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Making It Count: A Computational Approach To Attribution

Authors: Holmes, Kristi; Gutzman, Karen; Smith, Patricia; Eichmann, David; Haendel, Melissa;

Making It Count: A Computational Approach To Attribution

Abstract

People from a diverse array of backgrounds play important roles in research, often in ways that cannot be quantified through traditional metrics of scholarly impact. This demands better approaches to evaluate and ultimately communicate scholarly outcomes beyond the narrow criteria of publications and grants. It is imperative to develop a structure to track a much wider diversity of contributor roles and research objects - and do so in a manner that is easily populated with real data. This presentation will share details about the project, our team and approach, upcoming opportunities to collaborate, metrics for success, and integration and application of this work to date.

Preprint submitted to RO2018 workshop at IEEE eScience Conference 2018

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Keywords

data model, contributor roles, attribution, research objects, credit

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
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