1. The Development of Research Data Management Policies in Horizon 2020

Abstract

This article provides an overview of open research data and research data management in Horizon 2020. It describes the open research data pilot in Horizon 2020, which, as of 2017, has been extended to cover all thematic areas of Horizon 2020 (‘open data as the default’). However, the Commission also recognises that there are also good reasons to keep data closed and thus allows individual opt-outs. Good research data management in a broader sense has emerged as a key issue in this context. The link between openness and general management of research data is provided by a key document mandatory for all Horizon 2020 projects which do not opt-out: the data management plan (DMP). In the 2016 update of the Horizon 2020 guidelines on data management it was made clear that the DMP should outline how projects make their data FAIR: findable, accessible, interoperable and re-usable. Initial experience with DMP assessment by research data management (RDM) experts in H2020 reveals that additional guidance on data management is needed for all groups of actors in research projects. Aspects such as data preservation, IPR or standards are too often not well developed in the DMPs that have been submitted so far. However, improved guidance and tools are expected to improve these competences. Nevertheless research projects with excellent RDM performance are not rare. Some high quality DMPs from H2020 projects have already been published online1. While costs for data management can be covered by the beneficiaries and are fully eligible for reimbursement in Horizon 2020 many project participants need information about the adequate level of spending for data management in projects. At the moment, those projects opting out of opening their research data do not have to provide a DMP. The authors believe that in the future all projects should produce a DMP, even if they choose to keep some (or even all) of their data closed. In this case, the DMP should still address the curation and preservation of such data.

Full text

Daniel Spichtinger and Jarkko Siren
1 The Development of Research Data
Management Policies in Horizon 2020
Abstract: This article provides an overview of open research data and research
data management in Horizon 2020. It describes the open research data pilot in
Horizon 2020, which, as of 2017, has been extended to cover all thematic areas
of Horizon 2020 (‘open data as the default’). However, the Commission also
recognises that there are also good reasons to keep data closed and thus allows
individual opt-outs. Good research data management in a broader sense has
emerged as a key issue in this context. The link between openness and general
management of research data is provided by a key document mandatory for all
Horizon 2020 projects which do not opt-out: the data management plan (DMP).
In the 2016 update of the Horizon 2020 guidelines on data management it was
made clear that the DMP should outline how projects make their data FAIR:
findable, accessible, interoperable and re-usable. Initial experience with DMP
assessment by research data management (RDM) experts in H2020 reveals that
additional guidance on data management is needed for all groups of actors in
research projects. Aspects such as data preservation, IPR or standards are too
often not well developed in the DMPs that have been submitted so far. However,
improved guidance and tools are expected to improve these competences. Never-
theless research projects with excellent RDM performance are not rare. Some high
quality DMPs from H2020 projects have already been published online
1
.
While costs for data management can be covered by the beneficiaries and
are fully eligible for reimbursement in Horizon 2020 many project participants
need information about the adequate level of spending for data management in
projects. At the moment, those projects opting out of opening their research data
do not have to provide a DMP. The authors believe that in the future all projects
should produce a DMP, even if they choose to keep some (or even all) of their
data closed. In this case, the DMP should still address the curation and preser-
vation of such data.
1 Introduction
Data is becoming increasingly important for all aspects of the European economy
and society. More and more data is being generated and it has been estimated
1http://www.dcc.ac.uk/resources/data-management-plans/guidance-examples, accessed 06152017
DOI 10.1515/9783110365634-002
Bereitgestellt von | De Gruyter / TCS
Angemeldet
Heruntergeladen am | 01.03.18 09:56

that big and open data can potentially add 1.9% to the EU’s GDP by 2020
(Buchholtz et al. 2014, 6–7).
These gains can be derived from productivity increases, the opening up of
public sector data and better decision making thanks to data-driven processes.
The digital economy is therefore considered a key potential source for growth,
innovation and ultimately employment (European Policy Centre 2010, 4), a fact
that is reflected in the agenda of the Juncker Commission
2
, which has made
completing the digital single market a priority.
3
It is important to point out that
the trend of “datafication”does not only affect sectors traditionally associated
with the digital economy –such as IT –but that all parts of the economy are
producing or using computerised data. Big and Open Data have been estimated
to have an impact on sectors as diverse as agriculture, public administration,
health, retail, transportation and the work place. Data are a core asset that
can create a significant competitive advantage and drive innovation, sustainable
growth and development in all these sectors. In business, the exploitation of
data promises to create added value in a variety of operations, ranging from
optimising the value chain and manufacturing production to more efficient use
of labour and better customer relationships (Kounatze 2013, 4).
2 On definitions of data
The ubiquity and pervasiveness but at the same time the variety of what is con-
sidered ‘data’present important challenges in legislating on data related issues.
It is all the more surprising, then, that so few studies, reports and press articles
actually define what they mean when they discuss ‘data’. When, for instance, a
data protection activist talks about ‘usage data’from a social network this is
something very different from what a particle physicists at CERN has in mind.
On the most general level the Cambridge Dictionary defines data as “infor-
mation, especially facts or numbers, collected to be examined and considered
and used to help decision-making or information in an electronic form that can
be stored and processed by a computer”(Cambridge Dictionaries Online, 2014).
In a research context, examples of data include statistics, results of experiments,
measurements, observations resulting from fieldwork, survey results, interview
recordings and images. Nowadays, the focus is on research data that is available
in digital form. A further useful definition is provided by the United States Govern-
ment’sOffice of Science and Technology Policy (OSTP) in its Memorandum on
2https://ec.europa.eu/priorities/index_en, accessed 06152017
3Juncker 2014, 5.
12 Daniel Spichtinger and Jarkko Siren
Bereitgestellt von | De Gruyter / TCS
Angemeldet
Heruntergeladen am | 01.03.18 09:56

Increasing Access to the Results of Federally Funded Scientific Research where
data is defined “as the digital recorded factual material commonly accepted in
the scientific community as necessary to validate research findings including
data sets used to support scholarly publications”(OSTP 2013, 5). In a research
context, a further distinction can be made between
–Data generated primarily for research purposes –this is already an extremely
broad field covering different definitions of data. What is considered data
varies enormously, for instance in archaeology (e.g. pictures of a dig site),
medicine (e.g. clinical trial data) or particle physics (e.g. accelerator data).
–Data not primarily generated for research purposes, which can, however, be
used for research:
–So called “Public Sector Information”, that is data collected by public
authorities, such as statistics (e.g. census data, demographic and economic
indicators), geospatial data (e.g. maps, sensor data), transport data (e.g.
traffic information) or company and business registers.
–Data that is “out there”, that is on the internet –for instance on social
networks such as twitter or Facebook, including but not limited to usage
data of these sites. The Twitter DataGrants pilot program, for instance,
aims at giving a handful of research institutions access to Twitter’s public
and historical data (Twitter 2014).
For the research sector a further proposed classification of data is as follows:
a) Metadata / bibliographic data that describe data: metadata is found in online
catalogues, archives, repositories, etc.
b) Data underlying publications (i.e. the data needed to validate the findings
presented in scientific publications), often presented as part of publications
(“enriched publications”, with links to data).
c) Curated data, for example data collections, structured databases (held in
repositories and data centres, both institutional and discipline-based), in-
cluding relevant workflows and protocols.
d) Raw data and data sets: these are not curated and typically held on institute
hard drives and in drawers.
3 The development of research data policy in
Horizon 2020
The EU’s multiannual framework programme for Research and Innovation,
Horizon 2020, dedicates nearly 80 billion €for research funding. In addition to
The Development of Research Data Management Policies in Horizon 2020 13
Bereitgestellt von | De Gruyter / TCS
Angemeldet
Heruntergeladen am | 01.03.18 09:56

other results, it is expected to generate a significant amount of research data.
It is therefore in the interest of the EU to ensure that best possible use of this
data is guaranteed. One way to achieve this goal is by making the research
data collected or generated in H2020 projects findable, accessible, interoperable
and re-usable (FAIR).
However, while open access to scientific publications has been implemented
for a decade and is increasing in terms of acceptance and use
4
,efforts to achieve
open access to research data in EU research programs is more recent. The Com-
mission did not have a policy on research data in FP7 but started to proactively
address the issue in preparation for Horizon 2020. A 2011 online survey on scien-
tific information in the digital age
5
found that the vast majority of respondents
(87 %) disagreed or disagreed strongly with the statement that there is no access
problem for research data in Europe. The barriers to access research data con-
sidered very important or important by respondents were: lack of funding to
develop and maintain the necessary infrastructures (80%); insufficient credit
given to researchers for making research data available (80%); and insufficient
national/regional strategies/policies (79%). There was strong support (90% of
responses) for research data that is publicly available and results from public
funding to be, as a matter of principle, available for reuse and free of charge
on the Internet. Following up on the survey the Commission held a public con-
sultation on open research data on 2 July 2013 in Brussels, which was attended
by a variety of stakeholders from the research community, industry, funders,
libraries, publishers, infrastructure developers and others
6
.
Horizon 2020 contains both large scale calls for consortia of research organi-
sations and industrial companies as well as actions supporting individual re-
searchers, SMEs, public private partnership and many more. These varying
so-called “beneficiaries”of Horizon 2020 in principle own the results of the
research conducted and are free to exploit it. However, the Commission has
repeatedly highlighted the importance of optimising the circulation, access to
and transfer of scientific knowledge and stressed that research and innovation
benefit from scientists, research institutions, businesses and citizens accessing,
sharing and using existing scientific knowledge and the possibility to express
4More than 50% of research publications from 2012 were open access in 2014, according to
data collected by Science Metrix (counting both gold, green and other open access). See
http://science-metrix.com/en/publications/reports, accessed 06152017
5European Commission 2012a see http://ec.europa.eu/research/science-society/document_
library/pdf_06/survey-on-scientific-information-digital-age_en.pdf
6European Commission 2013.
14 Daniel Spichtinger and Jarkko Siren
Bereitgestellt von | De Gruyter / TCS
Angemeldet
Heruntergeladen am | 01.03.18 09:56

timely expectations or concerns on such activities.
7
This recognises that all
research builds on former work and depends on scientists’possibilities to access
and share scientific information. Fuller and wider access to scientific publica-
tions and data can therefore help to accelerate innovation, foster collaboration
and avoid duplication of effort by building on previous research results as well
as making research more accessible for companies (in particular SMEs) and non-
for profit organisation. This is particularly valuable if exploitation is not under-
taken by the primary beneficiary; added value can also be created through the
re-use of data already generated. Data re-use has the potential to further increase
the impact of the research funded by the European taxpayer and to support
Horizon 2020 in its contribution to economic growth and job creation.
The Horizon 2020 regulation stated that “open access to research data re-
sulting from publicly funded research under Horizon 2020 should be promoted,
taking into account constraints pertaining to privacy, national security and intel-
lectual property rights.”(Regulation (EU) No 1291/2013, Recital 28) In order to
“promote”open access to data, as stipulated by the legislator, the European
Commission set up a flexible pilot scheme for research data from EU funded
projects, anchored in the Horizon 2020 work programme (H2020 Open Research
Data Pilot aka ORD pilot).
8
The Commission considered it important that the
ORD pilot would be designed in a way that would allow wide acceptance and
uptake by the stakeholders in the research ecosystem. Issues and challenges
of access to research data were therefore extensively discussed with individual
researchers, industry, research funders, libraries, publishers, infrastructure
developers and others in the form of i) a one day event where individual presen-
tations and discussion could be heard and (ii) a written consultation period.
9
It
quickly became apparent that the ORD pilot would need to balance openness
with IPR and commercialisation issues, privacy concerns, security as well as data
management and preservation questions. Considerable efforts were therefore
undertaken in 2013 in designing a pilot scheme that would be ambitious, prag-
matic and flexible at the same time. The results led to a system which is very
clear on (i) which thematic areas of Horizon 2020 are included in the ORD pilot,
(ii) what kind of data is expected to be made open access and the implications
for data management.
Based on this initial structuring, two additional factors were then taken on
board: firstly it was recognised that there are also good reasons for NOT making
data available in open access (see above), and projects were therefore given
7European Commission 2012c.
8European Commission 2014f, 19.
9European Commission 2013.
The Development of Research Data Management Policies in Horizon 2020 15
Bereitgestellt von | De Gruyter / TCS
Angemeldet
Heruntergeladen am | 01.03.18 09:56

several options to “opt out”of the ORD pilot, namely in cases which would
create conflicts a) with the project’s obligation to protect results (in case of
commercialisation
10
), b) conflicts with confidentiality obligations, c) conflicts
with security obligations or d) with rules on protection of personal data. Finally,
projects can also opt out if achieving the action’s main aim is jeopardised by
making specific parts of the research data openly accessible. Secondly, due
account was taken of the fact that project applicants might like to participate in
the ORD pilot even when their project is not part of the so-called “core areas”
(in 2014–16 some areas of the H2020 Work Programme participated ‘by default’
in the ORD pilot; starting from 2017 proposals from all areas participate unless
they opt-out explicitly). In that case they will be given an opt-in possibility on
a voluntary project by project basis. The options to “opt-out”or to “opt-in”are
implemented as part of the electronic proposal submission process, through an
easily clickable form.
The second issue to be resolved concerned the kind of data that was to be
made available. An initial scoping exercise showed the enormous amount and
variety of objects which have been classified as “data”(see section 2 above). It
was therefore decided that the ORD pilot would primarily apply to data underly-
ing scientific publications because (i) this data is presumed to be cleaned and
structured since it has been used to create a publication (ii) there is a need to
increase the reproducibility of the results reported in scientific articles. Projects
can of course go beyond this initial requirement and also publish curated data
not connected to a publication, or raw data; but they are not obliged to do so.
Projects participating in the ORD pilot are obliged to outline which data they
want to make open as part of a data management plan (DMP), which is a docu-
ment outlining how the research data collected or generated will be handled
during a research project, and after it is completed. It should be noted that
both the decision on whether to participate in the ORD pilot or not is not part
of the evaluation and selection for funding. In other words, proposals are not
evaluated more favourably because they are part of the ORD pilot and will not
be penalised for opting out of the pilot.
In the Work Programmes 2014–2016, this ORD pilot concerned selected areas
of Horizon 2020. However, in the Communication “a European Cloud Initiative –
Building a competitive data and knowledge economy in Europe”the Commis-
sion commits itself to “make open research data the default option, while ensur-
ing opt-outs, for all projects of the Horizon 2020 programme”as of 2017. As of
10 “Horizon 2020 is the financial instrument implementing the Innovation Union, a Europe
2020 flagship initiative aimed at securing Europe’s global competitiveness”. http://ec.europa.
eu/programmes/horizon2020/en/what-horizon-2020, accessed 06152017
16 Daniel Spichtinger and Jarkko Siren
Bereitgestellt von | De Gruyter / TCS
Angemeldet
Heruntergeladen am | 01.03.18 09:56

the revised version of the Work Programme 2017, the ORD pilot has therefore
been extended to all thematic areas of the Horizon 2020 Research and Innova-
tion Programme.
For the uptake of the ORD pilot from 2014 to 2016 (when its scope was more
restricted), figures show an opt-out rate of 35% in the core areas of the pilot.
In other words 65% of projects in the core areas participate in the ORD pilot.
The most important reasons for opt-outs were IPR concerns followed by privacy
concerns and projects which do not expect to generate data. Outside the core
areas, 14% of projects make use of the voluntary opt-in possibility.
11
4 Research data management in Horizon 2020
While the main aspect of the EU research data policy refers to open access to
data, attention to data management has not been left out. The idea to focus on
research data management in EU funding programs was first spelled out in the
recommendations of the “Riding the Wave”report from the High Level Expert
Group on scientific data (2010) which called for “EU and national agencies
mandate that data management plans be created”
12
in order to gain strategic
view of data value.
The 2011 online survey on scientific information in the digital age
13
showed
a wide support for data management among all stakeholder groups (national
governments, regional and local governments, research funding organisations,
university/research institutes, libraries, publishers, international organisations,
individual researchers, citizens and respondents identified as ‘other’,amongwhich
there were NGOs, industries, charities, learned societies and scientific and pro-
fessional associations). The majority of respondents considered the lack data
management requirements as a barrier to enhanced access to research data. In
particular this was the opinion of all respondents from research funding organ-
isations, 80.8% of library respondents and 75.3% of publisher respondents. The
issue of data management plans (DMP) in research projects was already present
11 Details on call specific uptake are available on the Open Data Portal at https://data.europa.
eu/euodp/data/dataset/open-research-data-the-uptake-of-the-pilot-in-the-first-calls-of-horizon-
2020, accessed 15062017
12 http://ec.europa.eu/information_society/newsroom/cf/document.cfm?action=display&
doc_id=707, accessed 06152017
13 European Commission 2012a, see: http://ec.europa.eu/research/science-society/document_
library/pdf_06/survey-on-scientific-information-digital-age_en.pdf
The Development of Research Data Management Policies in Horizon 2020 17
Bereitgestellt von | De Gruyter / TCS
Angemeldet
Heruntergeladen am | 01.03.18 09:56

in some contributions by stakeholders. In particular the question on adequate
timing of DMP submission was mentioned (whether in the research proposal or
later as a project output). It is interesting to note that some respondents identified
lack of skills and capacity for data management as additional barriers to access.
Both aspects were addressed 1 year later in a Commission Recommendation to
Member States
14
.
The inclusion of RDM in the EU research data policy is also supported by
the fact that access to research data can be significantly improved by adequate
data management, understood as proper data curation, storage and preserva-
tion, provision of metadata and documentation, etc. From the point of view of
policy and project monitoring, the inclusion of DMP as part of the Horizon 2020
ORD pilot, provides additional means to assess project outcomes (among other
aspects, DMPs are expected to define data sharing policies). The quality of DMPs
delivered to the Commission and its agencies can be used to measure the evolu-
tion of data management skills in EU research, as well as to assess data sharing
practises.
However, a policy is only as good as its implementation which means that
data management policy needs to be backed by some guidance and tools. This is
why the European Commission developed specific guidelines on Data Manage-
ment, including a template for DMPs. As defined in the H2020 Model Grant
Agreement
15
, project partners can use DMPs as a flexible tool to define what
data will be shared, how and when.
The EC is one of many research funders currently requiring a DMP from its
grantees. While there are clear advantages in requesting a DMP either at pro-
posal stage or during project lifetime, the EC decided to apply a compromise
approach: proposals are required to define an outline of their data management
policy in general terms and this is evaluated as a sub criterion of the impact
criterion. However, a full DMP is not required at the time of submission but
only if the proposal is selected for funding and after start of the project (first
version at month 6 after start and final version before end of the project)
16
.
The flexible approach of the ORD pilot thus extends to the DMP mandate,
which is closely linked to it. While the DMP is obligatory for all research projects
participating in the ORD Pilot, it can evolve and adapt to the project’s needs
during its lifetime. In addition, although a DMP template is provided in H2020
14 European Commission 2012e.
15 The H2020 MGAs can be found in the Participant Portal: http://ec.europa.eu/research/
participants/portal/desktop/en/funding/reference_docs.html, accessed 06152017
16 The requirements referring to the proposal stage apply to all proposal, while those referring
to selected projects apply only to Open Research Data Pilot projects.
18 Daniel Spichtinger and Jarkko Siren
Bereitgestellt von | De Gruyter / TCS
Angemeldet
Heruntergeladen am | 01.03.18 09:56

and its use is recommended, it is currently not mandatory. While the data
management aspects covered in the template are common to most research
projects, the flexibility of the template allows discipline specific practices to be
included. As a result of feedback received from stakeholders, independent experts,
project coordinators and project officers, the guidelines and the DMP template
were updated in July 2016
17
. The adaptation of the FAIR principles to data
management is now part of the EC policy on research data as described in the
updated guidelines. This change required also substantial modifications to the
DMP template, which nevertheless continues to be optional. Therefore the use
of the previous templates as well as other templates is allowed, if considered
appropriate in the context of the project.
5 Experiences and preliminary lessons learned
from Horizon 2020 RDM
A major change in the 2016 update of the H2020 guidelines on Data Management
was the adaptation of the Fair Data Principles to the domain of RDM. In the
Horizon 2020 context the FAIR principles provide also a strong connection
between open research data policy and RDM practise. Since the DMP template
is an integral part of the H2020 RDM approach, it became clear that this
template should also be adapted. However, the H2020 DMP template is not a
“strict technical implementation of the FAIR principles, it is rather inspired by
FAIR as a general concept”.
Initial experience with DMP assessment by RDM experts in H2020
18
reveals
that additional guidance on data management is needed for all groups of actors in
research projects (researchers, peer reviewers and funder administrators (“project
officers”)) including roles supporting researchers with data management tasks
(data librarians or IT professionals working in data centres). Aspects such as
data preservation, IPR or standards are too often not well developed in the
DMPs that have been submitted so far. However, improved guidance and tools
are expected to improve these competences. While the UK DCC DMPOnline
19
was
17 European Commission (2016). http://ec.europa.eu/research/participants/docs/h2020-funding-
guide/cross-cutting-issues/open-access-data-management/data-management_en.htm, accessed
06152017
18 These conclusions are based almost entirely on a DMP assessment pilot conducted by the
EC Research Executive Agency (unit B3) in 2016 with H2020 Societal Challenge 6 projects.
19 http://www.dcc.ac.uk/dmponline, accessed 06152017
The Development of Research Data Management Policies in Horizon 2020 19
Bereitgestellt von | De Gruyter / TCS
Angemeldet
Heruntergeladen am | 01.03.18 09:56

the first available DMP tool, some alternatives, often based on the DMPOnline,
already exists, such as DMPTuuli
20
in Finland. Examples of DMPs can be found
in the DMPOnline site as well as in the Zenodo repository
21
. Nevertheless research
projects with excellent RDM performance are not rare. Some high quality DMPs
from H2020 projects have already been published online
22
.
While costs for data management can be covered by the beneficiaries and
are fully eligible for reimbursement in Horizon 2020 many project participants
need information about the adequate level of spending for data management in
projects. Decisions on data management costs call for better understanding of
the value of research data, or for a “strategic view of data value”.
In H2020 DMP is mandatory for all projects participating in the ORD pilot.
Due to the large flexibilities built in the pilot, total opt-out from it for reasons
such as IPR or personal data protection seem to raise questions about data
management. If no data sharing is possible, e.g. for serious data protection
issues, excellent data management and hence a DMP, seem even more necessary.
Following the “as open as possible, as closed as necessary”principle and the
possibility to participate even if no data is shared beyond the project consortium
members, the DMP remains the only strong mandate for pilot projects. But
for any project with strong protection requirements (whether for privacy or
commercial reasons), it seems even more important to manage research data
professionally which will require adequate planning activities. Therefore the
authors believe that a DMP is a necessary component in all research projects
dealing with data.
Disclaimer: All views expressed herein are entirely of the authors, do not
reflect the position of the European Institutions or bodies and do not, in any
way, engage any of them.
References
Boston Consulting Group. 2012. The Value of Our Digital Identity. https://www.bcgperspectives.
com/content/articles/digital_economy_consumer_insight_value_of_our_digital_identity/,
accessed 06152017.
Buchholtz, Sonia, Maciej Bukowski, Aleksander Sniegocki. 2014. Big and open data in Europe:
A growth engine or a missed opportunity? Warsaw Institute of Economic Studies / Demos
Europe http://www.bigopendata.eu/wp-content/uploads/2014/01/bod_europe_2020_full_
report_singlepage.pdf, accessed 06152017.
20 https://www.dmptuuli.fi/, accessed 06152017
21 https://www.zenodo.org/, accessed 06152017
22 http://www.dcc.ac.uk/resources/data-management-plans/guidance-examples, accessed 02082017
20 Daniel Spichtinger and Jarkko Siren
Bereitgestellt von | De Gruyter / TCS
Angemeldet
Heruntergeladen am | 01.03.18 09:56

Cambridge Dictionaries Online. 2014 English definition of data http://dictionary.cambridge.org/
dictionary/british/data
Data Protection Working Party. 2007. Opinion 4/2007 on the concept of personal data.01248/07/
EN. WP 136. http://ec.europa.eu/justice/policies/privacy/docs/wpdocs/2007/wp136_en.pdf,
accessed 06152017.
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.Official Journal L 281, 23/11/1995 P. 0031 –0050. http://eur-lex.
europa.eu/LexUriServ/LexUriServ.do?uri=CELEX:31995L0046:en:HTML
Directive 2001/29/EC of the European Parliament and of the Council of 22 May 2001 on the
harmonisation of certain aspects of copyright and related rights in the information society.
http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32001L0029&from=EN
Dragland, Aase. 2013. Big Data –for better or worse. SINTEF News Room. http://www.sintef.no/
home/Press-Room/Research-News/Big-Data-for-better-or-worse/
European Commission High level Expert Group on Scientific Data. 2010. Riding the wave. How
Europe can gain from the rising tide of scientific data. Final report. A submission to the
European Commission. http://cordis.europa.eu/fp7/ict/e-infrastructure/docs/hlg-sdi-report.
pdf
European Commission. 2012a. On content in the Digital Single Market. Communication from the
Commission. COM (2012) 789 final http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=
COM:2012:0789:FIN:EN:PDF
European Commission. 2012b. Commission proposes a comprehensive reform of data protection
rules to increase users’control of their data and to cut costs for businesses. Press release.
IP12-46. http://europa.eu/rapid/press-release_IP-12-46_en.htm?locale=en
European Commission. 2012c. Communication on a ‘Reinforced European Research Area Partner-
ship for Excellence and Growth’. COM (2012) 392 final http://ec.europa.eu/euraxess/pdf/
research_policies/era-communication_en.pdf
European Commission. 2012d. Proposal for a Directive of the European Parliament and of the
Council on the protection of individuals with regard to the processing of personal data by
competent authorities for the purposes of prevention, investigation, detection or prosecu-
tion of criminal offences or the execution of criminal penalties, and the free movement
of such data. COM (2012) 10 final 2012/0010 (COD). Brussels, 25.1.2012 http://eur-lex.
europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:52012PC0010&from=en
European Commission. 2012e. COMMISSION RECOMMENDATION of 17.7.2012 on access to and
preservation of scientific information. C(2012) 4890 final. Brussels, 17.7.2012. https://ec.
europa.eu/research/science-society/document_library/pdf_06/recommendation-access-
and-preservation-scientific-information_en.pdf
European Commission. 2013. Report of the European Commission Public consultation on open
research data. https://ec.europa.eu/digital-agenda/sites/digital-agenda/files/Report_2013-
07-OpenResearchData-Consultation-FINAL1.pdf
European Commission. 2014a. Towards a thriving data-driven economy. COM (2014) 442 final.
http://ec.europa.eu/information_society/newsroom/cf/dae/document.cfm?doc_id=6210
European Commission. 2014b. European Commission and data industry launch €2.5 billion
partnership to master big data. Press release http://europa.eu/rapid/press-release_IP-
14-1129_en.htm, accessed 06152017.
European Commission. 2014c. Standardisation in the area of innovation and technological
development, notably in the field of Text and Data Mining. Report from the Expert Group.
The Development of Research Data Management Policies in Horizon 2020 21
Bereitgestellt von | De Gruyter / TCS
Angemeldet
Heruntergeladen am | 01.03.18 09:56

http://ec.europa.eu/research/innovation-union/pdf/TDM-report_from_the_expert_group-
042014.pdf
European Commission. 2014d. Report on the responses to the Public Consultation on the Review
of the EU Copyright Rules. Directorate General Internal Market and Services Directorate
D–Intellectual property http://ec.europa.eu/internal_market/consultations/2013/copyright-
rules/docs/contributions/consultation-report_en.pdf
European Commission. 2014e. Progress on EU data protection reform now irreversible following
European Parliament vote. MEMO-14-186-EN. http://europa.eu/rapid/press-release_MEMO-
14-186_de.htm
European Commission. 2014f. Horizon 2020 Work Programme 2014–2015. European Commis-
sion Decision C (2014)4995 of 22 July 2014. http://ec.europa.eu/research/participants/
data/ref/h2020/wp/2014_2015/main/h2020-wp1415–intro_en.pdf
European Commission. 2016. Data management in H2020 Online Manual. http://ec.europa.eu/
research/participants/docs/h2020-funding-guide/cross-cutting-issues/open-access-data-
management/data-management_en.htm
European Parliament. 2013. Public and Commercial Models of Access in the Digital Era.
Directorate-General for Internal Policies. Policy Departement B. Structural and Cohesion
Policies. http://www.europarl.europa.eu/RegData/etudes/etudes/join/2013/495858/IPOL-
CULT_ET%282013%29495858_EN.pdf
European Policy Centre. 2010. The Economic Impact of a European Digital Single Market. Copen-
hagen Economics. Final Report. http://www.epc.eu/dsm/2/Study_by_Copenhagen.pdf,
accessed 06152017.
Filippov, Sergey. 2014. Data Driven Business Models. Powering Startups in the Digital Age.
Digital Insights. European Digital Forum https://www.researchgate.net/publication/
270508502_Data-Driven_Business_Models_Powering_Startups_in_the_Digital_
Age../../../Desktop/AppData/Local/Temp/Sergey Filippov.pdfaccessed 06152017, accessed
06152017.
Forschungsunion Wirtschaft –Wissenschaft /acatech –Deutsche Akademie der Technikwissen-
schaften. 2013. Deutschlands Zukunft als Produktionsstandort sichern. Umsetzungs-
empfehlungen für das Zukunftsprojekt Industrie 4.0. Abschlussbericht des Arbeitskreises
Industrie 4.0. http://www.bmbf.de/pubRD/Umsetzungsempfehlungen_Industrie4.0.pdf, ac-
cessed 06152017.
Fromholz, Julia. 2000. “The European Union Data Privacy Directive”. Berkeley Technology
Law Journal Volume 15 | Issue 1 Article 23. http://scholarship.law.berkeley.edu/cgi/
viewcontent.cgi?article=1281&context=btlj, accessed 06152017.
Hudson (w.d.), “Open Infrastructures for Open Science. Horizon. 2020. consultation report”
http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=2511DC9A5741029A5-
DEE5444BCAA7B73?doi=10.1.1.422.6056&rep=rep1&type=pdf, accessed 06152017.
Juncker, Jean Claude. 2014. “New Start for Europe: My Agenda for Jobs, Growth, Fairness and
Democratic Change. Political Guidelines for the next European Commission. Opening
Statement in the European Parliament Plenary Session.”Strasbourg, 15 July 2014 http://
ec.europa.eu/about/juncker-commission/docs/pg_en.pdf
Ligue des Bibliothèques Européennes de Recherche (LIBER), League of European Research Uni-
versities (LERU). 2014. Letter on EPC Copyright Vision Paper 2014: Copyright enabled on the
network. http://libereurope.eu/blog/2014/06/23/liber-leru-tdm-epc-copyright-vision-2014/,
accessed 06152017.
22 Daniel Spichtinger and Jarkko Siren
Bereitgestellt von | De Gruyter / TCS
Angemeldet
Heruntergeladen am | 01.03.18 09:56

Office of Science and Technology Policy (OSTP). 2013. Increasing Access to the Results of
Federally Funded Scientific Research. Memorandum for the Heads of Executive Depart-
ments and Agencies. https://www.epa.gov/sites/production/files/2015-01/documents/
ostp_memo_increasing_public_access.pdf, accessed 06152017.
“Regulation (EU) No 1291/2013 of the European Parliament and of the Council of 11 December
2013 establishing Horizon 2020 –the Framework Programme for Research and Innova-
tion (2014–2020) and repealing Decision No 1982/2006/EC.”http://eur-lex.europa.eu/
LexUriServ/LexUriServ.do?uri=OJ:L:2013:347:0104:0173:EN:PDF, accessed 06152017.
Reimsbach-Kounatze, C. 2013. Exploring data-driven innovation as a new source of growth.
Mapping the Policy Issues Raised by “Big Data”. OECD. http://www.oecd.org/
officialdocuments/publicdisplaydocumentpdf/?cote=DSTI/ICCP%282012%299/FINAL&
docLanguage=En, accessed 06152017.
Twitter. 2014. Twitter #DataGrants selections. https://blog.twitter.com/2014/twitter-datagrants-
selections
Welcome Trust et. al. 2014. Protecting health and scientific research in the Data Protection
Regulation (2012/0011(COD)). Position of non-commercial research organisations and
academics –July 2014 http://www.scienceeurope.org/wp-content/uploads/2014/05/LIBE_
report_joint_statement_January2014.pdf, accessed 06152017.
Wired 2014, November 14 Issue. Title page, p. 98–105.
The Development of Research Data Management Policies in Horizon 2020 23
Bereitgestellt von | De Gruyter / TCS
Angemeldet
Heruntergeladen am | 01.03.18 09:56

Bereitgestellt von | De Gruyter / TCS
Angemeldet
Heruntergeladen am | 01.03.18 09:56