Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
5
datasets available to search
ShareScore release 0.9.0
Dataset results
5 results for “comet assay”
Figure 3 from: Gonçalves MW, Gambale PG, Godoy FR, Alves AA, Rezende PH, Cruz AD, Maciel NM, Nomura F, Bastos RP, de Marco-Jr P, Silva DM (2017) The agricultural impact of pesticides on Physalaemus cuvieri tadpoles (Amphibia: Anura) ascertained by comet assay. Zoologia 34: 1-8. https://doi.org/10.3897/zoologia.34.e19865
Figure 3 - Principal component analysis (PCA) of the descriptor variables and the covariate pond area. Relationship between the PCA 1 and PCA 2, points were grouped according to the soybean (white triangle), corn (white circles) and non-agricultural lands (black circles). (TL) Tail Length, (% DNA) percentage of DNA in Tail, (OTM) Olive tail moment, (RNV) Remnant native vegetation.
Figures 4-6 from: Gonçalves MW, Gambale PG, Godoy FR, Alves AA, Rezende PH, Cruz AD, Maciel NM, Nomura F, Bastos RP, de Marco-Jr P, Silva DM (2017) The agricultural impact of pesticides on Physalaemus cuvieri tadpoles (Amphibia: Anura) ascertained by comet assay. Zoologia 34: 1-8. https://doi.org/10.3897/zoologia.34.e19865
Figures 4-6 - Relationship between comet assay parameters and the pond area according to soybean (gray squares), corn (white circles) and non-agricultural lands (black circles); 4) TL with pond area; 5)% DNA; 6) OTM with pond area. (TL) Tail Length, (% DNA) percentage of DNA in Tail, (OTM) Olive tail moment.
Figure 2 from: Gonçalves MW, Gambale PG, Godoy FR, Alves AA, Rezende PH, Cruz AD, Maciel NM, Nomura F, Bastos RP, de Marco-Jr P, Silva DM (2017) The agricultural impact of pesticides on Physalaemus cuvieri tadpoles (Amphibia: Anura) ascertained by comet assay. Zoologia 34: 1-8. https://doi.org/10.3897/zoologia.34.e19865
Figure 2 - Differences between the average scores of Soybean, Corn and Non-agricultural lands, generated by the discriminant function scores.
Comet assay annotation data for comet segmentation and scoring (1037 comet assay images)
<p>Comet assay annotation images used in the following paper</p> <p>Hong, Y., Han, HJ., Lee, H. <em>et al.</em> Deep learning method for comet segmentation and comet assay image analysis. <em>Sci Rep</em> <strong>10, </strong>18915 (2020). https://doi.org/10.1038/s41598-020-75592-7</p> <p>DeepComet online service website: <strong>https://ad3.io/</strong></p> <p>sign up->dashboard->APPs->Deep Comets->new job</p> <p> </p> <p>Two biology researchers who were experienced in the comet assay for over half a year under the guidance of a toxicology expert in the assay annotated the images independently and compared each other’s work to ensure consistency. Here, the <strong>VGG Image Annotator sofware</strong> was used to annotate the images manually. We annotated the intact comet heads without a tail using a circular region tool that outputs the center and radius of the circle. Further, comets with a tail and DNA damage were annotated with a polyline tool, where the frst dot was marked at the center of a comet head, and the second dot was marked at the bottom of the comet head. Tese two points were used to identify the head of a comet. Subsequently, dots were marked along the boundary of the comet tail counterclockwise from the second dot. Te total number of dots were at least eight per each comet.</p> <p>Comets can be sorted into non-ghost cells with a distinct head and tail, and ghost cells with a small or no nucleoid head and a broad tail. The ghost cells are usually classifed as the highest degree of DNA damage by visual scoring. Until now, there is a controversy over the cause of ghost cells and how they should be analyzed appropriately. Therefore, our method is designed to classify these two kinds of cells, so one can use some custom analysis techniques for ghost cells.</p> <p>The comets in our datasets were all classifed as non-ghost or ghost cells. Furthermore, we assigned tags to the comets if they were overlapped or located at the boundary of an image (i.e., only a part of the comet is visible). After manual annotation with dots and tags (in the red box), each dotted contour was flled up to give mask images.</p>
Figure 1 from: Gonçalves MW, Gambale PG, Godoy FR, Alves AA, Rezende PH, Cruz AD, Maciel NM, Nomura F, Bastos RP, de Marco-Jr P, Silva DM (2017) The agricultural impact of pesticides on Physalaemus cuvieri tadpoles (Amphibia: Anura) ascertained by comet assay. Zoologia 34: 1-8. https://doi.org/10.3897/zoologia.34.e19865
Figure 1 - Spatial distribution of the ponds sampled at five municipalities in the state of Goiás.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.