By Jose Luis Ortega
Academic se's: intends to run during the present landscape of the tutorial se's via a quantitative method that analyses the reliability and consistence of those companies. the target is to explain the most features of those engines, to focus on their benefits and disadvantages, and to debate the results of those new items sooner or later of clinical communique and their impression at the study dimension and assessment. in brief, educational se's offers a precis view of the recent demanding situations that the net set to the medical job in the course of the such a lot novel and leading edge looking prone to be had on the net.
- This is the 1st method of examine se's completely addressed to the learn neighborhood in an integrative instruction manual. the newness, expectation and usability of a lot of those prone justify their analysis.
- This publication isn't only an outline of the net functionalities of those companies; it's a medical evaluation of the main remarkable features of every platform, discussing their value to the scholarly communique and learn evaluation.
- This e-book introduces an unique method in response to a quantitative research of the lined information throughout the wide use of crawlers and harvesters which enable getting in intensity into how those engines are operating. Beside of this, a close descriptive evaluate in their functionalities and a serious dialogue approximately their use for medical neighborhood is displayed.
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Extra info for Academic Search Engines. A Quantitative Outlook
Perhaps, from a scientometric point of view, this application can help indicate the probability of one author collaborating with another one and hence forecast scientific dynamics, but I have doubts about the usefulness of CollabSeer in searching for future partners. SeerSeer is an experimental development authored by Pucktada Treeratpituk, a member of the CiteSeerx research team. Based on the CiteSeerx digital library, it acts as a search engine to locate experts in certain knowledge areas (although the majority of the documents come from the field of computer science).
The results page shows the acknowledgements that correspond to the queried entity. These are sorted by citations and present the document title, the acknowledgement text and the entities that were indexed from each document. It is interesting to view the timeline option because this shows a longitudinal graph where the user can see the number of times that a person or organization is mentioned in acknowledgements each year. However – and as is the case with the CiteSeerx main project – there is no standardization nor cleaning process, meaning that entities are presented in different forms, which leads to long and complex queries to retrieve the acknowledgements to one specific organization.
This weakness also influences the citation extraction and count, a critical aspect of the research evaluation. The chapter concludes by suggesting that this service has lost strength and appears to be at something of a standstill in terms of both its functioning and its coverage. Key words: CiteSeerx, autonomous citation index, digital library, parsing. ist. edu), was born, understood to be the first web-based search engine specializing in scientific information. It was developed at the NEC Research Institute in Princeton, by Steve Lawrence, Lee Giles and Kurt Bollacker.
Academic Search Engines. A Quantitative Outlook by Jose Luis Ortega