#  Chapter 1 Introduction

The SAFURE Data Management Plan (further on referred as DMP) is required for
H2020 projects participating in the Open Research Data Pilot and describes the
data management lifecycle for all data sets that will be generated, collected,
and processed by the research project SAFURE. Being more specific, it outlines
how research data will be handled, what methodology and standards will be
used, whether and how the data will be exploited or made accessible for
verification and reuse and how it will be curated and preserved during and
even after the SAFURE project is completed. The DMP can be considered as a
checklist for the future, as well as a reference for the resource and budget
allocations related to the data management.

However, to explain the **reason** why a DMP gets elaborated during the
lifespan of a research project, the European Commission’s vision is that
information already paid for by the public purse should not be paid again each
time it is accessed or used. Thus, other European companies should benefit
from this already performed research.

To be more specific, _“**research data** refers to information, in particular
facts or numbers, collected to be examined and considered and as a basis for
reasoning, discussion, or calculation. In a research context, examples of data
include statistics, results of experiments, measurements, observations
resulting from fieldwork, survey results, interview recording and images. The
focus is on research data that is _

_available in digital form.”_ 1

The DMP is not a fixed document. It will evolve and gain more precision and
substance during the lifespan of the SAFURE project. Figure 1 envisions the
Data Management Lifecycle in a graphical view.

Figure 1: Data Management Lifecycle

The first version of the DMP includes data management information from the
first six months of the project. Furthermore, it addresses how the consortium
plans to handle the following topics:

  * Description of Data 

  

  * Data Collection 

  

  * Data Documentation and Metadata 

  

  * Intellectual Property Rights 

  

  * Access and Sharing 

  

  * Archiving and Preservation 

  

The rest of this report is structured as follows. Chapter 2 introduces the
general methodology according to which this DMP has been derived. Chapters 3
and 4, describe, in a more detailed manner, how each of the above topics will
be addressed by each partner.

#  Chapter 2 Methodology

In order to get a detailed view regarding the data management topics
identified in the introduction and to collect the requirements and constraints
of each partner regarding the DMP, a data management questionnaire has been
designed. The additional purpose of this questionnaire was to raise awareness
among the project partners regarding the guidelines on data management in
Horizon 2020 projects.

The questionnaire has been divided into five main chapters regarding data
description, management, identification, intellectual property rights, and
accessibility, each comprising a series of questions to help address the
topics identified in the introduction. A template of the questionnaire is
provided in the Appendix of this report.

As the project is by now within its first months, some information remains
undefined at the moment. Since this DMP is panned to be a living document, it
will be updated as soon as more details are available and a more detailed and
elaborated version of the DMP will be delivered at later stages of the
project. Moreover, the DMP will be updated at least by the mid-term and final
review to be able to fine-tune it to the data generated and the uses
identified by the consortium.

#  Chapter 3 Data Management

The term ‘Data Management’ stands for an extensive strategy to make
project/research data available to interested target groups via a set of well-
defined policies. Before making data available to the public, the published
data needs to be defined, collected, documented and addressed properly. The
following sections define this process within SAFURE and will be led by the
following questions:

  * **3.1 Description of data** – Which type of data will be generated? Which formats will be chosen and can it be reused? 

  

  * **3.2 Data generation & collection ** – How can the data set be described? To whom might be the data useful? How can it be identified as research data? 

  

  * **3.3 Data documentation & metadata ** – Does the project data comply with international research standards? 

  

  * **3.4 Intellectual Property Rights** – Will the public availability be restricted due to the adherence to Intellectual Property Rights? 

  

##  3.1 Description of data

The consortium will generate data throughout the lifespan of the SAFURE
project. The generated data is expected to cover a large range of areas
including performance measures (code size, loading time, execution
performance,

temperature, power and clock frequency of MPSoCs, latency, jitter, bitrate),
measures obtained from worst-case and distribution analysis (e.g. network and
ECU load, frame and task latencies), as well as qualitative data (platform
requirements) and specifications (DOC/PDF). Furthermore, various types like
source code (C language), object code, software and hardware architecture
models (ARXML, SYSML, SymTA/S XML), network packets and type formats used by
network analysis tools.

##  3.2 Data generation & collection

Data generation and collection phase is concerned with the project data
generated or collected, including its origin, nature and scale, and to whom it
might be useful.

Data will be mostly generated by the SAFURE beneficiaries themselves or among
the consortium. Therefore, different methodologies come into operation. Almost
all partners will execute performance measures, which leads to high amount of
generated data. The consortium agrees in prospectively seeing the possibility
to integrate or reuse the generated data. They further agree that the data
will be useful for universities, research organizations, SMEs and scientific
publications. Moreover, it might be also beneficial for IP providers and to
design companies. Restrictions on data availability depend on the specific
type of data.

Based in the questionnaire we developed, Table 1 gives a per-partner overview
of the data which is expected to be generated within the SAFURE project,
including its description and identification. For each partner, more details
can be found in their respective questionnaires.

_D7.1_ – _Data Management Plan_  
  
<table>  
<tr>  
<th>

**Data Nr.**

</th>  
<th>

**SAFURE partner**

</th>  
<th>

**Data set reference and name**

**and used methodology**

</th>  
<th>

**Data set description**

</th>  
<th>

</th>  
<th>

</th>  
<th>

**Reserach data identification 2  **

</th>  
<th>

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

**End user (e.g. university, research organization,**

**SME’s, scientific publication)**

</th>  
<th>

**Existence of similar data (link, information)**

</th>  
<th>

**Possibility for integration and**

**reuse (Y/N) + information**

</th>  
<th>

**D 3  **

</th>  
<th>

**A 4  **

</th>  
<th>

**AI 5  **

</th>  
<th>

**U 6  **

</th>  
<th>

**I 7  **

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

1

</td>  
<td>

TRT

</td>  
<td>

Performance measures

</td>  
<td>

Universities, research organizations, SMEs

</td>  
<td>

None

</td>  
<td>

Yes

</td>  
<td>

D

</td>  
<td>

D

</td>  
<td>

N

</td>  
<td>

D

</td>  
<td>

N

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

2

</td>  
<td>

TTT

</td>  
<td>

Performance measures

</td>  
<td>

For internal use

</td>  
<td>

None

</td>  
<td>

Yes

</td>  
<td>

Y

</td>  
<td>

Y

</td>  
<td>

Y

</td>  
<td>

N

</td>  
<td>

N

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

3

</td>  
<td>

BSC

</td>  
<td>

Performance measures

</td>  
<td>

Academics, industry

</td>  
<td>

None

</td>  
<td>

Yes

</td>  
<td>

Y

</td>  
<td>

Y

</td>  
<td>

Y

</td>  
<td>

Y

</td>  
<td>

N

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

4

</td>  
<td>

TEC

</td>  
<td>

Performance measures

</td>  
<td>

Research organization, SMEs

</td>  
<td>

None

</td>  
<td>

Yes

</td>  
<td>

D

</td>  
<td>

D

</td>  
<td>

Y

</td>  
<td>

Y

</td>  
<td>

N

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

5

</td>  
<td>

ETHZ

</td>  
<td>

Performance measures

</td>  
<td>

Universities

</td>  
<td>

None

</td>  
<td>

No

</td>  
<td>

N

</td>  
<td>

Y

</td>  
<td>

Y

</td>  
<td>

N

</td>  
<td>

N

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

6

</td>  
<td>

SYM

</td>  
<td>

Performance measures obtained from worst-case and distribution analysis

</td>  
<td>

Universities, research organizations, SME’s, etc.

</td>  
<td>

Yes

</td>  
<td>

Yes

</td>  
<td>

D

</td>  
<td>

D

</td>  
<td>

Y

</td>  
<td>

D

</td>  
<td>

Y

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

7

</td>  
<td>

ESCR

</td>  
<td>

Qualitative data and performance measures

</td>  
<td>

Academics, SMEs

</td>  
<td>

None

</td>  
<td>

Yes

</td>  
<td>

Y

</td>  
<td>

Y

</td>  
<td>

Y

</td>  
<td>

N

</td>  
<td>

N

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

2 N – No; Y – Yes; D - Depends 3 Discoverable 4 Accessible

  5. Assessable and intelligible 

  

  6. Usable beyond the original purpose of which it was collected 

  

  7. Interoperable to specific quality standards 

  

  

SAFURE D7.1 Page 5 _D7.1_ – _Data Management Plan_  
  
<table>  
<tr>  
<th>

**Data Nr.**

</th>  
<th>

**SAFURE partner**

</th>  
<th>

**Data set reference and name**

**and used methodology**

</th>  
<th>

**Data set description**

</th>  
<th>

</th>  
<th>

</th>  
<th>

**Reserach data identification 2  **

</th>  
<th>

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

**End user (e.g. university, research organization,**

**SME’s, scientific publication)**

</th>  
<th>

**Existence of similar data (link, information)**

</th>  
<th>

**Possibility for integration and**

**reuse (Y/N) + information**

</th>  
<th>

**D 3  **

</th>  
<th>

**A 4  **

</th>  
<th>

**AI 5  **

</th>  
<th>

**U 6  **

</th>  
<th>

**I 7  **

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

8

</td>  
<td>

MAG

</td>  
<td>

MAG Sw Code, SW

Specs, Sw Architecture

Models

</td>  
<td>

All code products for internal use, other data to academics.

</td>  
<td>

None

</td>  
<td>

Yes

</td>  
<td>

N

</td>  
<td>

D

</td>  
<td>

D

</td>  
<td>

N

</td>  
<td>

N

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

9

</td>  
<td>

SSSA

</td>  
<td>

Sample models

</td>  
<td>

Universities, industry, standardization bodies

</td>  
<td>

TBD

</td>  
<td>

Yes

</td>  
<td>

D

</td>  
<td>

D

</td>  
<td>

D

</td>  
<td>

D

</td>  
<td>

D

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

10

</td>  
<td>

SYS

</td>  
<td>

Performance measures

</td>  
<td>

SMEs, academics

</td>  
<td>

None

</td>  
<td>

Yes

</td>  
<td>

Y

</td>  
<td>

Y

</td>  
<td>

Y

</td>  
<td>

D

</td>  
<td>

N

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

11

</td>  
<td>

TCS

</td>  
<td>

Network packets of the Wireshark tool, performance and timing data

</td>  
<td>

Internal use only

</td>  
<td>

None

</td>  
<td>

Not at present

</td>  
<td>

N

</td>  
<td>

D

</td>  
<td>

D

</td>  
<td>

N

</td>  
<td>

N

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

12

</td>  
<td>

TUBS

</td>  
<td>

Performance measures obtained from worst-case

analysis

</td>  
<td>

Universities, research organizations, SME’s, scientific publishing

</td>  
<td>

None

</td>  
<td>

Yes

</td>  
<td>

D

</td>  
<td>

Y

</td>  
<td>

Y

</td>  
<td>

N

</td>  
<td>

D

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

Table 1: Data Overview

SAFURE D7.1 Page 6

##  3.3 Data documentation & metadata

Data documentation ensures that the given dataset or set of documents can be
understood, citied properly and interpreted correctly by any interested party.
Where ever possible, we will use metadata standards to document the generated
data. At this point, AUTOSAR (ARXML), SYSML, XMI standards, and ISO 26262 have
been identified suitable by some partners. Most partners generate very
specific data for which no suitable metadata standard has been identified,
yet. These partners plan to store/organize their generated data in a
standardized format (e.g. CSV, XML, or Microsoft Excel) and plan to provide an
accompanying description to interpret the data. This description and metadata
will be produced manually and might also include scientific publications and
technical reports. Also, this process can be automated by scripts and specific
(software architecture) modelling tools.

Some tools are nonetheless required to access the given data sets, e.g. XML
parsing, tools to read XMI/ARXML formats, tools with CSV input support (e.g.
Microsoft Excel).

##  3.4 Intellectual Property Rights

Even though IPR issues mainly arise during the project lifetime or even after
project end due to the dissemination (scientific and non-scientific
publications, conferences etc.) and exploitation (licensing, spin-offs etc.)
of project results, the SAFURE consortium considered the handling of IPR right
from the beginning, i.e. during the project planning phase. Therefore the
Consortium Agreement (CA) clearly states the background, foreground, and
sideground of each partner and defines rules regarding patents, copyrights,
(un-)registered designs and other similar or equivalent forms of statutory
protection.

Within the SAFURE project most data will be generated within internal
processes at partner level through measurement and/or analysis. Close
cooperation within the consortium may lead to joint generation of data, which
is clearly handled in terms of IPR issues within the CA.

At this stage of the project, no licenses are required. Raw data and results
extracted from the performed studies will be public. The reuse of synthetic
data or collected data is covered by the CA and will be depending on hardware
and software targets of the consortium.

No third party data is reused in the current project phase. In case third-
party data will be reused, confidentiality restrictions might apply in
specific cases, which will be analyzed per case in detail.

Neither time lag nor restrictions for the publication of results are planned.
Publishable data will be posted and published in due course.

#  Chapter 4 Accessibility

While Chapter 3 focuses on the internal project processes before publication
including the compliance with the project rules for IPR, Chapter 4 describes
how the generated data will be made accessible for public (re-)use (Section
Chapter 4) and how availability will be ensured permanently, whether data
needs to be destroyed/retained for any contractual, legal or regulatory
purpose as well as how long the data should be preserved, what costs will
occur and how they will be covered (Section 4.2).

##  4.1 Access and Sharing

Access to and sharing of data helps to advance science and to maximize the
research investment. A recent paper  2  reported that when data is shared
through an archive, research productivity and often the number of publications
increases. Protecting research participants and guarding against disclosure of
identities are essential norms in scientific research. Data producers should
take efforts to provide effective informed consent statements to respondents,
to identify data before deposit when necessary, and to communicate to the
archive any additional concerns about confidentiality. With respect to the
timeliness of the data deposit, archival experience has demonstrated that the
durability of the data increases and the cost of processing and preservation
decreases when data deposits are timely. It is important that data is
deposited while the producers are still familiar with the dataset and able to
fully transfer their knowledge to the archive.

In particular, potential users can find out about generated and existing data
most likely through scientific publications and deliverables. During the
project lifetime these documents will be published on the official project
website (www.safure.eu) were a broad community has access to the project
information. Our SME/Industry partners will also conduct product marketing in
order to draw attention to the SAFURE results and data. The consortium will
provide data also through search engines or are willing to provide information
also upon requests of interested users, potential customers, etc. These
requests will be handled directly with them. Besides public websites, also
marketing flyers or the SVN repository will be used as a tool to provide
requested data.

The partners indicated to provide the generated data after the project end or
upon request. Public deliverables will be made available as soon as they have
been approved by the European commission. The consortium itself will receive
data as soon as it is available.

Once the interested users have received the information which data was
generated and is available, it depends on the dissemination level if this data
will be shared and made available without any restrictions. The consortium is
willing to share their produced data with researchers (from academia and
industry) and potential customers/business partners provided that
confidentiality restrictions are met. Of course the data will be shared within
the SAFURE consortium without any restrictions to obtain synthetic data.

In this early stage of the project most of the partners don’t pursue to get a
persistent identifier for their data.

##  4.2 Archiving and Preservation

Generally, the partners believe that it will not be necessary to destroy any
data. However, it might be the case that some confidential data may need to be
restricted. This will be decided on a case by case basis. At this early stage,
some partners could not yet identify whether data destroying will be necessary
at all, as this also depends on the software and hardware targets that still
need to be decided.

Along with the project progress it will be agreed what data will be kept and
what data will be destroyed. This will be done according to the SAFURE project
rules, agreements and discussion within the consortium. So far, the partners
have already expressed that data that is relevant for scientific evaluation
and publication should certainly be kept.

The data generated will serve as basis for future scientific research work and
projects. For the consortium it is clear that foreseeable research uses for
the data can be, for instance, performance comparisons, in SAFURE particularly
with future systems and other hardware and software. Furthermore, the data may
even define the starting point for new standards and provide benchmarks for
research.

Regarding the retention and preservation of the data, SAFURE partners will
retain and/or preserve the produced data for several years, three years at
least.

As to the location of the storage, the SAFURE partners prefer to hold data in
internal repositories and/or servers. Further, they can be hold in marketing
repositories. Another option indicated by the partners is the storage in
public or institutional websites. Furthermore, it has been suggested to
establish a commodity cloud by using internal cloud infrastructure or,
depending on the confidentiality, an external platform.

For SAFURE the costs for data storage and archiving will occur, in particular
for server provision (infrastructure) and maintenance. The coordinator,
Technikon, has already foreseen this in the project budget. The expected
amount at this stage will be approximately € 2,000 for the servers. At a later
stage of the project it can be better assessed, if further costs for data
storage will occur. These costs will then be covered by the partners with
their own resources.

#  Chapter 5 Conclusion

This data management plan outlines the handling of data generated within the
SAFURE project, during and after the project lifetime. As this document will
be kept as a living document, it will be updated regularly by the consortium.

This report defines the data management policy within the SAFURE project
addressing data description, collection, documentation and metadata,
intellectual property rights, access and sharing, and archiving and
preservation. A questionnaire has been developed to collect detailed
information from each partner regarding these topics (see Appendix for the
questionnaire template).

The main data collected within SAFURE will be various performance measurements
ranging from ECU to network performance measures. Also, platform requirements
and specifications will be derived. Additionally, a description what data is
collected by each partner is provided in this report. This data is anticipated
to be useful to universities, research organizations, SMEs and for scientific
publication. The partners have identified some metadata standards (AUTOSAR
(ARXML), SYSML, XMI standards) to help understanding for the collected data by
third parties. Data, for which no suitable metadata standard could be
identified at present, will be described and documented manually. The
consortium agreement specifies also how intellectual property rights can be
preserved and covers clear rules for dissemination and exploitation of project
data.

Data availability will be mainly advertised via publications, deliverables,
and marketing. This data will be made available through the project’s SVN
repository. Furthermore, some partners plan to make data available through
their websites as well. Access to this data will mostly be handled upon
request provided that confidentiality requirements are met. Also, if
confidentiality allows, data will be archived and preserved for at least three
years after the project ended.

The SAFURE consortium is convinced that this data management plan ensures that
project data will be provided for further use timely, available and in
adequate form, taking into account the IPR restrictions of the project.