#  1 Introduction

This document outlines the principles and processes for data collection,
annotation, analysis and distribution as well as storage, security and final
destroying of data within the Breaking the Nonuniqueness Barrier in
Electromagnetic Neuroimaging (BREAKBEN) project. The procedures will be
adopted by all project partners and third parties throughout the project in
order to ensure that all project-related data are well-managed according to
contractual obligations as well as applicable legislation both during and
after the project.

BREAKBEN shall participate in the Open Data Pilot according to the Grant
Agreement and Research Commitment. This document details the practices and
solutions regarding the storage and re-usability of the research data, which
will be made accessible for other researchers and the public for further use
and analyses.

One of the most important aspects of this document is to ensure that the data
are not opened if this would violate privacy, safety, security, terms of
project agreements or legitimate concerns of private partners.

The Grant Agreement of the BREAKBEN project as an Open Data Pilot participant
obligates the project to:

  1. _deposit in a research data repository and take measures to make it possible for third parties to_ access, mine, exploit, reproduce and disseminate — free of charge for any user — the following: _(i) the data, including associated metadata, needed to validate the results presented in scientific publications as soon as possible; (ii) other data, including associated metadata, as specified and within the deadlines laid down in the 'data management plan', i.e. this document._

  

  2. _provide information — via the repository — about tools and instruments at the disposal of the_ beneficiaries and necessary for validating the results (and — where possible — provide the tools and instruments themselves). 

  

  

However, the obligation to deposit research data in a databank does not change
the obligation to protect results, take care of the confidentiality and the
security obligations or the obligations to protect personal data. The Data
Management Plan addresses these topics and details how the seemingly
contradicting commitments to share and protect are implemented within the
project.

The Grant Agreement also contains an option to discard the obligation to
deposit a part of research data in the case where the achievement of the
action's main objective, described in Annex 1 of the Grant Agreement, would be
jeopardized. In such case, the Data Management Plan must contain the reasons
for not giving access.

The Data Management Plan has, on the other hand, also served the purpose of
acting as a tool to agree on the data processing of the BREAKBEN project
consortium. The production of the Data Management Plan has helped the
consortium to identify situations where the practices were thought to be
agreed upon and where a common understanding on practices was thought to have
been achieved, but where such in fact did not exist.

This Data Management Plan is a living document that will be submitted at the
end of June 2016 (M6) and will be updated and complemented as the project
evolves.

When creating the BREAKBEN Open Data policy the following principles should be
considered:

##  1\. Discoverability

The location of research data and the necessary software to access the data
are known, using a standard international identification mechanism.

##  2\. Accessibility

Research data and the necessary software to access the data shall be easily
accessible, an embargo period can be agreed upon, in order to achieve
strategic advantage for the creator. The licenses that may be considered for
research data are Creative Commons Attribution 4.0 International (CC BY 4.0),
for metadata CC0 1.0 Universal (CC0 1.0) Public Domain Dedication and for
software the MIT License.

##  3\. Assessability and intelligibility

The research data and the necessary software to access the data shall be
assessable for and intelligible to third parties for scientific scrutiny and
peer review. They shall be published together with related scientific
publications.

##  4\. Usability

The research data and the necessary software to access the data shall also be
usable for purposes beyond the original project. The research data chosen for
long-term preservation shall be safely stored and cured.

##  5\. Interoperability

Allows data exchange between researcher groups, higher education institutions
and research institutions in different countries. Interoperability will also
allow for the re-combination of different datasets from different origins.

#  2 Data Types

For each partner of the BREAKBEN consortium, there is a different profile of
data produced within the project. The main outcome of the project, however, is
not data, as the aims are focused towards developing methods, techniques and
instrumentation for research and clinical use. Therefore, a large share of the
results are in the documents and publications that describe the technological
advances, as well as in the prototype instrumentation. Nevertheless, some
phases of the project generate experimental data that may be able to produce
additional value under further analysis or be relevant to the reproducibility
of the science. From the scientific reproducibility point of view,
computational results are typically best reproduced by reapplying the
described theory and algorithm and by analyzing the validity of the model
used.

Data and documents in project BREAKBEN are described in terms of six basic
types:

  1. **Reports and publications** ; e.g., deliverables, presentations, articles 

  

  2. **Project data** ; e.g., agreements, financial statements 

  

  3. **Design data** ; e.g., MRI–MEG equipment structures 

  

  4. **Research data** ; e.g., time series of physical quantities 

  

  5. **Analyzed research data** ; e.g., results of statistical analysis of research data 

  

  6. **Software** ; e.g., computer-programmed data-analysis pipelines 

  

  

**Reports and publications** includes deliverables, presentations and for
example journal articles. This data type also refers to the contents of the
BREAKBEN project website.

For deliverables, a simple filename metadata scheme is used within the
consortium. Drafts and final deliverable documents will be named according to
the format (X = work package, Y = deliverable number):

BREAKBEN-DX.Y_Deliverable-Name_More-info_And-more.docx

It is also advised within the consortium that any email communication
regarding deliverables will have BREAKBEN-DX.Y in the subject line to help
manage the email traffic related to deliverables. When dates are added to file
names, they should follow the international standard for date format (ISO
8601) with the form YYYY-MM-DD. When this date format is inserted at the
beginning of a file name, or at a fixed position (such as after “Deliverable-
Name” for deliverables), automatic file name sorting will sort the files in
chronological order. It is further recommended to include the project name
BREAKBEN in the Subject line of email conversations, when the communication is
specific to the BREAKBEN project.

In order to ease the version handling of project deliverables and to diminish
the need of sending them via email, the project workspace Eduuni will be used.
Eduuni is a password-protected workspace, administrated by Aalto University.
There, the deliverables will be uploaded under the appropriate Work Package.
Eduuni allows the simultaneous editing of the document.

**Project data** include administrative and financial project data, including
contracts, partner information and reports, as well as accumulated data on
project meetings, teleconferences and other internal materials. These data are
confidential to the project consortium. Project data include mainly MS Office
documents, in English, which ensures ease of access and efficiency for project
management and reporting. Project data are stored in Eduuni workspace,
administrated by Aalto University.

There are differences in produced data types between different partners, as
described below:

##  AALTO

The main role of Aalto University in the project is building expertise and
developing ULF MRI and MEG methods and instrumentation. However, also data are
produced as part of that development.

**Design data** : descriptions of designs, written documents and/or schematic
drawings; data types including LaTeX-generated pdf, Scalable Vector Graphics
and CAD formats; metadata written inline, in format-specific metadata fields
or in the file name.

**Research data** : stored neuroimaging or phantom data that are recorded
within the project; FIFF data format; metadata in format-specific fields or
additionally in separate text files using JSON or other suitable structuring
where applicable.

**Analyzed research data** : results from data-analysis pipelines; various
data formats, depending on the kind of analysis, sometimes in the form of
figure or table.

**Software** : software written to produce results or automate procedures,
including data-analysis pipelines;

e.g., written in Python 3.5 (file type .py or .ipynb, using the Jupyter
Notebook for describing the recipe for reproducing the results, including
documentation).

##  ELEKTA

Elekta is a medical technology company with a quality system that complies
with ISO 9001 (2015), ISO 13485 (2015), and FDA QSR.

The role of Elekta in the BREAKBEN project is not to collect research data,
but to provide knowledge of commercial exploitation of medical technology and
regulatory requirements, and to provide technical expertise in writing
software and building instrumentation. Table 1 lists and describes the data
types generated by Elekta in the project.  
  
<table>  
<tr>  
<th>

**Description**

</th>  
<th>

**Data type**

</th>  
<th>

**Tools**

</th>  
<th>

**Volume**

</th>  
<th>

**Metadata**

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

Project data

</td>  
<td>

Text files

</td>  
<td>

MS office or equivalent

</td>  
<td>

TBD

</td>  
<td>

TBD

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

Reports and communication data

</td>  
<td>

Text files

</td>  
<td>

MS office or equivalent

</td>  
<td>

TBD

</td>  
<td>

TBD

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

Design data

</td>  
<td>

Text files, CAD

files

</td>  
<td>

MS office or equivalent, CAD programs

</td>  
<td>

TBD

</td>  
<td>

As required by

Elekta

document

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

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

</td>  
<td>

management system

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

Software

</td>  
<td>

Source binaries

</td>  
<td>

code,

</td>  
<td>

SW development tools

</td>  
<td>

TBD

</td>  
<td>

As required by Elekta software

development tools

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

**Table 1.**

In the BREAKBEN project, there are four basic types of data generated by
Elekta: project data, reports and communication data, design data, and
software. No research data are collected by Elekta in the project.

**Project data** and **Reports and communication data** is described above.
**Design data** includes all data created in the development process of the
software and hardware modules developed in the project. **Software** includes
source codes and binaries of the software generated in the project.

The Metadata concerning design data and software will be handled as required
by Elekta document management system.

**HUS**

##  Recorded data

**Data types:** MEG, MRI and MEG-MRI signals of 5 healthy subjects and 2
patients with epilepsy. MRI images of four additional healthy subjects and 3
tumor patients.

**MEG** : approximately 5 files of spontaneous MEG per subject/patient

**MRI** : One MRI image stack from each subject and patient

**MEG-MRI** approximately 5 files of spontaneous MEG per subject/patient

**Analyzed research data:** The sources of somatosensory evoked oscillatory
activity will be analyzed with standard commercial MEG methods. The occurrence
of fast oscillatory activity in MEG of patients with epilepsy will be screened
in a manner routine for clinical evaluation. MRIs of the healthy subjects will
be analyzed by a neuroradiologist. The MRI images of the patients will be
analyzed in connection with their clinical workup.

##  PTB

PTB will design, construct and operate ULF MR hardware for NCI and DCI. In
addition to “Reports and Publication” and “Project data”, further data types
will be generated.

**Design data:** CAD files generated by Autodesk Inventor, Text files.

**Research data:** These primary data include digitised voltages and personal
information on subjects. For subject data, personal data will be saved and
protected from unauthorized access by a third party.

**Analysed research data:** These constitute the measurement data and records
which are part of a measurement, are the result of the execution of a
measurement or can be derived from a measurement. These include post-processed
and/or analysed research data obtained on phantoms and subjects within the
project.

##  UDA

The main role of UDA is to quantitatively assess the impact on the large scale
connectivity results induced by the new device built in the project. This
assessment will be carried out at the in-silico, phantom, and invivo levels.
This will result in the production of a small set of real data and
connectivity maps. Additionally, scripts to generate surrogate data and to
evaluate the impact on the large scale connectivity will be produced.

**Research data:** Time series containing MEG data will be stored in a
proprietary format recognized by several software tools freely available
(e.g., FieldTrip, Brainstorm). MEG data will be collected using the MEG device
operating at ITAB-UDA during rest and the auditory protocols that will be
specified along the project. Average data set size is 1 GB. Surrogate data
generated simulating real data acquisition will be also produced. In the
latter case, data will not be stored as individual files; as an alternative,
the generating algorithm will be given.

**Analyzed research data** : Results from real data-analysis pipelines (e.g.,
ERF, source activity, connectivity) and surrogate data results (e.g.,
connectivity values) will be saved using different data formats, depending on
the kind of analysis, including figures (e.g., png).

**Software:** software written to produce results from real and simulated data
from the data-analysis pipelines; e.g., written in Matlab and/or Python.
Documentation will be provided in the native editors of the programming
environments.

##  VTT

The role of VTT is design and manufacture of superconducting sensors and their
front-end interface electronics. Sensors are fabricated in the VTT Micronova
clean room, whose operations are ISO9001:2008 certified.

**Design data** : The raw design data will be in various CAD and engineering
formats, primarily (i) GDS for the detector layout, (ii) PADS for electronics
design, and (iii) AWR APLAC for behavioural models. Because raw designs will
combine BREAKBEN-funded results with other results, both internally-funded and
from earlier externally funded projects, the raw engineering files will not be
made available. The publishable files will be edited to remove details which
may infringe company secrets, other agreements, or may reveal other
proprietary information, and get converted into MS-Office –compatible formats,
SVG, and GDS.

**Research data:** Detector and electronics performance data will be collected
by (i) commercial measurement instruments taken from VTT instrument inventory,
and (ii) home-built data acquisition setups. Commercial instruments have
proprietary data formats, whose variety is too wide to be listed here. Data
acquisition setups have not been designed or constructed yet, hence their data
formats cannot be described yet. This information will be updated later in the
document. For the sake of making the data accessible, they will be converted
into the CSV format or MS Office compatible formats.

**Analyzed research data:** Will be made available in MS Office compatible
formats.

#  3 Collecting the data

Different data collection principles apply in different institutions, and not
all partners collect research data. Since the project is developing new
technologies, many kinds of data collection do not have any applicable
standards. Therefore, the data are often collected using the most applicable
methods available or most convenient to implement. On the other hand, it is
among the aims of the project to move towards more standardized data handling
(e.g., Deliverable 4.1: Software architecture). Details are given regarding
each partner institution below. Good scientific practices will be applied
according to the guidelines used by each institution.

##  AALTO

Data will be collected using in-house techniques fully or partially automated
using Python scripts and Jupyter Notebooks. Data will be collected from
phantoms, human subjects and non-imaging studies. Low-quality data are
rejected in-house by automatic or semiautomatic procedures.

##  HUS

The MEG data are collected in BioMag Laboratory in standardized commercial MEG
recording setup. The MRI data of epilepsy and tumor patients are collected in
HUS in conjunction with their standard clinical examinations.

The quality control standards are those used for clinical MEG and MRI
recordings in HUS. No pre-existing data are used for this project.

##  PTB

Personal data will be obtained via a questionnaire. Standards and protection
are described in the section 4.2.

Raw research data will be obtained with data acquisition cards, DAQs, and
other electronic equipment and stored in a proprietary format. PTB follows its
in-house quality management system with procedural requirements for data
handling and an additional directive for data storage. For testing purposes,
surrogate data will be generated, simulating real data acquisition.

##  TUIL

Phantom data collection will be obtained according to: Tenner,U., Haueisen,J.,
Nowak,H., Leder,U., Brauer,H.: Source Localization in an Inhomogeneous
Physical Thorax Phantom. Physics in Medicine and Biology, 44, 1969 - 1981,
1999.

No existing data will be used. Quality assurance processes will be applied
according to the DFG guidelines for good scientific practice.

##  UDA

Raw data on human subjects will be collected as time series using in-house
techniques and stored in a proprietary format. Personal metadata consisting of
consensus and questionnaire will be stored together with the raw data. To test
the impact of the project achievements on connectivity, surrogate data will be
generated simulating real data acquisition. Quality assurance processes will
be assessed according to national ethical committee guidelines.

##  VTT

Due to the innovative and ground-breaking nature of the BREAKBEN project,
steps in the detector development depend strongly on the results obtained in
the previous step and on the difficulties encountered along the way. Data will
be collected whenever a new generation of detectors or electronics is finished
and its/their performance needs to be assessed. The data collection will take
place in ad hoc manner, following the general principles of scientific inquiry
and the craftsmanship of long-time practitioners of the art.

A large part of the data from the measurement instruments or data acquisition
systems will be a part of debugging work spent on the system. Only the data
obtained with devices in a proper working condition is counted as publishable
here.

#  4 Levels of Confidentiality and Flow of the data

##  4.1 Confidentiality

Overall, there are three basic levels of confidentiality, namely Public,
Confidential to consortium (including Commission Services), and Confidential
to the Partner / Subcontractor.

Each data type is treated differently with regard to the level of
confidentiality, e.g., the untreated research data such as personal data of
patients and research subjects, whereas most project deliverables are actively
disseminated. Some of the data falls under the EU and national laws on data
protection and for this reason the project is obliged to seek necessary
authorizations and to fulfill notification requirements.

The project will assume the principle of using commonly used data formats for
the sake of compatibility, efficiency and access. The preferred data types are
in MS Office compatible formats, where applicable.

**Figure 1. Data types displayed in three levels of confidentiality, as
applicable in most cases. There are exceptions to this confidentiality
classification. Software and design data are not displayed, because their
confidentiality may be in any of the three levels. See section 4 for more
information.**

Figure 1 displays how the previously mentioned data types are positioned in
the level of confidentiality context. Only one data type – (untreated or raw)
research data – is totally situated in one level of confidentiality, which
means that it solely remains with the partner or third party responsible for
collecting it. Three types (project data, analyzed data and reports and
communication) contain data of two different confidentiality levels. The
remaining two types, software and design data, may belong to any of the three
levels depending on the contents of the data and on potential restrictions due
to contracts and other policies. In general, there are exceptions to the given
confidentiality levels. For instance, there may be research data for which
public distribution for scientific purposes is possible and desirable. More
information is found later in this section.

The data flow in most cases starts at the bottom of the confidentiality levels
(Figure 1) and as the data moves up in levels, it is ensured that no data are
included that cannot be placed in the next level. This may involve reduction
of the data or anonymization, as necessary. However, anonymization is not
always possible, which may restrict the possibilities of changing
confidentiality level. Similarly, other privacy requirements may prevent
publishing data, including analyzed data (see below).

##  AALTO/HUS

Confidentiality: The data from the control subjects and patients are available
in anonymized form for the members of the research consortium. The averaged
data and anonymized MRI images can be used in scientific publications provided
that the individuals taking part into the research cannot be identified from
them.

##  ELEKTA

Again, there are three basic levels of confidentiality (Figure 2), namely
public, confidential to consortium, and confidential to the Partner (Elekta).

**Figure 2. Data types at Elekta displayed in three levels of
confidentiality**

**Project data** include agreements and financial data, and, therefore, are
confidential either to the project consortium or to the partner, depending on
the scope and content of the data.

**Reports and other communication data** are either public or confidential to
the project consortium, depending on the scope and content of the data.

**Design data** include information on Elekta’s development process and
background IP, and, therefore, are confidential either to the project
consortium or to the partner depending on the scope and content of the data.

**Software** include information on Elekta’s development process and
background IP, and, therefore, are confidential either to the project
consortium or to the partner depending on the scope and content of the data.

**Figure 3. Elekta data flows within and between the three levels of
confidentiality**

##  PTB

Research data and analyzed research data obtained by performing experiments on
humans cannot be opened to the public but to consortium level of
confidentiality. The subjects are recruited within PTB, Institute Berlin. PTB
intends to use maximum 50 subjects out of the about 400 strong workforce.
There is a high risk of violation of data privacy laws as the use of metadata
is very likely to allow the identification of the subject.

Research data obtained on phantoms are open to the consortium level of
confidentiality. Design data are confidential to the partner level only and
software is open to the consortium level of confidentiality.

##  UDA

Raw MEG and MRI recordings are available in anonymized form for the members of
the research consortium only upon request. Processed data are publicly
available after scientific publication. Reports are either public or
confidential to the project consortium, depending on the specific report as
indicated in the Grant Agreement.

##  VTT

Unedited design data will be kept confidential. The edited design data which
contains the findings obtained within the BREAKBEN project will be made
Consortium confidential or Public, depending on the scope or content of the
data. Editing involves hiding or removing those design items which are results
of internallyfunded development work or which may infringe earlier
confidentiality agreements.

Research data and analyzed research data will be made public.

###  4.2 Data Storage and Protection

Each partner has its own policies in storing and protecting data. As general
project guidelines, it is recommended that git is used for version control
where applicable and that the data are pushed into a repository with regular
backups. One such repository is the GitLab service maintained by Aalto
University IT Services, to which also personnel of non-Aalto partners can be
given password-protected access. It is also possible to turn a GitLab data
repository into a public repository when appropriate according to the data
flow. For documents that do not support proper version control, such as
Microsoft Office documents, the Eduuni workspace, is often the proper space
for storage and sharing. For deliverables, and other consortiumwide written
documents, Eduuni is always used.

When date information is included in file and folder names, it is recommended
that the date is positioned In the beginning of the name according to the ISO
8601 standard, YYYY-MM-DD, followed by an underscore (_) if other information
is appended before the filename extension. However, policies at individual
partner organizations may override these general guidelines.

##  AALTO

Data will be stored in regularly backed up storage maintained by the Aalto
University IT Services. In addition, the department GitLab service is used for
versioned data, which is also maintained by the local IT services. Data will
be anonymized, when applicable, before publishing.

Most data will be either versioned with git or saved under folder and/or file
names beginning with an ISO 8601 date stamp, or both.

##  ELEKTA

All the data will be generated according to standard Elekta procedures, and
stored to Elekta’s archives. The archives are backed up regularly. The
retention time and disposal of the data follow the guidelines of Elekta’s
procedure _EOYbms00015 Management of quality records._

The data will be copied to consortium members or made public when applicable
(see 4.1 Confidentiality above).

##  HUS

The MEG data are stored in the data storage system of the BioMag Laboratory.
The data are coded in recording phase and are protected by a user name and
password. The code key is kept in locked space separate from the data.

The MRI data of healthy subjects are stored in the Science PACS of HUS Medical
Imaging Center. The MRIs of the patients are stored in the Clinical PACS
system of HUS.

The data storage capacity for MEG and MRI images are adequate. No additional
services are needed.

Automatic tape backup for MEG data is used. The MRI data are backed up in the
usual hospital procedure. No new hardware or software is required. No charges
are required for data storage. The MEG-MRI data are stored by Aalto
University.

##  PTB

For human experiments, the names of the subjects will be pseudonymised using
an algorithm and the data saved only under pseudonames. Contact data will be
separately securely stored. All data will be saved on the PTB internal server.
Such data will be protected in compliance with the EU's Data Protection
Directive 95/46/EC aiming at protecting personal data. Compliance with privacy
rules as stated by the local ethics committee and national laws will be
ensured.

Analyzed research data that will be accessible at consortium and at public
level will be stored into BREAKBEN repository in the Aalto University’s GitLab
data repository.

PTB has a quality management system with procedural requirements for data
handling (QM-VA-22) and an additional directive for data storage. Both
directives clarify the responsibility of divisions for data storage and
central data backup at PTB. These directives are based on the rules specified
by Bundesamt für Sicherheit in der Informationstechnik (BSI). PTB data
handling is governed by the DFG rules for good scientific practice and in
accordance with the in-house quality management system. In particular, primary
data will be archived for 10 years after acquisition. Archiving will be
password protected and anonymous.

##  TUIL

Local TUIL repositories are used with no anonymization. The data will be
preserved on local computers. Useful conventions will be chosen for folder
structure and file naming. Versioning needs are minor and dealt with by
labeling data according to the date of the recording. The central service of
the computing center of TUIL takes care of backups. The TUIL staff is at
expert level. No hardware or software is required which is additional or
exceptional to existing institutional provision, nor will charges be applied
by data repositories.

##  UDA

**Anonymization of data:** For some of the activities to be carried out by the
project, it may be necessary to collect basic personal data (e.g., full name,
contact details, background), even though the project will avoid collecting
such data unless deemed necessary. Such data will be protected in compliance
with the EU's Data Protection Directive 95/46/EC aiming at protecting personal
data. Compliance with privacy rules as stated by the local Ethics Committee
and National laws will be ensured.

**Preserving and archiving data:** Data will be stored in two different
repositories, according to level of confidentiality. Raw data that are open
only at Partner Level will be stored in a local data storage server using
RAID-6 technology. Analyzed research data that are accessible at consortium
and at public level will be stored into a different local data server (using
ISO 8601 standard) and in the Aalto University GitLab service.

##  VTT

VTT will obtain an account in the commercial GitHub repository for the
duration of the project. The longer persistence of the data cannot be
guaranteed at the present date, as it is not clear which mechanisms could be
taken advantage of after the Breakben funding ends. Technical details of the
data storage are those provided by the GitHub service.

#  5 Opening the Data

As stated in previous sections, the project aims at providing open data where
applicable, although the nature of the data, or other requirements may prevent
publication.

All parties have signed/accessed to the project Grant Agreement and Consortium
Agreement, which detail the parties’ rights and obligations, including – but
not limited to – obligations regarding data security and the protection of
privacy. These obligations and the underlying legislation will guide all of
the data sharing actions of the project consortium.

The BREAKBEN project is participating in the Open Research Data Pilot, which
is an expression of the larger Open Access initiative of the European
Commission. Participation in the pilot is manifested on two levels: a)
depositing research data in an open access research database or repository and
b) choosing to provide open access to scientific publications which are
derived from the project research. At the same time, the consortium is
obligated to protect personal data and results.

The instructions to download and open the research data, together with
description of the contents of the files containing the data will be uploaded
in the same directory as the data themselves. A readme file describing the
data will be placed in the same repository as the data and identify the data
at the partner and consortium level. In most cases, publicly available
software and a desktop computer will be sufficient for validating the results.

To help potential users find the published data, the availability of the data
will be posted on the project website and the distribution of data will be
mentioned/cited in publications. The data will either be available to anyone
or only on a case-by-case basis with applicable conditions. Privacy
requirements may sometimes prohibit publication altogether, and a data sharing
agreement may be required in special cases. Data storage updates and backups
are managed by the system administrators or maintainers in order to prolong
the lifetime of the data.

Below are the policies designed for each participating organization.

##  AALTO/ VTT

Regarding the time of data publication, the aim is for article-related data to
be public at the time of publication of the article. The data types used are
selected so that they have specifications available so that opening the data
is possible also in the future if the same software is no longer available.
However, for some data, pieces of software may even be added as metadata along
with the data itself to help others handle the data. Sometimes, it may be
sufficient to refer to the software or specifications provided elsewhere.

Where applicable, data will be shared in a public repository accessible over
the internet. When (analyzed) research data are published for further
scientific analysis, a DOI may be assigned using a service such as Zenodo.

Licensing and conditions for such data will be determined based on the purpose
of the data. The data are released under a Creative Commons Attribution
license, unless the purpose or nature of the data requires otherwise. If the
situation is unclear regarding risks related to data security or privacy, the
partner will not publish data. To keep private data secure, it is accessed
over a secured SSL connection when accessing from outside networks.

##  ELEKTA

**The project data** will be stored in Elekta’s document archives and, when
applicable, copied to the project password protected Eduuni workspace
(confidential to the project consortium). When shared, the data will be made
available either according to external schedule or when requested.

**Reports and other communication data** include mainly MS Office documents,
in English. Public data will be published, for example, via the BREAKBEN
website. **Design data** include MS Office documents and CAD files and
**Software data** include the source code and the binaries of the software.
These data will be stored in Elekta’s document management system and when
applicable, shared, for example, via password protected Druva. When shared,
the data will be made available either according to external schedule or when
requested.

##  HUS

The present Finnish legislation enables data banks of patient samples (such as
blood or tissue samples) but forbids the use of signals such as MEG or MRI
data from individuals by parties not defined in the ethical permission of the
project. The averaged data and anonymized MRI images can be used in scientific
publications provided that the individuals taking part into the research
cannot be identified from them.

The data collected in HUS will be owned by HUS. The collected data can be used
by the members of the consortium as specified in the data management plan and
in the ethical permission.

##  TUIL

Data will be made available at the end of the publication process in formats
that enable sharing and longterm access to the data. Web page Typo3 will be
used for sharing the data, also beyond the original purpose of the data. Open
data will be shared with no conditions. Documentation will provide the
information needed for reading and interpreting the data in the future. In
addition, other imaging phantoms may be needed to validate the results.

Regarding managing risks to data security, no unacceptable risk is expected,
although normal backup policies are applied. Potential users (anyone) will
find out about data via a link in publications, and no data sharing agreement
or equivalent will be required.

##  UDA/PTB

Data sharing and long-term access is then regulated as follows:

  * Partner level: research data format: compatible with in-house or freeware tools made available to all the partner participants; shared through standard commercial programs (e.g. Office, Matlab). An internal repository available within the first deliverable will enable the sharing of the data. 

  

  * Consortium level: analyzed research data format will enable sharing through standard commercial software or software tools (e.g. scripts) made available to the Consortium members. A free of charge cloud system will be used to share the data. 

  

  * Public level: research result formats will enable sharing through standard commercial software or software tools (e.g. scripts) placed in the same repository as the results accessible via a free of charge repository. 

  

Beyond the original purpose the data can be shared as follows:

  * Partner level: Research data will be shared between researchers involved in specific tasks to ensure fulfillment of the project tasks and to internally discuss intermediate results; 

  

  * Consortium level: Analyzed research data will be shared between partners as required by specific project tasks, involving this and other partners. 

  

Conditions on shared data and minimizing restrictions are:

  * Third parties level: Access to research and analyzed data is restricted to users belonging to research entities. A formal request must be submitted by these users’ PIs. Specifically, re-use re-distribution, creation and publication of derivatives of the data with a third party can be granted within a cooperation after personal data protection/data privacy is guaranteed within the cooperation agreement. 

  

#  6 Data ownership

At Aalto and VTT, the ownership of data will be will be handled according to
applicable policies depending on the situation and/or the guidelines presented
in the section 3 of the grant agreement. Attribution will mainly be received
via citations to the work. While no research data are collected or produced by
Elekta, the data types presented in section 2 will be handled according to the
guidelines presented in the section 3 of the grant agreement. Regarding PTB,
data are owned by the research institution collecting the data (PTB).

At TUIL, the owner of collected data will be TUIL. Re-use of the data will be
permitted with no conditions. Redistribution of the data as well as creation
and publication of derivatives from the data are permitted either with or
without conditions. Others will also be permitted to use the data to develop
commercial products or in ways that produce a financial benefit for
themselves, either with or without conditions. The people who generated the
data sets will receive attribution for their work via citations.

At UDA, research data will be owned by UDA. Re-use of the data, re-
distribution of the data as well as creation and publication of derivatives
from the data will be permitted either with or without conditions. The people
who generated the data sets will receive attribution for their work via
citations.