#  Executive Summary

The WhoLoDancE Work Package 5 is responsible for the overall data management
infrastructure to be built and deployed by ATHENA RC with the objective to
collect, store, pre-process and manage the multimodal data acquired in the
project. Deliverable 5.1 provides technical information about the type of data
that will be produced, managed and maintained by the data management platform
and the methodologies which will be applied for the data integration and
management in order to deliver the various applications of the project.

The process of comprehending and driving conclusions on data sources related
to the project is an integral part of the WhoLoDancE data management approach
and is presented in this report. For gathering information from partners, a
special questionnaire has been designed and implemented by the project’s data
management team and has been populated by the individual data-providing
partners. More information on questionnaire structure the can be found in §3,
“Recorded dataset information”, while the results are presented in §7,
“Datasets”.

A set of dataset management practices and tools that can be used for storing,
delivering, preserving and licensing the data evaluated for the needs of the
project, complying with best practices, and generally acceptable paradigms in
the context that the project activates is then defined. The results of this
work are presented in §4, “Data Management”. Subsequently, the data model of
the project data is presented in §5, “Motion Capture Dataset ”. Finally, the
policies for ensuring data interoperability and integration across project’s
services, be it data management or end-user ones, is covered in section §6,
“Data Integration”.

#  Introduction

The WhoLoDancE work package 5 is responsible for the overall data management
infrastructure to be built and deployed by ATHENA RC with the objective to
collect, store, pre-process and manage the multimodal data acquired. The data
management infrastructure will be able to deliver that data as input to
various similarity search algorithms, multimodal analysis and modeling tools
and promote data exchange between different components and modalities of
interaction to support the realization of a variety of learning scenarios. WP5
is responsible for integrating the ”groundtruth” data selected in WP2,
indexing and annotating based on the models and high-level descriptors
prepared in WP3, and creating a Learning content repository of movement data
following the requirements and theoretical guidelines defined in WP1 and
modeled in WP3.

This deliverable outlines how the data “produced” (either generated or
collected) during the WhoLoDancE project will be managed during the project
and after the project completion. In particular, it describes the practices
characterizing research data handling during and after the project, what data
will be collected, processed or generated, what methodology and standards will
be applied, whether data will be shared / made open access and how, how data
will be curated and preserved.

#  Data management general approach

In the WhoLoDancE project, the produced data sets are a first class citizen
for empowering the project’s research, technological development, usage and
outreach activities. To facilitate this enhanced role of the data in the
project, the management approach of WhoLoDancE is to lay the foundations for
processes, technologies and policies related to data so that their production
and consumption via stakeholders is streamlined in a smooth and effective way.

In this direction, it is important to view data elements from various
perspectives and not as standalone artefacts but rather as integral part of
the project platform. This requires that both technological facets of data
(model, manifestation, volume, protocols, etc) and as well as policies around
them (availability, preservation, volume, etc) need to be handled when
planning their incorporation in the project platform and work plan.

The WhoLoDancE data management approach lays its foundations on the following
sources:

  * The Description of Action of the Project, which describes the principles for data management as well as the baseline plan (be it explicit or implicit) for data collection and usage in the project’s lifetime. 

  

  * The availability and capacity of project partners to generate, describe and provide data, which relates to partner role, equipment, data sizing etc. 

  

  * The learning scenarios and user needs defined in D1.4, that guide the production and consumption of data in overall. 

  

  * Common practices regarding data management in the context of H2020 datasets, which relates to provenance, preservation, policies etc. 

  

The process of comprehending and driving conclusions on those sources, is
herein called Conceptual Analysis of Datasets, and is an integral part of
WhoLoDancE data management approach and is presented in this Report.

Collecting and understanding the project’s datasets gathers information from
two sources:

  * Partner statement on dataset generation: i.e. listing of datasets that will become available in the process of the project, along with several data attributes that characterize their nature, use and policies. 

  

  * Specific dataset analysis: i.e. analysis of initial datasets gathered prior to the finalization of project’s Conceptual Analysis of Datasets. 

  

For gathering information from partners, a special questionnaire has been
designed and implemented by the project’s data management team and has been
populated by individual dataproviding partners. More information on
questionnaire structure the can be found in §3, “Recorded dataset
information”, while the results are presented in §7, “Datasets”.

The outcome of the conceptual analysis of datasets, covers a number of topics
and delivers the results presented below.

First we define the **set of dataset management practices and tools** that can
be used for storing, delivering, preserving and licensing the data evaluated
for the needs of the project, complying with best practices, and generally
acceptable paradigms in the context that the project activates. The results of
this work are presented in §4, “Data Management”.

Subsequently, the **data model** of the project motion capture dataset,
presented in §5, “Motion Capture Dataset ”, which covers:

 aspects of dataset internal models (i.e. how data is structured inside
datasets)  the WhoLoDancE data model which covers:

  * element referencing approach (i.e. how dataset/metadata elements are cross referenced) 

  

  * descriptive metadata that allow datasets to be discovered and consumed by end users (and services) 

  

  * structural metadata that allow datasets to be handled by the system and consumed and explored by services (and users) 

  

  * semantic extensions: the approach of project for handling semantic metadata 

  

Finally, the policies for ensuring data interoperability and integration
across project’s services, be it data management or end-user ones, is covered
in section §6, “Data Integration”.

#  Recorded dataset information

To record information for the foreseen datasets to be created during the
project lifetime, a questionnaire has been created in an on-line form format
and each partner has been asked to contribute by providing information on the
datasets they foresee to generate. For each dataset, the classes of
information that have been collected are presented in the remainder of this
section.

##  Dataset description

The provided description should characterise the dataset, its origin (in case
it is collected), nature and scale and to whom it could be useful, and whether
it underpins a scientific publication. Information on the existence (or not)
of similar data and the possibilities for integration and reuse. Requested
information includes:

  * _**Label:** _ Please provide a (tentative) label for the dataset;  _**Description:** _ Please provide a brief description of the dataset;  _**Generated/Collected:** _ Method of acquisition: 

    * Is the dataset genuinely generated within the project, transformed or produced by aggregating content out of existing datasets / data sources? 

  

    * In the case the dataset is derived from existing datasets or imported, please indicate its origin(s). Add the dataset download URLs if available, otherwise the origin URL. 

  

  

  * _**Origin:** _

    * In the case the dataset is derived from existing datasets or imported, please indicate its origin(s). Add the dataset download URLs if available, otherwise the origin URL. 

  

    * In the case the dataset is derived from existing datasets or imported, please indicate the licenses of the original data. Add URLs if available. 

  

  

  * **_Nature_ : **

    * What is the nature of the dataset content? The options provided include: Optical motion capture data, audio data, biometric sensing data, 3D data, Video data, programming and code data, documentation and reporting data 

  

    * Please provide details on the nature of the data (for example "greek dance video data") 

  

  

  * **_Scale/Size_ : ** What is the estimated scale (size) of the dataset? (indicate size of its constituents if applicable); 

  

  * _**Potential use:** _ What is the foreseen use of the dataset by different communities for specific applications or research purposes? 

  

  * **_Scientific publications / references_ : ** Please indicate publications that are related to the dataset; 

  

  * _**Availability:** _ When will the dataset be made available to the project (indicate month/year)? 

  

##  Content and Metadata Types

  * Is the dataset comprised by items of the same or different typologies? (e.g. video streams, audio streams, textual documents, tabular data etc) 

  

  * What are the data formats used? (For example, XML, CSV, FBX) Please refer to possible standards used. 

  

  * What are the metadata formats used? Please refer to possible standards used. 

  

##  Dataset use and sharing

This section includes a description of how data will be shared, including
access procedures, embargo periods (if any), outlines of technical mechanisms
for dissemination and necessary software and other tools for enabling re-use,
and definition of whether access will be widely open or restricted to specific
groups. In case the dataset cannot be shared, the reasons for this should be
mentioned (e.g. ethical, rules of personal data, intellectual property,
commercial, privacyrelated, security-related). Identification of the
repository where data will be stored, if already existing and identified,
indicating in particular the type of repository (institutional, standard
repository for the discipline, etc.). Requested information includes:

  * What are the repositories the dataset or/and its metadata have been published in? 

  

  * Is there specific software required for consuming the dataset? 

  

  * Indicate the dissemination mechanisms through which the availability of the dataset will be announced (e.g. metadata catalogs). 

  

  * What is the license under which the dataset is provided (e.g. Creative Commons Attribution 4.0 ) ? 

  

  * What are the policies governing access to the dataset (e.g. the dataset is “open”, the dataset is made available to authorised users only)? 

  

  * What is the procedure for a user to consume to the dataset (e.g. standard content access API, web site download / view, behind a service API)? 

  

  * If this is a generated dataset, will there be an embargo period for providing (open) access to the dataset? If yes, how long (in months)? 

  

  * If creating or collecting data in the field how will you ensure its safe transfer into the main data management system? 

  

##  Dataset handling and synchronization

  * What is the preservation strategy for the dataset? 

  

  * What is the foreseen preservation period duration? 

  

  * What are the instruments (tools) put in place to implement the preservation strategy ? 

  

  * Please provide any additional information on issues related to the handling of the dataset (synchronization, referencing, etc). 

  

#  Data Management

WhoLoDancE defines a series of practices and approaches for data storage, data
access and data preservation. These practices are briefly described in the
following sections.

##  Data Storage and Repositories

WhoLoDanceE preliminary data analysis shows that there is no single data
manifestation that may cover all project’s pilot cases and data generation
needs and as such has to adopt a rather more generic approach to data and
metadata storage. As per example, datasets may contain motion capture data in
various manifestation, video or audio streams, graphs, tables etc. Furthermore
those datasets shall be described, in order to facilitate discoverability, and
interconnected in order to facilitate their consumption. As a result the data
storage elements and repositories, , presented in Figure 1 are included in the
WhoLoDancE platform. More specifically, these include:

  * **Storage Layer** : A file-based object store for depositing binary data objects. The repository is implemented over a redundant store with one delayed replica and is accessible via a number of standard protocols such as FTP and HTTP, while special protocols are also available depending on the data type (e.g. streams for media objects). Items in the repository obtain URLs that can be disseminated via standard web means, yet access may be provided only with granted credentials. 

  

  * CKAN metadata repository tailormade w.r.t. configuration and plugins, to fit WhoLoDancE project data and metadata servicing needs. Offers full web UI for managing and accessing metadata and a rich set of REST web services for consuming/exploring projects datasets. 

  

  * A relational database management system (PostgreSQL) for managing dataset metadata, behind the CKAN repository and pilot-specific services. 

  

  * **An ontology management system** (Protégé) to provide management functionalities for the WhoLoDancE ontologies and access through appropriate APIs. 

  

**Figure 1 WhoLoDancE data management infrastructure**

##  Data Dissemination and Catalogues

Data dissemination has two aspects, referring either to end-users or to other
external systems / catalogues. To support both, a data catalogue based on the
CKAN  1  technology is available offering rich user interfaces to end users
for data search and discovery, as well as a REST Application Programming
Interface (API  2  ) for machine interaction.

In addition, aiming on being interoperable with external catalogues the system
is compatible to the most known and adopted interoperable standard, the  Open
Archives Initiative  Protocol for Metadata Harvesting (OAI-PMH  3  ).

Interoperability is the ability of two or more information systems to exchange
metadata with minimal loss of information.

The OAI-PMH is a protocol developed for  harvesting  metadata descriptions of
records in an archive so that services can be built using metadata from many
archives. An implementation of OAI-PMH must support representing metadata in
Dublin Core  4  schema, but may also support additional representations
depending on their nature and origin. In the current implementation, only the
default schema is supported.

Moreover, every data object residing in the WhoLoDancE platform is accessible
via a URL, which allows accessing its payload via a standard web protocol.
However, this does not preclude the involvement of an
authentication/authorization mechanism before accessing the actual dataset.
Due to the large size of project’s datasets it is required to protect
infrastructure availability via several means, one of those being limiting the
access to datasets.

##  Data Preservation

In essence, every data is archived in a secure manner. This is done in two
different yet complementary ways. Every data is constantly copied in a backup
area (the periodicity of the backup procedures varies from case by case yet it
is never longer than one day). Certain storage solutions automatically store
the content in multiple copies, e.g. this is the case of the technologies
behind the file-oriented storage.

No format migration strategy or approach is in place, the data are managed
with their native format.

##  Licenses

The library of motion capture data produced in the contect of WhoLoDancE is a
collection of high quality dance movement content that would be a valuable
asset for the researchers of the community. It will be decided in the
consortium what will be the approach to be taken for the access provided to
external parties to this data. A common approach would be to make the data,
available through a Creative Commons  5  license, possibly CC BY-NC-SA or CC
BY-NC-ND.

Normally the data would be made available upon request to interested parties
after registration.

#  Motion Capture Dataset Modeling

Although WhoLoDancE will produce a variety of Datasets, ranging from motion
capture files to text reports, the main content outcome that will be the basis
of the project research and development work is the Dataset of motion capture
dance segments, along with the multimodal data that accompany it.

This Dataset is presented in more detail in Section 7.1 - Table 5. This
section presents the metadata modelling approach employed for it.

The Dataset is organized in the following structures:

  * **Motion capture recording** : contains resources relevant to a particular motion capture segment and its relevant multimodal data, more specifically: .c3D, .fbx, .json, .txt, .mp4, .mp3 

  

  * **Collection** : a conceptual grouping of recordings, included as a metadata field in each recording, for example the Ballos Collection. A collection contains 1 to many recordings  **Dance Genre** : set of collections relevant to a particular dance genre. 

  

  * **Dataset** : it contains all motion capture data captured in the motion capture sessions organized by Motek and Unige, along with the data supporting multimodal material (music, videos, etc). 

  

The motion capture recordings have been stored in the FTP server and annotated
with metadata available through a CKAN metadata server  6  .

The metadata server offers a view to the user that emphasizes the four dance
genres. (Figure 2).

**Figure 2 The C-KAN view of the content organized by dance genre**

The first step for organizing the collected data was to create four recording
schemas corresponding to the four dance genres represented in the project
(Ballet, Contemporary, Greek Folk, Flamenco).

The recording schemas have some common fields that describe both the dances
and the files related to them (Table 1).

Table 2 presents the recording fields that are specific to Greek folk dance.

Each file (or resource) within the recording is described by a set of fields.
There is a subset that is common among the four genres (Table 3) as well as a
subset with is genre-specific and thus containing different fields for each of
the dance genres (Table 4).

The fields that are specific to each genre schema are presented in Table 4.

**Table 1 Fields that describe each recording**  
  
<table>  
<tr>  
<th>

**Field name**

</th>  
<th>

**Field type**

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

Source

</td>  
<td>

URL to the FTP server

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

Author

</td>  
<td>

Text

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

Author email

</td>  
<td>

Text

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

Maintainer

</td>  
<td>

Text

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

Maintainer Email

</td>  
<td>

Text

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

Dance Genre

</td>  
<td>

Text

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

Description

</td>  
<td>

Text

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

**Table 2 Greek folk dance - specific recording fields**  
  
<table>  
<tr>  
<th>

**Field name**

</th>  
<th>

**Field type**

</th>  
<th>

**Genre**

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

Dance name

</td>  
<td>

Text

</td>  
<td>

Greek folk

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

Local name

</td>  
<td>

Text

</td>  
<td>

Greek folk

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

Region

</td>  
<td>

Text

</td>  
<td>

Greek folk

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

Dance type

</td>  
<td>

Selection from the following values: circle, face to face

</td>  
<td>

Greek folk

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

Dance gender

</td>  
<td>

Selection from the following values: male, female, mixed

</td>  
<td>

Greek folk

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

Time signature

</td>  
<td>

Text

</td>  
<td>

Greek folk

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

**Table 3 Common resource fields for all genres**  
  
<table>  
<tr>  
<th>

**Field name**

</th>  
<th>

**Field type**

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

Last updated

</td>  
<td>

Date

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

Created

</td>  
<td>

Date

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

Format

</td>  
<td>

Text

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

License

</td>  
<td>

Text

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

File Type (e.g. Motion

Capture)

</td>  
<td>

Text

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

Dance Genre

</td>  
<td>

Text

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

Movement Principle

</td>  
<td>

Multiple selection from the following values: Alignment and Posture, Balance,
Coordination, Directionality, Motion

Through Space, Motorics, Rhythm and Phrasing, Stillness, Symmetry, Weight
bearing vs. Gesturing

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

Capture Venue

</td>  
<td>

Text

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

Capture day

</td>  
<td>

Date

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

Capture Quality

</td>  
<td>

Text

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

Capture Comments

</td>  
<td>

Text

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

Duration

</td>  
<td>

Text

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

Frames

</td>  
<td>

Text

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

Performer

</td>  
<td>

Text

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

Company

</td>  
<td>

Text

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

Dataset name

</td>  
<td>

Text

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

**Table 4 Dance-genre specific resource fields**  
  
<table>  
<tr>  
<th>

**Field name**

</th>  
<th>

**Field type**

</th>  
<th>

**Dataset /genre**

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

Type of Segment

</td>  
<td>

Text

</td>  
<td>

Greek folk

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

Actions in Segment

</td>  
<td>

Text

</td>  
<td>

Greek folk

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

Actions in Segment

</td>  
<td>

Text

</td>  
<td>

Contemporary

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

Relation in space

</td>  
<td>

Text

</td>  
<td>

Contemporary

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

Orientation

</td>  
<td>

Text

</td>  
<td>

Contemporary

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

Body Parts Leading

</td>  
<td>

Text

</td>  
<td>

Contemporary

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

Planes

</td>  
<td>

Text

</td>  
<td>

Contemporary

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

Axis

</td>  
<td>

Text

</td>  
<td>

Contemporary

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

Other Characteristics in

Motion

</td>  
<td>

Text

</td>  
<td>

Contemporary

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

Actions in Segment

</td>  
<td>

Text

</td>  
<td>

Ballet

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

**Figure 3 The metadata values for a motion capture resource (file)**

Having defined the recording and resource schemas according to the dance
types, it was decided to create a set of recordings per genre, to reflect a
categorization which is meaningful for each genre.

In total 111 recordings have been created with more than 1400 dance movement
sequences in two different formats. For each dance genre,

  * Ballet: 33 recordings, according to various ballet exercises, containing 185 .fbx resources and 216 .c3d ones 

  

  * Greek folk dance: 53 recordings, to reflect different dances, containing 189 .fbx resources and 302 .c3d ones 

  

  * Contemporary dance: 13 recordings, to reflect the movement principles and improvisations, containing 727 .fbx resources and 877 .c3d ones 

  

  * Flamenco: 12 recordings, to reflect movement principles, containing 99 .fbx resources and 65 .c3d ones 

  

More information on the resources (files) per created recording can be found
in Appendix A.

#  Data Integration

Data integration will be based on a number of principles:

  * Accessibility 

  

  * Discoverability 

  

  * Consumability 

  

In line with the first principle, data access will be provided via standards’
compliant methods. For instance, where adequate formal or defacto standards
exist, those will be utilized. This may be baseline standards such as FTP,
HTTP, XML, MPEG4 etc or higher level protocols such as WebDav, OAI-ORE, etc.
In areas where no applicable standards exist, which may be the case in higher
complexity interactions, access to data will be provided by standard REST web
services.

In the direction of the second principle, data need to be described adequately
in order to support discoverability. A thorough investigation of metadata
descriptors adequate to cover the needs of the project is performed and the
results are presented in Section 5. Yet, the required supporting mechanism is
the delivery of a registry which allows data consumers to locate the dataset
in need. Discoverability will be greatly supported by standard’s compliance,
as for example OAI-PMH support will allow the diffusion of data descriptions
in other registries, promoting their recognition and reuse.

Regarding consumability, the objective is multifold: (a) completeness of data
access services and (b) facilitation of machine-to-machine exchange of data.
In this direction, full coverage of services for locating and obtaining the
dataset element in need will be provided, under the manifestation preferred
(if many) and, in particular cases the granularity of access required.
Furthermore data model will be complete and will have enough metadata, to
allow locating data via search or direct transversal of data linking and
grouping descriptors, and will utilize common, machine readable, data formats
that allow consumption of the dataset payload. Documentation will be provided
on the data model, further facilitating its utilization.

#  Datasets

The datasets WhoLoDancE is called to manage belong to the following
categories:

  * Motion Capture datasets, i.e., the results of the motion capture activities in the project, in .fbx, .c3d, .json and/or .csv format as well as accompanying files with music or physiometric data; 

  

  * Videos, collected to either prepare for the motion capture process or record it; 

  

  * Questionnaire and interview results 

  

  * Software, i.e. datasets resulting from the software enabling WhoLoDancE. 

  

  * Reports and deliverables 

  

##  Motion capture related datasets

**Table 5 Motion capture data dataset description**  
  
<table>  
<tr>  
<th>

**Dataset name:** Motion capture data

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

_**Dataset description:** _ Motion capture data produced in the motion capture
sessions organized by Motek and UniGE, processed and rigged to inverse
kinematic skeleton, along with accompanying files with relevant music and/or
physiometric data . The dataset includes motion capture data from the four
dance genres, Greek folk, contemporary, ballet and flamenco.

_**Generated/Collected:** _ Generated.

_**Origin(s):Download URL:** _ N/A

_**Origin(s):Licenses:** _ N/A

_**Nature:** _ Optical motion capture data; 3D data

_**Size/Scale:** _ 11 GB - More than 1400 dance movement sequences in two
different formats. For each dance genre,

  * Ballet: 33 recordings, according to various ballet exercises, containing 185 .fbx resources and 216 .c3d ones 

  

  * Greek folk dance: 53 recordings, to reflect different dances, containing 189 .fbx resources and 302 .c3d ones 

  

  * Contemporary dance: 13 recordings, to reflect the movement principles and improvisations, containing 727 .fbx resources and 877 .c3d ones 

  

  * Flamenco: 12 recodings, to reflect movement principles, containing 99 .fbx resources and 65 .c3d ones 

  

_**Potential use:** _ Use for the WhoLoDancE research purposes: design of
dance learning material, HLF/LLF extraction, similarity search, etc.

_**References:** _ None at the moment

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

**Content and Metadata Types**

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

_**Typologies:** _ Files in this dataset are of the same typology, motion
capture dance segments in two different formats

_**Data Formats: .** _ fbx,.c3d, .csv, .json motion capture formats and .mp3
and .mp4 relevant files

_**Metadata formats:** _ Available in json format through CKAN API and XML
through OAI publisher

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

_**Availability date:** _ December 2016

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

**Dataset Use and Sharing**

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

_**Repositories:** _ Internal WhoLoDancE repository

_**Software and tools for re-use** _ : MotionBuilder, Unity, fbx viewer,
custom viewers

_**Dissemination Mechanisms** _ : Not available yet, will include the project
blending engine and learning tools for parts of the dataset.

_**License:** _ Currently internal use for the consortium research purposes.
To be decided if the data will be released under a licensing schema, possibly
Creative Commons CC BY-NC-SA or CC BY-NC-ND

_**Access policies:** _ Currently accessible only to the members of the
consortium for research purpose. According to the selected licensing schema
they could be available to external parties through a registration process
_**.** _

_**Access Procedure** _ : Web access after a registration process

_**Embargo Periods** _ : To be decided.

_**Transfer process:** _ Directly uploaded by Motek to the ftp server central
storage system

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

**Dataset handling and synchronization**

_**Preservation strategy:** _ Backup at the WhoLoDancE repository and in the
interested partners’ individual storage facilities

_**Preservation period:** _ indefinite

_**Preservation implementation instruments:** _ Multiple Backups in different
locations online and offline

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

**Table 6 Motion capture preparation videos dataset descrption**  
  
<table>  
<tr>  
<th>

**Dataset name:** Motion capture preparation audio and videos

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

_**Dataset description:** _ videos for preparation for motion capturing of
dance (movement principles, qualitative modules), recorded by the dance
partners prior to the motion capture sessions. For example, for the greek
dances, different videos have been recorded: full dance or parts, with and
without costumes, a) danced by a group of dancers, men and/or women, b) danced
by one or two dancers. The scope of the videos was focused on the variety of
dances and kinetic patterns and on the relation with the dance principles. In
some cases audio files with the accompanying music

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

has been provided

_**Generated/Collected:** _ Generated.

_**Origin(s):Download URL:** _ N/A

_**Origin(s):Licenses:** _ N/A

_**Nature:** _ Audio data, video data

_**Size/Scale:** _ Appr. 1,3 GB music audio files and 50GB video files

_**Potential use:** _ Use for the WhoLoDancE research purposes: to generate
the short list for motion capture; for the greek dances specifically, to be
used as a repository of Greek traditional dances and their kinetic patterns
for study by researchers of the field, teachers/students and use by
choreographers of any genre of dance.

_**References:** _ For Greek dances: “Improvisation in the Greek folk dances"
by Lefteris Drandakis

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

**Dataset Use and Sharing**

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

_**Repositories:** _ Internal WhoLoDancE repository

_**Software and tools for re-use** _ : Video and audio players

_**Dissemination Mechanisms** _ : For the project internal use only. An
exception is greek folk dances _**License:** _ For the project internal use
only.

_**Access policies:** _ For the project internal use only. For greek dances
available as supplementary material in the learning scenarios

_**Access Procedure** _ : Through the WhoLoDancE web applications

_**Embargo Periods** _ : N/A

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

_**Transfer process:** _ Directly uploaded by Motek to the ftp server central
storage system

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

**Dataset handling and synchronization**

_**Preservation strategy:** _ Backup at the WhoLoDancE repository and in the
interested partners’ individual storage facilities

_**Preservation period:** _ to be decided

_**Preservation implementation instruments:** _ Multiple Backups in different
locations online and offline

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

**Table 7 Motion capture features dataset description**  
  
<table>  
<tr>  
<th>

**Dataset name:** Motion capture, video and audio features

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

_**Dataset description:** _ The dataset will contain features extracted from
audio, video and motion capture recordings, along with relevant motion capture
segments. Depending on the project needs, it will be around 5 to 50% of the
original data from which features will be extracted

_**Generated/Collected:** _ Generated.

_**Origin(s):Download URL:** _ N/A

_**Origin(s):Licenses:** _ N/A

_**Nature:** _ Raw and structured textual and numerical data extracted from
the computation of features from audio, video and motion capture recordings. I
will include the original video, audio and motion capture content.
_**Size/Scale:** _ 5GB

_**Potential use:** _ The dataset will be used for data analysis and machine
learning purposes. _**References:** _ None at the moment

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

**Dataset Use and Sharing**

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

_**Repositories:** _ Internal WhoLoDancE repository

_**Software and tools for re-use** _ : Depending on the format, Matlab or
Python with the Numpy library might be needed

_**Dissemination Mechanisms** _ : Publications on conferences and journals,
social networks and mailing lists

_**License:** _ To be discussed, possibly available under Creative commons
license

_**Access policies:** _ To be discussed

_**Access Procedure** _ : Web site download or ftp access

_**Embargo Periods** _ : The dataset will be available for the partners from
the moment of its production, they will become publicly available from the
moment of the publication of the related work in a conference / journal

_**Transfer process:** _ Secure transfer protocol, such as SFTP

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

**Dataset handling and synchronization**

_**Preservation strategy:** _ Backup at the WhoLoDancE repository and in the
interested partners’ individual storage facilities

_**Preservation period:** _ indefinite

_**Preservation implementation instruments:** _ Multiple Backups in different
locations online and offline

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

**Table 8 Motion capture videos dataset description**  
  
<table>  
<tr>  
<th>

**Dataset name:** Motion capture videos

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

_**Dataset description:** _ Two camera views recording all motion capture
takes

_**Generated/Collected:** _ Generated.

_**Origin(s):Download URL:** _ N/A

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

_**Origin(s): Licenses:** _ N/A

_**Nature:** _ Video data

_**Size/Scale:** _ 160GB

_**Potential use:** _ Use for the WhoLoDancE research purposes, for example,
semi-automatic finger tracking on mocap

_**References:** _ None at the moment

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

**Dataset Use and Sharing**

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

_**Repositories:** _ Internal WhoLoDancE repository

_**Software and tools for re-use** _ : Video players

_**Dissemination Mechanisms** _ : For project internal use only

_**License:** _ For project internal use only

_**Access policies:** _ For project internal use only _**Access Procedure** _
: For project internal use only

_**Embargo Periods** _ : N/A.

_**Transfer process:** _ Directly uploaded to the ftp server central storage
system

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

**Dataset handling and synchronization**

_**Preservation strategy:** _ Backup at the WhoLoDancE repository and in the
interested partners’ individual storage facilities

_**Preservation period:** _ indefinite

_**Preservation implementation instruments:** _ Multiple Backups in different
locations online and

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

offline

##  Survey results datasets  
  
<table>  
<tr>  
<th>

**Dataset name:** Movement principles interview and questionnaire data

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

_**Dataset description:** _ The information will be collated from a number of
interviews conducted by the COVUNI team and a series of paper questionnaires
collected. All of the information gathered comes from professional
dancers/teachers/choreographers from a diverse range of disciplines.

The aim is to have an equal number of men and women.

_**Generated/Collected:** _ Generated.

_**Origin(s): Download URL:** _ N/A

_**Origin(s): Licenses:** _ N/A

_**Nature:** _ Documentation and reporting data; Questionnaire results data

_**Size/Scale:** _ 50 MB

_**Potential use:** _ For internal research purposes only

_**References:** _ None at the moment

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

**Dataset Use and Sharing**

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

_**Repositories:** _ Internal university private shared drive.

_**Software and tools for re-use** _ : Document viewers and sound players

_**Dissemination Mechanisms** _ : For project internal use only

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

_**License:** _ For project internal use only

_**Access policies:** _ For project internal use only. Confidential and
following COVUNI ethics guidelines.

_**Access Procedure** _ : For project internal use only. Data is confidential
and only COVUNI team has permission to hear the audio recordings.

_**Embargo Periods** _ : N/A.

_**Transfer process:** _ N/A.

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

**Dataset handling and synchronization**

_**Preservation strategy:** _ Backup at the WhoLoDancE repository and in the
interested partners’ individual storage facilities

_**Preservation period:** _ To be decided

_**Preservation implementation instruments:** _ N/A

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

**Dataset name:** Dance learning on-line survey results

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

_**Dataset description:** _ The dataset includes results in spreadsheet format
by the on-line surveys conducted by ATHENA and COVUNI

_**Generated/Collected:** _ Generated.

_**Origin(s):Download URL:** _ N/A

_**Origin(s):Licenses:** _ N/A

_**Nature:** _ Questionnaire results data

_**Size/Scale:** _ 5MB

_**Potential use:** _ For internal research purposes only, to extract
conclusions on user requirements and potential learning scenarios to be
implemented within the project. _**References:** _ None at the moment

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

**Dataset Use and Sharing**

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

_**Repositories:** _ Internal university private shared drive.

_**Software and tools for re-use** _ : Spreadsheet viewers

_**Dissemination Mechanisms** _ : For project internal use only

_**License:** _ For project internal use only

_**Access policies:** _ For project internal use only.

_**Access Procedure** _ : For project internal use only.

_**Embargo Periods** _ : N/A.

_**Transfer process:** _ N/A.

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

**Dataset handling and synchronization**

_**Preservation strategy:** _ Backup at the WhoLoDancE repository and in the
interested partners’ individual storage facilities

_**Preservation period:** _ To be decided

_**Preservation implementation instruments:** _ N/A

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

##  Software and reports

The software to be developed within WhoLoDancE will be deposited in Github and
published in repositories like Zenodo.

For the day to day management of project reports and deliverables, the project
coordinator has set up and maintains a Dropbox folder. All partners have
access to the folder and use it to exchange project reports and disseminate
the deliverables to the rest of the consortium.

#  Conclusions

This deliverable contains a description of data management processes that have
been set up within the project as well as the description of the datasets
collected, processed or generated by the project and an initial plan on how
sharing, archiving and preservation of these datasets will be guaranteed.

The motion capture datasets generated within the project comprise a library of
dance movements with high quality, diverse content, valuable for a variety of
research purposes. The project consortium will use this rich data through the
data modeling and management infrastructure described in this document to
perform research in different fields, from feature extraction, conceptual
modeling and semantic analysis to advanced search algorithms and innovative
presentation approaches for the content, in the general context of a dance
learning system.

#  Appendix A – Motion capture resources

##  Ballet

**Table 9 Ballet motion capture .fbx and .c3d files per recording**  
  
<table>  
<tr>  
<th>

**Dataset name**

</th>  
<th>

**.fbx resources**

</th>  
<th>

**.c3d resources**

</th>  
<th>

**Total**

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

Adagio

</td>  
<td>

4

</td>  
<td>

4

</td>  
<td>

8

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

Attitude

</td>  
<td>

2

</td>  
<td>

2

</td>  
<td>

4

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

Ballet Center

Variations

</td>  
<td>

5

</td>  
<td>

5

</td>  
<td>

10

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

Cou de pied

</td>  
<td>

2

</td>  
<td>

2

</td>  
<td>

4

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

Coupe

</td>  
<td>

2

</td>  
<td>

2

</td>  
<td>

4

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

Developpe

</td>  
<td>

4

</td>  
<td>

4

</td>  
<td>

8

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

Enchainement

</td>  
<td>

5

</td>  
<td>

5

</td>  
<td>

10

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

Flic Flac

</td>  
<td>

2

</td>  
<td>

2

</td>  
<td>

4

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

Fondue

</td>  
<td>

6

</td>  
<td>

8

</td>  
<td>

14

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

Fouette

</td>  
<td>

1

</td>  
<td>

1

</td>  
<td>

2

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

Frappe

</td>  
<td>

8

</td>  
<td>

8

</td>  
<td>

16

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

Grand Battement

</td>  
<td>

4

</td>  
<td>

6

</td>  
<td>

10

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

Grand Jete

</td>  
<td>

2

</td>  
<td>

2

</td>  
<td>

4

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

Grand Rond de Jambe

</th>  
<th>

4

</th>  
<th>

4

</th>  
<th>

8

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

Improvisation

</td>  
<td>

7

</td>  
<td>

7

</td>  
<td>

14

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

Jump

</td>  
<td>

15

</td>  
<td>

15

</td>  
<td>

30

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

Other

</td>  
<td>

3

</td>  
<td>

10

</td>  
<td>

13

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

Pad de deux

</td>  
<td>

2

</td>  
<td>

3

</td>  
<td>

5

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

Pas de Cheval

</td>  
<td>

2

</td>  
<td>

2

</td>  
<td>

4

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

Pas Marche

</td>  
<td>

6

</td>  
<td>

6

</td>  
<td>

12

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

Passe

</td>  
<td>

2

</td>  
<td>

2

</td>  
<td>

4

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

Petit Battement

</td>  
<td>

6

</td>  
<td>

6

</td>  
<td>

12

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

Pied a la main

</td>  
<td>

2

</td>  
<td>

2

</td>  
<td>

4

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

Pirouette

</td>  
<td>

18

</td>  
<td>

18

</td>  
<td>

36

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

Plie

</td>  
<td>

12

</td>  
<td>

16

</td>  
<td>

28

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

Port de Bras

</td>  
<td>

3

</td>  
<td>

11

</td>  
<td>

14

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

Releve

</td>  
<td>

6

</td>  
<td>

9

</td>  
<td>

15

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

Rond de Jambe

</td>  
<td>

12

</td>  
<td>

12

</td>  
<td>

24

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

Rond de Jambe en

l'air

</td>  
<td>

1

</td>  
<td>

1

</td>  
<td>

2

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

Soutenu

</td>  
<td>

3

</td>  
<td>

3

</td>  
<td>

6

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

Temps Lie

</td>  
<td>

2

</td>  
<td>

2

</td>  
<td>

4

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

Tendu

</td>  
<td>

15

</td>  
<td>

18

</td>  
<td>

33

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

Tendu Jete

</td>  
<td>

17

</td>  
<td>

18

</td>  
<td>

35

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

**Total**

</td>  
<td>

**185**

</td>  
<td>

**216**

</td>  
<td>

**401**

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

</td>  
<td>

</td>  
<td>

</td>  
<td>

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

##  Contemporary

**Table 10 Contemporary dance motion capture .fbx and .c3d files per
recording**  
  
<table>  
<tr>  
<th>

**Dataset name**

</th>  
<th>

**.fbx resources**

</th>  
<th>

**.c3d resources**

</th>  
<th>

**Total**

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

Alignment and

Posture

</td>  
<td>

60

</td>  
<td>

52

</td>  
<td>

112

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

Balance

</td>  
<td>

73

</td>  
<td>

74

</td>  
<td>

147

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

Coordination

</td>  
<td>

62

</td>  
<td>

62

</td>  
<td>

124

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

Directionality

</td>  
<td>

237

</td>  
<td>

235

</td>  
<td>

472

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

Emotions

</td>  
<td>

0

</td>  
<td>

11

</td>  
<td>

11

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

Motion Through

Space

</td>  
<td>

32

</td>  
<td>

31

</td>  
<td>

63

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

Motorics

</td>  
<td>

76

</td>  
<td>

76

</td>  
<td>

152

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

Multumodal_Genoa

</td>  
<td>

0

</td>  
<td>

110

</td>  
<td>

110

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

Other

</td>  
<td>

9

</td>  
<td>

44

</td>  
<td>

53

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

Rhythm and Phrasing

</td>  
<td>

19

</td>  
<td>

11

</td>  
<td>

30

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

Stillness

</td>  
<td>

11

</td>  
<td>

11

</td>  
<td>

22

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

Symmetry

</td>  
<td>

78

</td>  
<td>

90

</td>  
<td>

168

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

Weight bearing vs.

Gesturing

</td>  
<td>

70

</td>  
<td>

70

</td>  
<td>

140

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

**Total**

</td>  
<td>

**727**

</td>  
<td>

**877**

</td>  
<td>

**1604**

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

##  Flamenco

**Table 11 Flamenco motion capture .fbx and .c3d files per recording**  
  
<table>  
<tr>  
<th>

**Dataset name**

</th>  
<th>

**.fbx resources**

</th>  
<th>

**.c3d resources**

</th>  
<th>

**Total**

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

Alignment and

Posture

</td>  
<td>

6

</td>  
<td>

6

</td>  
<td>

12

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

Asymmetry

</td>  
<td>

1

</td>  
<td>

1

</td>  
<td>

2

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

Balance

</td>  
<td>

2

</td>  
<td>

2

</td>  
<td>

4

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

Coordination

</td>  
<td>

3

</td>  
<td>

3

</td>  
<td>

6

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

Directionality

</td>  
<td>

3

</td>  
<td>

3

</td>  
<td>

6

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

Flamenco combinations

</td>  
<td>

20

</td>  
<td>

36

</td>  
<td>

56

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

Motion Through

Space

</td>  
<td>

3

</td>  
<td>

3

</td>  
<td>

6

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

Motorics

</td>  
<td>

2

</td>  
<td>

2

</td>  
<td>

4

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

Rhythm and Phrasing

</td>  
<td>

2

</td>  
<td>

2

</td>  
<td>

4

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

Stillness

</td>  
<td>

3

</td>  
<td>

3

</td>  
<td>

6

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

Symmetry

</td>  
<td>

3

</td>  
<td>

3

</td>  
<td>

6

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

Weight bearing vs.

Gesturing

</td>  
<td>

1

</td>  
<td>

1

</td>  
<td>

2

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

**Total**

</td>  
<td>

**49**

</td>  
<td>

**65**

</td>  
<td>

**114**

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

##  Greek folk dance

**Table 12 Greek folk dance motion capture .fbx and .c3d files per recording**  
  
<table>  
<tr>  
<th>

**Dataset name**

</th>  
<th>

**.fbx resources**

</th>  
<th>

**.c3d resources**

</th>  
<th>

**Total**

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

Baintouska

</td>  
<td>

0

</td>  
<td>

5

</td>  
<td>

5

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

Ballos

</th>  
<th>

12

</th>  
<th>

23

</th>  
<th>

35

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

Basic Steps

</td>  
<td>

0

</td>  
<td>

9

</td>  
<td>

9

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

Chaniotikos

</td>  
<td>

1

</td>  
<td>

6

</td>  
<td>

7

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

Chassapiko

</td>  
<td>

2

</td>  
<td>

2

</td>  
<td>

4

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

Enteka

</td>  
<td>

4

</td>  
<td>

4

</td>  
<td>

8

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

Forlana

</td>  
<td>

1

</td>  
<td>

1

</td>  
<td>

2

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

Gaida

</td>  
<td>

3

</td>  
<td>

4

</td>  
<td>

7

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

Ikariotiko

</td>  
<td>

6

</td>  
<td>

6

</td>  
<td>

12

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

Issos

</td>  
<td>

5

</td>  
<td>

5

</td>  
<td>

10

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

Kalamatianos

</td>  
<td>

1

</td>  
<td>

1

</td>  
<td>

2

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

Kaneloriza

</td>  
<td>

4

</td>  
<td>

4

</td>  
<td>

8

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

Karatzova

</td>  
<td>

8

</td>  
<td>

11

</td>  
<td>

19

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

Karsilamas

</td>  
<td>

0

</td>  
<td>

7

</td>  
<td>

7

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

Kastrinos

</td>  
<td>

7

</td>  
<td>

16

</td>  
<td>

23

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

Katsivelikos

</td>  
<td>

6

</td>  
<td>

6

</td>  
<td>

12

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

Kotsari

</td>  
<td>

2

</td>  
<td>

1

</td>  
<td>

3

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

Koutsos

</td>  
<td>

0

</td>  
<td>

9

</td>  
<td>

9

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

Letsina

</td>  
<td>

4

</td>  
<td>

4

</td>  
<td>

8

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

Leventikos

</td>  
<td>

11

</td>  
<td>

14

</td>  
<td>

25

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

Nisamikos

</td>  
<td>

4

</td>  
<td>

4

</td>  
<td>

8

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

Other greek dance

</td>  
<td>

0

</td>  
<td>

13

</td>  
<td>

13

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

Papadia

</td>  
<td>

13

</td>  
<td>

13

</td>  
<td>

26

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

Patima

</td>  
<td>

6

</td>  
<td>

6

</td>  
<td>

12

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

Patinada

</th>  
<th>

3

</th>  
<th>

3

</th>  
<th>

6

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

Patrouninos

</td>  
<td>

0

</td>  
<td>

4

</td>  
<td>

4

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

Pentozali

</td>  
<td>

2

</td>  
<td>

2

</td>  
<td>

4

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

Pidiktos

</td>  
<td>

7

</td>  
<td>

7

</td>  
<td>

14

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

Pousnitsa

</td>  
<td>

1

</td>  
<td>

1

</td>  
<td>

2

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

Proskynitos

</td>  
<td>

0

</td>  
<td>

6

</td>  
<td>

6

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

Pyrgousikos

</td>  
<td>

1

</td>  
<td>

1

</td>  
<td>

2

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

Raiko

</td>  
<td>

1

</td>  
<td>

9

</td>  
<td>

10

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

Sera

</td>  
<td>

6

</td>  
<td>

10

</td>  
<td>

16

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

Seranitsa

</td>  
<td>

4

</td>  
<td>

4

</td>  
<td>

8

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

Sfarlys

</td>  
<td>

1

</td>  
<td>

1

</td>  
<td>

2

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

Sousta

</td>  
<td>

4

</td>  
<td>

4

</td>  
<td>

8

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

Sta Dio

</td>  
<td>

1

</td>  
<td>

2

</td>  
<td>

3

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

Sta Tria

</td>  
<td>

2

</td>  
<td>

3

</td>  
<td>

5

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

Streis

</td>  
<td>

6

</td>  
<td>

6

</td>  
<td>

12

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

Sygathistos

</td>  
<td>

8

</td>  
<td>

8

</td>  
<td>

16

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

Syrtos

</td>  
<td>

1

</td>  
<td>

1

</td>  
<td>

2

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

TikPal

</td>  
<td>

3

</td>  
<td>

3

</td>  
<td>

6

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

TikTrom

</td>  
<td>

1

</td>  
<td>

1

</td>  
<td>

2

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

Trigona

</td>  
<td>

6

</td>  
<td>

3

</td>  
<td>

9

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

Tritepati

</td>  
<td>

0

</td>  
<td>

4

</td>  
<td>

4

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

Tsamiko

</td>  
<td>

13

</td>  
<td>

13

</td>  
<td>

26

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

Vagelitsa

</td>  
<td>

1

</td>  
<td>

1

</td>  
<td>

2

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

Vlaha Naxos

</td>  
<td>

3

</td>  
<td>

3

</td>  
<td>

6

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

Zagorisio

</td>  
<td>

5

</td>  
<td>

14

</td>  
<td>

19

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

Zervodexos

</td>  
<td>

0

</td>  
<td>

5

</td>  
<td>

5

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

Zervos

</td>  
<td>

2

</td>  
<td>

2

</td>  
<td>

4

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

Zonaradikos

</td>  
<td>

6

</td>  
<td>

6

</td>  
<td>

12

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

Zorbas

</td>  
<td>

1

</td>  
<td>

1

</td>  
<td>

2

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

**Total**

</td>  
<td>

**189**

</td>  
<td>

**302**

</td>  
<td>

**491**

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