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ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2022
License: CC BY
Data sources: ZENODO
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cropped diBas dataset for machine learning

Authors: Qile Yang;

cropped diBas dataset for machine learning

Abstract

This zip file contains a collection of 244 x 244 images of various gram-stained bacteria strains, cropped from the diBas dataset (Bartosz Zieli ́nski et al. 2017). Each folder is named by the species of the bacteria images it contains. Potential uses for the dataset include machine learning. An example neural network for bacteria classification using this dataset is deployed at https://huggingface.co/spaces/qile0317/Bacteria-Classification Note that the data is not completely optimized, as it contains trace amounts of almost blank images.

To further improve viability in machine learning applications, a future version will be implemented to incorporate varying degrees of tint (warmth), size, and warping of the images. Of course, image augmentation of the original data via basic image manipulation code is also possible.

{"references": ["Bartosz Zieli \u0301nski et al. \"Deep learning approach to bacterial colony classification\". In: PloS One 12.9 (2017), e0184554"]}

Keywords

machine learning, gram stain, bacteria, computer vision

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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).
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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.
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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).
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.
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