#  Executive Summary

This Data Management Plan (DMP) defines effective _governance_ and
_management_ of research data generated and or used with the SUITCEYES
project. It addresses issues of data generation, ownership, storage, access,
exchange, use, openness, protection, preservation and destruction.

This document (and its subsequent updated versions) will act as a guideline
and provides an overview of research-data-related procedures within the
project. It aims to facilitate collaboration and help avoid unnecessary
duplications of work and data creations. It further defines procedures and
routines for easy and effective information sharing during the project time
and beyond. It is also a useful tool in ensuring continuity and bridging gaps
even in the cases of additional new members in the project.

After the introductory parts, Chapter 3 of the document presents an adaptation
of five (5) Data Governance Domains of _Data principles_ , _Data quality_ ,
_Metadata_ , _Data access_ , and _Data lifecycle_ which aim at defining the
decision-making structures that govern data-related issues within the project.
Following these, chapter 4 provides further description of routines for data
management. The approach of reaching informed consent and other agreements
with users taking part in requirements, user studies, video-recordings and
other R&D activities has also been included. The document is concluded with a
short summary and two appendices.

#  Introduction and Rationale

This deliverable – Data Management Plan (D8.14) – incorporates both Data
Governance and Data Management and accordingly defines the principals,
procedures, and routines that are put in place for the management of research
data within the SUITCEYES project.

As such, this document and its subsequent versions, act as a guide to help
form an up-to-date overview of the project-related data and related
procedures. This DMP is produced with the aim of facilitating information
sharing and collaboration, while avoiding unnecessary duplications of work. It
also defines the basis for various choices and is meant to help the members to
make sound and appropriate decisions when needed. This DMP is also meant to
create continuity even in potential cases of membership change.

This document presents the data governance approach adopted, and describes the
research data that will be collected, generated and or used. It outlines the
related data types and the ways in which the data will be handled both during
and after the project. Furthermore, it will describe which data will be made
available openly and which date will be kept protected, and the reasons why.
This DMP will remain a living document and will be updated when needed as the
project progresses. This deliverable is licensed under the Creative Commons
License CC BY-NC-SA (AttributionNonCommercial-ShareAlike).

##  SUITCEYES and Open Data

SUITCEYES is a three-year long (2018-2020) Horizon 2020 RIA project with a
focus on facilitating communication in cases of deafblindness through a smart
haptic interface. The project will address three challenges of (a) improved
perception of the physical surroundings, (b) improved exchanges of semantic
contents, and (c) enjoyable learning through gamification.

The project involves many areas of research including disability studies, user
studies, psychophysics, sensor technologies, face and object recognition,
semantics and knowledge management, social media studies, gamification and
affective computing, and smart textiles. As such, much data will be accessed
used and or generated during the project.

Open data, typically refers to online free distribution of research data and
results for access and reuse by third parties towards benefiting both future
research and society. With the advances in the “open” movement, a growing
demand for open and interoperable research data has emerged.

The view held in SUITCEYES is that all research, and especially those funded
by public money, should benefit the whole society, be instrumental for
progress, and act as a stepping stone for further research. We will therefore
make the research results available through different channels and strive to
provide open access to the project data, as far as possible. However, not all
data generated and used within SUITCEYES are suitable or appropriate for
sharing and reuse, and hence, SUITCEYES has chosen not to participate in the
Open Research Data Pilot. The decision to opt out has been based on two
factors, the potential for exploitation of results by some partners, but more
importantly, the vulnerability of some of the project’s study participants and
the sensitivity of the data that will be generated in the project. SUITCEYES
involves user-studies (including interviews and observations) of sensitive
nature. The participants, due to the small population and specific
circumstances of each participant, are potentially easily recognizable.
Although we anonymize user-study-generated data soon after collection and
before sharing among the project members, this data is still not suitable for
making openly available for wider access and use. This decision is supported
by the results of multiple studies  1  2  3  that have shown the ease of
deanonymisation even in areas where stringent efforts have been made in
removing identifying data.

##  Data Governance and Data Management

In this document we have broadened the scope to also include data governance
based on the structural features outlined by Khatri and Brown (2010)  4  where
in their state-of-the-art contribution they have defined five data governance
domains. Through the use of data governance, there is a distinction made
between governance of data and management of data. Management concerns making
and implementing decisions. Governance is concerned with the creation of a
structure that allows for a decision-making structure. As they exemplify,
“governance includes establishing who in the organization holds decision
rights for determining standards for data quality. Management involves
determining the actual metrics employed for data quality” (Khatri & Brown,
2010: 148). By extending the scope of this document to go beyond data
management issues and to also include data governance a more comprehensive
approach is adopted.

#  Data Governance

Khatri and Brown (2010) have identified five (5) decision domains for data
governance comprising of (i) _Data principles_ , (ii) _Data quality_ , (iii)
_Metadata_ , (iv) _Data access_ and (v) _Data life cycle_ . Khatri and Brown
(2010) propose that there is a need for creating a decision-making structure
for each of these domains, where the full table with more detail is provided
in Appendix 1.

In the following subsections, we outline the plan for SUITCEYES data
governance according to these five domains.

##  Data principles

Data principles concern the overarching ideas about the kind of decisions that
are to be made relative to the four other domains. These principles also
introduce boundary requirements for the use of data as well as standards for
data quality. The role of this domain is to clarify the role of data as an
asset. For the current DMP the following data principles have been established
as presented in Table 1.

Table 1: Adaptation of Khatri and Brown (2010)’s Domain of Data Principles in
SUITCEYES  
  
<table>  
<tr>  
<th>

**Domain Decisions**

</th>  
<th>

**Potential Roles or Locus of Accountability**

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

  * In SUITCEYES data is used for multiple purposes, including forming an understanding of the user needs, preferences and aspirations; forming an informed overview of the related policies; experimentations and conduct of research towards project goals and production of haptic, intelligent, personalized interface (HIPI). 

  

  * The various uses of data are communicated continuously at regular meetings which are held at various levels and in different formats. Furthermore, written documentations and an information sharing tool are further mechanisms for communicating uses of the data. 

  

  * Datasets are seen as assets and are therefore valuable and should be managed accordingly. 

  

  * Ownership implies responsibility and accountability for keeping data assets securely stored. 

  

  * As stipulated in related agreements, some of the data generated in SUITCEYES is of sensitive nature the use of which is regulated by the projects internal operational and ethical guidelines. 

  

  * Such data assets should be handled through principles of privacy by design and data minimization. 

  

  * All practices should be compliant to General Data Protection Regulation (GDPR). 

  

  * The current DMP should be used to guide all handling of data assets. It should also be revised regularly to accommodate the 

  

</td>  
<td>

  * The Project Management Board (PMB) is the ultimate decision-making body within 

  

SUITCEYES, and it is responsible to oversee the existence of appropriate data
governance structures within the project.

  * The PMB is responsible for decisions made on data management issues. 

  

  * The PMB should refer difficult issues of data management to the Ethical Advisory Board (EAB) for advice. 

  

  * The project DMP (the text at hand and its subsequent updates) will also be reviewed by the project’s EAB. 

  

  * The default structure for ownership is that the partner creating the data also owns it as defined in Grant Agreement (GA) article 26 where some data may be subject to JOINT ownership governed by GA Article 26.2 with the addition of further stipulated in PCA section 8. 

  

  * Data assets and their ownerships are clearly defined in a related tool share with all project members. 

  

  * Securely stored refers both to protection against breach and to instances of force majeure, i.e. fire, data crash etc. 

  

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

development of new insights, challenges and problems.

</td>  
<td>

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

##  Data quality

Data quality is connected to accurate, complete and trustworthy data being
available for various research tasks in a timely fashion. Lack of data quality
is a fundamental problem for most data intensive work and one of the core
issues that can be attended to through the DMP. There are multiple dimensions
involved in data quality which will be presented with the help of the
following table (Table 2). The role of data quality domain is to establish
requirements of intended uses of data.

Table 2: Adaptation of Khatri and Brown (2010)’s Domain of Data Quality in
SUITCEYES  
  
<table>  
<tr>  
<th>

**Domain Decisions**

</th>  
<th>

**Potential Roles or Locus of Accountability**

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

  * **Accuracy** refers to the correlation between recorded value, actual value and the kind of value needed for the research task. A number of crucial questions emerge regarding the user study data. For example: Will the interview transcripts reflect correctly the responses of the participants? Do the local conventions vary at different partner countries? How will the principles of data minimization and privacy by design affect the data sharing and interpretation as cross referenced in different countries of the studies? These accuracy-concerns are addressed by regular meeting and joint analyses among the researchers participating in user studies and collaboration with the User-Data Working Group. For the technical aspects of the project, experimental data, ontologies and so on, there will be other questions asked, and the accuracy of that data will be verified based on domain specific scientific measures and in collaboration with an Analytical-Data Working Group. 

  

  * **Timeliness** refers to up-to-date values being available at the right time. Timeliness is typically a challenge in complex projects with many dependencies and potential bottle necks. It is crucial for the project members across the board to be aware of the relationships between the different tasks and deadlines to ensure smooth and timely deliverance of results that are needed for the next phase in the project in their own and other work packages in the project. 

  

  * **Completeness** defines the need for the data to be as detailed, deep and broad as necessary for the research tasks. Related to the user studies, the ambitions for adherence to GDPR legislation and the principles of data minimization and privacy by design will be carefully balanced with the need to capture the data that is necessary for the design of the HIPI and an informed understanding of user needs and preferences. For Analytical data, while collection of broad data may not be bound by the same concerns, a challenge in cases of machine learning may be the lack of enough data. Such a challenge is carefully considered and potential solutions are investigated. 

  

  * **Credibility** refers to the need that the sources of data assets must be trustworthy. In the user studies utmost care is taken to ensure that most relevant participants are recruited and 

  

</td>  
<td>

  * The PMB will develop and assign responsibilities to a _User-Data_

  

_Working Group_ (UDWG) to oversee **accuracy** , **timeliness** ,
**completeness** and **credibility** of user data.

  * The PMB will develop and assign responsibilities to an _AnalyticalData Working Group_ (ADWG) to oversee accuracy, timeliness, completeness and credibility of analytical and technical data. 

  

  * There are a number of guidelines and tools devised and routines put in place in the project in order to ensure the **timely** conduct of all project tasks. The project PCA defines the members’ responsibility towards one another, the need for timely deliverance and measures to ensure compliance. 

  

  * The project has established a large network of contacts to allow collection of rich set of user data which will promote data completeness and credibility. 

  

  * Similarly, continued environ- mental scanning keeps the project members informed of emerging data sources and recent research to promote access to most relevant and appropriate set of data for conduct of research tasks within 

  

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

best user-study practices are put in place to ensure high levels of
trustworthiness in the results. Regarding the technical and analytical data,
in general Best Available Technology (BAT) and evaluation methods will be
utilized.

▪ A set of measures defined in task and work package meetings in collaboration
with UDWG and ADWG will be used for **evaluation** of data quality and
associated data collection procedures.

</td>  
<td>

SUITCEYES.

▪ Measures for data quality will be discussed, set, and documented in regular
task and work package related meetings. These measures will be promoted and
followed up by the two data working groups UDWG and ADWG.

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

##  Metadata

Metadata includes descriptions of data assets. Proper use of metadata
facilitates findability and, in the long run, quality of research. The role of
the metadata domain is given as Establishing the semantics or “content” of
data so that it is interpretable by the users. Khatri and Brown (2010) make a
distinction between three types of metadata. These will be reviewed below
according to the way that they are seen as relevant for the project.

Table 3: Adaptation of Khatri and Brown (2010)’s Domain of Metadata in
SUITCEYES  
  
<table>  
<tr>  
<th>

**Domain Decisions**

</th>  
<th>

**Potential Roles or Locus of Accountability**

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

  * For describing and documenting different datasets a tool in the form of a project-wide spreadsheet has been devised with multiple columns that each describe an attribute of the dataset at hand. This tool generally includes the following sub-categories of information. 

  

  * **Content metadata** describes the contents of different datasets, including whether data has been generated through user studies, policy studies, or technical experimental research streams. The list of related attributes includes (but is not limited to) dataset identifier, data description, source and mode of creation. It also describes whether the data can be shared openly or is of sensitive nature and special care is required. 

  

  * **Storage metadata** involves information about means of data storage. This involves the choice of local and cloud-based as well as level of cryptology necessary for different kinds of data. For each dataset and based on the level of sensitivity (whether it can be openly shared or not), ownership and data type, the appropriate means of storage is defined. 

  

  * **User metadata** relates to various annotations that different project members may associate with various data assets. This can involve notations on usage, findability, preferences and user history. 

  

  * **General metadata** refers to all the other attributes and information recorded about each dataset, these include area of use, ownership, date of creation, history of change, the standards used, general technical format of data, compatibility level with different analysis tools, the procedure for metadata and data update, and more. 

  

</td>  
<td>

  * The UDWG and the ADWG in collaboration with different instances in the project will develop a plan for the types of data used and create and provide guidelines related to appropriate storage procedure for each set of data. 

  

  * UDWG and ADWG oversee that the metadata tool is kept updated (as potentially new types and sets of data may emerge) and includes sufficient details to provide appropriate information towards effective further data generation, use, and potential reuse, storage and long-term preservation. 

  

  * The members collaborate closely with the two data work groups to facilitate their task of overseeing the upkeep of metadata information. 

  

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

##  Data access

Building upon compliance with GDPR as well as principles of data minimization
and privacy by design, there needs to be a clear plan for data access in
place. The role of this domain is to specify access requirement of data. The
UDWG and the ADWG will be tasked with development of a plan for data rights to
various data assets. The PMB will monitor development of this plan. The plan
will also be evaluated by the EAB as elaborated in the following table.

Table 4: Adaptation of Khatri and Brown (2010)’s Domain of Data access in
SUITCEYES  
  
<table>  
<tr>  
<th>

**Domain Decisions**

</th>  
<th>

**Potential Roles or Locus of Accountability**

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

  * **Risk assessment** related to data value and sensitivity will conducted on regular basis. 

  

  * **Data access** related to sensitive material within the project is based on a clearly defined need for purposes of research as monitored and decided upon by the PMB. 

  

  * Mechanisms for sharing such data should be through cryptology technology **.**

  

  * Other data assets can be shared publicly ( **Open Data** ) but decisions on such initiatives will be taken at a later stage of the project when all of the research needs of the project are clearly understood. 

  

  * Appropriate **naming conventions** as well as use of **standards** for data sets should be adopted to ensure interoperability within the project. 

  

</td>  
<td>

  * Sharing of sensitive data will be monitored and decided upon by the PMB. 

  

  * UDWG and ADWG will be tasked to oversee the procedures for information security and alert of deviations and potential risks. 

  

  * Continued dialogue with partner organisation IT support centres will take place to ensure being kept updated on security and preservation issues. 

  

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

##  Data lifecycle

All data moves through various lifecycle stages and this DMP is designed with
an awareness of this. The role of this domain is to determine the definition,
production, retention and retirement of data. Informed decisions related to
each stage of data lifecycle has gained increased importance in the light of
compliance with the GDPR principles of data minimization and privacy by
design. Some of the measures and related decisions are outlined in Table 5.

Table 5: Adaptation of Khatri and Brown (2010)’s Domain of data lifecycle in
SUITCEYES  
  
<table>  
<tr>  
<th>

**Domain Decisions**

</th>  
<th>

**Potential Roles or Locus of Accountability**

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

  * **Data inventory** will be conducted on multiple occasions during the project. 

  

  * **Data lifecycle plan** will be defined as part of the metadata tool, mentioned above. That is, at the time of data creation, not only the data will be defined, but plans will be put in place for data lifecycle, including long-term retention and even future destruction if appropriate. 

  

  * Towards **compliance with legislations** and other regulations, some data and records are required to be kept for given periods of times. An overview of such regulation is formed through access to related guidelines and in collaboration with partner organisations’ archival departments. 

  

  * At the time of project conclusion, the sensitive data will be securely archived based on the timelines specified in the metadata tool or made publicly available through different channels. 

  

</td>  
<td>

  * During the project, the UDWG and the ADWG will be tasked with recommendations for data life cycle management. 

  

  * The UDWG and the ADWG will also supply recommendations on data that might at some stage be made available as open data. 

  

  * The UDWG and the ADWG might also make recommendations of changing physical storage and alternating practices of metadata during various stages of data lifecycles. 

  

  * Formal decisions related to the recommendations made by the UDWG and the ADWG will be made by the PMB. 

  

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

#  Data Management

Based on the data governance structures described above, the following
sections describe the project-related datasets and actual measures and steps
employed to ensure effective production, access, use, reuse, storage and
preservation of data within SUITCEYES making it FAIR – findable, accessible,
interoperable and re-usable.

##  Data collected/generated within SUITCEYES

There are a number of different data types and datasets either collected or
generated within SUITCEYES. Some of this data is defined as sensitive and not
suitable for sharing or openly making available to third parties. Others will
be deemed as intellectual assets of partners and intended for future
exploitation or are subject to other copyright issues and will not be shared
openly or not at least before adequate measures have been taken. A third group
of data will be made available under the open data principals for use and
reuse by third parties.

The main datasets in SUITCEYES contain data resulting from user-studies,
policy studies, bibliographic searches, collections of semantic vocabularies,
algorithms, technical experiments in the project. For administrative purposes
we have grouped these data under two broad categories of User Data and
Analytical Data.

All sensitive data that will include personal information or result from
interviews and observations are placed in the first group. All the other data-
sets (although some not related to experiments) fall in the same category.
Table 6 provides a summary of these data and its main categories. The separate
group of Social Data is also provided in the table as a data about the
potential interest of different groups, social media data and disseminated
information about the project.

Table 6: Main categories of data and the methods of its collection/generation  
  
<table>  
<tr>  
<th>

Category

</th>  
<th>

Type of Study

</th>  
<th>

Methods of collection/ generation

</th>  
<th>

Data

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

User-Data

</td>  
<td>

User studies

</td>  
<td>

Interviews, observations, reaction tracking, audio visual recording,
qualitative data analysis tools

</td>  
<td>

Transcripts, psychophysical data and informed consent forms

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

User-Data

</td>  
<td>

Policy studies

</td>  
<td>

Interviews with decision makers

</td>  
<td>

Transcripts

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

AnalyticalData

</td>  
<td>

Policy studies

</td>  
<td>

Policy documents collection

</td>  
<td>

Policy documents

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

AnalyticalData

</td>  
<td>

Literature studies

</td>  
<td>

Searches in bibliographic databases (e.g. Web of Science).

</td>  
<td>

Bibliographic metadata, collections of articles and other publications

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

AnalyticalData

</td>  
<td>

Semantics

</td>  
<td>

Searches for and collection of a set of sign language vocabularies

</td>  
<td>

Sets of sign language vocabularies

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

AnalyticalData

</td>  
<td>

Semantics

</td>  
<td>

Searches for collections of social haptic signals

</td>  
<td>

Sets of social haptic signals

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

AnalyticalData

</td>  
<td>

Deploying visual understanding algorithms

</td>  
<td>

Wearable RGB-D (Red, Green, Blue and Depth) cameras or RGB and depth sensors

</td>  
<td>

Benchmark datasets from wearable cameras for activity recognition, object
detection, face and hands detections,

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

</td>  
<td>

</td>  
<td>

</td>  
<td>

navigation

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

AnalyticalData

</td>  
<td>

User studies

</td>  
<td>

Scientific instruments (temperature loggers, am-meters, thermography, optical
microscopy, video, tensiometers, martindale etc.)

</td>  
<td>

Technical

measurement data (temperature, time, vibration amplitudes, frequencies)

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

Social-Data

</td>  
<td>

Social interactions

</td>  
<td>

Appropriate accounts on the social media

</td>  
<td>

Data about potential interest of groups, disseminated information about the
project

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

##  Standards for collection, creation, and reuse

There are a set of standards and guidelines related to each type of data
collected or generated within the project. For example, there is a detailed
interview protocol that defines clearly the aim of the interviews; the
procedure for the interview, analysis, collaboration between the researchers
who conduct the user-studies; instruction about how the interviews are to be
conducted, the instructions for ensuring informed consent by the participants;
the interview questions and more. In other cases, for example for
bibliographic studies, the information scientist within the project will apply
best practice for data collection, pre-processing, analysis and
visualizations. Similarly, each of the researchers in the project will apply
their field expertise to ensure compliance with standards and data quality.

Furthermore, information is recorded about each sets of data, in a descriptive
accompanying document. The type of information included may vary from one
descriptive overview document to the next, but in general the types of
information that are included may comprise of information about the contents,
the source, means of data collection or generation, privacy level, storage
details, retention instructions, member(s) involved in data creation, notes,
dates of creation, use, versions, tools involved, methods used, and so on.

Additionally, a select set of such information is uniformly captured in a
Metadata Tool (Appendix 2), which for each dataset within the project defines
the dataset’s unique id and name, data type, description, ownership, purpose,
area of use, size, level of data sensitivity, depository, duration of
preservation, reuse instructions, accompanying metadata, required tools and
methods, data quality assurance process, and pertinent ethical considerations.

The information provided in the Metadata Tools therefore provides guidelines
as to whether each specific set of data can be shared for reuse or will be of
sensitive nature and would need to be protected and not shared.

##  Ownership and responsibilities

In SUITCEYES, some data will be generated and other data may be captured for
further analysis and use within the project. The ownership of the collected
data will remain with the original owner. For the rest, the default structure
for data ownership in SUITCEYES is that the partner creating the data also
owns it as defined in GA article 26. Where some data is subject to JOINT
ownership, this is governed by Grant Agreement Article 26.2. Further
stipulated are listed in the project’s consortium agreement, section 8.

For the sake of clarity and future reference, the name(s) of data generator(s)
and owner(s) is (are) clearly stated in the Metadata Tool. The data owners are
responsible to provide the required information that informs the project of
the level of data sensitivity and means of storage and retention.

The PMB is the ultimate decision-making body within SUITCEYES and as such it
is also responsible for decisions made on data management issues. The PMB is
also responsible to oversee the existence of appropriate data governance
structures within the project. Towards this, the PMB will develop and assign
responsibilities to two Data Working Groups one for User-Data and one for
Analytical-Data. These working groups will collaborate with project members
and will oversee accuracy, timeliness, completeness and credibility of data.

##  Dataset labelling convention

Each dataset will be assigned a unique identifying number (each dataset
receiving the next consecutive available number as indicated on the Metadata
Tool).

To facilitate labelling of datasets, much of the identifying information about
each dataset will be provided in the Meta-Data Tool and potential accompanying
data descriptive file. Therefore, the actual labelling of the dataset will be
as follows:

####  SC-DS_ID_Name

Where SC-DS is indicative of SuitCeyes-DataSet. ID refers to the dataset’s
unique identifying number. Finally, Name is to be formed in a way to provide
immediate meaningful information about the content of the dataset.

Example: **SC-DS_1_WoS-809 items-deafblindness-2018-02-02**

##  Storage and sharing (during and after the project)

Currently, the main depository for SUITCEYES data is BOX, which is a data
storage and sharing solution, procured nationally within Sweden for use by
Swedish universities and their collaborators. This storage facility has been
approved as meeting the standards set by the GDPR requirements. Some data may
be stored locally at partner organisations in accordance to secure GDPR-
compliant guidelines. The consortium identifies the level of security;
sensitiveness; storage requirements; retention instructions; sharing routines;
and the specifics of archiving, preservation, and or destruction for each set
of data as they emerge and as the project progresses. These parameters will
define how the data within the project are to be handled.

##  Data quality and evaluation

The consortium has defined guidelines, procedures, and routines to ensure a
general level of quality of data and research work by the means of the
structures that are put in place. In addition to this, each member of the
project is competent in their his or her specific area of research and well
familiar with related guidelines and best practices to ensure quality of work
and to apply appropriate evaluations measures. Furthermore, the internal
review structures and collaborative feedback by colleagues is a further means
for ensuring quality of work, data and research.

##  Ethical and legal compliance

As mentioned earlier, SUITCEYES involves user studies. The partners involved
in these studies either hold or will seek and obtain ethics approval from
their national ethics review boards. Based on the procedures described above,
the sensitive data generated or used within the project will be subject to
guidelines and related best practices. After assigning codes for future cross-
referencing purposes, personal identification information will be removed from
the interview and observation data, soon after data collection and before
sharing (only if needed) with other project members.

The consortium partners are also aware of the GDPR (EU) 2016/679  5  which is
a regulation in EU law on data protection and privacy for all individuals
within the European Union. It also addresses the

export of personal data outside the EU. The GDPR aims primarily to give
control over personal data and to simplify the regulatory environment for
international business by unifying the regulation within the EU. It also
involves decisions regarding which datatypes can be characterized as
containing sensitive information and how such information is stored and
shared. Each partner is aware to examine the possibility of protecting its
results and must adequately protect them within the project, and after its
finishing. It is especially important if the results can reasonably be
expected to be commercially or industrially exploited and protecting them is
possible, reasonable, and justified. The consortium considers its own
legitimate interests and the legitimate interests (especially commercial) of
the other partners. Already on the stage of submitting the project application
the consortium decided to opt out of the Pilot on Open Research Data in
Horizon 2020. It is connected with:

  * allowing the protection of results (e.g. patenting) 

  

  * incompatibility with privacy/data protection 

  

Where possible within the SUITCEYES project, the partners are using healthy
adults, so that one can minimise the burden on the target user group. In other
words, people with deafblindness are only being called upon where their
“expert user” perspective is required. For healthy users, ethical risks are
low – there is no sensitive data being recorded, so the only concerns are
around data protection and health and safety. The partners will only work with
those people with deafblindness who have the capacity to consent, and who have
the communication skills to carry out an interview. Interviews will be
conducted via a caregiver, so that the participant is with someone familiar
and with someone skilled in acting as an intermediary. Interviews will be
conducted in a location of the participants' choice – in this way they will be
in a familiar environment. Naturally, informed consent forms will always be
applied.

##  The approach and format of reaching informed consent

The approach to be used to reach informed consent, or any other agreement with
end users taking part in requirements and other user studies, has been
proposed and performed from the beginning of the project. This approach and
the format of reaching informed consent for this target group is of usage also
for other R&D activities and are made available / published as an important
output (the templated used by the consortium from the beginning of the project
are shown in Appendix 3). Moreover non-sensitive data are stored in a Google
drive folder (according to D1.1 Quality Assurance Plan). The YouTube videos
and other dissemination materials involving the presence of users and outside
participants require reaching informed consent for the target group presented
on the video, materials etc. In case the consortium will decide to video-
record the interviews, iteration of the agile process for WP7 or other
activities and applications involving persons outside the consortium, the DMP
requires to use the consent form that includes reference on how to treat these
materials (please check the forms in Appendix 3).

At the start, the consortium informs that giving consent to process any
personal data collected in the context of SUITCEYES is entirely voluntary and
that the consortium commits to protect personal data, and process it only
according to applicable laws and regulations such as the GDPR. The consent
might be withdrawn at any time, however withdrawing it might not effect
immediate stopping of using the material and that it does usually not affect
material that has already been made public. One is informed that the material
collected will be used in internal training of project members and/or
published in which media channels, e.g. billboards, newspapers, website, TV
programs, Twitter, YouTube, LinkedIn etc. It is important to note that the
consortium informs that published material probably reaches large audience and
that the consortium is not able to control other use of the material. It is
also mentioned that publishing the material on social media means that the
material is transferred to companies based in the United States. These
companies are members of the "Privacy Shield"- agreement and are thus
considered to ensure an adequate level of protection of personal data.

SUITCEYES ensures transparency with processing personal data. There is a
possibility of receiving information about the way of processing and copying
personal data. It will be received in a structured, commonly used and machine-
readable format. The consortium can rectify or supplement personal data that
is inaccurate or incomplete.

It is possible to erase personal data under certain circumstances, however
personal data that has been already made public, e.g. published on social
media is usually not affected by a withdrawn consent. Because of legal
provisions we may also be prevented from immediately erasing personal data.
Lodging a complaint to the supervisory authority is also possible. Therefore,
the privacy is an important issue for SUITCEYES consortium and we do the best
to protect personal data of internal and external members of the project.

Various formats of reaching informed consent have been used in SUITCEYES from
the beginning of the project, e.g.:

  * The consent forms for the interviews with the users within WP2 in Greek, German, Swedish, Dutch and English 

  

  * Non-Disclosure Agreements that were signed with the advisors, symposia participants, and 

  

e.g. persons who helped in transcribing some parts of the interviews

  * The informed consent forms used for experiments in the Netherlands (in Dutch) 

  

  * The informed consent form (in English) used by HB for taking photos, filming and publishing 

  

(for various university applications, also used in the project)

  * A letter of consent regarding video/audio recording or photos in German 

  

  * Universal consent form created in the second year of project in accordance with the project identity. 

  

The templates of these informed consent forms can be found in Appendix 3.

#  Summary

This DMP incorporates both Data Governance and Data Management and accordingly
defines the principals, procedures, and routines that are put in place for the
management of research data within the SUITCEYES project. The SUITCEYES
partners will make the research results available through different channels
and strive to provide open access to the project data, as far as possible.
However, not all data generated and used within the project are suitable or
appropriate for sharing and reuse, and hence, SUITCEYES has chosen not to
participate in the Open Research Data Pilot. The main datasets in SUITCEYES
contain data resulting from user-studies, policy studies, bibliographic
searches, collections of semantic vocabularies, algorithms, technical
experiments in the project. For administrative purposes these data has been
grouped under two broad categories of User Data and Analytical Data. For the
sake of clarity and future reference, the name(s) of data generator(s) and
owner(s) is (are) clearly stated in the Metadata Tool. The data owners are
responsible to provide the required information that informs the project of
the level of data sensitivity and means of storage and retention (relevant
informed consent forms are also used). The PMB as the ultimate decisionmaking
body within SUITCEYES is also responsible for decisions made on data
management issues. Each dataset is assigned a unique identifying number (each
dataset receiving the next consecutive available number as indicated on the
Metadata Tool) and the main depository for SUITCEYES data is BOX, which is a
data storage and sharing solution. This storage facility has been approved as
meeting the standards set by the GDPR requirements. Some data may be stored
locally at partner organisations in accordance to secure GDPR-compliant
guidelines.

The DMP will be updated over the course of the project whenever significant
changes arise, such as (but not limited to):

  * new data 

  

  * changes in consortium policies (e.g. new innovation potential, decision to file for a patent) 

  

  * changes in consortium composition and external factors (e.g. new consortium members joining or old members leaving). 

  

#  Appendices

###  Appendix 1

Framework for data decision domains (Khatri & Brown, 2010: 149)

###  Appendix 2

**SUITCEYES Metadata Tool**

The following information is recorded for each set of data collected or
generated within the project. (One dataset item is included here to
exemplify.)

78081416

###  Appendix 3

**Informed consent forms used in SUITCEYES**

The consent form used for the interviews with the users within WP2 (in Greek)

The consent form used for the interviews with the users within WP2 (in German)

The consent form (last page) used for the interviews with the users within WP2
(in Swedish)

The consent form used for the interviews with the users within WP2 (in Dutch)

The consent form used for the interviews with the users within WP2 (in
English)

The form of Non-Disclosure Agreements that were signed with the advisors,
symposia participants,

and e.g. persons who helped in transcribing some parts of the interviews

The informed consent forms used for experiments in the Netherlands (in Dutch)

The informed consent form (in English) used by HB for taking photos, filming
and publishing (for

various university applications, also used in the project)

Letter of consent regarding video/audio recording or photos in German

Universal consent form created in the second year of project in accordance
with the project identity