Journal:Compliance culture or culture change? The role of funders in improving data management and sharing practice amongst researchers

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Full article title Compliance culture or culture change? The role of funders in improving data management and sharing practice amongst researchers
Journal Research Ideas and Outcomes
Author(s) Neylon, Cameron
Author affiliation(s) Curtin University
Primary contact Email: cn at cameronneylon dot net
Year published 2017
Volume and issue 3
Page(s) e21705
DOI 10.3897/rio.3.e21705
ISSN 2367-7163
Distribution license Creative Commons Attribution 4.0 International
Download (PDF)


There is a wide and growing interest in promoting research data management (RDM) and research data sharing (RDS) from many stakeholders in the research enterprise. Funders are under pressure from activists, from government, and from the wider public agenda towards greater transparency and access to encourage, require, and deliver improved data practices from the researchers they fund.

Funders are responding to this, and to their own interest in improved practice, by developing and implementing policies on RDM and RDS. In this review we examine the state of funder policies, the process of implementation and available guidance to identify the challenges and opportunities for funders in developing policy and delivering on the aspirations for improved community practice, greater transparency and engagement, and enhanced impact.

The review is divided into three parts. The first two components are based on desk research: a survey of existing policy statements drawing in part on existing surveys and a brief review of available guidance on policy development for funders. The third part addresses the experience of policy implementation through interviews with funders, policy developers, and infrastructure providers.

In our review we identify, in common with other surveys, that RDM and RDS policies are increasingly common. The most developed are found among funders in the United States, United Kingdom, Australia, and European Union. However many other funders and nations have inspirational statements or are developing policy. There is a broad pattern of policy development moving from aspiration to recommendations, requirements, and finally reporting and auditing of data management practice.

There are strong similarities across policies: a requirement for data management planning, often in grant submissions; expectations that data supporting published articles will be made available; and, in many cases, requirements for data archiving and availability over extended periods beyond grants. However there are also important differences in implementation.

There is essentially no information available on the uptake and success of different policies in terms of compliance rates, or degrees of data availability. Many policies require a data management plan as part of grant submission. This requirement can be enforced, but there is disagreement on the value of this. One view is that requirements such as DMPs are the only way to force researchers to pay attention to these issues. The other is that such requirements lead to a culture of compliance in which the minimal effort is made and planning is seen as a “tick-box” exercise that has no further value. In this view, requirements such as DMPs may actually be damaging the effort to affect culture change towards improved community practice.

One way to bring these two views together is to see DMPs as living documents that form the basis of collaboration between researchers, funders, and data managers throughout the life of a research project. This approach is reflected in guidance on policy development that emphasizes the importance of clarifying responsibilities of various stakeholders and ensuring that researchers are recognized for good practice and see tangible benefits.

More broadly, this points to the need for a program of improving RDM and RDS to be shared projects, with the incentives for funders and researchers aligned as far as is possible. In the interviews, successful policy implementation was often seen to be dependent on funders providing the required support, both in the form of infrastructure and resourcing, and via the provision of internal expertise among program managers. Where resources are limited, leveraging other support — especially from institutional sources — was seen as being as important as was ensuring the scope of policy requirements were commensurate with the support available and readiness of research communities.

Throughout the desk research and interviews, a consistent theme is the desire for cultural change, where data management and sharing practices are embedded within the norms of behavior for research communities. There is general agreement that progress from inspirational policies to actually achieving compliance is challenging and that broad cultural change, with the exception of specific communities, is a long way off. It is interesting to note that discussion of cultural change is largely externalized. There is little engagement with the concept of culture as an issue to consider or work with and very little engagement with models of how cultural change could be enabled. The disagreement over the value of DMPs is one example of how a lack of active engagement with culture and how it changes is leading to problems.

Key findings:

  • Policies on RDM and RDS are being developed by a number of agencies, primarily in the Global North. These policies are broadly consistent in inspiration and outlines but differ significantly in details of implementation.
  • Policies generally develop along a path starting with inspirational statements, followed by recommendations, then requirements, and finally auditing and compliance measures.
  • Measurement of policy adoption and compliance in terms of the overarching goals of increased availability and re-use of data is not tracked and is likely unmeasurable currently.
  • Data management plans are a central requirement for many policies, in part because they can be made compulsory and act as a general focus for raising awareness.
  • There are significant differences in the views of stakeholders on the value of data management planning in its current form.
  • Some stakeholders regard them as successful in raising awareness, albeit with some limitations.
  • Some regard them as actively damaging progress towards real change in practice by making RDM appear as one administrative activity among the many required for grant submission.
  • Successful policy implementation is coupled with funder support for infrastructure and training. Seeing RDM as an area for collaboration between funders and researchers may be valuable.
  • Internal expertise and support from a funder often leads to a gap, which becomes a problem with monitoring and implementation.
  • DMPs can be a helpful part of the process, but it will be important to make them useful documents throughout and beyond the project.
  • If the objective of RDM and RDS policy is cultural change in research communities, then direct engagement with and understanding of the various cultures of researchers and other stakeholder communities, alongside frameworks of how they change, is an important area for future focus.

Keywords: review, data management planning, data sharing, policy, implementation, culture change, policy design

Introduction: Aims and scope

This review was written in support of the International Development Research Center (Canada) program Exploring the opportunities and challenges of implementing open research strategies within development institutions.[1] The project was constructed as a pilot in which the proposed IDRC data sharing policy is tested in the context of eight funded research projects. The intent of the review is to support the project and its participants by examining the existing literature on funder policies on research data management and sharing, and, using interviews with relevant experts (see Neylon's project data package for recordings and transcripts[2]), to develop an understanding of the current state of policy implementation and its challenges.

The review is focused on funder policy, as opposed to policies of research institutions or disciplinary communities. It aims to develop an overview of the funder policy landscape as a whole, examine existing guidance for funders on policy development, and to probe the issues that funders, and the researchers and institutions that they fund, are facing in the implementation and adoption of policies.

As the development of data sharing and data management policy and practice among funders of development research has been limited to date, the review does not focus specifically on data sharing in a development research context. However it does draw out issues that are likely to be relevant in this context, including the provision of infrastructure; (mis)alignment among the culture of research disciplines, institutions, and stakeholders; and motivations of researchers and funders. The surfacing of these issues is intended to guide the research program in its examination of data sharing in the real world of the projects that are taking part.

Research funder policies on data sharing and management

Research funders are increasingly developing policies on research data management (RDM) and research data sharing (RDS) or open data. The oldest and most developed policies are in the United States, Australia, and the United Kingdom. The European Union has imposed new requirements for the Horizon 2020 program, and various member states are developing policies. Canada’s Tri-Councils have been developing policy and infrastructure over the past several years.

A range of development research funders have also developed RDM and RDS policies. The U.K. Department for International Development included requirements for data management and sharing within the DFID Research Open and Enhanced Access Policy that came into effect in November 2012. The requirements included the provision of a data management plan when proposals were submitted, and datasets were to be made available through institutional and subject repositories within 12 months of final data collection.

The World Bank Open Access Policy also includes requirements for datasets associated with formal publications to be made available through the Bank’s Open Knowledge Repository. The World Bank also has a strong record of making general economic and research data available through its data portal and has a very strong disclosure policy in general, relative to other similar organizations.

Review methodology

Information on funder policies was sourced from the Comprehensive Brief on Research Data Management[3], Current Best Practice for Research Data Management Policies[4], as well as two online collations of funder policies with a United States federal agency[5] and United Kingdom focus.[6] Specific policy documents for the U.K. Department for International Development[7], the World Bank[8], the Australian Research Council, and National Health and Medical Research Council were also examined.

A focus was retained on funder policies as opposed to those of research institutions. National policies (with the exception of the United States OSTP Memo) and statements from trans-national bodies are high-level and generally inspirational rather than specific. As the review focuses on the impact of implementation details, these were not considered, except where they had a direct effect on the details of research funder requirements.

Funder motivations

The motivations for funders to develop data management and sharing policies are generally focused around two broad issues. The first of these is maximizing the impact and reach of research, and the second is concerns around citizen access and engagement with research. Fecher and Friesike[9] identified five categories of framing within the broader open science movement: democratic, pragmatic, infrastructure, public, and measurement. While issues might be raised with this classification, these framings are a useful lens for examining funder motivations to support data management and sharing. Funder policies and the documents around them often refer to the motivations for developing policy and guidance, and in the area of data, these public motivations fall largely into the "democratic" and "pragmatic" categories, with gestures towards the "public" category. These motivations tend to also align with public statements of governments addressing broader open data agendas.

Other categorizations of drivers have been described[10][11], and policy documents enumerate a range of positive outcomes for RDS.[12][13][3] The value of the categorization of Fecher and Friesike is its basis in the analysis of the discourse of these documents as a means of interrogating the underlying motivations.

The "democratic" framing is described as being concerned that access to knowledge, and the ability to reuse it, should be equitably distributed. It is distinguished from the "public" framing, which is concerned with the public, or rather non-professional engagement in the consumption and production of knowledge, rather than equity per se. Reference to these framings in policy statements is generally political, aligning with government language on democratization and engagement.

For instance the U.K. Department for International Development in its Research Open and Enhanced Access Policy[7] states that “DFID is committed to greater transparency in its activities and spending, and is working to make data more accessible to the public” and that “[the U.K. g]overnment is also committed to expanding access to publicly-funded research.” Similarly the World Bank policy on access to formal publications states that the bank “supports the free online communication and exchange of knowledge as the most effective way of ensuring that the fruits of research, economic and sector work, and development practice are made widely available, read, and built upon” and that it “is therefore committed to open access, which, for authors, enables the widest possible dissemination of their findings and, for readers, increases their ability to discover pertinent information.”[8]

In most cases, the focus of policy language aligns strongly with the "pragmatic" framing, and elements of this are also seen in the examples above. This is often also described as “the impact agenda”; the goal of maximizing value creation through funder investment in research. In the Concordat on Open Research Data[14], a U.K. document developed by a multi-stakeholder group and endorsed by the U.K. Research Councils, the first motivation for data sharing is that “[t]he societal benefits from making research data open are potentially very significant; including economic growth, increased resource efficiency, securing public support for research funding and increasing public trust in research.”

The Office of Science and Technology Policy of the U.S. Executive Administration starts its memorandum Increasing Access to the Results of Federally Funded Scientific Research[15] with the statement that the “Administration is committed to ensuring that, to the greatest extent and with the fewest constraints possible and consistent with law and the objectives set out below, the direct results of federally funded scientific research are made available to and useful for the public, industry, and the scientific community” [emphasis added]. In the European Union the focus on “big data” as an economic good, in particular Vice President Kroes’ description of it as “the new oil,” is also an element of this "pragmatic" framing.

Funder policy statements less frequently involve language that invokes the "infrastructure" or "measurement" framing identified by Fecher and Friesike. This raises questions, as infrastructure and technology are clearly required to deliver on the goals of the "democratic" and "pragmatic" agendas, and measurement of policy compliance is a key tool for driving uptake, or at least monitoring adoption.

A useful analysis is therefore to consider how, for any given policy, the language reflects the discourse associated with each of the five sets of discourse, the extent to which the substance of the policy addresses each school, and whether a funder is tracking success against the goals of each framing. A well-balanced policy would require actions that align with the stated goals and measure success against those same goals. In practice, most policies invoke the core of the "democratic" and "pragmatic" discourse, require actions more aligned with the "infrastructure" discourse, and have weak reporting requirements. Where reporting requirements are stronger, they rarely provide data that would support assessment of progress towards the goals of the discourse, describing the "democratic" and "pragmatic" framing.

Specific funder policies

A series of summaries of funder policies and their implementation exists, generally with a geographical focus.[6][3][5] The latter two of these are currently committed to maintaining updates and therefore will be a more reliable future source of information than the current review. Appendix 1 contains a table with summary information for a number of funders. Here a small selection of specific funder policies are discussed, in detail, as examples, focusing on requirements for data management planning, the support of costs for data management, and reporting and compliance requirements.

Engineering and Physical Sciences Research Council (U.K.)

The United Kingdom’s Engineering and Physical Sciences Research Council (EPSRC) differs from most other funders in the focus of their policy implementation. It is unique in placing obligations for policy compliance firmly on the research institution as opposed to the researcher. EPSRC expects that data management planning will take place but does not require a DMP as part of a grant proposal. The institution is responsible for ensuring that data is stored appropriately and made available for a period of 10 years, or for 10 years after any third-party request for access is made. The costs of data management are allowable expenses on grants; however, the provision is necessarily institutional.

This focus on institutional provision, and the consequent threat that funding might be withdrawn at the institutional level for non-compliance, is unique. The policy has definitely driven institutions to respond, although the effectiveness of those responses has been questioned by some. The EPSRC case demonstrates how the details of implementation, and particularly the assignment of responsibility, can change outcomes even when the actual requirements of the policy in terms of practice are very similar.

It is also instructive to consider the timing of responses. The council notified institutions in 2011 of the requirement to have a roadmap in place by May 2012 to deliver on the policy by May 2015. Nonetheless, researchers and institutions largely did not engage with this until very late. Researchers are only now becoming aware of the requirements. This may in part be a consequence of not using a requirement for DMPs as part of grant submission to raise awareness among researchers.

European Commission Horizon 2020 Program

Horizon 2020 is a seven-year research program supporting around €70B of research across the European Research Area. The legislation framing the program called for a data sharing pilot program, following the success of an open-access pilot in the previous research program (Framework Program 7).

Some data management planning is required for all proposals submitted within the main research programs of Horizon 2020. This is described as a “short, general outline” of the plans and is included with the impact assessment for the proposal, i.e., not as part of the assessment of research interest or quality. For grant calls included within the pilot (roughly 20 percent of the overall program) a more extensive DMP is required within six months of the project commencing.[16] Horizon 2020 emphasizes the role of the DMP as a living document to support the research project throughout its life.

The requirements placed on the project are then to observe the DMP (although mechanisms for monitoring compliance are not specified), to deposit data in an appropriate repository, and to “as far as possible[...] take measures to enable for third parties to access, mine, exploit, reproduce and disseminate (free of charge for any user) this research data.”[17] Timing of data release is not specified. Costs of data management and sharing are allowed as part of the grant, and the commission has supported a range of relevant data infrastructures.

As the program is still relatively new, there have been few reports on progress within the Horizon 2020 Open Data pilot. While a proportion of projects have opted out of the pilot, a substantial, though not equal, number have also opted in.[18] It is likely too early in the implementation process to expect detailed information on progress, but data is currently being collected on DMPs.[19]

Department for International Development (U.K.)

The United Kingdom’s main development funder, the Department for International Development (DFID), developed a general policy on access to research outputs, including data in 2012.[7] The policy requires datasets to be placed in an appropriate repository within 12 months of final data collection or on the publication of outputs underpinned by that data. Researchers are also “required to retain raw datasets for a minimum of five years after the end of a project, and make them available on request, for free, any time after 12 months from final data collection (unless exempted by DFID).”[7]

DFID provides a repository, R4D for published outputs, which can accept “simple data sets” in some cases, but the responsibility for storage and management is left to researchers and their institutions. An access and data management plan is required as part of proposals, and it should include sections on data management. Publications are also required to have a data availability statement stating where data is available and any restrictions on access.

As with many other policies, the open data provisions are found within a wider access policy that includes access to other research outputs. The guidance provided specifically for data is limited, and there is limited discussion of monitoring or compliance. A brief search for articles supported by DFID published in 2015 provided no examples where links were provided to data freely available in a repository, and only one case where there was a formal third party to request access from. However a large proportion of articles appeared to provide free-to-read access to the full text in some form, thus complying, in spirit at least, to that aspect of the policy.

World Bank

The World Bank has a strong track record of making data available through its data portal. However its policy on access to data generated by funded external research is limited to the inclusion of “associated data sets” in the definition of manuscripts that are included in its open access policy.[8] This means requirements on external researchers are limited. The World Bank has both an extremely strong policy on public access to its internal information and records, and, as noted, a strong program on making internally generated data available. A similar informal test, like the DFID, found no cases where links to data were made clearly available in World Bank-funded research.

National Institutes of Health (U.S.) and the OSTP Memorandum

The U.S. National Institutes of Health (NIH) has a long history as a provider of data infrastructures and, through its intramural research, the generation of important research data for the community. It has a general policy on data sharing dating back to 2003, requiring the preparation of a data management plan for sufficiently large grants. The NIH, with its strong history of funding the production of and access to specific types of data – common with many other large biomedical funders – focuses more than most other policies on the existing repositories for specific data types.

Like other large U.S. federal funders, the NIH is subject to the Office of Science and Technology Policy (OSTP) memorandum Increasing Access to the Results of Federally Funded Research.[15] The memorandum is best known for its requirements on open access to articles, but it also includes a series of directives on the availability and management of research data. In its response, the NIH commits to expanding the requirement for DMPs to all grants.

The NIH already has a substantial investment (several hundred million dollars) in providing technical and infrastructural support for data management and sharing. It is unusual in this respect in having the scale and resources to tackle the support requirements head-on. The response to the OSTP memorandum also notes that data management plans can form part of the formal Notice of Award and therefore be the basis for compliance action, including withholding funding. However, monitoring is still a challenge, and the response notes the importance of enhancing discoverability, particularly through metadata that “can be used to verify that novel data sets are registered in accordance with the applicable NIH policy.”[20] It is notable that even the NIH has challenges in tracking data outputs of its funded research.

The OSTP memorandum also includes a directive to “develop...appropriate attribution to scientific data sets that are made available under the plan,” focusing attention on the need for credit and recognition of good practice in data management and sharing (see also Hodson and Molloy[4], Shearer[3], and the following section on issues of credit and attribution). This is connected to the discoverability issue, and again the challenges that an organization the scale of NIH faces in doing this are well worth noting.

Australian Research Council and National Health and Medical Research Council (Australia)

Australia’s two main government funders, the Australian Research Council (ARC) and National Health and Medical Research Council (NHMRC) co-developed the Australian Code for the Responsible Conduct of Research[21], which provides the main policy framework for data sharing in Australia. This places limited requirements on researchers or institutions. Both have obligations to ensure the management and care of data, but in terms of sharing they have greater focus on ethical and ownership issues than data access. The single recommendation on data availability is that “[r]esearch data should be made available for use by other researchers unless this is prevented by ethical, privacy or confidentiality matters.”

Nonetheless as Shearer[3] notes, Australia is generally regarded as a leader in the data sharing space due to provision of infrastructure through the Australian National Data Service and good connections between institutions. The code is in practice not directly monitored or enforced, with expectations on detailed policy implementation deferred to institutions. Australia has some of the best discovery infrastructure for data at a national level through Research Data Australia and the National Library. Nonetheless, it is still challenging to determine levels of uptake or compliance in what is a complex policy landscape.

Patterns in the policy landscape

Within the set of funder policies there are two main patterns. There are consistent patterns in how policies and implementation evolve over time and there are patterns that relate to disciplinary differences. These patterns are logical and expected but they also are the root of a range of implementation challenges.

Policies develop over time

Funder policies follow a consistent development pattern. They start as an articulation of aspirations or points of principle. These policy articulations are often ambitious in aspiration but very limited in implementation details, often limited to statements that researchers “should” share data with limited specifications, if any. These policies achieve relatively little uptake and are essentially never monitored. The second stage of development is to specify requirements in more detail, often focused on data supporting published reports and articles. Sometimes, but not in all cases, this includes a requirement for data management plans at the point of grant submission, but often with limited clarity on how such plans are judged as part of the grant review process. In a few cases, DMPs are required but data sharing is not.

A problem with this pathway of development is that the early stages are performative. To the extent that new requirements are imposed, they are additional to, and generally separate from, the main flow of the research process. This means that monitoring is challenging, and recording and reporting are minimal. Often DMPs are required but do not actually play any significant part in the review process. This can contribute to a researcher view of requirements as not being connected to their core concerns and any requirements as a tick box administrative exercise to be completed.

This can create problems as policies move to the next state, where requirements are imposed and some process of monitoring is introduced. Firstly, because in previous stages most researchers have not engaged deeply, there has been little serious preparation to support data management and sharing, and little consideration of the changes in practice required. The frantic scrabbling of U.K. institutions in response to the EPSRC requirements is an example of this. On the funder side, there has generally been little consideration of how to monitor progress, and the systems for monitoring are generally inadequate.

The final phase is one where monitoring and strict requirements are put in place, often with the threat of sanctions. To date, there have not been explicit reports of sanctions being carried out. This is the stage at which it is generally realized that compliance monitoring is challenging or impossible across the community. Generally some form of audit process is suggested with limited random checks. A number of funders also report that at this stage the capacity and expertise of staff to monitor policy uptake and application is a challenge (Fig. 1).

Fig1 Neylon ResIdeasOut2017 3.jpg

Figure 1. Illustration of the categories through which many research data management and sharing policies develop, with examples of the language used

Disciplinary variation Strong policies are most common in areas of the biomedical sciences with a history of data sharing, most notably human genomics and structural biology, and areas of social sciences. These areas of strength are associated with the existence of long term data archives within the disciplines (ENA, NCBI, PDB, UKDA, ICPSR), often dating back to the 1980s and '90s. Within the biomedical and structural sciences, successful sharing is focused on very specific data types, and data archives often specialize in a very small number of data types.

Funders in the biomedical sciences tend to have some of the strongest data sharing requirements and some of the most specific requirements in terms of appropriate data forms and archives. However, in practice effective data sharing is largely focused around those specific data types for which repositories already exist. Other forms of data are often seen as unimportant or too difficult to manage. For example, while there is extensive data supporting the claimed structures of biomolecules, the data that describes the purification of samples prior to structure determination is rarely made available.

In the social sciences, where more heterogeneous data is common, a larger proportion of the core data is often available. However, here effort has often been particularly focused on data series, which often have long-term funding or at least grant support. An exception is the ESRC (U.K.) requirements. Until recently, all ESRC grant recipients were required to offer data collected for curation and management by UKDA. However, the UKDA is not obliged to take it. In 2015, the policy was updated to require the provision of metadata via UKDA, while the data can be housed elsewhere, provided FAIR requirements are met.[22] The Social Sciences and Humanities Research Council (Canada) also has a long-standing policy on data archiving and availability.

At the other end of the scale, data sharing in the humanities is extremely limited. This is due in part to humanities scholars having the sense that they do not work with data as it is understood in the social and natural sciences. However, digital data are increasingly being generated in humanities scholarship, and more traditional approaches also generate notes and archives that could be seen as falling into a similar category. The International Digging into Data Challenges (, and the development of digital humanities more generally, are an important signal of developing change. However, the broader engagement of the humanities disciplines with what data sharing might be relevant or appropriate for them has been limited by comparison with other disciplines to date.

Interdisciplinary researchers and funders crossing a wide range of disciplines therefore face a substantial challenge in working across communities that have different histories, expectations, and infrastructure in place. Differing cultures have entirely different perspectives on what data is important, how it might be shared, and indeed what data even is. Therefore, care is needed in developing policy to support these diverse groups and communities.

Likely future directions


  1. Neylon, C.; Chan, L. (2016). "Exploring the opportunities and challenges of implementing open research strategies within development institutions". Research Ideas and Outcomes 2: e8880. doi:10.3897/rio.2.e8880. 
  2. Neylon, C. (2017). "Dataset for IDRC Project: Exploring the opportunities and challenges of implementing open research strategies within development institutions. International Development Research Center". Zenodo. doi:10.5281/zenodo.844394. 
  3. 3.0 3.1 3.2 3.3 3.4 Shearer, K. (7 April 2015). "Comprehensive Brief on Research Data Management Policies". Government of Canada. Archived from the original on 01 October 2015. Retrieved 04 December 2017. 
  4. 4.0 4.1 Hodson, S.; Molloy, L. (2015). "Current Best Practice for Research Data Management Policies". Zenodo. doi:10.5281/zenodo.27872. 
  5. 5.0 5.1 Whitmire, A.; Briney, K.; Nurnberger, A. et al. (2016). "A table summarizing the Federal public access policies resulting from the US Office of Science and Technology Policy memorandum of February 2013". Figshare. doi:10.6084/m9.figshare.1372041.v5. 
  6. 6.0 6.1 "Overview of funders' data policies". Digital Curation Center. 2012. Retrieved 16 August 2015. 
  7. 7.0 7.1 7.2 7.3 Department for International Development (January 2013). "DFID Research Open and Enhanced Access Policy v1.1" (PDF). Retrieved 18 January 2016. 
  8. 8.0 8.1 8.2 World Bank (1 April 2012). "World Bank Open Access Policy for Formal Publications (English)". Retrieved 28 January 2016. 
  9. Fecher, B.; Friesike, S. (2013). "Open Science: One Term, Five Schools of Thought". SSRN. doi:10.2139/ssrn.2272036. 
  10. Borgman, C.L. (2012). "The conundrum of sharing research data". Journal of the Association for Information Science and Technology 63 (6): 1059–1078. doi:10.1002/asi.22634. 
  11. Leonelli, S. (2013). "Why the Current Insistence on Open Access to Scientific Data? Big Data, Knowledge Production, and the Political Economy of Contemporary Biology". Bulletin of Science, Technology & Society 33 (1–2): 6–11. doi:10.1177/0270467613496768. 
  12. OECD. "Open Science". Retrieved 17 September 2015. 
  13. The Royal Society Science Policy Centre (June 2012). "Science as an open enterprise" (PDF). The Royal Society. Retrieved 27 January 2016. 
  14. RCUK (17 July 2015). "Draft Concordat on Open Research Data". Retrieved 29 January 2016. 
  15. 15.0 15.1 Holdren, J.P. (22 February 2013). "Increasing Access to the Results of Federally Funded Scientific Research" (PDF). Office of Science and Technology Policy. Retrieved 29 January 2016. 
  16. European Commission (21 March 2017). "Guidelines to the Rules on Open Access to Scientific Publications and Open Access to Research Data in Horizon 2020" (PDF). 
  17. "Guidelines on FAIR Data Management in Horizon 2020" (PDF). European Commission. 26 July 2016. 
  18. Spichtinger, D. (14 November 2015). "Open Access in a European Policy Context". Slideshare. Retrieved 28 January 2016. 
  19. Open Access EC (28 January 2016). "Twitter Status". Retrieved 28 January 2016. 
  20. National Institutes of Health (21 October 2014). "NIH Data Sharing Policies". U.S. National Library of Medicine. Retrieved 01 April 2016. 
  21. NHMRC and ARC (2007) (PDF). Australian Code for the Responsible Conduct of Research. Australian Government. pp. 41. ISBN 1864964324. 
  22. ESRC (March 2015). "Research Data Policy". Retrieved 17 October 2017. 


This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added. The original article lists references alphabetically, but this version — by design — lists them in order of appearance.