**Introduction**

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

This document describes the data collected during the REVEAL project and how
it has been made Open Access in accordance with the H2020 Open Research Data
Pilot.

For more information about REVEAL data, including Fair Data policy, data
volume, allocation of resources, data security and ethical aspects, see REVEAL
Deliverable D1.5 Data Management Plan.  
  
<table>  
<tr>  
<th>

**2.**

</th>  
<th>

**Data Summary**

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

</td>  
<td>

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

**2.1.**

</td>  
<td>

**Locomotion Data**

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

In the first year of the REVEAL project, experiments were carried out during
game development to inform the most effective VR usability and immersion
techniques. Data was collected in order to evaluate alternative virtual
reality locomotion techniques for use in the REVEAL project. Locomotion is
essential to the creation of Environmental Narrative games (the primary goal
of REVEAL's technologies), but the resulting feelings of motion sickness are
an unresolved problem within the research literature. Questionnaires were used
to collect the preferences of participants in terms of their self-reported
levels of motion-sickness and immersion for different locomotion techniques,
and the software recorded interaction data about player's performance in the
game. Approximately 12MB of game interaction data was recorded in JSON format,
and detailed all player movement within the game accompanied by positional co-
ordinates allowing the player's overall movement through the level to be
visualised graphically. Questionnaire data was collated and entered into an
Excel spreadsheet for statistical analysis (~50k). This data could be useful
to other researchers interested in performing a meta-analysis of studies
investigating virtual reality locomotion techniques.  
  
<table>  
<tr>  
<th>

**2.2.**

</th>  
<th>

**Game Rating Data**

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

A separate data-set was collected of publicly available game-rating data for
PlayStation VR titles on the PlayStation Network Store, consisting of
anonymous scores from players, rating games from 0 to 5. This was collected to
complement an analysis of existing locomotion techniques employed by
commercial developers for PlayStation VR. It represents a snapshot of publicly
available data at a specific point in the evolution of a new console
peripheral (PSVR). This was less than 15k of data stored in Excel format and
would be useful to future researchers seeking to examine the evolution of
virtual reality platforms.  
  
<table>  
<tr>  
<th>

**2.3.**

</th>  
<th>

**Knowledge Test Data**

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

In the second year of the REVEAL project, Knowledge Test Data was collected to
evaluate the effectiveness of Educational Environmental Narrative games on
learning. A knowledge test was used to examine how well the player has learned
the topics in the in-game story. At least 50MB of game interaction data was
recorded in JSON format, detailing all interactions that the player carries
out in the

#  D5.6 OPEN DATA PUBLICATION

This project has received funding from European’s Union’s Horizon 2020
research and innovation programme under grant agreement No 732599. No part of
this document may be used, reproduced and/or disclosed in any form without the
prior written permission of the REVEAL project partners. © 2017 – All rights
reserved.

game, including picking up items and unlocking elements of the story. Again,
questionnaire and knowledge test data was collated and entered into an Excel
spreadsheet for statistical analysis (~200k).  
  
<table>  
<tr>  
<th>

**2.4.**

</th>  
<th>

**Game Evaluation and Interaction Data**

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

At the same time as the knowledge test, additional questionnaires were used to
collect data from the players on how present they felt within the game, how
engaged they were and their cognitive interest whilst the software recorded
interaction data about player's performance in the game.  
  
<table>  
<tr>  
<th>

**2.5.**

</th>  
<th>

**Museum Studies Data**

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

In year two of REVEAL there was also a series of museum-based studies in order
to see if there is potential for narrative-based video games use in the
context of museums that are considered places for “informal learning” and what
such technology could bring to the visiting experience. The data collected
consisted of data gathered from post-game interviews and questionnaires. The
studies were designed and were conducted in accordance with the new GDPR, with
appropriate consent, Participant Information Sheet and anonymization.  
  
<table>  
<tr>  
<th>

**3.**

</th>  
<th>

**Open Access**

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

The REVEAL data are shared using the Creative Commons, CC-BY licence:

_https://creativecommons.org/licenses/by/4.0/_

The data are stored in the Sheffield Hallam University Research Data Archive,
in accordance with REVEAL Deliverable D1.5 Data Management Plan.  
  
<table>  
<tr>  
<th>

**3.1.**

</th>  
<th>

**Locomotion Data**

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

The REVEAL Locomotion Data is Open Access at  _https://shurda.shu.ac.uk/95/_

The associated publication is also Open Access via the same link:

HABGOOD, Jacob, MOORE, David, WILSON, David and ALAPONT, Sergio (2018). Rapid,
continuous movement between nodes as an accessible virtual reality locomotion
technique. In: IEEE VR 2018 Conference. IEEE, 371-378,  
  
<table>  
<tr>  
<th>

**3.2.**

</th>  
<th>

**Game Rating Data**

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

The REVEAL Game Rating Data is Open Access at  _https://shurda.shu.ac.uk/94/_

The associated publication is also Open Access via the same link:

#  D5.6 OPEN DATA PUBLICATION

This project has received funding from European’s Union’s Horizon 2020
research and innovation programme under grant agreement No 732599. No part of
this document may be used, reproduced and/or disclosed in any form without the
prior written permission of the REVEAL project partners. © 2017 – All rights
reserved.

HABGOOD, Jacob, WILSON, David, MOORE, David and ALAPONT, Sergio (2017). HCI
Lessons From PlayStation VR. In: Proceeding CHI Play '17 extended abstracts.
New York, ACM, 125-135.  
  
<table>  
<tr>  
<th>

**3.3.**

</th>  
<th>

**Knowledge Test Data**

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

The scholarly article that this data relates to has not yet been published.
Upon publication of the article, the data will be made Open Access and this
document will be updated accordingly.  
  
<table>  
<tr>  
<th>

**3.4.**

</th>  
<th>

**Game Evaluation and Interaction Data**

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

The scholarly article that this data relates to has not yet been published.
Upon publication of the article, the data will be made Open Access and this
document will be updated accordingly.  
  
<table>  
<tr>  
<th>

**3.5.**

</th>  
<th>

**Museum Studies Data**

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

The scholarly article that this data relates to has not yet been published.
Upon publication of the article, the data will be made Open Access and this
document will be updated accordingly.

#  D5.6 OPEN DATA PUBLICATION

This project has received funding from European’s Union’s Horizon 2020
research and innovation programme under grant agreement No 732599. No part of
this document may be used, reproduced and/or disclosed in any form without the
prior written permission of the REVEAL project partners. © 2017 – All rights
reserved.