Compliance Culture or Culture Change? The role of funders in improving data management and sharing practice amongst researchers

Neylon, Cameron

doi:10.3897/rio.3.e14673

Cited by 11 publications

(21 citation statements)

References 7 publications

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“…Seeing RDM as an area for collaboration between funders and researchers may be valuable Internal expertise and support within a funder is often a gap which becomes a problem with monitoring and implementation DMPs can be a helpful part of process but it will be important to make them useful documents throughout and beyond the project If the object of RDM and RDS policy is cultural change in research communities then direct engagement with understanding the various cultures of researcher and other stakeholder communities, alongside frameworks of how they change is an important area for future focus. (Neylon and Chan 2016). 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.…”

Section: Key Findingsmentioning

confidence: 99%

Compliance Culture or Culture Change? The role of funders in improving data management and sharing practice amongst researchers

Neylon¹

2017

RIO

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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. ‡ © Neylon C. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.In our review we identify, in common with other surveys, that RDM and RDS policies are increasingly common. The most developed are found amongst funders in the United States, United Kingdom, Australia, and European Union. However many other funders and nations have aspirational statements or are developing policy. There is a broad pattern of policy development moving from aspiration, to recommendations, to 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 effect culture change towards improved co...

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Section: Key Findingsmentioning

confidence: 99%

Compliance Culture or Culture Change? The role of funders in improving data management and sharing practice amongst researchers

Neylon¹

2017

RIO

Self Cite

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“…The individual case studies are published separately (Neylon 2017a, Neylon 2017b, Neylon 2017c, Neylon 2017d, and the underlying data is available in the project data package on Zenodo (Neylon 2017i).…”

Section: Case Study Analysismentioning

confidence: 99%

“…Data sharing, open data, and data management policies are being implemented by many funders with a range of variation in their approaches (see Neylon (2017h), review for this project). Underpinning these differing policies are a range of motivations.…”

Section: Goals Of Idrc In Developing a Data Sharing Policymentioning

confidence: 99%

“…The Pilot was designed around seven case studies of existing IDRC funded projects (Neylon 2017a, Neylon 2017b, Neylon 2017c, Neylon 2017d. These projects were selected to span a range of geographies, scales and subjects.…”

Section: Pilot Design and Analysismentioning

confidence: 99%

“…Three groups (HMP, DED, ECV) sought to build a local data infrastructure for sharing (Neylon 2017e, Neylon 2017a, Neylon 2017b, Wael 2017. To achieve this required bringing in external support and the internal expertise to decide between options was not consistently available.…”

Section: Local Technical Capacities and Expertisementioning

confidence: 99%

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Building a Culture of Data Sharing: Policy Design and Implementation for Research Data Management in Development Research

Neylon¹

2017

RIO

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A pilot project worked with seven existing projects funded by the International Development Research Center of Canada (IDRC) to investigate the implementation of data management and sharing requirements within development research projects. The seven projects, which were selected to achieve a diversity of project types, locations, host institutions and subject areas, demonstrated a broad range of existing capacities to work with data and access to technical expertise and infrastructures. The pilot project provided an introduction to data management and sharing concepts, helped projects develop a Data Management Plan, and then observed the implementation of that plan.In examining the uptake of Data Management and Sharing practice amongst these seven groups the project came to question the underlying goals of funders in introducing data management and sharing requirements. It was established that the ultimate goal was a change in culture amongst grantees. The project therefore looked for evidence of how funder interventions might promote or hinder such cultural change.The project had two core findings. First that the shift from an aim of changing behaviour, to changing culture, has both subtle and profound implications for policy design and implementation. A particular finding is that the single point of contact that many data ‡ © Neylon C. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. management and sharing policies create where a Data Management Plan is required at grant submission but then not further utilised is at best neutral and likely counter productive in supporting change in researcher culture.As expected, there are significant bottlenecks within research institutions and for grantees in effectively sharing data including a lack of resources and expertise. However, a core finding is that many of the bottlenecks for change relate to structural issues at the funder level. Specifically, the expectation that policy initiatives are implemented, monitored, and evaluated by Program Officers who are the main point of contact for projects. The single most productive act to enhance policy implementation may be to empower and support Program Officers. This could be achieved through training and support of individual POs, through the creation of a group of internal experts who can support others, or via provision of external support, for instance by expanding the services provided by the pilot project into an ongoing support mechanism for both internal staff and grantees.Other significant findings include: the importance of language barriers and the way in which assumptions of English language in materials, resources, services and systems permeate the entire system; that data infrastructures are poorly served by current funding arrangements and tools, particularly where they are obliged to seek continuing fund...

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Can LCA be FAIR? Assessing the status quo and opportunities for FAIR data sharing

Ghose

2024

Int J Life Cycle Assess

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Aim The purpose of this study is to assess the status quo of data sharing in LCA in relation to the FAIR (findability, accessibility, interoperability, and reuse) data principles. Methods This study investigates how is LCA data from publicly funded research currently shared. Firstly, the focus is on life cycle inventory data shared in journal articles. Given that FAIR data sharing is not only the responsibility of the LCA practitioner, this study further investigates guidelines (e.g., data sharing standards and data management plans) and infrastructure (repositories, data formats, and nomenclature) to identify the tools and services available to LCA community which are essential to enable FAIR data sharing. Results The study identifies that although there is growing awareness to improve data sharing practices, implementation of FAIR guidelines for data sharing is seldom seen in practice. LCA studies that adhere to FAIR principles are primarily due to use of generic data repositories which provide tools to support data sharing. However, there is no guidance on how LCA specific data should be shared to ensure its findability, accessibility, interoperability, and reusability. This study suggests a workflow to enable FAIRification of LCA data. In addition, the study recommends further efforts within the LCA community on skill and technology development, strategic funding, and recognition of the best practices in relation to data sharing. Conclusion In conclusion, this study highlights the necessity of data sharing incentives, guidelines, and platforms/repositories specific for the LCA community.

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Compliance Culture or Culture Change? The role of funders in improving data management and sharing practice amongst researchers

Cited by 11 publications

References 7 publications

Compliance Culture or Culture Change? The role of funders in improving data management and sharing practice amongst researchers

Compliance Culture or Culture Change? The role of funders in improving data management and sharing practice amongst researchers

Building a Culture of Data Sharing: Policy Design and Implementation for Research Data Management in Development Research

Can LCA be FAIR? Assessing the status quo and opportunities for FAIR data sharing

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