1 EXECUTIVE SUMMARY  7

2 INTRODUCTION  8

3 PRINCIPLES  8

3.1 PARTICIPATION IN THE PILOT ON OPEN RESEARCH DATA  8

3.2 THE I-PROGNOSIS DATA MANAGEMENT PORTAL  9

3.3 COMMON PROCEDURES  11

3.3.1 Data Collection  11

3.3.2 Data Access Procedures  12

4 I-PROGNOSIS DATASETS  12

4.1 DATASETS NAMING  12

4.2 SUMMARY OF THE I-PROGNOSIS DATASETS  13

4.3 DATASETS BREAKDOWN  15

4.3.1 Personal & Clinical Data  15

4.3.2 Sensed and Captured GData/SData  18

4.3.3 Intervention Data  46

4.3.4 Requirements Data  63

APPENDIX I – DATASET DESCRIPTION TEMPLATE  70

#  LIST OF MAIN ABBREVIATIONS

API Application Programming Interface

BDI Beck Depression Inventory

CSV Comma Separated Values

DMP Data Management Plan

DoA Description of the Action

EC European Commission

EDF European Data Format

GData Generic Data

HDF5 Hierarchical Data Format 5

HRA Health Research Authority

JSON JavaScript Object Notation

MoCA Montreal Cognitive Assessment

NMSQuest Parkinson’s Disease Non Motor Symptoms Questionnaire

PDSS Parkinson’s Disease Sleep Scale

PGS Personalised Game Suite

RBD-SQ REM Sleep Behaviour Questionnaire

SData Specific Data

UPDRS Unified Parkinson Disease Rating Scale

XML Extensible Markup Language

#  EXECUTIVE SUMMARY

This deliverable is the initial version of the Data Management Plan (DMP) of
the iPROGNOSIS project, in accordance to the regulations of the Pilot action
on Open Access to Research Data of the Horizon 2020 programme (H2020). It
contains provisional information about the data that will be produced and
collected within the project, whether and how it will be made accessible for
re-use and further exploitation, and how it will be curated and preserved.

Based on the guidelines on Data Management in Horizon 2020  1  , a dataset
description template was initially drafted to provide the main pillar for the
dataset descriptions (see Appendix I). All relevant datasets were recognized
and a detailed list was produced, based on the datasets that have been
described within the DoA to be produced within the life span of the project.
The present deliverable consolidates all the partners’ feedback and provision
for the datasets they contribute to.

The project at its present stage is foreseen to develop a series of datasets,
related to issues ranging from user requirements to the intervention and
sensor captured data stemming from patients with PD and also healthy
participants. Specifically, datasets are planned to be collected in two ways:
the development data collection and the data that will be collected during the
deployment of i-PROGNOSIS system. The former dataset will help the development
and improvement of the algorithms and systems of the i-PROGNOSIS, while the
latter will constitute the actual datasets that are foreseen to be the main
input of the i-PROGNOSIS system.

Given that the majority of the i-PROGNOSIS datasets involve data collection
from human participants, the respective data produced either raw or processed,
should be carefully handled, under thorough consideration of ethical and
privacy issues involved in such datasets. For all the identified i-PROGNOSIS
datasets, specific parts that can be made publicly available have been
identified in the current first version of the project’s DMP. The public
datasets of the i-PROGNOSIS project will become available through a common
repository that will be formulated on the basis of the iPROGNOSIS “data
management portal” based on the CKAN open source platform  2  for open
datasets or similar system.

This is an initial version of the Data Management Plan of the i-PROGNOSIS
system. Having said that, the datasets described at this stage, represent an
early reflection on the data that we foresee to be collected. During the
evolution of the project, we expect that there will some changes either to the
content of the datasets or the information classification. However, main
principles - as described within this deliverable - is expected to remain
intact until the end of the project, thus forming the main strategic axes of
the overall Data Management Plan.

The overall DMP will be delivered at the end of the i-PROGNOSIS project (M48)
within the deliverable “D5.7 Report on open research data management”.

#  INTRODUCTION

During the lifetime of i-PROGNOSIS, data of different nature will be generated
and collected. These data are user-related and thus require a clear plan on
how they are to be managed, i.e., stored, accessed, protected against
unauthorized or improper use, etc. Thus, the main goals of i-PROGNOSIS Data
Management Plan (DMP) are:

  1. Outline of the types of data foreseen for generation at this stage of the project, including the context and procedures of this generation, as well as the degree of privacy and confidentiality of the data. 

  

  2. Outline of the protocols that will be followed to assess the generated/collected data with respect to their sensitiveness. 

  

  3. Outline of the data acquisition plan for the duration of the project. 

  

  4. Outline of the measures that are foreseen for the adequate management of the data from the ethical and security points of view. 

  

  

The remainder of the deliverable is structured as follows. In Section 3, the
common data collection procedures are outlined. A short description about
specifications of the data management portal is provided, in addition to
different types of classified information. Finally, Section 4 elaborates on
each dataset, based on the provided template of Appendix I.

#  PRINCIPLES

##  PARTICIPATION IN THE PILOT ON OPEN RESEARCH DATA

The Data Management Plan will guide all activities regarding the
anonymisation, exchange and release of data gathered during the project, as
required for participating in the Open Research Data Pilot of the H2020
framework. Datasets that are to be produced within the i-PROGNOSIS project
span from users' demographic and clinical data to sensor data, intervention
data and outcome measures of several interventions. These data will allow the
research community to benchmark algorithms with respect to several aspects of
Parkinson's disease (PD) detection and treatment, providing a common basis for
augmented policy decision making. Since iPROGNOSIS data collection phases
involve human participants, data collected will contain sensitive, personal
information, and, as a result, the focus is also placed on possible ethical
issues and access restrictions regarding personal data, so that no regulations
on sensitive information are violated (see also D1.2 "Ethics and safety
manual").

In this scope, this version of the deliverable describes in detail the
datasets that are to be collected in each data collection phase, by each
different technical or medical partner. Individual data collection mechanisms
are treated as individual cases and therefore individual data management plans
are foreseen, including the following information - as described in the
available data management plan template and guidelines of the European
Commission (EC)  1  : i) dataset reference and name, ii) dataset description,
iii) standards and metadata, iv) data sharing, v) archiving and preservation
(including storage and backup).

As part of the Data Management Plan, the i-PROGNOSIS consortium aims at
developing a central database station, where data and evaluation outcomes of
all relevant data collection phases will be deposited. Furthermore, a central
Data Management Portal will be developed, in order to serve for the main
access point to the publicly available datasets. The i-PROGNOSIS Data
Management Portal will be based on open-source data portal platforms, such as
CKAN and/or Zenodo  3  , allowing for each partner to publish its datasets to
the research community.

##  THE i-PROGNOSIS DATA MANAGEMENT PORTAL

The i-PROGNOSIS consortium will explore the possibility to develop its data
management portal based on the popular open source software CKAN  2  , and it
will be accessible through a portal (endpoint) at the following address:

"ckan.i-prognosis.eu"

CKAN may offer a better control over the stored data as they can be installed
and run completely under the control of the research institution.

CKAN is a powerful data management system that makes data accessible – by
providing tools to streamline publishing, sharing, finding and using data.
CKAN is aimed at data publishers (national and regional governments, companies
and organisations) wanting to make their data open and available. The software
supports strong integration with third-party content management systems
(CMSs), such as Drupal and WordPress.

The portal’s features include among others:

  * Complete catalogue system with easy to use web interface and a powerful 

  

API.

  * Integration with the profile database system. 

  

  * Data visualisation and analytics. 

  

  * Fine access controls. 

  

  * Store the raw data and metadata. 

  

  * Search by keyword or filter by tags. See dataset information at a glance. 

  

  * Rich application programming interface (API), and over 60 extensions including link checking, comments, and analytics. 

  

  * Records datasets’ change logs and versioning information. 

  

  * Allows deep customization of its features. 

  

  * Supports unrestricted (non standards-compliant) metadata. 

  

In addition, i-PROGNOSIS will investigate the possibility to publish a subset
of the data within the Zenodo repository service. Zenodo builds and operates a
simple and innovative service that enables researchers, scientists, EU
projects and institutions to share, preserve and showcase multidisciplinary
research results (data and publications) that are not part of the existing
institutional or subject-based repositories of the research communities.

Zenodo enables researchers, scientists, EU projects and institutions to:

  * Easily share the long tail of small research results in a wide variety of formats including text, spreadsheets, audio, video, and images across all fields of science. 

  

  * Display their research results and receive credit by making the research results citable and integrating them into existing reporting lines to funding agencies like the EU. 

  

  * Easily access and reuse shared research results. 

  

Some of the features provided by the Zenodo service are:

  * An open digital repository for everyone and everything not served by a dedicated service; the so-called “long tail” of research results. 

  

  * A modern look and feel in line with current trends in state-of-the-art online services. 

  

  * Integration with OpenAIRE infrastructure and assured inclusion in OpenAIRE corpus. 

  

  * Easy upload and semi-automatic metadata completion by communication with existing online services such as DropBox for upload, 

  

Mendeley/ORCID/CrossRef/OpenAIRE for upload and pre-filling metadata.

  * Easy access to research results via an innovative viewing option, open APIs, integration with existing online services, and the preservation of community independent data formats. 

  

  * A safe and trusted service by combining community-based curation with short- and long-term archival and digital preservation strategies in accordance with best practices. 

  

  * Persistent identifiers, Digital Object Identifiers (DOIs), for sharing research results. 

  

  * Service hosting according to industry best practices in CERN’s professional data centres. 

  

  * An easy way to link research results with other results and products, funding sources, institutions, and licenses. 

  

  * Supports Dublin Core, MARC and MARCXML for metadata exporting. 

  

  * Complies with OAI-PMH for data dissemination. 

  

##  COMMON PROCEDURES

###  Data Collection

3.3.1.1 Development Data Collection

The newly introduced development data acquisition period is the first and the
earliest collection period and it is highly correlated with software
development and implementation. The existence of non-artificial data is
mandatory for the effective development of software-related, and not limited,
to minor tremor or dysphonia detection. Moreover, the need for data is further
augmented due to the fact that only limited publicly available corpora exist,
and the existing ones incorporate different data capturing methods than
i-PROGNOSIS, constituting them not suitable for our needs.

Furthermore, it is crucial for software implementation to capture and have
access to raw data (e.g., unprocessed speech signals). The above is one of the
main differences with the rest of the data collection periods, where sensitive
data will be manipulated with proper methods.

_Goals of the development data collection period:_

  * Aid the implementation of the processing algorithms (machine learning approaches require data during their training phase). 

  

  * Evaluate the effectiveness of various data descriptors. The latter is essential in order to successfully analyse, understand and translate the raw signals to meaningful information, as well as, to initially train the machine learning algorithms. 

  

Development datasets will only be publicly available in the form of processed
data. Raw signal data will remain confidential and will be available within
the consortium, only after the data collectors provide their approval to share
the data.

3.3.1.2 Data to be collected during the deployment of i-PROGNOSIS

The goal of the data collection at this stage of the project is to build the
predictive algorithm for PD detection and evaluate it against the medical
golden standard. In addition, the efficacy of the interventions regimen will
be measured by medical partners, in terms of the specified medical evaluation
protocol (see D2.2 "Data collection and medical evaluation protocol").

Data collected during the deployment of i-PROGNOSIS will be fully anonymised
and will be offered to third parties in a processed format. Opening data
during this stage of the project in a raw format will not be an option, due to
privacy issues. Clinical data will be used to label the processed data, thus
forming the required “semantic ground truth” for other researchers to apply
their own machine learning techniques and statistical analyses and to compare
their findings.

###  Data Access Procedures

3.3.2.1 Public

For the sections of the dataset that will be made publicly available, a
respective Web page will be created on the data management portal that will
provide a description of the dataset. External researchers will have
unrestricted access, following the steps as indicated by the data management
portal of the i-PROGNOSIS project. In addition, any third party stakeholder
will be explicitly informed about the publication that need to be cited if
part of the dataset has been used for publications. All public datasets should
be strictly anonymised.

3.3.2.2 Protected

Data denoted as protected can be shared out of the consortium, as long as
interested parties _a priori_ request access from the consortium, explaining
how these datasets will be used, e.g., research or commercial purposes.
Appropriate forms will be created and be available through the data management
portal, from where interested parties can use them and request for explicit
access to certain datasets. Upon the approval by the i-PROGNOSIS partners,
interested parties will be provided with credentials, in order to download the
requested datasets.

3.3.2.3 Confidential/Private

The private part of the datasets will be stored at a specific and designated
private space of the partner responsible for the dataset, namely the data
collector, on dedicated hard disk drives, to which only members of the data
collector, whose work directly relates to these data will have access. In
order for other i-PROGNOSIS partners to obtain access to these data, each
partner must provide a proper written request to the data collector, including
a justification over the need of access to the particular data. Once deemed
necessary, the data collector will provide the respective data to the partner.

#  i-PROGNOSIS DATASETS

##  DATASETS NAMING

Concerning the convention followed for naming the project datasets, it should
be noted that the name of each dataset comprises:

  1. A prefix " **DS** " indicating a dataset. 

  

  2. Its unique identification number depending on the dataset category (see next Section), e.g., " **DS1** " for datasets belonging to the category of personal and clinical data, " **DS2** " for sensed and captured GData or SData, " **DS3** " for interventions data and “ **DS4** ” for requirements data. 

  

  3. Since sensed and captured GData or SData or interventions data contain different types of data, such as physiological recordings, audio, and visual features, further distinction takes place as follows: " **DS2.1** " for physiological features, " **DS2.2** " for visual features, " **DS3.1** " for serious games’ metrics, etc. 

  

  4. A short name indicative of its content and purpose. 

  

  

For example, body and gesture-related features collected during the
interventions data collection phase form a dataset named:  
  
<table>  
<tr>  
<th>

_Prefix_

</th>  
<th>

_Category identification number_

</th>  
<th>

_Type of data discriminator_

</th>  
<th>

_Indicative Name_

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

" **DS** "+

</td>  
<td>

" **3.** "+

</td>  
<td>

" **1-** "+

</td>  
<td>

" **BodyAndGesture** "+

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

i.e., " **DS3.1-BodyAndGesture** "

##  SUMMARY OF THE I-PROGNOSIS DATASETS

The i-PROGNOSIS datasets are divided in the following categories:

  1. Personal and clinical data. 

  

  2. Sensed and captured GData or SData. 

  

  3. Interventions data. 

  

  4. Requirements data. 

  

  

Personal and clinical data will accompany and annotate Sensed and captured
GData or SData and Interventions data, as essential metadata, thus allowing
for the semantic annotation of the collected datasets.

**TABLE 1** presents cumulatively all datasets that are initially planned to
be collected within the i-PROGNOSIS project with respect to each
aforementioned category.

**TABLE 1** Indicative Datasets to be collected within the i-PROGNOSIS project  
  
<table>  
<tr>  
<th>

**Dataset**

**#**

</th>  
<th>

**Dataset Name**

</th>  
<th>

**Description**

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

</td>  
<td>

**PERSONAL & CLINICAL DATA **

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

1.1

</td>  
<td>

**DS1.1-**

**ElectronicHealthRecordData**

</td>  
<td>

It contains information related to medical evaluation protocols, such as
health screening results and interventions’ outcome measures.

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

</td>  
<td>

**GDATA / SDATA**

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

2.1

</td>  
<td>

**DS2.1-VoiceQualityAnalysis**

</td>  
<td>

It contains voice data and vocal features captured using the smartphone
microphone.

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

2.2

</td>  
<td>

**DS2.2-PhotosFacialAnalysis**

</td>  
<td>

It contains facial features extracted from photos taken with the mobile phone
camera (selfies for masked face etc.).

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

2.3

</td>  
<td>

**DS2.3-ActivityAnalysis**

</td>  
<td>

It contains activity-related (steps, heart rate, skin temperature) features
derived from the smartwatch.

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

2.4

</td>  
<td>

**DS2.4-PhysioSignalAnalysis**

</td>  
<td>

It contains features derived from physiological data acquisition such as
electrocardiography derived from the

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

</th>  
<th>

</th>  
<th>

Smart TV remote.

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

2.5

</td>  
<td>

**DS2.5-TypingPatternAnalysis**

</td>  
<td>

It contains keystroke dynamics-related features collected during typing on a
virtual keyboard of a touch screenenabled smartphone.

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

2.6

</td>  
<td>

**DS2.6-**

**ExploratoryWalkabilityAnalysis**

</td>  
<td>

It contains de-personalised location information and derived features from
GPS, Wi-Fi, Mobile Network and IMU originating from the users’ smartphone and
smartwatch.

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

2.7

</td>  
<td>

**DS2.7-TextSentimentAnalysis**

</td>  
<td>

It contains SMS text and/or tweets collected from the users’ smartphone along
with associated sentiment classification

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

2.8

</td>  
<td>

**DS2.8-FoodIntakeAnalysis**

</td>  
<td>

The dataset is composed of objective quantification of meal mechanics as well
as derivative information about the user’s eating behavioural elements (meal
total intake and duration, number of bites, eating rate and eating rate
changes across the meal).

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

2.9

</td>  
<td>

**DS2.9-BowelSoundsAnalysis**

</td>  
<td>

It contains pre-processed bowel sound signals captured from the smart belt.

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

2.10

</td>  
<td>

**DS.10-TremorAnalysis**

</td>  
<td>

It contains IMU based (accelerometer, gyroscope and magnetometer) derived
features, originating from the user’s smartphone and smartwatch.

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

**INTERVENTIONS**

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

3.1

</td>  
<td>

**DS3.1-SeriousGamesMetrics**

</td>  
<td>

In-game metrics and performance such as scores, achievements, difficulty
level, etc. of all the gaming sessions (exergames, dietary, handwriting,
emotion and voice games).

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

3.2

</td>  
<td>

**DS3.2-BodyGestureAnalysis**

</td>  
<td>

The dataset consists of postures and gestures tracking experiments, as well as
balance and gait features, especially during the Exergames where Kinect will
be the main sensor.

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

3.3

</td>  
<td>

**DS3.3-SleepStageAnalysis**

</td>  
<td>

It comprises sleep stage data based on accelerometer, heart rate, and skin
temperature measurements of users, captured by the smartwatch during their
sleep (and provided they are wearing the smartwatch on their wrist).

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

3.4

</td>  
<td>

**DS3.4-GaitAnalysis**

</td>  
<td>

It contains accelerometer, gyroscope and pedometer data captured from the
smart watch/band with respect to rhythm guidance in terms of sound cues

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

3.5

</td>  
<td>

**DS3.5-VoiceEnhancementAnalysis**

</td>  
<td>

It contains voice data captured using the smartphone microphone.

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

**REQUIREMENTS**

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

4.1

</td>  
<td>

**DS4.1-FocusGroupsDataset**

</td>  
<td>

It contains data gathered within focus groups during the requirements
elicitation phase.

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

4.2

</td>  
<td>

**DS4.2-WebSurveyDataset**

</td>  
<td>

It contains the questions and the qualitative answers to the questions of the
i-PROGNOSIS Web survey, conducted within the context of the identification of
user requirements and system specifications, from ~2000 anonymous survey
participants.

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

##  DATASETS BREAKDOWN

In the following, datasets are described based on the template of Appendix I.

###  Personal & Clinical Data  
  
<table>  
<tr>  
<th>

**DATA SET REFERENCE**

**NAME**

</th>  
<th>

**DS1.1-ElectronicHealthRecordData**

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

**DATA SET DESCRIPTION**

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

**Generic description**

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

This dataset will contain the i-PROGNOSIS users' personal information,
demographics and health related data (assessment batteries’ scores,
comorbidities, medication).

This dataset will facilitate subsequent analysis with respect to the wide
GData and SData collection, as well as to clinical assessment tests towards
research on the stealth assessment and screening of early signs of PD. In
addition, results of the medical evaluation as represented within these
datasets, will allow to evaluate the effectiveness of the user participation
in the interventions (i.e., their involvement in the Serious Games).

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

**Origin of data**

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

This dataset a) will be collected by i-PROGNOSIS App and b) will be entered
into the i-PROGNOSIS system by the clinical sites. The data collected through
the mobile App will be taken during the first use of the mobile phone app; the
user will be asked to provide short personal information, demographics and
health related data by multiple choice questions. During the SData collection
and intervention phases, the relevant clinical data for each user will be
entered into the system from the recruiting clinical site (AUTH, KCL or TUD).
In case of participants who will follow all the projects’

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

phases, multiple instances of follow up assessment tests’ scores will be
available.

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

**Nature and scale of data**

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

The dataset will be collected during all phases of the project: the
GData/SData collection and the intervention phases. The questions delivered
through the iPROGNOSIS App will be designed in a multiple choice format, so
that the obtained information will be in a numeric format. Health related data
at this stage will include: 1. Diagnosis of Parkinson`s disease: yes / no, 2.
Physical handicap: yes / no, 3. Family history of Parkinson`s disease: yes /
no.

The process of clinical assessment will take place in three countries (UK,
Germany, and Greece), where health-centric partners are based, in order to
facilitate the medical monitoring of the users and the subsequent clinical
validation of PD risk. The clinical data will include all the relevant medical
information collected by the recruiting clinical centres. Some of the clinical
examinations include among others:

Unified Parkinson Disease Rating Scale (UPDRS), Parkinson’s Disease Non Motor
Symptoms Questionnaire (NMSQuest), Montreal Cognitive Assessment (MoCA),
Parkinson’s Disease Sleep Scale (PDSS), REM Sleep Behaviour Questionnaire
(RBDSQ), Beck Depression Inventory (BDI), Parkinson Fatigue Scale, Senior
fitness test and BERG balance scale.

During the initial pilot phase more than 5000 older adults (above 55 years of
age) is expected to participate, whereas some 80 and 60 will take part in the
SData phase and the intervention phases.

The data will be stored as records of a profile database, part of the
i-PROGNOSIS repository.

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

**To whom the dataset could be useful**

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

The dataset will be used to classify and rate the obtained sensed and captured
further GData and SData, intervention data and requirements data in respect to
age, gender and health state dependent values.

In addition, clinical data will be used for ground truth purposes, to train
the machine learning algorithms responsible for the second stage early PD
detection.

Also, part of this dataset will be each intervention user’s baseline and
follow up data, for the evaluation of their health state progress through the
use of the serious games Personalised Game Suite.

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

**Related scientific publication(s)**

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

This kind of dataset is collected in every type of clinical trial in the field
of PD or otherwise. Example:

Martinez-Martin et al. EuroInf: A Multicenter Comparative Observational Study
of Apomorphine and Levodopa Infusion in Parkinson's Disease. Mov Disord. 2015
Apr;30(4):510-6.

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

**Indicative existing similar data sets** (including possibilities for
integration and reuse)

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

Not applicable within the application context of the novel technologies used
in iPROGNOSIS.

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

**STANDARDS AND METADATA**

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

Clinical data will not contain any metadata, instead the name of the
examination and the measurement score will be provided. There would be a
possibility to support electronic health data exchange, via partial
implementation of the CEN/ISO 13606 standard as declared the base standard of
the European Interoperability Framework for Health.

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

**DATA SHARING**

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

**Access type**

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

Personal identification as stated in the consent forms will be kept separate
from any research and health-related data, which will be pseudonymised (only
initials and date of birth will be kept). Furthermore no personal data that
could identify an individual person e.g., name, date of birth is obtained. The
data for personal identification of the users will be kept locally at each
recruitment clinical centre, outside this dataset. This dataset will only
include personal and clinical data relevant to the i-PROGNOSIS protocols. Due
to ethical reasons, only averaged group data could become publicly available,
while individual data will be private to serve the i-PROGNOSIS R&D objectives.

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

**Access Procedures**

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

For the parts of the dataset that will be made publicly available, a
respective Web page will be created on the data management portal that will
provide a description of the dataset and links to a download section. The
private part of this dataset will be stored at a specifically designated
private space of clinical partners, in dedicated hard disk drives, on which
only members of the clinical research teams will have access.

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

**Embargo periods** (if any)

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

The applicable datasets will be publicly available 2 years after the end of
the project to allow the consortium prepare and submit the scientific
publications.

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

**Technical mechanisms for dissemination**

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

For the public part of the dataset, a link will be provided from the Data
management portal. The link will be provided in all relevant i-PROGNOSIS
publications. A technical publication describing the dataset and acquisition
procedure will be published.

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

**Necessary S/W and other tools for enabling re-use**

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

The dataset will be in a numeric manner and therefore designed to allow easy
reuse with commonly available tools.

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

**Repository where data will be stored**

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

The public part of this dataset will be accommodated at the data management
portal of the project Website, hosted by Microsoft Azure-based i-PROGNOSIS
Cloud infrastructure.

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

**ARCHIVING AND PRESERVATION** (including storage and backup)

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

**Data preservation period**

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

The dataset will be preserved as long as there are regular downloads. After
that it would be made accessible by request and preserved by AUTH at least
until the end of

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

the project. Locally, the personal and clinical data will be preserved based
on the national and departmental practises about scientific data handling.

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

**Approximated end volume of data**

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

Some MBs.

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

**Indicative associated costs for data archiving and preservation**

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

There are no costs associated with data preservation in institutional servers.
However, data publicly archived and preserved in the Microsoft Azure-based
i-PROGNOSIS Cloud infrastructure, will cost approximately 20 Euros (€) per
month for one year (at least) after the end of the project.

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

**Indicative plan for covering the above costs**

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

The costs associated with the data archiving and preservation will be covered
by the i-PROGNOSIS project budget.

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

**PARTNERS ACTIVITIES AND RESPONSIBILITIES**

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

**Partner Owner / Data**

**Collector**

</td>  
<td>

**TUD, KCL, AUTH, MICROSOFT**

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

**Partner in charge of the data analysis**

</td>  
<td>

**Both clinical and technical partners**

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

**Partner in charge of the data storage**

</td>  
<td>

**TUD, KCL, AUTH**

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

**WPs and Tasks**

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

The data are going to be collected within activities of WP4, WP6 and WP7, to
mainly serve the research efforts of T4.1 - T4.5, T6.1 - T6.5, T7.1, T7.3 and
T7.4.

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

###  Sensed and Captured GData/SData  
  
<table>  
<tr>  
<th>

**DATA SET REFERENCE NAME**

</th>  
<th>

**DS2.1-VoiceQualityAnalysis**

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

**DATA SET DESCRIPTION**

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

**Generic description**

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

Speech dataset for the detection and identification of voice-related PD
symptoms for early PD detection. The database will contain speech data
collected from phone calls recorded from the i-PROGNOSIS smartphone dialler
application. The data set includes extracted voice features and several
annotations about the speech data and the underlying speakers.

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

**Origin of data**

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

The dataset will be collected by recording conversations using the i-PROGNOSIS
smartphone dialler application during GData and SData collection phase.

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

**Nature and scale of data**

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

The goal of this dataset is to enable the detection and identification of
voice-related PD symptoms for early PD detection. The dataset will contain the
raw speech signals recorded by the i-PROGNOSIS smartphone application during
the GData and SData

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

collection phase as well as extracted features. Annotations including date and
time of the recordings along with speaker information (e.g., ID, gender, PD
scale) will provide the ground truth and will facilitate the subsequent
analysis of the speech recordings. The dataset will contain raw speech data
(e.g. 44.1 kHz sampling rate, wav unencoded) and text files for the
annotations.

_Data Format:_ Encoded (mp3, aac) or unencoded (wav, raw) audio waveforms; XML
or plain text files for annotations/metadata; HDF5 for feature files.

The dataset will be on the order of 0.5 MB to 5 MB per minute of speech
depending on the encoding and sampling rate.

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

**To whom the dataset could be useful**

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

The dataset will be used within the project for the development and evaluation
of automatic signal processing and pattern recognition algorithms for the
extraction of discriminative features for the detection and identification of
voice-related PD symptoms for the early detection of PD. This data will be
used in WP3 and WP6, supporting the early PD symptoms detection and for the
overall assessment of the iPROGNOSIS system. This dataset may also be useful
in the future for other researchers who want to explore voice-related PD
symptoms.

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

**Related scientific publication(s)**

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

This database will build the foundation of our research and development of
algorithms towards the identification and detection of voice-related PD
symptoms for early PD detection. We plan to propose our findings on ICASSP,
Interspeech or other voice and biomedical-related conferences.

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

**Indicative existing similar data sets** (including possibilities for
integration and reuse)

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

To the best of our knowledge no long-term speech database covering voice
recordings of healthy and PD subjects is (publicly) available.

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

**STANDARDS AND METADATA**

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

The dataset will be accompanied with detailed documentation of its contents.
Indicative metadata include: (a) description of the experimental setup and
procedure that led to the generation of the dataset and (b) documentation of
the variables recorded in the dataset.

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

**DATA SHARING**

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

**Access type**

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

Due to ethical reasons, only the raw data, captured by normal healthy control
subjects, (during the development data collection phase) as well as a subset
of the extracted features of the collected datasets could become publicly
available, while the rest of them will be private to serve the i-PROGNOSIS R&D
objectives.

The inclusion of a (normal healthy control) subject’s data in the public part
of this dataset will be done on the basis of appropriate informed consent to
data publication.

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

**Access Procedures**

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

For the parts of the dataset that will be made publicly available, a
respective web

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

page will be created on the data management portal that will provide a
description of the dataset and links to a download section. The private part
of this dataset will be stored at a specifically designated private space of
FRAUNHOFER, in dedicated hard disk drives, on which only members of the
FRAUNHOFER research team will have access.

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

**Embargo periods** (if any)

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

The applicable datasets will be publicly available two years after the end of
the project to allow the consortium prepare and submit the scientific
publications.

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

**Technical mechanisms for dissemination**

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

For the public part of the dataset, a link will be provided from the Data
management portal. The link will be provided in all relevant i-PROGNOSIS
publications. A technical publication describing the dataset and acquisition
procedure will be published.

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

**Necessary S/W and other tools for enabling re-use**

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

The dataset will be designed to allow easy reuse and access with commonly
available tools (e.g. Matlab, Python, VLC, GVIM) and software libraries (e.g.
Tensorflow, FFMPEG, HDF5 C++ API, C++ STL), because the data will be stored
primarily in common file formats and generic data containers.

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

**Repository where data will be stored**

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

The public data will be hosted on the central database (Microsoft Azure-based
iPROGNOSIS Cloud infrastructure) that will serve the needs of the Data
Management Portal of the i-PROGNOSIS project.

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

**ARCHIVING AND PRESERVATION** (including storage and backup)

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

**Data preservation period**

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

The public part of the dataset will be preserved as long as there are regular
downloads. After that it would be made accessible by request and preserved by
AUTH at least until the end of the project. The private part of the dataset
will be preserved by FRAUNHOFER at least until the end of the project.

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

**Approximated end volume of data**

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

The dataset is expected to consume up to 100 GB depending on the encoding and
the length and quantity of the speech signals. (E.g. single channel audio
waveform 16 bit, 44.1 kHz is of size 5.3 MB/min, single channel mp3 192 Kbps
is of size 1.44 MB/min)

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

**Indicative associated costs for data archiving and preservation**

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

There are no costs associated with data preservation in institutional servers.
However, data publicly archived and preserved in the Microsoft Azure-based
i-PROGNOSIS Cloud infrastructure, will cost approximately 20 Euros (€) per
month for one year (at least) after the end of the project..

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

**Indicative plan for covering the above costs**

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

The costs associated with the data archiving and preservation will be covered
by the i-PROGNOSIS project budget.

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

**PARTNERS ACTIVITIES AND RESPONSIBILITIES**

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

**Partner Owner / Data Collector**

</td>  
<td>

**TUD, KI, FRAUNHOFER**

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

**Partner in charge of the data analysis**

</td>  
<td>

**FRAUNHOFER**

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

**Partner in charge of the data storage**

</td>  
<td>

**FRAUNHOFER**

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

**WPs and Tasks**

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

The data is going to be collected within activities of WP3 and WP6, to mainly
serve the research efforts of T3.3, T6.1, T6.2 and T6.3.

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

**DATA SET REFERENCE NAME**

</th>  
<th>

**DS2.2-FacialAnalysis**

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

**DATA SET DESCRIPTION**

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

**Generic description**

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

Facial image dataset for the detection and identification of masked face
symptoms for early PD detection. The database will contain selfie images
collected from the frontal camera of the smartphone of the user. The data set
will include annotations about a) the bounding box of the recognized face of
the user b) self-assessment of the emotional state of the user.

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

**Origin of data**

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

The dataset will be collected from the user smartphone by collecting the
(selfie) images obtained during GData and SData collection phases and
detecting/recognizing the face of the user.

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

**Nature and scale of data**

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

The goal of this dataset is to enable the detection and identification of
masked face symptoms for early PD detection. The dataset will contain the
images (selfie) images obtained during GData and SData collection phases.
Annotations including date and time of the recordings along with bounding box
of the identified user face in the selfie will provide the ground truth and
will facilitate the subsequent face expression analysis task. The dataset will
contain JPEG selfie images and XML (or text) files for the annotations.

Data Format: JPEG files for selfie images from the frontal camera of the
smartphone, XML or plain text files for annotations/metadata.

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

**To whom the dataset could be useful**

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

The dataset will be used within the project for the development and evaluation
of automatic facial expression recognition algorithms which will then be used
to detect masked face symptoms for the early detection of PD. This data will
be used in WP3 and WP6, supporting the early PD symptoms detection and for the
overall assessment of the i-PROGNOSIS system. This dataset may also be useful
in the future for other researchers who want to explore masked face PD
symptoms.

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

**Related scientific publication(s)**

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

This database will build the foundation of our research and development of
algorithms towards the masked face PD symptoms for early PD detection. We plan
to propose our findings on computer vision/image processing- and biomedical-
related conferences and journals (e.g., ICIP conference).

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

**Indicative existing similar data sets** (including possibilities for
integration and reuse)

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

To the best of our knowledge no image database covering face expressions of
healthy and PD subjects is (publicly) available.

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

**STANDARDS AND METADATA**

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

The dataset will be accompanied with detailed documentation of its contents.
Indicative metadata include: a) description of the procedures that led to the
generation of the dataset and b) documentation of the contents of the dataset.

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

**DATA SHARING**

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

**Access type**

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

Due to ethical reasons, only the data captured by a subset of the patients,
during the GData and SData collection phases, as well as normal healthy
control subjects could become publicly available, while the rest of them will
be private to serve the iPROGNOSIS R&D objectives.

The inclusion of a (normal healthy control) subject’s data in the public part
of this dataset will be done on the basis of appropriate informed consent to
data publication.

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

**Access Procedures**

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

For the portions of the dataset that will be made publicly available, a
respective web page will be created on the data management portal that will
provide a description of the dataset and links to a download section. The
private part of this dataset will be stored at a specifically designated
private space of CERTH, in dedicated hard disk drives, on which only members
of the CERTH research team will have access.

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

**Embargo periods** (if any)

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

The applicable datasets will be publicly available two years after the end of
the project to allow the consortium prepare and submit the scientific
publications.

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

**Technical mechanisms for dissemination**

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

For the public part of the dataset, a link will be provided from the Data
management portal. The link will be provided in all relevant i-PROGNOSIS
publications. A technical publication describing the dataset and its
annotations will be produced.

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

**Necessary S/W and other tools for enabling re-use**

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

The dataset will be designed to allow easy reuse and access with commonly
available tools and software libraries.

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

**Repository where data will be stored**

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

The public part of this dataset will be accommodated at the data management
portal of the project Website, hosted by Microsoft Azure-based i-PROGNOSIS
Cloud

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

infrastructure.

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

**ARCHIVING AND PRESERVATION** (including storage and backup)

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

**Data preservation period**

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

The public part of the dataset will be preserved on the Data Management Portal
of the i-PROGNOSIS project at least until the end of the project. The private
part of the dataset will be preserved by CERTH at least until the end of the
project.

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

**Approximated end volume of data**

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

Each selfie image in JPEG format has size 1-2 MBs, so a dataset of e.g., 1000
images is expected to consume up to 2 GB.

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

**Indicative associated costs for data archiving and preservation**

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

There are no costs associated with data preservation in institutional servers.
However, data publicly archived and preserved in the Microsoft Azure-based
i-PROGNOSIS Cloud infrastructure, will cost approximately 20 Euros (€) per
month for one year (at least) after the end of the project.

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

**Indicative plan for covering the above costs**

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

The costs associated with the data archiving and preservation will be covered
by the i-PROGNOSIS project budget.

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

**PARTNERS ACTIVITIES AND RESPONSIBILITIES**

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

**Partner Owner / Data Collector**

</td>  
<td>

**CERTH**

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

**Partner in charge of the data analysis**

</td>  
<td>

**CERTH**

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

**Partner in charge of the data storage**

</td>  
<td>

**CERTH, AUTH**

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

**WPs and Tasks**

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

The data is going to be collected within activities of WP3 and WP6, to mainly
serve the research efforts of T3.4, T6.1 and T6.2.

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

**DATA SET REFERENCE NAME**

</th>  
<th>

**DS2.3-ActivityAnalysis**

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

**DATA SET DESCRIPTION**

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

**Generic description**

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

This dataset will include accelerometer, gyroscope, pedometer, estimated
calories, distance travelled, motion type (walking, jogging or running), heart
rate, skin temperature as well as altimeter and barometer when available to
identify steps climbing. The origin of the data will be from a smart
watch/band regarding activity analysis.

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

**Origin of data**

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

The dataset will be captured by the accelerometer, gyroscope, pedometer heart
rate and skin temperature sensors of the smart watch that will be worn by the
participants

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

during all the i-PROGNOSIS data collection phases.

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

**Nature and scale of data**

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

The first part of the dataset is going to be collected during the development
data collection phase where the subjects will follow specific activity
scenarios. The second part of the dataset will be collected mainly during the
SData and Interventions phases, where smart watches/bands will be provided to
80 and 60 participants, respectively. Moreover, the GData phase will
contribute to the dataset, however, the contribution is expected to be limited
since the smart watch/band is optional at GData. At these phases, i.e., GData,
SData and Interventions, the subjects will not perform specific activity
tasks, instead, the data will be captured throughout their daily routine
activities.

The dataset will include: i) the measures (double precision) of the
acceleration force in g units (9,81 m/s  2  ) that is applied to the smart
watch/band on all three physical axes (x, y, and z), ii) the measures (double
precision) of the smart watch/band’s rotation in rad/s around each of the
three physical axes, and iii) the absolute number of steps taken, the steps
ascended and descended (based on the smart watch’s altimeter), the number of
kilocalories (kcals) burned, the distance travelled (in cm) along with the
speed (cm/s), the pace (ms/m) and the motion type (walking, jogging, running,
etc.), the heart rate (beats/min) and the skin temperature.

The minimum sampling frequency for each sensor will be based on the outcome of
the analysis of the development data (where the sampling frequency will be the
highest possible).

Data format: TXT or CSV file.

The dataset will be in the order of ~5 MB per participant per hour of usage.
Usually the smart watch/band owners use it for at least 12 hours per day.

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

**To whom the dataset could be useful**

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

The collected data will be used for the development and evaluation of the
activity analysis of the i-PROGNOSIS project. The dataset could be useful in
analysing the activity levels and patterns over the course of the day.
Moreover, it may be possible to identify symptoms and signs related to
Parkinson’s Disease, such as tremor.

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

**Related scientific publication(s)**

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

The dataset will accompany the research results in the field of activity
analysis through a smart watch/band of people with PD. At least one
publication is intended to be made in the IEEE Biomedical Engineering journal
(or similar).

The following publication will be taken into account:

Sharma, Vinod, et al. "SPARK: personalized Parkinson disease interventions
through synergy between a smartphone and a smartwatch." International
Conference of Design, User Experience, and Usability. Springer International
Publishing, 2014.

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

**Indicative existing similar data sets** (including possibilities for
integration and reuse)

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

To the best of our knowledge, there is no available smart watch/band-based
activity dataset for PD-related. Related datasets include data captured from
smartwatches (some of them along with datasets originating from smartphones).
For example:

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

_Crowdsignals.io_ ( _http://crowdsignals.io_ )  . Crowdsignals creates the
largest set of rich, longitudinal mobile and sensor data recorded from
smartphones and smartwatches available to the community.

_Dbworld_ ( _http://permalink.gmane.org/gmane.comp.db.dbworld/54394_ )  :
Dbworld dataset contains free dataset for downloads originating from
smartphone+smartwatch mobile, sensor, and human activity. It is part of the
CrowdSignals.io data collection campaign.

_Heterogeneity Activity Recognition Dataset_

( _https://archive.ics.uci.edu/ml/datasets/Heterogeneity+Activity+Recognition_
)  . The Heterogeneity Human Activity Recognition (HHAR) dataset from
smartphones and smartwatches is a dataset devised to benchmark human activity
recognition algorithms (classification, automatic data segmentation, sensor
fusion, feature extraction, etc.) in real-world contexts; specifically, the
dataset is gathered with a variety of different device models and use-
scenarios, in order to reflect sensing heterogeneities to be expected in real
deployments.

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

**STANDARDS AND METADATA**

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

The dataset will be accompanied with detailed documentation of its contents.
Indicative metadata include: a) description of the experimental setup and
procedure that led to the generation of the dataset, b) health status of the
subject, and c) documentation of the variables recorded in the dataset.

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

**DATA SHARING**

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

**Access type**

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

Due to ethical issues, only part of the dataset will be publicly available.
More specifically, the data that correspond to a subset of the PD patients, as
well as the healthy control subjects, captured at the development phase of the
i-PROGNOSIS project will become publicly available. The rest of the data will
be private to serve the i-PROGNOSIS R&D objectives. The inclusion of a
subject’s data in the public part of this dataset will be done on the basis of
appropriate informed consent to data publication.

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

**Access Procedures**

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

The access procedures for the publicly available sub-dataset and the private
subdataset that will serve the project’s R&D objectives are described in
Sections 3.3.2.1 and Section 3.3.2.3 respectively.

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

**Embargo periods** (if any)

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

The applicable datasets will be publicly available 2 years after the end of
the project to allow the consortium to prepare and submit the scientific
publications.

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

**Technical mechanisms for dissemination**

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

For the public part of the dataset, a respective link will be provided through
the Data management portal. The link will be provided in all relevant
i-PROGNOSIS publications. A technical publication describing the dataset and
acquisition procedure will be published.

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

**Necessary S/W and other tools for enabling re-use**

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

The dataset will be designed to allow easy reuse with commonly available tools
and software libraries, such as Excel, Matlab, .NET etc.

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

**Repository where data will be stored**

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

The dataset will be accommodated at the data management portal of the project
Website, hosted by Microsoft Azure-based i-PROGNOSIS Cloud infrastructure.

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

**ARCHIVING AND PRESERVATION** (including storage and backup)

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

**Data preservation period**

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

The public part of this dataset will be preserved online for as long as there
are regular downloads. After that it would be made accessible by request. The
private part of the dataset will be preserved by AUTH at least until the end
of the project.

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

**Approximated end volume of data**

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

The dataset is expected to be several Gigabytes, provided that each
participant is expected to produce at least 20MB per day and the duration is
at least 22 months (SData and Interventions phase).

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

**Indicative associated costs for data archiving and preservation**

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

There are no costs associated with data preservation in institutional servers.
However, data publicly archived and preserved in the Microsoft Azure-based
i-PROGNOSIS Cloud infrastructure, will cost approximately 20 Euros (€) per
month for one year (at least) after the end of the project.

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

**Indicative plan for covering the above costs**

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

The costs associated with the data archiving and preservation will be covered
by the i-PROGNOSIS project budget.

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

**PARTNERS ACTIVITIES AND RESPONSIBILITIES**

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

**Partner Owner / Data Collector**

</td>  
<td>

**AUTH, MICROSOFT**

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

**Partner in charge of the data analysis**

</td>  
<td>

**AUTH**

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

**Partner in charge of the data storage**

</td>  
<td>

**AUTH, MICROSOFT**

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

**WPs and Tasks**

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

The data are going to be collected within the activities of WP3, WP4 and WP6,
to mainly serve the research efforts of T3.2, T4.3, T6.1, T6.2, T6.3 and T6.4.

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

**DATA SET REFERENCE NAME**

</th>  
<th>

**DS2.4-PhysioSignalAnalysis**

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

**DATA SET DESCRIPTION**

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

**Generic description**

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

This dataset will contain features derived from physiological data acquisition
(e.g. electrocardiography), as well as raw data. Whenever suitable,
annotations will be associated with the data to provide ground truth and
facilitate the subsequent analysis of pre-recorded data. Annotations may be
generated by the users in real

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

time and recorded simultaneously with the biosignal data (e.g., by means of a
manual trigger) or produced by a human expert upon revision of pre-recorded
data.

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

**Origin of data**

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

The dataset will be collected using the i-PROGNOSIS physiological data
acquisition devices, such as the Smart TV remote and/or the Microsoft Band.

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

**Nature and scale of data**

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

The datasets will be collected in guided and controlled testing scenarios,
producing SData, and include data acquired from interaction between the elder
and everyday living sensorial artefacts. The goal is to identify changes in
the cardiac and motion (i.e. tremor) patterns, which may relate to changes in
health status, behaviours or other aspects relevant to PD.

The dataset will contain raw data, as well as temporal, spectral and non-
linear features extracted from the raw time series. Examples of such features
are the Very Low Frequency (VLF), Low Frequency (LF), High Frequency (HF) and
related spectral indicators associated with the Heart Rate Variability (HRV),
instant heart rate, interbeat intervals, heart rate histogram, amongst others.

_Data Format:_ Comma-Separated Values (CSV) or Hierarchical Data Format 5
(HDF5)

The dataset is expected to include individualized records per user per device
usage session, possibly segmented in files of duration compatible with easily
manageable post-processing (e.g. 1 hour segments).

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

**To whom the dataset could be useful**

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

The collected data will be used within the project for the development and
evaluation of data mining and signal processing algorithms that incorporate
cardiac and motion data in the identification of early indicators of PD.
Physiological data can also be useful to follow-up the interventions, and also
to assess the progress and status of the disease (if possible). This data will
primarily feed WP4 and WP7, supporting the design of the interventions and to
the overall assessment of the i-PROGNOSIS system. Together with the
annotations, it may also be useful in the future for other researchers and
practitioners working not only in PD but in other medical specialties as well.

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

**Related scientific publication(s)**

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

The dataset will contribute to extend our research results in the field of
physiological data sensing to the specific case of people with PD. We envision
part of the dataset to include data collected from healthy controls. Recent
publications from the project team concerning multimodal physiological
datasets include:

. P. da Silva, C. Carreiras, A. ouren o, A. Fred, R. C. das Neves, and R.
Ferreira, “Offthe-person electrocardiography: Performance assessment and
clinical correlation,”

Health and Technology, vol. 4, no. 4, pp. 309–318, 2015.

_http://link.springer.com/article/10.1007/s12553-015-0098-y_

. P. da Silva, A. ouren o, A. Fred, N. Raposo, and M. A. de Sousa, “Check Your
Biosignals Here: A new dataset for off-the-person ECG biometrics,” Computer
Methods and Programs in Biomedicine, vol. 113, no. 2, pp. 503–514, 2014.

_http://www.sciencedirect.com/science/article/pii/S0169260713003891_

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

. Gamboa, . P. da Silva, and A. Fred, “ iMotion: a new re- search resource for
the study of behavior, cognition, and emotion,” Multimedia Tools and
Applications, vol. 73, no. 1, pp. 345–375, 2014

_http://link.springer.com/article/10.1007%2Fs11042-013-1602-x_

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

**Indicative existing similar data sets** (including possibilities for
integration and reuse)

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

To the best of our knowledge there are no datasets available (at least
publicly) that simultaneously target the specific case of PD and contain the
physiological data sources envisioned by the project.

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

**STANDARDS AND METADATA**

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

The dataset will be accompanied with detailed documentation of its contents.
Indicative metadata include: a) description of the experimental setup and
procedure that led to the generation of the dataset, b) type of activity
performed c) documentation of the variables recorded in the dataset, d) manual
annotations provided by the subjects, and e) manual annotations provided by
human experts.

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

**DATA SHARING**

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

**Access type**

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

Due to ethical reasons, only the raw data, captured by normal healthy control
subjects, (during the development data collection phase) as well as a subset
of the extracted features of the collected datasets could become publicly
available, while the rest of them will be private to serve the i-PROGNOSIS R&D
objectives. The inclusion of a (normal healthy control) subject’s data in the
public part of this dataset will be done on the basis of appropriate informed
consent to data publication.

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

**Access Procedures**

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

The recorded data will be primarily stored as CSV or HDF5 files containing the
raw data streams and the features derived from the data. The files will hosted
on the private i-PROGNOSIS central database that will serve the needs of the
Data Management Portal of the project, or in a suitable analogous digital
space, with protected access reserved only to the relevant members of the
i-PROGNOSIS project team or people for which the consortium partners decide
that access to the data is of relevant interest to the execution of the
project. Only anonymised data will be provided, unless otherwise deemed
necessary for the adequate pursuit of the project goals.

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

**Embargo periods** (if any)

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

The applicable datasets will be available two years after the end of the
project to allow the consortium the preparation and submission of scientific
publications.

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

**Technical mechanisms for dissemination**

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

A technical publication describing the dataset and acquisition procedure will
be published is expected. Dissemination will be mostly performed through
postprocessed data and result analysis by means of relevant i-PROGNOSIS
publications.

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

**Necessary S/W and other tools for enabling re-use**

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

The dataset will be designed to allow easy reuse and access with commonly
available

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

tools and software libraries.

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

**Repository where data will be stored**

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

The data will be hosted on the central database (Microsoft Azure-based
iPROGNOSIS Cloud infrastructure) that will serve the needs of the Data
Management Portal of the i-PROGNOSIS project.

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

**ARCHIVING AND PRESERVATION** (including storage and backup)

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

**Data preservation period**

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

The private part of the dataset will be preserved on the Data Management
Portal of the i-PROGNOSIS project at least until the end of the project.

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

**Approximated end volume of data**

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

The dataset is expected to be several hundreds of MB, considering that the
devices can produce up to 5KB of data per second.

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

**Indicative associated costs for data archiving and preservation**

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

There are no costs associated with data preservation in institutional servers.
However, data publicly archived and preserved in the Microsoft Azure-based
i-PROGNOSIS Cloud infrastructure, will cost approximately 20 Euros (€) per
month for one year (at least) after the end of the project.

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

**Indicative plan for covering the above costs**

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

The costs associated with the data archiving and preservation will be covered
by the i-PROGNOSIS project budget.

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

**PARTNERS ACTIVITIES AND RESPONSIBILITIES**

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

**Partner Owner / Data Collector**

</td>  
<td>

**AUTH, PLUX, MICROSOFT**

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

**Partner in charge of the data analysis**

</td>  
<td>

**AUTH, PLUX**

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

**Partner in charge of the data storage**

</td>  
<td>

**AUTH, PLUX, MICROSOFT**

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

**WPs and Tasks**

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

The data are going to be collected within activities of WP4, WP6 and WP7, to
mainly serve the research efforts of T4.1, T4.5, T6.1, T6.4, T7.3 and T7.4.

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

**DATA SET REFERENCE NAME**

</th>  
<th>

**DS2.5-TypingPatternAnalysis**

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

**DATA SET DESCRIPTION**

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

**Generic description**

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

This dataset will include typing patterns in the form of keystroke dynamics-
related features, extracted during the users' typing on a virtual keyboard (of
a touch screenenabled smartphone) so as to be used as indicators towards the
formulation of early PD detections tests. Features will be accompanied by
metadata in order to facilitate the analysis within the i-PROGNOSIS project,
as well as, by other researchers outside of the project.

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

**Origin of data**

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

The dataset will be collected as part of the development of the i-PROGNOSIS
first stage of early PD detection. It will comprise a development sub-dataset
and corresponding metadata collected by a small number of users, as well as, a
deployment sub-dataset and corresponding metadata collected by a larger number
of users (in the range of hundreds). Both data collection procedures will be
accompanied by ethics approval.

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

**Nature and scale of data**

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

The dataset will be collected during the interaction of users with the virtual
keyboard of a touch screen-enabled smartphone, i.e., when they will be typing
on the keyboard. The development sub-dataset will be collected when
development users' will be typing specific text excerpts in a controlled
environment using specific smartphones. Indicative features that will comprise
the dataset are the hold time (the time a key is pressed), the press latency
(the time between pressing a key and fully releasing it), flight time (the
time in-between releasing a key and pressing the next key) and release latency
(the time between releasing a key and pressing the next key), all measured in
milliseconds (ms). The deployment sub-dataset will comprise the same features
as the development sub-dataset, but it will be collected through the
iPROGNOSIS PD detection application [via the **typing pattern service** (see
Section 6 of D2.1)] that will be released to the public for download. As a
result, it is expected that the dataset will be formulated through a
significantly larger number of users' typing on their smartphone keyboard. In
the case of the deployment dataset, keystroke dynamics will be collected from
each typing session, i.e., starting when the keyboard becomes active and
finishing when the keyboard is suppressed. In the case of the deployment
dataset, _keys pressed will not be recorded_ in order to comply with personal
data privacy and protection regulations.

The dataset is expect to be composed of 40 records for the development
subdataset, while the deployment sub-dataset scale cannot be defined as it
depends on the number of users that will download the i-PROGNOSIS PD detection
application, the number of those that will allow for the respective service to
collect typing pattern data and their frequency of actions that require typing
(e.g., composing SMSs/Tweet or e-mails).

_Data Format:_ For each text excerpt typed (development sub-dataset) a CSV
file will be generated, including the values of features and metadata. For
each typing session (deployment sub-dataset) a structured XML file will be
generated, including values of features and metadata.

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

**To whom the dataset could be useful**

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

The development sub-dataset will be used to initialise the i-PROGNOSIS machine
learning algorithms as far as the typing pattern inference is concerned, that
will be further trained by the deployment sub-dataset.

The deployment sub-dataset will be used for training the i-PROGNOSIS machine
learning algorithms in order for the i-PROGNOSIS investigators to examine
whether it is useful to employ this type of information towards the
realisation of early PD detection tests based on the users' interaction with
their everyday digital devices,

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

such as the smartphone.

Moreover, the dataset could be useful for biomedical researchers that are
interested in studying the relationship between the interfacing (requiring
motor skills) of people with digital devices and psychomotor impairments, such
as PD. The later constitutes a relatively new research subject. Adequate use
of these data presupposes at least basic background regarding PD
symptomatology, as well as, basic knowledge in data analysis methodology and
experience in the use of statistical software packages.

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

**Related scientific publication(s)**

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

The development sub-dataset will accompany the research results regarding the
feasibility of early PD detection tests based on users' interaction with
everyday digital devices. Research results are planned to be published
initially in the indicative journals and/or conferences provided below:

  * IEEE Transactions on Biomedical Engineering (Journal) 

  

  * Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Conference) 

  

  * Human Computer Interaction International (Conference) 

  

The respective research field is relatively new. A recent publication has been
made targeting the psychomotor impairment detection based on users' natural
typing (see Giancardo et al. (2015) at _Indicative existing similar data sets_
)

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

**Indicative existing similar data sets** (including possibilities for
integration and reuse)

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

A similar dataset has been made available by Giancardo et al. (2015), linked
to the publication:

Giancardo, ., Sánchez-Ferro, A., Butterworth, I., Mendoza, C. S., & Hooker, J.
M. (2015). Psychomotor impairment detection via finger interactions with a
computer keyboard during natural typing. Scientific reports, 5, article no:
9678, doi:10.1038/srep09678

The dataset includes keystroke dynamics and it is part of the supplementary
information accompanying the publication and can be found through:

_http://www.nature.com/articleassets/npg/srep/2015/150409/srep09678/extref/srep09678-s1.pdf_

i-PROGNOSIS investigators plan to use this dataset and the respective
experiment protocol in order to configure the framework for the collection and
analysis of the development sub-dataset.

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

**STANDARDS AND METADATA**

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

The European Data Format+ (EDF+)  (
_http://www.edfplus.info/specs/edfplus.html_ )  will be exploited for the
production of the metadata files.

The development sub-dataset will be accompanied by a detailed description of
its contents. Indicative metadata include: a) description of the experiment
set-up and the procedure that led to the generation of the dataset, b)
demographics, anthropometrics and basic health-record data of the experiment
participants, c) description of the keystroke dynamics-related features, and
d) annotation of features

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

based on (b).

The deployment sub-dataset will be annotated based on the following indicative
metadata: a) basic health record data of the users and b)
smartphone/smartwatch device model.

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

**DATA SHARING**

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

**Access type**

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

The development sub-dataset will be publicly available (see Section 3.3.2.1),
accompanied by the respective ethics approval. Due to ethical compliance, the
deployment sub-dataset will be confidential and only i-PROGNOSIS partners will
be able to access it after following the appropriate procedure (see Section
3.3.2.3)

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

**Access Procedures**

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

The deployment sub-dataset that will be confidential will be handled according
the framework reported in Section 3.3.2.3. The development sub-dataset that
will be publicly available will be open for third-party stakeholders to
download based on the procedure described in Section 3.3.2.1.

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

**Embargo periods** (if any)

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

The development sub-dataset is planned to be publicly available after month 24
of the project to allow i-PROGNOSIS investigators to prepare and submit the
respective scientific publications, but allow also for third-party researchers
to conduct further analysis and comment on the i-PROGNOSIS results before the
end of the project.

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

**Technical mechanisms for dissemination**

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

The development sub-dataset will be available through the i-PROGNOSIS data
management portal (see Section 3.2). Links redirecting to the portal and the
available dataset will be provided through a dedicated page of the i-PROGNOSIS
project website (  _www.i-prognosis.eu_ )  .

A technical description providing information on the experiment protocol
through which the development sub-dataset was captured will accompany the
development sub-dataset.

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

**Necessary S/W and other tools for enabling re-use**

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

As the publicly available compressed dataset (development sub-dataset) will
comprise standard CSV files, no specialised software or tools will be required
for the dataset to be parsed and reused, other than a (de-)compression
software and a basic text editor (minimum requirement to access the content of
the CSV file).

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

**Repository where data will be stored**

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

The development sub-dataset will be stored in AUTH secure servers, as well as
in the Microsoft Azure-based i-PROGNOSIS Cloud infrastructure (data centres
are located in Ireland) where the data management portal will be installed.
The deployment subdatasets will be also stored in the i-PROGNOSIS Cloud
infrastructure, but access will be restricted (Section 3.3.2.3).

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

**ARCHIVING AND PRESERVATION** (including storage and backup)

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

**Data preservation period**

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

The public part of this dataset will be preserved online for as long as there
are regular downloads and at least one year after the end of the project.
After that it would be made accessible by direct request to the data
collector. The confidential part of the dataset will be preserved by the
owners at least one year after the end of the project.

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

**Approximated end volume of data**

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

The development sub-dataset is expected to be approximately 4 MB (including
metadata), based on the number of participants (40) and the approximate size
of features recorded during a 10 min typing session (~ 70 KB).

The deployment dataset is expected to be in the range of hundreds of Gigabytes
(GB), based on the expected number of the i-PROGNOSIS App users (in the range
of thousands), an average of 10 typing activities per day for at least six
months, an average typing activity duration of 5 min, and a 7 KB size of
typing pattern features per one minute of typing.

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

**Indicative associated costs for data archiving and preservation**

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

The development sub-dataset will be stored on AUTH servers. There are no costs
associated with the latter means of archiving and preservation. The
development and deployment sub-datasets will be also archived and preserved in
the Microsoft Azurebased i-PROGNOSIS Cloud infrastructure. The indicative cost
of the latter means of archiving and preservation is 20 Euros (€) per month
for one year (at least) after the end of the project.

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

**Indicative plan for covering the above costs**

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

The costs associated with the data archiving and preservation will be covered
by the i-PROGNOSIS project budget.

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

**PARTNERS ACTIVITIES AND RESPONSIBILITIES**

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

**Partner Owner / Data Collector**

</td>  
<td>

**AUTH, MICROSOFT**

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

**Partner in charge of the data analysis**

</td>  
<td>

**AUTH**

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

**Partner in charge of the data storage**

</td>  
<td>

**AUTH, MICROSOFT**

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

**WPs and Tasks**

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

The development sub-dataset will be collected within WP3 and WP6, to mainly
serve the research efforts of T3.2, T3.7, T6.2 and T6.3.

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

**DATA SET**

**REFERENCE NAME**

</th>  
<th>

**DS2.6-ExploratoryWalkabilityAnalysis**

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

**DATA SET DESCRIPTI**

</td>  
<td>

**ON**

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

**Generic description**

</td>  
<td>

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

Dataset for building an anonymous and depersonalised user sociability profile,
based on information (derived features) originating by location
sensors/modules (e.g. Wi-Fi, GPS).

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

**Origin of data**

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

The derived information will originate from data by the smartphone and
smartwatch embedded location and IMU (GPS, GSM, Wi-Fi, accelerometer,
gyroscope, and magnetometer) sensors and modules during the GData and SData
collection periods.

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

**Nature and scale of data**

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

The collected data will be captured while the user carries her/his smartphone
or wears her/his smartwatch while commuting. More specifically, the dataset
will contain derived de-personalised features based on the fusion of multiple
sensors and modules such as: Wi-Fi, GSM, GPS and IMU from the user’s
smartphone and GPS and IMU from the user’s smartwatch device. Since
anonymisation is of outmost importance when dealing with location information,
all derived features will be free of any absolute world coordinates or any way
to derive them by processing.

_Data format:_ JSON/XML for data representation as well as for any relevant
annotations.

It is expected that the dataset, will be composed of an equal number of PD
patients and healthy subjects; however, the number of subjects is heavily
related with the overall user participation.

The data volume per subject is anticipated to be in the order of ~10-15 MB per
subject.

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

**To whom the dataset could be useful**

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

The collected data will be used for the development and evaluation of the
location and the physical activity service (i.e. background services, see D2.1
for additional information) as well as the module responsible for building the
user’s behavioural profile.

Furthermore, the dataset will be of particular interest to the researchers and
health professionals that are willing to explore the underlying information
contained in location data regarding the sociability of potential PD patients.

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

**Related scientific publication(s)**

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

A paper describing the dataset is expected to be published. Furthermore, a
paper describing the approach of de-personalising location information and
fusing different location data sources is also target for publication. More
information on the latter can be found in D8.3 (Dissemination plan; “A paper
presenting an approach for generating user profiles based on de-personalized
location information”).

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

**Indicative existing similar data sets** (including possibilities for
integration and reuse)

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

To this day, publicly available location based PD datasets are non-existent.

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

**STANDARDS AND METADATA**

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

The dataset will be accompanied by a complete documentation containing:

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

  1. A description of the overall procedure regarding the collection of the data. 

  

  2. A brief definition of how the derived location-based features were generated. 

  

  3. A brief definition of the de-personalisation processes. 

  

  4. An annotation file, indicating if the subject (indicated solely by a subject ID) is a diagnosed PD patient or a healthy individual. If deemed informative or necessary, additional non-identifiable information may be included in this section. 

  

  

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

**DATA SHARING**

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

**Access type**

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

Since all identifiable information, like correlation to absolute world
coordinates, will be stripped clean off of the dataset, access type can be
classified as publicly available (i.e. can be publicly shared).

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

**Access Procedures**

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

A respective webpage will be created on the data management portal that will
include a brief description of the dataset as well as the publication(s) that
need to be cited in case any part of the dataset is used.

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

**Embargo periods**

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

The applicable dataset will be publicly available two years after the end of
the project to allow the consortium prepare and submit the scientific
publications.

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

**Technical mechanisms for dissemination**

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

The data management link leading to the dataset will be included in all
relevant publications. A complete technical publication describing in-depth
the dataset as well as the data acquisition procedures will be published.

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

**Necessary S/W and other tools for enabling re-use**

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

A typical JSON/XML (e.g. libxerces, libJSON, JSON-java) parsing library is the
only requirement for accessing the content of the dataset. Such libraries are
typical in most (if not all) programming languages.

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

**Repository where data will be stored**

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

The entirety of the dataset will be accommodated at the data management portal
of the project’s website, hosted by Microsoft Azure-based i-PROGNOSIS Cloud
infrastructure.

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

**ARCHIVING AND PRESERVATION** (including storage and backup)

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

**Data preservation period**

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

The dataset will be preserved online for as long as there are regular
downloads. After that it would be made accessible by request.

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

**Approximated end volume of data**

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

Depending on the magnitude of user participation, the end volume of data is
expected to be in the range of ~15-25 GB.

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

**Indicative associated costs for data archiving and preservation**

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

There are no costs associated with data preservation in institutional servers.
However, data publicly archived and preserved in the Microsoft Azure-based
i-PROGNOSIS Cloud infrastructure, will cost approximately 20 Euros (€) per
month for one year (at least) after the end of the project.

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

**Indicative plan for covering the above costs**

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

The costs associated with the data archiving and preservation will be covered
by the i-PROGNOSIS project budget.

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

**PARTNERS ACTIVITIES AND RESPONSIBILITIES**

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

**Partner Owner / Data Collector**

</td>  
<td>

**AUTH**

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

**Partner in charge of the data analysis**

</td>  
<td>

**AUTH**

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

**Partner in charge of the data storage**

</td>  
<td>

**AUTH**

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

**WPs and Tasks**

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

The entire “ExploratoryWalkabilityAnalysis” dataset will be collected during
WP3, to mainly serve the research efforts of T3.2, T3.7 and T4.4.

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

**DATA SET REFERENCE**

**NAME**

</th>  
<th>

**DS2.7-TextSentimentAnalysis**

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

**DATA SET DESCRIPTION**

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

**Generic description**

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

SMS/Tweet(Twitter message) datasets in different languages will be collected
from the users’ smartphones. These messages will be manually annotated by a
value indicating the sentiment classification (at minimum three classes will
be used: positive, negative, neutral or n/a).

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

**Origin of data**

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

The dataset will be collected from i-Prognosis users’ smartphones SMS or
Twitter messages (User consent will be provided).

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

**Nature and scale of data**

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

The goal of this dataset is to enable the detection of depression symptoms for
early PD detection.

Data Format: plain text files of SMS/Tweets with annotations/metadata.

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

**To whom the dataset could be useful**

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

The dataset will be used within the project for the development and evaluation
of depression recognition algorithms as a symptom for the early detection of
PD. This data will be used in WP3 and WP6, supporting the early PD symptoms
detection and for the overall assessment of the i-PROGNOSIS system. This
dataset may also be useful in the future for other researchers who want to
explore depression symptoms from short messages.

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

**Related scientific publication(s)**

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

This database will build the foundation of our research and development of
algorithms towards detecting depression symptoms from short messages for early
PD detection. We plan to publish our findings on natural language processing-
related conferences and journals.

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

**Indicative existing similar data sets** (including possibilities for
integration and reuse)

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

Many manually annotated databases are already available for SMS/twitter
sentiment analysis. To name a few:

  * Stanford Twitter Corpus:  _http://help.sentiment140.com/for-students_

  

  * HCR and OMD datasets:  _https://bitbucket.org/speriosu/updown_

  

  * Sentiment Strength Corpora:  _http://sentistrength.wlv.ac.uk/_

  

  * Sanders:  _http://www.sananalytics.com/lab/twitter-sentiment/_

  

  * SemEval:  _http://www.cs.york.ac.uk/semeval-2013/task2/_

  

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

**STANDARDS AND METADATA**

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

SMS/tweet messages are ASCII text with a length of 160/140 characters
respectively. The available metadata will be the available sentiment
labels(classes).

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

**DATA SHARING**

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

**Access type**

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

Due to ethical reasons, only the SMS/Tweets collected by a subset of the
patients during the initial phases by normal healthy control subjects could
become publicly available, while the rest of them will be private to serve the
i-PROGNOSIS R&D objectives.

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

**Access Procedures**

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

For the portions of the dataset that will be made publicly available, a
respective web page will be created on the data management portal that will
provide a description of the dataset and links to a download section. The
private part of this dataset will be stored at a specifically designated
private space of CERTH, in dedicated hard disk drives, on which only members
of the CERTH research team will have access.

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

**Embargo periods** (if any)

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

The applicable datasets will be publicly available two years after the end of
the project to allow the consortium prepare and submit the scientific
publications.

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

**Technical mechanisms for dissemination**

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

For the public part of the dataset, a link will be provided from the Data
management portal. The link will be provided in all relevant i-PROGNOSIS
publications. A technical publication describing the dataset and its
annotations will be produced.

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

**Necessary S/W and other tools for enabling re-use**

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

No specialised software or tools will be required for the dataset to be parsed
and reused, other than a basic text editor.

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

**Repository where data will be stored**

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

The public data will be hosted on the central database that will serve the
needs of the Data Management Portal of the i-PROGNOSIS project, hosted by
Microsoft Azurebased i-PROGNOSIS Cloud infrastructure.

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

**ARCHIVING AND PRESERVATION** (including storage and backup)

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

**Data preservation period**

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

The public part of the dataset will be preserved on the Data Management Portal
of the i-PROGNOSIS project at least until the end of the project. The private
part of the dataset will be preserved by CERTH at least until the end of the
project.

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

**Approximated end volume of data**

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

Each text/SMS message is less than 500 bytes (assuming utf-8 encoding for
tweets), so a corpus of 10000 SMS/Tweets is expected to consume up to 5 MB
(per language).

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

**Indicative associated costs for data archiving and preservation**

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

The private part of the dataset will be stored on a dedicated hard drive, with
very small costs for its preservation. Data publicly archived and preserved in
the Microsoft ,Azure-based, i-PROGNOSIS Cloud infrastructure, will cost
approximately 20 Euros (€) per month for one year (at least) after the end of
the project.

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

**Indicative plan for covering the above costs**

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

The costs associated with the data archiving and preservation will be covered
by the i-PROGNOSIS project budget.

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

**PARTNERS ACTIVITIES AND RESPONSIBILITIES**

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

**Partner Owner / Data Collector**

</td>  
<td>

**CERTH**

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

**Partner in charge of the data analysis**

</td>  
<td>

**CERTH**

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

**Partner in charge of the data storage**

</td>  
<td>

**CERTH, AUTH**

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

**WPs and Tasks**

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

The data is going to be collected within activities of WP3 and WP6, to mainly
serve the research efforts of T3.4, T6.1 and T6.2.

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

**DATA SET REFERENCE NAME**

</th>  
<th>

**DS2.8-FoodIntakeAnalysis**

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

**DATA SET DESCRIPTION**

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

**Generic description**

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

Dataset for the objective quantification of meal mechanics, based on the real-
time recording of the cumulative intake curve during a meal, extrapolated by
the weightloss recording from a personal weighting scale placed under the
user’s plate. This dataset will also include derivative information about the
specific values of the user’s eating behavioural elements, e.g.,: meal total
intake and duration, number of bites,

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

eating rate and eating rate changes across the meal.

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

**Origin of data**

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

The dataset will be produced by use of Mandometer electronic plate scales
during the SData collection phase. It is possible that the dataset will be
complemented by data collected during the intervention phase if/when the
Mandometer is used.

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

**Nature and scale of data**

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

It is expected that the datasets will be collected mainly during the SData
phase, and potentially during the intervention phase. The users will be asked
to use the Mandometers during their main (breakfast, lunch, dinner) meals
across a pre-set number of days (e.g., 5 times/week). The Mandometers will be
supposed to be used throughout the duration of a main meal.

The dataset will contain the raw weight-loss data series generated by the
removal of food from a plate during the meal, the corrected cumulative intake
curve of the user for each meal, the extracted meal behavioural indicators
(e.g., the meal total intake and duration, the recorded number of bites, the
total eating rate and the modelled eating rate changes across the meal).
Potentially, the dataset will also include averaged values per user across
multiple recorded meals. Additionally, the dataset will contain around-the-
meal self-rated subjective information concerning the meal, e.g., perceived
fullness before/after, food taste evaluation etc.

_Data Format:_ XML files for all the described measures.

The dataset will be on the order of 0.5 MB per recorded meal. The size will be
significantly higher if the food pictures are also included in the dataset.

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

**To whom the dataset could be useful**

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

The dataset will facilitate evaluation of the progress of each individual and
their comparison with healthy, age-matched control populations. This
comparison can be valuable for health professionals who care for patients with
Parkinson patients, the patients themselves and researchers who want to
explore the underlying shifts that happen in eating behaviour due to
Parkinson’s disease.

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

**Related scientific publication(s)**

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

The dataset will complement our research and clinical results in the field of
eating behaviour quantification. A subset of the investigated population,
participating in the SData phase, without having diagnosed Parkinson disease
will be the reference control population. Corresponding publications
describing and validating different components of the described methodology
are:

Ioakimidis I, Modjtaba Z, Eriksson-Marklund L, Bergh C, Grigoriadis A, &
Södersten P. (2011). “Description of Chewing and Food Intake over the Course
of a Meal.” Physiology & Behavior,104 (5,: 761–769.

Papapanagiotou V, Diou C, Langlet B, Ioakimidis I, & Delopoulos A. (2015). “A
parametric Probabilistic Context-Free Grammar for food intake analysis based
on continuous meal weight measurements.” Conf Proc IEEE Eng Med Biol
Soc.,78537856.

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

**Indicative existing similar data sets** (including possibilities for
integration and reuse)

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

While there are many age stratified datasets about qualitative measures
pertaining to the nutritional profile of national or local populations, to the
best of our knowledge there exists no comparable dataset for Parkinson
patients or aged-matched control group.

Available national (Sweden) dataset example:

Riksmaten 2010  ( _http://www.livsmedelsverket.se/matvanor-halsa--
miljo/kostrad-ochmatvanor/matvanor---undersokningar/riksmaten-2010-11---
vuxna/_ )  : In this dataset, a representative sample of 1800 individuals
between 18–80 years, living in Sweden, were invited to participate in the
survey. The data collection took place between May 2010 and July 2011\.

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

**STANDARDS AND METADATA**

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

The dataset will be accompanied with detailed documentation of its contents.
Indicative metadata include: a) description of the experimental setup and
procedure that led to the generation of the dataset, b) type of consumed food
(probably identified through mobile-recorded pictures), c) documentation of
the variables recorded in the dataset, and d) the relative “positioning” of an
individual in the corresponding population distribution.

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

**DATA SHARING**

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

**Access type**

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

Due to ethical reasons, only the data captured by a subset of the patients and
normal healthy control subjects, could become publicly available, while the
rest of them will be private to serve the i-PROGNOSIS R&D objectives.

The inclusion of a (normal healthy control) subject’s data in the public part
of this dataset will be done on the basis of appropriate informed consent to
data publication. It will be investigated further whether the complementary
food pictures can also become publicly available.

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

**Access Procedures**

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

For the portions of the dataset that will be made publicly available, a
respective web page will be created on the data management portal that will
provide a description of the dataset. The private part of this dataset will be
stored at a specifically designated private space of KI, in encrypted hard
disk drives, on which only members of the KI research team, whose work
directly relates to these data will have access. For further i-PROGNOSIS
partners to obtain access to these data, they should provide a proper request
to the KI primarily responsible, including a justification over the need to
have access to these data. Once deemed necessary, KI will provide the
respective data portions to the partner.

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

**Embargo periods** (if any)

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

The applicable datasets will be publicly available 2 years after the end of
the project to allow the consortium prepare and submit the scientific
publications.

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

**Technical mechanisms for dissemination**

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

For the public part of the dataset, a link to this will be provided from the
Data management portal. The link will be provided in all relevant i-PROGNOSIS

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

publications. A technical publication describing the dataset and acquisition
procedure will be published.

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

**Necessary S/W and other tools for enabling re-use**

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

A typical XML parsing library is the only requirement for accessing the
content of the dataset. Such libraries are typical in most (if not all)
programming languages.

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

**Repository where data will be stored**

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

The public part of this dataset will be accommodated at the data management
portal of the project website, hosted within Microsoft Azure-based i-PROGNOSIS
Cloud infrastructure.

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

**ARCHIVING AND PRESERVATION** (including storage and backup)

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

**Data preservation period**

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

The public part of this dataset will be preserved online for as long as there
are regular downloads. After that it would be made accessible by request. The
private part of the dataset will be preserved by KI at least until 10 years
after the publication of the scientific results.

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

**Approximated end volume of data**

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

The dataset will be on the order of 0.5 MB per recorded meal. The size will be
significantly higher if the food pictures are also included in the dataset. A
first estimation of the dataset, including food pictures would be in the range
from ~2-7 GB.

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

**Indicative associated costs for data archiving and preservation**

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

Probably parts from three hard disk drives in the KI lab (primary, backup 1
and backup 2) will be allocated for the dataset. There are no costs associated
with its preservation. Data publicly archived and preserved in the Microsoft
Azure-based iPROGNOSIS Cloud infrastructure, will cost approximately 20 Euros
(€) per month for one year (at least) after the end of the project.

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

**Indicative plan for covering the above costs**

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

The costs associated with the data archiving and preservation will be covered
by the i-PROGNOSIS project budget.

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

**PARTNERS ACTIVITIES AND RESPONSIBILITIES**

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

**Partner Owner / Data Collector**

</td>  
<td>

**KI**

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

**Partner in charge of the data analysis**

</td>  
<td>

**KI**

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

**Partner in charge of the data storage**

</td>  
<td>

**KI**

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

**WPs and Tasks**

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

The data are going to be collected within activities of WP3, WP4 and WP6, to
mainly serve the research efforts of T3.2, T3.7, T4.1, T4.5, T6.1 and T6.4.

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

**DATA SET REFERENCE NAME**

</th>  
<th>

**DS2.9-BowelSoundsAnalysis**

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

**DATA SET DESCRIPTION**

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

**Generic description**

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

This dataset will include bowel sound data captured from a smart belt towards
bowel immobility related to increased constipation detection, since
constipation is one of the earliest non-motor symptoms in PD patients.

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

**Origin of data**

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

The dataset will be captured by a custom-made smart belt that will carry
distributed microphones covering the abdomen area and will be worn either
directly on the skin or above clothes.

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

**Nature and scale of data**

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

The first part of the dataset is expected to be collected during the
development data collection phase where the subjects (healthy control and PD
patients) will wear the smart belt for at least 1 hour in laboratory
environment. The second part of the dataset will be collected mainly during
the SData and Interventions phases, where smart belts will be given to 80
(potential PD patients and healthy controls) and 60 (identified PD patients)
subjects, respectively. The recording will take place at each subject’s
indoor/outdoor environment. Since the bowel sounds, according to the
literature, exist in the range of 100 – 1000/1500 Hz, the raw signal will be
filtered (high-pass filtering at 80 Hz in order to eliminate the influence of
cardiac and pulmonary sounds).

Data format: TXT or CSV file

Considering three recording channels, an average usage of 4 hours per day and
3 kHz sampling frequency, the dataset is estimated to be on the order of ~500
MB per subject per day.

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

**To whom the dataset could be useful**

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

The collected data will be used for the development and evaluation of
constipation detection algorithms (through sound-based intestinal motility
assessment). Moreover, the dataset may be useful to identify other
gastrointestinal disorders, such as obstructions, ascites, infections and
trauma. The fact that there are not any available datasets of this kind, makes
this dataset valuable to the research community, especially to the
gastroenterologists.

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

**Related scientific publication(s)**

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

The dataset will accompany the research results in the field of bowel sounds
analysis for constipation detection of people with PD. One publication is
intended to be made in the World Journal of Gastroenterology (or similar).

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

**Indicative existing similar data sets** (including possibilities for
integration and reuse)

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

To the best of our knowledge, there is no available bowel sounds dataset. To
this end, the dataset that will be collected during i-PROGNOSIS project is
expected to have major impact.

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

**STANDARDS AND METADATA**

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

The dataset will be accompanied with detailed documentation of its contents.

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

Indicative metadata include: a) description of the experimental setup and
procedure that led to the generation of the dataset, b) health status of the
subject, and c) documentation of the variables recorded in the dataset.

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

**DATA SHARING**

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

**Access type**

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

Due to ethical issues, only part of the dataset will be publicly available.
More specifically, the data that correspond to a subset of the PD patients as
well as the healthy control subjects, captured at the initial phase of the
i-PROGNOSIS project will become publicly available. The rest of the data will
be private to serve the iPROGNOSIS R&D objectives. The inclusion of a
subject’s data in the public part of this dataset will be done on the basis of
appropriate informed consent to data publication.

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

**Access Procedures**

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

The access procedures for the publicly available sub-dataset and the private
subdataset that will serve the project’s R&D objectives are described in
Sections 3.3.2.1 and Section 3.3.2.3 respectively.

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

**Embargo periods** (if any)

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

The applicable datasets will be publicly available 2 years after the end of
the project to allow the consortium prepare and submit the scientific
publications.

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

**Technical mechanisms for dissemination**

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

For the public part of the dataset, a link to this will be provided from the
Data management portal. The link will be provided in all relevant i-PROGNOSIS
publications. A technical publication describing the dataset and acquisition
procedure will be published.

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

**Necessary S/W and other tools for enabling re-use**

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

The dataset will be designed to allow easy reuse with commonly available tools
and software libraries, such as Excel, Matlab, .NET etc.

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

**Repository where data will be stored**

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

The public part of this dataset will be accommodated at the data management
portal of the project website, hosted by Microsoft Azure-based i-PROGNOSIS
Cloud infrastructure.

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

**ARCHIVING AND PRESERVATION** (including storage and backup)

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

**Data preservation period**

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

The public part of this dataset will be preserved online for as long as there
are regular downloads. After that it would be made accessible by request. The
private part of the dataset will be preserved by AUTH at least until the end
of the project.

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

**Approximated end volume of data**

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

The dataset is expected to be several Terabytes, provided that each
participant is expected to produce at least 500MB per day and the duration is
at least 22 months

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

(SData and Interventions phase).

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

**Indicative associated costs for data archiving and preservation**

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

Probably four dedicated hard disk drives will be allocated for the private
dataset. There are no costs associated with its preservation. Data publicly
archived and preserved in the Microsoft Azure-based i-PROGNOSIS Cloud
infrastructure, will cost approximately 20 Euros (€) per month for one year
(at least) after the end of the project.

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

**Indicative plan for covering the above costs**

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

The costs associated with the data archiving and preservation will be covered
by the i-PROGNOSIS project budget.

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

**PARTNERS ACTIVITIES AND RESPONSIBILITIES**

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

**Partner Owner / Data Collector**

</td>  
<td>

**PLUX, AUTH**

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

**Partner in charge of the data analysis**

</td>  
<td>

**PLUX, AUTH**

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

**Partner in charge of the data storage**

</td>  
<td>

**PLUX, AUTH**

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

**WPs and Tasks**

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

The data are going to be collected within the activities of WP3 and WP6, to
mainly serve the research efforts of T3.6, T6.1, T6.3 and T6.4.

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

**DATA SET REFERENCE NAME**

</th>  
<th>

**DS2.10-TremorAnalysis**

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

**DATA SET DESCRIPTION**

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

**Generic description**

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

Dataset for detecting the presence of PD minor tremor in the upper limbs. The
dataset will include derived features from 3-axis acceleration, orientation as
well as magnetic data streams.

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

**Origin of data**

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

The data will originate from the smartphone and smartwatch embedded IMU
(gyroscope, accelerometer and magnetometer) sensors during the GData and SData
collection periods.

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

**Nature and scale of data**

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

The collected data will be captured during the user’s interaction with the
smartphone/smartwatch devices. More specifically, smartphone generated data
will be collected while the user performs certain actions while holding the
device (e.g., during a voice call). On the other hand, data originating from
the smartwatch device will be captured while the user performs everyday
activities while wearing the smartwatch.

_Data format:_ JSON/XML for data representation as well as for any relevant
annotations.

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

It is expected that the dataset, will be composed of an equal number of PD
patients and healthy subjects; however, the number of subjects is heavily
related with the overall user participation.

The data volume per subject is anticipated to be in the order of ~5-10 MB per
subject.

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

**To whom the dataset could be useful**

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

The collected data will be used for the development and evaluation of the
minor tremor detection module (i.e. handling service, see D2.1 for more
information) of the i-PROGNOSIS smartphone detection application.

Furthermore, the dataset will be of particular interest to the researchers and
health professionals that are willing to explore the underlying information
contained in IMU data regarding PD tremor.

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

**Related scientific publication(s)**

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

A paper describing the dataset is expected to be published. Furthermore, a
paper describing the approach for detecting PD minor tremor is also target for
publication. More information on the latter can be found in D8.3
(Dissemination plan; “A paper presenting a method for detecting the presence
of minor-tremor, based on IMU data collected by the smartphone/smartwatch”).

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

**Indicative existing similar data sets** (including possibilities for
integration and reuse)

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

To this day, publicly available IMU based PD tremor datasets are non-existent.

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

**STANDARDS AND METADATA**

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

The dataset will be accompanied by a complete documentation containing: a) a
description of the overall procedure regarding the collection of the data, b)
a brief definition of how the derived IMU-based features were generated and c)
an annotation file, indicating if the subject (indicated solely by a subject
ID) is a diagnosed PD patient or a healthy individual. If deemed informative
or necessary, additional non-identifiable information may be included in this
section.

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

**DATA SHARING**

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

**Access type**

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

Since all identifiable information will be stripped clean off of the dataset,
access type can be classified as publicly available (i.e. can be publicly
shared).

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

**Access Procedures**

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

A respective webpage will be created on the data management portal that will
include a brief description of the dataset as well as the publication(s) that
need to be cited in case any part of the dataset is used.

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

**Embargo periods**

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

The applicable dataset will be publicly available two years after the end of
the project to allow the consortium prepare and submit the scientific
publications.

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

**Technical mechanisms for dissemination**

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

The data management portal will provide a link leading to the dataset, which
will be

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

included in all relevant publications. A complete technical publication
describing indepth the dataset as well as the data acquisition procedures will
be published.

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

**Necessary S/W and other tools for enabling re-use**

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

A typical JSON/XML (e.g., libxerces, libJSON, JSON-java) parsing library is
the only requirement for accessing the content of the dataset. Such libraries
are typical in most (if not all) programming languages.

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

**Repository where data will be stored**

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

The entirety of the dataset will be accommodated at the data management portal
of the project’s website, hosted by Microsoft Azure-based i-PROGNOSIS Cloud
infrastructure..

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

**ARCHIVING AND PRESERVATION** (including storage and backup)

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

**Data preservation period**

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

The dataset will be preserved online for as long as there are regular
downloads. After that it would be made accessible by request.

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

**Approximated end volume of data**

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

Depending on the magnitude of user participation, the end volume of data is
expected to be in the range of ~15-25 GB.

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

**Indicative associated costs for data archiving and preservation**

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

Data archived and preserved in the Microsoft Azure-based i-PROGNOSIS Cloud
infrastructure, will cost approximately 20 Euros (€) per month for one year
(at least) after the end of the project.

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

**Indicative plan for covering the above costs**

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

The costs associated with the data archiving and preservation will be covered
by the i-PROGNOSIS project budget.

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

**PARTNERS ACTIVITIES AND RESPONSIBILITIES**

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

**Partner Owner / Data Collector**

</td>  
<td>

**AUTH**

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

**Partner in charge of the data analysis**

</td>  
<td>

**AUTH**

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

**Partner in charge of the data storage**

</td>  
<td>

**AUTH**

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

**WPs and Tasks**

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

The entire dataset will be collected during WP3, to mainly serve the research
efforts of T3.2, T3.7 and T4.4.

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

###  Intervention Data  
  
<table>  
<tr>  
<th>

**DATA SET REFERENCE NAME**

</th>  
<th>

**DS3.1-SeriousGames**

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

**DATA SET DESCRIPTION**

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

**Generic description**

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

This dataset will include in-game metrics and games’ performance of all the
sessions

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

in a semantically annotated way (by adopting proper ontologies for each
specific domain) so as to facilitate subsequently analysis with respect to the
clinical assessment tests towards research on stealth assessment and screening
of PD early signs and disease progress through the suite (linear correlation
of in-game metrics with clinical assessment tests).

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

**Origin of data**

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

The dataset will be collected using the i-PROGNOSIS Personalised Game Suite
(PGS) which will accommodate different types of serious gaming interventions.

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

**Nature and scale of data**

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

The datasets will be collected during the serious gaming interventions of
iPROGNOSIS. The serious games will ask the user to go under specific exercises
or gaming tests while mechanisms in the background will track and collect the
user’s performance. In the case of Exergames, the user will be asked to
perform specific exercises which will be captured by the Kinect sensor.

The dataset will contain metrics like reaction time, player’s path / optimum
path, goal time, movement range, balance, min and max angles of movements,
wrong choices and much more in-game metrics that will arise during the design
requirements of the serious games.

_Data Format:_ RDF triples or JSON

The dataset is expected to be composed of 180 records per serious gaming
session.

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

**To whom the dataset could be useful**

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

The collected data will be used for the development and evaluation of the
Personalized Game Suite in terms of usability, acceptance, effectiveness and
user’s assessment. The different parts of the (semantically annotated where
applicable) dataset could be useful in the benchmarking of a series of serious
games, focusing either on the effectiveness axis as the primary role of the
interventions, as well as in detecting and assessing, if possible, the
disease’s progress and status. The latter will feed the T4.5 which is intended
to dynamically recommend game adaptations for personalised and optimised use
as well as keeping the users in the “flow zone” which represents the feeling
of being complete and energized focus in an activity with a high level of
enjoyment and fulfilment towards increased adherence. Finally, this dataset
will be part of the overall evaluation of the i-PROGNOSIS contributing to the
validation of the pilot applications and interventions.

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

**Related scientific publication(s)**

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

The dataset will accompany our research results in the field of human activity
monitoring of people with Parkinson’s. A subset of similar dataset, including
recordings from elderly people without Parkinson’s going through Exergames,
will be used as initial input to this dataset. Corresponding publications are:

Bamparopoulos, G., Konstantinidis, E., Bratsas, C., & Bamidis, P. D. (2016).
Towards exergaming commons: composing the exergame ontology for publishing
open game data. Journal of Biomedical Semantics, 7(1), Article nr 4.
http://doi.org/10.1186/s13326-016-0046-4

Konstantinidis, E., Bamparopoulos, G., & Bamidis, P. (2016). Moving Real
Exergaming

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

Engines on the Web: The webFitForAll case study in an active and healthy
ageing living lab environment. IEEE Journal of Biomedical and Health
Informatics, 1–1.

http://doi.org/10.1109/JBHI.2016.2559787

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

**Indicative existing similar data sets** (including possibilities for
integration and reuse)

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

It should be noted that there are no serious games datasets available online.
To the best of our knowledge, the only open dataset regarding serious games,
and more specifically Exergames performed by elderly people, is the one that
AUTH has published a couple of years before described in:

Bamparopoulos, G., Konstantinidis, E., Bratsas, C., & Bamidis, P. D. (2016).
Towards exergaming commons: composing the exergame ontology for publishing
open game data. Journal of Biomedical Semantics, 7(1), Article nr 4.
http://doi.org/10.1186/s13326-016-0046-4

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

**STANDARDS AND METADATA**

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

The dataset will be accompanied with detailed documentation of its contents.
Indicative metadata include: a) description of the experimental setup and
procedure that led to the generation of the dataset, b) type of exercise or
game, c) documentation of the variables recorded in the dataset, and d)
semantic annotation based on existing ontologies.

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

**DATA SHARING**

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

**Access type**

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

Due to ethical reasons, only the data captured by a subset of the patients
during the initial phases by normal healthy control subjects could become
publicly available, while the rest of them will be private to serve the
i-PROGNOSIS R&D objectives.

The inclusion of a (normal healthy control) subject’s data in the public part
of this dataset will be done on the basis of appropriate informed consent to
data publication.

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

**Access Procedures**

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

An ontology that describes Exergames using the Web Ontology Language (OWL) is
available at  _http://purl.org/net/exergame/ns#_ .  The acquired game results
will be automatically converted to RDF triples and published on the web as
open data, accessible through a SPARQL Endpoint. The data will be accessible
from a SPARQL endpoint that is available at  _http://www.fitforall.gr/sparql_
,  where queries can be made using the GET or POST method. In order to
facilitate access, links to a download section where the datasets will be
downloaded as JSON files will be provided if required. The private part of
this dataset will be stored at a specifically designated private space of
AUTH, in dedicated hard disk drives, on which only members of the AUTH
research team whose work directly relates to these data will have access. For
further i-PROGNOSIS partners to obtain access to these data, they should
provide a proper request to the AUTH primarily responsible, including a
justification over the need to have access to these data. Once deemed
necessary, AUTH will provide the respective data portions to the partner.

Another option is to provide a public link to download the dataset through the
iPROGNOSIS data management portal.

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

**Embargo periods** (if any)

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

The applicable datasets will be publicly available two years after the end of
the project to allow the consortium prepare and submit the scientific
publications.

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

**Technical mechanisms for dissemination**

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

For the public part of the dataset, a link to this, as well as to the SPARQL
endpoint  _http://www.fitforall.gr/sparql_ ,  will be provided from the Data
management portal. The link and the SPARQL endpoint will be provided in all
relevant i-PROGNOSIS publications. A technical publication describing the
dataset and acquisition procedure will be published.

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

**Necessary S/W and other tools for enabling re-use**

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

The dataset will be designed to allow easy reuse with commonly available tools
and software libraries.

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

**Repository where data will be stored**

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

The public part of this dataset will be accommodated at AUTH servers,
accessible by the SPARQL endpoint  _http://www.fitforall.gr/sparql_ .  In
addition, a subset of these data will be hosted to the central database that
will serve the needs of the Data Management Portal of the i-PROGNOSIS project.

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

**ARCHIVING AND PRESERVATION** (including storage and backup)

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

**Data preservation period**

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

The public part of this dataset will be preserved online for as long as there
are regular downloads. After that it would be made accessible by request. The
private part of the dataset will be preserved by AUTH at least until the end
of the project.

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

**Approximated end volume of data**

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

The dataset is expected to be several hundreds of MB, provided that each
session is expected to produce a volume of ~80 KB of data.

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

**Indicative associated costs for data archiving and preservation**

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

The dataset will be stored in the serious gaming server hosted by AUTH. There
are no costs associated with its preservation. In the case that Microsoft
Azure will accommodate the serious gaming server, there will be an additional
cost of approximately 20 Euros (€) per month for one year

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

**Indicative plan for covering the above costs**

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

The costs associated with the data archiving and preservation will be covered
by the i-PROGNOSIS project budget.

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

**PARTNERS ACTIVITIES AND RESPONSIBILITIES**

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

**Partner Owner / Data Collector**

</td>  
<td>

**AUTH**

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

**Partner in charge of the data analysis**

</td>  
<td>

**AUTH**

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

**Partner in charge of the data storage**

</td>  
<td>

**AUTH**

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

**WPs and Tasks**

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

The data are going to be collected within activities of WP4, WP6 and WP7, to
mainly serve the research efforts of T4.1, T4.5, T6.1, T6.4, T7.3 and T7.4.  
  
<table>  
<tr>  
<th>

**DATA SET REFERENCE NAME**

</th>  
<th>

**DS3.2-BodyAndGesture**

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

**DATA SET DESCRIPTION**

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

**Generic description**

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

Dataset for human identification, postures and gestures tracking experiments,
especially during the Exergames where Kinect will be the main sensor. The
dataset is also planned to include balance and gait features.

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

**Origin of data**

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

The dataset will be collected using a Kinect2 sensor during the exergaming
intervention.

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

**Nature and scale of data**

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

It is expected that the datasets will be collected mainly during the
exergaming interventions. The Exergames will ask the user to go under specific
exercises which will be captured by Kinect. The approximate duration of each
session is expected to be close to 45min.

The dataset will contain the user’s silhouette as this is provided by the
Kinect SKD (Skeleton with bones and joints). It will be investigated further
whether the RGB and depth image will be collected.

_Data Format:_ PNG/JPG for images (both RGB and depth), JSON for the
coordinates of the joints and bones, XML or TXT for annotations.

The dataset will be on the order of ~2-5 GB per recording hour.

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

**To whom the dataset could be useful**

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

The collected data will be used for the development and evaluation of the
human activity monitoring and the Exergames intervention of the i-PROGNOSIS
project. The different parts of the dataset could be useful in the
benchmarking of a series of human tracking methods, focusing either on human
identification, on posture and gesture analysis and tracking as well as in
detecting, if possible, symptoms and signs of those appear at people with
Parkinson’s.

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

**Related scientific publication(s)**

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

The dataset will accompany our research results in the field of human activity
monitoring of people with Parkinson’s. A subset of similar dataset, including
recordings from elderly people without Parkinson’s going through Exergames,
will be used as initial input to this dataset. Corresponding publications are:

Konstantinidis, E. I., Antoniou, P. E., Bamparopoulos, G., & Bamidis, P. D.
(2014). A lightweight framework for transparent cross platform communication
of controller data in ambient assisted living environments. Information
Sciences, 300, 124–139. http://doi.org/10.1016/j.ins.2014.10.070

Konstantinidis, E. I., Billis, A. S., Bratsas, C., & Bamidis, P. D. (2016).
Active and Healthy

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

Ageing Big Dataset streaming on demand. In Proceedings of the 18th
International Conference on Human-Computer Interaction. Toronto, Canada.

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

**Indicative existing similar data sets** (including possibilities for
integration and reuse)

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

It should be noted that although several RGB-D datasets stemming from Kinect
sensor dealing with human activity analysis are publicly available (see
datasets below), to the best of our knowledge, there is no available any
Parkinson monitoring dataset yet.

_G3D_ ( _http://dipersec.king.ac.uk/G3D/G3D.html_ )  : G3D dataset contains a
range of gaming actions captured with Microsoft Kinect. The Kinect enabled us
to record synchronised video, depth and skeleton data. The dataset contains 10
subjects performing 20 gaming actions: _punch right, punch left, kick right,
kick left, defend, golf swing, tennis swing forehand, tennis swing backhand,
tennis serve, throw bowling ball, aim and fire gun, walk, run, jump, climb,
crouch, steer a car, wave, flap and clap_ .

_MSRC-12 Kinect gesture dataset_ (
_http://research.microsoft.com/enus/um/cambridge/projects/msrc12/_ )  : The
Microsoft Research Cambridge-12 Kinect gesture data set consists of sequences
of human movements, represented as bodypart locations, and the associated
gesture to be recognized by the system.

_RGB-D Person Re-identification Dataset_ ( _http://old.iit.it/en/datasets-
andcode/datasets/rgbdid.html_ )  : A new dataset for person re-identification
using depth information. The main motivation is that the standard techniques
(such as  _SDALF_ )  fail when the individuals change their clothing,
therefore they cannot be used for longterm video surveillance. Depth
information is the solution to deal with this problem because it stays
constant for a longer period of time.

_DGait Database_ ( _http://www.cvc.uab.es/DGaitDB/Summary.html_ )  : DGait is
a new gait database acquired with a depth camera. This database contains
videos from 53 subjects walking in different directions.

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

**STANDARDS AND METADATA**

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

The dataset will be accompanied with detailed documentation of its contents.
Indicative metadata include: a) description of the experimental setup and
procedure that led to the generation of the dataset, b) type of exercise in
case the dataset produced during exergames, c) documentation of the variables
recorded in the dataset, and d) annotated posture, action and activity.

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

**DATA SHARING**

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

**Access type**

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

Due to ethical reasons, only the data captured by a subset of the patients
during the initial phases by normal healthy control subjects could become
publicly available, while the rest of them will be private to serve the
i-PROGNOSIS R&D objectives.

The inclusion of a (normal healthy control) subject’s data in the public part
of this dataset will be done on the basis of appropriate informed consent to
data publication. It will be investigated further whether the silhouette
(coordinates of joints and bones) subset of the dataset could be publicly
available.

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

**Access Procedures**

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

For the portions of the dataset that will be made publicly available, a
respective web page will be created on the data management portal (the use of
the CAC-playback manager  4  will be assessed) that will provide a description
of the dataset, links to a download section and a playback possibility in case
the playback manager approach is followed. The private part of this dataset
will be stored at a specifically designated private space of AUTH, in
dedicated hard disk drives, on which only members of the AUTH and CERTH
research team whose work directly relates to these data will have access. For
further i-PROGNOSIS partners to obtain access to these data, they should
provide a proper request to the AUTH/CERTH primarily responsible, including a
justification over the need to have access to these data. Once deemed
necessary, AUTH/CERTH will provide the respective data portions to the
partner.

Cac-playback manager: Konstantinidis, E. I., Billis, A. S., Bratsas, C., &
Bamidis, P. D. (2016). Active and Healthy Ageing Big Dataset streaming on
demand. In

Proceedings of the 18th International Conference on Human-Computer

Interaction. Toronto, Canada.

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

**Embargo periods** (if any)

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

The applicable datasets will be publicly available 2 years after the end of
the project to allow the consortium prepare and submit the scientific
publications.

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

**Technical mechanisms for dissemination**

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

For the public part of the dataset, a link to this will be provided from the
Data management portal. The link will be provided in all relevant i-PROGNOSIS
publications. A technical publication describing the dataset and acquisition
procedure will be published.

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

**Necessary S/W and other tools for enabling re-use**

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

The dataset will be designed to allow easy reuse with commonly available tools
and software libraries. In case of supporting the online playback of the
datasets, libraries for a variety of programming languages will be released
(e.g.  _http://www.cacframework.com/_ )  
</td> </tr>  
<tr>  
<td>

**Repository where data will be stored**

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

The public part of this dataset will be accommodated at the data management
portal of the project website.

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

**ARCHIVING AND PRESERVATION** (including storage and backup)

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

**Data preservation period**

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

The public part of this dataset will be preserved online for as long as there
are regular downloads. After that it would be made accessible by request. The
private part of the dataset will be preserved by AUTH at least until the end
of the project.

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

**Approximated end volume of data**

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

The dataset is expected to be several gigabytes, provided that each recording
hour is expected to be ~2-5 GB.

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

**Indicative associated costs for data archiving and preservation**

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

Probably two dedicated hard disk drives will be allocated for the dataset; one
for the public part and one for the private. In this case the costs associated
with its preservation will be according to the hardware cost (hard drives) .
Azure will be also considered as candidate data archiving means although the
volume of the data (1-2 TB) would make this choice very expensive.

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

**Indicative plan for covering the above costs**

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

Small one-time costs covered by i-PROGNOSIS. In case of Azure, the cost will
be higher.

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

**PARTNERS ACTIVITIES AND RESPONSIBILITIES**

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

**Partner Owner / Data Collector**

</td>  
<td>

**AUTH, CERTH**

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

**Partner in charge of the data analysis**

</td>  
<td>

**AUTH, CERTH**

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

**Partner in charge of the data storage**

</td>  
<td>

**AUTH**

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

**WPs and Tasks**

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

The data are going to be collected within activities of WP3, WP4 and WP6, to
mainly serve the research efforts of T3.2, T3.7, T4.1, T4.5, T6.1 and T6.4.

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

**DATA SET REFERENCE NAME**

</th>  
<th>

**DS3.3-SleepStageAnalysis**

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

**DATA SET DESCRIPTION**

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

**Generic description**

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

This dataset will include physiological (heart rate and skin temperature) data
and IMU (accelerometer and gyroscope) data, captured by the smartwatch
(Microsoft Band 2) collected during the users' sleep, to be i) used for the
development of or ii) collected during the targeted nocturnal intervention
(TNI), i.e., an i-PROGNOSIS intervention that will use pacifying sounds in
order to reinstate a satisfactory sleep stage when a sleep disturbance episode
is detected. Features will be accompanied by metadata in order to facilitate
the analysis within the i-PROGNOSIS project, as well as, by other researchers
outside of the project.

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

**Origin of data**

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

The dataset will be collected as part of the development of the i-PROGNOSIS
supportive interventions and the respective data collection phase. It will
comprise a development sub-dataset and corresponding metadata, collected by
experiment participants during a short period of time (~2 days), as well as, a
deployment subdataset and corresponding metadata collected by users during a
significantly longer period (in the range of months) during the interventions
data collection period. Both data collection procedures will be accompanied by
ethics approval.

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

**Nature and scale of data**

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

The data collection leading to the formulation of the dataset will take place
during the users' sleep. The development sub-dataset will be collected based
on an

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

experiment protocol, i.e., the participants (PD patients) will be asked to
wear the smartwatch during their sleep for ~2 consecutive nights and
afterwards report on their sleep quality. Indicative features that will
comprise the dataset are the heart rate (beats per minute - 1 Hz sampling
frequency), the skin temperature (degrees Celsius - 1 Hz sampling frequency),
accelerometer data (X, Y, and Z acceleration in g units or meter per second
squared, 62 Hz sampling frequency) and gyroscope data (X, Y, Z angular
velocity in degrees per second, 62 Hz sampling frequency) as captured and
outputted by the Microsoft Band 2. The deployment sub-dataset will comprise
the same features as the development sub-dataset, but it will be collected
through the iPROGNOSIS PD interventions application [via the **targeted
nocturnal intervention (TNI) service** (see Section 6 of D2.1)] that will be
provided to a subset of interventions users to use (~10 PD patients out of 60
interventions users) based on a pre-interventions medical evaluation.

The dataset is expected to be composed of 60 (30 participants × 2 nights)
records for the development sub-dataset, while the deployment sub-dataset will
comprise approximately 1800 (~6 months interventions period × ~30 nights × ~10
interventions users) records.

_Data Format:_ For each sleep session (development sub-dataset) four CSV
files, one per feature (heart rate, skin temperature, accelerometer and
gyroscope data), will be generated, including the values of features and
timestamps, as well as a CSV or EDF+ file including metadata. For each sleep
session (deployment sub-dataset) a structured XML file will be generated,
including all the aforementioned features and metadata.

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

**To whom the dataset could be useful**

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

The development sub-dataset will be used to develop a sleep stage/disturbances
recognition model focusing on sleep disturbances that PD patients experience.
The latter model will be used as part of the targeted nocturnal intervention
(TNI) and the triggering of pacifying sounds will occur based on the model
outputs.

The deployment sub-dataset will be used for validation and fine-tuning of the
model produced during development, as well as for evaluating the effectiveness
of the TNI in real life scenarios.

Moreover, the dataset could be useful for biomedical researchers that are
interested in studying the inference of sleep disturbances, experienced by PD
patients, based on data captured by commercially available wearable sensors.
Adequate use of these data presupposes at least basic background regarding PD
symptomatology, as well as, basic knowledge in data analysis methodology and
experience in the use of statistical software packages.

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

**Related scientific publication(s)**

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

The development sub-dataset will accompany the research results regarding the
feasibility of a sleep stage/disturbances recognition model based on data
captured by commercially available wearable sensors, such as smartwatches.
Research results are planned to be published initially in the indicative
journals and/or conferences provided below:

  * IEEE Transactions on Biomedical Engineering (Journal) 

  

  * Elsevier International Journal of Medical Informatics (Journal) 

  

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

\- Annual International Conference of the IEEE Engineering in Medicine and
Biology

Society (Conference)

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

**Indicative existing similar data sets** (including possibilities for
integration and reuse)

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

To the researchers' knowledge there are no similar datasets publicly available
at the moment. However, it is expected that such dataset will be available in
the near future through the Michael J. Foundation for Parkinson's Research and
Intel collaborative "Fox Insight" clinical study (
_https://foxinsight.michaeljfox.org/_ )  that started in 2015 and aims at
collecting big data arising from users' (healthy and PD patients) interaction
with smartphones and smartwatches. The latter interaction will also lead to
the generation of smartwatch data during the users' sleep.

i-PROGNOSIS investigators will proceed to all the necessary actions to access
the latter dataset (when available) and exploit it for the development of the
sleep stage/disturbance model of the TNI in conjunction with the development
subdataset.

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

**STANDARDS AND METADATA**

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

The European Data Format+ (EDF+)  (
_http://www.edfplus.info/specs/edfplus.html_ )  will be exploited for the
production of the metadata files.

The development sub-dataset will be accompanied by a detailed description of
its contents. Indicative metadata include: a) description of the experiment
set-up and the procedure that led to the generation of the dataset, b)
anthropometrics and basic health-record data of the experiment participants,
c) description of the sleep-stagerelated features, d) annotation of features
based on (b), and e) ground truth based on self-reported sleep quality by
participants and sleep stage evaluation as provided by the Microsoft Health
mobile application.

The deployment sub-dataset will be annotated based on the following indicative
metadata: a) basic health record data and anthropometrics of the interventions
users, b) timestamps and duration of sleep disturbances detected, c)
timestamps of starting/stopping the playback of pacifying sounds, and d)
statistics on the usage of the TNI.

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

**DATA SHARING**

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

**Access type**

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

The development sub-dataset will be protected (see Section 3.3.2.2),
accompanied by the respective ethics approval. Due to ethical compliance, the
deployment subdataset will be confidential and only i-PROGNOSIS partners will
be able to access it after following the appropriate procedure (see Section
3.3.2.3)

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

**Access Procedures**

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

The deployment sub-dataset that will be confidential will be handled according
the framework reported in Section 3.3.2.3. The development sub-dataset that
will be protected will be available for third-party stakeholders to download
based on the procedure described in Section 3.3.2.1.

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

**Embargo periods** (if any)

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

The development sub-dataset is planned to be available as protected after
month 32

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

of the project to allow i-PROGNOSIS investigators to prepare and submit the
respective scientific publications, but enable also third-party researchers to
conduct further analysis and comment on the i-PROGNOSIS results before the end
of the project.

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

**Technical mechanisms for dissemination**

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

The development sub-dataset will be available through either the CKAN-based
iPROGNOSIS data management portal (see Section 3.2). Links redirecting to the
portal and the available dataset will be provided through a dedicated page of
the iPROGNOSIS project website (  _www.i-prognosis.eu_ )  .

A technical description providing information on the experiment protocol
through which the development sub-dataset was captured will accompany the
development sub-dataset.

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

**Necessary S/W and other tools for enabling re-use**

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

As the publicly available compressed dataset (development sub-dataset) will
comprise standard CSV files, no specialised software or tools will be required
for the dataset to be parsed and reused, other than a (de-)compression
software and a basic text editor (minimum requirement to access the content of
the CSV file).

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

**Repository where data will be stored**

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

The development sub-dataset will be stored in AUTH secure servers, as well as
in the Microsoft Azure-based i-PROGNOSIS Cloud infrastructure (European data
centres are located in Ireland) where the CKAN-based data management portal
will be installed. The deployment sub-datasets will be also stored in the
i-PROGNOSIS Cloud infrastructure, but access will be restricted (Section
3.3.2.3).

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

**ARCHIVING AND PRESERVATION** (including storage and backup)

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

**Data preservation period**

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

The public part of this dataset will be preserved online for as long as there
are regular downloads and at least one year after the end of the project.
After that it would be made accessible by direct request to the data
collector. The confidential part of the dataset will be preserved by the
owners at least for one year after the end of the project.

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

**Approximated end volume of data**

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

The development sub-dataset is expected to be approximately 600 MB (including
metadata), based on the number of experiment participants (30) and the
approximate size of features recorded during an average 7-hour sleep (~10 MB),
for 2 nights.

The deployment dataset is expected to be in the range of tens of gigabytes
(GB), based on the expected number of the i-PROGNOSIS interventions users that
will test the TNI (~10 users), an average 7-hour sleep per night for ~6 months
(~30 days per month), and an average size of features of ~10 MB per 7-hour
sleep.

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

**Indicative associated costs for data archiving and preservation**

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

The development sub-dataset will be stored on AUTH servers. There are no costs
associated with the latter means of archiving and preservation. The
development and

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

deployment sub-datasets will be also archived and preserved in the Microsoft
Azurebased i-PROGNOSIS Cloud infrastructure. The indicative cost of the latter
means of archiving and preservation is 20 Euros (€) per month for one year (at
least) after the end of the project.

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

**Indicative plan for covering the above costs**

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

The costs associated with the data archiving and preservation will be covered
by the i-PROGNOSIS project budget.

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

**PARTNERS ACTIVITIES AND RESPONSIBILITIES**

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

**Partner Owner / Data Collector**

</td>  
<td>

**AUTH, MICROSOFT**

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

**Partner in charge of the data analysis**

</td>  
<td>

**AUTH**

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

**Partner in charge of the data storage**

</td>  
<td>

**AUTH, MICROSOFT**

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

**WPs and Tasks**

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

The development sub-dataset will be collected within WP4 and WP6, to mainly
serve the research efforts of T4.2 and T6.4.

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

**DATA SET REFERENCE NAME**

</th>  
<th>

**DS3.4-GaitAnalysis**

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

**DATA SET DESCRIPTION**

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

**Generic description**

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

This dataset will include accelerometer, gyroscope and pedometer data captured
from a smart watch/band regarding gait rhythm identification and analysis so
as to provide personalised cueing and guidance in case of gait freezing
episodes.

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

**Origin of data**

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

The dataset will be captured by the accelerometer, gyroscope and pedometer
sensors of the smart watch/band that will be worn by the participants mainly
during the GData, SData and Interventions phases.

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

**Nature and scale of data**

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

The first part of the dataset is going to be collected during the development
data collection phase where the subjects will follow specific walking
scenarios. The second part of the dataset will be collected mainly during the
SData and Interventions phases, where smart watches/bands will be provided to
80 and 10 participants, respectively. Moreover, the GData phase will
contribute to the dataset; however, the contribution is expected to be limited
since the smart watch/band is optional at GData. At these phases, i.e., GData,
SData and Interventions, the subjects will not perform specific walking tasks,
instead, the data will be captured throughout their daily routine activities.

The dataset will include: i) the measures (double precision) of the
acceleration force in g units (9,81 m/s  2  ) that is applied to the smart
watch/band on all three physical axes (x, y, and z), ii) the measures (double
precision) of the smart watch/band’s

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

rotation in rad/s around each of the three physical axes, and iii) the
absolute number of steps taken. Since the pedometer sensor provides the total
number of steps the wearer has taken since the last factory-reset of the smart
watch/band, the absolute number of steps, that will be contained in the
dataset, will be determined by taking the difference between the sensors
returned values. The above data will be annotated with the health status of
the subject, i.e., healthy control or Parkinson’s disease patient.

The minimum sampling frequency for accelerometer and gyroscope data is 8Hz.
However, this value is too high for pedometer data. Consequently, pedometer
data will be recorded every 15 seconds.

Data format: TXT or CSV file.

The dataset will be on the order of ~1.3 MB per participant per hour of usage
and usually the smart watch/band owners use it for at least 12 hours per day.

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

**To whom the dataset could be useful**

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

The collected data will be used for the development and evaluation of the
personalised gait rhythmic guidance intervention of the i-PROGNOSIS project.
The dataset could be useful in analysing the gait patterns, speed,
periodicity, complexity and habits as well as in detecting sudden freezing
episodes and falls. Moreover, it may be possible to identify symptoms and
signs of those appear at people with Parkinson’s Disease, such as tremor.

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

**Related scientific publication(s)**

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

The dataset will accompany the research results in the field of rhythm gait
analysis and freezing episodes’ identification of people with PD. At least one
publication is intended to be made in the IEEE Biomedical Engineering journal
(or similar).

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

**Indicative existing similar data sets** (including possibilities for
integration and reuse)

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

To the best of our knowledge, there is no available smart watch/band-based
accelerometer/gyroscope and pedometer dataset for PD-related gait freezing
detection. Related datasets include data captured from standalone inertia
sensors attached to specific parts of the body, and/or accelerometer/gyroscope
of a smartphone. For example:

_Daphnet_ ( _https://archive.ics.uci.edu/ml/datasets/Daphnet+Freezing+of+Gait_
)  : Daphnet dataset contains the annotated readings of 3 acceleration sensors
at the hip, thigh and ankle of 10 Parkinson's disease patients that experience
freezing of gait during walking tasks. Users performed three kinds of tasks:
straight line walking, walking with numerous turns, and finally a more
realistic activity of daily living (ADL) task, where users went into different
rooms while fetching coffee, opening doors, etc.

_OU-ISIR_ ( _http://www.am.sanken.osaka-u.ac.jp/BiometricDB/InertialGait.html_
)  : OU-ISIR database contains accelerometer and gyroscope data captured from:
i) 3 inertia sensors located around user’s waist and ii) a smartphone which
includes only a triaxial accelerometer located at the back waist of the users.
There were three walking scenarios: flat level, slope up and slope down.

_ZJU-GaitAcc_ ( _http://www.cs.zju.edu.cn/~gpan/database/gaitacc.html_ )  :
The ZJUGaitAcc dataset contains the gait acceleration series of 175 subjects,
who were

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

equipped with 5 Wii remotes, acting as the inertia sensors, fastened at 5 body
locations: the left upper arm, the right wrist, the right side of the pelvis,
the left thigh, and the right ankle.

_Gait Dataset_ ( _http://www.cs.mcgill.ca/~jfrank8/data/gait-dataset.html_ )
: This dataset was collected at McGill University using the HumanSense open-
source Android data collection platform. It contains the raw sensor data
collected from a mobile phone in the pocket of 20 individuals, performing two
separate 15 minute walks.

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

**STANDARDS AND METADATA**

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

The dataset will be accompanied with detailed documentation of its contents.
Indicative metadata include: a) description of the experimental setup and
procedure that led to the generation of the dataset, b) health status of the
subject, and c) documentation of the variables recorded in the dataset.

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

**DATA SHARING**

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

**Access type**

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

Due to ethical issues, only part of the dataset will be publicly available.
More specifically, the data that correspond to a subset of the Parkinson’s
Disease patients as well as the healthy control subjects, captured at the
initial phase of the iPROGNOSIS project will become publicly available. The
rest of the data will be private to serve the i-PROGNOSIS R&D objectives. The
inclusion of a subject’s data in the public part of this dataset will be done
on the basis of appropriate informed consent to data publication.

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

**Access Procedures**

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

See Section 3.3.2.1 and Section 3.3.2.3.

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

**Embargo periods** (if any)

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

The applicable datasets will be publicly available 2 years after the end of
the project to allow the consortium prepare and submit the scientific
publications.

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

**Technical mechanisms for dissemination**

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

For the public part of the dataset, a link to this will be provided from the
Data management portal. The link will be provided in all relevant i-PROGNOSIS
publications. A technical publication describing the dataset and acquisition
procedure will be published.

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

**Necessary S/W and other tools for enabling re-use**

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

The dataset will be designed to allow easy reuse with commonly available tools
and software libraries, such as Excel, Matlab, .NET etc.

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

**Repository where data will be stored**

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

The public part of this dataset will be accommodated at the data management
portal of the project website, hosted by Microsoft Azure-based i-PROGNOSIS
Cloud infrastructure.

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

**ARCHIVING AND PRESERVATION** (including storage and backup)

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

**Data preservation period**

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

The public part of this dataset will be preserved online for as long as there
are regular downloads. After that it would be made accessible by request. The
private part of the dataset will be preserved by AUTH at least until the end
of the project.

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

**Approximated end volume of data**

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

The dataset is expected to be several gigabytes, provided that each
participant is expected to produce at least 6 MB per day and the duration is
at least 22 months (SData and Interventions phase).

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

**Indicative associated costs for data archiving and preservation**

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

Probably two dedicated hard disk drives will be allocated for the dataset; one
for the public part and one for the private. There are no costs associated
with its preservation. Azure will be also considered as candidate data
archiving means.

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

**Indicative plan for covering the above costs**

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

The costs associated with the data archiving and preservation will be covered
by the i-PROGNOSIS project budget.

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

**PARTNERS ACTIVITIES AND RESPONSIBILITIES**

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

**Partner Owner / Data Collector**

</td>  
<td>

**AUTH, MICROSOFT**

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

**Partner in charge of the data analysis**

</td>  
<td>

**AUTH**

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

**Partner in charge of the data storage**

</td>  
<td>

**AUTH, MICROSOFT**

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

**WPs and Tasks**

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

The data are going to be collected within the activities of WP3, WP4 and WP6,
to mainly serve the research efforts of T3.2, T4.3, T6.1, T6.2, T6.3 and T6.4.

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

**DATA SET REFERENCE**

**NAME**

</th>  
<th>

**DS3.5-VoiceEnhancementAnalysis**

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

**DATA SET DESCRIPTION**

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

**Generic description**

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

Speech database for the design and implementation of the voice enhancement
algorithms within the assistive interventions software. The database will
contain speech data collected from phone calls recorded from the i-PROGNOSIS
smartphone dialler application. The data set includes several annotations
about the speech data and the underlying speakers.

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

**Origin of data**

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

The dataset will be collected by recording conversations using the i-PROGNOSIS
smartphone dialler application during SData and Intervention Data collection
phase.

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

**Nature and scale of data**

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

The goal of this dataset is to enable the development and implementation of
voice enhancement algorithms for speech from PD patients. The dataset will
contain the raw speech signals recorded by the i-PROGNOSIS smartphone dialler
application

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

during the SData and Intervention data collection phase. Annotations including
date and time of the recordings along with speaker information (e.g. ID,
gender, PD scale) will provide the necessary information and will facilitate
the development and evaluation of the speech enhancement algorithms. The
dataset will contain raw speech data (e.g. 44.1 kHz sampling rate, wav
unencoded) and text files for the annotations.

_Data Format:_ Encoded (mp3, aac) or unencoded (wav, raw) audio waveforms; XML
or plain text files for annotations/metadata.

The dataset will be on the order of 0.5 MB to 5 MB per minute of speech
depending on the encoding and sampling rate.

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

**To whom the dataset could be useful**

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

The dataset will be used within the project for the development and evaluation
of automatic signal processing speech enhancement algorithm for speech from PD
patients. This data will be used in WP4 and WP6, supporting the assistive
intervention software and for the overall assessment of the i-PROGNOSIS
system. This dataset may also be useful in the future for other researchers
who want to explore PD patients’ speech and develop speech enhancement
algorithms.

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

**Related scientific publication(s)**

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

This database will build the foundation of our research and development of
algorithms for the automatic enhancement of the speech from PD patients within
the assistive intervention software. We plan to propose our findings on
ICASSP, Interspeech or other voice and biomedical-related conferences.

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

**Indicative existing similar data sets** (including possibilities for
integration and reuse)

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

To the best of our knowledge no speech database for the development and
evaluation of speech enhancement algorithms for the speech of PD patients is
available.

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

**STANDARDS AND METADATA**

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

The dataset will be accompanied with detailed documentation of its contents.
Indicative metadata include: (a) description of the experimental setup and
procedure that led to the generation of the dataset and (b) documentation of
the variables recorded in the dataset.

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

**DATA SHARING**

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

**Access type**

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

Due to ethical reasons, only the data captured by a subset of the patients
during the initial phases by normal healthy control subjects could become
publicly available, while the rest of them will be private to serve the
i-PROGNOSIS R&D objectives.

The inclusion of a (normal healthy control) subject’s data in the public part
of this dataset will be done on the basis of appropriate informed consent to
data publication.

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

**Access Procedures**

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

For the portions of the dataset that will be made publicly available, a
respective web

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

page will be created on the data management portal that will provide a
description of the dataset, links to a download section and a playback
possibility in case the playback manager approach is followed. The private
part of this dataset will be stored at a specifically designated private space
of FRAUNHOFER, in dedicated hard disk drives, on which only members of the
FRAUNHOFER research team will have access.

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

**Embargo periods** (if any)

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

The applicable datasets will be publicly available two years after the end of
the project to allow the consortium prepare and submit the scientific
publications.

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

**Technical mechanisms for dissemination**

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

For the public part of the dataset, a link will be provided from the Data
management portal. The link will be provided in all relevant i-PROGNOSIS
publications. A technical publication describing the dataset and acquisition
procedure will be published.

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

**Necessary S/W and other tools for enabling re-use**

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

The dataset will be designed to allow easy reuse and access with commonly
available tools (e.g. Matlab, Python, VLC, GVIM) and software libraries (e.g.
Tensorflow, FFMPEG, HDF5 C++ API, C++ STL), because the data will be stored
primarily in common file formats and generic data containers.

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

**Repository where data will be stored**

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

The public data will be hosted on the central database that will serve the
needs of the Data Management Portal of the i-PROGNOSIS project.

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

**ARCHIVING AND PRESERVATION** (including storage and backup)

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

**Data preservation period**

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

The public part of the dataset will be preserved on the Data Management Portal
of the i-PROGNOSIS project. The private part of the dataset will be preserved
by FRAUNHOFER at least until the end of the project.

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

**Approximated end volume of data**

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

The dataset should be expected to consume up to 10 GB depending on the
encoding and the length and quantity of the speech signals. (E.g. single
channel audio waveform 16 bit, 44.1 kHz is of size 5.3 MB/min, single channel
mp3 192 Kbps is of size 1.44 MB/min)

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

**Indicative associated costs for data archiving and preservation**

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

The public data will be archived and preserved in the Microsoft Azure-based
iPROGNOSIS Cloud infrastructure, will cost approximately 20 Euros (€) per
month for one year (at least) after the end of the project. The private
dataset will be stored on a dedicated hard drive.

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

**Indicative plan for covering the above costs**

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

The costs associated with the data archiving and preservation will be covered
by the i-PROGNOSIS project budget.

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

**PARTNERS ACTIVITIES AND RESPONSIBILITIES**

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

**Partner Owner / Data Collector**

</td>  
<td>

**TUD, KI**

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

**Partner in charge of the data analysis**

</td>  
<td>

**FRAUNHOFER**

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

**Partner in charge of the data storage**

</td>  
<td>

**FRAUNHOFER**

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

**WPs and Tasks**

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

The data is going to be collected within activities of WP4 and WP6, to mainly
serve the research efforts of T4.3, T6.3 and T6.4.

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

###  Requirements Data  
  
<table>  
<tr>  
<th>

**DATA SET REFERENCE**

**NAME**

</th>  
<th>

**DS4.1-FocusGroupsDataset**

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

**DATA SET DESCRIPTION**

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

**Generic description**

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

This dataset aims at providing a non-exhaustive list of end-user requirements,
as these have been elicited through a series of focus groups and personal
interviews with experts (both patient groups and clinicians) in PD.

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

**Origin of data**

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

KCL and relevant partners gathered pertinent data and established focus groups
data set, so as to then design a final paper-based user requirement
questionnaire that was used as an acquisition means in four focus groups. This
dataset contains information from all stakeholders (developers and clinicians)
involved in the development of the iPROGNOSIS system and its potential users
(healthcare professionals, and patients). The main components of the focus
groups questionnaire included: a) demographics, e.g. gender, age, health
problem related to PD, b) technology adoption, c) iPROGNOSIS App design-
oriented questions, d) i-PROGNOSIS App delivery through existing smart phones,
e) usability aspects of the specialized gadgets, such as smartwatch (Microsoft
Band was provided as an example), smart belt, Mandometer and a ECG-enabled TV
remote control, f) general questions about the PGS interventions and finally,
g) some specialized questions about specific interventions, such as the sleep
and gait interventions. For a more detailed description on the questions and
the results produced from the analyses, one is referred to D2.1 _\- First
version of user requirements analysis_ .

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

**Nature and scale of data**

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

The focus groups were conducted among health professionals such as nurses,
therapists, clinicians and researchers as well as patients and carers. The
subjects of the focus groups were about the design of the i-PROGNOSIS project
and expectations of it, the design of the application and special gadgets that
will be used in i-PROGNOSIS (smart belt, Mandometer, TV smart remote control,
etc.). Finally, the questions focus on the i-PROGNOSIS interventions. The
participants were carefully recruited for the face-to-face focus groups. The
dataset contains the output of the systematic analysis

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

Data format: The dataset will be made available as a single Microsoft Excel
file (.xlsx) by merging the CSV files of all languages. All questions and
answers will be translated in English.

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

**To whom the dataset could be useful**

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

The dataset and the respective outcomes and findings will be used to shape the
user requirements and the technical specifications of the i-PROGNOSIS system.

In addition, this kind of datasets could be exploited by researchers that
investigate to build new technologies of patients with PD relating to self-
care, as well as, developers, system architects and user experts that are
interested to develop a similar to i-PROGNOSIS ICT-based system for health
management.

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

**Related scientific publication(s)**

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

The dataset will accompany the research results regarding the prospective
users' attitude towards ICT-based self-care solutions and the i-RPOGNOSIS
system against PD. Research results and findings is planned to be published
initially in one of the indicative conferences provided below:

  * International Conference on e-Health (Conference) 

  

  * Human Computer Interaction International (Conference) 

  

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

**Indicative existing similar data sets** (including possibilities for
integration and reuse)

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

There are no similar datasets with open access. Results of relevant end-user
focus groups target to participatory design of symptomatic domains to be
measured as well as interventions (through serious games) design., i.e.,

Serrano, J. A., Larsen, F., Isaacs, T., Matthews, H., Duffen, J., Riggare, S.,
... & Graessner, H. (2015). Participatory design in parkinson's research with
focus on the symptomatic domains to be measured. Journal of Parkinson's
disease, 5(1), 187196.

McNaney, R., Balaam, M., Holden, A., Schofield, G., Jackson, D., Webster, M.,
... & Olivier, P. (2015, April). Designing for and with People with
Parkinson's: A Focus on Exergaming. In Proceedings of the 33rd annual ACM
conference on Human Factors in Computing Systems (pp. 501-510). ACM.

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

**STANDARDS AND METADATA**

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

For this dataset, the metadata correspond to the participant profile as well
as the settings of the focus group, and mainly include: a) occupational and
expertise of the participants (nurse, therapist, clinician, researcher,
patient, carers), b) the country where the focus group conducted, c) number of
the participants and d) health status relating to PD.

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

**DATA SHARING**

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

**Access type**

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

Personal identification as stated in the consent forms will be kept separate
from any research and health-related data, which will be pseudonymised (only
initials and date of birth will be kept). All paper based data will be stored
in an access restricted, locked building with access for the research team.
Electronic data will be saved on a

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

secured NHS server and CKAN-based i-PROGNOSIS data management portal.

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

**Access Procedures**

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

Personal and clinical data will be accessible by technical, medical and
scientific personnel being involved in the i-PROGNOSIS project and not by any
third-party users. When applicable, the publicly available dataset will be
open for third-party stakeholders to download based on the procedure described
in Section 3.3.2.1.

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

**Embargo periods** (if any)

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

The dataset is planned to be publicly available two (2) years after the end of
the project to allow i-PROGNOSIS investigators to prepare and submit the
respective scientific publications regarding the final version of user
requirements.

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

**Technical mechanisms for dissemination**

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

The dataset will be available either through the CKAN-based i-PROGNOSIS data
management portal (see Section 3.2) or other out of the project open access
repository. Links redirecting to the portal and the available dataset will be
provided through a dedicated page of the i-PROGNOSIS project website (
_www.i-prognosis.eu_ )  .

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

**Necessary S/W and other tools for enabling re-use**

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

The dataset will be kept in a numeric manner and therefore designed to allow
easy reuse with commonly available tools.

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

**Repository where data will be stored**

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

The data will be hosted to the central database that will serve the needs of
the Data Management Portal of the i-PROGNOSIS project and part of them in a
secured NHS server.

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

**ARCHIVING AND PRESERVATION** (including storage and backup)

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

**Data preservation period**

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

The dataset will be preserved as long as there are regular downloads. After
that it would be made accessible by request and preserved by AUTH at least
until the end of the project. Data set will be stored as per HRA regulations
and requirements.

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

**Approximated end volume of data**

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

The approximate size of the dataset is a couple of tens of MB.

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

**Indicative associated costs for data archiving and preservation**

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

The downloaded CSV files and the cumulative Microsoft Excel file will be
stored in an AUTH server, both not imposing any additional costs. The
Microsoft Excel file that will be made publicly available will be also
archived and preserved in the Microsoft Azure-based i-PROGNOSIS Cloud
infrastructure. The indicative cost of the latter means of archiving and
preservation is 20 Euros (€) per month for one year (at least) after the end
of the project.

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

**Indicative plan for covering the above costs**

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

The costs associated with the data archiving and preservation will be covered
by the i-PROGNOSIS project budget.

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

**PARTNERS ACTIVITIES AND RESPONSIBILITIES**

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

**Partner Owner / Data Collector**

</td>  
<td>

**TUD, KCL, AUTH**

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

**Partner in charge of the data analysis**

</td>  
<td>

**All**

</td> </tr>  
<tr>  
<td>

**Partner in charge of the data storage**

</td>  
<td>

**AUTH**

</td> </tr>  
<tr>  
<td>

**WPs and Tasks**

</td> </tr>  
<tr>  
<td>

The data are going to be collected within the activities of WP2, to mainly
serve the research efforts of T2.1 and T2.4.

</td> </tr> </table>  
<table>  
<tr>  
<th>

**DATA SET REFERENCE NAME**

</th>  
<th>

**DS4.2-WebSurveyDataset**

</th> </tr>  
<tr>  
<td>

**DATA SET DESCRIPTION**

</td> </tr>  
<tr>  
<td>

**Generic description**

</td> </tr>  
<tr>  
<td>

The dataset contains the questions and the qualitative answers to the
questions of the i-PROGNOSIS Web-based questionnaire (Web survey), conducted
within the context of the identification of user requirements and system
specifications, from ~2000 anonymous survey participants.

</td> </tr>  
<tr>  
<td>

**Origin of data**

</td> </tr>  
<tr>  
<td>

The dataset is populated by the answers of participants to the questions of
the iPROGNOSIS Web survey. The Web survey consists of three sections
corresponding to three groups of survey participants, i.e., healthy adults, PD
patients, and experts in PD (including physicians and carers). Each
participant answers the questions of her/his corresponding section based on
the group s/he belongs to. Each section is further divided in three parts,
i.e. demographics, PD detection and interventions. The section corresponding
to the group of healthy adults does not include the interventions part. The
participant is redirected to the corresponding section based on a question
allowing her/him to denote the group s/he belongs to. If no option is
selected, the participant is redirected to the survey section corresponding to
healthy adults. There are six versions of the Web survey corresponding to six
languages, i.e., English, Greek, French, German, Portuguese and Swedish.

</td> </tr>  
<tr>  
<td>

**Nature and scale of data**

</td> </tr>  
<tr>  
<td>

The Web survey is conducted electronically and it is available online. The
questionnaires were designed using the Google Forms online software. The
answers to the questions are qualitative and are stored in Google Sheets files
(one per language) that are updated with new entries (an entry corresponds to
a participant in the survey) automatically. The current dataset consists of
six Google Sheets files with a total of ~2000 entries, but it is updated
frequently as the Web survey is planned to be online until the updated user
requirements will be produced (month 28 of the project). The dataset (Google
Sheets files) can be downloaded manually as CSV files.

_Data format:_ The dataset will be made available as a single Microsoft Excel
file (.xlsx) by merging the CSV files of all languages. All questions and
answers will be translated

</td> </tr> </table>  
<table>  
<tr>  
<th>

in English.

</th> </tr>  
<tr>  
<td>

**To whom the dataset could be useful**

</td> </tr>  
<tr>  
<td>

The dataset and the respective results will be used to shape the identified
user requirements and the technical specifications of the i-PROGNOSIS system.

The dataset could be of interest to researchers that investigate the
relationship of similar groups of users with new technology relating to self-
care, as well as, developers, system architects and user experts that are
interested to develop a similar to i-PROGNOSIS ICT-based system for health
management.

Adequate use of these data presupposes at least basic knowledge in data
analysis methodology and experience in the use of statistical software
packages.

</td> </tr>  
<tr>  
<td>

**Related scientific publication(s)**

</td> </tr>  
<tr>  
<td>

The dataset will accompany the research results regarding the prospective
users' attitude towards ICT-based self-care solutions and the i-RPOGNOSIS
system against PD. Research results is planned to be published initially in
one of the indicative journals and/or conferences provided below:

  * Elsevier International Journal of Medical Informatics (Journal) 

  

  * IOS Journal of Parkinson's Disease (Journal) 

  

  * International Conference on e-Health (Conference) 

  

  * Human Computer Interaction International (Conference) 

  

</td> </tr>  
<tr>  
<td>

**Indicative existing similar data sets** (including possibilities for
integration and reuse)

</td> </tr>  
<tr>  
<td>

There are no similar datasets with open access. Results of similar surveys are
available through publications and may be used for shaping the i-PROGNOSIS
user requirements, i.e.,

Zhao, Y., Heida, T., van Wegen, E. E., Bloem, B. R., & van Wezel, R. J.
(2015). E-health support in people with Parkinson’s disease with smart
glasses: a survey of user requirements and expectations in the Netherlands.
Journal of Parkinson's disease, 5(2), 369-378.

</td> </tr>  
<tr>  
<td>

**STANDARDS AND METADATA**

</td> </tr>  
<tr>  
<td>

For this dataset, the metadata correspond to the participant-provided
information via the first part of each web survey section, i.e., the
demographics part, and mainly include: a) the age of the participant, b) the
country of residence, c) their occupational category, d) health status
relating to PD, e) expertise (in case of health care professionals), and (f)
familiarity with new technology and new means of communication. Thus, no
standard is followed.

</td> </tr>  
<tr>  
<td>

**DATA SHARING**

</td> </tr>  
<tr>  
<td>

**Access type**

</td> </tr>  
<tr>  
<td>

The dataset will be publicly available (see Section 3.3.2.1).

</td> </tr>  
<tr>  
<td>

**Access Procedures**

</td> </tr>  
<tr>  
<td>

The publicly available dataset will be open for third-party stakeholders to
download based on the procedure described in Section 3.3.2.1.

</td> </tr> </table>  
<table>  
<tr>  
<th>

**Embargo periods** (if any)

</th> </tr>  
<tr>  
<td>

The dataset is planned to be publicly available after month 28 of the project
to allow i-PROGNOSIS investigators to prepare and submit the respective
scientific publications regarding the final version of user requirements.

</td> </tr>  
<tr>  
<td>

**Technical mechanisms for dissemination**

</td> </tr>  
<tr>  
<td>

The dataset will be available through either the CKAN-based i-PROGNOSIS data
management portal (see Section 3.2). Links redirecting to the portal and the
available dataset will be provided through a dedicated page of the i-PROGNOSIS
project website (  _www.i-prognosis.eu_ )  .

A technical description providing information on how the web-survey was
structured and conducted will accompany the dataset.

</td> </tr>  
<tr>  
<td>

**Necessary S/W and other tools for enabling re-use**

</td> </tr>  
<tr>  
<td>

As the compressed dataset will consist of a Microsoft Excel file (.xlsx), only
a (de)compression software and the Microsoft Excel software (or other
compatible with this type of file software) will be required for data parsing
and re-use.

</td> </tr>  
<tr>  
<td>

**Repository where data will be stored**

</td> </tr>  
<tr>  
<td>

The dataset will be stored in AUTH secure servers, as well as in the Microsoft
Azurebased i-PROGNOSIS Cloud infrastructure (data centres are located in
Ireland) where the CKAN-based data management portal will be installed.
External repositories will also be considered as alternatives, to increase
visibility and dissemination efforts.

</td> </tr>  
<tr>  
<td>

**ARCHIVING AND PRESERVATION** (including storage and backup)

</td> </tr>  
<tr>  
<td>

**Data preservation period**

</td> </tr>  
<tr>  
<td>

The dataset will be preserved online for as long as there are regular
downloads and at least for one year after the end of the project. After that
it would be made accessible by request.

</td> </tr>  
<tr>  
<td>

**Approximated end volume of data**

</td> </tr>  
<tr>  
<td>

The approximate size of the dataset is ~ 3 MB based on the size of each entry
(~ 1.2 KB) and the expected final number of entries (participants in the
survey) (~ 2500 to 3000 participants - entries).

</td> </tr>  
<tr>  
<td>

**Indicative associated costs for data archiving and preservation**

</td> </tr>  
<tr>  
<td>

The original Google Sheets files comprising the dataset are stored in the
Google drive account of the i-PROGNOSIS project that is free of charge. The
downloaded CSV files and the cumulative Microsoft Excel file are stored in an
AUTH server, both not imposing any additional costs. The Microsoft Excel file
that will be made publicly available will be also archived and preserved in
the Microsoft Azure-based iPROGNOSIS Cloud infrastructure. The indicative cost
of the latter means of archiving and preservation is 20 Euros (€) per month
for one year (at least) after the end of the project.

</td> </tr>  
<tr>  
<td>

**Indicative plan for covering the above costs**

</td> </tr>  
<tr>  
<td>

The costs associated with the data archiving and preservation will be covered
by the

</td> </tr>  
<tr>  
<td>

i-PROGNOSIS project budget.

</td> </tr>  
<tr>  
<td>

**PARTNERS ACTIVITIES AND RESPONSIBILITIES**

</td> </tr>  
<tr>  
<td>

**Partner Owner / Data Collector**

</td>  
<td>

**AUTH**

</td> </tr>  
<tr>  
<td>

**Partner in charge of the data analysis**

</td>  
<td>

**AUTH**

</td> </tr>  
<tr>  
<td>

**Partner in charge of the data storage**

</td>  
<td>

**AUTH**

</td> </tr>  
<tr>  
<td>

**WPs and Tasks**

</td> </tr>  
<tr>  
<td>

The data are going to be collected within the activities of WP2, to mainly
serve the research efforts of T2.1 and T2.4.

</td> </tr> </table>

#  APPENDIX I – DATASET DESCRIPTION TEMPLATE

The table template used to describe each dataset along with clarifications on
each field:  
  
<table>  
<tr>  
<th>

**DATA SET REFERENCE NAME**

</th>  
<th>

**DS#.#- <DatasetName> **

</th> </tr>  
<tr>  
<td>

**DATA SET DESCRIPTION**

</td> </tr>  
<tr>  
<td>

**Generic description**

</td> </tr>  
<tr>  
<td>

_ <Provide a summary of data to be collected> _

</td> </tr>  
<tr>  
<td>

**Origin of data**

</td> </tr>  
<tr>  
<td>

_ <How will the dataset be produced, e.g. mobile phone app logs or Microsoft
Band and a sampling rate of xx Hz etc.> _

</td> </tr>  
<tr>  
<td>

**Nature and scale of data**

</td> </tr>  
<tr>  
<td>

_ <Description of file format, e.g. TXT files, estimated size, number of
participants> _

</td> </tr>  
<tr>  
<td>

**To whom the dataset could be useful**

</td> </tr>  
<tr>  
<td>

_ <What research purposes will the dataset facilitate, e.g. The dataset will
be valuable for benchmarking algorithms for activity analysis etc.> _

</td> </tr>  
<tr>  
<td>

**Related scientific publication(s)**

</td> </tr>  
<tr>  
<td>

_ <Either existing ones in case we get open data from external to the project
repositories, or future publications that we intend to make, e.g. description
of indicative research area like neuroscience or name a few scientific
conferences or journals that will be targeted> _

</td> </tr>  
<tr>  
<td>

**Indicative existing similar data sets** (including possibilities for
integration and reuse)

</td> </tr>  
<tr>  
<td>

_ <List other already existing datasets or repositories> _

</td> </tr>  
<tr>  
<td>

**STANDARDS AND METADATA**

</td> </tr>  
<tr>  
<td>

_ <Reference to existing suitable standards of the discipline. Format, e.g.
EDF format for biosignals. _Metadata_ should contain information such as: (a)
description of the experimental setup and procedure that led to the generation
of the dataset, (b) documentation of the variables recorded in the dataset and
(c) annotated experiment state of the monitored person per time interval. If
these do not exist, provide an outline on how and what metadata will be
created.> _

</td> </tr>  
<tr>  
<td>

**DATA SHARING**

</td> </tr>  
<tr>  
<td>

**Access type**

</td> </tr>  
<tr>  
<td>

_ <E.g., open - can be publicly shared, protected - can be shared but the
participants have to provide their consent, or confidential - cannot be shared
outside the project> _

</td> </tr>  
<tr>  
<td>

**Access Procedures**

</td> </tr>  
<tr>  
<td>

_ <Explain how will you handle private (if any) and publicly available
datasets, e.g., depending on the access type account for each dataset a
relevant access procedure should be defined. Access procedure should contain
information about the developed _

</td> </tr>  
<tr>  
<td>

_area within the portal that will let third-party users download files > _

</td> </tr>  
<tr>  
<td>

**Embargo periods** (if any)

</td> </tr>  
<tr>  
<td>

_ <When will the data be published and access provided> _

</td> </tr>  
<tr>  
<td>

**Technical mechanisms for dissemination**

</td> </tr>  
<tr>  
<td>

_ <Accompany datasets with a technical description of the dataset and the way
data were captured> _

</td> </tr>  
<tr>  
<td>

**Necessary S/W and other tools for enabling re-use**

</td> </tr>  
<tr>  
<td>

_ <Code/libraries/open source software to read and process data so as to allow
for reproducible research> _

</td> </tr>  
<tr>  
<td>

**Repository where data will be stored**

</td> </tr>  
<tr>  
<td>

_ <i-PROGNOSIS portal dedicated download section, institutional portals or
standard repository for the discipline> _

</td> </tr>  
<tr>  
<td>

**ARCHIVING AND PRESERVATION** (including storage and backup)

</td> </tr>  
<tr>  
<td>

**Data preservation period**

</td> </tr>  
<tr>  
<td>

_ <For how long should the data be preserved? We should take into account any
national regulations as well> _

</td> </tr>  
<tr>  
<td>

**Approximated end volume of data**

</td> </tr>  
<tr>  
<td>

_ <E.g., ### GB raw signal> _

</td> </tr>  
<tr>  
<td>

**Indicative associated costs for data archiving and preservation**

</td> </tr>  
<tr>  
<td>

_ <Hard disk drives, servers> _

</td> </tr>  
<tr>  
<td>

**Indicative plan for covering the above costs**

</td> </tr>  
<tr>  
<td>

_ <Within project, whole consortium, local partner, centrally> _

</td> </tr>  
<tr>  
<td>

**PARTNERS ACTIVITIES AND RESPONSIBILITIES**

</td> </tr>  
<tr>  
<td>

**Partner Owner / Data Collector**

</td>  
<td>

_ <Partner Acronym> _

</td> </tr>  
<tr>  
<td>

**Partner in charge of the data analysis**

</td>  
<td>

_ <Partner Acronym> _

</td> </tr>  
<tr>  
<td>

**Partner in charge of the data storage**

</td>  
<td>

_ <Partner Acronym> _

</td> </tr>  
<tr>  
<td>

**WPs and Tasks**

</td> </tr>  
<tr>  
<td>

_ <During which stage of the project will data be captured?> _

</td> </tr> </table>