Data interoperability
Data Science Journal, ISSN: 1683-1470, Vol: 12, Issue: 0, Page: GRDI19-GRDI25
2013
- 29Citations
- 107Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Data interoperability continues to be a significant challenge for researchers to address several issues. The 'data' and 'interoperability' concepts are difficult to be fully perceived and actually lead to different perceptions in diverse communities. This problem is further amplified when considered in the context of research data infrastructures that are expected to serve a number of communities of practice potentially involved in diverse application scenarios, each characterized by a specific sharing problem. The term 'interoperability' does not have a clear definition shared by the overall community despite being used to describe a core class of problems in many systems and application scenarios. Data integration, and data exchange are confused, as they share some commonalities in terms of issues and goals. Implementing data interoperability requires realizing data integration and data exchange along with an enabling effective use of the data that become available.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84882954494&origin=inward; http://dx.doi.org/10.2481/dsj.grdi-004; http://datascience.codata.org/articles/abstract/10.2481/dsj.GRDI-004/; https://www.jstage.jst.go.jp/article/dsj/12/0/12_GRDI-004/_article/-char/en/; https://www.jstage.jst.go.jp/article/dsj/12/0/12_GRDI-004/_article/-char/ja/; https://dx.doi.org/10.2481/dsj.grdi-004; https://datascience.codata.org/articles/abstract/10.2481/dsj.GRDI-004/
Ubiquity Press, Ltd.
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