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Other literature type . 2024
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Presentation . 2024
License: CC BY
Data sources: Datacite
ZENODO
Presentation . 2024
License: CC BY
Data sources: Datacite
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Presentation - Making FAIR Fair to the Researchers (Force11 Conference - Force2024)

Authors: Patel, Bhavesh;

Presentation - Making FAIR Fair to the Researchers (Force11 Conference - Force2024)

Abstract

This is the archive of the slides and information associated with Bhavesh Patel's presentation at the FORCE11 Annual Conference (FORCE2024), called "Making FAIR Fair to the Researchers". Abstract: The FAIR (Findable, Accessible, Interoperable, Reusable) Principles provide a framework for sharing research outcomes like data and software such that they are optimally reusable by both humans and machines. This is critical for allowing reproducibility of research results, promoting transparency of research processes, enabling reuse of research outcomes, and ultimately increasing the pace of discoveries in science. These Principles have been widely promoted and supported by all stakeholders in research since their publication in 2016 including researchers, funders, and scientific publishers. Yet, their implementation continues to be a challenge as sharing research results, i.e. manuscript publication, continues to be the main focus of researchers. Stakeholders have tested many approaches combining reward and punishment to overcome this challenge. Here, we present our approach that focuses more on supporting researchers. Specifically, we develop computer tools and software, such as SODA and codefair, that make it easier for researchers to prepare and share FAIR biomedical data and software. They are based on our own experience as biomedical researchers where we observed that most are in support of sharing their data and software in line with the FAIR Principles but often view it as an added burden as they lack time and knowledge to comply. We believe that such researcher-oriented tools can therefore play a significant role in making FAIR fair to the researchers and ultimately achieve the sharing of FAIR data, software, and other research outcomes at scale.

Related Organizations
Keywords

Data, Biomedical, Reusable, Software, FAIR

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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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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!
1
Average
Average
Average