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To improve the standardisation and comparability of economic evaluations among different physical activity programmes and among countries, high methodological quality and explicit reporting of a minimal dataset are important, which is a big challenge for health economists.
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In machine learning, especially for classification, a high-quality training dataset is important for training the classification model.
Using an already published dataset was important to us since it allows us to provide a publicly available Kvik installation for others to use.
The inclusion of PFT parameters in a test dataset was important due to FEV1 being an fairly objective and easily to obtain measurement to assess clinical CF severity and/or improvement in clinical trials.
Benchmark datasets are important for the validation and optimization of the analysis routes.
These datasets are important for two reasons: first, they have been used to derive both clinically relevant signatures and to investigate underlying biological processes [8], [9], [18], [20] [22], [24].
The BBSRC Data Sharing Policy [ 57] opines that bioscience datasets are important to the wider scientific community, and that re-use of data can lead to new scientific understanding.
Assigning DOIs to datasets is important.
Objective: Finding relevant datasets is important for promoting data reuse in the biomedical domain, but it is challenging given the volume and complexity of biomedical data.
The availability of common datasets is important to the scientific community.
Repeating this research on a wide variety of datasets is important.
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CEO of Professional Science Editing for Scientists @ prosciediting.com