#  Introduction

This document outlines the principles and processes for data collection,
annotation, analysis and distribution as well as storage, security and final
destroying of data within the Industrial Innovation in Transition (IIT)
project. The procedures will be adopted by all project partners and third
parties throughout the project in order to ensure that all project related
data is well-managed according to contractual obligations as well as
applicable legislation both during and after the project.

As the IIT project has opted to participate in the Open Data Pilot, this
document also details the practices and solutions regarding the storage and
re-usability of the research data, which will be made accessible for other
researchers and the public for further use and analyses. The Grant Agreement
of the IIT project as an Open Data Pilot participant obligates the project to:
a) deposit [digital research data generated in the project] in a research data
repository and take measures to make it possible for third parties to access,
mine, exploit, reproduce and disseminate — free of charge for any user — the
following: (i) the data, including associated metadata, needed to validate the
results presented in scientific publications as soon as possible; (ii) other
data, including associated metadata, as specified and within the deadlines
laid down in the 'data management plan', i.e. this document. The Grant
Agreement contains an option to discard the obligation to deposit a part of
research data in the case where the achievement of the action's main
objective, described in Annex 1 of the Grant Agreement, would be jeopardised.
In such case, the Data Management Plan must contain the reasons for not giving
access.

As the obligation to deposit research data in a databank does not change the
obligation to protect results, take care of confidentiality and security
obligations, or the obligations to protect personal data, the Data Management
Plan addresses these topics. This document details, how the seemingly
contradicting commitments to share and protect are implemented within the
project.

The Data Management Plan has, on the other hand, also served the purpose of
acting as a tool to agree on the data processing of the IIT project
consortium. The production of the Data Management Plan has helped the
consortium to identity situations, where the practices were thought to be
agreed upon and where a common understanding on practices was thought to have
been achieved, but where such in fact did not exist. For that reason the
process of producing a Data Management Plan can be recommended for other
projects as well.

Documents related to the Data Management Plan are the IIT project Grant
Agreement, the Consortium Agreement and the Project Handbook. Some of the
deliverables also contain information which links to the Data Management Plan.
The relationships are described below:  
  
<table>  
<tr>  
<th>

Related document

</th>  
<th>

Relationship to the Data Management Plan

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

The Grant Agreement

</td>  
<td>

  * Article 27 details the obligation to protect results 

  

  * Article 36 details confidentiality obligations 

  

  * Article 37 details security obligations 

  

  * Article 39 details obligations to protect personal data 

  

  * Annex 1, Chapter 1.4 details the ethics requirements, which in the case of the IIT project link to the obligation to protect personal data, the 

  

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

</td>  
<td>

obligation to get informed consent from persons participating in the research
and the obligation to get the ethical approvals for the collection of personal
data from relevant sources.

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

Consortium Agreement

</td>  
<td>

  * Chapter 4.1 on the General principles: “ _Each Party undertakes to take part in the efficient implementation of the Project, and to cooperate, perform and fulfil, promptly and on time, all of its obligations under the Grant Agreement and this Consortium Agreement as may be reasonably required from it and in a manner of good faith as prescribed by Belgian law_ .”. This is a general declaration of the partners to abide by the rights and obligations set out in the Grant Agreement. 

  

  * Chapter 4.3 on the Involvement of third parties: “ _A Party that enters into a subcontract or otherwise involves third parties (including but not limited to Affiliated Entities) in the Project remains responsible for carrying out its relevant part of the Project and for such third party’s compliance with the provisions of this Consortium Agreement and of the Grant Agreement. It has to ensure that the involvement of third parties does not affect the rights and obligations of the other Parties under this Consortium Agreement and the Grant Agreement_ .”. In the context of the Data Management Plan this chapter explains that the Partner extends the rights and obligations of the Grant Agreement to the subcontractor who implements the company interviews of WP2. 

  

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

The Project Handbook

</td>  
<td>

The Project Handbook defines the quality criteria for all work conducted in
IIT project.

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

D2.2

</td>  
<td>

D2.2 Interview guidelines also describes some aspects of the collection,
analysis, storage and reporting of IIT research data.

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

D3.3

</td>  
<td>

D3.3 describes the data processing related to the in-depth case studies. The
Description of the Scientific Research Data File will be an Annex to D3.3.

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

D3.4

</td>  
<td>

D3.3 which describes the validation web survey will in more detail whether
personal data laws affect the practical solutions related to the survey. In
case personal data is collected, the Description of the Scientific Research
Data File will be an Annex to D3.4.

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

#  Data Types

##  Data Types of the Project

In the IIT project there are four basic types of data (Figure 1): research
data, analysed research data, project data and reports and communication data.

**Research data** covers the data collected on the project subject matter,
namely industrial innovation practices and related innovation policies. The
data is mainly collected through company interviews and the data types are
e.g. audio records, transcriptions and possibly handwritten interviewer notes
from the interviews. Research data also includes web survey responses.

**Analysed research data** means the reports composed by the interviewee on
the main content of the interviews. Analysed data also refers to qualitative
and quantitative data analyses conducted on the data. Reviews of earlier
published data and records will be utilised to some degree. This data will be
considered as analysed research data for the purposes of this document.
Project related workshops and stakeholder engagement events are public events
and the workshop notes of project partners will be treated in the same way as
analysed research data (i.e. the notes will be shared within the consortium).

**Figure 1. Data types.**

**Project data** includes administrative and financial project data, including
contracts, partner information and periodic reports, as well as accumulated
data on project meetings, teleconferences and other internal materials. This
data is confidential to the project consortium and to the European Commission.
Project data includes mainly MS Office documents, in English, which ensures
ease of access and efficiency for project management and reporting. Most of
the project data is stored in the password protected Eduuni workspace,
administrated by Aalto University.

**Reports and other communication data** includes deliverables, presentations
and for example articles. This data type also refers to the contents of the
IIT project website.

Each data type is treated differently with regard to the level of
confidentiality (see Chapter 2.2). E.g. the untreated research data such as
audios of company interviews are treated as highly confidential data, whereas
most project deliverables are actively disseminated. Some of the data falls
under the EU and national laws on data protection and for this reason the
project is obliged to seek necessary authorisations and to fulfil notification
requirements.

The data will partly be in native languages, but all summary documents will be
translated in English. The project will assume the principle of using commonly
used data formats for the sake of compatibility, efficiency and access. The
preferred means of data types is MS Office compatible formats, where
applicable.

##  Levels of Confidentiality and Flow of Data

Overall, there are three basic levels of confidentiality, namely Public,
Confidential to consortium (including Commission Services), and Confidential
to the Partner / Subcontractor.

**Figure 2. Data types displayed in three levels of confidentiality.**

Figure 2 displays how the previously mentioned data types are positioned in
the level of confidentiality context. Only one data type – (untreated or raw)
research data – is totally situated in one level of confidentiality, which
means that it solely remains with the partner or third party responsible for
collecting it. The other three types contain data of two different
confidentiality level. For this reason Figure 3 displays the data in more
granularity.

**Figure 3. Data distributed into the three levels of confidentiality in more
detail.**

Figure 4 describes the data flows in time and also describes the transitions
of data – through processing – from one level of confidentiality to another.
The project team is aware of the effect of transitions, e.g. keeps in mind
that untreated data which has not been anonymized will not flow from Partner /
Subcontractor level of confidentiality to the Consortium level of
confidentiality. The Consent Forms signed by the interviewee and the Non-
Disclosure Agreements (NDAs) signed by the interviewers have been drawn in
this effect. In addition, the Description of the Scientific Research Data File
1  has also been composed according to the previously described data transfer
principle.

The data flows have been designed with the objective of maximizing personal
data protection: personal data remains within one partner or subcontractor and
within one country. This also makes the practical interpretation of data
protection laws more feasible.

**Figure 4. Data flows within and between the three levels of
confidentiality.**

It should be noted that workshop notes taken by the project partners will not
contain the names of persons, or any other information that would make it
possible to identify the origin of a comment or an opinion voiced in a
workshop. This being the practice, the data collected in the workshops is not
personal data. The workshop notes will be shared within the project consortium
and only aggregate reports and analyses will be made public.

##  Personal Data under the Data Protection Directive

A part of the data gathered in the IIT falls under the definition of personal
data. To avoid any misunderstandings, the central concept definitions have
been included in the IIT Data Management Plan. The following definitions have
been derived from the unofficial translation of the Finnish Personal Data Act
(523/1999)  2  . The Finnish Personal Data Act has been derived from the
constitutional reform and the EU Data

Protection Directive (Directive 95/46/EC of the European Parliament and of the
Council of 24 October 1995 on the protection of individuals with regard to the
processing of personal data and on the free movement of such data), so the
principles of it are applicable for the other IIT partner countries as well.

**Personal data** means any information on a private individual and any
information on his/her personal characteristics or personal circumstances,
where these are identifiable as concerning him/her or the members of his/her
family or household.

**Processing of personal data** means the collection, recording, organisation,
use, transfer, disclosure, storage, manipulation, combination, protection,
deletion and erasure of personal data, as well as other measures directed at
personal data.

**Personal data file** means a set of personal data, connected by a common use
and processed fully or partially automatically or sorted into a card index,
directory or other manually accessible form so that the data pertaining to a
given person can be retrieved easily and at reasonable cost.

**Controller** means a person, corporation, institution or foundation, or a
number of them, for the use of whom a personal data file is set up and who is
entitled to determine the use of the file, or who has been designated as a
controller by an Act.

**Data subject** means the person to whom the personal data pertains.

**Third party** 3  means a person, corporation, institution or foundation
other than the data subject, the controller, the processor of personal data or
someone processing personal data on the behalf of the controller or the
processor.

**Consent** means any voluntary, detailed and conscious expression of will,
whereby the data subject approves the processing of his/her personal data.

The next chapters of this deliverable have been organised as follows: first,
the different data types and collection methods are described. The second
section details the data management systems, categorisation and organisation
of data in the systems. The third and last part of the document focuses on
data sharing and protection of the privacy of interviewees and survey
respondents.

#  Collection, Storage and Use of Research Data and Analysed Research Data

The research data will be collected following jointly agreed guidelines and
principles in order to guarantee the achievement of sound research data and to
make the reusability of the research data possible. The project consortium
places a strong emphasis on data quality, and consequently there are dedicated
tasks for joint development of a conceptual framework (Task 2.1) and the
methodology for company interviews (Task 2.2). In addition, workshops on
interview principles and codification have been organised, in order to adopt
common practices. The developed guidelines and principles are discussed and
reviewed regularly in monthly project meetings, as well as in Work Package
meetings. Further tasks have been devoted to data collection and analysis,
both for company data and public policy data.

In the project, interviews and case studies are used for descriptive and
exploratory, theory building purposes, whereas a web-survey will be conducted
for theory testing and generalisation purposes. With this, the three different
methods enable comparison and authentication of data collected from different
sources in order to increase the reliability and validity of the research
results. This is illustrated in Figure 1. The three methods are briefly
summarised below.

**Figure**

**2**

**. IIT data collection methods.**

Data

triangulation

INTERVIEWS

CASE STUDIES

WEB SURVEY

_Theory building_

_Theory_

_testing_

Data collection methods, in summary, include:

  * literature reviews on innovation practices and national innovation policies, 

  

  * semi-structured interviews with standardised interview guideline throughout the interviews of companies, 

  

  * in-depth case studies from five selected sectors, 

  

  * a web survey, and 

  

  * policy and innovation management best practices dissemination workshops and events on national and European level. 

  

The interview questions and templates will be made available for the future
replication of the study in the project toolbox, which will be developed for
the benefit of future projects. The toolbox will include the methodology and
methods for data collection and analysis, the interview guidelines, and
descriptions of the case study procedures (Work Package 5). The toolbox
development is a key initiative to increase the impact of the IIT project.

##  Literature Review

There has been a renewed interest in innovation research following the fiscal
crisis in Europe and a vast amount of research has been conducted on
industrial innovation and innovation management practices by the European
Commission and different European universities, companies and innovation
agencies. Due to its fluid nature, innovation can be approached through a
plethora of approaches and disciplines. The IIT project takes a multi-
disciplinary approach to innovation, yet approaches the phenomenon from the
perspective of industrial companies. The IIT project partners have a strong
command of the current ‘state of the art’ in the field, and have reviewed the
existing body of research and best practice cases available on the innovation
practices of companies. As the project progresses, it will also collect
relevant background information regarding the target companies using public
sources of information.

The project will also conduct literature and policy reviews at the European,
regional and national levels through desktop research. This will generate data
that will be further elaborated in policy workshops, and used for summary
policy reports. The literature review will be carried out using publicly
available research results, publications and policy documents. This state of
the art review will serve as background for constructing the workshop agendas
for validation and extending the assumptions.

##  Company Interviews

The data collection for theory building purposes will begin with semi-
structured interviews, organised under Task 2.3 Data Gathering. The interviews
will be conducted among e.g. Chief Technology Officers, or managers in
equivalent position, of the target companies. Also, to ensure the
trustworthiness of the data, the IIT target is to carry out 800 interviews.
The IIT covers and compares five industrial sectors, and the ideal is to
achieve a balanced sample size between sectors. The purpose of the overall
sample size is also to enable different kinds of comparisons (e.g. innovation
practices between countries, company sizes, companies operating in high/low
innovation performer countries, etc.) and to ensure fulfilling the data
richness demands of qualitative research as well as the saturation of
understanding in each comparison. Also, in order to ensure the inclusion of
all the relevant companies (i.e. to ensure that all relevant ‘voices’ are
heard), and in that way to ensure the saturation of understanding, this sample
size will lead to conducting one interview per company

(cf. ibid.).

Each partner will conduct 150 interviews, mainly in two target member states
per partner (AT, CZ, DE, EE, ES, FI, IE, IT, NL, PT, UK). As an exception,
ZABALA will conduct interviews in three countries (ES, PT and IT). In
combination with the planned number of interviews, this will allow for
comparisons made between countries. In addition, to ensure that the best
innovation performers are reached, 10 additional interviews per partner are
assigned as result of the collaboration with the European Round Table of
Industrialists (ERT): this set of companies will be selected on the basis of
proven innovation capability, assessed either by their level of profitability,
growth or generally perceived innovative product and/or service portfolio.
Finally, and also to minimise possible over-/under sampling biases, the target
countries represent different type of innovation performers: of the 11 EU
member states targeted in the study, two are innovation leaders, five are
innovation followers and four are moderate innovators.

All interviews will be conducted following the guidelines documented in the
research methodology, which has been developed in the beginning of the project
(Task 2.2 Methodology for Company Interviews). Methodological guidelines
define the selection of industries and sectors, the type and size of the
interviewed companies, as well as the profile of their representatives. Data
will be collected on interviewee perceptions regarding external trends and
innovation drivers and on company internal innovation processes and
initiatives. The interview questions used across all sectors and countries are
detailed in the interview guidelines.

The initial preparation for the interviews contains contacting companies by
letter or email (which may be followed up by a phone call. The interviewees
will be asked for a two-hour-slot so that all of the main issues of the
interview guideline can be covered. The information given to the proposed
interviewee should contain:

  1. A description of the project objectives and indications of the areas in which questions will be asked (this can be a summary of the main questions set out as issues). 

  

  2. The motivations and hence potential benefits of the study to companies will also be made explicit. 

  

  3. The ethical and data protection related issues are addressed. This includes explaining that the interviews will be recorded and transcribed (unless the interviewee does not wish for the interview to be recorded), but that the transcript will be used only for coding purposes, and is confidential only to the partner / third party organization conducting the interview. Companies will be informed that the results will be used only statistically, and that any attribution of answers to a company or person will require their explicit acceptance and clearance. Where necessary, the interviewees will also be advised to seek the consent from their organisation to present their views before the interviews are arranged. A Consent Form (Annex I) will be sent to the interviewee to be reviewed and signed. The Description of the Scientific Research Data File (Annex II), which is required by the legislation regarding personal data  4  , will be presented to the interviewee. If the interviewee/company requires a Non-Disclosure Agreement, a model NDA is offered to project partners as part of the Data Management Plan (Annex III). 

  

  

The interviews will be conducted in line with the principles described in the
Data Management Plan and the related documents listed in Chapter 1 (e.g. Grant
Agreement, Consortium Agreement, Project Handbook), as well as the preceding
deliverables (D2.1 and D2.2) which guide the interview implementation work.
Most partner organizations have additional organization-specific principles
e.g. regarding research work, data protection and ethical code of conduct.
Each partner is responsible to observe the organisation specific guidance as
well as national legislation, in addition to the above mentioned documents. In
the cases where third parties have been contracted by project partners to
conduct interviews, the project partner responsible for contracting a third
party is responsible for contractually obligating these third parties to abide
by the same legal, ethical and project related documents and principles as
which direct the research work of the project partners themselves.

At the beginning of the interviews the purpose of the interview, the processes
for the management and use of data, and sharing of the results will be
discussed and explained to the interviewees. The Consent Form and the
Description of the Scientific Research Data File contain the key facts. The
signed Consent Forms will be collected from the interviewees prior to
conducting the interview.

The interviews will be conducted in the native language of the interviewees.
The interviews will be taped unless the interviewee requests to not be taped.
The taped interviews will be transcribed by third parties, which will be
contractually obligated to adopt the same principles as the consortium
partners with regard to personal data. Interview Summary Reports will be
composed based on the transcripts or written hand notes in the cases where the
interview has not been taped. The Interview Summary Reports will be sent to
the interviewees for review and approval prior to archiving, if the
interviewee requests this, or if the interviewer is unsure of whether the
report contains information which the company considers confidential and
harmful to be published even after aggregation and anonymization. The
interview data will also be used to produce an anonymised Codified Data
Catalogue.

All interview data will be confidential to the partner / third party
conducting the interview, and will not be disclosed even within the IIT
consortium. Access to raw data will be granted only to nominated persons in
the organisations which collected the data. The data will be stored in the
respective organisations’ secure databases. The Anonymized Interview Summary
Reports and Anonymized Codified Data Catalogues of each interview will be
shared within the consortium (M7 onwards). In addition, Anonymized Interviewer
notes will shared with the other consortium members, on a need-basis.

The Anonymized Interview Summary Reports and Anonymized Codified Data
Catalogues will be aggregated and made public at the end of the project (M24).
Depositing the research data into a publically accessible database comes from
the participation of the project in the Open Research Data Pilot, which is a
part of the larger Open Access initiative  5  . Open access can be defined as
the practice of providing on-line access to scientific information free of
charge to the end-user. Open access promotes the re-use of data. Scientific
information in this context refers to peer-reviewed scientific research
articles (published in scholarly journals) or research data (data underlying
publications, and e.g. raw data). The underlying principle of the vision is
that “information already paid for by the public purse should not be paid for
again each time it is accessed or used, and that it should benefit European
companies and citizens to the full.”  6  .

Deliverable 2.5 Best practices of company innovation management, which is
derived from the interview data, will be a public document, published
according to the schedule detailed in the Grant Agreement.

##  In-depth Case Studies

In order to deepen the understanding on the different innovation practices and
their alignment with innovation policies, 10-15 companies will be selected for
case studies. The purpose of the case studies is to enrich the findings and
strengthen the emerging understanding (and to fill the ‘gaps’ in
understanding) achieved with the interviews. The case studies will also
contribute to exploratory theory building. The IIT case study approach will be
developed in more detail based on the interviews described in the previous
section. However, two key principles are outlined here: Firstly, the case
studies will build on the interviews and prior theoretical understanding for
developing an understanding of the key variables and constructs examined in
the IIT project, and for outlining a rudimentary understanding of their
relationships.

Secondly, and in line with the _theoretical sampling_ principle, contrasting
cases will be selected in different sectors and countries. Therefore, the
selected cases will differ according to their innovation practices and the
extent to which these companies are supported or constrained by national
innovation policies. In order to increase the data richness and the depth of
understanding, additional interviews with key actors in the organisation, and
possibly at government level, will also be conducted. Also, data from public
annual reports, policy documents, and other written public sources may be
collected and used as additional sources of information.

The data will be collected in the form of interviews, company annual reports
and other public documentation, policy documents and other written sources.
The collected data will be confidential to partners Twente, Uniman and Aalto,
which are the partners conducting the case studies. The partners conducting
the case studies will compile Case Study Reports of the studies for which they
are responsible. The aim of the reports is to give additional insights and
e.g. quotes  7  regarding the innovation strategies, internal innovation
practices and e.g. collaborative arrangements of the companies. The Case Study
Reports as well as the Notes of interviewers will be anonymized and shared
within the consortium (i.e. the data is Confidential to the Consortium). These
documents will result in Deliverable 3.3 In-depth case study findings, which
is Confidential to the Consortium. The lessons learned will be utilized in
workshops and dissemination activities.

As the Case Study work will in all likelihood at some point result in research
work where the data protection laws related obligations will need to be
followed, D3.3 will contain as an annex the Description of the Scientific
Research Data File regarding the case study work (a model of such file can be
seen in Annex II of this document). Partner Twente as the task leader will be
responsible for the fulfilling this notification requirement.

##  Web Based Survey

The interviews and the case studies provide the basis for deductive,
hypotheses testing quantitative data collection. This will be carried out by
conducting a web survey. The primary motive of the web survey is to validate
the findings and hypotheses rising from the interviews and case studies,
therefore, contributing to developing _statistical generalisations_ . The web
survey will also further widen the respondent base to take into examination
the perspectives of key stakeholders. The survey will cover the same topics as
the interview guideline with the possibility of additions e.g. in the form of
tables usable in web based surveys.

The web surveys will be translated into the national languages of IIT in order
to cater to the needs of SMEs especially. Partner Joanneum will implement the
web survey and the data will be stored in a secure server of Partner Joanneum.
The methodology for the web survey has been developed within Task 2.2
Methodology for Company Interviews, and will be further reflected on as part
of Task 3.2 Data Analysis. The survey will complement the data collected
through interviews and case studies. The data from the survey is confidential
to the partner conducting the survey. The anonymised survey analyses will be
confidential to the consortium for the purpose of comparisons and further
analyses. The reports and the results of the analysis will be made public.

As the web survey implementation choices may at some point result in a
situation where the data protection law related obligations will need to be
followed, D3.4 will contain as an annex the Description of the Scientific
Research Data File regarding the web survey (if personal data will indeed be
collected). A model of such file can be seen in Annex II of this document.
Partner Joanneum as the task leader will be responsible for the fulfilling the
possibly emerging notification requirement.

##  National and European Level Workshops and Focus Groups

For further analysis, a two level workshop concept will be developed. This
includes national level focus groups in 11 countries that have been selected
for the analysis, and European level workshops. The focus groups at national
level will include representatives from relevant ministries, funding
authorities and agencies, NCPs, industrial associations as well as other
policy intermediaries. The workshops will also include representatives from
all the five main sectors covered by the IIT project: 1) ICT and ICT services,
2) Manufacturing, 3) Biopharma, 4) Agro-food, and 5) Clean technologies. The
outcomes of the national focus groups will be public summary reports and
policy briefings based on the discussions. European level workshops will
discuss the specific role, appropriateness and coherence of national and
European instruments in order to support industrial transition. The results of
the workshops will be reported in a public deliverable D4.2 Briefing paper for
the European policy workshop.

Further to the focused policy workshops, the IIT project will organise public
workshops for dissemination purposes. These workshops provide an opportunity
for further feedback and thus build on the final reports and recommendations
by the project.

Project partners will take notes in the workshops. The workshop notes will
neither list the names of the participants nor record the expressed opinions
of the participants in connection with the names of the persons expressing the
opinions. This being the practice, the data gathered does not fall under the
category of personal data as meant in the personal data protection
legislation.

All data collected in the workshops will be public and made available through
e.g. the IIT website. The data will also be summarised in the IIT project
deliverables D6.2 National level workshops documentation and D6.3 Workshop
documentation and Output paper.

##  Destroying of Data

The data of the IIT project will be destroyed in January 2022. The Grant
Agreement states that the project needs to be prepared for an audit within two
years after the payment of the balance. The obligation to provide
documentation for a possible investigation, however, is valid for five years
after the payment of the balance. As the exact date of the payment of the
balance is unknown at this stage, the consortium will adopt a security time
slot of one year, starting from the first possible date of the payment of the
balance. January 2022 has been derived with this principle.

Each partner will be responsible for destroying the data in their possession.
The coordinator will be responsible for destroying data from Eduuni and any
other coordinator servers. Each partner is obliged to make such arrangements
for this task which are not dependent of the availability of current project
personnel.

#  Collection, Storage and Use of Project Data

The data accumulated in the IIT project will be analysed and stored according
to the principles detailed in the Project Handbook and Interview Guidelines.
Overall the detailed data will be stored by the organisations which has
collected it, and the anonymised Interview Summary Reports and Codified Data
Catalogues will be stored at a database provided by partner Uniman. Both the
central repository and the databases of the individual organisations will be
secured using the latest security protocols, and access to data will be
granted only for persons nominated by the project partners. Each partner will
produce a Description of the Scientific Research Data File to fulfil the
obligations rising from data national protection laws. The partners are
offered a model Description of the Scientific Research Data File (Annex II),
but are advised to check whether it fulfils the national requirements.

All project administrative data will be stored at a dedicated database for the
IIT project. The project uses the Eduuni workspace (https://www.eduuni.fi/),
which is a secure, password protected document workspace and archive system.
The Eduuni workspace consists of Microsoft SharePoint 2013 Workspace and
Office Web Apps functionalities. It further includes a wiki functionality.
Access to the database is managed by the coordinator and provided for project
consortium and other parties as deemed necessary by the project team. The
project data is stored on Aalto University servers, not in the cloud, for
added security. The data is organised in the database following the e.g. Work
Package, Task and Deliverable structure as defined in the project plan and
contract. This ensures the ease of access and provides a logical structure for
the data. The following table details the project data management structure
and categories.  
  
<table>  
<tr>  
<th>

WP1 Management

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

T1.1 Coordination actions within the consortium

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

T1.2 Data management

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

T1.3 Project handbook

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

T1.4 Advisory board facilitating and stakeholder liaising

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

WP2 Current company innovation practices

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

T2.1 Conceptual framework development

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

T2.2 Methodology for company interviews

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

T2.3 Data gathering

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

T2.4 Data analysis (national, industry specific, other)

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

T2.5 Best practices of company innovation management

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

WP3 Innovation policy implications rising from current company innovation
practices

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

T3.1 Review of national innovation policies

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

T3.2 Data analysis (comparison of company practices against national policies)

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

T3.3 In-depth case studies to validate and understand findings

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

T3.4 Validation via web survey

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

WP4 Assessment of current innovation policies

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

T4.1 Methodology development

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

T4.2 Innovation policy assessment workshops

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

WP5 Toolkit for the replication of the study

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

T5.1 Toolkit development

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

T5.2 Toolkit introductory workshops

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

WP6 Dissemination

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

T6.1 Best innovation practices dissemination

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

T6.2 National market-to-policy workshops and related dissemination

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

T6.3 European innovation policy workshops and related dissemination

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

T6.4 Toolkit dissemination

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

Table 1. Structure of the IIT Project Work Packages and Tasks

The IIT Project Handbook details the project internal management structure and
processes, as well as quality and reporting practices. Related to project data
management, best practices for data generation and sharing have been applied.
This includes set rules for version control, whereby the partners are
encouraged to use unified methods for naming the documents by the Task or
Deliverable name, with a corresponding version number 01., 02., 03.. The
documents are stored in the database and preferably shared within the
consortium via a link to the database rather than e-mail attachments. All
deliverables have a unified look and feel with a unified template which helps
the reviewers in their project evaluation.

The project coordinator assumes the responsibility for timely documentation
and sharing of project management related documents and materials. Each Work
Package (WP) leader monitors the timely documentation of WP related
requirements within the consortium. Each task leader ensures the timely
production of the deliverable for which he/she is responsible. With the
strongly inter-related and intertwined Tasks in the different Work Packages,
the same previously described principles regarding e.g. confidentiality levels
and data types will be applied.

#  Data Sharing

All parties have signed/accessed to the project Grant Agreement and Consortium
Agreement, which detail the parties’ rights and obligations, including – but
not limited to – obligations regarding data security and the protection of
privacy. These obligations and the underlying legislation will guide all of
the data sharing actions of the project consortium.

The IIT project has opted to support and join the Open Research Data Pilot,
which is an expression of the larger Open Access initiative of the European
Commission  8  . Participation in the pilot is manifested on two levels: a)
depositing research data in an open access research database or repository and
b) choosing to provide open access to scientific publications which are
derived from the project research. At the same time the consortium is
dedicated to protect the privacy of the informants and companies.

**Depositing research data in an open access research database or
repository:** Following the principles of the European Commission Open Data
pilot, the applicable anonymised and aggregated data gathered in the project
will be made available to other researchers, in order to increase the
potential exploitation of the project work. The aggregated and anonymized
Interview Summary Reports and Codified Data Catalogues will be the key
contribution to the Open Access initiative, as they will be made available
according to the schedule detailed in the Grant Agreement. The IIT project
will further establish a toolbox which future users of the project methodology
can access and continue to use in their own respective countries, and in doing
so enrich the existing data with their corresponding national data. The
toolbox will be made available via the project website,  _www.IIT-project.eu_
.

**Choosing to provide open access to scientific publications which are derived
from the project** : All peerreviewed scientific publications relating to
results are published so that open access (free of charge, online access for
any user) is ensured. Publications will either immediately be made accessible
online by the publisher (Gold Open Access), or publications are available
through an open access repository after an embargo period, which is usually
from six to twelve months (Green Open Access). Possible Gold Open Access
journals include Research Policy, Technovation, Technological Forecasting and
Social Change, Industry and Corporate Change. For all other articles, the
researchers aim at publishing them in a Green Open Access repository. The
coordinator, Aalto University, has a Green Open Access repository the IIT
consortium can use, at https://aaltodoc.aalto.fi/?locale-attribute=en.

A machine-readable electronic copy of the published version or final peer-
reviewed manuscript accepted for publication will be available in a repository
for scientific publications. Electronic copies of publications will have
bibliographic metadata in a standard format and will include "European Union
(EU)" and "Horizon 2020", the name of the action, acronym and grant number;
publication date, and length of embargo period if applicable, and identifier.

In addition to the previous sharing of the project results, the IIT project
will disseminate the best practice experiences among the participating
companies and broader European audiences. It will also evaluate existing
innovation policy portfolios at national and European levels, and analyse the
differences between innovation processes and management practices in different
industrial sectors. The best practices and other results will be disseminated
widely both to the European business community and governments in order to
improve Europe’s innovation potential.

The IIT project will publish summary reports of the project findings, as well
as other reports and recommendations on how to accelerate the deployment of
best innovation practices in Europe. These publications will be made publicly
available through the project website, as well as through the participating
organisations’ and their partners’ websites. The reports will be accessible
for everyone and can be freely quoted in subsequent research and publications.

The project will provide three key reports, namely an Innovation Policy
Report, Report on Companies Innovation Practices, and a Toolkit for the
Replication of the Study. The reports will be published at national and
regional levels and further disseminated through events and workshops for the
benefit of European companies and the research community. There is a dedicated
Work Package 6 for dissemination activities, and further dissemination will be
done on a national level. Details regarding dissemination channels and events
will be a part of the project Dissemination and Communication Plan.

#  Living Document

This Data Management Plan is a living document which will be submitted at the
end of July 2015 (M6) but which will be updated and complemented as the
project evolves.

Examples of data related issues which remain to be decided at later stages of
the project:

  * Type of metadata on the research data deposited to an open access research database or repository 

  

  * Aggregation method / technology applied on the anonymised research data prior to depositing the research data in an open access research database or repository 

  

  * Practical implementation of the Grant Agreement obligation to submit copies of ethical approvals for the collection of personal data by the competent University Data Protection Officer / National Data Protection (Grant Agreement Annex 1, 1.4. Ethics Requirements)