each
group:

###  Dataset 1: Taxonomy of SMIs

To develop a draft taxonomy of SMIs, a literature review will be conducted and
taxonomies of good practices from the EU project PRO-STEP  1  will be used.
The use of a taxonomy increases clarity in defining and comparing complex
phenomena and supports the development of a theoretical framework. Experts on
self-management and stakeholders will be consulted via an online survey in
order to refine and validate the draft taxonomy developed through literature
searches. The data set of the online survey, including both labels and data,
will be maintained for the final _COMPAR-EU data dictionary_ . Data collected
by this online survey will not be for public use.

For the literature review, a small database of approx. 40 kB will be created
and stored in an interoperable format to allow re-use irrespective of the
software used in the original application. For each literature review,
including those conducted for Datasets 2 to 8, the same interoperable
principle applies; in addition, we will maintain a log file including the
final search strategy that led to the final selection of references.

Deliverable D2.1 will contain a validated and refined taxonomy of SMIs for
patients with type 2 diabetes, obesity, COPD and heart failure (WP leader:
FAD). The development of the taxonomy of SMIs will be useful to researchers
and guideline developers. It will provide a framework for subsequent research
tasks and will be made publicly available.

###  Dataset 2: Core Outcome Set

To develop core outcome sets (COS), we will create an exhaustive database
listing relevant health outcomes using data from previous EU projects (PRO-
STEP and EMPATHiE), existing COS databases such as COMET  2  or the
International Health Outcomes Consortium  3  and searching (via snowballing)
other relevant organisations.

This is the basis for the development of a Delphi consensus process which will
collect patients´ preferences on outcomes. A literature review of qualitative
and quantitative studies on the relative importance patients attach to
outcomes for the relevant conditions will be made. We will use the Confidence
in the Evidence from Reviews of Qualitative research (CERQual) approach. In
addition, a grey literature search (e.g. reports from patient organisations
and projects that have addressed patients´ priorities related to self-
management, patient empowerment, and living well with chronic conditions) will
be performed. To achieve consensus on the final COS, an outcomes
prioritisation workshop will be organised involving equal split between
patients and healthcare professionals. In a modified Delphi consensus process,
four panels of patients and carers will evaluate which outcomes linked to
chronic diseases are considered as most important. In a final workshop,
patients and other stakeholders (healthcare professionals, guideline
developers and researchers) will discuss the final COS to be included in the
project. Any standardised assessment of COS, for example by Delphi consensus
process, will be stored in appropriate databases to support transparency of
the research process. Any non-standardised assessment of COS data, for example
through workshop discussion, will be documented through workshop reports and
minutes, which will be shared amongst participating researchers.

The development of COS will feed into subsequent research phases. Deliverable
D3.1 will contain a report on each condition COS incorporating patients´
preferences (WP leader: EPF). We will report on the development process to
inform other researchers and guideline developers.

###  Dataset 3: Summary of evidence of self-management interventions (SMIs)

In this dataset, all necessary data from existing randomised controlled trials
(RCTs) on SMIs will be synthesised for the comparative analyses of SMIs. We
will build on existing information from PRO-STEP and adjust and update it for
COMPAR-EU. We will review around 4000 RCTs from peer-reviewed journals in
PubMed, MEDLINE, and Cochrane library databases. A detailed data extraction
sheet is being prepared by the research team and will form the basis for data
extraction.

As part of our quality assurance process, researchers reviewing the literature
and extracting data will participate in a training workshop. Deliverable D4.1
will contain a report on descriptive findings on the effectiveness of specific
SMIs by each studied outcome and condition and includes information on type
and number of interventions, outcome results, patient characteristics, self-
management components, and comorbidity profiles (WP leader: NIVEL).

This output will be the basis for the quantitative network meta-analysis in
this project. However, the database derived from this major effort of
literature review and data extract potentially informs other research
applications. The standardised dataset including all data extracted and search
strategies in the accompanying literature databases in interoperable format
will be made available to other researchers if approved by the steering
committee.

###  Dataset 4: Effectiveness of SMIs

To compare the relative effectiveness of SMIs, network meta-analysis (NMA)
will be applied. NMA statistically combines direct and indirect evidence from
RCTs in a single analysis in order to compare and rank SMIs. For all four
chronic diseases, the relative effectiveness will be estimated for each SMI
and for each component of the outcomes of interest. The confidence in the
output of the analysis will be determined both overall and for each
comparison. In order to perform NMA, data synthesised in Dataset 3 will be
used.

The generated list of ranked SMIs according to their relative effectiveness
organised by condition and outcome measures, which will be our deliverable
D5.1 (WP leader: UOI), might be useful for policymakers, researchers and the
public. The process of producing the ranked list of SMIs (output) will be able
to be traced back to the input “Dataset 3” by providing details on the
statistical protocol.

###  Dataset 5: Cost-effectiveness of SMIs

To model the impact of SMIs from the perspective of cost-effectiveness,
existing data will be used by collecting secondary data (i.e. meta-data in
summarised form taken from the literature) and primary data from anonymised
open-access online data sources (such as EU SHARE data). These data are used
to estimate the relationship between patient characteristics and public
expenditures. Dataset 5 combines effectiveness data from the NMA with cost
data from separate literature reviews in order to perform the cost-
effectiveness analysis.

The ranking of SMIs according to their relative cost-effectiveness, which will
be our deliverable D6.1 (WP leader: iMTA), will also inform policymakers,
researchers and the public.

###  Dataset 6: Contextual implementation factors

To analyse contextual and implementation factors with the aim to improve the
implementability of SMIs, we will search for qualitative studies on the most
effective SMIs in PubMed and CINAHL database. A qualitative content analysis
will be applied to these studies and outcomes will be synthesised. The aim of
this analysis is to determine the key contextual factors, such as intervention
settings and mechanism that produce specific outcomes.

A framework will be developed for understanding the success of the selected
SMIs for patients with one of the four chronic conditions. Data-analysis and
synthesis will be conducted using a narrative approach initially. Due to the
heterogeneous nature of the studies, thematic analysis will be employed. The
codes/thematic framework structure and outputs of the content analysis will be
documented for further research applications.

A modified Delphi process with a panel of experts on self-management and/or
implementation of healthcare interventions will be organised in order to
prioritise the influence and importance of the contextual factors on a
European level. The items assessed by panel members will be documented in the
projects data dictionary, the raw data will be kept by the project partner for
secondary research use. Participant expertise and knowledge of the healthcare
field can add information that cannot be found in scientific articles but are
of value for daily practice.

Deliverable D7.1 will contain a report on contextual factors for the
implementation of the most effective SMIs for the studied conditions (WP
leader: NIVEL). The generated list of facilitators and barriers of the
implementation of SMIs will inform the design of decision-making tools and
inform the business case development for SMIs.

###  Dataset 7: Decision-making tools

Decision-making tools will be developed to facilitate and disseminate the use
of the most effective self-management interventions among key target end users
(patients, healthcare professionals, policymakers, researchers, and SMEs).

Development of decision-making tools

Using the GRADE pro GDT software, we will generate interactive format tables
(iSoF), which presents results per outcome and comparison succinctly, and
interactive evidence to decision framework tabulated (iEtD), which include
draft recommendations/decisions that can be adopted or adapted to different
settings based on the magnitude of desirable and undesirable effects of the
SMIs, stakeholders’ views about outcome importance and
acceptability/feasibility aspects, and information on resource use and cost-
effectiveness. iSof and iEtDs are designed to be used by policymakers and
guidelines developers. For both, a small scale pilot tests in the five
participating countries will be run inviting a group of end users
(policymakers, guidelines developers and researchers according to the type of
tools) to test them and give feedback by a semi-structured online
consultation. We will strive to include at least 60 end users. Questions will
include issues related to content, functionality and applicability,
transferability and scalability (participating partners: OptiMedis, NIVEL,
EPF). Development of patient decision aids

We will generate interactive semiautomatic decision aids based on GRADE
evidence profiles, it consists in plain language summary and, where
applicable, an interactive graphic to be used in the patient physician
interaction. To pilot decision aids, we will organise small focus groups and
user experience tests in simulated settings using techniques such as thinking-
out-loud. For focus groups, we will invite patients living with the condition
and physicians. All focus group discussions will be audio-recorded,
transcribed, and analysed by theme.

The pilot-testing activities and focus groups will be based on previous
material used for similar purposes. Quantitative data resulting from this
assessment will be maintained by the project partner in source format,
qualitative data will be summarised by means of reports and minutes.

Deliverable D8.1 will contain a refined set of evidence profiles and
interactive Summary of

Findings tables, Evidence to Decision frameworks and patient decision aids (WP
leader: IRHSCSP). Subsequently, deliverable D8.2 will be the set-up of the
refined COMPAR-EU platform after piloting (WP leader: IR-HSCSP).

Generated data might be useful to patients, physicians, carers, policymakers
and other stakeholders in the field of self-management and chronic diseases
and will inform the design of dataset 8.

###  Dataset 8: Dissemination and exploitation

As part of the comprehensive dissemination, communication, and exploitation
plan to maximise the impact of the COMPAR-EU project, we will conduct reviews
of the grey literature to identify relevant policy and regulatory frameworks
that will influence the adoption of selfmanagement and decision-making tools
(such as WHO´s global strategy on people-centred and integrated health
services, HTA agencies standards on shared-decision-making and selfmanagement,
and eHealth/mHealth strategies). The process for literature review and
maintaining the datasets will be the same as for other datasets. Dataset 8
will also include insight generated through workshops with industry, pharma
and SMEs to identify business opportunities. These data are collected through
in-depth interviews with relevant managers, clinical decision makers,
policymakers and users. Transcripts of these interviews will be maintained in
anonymous format and inform the development of case studies for business
development of SMIs in various health systems. Deliverable D9.2 will contain
business cases for decision-making tools developed (WP leader: OptiMedis AG).

#  FAIR Data

In order to make the research data generated by COMPAR-EU findable,
accessible, interoperable and re-usable we apply the FAIR principles  4  .  
  
<table>  
<tr>  
<th>

**To be Findable:**

F1. (meta)data are assigned a globally unique and persistent identifier

F2. data are described with rich metadata (defined by R1 below)

F3. metadata clearly and explicitly include the identifier of the data it
describes

F4. (meta)data are registered or indexed in a searchable resource **To be
Accessible:**

A1. (meta)data are retrievable by their identifier using a standardized
communications protocol

A1.1 the protocol is open, free, and universally implementable

A1.2 the protocol allows for an authentication and authorization procedure,
where necessary A2. metadata are accessible, even when the data are no longer
available **To be Interoperable:**

I1. (meta)data use a formal, accessible, shared, and broadly applicable
language for knowledge representation.

I2. (meta)data use vocabularies that follow FAIR principles I3. (meta)data
include qualified references to other (meta)data **To be Reusable:**

R1. meta(data) are richly described with a plurality of accurate and relevant
attributes

R1.1. (meta)data are released with a clear and accessible data usage license

R1.2. (meta)data are associated with detailed provenance

R1.3. (meta)data meet domain-relevant community standards

</th> </tr> </table>

##  Making data findable, including provisions for metadata

We will create Metadata to describe each of the datasets 1 to 8 as described
above. For each dataset we will maintain unique identified and a log of the
data generating mechanisms and the process of their development, and
accompanying documents, such as data dictionaries to facilitate interpretation
of the dataset (see criteria re-usable). Metadata and any derived documents
such as data codebooks will be registered in updated versions of this
document.

We will use a standard convention to name electronic files, consisting of the
elements PROJECT ACRONYM + Type of document + Name of document + work package
it relates to + partner organisation name + name of research partner, for
example for this DMP:

COMPAR-EU_DELIVERABLE9.3_DataManagementPlan_WP9_OptiMedis_GroeneAdrion_v1.0

Following recommended practice, we will produce a ´readme´ text file including
information on the content of the dataset and instructions for its use. The
Research Data Alliance provides a Metadata Standards Directory that can be
searched for discipline-specific standards and associated tools.  5  For
future iterations of the DMP we will assess whether to adopt any such Meta
Standards to our dataset.

Each research task leading to a literature reviews will create Endnote files
(or interoperable format) - for the selected RCTs for each of the four
conditions separately. Files contain all information necessary to easily find
the original papers, including DOI numbers and PDFs of full text when
available.

For other datasets, in particular the NMA and cost-effectiveness analysis, we
will provide information on the research methodology used, detailed
information on collection methods, sample size, variable definition,
assumptions made, format and file type of the data, explanation of data coding
and analysis performed.

The results from each WP will be published in scientific journals indexed in
the major databases. There will be disclaimers that all underlying data will
be available upon request.

All publications coming from these WP’s (papers, reports) will also be
referred to – and if permitted under the publication license – be published on
the COMPAR-EU website with their DOI and/or ISBN.

We will facilitate search strategies to include our research output by
including relevant keywords. In addition, bibliographic metadata that identify
the deposited publication will be provided in a standard format and will
include the standard disclaimer “This project has received funding from the
European Union’s Horizon 2020 research and innovation programme under grant
agreement No 754936”. In addition, we will add the publication date, and
length of embargo period if applicable, and a persistent identifier.

##  Making data openly accessible

A number of project deliverables have been classified as `confidential´ (see
below, page 6 ff., ANNEX 1 (part A) of the Grant Agreement). The deliverables
have been classified as such because they are either intermediate project
outputs whose early publication might interfere with future scientific
publications or they reflect key outputs of the project, which need to be
protected by the research team under their consortium agreement´s IP
regulations.

Nevertheless, COMPAR-EU pursues the policy to make data openly accessible for
noncommercial use. To that extent, we have planned for GOLD Open Access for
all key publications of the project and all scientific publications published
by the COMPAR-EU consortium partners will be available online (open access).
Those publications will also be published on the COMPAR-EU website together
with further research outputs, such as summary datasets, book chapters,
theses, conferences papers, and other unpublished articles.

Following the search terms facilitated under the 2.1. rules, researchers will
be able to identify our research output/publications, access the final data
management plan and therefore assess which dataset was used to produce the
research. In this regard, the data management plan including all its annexes
serve as the standardised communications protocol for COMPAR-EU. It will be
accessible free of charge on the projects web pages.

In order for researchers to request further information on the data structure
or even data itself, we will establish an authentication and authorisation
procedure. This procedure will be included in the data management plan and
also refer to directly on our webpages to clarify the rules and steps under
which data can be requested for research and non-research use. During the
project timeframe, any such request will be discussed at the steering
committee.

To ensure transparency and validity of the research, interested parties may
during the timeframe request insight into the underlying raw data. It is the
assumption that this may be needed in some cases for a journal to validate the
data included in a research article or where questions regarding the integrity
of the data have been raised. During the timeframe of the project, such
queries will be strictly dealt with by the projects´ steering committee.

After termination of the project, one of the project partners will act on
behalf of the steering committee to ensure data requests can be processed for
at least as long as legal requirements warrant storage of the research data.
This partner will also be in charge of maintaining all research data and,
where appropriate and legally required, procedures to access the raw data from
partner organisations. Contact details for the research partner in charge of
long-term data handling will be available via the project website, which has
been reserved for 10 years initially.

Due to the diversity of institutional arrangements and the heterogeneity of
the underlying data sources, some partners have additional arrangements for
data archiving and access:

  * NIVEL will make their data open access upon request by using repository van Data Archiving and Networked Services (  www.dans.knaw.nl)  . 

  

  * IR-HSCSP will make the underlying summarised quantitative and qualitative data available upon request from its agency internal databases. No specific software is needed to access the data. 

  

  * Software codes used by UOI will be made available either in the publications or in the program’s or personal webpages. 

  

  * A cost-effectiveness model will be developed using analysed secondary data. Source code (most likely in the open source R programming language) for the estimates of effectiveness and costs will be made publicly available by iMTA after registering with the research group so that use can be monitored, and misuse can be prevented, but only for non-commercial use. Evaluation of the accesses will be overseen by the research team and a clear a-priori defined research question will be requested as prerequisite. 

  

##  Making data interoperable

As noted above, the data management plan and its future iterations will serve
as the standardised communications protocol for COMPAR-EU. It provides access
to all relevant metadata to make the research outputs – as much as possible –
interoperable with aligned research tasks. This includes standardised
databases and methodological documentation for literature reviews, qualitative
work, network meta-analysis and cost-effectiveness analysis. As part of the
ongoing work in building the data management plan, data dictionaries will be
established to allow researchers within the consortium to ensure efficiency in
the exchange of data. Metadata will be established as described under 2.1.
applying standard terms, templates and processes. These standards will then
ensure interoperability of the data generated by the consortium.

##  Increase data re-use

The FAIR criteria are overlapping, therefore some of our plans under re-use
are also related to the criteria findable, accessible and interoperable.

The data management plan serves as standardised communications protocol,
providing a rich description of all data sets and their attributes, providing
procedures for data access requests, and are described to the level of rigor
as expected in the pertinent scientific community.

In general, during the lifespan of the project, exclusive use of all project-
related data is expected by partners of the consortium. However, the steering
group would consider requests for data, for example if another EU project with
similar aims and tasks was funded and an exchange of data was mutually
convenient. All other data requests during the project lifetime will not a
prior be rejected, but discussed by the steering board.

After the active project phase, external partners can request access to data
for noncommercial use. Updated versions of the DMP will propose a process for
such data requests. It is envisaged that COMPAR-EU datasets are made available
to the scientific community after project closure, subject to ongoing use by
the consortium members and the urgency of making data accessible.

The intellectual property arrangements specified in the consortium agreement
-and the decisions taking by the steering group- will guide the timing and
scope of making data available for re-use.

Due to the diversity of institutional arrangements and the heterogeneity of
the underlying data sources, some partners have additional arrangements for
data re-use:

  * NIVEL: The data will be licensed via publications in scientific journals indexed in the major databases. At the end of the project, data will be made available for re-use. Data possessed by NIVEL will stay in archives for 10 years. NIVEL works according to an internal quality system (ISO-9001:2015 certified). This quality system concerns NIVEL’s primary task: doing research. Besides that, NIVEL has itself committed to the Dutch behavioural standards for scientific practice following the principles of: honesty, accuracy, reliability, controllability, independence and responsibility. 

  

  * IR-HSCSP: The data will be licensed via publications in scientific journals indexed in the major databases, data will be archived for 5 years. As part of the local ethics approval a quality management plan including random checks of consistency and data transcription accuracy is required. 

  

  * FAD: Confidential, restricted under conditions set out in Model Grant Agreement. It will be made available as part of publication to protect the intellectual property of participating partners. 

  

  * iMTA: data will be made available under a creative common open access agreement, with limitations for commercial re-use (i.e. re-use by commercial entities for-profit reasons). Data will remain available for a period of 5 years. 

  

#  Allocation of resources

Resources to ensure efficient translation of the DMP are included in the
research budget of each partner, as the creation of datasets and quality
assurance procedures to ensure accuracy and rigor in dataset design, execution
of analysis and reporting cannot be disentangled from the integral research
process.

However, we have allocated a budget for open-access publications as part of
the Horizon 2020 grant (see grant agreement).

Each COMPAR-EU partner is responsible for the creation, management and
appropriate storage of the datasets they create. Validation and registration
of datasets and metadata is the responsibility of the partner that generates
the data in the WP. These partners will liaise with OptiMedis to ensure that
standard nomenclature and reporting templates are designed in a participatory
manner and implemented efficiently.

The overall responsibility for the future development of this data management
plan rests with OptiMedis AG. When datasets are updated, WP leader that
possesses the data have the responsibility to manage the different versions
and to make sure that the latest version is made available to OptiMedis AG.
OptiMedis AG will align the changes with other datasets and update the DMP as
necessary.

Future iterations will advance definitions for data capture, metadata
production, data quality, storage and backup, data archiving & data sharing.

The costs and potential value of long term preservation will be assessed in
the last year of the project.

#  Ethical aspects

As indicated in the project proposal (section 5.1., page 125) the project does
not involve primary human data collection, processing of personal data or
observation of people. The only involvement of patients and other stakeholders
will be participating in the pilot of the IT platform through interviews or
surveys. No physical injury, financial, social or legal harm will be posed to
the participants, and potential psychological risks will not exceed the daily
life standard. See also deliverable D10.5 `Ethical issues report´ for further
information and the ethical approval.

There are no major ethical issues regarding the transfer of sensitive data as
most of the project´s data is extracted from the literature or pertains to
assessment of outcome measures available in databases. Data from individual
interviews, focus groups or consensus groups in which patients and
professionals will take place will not directly be made available publicly.
Anonymised data sets might be provided for future research applications. Raw
data bases including recordings or transcripts which might include person
identifiers (but not names) will be secured in line with institutional,
national and EU legislation and be kept out of reach of non-authorised
persons. This data will only be kept to facilitate an audit of the integrity
of the research process, if so initiated by an authorised party.

#  Data protection and security

Patients and other stakeholders who are asked to participate in COMPAR-EU
research project will be completely free to decide whether or not they would
like to take part. Their participation will start after they have signed the
provided informed consent (see annex 1). Prior to deciding to participate,
they will get information in oral and written form (in paper or online,
depending on the method of participation) by the researchers with regards to
the nature, scope and possible consequences of the study, and all potential
participants will have full opportunity for asking questions and withdrawing
without prejudice at any point. Written information (in paper or online) will
be given in the form of patient information leaflets and informed consent
leaflets. The patient information leaflet will provide a full explanation of
the purposes and processed of the study and what participation involves.
Declining to participate will be possible at all stages of the project. The
consent form will be signed and kept in the records of the partners’
institution. Each partner involved in this particular part of the study will
be responsible for adequate storage of informed consents according to good
clinical practice and national regulations. The consent forms together with
any potential temporal patient identifying data will be kept locally, and
disentangling patient identifying data and other data.

In our study, no names of patients or other information that will permit the
identification of persons will be kept after the follow-up period of four
weeks after conclusion of the survey. Information will be coded and
economised, distinguishing between identifying data and nonidentifying data,
so that individual information cannot be traced back to that patient and
furthermore, it will not be possible to enter patient names and other personal
identifiers on the main COMPAR-EU database. Identifying data will not be
stored in the main COMPAR-EU database.

For piloting the IT platform, users are asked to log in with self-chosen
username and a nontrivial password of at least 8 characters.

All data gathered will be transferred using encryption (SSL and/or HTTPS as
technical protocols [HTTPS], [SSL3]).

Updated versions of the DMP will include specific data protection protocols,
which will be prepared for each WP. Anonymisation protocols will be attached
in the annex of updated versions once stakeholders´ participation have taken
place.

Data security is fundamentally the responsible of each partner collecting,
processing and reporting data. All partners declare that they abide to their
institutional, national and EU standards in this regard, in particular in
relation to any secure storage and transfer of sensitive data.

Standards for long term preservation and curation, including the use of
certified repositories, will be discussed by the consortium before closure of
the project.

#  Other

Other than the procedures described here, COMPAR-EU does not make use of other
national/funder/sectorial/departmental procedures for data management.