#  Chapter 1 Introduction

The Data Management Plan (DMP) outlines how research data will be handled
during a research project and after it is completed. It includes clear
descriptions and rationale for the access regimes that are foreseen for
collected data sets.

This DMP describes the data management life cycle, see Figure 1, for all data
sets that will be collected, processed or generated by the SERENA project.
This is done by outlining how research data will be handled during the SERENA
project, and after the project is completed, describing what data will be
collected, processed or generated and following what methodology and
standards, whether and how this data will be shared and/or made open, and how
it will be curated and preserved.

The DMP is not a fixed document; it evolves and gains more precision and
substance during the lifespan of the SERENA project.

More elaborated versions of the DMP will be delivered at later stages of the
project. The DMP will be updated at least by the mid-term and final review to
fine-tune it to the data generated and the uses identified by the consortium
since not all data or potential uses are clear from the start.

Figure 1 Data Management Lifecycle

The outline of this report is as follows; after the introduction a description
about the data will be given, which includes what type of datasets are
expected to be collected, how the ownership of IP is arranged, how this data
can be accessed and shared, within, as well as outside of the SERENA
consortium. Followed by how the data will be archived after the end of the
project. Finally, an overview will be given of the collected datasets.

#  Chapter 2 Data description

A description about the data will be given, which includes what type of
datasets are expected to be collected, how the ownership of IP is arranged,
how this data can be accessed and shared, within, as well as outside of the
SERENA consortium.

##  2.1 Datasets

The different academic and industrial partners will create different kinds of
datasets. We expect datasets of the following types:

  * Proof-of-concept specifications 

  

  * Economical aspects and impact data 

  

  * Market data 

  

  * Design data 

  

  * Measurement data 

  

  * Validation data 

  

  * Simulation data 

  

  * Algorithm descriptions 

  

  * Source code 

  

SERENA will investigate algorithms and architectures for the hybrid analog-
digital beamforming system. These algorithms will be implemented (as source
code) and also tested with the proof-of-concept system. The test with the
proof-of-concept system will generate measurement data, as well as validation
data. Currently, we are investigating on the specifications of our proof-of-
concept platform and beamforming system and comparing them to current state-
of-the-art specifications. The specifications for the SERENA outputs will be
confidential. Deliverables and reports are also part of the produced datasets
and may contain data from any of the above-mentioned datasets.

So far, no specification is set for formats and therefore no special
requirements set how to read and interpret data/source code. However, since
the implementations will be based on well-used software stacks, a common
format shall be realized. Examples of formats used are typically data
dependent. Measurement data will be either made available in a machine-
readable text format (like CSV) or in a binary format compatible with the
software MATLAB. Source code and algorithm descriptions will be in text
format. Part of the data will need Mathworks MATLAB or an equivalent
application to be read. Other necessary information to interpret the data will
be made available as documentation respectively metadata. In specific
situations simulation and measurement data will generally be a collection of
complex variables as function of either stimuli frequency and/or input power.
The data formats will vary but most are in CSV (or similar format) with
descriptive headers. For design data some of the CAD systems store their data
in XML-format using an industry standard format (OpenAccess). For proprietary
data formats it is important that sufficient information is stored in an open
format to make it possible to recreate datasets in future, in SERENA this
especially relates to circuit designs, which may need to be re-simulated in
the future using newer simulation software tools. Thus, it is essential the
circuits and their device details and simulation settings are stored in an
open format.

To read the simulation and measurement data the access to the original files
should be sufficient. To interpret the data, companying design/measurement
reports are necessary as the data files do not provide enough context. To read
and interpret the design data bases the original CAD software must be
available (having exactly the same version used for generating the data). Some
of the data can be stored in industry standard formats (GDS or OpenAccess) but
this is not a guarantee that the data can be read or interpreted in the far
future.

The amount of data cannot yet be estimated as it depends on the investigations
during the project lifetime.

##  2.2 Design data example

Over the length of the project at least three types of data for designs will
be created in the SERENA project. A specific design dataset example is given
here: The first is the design databases for the GaN-on-Si MMICs and the 39 GHz
proof-of-concept demonstrator. These databases are stored in vendor
proprietary formats (Keysight ADS, Ansoft HFSS, and Cadence Allegro/OrCAD).
The second type is simulation data from the electronic CAD tools (Keysight
ADS/Momentum/EMPro and Ansoft HFSS). The simulation data is stored in vendors
specific proprietary formats with the option of exporting to open data formats
or industry standard formats. The third kind of data is measurement data of
the designed GaN-on-Si MMIC, antennas, and proof-of-concept demonstrators.
This data will be stored in partners proprietary databases.

Examples of simulation and measurement data are:

  * scattering parameters as function of frequency and/or bias 

  

  * output power as function of input power and frequency (and also bias) 

  

  * antenna radiation diagrams 

  

  * Error Vector Magnitude as function of input power and frequency (and also bias). 

  

Due to the nature of the generated research data (specific to certain circuit
designs and system designs) will limit the value of said data to a wide
audience. The data that is of value will, typically, be published in
scientific journals and at scientific/engineering conferences. These mediums
provide the necessary context to correctly interpret the data.

##  2.3 Intellectual property rights

The owner of the data will be the project partners who act as creators and
contributors. At a later stage plans and or specifications for licensing will
be decided, therefore at this stage of the SERENA project no licensing is
required. One partner expects to make data available, licensed under the
Creative Commons CC BY-NC-SA. Other partners expect to license data according
to their global strategies. At this stage there a no plans on restrictions on
the reuse of third-party data.

Most of the data will be restricted due to confidentiality and or patent
rights. This depends on the exploitation plans of the involved partners and
the corresponding choices as described in the Description of Action (DoA).
Signal processing algorithms and related specifications may be published. Some
data will be made public, in particular when this data accompanies published
results for validation purposes.

##  2.4 Access and sharing

Primarily potential users can find data via the partners websites, the
consortium website, as well as conferences, workshops and the public
deliverables.

Relevant data will be published in repositories, such as Zenodo  (
_https://zenodo.org/_ )  . The first dataset is already uploaded there by TUB,
see the table in Chapter 3 on datasets. Datasets will be linked on the project
website. Furthermore, this will partially be done through SERENA public
deliverables. Also, part of the data will be published in open access
publications or presented on conferences and workshops.

There won’t be any restrictions within the SERENA consortium.

Sharing data with the public (e.g. universities, research organisations, SMEs,
industries, etc.) will depend on the dissemination level of the deliverables.
Publications will be open access. Besides that, SERENA will follow and comply
with the FAIR (Findable, Accessible, Interoperable, and Re-usable) regulations
for data within the H2020 data management.

###  _2.4.1 Means of internal data sharing_

With the consortium the data will be shared via the project-internal SVN
repository. This repository is restricted to the SERENA consortium. The
partners each have their own internal IT infrastructures with their respective
IT guidelines. The data will be shared within the consortium as soon as the
data is available, to the public after the due date of the deliverable given
that it is public.

Our chosen types, formats and software enable sharing and long-term access of
the data. There are open and freely available alternatives to the often-chosen
software package Matlab, with which the data can be read and interpreted. The
amount of data does not imply any restrictions on the storage, backup and
access. A persistent identifier will be obtained for scientific publications
and their underlying research datasets.

Data will be shared and archived within the project’s SVN repository. Apache
Subversion (often abbreviated as SVN) is a software versioning and revision
control system distributed as open source.

Whenever research data are relevant for the consortium and will be used in
cooperation among several partners, data storage in the project SVN will be
considered. Data that are individually owned and individually used will
preferentially be stored by the owning partner. The SVN allows easy
synchronization of documents between the server (hosted at the premises of the
coordinator TEC in Austria) and a participant’s local file storage for sharing
documents and data. The system includes tools for retrieving older versions of
a particular file, which means that all former versions of a file are
available and reproducible. Therefore, no conventional backup system is
necessary. Regarding the preservation of the data, there is no expiration date
until which the data is available. Even years after the project end the data
will still be available. Hence, long term preservation of the data is secured.
Due to the fact that the system is set up and maintained by the coordinator in
their IT infrastructure in Austria, we have full control over the data at any
time, which is a significant advantage over cloud-based solutions.

Research data submitted to the SERENA SVN is protected by means of mandatory
authorization. Partners, who wish to have access to SVN (both read only and
read/write access) need to obtain user credentials from TEC. A username will
be generated and the user has to define a password. Only with these
credentials, the user get access to any documents on the SVN. All
communication between the clients and the SVN server use SSL encryption.

##  2.5 Archiving and preservation

Preserved data can form the basis for future scientific research work, thus
the data will be retained and preserved for at least 3 years after project
end. Other data, for example data that are stored in public repositories with
a persistent identifier will be preserved much longer. Locally data
preservation will be handled by the individual consortium partner, examples of
archives are the TUB backup system.

We have foreseen to retain data for 3 years after the project end. The
expected costs are those that arise through server provision and maintenance.
For some partners this is standard offered functionality within their
institute or company, for which no extra fee needs to be paid.

##  2.6 Documentation and metadata

Appropriate documentation in the form of public deliverables and/or
publications will be provided. There will also be data specification sheets
for data sets underlying scientific publications. Any additional and helpful
data or metadata will be logged.

There are no special requirements set yet for this additional data or
metadata, however it will be done in a human-readable form. The metadata is
purely descriptive to access the data. As described in this document, SERENA
will comply with the FAIR regulations stated for H2020 projects and their data
management. Therefore, SERENA will follow those rules and ensure that the data
will be **F** indable, **A** ccessible, **I** nteroperable, and **R**
e-usable.

For measurement data the documentation will include a description of the
measurement setup, used devices, and settings / parameters. For source code
the documentation will include the name and version of the software the code
is written for. The documentation will be made available in human readable
text form along the data.

#  Chapter 3 Data collection

This chapter will be updated throughout the project, please find descriptions
of the datasets, as well as a tabled overview below.

##  3.1 Dataset 1

Measurement data and processing code for Mathworks Matlab underlying the beam
alignment results of the publication “An Analog Module for Hybrid Massive MIMO
Testbeds Demonstrating Beam Alignment Algorithms”. The results compare three
algorithms for beam alignment and are measured at 2.4 GHz using the Hybrid
Massive MIMO testbed of the CommIT chair of the Technische Universität Berlin.
The measurement data is in the binary mat format and the source code in text
format. The choice is due to the usage of Matlab to generate the data. The
data is available on Zenodo and has a persistent identifier.

In Table 1 we will track our datasets, please find a description of the
content of Table 1 below.

D7.2

–

Data Management Plan  
  
<table>  
<tr>  
<th>

**Data Nr.**

</th>  
<th>

**Responsible**

**Beneficiary**

</th>  
<th>

**Data set reference and name and**

**[used methodology]**

</th>  
<th>

**Data set description**

</th>  
<th>

</th>  
<th>

</th>  
<th>

**Research data identification**

</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 1  **

</th>  
<th>

**A 2  **

</th>  
<th>

**AI 3  **

</th>  
<th>

**U 4  **

</th>  
<th>

**I 5  **

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

1

</td>  
<td>

TUB

</td>  
<td>

DOI:

10.5281/zenodo.121 7533

Title:

Beam Alignment

Measurements using a Hybrid Massive

MIMO Testbed

</td>  
<td>

Researchers (university and others)

</td>  
<td>

</td>  
<td>

Y

</td>  
<td>

X

</td>  
<td>

X

</td>  
<td>

X

</td>  
<td>

X

</td>  
<td>

X

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

2

</td>  
<td>

</td>  
<td>

[please fill-in]

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

3

</td>  
<td>

</td>  
<td>

[please fill-in]

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

4

</td>  
<td>

</td>  
<td>

[please fill-in]

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

5

</td>  
<td>

</td>  
<td>

[please fill-in]

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

6

</td>  
<td>

</td>  
<td>

[please fill-in]

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

Table 1: Data Overview

  1. Discoverable 

  

  2. Accessible 

  

  3. Assessable and intelligible 

  

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

  

  5. Interoperable to specific quality standards 

  

  

SERENA D7.2 Public Page 7 of 10

###  Responsible Beneficiary

• Who owns the data?

**_Data set reference and name and [used methodology]:_ **

Identifier for the data set to be produced, as well it’s used methodology
**_Data set description:_ **

Description of the data that will be generated or collected, its origin (in
case it is collected), nature and scale to whom it could be useful, and
whether it underpins a scientific publication. Information on the existence
(or not) of similar data and the possibilities for integration of reuse.

###  Research Data Identification

The boxes (D, A, AI, U and I) symbolize a set of questions that should be
clarified for all datasets produced in this project.

**Discoverable:**

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

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

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 judgements can be made about reliability and the
competence of those who created them)?

####  Useable beyond the original purpose for which it was collected

Are the data and associated software produced and/or used in the project
usable 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 for the likely purposes of non-specialists)?

####  Interoperable to specific quality standards

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?)

It is recommended to make a “X” to each applicable box and explain it
literally in more detail afterwards.

Public Page 8 of 10

#  Chapter 4 Summary and conclusion

This DMP outlines how research data will be handled during the SERENA research
project and after its completion. The DMP is not a fixed document; it evolves
and gains more precision and substance during the lifespan of the SERENA
project.

Within the SERENA project several different datasets are expected to be
created. As discussed, these datasets will be stored and archived by the
individual partners on their local IT infrastructure. Shared data will be
stored and archived on the project closed repository, managed by TEC. Public
data will be stored in public repositories, with a persistent identifier. All
these datasets will be accompanied by descriptive metadata, deliverables,
presentations, and publications. All datasets will be stored for at least
three years, datasets at partners may be stored for much longer. Public
datasets will be provided with a persistent identifier and may exist
indefinitely.

One of the key points identified in this document is that where the use of
proprietary formats is unavoidable, it is important to store sufficient
information to at a later stage recreate, especially, simulation data. This
DMP outlined what information is sufficient for this purpose.

The Consortium Agreement (CA) forms the legal basis on how to handle issues
related to IP and has defined rules with relation to dissemination and
publications. The SERENA consortium is convinced this DMP will ensure proper
handling according to the open data pilot, during and after the duration of
the SERENA project.

Public Page 9 of 10