#  INTRODUCTION AND OVERVIEW

##  General overview of data collection activities in SMARTEES

SMARTEES is a multimethod project where the established methodologies of
social simulation and social innovation research are merged to conduct
multimethod analyses of the socio-political conditions – including contextual
socioeconomic structures – that encourage and discourage collective energy use
patterns. Its key methodological strength comes from combining both
qualitative and quantitative methodologies with a user-centred approach from
the perspectives of sociology, psychology, economics, political science,
social simulation, and social innovation. This allows us to integrate
interdisciplinary knowledge into a comprehensive modelling framework, a unique
policy analysis tool – the SMARTEES policy sandbox, providing support to the
success of the Energy Union by developing alternative, more robust policy
pathways that contribute to identifying ways to overcome citizen resistance
and increase acceptability. This robust and transparent analytical tool, in
turn, will meet the pressing need identified in the SET-plan roadmap by
offering practitioners new ways of developing, testing and adapting policy
measures and technology diffusion strategies to implement the SET-plan. The
work in the project is delivered in different work packages, which have their
own methodological approaches.

##  Purpose and scope of this document

This DMP first gives a comprehensive overview of all data types collected in
SMARTEES, including a detailed mapping of data collection activities to work
packages and SMARTEES partners (see Section 2). Afterwards, each partner’s
responsibilities with respect to data collections, data handling and analysis,
as well as data storage are identified (Section 3). In the final section, the
document defines SMARTEES standards for all data collections, (including
research ethical standards and GDPR procedures), data storage and handling,
data documentation, pseudonymization or anonymization, access to data for
exploitation and future use, and data deletion. It needs to be noted, that a
DMP is understood to be a dynamic document that will be adjusted and adapted
during the course of the project. It will be integrated into the SMARTEES
project handbook (Deliverable 1.3) which will be implemented in SMARTEES as a
WIKI-based knowledge / procedures database constantly updated. In that
respect, the DMP describes the SMARTEES data management at the point in time
it was delivered to the European Commission. The document was written with
reference to the Guidelines to FAIR data management in Horizon 2020 (
_http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-
oa-datamgt_en.pdf_ )  and the GDPR (Regulation (EU) 2016/679).

##  Future revisions of this document

The DMP is a dynamic document, which will be constantly updated. Since the DMP
will become part of the dynamic project handbook (Deliverable D1.3), it will
be integrated in the latter´s revision cycles. However, a formal revision of
the DMP will be provided on an annual basis, which means in July 2019 and July
2020.

#  DATA COLLECTION

SMARTEES is a mixed methods project and its methodological strength comes from
combining qualitative and quantitative techniques with an interdisciplinary
approach. This allows us to integrate interdisciplinary knowledge into a
comprehensive modelling framework, which provides practitioners a unique
analysis tool for policy measures and technology diffusion strategies, by
conducting multimethod analysis of the socio-political conditions encouraging
/ discouraging collective energy use patterns across European member states.
SMARTEES includes data collection and handling activities in most of the WPs
(excluding WP1 and WP9), which strongly depend on each other. This complexity
demands a strict coordination between the different tasks and WPs as they
depend on one another, and input from preceding tasks is not only required
within the same WP but also in other WPs. Furthermore, SMARTEES is a project
that depends strongly of secondary data provided by the case partners (and
other sources). This makes it necessary to define procedures for how data
access rights for secondary data are achieved and how that data is used and
matched with primary data.

Table 1 presents an overview of the various data collections that are part of
SMARTEES (see the first column) and indicates which WP(s) participate in each
data collection or data handling. For example, the first data collection is
“Documents and Records” and WPs 3 and 4 contribute to this data collection.

####  _Table 1:_ _Data collection methods used in different WPs_  
  
<table>  
<tr>  
<th>

**Method/WP**

</th>  
<th>

**WP1**

</th>  
<th>

**WP2**

</th>  
<th>

**WP3**

</th>  
<th>

**WP4**

</th>  
<th>

**WP5**

</th>  
<th>

**WP6**

</th>  
<th>

**WP7**

</th>  
<th>

**WP8**

</th>  
<th>

**WP9**

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

Secondary data acquisition (i.e.

documents and records)

</td>  
<td>

Overall data management and

acquisition procedures for

secondary data

</td>  
<td>

Sets the framework and data

collection guidelines

</td>  
<td>

✓

</td>  
<td>

✓

</td>  
<td>

</td>  
<td>

Synthesizes/

analyses

data

</td>  
<td>

Sets the standards for data

collection/curation

</td>  
<td>

Di

sseminates/exploits results

</td>  
<td>

Sets ethical requirements

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

Interviews

</td>  
<td>

✓

</td>  
<td>

✓

</td>  
<td>

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

“On site” visits and observations

</td>  
<td>

✓

</td>  
<td>

</td>  
<td>

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

Questionnaire surveys

</td>  
<td>

</td>  
<td>

✓

</td>  
<td>

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

Focus groups

</td>  
<td>

✓

</td>  
<td>

✓

</td>  
<td>

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

Discussions

</td>  
<td>

</td>  
<td>

✓

</td>  
<td>

✓

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

Workshops

</td>  
<td>

✓

</td>  
<td>

</td>  
<td>

✓

</td>  
<td>

✓

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

##  Research data and personal information for non-research purposes

We define data as all research data obtained from respondents directly or
indirectly, through various research methods, for the project research
purposes. We distinguish data, as defined, from personal information used for
external communication purposes in WP8 (for example quotes from experts or
interviewees for project videos, blogs, etc. meant for communication). Such
personal information will be published only after informed written consent of
the involved individuals has been given  1  . In this latter instance,
personal information used for communication will not be anonymized, encrypted
or pseudonymized.

##  Data types

Table 2 presents key characteristics of each data collection in SMARTEES. The
first column indicates the type of data collection, the second column
indicates where the data, for each data collection method, come from, the
third column indicates how the data are collected, the fourth column indicates
whether data, from a given data collection type, will be published in an open
access mode at the end of the project, the next column lists the tasks and/or
WPs that contribute to a given data collection type, and finally the last
column names all partners involved in a given data collection. Note that the
same data collection type may be used in several independent data collections
in different WPs.

#####  _Table 2:_ _Details of data collection_  
  
<table>  
<tr>  
<th>

**Type of data collection**

</th>  
<th>

**Source of data**

</th>  
<th>

**How data is collected**

</th>  
<th>

**Open access**

</th>  
<th>

**WP/task**

</th>  
<th>

**Partners** (lead partners underlined)

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

Documents and records / secondary data acquisition

</td>  
<td>

Available documents and data (previous studies and research; case evaluations/
assessments; plans of action and periodical reports implemented in each case
study; other relevant documents in each case studies; social innovation
cluster assessments)

</td>  
<td>

Documents published by relevant stakeholders are identified through our case
contacts, in the European Commission’s document database, and through search
engines (e.g. Google, Google Scholar) using appropriate search words (e.g.,
“energy poverty”, “everyday mobility”).

In addition, relevant scientific and policy papers are identified through
databases (e.g. Web of Science, Scopus). Other papers indicating additional
potentially useful sources of information will be subsequently explored as
well.

Existing quantitative datasets will also be acquired, following permission
procedures where necessary.

</td>  
<td>

Y, if no restrictions by the owners of the data are made

</td>  
<td>

1.3, 2.3,

WP3,

4.1, 4.2,

4.9,

WP5,

6.1-6.3, WPs 7 and 8

</td>  
<td>

_EI-JKU_ , _JH_ ,

_K &I _ , _NTNU_ ,

_UG_ , _UOT_ ,

ACC, ICLEI,

SAM-EA,

UDC, UI

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

Individual indepth interviews

</td>  
<td>

Responses of key informants and

stakeholders

(e.g.,

policymakers, social innovation pioneers/

</td>  
<td>

In-depth interviews with the case studies’ main actors will be conducted as
part of WP3.

Interviewees will be selected by taking into account the features of each case
study.

</td>  
<td>

Y, after

pseudonymization with consent of the interviewees

</td>  
<td>

1.3, 2.3,

WP3,

4.4,

WP5,

6.1-6.3, WPs 7 and 8

</td>  
<td>

_EI-JKU_ , _K &I _ ,

_NTNU_ , _UDC_ ,

_UG_ , _UOT_ ,

ICLEI, JH,

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

</th>  
<th>

frontrunners, social actors, sustainable entrepreneurs / business actors)

</th>  
<th>

A detailed interview protocol appropriate to use individual interviews will be
developed in task 4.4 (WP4).

</th>  
<th>

</th>  
<th>

</th>  
<th>

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

“On site” observations

</td>  
<td>

Observational data from site visits

</td>  
<td>

Direct observations through “on site” visits and field visits by project
partners and representatives from learning case cities/regions

</td>  
<td>

Y, as brief case study reports

</td>  
<td>

1.3, 2.3,

WP3,

4.4,

WP5,

6.1-6.3, WPs 7 and 8

</td>  
<td>

_EI-JKU_ , _ICLEI_ ,

_K &I _ , _NTNU_ ,

_UG_ , _UOT_ ,

ACC, JH,

SAM-EA,

UDC, UI

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

Questionnaire surveys

</td>  
<td>

Responses of participants across all case study clusters

</td>  
<td>

WP4 will coordinate the overall efforts related to the surveys.

A core questionnaire, allowing a level of comparability across all case
studies, will be developed and later adapted for each case cluster.

The case responsible partner will lead data collection in each case study in a
subtask for each case.

</td>  
<td>

Y, after

pseudonymi-

zation; respondents will be informed prior to data collection

</td>  
<td>

1.3, 2.3,

3.4,

WP4,

WP5,

6.1-6.3, WPs 7 and 8

</td>  
<td>

_EI-JKU_ , _JH_ ,

_K &I _ , _NTNU_ ,

_UG_ , _UOT_ ,

ICLEI, UDC,

UI

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

Focus group interviews

</td>  
<td>

Key informants and

stakeholders

(e.g.,

policymakers, social innovation pioneers, social actors,

sustainability entrepreneurs /business actors)

</td>  
<td>

Focus group interviews with case-studies main actors will be conducted as part
of WP3.

Individuals belonging to each groups will be selected by taking into account
the features of each case study.

A detailed focus group interview protocol will be developed in task 4.4 (WP4).
Focus group interviews with case

</td>  
<td>

Y, after

pseudonymization with consent of the interviewees

</td>  
<td>

1.3, 2.3,

WP3,

4.4,

WP5,

6.1-6.3, WPs 7 and 8

</td>  
<td>

_EI-JKU_ , _K &I _ ,

_NTNU_ , _UDC_ ,

_UG_ , _UOT_ ,

ICLEI, JH,

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

</td>  
<td>

</td>  
<td>

studies main actors will be conducted as part of WP3.

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

Discussion events

</td>  
<td>

Responses of project partners, case study teams and actors

</td>  
<td>

JH will arrange a series of in-depth discussions with project partners and
casestudy teams regarding requirements for primary data in each case.

UOT will engage a sample of citizens, consumers, social and business actors,
including social innovators to discuss forthcoming energy policy
implementation.

</td>  
<td>

Y, in the form of brief reports

</td>  
<td>

1.3, 2.3, WP4,

WP5,

6.1-6.3, WPs 7 and 8

</td>  
<td>

_EI-JKU_ , _JH_ ,

_NTNU_ , _UG_ ,

_UOT_ , ACC,

ICLEI, K&I,

SAM-EA,

UDC, UI

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

Workshops

</td>  
<td>

Responses of workshop

participants (project partners, case study actors teams and follower city
represen-

tatives)

</td>  
<td>

Data on views of relevant experts and stakeholders are collected during
research workshops, during data analysis workshop, during multi-stakeholders
workshops, and during workshops with follower cities/regions.

</td>  
<td>

Y, in the form of brief reports

</td>  
<td>

1.3,

WP2,

3.2,

WP4,

WP5,

6.1-6.3, WPs 7 and 8

</td>  
<td>

_EI-JKU_ , _ICLEI_ ,

_JH_ , _NTNU_ ,

_UG_ , _UOT_ ,

ACC, K&I,

SAM-EA,

UDC, UI

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

#  PARTNER RESPONSIBILITIES

The data collections and data processing, each WP is responsible for, is
described in this section. The data collection/processing tasks in WP4 are
described in more detail than the tasks in other WPs, because WP4 deals with
the main data collection/handling and coordination tasks that support the work
in other WPs. For a complete overview of data collection responsibilities see
_**Appendix I** _ (constantly updated during the project).

##  WP1 (NTNU)

In the SMARTEES project, NTNU (WP1 lead beneficiary) is responsible for
management of the project. SMARTEES will use a large amount of existing data
and data collected specifically for the purpose of the study. This data needs
to be integrated, monitored, securely stored, and made available for analysis
within and beyond the project. Even though data collection, curation,
analyses, and exploitation will be conducted in WPs 4, 6, 7, and 8, WP1 will
have the overall responsibility to secure the compliance of the data
collection and handling with data protection laws (national and GDPR) and the
open data pilot regulations. WP1 has developed the DPM for SMARTEES – this
document (task 1.3), which will be updated in M14 and M26. All information
about data collection methodology and standards, data coding, referencing and
processing, and about the exploitation of the data during the project and
beyond is included in this DMP. Furthermore, WP1 will be responsible for
acquiring access to secondary datasets and engaging in legal agreements with
the providers of the secondary data.

##  WP2 (EI-JKU)

A methodological cornerstone of SMARTEES is its research strategy to carry out
empirical work meeting the scientific excellence criteria for each of the
involved disciplines separately, but at the same time, defining a joint new
best practice methodology for cooperative multi-disciplinary policy research.
For this aim, EI-JKU (WP2 lead beneficiary) will prepare the common
theoretical framework for WP4-WP7 (task 2.1), which will be scrutinized and
refined in a subsequent public workshop (task 2.1) and interdisciplinary
research workshops on theory and methods (task 2.4). This will ensure a
thorough multi-method analysis of socio-economic and structural drivers of
five types of energy related social innovations in relation to smart
technology diffusion, energy behaviour, consumer engagement and prosumerism.
EI-JKU will also prepare the background for case study analysis (literature,
desk research), and ensure consistent data collection by defining the process
with which the theoretical findings can be fully considered in all data
collection activities (task 2.3).

##  WP3 (K&I)

By thoroughly analysing five carefully selected case clusters that cover
different social innovations, European regions and cultures, and different
sub-groups driving the innovations, SMARTEES secures the policy relevance and
up-scalability of the findings. K&I (WP3 lead beneficiary) is responsible for
resuming contact with these actors, and preparing, for each reference and
supporting case, a plan of work with the correspondent timeline (task 3.1).
Task 3.2 will then prepare a description of the social dynamics characterizing
different cases, and a systematic analysis of each social innovation cluster
with its reference framework. For each case cluster, issues such as the
following will be examined: actors involved with their specific role; phases
of development of the social innovation, (how the social innovation
originated, how the social innovation has been consolidated, etc.); features
of each kind of actors’ participation and empowerment dynamics; local social,
economic, and environmental dynamics; local resources and assets; regulations;
incentives (including but not only economic); barriers, conflicts,
resistances, stress factors and negative effects in specific groups on one
hand and facilitating factors and success elements on the other, (including
acceptance and rejections dynamics); the co-existence of bottom-up and top-
down dynamics; and finally how the up-scaling process has worked and is
working. Sources of information will be available documents and data (previous
studies and research; assessments, plans of action and periodical reports
implemented in each case study; other relevant documents in each case studies;
social innovation cluster assessments; available datasets about the behaviour
of the actors, etc.); stakeholders’ interviews and direct observation through
“on site” visits. Task 3.2 will be responsible for planning and conducting the
interviews and visits. Interview protocols will be developed in Task 4.4 to
ensure their compatibility with the ABM methodology and each case partner’s
contact institution in SMARTEES is responsible for conducting the interviews.
To understand overall “prima facie” (i.e. before the implementation of the
survey – WP4 – and the draft of the scenarios – WP5) how social innovations in
energy transitions works “in action”, an overall analysis will be conducted
(Task 3.3). Based on the social innovation profiles and the overall analysis
of these, task 3.4 will identify knowledge gaps to be filled through the
surveys in WP4 (e.g. questions to be considered in the questionnaire survey),
and will propose a set of indicators to be considered in the preparation of
scenarios in WP5.

##  WP4 (JH)

SMARTEES will make as much use of existing data as possible, though it is
anticipated that there is a need, on a case by case basis, to supplement these
existing data to provide additional insights. Therefore, the project
scrutinizes a large amount of existing and new data. The dedicated objective
of JH (WP4 lead beneficiary) is to collect existing and new data necessary for
WP 5-7 and make them available for further use also after the end of SMARTEES.
The tasks in WP4 are hence associated with coordinating requirements for new
data, and ensuring that all data, new and existing, are properly annotated
with metadata to enable their consistent application. Datasets we expect to
obtain access to, as a direct result of the collaboration with the case
cluster cities/regions, are results from previous citizen surveys, case
evaluations, energy use data, mobility patterns, socio-demographics in the
respective regions, consumption data, relevant local business indicators, etc.
Contracts, about the conditions under which these datasets will be used by the
partners in accordance to GDPR, will be made with the owners of the datasets.

JH will start by developing an initial schema for discussion with all
partners, along with a method for continual synthesis of new metadata into
that schema as the project progresses (task 4.1). The methods will be based on
the tools of knowledge elicitation and integration with the other project
partners as domain experts, such as telephone interviews, questionnaires, and
structured document analysis. The work done here will draw on experience in
the GLAMURS project developing a data integration protocol. This metadata
specification facilitates data integration among the various data sets used by
the project. WP4 will then provide a protocol for capturing sources of data to
ensure that all sources of data used by the project are sufficiently
documented, and that all information regarding raw data, metadata, conditions
of use, and any licences are recorded in such a way as to facilitate efficient
retrieval by members of the project consortium (task 4.2).

JH will also arrange a series of in-depth discussions with project partners,
and case-study teams to first generate a long list of potential requirements,
and then in discussion, distil this list into a realistic set of requirements
for primary data collection (task 4.3). Although the initial description of
work provided some indicative estimates for survey samples, the actual sample
size calculations will be performed within this task. The requirements of the
models, and the data needs of the case study partners themselves will be taken
into consideration during these discussions. Following from task 4.3, a
relatively short core questionnaire will be developed (task 4.5) that will
allow a level of comparability across all case studies surveyed. Beyond this
core, this task will develop a collaborative process whereby partners can
contribute to a battery of measures and items to be drawn on in task 4.6, i.e.
adaptation of questionnaire framework for each case cluster. In many cases the
constructs to be measured will be established and validated, but where this is
not the case, smaller pilot studies may be required to ensure a high level of
construct and content validity for any newly developed measure in the SMARTEES
project. Which questions are asked in each case study and to which types of
respondents (beyond the agreed core), will depend on requirements and context
of that case study. Details of the sample requirements for each questionnaire
survey will be developed following earlier discussions in task 4.3, and will
form part of the survey brief to be provided to the professional companies
subcontracted to undertake the empirical survey work. Each case study cluster
will be required to develop its own briefing document for this survey
subcontract. The lead partner for each case study cluster (i.e., K&I. NTNU,
UDC) will work with the overall coordinating partner for this task (JH) in
developing the sampling details, the questionnaire tool, and any data
management issues associated with each case. As the data from these surveys
will be fed into the agent-based models, the development of the questionnaire
will require detailed discussion with project partners intending to utilise
the data being generated by these surveys.

WP4 will as well develop detailed interview protocols depending on
requirements and context of each social innovation case study (task 4.4) to be
utilised in task 3.2. For example, it may be appropriate to use individual
indepth interviews in some cases, whereas in others, group interviews or
workshops may be deemed more appropriate. The development of the case study
interview protocols will be discussed with the agent-based modelling teams
(WP7) to ensure compatibility of thematic content with the requirements of the
models. The interviews themselves will be conducted under Task 3.2. The
interview data itself (recordings/transcripts) however, forms part of the
overall data of the project, and it therefore included in task 4.7 as a
subsection of the data collection. Data collection in each case study in a
subtask for each case will be led by the case responsible partner (i.e., K&I.
NTNU, UDC). The survey work will be subcontracted to professional survey
companies (including participant recruiting, printing, postage, data
processing), using the briefing document generated in task 4.6. Any further
data collection (e.g. additional empirical studies) will follow directly from
the specific requirements determined in task 4.3.

After collection of the primary and secondary data, JH will host a workshop
(task 4.8) to discuss issues such as ensuring proper interoperability for use
in the models and smooth transfer to task 6.4 with project partners (i.e.,
NTNU, K&I, UDC, UOT, UG). Based on the discussions at the workshop and
collaboration with task 6.4 and WP7, JH will produce data summaries for each
case cluster by carrying out initial exploratory data analysis (task 4.9), and
prepare a data report giving an integrated overview of the case studies as a
draft paper for submission to a journal (task 4.10).

##  WP5 (UDC)

In the SMARTEES project, UDC (WP5 lead beneficiary) will develop policy
insights and future policy scenarios that are plausible, theoretically solid,
and empirically grounded on qualitative and quantitative data (task 5.3). This
task draws upon the theoretical framework developed in WP2 (tasks 2.2, 2.3)
and the empirical research insight on conducted case-studies (WP3, WP4 and
WP6). UDC is also responsible for establishing scenario logics, testing policy
alternatives and identifying tipping points to co-produce a set of dynamic
simulations of policy implementations for each case study involved through
first phase of multi-stakeholders deliberative workshops (task 5.4). Policy
scenarios will be co-created through iterative phases engaging policy and
local actors in reflexivethinking activities with SMARTEES researchers.
SMARTEES’ researchers will provide a formally represented model for each case-
study policy scenario, considering the interactions among actors and networks
within it and with its context. Following the integration of inputs from
participatory workshops and elaboration of realistic policy scenarios by
agent-based modelling techniques (task 5.5), SMARTEES researchers will prepare
and execute second phase of multi-stakeholders deliberative workshops for each
local case-study involved (task 5.6). In this task, SMARTEES will engage a
sample of citizens, consumers, social and business actors, including social
innovators to discuss forthcoming energy policy implementation.

##  WP6 (NTNU)

WP6 (NTNU as lead beneficiary) will synthesize, structure, harmonize and
analyse primary and secondary data with a special focus on energy equality and
utilize them for an analysis of policy relevant social innovation drivers and
barriers, and a structured input for the agent-based modelling in WP7. In
doing so, WP6 is directly dependent on input from WP2 providing the
theoretical framework for the analyses in WP6, and WP4, providing the raw data
for the integration in WP6, and WP5 providing the scenarios for the analyses.
WP6 feeds then the synthesized data into the agent-based simulations in WP7
and the theory relevant implications of the results back into WP2.
Furthermore, WP6 feeds into WP8 by providing reports about the policy
implications of the analyses.

With input from task 2.2 and task 3.2, the first task in WP6 (i.e. task 6.1)
is to identify all relevant agent types, stakeholders and subpopulations (e.g.
household members from a high diversity of households, decision-makers in
energy companies, network utilities, building companies, city planners,
policymakers), and to document their characteristics and social networks
through social network analysis. The results of this task will feed into WP7.
Based on theoretical framework provide by WP2 and the agent types identified
in task 6.1, task 6.2 will then synthesize and analyse primary data and
secondary data provided by WP4 to identify the drivers and barriers for the
diffusion of the social innovations analysed in all case clusters. This task
will directly fed into the ABM in WP7. NTNU will be responsible for
identification of the decision-making mechanisms (task 6.3), which together
with relevant drivers and barriers identified in task 6.2 are crucial for a
realistic modelling of agent behaviour, based on the data delivered from WP4
and interviews with key stakeholders and insights from WP2 and WP3. The
results of this task will also directly feed into the programming of the
agents in WP7. Both the analysis of empirical data in tasks 6.1-6.3 and the
input from the simulations in WP7 will then be fed back to theory development
about social innovation diffusion in task 2.3 (task 6.5). WP6 will also
integrate data, monitor the process of data generation and model development
in WPs 2-4 with a special focus of providing the ABM in WP7 with the necessary
values for model parameterization, calibration, and validation based on data
synthesized and pre-analysed in WP4 (task 6.4).

##  WP7 (UG)

UG (WP7 lead beneficiary) will be responsible for defining the structural
requirements of the data to enhance compatibility between the modelling
infrastructure and all data collection and curation activities in WP4 (task
7.2). UG will then parameterise the SMARTEES agent-based social simulation
model, which is formalized in task 7.3, for selected cases using a variety of
data from the case studies integrated by WP6 in task 7.4. In this iterative
process, the selected case models will be calibrated to create realistic
simulations of the social innovation processes in each case.

##  WP8 (ICLEI)

In the SMARTEES project ICLEI (WP8 lead beneficiary) will provide a group of
follower cities, which is committed to learning from the experiences of the
SMARTEES reference cases, direct access and first-hand information in a
confidential setting. During the course of the project, these follower cities
are invited to two field visits/ workshops to the main and/or supporting
reference case and three online training sessions (webinar or similar format).
The feedback from these follower cities will provide input for the simulation
tool and datasets as exploitation material in the SMARTEES. Specifically, for
each of the five case study clusters, one follower city will be selected for
further in-depth analysis aimed at preparing this city for implementing the
social innovation themselves. Furthermore, as of task 8.5, workshops utilizing
the pro version of the SMARTEES policy sandbox IT tool, will be developed and
conducted in all 10 participating case cities/regions, 1 in Brussels for
policymakers at the EU level, and in 5 follower cities (1 per cluster).

#  DATA MANAGEMENT

This chapter describes the procedures applied in SMARTEES for the different
steps of data collection, management, storage, and publication in detail.

##  Formal ethics approval

SMARTEES involves human participants, data collection and processing, and
involvement of a non-EU country, which raises ethical issues. The SMARTEES
consortium will comply with all relevant national, European and international
ethical regulations and professional codes of conduct. All partners will also
conform to Horizon 2020 ethical guidelines, including General Data Protection
Regulation, “Data protection and privacy ethics guidelines”, the “Guidance for
Applicants on Informed Consent”, and national regulations. Table 3 identifies
the data protection officer or national agency responsible for the approval of
every data collection activity or inquiry for secondary data.

Their opinion will be guiding for how SMARTEES handles the different types of
data.

###  Involvement of human participants

There are three sub-points related to the involvement of human participants to
be managed:

  1. Details on the procedures and criteria that will be used to identify/recruit research participants must be provided. 

  

  2. Detailed information must be provided on the informed consent procedures that will be implemented for the participation of humans. 

  

  3. Templates of the informed consent forms and information sheet must be submitted on request. 

  

  

######  Details on the procedures and criteria that will be used to
identify/recruit research participants

Participants to the primary quantitative data surveys will be recruited from
already registered members of national or local web-panels or specifically
recruited by the subcontracted survey companies. The participants will be
sampled to be representative for each case. Participants are 18 years or older
and must be able to give informed consent. They will be informed about the aim
of the study, the collected data, the aim of connecting the survey data with
secondary datasets (such as for example energy consumption data) via a
pseudonymized key table, data handling, storage and pseudonymization and
anonymization procedures as well as publication of the anonymized data and its
inclusion in the Open Data Pilot. This will be done – in accordance to GDPR –
in simple language, easy to understand for the participants. In cases the data
collection will be conducted online, by following the link to participate they
explicitly give their consent to participate. In cases the data collections
are conducted in personal interviews the participants will give written
consent (templates for the consent/information sheet see point 3). If
participants are recruited from existing panels, they will earn points in the
point system of their panel operator as reward for their participation. If
they are recruited specifically for this study, they will participate in a
lottery of rewards for their participation. Participants are also informed
that they can withdraw their consent until the data is anonymized without any
disadvantages and without having to give a reason. A contact (telephone and
e-mail) will be given where they can request to be informed about all data
that is stored about them in the project. From the point of anonymizing the
data is no longer personal information.

For the other empirical studies like in-depth interviews and focus groups,
information is presented in written form when participants are recruited. It
will be repeated immediately before the data collections are started and the
consent form is signed by the participant before the interview. Participants
are also informed that they can withdraw their consent until the data is
anonymized without any disadvantages and without having to give a reason. A
contact (telephone and e-mail) will be given where they can request to be
informed about all data that is stored about them in the project. From the
point of anonymization the data is no longer personal information.
Participants of this part of the empirical work will be recruited locally from
the general population older than 18 years and from experts in the fields
studied. Only participants will be recruited that are able to give informed
consent and information sheets and consent forms will be designed according to
the templates provided by the Norwegian Center for Research Data. Participants
will be recruited through mailing lists, newspaper advertisement, snowball
systems, posters, or the like. Expenses that they have for participating will
be reimbursed.

Table 3: Data protection officer or national agency in each task  
  
<table>  
<tr>  
<th>

**Name of task**

</th>  
<th>

**Task lead partner**

</th>  
<th>

**Data protection officer / National agency** (responsible for the approval of
data collection/use/inquiry)

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

T 1.3 Overall data management

</td>  
<td>

NTNU

</td>  
<td>

Norwegian Centre for Research Data (NSD)
http://www.nsd.uib.no/nsd/english/index.html  
</td> </tr>  
<tr>  
<td>

T 2.4 Interdisciplinary research workshops

</td>  
<td>

EI-JKU

</td>  
<td>

Marie Holzleitner

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

T 3.1 Case-studies main actors involvement

</td>  
<td>

K&I

</td>  
<td>

Giovanna Murari

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

T 3.2 Profiles of types of social innovation

</td>  
<td>

K&I

</td>  
<td>

Giovanna Murari

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

T 3.3 Overall analysis of social innovation

</td>  
<td>

K&I

</td>  
<td>

Giovanna Murari

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

T 3.4 Models of social innovation

</td>  
<td>

K&I

</td>  
<td>

Giovanna Murari

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

T 4.1 Metadata specification

</td>  
<td>

JH

</td>  
<td>

Doug Salt, Katherine McBay, Gary Polhill, Tony Craig

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

T 4.2 Data curation

</td>  
<td>

JH

</td>  
<td>

Doug Salt, Katherine McBay, Gary Polhill, Tony Craig

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

T 4.3 Identifying requirements for primary data

</td>  
<td>

JH

</td>  
<td>

Katherine McBay, Gary Polhill, Tony Craig

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

T 4.4 Development of interview protocols

</td>  
<td>

UDC

</td>  
<td>

Adina Dumitru/Amparo Alonso

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

T 4.5 Development of the questionnaire framework for the surveys

</td>  
<td>

JH

</td>  
<td>

Katherine McBay, Gary Polhill, Tony Craig

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

T 4.6.1 Adaptation of questionnaire framework for Case Cluster 1

</td>  
<td>

K&I

</td>  
<td>

Giovanna Murari

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

T 4.6.2 Adaptation of questionnaire framework for Case Cluster 2

</td>  
<td>

NTNU

</td>  
<td>

Norwegian Centre for Research Data (NSD)

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

T 4.6.3 Adaptation of questionnaire framework for Case Cluster 3

</td>  
<td>

NTNU

</td>  
<td>

Norwegian Centre for Research Data (NSD)

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

T 4.6.4 Adaptation of questionnaire framework for Case Cluster 4

</td>  
<td>

UDC

</td>  
<td>

Adina Dumitru/Amparo Alonso

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

T 4.6.5 Adaptation of questionnaire framework for Case Cluster 5

</td>  
<td>

JH

</td>  
<td>

Katherine McBay, Gary Polhill, Tony Craig

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

T 4.7.1 Data collection in Case Cluster 1

</td>  
<td>

K&I

</td>  
<td>

Giovanna Murari

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

T 4.7.2 Data collection in Case Cluster 2

</td>  
<td>

NTNU

</td>  
<td>

Norwegian Centre for Research Data (NSD)

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

T 4.7.3 Data collection in Case Cluster 3

</td>  
<td>

NTNU

</td>  
<td>

Norwegian Centre for Research Data (NSD)

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

T 4.7.4 Data collection in Case Cluster 4

</td>  
<td>

UDC

</td>  
<td>

Adina Dumitru/Amparo Alonso

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

T 4.7.5 Data collection in Case Cluster 5

</td>  
<td>

JH

</td>  
<td>

Katherine McBay, Gary Polhill, Tony Craig

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

T 4.8 Data analysis workshop

</td>  
<td>

JH

</td>  
<td>

Katherine McBay, Gary Polhill, Tony Craig

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

T 4.9 Preliminary data analysis

</td>  
<td>

JH

</td>  
<td>

Katherine McBay, Gary Polhill, Tony Craig

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

T 4.10 Integrated data report

</td>  
<td>

JH

</td>  
<td>

Katherine McBay, Gary Polhill, Tony Craig

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

T 5.1 Developing a common criteria list for defining policy scenarios

</td>  
<td>

UDC

</td>  
<td>

Adina Dumitru/Amparo Alonso

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

T 5.2 Building a common framework for the development of local-embedded future
policy scenarios

</td>  
<td>

UDC

</td>  
<td>

Adina Dumitru/Amparo Alonso

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

T 5.3 Definition of future policy scenarios

</td>  
<td>

UDC

</td>  
<td>

Adina Dumitru/Amparo Alonso

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

T 5.4 Exploration of future policy scenarios through multi-stakeholders
deliberative workshops

</td>  
<td>

UOT

</td>  
<td>

IT analyst Oana Velcota, IT&C Department,

UVT

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

T 5.5 Integration of inputs from participatory workshops and elaboration of
realistic policy scenarios to be tested by Agent-based Modelling techniques

</td>  
<td>

UDC

</td>  
<td>

Adina Dumitru/Amparo Alonso

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

T 5.6 Refinement phase: Analysis of energy future scenarios and transforming
them into strategic interventions

</td>  
<td>

UOT

</td>  
<td>

IT analyst Oana Velcota, IT&C Department,

UVT

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

T 5.7 Elaboration of policy recommendations and guidelines for the
implementation and assessment of new locally embedded low carbon policies

</td>  
<td>

UDC

</td>  
<td>

Adina Dumitru/Amparo Alonso

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

T 6.1 Identifying stakeholders and subpopulations and their social networks

</td>  
<td>

UOT

</td>  
<td>

IT analyst Oana Velcota, IT&C Department,

UVT

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

T 6.2 Identifying drivers of and barriers towards social innovation

</td>  
<td>

UOT

</td>  
<td>

IT analyst Oana Velcota, IT&C Department,

UVT

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

T 6.3 Analysing decision-making mechanisms of actors

</td>  
<td>

NTNU

</td>  
<td>

Norwegian Centre for Research Data (NSD)

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

T 6.4 Integrating data and providing the input for

ABM in WP7

</td>  
<td>

UOT

</td>  
<td>

IT analyst Oana Velcota, IT&C Department,

UVT

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

6.5 Scrutinizing the results of the simulations for theory development

</td>  
<td>

NTNU

</td>  
<td>

Norwegian Centre for Research Data (NSD)

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

T 6.6 Energy equality through social innovation

</td>  
<td>

UOT

</td>  
<td>

IT analyst Oana Velcota, IT&C Department,

UVT

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

T 7.4 Parameterising selected cases in model

</td>  
<td>

UG

</td>  
<td>

Wander Jager

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

T 7.5 Experimentation with simulated scenarios in selected cases

</td>  
<td>

ICLEI

</td>  
<td>

Wolfgang Teubner

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

T 8.1 Developing and implementing a communication and dissemination strategy

</td>  
<td>

ICLEI

</td>  
<td>

Wolfgang Teubner

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

T 8.3 Follower city network

</td>  
<td>

ICLEI

</td>  
<td>

Wolfgang Teubner

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

T 8.4 Project video blog

</td>  
<td>

ICLEI

</td>  
<td>

Wolfgang Teubner

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

T 8.5 Developing and programming policy sandbox

tool

</td>  
<td>

ICLEI

</td>  
<td>

Wolfgang Teubner

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

_T 9. Compliance with ethics requirements_

</td>  
<td>

_NTNU_

</td>  
<td>

_Norwegian Centre for Research Data (NSD)_

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

######  Informed consent procedures

Before participation of an online survey, panel members will be invited to
participate in the survey via e-mail. In the e-mail the information from the
informed consent form will be presented and a link to the survey will be
included. The participants will be instructed that by clicking the link they
consent to participate in the study as described in the information included
in the mail. A contact (telephone and e-mail) will be given where they can
request to be informed about all data that is stored about them in the
project. From the point of anonymization the data no longer includes personal
information.

To the extent that interviews are conducted by project researchers,
respondents will be informed prior to being interviewed and are asked for
their consent. The principles of written informed consent will be applied.
Their participation in research activities (e.g., interviews, focus groups) is
entirely voluntary. They may give notice of their withdrawal from research
activities at any time. Participants are also informed that they can retract
their consent until the data is anonymized without any disadvantages and
without having to give a reason

######  Informed consent forms and information sheet

The information sheets and consent forms will be based on the standard form
provided by the Norwegian Centre for Research Data (NSD), which is the
Norwegian organization acting as Data Protection Office in regard to GDPR for
social science research organizations, and will be in line with national
regulations. Future updates of the DMP will include a documentation of all
local consent forms/information sheets used in SMARTEES (see _**Appendix II**
_ )

###  Data collection and Processing

There are three sub-points related to Data collection and Processing

  1. Copies of opinion or confirmation by the competent Institutional Data Protection Officer and/or authorization or notification by the National Data Protection Authority must be submitted (which ever applies according to the GDPR and the national law). 

  

  2. If the position of a Data Protection Officer is established, their opinion/confirmation that all data collection and processing will be carried according to EU and national legislation, should be submitted. 

  

  3. Detailed information must be provided on the procedures that will be implemented for data collection, storage, protection, retention and destruction and confirmation that they comply with national and EU legislation. 

  

  

######  Copies of opinion (sub-points 1 and 2)

All partners that collect and process data have confirmed they will do so
according to the GDPR and the national law. All confirmations by the
institutional or national data protection officers regarding the conduction of
data collections in SMARTEES in accordance with EU and national legislation
will be collected in _**Appendix III** _ of the DMP as soon as they are
received. They will be provided at the earliest possible time point prior to
starting the data collections.

**Information on the procedures for data collection, storage, protection,
retention and destruction.**

This document (D1.2), The Work Plan (D1.1) as well as the Project Handbook
(D1.3) provides this information. All procedures are in accordance the
principles of the “Guidelines on Data Management in Horizon 2020”.

######  Further data collection and procession standards

Procedures in this section, on SMARTEES data collection, storage, protection,
retention and destruction, are based on the “Guidelines on Data Management in
Horizon 2020”.

The data collection, storage, protection, retention and destruction will be
conducted according to National and EU legislation (GDPR), as all partners
dealing with data have declared. All partners have identified a Data
Protection Officer (see table above). SMARTEES includes different forms of
data collection (primary data survey based on panels by professional survey
providers, surveys with specifically recruited participants, interviews, focus
groups, on site observations, workshops, discussions, and secondary data
sources). These datasets need in some cases to be linked by a pseudonymized
key table to be able to identify the same unit of analysis (e.g. a respondent
or a household). The main principles for all types of data collections is to
pseudonymize the datasets at the earliest possible point in the process by
separating the identifying information from the rest of the data and store the
identifying information in a key table (the pseudonyms) separately from the
rest of the data. This key table will be stored at the level of the research
partner responsible for supporting the respective case. The key tables will
not be shared with other partners in the project or externals. This personal
data will be stored under strictest precautions using for example a service
for storing sensitive data such as provided by the University of Oslo  2  or
an equivalent service being established by NTNU at the moment, which is
approved for storing sensitive data. It will under no circumstances be made
public or shared with the other project partners. The conducting party will
ensure that data collection and pseudonymization will comply with EU
legislation and the SMARTEES project handbook. Furthermore, data produced in
one WP will always be pseudonymized before it is shared with the other WPs.
Consent is sought before storing collected material in electronic form. Data
collection, processing and storage will comply with the GDPR and national
rules of each of the other countries where interviews or surveys are to take
place. At the end of the project, the key tables will be deleted and the
pseudonymized data will be fully anonymized by deleting the pseudonyms and
indirectly identifying information. The latter will be done by checking if
combinations of for example sociodemographic information leads to groups with
unique combinations smaller than three units (e.g., respondents or
households), in which case sociodemographic data will be aggregated to larger
categories until no group with less than three units exists.

In more detail, the main quantitative survey data will be pseudonymized when
it is received from the survey companies by SMARTEES. SMARTEES will only
collaborate with survey companies which can document the clearance by all
national and EU legislative bodies relevant for the study and their procedures
for the GDPR. The contracts with the survey companies will specify the consent
procedures (only panel members who have given their consent will be recruited,
it will be explicitly informed that clicking the link to the online survey
equals consent to participate, this consent can be retracted at any point
until the data is anonymized). The data set will be pseudonymized by removing
all directly or indirectly identifying information and storing it in a key
table. After the end of the project, the survey company will delete the key
table.

All informants and subjects in interviews, focus groups and workshops will
give a written informed consent to their participation, either by receiving
and signing a _consent and information_ letter or email, ensuring they
understand what the consent is concerning and what the consequences of
participation will be. This will explicitly include information about which
secondary data is processed about the same informant and linked to the answers
in the surveys or interviews and how the procedures for pseudonymizing the
data are. Identifying information will be stored separated from the study
data. After the project has ended, the pseudonyms and the key tables will be
deleted, thus fully anonymizing the data. Video and audio material from
research interview sessions will be deleted after transcription and quality
control. Besides those new data collection activities, SMARTEES will also
reanalyse existing data sources, such as datasets provided by the case cluster
cities. Conditions for use of this data in relation to GDPR will be fixed in
use licences or contracts (WP1 coordinates this work), also regulating how the
secondary data and the primary datasets will be linked through key tables.

All raw data will be stored and protected on SMARTEES encrypted servers for
secure data storage that meet the Norwegian Act relating to Personal Data
Filing Systems. Personal data directly linking person and data will always be
kept separate from the datasets. Identifying personal data will be retained
for a maximum of 1 year after collection completion to allow for thorough
quality control. All such data will thus be deleted by January 2021 the
latest.  
  
<table>  
<tr>  
<th>

**Procedures for data protection**

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

  * The project will follow the principle of “data minimization” by restricting acquisition of primary or secondary data to the absolute necessary for the project 

  

  * Data will be pseudonymized at the earliest possible point in time where linking of datasets is necessary, otherwise data will be anonymized as early as possible. 

  

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



</td>  
<td>

Pseudonymization will be conducted by the partners responsible for a
respective case by separating identifying information from the rest of the
data and storing it in a file, separated from the other data. Anonymization
will also be conducted by the same partners, but here no identifying
information will be retained.

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



</td>  
<td>

Personal data in key tables will be stored under strictest precautions and
under no circumstances made public

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



</td>  
<td>

Data will always be pseudonymized before it is shared with the other partners

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



</td>  
<td>

Consent is sought before storing collected material in electronic form

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



</td>  
<td>

When the data collected through interviews generates personal information,
this data will be pseudonymized during transcription

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



</td>  
<td>

Any text produced based on research results will respect the principle of
privacy and anonymity, not identifying any informants directly or indirectly
without their explicit consent

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



</td>  
<td>

All secondary data sources shall contain already pseudonymized datasets where
linking them with other data is necessary, anonymized secondary dataset will
the acquired wherever possible.

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



</td>  
<td>

Licences and contracts for use of secondary data will be made, specifying
their use in relation to the GDPR

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



</td>  
<td>

Activities carried out outside the EU will be executed in compliance with the
legal obligations in the country where they are carried out and the EU

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



</td>  
<td>

The activities must also be allowed in at least one EU Member State

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



</td>  
<td>

All data transferred between project partners (within or outside the EU) will
be restricted to pseudonymized or anonymized data and transfer will only be
made in encrypted form via secured channels

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



</td>  
<td>

At the end of the project, all data will be anonymized before making it
available for open access. The pseudonyms and according key tables will be
deleted at the end of the project.

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



</td>  
<td>

Since Agent Based Models often are geographically explicit, the visual
representation of the data will be provided in a form that does not allow
identifying individual analysis units (e.g., households), for example by
dividing the region of analysis in sections which contain at least three
response units and placing these units randomly in the section in the visual
output.

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

###  Involvement of non-EU countries

SMARTEES’ non-EU partner (NTNU - the coordinator) has confirmed that the
ethical standards and guidelines of Horizon2020 will be rigorously applied,
regardless of the country in which the research is carried out. Activities
carried out outside the EU will be executed in compliance with the legal
obligations in the country where they are carried out, with an extra condition
that the activities must also be allowed in at least one EU Member State.

In SMARTEES data will be transferred between the named non-EU country (Norway)
and countries in the European Union to allow for joined analyses and storage
of all data in the common database. All data transferred between project
partners (within or outside the EU) will be restricted to pseudonymized or
anonymized data and transfer will only be made in encrypted form via secured
channels.

Two partners (JH and ACC) are located in the UK, which at the time of start of
the project is an EU country. Should this status change throughout the project
period, the same rules as for the Norwegian partner will apply.

###  Survey questionnaire for each case cluster

The SMARTEES survey effort is coordinated and administrated in WP4, while the
process with which the theoretical framework in all data collection
activities, scientific design of the questionnaires survey (i.e., questions
and items to be considered) and structural requirements of the data collection
are defined in WP2, WP3 and WP7. First, a relatively short core questionnaire
will be developed that will allow a level of comparability across all case
studies surveyed. Later, tailored questionnaires for each case cluster,
whereby partners can contribute to a battery of measures and items, will be
developed by the lead partner for each case study cluster, who will work with
the overall coordinating partner for the survey questionnaire in developing
the sampling details, the questionnaire tool, and any data management issues
associated with each case. Data collection in each case study in a subtask for
each case will be led by the case responsible partner. The survey work will be
subcontracted to a professional survey company (including participant
recruiting, printing, postage, data processing), using the briefing document
that is developed for each case study cluster. A professional company
subcontracted to undertake the empirical survey work is needed to a) translate
the questionnaire into local languages, b) program the questionnaire in an
adequate online-survey tool if necessary, c) recruit survey participants
(according to the selection criteria defined by the consortium) and d) send
out the questionnaire to participants. Emphasis is put on hiring companies
that specialize in the above-mentioned tasks and that have a proven record of
accomplishment of multi-national efforts to ensure the highest quality data
material. We will collect several quotes for the SMARTEES survey service, and
choose the one offering the best value for money.

Companies in question must document their routines and procedures for data
management, sensitive information and personal data – and thus prove that all
data collection services and processing will be carried out according to the
EU GDPR and national legislation. The chosen company will transfer
pseudonymized data to the SMARTEES project server through a secure file
transferring system.

##  Data collection procedures

In order to achieve quality assurance, quality control and consistency
throughout the project; specific data collection procedures will be added to
the DMP as they are developed by the involved partners ahead of the different
data collections. All procedures will be developed to meet general scientific
quality criteria for data collections  3  as indicated in the following table:  
  
<table>  
<tr>  
<th>

**Quality standards**

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

**Accuracy**

Is the data collected correct and complete? Are the data entry procedures
reliable?

**Efficiency**

Are the resources used to collect data the most economical available to
achieve those objectives?

**Effectiveness**

Have the objectives been achieved?

Have the specific results planned been achieved?

**Feasibility and timeliness**

Can data be collected and analysed cost effectively? Can it provide current
information in a timely manner?

**Relevance**

What is the relevance of the data/information/evidence for primary
stakeholders?

Is data collection compatible with other efforts? Does it complement,
duplicate or compete?

**Security**

Is the confidentiality ensured?

**Utility**

Does data provide the right information to answer the questions posed?

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

###  Document and record study

The document and record (data) study procedure will be developed ahead of the
data collection and documented in an updated DMP. The first steps in this
respect are identifying the need for data in the different cases and the
availability of secondary data.

###  Individual in-depth interviews

Interview data procedure will be developed ahead of the data collection and
documented in an updated DMP.

###  Case study observations

Case study procedure will be developed ahead of the data collection and
documented in an updated DMP.

###  Questionnaire surveys

The questionnaire survey procedures will be developed ahead of the data
collection and documented in an updated DMP.

###  Focus group interviews

Focus group data collection procedure will be developed ahead of the data
collection and documented in an updated DMP.

###  Discussions

Discussion event data collection procedure will be developed ahead of the data
collection and documented in an updated DMP.

###  Workshops

Workshop data collection procedure will be developed ahead of the data
collection and documented in an updated DMP.

##  Data documentation

All collected data shall include a metafile when stored on the SMARTEES secure
storage solution and/or the SMARTEES SharePoint server. The file will later be
made available for external users of the data. This metafile shall explain the
kind of data included, involved personnel, date and duration of the data
collection, variable names/labels, recruiting procedures, response rates,
whether or not it is pseudonymized or anonymized, related WPs and tasks, and
finally a summary. _**Appendix IV** _ provides two templates for qualitative
and quantitative data sets that will be adapted during the course of SMARTEES,
especially after the initial activities in WP 4, responsible for the curation
of datasets and providing their metadata. The templates might be adapted
according to the needs identified in WP4.

##  Data storage and curation

All personal data will be stored and protected on SMARTEES encrypted server
space for secure data storage, described in 4.4.1. Pseudonymized and
anonymized data will be stored in the SMARTEES SharePoint solution described
in 4.4.2 in encrypted form (see 4.4.3). WP1 (NTNU) and WP4 (JH) are
responsible for the curation of all data collected in SMARTEES and its safe
storage. The storage solutions for personal raw-data, pseudonymized and
anonymized data include daily backup routines to prevent data loss. All data
files will be assigned a persistent and unique Digital Object Identifier (DOI)
by the end of the project through services such as Figshare, Zenodo or Dryad.

###  Personal raw-data and pseudonym key tables

The non-anonymized raw-data and the key tables for pseudonymized data will be
stored on secure server solutions, such as the one hosted and operated by the
University of Oslo (UiO) and their _Services for sensitive data_ (TSD)  (
_https://www.uio.no/english/services/it/research/storage/sensitive-data/_ )  ,
which complies with the Norwegian regulation regarding individual privacy.
This secure storage solution is NTNUs standard for sensitive data (NTNU is at
the moment establishing a similar internal service, so it might be, SMARTEES
will use NTNU’s own service instead). Each contact partner for the cases is
responsible for storing these data and pseudonymizing the datasets for the
work in SMARTEES. SMARTEES will establish these server spaces as soon as first
data is produced. Our server solutions will comply with the regulations set by
the TSD. Backup is performed through UiO`s regular backup system with the
addition of encryption. The encryption key is only available on the dedicated
terminal server with a copy stored in safes on two separate locations. Data
transfers (import/export) to and from the services is handled by a special
purpose file staging service and the project administrator controls access
rights for all project members. By default, all project members are able to
transfer data in, but only the project administrator can do a data transfer
out. For security reasons the TSD infrastructure is accessible only with a
2-factor login, i.e. the username, password and electronically generated
secure code (like in internet banking). Connecting to the system is first done
by accessing a login server via an encrypted SSH tunnel. From the login server
users will connect to project VMs via PCoIP (Windows)/Thinlinc (Linux). The
login procedure requires a one-time password that you get from a
smartphone/yubikey. User guides are available at the TSD homepage  4  . The
folder structure on the server will be based on the SMARTEES case structure,
and separate folders for each case and WP. Datasets that belong to multiple
WPs will be stored in sub-folders of WP1 for the cases.

###  Pseudonymized data

In cases where the research questions of SMARTEES make it necessary to link
several datasets (e.g., primary survey data and secondary data such as energy
consumption data), this linking of datasets will be conducted through
pseudonyms (e.g., a randomly generated participant code). A table matching the
pseudonyms with identifying information (e.g., names or e-mail addresses) will
be produced by the SMARTEES partner responsible for the respective case. All
identifying information will be replaced in the dataset by the pseudonyms. The
key table will be stored separately from the dataset on the secure server
solution as described under 4.4.1. At the end of the project, the key tables
will be deleted and the data fully anonymized.

Pseudonymized data will be stored at the SMARTEES SharePoint solution in
encrypted and password-protected form (see Section 4.4.4). SMARTEES’ partners
have access to this solution through personal logins provided by NTNU. The
overall folder structure is based on the SMARTEES WP and case structure; each
WP folder includes a data sub-folder and these will include folders for the
specific kinds of data produced.

###  Anonymized data

All data collection and processing that will be done during SMARTEES will be
carried out according to national legislation and the EU General Data
Protection Regulation (Regulation (EU) 2016/679). The consortium and the
partners are responsible for following the ethical procedures in their
respective countries (see Section 4.1).

In cases where linking of datasets is not necessary, the data will be
anonymized at the earliest possible point in time. This will be done by
deleting directly identifying information from the file and aggregating
indirectly identifying information such as sociodemographic data to a level,
where no unique combination of such data identifies groups of less than three
respondents. At the end of the project, all pseudonymized data will be
anonymized and the key tables will be deleted.

Anonymized data will be stored at the SMARTEES SharePoint solution in
encrypted and password-protected form

(see Section 4.4.4). SMARTEES’ partners have access to this solution through
personal logins provided by NTNU.

The overall folder structure is based on the SMARTEES WP and case structure;
each WP folder includes a data sub-folder and these will include folders for
the specific kinds of data produced.

###  Encryption standards and procedures

All data files will be transferred via secure connections and in encrypted and
password-protected form (for example with the open source 7-zip tool providing
full AES-256 encryption:  _http://www.7-zip.org/_ _or the encryption options_
_implemented in MS Windows_ ). Passwords will not be exchanged via e-mail but
in personal communication between the partners. The encryption solutions will
be chosen in accordance with the SMARTEES partners’ IT supports.

###  File name standards

To ensure that data files as well as any other file in SMARTEES has a clear
name identifying the content, the following file name standards are used. All
documents shall be numbered by their type of document, and the assigned
subsequent numbering within each WP (first deliverable of WP1: **D1.1,** first
deliverable of WP 2: **D2.1** ).

**XXX** : Identifies which main category the document belongs to. In order to
always easily identify the files, the project name **SMARTEES-** shall be
included as a prefix to all document categories.

**YYY** : Will always be a number assigned subsequently for each new doc in
the XXX category and WP. **ZZZ** : Issue number  
  
<table>  
<tr>  
<th>

**XXX**

</th>  
<th>

**XXX explanation**

</th>  
<th>

**YYY**

</th>  
<th>

**ZZZ**

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

D

</td>  
<td>

Deliverable

</td>  
<td>

1.1, 1.2, 2.1, 2.2 etc.

</td>  
<td>

1,2,3,etc.

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

MAN

</td>  
<td>

Management

</td>  
<td>

1.1, 1.2, 2.1, 2.2 etc.

</td>  
<td>

1,2,3,etc

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

DAT

</td>  
<td>

Data files

</td>  
<td>

1.1, 1.2, 2.1, 2.2 etc.

</td>  
<td>

1,2,3,etc

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

DOC

</td>  
<td>

Data documentation

file

</td>  
<td>

1.1, 1.2, 2.1, 2.2 etc.

</td>  
<td>

1,2,3,etc

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

NOT

</td>  
<td>

Notes

</td>  
<td>

1.1, 1.2, 2.1, 2.2 etc.

</td>  
<td>

1,2,3,etc

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

MOM

</td>  
<td>

Minutes of meeting

</td>  
<td>

1.1, 1.2, 2.1, 2.2 etc.

</td>  
<td>

1,2,3,etc

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

PRE

</td>  
<td>

Presentations

</td>  
<td>

1.1, 1.2, 2.1, 2.2 etc.

</td>  
<td>

1,2,3,etc

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

PAP

</td>  
<td>

Journal paper manuscript

</td>  
<td>

1.1, 1.2, 2.1, 2.2 etc.

</td>  
<td>

1,2,3,etc

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

The file-name shall always consist of: document number, document title and
issue (in this order). Underscore shall be used between document number, issue
number and document title. There shall be no open spaces in the document
title. Logical short versions of words can be used in the document title part
of the filename in order to shorten the filename. If the document is a draft
version, this is indicated by “DR” after Issue# and underscore.

Example (this document, first issue): **SMARTEES-D1.2_DMP_1**

##  Open access to SMARTEES Data

An open access database (e.g., Zenodo or EUDAT) will be used to grant access
to anonymized quantitative and qualitative data after the project is ended.
Data based on the empirical results from this project will be stored in this
database (e.g., data and information collection via a web-based survey(s),
workshops, interview, site visits etc.). The researchers have a duty of
transparency to fully inform how the data will be used and to what purpose the
data is for. Thus, the ethically compliant data collection will be guided by
proportionality and follow the legal safeguards (described in WP9) to minimize
any risks related to unauthorized release of personal and private information.
The empirical work of WP3 and WP4 will be coordinated (for data management
issues) by WP1 and then transferred into the database created by WP4
(qualitative and quantitative interview data). Furthermore, SMARTEES will in
WP8 provide a policy sandbox tool utilizing the data curated and collected in
SMARTEES. Secondary data will only be made available to open access if the
licensing with the owners of the data allows for that.

##  Deletion of data

Identifying personal data and key tables will be retained for a maximum of 1
year after collection completion to allow for thorough quality control. All
such data will thus be deleted by January 2021 the latest. At this point all
data will be anonymized. Anonymized data will not be deleted but stored and
made available for future use through the Open Data Pilot.

##  Dissemination and exploitation of the data

Although the dissemination and exploitation strategy of the project is yet
under development and will be published through deliverables D8.1, D8.4 and
D8.5, different type of data or data packages are likely to be used for
dissemination and exploitation purposes:

  * _The policy sandbox IT tool_ : a policy sandbox allowing a realistic prospective analysis of existing and future policy and market incentive scenarios with users on the case level and in policy-makers workshops on the European level is one of the major achievements in SMARTEES. This is an IT-based tool to allow policymakers to test policy scenarios on the simulated populations in the SMARTEES agent-based models. The tool will be provided in two versions: A light version (SMARTEES toolbox light), which will be available on the internet to allow manipulation of predefined scenarios based on the work done in WP5, WP6 and WP7, exploring the boundary conditions of these scenarios. A more powerful professional version of the tool allowing for more fundamental variations in the policy scenarios will be programmed as a tool that is provided in a package together with a scenario analysis workshop (SMARTEES toolbox pro). 

  

  * _Data in a public database (see Section 4.5)_ : the data in the public database is expected to be one of the main exploitable results of the SMARTEES project. The selected database will be open source and operate under the regulations of the pilot on open research data in H2020. This open access character’s compatibility with SMARTEES’ exploitation strategy will be analysed during the project (WP8) and restrictions to open access use will be kept at a minimum and only implemented if strong exploitation benefits stand against or is restricted by licences on secondary data. In any case, the procedures for data management set in this document, particularly regarding WP4, will be followed and respected. 

  

##  Visual representation of data

Whenever relevant for research questions to represent the “neighbourhoods”
present in cities, SMARTEES will make use of available GIS data. In
computational models of the cities, the GIS data may be linked to other
variables. For example, represented clusters of households (i.e. city
neighbourhoods) may be assigned values of other relevant variables (e.g. mean
energy use per month). This will be done by using central tendency measures
for aggregations of units, so that at no stage individual household data, or
data concerning geographical locations of individual respondents is made
public.

##  Open data pilot

SMARTEES provides access to all primary data collected and secondary data
aggregations where publication of the data does not collide with copyrights of
the initial data providers. This is in line with the H2020 open data pilot
openaire (se  e _https://www.openaire.eu/opendatapilot_ )  . Data will be made
available as soon as SMARTEES primary research and publication interests are
fulfilled. No embargo period is implemented once the SMARTEES publications are
finished and no restrictions are foreseen to be put on the re-use of the data
at this point. WP1 and WP4 are responsible for providing open access to the
data.

###  General principles

All data in SMARTEES shall be open access if no other important principles
stand against it (such as for example restrictions on secondary data). In this
respect, the Consortium Agreement and the Grant Agreement are binding,
especially Section 9 (“Access rights”) and Section 10 (“Non-disclosure of
information”) of the Consortium Agreement are relevant for determining
potential need for access restriction to SMARTEES data.

###  Size of the data

The size of the data files is not determined at this point of the SMARTEES
project yet. The questionnaire surveys are expected to have at least 5000
participants in total. A single focus group interview will typically have
between 6 and 12 participants. This section will be updated as soon as more
information is available.

###  Target group for the data use

The data provided in SMARTEES will be of interest for policy makers,
businesses in the energy sector, stakeholder groups and other researchers.
They will be documented and presented in a way that makes them accessible for
non-scientists.

###  Access procedures

The data made available through the open data pilot will be fully accessible
without any restrictions (if not exploitation benefits require an embargo
period, see Section 4.7).

###  Documentation procedures

All data files provided by SMARTEES include a documentation of the content of
the data file and the context the data was collected in (see Section 4.2.2).
It is important to ensure usefulness of the data for researchers and analysist
not foreseen in the data collection. The documentation procedures will be
constantly updated during the SMARTEES project.

###  Securing interoperability

For social science data it is essential to document the use and source of
theoretical concepts leading to data collections to ensure interoperability
across different user groups. SMARTEES will create a glossary defining key
terms and concepts used in the project and an ontology (WP2). This glossary
and the theoretical work conducted on WP2 will be part of the data
documentation. Furthermore, sources for theoretical concepts and variables
measures will be documented to ensure comparability with previous and future
use. For quantitative data, the psychometric performance of the variables will
be documented. The use of theoretical concepts will be standardized within
SMARTEES in WP2 and with previous use of the variables and concepts wherever
possible (there might be a number of newly developed concepts in SMARTEES,
which cannot be aligned with previous work).

###  Search keywords and data identification

Each data set will be assigned a unique and persistent Digital Object
Identifier (DOI) to make it identifiable when stored in a data repository.
Each fill will be tagged with keywords for search purposes: SMARTEES is always
a keyword, furthermore, keywords describing the type of the data (e.g.,
“interview”, “survey”), the participants (“representative sample”), the type
of topics included (e.g., “energy poverty”, “consumer empowerment”).

###  File types

Each data file in SMARTEES will be made available with an accompanying
documentation of its content (see Section 4.2.2). Qualitative data such as
interview transcripts will be made available in its entirety in the form of
text documents (e.g. in PDF, TXT, RTF or DOCX format) in their original
language. In addition, excerpts of transcripts and other qualitative data will
be made available in English. Quantitative data will be made available in
standard data formats for popular statistical program packages to make reuse
as easy as possible (e.g. CSV, SAV, or R with popular character encoding such
as ASCII or UTF-8 without BOM).

###  Curation of the data after the end of SMARTEES

All data in SMARTEES will be transferred into a data repository that is the
state of the art.

#  APPENDIX I: DETAILED DATA COLLECTION RESPONSIBILITIES

The following table presents a complete summary of all data collections in
SMARTEES with the responsibilities indicated. The table will be constantly
updated during the project as soon as data collections are started.  
  
<table>  
<tr>  
<th>

ID

</th>  
<th>

DATA

COLLECTION

</th>  
<th>

SOURCE

</th>  
<th>

DATA

</th>  
<th>

WHEN

</th>  
<th>

FORMAT

</th>  
<th>

DATA FLOW

</th>  
<th>

RESPONSIBLE FOR

DATA PRODUCTION

& MONITORING

</th>  
<th>

RESPONSIBLE FOR

DATA PREPARATION

(TRANSCRIPTION,

DATA CLEANING)

</th>  
<th>

RESPONSIBLE FOR

STORAGE /

PSEUDONYMIZATION

</th>  
<th>

RESPONSIBLE

FOR TRANSFER

TO LONG-TERM

STORAGE

</th>  
<th>

DATA TO BE

USED BY

</th>  
<th>

WP/TASK

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

ID1

</td>  
<td>

Documents and records

</td>  
<td>

Documents published by relevant stakeholders

</td>  
<td>

Existing data and records on relevant case study clusters

</td>  
<td>

M3-M10

</td>  
<td>

Text file, numerical data

</td>  
<td>

WP3 identify documents/records. WP4 supplement these existing data with new
data and integrate them. WP5 will define future policy scenarios based in
insight from WP4. WP6 analyses the data provided by WP4, and provides input to
WP7.

Further utilities of the data will be carried out in WP8.

</td>  
<td>

WPs 1, 3 and 4

</td>  
<td>

WPs 3 and 4

</td>  
<td>

WPs 1, 3 and 4

</td>  
<td>

WPs 1, 3 and 4

</td>  
<td>

WPs 4-8

</td>  
<td>

1.3, 2.3, WP3,

4.1, 4.2, 4.9,

WP5, 6.1-6.3,

WPs 7-8

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

ID2

</td>  
<td>

Interviews

</td>  
<td>

Stakeholders interviews

</td>  
<td>

Responses of key informants and stakeholders

</td>  
<td>

M1-M10

</td>  
<td>

Text file

</td>  
<td>

WP4 will develop interview protocol. Interview with relevant stakeholders will
be conducted as part of WP3. Data are exploited in WPs 4, 5, 6 and 7 (analyses
and synthesis), and further utilized in WP8 as part of the dissemination
efforts.

</td>  
<td>

WPs 1 and 3

</td>  
<td>

WP 3

</td>  
<td>

WPs 1, 3 and 4

</td>  
<td>

WPs 1, 3 and 4

</td>  
<td>

WPs 4-8

</td>  
<td>

1.3, 2.3, WP3,

4.4, WP5, 6.1-

6.3, WPs 7-8

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

ID3

</td>  
<td>

“On site” visits and observations

</td>  
<td>

Observations, interviews

</td>  
<td>

Observations, interviews with relevant stakeholders at the site visited

</td>  
<td>

M1-M10

</td>  
<td>

Text file

</td>  
<td>

"On site” visits and observations are carried out as part of the task in WP3.
Data are exploited in WPs 4, 5, 6 and 7 (analyses and synthesis), and further
utilized in WP8 as part of the dissemination efforts.

</td>  
<td>

WPs 1 and 3

</td>  
<td>

WP 3

</td>  
<td>

WPs 1, 3 and 4

</td>  
<td>

WPs 1, 3 and 4

</td>  
<td>

WPs 4-8

</td>  
<td>

1.3, 2.3, WP3,

4.4, WP5, 6.1-

6.3, WPs 7-8

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

ID4

</td>  
<td>

Questionnaire surveys

</td>  
<td>

Questionnaire

</td>  
<td>

Participants’ responses to questionnaire(s)

</td>  
<td>

M7-M15

</td>  
<td>

Numerical data

</td>  
<td>

WP3 provides input to the designing of questionnaire in WP4. Data collection
is performed by hired data collection company(s) under the supervision of WP 1
and 4. Data are exploited in WPs 4, 5, 6 and 7 (analyses and synthesis), and
further utilized in WP8 as part of the dissemination efforts.

</td>  
<td>

WPs 1 and 4

</td>  
<td>

WP4

</td>  
<td>

WPs 1 and 4

</td>  
<td>

WPs 1 and 4

</td>  
<td>

WPs 4-8

</td>  
<td>

1.3, 2.3, 3.4,

WPs 4-5, 6.1-6.3,

WPs 7-8

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

ID5

</td>  
<td>

Focus groups

</td>  
<td>

Focus group discussions

</td>  
<td>

Responses of focus group participants

</td>  
<td>

M1-M10

</td>  
<td>

Text file

</td>  
<td>

WP4 will develop interview protocol. Focus group interview with relevant
stakeholders will be conducted as part of WP3. Data are exploited in WPs 4, 5,
6 and 7 (analyses and synthesis), and further utilized in WP8 as part of the
dissemination efforts.

</td>  
<td>

WPs 1 and 3

</td>  
<td>

WP 3

</td>  
<td>

WPs 1, 3 and 4

</td>  
<td>

WPs 1, 3 and 4

</td>  
<td>

WPs 4-8

</td>  
<td>

1.3, 2.3, WP3,

4.4, WP5, 6.1-

6.3, WPs 7-8

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

ID6

</td>  
<td>

Discussions of events

</td>  
<td>

Discussion with stakeholders

</td>  
<td>

Responses of stakeholder/ participants during discussion

of events

</td>  
<td>

M3-M36

</td>  
<td>

Text file

</td>  
<td>

WP4 will arrange a series of discussions with project partners, case-study
teams and agent-based modelling teams about requirements for primary data
collection. WP5 will engage a sample of citizens, consumers, social and
business actors to discuss future energy policy scenarios. Data are exploited
in WPs 3, 4, 5, 6 and 7 (analyses and synthesis), and further utilized in WP8
as part of the dissemination efforts.

</td>  
<td>

WPs 1, 4 and 5

</td>  
<td>

WPs 4 and 5

</td>  
<td>

WPs 1, 4 and 5

</td>  
<td>

WPs 1, 4 and 5

</td>  
<td>

WPs 3-8

</td>  
<td>

1.3, 2.3, WPs 4-5

, 6.1-6.3, WPs 7-

8

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

ID7

</td>  
<td>

Workshops

</td>  
<td>

Workshop discussions

</td>  
<td>

Responses of workshop

participants

</td>  
<td>

M5-M36

</td>  
<td>

Text file

</td>  
<td>

Data on views of relevant experts and stakeholders are collected during three
interdisciplinary research workshops on theory and method (organized by
EIJKU), a data analysis workshop (organized by JH), a series of multi-
stakeholders deliberative workshops on future policy scenarios (organized by
UOT & UDC), Data are exploited in WPs3, 4, 5, 6 and 7 (analyses and
synthesis), and further utilized in WP8 as part of the dissemination efforts.

</td>  
<td>

WPs 1, 2, 4 and 5

</td>  
<td>

WPs 2, 4 and 5

</td>  
<td>

WPs 2, 4 and 5

</td>  
<td>

WPs 2, 4 and 5

</td>  
<td>

WPs 3-8

</td>  
<td>

1.3, WP2, 3.2,

WPs 4-5, 6.1-6.3,

WPs 7-8

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

**PROJECT NO. REPORT NO. VERSION**

Project No. 763912 SMARTEESD1.2 Data Management Plan-1.2 01 27 of 34

#  APPENDIX II: CONSENT FORMS

This appendix collects all consent forms and information sheets used in
SMARTEES. The first included document is the template for SMARTEES information
sheets based on the general template provided by the Norwegian Centre for
Research Data (NSD).

(1) Consent form template from NSD.

####  Request for participation in research project

_**[This template serves as an example of an information letter.** _

_**Clear text and insert your own.** _

_**NB - all information should be concise and easy to understand]** _

#####  "[Insert _project title_ ]"

**Background and Purpose**

[ **Describe** the purpose of the project and briefly sketch the main research
topics. Indicate whether the project is a master’s or Ph.D. project at the
institution, whether it is implemented as commissioned research, in
cooperation with other institutions, etc.]  
  
<table>  
<tr>  
<th>

[ **Describe** how the sample has been selected and/or why the person has been
requested to

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

participate.]

</td>  
<td>

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

**What does participation in the project imply?**

[ **Describe** the main features of the project: data collection that requires
active participation (surveys, interviews, observation, tests, etc.,
preferably describing approximate duration), and any collection of data about
the participant from other sources (registers, records, student files, other
informants, etc.). **Describe** the types of data to be collected (e.g.
"questions will concern..."), and the manner(s) in which data will be
collected (notes, audio/video recordings, etc.).

If parents are to give consent on behalf of their children, inform that they
can request to see the questionnaire/interview guide etc. If multiple sample
groups are to be included in the project, it must be explicitly indicated what
participation entails for each group, alternatively, a separate information
letter must be made for each group.]

**What will happen to the information about you?**

All personal data will be treated confidentially. [ **Describe** who will have
access to personal data (e.g. only the project group, student and supervisor,
data processor, etc.), and how personal data/recordings will be stored to
ensure confidentiality (e.g. if a list of names is stored separately from
other data).]

[ **Describe** whether participants will be recognizable in the publication or
not.]

The project is scheduled for completion by [ **insert** date]. [ **Describe**
what will happen to personal data and any recordings at that point. If the
data will not be made anonymous by  
  
<table>  
<tr>  
<th>

project completion: state the purpose of further storage/use, where data will
be stored, who

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

will have access, as well as the final date for anonymization (or information
about personal

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

data being stored indefinitely).]

</td>  
<td>

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

**Voluntary participation**

It is voluntary to participate in the project, and you can at any time choose
to withdraw your consent without stating any reason. If you decide to
withdraw, all your personal data will be made anonymous. [For patients and
others in dependent relationships, it must be stated that it will not affect
their relationships with clinicians or others, if they do not want to
participate in the project, or if they at a later point decide to withdraw.]

If you would like to participate or if you have any questions concerning the
project, please contact [ **Insert** name and telephone number of the project
leader. In student projects contact information of the supervisor should also
be inserted].

The study has been notified to the Data Protection Official for Research, NSD
- Norwegian Centre for Research Data.

####  Consent for participation in the study  
  
<table>  
<tr>  
<th>

**[Consent may be attained in writing or verbally. If consent is obtained in
writing from**

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

**the participant, you can use the formulation below. If parents/guardians are
to give**

</td>  
<td>

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

**consent on behalf of their children or others with reduced capacity to give
consent, the**

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

**consent form must be adapted, and the participant’s name should be
stated.]**

</td>  
<td>

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

I have received information about the project and am willing to participate

\-------------------------------------------------------------------------------------------------------------
(Signed by participant, date)

**[Checkboxes can be used (in addition to signature) if the project is
designed in such a way that the participant can choose to give consent to some
parts of the project without participating in all parts (e.g. questionnaire,
but not interview), or if information is to be obtained from other sources,
especially when the duty of confidentiality must be set aside in order for the
information about the participant to be disclosed._Examples: - I agree to
participate in the interview / - I agree that information about me may be
obtained from teacher/doctor/register - I agree that my personal information
may be published/saved after project completion]_ **

#  APPENDIX III: CONFIRMATIONS BY DATA PROTECTION OFFICERS

All confirmations regarding the conduction of data collection in accordance
with national and international law, especially the General Data Protection
Regulation (Regulation (EU) 2016/679) will be collected in this appendix as
soon as they are available.

#  APPENDIX IV: DATA DOCUMENTATION TEMPLATES

The following two templates shall be used to document the necessary background
of the data files for internal and external use in SMARTEES.

  1. Data documentation template for qualitative data in SMARTEES 

  

  2. Data documentation template for quantitative data in SMARTEES 

  

  

_Data documentation template for qualitative data in SMARTEES_

Name of the data set: ___________ Date the data set was finalized:
_________________

Date/time period the data was collected: ____________ to ____________.

Responsible partner for the collection of the data:

_________________________________ (name) ____________________________
(institution)

Data produced in WP: ____________ Task: ____________

Data pseudonymized on (date): _______________ by __________________________

Data anonymized on (date): _______________ by __________________________

_Information about the participants:_

Number: __________ Age: _________________ Sex: ___________________

Participants’ background: ____________________________________________________

Recruitment procedure: _____________________________________________________

Original language of the material:
_____________________________________________

Data collected by (interviewer):
_______________________________________________

Transcribed by: ___________________________________________________________

Transcription rules: ________________________________________________________

Translated to English by: ___________________________________________________

Ethically cleared by: _____________________________________ on (date):
_______________

Interview guidelines (or the like):
_______________________________________________________

Size of the data (e.g. number of words):
_________________________________________________

Short summary: ______________________________________________ _Data
documentation template for quantitative data in SMARTEES_

Name of the data set: ___________ Date the data set was finalized:
_________________

Date/time period the data was collected: ____________ to ____________.

Responsible partner for the collection of the data:

_________________________________ (name) ____________________________
(institution)

Data produced in WP: ____________ Task: ____________

Data pseudonymized on (date): _______________ by __________________________

Data anonymized on (date): _______________ by __________________________

_Information about the participants:_

Number: __________ Age: _________________ Sex: ___________________

Participants’ representative for which population:
__________________________________________

Recruitment procedure: _____________________________________________________

Response rate: ___________________________________________________________

Original language of the material:
_____________________________________________

Translated to English by: ___________________________________________________

Ethically cleared by: _____________________________________ on (date):
_______________

Variables in the dataset:  
  
<table>  
<tr>  
<th>

**Variable name**

</th>  
<th>

**Variable type**

</th>  
<th>

**Variable label**

</th>  
<th>

**Answering format / value labels**

</th>  
<th>

**Comments**

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

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

_Variable types:_

  * T = text 

  

  * D = date / time 

  

  * B = binary / dichotomous 

  

  * C = categorical 

  

  * O = ordered categorical / ordinal 

  

  * I = interval / ratio / Likert scales with 5 or more categories 

  

Short summary: ______________________________________________