**1 Executive Summary**

The results of the UNICORN project will be openly published to communicate and
spread the knowledge to all interested communities and stakeholders. Published
results generate wider interest towards the improvements achieved by the
project in order to facilitate and potentiate exploitation opportunities. The
goal of this deliverable is listing publishable results and research data and
investigating the appropriate methodologies and open repositories for data
management and dissemination. The UNICORN's partners aim to offer as much
information as possible generated by the project through open access. Such
information includes scientific publications issued by the UNICORN consortium,
white papers published, open source code generated, anonymous interview
results, or mock-up datasets used for gathering customer feedback.

In general, there are two types of project results which differ in the way
they are published; namely publications and other research data. Our
publication strategy follows the ideas of open access and open research data.
Figure 1 depicts how research activities lead to different results and how
these results are disseminated and exploited. Scientific publications and
related research data is as far as possible published openly. On the other
hand, not all data collected or generated can be published openly, as it may
contain private information or interfere with legal aspects. This kind of data
must be identified and protected accordingly. One of the targets of the
project is to deliver a specification/standard which, however, will not be
publicly available.

  2. **Introduction**

    1. **Purpose of The Document**

  
  

  

  

Each project in the EC's Horizon 2020 program has to define what kind of
results are generated or collected during the project's runtime and when and
how it is published openly. This document describes which results will be
published in UNICORN within the whole project duration. For all results
generated or collected during UNICORN a description is provided including the
purpose of the document, the standards and metadata used for storage, the
facility used for sharing openly, based on the EC template recommended
(European Commission, 2013). This document is updated on a regular basis.
However, it does not describe how the results are exploited, which is part of
D5.1, D5.4 and D5.5.

  2. **Terminology**

  

  

**Open Access** : Open access means unrestricted access to research results.
Often the term open access is used for naming free online access to peer-
reviewed publications. Open access is expected to enable others to a) build on
top of existing research results,

  2. avoid redundancy, 

  

  3. participate in open innovation, and 

  

  4. read about the results of a project or inform citizens. 

  

  

All major publishers in computer science - like ACM, IEEE, Elsevier, or
Springer - participate in the idea of open access. Both green, or gold open
access levels are promoted. Green open access means that authors eventually
are going to publish their accepted, peer-reviewed articles themselves, e.g.
by deposing it to their own institutional repositories. Gold open access means
that a publisher is paid (e.g. by the authors) to provide immediate access on
the publishers website and without charging any further fees to the readers.

**Open Research Data** : Open research data is related to the long-term
deposit of underlying or linked research data needed to validate the results
presented in publications. Following the idea of open access, all open
research data needs to be openly available, usually meaning online
availability. In addition, standardized data formats and metadata has to be
used to store and structure the data. Open research data is expected to enable
others to:

  1. understand and reconstruct scientific conclusions, and 

  

  2. to build on top of existing research data. 

  

  

**Metadata** : Metadata defines information about the features of other data.
Usually metadata is used to structure larger sets of data in a descriptive
way. Typical metadata are names, locations, dates, storage data type, and
relations to other data sets. Metadata is very important when it comes to
index and search larger data sets for a specific kind of information.
Sometimes metadata can be retrieved automatically from a dataset, but often it
needs some manual classification also. The well-known tags in MP3-recordings
are a good example why metadata is necessary to find a specific kind of genre
or composer in a larger number of songs.

  3. **Structure of The Document**

  

  

The rest of the document is structured into two sections. Section 2 defines
the process that will be applied to all results collected or generated during
UNICORN. The process defines if a result has to be published or not. In
addition, we provide a summary of all publishing platforms to be used by the
UNICORN consortium. Section 3 lists publications and other public related data
that is already or may be generated or collected during UNICORN. For each
result, a short description, the chosen way of open access, and a long-term
storage solution are specified according to the EC's data management
guidelines (European Commission, 2013).

  3. **Publishing Infrastructure for Open Access**

  

  

The UNICORN publication infrastructure consists of a process and several web-
based publication platforms that together provide long-term open access to all
publishable, generated or collected results of the project. The implementation
of the project fully complies with law in national and EU level and especially
with the **Directive 95/46** related to the Protection of personal data. More
specifically, there are not cases where personal data information or sensitive
information of internet users is collected (IP addresses, email addresses or
other personal information) or processed. For the whole duration of the
project, from the beginning to its end, the Data Protection Officer (DPO – Ms.
Meltini Christodoulaki (FORTH)) will carefully examine the legality of the
activities and the tools (including platforms) that will be produced for not
violating the personal data of internet users. In the potential future case
where the UNICORN consortium will collect, record, store or process any
personal information, it will be ensured that this will be done on a basis of
respecting citizens’ rights, preventing their identification and keeping their
anonymization. Both the process and the used web-based platforms are described
in the following subsections. The section ends with some research items and
data that they project partners foresee that they will be reflected on
respective publication actions. The research data will be the main subject of
analysis with respect to data management in the next section.

  1. **Publishing Process**

  

  

UNICORN partners defined a simple, deterministic process that decides if a
result in UNICORN must be published or not. The term result is used for all
kind of artefacts generated during UNICORN like white papers, scientific
publications, and anonymous usage data. By following this process, each result
is either classified public or nonpublic. Public means that the result must be
published under the open access policy. Non-public means that it must not be
published.

For each result generated or collected during UNICORN runtime, the following
questions must be answered to classify it:

  1. _Does a result provide significant value to others or is it necessary to understand a scientific conclusion?_

  

  

If this question is answered with yes, then the result is classified as
public. If this question is answered with no, the result is classified as non-
public. Such a result could be code that is very specific to UNICORN platform
(e.g., a database initialization) which is usually of no scientific interest
to anyone, nor does it add any significant contribution.

  2. _Does a result include personal information that is not the author's name?_

  

  

If this question is answered with yes, the result is classified as non-public.
Personal information beyond the name must be removed if it should be
published. This also bares witness on the repetitive nature of the publishing
process, where results which are deemed in the beginning as non-publishable
can become publishable once privacy-related information is removed from them.

  3. _Does a result allow the identification of individuals even without the name?_

  

  

If this question is answered with yes, the result is classified as non-public.
Sometimes data inference can be used to superimpose different user data and
reveal indirectly a single user's identity. As such, in order to make a result
publishable, the included information must be reduced to a level where single
individuals cannot be identified. This can be performed by using established
anonymisation techniques to conceal a single user's identity, e.g.,
abstraction, dummy users, or non-intersecting features.

  4. _Does a result include business or trade secrets of one or more partners of UNICORN?_

  

  

If this question is answered with yes, the result is classified as non-public,
except if the opposite is explicitly stated by the involved partners. Business
or trade secrets need to be removed in accordance to all partners'
requirements before it can be published.

  5. _Does a result name technologies that are part of an ongoing, project-related patent application?_

  

  

If this question is answered with yes, then the result is classified as non-
public. Of course, results can be published after patent has been filed.

  6. _Can a result be abused for a purpose that is undesired by society in general or contradict with societal norms and UNICORN’s ethics?_

  

  

If this question is answered with yes, the result is classified as non-public.
_7\. Does a result break national security interests for any project partner?_

If this question is answered with yes, the result is classified as non-public.

**3.2 Publishing Platforms**

In UNICORN, we use several platforms to publish our results openly. The
following list presents the platforms used during the project and describes
their concepts for publishing, storage, and backup.

  1. **The project Website**

  

  

The partners in the project consortium decided early to setup a project-
related website. This website describes the mission and the general approach
of UNICORN and its development status. A blog informs about news on a regular
basis. Later in the project the developed UNICORN platform will be announced.
A dedicated area for downloads is used to publish reports and white papers as
well as scientific publications (in pre-camera ready form, or through links to
the publisher’s websites in case these are not open access). All documents are
published using the portable document format (PDF). All downloads are enriched
by using simple metadata information, such as the title and the type of the
document. The website is hosted by partner FORTH. All webpage-related data is
backed on a regular basis. All information on the project website can be
accessed without creating an account. The webpage is backed up once per month.

Web-Link:  _http://www.UNICORN-project.eu/_

  2. **Zenodo**

  

  

Zenodo is a research data archive / online repository which helps researchers
to share research results in a wide variety of formats for all fields of
science. It was created through EC's OpenAIRE+ project and is now hosted at
CERN using one of Europe's most reliably hardware infrastructures. Data is
backed nightly and replicated to different locations. Zenodo not only supports
the publication of scientific papers or white papers, but also the publication
of any structured research data (e.g., using XML). Zenodo provides a connector
to GitHub that supports open collaboration for source code and versioning for
all kinds of data. All uploaded results are structured by using metadata, like
for example the contributors' names, keywords, date, location, kind of
document, license, and others. Considering the language of textual metadata
items, English is preferred. All metadata is licensed under CC0 license
(Creative Commons 'No Rights Reserved'). The property rights or ownership of a
result does not change by uploading it to Zenodo.

All public results generated or collected during the project lifetime will be
uploaded to Zenodo for long-term storage and open access.

Web-Link:  _http://zenodo.org_

  3. **GitHub**

  

  

GitHub is a well-established online repository which supports distributed
source code development, management, and revision control. It is primarily
used for source code data. It enables world-wide collaboration between
developers and provides also some facilities to work on documentation and to
track issues. GitHub provides paid and free service plans. Free service plans
can have any number of public, open-access repositories with unlimited
collaborators. Private, non-public repositories require a paid service plan.
Many open-source projects use GitHub to share their results for free. The
platform uses metadata like contributors' nicknames, keywords, time, and data
file types to structure the projects and their results. The terms of service
state that no intellectual property rights are claimed by the GitHub Inc. over
provided material. For textual metadata items, English is preferred. The
service is hosted by GitHub Inc. in the United States. GitHub uses a rented
Rackspace hardware infrastructure where data is backed up continuously to
different locations.

All source-code components that are implemented during this project and
decided to be public will be uploaded to an open access GitHub repository.

Web-Link:  _https://github.com/_

**3.3 Project Artefacts**

This section just attempts to enumerate specific datasets, software and
research items (from which publications can be produced) and whether all these
artefacts can be publishable or not according to which means. More detailed
analysis spanning the different categories of artefacts is provided in the
next chapter.

#  Table 1: Project Artefacts  
  
<table>  
<tr>  
<th>

**Artefact Type**

</th>  
<th>

**Artefact**

</th>  
<th>

**Publication Means**

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

**Research Item**

</td>  
<td>

Cloud Governance Mechanisms

</td>  
<td>

Zenodo, open-access journals, project web site, research-oriented social media

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

**Research Item**

</td>  
<td>

Continuous Security and Data Privacy Enforcement Mechanisms

</td>  
<td>

Zenodo, open-access journals, project web site, research-oriented social media

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

**Research Item**

</td>  
<td>

Risk, Cost and Vulnerability Assessment

</td>  
<td>

Zenodo, open-access journals, project web site, research-oriented social media

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

**Research Item**

</td>  
<td>

Policy Validation & Identity Signing

</td>  
<td>

</td>  
<td>

Zenodo, open-access journals, project web site, research-oriented social media

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

**Software**

</td>  
<td>

Privacy by Design Libraries

</td>  
<td>

</td>  
<td>

Github, Zenodo

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

**Software**

</td>  
<td>

Orchestrator for Microservices

</td>  
<td>

</td>  
<td>

Github, Zenodo

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

**Software**

</td>  
<td>

Cloud Monitoring specialized

Microservices

</td>  
<td>

for

</td>  
<td>

Github, Zenodo

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

**Software**

</td>  
<td>

Elasticity Control specialized Microservices

</td>  
<td>

for

</td>  
<td>

Github, Zenodo

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

**Software**

</td>  
<td>

Eclipse Che Plugin

</td>  
<td>

</td>  
<td>

Github, Zenodo

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

**Dataset**

</td>  
<td>

Anonynous usage statistics

</td>  
<td>

</td>  
<td>

Zenodo

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

**Dataset**

</td>  
<td>

UseCase data

</td>  
<td>

</td>  
<td>

Non-publishable

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

**Dataset**

</td>  
<td>

...

</td>  
<td>

</td>  
<td>

Zenodo

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

**4 Project Results / Data Sets**

In this section, we first introduce the template that has been proposed by the
EC in order to form the description of the data that will be produced in the
context of UNICORN. Every use case will fill in such a template and
subsequently all the templates will be collected with the beginning of WP6,
the demonstration applications work package of the project.

Then, two sub-sections will shortly focus on two types of specific artefacts
produced by UNICORN, i.e., publications and software and finally the third one
will be dedicated to the analysis of all data produced in UNICORN according to
the previously presented template.

#  Table 2: Initial Dataset Template  
  
<table>  
<tr>  
<th>

**Initial Dataset Template**

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

**Dataset reference name**

</td>  
<td>

Identifier for the data set to be produced.

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

**Dataset description**

</td>  
<td>

Description of the data that will be generated or collected, its origin (in
case it is collected), nature and scale and to whom it could be useful, and
whether it underpins a

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

</td>  
<td>

scientific publication. Information on the existences (or not) of similar data
and the possibilities for integration and reuse.

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

**Standards and metadata**

</td>  
<td>

Reference to existing suitable standards of the discipline. If these do not
exist, an outline on how and what metadata will be created.

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

**Data sharing**

</td>  
<td>

Description of how data will be shared, including access procedures, embargo
periods (if any), outlines of technical mechanisms for dissemination and
necessary software and other tools for enabling reuse, and definition of
whether access will be widely open or restricted to specific groups.
Identification of the repository where data will be stored, if already
existing and identified, indicating in particular the type of repository
(institutional, standard repository for the discipline, etc.).

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

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

</td>  
<td>

Description of the procedure that will be put in place for long-term
preservation of the data. Indication of how long the data should be preserved,
what is its approximated end, volume, what the associated costs are and how
these are planned to be covered.

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

**Additional Dataset explanation**

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

**Discoverable**

</td>  
<td>

Are the data and associated software produced and / or used in the project
discoverable (and readily located), identifiable by means of a standards
identification mechanism (e.g. Digital Object Identifier)?

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

**Accessible**

</td>  
<td>

Are the data and associated software produced and / or used in UNICORN
accessible and in what modalities, scope, licenses (e.g. licencing framework
for research and education, embargo periods, commercial exploitation, etc.)?

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

**Assessible and intelligible**

</td>  
<td>

Are the data and associated software produced and / or used in the project
assessable for and intelligible to third parties in contexts such as
scientific scrutiny and peer review (e.g. are the minimal datasets handled
together with scientific papers for the purpose of peer review, are data is
provided in a way that judgments can be made about their reliability and the
competence of those who created them)?

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

**Usage beyond the original purpose for which it was collected**

</td>  
<td>

Are the data and associated software produced and / or used in UNICORN useable
by third parties even long time after the collection of the data (e.g. is the
data safely stored in certified repositories for long term preservation and
curation; is it stored together with the minimum software, metadata and
documentation to make it useful,; is the data useful for the wider public
needs and usable of the likely purpose of nonspecialists.

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

**Interoperable to specific quality standards**

</td>  
<td>

Are the data and associated software produced and / or used in the project
interoperable allowing data exchange between researchers, institutions,
organisations, countries, etc. (e.g. adhering to standards for data
annotation, data exchange, compliant with available software applications, and
allowing re-combinations with different datasets from different origins)?

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

**4.1 Publications**

The management of the dissemination and publication process will be performed
in WP 7 and will be reported in deliverables D7.1, D7.2, D7.3, D7.4 and D7.6.
Publications will be related to main research results that will be achieved
within UNICORN which will fall in the following research directions:

All scientific publications will be indexed, thus also obtaining a DOI, and
will be made available to:  Zenodo,

  * respective literature databases, such as Scopus and DBLP, 

  

  * research databases like ResearchGate and Academia.edu. 

  

**4.2 Software**

WP7 will guide the management and exploitation of the software produced within
this project and the respective reporting on this will be reflected on D7.5
deliverable leaded by STW.

The distribution of software mainly depends on the intended business model of
the respective owning parties. In UNICORN, we foresee that there will be two
types of software distribution:

  1. open-source software distributed under a public license and mainly related to research/academic institutions as well as organisations with open-source distribution policies; 

  

  2. licensed software distributed with an appropriate (e.g. commercial) license and mainly related to commercial organisations. 

  

  

Licensed software will be announced according to the respective dissemination
channels of the commercial organisation as well as on UNICORN web site. Open-
source software will be made available both in the UNICORN web site and will
also be published in respective repositories like Zenodo and github. This will
enable also the further development and sustainability of the software even
beyond the lifetime of UNICORN. The publishing of the open-source software
will depend on the respective community targeted by the partner organisation.
Respective dissemination actions will be performed to also reach such a
community.

The repository on which software will be distributed will handle the creation
of respective meta-data, such as author and commit history. Dependencies over
third-party tools, operating systems, runtime environments will also be
included, along with respective versioning information. Usage-related material
like documentation and tutorials will also be developed and published to ease
the exploitation of the software.

Depending on the publication medium (e.g., Zenodo vs github), each partner in
UNICORN will use its own state of the art version management mechanisms to
store its source code contributions and will guarantee the proper back up of
the code on a regular basis.

Software published in github will obtain a URL via which it can then be
accessed and exploited. It can also be made discoverable via respective web-
based search engines, like Google. If software is also published in Zenodo, a
proper DOI will be assigned to it.

**4.3 Research Data**

**4.3.1 Template Example**

#  Table 3: Dataset template example  
  
<table>  
<tr>  
<th>

**Initial Dataset Template**

</th>  
<th>

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

**Dataset reference name**

</td>  
<td>

Dataset XXX.

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

**Dataset description**

</td>  
<td>

The dataset covers the description of ...

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

**Standards and metadata**

</td>  
<td>

Dataset description was performed in standard language X.

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

**Data sharing**

</td>  
<td>

Dataset will be published in Zenodo to be made freely available for everyone.

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

**Archiving and preservation**

</td>  
<td>

This is mainly responsibility of the open-access repository (e.g., Zenodo).

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

**Additional Explanation**

</td>  
<td>

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

**Discoverable**

</td>  
<td>

The dataset will be assigned a DOI via which it can be discoverable.

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

**Accessible**

</td>  
<td>

A respective public licence will be associated to this dataset to govern its
accessing.

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

**Accessible and Intelligible**

</td>  
<td>

N.A

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

**Usage beyond original collection purpose**

</td>  
<td>

Dataset can be exploited during and even beyond the course of UNICORN.

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

**Interoperable to specific quality standards**

</td>  
<td>

Respective methodologies, best practices and standards

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

**4.3.2 More Concrete Example - Anonymous User Statistics**

#  Table 4: Anonymous User Statistics example  
  
<table>  
<tr>  
<th>

**Initial Dataset Template**

</th>  
<th>

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

**Dataset reference name**

</td>  
<td>

Anonymous User Statistics.

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

**Dataset description**

</td>  
<td>

UNICORN platform will provide online services not only to developers but also
to customers (end-users). The idea of UNICORN includes social collaboration
and anonymous customer profiles that help developers to recruit potential
users for a project or to customize software for a market. After UNICORN
platform has gone online, real users will interact with our system. The
behaviour of these users will be studied and used for improving our platform.
Any results that are published will be reduced to anonymous user statistics.
The details of this data set will be decided during the implementation and
updated accordingly in the following versions of the data management plan.

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

**Standards and metadata**

</td>  
<td>

The data is stored in a platform-independent format (e.g. XML). Any names or
features that may be used for single user identification (e.g. e-mail address,
phone number, date of birth) will be removed and summarized in a more abstract
way (e.g. 300 users in the age of 30-

40).

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

**Data sharing**

</td>  
<td>

User-related data that may lead to single user identification will not be
published. Only abstract user statistics that do not allow for single user
identification will be made openly accessible, but only if it contributes to
the results of a scientific publication. Any anonymous user data (e.g.
statistics) will be published openly using either existing institutional

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

</td>  
<td>

repositories or Zenodo. The access is free for everyone and without
restrictions.

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

**Archiving and preservation**

</td>  
<td>

Any user-related data is stored in project-related databases, which are hosted
by UNICORN partners. Each partner restricts access to private information by
implementing a security policy. Databases are backed on a regular basis by
each partner. All public user statistics will be added to Zenodo for long term
preservation during or at the end of UNICORN.

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

**Additional Explanation**

</td>  
<td>

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

**Discoverable**

</td>  
<td>

The dataset will be assigned a DOI via which it can be discoverable.

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

**Accessible**

</td>  
<td>

A respective public licence will be associated to this dataset to govern its
accessing.

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

**Accessible and Intelligible**

</td>  
<td>

N.A

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

**Usage beyond original collection purpose**

</td>  
<td>

Dataset can be exploited during and even beyond the course of UNICORN.

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

**Interoperable to specific quality standards**

</td>  
<td>

Respective methodologies, best practices and standards

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