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ShareScore release 0.9.0
Dataset results
12 results for “Materials chemistry”
Vector-LabPics dataset for images of materials in vessels in the chemistry lab
<p><strong>LabPics 2: A newer and larger a version (But harder to use) can be found here: <a href="../record/4736111"> https://zenodo.org/record/4736111</a></strong></p> <p>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.</p> <p>For more details see:</p> <p><a href="https://pubs.acs.org/doi/10.1021/acscentsci.0c00460">https://pubs.acs.org/doi/10.1021/acscentsci.0c00460</a></p> <p> </p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>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 (@<a href="https://twitter.com/SuperScienceGrl?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor">SuperScienceGrl</a> Twitter), <a href="https://cen.acs.org/sections/chemistry-in-pictures.html">Chemical and Engineering Science chemistry in pictures</a>, YouTube channels dedicated to chemistry experiments: <a href="https://www.youtube.com/channel/UCIgKGGJkt1MrNmhq3vRibYA">NurdRage</a>, <a href="https://www.youtube.com/user/TheRedNile">NileRed</a>, <a href="https://www.youtube.com/user/DougsLab">DougsLab</a>, <a href="https://www.youtube.com/channel/UCsJHe4uMbquncMpe1PiLa2A/videos">ChemPlayer</a>, and <a href="https://www.youtube.com/user/koen2all">Koen2All</a>. Additional sources for images include Instagram channels <a href="https://www.instagram.com/chemistrylover_/">chemistrylover_</a>(Joana Kulizic),<a href="https://www.instagram.com/chemistry.shz/?hl=en">Chemistry.shz</a> (Dr.Shakerizadeh-shirazi), <a href="https://www.instagram.com/ministryofchemistry/?hl=en">MinistryOfChemistry</a>, <a href="https://www.instagram.com/chemistryandme/?hl=en">Chemistry And Me</a>,<a href="https://www.instagram.com/explore/tags/chemistrylifestyle/?hl=en"> ChemistryLifeStyle</a>, <a href="https://www.instagram.com/explore/tags/vacuumdistillation/?hl=en">vacuum_distillation</a>, and <a href="https://docs.google.com/document/d/16QkXuIesB80gDONX-YVNFP4C6Mmj-14ZDrTmLrnNfms/edit#organic_chemistry_lab">Organic_Chemistry_Lab</a>. 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.</p> <p>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.</p> <p>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 (@<a href="https://twitter.com/SuperScienceGrl?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor">SuperScienceGrl</a> Twitter), <a href="https://cen.acs.org/sections/chemistry-in-pictures.html">Chemical and Engineering Science chemistry in pictures</a>, YouTube channels dedicated to chemistry experiments: <a href="https://www.youtube.com/channel/UCIgKGGJkt1MrNmhq3vRibYA">NurdRage</a>, <a href="https://www.youtube.com/user/TheRedNile">NileRed</a>, <a href="https://www.youtube.com/user/DougsLab">DougsLab</a>, <a href="https://www.youtube.com/channel/UCsJHe4uMbquncMpe1PiLa2A/videos">ChemPlayer</a>, and <a href="https://www.youtube.com/user/koen2all">Koen2All</a>. Additional sources for images include Instagram channels <a href="https://www.instagram.com/chemistrylover_/">chemistrylover_</a>(Joana Kulizic),<a href="https://www.instagram.com/chemistry.shz/?hl=en">Chemistry.shz</a> (Dr.Shakerizadeh-shirazi), <a href="https://www.instagram.com/ministryofchemistry/?hl=en">MinistryOfChemistry</a>, <a href="https://www.instagram.com/chemistryandme/?hl=en">Chemistry And Me</a>,<a href="https://www.instagram.com/explore/tags/chemistrylifestyle/?hl=en"> ChemistryLifeStyle</a>, <a href="https://www.instagram.com/explore/tags/vacuumdistillation/?hl=en">vacuum_distillation</a>, and <a href="https://docs.google.com/document/d/16QkXuIesB80gDONX-YVNFP4C6Mmj-14ZDrTmLrnNfms/edit#organic_chemistry_lab">Organic_Chemistry_Lab</a>. 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 (<a href="http://cen.chempics.org/">cen.chempics.org</a>) used here with permission from C&EN and ACS. All rights reserved. Please contact cenchempics@acs.org to inquire about republishing.</p>
The contribution of Fe(III) reduction to soil carbon mineralization in montane meadows depends on soil chemistry, not parent material or microbial community
<p>The long-term stability of soil carbon (C) is strongly influenced by organo-mineral interactions. Iron (Fe)-oxides can both inhibit microbial decomposition by providing physicochemical protection for organic molecules and enhance rates of C mineralization by serving as a terminal electron acceptor, depending on redox conditions. Restoration of floodplain hydrology in montane meadows has been proposed as a method of sequestering C for climate change mitigation. However, dissimilatory microbial reduction of Fe(III) could lead to C losses under increased reducing conditions. In this study, we explored variations in Fe-C interactions over a range of redox conditions and in soils derived from two distinct parent materials to elucidate biochemical and microbial controls on soil C cycling in Sierra Nevada montane meadows. Differences in parent material were associated with different rates of Fe(III) reduction at increasing soil moisture levels, but not with differences in soil C mineralization. Known Fe(III)-reducing taxa were present in all samples but neither the relative abundance nor richness of Fe(III) reducers corresponded with measured rates of Fe(III) reduction. Under reducing conditions, our results suggest that Fe(III) reduction contributes to C mineralization only when Fe-bound C is present. However, Fe-bound C was not present in all of our soils and was below theoretical limits for C sorption onto Fe-oxides where it was found. Overall, our results suggest that meadow-specific soil chemistry drives Fe-C interactions and that the impact of Fe on C cycling in montane meadows may be smaller than in other ecosystems.</p>
The contribution of Fe(III) reduction to soil carbon mineralization in montane meadows depends on soil chemistry, not parent material or microbial community
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Videos of computer vision based recognition/segmentation/classification of materials inside vessels in chemistry lab and other setting
<p>These videos contain materials in vessels in various settings related to the chemistry lab, medical samples and handling liquids in everyday life settings. The region and type of each vessel and material phase found by the computer vision (neural net) are marked in purple, the class of each phase appears above each panel in green (each panel corresponds to a different class). The work is part of the computer vision for the chemistry lab project.</p> <p>For details on the project see:</p> <p><a href="https://chemrxiv.org/articles/Computer_Vision_for_Recognition_of_Materials_and_Vessels_in_Chemistry_Lab_Settings_and_the_Vector-LabPics_Dataset/11930004">https://chemrxiv.org/articles/Computer_Vision_for_Recognition_of_Materials_and_Vessels_in_Chemistry_Lab_Settings_and_the_Vector-LabPics_Dataset/11930004</a></p> <p>For Details on the videos See:</p> <p><a href="https://www.youtube.com/watch?v=K7I2QJcIyBQ&list=PLRiTwBVzSM3B6MirlFl6fW0YQR4TtQmtJ">https://www.youtube.com/watch?v=K7I2QJcIyBQ&list=PLRiTwBVzSM3B6MirlFl6fW0YQR4TtQmtJ</a></p> <p>Basically, the videos contain process such as pouring, mixing, foam formation precipitation, phase separation, melting freezing, dissolving, etc.., where the region of each vessel and material phase and their type (liquid, solid, powder, suspension, foam) is found by the neural net and marked purple.</p> <p>For code see:</p> <p><a href="https://github.com/aspuru-guzik-group/Computer-vision-for-the-chemistry-lab">https://github.com/aspuru-guzik-group/Computer-vision-for-the-chemistry-lab</a></p> <p> </p> <p> </p> <p> </p>
An investigation of data repositories in materials chemistry
<p>Detailed lists of articles used for testing accessibility and raw data used for analysis of data repositories in materials chemistry literature.</p>
Supplementary material 3 from: Ossowska E, Guzow-Krzemińska B, Kolanowska M, Szczepańska K, Kukwa M (2019) Morphology and secondary chemistry in species recognition of Parmelia omphalodes group – evidence from molecular data with notes on the ecological niche modelling and genetic variability of photobionts. MycoKeys 61: 39-74. https://doi.org/10.3897/mycokeys.61.38175
: Data type: multimedia
Supplementary material 1 from: Ossowska E, Guzow-Krzemińska B, Kolanowska M, Szczepańska K, Kukwa M (2019) Morphology and secondary chemistry in species recognition of Parmelia omphalodes group – evidence from molecular data with notes on the ecological niche modelling and genetic variability of photobionts. MycoKeys 61: 39-74. https://doi.org/10.3897/mycokeys.61.38175
: Data type: occurrence
Supplementary material 2 from: Ossowska E, Guzow-Krzemińska B, Kolanowska M, Szczepańska K, Kukwa M (2019) Morphology and secondary chemistry in species recognition of Parmelia omphalodes group – evidence from molecular data with notes on the ecological niche modelling and genetic variability of photobionts. MycoKeys 61: 39-74. https://doi.org/10.3897/mycokeys.61.38175
: Data type: multimedia
Supplementary material 4 from: Ossowska E, Guzow-Krzemińska B, Kolanowska M, Szczepańska K, Kukwa M (2019) Morphology and secondary chemistry in species recognition of Parmelia omphalodes group – evidence from molecular data with notes on the ecological niche modelling and genetic variability of photobionts. MycoKeys 61: 39-74. https://doi.org/10.3897/mycokeys.61.38175
: Data type: multimedia
Supplementary Material: Intellectual hubs and authorities, and Scientific Specialties of Green Chemistry
<p>Supplementary material to co-citation analysis of Green Chemistry output (1990-2017), comprising: best-fitting criteria to generate the co-citation network; composition of research front and intellectual base (in general and by period); analysis of contributions of authors to the network. </p>
Supplementary Material for Publication: Comparing Image-to-Chemistry Tools
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Data for "Resolving Structures of Paramagnetic Systems in Chemistry and Materials Science by Solid-State NMR: the Revolving Power of Ultra-Fast MAS"
<p>Raw NMR data</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.