#  Introduction

##  **Project Summary**

ReMAP “Real-time Condition-based Maintenance for adaptive Aircraft Maintenance
Planning” (hereinafter also referred as “ReMAP” or “the project”), is a
European project started on the 1  st  of June 2018 and has a duration of four
years. The project addresses the specific challenge to take a step forward
into the adoption of Condition-Based Maintenance in the aviation sector. In
order to achieve this, a datadriven approach will be implemented, based on
hybrid machine learning & physics-based algorithms for systems, and data-
driven probabilistic algorithms for systems and structures. A similar approach
will be followed to develop a maintenance management optimisation solution,
capable of adapting to real-time health conditions of the aircraft fleet.
These algorithms will run on an open-source IT platform, for adaptive fleet
maintenance management. The proposed Condition-Based Maintenance solution will
be evaluated according to a safety risk assessment, ensuring its reliable
implementation and promoting an informed discussion on regulatory challenges
and concrete actions towards the certification of Condition-Based Maintenance.

##  **Purpose of this Document**

Deliverable D9.3 Data Management Plan (DMP) addresses the way research data is
managed in the ReMAP project within the Open Research Data Pilot (ORD Pilot).
The ORD pilot aims to improve and maximise access and re-use of research data
generated by Horizon 2020 projects, considering the need to balance openness
and protection of sensitive information, commercialisation and Intellectual
Property Rights (IPR), privacy concerns, as well as data management and
preservation of questions.

DMPs are a key element for good data management, as they describe the
management of the data to be collected, processed and published during a
research project, creating awareness about research data management topics
such data storage, backup, data access, data sharing, archiving and licensing.
ReMAP hereby states the adherence to the FAIR  1  data principles, whereby
research data is made Findable, Accessible, Interoperable and Re-usable for
the community, responsibly considering possible data restrictions on public
sharing.

##  **Context**

This Data Management Plan is closely linked to the report on D10.1 Ethics
Requirements – POPD, submitted to the European Commission at the end of
October 2018, whereby in section 4, a general overview of ReMAP data
management strategy regarding interview data was described.

In the following chapters, we enfold the ReMAP DMP making use of the UK
Digital Curation Centre template for _Initial DMP_ . It is acknowledged that a
DMP is a living document and, therefore, as the implementation of the project
progresses and significant changes occur, we will update this plan accordingly
on a finer level of granularity at the end of each project period (M18, M36
and M48) using the templates for the _Detailed DMP_ and _Final Review DMP_ .

It is important to mention that, at the outset of the project, we have engaged
the Data Steward of the Faculty of Aerospace Engineering at Delft University
of Technology (TUD), Dr. Heather Andrews (H.E.AndrewsMancilla@tudelft.nl). TUD
has appointed dedicated  Data Stewards  2  at every faculty with the goal of
improving awareness of good research data management practices in a
disciplinary manner. Data Stewards are then the first point of contact for
research data management advice. The input and guidance of the faculty’s Data
Steward are at the basis of this plan.

#  Data summary

###  Purpose

The aim of ReMAP is to develop a maintenance management optimization solution
to monitor the real-time health conditions of aircrafts. The algorithms
resulting from this project will run on an open-source IT platform built by
the consortium.

The data collected, stored, protected and analysed throughout this project
consist of:

  * personal data from interviews and workshop participants (see D10.1 Ethics Requirements – POPD deliverable) and, 

  

  * technical data (KLM operations, aircraft sensors and structural laboratory tests). 

  

Personal data from interviews and workshop participants will be used for both
dissemination purposes and research purposes. Data collection, storage,
protection and analysis procedures regarding personal data used for
dissemination purposes has been already presented in the D10.1 Ethics
Requirements – POPD deliverable. In this DMP, only the management of
interviews data used for research purposes will be discussed.

Regarding the technical data, this consists of: i) data provided by KLM on
aircraft operations, health monitoring and flight information; ii) laboratory
test data on aircraft structural elements; iii) programming algorithms (code)
to analyse and model different aspects of the maintenance management of
aircrafts; iv) technology design and assessment results; and v) external data
collected from multiple sources relevant for the project (e.g., EUROCONTROL
Monoradar Weather Data or EASA Air quality data repository).

_Table 1. Research activities to be done per partner_ lists a description of
the research that will be carried out by each collaborating partner, and its
purpose, focusing on the technical data mentioned above.  
  
<table>  
<tr>  
<th>

ATOS

</th>  
<th>

_IT Platform for Integrated Fleet Health Management solution (IFHM)_

Development of an IT platform to collect data from systems and sensors, and
provide it to the different algorithms and decission support solutions.

Collaborating partners: ENSAM , IPN, KLM, TUD

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

CTEC & STEC

</td>  
<td>

_Development of sensor technology for Structural Health Management (SHM)_

Procurement, development and integration of promising sensor technologies for
damage monitoring in aeronautical composite structures.

Collaborating partners: ENSAM, UPAT

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

ENSAM

</td>  
<td>

_Damage monitoring of complex aeronautic structures by means of Ultrasonic
Lamb Waves_

Develop hardware and software systems able to monitor damages in composite
structures by means of Lamb waves emitted and received by piezoelectric
elements.

Collaborating partners: CTEC

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

EMB

</td>  
<td>

_Development of predictive algorithms for aircraft systems Prognostics &
Health Monitoring (PHM) _

Develop algorithms for predicting the Remaining Useful Life (RUL) of aircraft,
based on data from aircraft models provided by KLM (KLC).

Collaborating partners: ATOS, KLM, ONERA, UTRCI, UC

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

KLM

</td>  
<td>

_Development, verification and test of an IFHM_

KLM will provide the data to the rest of the partners. Data provided by KLM
consist on operations data, aircraft health monitoring data, and flight data
for different aircraft models. These datasets are commercially and safety-
sensitive data. Thus, they are subject to strict institutional and national
rules and protocols (see Annex A).

Collaborating partners: ATOS, EMB, ONERA, TUD, UTRCI, UC

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

ONERA

</td>  
<td>

_Safety risk assessment of the IFHM_

Identification of hazards and safety barriers related with CBM technologies.
Future CBM regulations and industrial processes discussion.

Collaborating partners: EMB, KLM, ONERA, TUD

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

OPT

</td>  
<td>

_Design and manufacture of aircraft structure cupons and study of ReMAP’s
impact on aircraft weight_

Design, test and manufacture components for experimental tests (WP4). Study of
the impact of a Condition-based Maintenance (CBM) approach in weight reduction
of aircraft structures.

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

</td>  
<td>

Collaborating partners: -EMB, TUD, UPAT

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

TUD

</td>  
<td>

_Development of predictive algorithms for aircraft structures and systems &
maintenance scheduling decision support _ _tool & safety risk assessment _

Develop validated multi-disciplinary SHM system methodologies towards
remaining useful life estimation (prognosis) in the presence of adverse
conditions during flight. Several sensing technologies are going to be used
along with an ambitious extended test campaign. This campaign will result in a
massive SHM database upon which the diagnostic and prognostic methodologies
are going to be developed and validated.

Development of the adaptive aircraft fleet maintenance schedule solution,
including the definition of an uncertainty mapping.

Model development for safety assessment of CBM technologies included in the
_IFHM._

Collaborating partners: CTEC, EMB, ENSAM, OPT, STEC, TUD

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

UTRCI

</td>  
<td>

_Development of system level Prognostics & Health Monitoring (PHM) &
Condition-Based Monitoring (CBM) _ _technologies_

Develop PHM models to predict and detect degradation and failures in aircraft
systems and components by using data on aircrafts, weather conditions,
component removals, among others.

Collaborating partners: ATOS, EMB, KLM, ONERA, UC

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

UC

</td>  
<td>

_Development of system level Prognostics & Health Monitoring (PHM) &
maintenance planning decision support tool _ Enabling edge computing and

Actionable information extraction and visualization for optimal maintenance

Development of efficient machine learning algorithms for optimal maintenance.

Development of a user interface for the maintenance planning decision support
tool. Development of an adaptive plan and uncertainty mapping.

Collaborating partners: ATOS, EMB, IPN, KLM, ONERA, TUD

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

UPAT

</td>  
<td>

_Structural Health Management: Diagnostics & Remaining Useful Life (RUL)
Prognostics _

Develop validated multi-disciplinary SHM system methodologies towards
remaining useful life estimation (prognosis) in the presence of adverse
conditions during flight. Several sensing technologies are going to be used
along with an ambitious extended test campaign. This campaign will result in a
massive SHM database upon which the diagnostic and prognostic methodologies
are going to be developed and validated.

Collaborating partners: CTEC, EMB, ENSAM, OPT, STEC, TUD

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

**Table 1. Research activities to be done per partner**

###  Data Types and Formats

As explained above, there are two main sets of data in this project: technical
data (KLM data, laboratory data, programming algorithms, design and assessment
data, external data) and personal data.

_Technical data:_

The IFHM solution resulting from ReMAP will be developed, validated and
demonstrated based on KLM’s operational data. KLM will follow internal
protocols to anonymize all data before sharing it with the consortium
partners, meaning that the research team will not have access to personal
data. The KLM operational data includes:

— Aircraft Health Monitoring data, used as input and/or validation data for
to-be-developed model for assessing condition and prognosis of individual
aircraft systems. This data is owned by KLM and it is restricted data under
governmental and company regulations.

— Aircraft Maintenance data, used to support the development of the
maintenance schedule solution and as validation data for tobe-developed model
for assessing condition and prognosis of individual aircraft system. This data
is owned by KLM and it is restricted data under governmental and company
regulations.

— Risk assessment, used to map and mitigate operation, technical, commercial,
economical and health & safety risks that are associated with aircraft
maintenance. This data is owned by KLM and it is restricted data under
governmental and company regulations.

Most of these datasets correspond to log files, reporting documents and
tabular data. Health monitoring data and flight data from aircraft models are
observational data from sensor measurements. KLM will provide the monitoring
data to partners in .csv format, while log files and reports will be provided
in .pdf format. It is important to mention no personal information (e.g.
ground staff, flight crew, etc.) will be disclosed by KLM. No information will
be disclosed that can, directly or indirectly, be linked to KLM staff either.

Aside KLM’s data, there will be experimental data from laboratory tests on
aircraft structural elements and composite generic elements and subcomponents,
typically found in modern commercial aircraft. This type of data will be
generated during the project at UPAT and TUD premises. The data correspond to
sensor recordings obtained during the tests, as well as Finite Element
Analysis outputs from simulation endeavors. Various file formats will be
involved depending on the monitoring technique and the associated software
that is utilized to record the data. However, all data will be converted to
.txt, .csv, or .dat files after some raw data processing to increase data
interoperability.

The codes that will be generated throughout the project will be mainly in
MATLAB, Python and R languages. For their development and validation,
researchers will also make use of external data (e.g. on weather conditions,
pollution information, etc.) collected from multiple sources, which shall
include public repositories and other existing data gathering channels
available to partners (e.g., EUROCONTROL Monoradar Weather Data or EASA Air
quality data repository).

Finally, the information generated with the design and development of sensor
technology and aircraft structures will be produced during the project,
together with data related with the assessment of the performance of the
multiple technologies developed for the IFHM solution proposed.

_Personal Data:_

As mentioned in Section 3 of D10.1 Ethics Requirements – POPD deliverable, the
project will carry out interviews to team members and external workshop
participants. In order to collect, store and use the personal data from
interviews, the consortium shall seek the informed consent of each individual,
following the policy of the EU for Data Protection (see D10.1 Ethics
Requirements – POPD deliverable). The individual subjects will be informed
about all aspects of the research in which they are being asked to participate
and the future use of the data they might provide.

The interviews will be recorded as audio-visual footage. The recording of each
interview will be stored in the work laptop/computer of the IPN researcher in
charge of the interview. The data will be saved in a private password-
protected folder accessed only by the respective IPN researcher. The IPN team
or another partner will anonymise and transcribe the data (e.g., into .docx
files). The transcriptions will then be shared with relevant consortium
researchers via SURFdrive  ( _https://www.surfdrive.nl_ )  , which is a
password protected cloud storage service. The raw interview data will be then
transferred to a private repository in the DataverseNL environment ( _
https://dataverse.nl/)  . _ This environment is expected to be safe enough for
the recorded material. In case the raw interview data contains highly
sensitive information, then the data will be saved in a Project Data drive at
TUD. This is a drive maintained by IT TUD and it is meant for confidential
data. The folder containing the raw data will be managed by Dimitrios
Zarouchas ( _d.zarouchas@tudelft.nl_ ) from TUD, and accessible to the project
coordinator Bruno Santos ( _b.f.santos@tudelft.nl_ ) and to Mónica Ferreira (
_mferreira@ipn.pt_ ), the IPN coordinator for WP9.

If it becomes pertinent for the research purpose of the project, the interview
data might be released to the public. This will happen only if the respective
interviewee agrees on it via email, reacting to a consent request sent by
either IPN (WP9 leader) or TUD (project coordinator) explaining the context,
purpose, content to be made public and the right to reject this request, which
is assumed by default. If any of the interviewees request not to keep the
recordings but for getting notes, they will be deleted from the repositories
and only the notes will be circulated among the consortium.

###  Data Size

The estimated size of the data delivered by KLM to partners is about 2 TB in
total, taking as a reference data from 30 aircraft operating during a time
frame of 3 years. Included in these 2 TB is also the external data (e.g.,
weather conditions and pollution information) taken from public repositories
and/or data gathering channels available to partners.

The estimated size of the laboratory data is expected to be in the order of
100 GB.

The processing of the data and its use for algorithm development might be on
the order of 1 TB.

The data regarding the design and development of sensor technologies and
aircraft technology, together with the data resulting from the technology
assessment, should be in the order of few tens GB.

The estimated size of the interview data considering the audio-visual footage
is undetermined, but it might be in the order of few hundred GBs (considering
they will mainly be audio files and the transcribed files into .docx
documents).

###  Data Utility

The final outcome of ReMAP will be useful for aircraft manufacturers (OEMs),
maintenance service providers and airlines around the world. The data
generated throughout the project will be useful for:

— The data from the laboratory tests will be useful to structural scientists,
OEMs and airline researchers, for the future development and training of SHM
prognostics and diagnosis data-driven algorithms.

— The sensor technology design and reliability performance data will be useful
to sensor companies and OEMs, for the analysis and development of sensor
solutions for SHM in future aircraft.

— The safety risk assessment data will be essential for the development of the
common roadmap towards the implementation of CBM in practice. In particular,
it will be useful for the discussion with EASA about continuing airworthiness
regulations part-M and part-145, and with the Maintenance Steering Group-3 (or
MSG-3) regarding aircraft maintenance procedures and they could be adapted to
CBM.

— The IFHM validation and test data will also be essential for the development
of the common roadmap. In this case, regarding the involvement of maintenance
service providers and airlines in a common solution to the implementation of
CBM. The results will test the overall capability of the multiple CBM
technologies developed, including the adaptive aircraft maintenance schedule
approach proposed.

#  FAIR data

##  **Making data findable, including provisions for metadata**

####  Metadata

During the research project, KLM will deliver the data to partners in a
structured way with proper documentation (README file) indicating (at least):

  1. Data origin and collection methodology 

  

  2. Structure and organisation of data files 

  

  3. Data manipulations applied prior to sharing 

  

  4. Variable names and descriptors (if applicable) 

  

  5. Definitions of terminology and acronyms 

  

  

At the moment there are no metadata standards defined, but KLM might select a
metadata standard at a later stage for partners to use when describing the
data. Even though KLM data will not be disclosed to the public (because of
safety and commercial reasons; see Annex A), such standard would be the one
used for other publishable datasets for consistency within the project. If no
discipline specific standard is used, then Dublin Core (  _www.dublincore.org_
)  metadata standard will be adopted (and further information on the data will
be delivered in file headers or in separate documentation files). This last
statement also applies to documentation files (e.g. reports) and other types
of data including experimental data and input/output tabular data for code
training, testing and validation.

Regarding codes, these will be developed and managed mainly via Gitlab
(partners may use their account or use an account provided by ATOS) and
Subversion (SVN), where both allow easy metadata attachment. The metadata
standard to be adopted for all technology codes will be discussed as part of
the IT platform requirements and specifications (WP2). As stated above, if no
discipline specific standard is used, then Dublin Core metadata standard will
be adopted (and further information on the data will be delivered in file
headers or in separate documentation files).

Once scientific journal publications are published (in Open Access),
publishable data (according to the Consortium Aggrement) will be publicly
archived for the long term via the 4TU.Centre for Research Data archive
(documentation, experimental data and tabular data;
_https://researchdata.4tu.nl/en/home/_ )  , or similar online archives,
following their metadata standards (Dublin Core). TUD researchers can,
nowadays, upload up to 1 TB of data per year free of charge. This should
suffice for the data that will be archived for the long term. The 4TU.Centre
for Research Data Archive ensures data will be well-preserved and findable in
the long term (each uploaded dataset is given a unique persistent digital
identifier). In order to allow for responsible public reuse of the data,
datasets will be publicly released under an open content license (CC-BY). More
specific, metadata of the files in the dataset will be given in XML format (if
necessary) following the standards agreed upon during the project.

####  Version Control and File Naming Conventions

Partners working on programming algorithms will use Gitlab repositories to
collaboratively work with their research team members. Gitlab allows for clear
code version management, and it is already available to the respective
partners.

Subversion tool will also be used by some partners to keep track of versioning
for documents related to other technical data files.

Reports and other types of data will be managed manually for which file naming
conventions will be followed as the project progresses. This will described in
D1.1 Project Handbook.

##  **Making data openly accessible:**

The data provided by KLM cannot be open to the public for safety and
commercial reasons (see Annex A for the provisions for KLM Operational Data
use). Processed and/or analysed data might be released to the public via the
4TU.Centre for Research Data Archive after proper discussion with KLM (KLC).
Codes and auxiliary scripts built upon the processed data provided by KLM
might be released via GitLab after proper discussion with all partners.

During the project, laboratory data will be accessible only to consortium
partners. Some processed data might be subject to Intellectual Property Rights
of the respective partner(s) that generate the data, and thus, will be
restricted for use within the consortium only. Whenever a journal article is
ready to be published (in Open Access), the laboratory data related to the
journal article will be released openly to the public via the 4TU.Centre for
Research Data Archive. This data includes all data necessary for the re-use of
the results, as well as the data needed to validate them.

The final technological outcome of ReMAP will be an open IT cloud platform
where the finalized algorithms can work in an interoperable manner. This IT
platform will be open source. The platform will be built in a modular way,
following an architecture that will allow the integration of third-party data
analytics and maintenance management solutions or the exploitation of
solutions developed by the ReMAP consortium (these ones, not necessarily
open).

Regarding the interviews to team members and workshop participants, the raw
interview data (audio visual footage) will not be released to the public. It
will only be accessible to relevant partners for a maximum period of 10 years
and erased afterwards. The data will be kept in the long term for recording
and auditing of the project and so it can be used for future research and
learning, unless the interviewee does not grant such a permission.
Nonetheless, as described, the participants have the “Right to be forgotten”.
They can request at any time the elimination of their personal data (such as
names, emails, contact details, information from interviews) stored in the
project data storage services and IPN servers.

If requested by the participants, when given consent for the interview, only
anonymized transcripts of the interviews might be used for research purposes.
The anonymized transcripts will be available only to relevant consortium
partners during the project. If the transcripts are used as material for a
journal article, then anonymized transcripts will be published via the
4TU.Centre for Research Data Archive at the same time the journal publication
is released.

The public outcomes of workshops that can be made public will be shared via
DataverseNL, with reference to it via the project’s website. Unless an
informed consent is given, the public outcomes from these workshops will be
free of any personal reference. The workshop coordinator will be responsible
for preparing these results and anonymize the information when necessary.

####  Documentation and Software for Data Access

As all openly publishable material will be made available via the 4TU.Centre
for Research Data Archive, DataverseNL or GitLab, the datasets’ models will be
easily downloadable from these platforms with the respective metadata.

Open and standard formats will be preferred for archived data files (e.g.,
.csv, .txt) and code (e.g., python). Proper documentation files will be
delivered together with the datasets in order to facilitate reuse of data.

In some cases, MATLAB files (.mat) will also be released, as MATLAB will be
used to record, process and visualize data in some research lines of ReMAP.
MATLAB is a licensed software, widely used in the engineering community, and
it is already available to all partners. CATIA software will also be used for
computer-aided engineering. This is a licensed software already available to
partners. Output design files of CATIA software will be converted to other
formats whenever the data can be open to the public, to facilitate reuse.

##  **Making data interoperable:**

As mentioned above, all publishable data will be delivered in open and
standard data formats. Discipline specific metadata is currently under
discussion. If applicable, metadata will be delivered in XML/JSON format
together with the data (depending on the chosen format).

Proper documentation (README) files will be delivered accordingly. Tabular
data and codes (auxiliary scripts) will be archived with informative and
explanatory headers to facilitate data re-use and interoperability.

All code will be managed via Gitlab and/or SVN, which are interoperable with
one another, and are platforms that encourage interoperability between
different workflows.

The final IFHM solution will gather different algorithms that will be
interoperable with each other. The open IT platform over which the algorithms
will be implemented, will have proper documentation and manuals for
researchers to use.

##  **Increase data re-use (through clarifying licenses):**

The data provided by KLM may never be disclosed because of safety and
commercial reasons, not after the end of the Project and not after the end of
the 4-year non-disclosure term stated in the Consortium Agreement (see Annex
A). All other data that cannot be disclosed (except KLM data) will be kept at
the respective institutional server for the long term (at least 4 years after
the end of the project); accessed only by team members within the institution,
for auditing and validation purposes. It is also acknowledged that, for some
of the outcomes, copyright and IPR rules as stated in the Consortium Agreement
may apply. This will include some of the _Prognostics & Health _ _Monitoring
(PHM) solutions to be developed_ . The following release of this data will be
determined based on an internal review and decision from the Steering
Committee, supported by the Project Board.

Since the results from this project will make a strong impact in the aviation
sector (airlines, manufactures, maintenance service providers, etc.), we find
it is extremely important to share the data responsibly. Hence datasets that
will be open to the public will be released along the journal scientific
publications after proper discussion with partners. The datasets will be
published via repositories such as GitLab (algorithms, code, auxiliary
scripts) and the 4TU.Centre for Research Data Archive (documentation, images,
tabular data, etc.) under open content licenses in order to increase data re-
use (e.g., CC-BY license for documentation and MIT license for software and
code). In the same way, and in order to motivate re-use of data, the journal
articles associated to these datasets will be published in open access and/or
selfarchived in ReMAP’s website and subject repositories, following the
publisher’s self-archiving policies.

Regarding the final IT platform, this will be distributed, deployed, and
explored according to an open source license. The platform itself, and the
users may integrate their data analytics solutions or use ReMAP’s proposed
solutions (both the ones made available in the public domain, and the ones
copyrighted subjected to usage fees).

#  Allocation of resources

###  Costs

In principle no costs are expected for the archiving of the publishable data
via the 4TU.Centre for Research Data Archive nor via Github. TUD researchers
can upload nowadays up to 1 TB of data to the 4TU.Centre for Research Data
Archive (per year) free of charge. TUD researchers also have free of charge
access to the DataverseNL environment. Also, most of the software used for
version control and data processing are already available at each institution,
as well as the storage capacity and privately accessed drives managed by each
institution’s IT department.

The IT platform will be developed and implemented in ATOS infrastructure,
using Cloud services with rental costs included in the budget of the project
(ATOS ‘other goods and services’ budget).

###  Responsibilities

The following table specifies the team members who will be in charge of the
management of the data, within each research line of study. It is important to
mention that each institution has support staff that can provide advice
whenever data management issues arise, and who will be contacted if it is
necessary.  
  
<table>  
<tr>  
<th>

Partner

</th>  
<th>

Name

</th>  
<th>

Email address

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

TUD

</td>  
<td>

Dimitrios Zarouchas

</td>  
<td>

d.zarouchas@tudelft.nl

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

ATOS

</td>  
<td>

Javier García Hernández

</td>  
<td>

javier.garcia@atos.net

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

ENSAM

</td>  
<td>

Nazih Mechbal

</td>  
<td>

nazih.mechbal@ensam.eu

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

CTEC

</td>  
<td>

Frank Claeyssen

</td>  
<td>

frank.claeyssen@cedrat-tec.com

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

EMB

</td>  
<td>

Rúben Menezes

</td>  
<td>

ruben.menezes@pt.embraer.com

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

KLM

</td>  
<td>

Floris Freeman

</td>  
<td>

Floris.Freeman@klm.com

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

OPT

</td>  
<td>

Nicole Cruz

</td>  
<td>

nicole.cruz@optimal.pt

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

UTRCI

</td>  
<td>

Anarta Ghosh

</td>  
<td>

ghosha@utrc.utc.com

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

UC

</td>  
<td>

Bernardete Ribeiro

</td>  
<td>

bribeiro@dei.uc.pt

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

UPAT

</td>  
<td>

Theodoros Loutas

</td>  
<td>

thloutas@upatras.gr

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

In case any of the above team members is unavailable, other research team
members (within one institution) will have access to the data, as all data
will be stored on the respective institutional servers, with provided access
only to team members.

#  Data security

KLM will take care of anonymizing the data before sharing it with partners.
Servers that are set-up by KLM will have a redundancy scheme (e.g. RAID5 or
alternative) or backup plan to mitigate risks of disk failure. KLM internal
servers will keep a copy of the original raw data, in case the processed data
is no longer available. Also, KLM will monitor ReMAP’s process through an
internal knowledge management system (e.g. Confluence). All data that KLM
staff generated for ReMAP will be stored on an Office365 or similar
environment, such that other KLM staff can pick up work in case of long-term
illness or unforeseen staff changes.

Some data will be processed in work laptops of research team members only when
allowed (given the sensitivity of the data). Master copies will be kept at the
drives of each respective institution. The IT departments of each institution
will maintain the data regarding backups (redundancy) and secure storage
(protected access to only team members). Only team members within each
institution will have access to the data during the research project. Such
data access will be set up by the respective IT departments of each
institution. The data that will remain close to the public will be archived at
each partner’s servers for at least 4 years after the end of the project.

Surfdrive will be used for temporal data storage and for data sharing among
different partners coordinated by TUD (coordinator). Google Drive might also
be used for the sharing and temporal storage of non-sensitive data.

#  Ethical aspects

Please refer to D10.1 Ethics Requirements – Protection of Personal Data (POPD)
deliverable for the management of personal data information for both
communicational and research purposes.

It is important to mention, in case there are ethics-related questions or
issues arising throughout the project, these will be reported to Bruno Santos
( _b.f.santos@tudelft.nl_ ) and will be discussed accordingly among team
members. Extra advise can be discussed with the Human Research Ethics
Committee of TUD (at _HREC@tudelft.nl_ ).

#  Other

ReMAP will make use of the TUD Research Data Framework Policy which can be
found following:
_https://www.tudelft.nl/en/2018/library/researchdatamanagement/tu-delft-
research-data-framework-policy-published/_