stands for Digital Object Identifier
and is the unique identifier for objects on the internet. It can be used to create persistent link and to cite articles.
Using DOI as a persistent link
To create a persistent link, add「http://dx.doi.org/」
before a DOI.
For instance, if the DOI of an article is 10.5297/ser.1201.002 , you can link persistently to the article by entering the following link in your browser: http://dx.doi.org/ 10.5297/ser.1201.002 。
The DOI link will always direct you to the most updated article page no matter how the publisher changes the document's position, avoiding errors when engaging in important research.
Cite a document with DOI
When citing references, you should also cite the DOI if the article has one. If your citation guideline does not include DOIs, you may cite the DOI link.
DOIs allow accurate citations, improve academic contents connections, and allow users to gain better experience across different platforms. Currently, there are more than 70 million DOIs registered for academic contents. If you want to understand more about DOI, please visit airiti DOI Registration （ doi.airiti.com ） 。
-  Douglas W. Hubbard (2011), “Pulse: The New Science of Harnessing Internet Buzz to Track Threats and Opportunities,” 73-84.
-  Ladislav Kristoufek (2013), “BitCoin Meets Google Trends and Wikipedia: Quantifying the Relationship between Phenomena of the Internet Era,” Scientific Reports, 3:3415. (Retrieved from http://www.nature.com/articles/srep03415)
-  Liwen Vaughan and Esteban Romero-Frías (2013), “Web Search Volume as a Predictor of Academic Fame: An Exploration of Google Trends,” Journal of the Association for Information Science and Technology, 65(4), 707-720. (Retrieved from http://onlinelibrary.wiley.com/doi/10.1002/asi.23016/abstract)
-  Sungjin Cho, Chang Hwan Sohn, Min Woo Jo, Soo-Yong Shin, Jae Ho Lee, Seoung Mok Ryoo, Won Young Kim and Dong-Woo Seo (2013), “Correlation between National Influenza Surveillance Data and Google Trends in South Korea,” Journal of PLOS ONE, 8(12), e81422. (Retrieved from http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0081422)
-  Damien Challet and Ahmed Bel Hadj Ayed (2014), “Do Google Trend data contain more predictability than price returns?,” SSRN Electronic Journal, March. (Retrieved from https://poseidon01.ssrn.com/delivery.php?ID=009020074123066092027083108004094091037009014088040058108071099025082081029094000007011034030127028123007076070078005103020016109073061058077064088018088031113124056028000027082070080126109006118100000112005003068113008097020088094001102121090125075&EXT=pdf)
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