##  1 Introduction

CICERONE brings together programme owners, research organizations and other
stakeholders to create a platform for efficient Circular Economy programming.
The priority setting and the organization of the future platform will be
driven by Programme Owners (POs), involved either as project partners, or via
a stakeholder network, and the work will be carried out in close cooperation
with research & technology organisations (RTOs), which contribute with their
expertise of the main scientific and technological challenges. Consultation
mechanisms will also ensure that all stakeholders will be able to actively
contribute (civil society, industry, innovative SMEs, startups, cities,
investors, networks, etc.).

###  1.1 Purpose of the Data Management Plan

The purpose of this document is to lay out a plan for the management of data
generated/collected in CICERONE. It covers the following:

  * Identification of data to be collected/processed/generated 

  

  * Methodology and standards to be applied 

  

  * Data handling during and after the project 

  

  * Sharing, curating ad preserving data 

  

At the time of this writing, CICERONE partners have identified 4 data sets to
be included in the DMP at this stage. All of these data sets identified at
this stage will be made openly accessible to the public through repositories
such as Zenodo, and they will be preserved after the end of the project. The
DMP is a living document – if necessary, it will be updated throughout the
project’s lifetime.

###  1.2 Data set properties

Following the guidelines of the EC (EC, 2016), this document contains the
following properties for each of the identified data sets:

  1. Name 

  

  2. Short description 

  

  3. Standards to be applied, metadata 

  

  4. Data sharing 

  

  5. Curation/archiving/preservation 

  

  

A short description of each of these properties is provided below.

_1.2.1 Name and reference code_

In order to imbue the names of datasets with easily identifiable meaning that
conveys important information, the following naming convention shall apply:

_CountryCode.DataOwner.Openness.Title_

_CountryCode_ : this string identifies the country to which the data
pertains/where the data was collected using the ISO 3166 Alpha-2 coding
system.

_DataOwner_ : this string identifies the project partner in CICERONE that is
associated with the dataset (data collector/custodian) using the official
abbreviated partner names.

_Openness_ : this string determines whether a given dataset is intended to be
shared with the public as Open Data. It may take the following values:

  1. Open: can be accessed, used and shared by anyone without limitations, accessible on the internet in a machine-readable format, free of restrictions on use in its licensing) 

  

  2. Shared: available to use, but not under an open data license. Restrictions on its use or reproduction may apply (limited to a given group of people or organisations, may not be reproduced without authorisation, etc.) 

  

  3. Closed: can only be accessed by its subject, owner or holder _Title_ : a short and descriptive string to identify the contents of the data Using these strings, the name of a dataset would look like this: 

  

  

_FR.LGI.Open.CommuteHouseholdSurvey_

A dataset with this name would describe a household survey on commuting
preferences conducted in France and curated by LGI.

###  1.3 Data licensing

Without a license to set out the terms of use, data is not truly open. Data
without a license may be publicly accessible, but users do not have the
certainty that they can use and share the data, leaving them in a legal grey
area. Data licensing standards are used to lay out the openness of data sets
in concrete terms, and an open data license gives explicit permission to use
the data both for commercial and non-commercial purpose. There are many types
of licenses to choose from, and this document will not cover them in depth.
The table below provides a summary of common data licenses that will be
considered for use in the project (based on definitions from
opendefinition.org):  
  
<table>  
<tr>  
<th>

**Name**

</th>  
<th>

**Domain**

</th>  
<th>

**Attribution**

</th>  
<th>

**Sharealike***

</th>  
<th>

**Notes**

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

Creative Commons CCZero (CC0)

</td>  
<td>

Content, data

</td>  
<td>

N

</td>  
<td>

N

</td>  
<td>

All rights (including those of attribution) waived

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

Open Data

Commons Public

Domain Dedication and Licence (PDDL)

</td>  
<td>

Data

</td>  
<td>

N

</td>  
<td>

N

</td>  
<td>

All rights (including those of attribution) waived

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

Creative Commons

Attribution 4.0 (CCBY-4.0)

</td>  
<td>

Content, data

</td>  
<td>

Y

</td>  
<td>

N

</td>  
<td>

Credit must be given, a link to the license must be provided, changes made
must be indicated. If these terms are not followed, license may

be revoked

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

Open Data

Commons Open

Database License (ODbL)

</td>  
<td>

Data

</td>  
<td>

Y

</td>  
<td>

Y

</td>  
<td>

Credit must be given, share-alike must be assured, data may be

redistributed using DRM as long as a

DRM-free version is also released

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

_*Share-alike is the requirement that any materials created using the given
dataset must be redistributed under the same license_

##  2 Description of the data

The following detailed information sheet will be produced for every dataset to
be produced/collected/curated in the project:  
  
<table>  
<tr>  
<th>

Name of the dataset

</th>  
<th>

A name to identify the data, see 1.2.1 for details.

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

Description of the dataset

</td>  
<td>

  * A brief, easy to understand description of what the dataset contains and what it will be used for in the project 

  

  * A list of institutions to whom the data set could be useful outside the project 

  

  * Whether the dataset has been/will be used for a scientific publication (if yes, brief details about the content and journal) 

  

  * If the dataset is collected, a brief description of its origin and how it was collected will be provided 

  

  * Openness of the dataset 

  

  * Whether the dataset is anonymised or not 

  

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

Format/license

</td>  
<td>

The format in which the data will be available (e.g. .xls, .csv, .txt) will be
provided. The license to be used will also be provided.

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

Archiving/preservation

</td>  
<td>

Efforts and means to keep the data available after the end of the project will
be described here, including where/how the data will be preserved, the
duration of preservation, the associated costs and the plans of the consortium
to cover these costs.

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

##  3 Summary of identified datasets

This DMP contains 4 datasets identified by the CICERONE partnership. The
following tables provide information on the various aspects of these datasets.
The sheets completed by partners are provided in Annex I.

###  3.1 Format/license  
  
<table>  
<tr>  
<th>

Name of the dataset

</th>  
<th>

Format

</th>  
<th>

License

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

FRLGIOpenPOsSurvey

</td>  
<td>

.xls

</td>  
<td>

ODbL

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

FIVTTKICOpenPOsSurvey

</td>  
<td>

.xls

</td>  
<td>

ODbL

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

ESC-KICOpenPOsSurvey

</td>  
<td>

.xls

</td>  
<td>

ODbL

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

World.Juelich.Open.International benchmark on CE(T1.2, D.1.3)

</td>  
<td>

.xls

</td>  
<td>

ODbL

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

###  3.2 Archiving/preservation  
  
<table>  
<tr>  
<th>

Name of the dataset

</th>  
<th>

Sharing medium

</th>  
<th>

Duration of preservation

</th>  
<th>

Costs

</th>  
<th>

How

costs

will be covered

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

FRLGIOpenPOsSurvey

</td>  
<td>

Zenodo.org

</td>  
<td>

Perpetual

</td>  
<td>

N/A

</td>  
<td>

N/A

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

FIVTTKICOpenPOsSurvey

</td>  
<td>

Zenodo.org

</td>  
<td>

Perpetual

</td>  
<td>

N/A

</td>  
<td>

N/A

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

ESC-KICOpenPOsSurvey

</td>  
<td>

Zenodo.org

</td>  
<td>

Perpetual

</td>  
<td>

N/A

</td>  
<td>

N/A

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

World.Juelich.Open.International benchmark on CE(T1.2, D.1.3)

</td>  
<td>

Zenodo.org

</td>  
<td>

Perpetual

</td>  
<td>

N/A

</td>  
<td>

N/A

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

##  4 Data Protection Officer

In accordance with applicable regulations, the host institution (Climate-KIC)
is not required to appoint a Data Protection Officer. A detailed data
protection policy for the project is kept on file.

##  5 Ethical aspects

This Data Management Plan (DMP) was drafted and updated taking into account
the General Data Protection Rules (GDPR) for the collection, storage and re-
use of the data, in line with the following general principles. :

Personal data shall be:

  1. processed lawfully, fairly and in a transparent manner in relation to the data subject (‘lawfulness, fairness and transparency’); 

  

  2. collected for specified, explicit and legitimate purposes and not further processed in a manner that is incompatible with those purposes; further processing for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes shall, in accordance with Article 89(1), however not be considered to be incompatible with the initial purposes (‘purpose limitation’); 

  

  3. adequate, relevant and limited to what is necessary in relation to the purposes for which they are processed (‘data minimisation’); 

  

  4. accurate and, where necessary, kept up to date; every reasonable step must be taken to ensure that personal data that are inaccurate, having regard to the purposes for which they are processed, are erased or rectified without delay (‘accuracy’); 

  

  5. kept in a form which permits identification of data subjects for no longer than is necessary for the purposes for which the personal data are processed; personal data may be stored for longer periods insofar as the personal data will be processed solely for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes, in accordance with Article 89(1) subject to implementation of the appropriate technical and organisational measures required by this Regulation in order to safeguard the rights and freedoms of the data subject (‘storage limitation’); 

  

  6. processed in a manner that ensures appropriate security of the personal data, including protection against unauthorised or unlawful processing and against accidental loss, destruction or damage, using appropriate technical or organisational measures (‘integrity and confidentiality’). 

  

  

##  6 Restrictions for re-use

Data generated through interviews and surveys will not be re-used directly due
to privacy concerns. To allow re-use and avoid loss of research data, two
different techniques could be used to disseminate its data while abiding by
regulations on privacy.

###  6.1 Anonymization of data

“Anonymization" of data means processing it with the aim of irreversibly
preventing the identification of the individual to whom it relates. Data can
be considered anonymised when it does not allow identification of the
individuals it is related to, and no individuals can be identified from the
data by any further processing of that data or by processing it together with
other information which is available or likely to be available.

There are different anonymization techniques. Here are the two most relevant:

  * Generalisation : generalising data means removing its specificity. For example, in the case of a table containing household income levels, with 4 figures mentioned: €135,000, €60,367, €89,556, and €365,784. One way of generalising this numbers would be to write that the values are “more than €150,000, less than €80,000, between €90,000 and €120,000, and more than €300,000” respectively. Essentially it means taking exact figures, establishing a baseline category, and then obfuscating the data by assigning it to one of the categories in order to remove any sense of specificity from it. 

  

  * K-anonymity; A release of data is said to have the k-anonymity property if the information for each person contained in the release cannot be distinguished from the other individuals whose information also appear in the release. For instance, in a table composed of six attributes (Name, Age, Gender, State of Domicile, Religion and Disease), removing the name and the religion column while generalising the age is a way to effectively k-anonymise the data. Other techniques, such as “masking” or “pseudonymisation”, which are aimed solely at removing certain identifiers, may also play a role in reducing the risk of identification. In many cases, these techniques work best when used together. 

  

###  6.2 Pseudonymisation of data

"Pseudonymisation" of data means replacing any identifying characteristics of
data with a pseudonym, or, in other words, a value which does not allow the
data subject to be directly identified. Although pseudonymisation has many
uses, it should be distinguished from anonymization, as it only provides a
limited protection for the identity of data subjects in many cases as it still
allows identification using indirect means. Where a pseudonym is used, it is
possible to identify the data subject by analysing the underlying or related
data.

Task leaders will be responsible for the anonymization of data in CICERONE for
all datasets where this is deemed necessary.

##  7 Personal data transfer and processing

In case personal data will be transferred from the EU to a non-EU country
(NCKU is an international partner in the project) or international
organisation, such transfers will be made in accordance with Chapter V of the
General Data Protection Regulation 2016/679, and such transfers will comply
with the laws of the country in which the data was collected.

In case of further processing of previously collected personal data, CICERONE
will ensure that the beneficiary has legal grounds for the data processing and
that the appropriate technical and organisational measures are in place to
safeguard the rights of the data subjects.