Research Data Management - A European Perspective

Filip Kruse, Jesper Boserup Thestrup, Filip Kruse, Jesper Boserup Thestrup

• Publisher: De Gruyter Saur
• Year: 2017
• Print ISBN: 9783110369441
• eBook ISBN: 9783110396065
• Edition: 1
• Topic: Languages & Linguistics
• Subtopic: Library & Information Science
• Index: Languages & Linguistics

Based on case studies this book offers an insight in various European activities and practices in data management and their interaction with policies and programs. The latter form the background for the following case studies, provide the conceptual framework, at the same time giving an exhaustive understanding of the specific subjects. The case studies share common themes and give a concrete insight into vital issues such as web archiving, digitization of analog archives, researchers' motivations for sharing data, and how libraries, archives and researchers can collaborate in creating research tools and services.

Part I

Daniel Spichtinger and Jarkko Siren

1The 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 online10.

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.

1Introduction

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 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 Commission11, which has made completing the digital single market a priority.12 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).

2On definitions of data

The ubiquity and pervasiveness but at the same time the variety of what is considered ‘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 “information, 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 Government’s Office of Science and Technology Policy (OSTP) in its Memorandum on 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), including relevant workflows and protocols.

d)Raw data and data sets: these are not curated and typically held on institute hard drives and in drawers.

3The 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 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 use13, efforts to achieve open access to research data in EU research programs is more recent. The Commission 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 scientific information in the digital age14 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 considered 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 consultation 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 others15.

Horizon 2020 contains both large scale calls for consortia of research organisations and industrial companies as well as actions supporting individual researchers, 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 timely expectations or concerns on such activities.16 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 publications 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 undertaken 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 resulting from publicly funded research under Horizon 2020 should be promoted, taking into account constraints pertaining to privacy, national security and intellectual 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).17 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 presentations and discussion could be heard and (ii) a written consultation period.18 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, pragmatic and flexible at the same time. The res...

Table of contents

• Cover
• Title Page
• Copyright
• Table of contents
• Introduction
• Part I
• Part II
• About the authors
• Index