**Initial Data Management Plan**

_A Data Management Plan created using DMPonline_

Creators: Marina Bloj, Sarah Jones, a.logan@bradford.ac.uk

Affiliation: University of Bradford

Template: European Commission (Horizon 2020)

ORCID iD: 0000-0001-9251-0750

Grant number: 765121 **Project abstract:**

Real world tasks as diverse as drinking tea or operating machines require us
to integrate information across time and the senses rapidly and flexibly.
Understanding how the human brain performs dynamic, multisensory integration
is a key challenge with important applications in creating digital and virtual
environments.

Communication, entertainment and commerce are increasingly reliant on ever
more realistic and immersive virtual worlds that we can modify and manipulate.

Here we bring together multiple perspectives (psychology, computer science,
physics, cognitive science and neuroscience) to address the central challenge
of the perception of material and appearance in dynamic environments. Our goal
is to produce a step change in the industrial challenge of creating virtual
objects that look, feel, move and change like ‘the real thing’. We will
accomplish this through an integrated training programme that will produce a
cohort of young researchers who are able to fluidly translate between the
fundamental neuro-cognitive mechanisms of object and material perception and
diverse applications in virtual reality. The training environment will provide
11 ESRs with cutting-edge, multidisciplinary projects, under the supervision
of experts in visual and haptic perception, neuroimaging, modelling, material
rendering and lighting design. This will provide perceptually-driven advances
in graphical rendering and lighting technology for dynamic interaction with
complex materials.

Central to the fulfilment of the network is the involvement of secondments to
industrial and public outreach partners.

Thus, we aim to produce a new generation of researchers who advance our
understanding of the ‘look and feel’ of real and virtual objects in a
seamlessly multidisciplinary way. Their experience of translating back and
forth between sectors and countries will provide Europe with key innovators in
the developing field of visual-haptic technologies.

Last modified: 08-08-2018

#  DATA SUMMARY

Provide a summary of the data addressing the following issues:

  * State the purpose of the data collection/generation 

  

  * Explain the relation to the objectives of the project 

  

  * Specify the types and formats of data generated/collected 

  

  * Specify if existing data is being re-used (if any) 

  

  * Specify the origin of the data 

  

  * State the expected size of the data (if known) 

  

  * Outline the data utility: to whom will it be useful 

  

##  Purpose

All data will be collected for the purpose of addressing a series of defined
research questions. The over-arching aim of the DyViTo network is to
investigate the creation and perception of complex materials- across senses-
in a dynamically changing environment. In order to do this, we will collect
three major types of data.

Firstly, we will collect data from human participants undertaking a range of
behavioural tests which are designed to measure both visual and haptic
sensitivity to materials. We will use these data to determine the properties
of materials which influence our perception of them. Additionally, we will
collect behavioural data on how our perception of materials is affected by
changes over time (e.g. changes in illumination or rotation angle).

Secondly, we will use fMRI, MEG and EEG techniques to collect neuroimaging
data from human participants while they are engaged in perception of
materials. These data will provide us with understanding of the neural
circuitry which underlies human perception of material properties. Linking
these data with the behavioural data outlined above will provide insight into
the link between human brain function and perception.

Thirdly, in order to collect the data outlined above, we will create novel
stimuli. These new stimuli will take two major forms: computer renderings of
object photographs and computer-generated, virtual materials. We will also
collect data on the physical characteristics of these stimuli which will
include optical measurements (e.g. light scatter) and data on the geometry of
a stimulus layout.

##  Relation to Objectives

The DyViTo network is focussed upon three major research objectives:

**RO1.** To understand how humans perceive dynamic changes in shape, material
properties and illumination using novel behavioural and brain imaging
techniques.

The behavioural data that we collect data from human participants performing
tests which measure visual and haptic sensitivity to materials will enable us
to understand material perception. We will collect data that quantifies
changes in performance on these tests which are associated with changes in
material properties (e.g. shape, illumination). These behavioural data will
enable us to understand human perception of dynamic changes in material
properties. To address the second part of this objective, we will employ three
brain imaging techniques: fMRI, MEG and EEG. These neuroimaging data will
enable us to link human perception of material appearance with the neural
circuitry, which underlies these processes.

**RO2.** To measure and model the sensory integration of vision and touch
information as we explore objects to determine their material properties.

We will collect behavioral and brain-imaging data associated with material
perception from two major domains: visual and haptic. Analysis of behavioural
and brain imaging data across these domains will enable us to understand how
the integration of information from both senses influences human perception of
materials. We will use these data to develop models which aim to explain
processing of information from the senses of vision and touch.

**RO3.** To exploit insights from perception science to optimise the
interactive rendering of virtual visual-haptic objects and to support advances
in lighting technology.

We will use the new knowledge generated by the behavioural and brain imaging
data to understand human perception of materials. Based on this understanding,
we will be able to make recommendations on how the parameters of virtual
objects can be manipulated to optimise their appearance and realism for the
human senses of vision and touch. Related to this, our results may be used to
inform the development of lighting technology used to illuminate materials.

##  Types and Formats

  1. Experimental Measurements 

    1. Behavioural data. These data comprise numerical measurements (e.g. accuracy, response time, rating scores on the appearance of materials). Data will be stored in ASCII format (i.e. as a .txt file) which is a widely-used method of storing quantitative behavioural data. To make the data as accessible as possible, this file type does not require proprietary software. Data will be organised with meaningful column headings and units. 

  

    2. MRI data. These data are large-scale numerical measurements consisting of time series of blood flow measurements obtained from multiple voxels within the participants brain. Raw data for brain-imaging measurements are stored in standard formats (e.g., DICOM, Nifti) and then processed using specalised software packages. Intermediate processing stages involve the use of widely used tools (e.g. Excel) and specialist software (e.g. Matlab) for which there are open source tools to ensure that data files can be read without proprietary software. Data will be meaningfully organised with appropriate file headers and filenames, corresponding to date and place of acquisition, experimental condition and anonymised participant ID. 

  

    3. EEG data. These data are large-scale numerical measurements consisting of time series of electrical potential amplitudes, typically recorded with millisecond time resolution at multiple spatial locations simultaneously (e.g. 32-, 64-, 128- or 256- channel locations on an individual participant's scalp). The raw data are typically stored as numerical arrays in binary format (e.g. Matlab *.mat format) which are easily converted to other format. The averaged or otherwise processed data are also readily made accessible in ASCII format with no loss of essential information. Data will be meaningfully organised with appropriate file headers and filenames, corresponding to date and place of acquisition, experimental condition and anonymised participant ID. 

  
  

  

  2. Audiovisual and haptic data 

    1. Audiovisual stimuli. These new real and virtual images and/or movies with or without associated sounds will be computer renderings and/or photographs of materials with lighting variations. They will be stored in an appropriate image/video file format. 

  

    2. Haptic stimuli. 3D Models of printed objects of natural touch stimuli, description of force patterns produced by haptic devices/actuators 

  

    3. Quantitative descriptions of the physical properties of the real world light, materials and object stimuli. 

  
  

  

  

##  Existing Data Re-Use

DyViTo’s research questions and scientific approach is novel and aims to
generate new knowledge. Accordingly, the scope for re-use of existing data is
minimal. Nevertheless, we have identified a small number of areas in which re-
use of existing data is appropriate.

  1. Existing behavioural data. To inform the development of new experimental protocols, existing behavioural data may be inspected, or subjected to computer simulations. These data were all collected by researchers within our network as part of previous studies. Accordingly, there are no issues surrounding copyright or ownership. 

  

  2. Publicly-available videos and images. We will use existing videos and images to analyse the optical properties of depicted materials (e.g. light scatter, reflectance). Media used for this purpose will be free from copyright restrictions and publically available, for example through services suck as Flickr and Wikimedia. 

  

  

##  Origin

  1. Behavioural data. These data will be collected as part of controlled experiments performed in a research laboratory. The data are generated by volunteer participants who provide responses to either visual or haptic stimuli. 

  

  2. Neuroimaging data. These data will be collected as part of controlled experiments performed in a research laboratory. The data are collected via neuroimaging techniques (fMRI, MEG or EEG) from volunteer participants while they are engaged in a material perception task. 

  

  

##  Size

  1. Behavioural data. The size of these text-based data will be small. We estimate that the total will not exceed 90MB (10 MB in each of the 9 institutions). 

  

  2. Neuroimaging data. The file size of the neuroimaging data is considerably larger. We estimate that the total will not exceed 500GB. 

  

  3. Audiovisual data. Video files of the haptic stimuli (500GB), video/images of visual stimuli (50GB). 

  

  

##  Utility

The behavioral data are of long-term value and will be useful to other
researchers investigating perception of materials. The broad scope of the
DyViTo network means that our data could be used across a range of disciplines
(e.g. psychology, neuroscience, computational modelling). Accordingly, all
behavioural data will be preserved and shared openly.

Similarly, the audiovisual data that we create (haptic stimuli videos and
visual stimuli images/videos) will also be useful to researchers in the fields
of visual and/or haptic sensory perception. We will preserve and make
available these audiovisual data.

We will also share data from our neuroimaging experiments, which will be
useful to researchers investigating the neural mechanisms, which underlie
visual and haptic perception. To respect participant confidentiality,
structural MRI scans are only shared once potentially identifiable information
(e.g., scalp and facial features) have been removed.

#  FAIR DATA

**2.1 Making data findable, including provisions for metadata:**

  * Outline the discoverability of data (metadata provision) 

  

  * Outline the identifiability of data and refer to standard identification mechanism. Do you make use of persistent and unique identifiers such as Digital Object Identifiers? 

  

  * Outline naming conventions used 

  

  * Outline the approach towards search keyword 

  

  * Outline the approach for clear versioning 

  

  * Specify standards for metadata creation (if any). If there are no standards in your discipline describe what metadata will be created and how 

  

We have identified the  _UK Data Service_ as a potentially suitable repository
and will follow their instructions regarding metadata. We are also considering
alternative respositories, in particular Zenodo, Psychdata and the University
of Cambridge's institutional repository. All repositories, which we are
considering, include provision for the creation of digital object identifiers,
and we will comply with these processes.

In order to make our data discoverable, we will use keywords that are
appropriate within our subject area.

We will using a naming convention which enables the names of data files to
include information pertaining to the date, time and anonymous participant
identifier.

The laboratory of each PI already operates standard practices, which ensure
version control of data files. With respect to code developed during the
project, the consortium will use version control tools (e.g., svn, git), and
will document this code, for a proper maintenance and reuse of it.

Our data will include documentation intended to support re-use. Specifically,
we will include detailed descriptions of experimental methods and set-up, and
use meaningful column headings and units for all data.

**2.2 Making data openly accessible:**

  * Specify which data will be made openly available? If some data is kept closed provide rationale for doing so  Specify how the data will be made available 

  

  * Specify what methods or software tools are needed to access the data? Is documentation about the software needed to access the data included? Is it possible to include the relevant software (e.g. in open source code)? 

  

  * Specify where the data and associated metadata, documentation and code are deposited  Specify how access will be provided in case there are any restrictions **Which**

  

  1. Behavioural data. We will seek consent from each participant to make available all behavioural data (e.g. 

  

  

accuracy measurements, reaction time, ratings). All data will be fully
anonymised such that individual participants cannot be identified.

  2. Audiovisual data. We will make available all audiovisual data (haptic stimuli videos and stimuli images/videos) created as part of the DyViTo project. We expect that these data will be re-used by other researchers to address new research questions. 

  

  3. Neuroimaging data. To respect confidentiality, we cannot make available MRI structural scans of individual participants unless they have been modified to remove information about the scalp and facial features – this is done upon receiving a request for the data. We will make available all other data collected with the MRI and EEG methods. 

  

  

##  How

We will make use of appropriate data repositories for sharing our data. We
have identified the  _UK Data Service_ as a potentially suitable repository,
and are also considering alternative despositories, in particular Zenodo,
Psychdata and the University of Cambridge's institutional depository. Using
established repositories will ensure that the data can be curated effectively
beyond the lifetime of the project. The depositories which we are considering
all have experience of curating data relevant to our discipline.

##  Methods/Tools Needed to Access Data

All data will be made available with meaningful labels, structured in
appropriate columns and rows and marked with units. Documentation which
describes the format of the data will be included.

Behavioural data will be made available in ASCII format (.txt file) which does
not require proprietary software to access.

Neuroimaging data (DICOM, Nifti) for which open source software tools are
available.

Audiovisual data will be made available in widely acceted and used file
formats (e.g. .tiff, .png, .avi).

**2.3 Making data interoperable:**

  1. Assess the interoperability of your data. Specify what data and metadata vocabularies, standards or methodologies you will follow to facilitate interoperability. 

  

  2. Specify whether you will be using standard vocabulary for all data types present in your data set, to allow interdisciplinary interoperability? If not, will you provide mapping to more commonly used ontologies? 

  

  

To ensure that our data can be re-used by as many researchers as possible,
data will be stored and made available in line with standard practices in
experimental psychology.

All data will be well described and supported with clear documentation, which
describes variable names and abbreviations.

We will make use of appropriate data repositories for sharing our data. We
have identified the  _UK Data Service_ as a potentially suitable repository,
and are also considering alternative despositories, in particular  _Zenodo_ ,
_Psychdata_ and the  _University of Cambridge's institutional depository_ .
Each of these depositories has experience of curating data relevant to our
discipline.

We will comply with the established standards and practices for metadata
implemented by these data repositories. We will take advice from data centres
on relevant metadata standards and documentation.

**2.4 Increase data re-use (through clarifying licenses):**

  * Specify how the data will be licenced to permit the widest reuse possible 

  

  * Specify when the data will be made available for re-use. If applicable, specify why and for what period a data embargo is needed 

  

  * Specify whether the data produced and/or used in the project is useable by third parties, in particular after the end of the project? If the re-use of some data is restricted, explain why 

  

  * Describe data quality assurance processes 

  

  * Specify the length of time for which the data will remain re-usable 

  

Data will be owned by the DyViTo partner responsible for collecting the data.

To permit the widest use possible, we will licence data using an appropriate
and standard licencing mechanism for research data, such as Creative Commons
Attribution-BY (CC-BY) or Open Data Commons Open Database Licence (ODC-ODbL)
licences.

We intend that all valuable data produced by the project will contribute to
publications. Accordingly, data will be made available on the publication date
of the associated article.

Our data will be available for use by third parties, and will remain available
after the end of the project.

#  ALLOCATION OF RESOURCES

Explain the allocation of resources, addressing the following issues:

  * Estimate the costs for making your data FAIR. Describe how you intend to cover these costs 

  

  * Clearly identify responsibilities for data management in your project 

  

  * Describe costs and potential value of long term preservation 

  

##  Costs

We will make use of established repositories, designed for the purpose of
depositing scientific data, which are appropriate within our discipline. There
is no fee for depositing data within these repositories.

The time required by Principal Investigators (PIs) for the preparation of data
was included in the agreed time commitment within the EU Horizon 2020 grant.

The Project Manager (Callie Lancaster) will support PIs and Early Stage
Researchers (ESRs) by assisting with the administrative aspects of outputs and
data deposits.

##  Responsibilities

The DyViTo network is overseen by the Network Co-ordinator (Lead PI), Prof
Marina Bloj. The Network Co-ordinator is supported by a project manager
(Callie Lancaster) who will direct communication and sharing of information
across the network, and manage depository of outputs and data as outlined in
the data management plan. The Network Coordinator and Project Manager are
supported by an Open Science Champion (Dr Andrew Logan) who has responsibility
for overseeing data management across the DyViTo network, and delivery of the
data management plan.

Data collection will take place within the 9 academic institutions which are
affiliated with the DyViTo network. The principal investigator (PI) at each
institution assumes overall responsibility for data management within their
institution.

Day-to-day data collection, processing and analysis will be undertaken by the
ESR assigned to each institution. The ESR will work under direct supervision
of their PI.

Each PI has responsibility for the storage, back-up and quality assurance of
the data collected within their institution.

##  Costs and Potential Value

The data collected throughout the DyViTo project will be of long-term value to
researchers investigating perception of materials in the domains of both
vision and touch. Accordingly, we aim to ensure that our data is curated and
preserved in the long-term.

We have identified a number of established data repositories, which are
appropriate for our discipline, as an appropriate method of achieving this
long-term preservation. There is no cost for depositing data within these
repositories. The time required for ESRs (under supervision of the PI) to
prepare data (time estimate: 1 day per publication) for depositing in these
repositories is directly funded by this EU Horizon 2020 grant.

#  DATA SECURITY

Address data recovery as well as secure storage and transfer of sensitive data

Data collected will initially be saved to the lab computer’s hard drive. At
the end of each day, all data will be copied to a secure institutional server
which is managed by the each beneficiaries’ insittutional IT team. The secure
institutional server is regularly backed-up. Responsibility for the transfer
of data from the lab computer to the secure institutional server will lie with
the PI at each institution.

In most beneficiary institutions, there is an automated file back-up system.
In cases where this is unavailable, a process is in place to ensure safe
transfer of data at the end of each day.

Lab computers are password-protected and housed in locked rooms. Data stored
on institutional servers are secured by password protection.

To minimise storage of personal data, participants will be identified by a
unique identifier number. This identifier number will be used to denote
individual participants within all data files. One additional file will be
created for each institution which links each unique identifier number with
the participant’s initials and date of birth. This decoder file will be
password-protected, encrypted and saved on the secure institutional server.

At the point of making data available, data can be directly uploaded from the
secure institutional server to the external data repository.

#  ETHICAL ASPECTS

To be covered in the context of the ethics review, ethics section of DoA and
ethics deliverables. Include references and related technical aspects if not
covered by the former

**Participant recruitment:** In general, healthy human adult subjects are
recruited from within the population in which the institution is based and
exclusion criteria are designed either to prevent participation of subjects
for whom the equipment is not safe (i.e. someone with back pain would not be
suitable subject for a study requiring them to sit still in front of a
computer responding to visual stimuli via a keyboard) or who suffer from
specific conditions that might compromise the results (colour deficient
individuals would not be suitable participants in a study were colour is one
of the material properties being studied).

In all studies, a gender balanced mix of participants will be sought. In some
experiments, age criteria and handedness will be used to maintain homogeneity
of the sample being studied. All partner institutions have established
mechanisms for recruiting volunteer participants in studies that take into
account issues of inducement (i.e. the financial remuneration offered covers
expenses and compensation for their time while not being so large as to become
a factor in driving participation) and dependant relationships are scrutinised
in accordance with ethics approval processes in place at each site. For each
study, details of their specific recruitment procedure, inclusion/exclusion
criteria and inducement/remuneration are detailed as part of the submission to
the institutional ethics review committee. The DyViTo Ethics Panel will review
these procedures and ethical statements prepared by researchers for their
local institutional ethics committees and keep copies of granted approvals
that will be submitted to Research Executive Agency (REA) upon request.

**Informed consent:** Written informed consent (based on the  _UK Data Archive
example consent form_ ) will be obtained from participants before experiments
begin and particular attention paid to ensure that research data can be
curated and made available for future use. At the outset of the experiment it
will be made clear to participants that they participate voluntarily and that
they have the right to withdraw from the research at any time without giving a
reason (while still receiving remuneration for expenses incurred and
compensation for their time). This is particularly important in the case where
volunteers might be participants in a dependant relationship. For example,
some of the volunteers might be students or individuals employed/line managed
by the researchers carrying out the study, in these instances it will be made
explicit via the informed consent procedures and forms that participation or
withdrawal from the study at any time will not lead to any more or less
advantageous treatment of the individuals in question.

In some cases, the ethics review and authorisation might explicitly disallow
the participation of volunteers in dependant relationship and this will be
noted in the informed consent form. All laboratories ensure that the
participants understand the considerations involved in informed consent. In
general, information about the experiment, its risks and exclusion criteria
will be given to the potential volunteers 24 hours before informed consent can
be given and again at the beginning of the experiment. By allowing a waiting
period between receiving the information and giving informed consent,
volunteers can carefully consider, without pressure, if they want to
participate. In the process of obtaining written consent, the experimenter
interviews the participant to ensure that they understand to what they have
agreed and that they are comfortable with the protocols as explained to them.
In addition to written information, participants will be given a verbal
explanation of the experiments.

For each study details of their specific informed consent procedures are
detailed as part of the submission to the institutional ethics review
committee. The DyViTo Ethics Panel will review these procedures and ethical
statements prepared by researchers for their local institutional ethics
committees and keep copies of granted approvals that will be submitted to
Research Executive Agency (REA) upon request. Template informed consent forms
and detailed information sheets will be prepared for each study and approved
by the appropriate institutional ethics committees and reviewed by the DyViTo
Ethics Panel. Templates of the informed consent forms and information sheets
will be kept in file by the DyViTo Ethics Panel and submitted to the REA on
request.

**Protection of personal data** : All participants in the DyViTo network are
fully committed to following EU, national (British, German, Dutch, Spanish and
Turkish), and institutional (Universities and Museums) ethical standards and
data protection legislation applicable to the collection and protection of
sensitive personal data, when such data is collected. The DyViTo Ethics Panel
will ensure that copies of opinion or confirmation by the competent
Institutional Data Protection Officer and/or authorization or notification by
the National Data Protection Authority will be obtained, kept in the file and
submitted upon request (whichever applies). The studies carried out by the
network do not involve extensive collection of personal data. This will
generally be limited to information such as name, age, gender, handness and in
some cases minimal details of relevant medical history. The later will be
collected for the sole purpose of establishing if inclusion/exclusion criteria
are met, for example to prevent participation of subjects for whom the
equipment is not safe (i.e. someone with back pain would not be suitable
subject for a study requiring them to sit still in front of a computer
responding to visual stimuli via a keyboard) or who suffer from specific
conditions that might compromise the results (colour deficient individuals
would not be suitable participants in a study were colour is one of the
material properties being studied).

In all cases, participants will give written informed consent of the storage,
preservation and anonymised use (including sharing) of their data. Data
privacy will be guaranteed, because the process of anonymisation will occur at
the level of the data-entry. Data will be held on the confidential password-
protected database. In all cases, the participants name will be replaced by a
code that will make any direct identification impossible. Therefore, no
medical or health records or other sensitive information can be re-identified
by a non- authorised administrator. No identifying personal information will
be stored in combination with actual data. Each participant will be assigned a
code. A data sheet, linking subject identifying information (initials and date
of birth) with codes, will be maintained in an encrypted file stored on the
secure institutional server.

Consent forms inform subjects that data can be shared, stripped of any
identifying information. Group average data will be published and made
available to the public under normal guidelines of good practice for
publishing and data sharing taking into account Data Protection Directive (EC
Directive 95/46/EC, and the national law) and will fully comply with national
and EU legislation. Some studies carried out by the DyViTo network involve the
collection of a small amount of personal data that might not be publicly
available. This will generally be limited to information such as name, age,
gender, handness and occasionally minimal details of the participants’
relevant medical history. In all cases we will seek relevant authorisation
before data collection and keep copy of authorisations for submission to the
REA on request.

Detailed information on the informed consent procedures that will be
implemented for the collection, storage and protection of personal data will
be prepared for each applicable study and approved by the appropriate
institutional ethics committees and reviewed by the DyViTo Ethics Panel. This
information will be kept in file by the DyViTo Ethics Panel and submitted to
the REA on request.

**Third countries:** The only third country were research will be carried out
is in Turkey, an associate state. The Project Legal Signatory for Bilkent
Üniversitesi confirms that all the EU ethical standards and guidelines as
described by the Horizon2020 program will be rigorously applied for all the
research carried out in Turkey.

No materials will be imported from Turkey to EU; and no materials will be
exported from EU to a third country. No local resources with ethical issues
(e.g. animal and/or human tissue, samples, genetic material, live, animals,
human remains, materials of historical value, endangered fauna or flora
samples, traditional knowledge etc.) will be used in the research.

The research data collected in Turkey and shared with EU network partners (or
collected in the EU and shared with our Turkish partners) as part of this
project does not include sensitive personal information. It will be anonymised
at the point of recording, managed and stored by Bilkent Üniversitesi in full
accordance with the DyViTo's Data Management Plan which takes into account
Data Protection Directive (EC Directive 95/46/EC).

#  OTHER

Refer to other national/funder/sectorial/departmental procedures for data
management that you are using (if any)

Our data management plan seeks to comply with the data management policies of
the beneficiaries, where one is available:

_University of Cambridge_

_Newcastle University_

_TU Delft_

_University of Southampton_

These data management policies may require beneficiaries to undertake
additional steps to those outlined within the DyViTo data management plan.

Institutional support and training with data management is available to
beneficiaries within the above institutions.