The History and Future of Data Citation in Practice

Parsons, M. A.; Duerr, Ruth; Jones, Matthew B.

doi:10.5334/dsj-2019-052

Cited by 19 publications

(16 citation statements)

References 53 publications

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“…In contrast to the common peer-reviewed publication in journals, it is not so easy to measure the impact of research data per se, because data citation is still not a common practice [ 46 ], although it is becoming more and more important and accepted [ 47 ]. This is not only a cultural problem but also a technical challenge and therefore an issue of practicability [ 48 ]. One of the first metrics to count data citations was the commercial Data Citation Index.…”

Section: Discussionmentioning

confidence: 99%

The on-premise data sharing infrastructure e!DAL: Foster FAIR data for faster data acquisition

et al. 2020

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Background The FAIR data principle as a commitment to support long-term research data management is widely accepted in the scientific community. Although the ELIXIR Core Data Resources and other established infrastructures provide comprehensive and long-term stable services and platforms for FAIR data management, a large quantity of research data is still hidden or at risk of getting lost. Currently, high-throughput plant genomics and phenomics technologies are producing research data in abundance, the storage of which is not covered by established core databases. This concerns the data volume, e.g., time series of images or high-resolution hyper-spectral data; the quality of data formatting and annotation, e.g., with regard to structure and annotation specifications of core databases; uncovered data domains; or organizational constraints prohibiting primary data storage outside institional boundaries. Results To share these potentially dark data in a FAIR way and master these challenges the ELIXIR Germany/de.NBI service Plant Genomic and Phenomics Research Data Repository (PGP) implements a “bring the infrastructure to the data” approach, which allows research data to be kept in place and wrapped in a FAIR-aware software infrastructure. This article presents new features of the e!DAL infrastructure software and the PGP repository as a best practice on how to easily set up FAIR-compliant and intuitive research data services. Furthermore, the integration of the ELIXIR Authentication and Authorization Infrastructure (AAI) and data discovery services are introduced as means to lower technical barriers and to increase the visibility of research data. Conclusion The e!DAL software matured to a powerful and FAIR-compliant infrastructure, while keeping the focus on flexible setup and integration into existing infrastructures and into the daily research process.

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Section: Discussionmentioning

confidence: 99%

The on-premise data sharing infrastructure e!DAL: Foster FAIR data for faster data acquisition

et al. 2020

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“…The use of DOIs has become more popular as a PDI for data since DataCite acts as an organization of DOIs for datasets (Callaghan, 2014). However, there are several other choices of PIDs, such as Compact URIs (Parsons, Duerr, & Jones, 2019). The principle of Unique Identification also explains machine actionability that enables computers to search data.…”

Section: Discussionmentioning

confidence: 99%

Examination of data citation guidelines in style manuals and data repositories

Yoon

Chung²,

Schalk

et al. 2020

Learned Publishing

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This study investigated the status of data citation guidelines by analysing 15 style manuals and 100 data repositories in various fields. Analysis results are presented through a comparison with eight items in the Joint Declaration of Data Citation Principles (JDDCP). Only nine style manuals and only 20 repositories contain instructions regarding data citation. The guidelines generally agree on the importance of the author, publication date, title, and publisher, but there is large variation between the different guides. Analysis of the guidelines revealed that they generally reflect the principles of the JDDCP. In particular, the principle of Credit and Attribution is significantly reflected in style manuals and data repositories and supports one of the major motivations for data sharing. The principles of Unique Identification, Access, and Persistence are less well supported in data citation guidelines. However, the principles of Importance, Evidence, Specificity and Verifiability, and Interoperability and Flexibility are rarely supported. The findings of this study reveal that better guidelines are required, and future work could investigate developing and assessing the standard guidelines for data citation in different fields.

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“…Moreover, a standard for measuring and displaying data user metrics is worked on. In [25], Parsons et al review the history and future of data citations. "We know how to cite most data in research publications.…”

Section: Plos Onementioning

confidence: 99%

“…"We know how to cite most data in research publications. We must only accelerate the implementation, and there does appear to be movement in that direction", [25].…”

Section: Plos Onementioning

confidence: 99%

Search, reuse and sharing of research data in materials science and engineering—A qualitative interview study

2020

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Open research data practices are a relatively new, thus still evolving part of scientific work, and their usage varies strongly within different scientific domains. In the literature, the investigation of open research data practices covers the whole range of big empirical studies covering multiple scientific domains to smaller, in depth studies analysing a single field of research. Despite the richness of literature on this topic, there is still a lack of knowledge on the (open) research data awareness and practices in materials science and engineering. While most current studies focus only on some aspects of open research data practices, we aim for a comprehensive understanding of all practices with respect to the considered scientific domain. Hence this study aims at 1) drawing the whole picture of search, reuse and sharing of research data 2) while focusing on materials science and engineering. The chosen approach allows to explore the connections between different aspects of open research data practices, e.g. between data sharing and data search. In depth interviews with 13 researchers in this field were conducted, transcribed verbatim, coded and analysed using content analysis. The main findings characterised research data in materials science and engineering as extremely diverse, often generated for a very specific research focus and needing a precise description of the data and the complete generation process for possible reuse. Results on research data search and reuse showed that the interviewees intended to reuse data but were mostly unfamiliar with (yet interested in) modern methods as dataset search engines, data journals or searching public repositories. Current research data sharing is not open, but bilaterally and usually encouraged by supervisors or employers. Project funding does affect data sharing in two ways: some researchers argue to share their data openly due to their funding agency's policy, while others face legal restrictions for sharing as their projects are partly funded by industry. The time needed for a precise description of the data and their generation process is named as biggest obstacle for data sharing. From these findings, a precise set of actions is derived suitable to support Open Data, involving training for researchers and introducing rewards for data sharing on the level of universities and funding bodies.

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The History and Future of Data Citation in Practice

Cited by 19 publications

References 53 publications

The on-premise data sharing infrastructure e!DAL: Foster FAIR data for faster data acquisition

The on-premise data sharing infrastructure e!DAL: Foster FAIR data for faster data acquisition

Examination of data citation guidelines in style manuals and data repositories

Search, reuse and sharing of research data in materials science and engineering—A qualitative interview study

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