A Review of Open Research Data Policies and Practices in China

Zhang, Lili; Downs, Robert R.; Li, Jianhui; Wen, Liangming; Li, Chengzan

doi:10.5334/dsj-2021-003

Cited by 12 publications

(9 citation statements)

References 35 publications

(31 reference statements)

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“…Second, there are internal limitations, such as a reluctance or even resistance towards open science within funding agencies, a lack of practical expertise, and a lack of resources. Some proposed solutions reported in our interviews, such as the collaboration with other stakeholders, the joint development of shared standards, and learning from other funders are also suggested in other studies [30,36,64]. Some of our interviewees suggested that dedicated governmental initiatives can also help raise awareness for the importance of data sharing and provide data sharing policies with political legitimacy.…”

Section: Plos Onementioning

confidence: 65%

Neither carrots nor sticks? Challenges surrounding data sharing from the perspective of research funding agencies—A qualitative expert interview study

et al. 2022

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Background Data Sharing is widely recognised as crucial for accelerating scientific research and improving its quality. However, data sharing is still not a common practice. Funding agencies tend to facilitate the sharing of research data by both providing incentives and requiring data sharing as part of their policies and conditions for awarding grants. The goal of our article is to answer the following question: What challenges do international funding agencies see when it comes to their own efforts to foster and implement data sharing through their policies? Methods We conducted a series of sixteen guideline-based expert interviews with representatives of leading international funding agencies. As contact persons for open science at their respective agencies, they offered their perspectives and experiences concerning their organisations’ data sharing policies. We performed a qualitative content analysis of the interviews and categorised the challenges perceived by funding agencies. Results We identify and illustrate six challenges surrounding data sharing policies as perceived by leading funding agencies: The design of clear policies, monitoring of compliance, sanctions for non-compliance, incentives, support, and limitations for funders’ own capabilities. However, our interviews also show how funders approach potential solutions to overcome these challenges, for example by coordinating with other agencies or adjusting grant evaluation metrics to incentivise data sharing. Discussion and conclusion Our interviews point to existing flaws in funders’ data sharing policies, such as a lack of clarity, a lack of monitoring of funded researchers’ data sharing behaviour, and a lack of incentives. A number of agencies could suggest potential solutions but often struggle with the overall complexity of data sharing and the implementation of these measures. Funders cannot solve each challenge by themselves, but they can play an active role and lead joint efforts towards a culture of data sharing.

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Section: Plos Onementioning

confidence: 65%

Neither carrots nor sticks? Challenges surrounding data sharing from the perspective of research funding agencies—A qualitative expert interview study

et al. 2022

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“…Calls for the sharing of code related to research publications are increasing; for example, see current policies from UNESCO [ 4 ], the European Commission via Horizon Europe funding scheme [ 5 ], and the French National Open Science plan [ 6 ]. However, code sharing policies are still not as common as data sharing policies [ 7 , 8 ]. The computational biology community is a leading example of sharing research methods to enable open science.…”

Section: Discussionmentioning

confidence: 99%

Advancing code sharing in the computational biology community

et al. 2022

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“…The range of human actors in a data ecology includes data curators, data submissions, support assistance, and data flow engineers (Nadim, 2016). Central to the notion of a data ecology then are relationships between data, people, technologies, in a particular data context and policy environment, and as a result of driving forces/interests (Zhang, et al, 2021).…”

Section: Data Ecologiesmentioning

confidence: 99%

Learning analytics as data ecology: a tentative proposal

2023

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Central to the institutionalization of learning analytics is the need to understand and improve student learning. Frameworks guiding the implementation of learning analytics flow from and perpetuate specific understandings of learning. Crucially, they also provide insights into how learning analytics acknowledges and positions itself as entangled in institutional data ecosystems, and (increasingly) as part of a data ecology driven by a variety of data interests. The success of learning analytics should therefore be understood in terms of data flows and data interests informing the emerging and mutually constitutive interrelationships and interdependencies between different stakeholders, interests and power relations. This article analyses several selected frameworks to determine the extent to which learning analytics understands itself as a data ecosystem with dynamic interdependencies and interrelationships (human and non-human). Secondly, as learning analytics increasingly becomes part of broader data ecologies, we examine the extent to which learning analytics takes cognizance of the reality, the potential and the risks of being part of a broader data ecology. Finally, this article examines the different data interests vested in learning analytics and critically considers implications for student data sovereignty. The research found that most of the analyzed frameworks understand learning analytics as a data ecosystem, with very little evidence of a broader data ecological understanding. The vast majority of analyzed frameworks consider student data as valuable resource without considering student data ownership and their data rights for self-determination.

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A Review of Open Research Data Policies and Practices in China

Cited by 12 publications

References 35 publications

Neither carrots nor sticks? Challenges surrounding data sharing from the perspective of research funding agencies—A qualitative expert interview study

Neither carrots nor sticks? Challenges surrounding data sharing from the perspective of research funding agencies—A qualitative expert interview study

Advancing code sharing in the computational biology community

Learning analytics as data ecology: a tentative proposal

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