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This dataset will serve as an invaluable resource for future RNA-based analyses.

This dataset will be published as a standalone result, which can be used as a set for future applications of deep learning enhanced algorithms in the nanoscience domain.

This dataset will not be analyzed in detail here.

Before the workshop, this dataset will be expanded with translations and summaries.

It is unclear if a model trained using this dataset will yield similar results when evaluated on real world data.

This dataset will be available upon publication by contacting the authors, restricted by Twitter Terms of Services.

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This new dataset will be a valuable starting point for understanding the hidden organ of plants.

This unique dataset will support efforts to reduce PM10 and PM2.5 concentrations in the area to below the National Ambient Air Quality Standards (NAAQS) for these pollutants.

We anticipate this curated dataset will facilitate machine learning research in heliophysics and the physical sciences generally, increasing the scientific return of the SDO mission.

This rich dataset will also contribute to the development of a numerical turbulence model, to investigate turbulence effects on bubble size distribution and predict the rate of air entrainment and the oxygen transfer occurring in the wake at different hydrodynamic conditions.

This updated dataset will be useful for future PCA comparisons like those described here (Figure 6a and 6b).

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