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InteractiveResource . 2024
License: CC BY
Data sources: Datacite
ZENODO
InteractiveResource . 2024
License: CC BY
Data sources: Datacite
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Scaling up computational reproducibility practices in research organisations - how to influence change

Authors: Barker, Michelle; Chue Hong, Neil; Martinez-Ortiz, Carlos;

Scaling up computational reproducibility practices in research organisations - how to influence change

Abstract

Attendees will learn how to use a framework created to assist research organisations in understanding how reproducibility (including computational reproducibility) can be scaled up internally. The framework can be used by a range of internal stakeholders with differing goals, such as institutional leaders seeking to align organisational strategies, or managers wishing to provide the support that staff in their part of the organisation may be seeking. The framework is a part of a report on how the practice of conducting research in a reproducible way can be scaled up from pioneers to the majority of researchers and research support staff. Attendees will be introduced to the types of practices that assist individual researchers, research support staff, and managers to scale up practices that improve research reproducibility. Note that this framework is focused on how well organised an organisation is at scaling up reproducibility practices (i.e., access and coordination), not the maturity of reproducibility practices and how well they adhere to what is commonly understood to be best practice in reproducibility. The framework can be used to assess current practice, and facilitate discussion about priorities and directions. This report was commissioned by Knowledge Exchange, a group of national organisations from six European countries supporting research infrastructure and services to enable the use of digital technologies to improve higher education and research: CSC in Finland, CNRS in France, DeiC in Denmark, DFG in Germany, Jisc in the UK and SURF in the Netherlands. A recording of this session is available on YouTube: https://youtu.be/Qq-kRGEJHAg

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    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.
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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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!
0
Average
Average
Average
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