Towards transdisciplinary impact of scientific publications: A longitudinal, comprehensive, and large-scale analysis on Microsoft Academic Graph

2022 ◽  
Vol 59 (2) ◽  
pp. 102859
Author(s):  
Yong Huang ◽  
Wei Lu ◽  
Jialin Liu ◽  
Qikai Cheng ◽  
Yi Bu
2018 ◽  
Author(s):  
Alberto Martín-Martín ◽  
Rodrigo Costas ◽  
Thed van Leeuwen ◽  
Emilio Delgado López-Cózar

This article uses Google Scholar (GS) as a source of data to analyse Open Access (OA) levels across all countries and fields of research. All articles and reviews with a DOI and published in 2009 or 2014 and covered by the three main citation indexes in the Web of Science (2,269,022 documents) were selected for study. The links to freely available versions of these documents displayed in GS were collected. To differentiate between more reliable (sustainable and legal) forms of access and less reliable ones, the data extracted from GS was combined with information available in DOAJ, CrossRef, OpenDOAR, and ROAR. This allowed us to distinguish the percentage of documents in our sample that are made OA by the publisher (23.1%, including Gold, Hybrid, Delayed, and Bronze OA) from those available as Green OA (17.6%), and those available from other sources (40.6%, mainly due to ResearchGate). The data shows an overall free availability of 54.6%, with important differences at the country and subject category levels. The data extracted from GS yielded very similar results to those found by other studies that analysed similar samples of documents, but employed different methods to find evidence of OA, thus suggesting a relative consistency among methods.


2018 ◽  
Vol 12 (3) ◽  
pp. 819-841 ◽  
Author(s):  
Alberto Martín-Martín ◽  
Rodrigo Costas ◽  
Thed van Leeuwen ◽  
Emilio Delgado López-Cózar

2021 ◽  
Author(s):  
Mehdi A. Beniddir ◽  
Kyo Bin Kang ◽  
Grégory Genta-Jouve ◽  
Florian Huber ◽  
Simon Rogers ◽  
...  

This review highlights the key computational tools and emerging strategies for metabolite annotation, and discusses how these advances will enable integrated large-scale analysis to accelerate natural product discovery.


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