#  Introduction

The current document constitutes the interim version of the Data Management
Plan (DMP) elaborated in the framework of INVITE, which has received funding
from the European Union’s Horizon 2020 Research and Innovation programme under
Grant Agreement No 763651.

INVITE is set on co-creating a well-connected European Open Innovation (OI)
ecosystem. It envisions an OI ecosystem in which knowledge meaningfully flows
across borders and is translated into marketable innovations, bringing
increased socio-economic benefits to EU citizens. To this end, INVITE will co-
design, pilot and demonstrate a pan-European service platform, the **Open
Innovation 2.0 Lab** , aiming to better link the currently fragmented
innovation systems of the EU by facilitating meaningful cross-border knowledge
flows; empower EU businesses with the skill-sets required to tap into Europe’s
knowledge-base and turn it into value; and increase the participation of
private investors in OI and collaborative innovation projects.

The Open Innovation 2.0 (OI2) Lab will experiment with novel bottom-up
collaborative models of innovation and leverage **OI support services** and
**ICT tools** to stimulate and support OI across Europe, building a vibrant
community of OI actors and stakeholders (including business, science and
research, public authorities and civil society) along the way. The valuable
knowledge, evidence and experiences gained through the experiments of the OI2
Lab will be diffused across the EU so as to fuel their replication and scale-
up for the benefit of the European economy and society as a whole.

To this end, INVITE brings together a well-balanced and complementary
**consortium** , that consists of **9 partners across 5 different European
countries** , as presented in the following table.

##  _Table 1: INVITE partners_  
  
<table>  
<tr>  
<th>

**No**

</th>  
<th>

**Name**

</th>  
<th>

**Short name**

</th>  
<th>

**Country**

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

1

</td>  
<td>

Q-PLAN INTERNATIONAL ADVISORS

</td>  
<td>

Q-PLAN

</td>  
<td>

Greece

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

2

</td>  
<td>

STEINBEIS INNOVATION

</td>  
<td>

SEZ

</td>  
<td>

Germany

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

3

</td>  
<td>

EUROPE UNLIMITED

</td>  
<td>

E-UNLIMITED

</td>  
<td>

Belgium

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

4

</td>  
<td>

RTC NORTH

</td>  
<td>

RTC NORTH

</td>  
<td>

United Kingdom

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

5

</td>  
<td>

NINESIGMA EUROPE

</td>  
<td>

NINESIGMA

</td>  
<td>

Belgium

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

6

</td>  
<td>

INTRASOFT INTERNATIONAL

</td>  
<td>

INTRASOFT

</td>  
<td>

Luxembourg

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

7

</td>  
<td>

CENTRE FOR RESEARCH AND TECHNOLOGY HELLAS

</td>  
<td>

CERTH/ITI

</td>  
<td>

Greece

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

8

</td>  
<td>

WIRTSCHAFTSFOERDERUNG REGION STUTTGART

</td>  
<td>

WRS

</td>  
<td>

Germany

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

9

</td>  
<td>

NORTH EAST LOCAL ENTERPRISE PARTNERSHIP

</td>  
<td>

NELEP

</td>  
<td>

United Kingdom

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

In this context, **all partners of INVITE’s consortium adhere to sound data
management** in order to ensure that the meaningful data collected, processed
and/or generated throughout the duration of the project is well-managed,
archived and preserved, in line with the  _Guidelines on Data Management in
Horizon 2020_ .

Along these lines, this **interim version of the DMP** builds upon and
significantly enriches the initial version of INVITE’s DMP in order to achieve
the following **objectives** :

  * Describe the data management lifecycle for the data to be collected and/or generated in the framework of INVITE, serving as a key element of good data management. 

  

  * Outline the methodology employed to safeguard the sound management of the data collected, and/or generated as well as to make them Findable, Accessible, Interoperable and Re-usable (FAIR). 

  

  * Provide information on the data that will be collected and/or generated and the way in which it will be handled during and after the end of the project along with the standards applied to this end. 

  

  * Describe how the data will be made openly accessible and searchable to interested stakeholders as well as its curation and preservation. 

  

  * Present an estimation of the resources allocated to make data FAIR, while also identifying the responsibilities pertaining to data management and addressing data security. 

  

In order to achieve its objectives, this interim version of the DMP takes into
account and builds upon common best practices and guidelines for sharing the
project’s data (such as the practices described by the  _Consortium of
European Social Science Data Archives tour guide on data management,_ _UK
Data_ _Service_ ,  etc.) and facilitating open access (through the use of
_openly accessible repositories_ )  to the data collected/generated while
taking into account the recommendations provided by the European Commission
(hereinafter referred to as Commission) based on the evaluation of INVITE’s
initial DMP.

With the above in mind, this interim version of **the DMP is structured in 7
distinct chapters** , as follows:

  * **Chapter 1** provides introductory information about the DMP, the context in which it has been elaborated as well as about its objectives and structure. 

  

  * **Chapter 2** presents a summary of the data to be collected/generated during the activities of INVITE including the purpose of its collection/generation as well as its types and formats. Additionally, it outlines its origin, expected volume and the stakeholders that may find it useful. 

  

  * **Chapter 3** describes the methodology that is applied in the framework of INVITE in order to safeguard the effective management of data across their entire lifecycle, making it FAIR. 

  

  * **Chapter 4** estimates the resources required for making the project’s data FAIR, while also identifying data management responsibilities. 

  

  * **Chapter 5** outlines the data security strategy applied within the context of INVITE along with the respective secure storage solutions employed. 

  

  * **Chapter 6** addresses ethical aspects as well as other relevant considerations pertaining to the data collected/generated during the implementation of the project. 

  

  * **Chapter 7** concludes on the next steps foreseen in the framework of the project with respect to its data management plan. 

  

Finally, the **Annex** of this document includes the privacy policy adopted by
the project’s Web Portal as well as an initial draft version of the privacy
policy that will be employed in the framework of OI2 Lab. Moreover, templates
for the informed consent form and information sheet used in the implementation
of the project’s activities that collect/generate data are settled in the
Annex of this document.

**The DMP is not a fixed document** . It evolves during the lifespan of the
project. More specifically, the DMP will be **updated at least once more
during INVITE** **(i.e. as D7.4 at M36)** . Additional ad hoc updates may also
be realised (if necessary), to include new data, better detail and/or reflect
changes in the methodology or other aspects relevant to its management (such
as costs for making data FAIR, size of data, etc.), changes in consortium
policies and plans or other potential external factors. Q-PLAN is responsible
for elaborating the DMP and with the support of all partners will update and
enrich it when required.

#  Data summary

INVITE will collect/generate meaningful non-sensitive data that do not fall
into any special categories  1  of personal data as those are described within
the General Data Protection Regulation  2  3  (GDPR). This data may be
quantitative, qualitative or a blend of those in nature and will be analysed
from a range of methodological perspectives with a view to producing insights
that will successfully feed INVITE’s activities, enable us to deliver
evidence-based results and ultimately achieve the objectives of the project.
With that in mind, the second chapter of the Data Management Plan (DMP) starts
by explaining the purpose for which this data will be collected/generated and
how it relates with INVITE. It proceeds by describing the different types and
formats of this data as well as its origin and expected volume, before
concluding with an overview of potential stakeholders for whom it may prove
useful for re-use.

##  Purpose of data collection/generation and its relation to the project

In order to successfully meet its objectives and ensure the production of
evidence-based results, INVITE entails several activities during which data
will be collected/generated. The purpose for which this data is
collected/generated is interrelated with the objective of the activity during
which it is produced.

In particular, these activities along with their objectives in the framework
of INVITE are as follows:

  * **Analysis of successful European Open Innovation (OI) support service providers and platforms** , which are interconnected with INVITE, in order to identify potential gaps and opportunities in the respective market targeted by the Open Innovation 2.0 (OI2) Lab and its value propositions. 

  

  * **Analysis of** **the needs and requirements of prospective users and stakeholders** , aimed at shedding ample light on their views on and experiences with the European OI ecosystem and ultimately at fuelling the demand-driven design and development of INVITE’s pilots and OI2 Lab. 

  

  * **Analysis of ideas and feedback** **collected during the INVITE Co-Creation Workshop** , with a view to informing the co-design of INVITE’s pilots and OI2 Lab as well as for elaborating recommendations on how they may be implemented and customised according the needs of users and stakeholders. 

  

  * **Monitoring, co-evaluation and validation of INVITE’s pilots and OI2 Lab** , in order to improve and fine-tune their design and offers based on data and feedback collected/generated by users of the OI2 Lab and stakeholders participating in the project’s pilot activities. 

  

  * **Improvement and validation of the business models designed for the post project market rollout of the OI2 Lab** aimed at producing a set of commercially viable and sustainable business models for the OI2 Lab taking into account the needs of potential users/customers as well as the interests and visions of INVITE’s consortium partners. 

  

  * **Monitoring and evaluation of the results produced by the project’s stakeholder engagement activities** in order to effectively measure and report the progress of these activities towards building up a vibrant community of diverse OI stakeholders around the OI2 Lab. 

  

  * **Monitoring and assessment of the dissemination and communication results** of the project with a view to measuring the impact of the project’s relevant activities, accordingly fine-tuning INVITE’s strategy in this respect as well as fulfilling its reporting requirements towards the Commission. 

  

On top of the aforementioned, data will be collected/generated by the
applicants of the Open Innovation Competitions (OICs) to be launched in the
framework of INVITE. The purpose of this data collection/generation is to
select the award participants to be provided with financial support in the
form of innovation vouchers for short-term virtual Human Capital Mobility.
Still, neither the platform that will host the OICs nor the exact data to be
collected/generated are determined from the consortium partners. With that in
mind, this category of data will be further elaborated in the final version of
the DMP (M36). Along these lines, consortium partners acknowledge the
importance of this data, and thus they will handle it according to the
national and European laws with respect to the processing of personal data.

The following section provides further details on the different types and
formats of data collected\generated during the project’s activities.

##  Types and formats of collected/generated data

During the activities of INVITE, data of different nature will be
collected/generated. The types of this data can be described in many different
ways depending on the source and physical format of the data. Nevertheless,
data is often seen as how it is created/captured  4  . Examples include
electronic text documents, spreadsheets, questionnaires and transcripts, among
others. Another way to think about data is the format in which different data
types (qualitative, quantitative, etc.) are stored. Along these lines,
INVITE’s data will be available in easily accessible formats, such as post
scripts (e.g. pdf, xps, etc.), machine readable formats (xml, html, etc.),
spreadsheets (e.g. xlsx, csv, etc.), text documents (e.g. docx, rtf, etc.),
compressed formats (e.g. rar, zip, etc.) or any other format required by the
objectives and methodology of the activity within the frame of which it is
produced.

In this respect, special attention will be paid in using **open formats** 5
(such as csv, pdf, zip, etc.) and/or **machine-readable formats** 6  (such as
xml, json, rdf, html, etc.) when possible in order to enhance the
**interoperability** and **re-use** of INVITE’s data. In doing so, we will be
providing data that is **easily readable** and **freely usable in any software
program** employed by third-parties interested in utilizing the data.

With that in mind, the type and formats of the data collected/generated in the
framework of INVITE can be divided into **3 distinct categories** , namely
**(i)** data collected/generated by direct input methods and **(ii)** users of
the OI2 Lab as well as **(ii)** data collected/generated from dissemination,
communication and stakeholder engagement activities, as further described in
the following subsections.

###  _Data collected/generated through direct input methods_

In the framework of INVITE, direct input methods encompass methodologies for
collecting data through interactions between consortium partners and external
stakeholders, with the latter providing data to the former. Along these lines,
external stakeholders assume the role of a data subject that is a natural
person whose personal data is being processed  7  . In particular, the
identification and selection of suitable data subjects are based on purposeful
sampling according to which, external stakeholders are identified and selected
by consortium partners based on their role within the OI ecosystem and the
objectives of the respective activity for which data is collected  8  .

In this context, quantitative and qualitative data will be collected/generated
during the course of INVITE:

  * **Quantitative data** is numerical and acquired through counting or measuring  9  . Examples of quantitative data are the yearly turnovers of a business, the hourly compensation of a worker, the number of SMEs in Europe, etc. This data may be represented by ordinal, interval or ratio scales and lend themselves to statistical manipulation. 

  

  * **Qualitative data,** sometimes referred to as categorical data, is data that can be arranged into categories based on physical traits, gender, colours or anything that does not have a number associated with it  10  . Moreover, written documents, interviews, and various forms of in-field observation are all sources of qualitative data. Examples of quantitative data are the preferences of learning, skillsets, country of origin, etc. 

  

With that in mind, further details with respect to the different types and
formats of data that will be collected through direct input methods under the
frame of INVITE are provided in the remainder of this subsection.

####  Market gaps and opportunities

This data has been collected in two phases, with a view to supporting the
analysis of successful OI support service providers and platforms, which are
interconnected with INVITE. The first phase involved a desk review of
secondary data sources that are available in the public domain. The second
phase was implemented through a series of semi-structured in-depth interviews
with respondents who work for either the Enterprise Europe Network (EEN),
NineSigma or Steinbeis. The data collected consists of a combination of
information extracted from the secondary data sources and information provided
by the respondents during the in-depth interviews. In both cases, the data
collected are mainly of a qualitative nature and recorded in the form of
interview transcripts, containing information regarding how the OI
platforms/service providers under study currently operate in terms of their
value propositions, target audiences, service offerings (both online and
offline) and commercial models.

####  User needs and requirements

This data was collected during the qualitative interview-based survey that was
conducted in the framework of INVITE, which aimed at revealing the needs and
requirements of prospective users and stakeholders of the OI2 Lab. In
particular, a stratified purposeful sampling methodology was employed in order
to include diverse OI stakeholders of the quadruple helix (i.e. from
businesses and investors in the private sector over to academia and public
authorities as well as civil society) and across different regions in Europe.
Data in the form of interview transcripts was collected by means of semi-
structured interviews and encompass information on the different ways in which
OI actors are currently engaged (or not) in OI, various enablers / barriers
that appear to be fostering / hindering their participation in OI as well as
insights into the perceived knowledge, skills and support that they may need
in order to successfully adopt and apply OI.

####  Ideas and feedback collected during the INVITE Co-Creation Workshop

In the framework of the INVITE Co-Creation Workshop, a diverse group of OI
stakeholders engaged in a series of co-creative brainstorming and ideation
sessions to co-define demand-driven designs and features for INVITE’s pilots
and OI2 Lab. In particular, the World Café process  11  was used during the
workshop and six tables were set up to discuss six themes, each of which
addressed key aspects of the pilots, services and tools to be deployed through
the OI2 Lab. The discussions followed a semi-structured approach and were
guided by six key questions, unique to each theme. Data of both qualitative
and quantitative nature, as derived from the participants’ responses to each
of these six questions per theme along with any additional ideas, were written
up and collected by means of post-it notes  12  .

####  Pilot monitoring, co-evaluation and validation data

Users and stakeholders who will participate in the pilot activities of INVITE
and its OI2 Lab will be required to provide feedback as part of an ongoing
monitoring framework that will be established to keep track of, coevaluate and
validate their performance, ultimately fuelling their demand-driven
improvement.

To this end, both qualitative and quantitative data will be collected from
pilot participants by means of questionnaire-based surveys aimed at capturing
customer experience metrics (e.g. a Net Promoter Score) as well as key general
information relating to how users found out about the OI2 Lab, the main
objectives that drove them to use it, the components and services used as well
as to the experiences and outcomes derived from their participation in the
pilots. Specific data on the impact of the pilots will also be collected,
revolving around themes such as the degree of integration of OI in the users’
business model, external knowledge search and acquisition, collaboration with
other stakeholders, occasional vs continuous engagement in OI activities,
disruptive vs incremental innovation, internal innovation capability, time-to-
market, level of proficiency gained in collaborative innovation, scale
achieved in terms of outreach (volume, sectoral and geographical), fundraising
capacity, staff impact, organizational impact and cost-benefit.

####  Feedback on the OI2 Lab business models

This data will be collected through a questionnaire-based survey of pilot
participants as well as members of the project’s Advisory Board identified as
potential early adopters / lead users, with a view to gaining meaningful
feedback on how the business models of the OI2 Lab may be refined and further
shaped to fit the needs of potential users and stakeholders. The data will be
of both qualitative as well as quantitative nature addressing the
appropriateness and acceptance of different elements of the different Business
Model Canvases and Value Propositions (e.g. customer relationships,
collaborators, revenue streams, cost structure, etc.) that will constitute the
business models designed for the OI2 Lab.

Data collected/generated through direct input methods will be **stored in
standard .docx** as well as . **xlsx** **formats** . These kinds of formats
allow the documentation of information coming from various files and documents
so that it exists in a single location. By doing so, it is possible to
circulate raw data from transcripts, as well as text, images and other objects
from other files to one document file or multiple tabs of a single
spreadsheet. Moreover, both formats can be immediately converted into open and
machinereadable formats (such as .xml and .csv) boosting the interoperability
and re-usability of the datasets produced during the implementation of INVITE.

###  _Data collected/generated by users of the Open Innovation 2.0 Lab_

The OI2 Lab platform aims to facilitate the needs of potential users and
stakeholders of an extended OI ecosystem. With that in mind, a suite of
tailored ICT tools will be leveraged with a view to structuring a vibrant web-
based community of OI actors and create value for all of them within the OI
ecosystem. The ICT tools include: **(i)** an **open multi-sided marketplace**
, **(ii)** an **online collaboration space** , **(iii)** an **e-learning
environmen** t as well as **(iv)** a **crowdfunding tool** . Within this
framework, users of various roles, such as SME representatives, advisors and
mentors with field expertise and account managers are expected to utilize the
functionalities offered by the ICT tools which in their turn will generate
valuable data for the consortium partners.

On another note, external data is expected to be sourced, reformatted and
presented accordingly from other open innovation initiatives such as, but not
limited, to the European Enterprise Network, NineSigma as well as data sourced
by local actors such consortium members active in the OI sphere in their
respective regions.

Along these lines, data collected by the users of the OI2 Lab incorporates:

  * Data provided by OI2 Lab participants including personal details such as name, contact details, social media accounts, organisation, date of birth and other. Additionally, personal preferences will be collected with respect to fields of interest, expertise, activities as suggested directly by users that will help the OI2 Lab platform personalise the content delivered to users during the pilot phases. 

  

  * Data based on tracking the user’s activity across the OI2 Lab platform and will be utilised towards further enhancing the personalisation of the user experience throughout the platform. The overarching goal will be to support: (a) a data driven reiteration and adaptation development process for the consortium in order to identify processes that need enhancements and/or (b) functionalities that are of low or no interest and could be deprecated, as they provide no additional value to participants. Activity data will be collected for all roles and stand to not only streamline processes, but also allow the consortium members to identify the most prominent features required and utilised by OI participants, which will subsequently support finalisation of business modelling and commercialisation efforts. 

  

Further details of data collected from the ICT tools are provided over the
following subsections.

####  Open multi-sided market place data

One of the main scopes of the overall architecture includes bringing together
all the ICT tools functionality under a well-designed Open multi-sided market
place. The main feature of this process is a global user registration and
profile creation, whereby potential users of the OI2 Lab will be able to
access the functional elements and subsequently the value that will be offered
by participating. As such, the registration process includes the following
data collection process:

  * **Required Data:** This set of data will be required for registration and include personal data for profile creation including: 

    * Full Name o City, country o Date of birth o Organisation/SME o Job Function o Notification settings (email, platform only or desktop, etc.) o Privacy settings (publicly available or private profile) 

  

  

  * **Optional Data:** The second set of data to be collected will be optional but highly important in allowing the matchmaking algorithms to match users with relevant content throughout the platform and drive engagement and user experience. Such data will include among others: 

    * Basic interests (e.g. Technology offer/request, Competitions, etc.) 

  

    * Fields of interest/expertise (e.g. machine learning, renewable sources, e-health, etc.) 

  

  

In addition to personal profile data, the Open multi-sided marketplace will
accommodate tracking of user activity to further inform the matchmaking engine
included in prioritizing notification settings and matching OI2 Lab users with
content or other users (via introductory notifications and search results
adaptive sorting) with common interests and activity. All personally
identifiable data will be tied with a unique userID per participant, which
will be utilised across the ICT tools and processes. In the case, whereby
users wish to withdraw their participation and delete their account, all
personally identifiable data will be deleted and the userID will no longer be
associated with a particular profile. The overall data collected will be
stored in the OI2 Lab MySQL database under the required schema to facilitate
process automation.

####  Online collaboration space data

In order to facilitate user interaction and collaboration the OI2 Lab will
foster an online collaboration space. This will leverage user profiles and
allow participants to:

  * Communicate directly with account managers assigned to them as part of the support process; 

  

  * Ask questions in a Q&A format; 

  

  * Create topics for discussion among platform users; 

  

  * Create work groups on collaborative projects. 

  

As such, the platform will have to store messages, attachments and activity in
an identifiable manner according to userID in the OI2 Lab MySQL database.
Furthermore, any registered user can tag the collaborative spaces or
discussions they initiated as private (i.e. among collaborating parties) or
public allowing open participation. UserIDs will link back to user profiles,
unless someone terminates its profile. As an extension to this, users apart
from being disassociated from content contributed, they may also have the
option to delete the relevant content they contributed to one of the
communication and collaboration spaces they participated.

####  E-Learning environment data

One aspect that stands to accelerate open innovation participation and
efficiency is access to learning material relevant to OI. The E-learning
environment to be integrated to the OI2 Lab, will be based on the  _Moodle_
open source e-learning environment adapted accordingly to facilitate the needs
of the platform. Course creators will be able to upload content, append fields
of relevance and all necessary details for final submission. Conversely, live
webinars will be created including date and participant participation data
that need to be circulated with the instructors and among other users
depending on the level of anonymity required. As such the e-learning
environment will not only include course or webinar specific data but also
personal data in some cases.

All data including personal preferences and activity will be stored in the OI2
Lab database and lend themselves for access by the matchmaking engine of the
Open multi-sided marketplace to facilitate notifications, content placement
and personalisation, ultimately supporting engagement and re-engagement of
platform participants. Similarly, to the other use cases, personally
identifiable data will only be accessible as long as users remain registered
in the OI2 Lab platform environment.

####  Crowdfunding tool data

An important function of any open innovation platform in support of
participating SMEs is access to financing options. Besides state or EU driven
financing tools listed, the OI2 Lab will also provide a crowdfunding tool
whereby participants will have a chance to showcase their projects in search
of sourcing financial support from the general public or investors. In this
respect, crowdfunding campaign creators will upload their proposed projects
and relevant contents, stored in the platforms database, and decide whether
the campaign should be publicly accessible or only visible to registered
users. Following that, users (SMEs, individuals, investors) that want to
participate will be able to pledge their support in an anonymised manner,
whilst only the campaign creator will have access to their personal contact
info.

The matchmaking engine will have access to the campaign details as registered
in the OI2 Lab database and subsequently match them with users with expressed
or relevant interest with notifications and emails in order to drive
engagement. Campaign data will also be conveyed to account managers to enhance
support extending beyond the platform participants. Crowdfunding campaign
creators will have full control over the timeframe of their campaign up to a
designated time limit, inherently coded in the platform, and may proceed with
editing or deleting their campaign and relevant content if they so choose.

####  Web portal analytics

The OI2 Lab will be supported by platform exclusive tracking and analytics
software based on the  _Matomo_ open source analytics platform. This
implementation will make sure that the project maintains 100% data ownership,
user privacy is protected and that user-centric insights can be generated and
leveraged across the board.

To track visitors, Matomo will be configured to use 1  st  party cookies, set
on the OI2 Lab domain. Cookies created by Matomo start with: _pk_ref,
_pk_cvar, _pk_id, _pk_ses. Users that wish to be excluded from being tracked
using the cookie method, will be allowed to do so via opting out which will
create a cookie piwik_ignore set on the domain of the Matomo server hosted in
the OI2 Lab server environment. In case of account deletion all first party
cookies will be disabled from being set — for example for privacy reasons.
First party cookies track among others the following data:

  * User IP address 

  

  * Optional User ID 

  

  * Date and time of the request 

  

  * Title of the page being viewed (Page Title) 

  

  * URL of the page being viewed (Page URL) 

  

  * URL of the page that was viewed prior to the current page (Referrer URL) 

  

  * Files that were clicked and downloaded (Download) 

  

  * Links to an outside domain that were clicked (Outlink) 

  

  * Pages generation time (the time it takes for webpages to be generated by the webserver and then downloaded by the user: Page speed) 

  

  * Location of the user: country, region, city, approximate latitude and longitude (Geolocation) 

  

Other optional data or events may also be tracked in order to further enhance
a data driven implementation plan along future iterations of the platform.
These may include: (i) custom dimensions, (ii) custom variables, (iii)
campaigns, (iv) site search, (v) goals, (vi) events, (vii) e-commerce, (viii)
viewing and clicking on content.

###  _Data collected/generated from dissemination, communication and
stakeholder engagement activities_

####  Social media statistics (including Facebook, Twitter, LinkedIn, YouTube)

This data will be collected/generated through a periodic monitoring of the
project’s social media statistics (including Facebook, Twitter, LinkedIn and
YouTube) with a view to measuring and assessing the performance and results of
the project’s social media activity in terms of dissemination and
communication. With that in mind, the data will be both qualitative as well as
quantitative in nature addressing the metrics reached on each channel (e.g.
followers, tweets impressions on twitter, friends, likes on Facebook etc.).
Additionally, this data will be followed by an analysis of the results
stemming from it and possible ways to improve the results so as to reach the
project’s targets. All in all, the data will be stored in a Microsoft excel
file (.xlsx) while at the same time the analysis of the results will be stored
in a standard word document (.docx).

_**Data collected from project events (e.g. co-creation workshop, stakeholder
engagement events, etc.)** _ This data will be collected in 2 ways during the
implementation of the project, that is:

  * The stakeholder engagement events organised by INVITE (such as the co-creation workshop, regional stakeholder engagement events, etc.) consisting of the participants lists that will enclose demographic information about the participants; 

  

  * The participation of INVITE consortium partners in third party relevant events to reach out and engage stakeholders, thus including general information about the events attended and their outreach. 

  

Along these lines, this data is collected so as to keep track of the results
of stakeholder engagement activities and provide the opportunity to project
partners to report on these activities. Moreover, this data will be updated
every time a partner attends an event, or a partner organises an event.
Finally, the data will be both quantitative and qualitative in nature and will
be stored in a standard spreadsheet (.xlsx).

####  Newsletter subscriptions (e.g. contact details of subscribers)

A subscription form hosted in the project’s  _web portal_ will aid the
collection of this data in which any interested stakeholder can freely provide
his/her contact details in a dedicated sign-up form so as to receive the most
up-to-date news and outcomes of the project. A newsletter will be sent to
subscribers once per 4 months while a short version of it will be distributed
every month via an e-mail message. With that in mind, this data will be
collected so as interested stakeholders can be informed about the INVITE
project as well as the OI2 Lab. Along these lines, the data will be comprised
of a list of stakeholders along with their personal information. In this
context, the data collected include the following information: (i) email
address, (ii) first and last name, (iii) country, (iv) type of organisation,
(v) region and (vi) gender. A copy of this contact list will be stored to
MailChimp’s server (  _http://mailchimp.com_ )  , which is used for e-mail
campaigns and newsletters distribution. All personal information included in
this contact list will be used and protected according to MailChimp’s Privacy
Policy.

####  Data from dissemination and communication

This data will be collected through a periodic monitoring of the project’s
miscellaneous dissemination activities such as publications in relevant
journals, posts in the blogs, etc. The data will consist of a list of
publications and posts published by the consortium partners. The purpose of
collecting this data is to assess the outreach and efficiency of the
dissemination activities during the implementation of the project. For this
purpose, a template has been shared with all partners to recommend activities
to be performed and log the activities they performed. The template is
provided also online so as the partners can directly update their input.
Finally, all the data will be integrated in a single excel file (.xlsx).

####  Data from monitoring stakeholder engagement

This data is collected during the project’s stakeholder engagement activities
with a view to effectively measuring and reporting the progress of these
activities. With that in mind, a dedicated methodological tool has been
designed and is being employed throughout the duration of INVITE, namely the
Stakeholder Matrix. Each project partner sets-up an internal Stakeholder
Matrix ensuring the confidentiality of the data included. In this respect,
this Stakeholder matrix includes data about key stakeholder groups and
individual stakeholders spanning across the quadrable helix innovation system.
These are classified by organisation name, contact person (incl. gender,
region/nation), contact details and activities in which they have been
involved. At least 120 stakeholders shall be identified by each partner
throughout the duration of the project in order to bring together at least
1000 members for the Open Innovation 2.0 Lab community. Finally, project
partners must only send an anonymised Stakeholder Matrix (with only data on
organization type, gender and region / country) to the project’s Innovation
Manager, that is SEZ, for aggregating the data and updating the aggregated
Stakeholder Matrix of the project at least on a semester basis as well as ad
hoc when deemed necessary. The Stakeholder Matrix is stored in a standard
excel file (.xlsx).

##  Origin of data and re-use of pre-existing data

In the context of INVITE, **new data** will be collected/generated by
consortium partners as well as external stakeholders participating in the
activities of the project and/or using the OI2 Lab. With that in mind and
aside consortium partners, **external groups of stakeholders from which new
data will originate include** :

  * Innovative entrepreneurs (social or not) as well as CEOs and (OI) managers of SMEs (including microfirms) as well as OI managers and practitioners in large enterprises. 

  

  * Knowledge, technology and innovation solution providers (e.g. within academic institutions and their technology/knowledge transfer offices, non-university public research organisations, research and technology organisations, high-tech SMEs and large enterprises, etc.). 

  

  * Policy designers and implementers at regional, national and EU level (e.g. in regional/national/EU authorities, development agencies, etc.). 

  

  * Financers from the private funding sector in both mainstream finance markets (e.g. venture capital, business angels, etc.) as well as more alternative ones (e.g. award-based/equity-based crowdfunding, peer-to-peer consumer lending, etc.). 

  

  * Staff members of non-governmental organisations as well as representatives of civil society groups active within the European OI ecosystem and aiming to address social challenges and needs; and 

  

  * Other stakeholders (e.g. OI practitioners in cluster organisations and science parks, e-learning providers, etc.) including individual citizens (general public) that may be interested in the project’s results. 

  

Moreover, specific **pre-existing data** may be utilised within the context of
the project as well. In particular, OI intermediaries, support networks and/or
relevant development agencies as well as online collaboration networks and
providers of OI, collective intelligence and knowledge platforms will be
provided with the opportunity to integrate with and enhance the accessibility
of their data-driven offers through the OI2 Lab (e.g. through its open multi-
sided market place or e-learning environment). Prime example of such
preexisting datasets is the sourcing of European Enterprise Network’s
profiles, including technology offers/requests, business offers/requests and
R&D requests. Other examples are inclusion of NineSigma’s competition or
challenges, textual content and redirects to local, national or European wide
financing options and lists of Venture Capital Funds throughout Europe. These
pre-existing datasets will foster an already populated environment with
engaging content that will resonate with the OI2 Lab’s targeted audience.

##  Expected size of data

INVITE entails a series of activities aiming at setting the stage for and
ultimately facilitating the demanddriven evidence-based development, piloting,
evaluation, validation and fine-tuning of its OI2 Lab and value propositions.
With that in mind, the table that follows presents the different activities
implemented during the course of the project in which data is
collected/generated, the types and formats of the data as well as the expected
size of the data.

###  _Table 2: Expected size of data_  
  
<table>  
<tr>  
<th>

**Νο**

</th>  
<th>

**Name of activity**

</th>  
<th>

**Data**

</th>  
<th>

**Type of data**

</th>  
<th>

**Format of data**

</th>  
<th>

**Expected size of data**

**(KB)***

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

1

</td>  
<td>

Analysis of European OI support service providers/platforms

</td>  
<td>

Market gaps and opportunities

</td>  
<td>

Interview transcripts

</td>  
<td>

.docx

</td>  
<td>

207 **

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

2

</td>  
<td>

Analysis of needs and requirements of prospective users and stakeholders

</td>  
<td>

User needs and requirements

</td>  
<td>

Interview transcripts

</td>  
<td>

.docx

</td>  
<td>

2,516.5 **

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

3

</td>  
<td>

Analysis of ideas and feedback collected during

INVITE Co-Creation

Workshop

</td>  
<td>

Ideas and feedback

collected during the INVITE

Co-creation Workshop

</td>  
<td>

Post-it notes

</td>  
<td>

.docx

</td>  
<td>

107 **

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

4

</td>  
<td>

Monitoring, co-evaluation and validation of INVITE’s pilots and OI2 Lab

</td>  
<td>

Data collected through direct input methods

</td>  
<td>

Questionnaires

</td>  
<td>

.docx

</td>  
<td>

100,000 *

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

Open multi-side market place data

</td>  
<td>

Machine & user generated

</td>  
<td>

MySQL dB

</td>  
<td>

80,000 *

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

Online collaboration space data

</td>  
<td>

Machine & user generated

</td>  
<td>

MySQL dB

</td>  
<td>

1,000,000 *

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

E-learning environment data

</td>  
<td>

Machine & user generated

</td>  
<td>

MySQL dB

</td>  
<td>

1,000,000 *

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

Crowdfunding tool data

</td>  
<td>

Machine & user generated

</td>  
<td>

MySQL dB

</td>  
<td>

100,000 *

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

Web portal analytics

</td>  
<td>

Machine generated

</td>  
<td>

MySQL dB

</td>  
<td>

20,000 *

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

5

</td>  
<td>

Improvement and validation

of the OI2 Lab’s Business Models

</td>  
<td>

Feedback on the OI2 Lab business models

</td>  
<td>

Questionnaires

</td>  
<td>

.xlsx

</td>  
<td>

250 *

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

6

</td>  
<td>

Monitoring and evaluation of the results produced by the project’s stakeholder
engagement activities

</td>  
<td>

Data for monitoring stakeholder engagement

</td>  
<td>

Stakeholder Matrix

</td>  
<td>

.xlsx

</td>  
<td>

61 *

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

7

</td>  
<td>

Monitoring and assessment

of the project’s dissemination and communication results

</td>  
<td>

Social media statistics

</td>  
<td>

Machine generated

</td>  
<td>

.xslx

</td>  
<td>

150 *

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

Project events data

</td>  
<td>

Spreadsheets

</td>  
<td>

.xlsx

</td>  
<td>

150*

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

Newsletter subscriptions

</td>  
<td>

Spreadsheets

</td>  
<td>

.xslx

</td>  
<td>

300*

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

Data for dissemination and communication reporting

</td>  
<td>

Spreadsheets

</td>  
<td>

.xlsx

</td>  
<td>

150*

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

* The estimated expected size of the data is based on the adjusted size of data generated via similar activities of project partners in the past unless otherwise indicated. 

** The collection/generation of this data has already been completed and the
size of the data represent real values (not estimations).

##  Data utility

The stakeholders that may find meaningful utility for the data to be
collected/generated by the project (both within as well as outside of INVITE’s
consortium) along with the benefits that could arise for them by utilizing
this data, are concisely presented in the table that follows.

###  _Table 3: Data utility_  
  
<table>  
<tr>  
<th>

_**Stakeholder group** _

</th>  
<th>

**_Data utility_ **

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

**Researchers in the field of Open Innovation**

</td>  
<td>

The field of OI, albeit having promising potential for generating sustainable
innovations with great market and social value, appears to still be relatively
under-researched and characterised by a limited evidence base of relevant
efforts worldwide  13  . Under this light, INVITE’s data can provide
researchers in the multi-disciplinary and cross-cutting field of OI with
valuable insights into how OI is currently taken place in Europe as well as
with empirical evidence generated from practical applications of OI and
collaborative models of innovation. Interested researchers may re-use the data
of INVITE as a basis to replicate similar studies within the same or different
contexts as well as to design and launch new studies, generating comparable
research findings to further advance the field and shed ample light on the
inner workings of OI within a quadruple helix innovation model.

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

**Policy makers, implementers and funders**

</td>  
<td>

Throughout its duration, INVITE is set on collecting and producing
quantifiable evidence on the effectiveness and impact of the support
mechanisms and measures to be piloted during the project (such as the
innovation voucher scheme or the e-learning interventions deployed through the
OI2 Lab), with a view to fostering their replication and scale-up beyond its
completion. Data generated to this end, may find great utility in the hands of
experts who design, implement and/or fund relevant innovation and business
support policies. Indeed, data on what really changed (or not), for whom and
why during the experiments conducted by the OI2 Lab, can provide them with
reliable input to analyse the potential successes (and failures) generated
under the pilot operation of the OI2 Lab. This can in turn help them gain a
better understanding of what could drive successful OI in their own context,
supporting them in facilitating knowledge flows from and to their respective
nations/regions, while also fostering OI, especially amongst SMEs.

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

**Project partners**

</td>  
<td>

The data collected/generated during INVITE is of paramount utility for project
partners in order to produce evidence-based results and ultimately achieve the
objectives of the project. Indeed, this data will enable the co-design,
development, validation and fine-tuning of the project’s pilots and OI2 Lab.
Moreover, the data will be used to design, improve and validate sustainable
business models for the rollout of the OI2 Lab, while also fostering the
replication and scale-up of its piloted solutions. At the same time, this data
may hold meaningful utility for project partners beyond the end of the project
as well, enabling them to build and capitalise upon interesting ideas and
opportunities that may emerge to ensure the long-term sustainability of the
OI2 Lab.

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

#  FAIR data

The  _Guidelines on Data Management in Horizon 2020_ of the Commission
emphasise the importance of making the data produced by projects funded under
Horizon 2020 **Findable, Accessible, Interoperable as well as Reusable
(FAIR)** , with a view to ensuring its sound management. This means using
standards and metadata to make data discoverable, specifying data sharing
procedures and which data will be open, allowing data exchange via open
repositories as well as facilitating the reusability of the data. With that in
mind, the following sections of the DMP lay out the methodology followed in
the framework of INVITE with respect to making data findable, accessible and
interoperable as well as ensuring their preservation and open access, with a
view to increasing its re-use.

##  Making data findable, including provisions for metadata

###  _Data discoverability and identification mechanisms_

INVITE places special emphasis in enhancing the discoverability of the data
collected/generated during the course of its activities. To this end, the
project follows a metadata-driven approach so as to increase the searchability
of the data, while also facilitating its understanding and re-use. Metadata is
defined as “data about data” or “information about information”  14  . It is
usually structured textual information that describes something about the
creation, content, or context of a digital resource – be it a single file,
part of a single file, or a collection of many files. Metadata is the glue
which links information and data across the world wide web. It is the tool
that helps people to discover, manage, describe, preserve and build
relationships with and between digital resources  15  .

In particular, three distinct types of metadata exist  16  , as presented
below:

  * **Descriptive metadata** , used to identify and describe collections and related information resources. Descriptive metadata at the local level helps with searching and retrieving. In an online environment, descriptive metadata helps to discover resources. Most of the times includes information such as the title, author, date, description, identifier, etc. 

  

  * **Administrative metadata** is used to facilitate the management of information resources. It is helpful for both short-term and long-term management and processing of data. This is information that will not usually be relevant to the public but will be essential for staff to manage collections internally. Such metadata may be location information, acquisition information, etc. 

  

  * **Structural metadata** enables navigation and presentation of electronic resources. Its documents how the components of an item are organized. Examples of structural metadata could be the way in which pages are ordered to form chapters of a book, a photograph that is included in a manuscript or a scrapbook or the JPEG and TIF files that were created from the original photograph negative, linked together. 

  

With that in mind, **data produced/used during INVITE is discoverable with
metadata** suitable to its content and format. To this end, the project
employs **metadata standards** to produce rich and consistent metadata to
support the long-term discovery, use and integrity of its data (see Subsection
3.1.5 for more details on the metadata standards adopted by INVITE).

In parallel, to further increase data discoverability, the **data produced by
INVITE and deemed open for sharing and re-use, will be deposited to Zenodo** (
_www.zenodo.org_ )  , **an open data repository.** This data repository,
created by OpenAIRE and CERN, has been chosen to enable open access to the
project’s open data free of charge. In fact, Zenodo builds and operates a
simple service that enables researchers, scientists, EU projects and
institutions, among others, to share and showcase research results (including
data and publications) that are not part of the existing institutional or
subject-based repositories of the research communities. It accepts any file
format, promotes peer-reviewed openly accessible research, allows the creation
of own collections and it is available free of charge both for INVITE to
upload and share data as well as for other stakeholders to explore, download
and re-use this data.

Moreover, by employing this data repository, the **data produced during the
implementation of the project is locatable by means of a standard
identification mechanism.** Indeed, INVITE will be able to assign globally
resolvable **Persistent Identifiers (PIDs)** on any data uploaded to Zenodo.
An identifier is a unique identification code that is applied to a dataset, so
that it can be unambiguously referenced  17  . For example, a catalogue number
is an identifier for a particular specimen and an ISBN code is an identifier
for a particular book. PIDs are simply maintainable identifiers that allow for
permanent reference to a digital object. In other words, PIDs are a way of
giving digital resources, such as documents, images and data records, a unique
and persistent reference number.

Moreover, as a digital repository, Zenodo registers **Digital Object
Identifiers (DOIs)** for all submitted data through  _DataCite_ ,  which is
the leading global non-profit organisation that provides PIDs (and
specifically DOIs) for research data, and preserves these submissions using
the safe and trusted foundation of CERN’s data centre, alongside the biggest
scientific dataset in the world, the LHC’s 100PB Big Data store  18  . This
means that the data preserved in Zenodo will be accessible for years to come,
and the DOIs will function as perpetual links to the resources. DOIs remain
valuable since they are future proofed against Uniform Resource Locator (URL)
or even protocol changes, through resolvers (such as  _DOI)_ . With that in
mind, an example of a DOI retrieved from this open repository follows the
structure illustrated by Figure 1.

_**Figure** _

_**1** _

_**:** _

_**Typical DOI created by Zenodo** _

At the same time, **datasets not uploaded to Zenodo will be deposited in a
searchable resource (i.e. the web portal of the project) and utilise well-
tailored identification mechanisms** as well, in the form of standard naming
conventions that will safeguard their consistency and make them **easily
locatable** for project partners within the framework of the project. The
following subsection provides further details in this respect.

###  _Naming conventions_

Following a consistent set of naming conventions in the development of the
project’s data files can greatly enhance their searchability. With that in
mind, INVITE creates consistent data file names that provide clues to their
content, status and versioning, while also increasing their discoverability.
In doing so, project partners as well as interested stakeholders can easily
identify a file as well as classify and sort them.

According to the UK Data Archive (  _UK Data Service, 2017b_ )  , a best
practice in naming convention is to create brief yet meaningful names for data
files, that facilitate classification. The naming convention should avoid the
utilisation of spaces, dots and special characters (such as & or !), whereas
the use of underscores is endorsed, to separate elements in the data file name
and make them understandable. At the same time, versioning should be a part of
a naming convention to clearly identify the changes and edits in a file.

With that in mind and to facilitate the reference of the datasets that will be
produced during its implementation, INVITE employs a **standard naming
convention** **that integrates versioning and takes into account the
possibility of creating multiple datasets** during an activity that entails
data collection/generation. Indeed, INVITE’s naming convention takes considers
this issue and addresses it by employing a unique element that captures the
number of datasets that are produced under the same activity.

In particular, the **naming convention employed by the project** **is
described below** .

**INVITE _ [Name of Study] _ [Number of dataset] _ [Issue Date] _ [Version
number]**

  * **INVITE:** The name of the project. 

  

  * **Name of Study:** A short version of the name of the activity for which the dataset is created. 

  

  * **Number of dataset:** An indication of the number assigned to the dataset. 

  

  * **Issue Date:** The date on which the latest version of the dataset was modified (YYYY.MM.DD.). 

  

  * **Version number:** The versioning number of a dataset. 

  

With the above in mind, some **indicative examples** to showcase the naming
structure applied in the context of INVITE are provided below:

  * **INVITE_NeedsAndRequirements_Dataset1_2017.10.31_v1 –** The first dataset generated within the framework of the survey conducted to identify the needs and requirements of diverse OI stakeholders. This is the first version of the dataset that was last modified on the 31  st  of October 2017 (31/10/2017). 

  

  * **INVITE_BMValidation_Dataset2_2018.02.01_v2 –** The second dataset created in the process of validating and improving the business models developed for the OI2 Lab with a view to feeding the elaboration of the business plan that will guide its market rollout beyond the end of the project. The last modification of this dataset, which in this case produced the second version of the dataset, was on the 1  st  of February 2018 (01/02/2018). 

  

###  _Search keywords_

The project’s data will be provided with search keywords with a view to
optimizing its re-use by interested stakeholders during its entire lifetime.
With that in mind, the metadata standards employed by INVITE provide
opportunities for tagging the data collected/generated and its content with
keywords. In general, keywords are a subset of metadata and include words and
phrases used to name data. In the context of INVITE, keywords are used to add
valuable information to the data collected/generated as well as to facilitate
the description and interpretation of its content and value.

Along these lines, the project’s strategy on keywords is underpinned by the
following principles:

  * The who, the what, the when, the where, and the why should be covered. 

  

  * Consistency among the different keyword tags needs to be ensured. 

  

  * Relevant, understandable and clear keywording ought to be sought. 

  

In general, the keywords will comprise terms related to open innovation, co-
creation, the quadruple helix as well as SMEs. The keywords will accurately
reflect the content of the datasets and avoid words used only once or twice
within them.

###  _Versioning_

Versioning of information makes a revision of datasets uniquely identifiable
and can be used to determine whether and how data changed over time and to
define specifically which version the creators/editors are working with.
Moreover, effective data versioning enables understanding if a newer version
of a dataset is available and which are the changes between the different
versions allowing for comparisons and preventing confusion. In this context,
**a clear version number indicator is used in the naming convention** of every
data file produced during the course of the INVITE in order to facilitate the
identification of different versions.

###  _Standards for metadata creation_

**INVITE employs standards for creating metadata** for the data
collected/generated by the project, with a view to describing it with **rich
metadata** and thus improving their discoverability and searchability. In
result, effective searching, improved digital curation and easy sharing will
be realized. In addition, the metadata standards applied enable the
integration of metadata from a variety of sources into other technical
systems.

With that in mind, **for INVITE’s openly available data the** **metadata
standards provided by Zenodo will be used** . Zenodo creates metadata to
accompany the datasets that are uploaded to its repository, extending their
reach to a wider audience of interested stakeholders. This metadata can be
exported in several standard formats, including open and machine-readable ones
(such as MARCXML, Dublin Core, and DataCite Metadata Schema), following the
guidelines of OpenAIRE and are stored by Zenodo in JSON-format according to a
defined JSON schema  19  .

Project **data not available for re-use, will also be annotated with open and
machine-readable metadata** following the **Dublin Core Metadata standard** .
The Dublin Core Metadata element set (certified with the ISO Standard 15836)
is a standard which can be easily understood and implemented and as such, is
one of the best known metadata standards. It was originally developed as a
core set of elements for describing the content of web pages and enabling
their search and retrieval. Among the reasons for selecting this standard is
also the fact that **Zenodo is compatible with Dublin Core metadata formats**
and thus any initially closed data, that may become open at a later stage
(e.g. due to a change in the consortium’s policy), will not lose its metadata.
With that said, the Dublin Core metadata standard is a simple yet effective
set for creating rich metadata that will describe a wide range of resources.
The fifteen element "Dublin Core" described in this standard is part of a
larger set of metadata vocabularies and technical specifications maintained by
th  e _Dublin_ _Core Metadata Initiative (DCMI)_ .  The full set of
vocabularies, also includes sets of resource classes, vocabulary encoding
schemes, and syntax encoding schemes. **An online metadata generator will be
used** to produce the different metadata elements required (
_dublincoregenerator.com_ )  .

##  Making data openly accessible

###  _Openly available and closed data_

INVITE is part of the H2020 Open Research Data Pilot (ORDP) that aims to “
_make the data collected/generated by selected projects openly available with
as few restrictions as possible, while at the same time protecting sensitive
data from inappropriate access_ ”  20  . Under this light, the project adopts
the good practice encouraged by the ORDP, namely that of making data as open
as possible and as closed as necessary  21  . This calls for project partners
to disseminate the project’s data that have the potential to offer long-term
value to external stakeholders and do not harm the confidentiality and privacy
of the stakeholders that contributed in the collection/generation of this
data, with a view to maximising the beneficial impact of INVITE.

**Only anonymised and aggregated data will be made open** to ensure that data
subjects cannot be identified in any reports, publications and/or datasets
resulting from the project. The project partner serving as **the data
controller** 22  **in each case will undertake all the necessary anonymisation
procedures** to anonymise the data in such a way that the data subject is no
longer identifiable (more details on data management responsibilities are
provided in Section 4.2).

To this end, it is important to keep in mind that during the process of data
anonymisation, data identifiers need to be removed, generalised, aggregated or
distorted. Moreover, **anonymisation is different than pseudonymisation** ,
which falls under a distinct category in the GDPR - anonymisation
theoretically destroys any way of identifying the data subject, while
pseudonymisation allows for the data subject to be reidentified with
additional information. Along these lines, the table below provides a **list
of good practices** for the anonymisation of quantitative and qualitative data
derived from the tour guide on data management of the Consortium of European
Social Science Data Archives (CESSDA).

####  Table 4: Good practices for data anonymisation  
  
<table>  
<tr>  
<th>

**Type of data**

</th>  
<th>

</th>  
<th>

**Good practices**

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

Quantitative data

</td>  
<td>

•

•

</td>  
<td>

_Removing or aggregate variables or reduce the precision or detailed textual
meaning of a variable._

_Aggregate or reduce the precision of a variable such as age or place of
residence. As a general rule, report the lowest level of geo-referencing that
will not potentially breach respondent confidentiality._

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

</td>  
<td>

•

</td>  
<td>

_Generalise the meaning of a detailed text variable by replacing potentially
disclosive free-text responses with more general text._

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

</td>  
<td>

•

</td>  
<td>

_Restrict the upper or lower ranges of a continuous variable to hide outliers
if the values for certain individuals are unusual or atypical within the wider
group researched._

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

Qualitative data

</td>  
<td>

•

•

•

</td>  
<td>

_Use pseudonyms or generic descriptors to edit identifying information, rather
than blanking-out that information;_

_Plan anonymisation at the time of transcription or initial write-up,
(longitudinal studies may be an exception if relationships between waves of
interviews need special attention for harmonised editing)._

_Use pseudonyms or replacements that are consistent within the research team
and throughout the project. For example, using the same pseudonyms in
publications and follow-up research;_

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

</td>  
<td>

•

</td>  
<td>

_Use 'search and replace' techniques carefully so that unintended changes are
not made, and misspelt words are not missed;_

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

</td>  
<td>

•

</td>  
<td>

_Identify replacements in text clearly, for example with [brackets] or using
XML tags such as <seg>word to be anonymised</seg>; _

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

</td>  
<td>

•

</td>  
<td>

_Create an anonymisation log (also known as a de-anonymisation key) of all
replacements, aggregations or removals made and store such a log securely and
separately from the anonymised data files._

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

Source: Tour guide on data management of the CESSDA  23

With that in mind, the following table presents the data collected/generated
during the course of the project that will be made openly available. In case
certain data cannot be shared (or need to be shared under restrictions), a
justification for that choice is provided.

####  Table 5: Data availability  
  
<table>  
<tr>  
<th>

**Νο**

</th>  
<th>

**Data**

</th>  
<th>

**Availability**

</th>  
<th>

**Notes**

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

1

</td>  
<td>

Market gaps and opportunities

</td>  
<td>

Open

</td>  
<td>

\-

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

2

</td>  
<td>

User needs and requirements

</td>  
<td>

Open

</td>  
<td>

\-

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

3

</td>  
<td>

Ideas and feedback collected during the INVITE Co-creation

Workshop

</td>  
<td>

Open

</td>  
<td>

\-

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

4

</td>  
<td>

Pilot monitoring, co-evaluation and validation data collected through direct
input methods

</td>  
<td>

Open

</td>  
<td>

\-

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

5

</td>  
<td>

Open multi-side market place data

</td>  
<td>

Closed

</td>  
<td>

Data provided and/or produced via the interaction of users with the OI2 Lab
will be closed and only accessible to platform account managers as they
include personally identifiable data. Furthermore, registered users will be
provided with options as to the privacy settings of their personal data.

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

6

</td>  
<td>

Online collaboration space data

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

7

</td>  
<td>

E-learning environment data

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

8

</td>  
<td>

Crowdfunding tool data

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

9

</td>  
<td>

Web portal analytics

</td>  
<td>

Open

</td>  
<td>

\-

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

10

</td>  
<td>

Feedback on the OI2 Lab business models

</td>  
<td>

Closed

</td>  
<td>

The data will remain closed (accessible only to members of the INVITE
consortium) so as to safeguard the commercial interests of project partners
with respect to the market rollout of the OI2 Lab.

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

**Νο**

</td>  
<td>

**Data**

</td>  
<td>

**Availability**

</td>  
<td>

**Notes**

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

11

</td>  
<td>

Data for monitoring stakeholder engagement

</td>  
<td>

Open

</td>  
<td>

\-

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

12

</td>  
<td>

Social media statistics

</td>  
<td>

Open

</td>  
<td>

\-

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

13

</td>  
<td>

Project events data

</td>  
<td>

Closed

</td>  
<td>

This data will remain closed (accessible only to consortium members) as it is
useful only for internal reporting purposes. On top of that, any anonymization
will leave no data within the dataset.

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

14

</td>  
<td>

Newsletter subscriptions

</td>  
<td>

Closed

</td>  
<td>

This data will remain closed (accessible only to consortium members) as it is
useful only for internal reporting purposes. On top of that, any anonymization
will leave no data within the dataset.

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

15

</td>  
<td>

Data for dissemination and communication reporting

</td>  
<td>

Open

</td>  
<td>

\-

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

It is important to note that **all personal data collected / generated will be
considered as closed data prior to their anonymisation and aggregation** to
safeguard the confidentiality of the data subjects.

This data will be securely stored by the consortium partners that collected
them to be **preserved in their respective records** only for as long as
necessary for them to comply with their contractual obligations to INVITE’s
funding authority, namely the Research Executive Agency (REA) of the
Commission, and **no longer than 5 years from the project’s completion** .
During this period the personal data will be accessible only by authorised
individuals of INVITE’s consortium partner that collected this data and of the
REA. After this period the personal data will be deleted from the respective
consortium partner’s records.

###  _Data accessibility and availability_

Public access to the open data will be made available through Zenodo, which
will automatically link to OpenAIRE. The data will be fully accessible thanks
to the included metadata and the search facility available on Zenodo. At the
same time, closed data will be stored and shared amongst authorised members of
the consortium through web portal of the project. With that in mind, the
following table presents where data will be made accessible in the context of
INVITE.

####  Table 6: Data accessibility  
  
<table>  
<tr>  
<th>

**Νο**

</th>  
<th>

**Data**

</th>  
<th>

**Accessibility**

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

1

</td>  
<td>

Market gaps and opportunities

</td>  
<td>

Zenodo

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

2

</td>  
<td>

User needs and requirements

</td>  
<td>

Zenodo

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

3

</td>  
<td>

Ideas and feedback collected during the INVITE Co-creation Workshop

</td>  
<td>

Zenodo

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

4

</td>  
<td>

Pilot monitoring, co-evaluation and validation data collected through direct
input methods

</td>  
<td>

Zenodo

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

5

</td>  
<td>

Open multi-side market place data

</td>  
<td>

\-

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

6

</td>  
<td>

Online collaboration space data

</td>  
<td>

\-

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

7

</td>  
<td>

E-learning environment data

</td>  
<td>

\-

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

8

</td>  
<td>

Crowdfunding tool data

</td>  
<td>

\-

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

9

</td>  
<td>

Web portal analytics

</td>  
<td>

Zenodo/Matomo *

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

10

</td>  
<td>

Feedback on the OI2 Lab business models

</td>  
<td>

\-

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

11

</td>  
<td>

Data for monitoring stakeholder engagement

</td>  
<td>

Zenodo

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

12

</td>  
<td>

Social media statistics

</td>  
<td>

Zenodo

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

13

</td>  
<td>

Project events data

</td>  
<td>

\-

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

14

</td>  
<td>

Newsletter subscriptions

</td>  
<td>

\-

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

15

</td>  
<td>

Data for dissemination and communication reporting

</td>  
<td>

Zenodo

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

*A subset of the web portal analytics relevant to the usage of the OI2 Lab tools and the overall user activity will be anonymised and (a) extracted in the form of .xlsx for upload to the Zenodo platform and (b) provided for open access via the Matomo analytics platform to interested stakeholders and the research community _._

###  _Methods, software tools and documentation to access the data_

INVITE emphasises the accessibility of the data collected/generated during the
course of the project. With that in mind, **no specialised method, software
tool and/or documentation are needed** , at the moment, in order to access the
data. Stakeholders can access the data by simply using their web browser (e.g.
Mozilla, Google Chrome, Internet Explorer, Safari, etc.) through their
computers (either desktop or laptop), smart phones and/or tablets. More
specifically, they first need to access Zenodo through its webpage (following
the lin  k _https://zenodo.org/_ )  and utilise the search engine of the
repository to search for interesting data. By typing the name of the project
(or any other relevant keyword connected to the data) the search engine will
direct the user to the project’s data, ready to be explored and re-used.
Moreover, since the data will be available in open formats we will be ensuring
that they can correctly be read by a range of different software programs that
most of the people employ in their everyday lives.

Closed data can be accessed only by authorised project partners through the
respective member section of INVITE’s web portal. Again, no specialised
method, software tool and/or documentation are needed to this end.
Nevertheless, the member section of the web portal is accessible only with the
provision of their unique username and password combination. Therefore, they
first need to login the INVITE web portal (following the lin  k
_http://invite-project.eu/user_ )  through their digital device (e.g.
computers, smartphones, tables, etc.) and provide their respective usernames
and passwords. Then, they can find the uploaded data categorised under the
file browser section.

###  _Data, metadata, code and documentation repositories_

INVITE’s open data along with their linking metadata as well as any relevant
code and documentation (if applicable) required to access this data, will be
deposited to and securely stored by Zenodo. It is quite unlike that Zenodo
will have to terminate its operation and stop providing its services, but in
such a case all data, metadata, code and documentation uploaded by INVITE will
be transferred and hosted to other suitable repositories  24  without undue
delay. In this respect, it is important to note that, since all of INVITE’s
openly available data will make use of PIDs (i.e. DOIs), the links to the data
will not be affected. In parallel, INVITE’s data that will not be openly
available for sharing will be deposited, together with their accompanying
metadata, code and documentation (if necessary), to the web portal of the
project.

###  _Restrictions_

By utilising Zenodo for sharing the project’s openly available data, INVITE
can apply **different levels of accessibility** for this data taking into
account any relevant issues (such as ethical, rules of personal data,
intellectual property, commercial, privacy-related, security-related, etc.).

More specifically, **Zenodo offers the following levels of data
accessibility** :

  * **Open access** : Data remains available for re-use. Nevertheless, the level in which this data can be reused is determined also by their accompanied licence for re-use (see subsection 3.4.1). 

  

  * **Embargoed** **status** : Access to the data will be restricted until the end of the embargo period, at which time, the content will automatically become publicly available. 

  

  * **Restricted access** : The data will not be made publicly available and sharing will be made possible only by the approval of the project partner that have the responsibility of the data. 

  

  * **Closed access** : The data is protected against unauthorized access at all levels and only members of the consortium have the right to access it. 

  

**Project partners will mainly use the open access level** to disseminate the
project’s data amongst the interested stakeholders. Nevertheless, in some
cases embargo periods or restricted access may be used as described in
Subsection 3.2.1. Data that will not be available for re-use will be
accessible only by authorised partners of INVITE’s consortium and /or
authorised personnel from the REA of the Commission.

Moreover, **INVITE will ensure open access to all peer-reviewed scientific
publications** that may be produced in the framework of the project. In
particular, according to the Grant Agreement, INVITE will:

  * As soon as possible and at the latest on publication, deposit a machine-readable electronic copy of the published version or final peer-reviewed manuscript accepted for publication in a repository for scientific publications as well as deposit, at the same time, the research data needed to validate the results presented in the deposited scientific publications. 

  

  * Ensure open access to the deposited publication — via the repository — at the latest on publication, if an electronic version is available for free via the publisher, or within six months of publication. 

  

  * Safeguard open access — via the repository — to the bibliographic metadata that identify the deposited publication. The bibliographic metadata shall be in a standard format and include the terms “European Union (EU)” and “Horizon 2020”; the name of the action, acronym and grant number; the publication date, and length of the embargo period if applicable; and a PID. 

  

Along these lines, this section has provided the methodology applied in the
framework of INVITE so as to ensure that its data is as openly accessible as
possible by any stakeholder that may find it interesting for reuse. In this
context, INVITE also focuses on providing metadata standards and appropriate
metadata vocabularies to increase its data interoperability. The following
section provides further details in this respect.

##  Making data interoperable

Data interoperability refers to the ability of systems and services that
create, exchange and use data to have clear, shared expectations for the
contents, context and meaning of that data  25  . With that in mind, INVITE
has adopted in its data management methodology the use of metadata
vocabularies, standards and methods that will increase the interoperability of
the data collected/generated through its activities.

More specifically, **the interoperability of the data that will not be
publicly shared will be facilitated by the use of the Dublin Core Metadata
standard.** This standard is a small “metadata element set” which accounts for
issues that must be resolved in order to ensure that data meet traditional
standards for quality and consistency, while still remaining broadly
interoperable with other data sources in the linked data environment. The
fifteen elements of the standard provide a vocabulary of concepts with
natural-language definitions (e.g. title, creator, author, etc.) that are
instantly converted into open machine-readable formats (such as XML, HTML,
etc.), enabling machine-processability. Each element is optional and may be
repeated, while the standard itself offer ways exist for refining them,
encouraging the use of encoding and vocabulary schemes. The vocabulary of the
Dublin Core Metadata standard is presented by the following table  26  :

###  _Table 7: Dublin Core Metadata standard vocabulary_  
  
<table>  
<tr>  
<th>

**Νο**

</th>  
<th>

**Element**

</th>  
<th>

**Element definition**

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

1

</td>  
<td>

Title

</td>  
<td>

A name given to the resource.

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

2

</td>  
<td>

Creator

</td>  
<td>

An entity primarily responsible for making the content of the resource.

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

3

</td>  
<td>

Subject

</td>  
<td>

The topic of the content of the resource.

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

4

</td>  
<td>

Description

</td>  
<td>

An account of the content of the resource.

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

5

</td>  
<td>

Publisher

</td>  
<td>

An entity responsible for making the resource available.

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

6

</td>  
<td>

Contributor

</td>  
<td>

An entity responsible for making contributions to the content of the resource.

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

7

</td>  
<td>

Date

</td>  
<td>

A date associated with an event in the life cycle of the resource

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

8

</td>  
<td>

Type

</td>  
<td>

The nature or genre of the content of the resource.

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

9

</td>  
<td>

Format

</td>  
<td>

The physical or digital manifestation of the resource.

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

10

</td>  
<td>

Identifier

</td>  
<td>

An unambiguous reference to the resource within a given context.

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

11

</td>  
<td>

Source

</td>  
<td>

A reference to a resource from which the present resource is derived.

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

12

</td>  
<td>

Language

</td>  
<td>

A language of the intellectual content of the resource.

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

13

</td>  
<td>

Relation

</td>  
<td>

A reference to a related resource.

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

14

</td>  
<td>

Coverage

</td>  
<td>

The extent or scope of the content of the resource.

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

15

</td>  
<td>

Rights

</td>  
<td>

Information about rights held in and over the resource.

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

Along similar lines, **the interoperability of openly available data will be
facilitated through Zenodo** , since its metadata will be stored internally in
JSON format according to a defined JSON schema. This encloses HTML microdata
that allows machine-readable data to be embedded in HTML documents in the form
of nested groups of name-value pairs. Moreover, the JSON schema provides a
collection of shared vocabularies in microdata format that can be used to
mark-up pages in ways that can be understood by the major search engines.
Moreover, all metadata linked to the open data is exported via the Open
Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) and can be
harvested. The OAI-PMH develops and promotes interoperability standards that
facilitate the efficient dissemination of data amongst diverse communities  27
.

##  Increase data re-use

###  _License schemes to permit the widest use possible_

The application of a licence to INVITE’s open data is a simple way to ensure
that any interested third-party can re-use it. In this context, licences are
the instrument which permit a third-party to copy, distribute, display and/or
modify the project’s data only for the purposes that are set by the licence.
Licences typically grant permissions on condition that certain terms are met.
While the precise details vary, three conditions are commonly found in
licences which are the attribution, non-derivative, and non-commerciality.

Along these lines, INVITE publishes its openly available data under the
**Creative Commons licencing scheme** to foster their re-use and build an
equitable and accessible environment for them. In fact, Zenodo provides INVITE
the **opportunity to publish its open data under five Creative Common
licences** as follows:

  * **Creative commons Attribution-Share Alike 4.0** (CC BY-SA 4.0) according to which any third party can freely copy, distribute, display and modify the datasets for any purpose. Remix, transform, or built upon data, must be distributed under the same license as the original. Third parties must give appropriate credit, provide a link to the license, and indicate if changes were made. 

  

_**Figure** _

_**2** _

_**:** _

_**CC** _

_**BY** _

_**-** _

_**SA 4.0** _

_**Figure** _

_**3** _

_**:** _

_**CC BY 4.0** _

  * **Creative Commons Attribution 4.0 International** (CC BY 4.0) according to which any third party can freely copy, distribute, display and modify the datasets for any purpose. Third parties must give appropriate credit, provide a link to the license, and indicate if changes were made. 

  

  * **Creative Commons Attribution-No Derivatives 4.0 International** (CC BY-ND 4.0) during which any third party can freely copy, distribute, display and modify the datasets for any purpose. Remix, transform, or built upon data, however must not be distributed. Third parties must give appropriate credit, provide a link to the license, and indicate if changes were made. 

  

_**Figure** _

_**4** _

_**:** _

_**CC BY** _

_**-** _

_**ND 4.0** _

  * **Creative Commons Attribution-NonCommercial 4.0 International** (CC BY-NC 4.0) based on which third parties can copy, distribute, display and modify the datasets for any purpose other than commercial unless they get a permission by project partners first. Third parties must give appropriate credit, provide a link to the license, and indicate if changes were made. 

  

_**Figure** _

_**6** _

_**:** _

_**CC BY** _

_**-** _

_**NC 4.0** _

  * **Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International** (CC BY-NC-ND 4.0) according to which third parties can copy, distribute, display and modify the datasets for any purpose other than commercial unless they get a permission by project partners first. Remix, transform, or built upon data, however, must not be distributed. Third parties must give appropriate credit, provide a link to the license, and indicate if changes were made. 

  

_**Figure** _

_**5** _

_**:** _

_**CC BY** _

_**-** _

_**NC** _

_**-** _

_**ND 4.0** _

With that in mind, **the INVITE consortium considers that the CC BY-NC 4.0 is
an appropriate licensing scheme to ensure the widest re-use of the data** ,
while also taking into account the importance of recognising both the source
and the authority of the data as well as safeguarding the commercial interests
of the OI2 Lab.

Nevertheless, different licensing schemes may be selected to better fit the
need of INVITE’s open data ensuring not only their long-term preservation and
re-use but also the interests of the consortium along with the rights of
individuals for whom the data is about. In such a case, this subsection of the
DMP will be updated accordingly.

###  _Availability for re-use_

The re-use of data is a key component of INVITE’s methodology for making data
FAIR. In fact, making data available for re-use ensures interested
stakeholders, other that project partners, can benefit from this data,
contributing towards maximising the impact of the project. **Rich metadata**
created based on metadata standards that enable proper discovery as well as
**appropriate licensing schemes** **facilitate the re-use of INVITE’s open
data** , allowing them to find valuable utility.

In principle, it is expected that data will become available for re-use no
later than 120 days after the end of its processing in the framework of the
project (i.e. collection, anonymisation, aggregation, etc.) to ensure that any
additional data management activities required to this end do not compete with
the timely delivery of the project’s planned outputs. Nevertheless, the data
that have been already collected/generated will be uploaded to Zenodo
immediately after the submission of the interim version of the Data Management
Plan, that is the 1  st  of September 2018. This data refers to the activities
performed during the course of Task 1.1, Task 1.2 and Task 1.3. The period for
which the data will remain available for re-use depends on the lifetime of
their repository. In the case of data deposited to Zenodo, this is the
lifetime of CERN’s relevant laboratory, which at the moment has an
experimental programme defined for the next 20 years. In case Zenodo
discontinuous the data, this will be transferred and hosted to other suitable
repositories.

With that in mind, the expected time that INVITE’s data will be made openly
accessible and uploaded to Zenodo is indicatively provided in the following
table:

####  Table 8: Expected time that data will be made open through Zenodo  28  
  
<table>  
<tr>  
<th>

**Νο**

</th>  
<th>

**Name of activity**

</th>  
<th>

**Expected time for making data open**

</th>  
<th>

**Notes**

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

**1**

</td>  
<td>

Market gaps and opportunities

</td>  
<td>

01/09/2018

</td>  
<td>

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

**2**

</td>  
<td>

User needs and requirements

</td>  
<td>

01/09/2018

</td>  
<td>

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

**3**

</td>  
<td>

Ideas and feedback collected during the INVITE Co-creation Workshop

</td>  
<td>

01/09/2018

</td>  
<td>

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

**4**

</td>  
<td>

Pilot monitoring, co-evaluation and validation data collected through direct
input methods

</td>  
<td>

1  st  version: 31/01/2020

2  nd  version: 31/10/2020

</td>  
<td>

This data will be collected/generated during the iterative implementation of
the project’s pilots. In this context, the respective dataset will be updated
twice during the course of the project, once per each pilot round.
Accordingly, the

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

</td>  
<td>

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

**5**

</td>  
<td>

Web portal analytics

</td>  
<td>

</td>  
<td>

datasets will be made openly available no later than 120 days after the
completion of each round.

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

**6**

</td>  
<td>

Data for monitoring stakeholder engagement

</td>  
<td>

1  st  version: 31/12/2018

2  nd  version: 31/10/2020

</td>  
<td>

The data will be updated regularly during the course of INVITE following the
monitoring of the project’s stakeholder engagement. An upto-date version of
the respective dataset will be uploaded after the end of each of the 2
reporting periods of INVITE.

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

**7**

</td>  
<td>

Social media statistics

</td>  
<td>

1  st  version: 31/12/2018

2  nd  version: 31/10/2020

</td>  
<td>

This data will be collected throughout the duration of INVITE as the
dissemination and communication activities of the project run their course. An
up-to-date version of the

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

</td>  
<td>

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

**8**

</td>  
<td>

Data for dissemination and communication reporting

</td>  
<td>

</td>  
<td>

respective dataset will be uploaded after the end of each of the 2 reporting
periods of INVITE.

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

###  _Data quality assurance processes_

**Quality Assurance** (QA) and **Quality Control** (QC) activities are an
integral part of INVITE’s data management methodology and are implemented
prior to the publication of any data to Zenodo, safeguarding the transparency,
consistency, comparability, completeness and accuracy of the data.

**QA** is a planned system of review procedures conducted outside the
framework of developing a dataset, by personnel not directly involved in the
dataset development process  28  . In the context of INVITE, it takes the form
of **peer-reviews of methods and/or data summaries** to assess the quality of
the dataset and identify any need for improvement, ensures that the dataset
correctly incorporates the scientific knowledge and data generated.

**QC** is defined as a system of checks to assess and maintain the quality of
the dataset being compiled  29  . The relevant procedures of INVITE are
designed to provide routine technical checks as they measure and control data
consistency, integrity, correctness and completeness as well as identify and
address errors and omissions. In this context, QC checks cover everything from
data acquisition and handling, application of approved procedures and methods,
and documentation. Some of the general quality checks undertaken in the
framework of the project include checking (i) for transcription errors in data
input; (ii) that scale measures are within the range of acceptable values; and
(iii) whether proper naming conversions are used.

  28. 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Vol. 1 General Guidance and Reporting, CHAPTER 6 Quality Assurance / Quality Control and Verification. 

  

  29. 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Vol. 1 General Guidance and Reporting, CHAPTER 6 Quality Assurance / Quality Control and Verification. 

  

  

#  Allocation of resources

##  Estimated costs for making data FAIR

The costs required for making the data collected/generated during the course
of INVITE’s activities FAIR are integrated within the budget of the project.
With that in mind, the table which follows provides an overview of the
estimated costs of making data FAIR as well as their budget source within the
framework of INVITE.

###  _Table 9: Estimated costs for making data FAIR_  
  
<table>  
<tr>  
<th>

**No**

</th>  
<th>

**Data Processing /**

**Management**

**Activity**

</th>  
<th>

**Budget source**

</th>  
<th>

**Total estimated effort in Person**

**Months** 29

</th>  
<th>

**Total estimated cost in Euro** 30  
</th> </tr>  
<tr>  
<td>

**1**

</td>  
<td>

Collection

</td>  
<td>

Budget allocated to the WP under which the respective data are processed

</td>  
<td>

25.60

</td>  
<td>

153,767.08

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

**2**

</td>  
<td>

Documentation

</td>  
<td>

Budget allocated to the WP under which the respective data are processed

</td>  
<td>

1.45

</td>  
<td>

8,694.63

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

**3**

</td>  
<td>

Storage

</td>  
<td>

Budget allocated to the WP under which the respective data are processed

</td>  
<td>

0.88

</td>  
<td>

5,261.79

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

**4**

</td>  
<td>

Access and security

</td>  
<td>

Budget allocated to the WP under which the respective data are processed

</td>  
<td>

0.88

</td>  
<td>

5,261.79

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

**5**

</td>  
<td>

Preservation

</td>  
<td>

Budget allocated to the WP under which the respective data are processed

</td>  
<td>

1.63

</td>  
<td>

9,816.50

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

**6**

</td>  
<td>

Availability and re-use

</td>  
<td>

Budget allocated to the WP under which the respective data are processed

</td>  
<td>

2.57

</td>  
<td>

15,413.03

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

**7**

</td>  
<td>

Overall data management

</td>  
<td>

WP7

</td>  
<td>

4.00

</td>  
<td>

24,024.72

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

</td>  
<td>

</td>  
<td>

</td>  
<td>

_**Total** _

</td>  
<td>

_**222,239.54** _

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

In order to produce the estimations of the costs for making data FAIR in the
context of INVITE, a series of **assumptions** were made, taking into account
the respective **guidelines** provided by the Research Data Management
Support, a multidisciplinary network of data experts within Utrecht University
31  , as well as of the UK Data Service and its data management costing tool
32  . With that in mind, the estimated costs for making INVITE’s data FAIR
cover **data-related activities and resources across the data lifecycle** ,
spanning from collection and documentation through storage and preservation
over to sharing and re-use.

In particular, costs for **data collection** cover activities necessary for
acquiring external datasets (if required), gathering/generating new data,
transcribing (if applicable), formatting and organising this data as well as
acquiring informed consent from data subjects. This data processing activity
reflects the majority of the costs required for making data FAIR as the
majority of INVITE’s data constitutes new data collected/generated over the
course of the project. At the same time, **data documentation** costs address
the effort required for describing data (e.g. marking data with variable and
value labels, code descriptions, etc.) as well as creating well-defined
metadata along with a meaningful description of the context and methodology of
how data was collected/generated and processed (where necessary).

Costs for **data storage** include both the resources required for ensuring
adequate storage space for the data as well as the effort necessary for
conducting data back-ups, while **data access and security** costs encompass
costs related to ensuring access to the data as well as for protecting it from
unauthorised access or use or from disclosure. Given that the storage of
INVITE’s data will not require the procurement of additional space (other than
what is already available to project partners) as well as that no special
measures or software are required to access and secure the data (other than
the what is inherently built in to the repositories of INVITE’s data), such
costs are kept to a minimum.

**Data preservation** costs, on the other hand, are estimated relatively
higher than data storage, access and security costs, as additional effort will
be required in several cases in order to convert the collected/generated data
from their original form (e.g. physical interview transcripts) to an open
and/or machine readable format suitable for long-term preservation (e.g. to an
.xlsx format.). Adequate effort for **data availability and re-use** costs is
also foreseen to safeguard the appropriate digitisation and anonymisation of
the data as well as cover any resources required for data sharing and
cleaning. Along the same lines, appropriate effort is foreseen **for overall
data management** as well, in order to cover the effort related with the
operationalisation of data management in the framework of INVITE

Finally, costs for **long-term preservation** in the framework of INVITE are
assumed to be negligible, since the open data of the project will be hosted in
the repository of Zenodo free of charge.

##  Data management responsibilities

For the effective, proper and secure handling of the data collected/generated
in the framework of INVITE, specific data management roles have been
established within the data management methodology and procedures of the
project. These responsibilities are outlined in this section of the DMP and
are as follows.

**Project Coordinator (PC)** : The PC, Q-PLAN, is responsible for overall data
management in the framework of INVITE, including the elaboration of the DMP
and its updates (when necessary and with support of all partners). At the same
time, the PC is responsible for the elaboration of proper templates for the
informed consent form and information sheet to be appropriately adjusted and
utilised by project partners during the relevant activities of the project.
Finally, the PC coordinates with Work Package and Task Leaders to determine
whether and how the data collected/generated by INVITE are shared and become
available for re-use, contributes to its quality assurance and uploads the
project’s openly available data to Zenodo.

**Work Package Leaders (WPL)** : The WPL is responsible for coordinating the
implementation of the data processing activities performed under the WPs they
are leading. Moreover, they align with the PC and the respective Work Task
Leader on whether and how the data gathered/produced under the tasks that fall
within the WP they are leading will be shared and/or re-used. This includes
the definition of access procedures as well as potential embargo periods along
with any necessary software and/or other tools which may be required for data
sharing and re-use. Finally, the WPL are the main responsible for assuring the
quality of the data stemming from the activities of the WP they are leading,
including assessing their quality and indicating any need for improvement to
the respective Work Task Leaders.

**Work Task Leaders (WTL)** : The WTL act as **data controllers** 33  of the
data collected/generated in the frame of the tasks that fall under their
leadership, determining the purposes and means of processing this data as well
as safeguarding its appropriate and timely processing. Moreover, they are
responsible for properly adjusting the templates for the informed consent form
and information sheet to the needs and specificities of the activities carried
out in the task they are leading. Finally, they undertake any necessary
actions to prepare the data collected/ generated through the tasks they are
leading for sharing either within the consortium or openly (including the use
of proper naming conventions, application of suitable anonymisation
techniques, creation of appropriate metadata and documentation, etc.).

**Data processors** : Data processors are project partners that are tasked to
collect, digitise, anonymise, store, destroy and/or otherwise process data for
the specific purpose of the activity in which it has been collected/generated
within the framework of the project. They are responsible for appropriately
collecting the necessary consent for processing data as well as for ensuring
that the informed consent form and information sheet used to this end is
properly adjusted to the needs of the activity they are participating and any
particularities applicable to their organisation. Moreover, they are also
responsible for managing the consents they have retrieved with a view to
demonstrating their compliance with the relevant applicable EU and national
regulation. Finally, they perform quality checks to assess and maintain the
quality of the dataset(s) held within their records.

**Data repositories** : Data repositories are tasked with the storage and
long-term preservation of the project’s data. In this respect, Zenodo
maintains and preserves the openly available data of INVITE, enabling its
sharing and re-use. To this end, Zenodo assigns metadata and DOIs to the data,
while also taking all the necessary measures to securely back-up the data and
be in a position to restore it, safeguarding its long-term preservation.
Accordingly, the Web Portal of INVITE shall securely store and preserve the
project’s data available for sharing amongst authorised consortium members in
the framework of the project.

In this context, the following table illustrates the allocation of data
management responsibilities amongst the members of the INVITE consortium per
data collected/generated under each WP.

_**Table 10: Data management responsibilities of INVITE partners per data
collected/generated under each WP** _  
  
<table>  
<tr>  
<th>

**WP**

</th>  
<th>

**WPL**

</th>  
<th>

**Data**

</th>  
<th>

**Tasks**

</th>  
<th>

**WTL** _**Data Controllers** _

</th>  
<th>

**Data Processors**

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

WP1

</td>  
<td>

CERTH/ITI

</td>  
<td>

Market gaps and opportunities

</td>  
<td>

Task 1.1

</td>  
<td>

RTC NORTH

</td>  
<td>

RTC NORTH

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

User needs and requirements

</td>  
<td>

Task 1.2

</td>  
<td>

CERTH/ITI

</td>  
<td>

All partners

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

Ideas and feedback collected during the INVITE Co-creation

Workshop

</td>  
<td>

Task 1.3

</td>  
<td>

RTC NORTH

</td>  
<td>

All partners

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

WP3

</td>  
<td>

RTC NORTH

</td>  
<td>

Pilot monitoring, co-evaluation and validation data collected through direct
input methods

</td>  
<td>

Task 3.2 &

Task 3.3

</td>  
<td>

RTC NORTH &

CERTH/ITI

</td>  
<td>

All partners

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

Open multi-side market place data

</td>  
<td>

Task 3.2 &

Task 3.3

</td>  
<td>

RTC NORTH &

CERTH/ITI

</td>  
<td>

INTRASOFT

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

Online collaboration space data

</td>  
<td>

Task 3.2 &

Task 3.3

</td>  
<td>

RTC NORTH &

CERTH/ITI

</td>  
<td>

INTRASOFT

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

E-learning environment data

</td>  
<td>

Task 3.2 &

Task 3.3

</td>  
<td>

RTC NORTH &

CERTH/ITI

</td>  
<td>

INTRASOFT

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

Crowdfunding tool data

</td>  
<td>

Task 3.2 &

Task 3.3

</td>  
<td>

RTC NORTH &

CERTH/ITI

</td>  
<td>

INTRASOFT

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

Web portal analytics

</td>  
<td>

Task 3.2 &

Task 3.3

</td>  
<td>

RTC NORTH &

CERTH/ITI

</td>  
<td>

INTRASOFT

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

WP4

</td>  
<td>

Q-PLAN

</td>  
<td>

Feedback on the OI2 Lab business models

</td>  
<td>

Task 4.2

</td>  
<td>

Q-PLAN

</td>  
<td>

All partners

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

WP5

</td>  
<td>

SEZ

</td>  
<td>

Data for monitoring stakeholder engagement

</td>  
<td>

Task 5.1

</td>  
<td>

SEZ

</td>  
<td>

All partners

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

WP6

</td>  
<td>

E-UNLIMITED

</td>  
<td>

Social media statistics

</td>  
<td>

Task 6.1

</td>  
<td>

E-UNLIMITED

</td>  
<td>

E-UNLIMITED

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

Project events data

</td>  
<td>

Task 6.1

</td>  
<td>

E-UNLIMITED

</td>  
<td>

E-UNLIMITED,

NINESIGMA,

WRS and NELEP

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

Newsletter subscriptions

</td>  
<td>

Task 6.1

</td>  
<td>

E-UNLIMITED

</td>  
<td>

E-UNLIMITED

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

Data for dissemination and communication reporting

</td>  
<td>

Task 6.1

</td>  
<td>

E-UNLIMITED

</td>  
<td>

E-UNLIMITED

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

#  Data security

INVITE will **securely handle any collected/generated data** throughout its
entire lifecycle as it is essential to safeguard this data against accidental
loss and/or unauthorised manipulation. Particularly, in case of personal data
collection/generation it is crucial that this **data can only be accessible by
those authorised to do so** . With that in mind, the project’s back-up and
data recovery strategy aims at ensuring that no data loss will occur during
the course and after the completion of INVITE, either from human error or
hardware failure, as well as inhibit any unauthorised access.

In particular, all project partners responsible for processing  34  data
within their private servers will ensure that this **data is protected** and
any **necessary data security controls have been implemented** , so as to
minimize the risk of information leak and destruction. This case refers to the
data that will be closed and therefore will not be shared and/or re-used
within the framework of the project. In this case and to avoid data losses,
the data will be **backed up on a daily basis** and the **backed-up files will
be stored in external hard disk drives** so as to safeguard their
preservation, while also enabling their recovery at any time. Moreover,
**integrity checks** 35  will be carried out once a month (or more often, if
deemed necessary) ensuring that the stored data has not been changed or
corrupted. The tool that will support partners in undertaking integrity checks
is the  **_MD5summer_ ** which generates and verifies MD5 checksums  36  .

Access to closed data will only be permitted to authorised project partners.
In case there is a personal data breach, project partners will notify, without
undue delay and, where feasible, not later than 72 hours after having become
aware of it, their competent national supervisory authorities (e.g. data
protection authorities) as well as the data subject(s) that may be affected by
the breach. Moreover, they will document any personal data breaches, including
information such as the facts relevant to the breach, its effects and the
remedial action(s) taken.

With that in mind, **identification and authentication access controls play an
important role** in the context of the project, as they help partners to
protect the data collected/generated during the course of INVITE and
especially personal data. To this end, each project partner is responsible for
and committed to ensuring the application of appropriate access controls to
the data they are processing within their private servers of their
organisation. At the same time, **technical access controls are built into the
Web Portal and OI2 Lab of INVITE** , setting out clear roles with access
rights to the data stored there, so that only authorised personnel have
access. Each project partner has been provided with unique accounts with one
or more roles assigned to them enforcing role-based security when its staff
processes the project’s data. These accounts are username/password protected
maximising access control. Moreover, in order to safeguard the privacy of the
users of the project’s Web Portal and OI2 Lab, dedicated **privacy policies**
have been created that clearly state the way in which these online spaces
collect, process and use personal data, the security procedures followed, the
users’ rights as well as the cookies policy employed (see Annex I and II of
this document).

On another note, INVITE’s **openly available** data will be stored safely for
long-term preservation on **Zenodo** , in the same cloud infrastructure as
research data from CERN’s Large Hadron Collider, using CERN’s **battletested
repository** **software** INVENIO, which is used by some of the world’s
largest repositories (such as INSPIRE HEP and the CERN Document Server). Along
these lines, data is stored and backed-up in CERN’s EOS service in an 18
petabytes disk cluster. Both data files and metadata are kept in **multiple
online replicas and independent replicas** ensuring their long-term
preservation as well as their recovery when necessary. Moreover, for each file
two independent MD5 checksums are stored. One checksum is stored by INVENIO,
used to detect changes to files made from outside of it whereas the other
checksum is stored by EOS, and used for automatic detection and recovery of
file corruption on disks. In this context, **access control is applied by the
different level of openness that Zenodo allows** (i.e. open, embargoed,
restricted and closed).

#  Ethical aspects and other procedures

INVITE entails activities which involve the **processing of data that does not
fall into any special category of personal data** 37  (i.e. non-sensitive
data). The collection/generation of this data from individuals participating
in the project’s activities is based upon a **process of informed consent** .
In fact, any personal data collected/generated in the framework of INVITE is
processed according to the principles laid out by the **Regulation (EU)
2016/679 of the European Parliament and of the Council** **of 27 April 2016**
on the protection of natural persons with regard to the processing of personal
data and on the free movement of such data which has entered into force in May
2018 aiming to protect individuals’ rights and freedoms in relation to the
processing of their personal data, while also facilitating the free flow of
such data within the European Union. Along these lines, **data is
collected/generated only for specified, explicit and legitimate purposes**
relative to project’s objectives. Moreover, all project partners tasked with
processing data during the course of INVITE fully abide with their respective
applicable national as well as EU regulations.

Under this light, further details about the **scope of the activities that
entail data collection/generation** in the frame of INVITE along with the
procedures for identifying/recruiting suitable stakeholders to take part in
them as well as for obtaining their informed consent are provided in “ **D8.1:
H - Requirement No. 1** ”. Moreover, **evidence that all data handling
procedures** carried out by project partners are **in line with relevant EU
and national regulations** are provided in “ **D8.2: H - Requirement No. 2**
”. The templates for the Information Sheet and the Informed Consent Form, used
in the implementation of the project’s activities, are compliant with the
General Data Protection Regulation and annexed to this DMP (see Annex II).

In this respect, it is important to highlight that **each project partner is
responsible for ensuring that the templates for the Information Sheet as well
as the Informed Consent Form are appropriately adjusted** according to (i) the
needs of the activity for which they are being used by them as well as to (ii)
the relevant regulations applicable to their respective countries and/or
organisation. Moreover, **all partners should keep records to demonstrate that
an individual has consented to processing of his / her personal data** and use
consent management mechanisms that make it easy for individuals to withdraw
their consent.

Finally, no other national/funder/sectoral/departmental procedures for data
management are currently used in the framework of INVITE.

#  Conclusions and way forward

The interim version of the DMP builds upon its initial version to further
elaborate on the methodology employed in the framework of INVITE. With that in
mind, it safeguards the sound management of the data collected/generated
during the course of the project’s activities across their entire lifecycle,
while also making them FAIR. Moreover, this version of the DMP provides an
estimation of the costs required for making data FAIR, outlines the provisions
pertaining to their security as well as addresses the ethical aspects
revolving around their collection/generation.

The DMP is considered to be a living document in the framework of INVITE and
is updated throughout the course of the project taking into account its latest
developments and available results. In fact, the interim version of the DMP
will be further elaborated and updated at least once more over the course of
INVITE, namely on M36 of the project. Ad hoc updates, may also be realised
when deemed necessary, with a view to delivering an accurate, up-to-date and
comprehensive DMP before the completion of the project.