#  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).

##  1.4 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 second report on DMP, submitted at M12 (30  th  September 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. Update D9.9 (second update on the Data management plan) which will
be delivered in M30, includes a final revision of the information presented in
D9.8. This revision will be mainly based on the feedback on D9.8 which will be
given in the plenary meeting in Nice (October 2018) and new insights that will
arise when data is actually being managed. Further on D9.9 will be a version
that includes the templates of D9.8 filled in with information about the data
that is being aggregated from the various demonstrator projects within the
IRIS framework.

D9.10 (M42) and D9.11 (M60) finally are mainly a continuation of D9.9 which
include the details of all new datasets that will be gathered or aggregated in
the period between D9.9 and the moment that each update is being delivered.

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._

##  1.5 Contributions of partners

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 D9.9 (Second update on the DMP which is due to be published
in M30).

RISE organised 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. Different than projected in D9.1 these workshops where more about
the LH solutions and the KPI’s itself. It was too early to define detailed
monitoring strategies in these sessions. Therefore, another session on this
topic is planned in the 3  rd  IRIS Consortium plenary Board meeting in Nice.
This meeting will take place on October 16, 17 and 18 of 2018. In the
interactive program that will be created for all consortium partner contacts a
workshop on the Data Management Plan will be organized.

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 an

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).

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 or collected during the project by using the templates listed in
this second report on the DMP.

##  1.6 Relation to other activities

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 this
document, 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).

WP9 and WP4 activities are connected (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.

The data management plan on a first glance might have large similarities with
D9.3 (Data model and management plan for integrated solutions). The main
differences are that data management plan D9.1 has its primary focus on the
definition of datasets. D9.3 defines the variables within these sets, and how
these variables determine the KPI’s.

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

##  1.7 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** : This section provides Table 1 which summarizes
the data to be generated/collected during the project. This table includes
standardised items, of which the contents are described in this section

**Section 3 FAIR data** : Besides this data summary, more information about
the data is required to meet the demands of FAIR- data. Section 3 shortly
describes what this means. It introduces another table with items that should
be added in the data management plan, together with a description.

**Section 4 Allocation of Resources:** Section 4 is about the costs of making
FAIR data.

**Section 5 Data security:** Refers to how each partner will make sure it
keeps its data secure.

**Section 6 Ethical aspects:** Refers to the ethical aspects that arise during
the production and utilization data in the IRIS project **.**

**Section 7 Other issues:** In this section, the project partners will report
the use of any other national/funder/sectorial/departmental procedures for
data management.

#  2 Data Summary

In Table 1 a summary is provided of the data to be generated or 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 M12.

Apart from some minor modifications, the main difference between the tables in
this document compared to the ones in D9.1 is the appearance. To facilitate
the collection of data, a large excel table is created, (Annex 4) including
all different tables in this document and additional space for data mentioned
in chapter 4 and 5.

A workshop in the plenary meeting in Nice (October 2018) will be organized to
improve, initiate and inform about the data collection process.

##  2.1 Explanation for the input of table 1

In **Column 1 ‘** Title of data set’: Each dataset should be named according
to the following model:

IRIS_XX_YYY_NAME

Where:

  * XX corresponds to the abbreviation of the lighthouse or follower city providing the data set as defined in the table below: 

  

  
<table>  
<tr>  
<th>

**Lighthouse city**

</th>  
<th>

**Abbreviation**

</th>  
<th>

**Follower City**

</th>  
<th>

**Abbreviation**

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

Gothenburg

</td>  
<td>

GO

</td>  
<td>

Alexandropoulis

</td>  
<td>

AL

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

Nice

</td>  
<td>

NI

</td>  
<td>

Focsani

</td>  
<td>

FO

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

Utrecht

</td>  
<td>

UT

</td>  
<td>

Santa Cruz de Tenerife

</td>  
<td>

SC

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

</td>  
<td>

</td>  
<td>

Vaasa

</td>  
<td>

VA

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

  * YYY is an abbreviation for the demonstrator project or integrated solution of which the dataset is part of (can be defined by the project leader) 

  

  * NAME specifies 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** New dataset is for administrative purposes, to specify if a
dataset is

  * New: No similar dataset is generated before) 

  

  * Edit: The dataset is an edited version of a previously generated set 

  

  * Addition: The dataset is a previously generated set with added data 

  

In **Column 3** ‘Relation to project objective’ select the objective of the
project (1-8) that relates to the purpose of the data to be generated or
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 4** ‘Data type’ select the type of data to be generated or
collected:

  * **integers**

  

  * **booleans**

  

  * **characters**

  

  * **floating-point numbers**

  

  * **alphanumeric strings**

  

  * **Other (please specify)**

  

  * **Not known yet**

  

In **Column 5** ‘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 6** ‘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 7** ‘Origin of the data’ please specify in plain text the origin
of the data.

In **Column 8** ‘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 9** ‘Data utility’ please specify to whom the data might be useful
in terms of Work Package (WP) and/or Task (T).

In **Column 10** ‘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 11** ‘City’ please specify the relevant city (Lighthouse or
Follower) for the corresponding data set. (this column could be

In **Column 12** ‘Contact person(s)’ please specify the name and e-mail of the
responsible contact person(s) for the corresponding data set.

H2020: First update of the Data Management Plan – 28-09-2018

_Table 1 Data Summary_  
  
<table>  
<tr>  
<th>

**Admin**

</th>  
<th>

</th>  
<th>

**Data Summary**

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

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

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

</th>  
<th>

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

Title of data set

</td>  
<td>

New dataset?

</td>  
<td>

Relation to project objective

</td>  
<td>

Data type

</td>  
<td>

Data format

</td>  
<td>

Re-use of existing data

</td>  
<td>

Origin of the data

</td>  
<td>

Expected size of the data

</td>  
<td>

Data utility (WP and Task)

</td>  
<td>

Other info

</td>  
<td>

City

</td>  
<td>

Contact person(s)

(name / email)

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

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

**D 9.8** Dissemination Level: **Public** Page **17** of **33**

H2020: First update of the Data Management Plan – 28-09-2018

#  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 after M12
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 next update of the DMP (D9.9
in M30).

##  3.1 Data identification

####  3.1.1 Title of dataset

The title of the dataset is similar as in table 1

####  3.1.2 Dataset description

Gives a short description of the dataset, use keywords: _what is monitored?
What is the purpose of the dataset? What kind of sensor is being used?_

##  3.2 Partners, services and responsibilities

Specify in this part of the table the partner who

  * Owns the device 

  

  * Collects the data 

  

  * Analyses the data 

  

  * Stores the data 

  

Also specify to which work package and task the dataset is related with
(similar as Table 1)

**D 9.8** Dissemination Level: **Public** Page **18** of **33**

##  3.3 Standards

####  3.3.1 Info about metadata and documentation

What kind of metadata is being provided with the data?

  * Has the metadata been defined? 

  

  * What is the status of the metadata so far 

  

  * What is the content of the metadata (Datatypes like images portraying an action, textual messages, sequences, timestamps, etc.) 

  

####  3.3.2 Data standards and formats

These columns specify data standards and formats similar as in table 1

##  3.4 Data exploitation and sharing

####  3.4.1 Data exploitation

What is the data going to be used for? What is the purpose, use of the data
analysis? For example: _Production process recognition and help during the
different production phases, avoiding mistakes_

####  3.4.2 Data access policy / Dissemination level

What policies adhere to the data? Is the data Public? Or is it confidential?
Only to be shared amongst the consortium members and the Commission services?

In case of public data, make sure that no potential ethical issues will arise
with the publication and dissemination.

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_

####  3.4.3 Data sharing, reuse and distribution

Has the data sharing policies been decided yet? What requirements exist for
sharing data? How will the data be shared? Who will decide what to be shared?

####  3.4.4 Embargo periods

In case there is any embargo period related to the data, it can be specified
here.

##  3.5 Archiving and preservation

Specify in these columns where and until when the data (and its backups) are
stored.

H2020: First update of the Data Management Plan – 28-09-2018

_Table 2 Format with the dataset description_  
  
<table>  
<tr>  
<th>

**Fair Data (Table2)**

**Partners, services and Responsibilities**

</th>  
<th>

</th>  
<th>

**Standards**

</th>  
<th>

</th>  
<th>

**Data exploitation and sharing**

</th>  
<th>

**Archiving and preservation**

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

**Title of data set**

</td>  
<td>

**Data set description**

</td>  
<td>

**Partner owner of the device**

</td>  
<td>

**Partner in charge of the data collection**

</td>  
<td>

**Partner in charge of the data analysis**

</td>  
<td>

**Partner in charge of the data storage**

</td>  
<td>

**WP's and**

**Tasks**

</td>  
<td>

</td>  
<td>

**Info about metadata**

</td>  
<td>

**Data type**

</td>  
<td>

</td>  
<td>

**Data format**

</td>  
<td>

**Data exploitatio n**

</td>  
<td>

**Data access policy /**

**Disseminati on level**

</td>  
<td>

**Data sharing, reuse and distribution**

</td>  
<td>

**Embargo**

**periods (if any)**

</td>  
<td>

**Location of**

**Data**

</td>  
<td>

**Location of**

**Backup**

</td>  
<td>

**Expiry date**

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

</td>  
<td>

</td>  
<td>

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<td>

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**D 9.8** Dissemination Level: **Public** Page **20** of **33**

#  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 1. 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 next update of the DMP (D9.9 in
M30).

#  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 1) the partners will state the provisions taken for data security.
This includes data recovery as well as secure storage and transfer of
sensitive data. Further on, it defines how long-term preservation and curation
in certified repositories will take place. This information will be
incorporated in the next update of the DMP (D9.9 in M30).

#  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 next update of the DMP (D9.9 in M30).

#  8 Conclusions

The Data Management Plan is a working document that is updated regularly
during the IRIS project. The plan provides templates that will be used by the
partners in the project, when data is being generated or gathered. To make use
of these templates properly, the document gives information on what is
expected in each column of the template. By managing all this data in a
structured way, the FAIR principles will be maintained. This first update is
the result of a revision of the content of the first version of the DMP
(D9.1), some changes have been made to the content of the tables and parts of
the text has been revised to make the information more clear and concise. The
next update of the DMP will consist of a revision of the templates presented
in this plan, but more significantly it will present the utilization of the
templates itself. Meaning that it will show all required details of the data
that is being managed during the IRIS project till the time of deliverable 9.9
(M30).