Tracing the footsteps of open research data in China

Li, Chengzan; Zhou, Yuanchun; Xiaohuan, Zheng; Zhang, Zeyu; Jiang, Lulu; Li, Zongwen; Wang, Pengyao; Li, Jianhui; Xu, Songyuan; Wang, Zhan Jie

doi:10.1002/leap.1439

Cited by 4 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among the criteria for restricting sharing are whether the scientific data contain personal information or concern national security. The adherence to the privacy laws is supposed to be implemented at the level of the data center policies which are currently evolving (Li et al, 2022). The complex and domain specific regulatory framework and its consequences for international sharing has been mostly analyzed in the field of human genetic data where China has considerable data resources.…”

Section: Chinamentioning

confidence: 99%

FAIR human neuroscientific data sharing to advance AI driven research and applications: Legal frameworks and missing metadata standards

et al. 2023

View full text Add to dashboard Cite

Modern AI supported research holds many promises for basic and applied science. However, the application of AI methods is often limited because most labs cannot, on their own, acquire large and diverse datasets, which are best for training these methods. Data sharing and open science initiatives promise some relief to the problem, but only if the data are provided in a usable way. The FAIR principles state very general requirements for useful data sharing: they should be findable, accessible, interoperable, and reusable. This article will focus on two challenges to implement the FAIR framework for human neuroscience data. On the one hand, human data can fall under special legal protection. The legal frameworks regulating how and what data can be openly shared differ greatly across countries which can complicate data sharing or even discourage researchers from doing so. Moreover, openly accessible data require standardization of data and metadata organization and annotation in order to become interpretable and useful. This article briefly introduces open neuroscience initiatives that support the implementation of the FAIR principles. It then reviews legal frameworks, their consequences for accessibility of human neuroscientific data and some ethical implications. We hope this comparison of legal jurisdictions helps to elucidate that some alleged obstacles for data sharing only require an adaptation of procedures but help to protect the privacy of our most generous donors to research … our study participants. Finally, it elaborates on the problem of missing standards for metadata annotation and introduces initiatives that aim at developing tools to make neuroscientific data acquisition and analysis pipelines FAIR by design. While the paper focuses on making human neuroscience data useful for data-intensive AI the general considerations hold for other fields where large amounts of openly available human data would be helpful.

show abstract

Section: Chinamentioning

confidence: 99%

FAIR human neuroscientific data sharing to advance AI driven research and applications: Legal frameworks and missing metadata standards

et al. 2023

View full text Add to dashboard Cite

show abstract

“…For example, as of 28 September 2021, there were over 1000 registered data repositories in the USA, 457 in Germany, and 300 in the UK. However, the current number of data repositories in China is only 48 (Li et al, 2022). Therefore, the FAIR principle requiring data to be findable, accessible, interoperable, and reproducible is proposed to guide scientists when sharing their research datasets, even in different repositories (Cannon et al, 2022; Koers et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Opening research data contributes to the citations of related research articles: Evidence from Data in Brief

Tian

Cheng-zhi

et al. 2023

Learned Publishing

View full text Add to dashboard Cite

This paper explores the effect of publishing a data paper in the Open Access journal Data in Brief (DIB) on the citation counts of the related research paper. Using regression analysis, citation content analysis and a survey method, we investigate whether research papers with a related data paper have higher citation counts and the potential reasons.After controlling variables that correlate with the citation counts, research papers with a related data paper were found to have higher citation counts than those published in the same issue of the same journal. Next, we explored the causal relationship between the two variables by surveying the corresponding authors of 618 papers who shared datasets in DIB from 2014 to 2021. The results show that the authors acknowledge the benefits of sharing data in DIB, including citation increase and career reputation enhancement. We further explored how the data papers in DIB increase the citations of the related research papers by using citation content analysis. We found that scientists co-cite the data papers and their related research papers for the purpose of reusing the underlying data or portraying a better understanding of the underlying data and related research articles.

show abstract

To share or not to share, that is the question: a qualitative study of Chinese astronomers’ perceptions, practices, and hesitations about open data sharing

Liu,

Zhao,

et al. 2024

Humanit Soc Sci Commun

View full text Add to dashboard Cite

Many astronomers in Western countries may have taken open data sharing (ODS) for granted to enhance astronomical discoveries and productivity. However, how strong such an assumption holds among Chinese astronomers has not been investigated or deliberated extensively. This may hinder international ODS with Chinese astronomers and lead to a misunderstanding of Chinese astronomers’ perceptions and practices of ODS. To fill this gap, we conducted a qualitative study comprising 14 semi-structured interviews and 136 open-ended survey responses with Chinese astronomers to understand their choices and concerns regarding ODS. We found that many Chinese astronomers conducted ODS to promote research outputs and respected it as a tradition. Some Chinese astronomers have advocated for data rights protection and data infrastructure’s further improvement in usability and availability to guarantee their ODS practices. Still, some Chinese astronomers agonized about ODS regarding the validity of oral commitment with international research groups and the choices between international traditions and domestic customs in ODS. We discovered two dimensions in Chinese astronomers’ action strategies and choices of ODS and discussed their descriptions and consequences. We also proposed the implications of our research for enhancing international ODS in future work.

show abstract

Tracing the footsteps of open research data in China

Cited by 4 publications

References 10 publications

FAIR human neuroscientific data sharing to advance AI driven research and applications: Legal frameworks and missing metadata standards

FAIR human neuroscientific data sharing to advance AI driven research and applications: Legal frameworks and missing metadata standards

Opening research data contributes to the citations of related research articles: Evidence from Data in Brief

To share or not to share, that is the question: a qualitative study of Chinese astronomers’ perceptions, practices, and hesitations about open data sharing

Contact Info

Product

Resources

About