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Vector-LabPics dataset for images of materials in vessels in the chemistry lab

Authors: Sagi Eppel; Haoping Xu; Mor Bismuth; Alan Aspuru-Guzik;

Vector-LabPics dataset for images of materials in vessels in the chemistry lab

Abstract

LabPics 2: A newer and larger a version (But harder to use) can be found here: https://zenodo.org/record/4736111 The Vector-LabPics V1 dataset contains 2187 images of chemical experiments with materials within mostly transparent vessels in various laboratory settings and in everyday conditions such as beverage handling. Each image in the dataset has an annotation of the region of each material phase and its type. In addition, the region of each vessel and its labels, parts, and corks are also marked. For more details see: https://pubs.acs.org/doi/10.1021/acscentsci.0c00460 Acknowledgments We like to thank the sources of the images used for creating this dataset without them this work was not possible. These sources include Nessa Carson (@SuperScienceGrl Twitter), Chemical and Engineering Science chemistry in pictures, YouTube channels dedicated to chemistry experiments: NurdRage, NileRed, DougsLab, ChemPlayer, and Koen2All. Additional sources for images include Instagram channels chemistrylover_(Joana Kulizic),Chemistry.shz (Dr.Shakerizadeh-shirazi), MinistryOfChemistry, Chemistry And Me, ChemistryLifeStyle, vacuum_distillation, and Organic_Chemistry_Lab. We are grateful to the Defense Advanced Research Projects Agency (DARPA) for funding this project under award number W911NF-18-2-0036 from the Molecular Informatics program. A.A.-G. Thanks Anders G. Frøseth for his generous support. Images of the dataset were taken from images and videos shared on Youtube and Instagram, Twitter and Tumblr channels and other contributors; we do not have copyright for the images. Any commercial or none academic use of the images depends on acquiring permission from the owner of the images. Note that the name of each image contains the image source. For any non-academic use of the images, please contact their sources for permission. We like to thank the following channels for sharing the images used in this dataset. We like to thank the sources of the images used for creating this dataset without them this work was not possible. These sources include Nessa Carson (@SuperScienceGrl Twitter), Chemical and Engineering Science chemistry in pictures, YouTube channels dedicated to chemistry experiments: NurdRage, NileRed, DougsLab, ChemPlayer, and Koen2All. Additional sources for images include Instagram channels chemistrylover_(Joana Kulizic),Chemistry.shz (Dr.Shakerizadeh-shirazi), MinistryOfChemistry, Chemistry And Me, ChemistryLifeStyle, vacuum_distillation, and Organic_Chemistry_Lab. We are grateful to the Defense Advanced Research Projects Agency (DARPA) for funding this project under award number W911NF-18-2-0036 from the Molecular Informatics program. A.A.-G. Thanks Anders G. Frøseth for his generous support. Images from C&EN's Chemistry in Pictures (cen.chempics.org) used here with permission from C&EN and ACS. All rights reserved. Please contact cenchempics@acs.org to inquire about republishing.

Keywords

Computer vision, Chemistry Laboratory, Deep Learning, Machine Learning

EOSC Subjects

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selected citations
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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).
BIP!Citations provided by BIP!
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.
BIP!Popularity provided by BIP!
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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