Difference between revisions of "Usage Based Discovery Tool"
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| + | November 4, 2022 | ||
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| + | Traditional methods of dataset discovery leverage dataset features (like platform or processing level) to perform search. This can be a difficult paradigm for non-experts and interdisciplinary scientists alike. | ||
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| + | Usage-Based Discovery takes a different approach by first allowing users to narrow their search by how the datasets are used in real-world applications and research (like an application that monitors forest fires or a publication that looks into the relationship between sea level rice and ice melt). By promoting these examples of data use (and their associated datasets), users can more easily get started with using the data that's suited for their goals, building off of the existing work of the scientific community. | ||
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| + | At its core, Usage-Based Discovery is a socially-driven process, where discovering data is an open and collaborative effort spread amongst a diverse community. | ||
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| + | <nowiki>https://raw.githubusercontent.com/ESIPFed/usage-based-discovery/main/static/ubd-app.png</nowiki> | ||
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The Discovery Cluster developed the beta Usage Based Discovery Tool under Chris Lynnes' leadership and with help from interns and a dedicated developer from NASA. This tool is available and runs on ESIP's Amazon Web Services infrastructure. | The Discovery Cluster developed the beta Usage Based Discovery Tool under Chris Lynnes' leadership and with help from interns and a dedicated developer from NASA. This tool is available and runs on ESIP's Amazon Web Services infrastructure. | ||
Back to [[Discovery Cluster]] Wiki Page. | Back to [[Discovery Cluster]] Wiki Page. | ||
Revision as of 07:13, November 4, 2022
November 4, 2022
Traditional methods of dataset discovery leverage dataset features (like platform or processing level) to perform search. This can be a difficult paradigm for non-experts and interdisciplinary scientists alike.
Usage-Based Discovery takes a different approach by first allowing users to narrow their search by how the datasets are used in real-world applications and research (like an application that monitors forest fires or a publication that looks into the relationship between sea level rice and ice melt). By promoting these examples of data use (and their associated datasets), users can more easily get started with using the data that's suited for their goals, building off of the existing work of the scientific community.
At its core, Usage-Based Discovery is a socially-driven process, where discovering data is an open and collaborative effort spread amongst a diverse community.
https://raw.githubusercontent.com/ESIPFed/usage-based-discovery/main/static/ubd-app.png
The Discovery Cluster developed the beta Usage Based Discovery Tool under Chris Lynnes' leadership and with help from interns and a dedicated developer from NASA. This tool is available and runs on ESIP's Amazon Web Services infrastructure.
Back to Discovery Cluster Wiki Page.
