1. **Executive Summary**

  

  

This document comprises deliverable D1.2 Data Management and Quality Assurance
Plan of WP1 Management Working Package. The document is prepared according to
“H2020 templates: Data management plan v1.0 – 13.10.2016”. In general terms,
the research data is aimed to follow the 'FAIR' principle that refers to **f**
indable, **a** ccessible, **i** nteroperable, and **r** e-usable data. A-WEAR
will participate in the Open Research Data Pilot of Horizon 2020 and hence
will make the research data publicly available.

This document is a living document in which information can be made available
on a finer level of granularity through updates as the implementation of the
A-WEAR project progresses and when significant changes occur. This document
would be updated in the context of the periodic evaluation/assessment of the
project if any changes appear.

  2. **Partners**

  

  

This section provides a list of A-WEAR partners and corresponding
abbreviations. We remark that the abbreviations are used here only for the
sake of compactness, but they may not be affiliated with or reflect company’s
official abbreviation.

**Table 2-1 A-WEAR Beneficiaries**  
  
<table>  
<tr>  
<th>

**Consortium Member**

</th>  
<th>

**Legal Entity**

**Short Name**

</th>  
<th>

**Country**

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

TAU

Tampere University (formerly Tampere University of Technology)

</td>  
<td>

TAU (formerly

TUT)

</td>  
<td>

Finland

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

UJI

Universitat Jaume I de Castellon

</td>  
<td>

UJI

</td>  
<td>

Spain

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

BUT

Brno University of Technology

</td>  
<td>

BUT

</td>  
<td>

Czech

Republic

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

UPB

University “Politehnica” of Bucharest

</td>  
<td>

UPB

</td>  
<td>

Romania

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

URC

Universita Mediterranea di Reggio Calabria

</td>  
<td>

UNIRC

</td>  
<td>

Italy

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

#  Table 2-2 A-WEAR partner organizations  
  
<table>  
<tr>  
<th>

**Partner Organization**

</th>  
<th>

**Legal Entity**

**Short Name**

</th>  
<th>

**Country**

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

NET

Netcope technologies

</td>  
<td>

Netcope

</td>  
<td>

Czech

Republic

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

CIT

CITST

</td>  
<td>

CITST

</td>  
<td>

Romania

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

NXP

NXP Semiconductors

</td>  
<td>

NXP

</td>  
<td>

Romania

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

WPS

Wirepas

</td>  
<td>

Wirepas

</td>  
<td>

Finland

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

DLI

Digital Living International Oy

</td>  
<td>

DLI

</td>  
<td>

Finland

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

BEIA

Beia Consult International

</td>  
<td>

BEIA

</td>  
<td>

Romania

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

S2G

S2 Grupo

</td>  
<td>

S2 GRUPO

</td>  
<td>

Spain

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

ERI

Ericsson

</td>  
<td>

Ericsson

</td>  
<td>

Finland

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

CPD

City of Castellón, police department

</td>  
<td>

\-

</td>  
<td>

Spain

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

IDOM

IDOM Consulting, Engineering, Architecture S.A.U.

</td>  
<td>

IDOM

</td>  
<td>

Spain

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

SWO

Sewio Networks

</td>  
<td>

SEWIO

</td>  
<td>

Czech

Republic

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

T6E

T6 Ecosystems

</td>  
<td>

T6-ECO

</td>  
<td>

Italy

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

The Working Package (WP) structure of the project is illustrated below.

**Figure 1 Working Packages in A-WEAR**

  3. **Data Management Plan**

  

  

A-WEAR is part of a flexible pilot under Horizon 2020 called the Open Research
Data Pilot (ORD pilot). The ORD pilot aims to improve and maximize access to
and re-use of research data generated by Horizon 2020 projects and it takes
into account the need to balance openness and protection of scientific
information, commercialization and Intellectual Property Rights (IPR), privacy
concerns, security as well as data management and preservation questions.

  1. **Data Summary**

    1. **Data Purpose**

  
  

  

  

All technical WPs, namely WP2-WP5 (see Figure 1) will rely on various data
types (simulated, measurements-based, etc.) in order to analyze, verify, test,
and improve the developed algorithms and methods.

  2. **Data Types and Formats**

  

  

The data will consist of software data developed by the (e.g., based on
Matlab, C/C++, Python, VHDL, Java, Android OS, Wear OS, etc.), raw data
measurements from the field experiments and testbed campaigns, mathematical
and statistical models, and channel traces and context-awareness metrics in
time, frequency or space domains. Data types can include multidimensional
time-series, structured data, and unstructured data – such as image analysis,
video analysis, audio analysis and machine generated data analysis. The data
types specific to eHealth studies in WP3 will be electronic health records,
clinical data based on HL7 standard, DICOM files.

  3. **Data Re-using**

  

  

Open-access datasets and other open-access data might also be used for the
scope of A-WEAR research. The main open-access repositories that we plan to
use are:

  1. EU Zenodo repository (  _www.zenodo.org_ )  : it contains research papers, datasets with measurements, software tools (Matlab, Python, etc.), etc. 

  

  2. EU OpenAIRE (  _https://www.openaire.eu/_ )  is the EU emerging repository for open science. We remark that the metadata records of the published data sets on Zenodo can be easily loaded into the OpenAIRE platform. 

  

  3. EU Open Data Portal (  _https://data.europa.eu/euodp/en/home_ )  : a repository of an expanding range of data from the European Union institutions and other EU bodies 

  

  4. Github repository (  _https://github.com/_ )  : it mostly contains software tools, but also reduced-scope datasets with measurements are available. Github and and Zenodo are tightly coupled and publishing majors releases of data in Zenodo from GitHub is trivial (almost automated). 

  

  5. Crawdad (  _https://crawdad.org/_ )  is an archive for wireless datasets at Dartmouth university 

  

  6. ArXiv (  _https://arxiv.org/_ )  is an archive of pre-print publications and unpublished work by research community in various fields (ICT, physics, mathematics, etc.) 

  

  7. Stanford Large Network Dataset Collection (  _https://snap.stanford.edu/data/#email_ )  is a library of relevant datasets for research on large social and information networks 

  

  8. Kaggle (  _https://www.kaggle.com/datasets_ )  : it provides a large collection of datasets and models in various areas, such as health domain (e.g., physiological parameters), demographics, data visualization tools, etc. 

  

  9. CodeOcean (  _https://codeocean.com_ )  is a collection of scientific codes (software) associated to papers published in IEEE venues, with the target of making the research results reproductible and reusable 

  

  10. UC Irvine machine learning repository (  _https://archive.ics.uci.edu/ml/index.php)_

  

  11. Finnish open data (  _https://www.avoindata.fi/en_ )  is a repository of open data sets from Finnish R&D units, covering all vertical industries, such as smart cities, agriculture, energy, health, etc. 

  

  12. US government open data (  _https://www.data.gov/_ )  is a huge repository of data, software tools, and other resources with the purpose to help the research in various areas and to develop web and mobile applications 

  

  13. Romanian government open data (  _http://data.gov.ro/_ )  is a Romanian repository of open-access data collected from public administration institutions in Romania 

  

  

All A-WEAR researchers will be highly encouraged to add all their publications
(and other relevant material) on Zenodo, in addition to other institutional or
personal repositories.

In addition to the above-mentioned open-access repositories, an open health
data repository will be available soon (managed by the Finnish National
Institute for health and Welfare), as an “Act on the Secondary Use of Health
and Social Data” has come to effect in Finland  1  since 1 May 2019.

The available open-source datasets can be used in various manners, such as:

  14. benchmark data to test the developed algorithms in A-WEAR; 

  

  15. benchmark unlabeled (blind) data to organize competitions in A-WEAR or other events (e.g., at IPIN annual events); 

  

  16. benchmark software codes to compare the developed algorithms with existing state-of-the-art from scientific literature; 

  

  17. benchmark calibration scenarios and parameters for the purposes of cross-validation; 

  

  18. PhysioNet offers free web access to large collections of recorded physiologic signals (PhysioBank) and related open-source software (PhysioToolkit). 

  

  

  4. **Data Origin 3.1.5. Data origin at Beneficiaries**

  

  

The data will be collected utilizing the hardware (HW) existing in the A-WEAR
team, such as Arduino, Raspberry Pi-s, wireless sensor/actuator devices of
various nature, RFID systems, Intel Galileo dedicated to medical devices,
brain computer interface systems, and Artix-7 development boards, as well as
software (SW) tools available on mobile devices (e.g. via Google play for
Android devices) or developed by the AWEAR team, software tools for network
traffic analyses (such as Wireshark or similar), external open source software
tools available online.

In order to attain A-WEAR objectives, large or massive wearable data might be
collected through crowdsensing approaches for the purpose of social and
consumer applications, such as eHealth and public safety (e.g., for studies in
WP3). Crowdsensed data can be either stored/processed in the aggregated
manner, or represented in a user-tailored form according to the target
application.

The eHealth data used in our studies come from PhysioBank that offers a
variety of medical signals and data. Also, some data come from different
paraclinical investigation obtained from emergency hospitals of Bucharest
based on signed protocols. During our future experiments, data collection will
also contain information coming from the wearable healthcare monitoring
systems that may consist of various types of biosensors. These sensors are
measuring significant physiological parameters such as blood pressure,
electrocardiogram (ECG), muscle electromyography (EMG), oxygen in the blood,
body temperature and patient position.

Sensors found or planned on top android devices will be: Accelerometer,
Gyroscope, Magnetomete, Barometric pressure sensor, ambient temperature, Heart
rate monitor, Oxymetry sensor, Skin conductance sensor, skin temperature
sensor, blood glucose, wrist force sensors, ECG. Some sports sensors (bicycle)
will also be considered: speed, cadence, power, heart rate.

The cloud context-aware platform will be based on the UJI’s experience in open
sensor platforms for the smart city context. Crowdsourcing positioning data
will be collected and used for reduced training efforts and for the study of
the device-free localization methods (e.g., addressed in WP4).

Data origin for WP5 studies will rely on the different laboratory as well as
field measurement campaigns, where the data will be collected via a variety of
personal wearable devices (e.g. AR/VR glasses, smart watches, smart wristband,
etc.) as well as industrial sensors (e.g. electricity meters, environmental
sensors, etc.). The research will have to tackle the analysis of various data
types including multidimensional timeseries, structured data, unstructured
data – such as image analysis, video analysis, audio analysis and machine
generated data analysis.

**3.1.6. Data origin at Partner Organisations**

During the secondment the researcher might have access to data sets which are
provided for commercial purposes and property of the partner organisations or
the third parties. In these cases, the access rights and the use of the data
for scientific purposes will be agreed separate. In any case, opening the data
sets and supporting the researchers’ possibilities to research and graduate
will be targeted, as well as extend their skills for advanced career
possibilities. Always, when possible these data sets will be opened to allow
the maximum exploitation of the outcome of the action as well for further
scientific purposes beyond the action as for the commercialization by the
industry.

**3.1.7. Estimated Data Sizes**

The data sizes are expected to be on average below 100 GB of data/year),
however occasionally huge amount of data may be captured via USRPs
measurements with high sampling frequencies and such data can easily reach few
snapshots of 100GB or more in size (e.g, hours of data collected at high
sampling frequencies which might be relevant for the 5G studies of ESR4 may
require in excess of several hundred GB of storage, also smaprtphone data such
as gyroscope data, accelerometer data, WiFi and BLE data, etc. typically can
reach about 150 MB/hour in uncompressed form).

**3.1.8. Data Utility**

The main bulk of generated data to be made available will be SW and
measurements data. Sharing platform for the consortium data is the Microsoft
OneDrive and the main sharing platforms for the open-source data to be created
in A-WEAR are the EU Zenodo and GitHub. As parts of the university
infrastructure at TAU, the vital components of hosting and sharing the data
are expected to stay in place in the long term (i.e., minimum15 years after
the project’s end), thus ensuring continued access to the collected data.

**3.1.9. Data sharing across the consortium**

Data not containing any personal information and not raising any ethical
concerns (e.g., such as simulated data) will be shared between the Consortium
units on-a-need basis. The sharing of any other data types across the
consortium will be based on anonymity of data. Where partners require access
to data to enable a synthesis of findings across studies, this will be
provided in strongly anonymized form only. We will comply with the EC Data
Protection Directive  2  and its newer amendments  3  at all steps in our
project and after its end.

**3.2. FAIR data**

**3.2.1. Making data findable, including provisions for metadata**

A-WEAR project will follow the EU FAIR data guiding principles in order to
make it Findable, Accessible, Interoperable, and Reusable. A-WEAR goals are to
follow FORCE11 FAIR data principles  4  where possible:

**Metadata** to be created within A-WEAR refers to any summaries and
documentations about data to be produced within the project (measurements, SW,
simulated data, analytical/mathematical model, etc.), publications, conference
slides, workshop presentations, etc. Most metadata will be available through
the project deliverables and open-access publications, as well as through the
project dissemination channels (webpage, blog, Twitter, Youtube channels,
etc.). We will be guided by the following FAIR principles:

In order to make the data findable in A-WEAR, the following procedures will be
followed:

  19. Clear version numbers will be used for the project deliverables. 

  

  20. The target publication venues of A-WEAR are open-access peer-reviewed journals and conferences, due to wide dissemination opportunities. Those are easy to access online and offer a simple keyword, author or DOI (digital object identifier) search through their homepages or publication search engines such as “sciencedirect.com” or “scopus.com”. 

  

  21. standard metadata generated for publications (Springer, SCOPUS, ISI, etc). 

  

  22. standard metadata for software on GIT. 

  

  

**3.2.2. Making data openly accessible**

27 out of 35 A-WEAR deliverables are to be open-access deliverables. Relevant
measurement data and simulation-based data generated within A-WEAR project
will also be made available at least on Zenodo open-access repository, and
possibly to the Beneficiaries’ relevant webpages. Relevant software codes
developed with AWEAR will be made available at least on GitHub repository, and
possibly to the Beneficiaries’ webpages. The **relevance** will be agreed upon
by discussions within the Advisory Board of AWEAR, by considering the
following aspects:

  23. the open-access data must be useful for the research community at large, e.g., by providing valuable benchmark solutions (SW, measurements, etc.). 

  

  24. the open-data might have a reasonable tradeoff between its size and its potential usefulness (e.g., huge datasets of radio frequency (RF) or intermediate frequency (IF) samples at high sampling frequency may be not relevant enough to be shared directly with the community or might not fit into the current upper size limits of existing repositories, but there might be made available on request to interested researchers). 

  

  

The main target groups of the dissemination are the scientific community, the
industrial stakeholders in wearables and IoT, the authorities and bodies
responsible for development national and EU knowledge societies and digital
economy, potential end users (including all population in contact with a
wearable device, primary end users, public service personnel, etc.), high-
school pupils (as the future users of wearables and current users of
Internet), and persons developing multinational PhD and cross-sector
trainings.

Several activities will be considered in A-WEAR to ensure that there is a
clear way of communication between the ESRs and both the scientific and
general public the target groups. The main goal of these activities will be to
share results and more in general to create awareness of the importance of
A-WEAR research themes to society and to raise awareness of the MSCA Actions
aiming to follow FAIR principles.

A-WEAR will use 10 dissemination and outreach activities listed in Table 3-1
and conference and journal publications and workshop participation.

#  Table 3-1 The 10-step involvement in social media in A-WEAR, in addition to
the project webpage  
  
<table>  
<tr>  
<th>

**Additional dissemination activities besides webpage, scientific
publications, conference & workshop participation, and patents. All ESRs will
be involved in all these activities. One or two ESRs/task will lead the
efforts **

</th>  
<th>

**Lead ESRs**

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

**Webropol** survey active all through the EJD where users and stakeholders
will be free to share their concerns and challenges regarding the technology
(on one hand) and applications (on the other hand) of wearables

</td>  
<td>

1,9

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

**Facebook** open group for A-WEAR public awareness

</td>  
<td>

10

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

**LinkedIn** open group regarding discussions in the areas of A-WEAR with
**blog** posts on LinkedIn, including ESRs’ blog inputs on their experiences
within the EJD (technical, social, experiences associated to mobility in other
country, lesson learnt and

</td>  
<td>

4,5

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

best practices) with at least two posts/quarter

</td>  
<td>

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

Adding A-WEAR open-source measurement data on **open repositories,** such as
EU **Zenodo, GitHub** or **Bitbucket** – Fellows 3 and 13 will be in charge
with finding out the distribution terms for the open repositories, informing
the other fellows of those and regularly reminding each of them to distribute
their open measurement data through those repositories

</td>  
<td>

3,13

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

ESRs will maintain a **youtube** channel with video clips and fellows
testimonies related to the main topic of the project, providing lessons and
general-purpose talks, to spread the relevance of the activities carried out
in the network

</td>  
<td>

6,14

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

**Twitter** 140-character postings with links to to results and elevator
pitches

</td>  
<td>

8

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

ESRs will attempt contact with **local mass-media** to spread the activities
of the consortium, the Marie Curie Actions, and of individual activities

</td>  
<td>

2,12

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

Each ESR will post his/her publications (at least the abstract) on
**ResearchGate** and participate in the ResearchGate discussions related to
A-WEAR topics

</td>  
<td>

7

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

ESRs from each beneficiary will organize a **A-WEAR Open Day** (one per
beneficiary) where general audience will be invited to visit the host
facilities and create attraction to the conducted research activities &
doctoral studies

</td>  
<td>

11

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

Each ESR will commit to act as Marie Curie Ambassadors and visit **local
schools and universities** , as well as **local councils** , exposing the
activities and results of the network. They will give at least 2 public
presentations per ESR within the 36 months of contract. The specific election
of places to give the talk will be left for decision of the ESRs with the
support of the nominated supervisors.

</td>  
<td>

15

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

Research papers will be published as open-access by taking up self-archiving
rights for journals and conferences that have them, or if necessary paying the
open-access fees where self-archiving and or free open access is not possible.
Also, online pre-publication in ArXiv will be recommended to fellows.

In addition, related to data accessibility, participants to the conferences
where A-WEAR fellows will present their work will have a direct access to the
information through oral presentations, posters and conference proceedings.

The A-WEAR webpage is hosted at TAU. The project will have a dedicated webpage
aims to promote the ESRs’ skills and progress in their career in order to be
available for the best possible employment opportunities, the training
network, disseminate the results achieved and announce the events organized
within this project. This website will be supported by a set of static content
pages (institutional content) and will integrate a more dynamic area,
eventually adding a blog and making easy to any participant in the network to
collaboratively update and create new content.

A-WEAR beneficiaries commit to make their results available in open–access as
much as possible, through at least the followings: a) majority of deliverables
in public access; b) publications via open-access option in IEEE and other
publication forums; c) dissemination of results on open-forums such as
ResearchGate, personal webpages, and open library pages (e.g., TUT has own
open-access portfolios: Dpub and TUTCRIS, UJI publish the papers also on their
webpages, etc.); d) less sensitive and privacy-preserving measurement data to
be provided in open access.

The next European Researchers’ Night will be organized on 27 September 2019
together with Europe and 11 other locations in Finland. During one day and
night, visitors from all walks of life can take part in different kinds of
workshops, panel discussions and exhibitions as researchers open their doors
to the public. The A-WEAR researchers will participate the events yearly
either as soon as possible talking about their work or visiting the relevant
scientist. All events have been earlier very popular in Finland. They are
organized in collaboration between the universities and research organisations
and are free of charge.

The webpages of the partner organisations will be linked to the project
webpage. The partner organisations are encouraged to refer the A-WEAR action
in their professional or commercial occasions always when possible.

For cryptography-related papers free repository (
_https://www.iacr.org/eprint/_ )  will be used.

**3.2.3. Making data interoperable**

In order to make the data interoperable in A-WEAR, standard open formats will
be used for storage. Additional metadata will be described and clarified.
Proprietary software and language-dependent formats will be avoided where
possible (e.g., during industrial secondments).

**3.2.4. Making data re-usable (through clarifying licenses)**

In order to make the data re-usable in A-WEAR, the following procedures will
be followed:

  25. The datasets will be typically shared under Creative Commons Attribution-NonCommercial 4.0 International License. Commercial licenses will be available upon request. Data is expected to be available as long as the service used for sharing data is operational. Data published in scientific journals in form of a journal article would be in open access. 

  

  26. We will encourage the use of the collected data in open challenges at dedicated conferences (e.g. IPIN annual open challenge on indoor localization). 

  

  27. We will include information how data were created and will describe experiment details. 

  

  

A-WEAR team recognizes the importance of software licensing from the outset of
the project, but given the uncertainty we have now of the potential value of
such tools in the future, the exact licenses to be used in case-by-case
situation will be refined through the project. This is because some of the
created data may only be demonstrators or proof of concepts. Other type of
created data, on the contrary, may be valuable tools to launch marketable
ideas via starts-ups. Once we will have a clearer idea of what type of data
will be produced by the ESRs during the project (e.g., only open sources
tools, mix of proprietary and software tools, etc.), the DMP will be updated
with relevant information. At this point, we are exploring the spectrum of
licenses as a preliminary step based on the information found at
_https://choosealicense.com/_ .

Regarding the WP3 studies (eHealth domain), we mention that the
interoperability of medical data is a key concept for the electronic health
records by measuring the communication and cooperation capacity between
different healthcare entities that allow the exchange of information through
electronic health records or other medical information systems. The
interoperability of medical data is realized by means of HL7 standard that
assure automated conversion of information into structured data.
Interoperability of medical data also mean interoperability with medical
devices that capture (generates) medical information from medical sensors and
different devices like Holter, ECG, MRI, ECOGRAF, etc., interoperability with
emergency support systems, with other systems that can quickly and efficiently
deliver the medicines needed for patients unable to move and interoperability
of medical information by creating medical social media portals for
physicians, where they can access studies, updated medical guides and patients
records.

**3.2.5. Data – related procedures at A-WEAR units**

**3.2.5.1. TAU**

The public datasets will be archived and shared through the Research data
storage IDA

( _https://openscience.fi/ida_ )  and Research data finder ETSIN (
_https://openscience.fi/etsin_ )  services provided by the Finnish IT Centre
for Science (SCS) and endorsed by the Finnish Ministry of Education and
Culture. In addition, the data will be promoted on the project web site,
through relevant publications (with related DOIs and keywords) and through
presentation in scientific and public events.

All the data collected during the subjective experiments will be anonymized
and aggregated. The created test databases will be made available through web
sites where applicable.

**3.2.5.2. UJI**

The public datasets will be archived published in Zenodo and the IndoorLoc
platform

( _http://indoorloc.uji.es_ )  provided by Universitat Jaume I. In addition,
the data will be promoted on the project web site, through relevant
publications (with related DOIs and keywords) and through presentation in
scientific and public events.

All the data collected during the subjective experiments will be anonymized
and aggregated. The created test databases will be made available through web
sites where applicable.

**3.2.5.3. BUT**

We will publish the data sets in Open data of the Czech Republic,
_https://opendata.gov.cz/_ .  In addition, the project outputs (generic data,
software, etc.) will be promoted on the project web site, through relevant
publications (with related DOIs and keywords) and through presentation in
scientific and public events.

All the data collected during the subjective experiments will be anonymized
and aggregated. The created test databases will be made available through web
sites where applicable.

**3.2.5.4. UPB**

UPB will have a local site linked to  _http://cs.pub.ro_ on which the
collected data will be available. The data will be also promoted on the
project web site, through relevant publications. Regarding ESR8 research, the
collected data will be non-personal data (e.g. radio measurements).

**3.2.5.5. URC**

After the execution of any test, the resulting collected are analyzed for
modelling, model verification or contribution purposes. The data collected
during any subjective experiment will be anonymized and aggregated.

Data is then kept in personal computers with password security and open access
for only the people involved in the study or co-authors of the relevant
article for scientific purposes. The created test databases will be made
available through web sites where applicable. The collected data will be
disseminated through relevant publications (with related DOIs and keywords)
and through presentation in scientific and public events. Publications will be
available on the project web site and through other channels.

Should experiment involve external subjects supplying data (such as position,
for example), although these data are anonymized, subjects are also informed
to have rights to have their collected results destroyed if they wish by
supplying the nick name they chose or the number they are assigned with.

**3.3. Allocation of resources**

The data will be prepared during regular working hours of the ESR. The data
will be stored on OneDrive provided by TAU. No additional cost to the project
is expected for the OneDrive repository. The costs for publications in Open
Access journals can be between 1000 and 4000 EUR per paper and this will be
covered by A-WEAR project allocated resources.

**3.4. Data security**

All data will be stored on secured password protected computers. Data will not
be stored on unencrypted flash drives. For possible vulnerable data (such as
the data regarding anonymous user traces and operator collected data) to be
commonly used by several Consortium partners, a password-protected space on
the project web server will be created and data will be stored in encrypted
form.

The sharing platform for the consortium data is the Microsoft OneDrive. As
parts of the university infrastructure at TAU, the vital components of hosting
and sharing the data are expected to stay in place in the long term, thus
ensuring continued access to the collected data in a secure way.

Raw data collected from volunteers will only be retained for the lifetime of
the research project and stored on OneDrive and password-protected computers
according to the participants’ information security policy. It will be stored
unless explicit permission is requested and given by the research participants
for an extension period (which may necessitate an appropriate consent form
amendment).

All research participants will be informed of the nature and limits of
confidentiality in accordance with the data protection and privacy legislation
in the jurisdiction where the research is to be carried out. The web surveys
to be organized within A-WEAR will be done with volunteers and fully
consenting to fill in the web surveys and the data will be collected
anonymously, and without storing the IP of the respondent (e.g., Webropol
survey tool to be used in Dissemination activities has such an option). No
data will be collected from children or vulnerable adults or any other person
deemed unable to express his/her full and free consent.

**3.5. Ethical aspects related to data management**

Detailed description of the ethical aspects is given in deliverable D1.3
Collection of ethical clearance procedure and forms available at each
beneficiary.

**3.5.1. Wearables data of individuals**

Making data anonymous and implementing privacy-enhancing mechanisms will
require access to nonanonymized or weakly anonymized data at some point. In
A-WEAR, all data sets used will come from informed and volunteer individuals
and approved databases that will only be used for training purposes. Anonymity
techniques will be implemented as soon as it is feasible and databases will
not be shared between institutions.

Ethical assessment is a key component for the adoption of new medical
technologies. Ethical problems resulting from the inherent risks of Internet
enabled devices can appear due to the sensitivity of healthrelated data, and
their impact on the delivery of healthcare. These issues can also come from
that fact that devices range from single-sensor wearable devices to complex
spatial networks capable of measuring and health-related behaviors for
management of health and well-being. When talking about ethical issues
concerning eHealth wearables, we call the ethics of devices and data. eHealth
wearables are generally carried by the user at home, at residential care,
workplace or public spaces. In each case, a door into private life is created,
enabling the collection of data about the user’s health and behaviors and the
analysis. The lives of users can be digitized, recorded, and analyzed,
creating opportunities for data sharing, processing and mining. That is why
the privacy should be respected and ethical forms will be signed with all the
individuals that will be involved in any experiments and medical reports.

**3.5.2. Assessing privacy intrusion**

In order to assess the acceptability and privacy intrusion of specific
privacy-enhancing solutions, partners may choose to conduct surveys. For web
surveys, the information will be collected anonymously and with full consent
of the participants, on a volunteer basis. For in-person surveys, if any,
information sheets and consent forms will be provided, storage of personal
data will be avoided whenever possible and data with potential for re-
identification will be safely deleted as soon as it is feasible.

**3.5.3. Webropol surveys**

If qualitative methodologies are used, participants will be duly informed in
writing of the nature of the research and their involvement, their rights
during and after their participation and the final goal of the study. Data
will be collected and stored anonymously, as Webropol survey tool has an
option for fully anonymous data collection.

**3.6. Other aspects**

**3.6.1. Issue register**

The “Issue Register” is a log of any issue which arises during the course of
the project, and will be maintained by the Project Coordinator on a separate
folder on OneDrive. The Issue Register will collate any issue as it arises,
issues will then be analyzed and escalated or dealt with accordingly.

An issue could be anything which is of concern to an ESR, beneficiary,
partner, supervisor, etc., this could be deviations from the project plan,
identification of new risks or just concerns.

For example it may be an issue such as Partner X is not responding to emails,
this may be a precursor to larger problems which may impact on A-WEAR
progress, therefore, as an issue is raised it may cause a trigger which causes
a new risk to be identified and added to the risk register.

This register will also allow an additional tool for keeping track on the
risks and means for responding immediately with mitigating activities.

**3.6.2. Lessons Learnt Log**

A lessons log will also be maintained by the Project Coordinator to record
lessons generated out of the “Issues Register” and any other lessons from the
project. The lessons log will categories the lessons for their significance to
different parties and the stage in future projects at which the lesson log
should be reviewed. The A-WEAR project lessons log will be available to all of
the consortium members for ensuring that lessons learnt in this project may be
applied to future projects.

As well the lessons Learnt Log will provide information relevant for realizing
the significant results

  * linked to dissemination, exploitation and impact potential of the outcome overall and the management and usage of data in particular, and 

  

  * with significant immediate or potential impact in science or industry. 

  

**4\. Quality Assurance Plan**

Internal quality assurance (QA) of all deliverables will be carried out prior
to submission to the Commission. First of all, each deliverable gets assigned
of up to two internal reviewers. To that purpose, a draft copy will be
delivered to the internal reviewers one month before its due date for comments
on technical as well as formal quality.

The reviewer has specific responsibility for providing feedback to the lead
authors on more detailed quality assurance in terms of presentation, quality
of writing, consistency, clarity, etc. Reviewing will be done by using the
reviewing form, provided in Annex 1, in order to ensure consistency in the
reviewing process.

At the same time, draft copies of deliverables will be circulated
electronically to all partners for additional comments. Review forms and
comments are to be sent in the agreed way within a maximum of two weeks to
those responsible for deliverables, which gives the latter one week correction
time before the final version is delivered to the PC team and submitted.

External quality assurance is to be gained from the various contacts within
the advisory board, during the dissemination moments, and with the various
other contacts that will be established, including any ongoing and future
academic and industrial collaborations. For the appointed external advisory
board member, one week of reviewing time will also be granted, and one week of
revision time for the deliverable authors. In addition, as ESRs will publish
in peer-reviewed open-access publication channels, the peer reviewed papers
are a reliable quality check.

The impact from the representatives from industry during meetings or
public/professional presentations, etc. or the possible implementation of the
outcome as a part of the product development of the partner organisations will
also prove of the good quality of the outcome

**4.1. Project Coordinator Involvement**

Project coordinator (PC) team is officially responsible for sending all
deliverables to the European Commission for the evaluation process. It
cooperates with the deliverable leaders and Training and Project Manager on
all relevant matters to ensure the quality of the project’s deliverables. PC
receives (in cc) the deliverables for peer reviewing from the respective
responsible party followed by the results of peer reviewing from each assigned
reviewer. Finally, PC receives the confirmation of the satisfactory
implementation of the recommendations.

PC team should send reminders and alerts in due time to the responsible
parties in order to remind them the deadlines for the delivery submission and
the procedure to be followed within the quality assurance phase. PC team
receives the deliverables for peer reviewing from the respective responsible
partner and organizes the quality assurance procedure. If necessary, OD team
is also in charge with

  28. Compiling the related peer review reports with recommendations or, if necessary, sending the deliverable to another partner who will be in charge for peer reviewing. 

  

  29. Delivering the results of peer reviewing to the deliverable author and other beneficiaries. 

  

  30. Verifying the satisfactory implementation of the recommendations of the peer review report, in cooperation with the responsible partner. 

  

  

PC team is also responsible for keeping track of the reviewer’s assignments
and storage of the related data.

**4.2. Review responsibilities and process**

A deliverable is sent by the responsible person 15 days prior the deadline for
any comments and revision.

The compiled peer review report (if any major comments) is sent to the
relevant author(s) within 7 calendar days. If no major comments, the minor
comments are provided via telco or email to the responsible person.

The Author(s) of the deliverable, in cooperation with the other partners (if
applicable), carries out the required improvements with the highest priority,
and sends it back to the project coordinator team within a further 5 calendar
days.

**4.3. Simplified quality assurance procedure**

In order to achieve the QA, each reviewer will provide his comments either in
a free form or with track changes on the deliverables or by filling the form
given in Annex 1.

The reviewers will keep in mind the following questions when reviewing a
deliverable:

  1. are the objectives/goals of the deliverable clearly presented? 

  

  2. does the work include references to relevant material and literature? (if applicable) 

  

  3. is there sufficient detail in all areas? 

  

  4. is the information technically sound? 

  

  5. are the findings clear and well argued? 

  

  6. does the deliverable provide inputs as expected for the subsequent work? 

  

  7. is the information clearly presented? 

  

  8. is the work cohesive and consistent? 

  

  9. is the writing style appropriate? 

  

  10. is the graphical content appropriate? 

  

  

**4.4. Risk management**

TAU as coordinator is in charge with the risk management procedure. Each
partner has the responsibility to report immediately to their respective WP
leader and to the PC any risky situation that may arise and may affect the
successful completion of A-WEAR objectives. In case of problems or delays, the
AB will be consulted and it may set up task forces in order to take the
necessary actions. In case there is no resolution, the PC together with AB
will establish mitigation plans to reduce the impact of risk occurring. Table
3.2c shows the implementation risk analysis.

#  Table 4-1 Implementation risks and mitigation procedures  
  
<table>  
<tr>  
<th>

**Type**

</th>  
<th>

**Risk No.**

</th>  
<th>

**Description of Risk**

</th>  
<th>

**Probabilit y /**

**Impact**

</th>  
<th>

**WP No**

</th>  
<th>

**Proposed mitigation measures**

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

**Technical**

</td>  
<td>

R1

</td>  
<td>

Input measurement data is unavailable in research literature at the time when
mathematical modelling work has to start

</td>  
<td>

High/Low

</td>  
<td>

2

</td>  
<td>

Conduct own measurements in BUT-TAU LTE test network similar to how it was
done in  _http://wislab.cz/our-work/lte-assisted-wifidirect_

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

R2

</td>  
<td>

Scarce availability of off-the-shelf devices for implementing& testing real
use cases

</td>  
<td>

Low/

Medium

</td>  
<td>

2

</td>  
<td>

A-WEAR team has a wide expertise in the implementation of simulators and
testbeds in order to overcome the considered issues

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

R3

</td>  
<td>

Insufficient crowdsensed data in WP3 studies

</td>  
<td>

Low/

Medium

</td>  
<td>

3

</td>  
<td>

Collecting data through all A-WEAR units as much as possible; using analytical
models & existing open-source data to supplement the missing measurements

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

R4

</td>  
<td>

Standardization efforts in wearables is highly dynamic; new emerging standards
may rely on privacy assumptions we have not considered

</td>  
<td>

High/ Low

</td>  
<td>

4

</td>  
<td>

Actively following the standardization efforts in wearables, IoT and future
wireless communications in in order to adjust the hypotheses and project work
accordingly.

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

R5

</td>  
<td>

Noisy mmWave and industrial data or inappropriate data format, not suitable
for machine learning analysis

</td>  
<td>

Low/

Medium

</td>  
<td>

5

</td>  
<td>

Collecting data through all A-WEAR units in both supervised and unsupervised
modes from very beginning of the project; supplementing unavailable data with
statistical models; discussing with industrial partners for finding out
suitable/standardized formats

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

R6

</td>  
<td>

Some of the envisioned tasks may require collaboration with experts in other
fields (user experience, control theory, SW engineering, etc.)

</td>  
<td>

Medium/ Medium

</td>  
<td>

5

</td>  
<td>

Utilize the rich contact network of the consortium units to seek prompt advice
in complex matters related to other fields of knowledge; proactive role of AB
in providing timely feedback on tasks planning and completion

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

**Administrative**

</td>  
<td>

R7

</td>  
<td>

Integration problems in building the SW and HW platforms

</td>  
<td>

Low/

Medium

</td>  
<td>

2-5

</td>  
<td>

A-WEAR team has a wide expertise in SW, HW and SoC and active discussions and
feedback from AB will help to overcome the problems.

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

R8

</td>  
<td>

Delays in recruitment process

</td>  
<td>

Medium/ Low

</td>  
<td>

1-7

</td>  
<td>

The positions will be actively advertised through various channels, in
addition to the joint network links of all partners

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

R9

</td>  
<td>

More than 36 months needed to complete the double/joint PhD degree

</td>  
<td>

High/ Medium

</td>  
<td>

1-7

</td>  
<td>

Each beneficiary commits to ensure all needed resources in terms of funding &
supervision to allow the ESRs to finish their joint/double degree.

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

R10

</td>  
<td>

Potential problems in leading a consortium of 17 partners

</td>  
<td>

Low/ High

</td>  
<td>

</td>  
<td>

PC has worked successfully before (projects, publications,…) with 47% of the
17 A-WEAR units; PC has experience in leading large national Consortia and she
gets strong support from TAU Research Services (having great experience in
ITNs and EU projects) to address promptly any issues that might appear

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

R12

</td>  
<td>

Scientific misconduct

</td>  
<td>

Low/

Medium

</td>  
<td>

1-5

</td>  
<td>

Termination of contract and recruitment of replacement

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

R13

</td>  
<td>

Some industrial partner going bankrupt

</td>  
<td>

Low/ Low

</td>  
<td>

1-5

</td>  
<td>

Replacing the industrial secondment unit with new industrial partners,
suitable to the addressed objectives.

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

R14

</td>  
<td>

Topic divergences from the scheduled A-WEAR network

events in table 1.2b

</td>  
<td>

Medium/ Low

</td>  
<td>

7

</td>  
<td>

If some of the forecast lecturers are not available, we invite new lecturers
to cover in a comprehensive non-overlapping manner the core topics

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