Augur

Abstract

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The impact of preprint servers in the formation of novel ideas

“…A number of studies propose different ideas to quantify originality in science and technology (Cozzens et al, 2010;Alexander et al, 2013;Rzhetsky et al, 2015;Rotolo et al, 2015;Wang and Chai, 2018;Shibayama and Wang, 2020) or their impact on the other works (Shi et al, 2010;Shahaf et al, 2012;Sinatra et al, 2016;Hutchins et al, 2016;Wesley-Smith et al, 2016;Herrmannova et al, 2018b,a;Zhao et al, 2019;Bornmann et al, 2019;Small et al, 2019). The prediction of breakthroughs, scientific impact and citation counts is a well developed area (Schubert and Schubert, 1997;Garfield et al, 2002;Dietz et al, 2007;Lokker et al, 2008;Shi et al, 2010;Uzzi et al, 2013;Alexander, 2013;Klimek et al, 2016;Tahamtan et al, 2016;McKeown et al, 2016;Clauset et al, 2017;Peoples et al, 2017;Salatino et al, 2018;Dong et al, 2018;Iacopini et al, 2018;Feldman et al, 2018;van den Besselaar and Sandström, 2018;Klavans et al, 2020). However, the question asked in these works is different from the one we ask.…”

Ontology Extraction and Usage in the Scholarly Knowledge Domain1

“…Some fields of research are comprehensively described by ontologies of research areas, e.g., MeSH in Biology and PhySH in Physics. These ontologies can provide support toward a multitude of tasks, such as integrating heterogeneous datasets [11], assisting users in exploring digital libraries [18], producing scholarly analytics [3], and forecasting research dynamics [24,20]. In this section, we will review the current state of the art with regards to developing and using ontologies of research areas.…”

“…Understanding and reacting timely to new developments in the research landscape is critical for a variety of stakeholders, such as funding bodies, academic publishers, companies and others. Augur [24] is a novel approach which uses CSO for anticipate the emergence of new research topics. Specifically, Augur analyses topic networks, i.e.…”

“…In the field of scholarly knowledge, ontologies are used to facilitate the integration of large datasets of research data [11], the exploration of the academic landscape [18,9], information extraction from scientific articles [8], and so on. More specifically, ontologies representing research topics and describing their relationships, have been employed in several tasks, such as making sense of research dynamics [18], classifying research publications [25], characterising [2] and identifying [21] research communities, studying the origin of research topics [23], and forecasting research trends [24]. However, not every domain of science has an ontology that comprehensively describes all research concepts and their relations.…”

Editorial: Special Issue on Scholarly Data Analysis (Semantics, Analytics, Visualisation)

“…In recent years, we have seen an increasing interest in analysing, describing, and improving the research process with the aim of tackling the reproducibility crisis [6], informing research policies [2], enriching representation of research papers semantically [7], identifying promising research directions [9], monitoring the evolution of research topics [4], and, ultimately, accelerating the scientific progress [3]. These efforts require the development and publishing of new forms of scholarly artifacts and their analysis which integrates lessons and approaches from the field of semantic technologies, science of science, digital libraries, and artificial intelligence.…”