The Emerging Scholarly Brain

Kurtz, Michael J.

doi:10.1007/978-1-4419-8369-5_3

Cited by 3 publications

(2 citation statements)

References 44 publications

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“…As brieflymentioned above, the Eigenfactorwas developed as part of aresearch project at the University of Washington, and the concept is available on apublic website. Bergstrom and colleagues tried to servet he needso fv arious stakeholders, among others the library community,f or example, to support librarians' decisionmaking on journal subscriptions ( Kurtz, 2011). One of the goals of the Eigenfactor is to help academicl ibrarians identify the most important journals when deciding which journals to subscribe to.…”

Section: Role Of the Eigenfactor Within The Scientific Communitymentioning

confidence: 99%

4.7 Eigenfactor

Fraumann¹,

D’Souza²,

Holmberg³

2021

Handbook Bibliometrics

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The Eigenfactor™ is aj ournal metric, which was developedb yB ergstrom and his colleagues at the Universityo fW ashington. They invented the Eigenfactor as aresponse to the criticism against the use of simple citation counts.The Eigenfactor makes use of the network structure of citations,i .e.c itations between journals, and establishes the importance, influenceo ri mpact of aj ournal based on its location in an etwork of journals.The importance is defined based on the number of citations between journals. As such, the Eigenfactoralgorithm is based on Eigenvector centrality. While journalbased metrics have been criticized, the Eigenfactor has also been suggested as an alternative in the widelyused San Francisco Declaration on Research Assessment (DORA).

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Section: Role Of the Eigenfactor Within The Scientific Communitymentioning

confidence: 99%

4.7 Eigenfactor

Fraumann¹,

D’Souza²,

Holmberg³

2021

Handbook Bibliometrics

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show abstract

“…Newer iterations of this system (Chyla et al 2015) have started to branch out and allow not only search algorithms but provide certain bibliometric statistics as well as a recommender system (named "Suggested Articles"). This recommender system is based on citations, text similarity, and co-readership (as described on the ADS 2.0 website and suggested in Henneken & Kurtz 2010;Kurtz 2011). Such recommender systems will be a first step to tackle a world in which the scientific literature has massively outgrown the memory capacity of human brains.…”

Section: Introductionmentioning

confidence: 99%

Knowledge discovery through text-based similarity searches for astronomy literature

Kerzendorf

2019

J Astrophys Astron

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The increase in the number of researchers coupled with the ease of publishing and distribution of scientific papers (due to technological advancements) has resulted in a dramatic increase in astronomy literature. This has likely led to the predicament that the body of the literature is too large for traditional human consumption and that related and crucial knowledge is not discovered by researchers. In addition to the increased production of astronomical literature, recent decades have also brought several advancements in computational linguistics. Especially, the machine-aided processing of literature dissemination might make it possible to convert this stream of papers into a coherent knowledge set. In this paper, we present the application of computational linguistics techniques to astronomy literature. In particular, we developed a tool that will find similar articles purely based on text content from an input paper. We find that our technique performs robustly in comparison with other tools recommending articles given a reference paper (known as recommender system). Our novel tool shows the great power in combining computational linguistics with astronomy literature and suggests that additional research in this endeavor will likely produce even better tools that will help researchers cope with the vast amounts of knowledge being produced.

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Second Order Operators in the NASA Astrophysics Data System

Kurtz

Chyla

2020

Bulletin of the AAS

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Second Order Operators (SOOs) are database functions which form secondary queries based on attributes of the objects returned in an initial query; they can provide powerful methods to investigate complex, multipartite information graphs. The NASA Astrophysics Data System (ADS) has implemented four SOOs, reviews , useful , trending , and similar which use the citations, references, downloads, and abstract text. This tutorial describes these operators in detail, both alone and in conjunction with other functions. It is intended for scientists and others who wish to make fuller use of the ADS database. Basic knowledge of the ADS is assumed.

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The Emerging Scholarly Brain

Cited by 3 publications

References 44 publications

4.7 Eigenfactor

4.7 Eigenfactor

Knowledge discovery through text-based similarity searches for astronomy literature

Second Order Operators in the NASA Astrophysics Data System

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