Starting the Process

Resources pertaining to
Guidelines for Interpretation

The four experiments carried out during the FIT4RRI project were primarily aimed at analysing the RRI and OS in action, thus generating direct knowledge on RRI-related processes (barriers, drivers, resistances, interests and values, feasibility and transferability conditions, etc.). The experiments have been conducted by the Instituto de Soldadura e Qualitade (ISQ)(Portugal), the University of Liverpool (the United Kingdom), the Sapienza University of Rome (Italy) and the Open University (United Kingdom). The experiments have been coordinated by the South-East European Research Centre (SEERC).

The experiment at ISQ

The Instituto de Soldadura e Qualidade (ISQ) is a Portuguese private, non-profit and independent technological institution running operations in more than 40 countries across the world in technical inspections, technical assistance for engineering projects, consultancy services and training activities, supported by transversal research and development activities and by 16 accredited laboratories (e.g.: chemical, bio and agro testing, non-destructive testing, Aerospatiale, etc).

The experiment was aimed at applying RRI and OS in ISQ R&D units, thus developing a self-tailored RRI model. This entailed the implementation of several workshops and internal meetings initially focused on RRI governance as well as the development of a set of procedures through participatory and consultation processes. By the end of the experiment implementation period, three RRI keys were covered, i.e., open science/open access, governance and science education. The process is continuing after the end of the experimentation period.

The experiment at the University of Liverpool

At the University of Liverpool, a research group is developing a technology-based project aimed at ensuring the safety and well-being of vulnerable people relating to Health and Social Care needs. The system uses two parts of the visual photonic spectrum to detect unusual patterns of behaviour (for example, in the case of elderly people at home or in protected environments).

The experiment aimed at collecting information about understandings and opinions of internal and external stakeholders in order to identify the ethical and science educational implications of this kind of monitoring systems. The participants were asked to consider this kind of implications starting from a monitoring system case study and drawing on their experiences and opinions. Each participant was invited to fill out two questionnaires, one at the start of the process and one at the end, attend a focus group and workshop and take part in a one-to-one interview. The information collected has been processed and interpreted with the aim of increasing the capacity of researchers to manage these issues in their research.

The experiment at Sapienza University of Rome

At the Sapienza University of Rome, a new research and innovation centre – Saperi&Co. – is going to be established. The centre is a distributed infrastructure based on interdisciplinarity, multi-actor approach and the sharing of competences and knowledge and has been developed according to a hub-model putting together several laboratories and a central physical R&I infrastructure hosting. The centre supports a fablab, co-working activities, enterprise incubation services and four on-demand labs dedicated to the cultural heritage, aerospace, life sciences and renewable energy.

The aim of the experiment is the development of a responsible governance in managing the new research centre for strengthening the university’s capacity of generating social impact and value. The experiment allowed to test in a pilot scale the setting up of a multi-actor, co-created responsible governance to understand its limits and opportunities, to involve different stakeholders (e.g. scientists, companies, civil society, policymakers) in an open debate about sustainability, circular economy and bio-materials with the attempt of shaping a common scientific agenda and to engage families and especially school children, letting them experiencing, through hands-on experiment, the role of sustainability in their daily life.

The experiment at the Open University

The experiment promoted at the Open University dealt with a specific problem related to text mining big scholarly data. While students and researchers can access research literature their university subscribes to quite easily, it is not possible for text and data miners to machine access research literature their university subscribes to effectively and at scale. To cope with this problem, a specific technical solution – eduTDM – has been developed with the aim of finding a pragmatic solution to arrange how this content can be delivered to text miners as easily as possible based on the subscription they have.

In the framework of the experiment, a working group has been created in order to reach a pragmatic solution and examine whether the stakeholders agree with the eduTDM vision. The working group consisted of commercial closed access publishers, openaccess publishers, companies performing TDM, organisations creating TDM corpora, policymakers, text and data miners and industry. This allowed to get feedback from all the actors involved and to activate a consultation process to support socially effective and consensus-based uses of the new proposed technological solution. A white paper report as a result of this consultation process has been developed.

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The most influential and comprehensive interpretive scheme of the changes occurring in science is undoubtedly the Mode 1 – Mode 2 model. Moreover, this model is probably the one that recognizes most the relationships between new modes of scientific knowledge production and the overall shift from modernity to post-modernity, even though the latter is referred to as “knowledge society” (Nowotny, Scott & Gibbons, 2001).

The main attributes distinguishing Mode 2 from Mode 1 have been summarised by the authors themselves (Nowotny, Scott & Gibbons, 2003) and can be schematised as follows.

Mode 1	Mode 2
Academic context	Context of application
Disciplinarity	Transdisciplinarity
Homogeneity	Heterogeneity
Autonomy	Reflexivity/Social accountability
Traditional quality control (peer review)	Novel quality control

These main trends can be summarised as follows.

Research context. Under Mode 2, knowledge is generated within a context of application, which influences all research steps (definition of the problems to address, methodologies to apply, outcomes to disseminate and results to be used). Under Mode 1, all these elements are generated in the academic context and transferred, if need be, to the context of application.

Disciplinary dynamics. Under Mode 2, research is used to solve problems and, therefore, it needs different theoretical perspectives and methodologies not necessarily derived from pre-existing disciplines (hence the concept of transdisciplinarity). Under Mode 1, research is generated under the internal impulse of specific disciplinary research dynamics.

Research community. Under Mode 2, research is conducted by communities (mainly virtual communities) which are different in nature and connected to each other in open ways, thanks to the huge development of ICTs. Thus, research is also carried out by new kinds of knowledge organisations, including think-tanks, NGOs, management consultants or activist groups, with the effect that science is becoming a heterogeneous practice. Under Mode 1, research is done almost exclusively by academic research institutions.

Actors involved. Under Mode 2, the research process becomes much more reflexive, i.e., it includes dialogue or “conversations” among many differentactors so as to incorporate different views. In this way, “problem-solving environments influence topic-choice and research-design as well as end-users” (Nowotny, Scott & Gibbons, 2003). Under Mode 1, the topics, research design and end-users are autonomously identified in the academic realm.

Quality control. Under Mode 2 conditions, new criteria come into play (not necessarily consistent with each other) of different kinds of quality (economic, social, political, etc.), strongly influencing prioritization processes. Under Mode 1, peer review, the use of disciplinary-based quality criteria was practically the only approach for quality assessment of scientific products.

BASIC SOURCES

Gibbons, M., Limoges, C., Nowotny, H., Schwartzman, S., Scott, P., & Trow, M. (1994).The new production of knowledge: the dynamics of science and research in contemporary societies. Sage.
Hessels, L.K., & Van Lente, H. (2008). Re-thinking new knowledge production: A literature review and a research agenda. Research Policy, 37(4), 740-760.
Nowotny, H., Scott, P. & Gibbons, M. (2001). Re-thinking Science: Knowledge and the Public in the Age of Uncertainty. Polity.
Nowotny, H., Scott, P., & Gibbons, M. (2003). Introduction: ‘Mode 2’ Revisited: The New Production of Knowledge. Minerva, 41(3), 179-194.

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Post-academic science is an expression coined by John Ziman (1994, 2000) to describe the emerging transformations of the ways in which scientific knowledge is produced. According to Ziman, the shift from academic to post-academic science is marked by a set of general trends. The main attributes distinguishing post-academic from academic science can be summarised as follows.

Academic science	Post-academic science
Academic sites	Multiple-site networks
Internal scrutiny	Public scrutiny
Scientific value of knowledge	Utility value of knowledge
Separation between scientific research and industrial research	Industrialisation of scientific research
Disciplinarity	Transdisciplinarity and specialisation
Autonomy, separation between research work and administrative work, institutional access to research funds	Political steering, bureaucratisation of the research work and competitive access to research funds

Multiplication of knowledge production sites. In post-academic science, research is a collective enterprise, involving large trans-disciplinary networks of scientific actors collaborating in multiple sites. Different kinds of institutions are involved and relations between them can be short-term and superficial. This “virtual lab” is made up of permanent employees and an increasing number of scientists working under fixed-term contracts. In academic science, research was carried out in single labs while the scope of cooperation with other institutions was smaller and based on long-term relations.

Openness to public scrutiny. This “virtual lab” is mainly web-based and research results are increasingly accessible to anyone on the web, even though there is still tension between the tendency to allow Open Access to scientific publications and data and the tendency to privatize this access. In any case, science,in post-academic conditions, is much more open (both potentially and concretely) to public scrutiny than it was in the academic era, where the same access to publications and data was extremely limited if not technically impossible for laypeople or non-scientific institutions.

The utility of scientific knowledge. Another trend is that science is increasingly under pressure to produce “useful knowledge”, i.e., knowledge which could have an economic value could be used by governments or could be applied toaddress social needs. One of the effects of this tendency is the decreasing role of fundamental curiosity-driven research in the scientific landscape and the increasing support given to applied research.

The industrialisation of scientific research. The stress placed on the utility of research products has fostered increased adoption of industry standards and organisational procedures in the scientific process. Paradoxically, while scientific publications and data are increasingly accessible to anyone, data and knowledge susceptible to economic exploitation are more and 52more privatised. In academic science, industrial research and scientific research are clearly separated

Transdisciplinarity and specialisation. In the context of post-academic science, transdisciplinarity and specialisation are both expanding. This is not a paradox (Kellogg, 2006). In fact, the increasing complexity of research activities is leading to a fragmentation of research tasks and, consequently, to increased specialisation. Thus, while a few have a truly interdisciplinary frame of inquiry, most researchers perform small and repetitive tasks without contacts with other researchers.

Political steering, bureaucratisation and competitive access to research funds. According to Ziman (1996), «science is becoming a too large and expensive enterprise. Governments are putting strict financial ceilings on their patronage and are trying to get better value for their money». Consequently, governments are taking a political steering stance over science, devising policies favouring the development of marketable technologies, leveraging also upon increasingly competitive access to research funds. This also entails a progressive bureaucratisation of research activities and an increasing impact of administrative work on research processes. Academic science is characterised by greater autonomy for researchers and scientific institutions, separation of research work and administrative work, and by the delivery of institutional funds to research institutions.

Basic sources

Kellogg, D. (2006). Toward a post-academic science policy: Scientific communication and the collapse of the Mertonian norms. International Journal of Communications Law & Policy, Special Issue, Access to Knowledge, Autumn.
Ziman, J. (1994). Prometheus Bound: Science in a Dynamic Steady State. Cambridge University Press.
Ziman J. (1996). “Postacademic science”: Constructing Knowledge with Networks and Norms. Science Studies, 1.
Ziman, J. (2000): Real Science. What it is, and what it means. Cambridge University Press.

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Another renowned model describing the changes occurring in the ways in which scientific knowledge is produced is the Triple Helix Approach (Etzkowitz & Leydesdorff, 2000), which, more recently, has also been proposed as the Quadruple Helix Approach (Carayannis, Barth & Campbell, 2012).

Rather than just knowledge production, the model focuses on innovation. In particular, the model observes the prominent role acquired by universities in the innovation process, which has transformed the previously dyadic industry/government relations into closer triadic interactions and coordination involving State, Academia and Industry (hence the image of the “Triple Helix”). Quadruple Helix enlarges the scope of the innovation dynamics to include civil society and the media in order to recognise and propel a direct connection between research and civil society, reinforcing co-production processes also encompassing end users.

For the sake of simplicity, we shall focus only on some of the main trends identified under the Triple Helix model.

The main attributes distinguishing Triple Helix from dyadic industry/government relations are as follows.

Dyadic industry-government relations	Triple Helix
Academia not involved in innovation	Academia involved in innovation
Separation of institutional spheres	Co-evolution and hybridisation of institutional spheres
Two university missions: teaching and research	Third mission and entrepreneurial research
Disciplinarity	Transdisciplinarity

Involvement of Academia with innovation. In the Triple Helix approach, academia is increasingly involved in innovation dynamics, leading to ever closer cooperation and coordination with Industry and State.

Relations among institutional spheres. The involvement of academic institutions in innovation is happening in a context of increasing levels of interdependence among the three institutional spheres, creating the premises for co-evolution. Interdependency and co-evolution are producing, at the interface between State, Academia and Industry, the spread and differentiation of an increasing number of “hybrid” organisations (spin-off firms, tri-lateral initiatives, strategic alliances, etc.), facilitating higher cooperation levels. This is also supported through internal differentiation at the institution level (for example, the creation of the liaison offices in universities).

University missions. At the level of academia, the triple helix approach emphasises the changes directly affecting universities, which are assuming new characteristics linked to their new role of proactive promoters of innovation, epitomised in the concept of “entrepreneurial university. The key concept that universities are being asked to pursue is a “third mission”, i.e., promoting socio-economic development, together with the traditional missions of teaching and research (Etzkowitz, Ranga, Benner, Guaranys, Maculan, & Kneller, 2008). Obviously, the definition of a third mission has structurally modified the ways in which the other two missions are pursued. For example, students should also be trained and encouraged to become entrepreneurs or to create new companies so they can contribute directly to the economic development of society.

Disciplinary dynamics. Finally, the Triple Helix approach emphasizes the increasing relevance of trans-disciplinary research, especially considering that the most advanced research sectors, such as nanotechnology, are to a great extent based on contributions, methodologies and interests emanating from different disciplinary fields.

BASIC SOURCES

Carayannis, E.G., Barth, T.D., & Campbell, D.F. (2012). The Quintuple Helix innovation model: global warming as a challenge and driver for innovation. Journal of Innovation and Entrepreneurship, 1(1), 2.
Etzkowitz, H., & Leydesdorff, L. (2000). The dynamics of innovation: from National Systems and ”Mode 2” to a Triple Helix of university–industry–government relations. Research Policy, 29(2), 109-123.
Etzkowitz, H., & Leydesdorff, L. (2014). The endless transition: a ‘Triple Helix’ of university industry-government relations. Minerva, 203-208.
Etzkowitz, H., Ranga, M., Benner, M., Guaranys, L., Maculan, A.M., & Kneller, R. (2008). Pathways to the entrepreneurial university: towards a global convergence. Science and Public Policy, 35(9), 681-695.

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Post-normal science is another model for interpreting changes affecting scientific knowledge production. This model is more limited in scope compared to the others. Developed by Silvio Funtowicz and Jerome Ravetz (1993), rather than describing a general turn in scientific production, it highlights the increasing need to investigate issues where «facts are uncertain, values in dispute, stakes high and decisions urgent». Thus, the concept of “post-normal science” refers to the kind of research which goes beyond the boundaries of usually applied research, since it entails higher decision stakes and a higher level of uncertainty of the facts under investigation.

Post-normal science necessarily requires new institutional arrangements, including:

The use of an extended peer community, involving all those who, for different reasons, are affected by the issues under investigation
The use of a language which is more comprehensible to all actors in the public arena
The development of new channels and ways to communicate science to facilitate political debate
Greater involvement of policy actors in all phases of the research process
The coexistence of competing interpretive proposals, from which competing solutions may derive.

BASIC SOURCES

Funtowicz, S.O., & Ravetz, R.J. (1993). Science for the post-normal age. Futures, September.
Ravetz, J.R. (2006). Post-normal science and the complexity of transitions towards sustainability. Ecological Complexity, 3(4), 275-284.

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The concept of innovation system was introduced by Lundvall (1985, 1992) and developed by the same author (Lundvall, 2016) and many others (for example, Patel & Pavitt, 1994; Metcalfe, 1995) including international agencies (such as OECD, 1997).

The model sees innovation as a process based on the interaction among many actors of different types (including research institutions), where knowledge-related dynamics play aprominent role in the development of new ideas and solutions and in the activation of learning processes involving the concerned organisations.

Innovation is also viewed as a non-linear process, which rarely follows a given series of phases (design, engineering, production, introduction in the market of the new product). Rather, it usually includes many feedback loops between the different stages which involve different actors each time.

The concept of innovation system can be applied at different levels, such as an international, regional, national, local or sectoral level.

BASIC SOURCES

Lundvall, B.Å. (1985). Product innovation and user-producer interaction, industrial development, Research Series 31. Aalborg University Press.
Lundvall, B.Å. (ed.) (1992). National Innovation Systems: Towards a Theory of Innovation and Interactive Learning. Pinter.
Lundvall, B.Å.(2016). The Learning Economy and the Economics of Hope. Anthem Press.
Metcalfe, S. (1995). The Economic Foundations of Technology Policy: Equilibrium and Evolutionary Perspectives. In P. Stoneman (ed.), Handbook of the Economics of Innovation and Technological Change. Blackwell.
Patel, P., & Pavitt, K. (1994). The Nature andEconomic Importance of National Innovation Systems. STI Review, No. 14. OECD Publishing.

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Many authors described the manychanges affecting contemporary societies from the 1960s onward in terms of a move from modern society to another kind of society, variably termed as “post-industrial society” (Bell, 1976), “late modernity” (Giddens, 1991), “risk society” (Beck, 1992), “liquid society” (Bauman, 2000), “network society” (Castells, 2000) or “high-speed society” (Rosa, 2013).

For the sake of simplicity, we will refer to this “new society” as a “post-modern society”, even though this concept is highly controversial (Beck, 1992).

Despite the difference among these theoretical approaches, a relatively broad convergence can be observed about some of the key trends characterising this transformation. The following seem to be particularly relevant here, i.e.:

Globalisation
Weakening of social structures
Individualisation
Risk and uncertainty
Diversification and fragmentation
Blurred cognitive and social boundaries.

Globalisation. Post-modern times are characterised by the emergence of a single interconnected world (made possible by the huge development of ICTs) producing complex and extended social configurations of mutual interdependences (De Swaan, 1988) of different natures (economic, social, cultural, but also cognitive and emotional). One of the main well-known effects of globalisation has been the rapid growth of economic competition at a global level, affecting both national economies and individual companies. Globalisationhas led to a systematic dis-embedding of social relations (Giddens, 1990), i.e., lifted out from their local embeddedness, based on specific space-time relations.

The weakening of social structures. Globalisation has produced a rapid weakening of social structures, i.e., the dominant patterns of action and social relationships (Berger & Luckmann, 1996; North, 1990; Nadel, 1951), legitimated by cognitive structures, such as socially supported views, representations, beliefs and stereotypes. In fact, any social structure, until then, was necessarily based on specific space-time frames and fully incorporated into the local dimension. Dis-embedding processes led to an overall weakening of culture (i.e., traditional worldviews and social norms) and its capacityto produce patterns and cognitive schemes orienting individual behaviours and led to an increased role of “self-reflexive” behaviours in personal and institutional life (Archer, 2007; Giddens, 1991; Beck, Giddens & Lash, 1994).

Individualisation. Connected to the weakening of social structures, a parallel acceleration of the process of individualisation (Elias, 1991) can be observed, deriving from and driving an increase in people’s subjectivity (Quaranta, 1986; d’Andrea, Declich & Feudo, 2014), i.e., their capacity and power to think and act more freely, as well as to “build up” their own lives, projects, and identities (Berger, Berger & Kellner, 1974; Giddens, 1991). Individualisation produced a set of general trends, including:

The tendency of individuals tobypass intermediated entities(associations, trade unions, political parties, etc.)
The tendency of individuals towards self-disclosure(in terms of opinions, ideas, personal attitudes, private feelings, intimate aspects of life, body, etc.) in public or semi-public environments (both physical and virtual)
The radical change in the usual mechanisms of social control (for example, the tendency of people towards self-steering, rejecting established values and beliefs and instead becoming sensitive to the opinions of their friends).

Risk and uncertainty. Risk profiles have changed too. Because of the weakening of social structures and of the institutions of modern society (see below), people have become more directly exposed to risks of different kinds (Beck, 1992; Giddens, 2001; Renn, 2008; Zinn, 2008), such as environmental risks, unemployment, lack of access to social protection and pension schemes, or health risks. Moreover, individuals are increasingly asked to manage their own lives by themselves, with no institutions or dominant social patterns to guide them. Finally, also technology, while used to control risks, produces, in turn, new risks (Beck, 1999; Giddens, 1990). Therefore, the sense of uncertainty appears to be a dominant characteristic both in social life and in the biographical dimension.

Diversification and fragmentation. The modified balance between individuals and social structures has produced great social and cultural diversification within society. It is more and more difficult to identify homogeneous social groups and classes or dominant behavioural patterns. Even the identity of individuals is more unstable, fragmented and inconsistent (Giddens, 2001; Bauman, 2005; Barglow, 1994). At the same time, diversification feeds a multitude of ideas, initiatives, behaviours and forms of knowledge, accelerating social changes (Rosa, 2013).

Blurred cognitive and social boundaries. Another consequence of the mix of weakening of social structure and individualisation is the blurring, if not the collapse, of social boundaries on which modernity was built (Beck, Bonss & Lau, 2003), including the most fundamental distinctions (nature/culture or past/present/future) (d’Andrea, Declich & Feudo, 2014), as well as distinctions among life domains and social spheres (for example, private/public or professional life/leisure). Even personal identity does not have stable boundaries. The effect is that new boundaries have to be constantly negotiated among actors so that common problems or public issues can be addressed.

BASIC SOURCES

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The concept of "big science" is used to refer to a change in scale and features occurred in science from World War II onward.

Using the model developed with WWII for the Manhattan Project, science has been increasingly involved in large development programmes sponsored by governments to cope with large-scale problems entailing highly advanced technologies and often strong theoretical advancements.

This change in scale made research also different in qualitative terms with respect to the "small science".

Differently from "small science", big science implies:

The involvement of hundreds of scientists, specialised in different research fields, but able to get involved in interdisciplinary activities
The participation of many research institutions working together
The development of technologies which no single institute usually can afford
Large budgets also requiring the development of more complex administrative structures.

SOURCE

de Solla Price, D. J. (1986). Little science, big science... and beyond. New York: Columbia University Press.

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There is a wide and fragmented scientific literature – often ignored or overlooked by in the debate on RRI and OS – which highlights the presence of critical changes in science involving science-society scientific relations as well as the research processes and the organisational structures on which the production of scientific knowledge is based.

The following tendencies can be mentioned here.

COMPETITION AND ACCELERATION

Increasing competition among research institutions and research systems on a global scale leading to an increased acceleration of all research and innovation processes, with impacts on the organisation of the academic life, researchers’ living and professional conditions, research quality, and research integrity.

SOURCES

Alberts, B., Kirschner, M.W., Tilghman, S., & Varmus, H. (2014). Rescuing US biomedical research from its systemic flaws. Proceedings of the National Academy of Sciences, 111(16), 5773-5777.
Bianchetti, L., & Quartiero, E.M. (2010). Researchers under Pressure: a comparative study of new forms of producing, advising and transmitting knowledge in Brazil and the European Union. European Educational Research Journal, 9(4), 498-509.
Fochler, M., Felt, U., & Müller, R. (2016). Unsustainable growth, hyper-competition, and worth in life science research: Narrowing evaluative repertoires in doctoral and postdoctoral scientists’ work and lives. Minerva, 54(2), 175-200.
Garforth, L. & Cervinková, A. (2009). Times and trajectories in academic knowledge production. In U. Felt (Ed.), Knowing and living in academic research. Convergence and heterogeneity in research cultures in the European Context. Institute of Sociology of the Academy of Sciences of the Czech Republic.
Müller, R. (2014). Racing for what? Anticipation and acceleration in the work and career practices of academic life science postdocs. In Forum Qualitative Sozialforschung/ Forum: Qualitative Social Research (Vol. 15, No. 3).
Pels, D. (2003). Unhastening Science: Autonomy and reflexivity in the social theory of knowledge. Liverpool University Press.
Schatz, G. (2014). The faces of big science. Nature Reviews Molecular Cell Biology, 15(6), 423-426.
Vostal, F. (2016). Accelerating Academia: The Changing Structure of Academic Time. Palgrave MacMillan.

SHRINKING OF RESEARCH FUNDS

Shrinking of research funds, combined with an increase in the research costs, producing an extremely competitive access to funds and publishing, a decline in the success rate for grant applicants, the activation of new forms of “delocalisation” of the research work (laboratory activities are moved in emerging countries where costs are lower) and an increase in the time researchers devote to look for new research fund.

SOURCES

Alberts, B., Kirschner, M.W., Tilghman, S., & Varmus, H. (2014). Rescuing US biomedical research from its systemic flaws. Op. cit.
Ehrenberg, R.G., Rizzo, M.J., & Jakubson, G.H. (2003).Who bears the growing cost of science at universities?(No. w9627). National Bureau of Economic Research.
OECD (2016). Science, Technology and Innovation Outlook 2016. OECD Publishing.
Stephan, P. (2012). How economics shapes science. Harvard University Press.

TASK DIVERSIFICATION

Diversification of tasks within research organisations, also due to an increased market-oriented organisation, leading researchers to devote time to a wide range of different types of activities (participation in extended research networks, direct involvement in innovation and technology transfer, activities related to accountability, transparency and public scrutiny, administrative work, etc.), with an inevitable decrease in the time devoted to scientific work.

SOURCES

Bozeman, B. (2015). Bureaucratization in academic research policy: perspectives from red tape theory. In 20th International Conference on Science and Technology Indicators, Lugano, Switzerland; FASEB (2013). Findings of the FASEB Survey on Administrative Burden (https://www.faseb.org/Portals/2/PDFs/opa/2014/6.7.13%20FASEB%20NSB%20Survey%20findings.pdf).
Kogan, M., Moses, I., & El Khawas, E. (1994). Staffing Higher Education. Jessica Kingsley.
Musselin, C. (2007). The transformation of academic work: Facts and analysis. HAL Archives Ouvertes .

INCREASED STAFFING

Increased staffing of research organisations, especially conducted by heightening the number of contingent staff (PhD students and postdocs) prevalently paid through soft money (i.e., money related to specific projects) in order to contain personnel costs; this is also determining an increasing pressure on young researchers to make more in less time with the aim of accessing permanent positions which are reducing in number.

SOURCES

Alberts, B., Kirschner, M.W., Tilghman, S., & Varmus, H. (2014). Rescuing US biomedical research from its systemic flaws. Op. cit.
Dijstelbloem, H., Huisman, F., Miedema, F., & Mijnhardt, W. (2014). Why science does not work as it should. And what to do about it. Science in Transition, Position Paper.
Ravetz, J. (2016). How should we treat science’s growing pains? The Guardian, 8 June 2016.
Stephan, P. (2012). How economics shapes science, Op. cit.

STAFF SEGMENTATION

A consequent segmentation of staffby age, sex,nationality, and contractual status, leading to, e.g., overtraining (tendency to retain PhD students and Postdocs longer than necessary), decrease in teaching quality (increasingly done by ever cheaper teaching staff), changes in internal labour relationships (research organisations are no longer a “community of peers” but a sort of “industry” employing high-qualified human resources), individualisation (researchers increasingly act as individual professionals and not as part of a staff), and attitude of self-promotion among scientists.

SOURCES

Dijstelbloem, H., Huisman, F., Miedema, F., & Mijnhardt, W. (2014). Why science does not work as it should. And what to do about it. Op. Cit.
Musselin, C. (2005). European academic labor markets in transition.Higher Education,49(1), 135-154.
Musselin, C. (2007). The transformation of academic work: Facts and analysis. Op. Cit.
Slaughter, S., & Leslie, L.L. (1997). Academic capitalism: Politics, policies, and the entrepreneurial university. The Johns Hopkins University Press, 2715 North Charles Street, Baltimore, MD 21218-4319.
Ylijoki, O., & Ursin, J. (2015). High-flyers and underdogs: The polarisation of Finnish academic identities. In L. Evans, & Nixon, J. (Eds.), Academic Identities in Higher Education: The Changing European Landscape. Bloomsbury Academic.

MOBILITY OF RESEARCHERS

Increase in the mobility of researchers, entailing, e.g., difficulties in returning to one’s home country, increased competition among researchers or problems in managing familylife, especially for women scientists.

SOURCES

Børing, P., Flanagan, K., Gagliardi, D., Kaloudis, A., & Karakasidou, A. (2015). International mobility: Findings from a survey of researchers in the EU. Science and Public Policy, 42(6), 811-826.
Dubois, P., Rochet, J.C., & Schlenker, J.M. (2014). Productivity and mobility in academic research: Evidence from mathematicians. Scientometrics,98(3), 1669-1701.
Franzoni, C., Scellato, G., & Stephan, P. (2014). The mover’s advantage: The superior performance of migrant scientists. Economics Letters, 122(1), 89-93.
Halevi, G., Moed, H.F., & Bar-Ilan, J. (2016). Does Research Mobility Have an Effect on Productivity and Impact? International Higher Education, (86), 5-6.
Marinelli, E., Pérez. S.E. & Fernandez-Zubieta, A. (2013). Research-Mobility and Job-Stability: Is There a Trade-Off? Paper presented at the 35th DRUID Celebration Conference 2013, Barcelona, Spain, June 17-19.

PRESSURE ON RESEARCH ASSESSMENT SYSTEMS

Growing pressure on research assessmentsystems, due to the hyper-production of scientific knowledge and the increased competition among researchers and research organisations, which emerge in phenomena like systematic problems and errors in peer review or increased use of quantitative indicators to assess researchers, research institutions and scientific journals, with distorting or at least questionable effects on science quality.

SOURCES

Gunsteren (van) W. (2015). On the pitfalls of peer review. F1000Research, 4.
Hicks, D., Wouters, P., Waltman, L., De Rijcke, S., & Rafols, I. (2015). The Leiden Manifesto for research metrics. Nature, 520(7548), 429.
Rothwell, P.M., & Martyn, C.N. (2000). Reproducibility of peer review in clinical neuroscience: Is agreement between reviewers any greater than would be expected by chance alone?. Brain, 123(9), 1964-1969.
Young, N.S., Ioannidis, J.P., & Al-Ubaydli, O. (2008). Why current publication practices may distort science. PLoS medicine, 5(10), e201; Osterloh, M., & Frey, B.S. (2015). Ranking games. Evaluation Review, 39(1), 102-129.

CRITICAL DYNAMICS IN THE QUALITY OF RESEARCH

Critical dynamics affecting the quality of research, such as decreasing reproducibility of scientific data, tendency of researchers to adopt safe and low-risk research strategies, to produce irrelevant science (for career advancement rather than producing advances in science) and redundant papers (publishing the same data or papers more than once), increasing malpractice or undesirable impacts of commercial interests on research quality.

SOURCES

Alberts, B., Kirschner, M.W., Tilghman, S., & Varmus, H. (2014). Rescuing US biomedical research from its systemic flaws. Op. cit.
Baker, M. (2016). Is there a reproducibility crisis? A Nature survey lifts the lid on how researchers view the 'crisis rocking science and what they think will help. Nature, g533(7604), 452-455.
Brochard, L. (2004). Redundant publications, or piling up the medals. Getting published is not the Olympic Games. Intensive care medicine, 30(10), 1857-1858.
Dijstelbloem, H., Huisman, F., Miedema, F., & Mijnhardt, W. (2014). Why science does not work as it should. And what to do about it. Op. cit.
Irzik, G.(2013). Introduction: Commercialization of academic science and a new agenda for science education. Science & Education, 22(10), 2375-2384.
Kaiser, M. (2014). The integrity of science–Lost in translation? Best Practice & Research Clinical Gastroenterology, 28(2), 339-347.
Stephan, P. (2012). How economics shapes science, Op.cit..
Vermeulen, N., (2010). The projectification of science: the case of virology. Paper presented at the annual meeting of the 4S Annual Meeting – Abstract and Session Submissions, Crowne Plaza Cleveland City Center Hotel, Cleveland, OH.

INCREASING OPENNESS TO EXTERNAL ACTORS

Rising complexity in managing research organisations due to the growing need to interact with external actors (political authorities, civil society, industry, etc.) for different reasons (innovation, providing expertise, public engagement, policy issues, societal engagement, science communication, etc.); need to find the right openness level; institutional undervaluation of openness-related initiatives; conceptual ambiguities and interpretive mismatches about openness; resistance and barriers to openness; decreasing trust in science.

SOURCES

Bauer, A., Bogner, A., & Fuchs, D. (2016). Report on the expert workshop “Contemporary experiences with societal engagement under the terms of RRI. Austrian Academy of Sciences, Institute of Technology Assessment, PROSO Project.
Boogaard, B.K., Schut, M., Klerkx, L., Leeuwis, C., Duncan, A., & Cullen, B. (2013). Critical issues for reflection when designing and implementing Research for Development in Innovation Platforms. Wageningen, CGIAR Research Program on Integrated Systems for the Humid Tropics (CRP Humidtropics).
Burchell, K. (2015). Factors affecting public engagement by researchers: literature review. Policy Studies Institute, London.
Ferrante, N. (2016). The Dubious Credibility of Scientific Studies. Intersect, Vol. 10, No. 1.
Nowotny, H., Scott, P. & Gibbons, M. (2001). Re-thinking Science: Knowledge and the Public in the Age of Uncertainty. Polity.

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The results of an opinion poll about the problems facing science was published in the website Voxon September 7, 2016, on the basis of interviews involving 270 scientists (including graduate students, senior professors, and laboratory heads) from different disciplines and research fields. The ranking based on the seriousness of the problems is as follows.

1. Academia has a big money problem Funds, in many fields, are shrinking and the way money is handed out puts pressure on labs to publish a lot of papers, breeds conflicts of interest, and encourages scientist to overhype their work.

2. Too many studies are poorly designed. Blame bad incentives Scientists are ultimately judged by the research they publish. And the pressure to publish means that scientists often design their studies poorly, to game them so they turn out to be a little more “revolutionary” through specific research decisions and cutting corners in how they analyse their data.

3. Replicating results is crucial. But scientists rarely do itScientists tend not to replicate scientific results as they should and, when they attempt to replicate a study, they often find they cannot do so.

4. Peer review is broken Numerous studies and systematic reviews have shown that peer review does not reliably prevent poor-quality science from being published and frequently fails to detect fraud and other problems.

5. Too much science is locked behindpaywall Many scientific works are not easily accessible, being locked away in paywalled journals, difficult and costly to access.

6. Science is poorly communicated to the public Lack of appropriate communication approaches leads many laypeople to hold on to completely unscientific ideas or have a crude view of how science works.

7. Life as a young academic is incredibly stressful Many tenured scientists and research labs depend on small armies of graduate students and Postdoctoral researchers to perform their experiments and conduct data analysis. However, young researchers are poorly paid, work very hard, encounter family problems, and have limited career prospects. This situation tends to disproportionately affect women.

SOURCE

Belluz, J., Plumer, B., & Resnick, B. (2016). The 7 biggest problems facing science, according to 270 scientists. Vox (www. vox. com/2016/7/14/12016710/science-challeges-research-funding-peer-review-process).

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Change	Examples of risks	Examples of opportunities/solutions
Hyper-competition and accelerated pace of research process	Distortions on the research process Decreased quality of the research process Decreased reproducibility of scientific data	More rapid research outputs Accelerated innovation New forms of co-operation/co-petition
Structural shrinking of public research funds in a context of increasing costs of research activities	Decreased quality of the research process Delocalisation of the experiments Decreased reproducibility of scientific data	Increased efficiency of research organisations New forms of co-operation/ co-petition among research actors More creative/effective forms of funding
Task diversification and decreasing time devoted to scientific work	Decreased quality of the research process Allocation of key tasks to less skilled staff members	More effective organisational solutions within research organisations Increased specialisation among research organisations
Increasing staffing	Increased job instability Decreased salaries Increased career interruptions Increased gender inequality Decreasing access ofyoung researchers to permanent positions	Updating of the structure of scientific careers Stronger connections between private and public sector New hybrid career paths
Staff segmentation and polarization on the basis of age and contractual status	Overexploitation and overtraining of young researchers Decline in teaching quality Weakening of researchers’ identity	More advanced interdisciplinary forms of scientific work Updating of the structure of scientific career
Increasing researchers’ mobility	Increased gender equality Decreased quality of life of young researchers	Increased circulation of knowledge and skills More flexible work-life balance and welfare solutions for researchers
Impacts of ICTs and open data on scientific practices	Decreased control over the quality of scientific products	More rapid research outputs Accelerated innovation New forms of co-operation/ co-petition Increased efficiency of research organisations
Increasing individualisation of scientific careers	Distortion in the use of publications due to the “publish or perish” syndrome (e.g., production of redundant or irrelevant publications) Increased diffusion of malpractices	Accelerated use of solutions based on open access and open data More effective cross-institutional forms of cooperation
Increasing pressure on research assessment systems	Decreased quality of peer reviewing Inappropriate use of quantitative indicators and rankings	Development of more advanced forms of research quality assessment Updating quality assessment system solutions based on open access and open data
Governance shift towards broader entrepreneurial models	Decreased relevance of fundamental research Increasing adoption of research strategies aimed at short-term marketable products	Highly diversified and more effective governance solutions Accelerated innovation New forms of co-operation/co-petition
Increasing openness of research institutions toward external actors	Work overload for researchers Decreased quality of the research process	Accelerated innovation New forms of co-operation/ co-petition Democratization of the research process and science governance

(Source: FIT4RRI Project)

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Responsible Research and Innovation (RRI) can be generally understood as a specific policy approach aimed at managing science and science-society relations. However, exactly defining what RRIis and which are its contents and dimensions is not actually simple. In the box below, a set of definitions of RRI are provided.

Some definitions of RRI

Process by which societal actors and innovators become mutually responsive to each other with a view to the (ethical) acceptability, sustainability and societal desirability of the innovation process and its marketable products (R. Von Schomberg, 2012)

A collective commitment of care for the future through responsive stewardship of science and innovation in the present (R. Owen et al., 2013)

An alignment ofR&I process and its outcomes to values, needs and expectations of European society (M. Georhean-Quinn, 2012)

Ways of proceeding in Research and Innovation that allow those who initiate and are involved in the processes of research and innovation at an early stage: (A) to obtain relevant knowledge on the consequences of the outcomes of their actionsand on the range of options open to them; (B) to effectively evaluate both outcomes and options in terms of moral values (including, but not limited to wellbeing, justice, equality, privacy, autonomy, safety, security, sustainability, accountability, democracy and efficiency); (C) to use these considerations (under A and B) as functional requirements for design and development of new research, products and services (Expert Group on the State of Art in Europe on RRI, 2013)

Reflection, analysis and (public) debate concerning the moral acceptability of new technology and innovation (J. Van den Hoven, 2013)

A higher-level responsibility or meta-responsibility that aims to shape, maintain, develop, coordinate and align existing and novel research and innovation-related processes, actors and responsibilities with a view to ensuring desirable and acceptable research outcomes (B.C. Stahl, 2013)

The European Commission developed a view of RRI as a way to align research and innovation to the needs, expectations, and values of society, which can be promoted especially reinforcing what it refers to as “keys” or “pillars” of RRI, i.e.,

Gender equality in science
Open access to research data and publications
Research ethics and integrity
Citizen engagement
Science education and
Governance (intended as a means for integrating the other five dimensions).

Many authors prefer to approach RRI, not in terms of specific contents, but in terms of specific conceptual dimensions of RRI which, separately or in combination with each other, are supposed to induce changes in research practices, science policies or scientific culture.

There is a general convergence among authors on four main dimensions of RRI.

Inclusion. It mainly refers to the engagement of different stakeholders from the early stages of research and innovation onward so as to give voice to all the concerned interests, values, needs, and beliefs.
Anticipation. It refers to the capacity of envisioning the future of R&I and understanding how current dynamics help design the future in order to prevent risks and to lead research to desirable impacts. Hence the importance recognised for implementing RRI to reliable and participatory forecasting techniques.
Responsiveness. It concerns the capacity to develop proactive management of new technologies so as to identify risks and develop ethically adequate responses. The concept of responsiveness also relates to transparency (responses should be open to the public debate) and accessibility (scientific results about risks and responses should be openly accessible to everyone). As it is easy to notice, responsiveness is partially overlapped with the dimension of anticipation.
Reflexivity. It is mainly seen as the capacity of the research system to keep control of its own activities and assumptions, to be aware of the limits of the knowledge produced and of the framing processes connected to the identification of the issues to be addressed as well as to reflect on values and beliefs connected with R&I. Reflexivity is linked to public dialogue and collaborative approaches in science.

SOURCES

Burget, M., Bardone, E., & Pedaste, M. (2017). Definitions and Conceptual Dimensions of Responsible Research and Innovation: A Literature Review. Science and engineering ethics, 23(1), 1-19.
European Commission (2012). Responsible Research and Innovation. Europe’s Ability to Respond to Societal Challenges. Publication Offices of the European Union.
Expert Group on the State of Art in Europe on RRI (2013). Options for strengthening responsible research and innovation. Luxembourg: Publications Office of the European Union.
Geoghean-Quinn, M. (2012). Science in Dialogue. Towards a European Model for Responsible Research and Innovation. Odense, Denmark.
Owen, R., Stilgoe, J., Macnaghten, P., Gorman, M., Fisher, E., & Guston, D.H. (2013). Framework for Responsible Innovation. In Owen, R., Heintz, M. & Bessant, J. (eds.) Responsible Innovation. Wiley.
Stahl, B.C. (2013). Responsible research and innovation: The role ofprivacy in an emerging framework. Science and Public Policy, 40(6), 708-716.
Van den Hoven, J. (2013). Value Sensitive Design and Responsible Innovation, in Owen, R., Hents, M. & Bessant, J. (eds). Responsible Innovation. Wiley.
Von Schomberg, R. (2012). Prospects for technology assessment in a framework of responsible research and innovation. In Technikfolgen abschätzen lehren (pp. 39-61). VS Verlag für Sozialwissenschaften.

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In comparison to RRI, Open Science is an approach apparently more focused in scope, addressing quite exclusively how the research process is done.

Different definitions of OS have been given. Some examples are given in the box below.

Some definitions of Open Science

Open Science represents a new approach to the scientific process based on cooperative work and new ways of diffusing knowledge by using digital technologies and new collaborative tools (European Commission, 2016)

Open science is the encounter between the age-old tradition of openness in science and the tools of information and communications technologies (ICTs) that have reshaped thescientific enterprise and require a critical look from policymakers seeking to promote long-term research as well as innovation (OECD, 2015)

Open Science is the practice of science in such a way that others can collaborate and contribute, where research data, lab notes and other research processes are freely available, under terms that enable reuse, redistribution and reproduction of the research and its underlying data and methods (FOSTER Project, on-line)

Open science is the concept of opening up all aspects of scientific research, to allow others to follow the process and collaborate. There is no formal definition of open science, but it usually incorporates aspects such as open access, open peer review, post-publication peer review, and open data. Additionally, it includes other ways to make science more transparent and accessible during the research process: open notebook science, citizen science, and aspects of open source software and crowdfunded research projects (Amsen, 2015)

Open science means the promotion of an open operating model in scientific research. The key objective is to publish research results, along with the data and methods used, so they can be examined and used by any interested party. Open science includes practices such as promoting open access publishing, open access publishing itself, harnessing open-source software and open standards, and the public documentation of research processes with ‘memoing’ (ATT –Open Science and Research Initiative, 2014)

The core of Open Science is the access of research process and products, made it possible now thanks to digital technologies. However, as some definitions highlight, opening up research procedures implies a wide range of changes inside the research process, such as: a transformation in the researchers’ culture and daily practices; new configurations among actors, allowing new forms of at-distance cooperation also including citizens; new professional figures, skills, and expertise; new regulations and legal frameworks; the management of conflicts among actors (for example, those related to the decreasing power of publishing houses); a modification of the mechanisms of scientific award and recognition; the adoption of new ethical and behavioural standards (forexample, related to transparency and data processing).

As it easy to observe, OS overlaps RRI in many ways:

RRI includes Open Access, which is part of Open Science too
RRI includes public engagement as one of its main component; OS requires widening mechanisms of cooperation up to encompass the public at large
RRI also focuses on ethical issues and research integrity; OS allows a higher level of transparency and research integrity in the research process, reducing the risks of frauds and plagiarism.

Other aspects of RRI are not considered in OS, such as anticipation, gender equality, or reflexivity.

An attempt to organise the different components of OS has been made under the FOSTER Project, which identified the following categories.

Component	Issues
Open access	Open access definition, open access initiatives, open access routes (Gold Route, Green Route), open access use and reuse
Open data	Open big data, open data definition, open data journals, open data standards, open data use and reuse, open government data
Open reproducible research	Definition of open reproducible research, irreproducibility studies, open lab/notebooks, open science workflows, open source in open science, reproducibility guidelines, reproducibility testing
Open science evaluation	Open metrics and impact (altmetrics, bibliometrics, semantometrics, webmetrics), open peer review
Open science policies	Organisational mandates (funders policies, governmental policies, institutional policies), subject policies (open access policies, open data policies)
Open science tools	Open repositories, open services, open workflows tools
Open science definition
Open science guidelines
Open science projects

According to Benedikt Fecher and Sascha Friesike, five different schools (or approaches) related to OS can be also identified, including:

The infrastructure school (concerned with the technological architecture), mainly aiming to create openly available platforms, tools and services for scientists
The public school (concerned with the accessibility of knowledge creation), mainly aiming at developing tools and procedures for making science accessible for citizens
The measurement school (concerned with alternative impact measurement), mainly aiming to developing new procedures for measuring scientific impacts (such as altmetrics, open peer review, impact factors, citation indexes, etc.)
The democratic school (concerned with access to knowledge), aiming to develop tools and measures for making knowledge freely available for everyone
The pragmatic school (concerned with collaborative research), aiming to make the process of knowledge creation more efficient and goal oriented

SOURCES

Fecher, B., & Friesike, S. (2014). Open science: one term, five schools of thought. In Opening science (pp. 17-47). Springer, Cham.
European Commission (2016).Open innovation, open science, open to the world. A vision for Europe, Luxembourg, Publication Office of the European Union.
Amsen, E. (2015). Guide to open science publishing. F1000 Research Open for Science.
OECD (2015), “Making Open Science a Reality”. OECD Science, Technology and Industry Policy Papers, No. 25, OECD Publishing, Paris.
Facilitate Open Science Training for European Research – FOSTER Project (on-line), Open Science Taxonomy, https://www.fosteropenscience.eu/foster-taxonomy/open-science-definition.
ATT (2014) Open Science and Research Handbook, https://avointiede.fi/sites/avointiede.fi/files/openscience%20handbook.pdf.

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The literature review conducted under FIT4RRI allowed to identify a large set of barriers to RRI, which overall fall into four main families.

Barriers resulting in a lack of awareness about RRI

Barriers leading RRI to be or to be perceived as little relevant

Excellence vs. RRI. Many researchers and research managers see RRI as an obstacle to the search for excellence since it introduces criteria which have nothing to do with the attainment of scientific results. The peer review system and reward mechanisms are also exclusively based on excellence and not on social impacts
Pressure to publish. RRI can be also seen as an obstacle to getting one’s research papers published in the shortest time possible (for example, because RRI implies additional activities to be carried out)
Creating growth and making a profit. RRI can be viewed by policymakers and industry as an impediment to the need of making a profit, to develop new patents and to commercially exploit research results, especially because it requires additional funds, time, and resources
Distrust in scientific institutions and in RRI. Different stakeholders feel a sense of scepticisms toward RRI as well as toward scientific organisations in general. This produces a “motivational deficit” to get involved in RRI
Lack of material incentives. RRI requires money and resources, which are rarely guaranteed
Lack of scientific recognition. Scientists are not rewarded nor receive institutional support for their engagement with RRI. RRI is also not considered, except episodically and marginally, in the research evaluation process
RRI as a disincentive for scientific recognition. Researchers’ involvement in RRI can be perceived by peers as belittling their capacity to do research
Lack of incentives for non-R&I actors. It is not clear what benefits derive for civil society organisations and the public at large to get involved with RRI
Unclear benefits of RRI. For researchers and other stakeholders, the benefits of RRI often remain often unclear or uncertain

Barriers leading RRI to be or to be perceived as little effective

Barriers leading RRI to be or to be perceived as little sustainable

Bureaucratisation. RRI can be merely understood or applied as a formal aspect of the life of the organisation, simply requiring ticking the appropriate boxes in a form, or a tokenistic practice, thus making RRI something that continues to cement existing norms, behaviours and power relations
Lack of investments. Lack of long-term investments at all level (funds, time, expertise, political willingness, political power, etc.) can make RRI little sustainable or not sustainable at all
Resistance and institutional barriers. Institutionally embedding RRI may trigger strong resistance to change from both staff and leaders (RRI Tools), due to the persistence of the existing institutional structures, specific interests and power relations, cultural gaps and lack of information, and consolidated behavioural patterns
Inadequate legal and regulatory framework. National legislation can be a serious obstacle to RRI because it is often inconsistent, unclear and scattered
Inadequate policy framework. Apart from some specific exceptions, EC member states have not developed adequate policy frameworks to promote the spread and consolidation of RRI
Difficulties in defining the objectives. RRI can address many different objectives which cannot be pursued all together. However, identifying the “right” objectives for a given organisation or research sector is a difficult and complex exercise, especially in a context where many players are concerned, often in a limited period of time
Difficulties in defining responsibilities and implementation procedures. Often it remains unclear who is responsible for RRI within the organisation
Lack of evidence and data about RRI. An important barrier to the “institutionalisation” of RRI is the lack of evidence, criteria and data about its impacts and benefits. The lack of this information makes it difficult to convince research managers and leaders to invest in RRI

SOURCES

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The 2017 Mallorca Declaration on Open Science, developed by the Research, Innovation and Science Expert group (RISE) serving as an advisory group for the European Commission, identifies four main families ofbarriers to the development of Open Science.

The 2016 Amsterdam Call for Action on Open Science, developed with the impulse of the Netherlands’ EU Presidency, also identified four families of barriers to Open Science, which provided the basis to those developed by the RISE.

Barriers related to assessment, evaluation, and reward systems. Present assessment, evaluation, and reward systems are heavily focused on publications, in terms of their number and of the prestige of the journals where they are published. This creates a competition which penalizes high-risk research and broad knowledgeexchange, ultimately inhibiting the progress of science and innovation and the optimal use of knowledge.
Barrier related to the copyright system. The present copyright systems oblige authors to transfer their copyrights before publication. The result is that the scientific community relinquishes control over the way in which the publications are used. This fact impedes or limits the use of text and data mining procedures (especially by private firms and SMEs) and the distribution of scientific knowledge beyond the scientific community.
Barriers related to the intellectual property right systems and to privacy-related issues. The intellectual property right systems are not destined to disappear with Open Science, but it should be modified so as to make it compatible with knowledge sharing. Moreover, new approaches to privacy protection should be developed which could take into account the new EU regulations in this field.
Barriers related to costs and conditions of academic communication. There are concerns that the current academic publication system is unsustainable for research organisations. In order to achieve a cost-effective, efficient and dynamic system of academic communication, stakeholders need to gain appropriate insight into its costs and conditions. This is particularly relevant in the transition phase to open access.

According a report published in 2018 by the US National Academies of Sciences, Engineering, and Medicine, several important barriers remain, as well as limitations on the extent and speed with which open science can be realized. The report describes them as follows.

Costs and infrastructure. There are significant remaining cost barriers to widespread implementation of open publication and open data. New technological and institutional infrastructure within specific disciplines and across disciplines needs to be developed.
Structure of scholarly communications. Most publications are still only available on a subscription basis, and some potential pathways to open publication may disrupt the current scholarly communications ecosystem, including scientific society publishers, or may disadvantage early career researchers, researchers working in the developing world, or those in institutions with fewer resources.
Lack of supportive culture, incentives and training. Open practices such as preparing datasets and code for sharing and making preprints available are not generally rewarded and may even be discouraged by current incentive and reward systems. This may have the unintended consequence of causing a disadvantage to early career researchers.
Privacy, security, and proprietary barriers to sharing. Sharing data, code, and other research products is becoming more common, but barriers related to ensuring patient confidentiality and the protection of national security information exist in some domains. Proprietary research also presents barriers. Ultimately, some parts of the research enterprise may not be open.
Disciplinary differences. The nature of research and practices surrounding treatment of data and code differ by discipline and even within a discipline. The size of datasets and the nature of some data may prevent immediate, complete sharing. Safeguards to prevent misuse or misrepresentation of data will be needed.

SOURCES

National Academies of Sciences, Engineering, and Medicine. (2018). Open science by design: Realizing a vision for 21st century research. National Academies Press.
RISE (2017) Mallorca Declaration on Open Science, European Commission. https://ec.europa.eu/research/openvision/pdf/rise/mallorca_declaration_2017.pdf.
The Netherlands EU Presidency (2016). Amsterdam call for action on open science. Publication of the Netherlands Presidency of the Council of the European Union, May, 7.

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Under FIT4RRI, a set of focus groups have been organised with researchers and stakeholders to grasp how the shift towards an open and responsible science is actually experienced by them. The focus groups only focused on RRI, even though some aspects of Open Science have been also dealt with.

Some trends emerged from the focus groups deserve to be highlighted.

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The number and kinds of actions and measures which can be developed for promoting RRI and OS is extremely wide. Moreover, the ways in which they can be implemented and combined with each other are highly variable, largely depending on their context of application. This is to say that it is impossible to provide a comprehensive list of possible items to consider while mapping the actions and measures pertaining to RRI and OS already in place or planned.

However, only as a source of inspiration, a list of action areas is presented below for the main components of RRI and OS.

Component	Items
Gender equality	Use of gendered data about the presence of women at different career levels and career paths; presence of women in leadership positions
	Gender-sensitive recruitment and promotion policies (including, e.g., the organisation of interview, the proactive search of candidates, the contents and language used in advertising job vacations, the training of committee, members)
	Gender-sensitive career development and training policies (including, for example, mentoring programmes, career development initiatives, women networks, specific training for improving the crosscutting skills, etc.)
	Gender-sensitive work-life balance policies (including, for example, support to parents and women, organisation of time and space compatible with family life, special measures for parents returning after parental leaves, in-house kindergartens and lactation rooms or facilitated access to external kindergartens)
	Involvement of men in gender equality issues (including, for example, awareness raising initiatives addressed to males, identifying and involving male “champions” of gender equality within the institution, involving men in all the initiatives aimed to gender equality or integrating men in units and teams in charge of promoting gender equality)
	Gender-sensitive work environment policies (including, for example, fight against sexual harassment, fight against gender bias, gender-sensitive communication and language, gender pay gap, activities aimed at increasing the visibility of women in research)
	Policies pertaining the gender dimension in research contents (including, for example, training, conferences and workshops, guidelines on how to incorporategender and sex as variables in research)
	Skills and competence on gender equality in the organisation
	Groups, networks and individuals engaged with gender equality
Public engagement	Science communication policies (including, e.g., initiatives like Open days or researchers’ nights; training to staff members on how communicating science; an officer, office, or unit specialised in communicating the activities of the organisation and its research programmes; web-based science communication activities)
	Involvement of stakeholders, citizens or other actors in discussion, dialogue and consultation initiatives, pertaining both the research process (for example, design or implementation of research programmes, use of the research products, etc.) and the decision-making process (e.g., on resource allocation, research programmes to launch, etc.)
	Involvement of stakeholders, citizens or other actors in deliberative initiatives
	Citizen science initiatives involving citizens or other actors in designing and implementing research programmes
	Skills and competence in public engagement in the organisation
	Groups, networks and individuals engaged with public engagement
Research ethics and integrity	Data protection, privacy issues and informed consent concerned with the implementation of research programmes (for example, in social research, in clinical trials, etc.) and the application of the EU General Data Protection Regulation (GDPR)
	Ethically sensitive research and application of the precautionary principle (for example, research on human embryos or foetuses, research developing technologies which can be used for both peaceful or military aims, research producing invasive technologies like those aimed at surveillance, etc.)
	Management of the ethical issues related to research involving animals
	Measures and procedures aimed at preventing, detecting and managing cases of misconduct in research (frauds, plagiarism, conflicts of interest, corruptions, and other acts endangering research integrity)
	Skills and competence in research ethics and integrity in the organisation
	Groups, networks and individuals engaged with research ethics and integrity
Science education	Initiatives or programmes for promoting science education in primary and secondary schools
	Science outreach, i.e., any education and teaching initiatives targeted on citizens, group of citizens, entities of any kind, also including lifelong learning programmes or initiatives and cooperation with science centres and science museums
	Training initiatives on RRI and OS addressing students, staff, and leaders
	Skills and competence in science education in the organisation
	Groups, networks and individuals engaged with science education
Open science	Open access publications (e.g., the creation of an institutional OA repository, allocation of a specific budget for OA publishing, set of rules about archiving, acknowledgement and documentation, etc.)
	Open data and reproducible research (initiatives aimed at sharing e.g., protocols, workflows, notebooks, codes, data, reference libraries or grant proposals)
	Open science evaluation (including, e.g., open peer review, webmetrics, bibliometrics, etc.)
	Skills and competence in Open Science in the organisation
	Groups, networks and individuals engaged with Open Science
Governance of RRI/OS	Initiatives or programmes connected to RRI and OS in general
	Awareness-raising initiatives on RRI and OS in general
	Governance structures (of any kind: units, officers, norms, website page, procedures, guidelines, etc.) in the organisation which are devoted to RRI, its keys or Open Science
	Skills and competence in RRI in the organisation
	Groups, networks and individuals engaged with RRI in general
Reflexivity	Including mechanisms inside the research process aimed at making it more sensitive towards societal issues (in terms of, e.g., contents, methods, and technological outputs)
Anticipation	Initiatives aiming at forecasting future risks and undesirable effects of research programmes

Starting the Process

Resources pertaining toGuidelines for Interpretation

Resources pertaining to
Guidelines for Interpretation