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Update text based on Nazeefa's comments
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lauportell authored Sep 16, 2024
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### Description

Discovering research data for re-analysis can occur at different levels of granularity. Initially, researchers browse online catalogues that describe studies, datasets, related publications, variables, and some data distributions. This basic discovery may suffice if the datasets meet all the criteria. However, to find dataset that meet specific combinations of attributes, such as adults diagnosed with COVID-19 in the last year, fully vaccinated, with no underlying health conditions,” researchers must either contact the authors or request data access and verify themselves. This process is feasible for a small number of datasets and cooperative data controllers but it is usually time-consuming and uncertain. To streamline this, data discovery at the source allows users to query data non-disclosively, determining its relevance before requesting full access.
Discovering research data for re-analysis can occur at different levels of granularity. Initially, researchers browse online catalogues that describe studies, datasets, related publications, variables, and some data distributions. This basic discovery may suffice if the datasets meet all the criteria. However, to find dataset that meet specific combinations of attributessuch as identifying datasets with particular combinations of attributes, like 'adults diagnosed with COVID-19 in the last year, fully vaccinated, with no underlying health conditions' (as an example) — researchers must either contact the authors or request data access and verify themselves. This process is feasible for a small number of datasets and cooperative data controllers but it is usually time-consuming and uncertain. To streamline this, data discovery at the source allows users to query data non-disclosively, determining its relevance before requesting full access.

### Considerations

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