#  EXECUTIVE SUMMARY

The present document is a deliverable of the SARAFun project, funded by the
European

Commission’s Directorate-General for Research and Innovation (DG RTD), under
its Horizon 2020 Research and innovation programme (H2020). It presents the
final version of the project Data Management Plan (DMP). The current document
explains in detail what data has been generated throughout the project’s
lifecycle, the means for sharing of this data in order to become accessible
for verification and reuse, as well as the ways in which it has been curated
and preserved.

Throughout the project, the team needed to manage a large number of datasets,
generated and collected by various means, i.e. sensors, cameras, robots and
direct interactions with users (e.g. interviews and questionnaires). By the
end of the project, 7 different datasets have been produced through the
SARAFun’s technical activities, with almost all the partners being data owners
and/or producers.

All SARAFun datasets have been handled considering the main data security and
privacy principles, respecting also the partners IPR policies. A dedicated
Data Management Portal, hosted on Zenodo, further supported the efficient
management, storage and sharing of the project’s datasets.

It is strongly emphasized that this is the result of an ongoing document that
has being evolved along with the project progress and has been updated in
order to reflect up-to-date information.

#  I  NTRODUCTION

##  PURPOSE

The SARAFun project has been formed to enable a non-expert user to integrate a
new bimanual assembly task on a robot in less than a day. This is accomplished
by augmenting the robot with cutting edge sensory and cognitive abilities as
well as reasoning abilities required to plan and execute an assembly task.

The purpose of this deliverable (D7.8 “Data Management Plan”) is to deliver a
detailed analysis of all the datasets generated by the SARAFun project. This
final version of the DMP includes an overview of the datasets that have been
produced by the project as well as the specific characteristics and their
management processes. It also includes additional information regarding the
dissemination of the project’s open access knowledge and datasets, aiming to
foster further exploitation of the SARAFun’s results by the scientific
community.

##  GENERAL PRINCIPLES

Through the activities of the SARAFun project [1], pioneer research has been
carried out in order to develop and deliver a next generation bi-manual robot
that can be exploited in the production lines for assisting human workers in a
safety manner through novel human demonstration and teaching algorithms. To
this end, human participants have been involved in the project and data have
been collected regarding their assembly’s movements, their ratings of the
system and assembly forces in a production line.

###  Participation in the Pilot on Open Research Data

SARAFun highly supports the Pilot on Open Research Data launched by the
European Commission along with the Horizon2020 programme, and therefore a
significant part of research data generated by the project has been made open
and it is offered to the Open Research Data Pilot, where SARAFun participates.
To this end, the Data Management Plan provided through the activities of this
deliverable, explains in detail what data has been generated by the project,
how it has been exploited or made accessible for verification and reuse, and
how it has been curated and preserved.

###  IPR Management & Security

Due to the high innovative nature of the SARAFun project, high level
technologies have been developed during the project’s lifecycle in order to be
afterwards released in the market. Therefore, foreground capable of industrial
or commercial application must be protected taking into account legitimate
interests. All involved partners have Intellectual Property Rights on the
technologies and data developed or collected with their participation. As the
partners’ economic sustainability highly depends on these technologies and
data, SARAFun Consortium will protect all data collected for SARAFun purposes.
Additionally, prior notice of dissemination has been given to other
participants, whereas any dissemination such as publications and patent
applications must indicate the Community financial assistance. Moreover,
appropriate measures have been taken for effectively avoiding a leak of data,
while all data repositories of this project are adequately protected.

###  Personal Data Protection

SARAFun involves the carrying out of data collection in order to assess the
technology and effectiveness of the proposed solution. This have been carried
out in full compliance of any European and national legislation and directives
relevant to the country where the data collections are taking place
(INTERNATIONAL/EUROPEAN):

  1. The Convention 108 for the Protection of Individuals with Regard to Automatic Processing of Personal Data; 

  

  2. Directive 95/46/EC & Directive 2002/58/EC of the European parliament regarding issues with privacy and protection of personal data and the free movement of such data; iii) The legislation in Sweden: The 1998 Personal Data Act; iv) The Spanish Organic Law 15/99 (amendments: 5/02 & 424/05); 

  

  

v) The Greek Law 2472/1997: Protection of Individuals with regard to the
Processing of Personal Data, and vi) The Greek Law 3471/2006: Protection of
personal data and privacy in the electronic telecommunications sector and
amendment of law 2472/1997.

More detailed information regarding data privacy issues can be found in
Deliverable 1.2 “Preliminary Ethics and Safety Manual for SARAFun technology”.

#  DATA MANAGEMENT PLAN

##  DATASET LIST  
  
<table>  
<tr>  
<th>

**#**

</th>  
<th>

**Dataset Name**

</th>  
<th>

**Status**

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

1

</td>  
<td>

DS.01.CERTH.FAIM2017Dataset

</td>  
<td>

“updated M36”

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

2

</td>  
<td>

DS.02.CERTH.IJERTCS2018Dataset

</td>  
<td>

“updated M36”

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

3

</td>  
<td>

DS.03.CERTH.CVPR2016Dataset

</td>  
<td>

“updated M36”

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

4

</td>  
<td>

DS.04.CERTH.SnapFitForceProfiles

</td>  
<td>

“updated M36”

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

5

</td>  
<td>

DS.05.CERTH.ContactEvaluationData

</td>  
<td>

“updated M36”

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

6

</td>  
<td>

DS.01.ULUND.TransientDetection

</td>  
<td>

“updated M36”

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

7

</td>  
<td>

DS.01.UNIBI.TactileData

</td>  
<td>

“updated M36”

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

8

</td>  
<td>

DS.01.ABB.ExperimentalVerification_GraspQuality

</td>  
<td>

“updated M36”

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

9

</td>  
<td>

DS.01.TECNALIA.Human_Performance_of_Bimanual_Assembly

</td>  
<td>

“updated M36”

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

##  PLANS PER DATASET  
  
<table>  
<tr>  
<th>

**Dataset reference and name**

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

**DS.01.CERTH.FAIM2017Dataset**

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

**Dataset description**

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

_**General Description** _

Dataset used for keyframe extraction in laboratory environment. An instructor
person will pick up two small objects and, afterwards will assembly them. This
dataset was used in “Teaching Assembly by Demonstration using Advanced Human
Robot Interaction and a Knowledge Integration Framework”

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

_**Origin of Data** _

Device type: RGBD sensor. Two aligned streams are used, extracted from one
depth sensor (640X480) and one RGB camera (640X480). The two sensors operate
in a low range area (20cm to 1.5m). Sampling rate: 10 fps.

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

_**Relation to project objectives** _

Objective 1: To develop a bi-manual robot that will be capable to learn the
assembly of two parts by human demonstration

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

_**To whom it would be useful** _

This dataset will be useful to key frame extraction algorithms.

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

_**Type and format** _

The data will be available in video format (e.g. image sequences).

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

_**Expected size** _

The volume of data is estimated at approximately 1.26GB/min for RGB and 1.08
GB/min for depth. 11 sequences have been captured ranging from 10 to 15
seconds each. Each sequence holds a volume of approximately 100 MB (70 MB for
color and 30 MB for depth).

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

_**Similar Data sets** _

No similar datasets have been found

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

**Discoverability and naming conventions**

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

_**Metadata provision** _

For the metadata RGBD sensors have been used. Two aligned streams are used,
one depth camera (640X480) and one RGB (640X480). Both sensors have low range
(20cm-1.5m). Sampling rate 10 fps. Annotation has been given based on the
outputs of the algorithms produced, in addition to the manually

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

selected (ground truth) key frames. The metadata is provided in xml format
with the respective xml schema. Indicative metadata include a) camera
calibration information, b) camera pose matrix for each viewpoint, c) 3D pose
annotation

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

_**Naming conventions** _

Each demonstrated assembly sequence will be labeled using the type of assembly
followed by an integer indicating the order of execution.

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

**Data sharing**

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

_**Access** _ Open

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

_**Reason for restricting access (if so)** _ No restriction

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

_**Access provision** _

A web portal has been created by CERTH on the Zenodo platform for the data
management that should provide a description of the dataset as well as links
to a download section.

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

_**Software to access data set** _

Commonly available tools and software libraries for enabling reuse of dataset
(e.g.OpenCV).

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

**Archiving, preservation and re-usability**

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

_**Duration of preservation** _

Data will be preserved for at least 2 years after the end of the project.

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

_**Repository of preservation** _

The dataset is preserved on zenodo as well as on CERTH servers and is
available for download. The portal is equipped with authentication mechanisms,
so as to handle the identity of the persons/organizations that download them,
as well as the purpose and the use of the dataset.

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

_**Cost of preservation** _

A USB disk drive (approximately 16GB) has been allocated for the dataset.
There are no costs associated with its preservation

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

_**Quality assurance** _

The available datasets have been validated by CERTH and included in the
relative publications.

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

**Dataset reference and name**

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

**DS.02.CERTH.IJERTCS2017Dataset**

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

**Dataset description**

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

_**General Description** _

Dataset of responses from users that had to rate the HRI system. The dataset
has been used in the paper with title “An Advanced Human-Robot Interaction
Interface for Collaborative Robotic Assembly

Tasks”

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

_**Origin of Data** _

Interviews and questionnaire answers of the test subjects that had to rate the
HRI system. Also the time it took them to teach an assembly to the robot.

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

_**Relation to project objectives** _

Objective 1, and WP2, T2.5: The design and prototyping the necessary
interfaces for the HRI in terms of controlling the teaching procedure.

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

_**To whom it would be useful** _

This dataset will be useful for HRI rating reference.

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

_**Type and format** _

The data’s type is spreadsheet and it’s available in excel format.

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

_**Expected size** _

The data is small in size, around 1MB.

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

_**Similar Data sets** _

There are many similar datasets that involve participant responses in UI
questionnaires, however the particular questionnaire on HRI was generated by
CERTH so there are no comparable datasets.

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

**Discoverability and naming conventions**

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

_**Metadata provision** _

There is no metadata available.

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

_**Naming conventions** _

The dataset is contained in a single .xls file and each question has a
corresponding column in the table which is clearly indicated by its number.

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

**Data sharing**

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

_**Access** _

Open

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

_**Reason for restricting access (if so)** _

No restriction

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

_**Access provision** _

A web portal has been created by CERTH on the Zenodo platform for the data
management that should provide a description of the dataset as well as links
to a download section.

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

_**Software to access data set** _

Commonly available tools and software libraries for enabling reuse of dataset
(e.g.MS Excel, LibreOffice Calc).

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

**Archiving, preservation and re-usability**

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

_**Duration of preservation** _

Data will be preserved for at least 2 years after the end of the project.

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

_**Repository of preservation** _

The dataset is preserved on zenodo as well as on CERTH servers and is
available for download. Of course, the portal is equipped with authentication
mechanisms, so as to handle the identity of the persons/organizations that
download them, as well as the purpose and the use of the downloaded dataset.

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

_**Cost of preservation** _

A USB disk drive (approximately 16GB) has been allocated for the dataset.
There are no costs associated with its preservation

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

_**Quality assurance** _

The available datasets have been validated by CERTH and included in relative
publications on the corresponding methods

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

**Dataset reference and name**

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

**DS.03.CERTH.CVPR2016Dataset**

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

**Dataset description**

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

_**General Description** _

Dataset of RGB and depth images reflecting two usage scenarios, one
representing domestic environments and the other a bin-picking scenario found
in industrial settings.

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

_**Origin of Data** _

Device type: RGBD sensor. Two aligned streams are used, extracted from one
depth sensor (640X480) and one RGB camera (640X480).

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

_**Relation to project objectives** _

Objective (2), WP3: To develop coarse-grained object tracking algorithms based
on privacy-preserving sensing (depth) and at different levels of granularity
(teaching mode versus real-time execution of the manipulation process)

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

_**To whom it would be useful** _

This dataset will be useful to object tracking algorithms

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

_**Type and format** _

The data will be available in video format (e.g. image sequences) and txt
formats for the annotation.

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

_**Expected size** _

The volume of data is estimated at approximately 500KB/image for RGB and 100
KB/image for depth. 15 scenes have been captured ranging from 20 to 60 images
each. Each scene holds a volume of approximately 15 MB (10 MB for color and 5
MB for depth).

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

_**Similar Data sets** _

1\. Princeton Tracking Benchmark

(http://tracking.cs.princeton.edu/dataset.html)

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

**Discoverability and naming conventions**

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

_**Metadata provision** _

For the metadata RGBD sensors have been used. Two aligned streams are used,
one depth camera (640X480) and one RGB (640X480). Annotation has been given
based on the outputs of the algorithms produced, in addition to the manually
defined (ground truth) object poses. The metadata is provided in

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

txt format. Indicative metadata include a) camera position information, b) 3D
pose annotation, c) 3D mesh files of the objects.

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

_**Naming conventions** _

Each sequence is labeled using the type of the objects followed by an integer
indicating the order of detection.

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

**Data sharing**

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

_**Access** _

Open

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

_**Reason for restricting access (if so)** _

No restriction

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

_**Access provision** _

A web portal has been created by CERTH on the Zenodo platform for the data
management that should provide a description of the dataset as well as links
to a download section.

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

_**Software to access data set** _

Commonly available tools and software libraries for enabling reuse of dataset
(e.g.OpenCV).

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

**Archiving, preservation and re-usability**

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

_**Duration of preservation** _

Data will be preserved for at least 2 years after the end of the project.

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

_**Repository of preservation** _

The dataset is preserved on zenodo as well as on CERTH servers and is
available for download. Of course, the portal is equipped with authentication
mechanisms, so as to handle the identity of the persons/organizations that
download them, as well as the purpose and the use of the downloaded dataset.

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

_**Cost of preservation** _

A USB disk drive (approximately 16GB) has been allocated for the dataset.
There are no costs associated with its preservation

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

_**Quality assurance** _

The available datasets have been validated by CERTH and included in relative
publications.

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

**Data set reference and name**

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

**DS.04.CERTH.SnapFitForceProfiles**

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

**Data set description**

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

_**General Description** _

The Dataset is used for training and testing a machine learning classifier in
order to achieve real-time detection of successful snap-fit assemblies.

The Dataset contains force profiles on the axis of motion (assembly), captured
during a robotic and a human assembly process of two different snap-fit
assembly types, namely cantilever and annular.

In robotic assembly, the process is done automatically where a robot holds one
of the two parts and pushes it against the other, until the process is
characterized as successful or failed. In the human assembly process, a human
assembles the two parts while the robot acts as a smart sensor and captures
the developed forces in the axis of assembly.

The data set is split into 8 files, 4 for each snap fit type. One containing
force profiles from the human based process (50 assembly cases) and one
containing force profiles from the robot based process (60 assembly cases).
Their labels (successful or failure) are also included in separate files.

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

_**Origin of Data** _

Device type: A 6 DoF KuKa robot is used for the assembly and force capturing,
along with a wrist forces torque sensor (ATI F/T Mini 40).

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

_**Relation to project objectives** _

Objective 2: To develop a bi-manual robot that enables teaching of assembly
with advanced physical human-robot interaction

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

_**To whom it would be useful** _

This dataset will be useful to analyze and evaluate snap fit assembly types
based on the developed force profiles. It can support any type of detection
and machine learning algorithm for assembly detection and fault prediction.

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

_**Type and format** _

The data will be available in .mat files

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

_**Expected size** _

The data set as described above incorporates 100 human based assemblies and
120 robot based assemblies along with their labels, and is of approximately
3.4 MB.

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

_**Similar Data sets** _

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

1\. Complementary material of the following research item

Huang, Jian, Yuan Wang, and Toshio Fukuda. "Set-Membership-Based Fault
Detection and Isolation for Robotic Assembly of Electrical Connectors." _IEEE
Transactions on Automation Science and Engineering_ (2016).

**Source** : http://ieeexplore.ieee.org/document/7572012/media

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

**Discoverability and naming conventions**

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

_**Metadata provision** _

There is no metadata available.

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

_**Naming conventions** _

Each snap fit assembly process is labeled indicating the order of experimental
execution.

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

**Data sharing**

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

_**Access** _

Open

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

_**Reason for restricting access (if so)** _

No restriction

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

_**Access provision** _

A web portal has been created by CERTH on the Zenodo platform for the data
management that should provide a description of the dataset as well as links
to a download section.

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

_**Software to access data set** _

Any software that can process .mat files such as Matlab, R, Python etc.

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

**Archiving, preservation and re-usability**

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

_**Duration of preservation** _

Data will be preserved for at least 2 years after the end of the project.

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

_**Repository of preservation** _

The dataset is preserved on zenodo as well as on CERTH servers and is
available for download. Of course, the portal is equipped with authentication
mechanisms, so as to handle the identity of the persons/organizations that
download them, as well as the purpose and the use of the downloaded dataset.

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

_**Cost of preservation** _

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

Storage space of approximately 3.4MB is required and there are no costs
associated with its preservation.

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

_**Quality assurance** _

The available datasets is validated by CERTH and is included in publications
currently under review.

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

**Dataset reference and name**

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

**DS.05.CERTH.ContactEvaluationData**

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

**Dataset description**

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

_**General Description** _

Dataset generated by logging wrench forces of the robot’s F/T sensor in
various contact configurations between the assembly parts.

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

_**Origin of Data** _

Device type: F/T sensor. Wrench sensor messages created by ROS
(sensor_msgs:WrenchStamped). TF data between the sensor link and the robot
base_link is included.

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

_**Relation to project objectives** _

Objective (1), WP5: To maintain contact stability.

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

_**To whom it would be useful** _

This dataset will be useful to center of pressure estimation algorithms.

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

_**Type and format** _

ROS bag files and txt formats for the annotation.

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

_**Expected size** _

The volume of data is estimated at approximately 1ΜB/bag file and there are
around 18 bag files for each one of the 3 assemblies. So the total size is
around 50MB.

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

_**Similar Data sets** _

No similar datasets found

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

**Discoverability and naming conventions**

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

_**Metadata provision** _

None

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

_**Naming conventions** _

Each bag file is named using the type of the assembly along with an integer id
and the timestamp of the time that the recording took place.

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

**Data sharing**

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

_**Access** _

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

Open

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

_**Reason for restricting access (if so)** _

No restriction

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

_**Access provision** _

A web portal has been created by CERTH on the Zenodo platform for the data
management that should provide a description of the dataset as well as links
to a download section.

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

_**Software to access data set** _

The Robot Operating System (ROS).

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

**Archiving, preservation and re-usability**

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

_**Duration of preservation** _

Data will be preserved for at least 2 years after the end of the project.

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

_**Repository of preservation** _

The dataset is preserved on zenodo as well as on CERTH servers and is
available for download. Of course, the portal is equipped with authentication
mechanisms, so as to handle the identity of the persons/organizations that
download them, as well as the purpose and the use of the downloaded dataset.

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

_**Cost of preservation** _

A USB disk drive (approximately 16GB) has been allocated for the dataset.
There are no costs associated with its preservation

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

_**Quality assurance** _

The available datasets have been validated by CERTH and have been used for
SARAFun’s contact evaluation.

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

**Data set reference and name**

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

**DS.01.ULUND.TransientDetection**

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

**Data set description**

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

_**General Description** _

Dataset used for evaluation of a recurrent neural network (RNN) for
recognition of transients, in order to detect events during robotic assembly.
Inputs are robot joint torque data. Outputs are probabilities that the event
is occurring, as estimated by the RNN.

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

_**Origin of Data** _

ABB YuMi robot. Joint torque measurements on the right arm with seven degrees
of freedom.

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

_**Relation to project objectives** _

Objective 1: To develop a bi-manual robot that will be capable to learn the
assembly of two parts by human demonstration. During assembly, it is necessary
that the robot detects key events, to determine when to switch between sub-
tasks. Not all assembly robots are equipped with force sensors, hence the
sensor-less approach where joint torques are used.

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

_**To whom it would be useful** _

Robot engineers and researchers that have to create and test transient
detection algorithms, for instance using statistical machine learning.

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

_**Type and format** _

There are 50 trials in total. 50 time series consist of input data, and
another 50 represent the output. These are stored in plain .txt format.

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

_**Expected size** _ 10 MB.

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

_**Similar Data sets** _

The MNIST dataset is similar in the sense that it has the purpose of
evaluating machine learning algorithms.

http://yann.lecun.com/exdb/mnist/

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

**Discoverability and naming conventions**

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

_**Metadata provision** _

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

Input consists of time series of robot joint torques on the arm side in Nm,
with seven channels; one for each robot joint. The output consists of
estimated transient probability in one dimension. The sampling frequency is
250 Hz.

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

_**Naming conventions** _ trq{i}.txt denotes input time series number {I}
snaplog{i} denotes output time series number {i}

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

**Data sharing**

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

_**Access** _

Open

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

_**Reason for restricting access (if so)** _ Not applicable.

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

_**Access provision** _

A web portal has been created by CERTH on the Zenodo platform for the data
management that should provide a description of the dataset as well as links
to a download section.

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

_**Software to access data set** _

Matlab, Python, Julia, or similar programming tools required for data
visualization.

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

**Archiving, preservation and re-usability**

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

_**Duration of preservation** _

Data will be preserved for at least 2 years after the end of the project.

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

_**Repository of preservation** _

The dataset is preserved on zenodo as well as on CERTH servers and is
available for download. Of course, the portal is equipped with authentication
mechanisms, so as to handle the identity of the persons/organizations that
download them, as well as the purpose and the use of the downloaded dataset.

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

_**Cost of preservation** _

The dataset is relatively small in size in the machine learning context. There
are no costs associated with the preservation.

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

_**Quality assurance** _

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

The dataset was included in the peer-reviewed, accepted paper _Detection and
Control of Contact Force_

_Transients in Robotic Manipulation without a Force Sensor,_ to be presented
at ICRA, Brisbane, May 2018.  
  
<table>  
<tr>  
<th>

**Data set reference and name**

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

**DS.01.UNIBI.TactileData**

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

**Data set description**

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

_**General Description** _

Tactile data for slip detection experiments. Various objects are hold by two
KuKa robots between two tactile sensors with different initial forces and
released to create slippage events.

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

_**Origin of Data** _

Two Myrmex sensors attached to KuKa LWR 4 arms.

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

_**Relation to project objectives** _

Objective 4:To develop strategies to improve and maintain grasp stability for
industrial grippers

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

_**To whom it would be useful** _

Comparison of different slip detection algorithms.

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

_**Type and format** _

Tactile data is recorded as ros sensor_msg/image stream @ 1kHz per sensor.

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

_**Expected size** _

230 Mb compressed, 2.4Gb uncompressed.

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

_**Similar Data sets** _

No similar datasets have been found

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

**Discoverability and naming conventions**

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

_**Metadata provision** _

ROS .bag metadata, e.g. timestamps.

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

_**Naming conventions** _

ROS naming convention.

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

**Data sharing**

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

_**Access** _ open

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

_**Reason for restricting access (if so)** _

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

none

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

_**Access provision** _

A web portal has been created by CERTH on the Zenodo platform for the data
management that should provide a description of the dataset as well as links
to a download section.

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

_**Software to access data set** _

Standard ROS tools and image processing software (e.g. OpenCV).

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

**Archiving, preservation and re-usability**

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

_**Duration of preservation** _

Data will be preserved for at least 2 years after the end of the project.

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

_**Repository of preservation** _

The dataset is preserved on zenodo as well as on CERTH servers and is
available for download. Of course, the portal is equipped with authentication
mechanisms, so as to handle the identity of the persons/organizations that
download them, as well as the purpose and the use of the downloaded dataset.

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

_**Cost of preservation** _

A USB disk drive (approximately 16GB) has been allocated for the dataset.
There are no costs associated with its preservation

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

_**Quality assurance** _

The dataset is validated with the standards set by the SARAFun consortium and
through various trials with the SARAFun system.

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

**Data set reference and name**

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

**DS.01.ABB.ExperimentalVerification_GraspQuality**

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

**Data set description**

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

_**General Description** _

Dataset used for measuring grasp quality of automatically design fingers for
industrial robots.

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

_**Origin of Data** _ the following equipment are used to measure the
resistant force and torques of the fingers designed by GAFD, MDF and eGrip
methods:

  * Torque/force sensor (MAGTROL SA – TMB 306/411) measures the torque resistance with analog voltage signal as output. 

  

  * Analog-to-digital convertor (PicoScope 2000) that converts the analog signal from the torque sensor to a laptop through a USB connection. 

  

  * Spring: Adjusting the component through a pull force (will only be used for the force experiment). The spring is attached between the component and the sensor with the purpose to give a certain elasticity to the pull force and prevent impact forces. 

  

  * Cables are used to attach the component to the sensor. 

  

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

_**Relation to project objectives** _

Objective 1: To automate the design process of fingers for industrial robot
grippers.

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

_**To whom it would be useful** _

This dataset will be useful to measure the grasp quality of fingers.

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

_**Type and format** _

The data will be available in Microsoft Excel format (i.e. .csv).

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

_**Expected size** _

The volume of data is estimated at approximately 1.5MB.

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

_**Similar Data sets** _

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

**Discoverability and naming conventions**

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

_**Metadata provision** _

There is no metadata available

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

_**Naming conventions** _

Each experiment iteration is labeled using “combination” followed by an
integer indicating the order of execution.

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

**Data sharing**

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

_**Access** _

Open

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

_**Reason for restricting access (if so)** _

No restriction

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

_**Access provision** _

A web portal has been created by CERTH on the Zenodo platform for the data
management that should provide a description of the dataset as well as links
to a download section.

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

_**Software to access data set** _

Commonly available tools and software libraries for enabling reuse of dataset
(e.g. Excel and Notepad).

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

**Archiving, preservation and re-usability**

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

_**Duration of preservation** _

Data will be preserved for at least 2 years after the end of the project.

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

_**Repository of preservation** _

The dataset is preserved on zenodo as well as on CERTH servers and is
available for download. Of course, the portal is equipped with authentication
mechanisms, so as to handle the identity of the persons/organizations that
download them, as well as the purpose and the use of the downloaded dataset.

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

_**Cost of preservation** _

A USB disk drive (approximately 16 Gigabyte) will be allocated for the
dataset. There are no costs associated with its preservation

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

_**Quality assurance** _

The dataset has been used in a peer-reviewed published paper with title
“Experimental verification of design automation methods for robotic finger”

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

**Data set reference and name**

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

**DS.01.TECNALIA .Human_Performance_of_Bimanual_Assembly**

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

**Data set description**

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

_**General Description** _

Recording of experiments in which volunteer human subjects performed a sliding
insertion task using instrumented objects to measure the kinematics and
interaction forces during unimanual and bimanual manipulation.

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

_**Origin of Data** _

Data were acquired using custom instrumented objects that included infrared
markers for 3D motion tracking by a CodaMotion tracking system and interaction
forces measured by OptoForce 6 d.o.f. force/torque sensors.

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

_**Relation to project objectives** _

Objective 5: To transfer to the robot, knowledge about human sensorimotor
performance during assembly.

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

_**To whom it would be useful** _

Researchers interested in developing biomimetic control policies for assembly

Researchers interested in identifying human behavior for teaching by
demonstration

Researchers interested in studying human sensorimotor behavior

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

_**Type and format** _

Raw data in the form of 3D markers positions and force/torque values

Processed data with computed object pose and wrench

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

_**Expected size** _

Typical size for an experiment with 5-10 subjects: 0.5 – 1 GB

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

_**Similar Data sets** _

These data sets are rather unique in their details, i.e. with respect to the
assembly tasks that are studied and the specific file formats. But the general
nature of the dataset is similar to many datasets collected by research
laboratories centered on the study of human motor control.

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

**Discoverability and naming conventions**

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

_**Metadata provision** _

Metadata includes alignment information from the CodaMotion tracking system

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

_**Naming conventions** _

Data is stored in an anonymous fashion, preventing the association of a given
dataset to an individual human volunteer.

Data from a given individual are grouped under a common filename tag, e.g.
user01, user02, etc.

Data are stored in directory structures denoted ‘raw’ for the original
recording files intrinsic to each data measurement system and ‘splitted’
corresponding to data that have been aligned across sensors and split into
individual trials.

Data are further split according to experiment conditions:

  * BI – bimanual, impaired vision 

  

  * BP – bimanual, normal vision 

  

  * UI – unimanual, impaired vision 

  

  * UIH – unimanual, impaired vision, haptic information 

  

  * UP – unimanual, normal vision 

  

  * UPH – unimanual, normal vision, haptic information 

  

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

**Data sharing**

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

_**Access** _

Embargoed

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

_**Reason for restricting access (if so)** _

The dataset is reserved to researchers involved in the project until the first
scientific report is published in a peer-reviewed journal. It will then be
released for public access.

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

_**Access provision** _

A web portal has been created by CERTH on the Zenodo platform for the data
management that should provide a description of the dataset as well as links
to a download section.

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

_**Software to access data set** _

Preprocessed (“splitted”) are recorded in ASCII text files for universal
access.

Raw data from the CodaMotion tracking system, in proprietary .mdf format, are
also available.

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

**Archiving, preservation and re-usability**

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

_**Duration of preservation** _

2 years past the publication of the first peer-reviewed scientific report.

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

_**Repository of preservation** _

The dataset is preserved on zenodo as well as on CERTH servers and is
available for download. Of course, the portal is equipped with authentication
mechanisms, so as to handle the identity of the persons/organizations that
download them, as well as the purpose and the use of the downloaded dataset.

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

_**Cost of preservation** _

A USB disk drive (approximately 16 Gigabyte) will be allocated for the
dataset. There are no costs associated with its preservation

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

_**Quality assurance** _

Datasets are provided “as is”, but validation of the datasets are assured
through the publication of peerreviewed reports in the international
scientific literature.

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

#  DISSEMINATION AND EXPLOITATION OF OPEN RESEARCH DATA

Data constitutes a strong asset of the SARAFun project, since the several
components of the system developed and tested in real environments throughout
the project have led to the production of a considerable volume of various
datasets. On top of that, considerable new applied knowledge has been produced
during the project, captured in the several SARAFun reports and scientific
publications (ANNEX I).

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 research data at a larger scale. By ensuring that the project’s
results are used by other research stakeholders, we will stimulate the
continuity and transfer of SARAFun outputs to further research and other
initiatives, allowing others to build upon, benefit from and be influenced by
them.

To this end, SARAFun participates in the **Open Research Data Pilot (ORD)**
launched by the European Commission along with the Horizon 2020 programme. In
this context, certain data produced by the project will be published with open
access – though this objective will obviously need to be balanced with IPR and
data privacy principles.

##  SARAFUN OPEN RESEARCH DATA

The main openly exploitable data assets of the project take the following
forms:

  * Open datasets;  Public deliverables; 

  

  * Scientific publications. 

  

###  Open Datasets

Throughout the SARAFun development period, various data has been generated to
aid in the development of different modules of the system or in the creation
of scientific publications. This data was generated by multiple sources, some
of them listed below:

  * Recordings from RGB and Depth sensors (images, video); 

  

  * Questionnaire responses; 

  

  * Measurements on the robot; 

  

  * Force sensor data; 

  

Such data can be anonymised and shared with open access in the form of
statistics, which could be analysed for evaluating algorithms or similar
systems and possibly extracting knowledge from them. Nearly every dataset is
accompanied by several metadata e.g. type, xml, object, etc., which could
support multiple kinds of analysis on the generated data.

###  Public Deliverables

The project has produced and updated more than 40 public reports which
incorporate public data and knowledge produced and integrated during the
3-year duration of the grant. This knowledge revolves around multiple research
fields and disciplines, such as:

  * End-user needs analysis; 

  

  * Industrial application scenarios/use cases; 

  

  * Robotic systems architecture; 

  

  * HRI interfaces; 

  

  * User experience optimization 

  

  * Semantics modelling; 

  

  * Data aggregation/integration techniques; 

  

  * Evaluation methodologies; 

  

  * Dissemination and exploitation of results;  etc. 

  

###  Scientific Publications

Multiple open access scientific publications have been produced in the
framework of the project, published either in conferences or relevant
journals/books. These publications summarize main achievements of the project
that can be further exploited by the scientific community.

##  OPEN DATA DISSEMINATION PLATFORMS

Visibility of the above mentioned assets is the key for allowing other
stakeholders to get inspired by the project and re-use the produced data and
knowledge, so as to fuel the open data economy. To ensure visibility of open
SARAFun resources, several platforms have been employed by the team, where
other researchers and the general public can find information on the project’s
results, but also to download project’s data and documents. These platforms
are listed below:

###  Zenodo

Zenodo is a widely used research data repository, allowing research
stakeholders to search and retrieve open data uploaded by other researchers.
The uploaded datasets can be accessible by anyone (open access) and the
project is provided with a dissemination platform. The project team ensures
that open project resources are regularly uploaded on Zenodo, such as public
deliverables, scientific papers and datasets.

**Figure 1. SARAFun zenodo page**

**Figure 2. The Zenodo page of the first dataset of CERTH**

**Figure 3. The Zenodo page of the first dataset of ULUND**

###  The OpenAIRE platform

Dissemination and exploitation of the project’s open data is supported through
the EC’s OpenAIRE platform, where visitors can access all types of SARAFun
data, searching by various keywords and metadata. Zenodo is linked with the
OpenAIRE platform and every uploaded dataset and publication can be accessed
through it.

**Figure**

**4**

**. The OpenAIRE platform**

#  CONCLUSIONS

The present report constitutes the final version of the SARAFun Data
Management Plan and provided an updated description of the datasets produced
throughout the project, the strategy put in place for their storage,
protection and sharing, as well as the infrastructure implemented to
efficiently manage them. In addition, it presented the project’s measures for
ensuring visibility, sustainability and dissemination of the SARAFun open
research data.

Throughout the project, the consortium needed to manage a large number of
datasets, collected by various means, i.e. sensors, cameras, manual inputs in
robotic systems and direct interactions with users (e.g. interviews and
questionnaires). Almost all the project partners have become SARAFun data
owners and/or producers. Similarly, all the technical work packages of the
project produced data. All datasets have been handled considering the main
data security and privacy principles, respecting also the partners IPR
policies.

As part of the Open Research Data Pilot (ORD), the project has taken measures
to promote the open data and knowledge produced by the project. Interested
stakeholders, such as researchers or industry actors, will be able to access
open resources generated by the project, through various platforms, even
beyond the project’s duration. This way, sustainability of the SARAFun
outcomes will be fostered. However, particular attention needs to be paid on
ensuring that the data made openly available violates neither IPR of the
project partners, nor the regulations and good practices around personal data
protection. For this latter point, systematic anonymization of data is
necessary.