#  1\. Introduction

The Data Management Plan (DMP) consists of a description of the data
management life cycle for the data to be produced, collected, and processed,
and will include information on the handling of data during and after the end
of the project, i.e. what data will be produced, collected, and processed,
which methodology and standards will be applied, whether data will be shared
and/or made open access, and how data will be curated and preserved (including
after the end of the project).

##  1.1. Scope, objectives and expected impact

The scope of this document is to provide the procedure to be adopted by the
project partners and subcontractors to produce, collect and process the
research data from the IRIS demonstration activities. The adopted procedure
follows the guidelines provided by the European Commission in the document
_Guidelines on FAIR Data Management in Horizon 2020_ .

This document has been built based on the Horizon 2020 FAIR DMP template
(Version: 26 July 2016), which actually provides a set of questions that the
partners should answer with a level of detail appropriate to the project. It
is not required to provide detailed answers to all the questions in this first
report on DMP. The DMP is intended to be a living document in which
information can be made available on a finer level of granularity through
updates as the implementation of the project progresses and when significant
changes occur. As a minimum, the DMP shall be updated in the context of the
periodic evaluation/assessment of the project.

This first report on DMP, submitted at M6 (31st March 2018), describes a
preliminary plan for data production, collection and processing, and will be
continuously updated until the end of the project, as part of WP9 activities.
Specifically, the DMP will be updated in M12 (D9.8: First update on the Data
management plan), in M30 (D9.9: Second update on the Data management plan), in
M42 (D9.10: Third update on the Data management plan), and in M60 (D9.11:
Fourth and final update on the Data management plan).

The availability and sharing of project data will raise the impact of IRIS
activities, allowing for access to a large number of stakeholders. The DMP
considers (see Figure 1):

  * Data Types, Formats, Standards and Capture Methods 

  

  * Ethics and Intellectual Property 

  

  * Access, Data Sharing and Reuse 

  

  * Resourcing 

  

  * Deposit and Long-Term Preservation 

  

  * Short-Term Storage and Data Management 

  

_Figure 1. Aspects considered in the data management plan._

##  1.2. Contributions of partners and relation to other activities

The main project partners in T9.2 are UU, RISE and CERTH. UU, as the leader in
T9.2, is responsible for coordinating the activities related to the definition
of the data model and the DMP for performance and impact measurement. RISE as
the WP9 leader ensures that all activities are in line with other related WPs
by establishing communication with the respective WP leaders. Part of this
work entails cooperation with ongoing projects, initiatives and communities in
WP2, such as the H2020-SCC CITYKEYS project for smart city performance
indicators, and facilitation for all performance data to be incorporated into
the database of the EU Smart City Innovation System (SCIS). Furthermore, RISE
as the leader in T9.1 ensures that all relevant data are addressed in D9.1,
based on the initial definition of the KPIs included in T9.1, as well as that
any new KPIs, being introduced if the need arises to modify them after review,
are addressed in D9.8 (First update on the DMP which is due to be published in
M12).

In Figure 2, the timeline for the DMP development within the IRIS project is
illustrated, pointing out interactions with other tasks and WPs. Next to the
D9.8 (First update on the DMP which is due to be published in M12), the DMP
will be further updated in M30 (D9.9: Second update on the Data management
plan), in M42 (D9.10: Third update on the Data management plan), and in M60
(D9.11: Fourth and final update on the Data management plan).

RISE will organise WP9 workshops in March and April 2018 in all the Lighthouse
Cities (LH), i.e. Gothenburg, Nice and Utrecht, to discuss LH solutions and
possible monitoring strategies for technologies, indicators and data
collection. Instead of directly supplying data collection sheets, all the LH
will be invited to provide input on relevant data to be collected, discuss the
purpose of utilisation of collected data and the project goals together with
IMCG representing WP3 Business models. These workshops will establish a
harmonised approach among the LH with respect to the DMP development and the
Pilot on Open Research Data  1  . CERTH as the leader in T9.3 ensures that the
development of the first report of the DMP and T9.2 activities are in line
with T9.3 activities and the development of the City Innovation Platform
(CIP), and thus connect WP9 with WP4 activities (including the linkage to
activities in T4.3 ‘Data Governance Plan’ which is meant to facilitate a
smooth, secure and reliable flow of data, including the description of
supporting processes and assets, and also addressing privacy and ethical
issues). The work in T9.2 will be performed in close and continuous
collaboration with WP 5-7 to ensure that the DMP addresses data and relevant
developments from the IRIS demonstration activities in the LH. Furthermore,
with respect to ethical aspects each LH and FC will have its own Ethics
Committee and one person will be nominated per site as responsible for
following the project’s recommendations and the National and European
legislations (See Section 6.1.2), thus linking WP9 to WP 5-7 and to WP8
(Replication by Lighthouse regions, Follower cities, European market uptake).
Finally, T9.2 will also ensure privacy and security of sensitive information,
for legal or ethical reasons, for issues pertaining to personal privacy, or
for proprietary concerns linking to WP3.

In the course of the project, the project partners will be guided by the T9.2
leader and the WP9 leader on how to provide input and report on data to be
generated/collected during the project by using the templates listed in this
first report on the DMP.

M6 M12

M18

M24 M30

M36

M42 M60

IRIS WP9 Workshops in

March -

April 2018 on

Monitoring Strategy

D9.9: Second

update on the DMP

D9.8: First update

on the DMP

D9.10: Third update

on the DMP

D9.11: Fourth and final

update on the DMP

T2.3 CITYKEYS and SCIS (M1-M60)

D9.1: First report

on the DMP

WP5 Utrecht Lighthouse City demonstration activities

WP7 Gothenburg Lighthouse City demonstration activities

WP6 Nice Lighthouse City demonstration activities

T9.1 Specification of

the monitoring and

evaluation

methodology and KPIs

definition (M1-M12)

T9.3 Establishment of a unified

framework for harmonized data

gathering, analysis and

reporting (M9-M24)

T9.2 Defining the data model and the data management plan for performance and
impact measurement (M4-M60)

_Figure 2. Timeline for the DMP development within the project duration,
indicating interactions with other work tasks and packages._

##  1.3. Structure of the deliverable

This document has been built based on the Horizon 2020 FAIR DMP template
(Version: 26 July 2016). Accordingly, the document is structured as follows:

  * **Section 2:** Data Summary 

  

  * **Section 3:** FAIR data 

  

  * **Section 4:** Allocation of resources 

  

  * **Section 5:** Data security 

  

  * **Section 6:** Ethical aspects 

  

  * **Section 7:** Other issues 

  

  * **Section 8:** Further support in developing your DMP 

  

#  2\. Data Summary

In Table 1, a summary is provided of the data to be generated/collected during
the project. This table includes standardised items and lists as described
below.

At this stage of the project it is still not possible to list in the exact
data that will be generated/collected during the project, since relevant
activities in T9.1 ‘Specification of the monitoring and evaluation methodology
and KPIs definition’ are running in parallel. A full overview of the data will
be possible after the completion of T9.1 and the submission of D9.2 ‘Report on
monitoring and evaluation schemes for integrated solutions’.

In **Column 1** ‘Title of data set’ please specify a name or a short title for
the corresponding data set. The name/title shall be self-explanatory regarding
the nature/purpose of the data set.

In **Column 2** ‘Relation to project objective’ select the objective of the
project (1-8) that relates to the purpose of the data to be
generated/collected:

  * **Objective 1:** Demonstrate solutions at district scale integrating smart homes and buildings, smart renewables and closed-loop energy positive districts 

  

  * **Objective 2:** Demonstrate smart energy management and storage solutions targeting Grid flexibility 

  

  * **Objective 3:** Demonstrate integrated urban mobility solutions increasing the use of environmentally-friendly, alternative fuels, creating new opportunities for collective mobility and lead to a decreased environmental impact 

  

  * **Objective 4:** Demonstrate the integration of the latest generation ICT solutions with existing city platforms over open and standardised interfaces enabling the exchange of data for the development of new innovative services 

  

  * **Objective 5:** Demonstrate active citizen engagement solutions providing an enabling environment for citizens to participate in co-creation, decision making, planning and problem solving within the Smart Cities 

  

  * **Objective 6:** Put in practice bankable business models over proposed integrated solutions, tested to reduce technical and financial risks for investors guaranteeing replicability at EU scale 

  

  * **Objective 7:** Strengthening the links and active cooperation between cities in a large number of Member States with a large coverage of cities with different size, geography, climatic zones and economical situations 

  

  * **Objective 8:** Measure and validate the demonstration results after a 3-years large-scale demonstration at district scale within 3 highly innovative EU cities 

  

In **Column 3** ‘Data type’ select the type of data to be generated/collected:

  * **integers**

  

  * **booleans**

  

  * **characters**

  

  * **floating-point numbers**

  

  * **alphanumeric strings**

  

  * **Other (please specify)**

  

  * **Not known yet**

  

In **Column 4** ‘Data format’ select the format of data to be
generated/collected:

  * **ASCII text-formatted data (TXT)**

  

  * **CAD data (DWG)**

  

  * **Comma-separated values (CSV)**

  

  * **dBase (DBF)**

  

  * **eXtensible Mark-up Language (XML)**

  

  * **Tab-delimited file (TAB)**

  

  * **Geospatial open data based upon JavaScript Object Notation (GeoJSON)**

  

  * **Geo-referenced TIFF (TIF, TFW)**

  

  * **Hypertext Markup Language (HTML)**

  

  * **Keyhole Markup Language (KML)**

  

  * **MS Word (DOC/DOCX)**

  

  * **MS Excel (XLS/XLSX)**

  

  * **MS Access (MDB/ACCDB)**

  

  * **OpenDocument Spreadsheet (ODS)**

  

  * **Open Document Text (ODT)**

  

  * **Rich Text Format (RTF)**

  

  * **SPSS portable format (POR)**

  

  * **Other (please specify)**

  

  * **Not known yet**

  

**Note:** When choosing the right **format** for **open data** 2  it is
recommended to start with comma separated values (CSV) files. CSV is perfect
for tabular data and can be easily loaded into and saved from applications
like Excel, making it accessible to users. For geospatial open data formats,
formats to be considered are geoJSON (based upon JavaScript Object Notation -
JSON) and Keyhole Markup Language (KML) which is based upon Extensible Markup
Language – XML. These formats are specifically designed with usability in mind
and can easily be imported and exported from specialist mapping tools like
Open Street Map and CartoDB.

In **Column 5** ‘Re-use of existing data’ select one of the following options
(in the case of re-use of existing data, please specify in plain text how to
re-use):

  * **Re-use of existing data (specify how)**

  

  * **Non re-use of existing data**

  

  * **Not known yet**

  

In **Column 6** ‘Origin of the data’ please specify in plain text the origin
of the data.

In **Column 7** ‘Expected size of the data’ please specify the expected size
of the data and add the appropriate units: Kilobytes (KB), Megabytes (MB),
Gigabytes (GB), and Terabytes (TB).

In **Column 8** ‘Data utility’ please specify to whom the data might be useful
in terms of Work Package (WP) and/or Task (T).

In **Column 9** ‘Other info’ please specify, if applicable, the **data units**
, **time resolution** and **the time period** that the data set covers in
DD/MM/YEAR, or any other relevant information that was not addressed in
columns 1-8. For example, for time-series of power measurement data mention
the units, time resolution and the time period that the data set covers (e.g.
measurements in kW with 15 minutes resolution from 01/01/2018 to 01/02/2018).

In **Column 10** ‘City’ please specify the relevant city (Lighthouse or
Follower) for the corresponding data set.

In **Column 11** ‘Contact person(s)’ please specify the name and e-mail of the
responsible contact person(s) for the corresponding data set.

IRIS: Data Management Plan v1.0 – 30.03.2018

_Table 1. Data Summary._  
  
<table>  
<tr>  
<th>

Title of data set

</th>  
<th>

Relation to project objective

</th>  
<th>

Data type

</th>  
<th>

Data format

</th>  
<th>

Re-use of existing data

</th>  
<th>

Origin of the data

</th>  
<th>

Expected size of the data

</th>  
<th>

Data utility

</th>  
<th>

Other info

</th>  
<th>

City

</th>  
<th>

Contact person(s)

(name / email)

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

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</td>  
<td>

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</td>  
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See explanation in pg. 11

</td>  
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See explanation in pg. 12

</td>  
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See explanation in pg. 13

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See explanation in pg. 13

</td>  
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See explanation in pg. 13

</td>  
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See explanation in pg. 13

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See explana tion in pg. 13

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D 9.1 Dissemination Level: **Public** Page **14** of **25**

#  3\. FAIR data

The IRIS project partners will ensure that the project research data will be
'FAIR', that is findable, accessible, interoperable and re-usable.

For all the data produced and/or used in the project, the project partners
will put effort in:

  * Making data findable, including provisions for metadata 

  

  * Making data openly accessible 

  

  * Making data interoperable 

  

  * Increase data re-use (through clarifying licences) 

  

More information about FAIR can be accessed through the FORCE11 community [1],
and the FAIR principles published as an article in Nature [2].

As a first step in making the project research data 'FAIR', the projects
partners involved in the LH demonstration activities will be asked during
M6-12 to fill in the template with the data set description (See Table 2).
This template will be filled in for each dataset summarised in Table 1. These
dataset descriptions will be incorporated in the first update of the DMP (D9.8
in M12).

_Table 2. Format of the data set description._  
  
<table>  
<tr>  
<th>

**Data Identification**

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

Data set description

</td>  
<td>

_Where are the sensor(s) installed? What are they monitoring/registering? What
is the dataset comprised of? Will it contain future sub-datasets?_

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

Source (e.g. which device?)

</td>  
<td>

_How will the dataset be collected? What kind of sensor is being used?_

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

**Partners services and responsibilities**

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

Partner owner of the device

</td>  
<td>

_What is the name of the owner of the device?_

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

Partner in charge of the data collection (if different)

</td>  
<td>

_What is the name of the partner in charge of the device? Are there several
partners that are cooperating? What are their names?_

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

Partner in charge of the data analysis (if different)

</td>  
<td>

_The name of the partner._

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

Partner in charge of the data storage (if different)

</td>  
<td>

_The name of the partner._

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

WPs and tasks

</td>  
<td>

_The data are going to be collected within activities of WPxx and WPxx._

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

**Standards**

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

Info about metadata (Production and storage dates, places) and documentation?

</td>  
<td>

_What is the status with the metadata so far? Has it been defined? What is the
content of the metadata (e.g. datatypes like images portraying an action,
textual messages, sequences, timestamps etc.)_

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

Standards, Format, Estimated volume of data

</td>  
<td>

_Has the data format been decided on yet? What will it look like?_

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

**Data exploitation and sharing**

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

Data exploitation

(purpose/use of the data analysis)

</td>  
<td>

_Example text:_

_Production process recognition and help during the different production
phases, avoiding mistakes_

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

Data access policy /

Dissemination level

(Confidential, only for members of the Consortium and the Commission Services)
/ Public

</td>  
<td>

_Example text:_

_The full dataset will be confidential and only the members of the consortium
will have access on it. Furthermore, if the dataset or specific portions of it
(e.g. metadata, statistics, etc.) are decided to become of widely open access,
a data management portal will be created that should provide a description of
the dataset and link to a download section. Of course these data will be
anonymized, so as not to have any potential ethical issues with their
publication and dissemination_

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

Data sharing, re-use and distribution (How?)

</td>  
<td>

_Has the data sharing policies been decided yet? What requirements exists for
sharing data? How will the data be shared? Who will decide what to be shared?_

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

Embargo periods (if any)

</td>  
<td>

\-

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

**Archiving and preservation (including storage and backup)**

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

Data storage (including backup): where? For how long?

</td>  
<td>

_Who will own the information that has been collected? How will it adhere to
partner policies? What kind of limitation are put on the archive?_

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

#  4\. Allocation of resources

Further to the FAIR principles, the DMP will also address the allocation of
resources. All the data produced and/or used in the project, will be described
by using the template included in Table 2. For each described dataset the
partners will report on the costs for making data FAIR in the IRIS project.
This information will be incorporated in the first update of the DMP (D9.8 in
M12).

#  5\. Data security

For all the data produced and/or used in the project, the project partners
will ensure data security. For each described dataset (based on the template
in Table 2), the partners will state the provisions taken for data security
(including data recovery as well as secure storage and transfer of sensitive
data), as well as for long term preservation and curation in certified
repositories. This information will be incorporated in the first update of the
DMP (D9.8 in M12).

#  6\. Ethical aspects

For all the data produced and/or used in the project, the project partners
will take into account ethical aspects. Specifically, the project partners
will address all obligations as described in the Description of the Action
(DoA)  3  , in ARTICLE 34 ‘ETHICS AND RESEARCH INTEGRITY’. Thus, the IRIS
project will assure the investigation, management and monitoring of ethical
and privacy issues that could be relevant to its envisaged technological
solution and will establish a close-cooperation with the Ethics Helpdesk of
the European Commission. The consortium is aware that a number of privacy and
data protection issues could be raised by the activities (in WP5, WP6 and WP7)
to be performed in the scope of the project. The project involves the carrying
out of data collection in all LH and FC in order to assess the effectiveness
of the proposed solutions. For this reason, human participants will be
involved in certain aspects of the project and data will be collected. This
will be done in full compliance with any European and national legislation and
directives relevant to the country where the data collections are taking
place, as well as with the EU General Data Protection Regulation (GDPR)  4  ,
which replaces the Directive 95/46/EC, with enforcement date the 25  th  May
2018.

###  6.1.1 IRIS Ethical Policy

IRIS will follow the opinions of various expert committees in the field (e.g.
the European group on ethics in science and new technologies to the European
Commission. In addition, all national legal and ethical requirements of the
Member States where the research is performed will be fulfilled. Any data
collection involving humans will be strictly held confidential at any time of
the research. This means in detail that:

  * All the test subjects will be informed and given the opportunity to provide their consent to any monitoring and data acquisition process that all the subjects will be strictly volunteers and all test volunteers will receive detailed oral information. 

  

  * No personal or sensitive data will be centrally stored. In addition, data will be scrambled where possible and abstracted in a way that will not affect the final project outcome. 

  

In addition, they will receive in their own language:

  * A commonly understandable written description of the project and its goals. 

  

  * The planned project progress and the related testing and evaluation procedures. ▪ Advice on unrestricted disclaimer rights on their agreement. 

  

On the other hand, an Ethics Helpdesk will scrutinise the research, to
guarantee that no undue risk for the user, neither technically nor related to
the breach of privacy, is possible. Thus, the Consortium shall implement the
research project in full respect of the legal and ethical national
requirements and code of practice. Whenever authorisations have to be obtained
from national bodies, those authorisations shall be considered as documents
relevant to the project. Copies of all relevant authorisations shall be
submitted to the Commission prior to commencement of the relevant part of the
research project.

###  6.1.2 IRIS Ethics Helpdesk

All used assessment tools and protocols within IRIS LH and FC will be verified
beforehand by its Ethics helpdesk regarding their impact to business actors
and end users before being applied to the sites. The helpdesk takes
responsibility for implementing and managing the ethical and legal issues of
all procedures in the project, ensuring that each of the partners provides the
necessary participation in IRIS and its code of conduct towards the
participants. Each LH and FC will have its own Ethics Committee and one person
will be nominated per site as responsible for following the project’s
recommendations and the National and European legislations.

###  6.1.3 Data to be collected within IRIS LH and FC

Data will be both manually and automatically collected by smart sensors and
other proprietary equipment installed at selected areas during the execution
of the demonstration activities and will be further investigated in (WP5, WP6
and WP7). In most cases the collected data will be data needed for monitoring
the contextual conditions of the pilot areas (energy consumption, energy
production, temperature, humidity, weather etc.). Since some of the collected
data in the latter case may involve sensitive personal data, all provisions
for data management will be made in compliance with national and EU
legislation: Including the European Network for Information and Security
Agency  5  security measures to minimise the risk to data protection arising
from smart metering and the British Sociological Association's Statement of
Ethical Practice as described in the following paragraphs.

The project research data will be collected in two phases:

  * Before the implementation of the demonstration activities in the LH (for baselines, references and design data). 

  

  * After the implementation of the demonstration activities in the LH (for evaluation purposes). 

  

The consent procedure for the pilot use case realisation at each of the
selected pilot sites will make use of a template of a consent form, to be
adopted as required per pilot use case. Such a template is included in Annex
3: Consent form template.

#  7\. Other issues

In this section, the project partners will report the use of any other
national/funder/sectorial/ departmental procedures for data management. This
information will be incorporated in the first update of the DMP (D9.8 in M12).