#  Executive Summary

This deliverable is the third version of the Data Management Plan (DMP) of the
RAMCIP project, in accordance with the regulations of the Pilot action on Open
Access to Research Data of the Horizon 2020 programme (H2020). It contains the
final information about the data the project has generated along with details
on how it will be exploited or made accessible for verification and re-use,
and how they are curated and preserved.

To develop the first version of the deliverable (D9.3. RAMCIP Data Management
Plan – v1), a “data identification form” template was first drafted on the
basis of the H2020 guidelines for the development of projects’ Data Management
Plans. This was circulated to all project partners so as to collect all
relevant information concerning the datasets that are planned to be developed
in the course of the project. On the basis of all partners’ feedback, the
preliminary data management plan of the project was established during the
first project year. During the second project year, the preliminary DMP of the
project, as had been reported in the deliverable D9.3, has been iterated among
all project partners and was revised, in order to better depict the
Consortium’s plans following the project developments achieved so far.

As shown from the description of the project datasets provided herein, the
project at its present stage has already developed a series of datasets,
related to issues ranging from user requirements analysis, through to
evaluation of the algorithms and methods that enable the target skills of the
RAMCIP robot. Specifically, datasets have been collected towards developing
and evaluating the RAMCIP robot’s object recognition algorithms, home
environment modelling and monitoring ones, as well as its human activity,
behaviour and skills modelling and monitoring methods. Moreover, given the
focus of the project on advanced, dexterous manipulations inside the user’s
home environment, datasets are being established concerning the modelling of
objects and appliances that should be handled by the foreseen robot through
its manipulations, as well as ones related to simulating the robot’s
manipulator kinematics.

The datasets that have been collected in RAMCIP helped the development and
improvement of the skills of the RAMCIP robot, while they can also serve as
for

e.g. benchmarking datasets to the scientific community of the RAMCIP-related
research fields, once made public. Nevertheless, as some of the project’s
datasets involve data collection from human participants, the respective data
collection experiments, as well as the data analysis procedures that will be
employed should be carefully handled, under thorough consideration of ethical
and privacy issues involved in such datasets. In this line, the present
deliverable, in parallel to the deliverable D2.4 “Ethics Protocol”, pays due
attention to ethical and privacy issues related not only to the above, but
also to whether the foreseen datasets can be made public. For all the
identified RAMCIP datasets, specific parts that can be made publicly available
have been identified.

The public datasets of the RAMCIP project became available through a common
repository that has been formulated on the basis of the RAMCIP “data
management portal”; this is a dedicated space of the RAMCIP project website,
which can aggregate descriptions of all project public datasets, and provide
links to respective dataset download sections to the interested public, as
well as centralized data management functionalities to project partners. The
data management portal of the RAMCIP project has been developed and made fully
operational during the second project year, as reported in the present
deliverable.

The present deliverable has been formulated at the end of the project’s
lifespan following the H2020 guidelines on Data Management Plans depicting
which of the datasets have been made publicly available and under which Data
Management framework.

#  1\. Introduction

The purpose of the Data Management Plan (DMP) deliverable is to provide
relevant information concerning the data that that have been collected and
used by the partners of the project RAMCIP. These datasets were required for
the development and evaluation of the methods that are have researched,
developed and used to address the particular research problems of the project.

RAMCIP aims at developing a domestic service robot capable of providing
proactive and discreet assistance to elderly people with MCI and at early AD
stages in their everyday life at home. Such kind of robot should develop
highlevel cognitive functions, advanced communication and manipulation skills
in order to interact with the patients as well as with its environment. The
process of training the robot to achieve such advanced skills require
capturing a variety of datasets regarding for instance large and small scale
object detection, localization and human tracking, while it is of equal
importance to simulate the robot kinematics and the patients’ behaviour to
capture synthetic data, instead of relying exclusively on real patients such
as the ones of the primary RAMCIP end user groups.

In this scope, this deliverable extensively describes the RAMCIP consortium
plans for each dataset collected throughout the project’s duration. It
provides final information about the origin and nature of each dataset
acquired during the RAMCIP lifespan, its standards, any similar datasets and
corresponding publications, data access and preservation policies.

RAMCIP participates to the Pilot action on Open Access to Research Data which
is part of the Horizon 2020 program. Our goal is to provide where possible,
accurate and high-quality data to the research community so that the project
will contribute to future advancements in the field of assistive robotics.
However, since data may contain personal information about human participants,
a focus is also given to possible ethical issues and access restrictions
regarding personal data so that no regulations on sensitive information are
violated.

The DMP, is considered fixed and compared to the previous two versions all the
foreseen datasets have been recorded and sufficiently uploaded to the Data
Management Portal. Hardly any deviations took place from the initial foreseen
plan all justifiable by the needs aroused in due course of the project. This
third version of the RAMCIP DMP summarizes the direction of the project
regarding the collection of the data, as well as the project progress made so
far to this end.

The overall plan of RAMCIP related to data management along the project
duration is as follows:

  * M6: Preliminary analysis and production of the first version of the Data Management Plan (D9.3). 

  

  * M16: Writing of the specifications for the project’s data management portal, where information over the project’s datasets and links to download locations shall be provided where applicable (e.g. where a publicly available version of a dataset exists). 

  

  * M17-M19: Development of the data management portal (to be carried out by CERTH), as a dedicated part of the RAMCIP website. 

  

  * M20: The data management portal is operational, as described in the present deliverable. 

  

  * M24: Second version of the Data Management Plan, describing actual, proven procedures implemented by the project during its data collection efforts, and preparing the sustainability of the data storage after the end of the project (updated Data Management Plan and developed Data Management Portal, as described in the present document – D9.8). 

  

  * M42: Final Data Management Plan, reflecting on the lessons learnt through the project, and describing the plans implemented by RAMCIP for sustainable storage and accessibility of the data (D9.9). 

  

In the Section 3 of the deliverable, we provide a list of the established
datasets during the project is provided, including detailed descriptions on
the aforementioned specifications.

##  1.1 Deliverable structure

In the rest of the deliverable, Chapter 2 first summarizes key general
principles that are involved in the Data Management Plan of the RAMCIP
project, such as ones related to data security and personal data protection,
whereas it also provides a description of the project’s plans toward the
development of the data management portal.

Chapter 3 serves as the core chapter of the present deliverable, as it
describes, in detail level the datasets that have been acquired during the
RAMCIP project and their current status.

Chapter 4 describes the Data Management Portal that has been developed during
the second project year, maintained and expanded during the third year,
following the initial DMP specifications.

Finally, Chapters 5 and 6 provide a discussion on this 3  rd  version of the
RAMCIP Data Management Plan and draw the conclusions of the present
deliverable.

#  2\. General Principles

##  2.1 Participation in the Pilot on Open Research Data

RAMCIP participates in the Pilot on Open Research Data launched by the
European Commission along with the Horizon2020 programme. The consortium
believes firmly in the concepts of open science, and the large potential
benefits the European innovation and economy can draw from allowing reusing
data at a larger scale. Therefore, all data produced by the project may be
published with open access – though this objective will obviously need to be
balanced with the other principles described below.

##  2.2 Security

The datasets that have been collected collected through RAMCIP are of high
value and may contain sensitive personal data. Special care should be taken to
prevent such datasets to leak or become hacked. This is another key aspect of
RAMCIP data management, and all data repositories used by the project will
include effective protection.

A holistic security approach will be followed, in order to protect the pillars
of information security (confidentiality, integrity, availability). The
security approach will consist of a methodical assessment of security risks
followed by their impact analysis. This analysis will be performed on the
personal information and data processed by the proposed system, their flows
and any risk associated to their processing.

Security measures will include the implementation of PAKE protocols, such as
the SRP protocol, and protection about bots such as captcha technologies.
Moreover, the pilot sites shall apply monitored and controlled procedures
related to the data collection, their integrity and protection. The data
protection and privacy of personal information will include protective
measures against infiltration as well as physical protection of core parts of
the systems and access control measures.

##  2.3 Personal Data Protection

RAMCIP activities will involve human participants for various human activity
and behaviour analysis –related data collection purposes. Therefore, it is
clear that in some cases personal data will have to be collected. Such data
will be protected in accordance with the EU's Data Protection Directive
95/46/EC “on the protection of individuals with regard to the processing of
personal data and on the free movement of such data”. Further information on
how personal data collection and handling should be approached in the RAMCIP
project are provided in the deliverable D2.4 “Ethics Protocol” of the project.

All personal data collection efforts of the project partners will be
established after giving subjects full details on the experiments to be
conducted, and obtaining from them a signed informed consent form, following
the respective guidelines set in the D2.4 deliverable.

##  2.4 The RAMCIP Data Management Portal

RAMCIP has developed a data management portal as part of its website. This
portal will provide to the public, for each dataset that will become publicly
available, a description of the dataset along with a link to a download
section. The portal will be updated each time a new dataset has been collected
and is ready of public distribution. The portal will however not contain any
datasets that should not become publicly available.

The initial version of the portal has become available during the 2  nd  year
of the project, in parallel to the establishment of the first versions of
project datasets that has been made publicly available. The RAMCIP data
management portal enables project partners to manage and distribute their
public datasets through a common infrastructure.

#  3\. Description of the established RAMCIP datasets

In this chapter detailed information about the datasets that have been
captured by the partners of the RAMCIP project are provided. In order to meet
the requirements of the DMP according to the Pilot of Open Access of the
Horizon 2020, each partner provided the description of their datasets using
the template given in Annex I, which was formed by following the EC guidelines
of the dataset aspects that should be reported in DMPs of the H2020 projects
1  .

In the present, third version of the RAMCIP Data Management Plan, all partners
have revisited the initial descriptions of their datasets (as provided in
D9.3) and have made any necessary changes so as to reflect the current status
of their DMPs, so as the uploaded data to precisely coincide with their
description herein.

**Datasets Naming Conventions**

Concerning the convention followed for naming the RAMCIP datasets, it should
be noted that the name of each dataset comprises: (a) a prefix 'DS' indicating
a dataset, along with its unique identification number, e.g. “DS1”, (b) the
name(s) of the partner(s) responsible to collect it, e.g. CERTH, along with an
identifier denoting the internal numbering of the dataset concerning the
specific partner,

e.g. -01, and (c) a short title of the dataset summarizing its content and
purpose,

e.g. Object Recognition Dataset.

**Summary of the RAMCIP datasets**

Within the RAMCIP project period all the foreseen datasets have been collected
and uploaded to the Data Management Portal covering a series of research
dimensions on the skills the RAMCIP robot had developed. A comprehensive
description of the uploaded datasets is also provided within the portal.

A summary of the developed datasets and those made publicly available through
the RAMCIP Data Management Portal is provided in the table below. All expected
outcomes have been established and the anticipated public parts of the RAMCIP
datasets have been uploaded at the RAMCIP Data Management Portal, according to
the updated data management plan presented in the second version of the
project’s DMP deliverable (D9.8; RAMCIP Data Management Plan – v2, M24).

####  Table 1\. Summary of datasets planned to be collected during the course
of the RAMCIP project and current status  
  
<table>  
<tr>  
<th>

**No**

</th>  
<th>

**Name**

</th>  
<th>

**Description**

</th>  
<th>

**Summary**

</th>  
<th>

**Current Status**

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

**DS1**

</td>  
<td>

DS1.CERTH

-01. Object 

Recognition

Dataset

</td>  
<td>

A large scale dataset of images and associated annotations will be collected
aiming at benchmarking object recognition and grasping algorithms in a
domestic environment.

</td>  
<td>

Object 3D models and test cases have been made publicly available through the

RAMCIP Data Management Portal (DMPo).

</td>  
<td>

**Uploaded Final**

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

**DS2**

</th>  
<th>

DS2.CERTH

-02. 

Domestic

Space

Modeling

Dataset

</th>  
<th>

A collection of RGB-D data with great spatial coherence using the Kinect2
sensor of multiple places concerning indoor scenarios both for large and small
scale circumstances.

</th>  
<th>

Data regarding metric mapping along with hierarchical semantic information for
the objects/ robot parking positions is publicly available through the

RAMCIP DMPo

</th>  
<th>

**Uploaded Final**

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

**DS3**

</td>  
<td>

DS3.ACCRE

A-01.

Interactive Environmen

tal

Component s Dataset

</td>  
<td>

A collection of CAD data containing models of

usable/interactive elements of RAMCIP user's surroundings, like light
switches, water taps, cooker knobs, door handles etc.

</td>  
<td>

Data with models of house elements from various environments along with full
house models are publicly available through the RAMCIP DMPo.

</td>  
<td>

**Uploaded Final**

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

**DS4**

</td>  
<td>

DS4.CERTH

-03. 

Human

Tracking

Dataset

</td>  
<td>

Dataset for human identification, pose and gestures tracking, facial

expressions

monitoring and activity tracking along obtained with

Kinect2 sensor mounted on a mobile robotic base (e.g.

Turtlebot).

</td>  
<td>

Dataset containing human skeleton tracking, with low level actions and high
level activities through data collection experiments at the premises of CERTH
and LUM is publicly available through the RAMCIP DMPo.

</td>  
<td>

**Uploaded Final**

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

**DS5**

</td>  
<td>

DS5.SSSA01. Human

Motion for

Fine

Biomechani

cal Analysis

Dataset

</td>  
<td>

Dataset for the training and evaluation of the Fine-grained Body Motion
Tracking Task by SSSA.

</td>  
<td>

Data collected through experiments at the premises of LUM from technical
partners of SSSA are publicly available through the RAMCIP DMPo.

</td>  
<td>

**Uploaded Final**

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

**DS6**

</td>  
<td>

DS6.SSSA02. Human Walking Dataset

</td>  
<td>

Dataset for characterizing the walking behaviour of subjects and
identification of changes in the motion patterns, based on RGB-D cameras.

</td>  
<td>

Data collected through experiments at the premises of LUM from technical
partners of SSSA are publicly available through the RAMCIP DMPo.

</td>  
<td>

**Uploaded Final**

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

**DS8**

</td>  
<td>

DS8.CERTH

.04. Virtual

User

Models

Dataset

</td>  
<td>

Virtual User Models (VUMs) of robot users (e.g. MCI patients), encoding their
cognitive and motor skills, behavioral aspects,

</td>  
<td>

VUMs dataset has been developed from users participated in RAMCIP preliminary
trials including both healthy and MCI users and are publicly

</td>  
<td>

**Uploaded Final**

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

</td>  
<td>

</td>  
<td>

as well as human-

robot interaction and communication preferences.

</td>  
<td>

available through the RAMCIP DMPo.

</td>  
<td>

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

**DS9**

</td>  
<td>

DS9.TUM.0 1. Lowerbody kinematic Dataset

</td>  
<td>

Dataset consisting of kinematics of lowerbody interaction by pairs of human
participants.

</td>  
<td>

Data collected at

laboratory

environment with ground truth measurements are publicly available through the
RAMCIP DMPo.

</td>  
<td>

**Uploaded Final**

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

**DS10**

</td>  
<td>

DS10.ACCR

EA.02 Manipulator kinematics chains Dataset

</td>  
<td>

A set of

Simulink/CAD/Gazeb

o models for

simulation,

optimization and

development purposes.

</td>  
<td>

The second version of the robot’s urdf model has been developed and is
publicly available through the RAMCIP DMPo.

</td>  
<td>

**Uploaded Final**

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

**DS11**

</td>  
<td>

DS11.LUM_

ACE.01

User

Requireme nts Dataset

</td>  
<td>

Dataset with the pictures and videos taken during the workshops with
stakeholders in

Lublin and Barcelona as well as

anonymized questionnaires, which were filled in by the different stakeholders
groups.

</td>  
<td>

Dataset established, used for user requirements analysis. Analysis results
made publicly available in the RAMCIP deliverable 2.1, which has been uploaded
at the RAMCIP DMPo.

</td>  
<td>

**Described in the**

**deliverabl e D2.1**

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

**DS12**

</td>  
<td>

DS12.CERT

H-01 3D

Force

Slippage PB

Dataset

</td>  
<td>

Dataset including 3D Force measurements from Optoforce 3axis Sensors during
experiments where slippage occurred including several surfaces.

</td>  
<td>

Data collected with optoforces from

SHADOW that involve multiple experimental setups are publicly available
through the RAMCIP DMPo.

</td>  
<td>

**Uploaded Final**

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

In the following sections detailed description of each dataset in accordance
to the H2020 DMP template is provided.

##  3.1 Dataset “DS1.CERTH-01.ObjectRecognitionDataset”

####  General Description

A large scale dataset of images and associated annotations will be collected
aiming at benchmarking object recognition and grasping algorithms in a
domestic environment.  
  
<table>  
<tr>  
<th>

**1.**

</th>  
<th>

**Data set reference and name**

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

**DS1.CERTH-01. Object Recognition Dataset**

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

**2.**

</td>  
<td>

**Data set description**

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

_**Origin of Data** _

The dataset includes a collection of RGB-D images of household objects
captured from various viewpoints using a Kinect1 and/or Kinect2 sensor. Small
sized objects have been captured by placing them on a turntable. Fiducial
markers have been used to obtain an accurate estimation of the camera pose for
each view point. Larger objects have been captured by moving the sensor around
the object and capturing as much views as possible. In cases where a large
object contains articulations, detailed models of all the articulated parts
are provided, accompanied with the corresponding annotated information
regarding joint types (revolute, prismatic), joint frame (position and
orientation), and joint limits.

_**Nature and scale of data** _

The data consist of 3D models of objects that have been created either with
CAD software, 3D scanner or by merging RGB-D point clouds as well as test
images depicting realistic scenarios for evaluation.

_Data Format:_ Training: PLY, OBJ for 3D models, Testing: PNG, JPG for images,
TXT for annotations

_**To whom could the dataset be useful** _

The dataset is valuable for benchmarking algorithms for object recognition,
robotics navigation and grasping.

_**Related scientific publication(s)** _

The dataset accompanied the research results in the field of object
recognition and grasping.

_**Indicative existing similar data sets** _

There are several public datasets containing RGB-D images of objects aimed at
object recognition.

The UW dataset (  _http://www.cs.washington.edu/rgbd-dataset/_ )

The Berkley's B3DO dataset (  _http://kinectdata.com/_ )

The Berkley's BigBird dataset (  _http://rll.berkeley.edu/bigbird/_ )

The Berkley’s YCB dataset (  _http://rll.eecs.berkeley.edu/ycb/_ )

_Part of our dataset will be considered for integration in the B3DO dataset
that is designed to be extensible._

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

**3.**

</th>  
<th>

**Standards and metadata**

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

Indicative metadata include a) foreground-background masks for training
images, b) camera calibration information, c) camera pose matrix for each
viewpoint, d) object identifier and description category label, e) 3D object
model in CAD format. The metadata are in a format that can be easily parsed
with open source software.

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

**4.**

</td>  
<td>

**Data sharing**

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

_**Access type** _ Widely open.

_**Access Procedures** _

A web page has been created on the RAMCIP data management portal (hosted at
the RAMCIP web site) that provides a description of the dataset and links to a
download section.

_**Embargo periods** _

Some datasets will be available only after the corresponding paper is accepted
and published.

_**Technical mechanisms for dissemination** _

A link to the dataset from the RAMCIP web site (RAMCIP data management
portal). The link is provided in all relevant RAMCIP publications. A technical
publication describing the dataset and acquisition procedure has been
published.

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

The dataset is designed to allow easy reuse with commonly available tools and
software libraries.

_**Repository where data will be stored** _

The dataset is accommodated at the data management portal of the project
website.

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

**5.**

</td>  
<td>

**Archiving and preservation**

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

_**Data preservation period** _

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

_**Approximated end volume of data** _ The dataset is approximately 150MB.

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

Probably a dedicated hard disk drive will be allocated for the dataset. No
costs are currently foreseen regarding its preservation.

_**Indicative plan for covering the above costs** _ Small, one-time costs
covered by RAMCIP.

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

**6.**

</td>  
<td>

**Partners activities and responsibilities**

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

_**Partner Owner / Data Collector** _

CERTH

_**Partner in charge of the data analysis** _

CERTH

_**Partner in charge of the data storage** _

CERTH

_**WPs and Tasks** _

The data have been collected within activities of WP3 in Task 3.1 and have
been mainly be used for analysis in the scope of WP3, WP5 and WP6 tasks

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

##  3.2 Dataset “DS2.CERTH02.DomesticSpaceModellingDataset”

####  General Description

The space modelling dataset comprises the collection of RGB-D data with great
spatial coherence using the Kinect2 ToF (Time of Flight) sensor. Multiple
places have been recorded concerning indoor scenarios both for large and small
scale circumstances. The collected dataset contain fully registered color
(RGB) images with their respective depth maps. The collection area concerns
domestic real home or home-like environment.

_http://vision.in.tum.de/data/datasets/rgbd-dataset_
_http://robotics.pme.duth.gr/kostavelis/Dataset.html_  
  
<table>  
<tr>  
<th>

**1.**

</th>  
<th>

**Data set reference and name**

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

**DS2.CERTH-02. Domestic Space Modeling Dataset**

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

**2.**

</td>  
<td>

**Data set description**

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

_**Origin of Data** _

During the acquisition procedure the sensor motion was as smooth as possible,
which combined with the high frame rate of the sensor ensuring great overlap
among the captured scenes. Therefore, the collected data are suitable for
mapping and navigation experimentation.

_**Nature and scale of data** _

The Domestic-Space-Modelling dataset is split into two parts:

In PART I the recordings in static environment have been obtained, providing
thus the required data to assess the developed solutions (mapping, navigation)
in their basis.

In PART II the recording has been carried out in a dynamic environment
including also human activity. Thus the acquired data have been used for the
assessment of the developed algorithms in their higher level (map recalling,
planning, and replanning) and their real performance in human inhabited
environments.

Moreover, the acquired dataset is accompanied with accurate ground-truth
measurements (of the robot location and pose, as well as of the modelled
space) for the evaluation of the mapping and localization algorithms. _Data
Format:_ PNG, JPG image format, PCD format for 3D models

_**To whom could the dataset be useful** _

The dataset is useful for the benchmarking of mapping and robotic navigation
solutions.

_**Related scientific publication(s)** _

The results of the developed algorithms along with the Domestic-Space-
Modelling dataset have been disseminated in International Conferences and
Journals of the robotics field.

_**Indicative existing similar datasets** _

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

Similar datasets have already been collected in the past such as:

( _http://vision.in.tum.de/data/datasets/rgbd-dataset_ )  provided by the
Technische Universität München.

( _http://robotics.pme.duth.gr/kostavelis/Dataset.html_ )  provided by
Laboratory of Robotics and Automation, DUTH.

Contrary to the aforementioned cases where the data have been collected with
the RGB-D sensor Kinect1, our Domestic-Space-Modelling dataset will be
captured with a Kinect2 sensor which is more accurate and retains greater
resolution.

Since the publicly available datasets are recorded with a Kinect1 sensor, a
direct integration with the Domestic-Space-Modelling dataset is problematic
mainly due to the fact that a) the data are collected in different
environments and b) the resolution is different between the acquired data.

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

**3.**

</td>  
<td>

**Standards and metadata**

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

The Domestic-Space-Modelling is accompanied by accurate ground-truth data
ensuring the validity of the developed algorithms as well as their reuse in
future research activities.

The metadata that have been produced are summarized as follows:

  * The point clouds (textured/pseudo-colored) of each instance transformed in real world coordinates (x, y, z). 

  

  * The produced 3D/2D map as a result of the developing procedure within the RAMCIP project, providing a benchmark 

  

  * The consecutive Visual Odometry (VO) transformations reproducing the trajectory of the robot, also for benchmarking. 

  

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

**4.**

</td>  
<td>

**Data sharing**

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

_**Access type** _

Only portions of the PART I of the dataset that contain a static environment
are publicly released. These portions concern data collected from the LUM
apartment, simulating a home-like environment, without human presence.

For the rest parts of this dataset, including e.g. data collected from real
apartments and those dynamically updated through human activities, only
private access is given. The access is granted to RAMCIP partners whose
research and development activities have a direct dependency (e.g. map
recalling, planning and re-planning), on the basis that a respective informed
consent has been taken from the human subjects participated in the data
collection.

The latter parts of the dataset, including models of real apartments and in
some cases dynamically updated through human activity, cannot become publicly
available. Regardless of the informed consent for publication, such data could
lead to a recognition of participant’s identity and details of his/her home
environment. Thus, it raises significant privacy and ethical concerns and
publication of such a dataset should be prevented, as further explained in the
project’s ethics protocol (deliverable D2.4).

On the contrary, as the LUM home-like environment concerns a public space, the
respective home environment modelling and monitoring dataset, without human
presence, would not be subject to such privacy and ethical issues.

The dataset is accompanied with a specific technical report describing the
calibration, the acquisition procedure as well as technical details of the
architecture of the robot.

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

_**Access Procedures** _

For the public part of this dataset a web page has been created on the RAMCIP
data management portal (hosted at the RAMCIP web site) that provides a
description of the dataset and links to a download section.

The private part of this dataset is stored at a specifically designated
private space of CERTH, in dedicated hard disk drives, on which only members
of the CERTH research team whose work directly relates to these data will have
access. For the other RAMCIP partners to obtain access to these data, they
should provide a formal request to the CERTH’s primarily responsible for the
data storage, including a justification over the need to have access to these
data. Once deemed necessary, CERTH will provide the respective data portions
to the partner.

_**Embargo periods (if any)** _

None

_**Technical mechanisms for dissemination** _

A link to the public part of this dataset from the RAMCIP web site (data
management portal). The link has been provided in all relevant RAMCIP
publications. A technical publication describing the dataset and acquisition
procedure has been published.

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

The dataset is designed to allow easy reuse with commonly available tools and
software libraries.

_**Repository where data will be stored** _

The public part of this dataset is accommodated at the data management portal
of RAMCIP.

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

**5.**

</td>  
<td>

**Archiving and preservation**

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

_**Data preservation period** _

The public part of the dataset is 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 CERTH at least until the
end of the project.

_**Approximated end volume of data** _

The dataset is approximately 5 Gigabytes.

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

Two dedicated hard disk drives will be allocated for the dataset; one
dedicated to the public part and one to the private. No costs are currently
foreseen regarding its preservation.

_**Indicative plan for covering the above costs** _ Small one-time costs
covered by RAMCIP.

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

**6.**

</td>  
<td>

**Partners activities and responsibilities**

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

_**Partner Owner / Data Collector** _

CERTH

_**Partner in charge of the data analysis** _

CERTH

_**Partner in charge of the data storage** _

#####  CERTH

_**WPs and Tasks** _

The have been collected within activities of WP3 in Task 3.1 and used in the
research efforts of same task, as well as in the context of WP5 activities.

##  3.3 Dataset “DS3.ACCREA01.InteractEnvComponentsDataset”

####  General Description

Interactive environmental components dataset comprises the collection of CAD
data. The prepared dataset contains models of usable/interactive elements of
RAMCIP user's surroundings, such as light switches, water taps, cooker knobs,
door handles etc.  
  
<table>  
<tr>  
<th>

**1.**

</th>  
<th>

**Data set reference and name**

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

**DS3.ACCREA-01. Interactive Environmental Components Dataset**

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

**2.**

</td>  
<td>

**Data set description**

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

_**Origin of Data** _

CAD models have been created using a 3D-scanning device and regular
calliper/ruler methods.

_**Nature and scale of data** _

Dataset is available in a form of SolidWorks library files package. _Data
Format:_ SolidWorks format

_**To whom could it be useful** _

The collected data have been used for simulations and development of the
RAMCIP manipulator, mobile platform, elevation mechanism and dexterous hand
kinematic chains. Models can be imported into the Gazebo environment
simulation, which have been used for testing components and system integration
by most of technical RAMCIP partners.

_**Related scientific publication(s)** _

N/A

_**Indicative existing similar data sets** _

Several websites provide free bases of everyday objects, although not all of
them are applicable for RAMCIP uses because of their artistic purposes instead
of mechanical/simulation ones.

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

**3.**

</td>  
<td>

**Standards and metadata**

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

The metadata that have been produced are be summarized as follows:

  * the parsing routines used to read and absorb the data for developing purposes, 

  

  * the 3D CAD maps of selected user environments with modelled objects placed on specified world coordinates 

  

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

**4.**

</td>  
<td>

**Data sharing**

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

_**Access type** _

A part of the dataset is publicly available. The public part is accessible
through the

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

data management portal of the RAMCIP project. The dataset is accompanied with
photographs and datasheets of chosen, more complex models.

_**Access Procedures** _

For the public part of this dataset a web page has been created on the RAMCIP
data management portal (hosted at the RAMCIP web site) that provides a
description of the dataset and links to a download section.

The private part of this dataset is stored at a specifically designated
private space of ACCREA and CERTH, in dedicated hard disk drives, on which
only members of the ACCREA/CERTH research team whose work directly relates to
these data will have access. For the other RAMCIP partners to obtain access to
these data, they should provide a formal request to the ACCREA’s primarily
responsible for the data storage, including a justification over the need to
have access to these data. Once deemed necessary, ACCREA will provide the
respective data portions to the partner.

_**Embargo periods** _

None

_**Technical mechanisms for dissemination** _

A link to the dataset from the Data management portal. The link is provided in
all relevant RAMCIP publications.

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

The dataset is designed to allow easy reuse with commonly available tools and
software libraries.

_**Repository where data will be stored** _

The public part of this dataset is accommodated at the data management portal
of the project website.

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

**5.**

</td>  
<td>

**Archiving and preservation**

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

_**Data preservation period** _

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

_**Approximated end volume of data** _

The data are expected to be several hundred of Megabytes.

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

A dedicated hard disk drive has been allocated for the dataset. No costs are
currently foreseen regarding its preservation.

_**Indicative plan for covering the above costs** _

The cost will have been covered by the local hosting institute in the context
of RAMCIP.

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

**6.**

</td>  
<td>

**Partners activities and responsibilities**

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

_**Partner Owner / Data Collector** _

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

ACCREA and CERTH

_**Partner in charge of the data analysis** _

ACCREA, TUM, CERTH, SHADOW

_**Partner in charge of the data storage** _

#####  ACCREA, CERTH

_**WPs and Tasks** _

The data have been collected within activities of WP5 in Task 5.1 and 5.4, to
serve the respective project tasks’ research efforts.

##  3.4 Dataset “DS4.CERTH-03.HumanTrackingDataset”

####  General Description

Dataset for human identification, pose and gestures tracking experiments,
along with high-level activities monitoring (e.g. Activities of Daily Living –
ADLs, such as cooking or eating), obtained with Kinect2 or Kinect1 or ASUS
Xtion sensor mounted on a mobile robotic base (e.g. Turtlebot).

The dataset includes facial expressions monitoring and activity tracking
during different affective states, to be used for WP4 affect-related analyses.  
  
<table>  
<tr>  
<th>

**1.**

</th>  
<th>

**Data set reference and name**

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

**DS4.CERTH-03. Human Tracking Dataset**

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

**2.**

</td>  
<td>

**Data set description**

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

_**Origin of Data** _

The dataset has been collected using a Kinect2 sensor mounted on a mobile
robotic base (e.g. Turtlebot robotic platform). During the acquisition
procedure the robot motion was as smooth as possible.

_**Nature and scale of data** _

The collection experiment has been carried out in two phases, that split the
dataset into three parts:

In PART I the recording focus on monitoring of low-level human activities,
such as pose, gestures and actions.

In PART II the recording deals with monitoring of high-level domestic
activities, such as cooking and eating.

In PART III the recording focus on facial expressions, biosignals and activity
monitoring during different affective states of the user.

_Data Format:_ PNG, JPG for images, XML or TXT for annotations

_**To whom could the dataset be useful** _

The collected data have been used for the development and evaluation of the
human activity monitoring and the affect recognition methods of the RAMCIP
project. The different parts of the dataset are useful in the benchmarking of
a series of human tracking methods, focusing either on human identification,
on pose and gesture analysis and tracking, on high-level activity recognition
and on affect-related human activity analysis.

_**Related scientific publication(s)** _

The dataset accompanies our research results in the field of human activity
monitoring and affect recognition.

_**Indicative existing similar datasets** _

HumanEva: Synchronized Video and Motion Capture Dataset and Baseline Algorithm
for Evaluation of Articulated Human Motion, IJCV 2010.

Cornell Activity Datasets: CAD-60 & CAD-120

(http://pr.cs.cornell.edu/humanactivities/data.php)

It should be noted that although several RGB-D datasets dealing with human

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

activity analysis are publicly available at present (e.g. the
MSRDailyActivity3D dataset - http://research.microsoft.com/en-
us/um/people/zliu/actionrecorsrc), to the best of our knowledge, no domestic
human activity tracking datasets, focusing on low-level actions, high-level
activities and affect, recorded through the Kinect2 sensor mounted on a mobile
robot base currently exist.

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

**3.**

</td>  
<td>

**Standards and metadata**

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

The dataset is 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) documentation of the
variables recorded in the dataset and (c) annotated pose, action, activity and
affective state of the monitored person per time interval.

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

**4.**

</td>  
<td>

**Data sharing**

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

_**Access type** _

Due to ethical reasons, only the data captured in the LUM premises by normal
healthy control subjects are publicly available, while the rest of them are
private to serve the RAMCIP R&D objectives.

Overall, the data that became publicly available will respect the principle of
anonymity. Therefore, in principle, data that can expose the identity of
participants, including RGB recordings of subjects, have been excluded from
publication. The inclusion of RGB data that could expose the identity of
normal healthy control subjects in the public part of this dataset has been
further investigated in the third project year and if done, it has been on the
basis of appropriate informed consent to data publication (see deliverable
D2.4).

_**Access Procedures** _

For the portions of the dataset that are publicly available, a respective web
page has been created on the data management portal (hosted at the RAMCIP web
site) that provides a description of the dataset and links to a download
section.

The private part of this dataset is stored at a specifically designated
private space of CERTH, in dedicated hard disk drives, on which only members
of the CERTH research team whose work directly relates to these data have
access. For further RAMCIP partners to obtain access to these data, they
should provide a proper request to the CERTH primarily responsible, including
a justification over the need to have access to these data. Once deemed
necessary, CERTH will provide the respective data portions to the partner.

_**Embargo periods** _

None

_**Technical mechanisms for dissemination** _

For the public part of the dataset, a link to has been provided from the Data
management portal. The link is provided in all relevant RAMCIP publications. A
technical publication describing the dataset and acquisition procedure has
been published.

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

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** _

The public part of this dataset will be accommodated at the data management
portal of the project website.

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

**5.**

</td>  
<td>

**Archiving and preservation**

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

_**Data preservation period** _

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 CERTH at least until the
end of the project.

_**Approximated end volume of data** _

The dataset is approximately 500 Mbs.

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

Two dedicated hard disk drives have been allocated for the dataset; one for
the public part and one for the private. There are no costs associated with
its preservation.

_**Indicative plan for covering the above costs** _ Small one-time costs
covered by RAMCIP.

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

**6.**

</td>  
<td>

**Partners activities and responsibilities**

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

_**Partner Owner / Data Collector** _

CERTH

_**Partner in charge of the data analysis** _

CERTH, SSSA, LUM

_**Partner in charge of the data storage** _

CERTH

_**WPs and Tasks** _

The data have been collected within activities of WP3 and WP4, to mainly serve
the research efforts of T3.2, T3.4 and T4.2.

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

##  3.5 Dataset “DS5.SSSA-01.HumanMotionFineDataset”

####  General Description

This dataset has beencreated for the purpose of characterizing the walking
behavior of MCI subjects based on RGB-D cameras (T3.3) in the activities not
covered by other datasets from RAMCIP.  
  
<table>  
<tr>  
<th>

**1.**

</th>  
<th>

**Data set reference and name**

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

**DS5.SSSA-01. Human Motion for Fine Biomechanical Analysis Dataset**

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

**2.**

</td>  
<td>

**Data set description**

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

_**Origin of Data** _

The dataset has been collected by asking people to perform up to 2 minutes of
normal walking motion, then to perform the same motion after a fatiguing
protocol. The trials have been recorded with Kinect1 RGB-D camera placed on a
fixed structure with the same point of view of the robot at short range
distance, together with inertial sensors.

_**Nature and Scale of Data** _

The dataset conists of two sets of data: leg motions before and after the
onset of physical fatigue, captured for 20 people each suffering from mild-
cognitive impairment

The size of the dataset is on the order of 200-300 GB.

_Data Format:_ ROS bag files, XML or TXT for annotations.

_**To whom could the dataset be useful** _

The dataset is helpful for research because it combines a marker-less tracking
with in specific short ranges shots, with inertial sensors. The biomechanical
measures provided by the sensors provide a means to assess differences in
walking patterns due to the onset of physical fatigue in MCI subjects.

_**Related scientific publication(s)** _

A scientific publication has be created for analyzing data and proposing a new
mechanism to detect the onset of physical fatigue from gait patterns in MCI
subjects, using deep learning. .

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

_http://www.cbsr.ia.ac.cn/users/szheng/?page_id=71_
_http://www.cvc.uab.es/DGaitDB/Summary.html_ http://mocap.cs.cmu.edu/

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

**3.**

</td>  
<td>

**Standards and metadata**

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

The dataset is be accompanied with detailed documentation of its contents.

Indicative metadata include: (a) description of the experimental setup and

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

procedure that led to the generation of the dataset, (b) documentation of the
variables recorded in the dataset and (c) statistics for every participant
with experimental notes.

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

**4.**

</td>  
<td>

**Data sharing**

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

_**Access type** _

Data from MCIsubjects as well as from healthy people have been acquired, and
such data do not contain personal information. This allows the release of
data, after anonymization, to the public. The collection of data from these
subject required the adoption a consent form that will followed the guidelines
of deliverable D2.4 (Ethics Protocol).

_**Access Procedures** _

For the portions of the dataset that have been made publicly available, a
respective web page has been created on CERTH's RAMCIP portal that provides a
description of the dataset and links to a download section.

The private part of this dataset is stored at a specifically designated
private space of SSSA, in dedicated hard disk drives, on which only members of
the SSSA research team whose work directly relates to these data have access.
For further RAMCIP partners to obtain access to these data, they should
provide a proper request to the SSSA’s primarily responsible, including a
justification over the need to have access to these data. Once deemed
necessary, SSSA will provide the respective data portions to the partner.

_**Embargo periods** _

None

_**Technical mechanisms for dissemination** _

For the public part of the dataset, a link to this has benn provided from the
Data management portal. The link is also provided in all relevant RAMCIP
publications. A technical publication describing the dataset and acquisition
procedure has been published.

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

The data are published as ROS bag and in a form easily loadable by MATLAB. The
ROS solution is quite good for existing tools but it is not good on the long
term due to the complexity of the representation and the associated
dependencies.

_**Repository where data will be stored** _

The dataset has also been made available over a dedicated website under the
domain of SSSA. The data management portal provides links to the dataset’s
download section.

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

**5.**

</td>  
<td>

**Archiving and preservation**

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

**Data preservation period**

The data are also available on the PERCRO SSSA website with an expected
lifetime of 10 years given the history of PERCRO and the backup procedures of
SSSA. The digital signature of the whole dataset, or the storage of the
dataset in a git repository provides support for the correct duplication and
preservation.

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

_**Approximated end volume of data** _ 200-300 GBs.

_**Indicative associated costs for data archiving and preservation** _ None,
they are kept on SSSA server.

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

**6.**

</td>  
<td>

**Partners activities and responsibilities**

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

_**Partner Owner / Data Collector** _

SSSA

_**Partner in charge of the data analysis** _

SSSA, CERTH

_**Partner in charge of the data storage** _

SSSA

_**WPs and Tasks** _

The data have been collected within activities of WP3 in Task 3.3

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

##  3.6 Dataset “DS6.SSSA-02.WalkingSkillsDataset”

####  General Description

This dataset has been created for the purpose of characterizing the walking
behavior of healthy subjects based on RGB-D cameras. This characterization is
part of the motor based skill assessment of the subject for the identification
of changes in the motion patterns.  
  
<table>  
<tr>  
<th>

**1.**

</th>  
<th>

**Data set reference and name**

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

**DS6.SSSA-02. Human Walking Dataset**

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

**2.**

</td>  
<td>

**Data set description**

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

_**Origin of Data (e.g. indicative collection procedure, devices used etc.)**
_

Actors have been asked to perform two different walking behaviors: before and
after the onset of physical fatigue. : Physical fatigue has been induced with
up to 45 minutes of walking on a threadmill. This recording has been performed
with a Kinect 2 sensor, a Kinect 1 sensor, inertial sensors and Vicon Motion
capture setup as ground truth. The data have been labelled by the presence of
physical fatigue.

_**Nature and scale of data** _

For this dataset, 20 subject have been recorded for 2 minute for each type of
walking behavior.

The dataset is approximately 50 GB.

_Data Format: ROS bag files_ , XML or TXT for annotations

_**To whom could the dataset be useful** _

This dataset is very valuable for research due to the validation with Vicon
and the use of Kinect 2 and Kinect 1.

The collected data have been used for the development and evaluation of the
human tracking and walking assessment. The different parts of the dataset are
useful in understanding different walking behaviors in fatigued subjects.

_**Related scientific publication(s)** _

Such dataset is not existent from literature, and it will can be used for
characterizing walking patterns and low cost solutions to assess gait
behaviors.

_**Indicative existing similar datasets** _

Various activity datasets do exist, but none deals with variability in walking
patterns. In addition this dataset will provide Vicon measures together with
the Kinect2, Kinect 1 and inertial sensors.

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

**3.**

</td>  
<td>

**Standards and metadata**

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

The dataset is 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) documentation of the
variables recorded in the dataset and (c) statistics of the participants,
experimental notes and biometrics statistics of the monitored person per time

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

interval.

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

**4.**

</td>  
<td>

**Data sharing**

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

_**Access type** _

Only data from normal healthy control subjects have been acquired, and such
data do not contain personal information. This allows the release of data,
after anonymization, to the public. The collection of data from these subjects
required the adoption of a consent form that followed the guidelines of
deliverable D2.4 (Ethics Protocol).

_**Access Procedures** _

For the portions of the dataset that are publicly available, a respective web
page has been created on the data management portal that provides a
description of the dataset and links to a download section.

The private part of this dataset has been stored at a specifically designated
private space of SSSA, in dedicated hard disk drives, on which only members of
the SSSA research team whose work directly relates to these data have access.
For further RAMCIP partners to obtain access to these data, they should
provide a proper request to the SSSA’s primarily responsible, including a
justification over the need to have access to these data. Once deemed
necessary, SSSA will provide the respective data portions to the partner.

_**Embargo periods** _

None

_**Technical mechanisms for dissemination** _

Publishing and RAMCIP project advertising. Eventually robotics mailing list
advertising.

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

The data is published as ROS bag and in a form easily loadable by MATLAB. The
ROS solution is quite good for existing tools but it is not good on the long
term due to the complexity of the representation and the associated
dependencies.

_**Repository where data will be stored** _

The public part of the dataset is available over a dedicated website under the
domain of SSSA. The RAMCIP data management portal provides links to the
respective dataset’s download section.

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

**5.**

</td>  
<td>

**Archiving and preservation**

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

**Data preservation period**

The data are available on the PERCRO SSSA website with an expected lifetime of
10 years given the history of PERCRO and the backup procedures of SSSA. The
digital signature of the whole dataset, or the storage of the dataset in a git
repository could provide support for the correct duplication and preservation.

_**Approximated end volume of data** _

200-300 GBs

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

_**Indicative associated costs for data archiving and preservation** _ None if
kept on SSSA server.

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

**6.**

</td>  
<td>

**Partners activities and responsibilities**

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

_**Partner Owner / Data Collector** _

SSSA

_**Partner in charge of the data analysis** _

SSSA, CERTH

_**Partner in charge of the data storage** _

SSSA

_**WPs and Tasks** _

The data have been collected within activities of WP3 in Task 3.3 and Task
3.5.

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

##  3.7 Dataset “DS8.CERTH.04.VirtualUserModelsDataset”

####  General Description

This dataset concerns the RAMCIP VUMs; these are Virtual User Models (VUMs) of
robot users (e.g. MCI patients), encoding their cognitive and motor skills,
behavioral aspects, as well as human-robot interaction and communication
preferences. The models are XML-based specification of parameters that are
taken into account in the context of the RAMCIP user modelling methodology.
The dataset includes for each indicative robot user case, a semantic
representation of a series of parameters related to the above.  
  
<table>  
<tr>  
<th>

**1.**

</th>  
<th>

**Data set reference and name**

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

**DS8.CERTH.04. Virtual User Models Dataset**

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

**2.**

</td>  
<td>

**Data set description**

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

_**Origin of Data** _

This dataset derived by analyzing the datasets of Human Tracking (2.4),
Walking Skills (2.6) and Human Cognitive Skills (2.7) described above, toward
modelling behavioural aspects as well as cognitive and motor skills of the
participants of the respective data collection experiments, into VUM
representations that hold statistical population values.

_**Nature and scale of data** _

The dataset is in the form of XML-based representations of the parameters
involved in the RAMCIP VUMs.

_Data Format**:** _ XML file format

_**To whom could the dataset be useful** _

This dataset has been used in the development of the RAMCIP user modelling
methodology of WP3. The dataset is also useful for researchers investigating
behavioral traits, as well as cognitive and motor skills correlates to MCI.

_**Related scientific publication(s)** _

The developed VUMs dataset are disseminated in International Conferences and
Journals of robotics and health (e.g. MCI-related) domains.

_**Indicative existing similar datasets** _

Virtual Human Models encoding anthropometric and kinematic parameters of the
human body, focusing on the elderly and disabled have derived from the VERITAS
FP7 project. Knowledge derived from the VERITAS VUMs could be integrated into
the RAMCIP VUMs which, however, will also focus on the cognitive and
behavioural traits of elderly with MCI.

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

**3.**

</td>  
<td>

**Standards and metadata**

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

The dataset is accompanied with detailed documentation of its contents;
detailed documentation of the variables involved in the RAMCIP VUMs are also
provided.

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

Guidelines for Virtual Human Modelling derived from the VUMS cluster
(http://vums.iti.gr/index8091.html?page_id=64) can be followed, as well as
related XSD and XML specifications will be followed. The relevance of
following also usiXML-based paradigms to develop respective (e.g. Human Robot
Communication -related) parts of the RAMCIP VUMs will be investigated.

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

**4.**

</td>  
<td>

**Data sharing**

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

_**Access type** _

Anonymized versions of the RAMCIP VUMs formulate open models encoding
behavioral traits and human robot communication preferences, cognitive and
motor skills of MCI patients.

_**Access Procedures** _

A web page has been created on the RAMCIP data management portal should
provides a description of the dataset and links to a download section.

_**Embargo periods (if any)** _

None

_**Technical mechanisms for dissemination** _

A link to the anonymized dataset from the Data management portal. The link is
provided in all relevant RAMCIP publications. A technical publication
describing the dataset and acquisition procedure could be published.

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

The dataset is designed to allow easy reuse with commonly available XML
editing tools and software libraries.

_**Repository where data will be stored** _

The public part of the dataset is accommodated at the data management portal
of RAMCIP.

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

**5.**

</td>  
<td>

**Archiving and preservation**

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

_**Data preservation time** _

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

_**Approximated end volume of data** _

The dataset’s end volume is expected are at the leve of 2 Megabytes.

_**Indicative associated costs for data archiving and preservation** _ There
are no costs associated with its preservation.

_**Indicative plan for covering the above costs** _

The cost will be covered at the local hosting institute as a part of the
standard network system maintenance.

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

**6.**

</td>  
<td>

**Partners activities and responsibilities**

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

_**Partner Owner / Data Collector** _

CERTH

_**Partner in charge of the data analysis** _

CERTH, SSSA, FORTH, LUM, ACE

_**Partner in charge of the data storage** _

#####  CERTH

_**WPs and Tasks** _

The data have been collected within activities of WP3 in Task 3.4, and served
the project’s research efforts within Task 3.4, Task 3.5 and Task 3.6.

##  3.8 Dataset “DS9.TUM01.LowerBodyKinematicsDataset”

####  General Description

This dataset contains kinematics of lower-body interaction by pairs of human
participants in which one human participant assists wearing a shoe of another
seated participant in line with a scenario description of the RAMCIP project.
The dataset has been used to train the predictive controller of the RAMCIP
system in R&D activities under T6.3. Furthermore, the dataset has been used to
provide ergonomic guidance for designing the control of the RAMCIP system.  
  
<table>  
<tr>  
<th>

**1.**

</th>  
<th>

**Data set reference and name**

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

**DS9.TUM.01. Lower-body kinematic Dataset**

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

**2.**

</td>  
<td>

**Data set description**

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

_**Origin of Data** _

The data have been collected from volunteers of healthy participants using a
Qualisys passive-marker motion tracking system. The small light-weight markers
have been placed on the foot, tibia, and femur of both legs to capture the
position and orientation of the lower-limb segments. Furthermore, the pose of
the torso has been captured with markers placed on the sternum. A separate set
of markers tracks the positions of the shoe and the hand of the assisting
person. The dataset has beenobtained in accordance with the local ethics
requirements at TUM, Germany, for human subject testing.

_**Nature and scale of data** _

The raw data are images of the reflective markers taken by each motion
tracking camera at a pre-set frequency. The cameras use reflections of
infrared light on the special markers to visualize their positions, thus the
raw data do not record any personal information. The centroid of each marker
image is then triangulated from multiple cameras to estimate its position in
the Cartesian coordinate. The position data are completely anonymous and will
be used as a dissemination material.

The dataset consists of repetitions of the same motions from multiple pairs of
participants. Each pair performed approximately 10 repetitions of a given
movement scenario. Data have been collected from 10 – 20 pairs of
participants.

_Data Format:_ PNG, JPG for images, XML or TXT for annotations

_**To whom could the dataset be useful** _

Roboticists, biomechanists, ergonomic designers.

_**Related scientific publication(s)** _

_Not Available_

_**Indicative existing similar datasets** _

CMU Graphics Lab Motion Capture Database Multisensor-Fusion for 3D Full-Body
Human Motion Capture

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

**3.**

</th>  
<th>

**Standards and metadata**

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

The marker position data obtained from the recording of human participants
have been processed in Matlab and then converted into a c3d file format
(www.c3d.org). The c3d format is a public domain, binary file supported by
most of major motion capture system and animation software. The anonymized
files are available with general information about the file including
participant's gender, age group, and a short description of movements being
performed.

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

**4.**

</td>  
<td>

**Data sharing**

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

_**Access type** _

In accordance with the ethical requirement regarding data obtained from human
participants, anonymized dataset are available to a restricted group. Personal
information regarding the participants are kept strictly private.

The description of data may is publically disseminated in a form of
publication. Published data including articles, book chapters, and conference
proceedings are available in print or electronically from publishers, subject
to subscription or printing charges. The source codes are retained at the
local site, open to access by a restricted group (e.g. consortium), subject to
privacy, confidentiality, and intellectual property right policy of the
developer(s) with respect to the local national registrations.

_**Access Procedures** _

The request form of the raw data can be submitted to the principal
investigator of the developing site, and upon approval, the data will be
electronically transferred.

Published materials may be accessed from the publishers, subject to
subscription or printing charges.

_**Embargo periods** _

None

_**Technical mechanisms for dissemination** _

A standard publication procedure is taken for dissemination.

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

The dataset are stored as MATLAB, c3d, and QTM (Qualisys Tracking Manager)
files.

_**Repository where data will be stored (institutional, etc., if already
existing and identified)** _

The dataset is accommodated at the data management portal of RAMCIP. The
network repository has also been used to host all relevant materials at the
local institutes where all data are periodically backed up.

The published materials is hosted by the publishers.

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

**5.**

</td>  
<td>

**Archiving and preservation**

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

_**Data preservation period** _

The datasets for publication will be stored up to 3 years at the local site
following publication for published material.

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

_**Approximated end volume of data** _

The dataset’s end volume is approximately 500 megabytes

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

A dedicated hard drive has been used to preserve the dataset. It is estimated
to be around 100 euros.

_**Indicative plan for covering the above costs** _

The cost will be covered by the local hosting institute

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

**6.**

</td>  
<td>

**Partners activities and responsibilities**

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

_**Partner Owner / Data Collector** _

TUM

_**Partner in charge of the data analysis** _

TUM, CERTH, LUM

_**Partner in charge of the data storage** _

TUM

_**WPs and Tasks** _

The data have been collected within activities of WP6 in Task 6.3

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

##  3.9 Dataset “DS10.ACCREA-2.ManipKinematicsDataset”

####  General Description

Set of Simulink/CAD/Gazebo models for simulation, optimization and development
purposes. The prepared dataset contains models of selected manipulator
kinematics, which allowed RAMCIP partners to choose the best solution for user
requirements and dexterous manipulation tasks.  
  
<table>  
<tr>  
<th>

**1.**

</th>  
<th>

**Data set reference and name**

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

**DS10.ACCREA.02 Manipulator kinematics chains Dataset**

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

**2.**

</td>  
<td>

**Data set description**

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

_**Origin of Data** _

Anthropomorphic kinematics of previously developed by ACCREA and commercial
manipulators have been considered, as well as new concept ones. Our goal is to
select solutions meeting user and safety requirements and also being capable
of performing RAMCIP manipulation tasks.

_**Nature and scale of data** _

Dataset are available in a form of Simulink/Solid Works/Gazebo/URDF files
package.

_Data Format:_ SolidWorks / URDF file format

_**To whom could it be useful** _

The collected data have been used for simulations and development of the
RAMCIP manipulator. Models can be imported into the Gazebo environment
simulation, which has been used for testing components and system integration
by most of technical RAMCIP partners.

_**Related scientific publication(s)** _

None

_**Indicative existing similar data sets** _

Several commercial manipulators' kinematics have been considered in the design
of the most suitable one for the RAMCIP project.

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

**3.**

</td>  
<td>

**Standards and metadata**

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

The metadata that have been produced are summarized as follows:

  * the parsing routines used to read and absorb the data for developing purposes 

  

  * selection of different object grasping/manipulating scenarios based on RAMCIP requirements along with results of simulations. 

  

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

**4.**

</td>  
<td>

**Data sharing**

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

_**Access type** _

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

A part of the dataset is publicly available. It has been uploaded to the main
site of the RAMCIP project.

_**Access Procedures** _

A web page has bee created on the project’s data management portal that
provides a description of the dataset and link to a download section.

_**Embargo periods** _

None

_**Technical mechanisms for dissemination** _

A link to the dataset from the data management portal. The link is provided in
all relevant RAMCIP publications.

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

The dataset are designed to allow easy reuse with commonly available tools and
software libraries.

_**Repository where data will be stored** _

The dataset is accommodated at the data management portal of RAMCIP, being
accessible through the RAMCIP website.

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

**5.**

</td>  
<td>

**Archiving and preservation**

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

_**Data preservation period** _

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

_**Approximated end volume of data** _

The data is approximately 500 Megabytes.

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

A dedicated hard disk drive has been allocated for the dataset. There are no
costs associated with its preservation.

_**Indicative plan for covering the above costs** _

The cost will be covered at the local hosting institute in the context of
RAMCIP.

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

**6.**

</td>  
<td>

**Partners activities and responsibilities**

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

_**Partner Owner / Data Collector** _

ACCREA

_**Partner in charge of the data analysis** _

ACCREA, SHADOW

_**Partner in charge of the data storage** _

ACCREA, SHADOW

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

_**WPs and Tasks** _

The data have been collected within activities of WP7

##  3.10 Dataset “DS11.LUM_ACE01.UserRequirementsDataset”

####  General Description

The user requirement dataset comprises the pictures and videos taken during
the workshops with stakeholders in Lublin and Barcelona as well as anonymized
questionnaires, which were filled in by the different stakeholders groups.
Since the raw data are collected in the local languages, the videos and
summary of the collected data have to be translated into English  
  
<table>  
<tr>  
<th>

**1.**

</th>  
<th>

**Data set reference and name**

_Identifier for the data set to be produced_

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

**DS11.LUM_ACE.01 User Requirements Dataset**

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

**2.**

</td>  
<td>

**Data set description**

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

_**Origin of Data** _

Materials collected for and during workshops conducted at LUM and ACE with
medical personnel and caregivers. The surveys were conducted by LUM and ACE
teams.

_**Nature and scale of data** _

  * Videos which were taken during the workshops with medical personnel and caregivers – in local languages. 

  

  * Pictures taken during the workshops 

  

  * Transcripts of videos and summary in English 

  

  * Completed questionnaires – paper versions and scans – in local languages 

  

  * Informed consents of the workshop participants in local languages – paper versions and scans. 

  

  * Excel sheets and summary of the survey results 

  

_Data Format:_ MPG, AVI format for videos, JPG for images, DOC/PDF for
transcripts, questionnaires and papers, XLS for survey results.

_**To whom could it be useful** _

Raw data – videos and questionnaires in local languages can be assessed and
summarized by local LUM and ACE teams in the scope of user requirements
analysis and definition of the RAMCIP use cases. These data should also be
available for the local Ethics Committees on their requests.

Some videos and pictures may be used for publications and presentations.

The transcripts, tables and summaries can be used by the entire RAMCIP
consortium for a preparation of the functional and technical specifications.

_**Related scientific publication(s)** _

The summaries of the dataset can to be published as user requirements analysis
- related publication. Some pictures can be also part of the scientific
publications.

_**Indicative existing similar data sets** _

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

No similar data sets are available for public.

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

**3.**

</td>  
<td>

**Standards and metadata**

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

The dataset is accompanied by the metadata describing the demographics of the
samples from which the questionnaires were collected and the data collection
process will be described analytically.

The results of the workshops have been described and categorized. The results
of the questionnaires are exhibited in an Excel data sheets with the
respective statistical analysis.

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

**4.**

</td>  
<td>

**Data sharing**

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

_**Access type (widely open, restricted to specific groups, private)** _

Based on the ethical rules and legal requirements, the data which contain
personal data such as images of people and their opinions, cannot be available
for public. Summaries of the data can be published as public deliverables and
scientific publication.

_**Access Procedures** _

The datasets with personal data of the workshop participants (videos,
pictures, informed consents) has been stored at the special locked cabinet
(paper) or servers (videos, pictures and scans) at LUM and ACE and only the
members of the RAMCIP team will have access to them.

_**Embargo periods (if any)** _

None

_**Technical mechanisms for dissemination** _

The summaries of the data have been published as user requirements in the
appropriate deliverables and scientific publications. Some videos and pictures
are part of the scientific publications and presentations, but for
dissemination of the videos and pictures, the written confirmation of LUM or
ACE (depend on where the data has been recorded) was acquired to ensure that
the publication does not violate personal rights of the participants of the
workshops.

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

N/A

_**Repository where data will be stored (institutional, etc., if already
existing and identified)** _

LUM’s and ACE’s internal servers for electronic data and locked cabinets at
LUM and ACE for paper documents.

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

**5.**

</td>  
<td>

**Archiving and preservation (including storage and backup)**

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

_**For how long should the data be preserved?** _

In Poland the videos and pictures will be kept for 5 years after the end of
the project and the paper documentations have to be kept for 20 years after
the end of the project as required by the local regulations.

In Spain there are no time limits for how long data should be kept. Therefore
all

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

source data will be kept as long as possible.

_**Approximated end volume of data** _ Videos and pictures – 8 GB.

Informed consents – 18 pages

Questionnaires – 789 pages

_**Indicative associated costs for data archiving and preservation** _ No
additional costs if kept on LUM and ACE servers and spaces.

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

**6.**

</td>  
<td>

**Partners activities and responsibilities**

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

_**Partner Owner / Data Collector** _

LUM, ACE

_**Partner in charge of the data analysis** _

LUM, ACE

_**Partner in charge of the data storage** _

LUM, ACE

_**WPs and Tasks** _

The collection of this dataset and its analysis is part of WP2 activities,
concerning the research efforts of Task T2.1 in the scope of user requirements
analysis.

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

##  3.11 Dataset “DS12.CERTH-01.3DForceSlippageDataset”

####  General Description

The 3D Force Slippage Dataset comprises 3D Force measurements from Optoforce
3-axis Sensors during experiments where slippage occurred including several
surfaces  
  
<table>  
<tr>  
<th>

**1.**

</th>  
<th>

**Data set reference and name**

_Identifier for the data set to be produced_

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

**DS12.CERTH-01 3D Force Slippage PB Dataset**

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

**2.**

</td>  
<td>

**Data set description**

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

_**Origin of Data** _

Measurements were collected with Optoforce 3D sensors (at 1000Hz without
filtering) during experimentation at CERTH with RAMCIP V2 Hand. The hand
established a 2-finger (pinch) grasp of a cylindric object, which was fixed on
a supporting surface. Data was collected while the arm’s end-effector moved
translationally upwards and downwards, resulting to the slippage of the
fingertips on the object’s surface. On total 6 different surfaces were
sampled, by 2 different fingers, for 3 different arm’s moving velocities, for
several different initial normal grasping forces per finger (in the range
between 1N and 2.5N), resulting in a dataset containing 72 samples in total.

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

_**Nature and scale of data** _

  * Raw data from Optoforce sensors (dataset name: f) 

  

  * Labels provided for each sample as slip or stable (dataset name: l) 

  

  * Short description of data acquisition and origination of each different sample (dataset name: fd) 

  

_Data Format:_ MAT formats for dataset and txt for documentation

_**To whom could it be useful** _

Data could be useful to researchers trying to address slippage detection,
either as evaluation or as training dataset.

_**Related scientific publication(s)** _

I. Agriomallos, S. Doltsinis, I. Mitsioni, & Z. Doulgeri, (2018). Slippage
Detection Generalizing to Grasping of Unknown Objects using Machine Learning
with Novel

Features. IEEE Robotics and Automation Letters, 3(2), 942–

948,  _https://doi.org/10.1109/LRA.2018.2793346_

_**Indicative existing similar data sets** _

No similar data sets that we know of are publicly available.

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

**3.**

</td>  
<td>

**Standards and metadata**

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

The dataset will be accompanied by short description of collection process.

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

**4.**

</td>  
<td>

**Data sharing**

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

_**Access type (widely open, restricted to specific groups, private)** _
Widely open as soon as a complete version of data is collected.

_**Access Procedures** _

A web page will be created on the RAMCIP data management portal (hosted at the
RAMCIP web site) that should provide a description of the dataset and links to
a download section.

_**Embargo periods (if any)** _

None

_**Technical mechanisms for dissemination** _

A link to the dataset from the RAMCIP web site (RAMCIP data management
portal). The link will be provided in all relevant RAMCIP publications. A
technical publication describing the dataset and acquisition procedure will be
published.

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

The dataset will be designed to allow easy reuse with commonly available tools
and software libraries. Since it is a .mat file it will be loaded by MATLAB or
GNU Octave (free) or whichever framework can load such files (e.g. python’s
scipy.io library).

_**Repository where data will be stored (institutional, etc., if already** _

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

_**existing and identified)** _

The dataset will be accommodated at the data management portal of the project
website.

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

**5.**

</td>  
<td>

**Archiving and preservation (including storage and backup)**

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

_**For how long should the data be preserved?** _

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

_**Approximated end volume of data** _

All Files ~ 50MB

_**Indicative associated costs for data archiving and preservation** _ Small,
one-time costs covered by RAMCIP.

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

**6.**

</td>  
<td>

**Partners activities and responsibilities**

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

_**Partner Owner / Data Collector** _

CERTH

_**Partner in charge of the data analysis** _

CERTH

_**Partner in charge of the data storage** _

CERTH

_**WPs and Tasks** _

The collection of this dataset and its analysis is part of WP5 activities and
Task T5.3 concerning grasp stability maintenance.

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

#  4\. The RAMCIP Data Management Portal

The present section provides an overview of the RAMCIP Data Management Portal
(DMPo). This is a web based portal, accessible at the URL:
_http://ramcipproject.eu/ramcip-data-mng/_ (Figure 1). The data management
portal can be accessed through the official website of the RAMCIP project, by
following the menu items “Results -> Data Management Portal”. The portal has
been developed with the purpose to enable project partners to manage and
distribute their public datasets generated in the course of the project,
through a common infrastructure.

**Figure 1: Welcome page of the RAMCIP Data Management Portal (
_http://ramcipproject.eu/ramcip-data-mng/_ )  **

Specifically, as defined in the deliverable D9.3 of RAMCIP (D9.3. “RAMCIP Data
Management Plan – v1”), the Data Management Portal, whose first functional
version became operational on M20, formulates a dedicated space of the RAMCIP
project website, which can aggregate descriptions of all project public
datasets and provide links to respective dataset download sections to the
interested public, as well as centralized data management functionalities to
project partners.

Based on the information detailed in the previous sections of the present
deliverable, the Data Management Portal will have to establish the above for a
series of datasets, which are planned to be collected by different project
partners throughout the project’s duration. For the datasets which have been
eventually be collected and comprise (in part or as whole) a portion that can
be made publicly available, the Data Management Portal offers the owner
parties with data management functionalities, enabling them to have an
aggregated space facilitating the public datasets distribution.

##  4.1 Specification of the core functionalities of the Data Management
Portal

The RAMCIP Data Management Portal supports a series of functionalities in
order to facilitate the management and distribution of the public datasets
formulated during the RAMCIP project. More specifically:

  * The Data Management Portal has been implemented through a **web based platform** which will enable its users to easily access and effectively manage the public datasets that have been created in the course of the project. 

  

  * Each dataset available through the DMP is accompanied by descriptive information, as well as a link to the dataset’s download section. 

  

  * Management functionalities (addition, editing) of datasets are provided to authorised members of the web platform, which have access to a corresponding private section of the DMP. 

  

  * Public access to the datasets registered in the DMP is provided through a “public space” of the portal, where information on the datasets is provided to the public, as well as links to the datasets download sections. 

  

Regarding the **authentication** procedures of the DMP as well as the
respective permission and access rights, the following three categories of
users are specified:

####   Administrator (Admin)

The Admin has access to all of the datasets and the functionalities offered by
the DMP. The Admin is also be able to provide permission and access rights to
the registered members as well as to determine and adjust the editing/access
rights of the members and the users (open access area). Finally, the Admin is
able to access and extract the analytics, concerning the visitors of the
portal.

####   Members

After someone has been successfully registered and given access permission by
the Admin, s/he isl considered as a “registered Member”. All the registered
members have access to all datasets and are able to manage the datasets that
they own (i.e. those that they have added to the portal). The “Members” role
is designated for the members of the RAMCIP Consortium who is capable of
adding public datasets in the portal.

####   Users

Apart from the admin and the registered members, an open access area became
available for users who are not need to register and they have access to the
public datasets. Users are capable of viewing the descriptive information of
the datasets provided through the DMP and are also capable of selecting to
“Download” a dataset that is of interest to them, being this way redirected to
the download section of the dataset.

##  4.2 Data Management Portal Architecture

Following the above specifications, the RAMCIP DMP comprises two sections, a
private space, accessible to Members, as well as a public space, accessible to
all user types.

**Figure 2: Conceptual Architecture of DMPo access roles and functionalities**

As shown in Figure 2 above, the public space allows users to see descriptive
information regarding the DMPo datasets, as well as to proceed with the
download process of a dataset that they are interested in.

On the other hand, the private space, in addition to the above
functionalities, allows users of the “Member” type, to also register a new
dataset in the DMPo repository, becoming thus the corresponding dataset’s
owner, and also apply modifications to all the datasets that are owned by
her/him.

The functional architecture of the RAMCIP Data Management Portal is a three-
tier architecture composed by the Data, Application and Presentation tiers, as
depicted in Figure 3 below and further explained in the following sub-
sections.

**Figure 3: Functional Architecture of the Data Management Portal**

###  4.2.1. Data Tier

The Data Tier is the one responsible for the storing of both the data that is
necessary for the overall operation of the DMPo Backend (DMPo Application
Tier), as well as the RAMCIP public datasets which are accessible through the
portal. More specifically:

The DMPo Backend DB contains all the tables related to the registered users
(members) details, as well as the information (metadata) that have been
registered to the portal for the added datasets. Moreover, the Backend DB
stores also information over the datasets owners (i.e. those who have
modification rights on the registered datasets).

The Datasets DBs correspond to the databases which perform the actual storing
of the public datasets that are made accessible through the Data Management

Portal.

###  4.2.2. Application Tier (Backend)

The Application Tier corresponds in essence to the RAMCIP DMPo application
server backend, which holds and applies all the business logic of the portal.
In this respect, it is responsible to handle user requests (derived through
the Presentation tier described below) for the provision of information on
specific datasets, as well as for their download. In addition, it is
responsible to provide to the users of the “Members” type the access rights of
adding and modifying the registered datasets. The application tier contains in
this scope a series of interfaces which enable the efficient communication of
the application logic with the datasets of the Data Tier.

###  4.2.3. Presentation Tier (Frontend)

The Presentation Tier comprises the Web-based interface of the Data Management
Portal. This is accessible by any PC, and provides to each of the user roles
(i.e. admin, members, users), all user interface mechanisms that are necessary
to fulfil the functionalities described in the previous section. In the
following Section, more details are provided for the Presentation Tier, along
with screenshots of the DMPo user interfaces. The core design principles that
are followed for the development of the DMPo frontend are as follows:

  * The “Look & Feel” of the DMPo Web Interface should follow the one of the official RAMCIP website 

  

  * The DMPo Web Interface should be easy to use, enabling the effective establishment of the portal’s target functionalities 

  

  * Emphasis should be put on developing a user friendly interface, which will allow easy access of the interested public, to the public datasets 

  

##  4.3 Overview of the DMPo Design and Functionalities

The home page of the developed Data Management Portal is shown in Figure 1
above. From that page, the user can be provided either with public access to
the datasets, or with private access, after providing her/his credentials so
as to be logged in as a “Member”. As soon as the user progresses, either by
selecting to get simple “Access” or, by logging in as member, s/he is
navigated to the DMPo’s introductory page (Figure 4).

At the “INTRODUCTION” page of the DMPo shown above, the user is presented with
some basic information on the RAMCIP project and the data that is provided
through the portal. In addition, links to H2020 guidelines on Data Management
are provided as well.

By clicking on the “DATA” tab, the user can then navigate to the core part of
the DMPo, which provides access and management capabilities (where
appropriate) on the RAMCIP DMPo datasets.

The DATA section comprises two sub-sections, one dedicated to public
“DOCUMENTS” of the RAMCIP project and one dedicated to “DATASETS”, which are
further described below.

###  4.3.1. Public Documents Section

The “DOCUMENTS” section of the “DATA” page of the DMPo (Figure 5) provides
access to public documents of the project. Specifically, it provides a single
access point to RAMCIP public deliverables and publications. A filter allows
the user to obtain a list including only project publications or public
deliverables (Figure 6). By selecting the “Get It” option for each document, a
direct download of that document starts.
Documents**
deliverables**

While the above functionalities are available to all users of the RAMCIP DMPo,
either registered members or simple users, the corresponding page provided to
the portal administrator allows her/him to also register and upload a new
public document, by clicking on the “+” button (Figure 7). The administrator
can also delete some document entry, and also obtain information for each
document related to its downloads.

**Figure 7: Overview of available public documents; administrator view**

###  4.3.2. Public Datasets Section

The main page of the public datasets section, as seen by the general public,
is shown in the Figure 8 below. This page provides a list with all public
datasets that are accessible through the Data Management Portal. By clicking
on a dataset, the user can view more detailed information about it (Figure 9).
By clicking on the “Get it” button shown in Figure 8, the user can proceed to
the process of downloading the desired dataset.

The downloading of a dataset can then be done either directly from the DMPo,
or through the dataset’s owner corresponding web page, in respect to which of
these two approaches has been followed by the owner while adding the dataset
to the DMPo. More details on the addition and downloading of a dataset, from
DMPo members and all users respectively, are provided in the two corresponding
subsections that follow.

**Figure 9: Viewing the details of an available public dataset**

#####  **4.3.2.1** Adding a dataset to the Data Management Portal

A DMPo Member and the administrator have the rights to add a new dataset to
the portal. This is achieved through the addition option (“+”) that is
provided in their view of the DMPo available datasets overview page (Figure
10).
view

The addition of a new dataset to the DMPo is performed through the UI shown in
Figure 11 below. Through that interface, the user specifies a title and
general description for the dataset, as well as further information which will
appear to the DMPo users. Notably, at this point the user must define the way
that the dataset will be made available to the public, by defining the
“storage location” details (Figure 11).

In this respect, the user has two options. The first option (“URL”, shown in
Figure 11) concerns the definition of an external link, from which the dataset
can be downloaded. The second option (“Upload file to server”, shown in Figure
12) concerns the uploading of a single file containing the dataset to the
portal. This file will subsequently be available for download, directly from
the DMPo server. This option can be applied in case that the dataset owner
wishes to use the DMPo server for storing the downloadable version of the
dataset, however, it can be applied to cases of datasets of relatively limited
size.

In order to proceed with concluding the addition of the dataset on the DMPo
server, the user should indicate that s/he agrees with the license agreement
of the RAMCIP DMPo, which is illustrated in Annex II of the deliverable and is
shown to the user by clicking on the corresponding “license agreement” link
(Figure 11).

Through a similar UI of Figure 11, shown in Figure 13 below, which appears to
a Member upon the selection of an owned dataset from the list of Figure 10,
the dataset owner can also edit the dataset after its initial addition. The
dataset owner has also the option to delete the dataset through the
corresponding option shown in the UI of Figure 10. In addition, the owner can
be provided with information on the amount of downloads that the dataset has
received through the DMPo, either from public users, or from registered
members; this information is provided by clicking the corresponding “i” button
at the last row of the datasets overview list (Figure 10).
Data Management Portal
uploading a dataset file to make it available for download through the DMPo

**Figure 13: Editing the information of an existing dataset**

#####  **4.3.2.2** Dataset download

In the current implementation of the DMP, two different download procedures
are supported. As explained above, the dataset owner may have uploaded a
single (.zip) file containing the full dataset to the portal, along with a
disclaimer notice specifying the terms and conditions for the dataset
download. Alternatively, the owner may have specified that the dataset can be
downloaded from a specific URL of the owner. In accordance, the two
corresponding ways that a dataset can be downloaded once the user selects the
“Get It” option for it (Figure 8), are further explained below.

######  _Case 1: Direct download_

In this case, the user directly downloads the dataset, which has been stored
as a single .zip file on the DMP server.

######  _Case 1: Download through the owner’s website_

In this case, the user is redirected to the web page of the URL that has been
specified by the dataset owner during the dataset’s addition (URL option), as
the one through which the dataset can be downloaded. The owner may provide
through that webpage additional information on the dataset, request specific
user details to be provided prior to the download, ask the user to consent to
specific terms and conditions related to the use of the dataset etc.

#  5\. Discussion

One of the main objectives of RAMCIP project is to make the datasets or the
portions of them that can become public, easily discoverable and accessible.
In several cases, a published scientific paper introduced a new dataset so
that the community can learn about it, and later evaluate and refer to it. In
this case, the dataset has also been related with a certain DOI (upon its
public avaialability). Publishing a scientific paper along with a new dataset
not only helps in making the dataset known to a wider community, but also the
peer review process ensures about its reliability and quality of the context.

All the metadata and various file formats used, adhere to commonly used
practices as much as possible, including commonly used software, while the
description ensures clarity and ease of use by third parties.

The datasets have been announced on the project’s website with extensive
description and with a download link; the RAMCIP “data management portal” of
the project’s website is responsible to enable such a centralized repository.
However, when a dataset could not be publicly available, it was accessible to
the members of the consortium only via internal servers.

As it can be seen from the analysis above, the total space required is on the
order of several TB. In order to balance the period of the datasets
availability and the preservation costs, the public datasets will be kept
available on dedicated servers (being accessible through a the Data management
portal), for as long as there is sufficient demand for them, under specific
licensing schemas which will be defined at subsequent project phases, when the
datasets will be established. In a later time, they will be distributed only
by request.

Lastly, any personal data of healthy controls and patients involved in the
data acquisition will be secured from being publicly leaked, while anonymity
will be exercised in all cases.

#  6\. Conclusions

In the first version of the RAMCIP Data Management Plan, reported in the
deliverable D9.3 on M6 of the project, a detailed preliminary analysis of the
datasets that the partners of the RAMCIP project planned to collect and use
has been performed, toward developing the various skills of the RAMCIP robot.
Those initial plans have been revisited in the second version of the RAMCIP
DMP, resulting to a more updated status of the RAMCIP Data Management Portal.
With the current version all the activities regarding the collection of the
data foreseen in the first version have been concluded. The initially
identified datasets have been created and uploaded in the RAMCI Data
Management Portal and the current status of it is reflected in Section 3 of
the present deliverable with a summary in Section 4.

The created datasets include captured models of objects and domestic
environments, human tracking and behavioural modelling, as well as
questionnaires related to the analysis of the RAMCIP user requirements. Each
dataset was separately analysed, with emphasis given on the nature of the
data, the accessibility and its possible access type, as well as any ethical
issues that may arise from manipulating sensitive personal information.

The first version of the RAMCIP DMP (D9.3) served as a preliminary guide to
build the infrastructure for efficiently managing, storing and distributing
the amount of data collected, especially concerning the portions of the RAMCIP
datasets that will be made publicly available. On that basis, the Data
Management Portal of the RAMCIP project was developed and became fully
operational in the second project year.

In the third year, the Data Management Plan has been further elaborated and
all the identified datasets have been uploaded and made publically available.
Consequently, with this deliverable, the DMP is considered as final, however
all the required maintenance activities to keep the Data Management Portal
alive and accessible for the scientific community will take place as long as
there is a scientific need for the uploaded data.