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FIGURE. Map of the region and sampling area showing the relative position of Livingston Island to Antarctic Peninsula (A), and the main sampling locations (B): 1—Hannah Point, 2—Mongolian (Reserve) Port, 3—Caleta Argentina. Map outlines are based on OpenStreetMap© contributors (www.openstreetmap.org), edited and arranged using Adobe Illustrator© and Adobe Photoshop®. Scale bars = 35 km (A); 2 km (B). in The genus Craspedostauros E.J.Cox (Bacillariophyta) on the coasts of Livingston Island, Maritime Antarctica
FIGURE. Map of the region and sampling area showing the relative position of Livingston Island to Antarctic Peninsula (A), and the main sampling locations (B): 1—Hannah Point, 2—Mongolian (Reserve) Port, 3—Caleta Argentina. Map outlines are based on OpenStreetMap© contributors (www.openstreetmap.org), edited and arranged using Adobe Illustrator© and Adobe Photoshop®. Scale bars = 35 km (A); 2 km (B).
Visualizing synaptic plasticity in vivo by large-scale imaging of endogenous AMPA receptors
<p>Elucidating how synaptic molecules such as AMPA receptors mediate neuronal communication and tracking their dynamic expression during behavior is crucial to understand cognition and disease, but current technological barriers preclude large-scale exploration of molecular dynamics in vivo. We have developed a suite of innovative methodologies that break through these barriers: a new knockin mouse line with fluorescently tagged endogenous AMPA receptors, two-photon imaging of hundreds of thousands of labeled synapses in behaving mice, and computer vision-based automatic synapse detection. Using these tools, we can longitudinally track how the strength of populations of synapses changes during behavior. We used this approach to generate an unprecedentedly detailed spatiotemporal map of synapses undergoing changes in strength following sensory experience. More generally, these tools can be used as an optical probe capable of measuring functional synapse strength across entire brain areas during any behavioral paradigm, describing complex system-wide changes with molecular precision.</p>
Data from: Chromosome-scale reference genome and RAD-based genetic map of yellow starthistle (Centaurea solstitialis) reveal putative structural variation and QTLs associated with invader traits
<p>The data directory here includes all of the data and scripts necessary to recreate the results and plots for the manuscript titled "Chromosome-scale reference genome and RAD-based genetic map of yellow starthistle (Centaurea solstitialis) reveal putative structural variation and QTLs associated with invader traits". These data include a genetic map, QTL analysis, paleolog analysis, gene synteny analysis, and assembly validation for yellow starthistle (Centaurea solstitialis).</p>
MonkeyPox2022Tweets: A Large-Scale Twitter Dataset on the 2022 Monkeypox Outbreak, Findings from Analysis of Tweets, and Open Research Questions
<p><strong>Please cite the following paper when using this dataset:</strong></p> <p>N. Thakur, “MonkeyPox2022Tweets: A large-scale Twitter dataset on the 2022 Monkeypox outbreak, findings from analysis of Tweets, and open research questions,” Infect. Dis. Rep., vol. 14, no. 6, pp. 855–883, 2022, DOI: https://doi.org/10.3390/idr14060087</p> <p><strong>Abstract</strong></p> <p>The mining of Tweets to develop datasets on recent issues, global challenges, pandemics, virus outbreaks, emerging technologies, and trending matters has been of significant interest to the scientific community in the recent past, as such datasets serve as a rich data resource for the investigation of different research questions. Furthermore, the virus outbreaks of the past, such as COVID-19, Ebola, Zika virus, and flu, just to name a few, were associated with various works related to the analysis of the multimodal components of Tweets to infer the different characteristics of conversations on Twitter related to these respective outbreaks. The ongoing outbreak of the monkeypox virus, declared a Global Public Health Emergency (GPHE) by the World Health Organization (WHO), has resulted in a surge of conversations about this outbreak on Twitter, which is resulting in the generation of tremendous amounts of Big Data. There has been no prior work in this field thus far that has focused on mining such conversations to develop a Twitter dataset. Therefore, this work presents an open-access dataset of <strong>571,831 Tweets</strong> about monkeypox that have been posted on Twitter since the first detected case of this outbreak on May 7, 2022. The dataset complies with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter, as well as with the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management.</p> <p> <strong>Data Description</strong></p> <p>The dataset consists of a total of <strong>571,831 Tweet IDs</strong> of the same number of tweets about monkeypox that were posted on Twitter from 7th May 2022 to 11th November (the most recent date at the time of uploading the most recent version of the dataset). The Tweet IDs are presented in 12 different .txt files based on the timelines of the associated tweets. The following represents the details of these dataset files.</p> <ul> <li>Filename: TweetIDs_Part1.txt (No. of Tweet IDs: 13926, Date Range of the associated Tweet IDs: May 7, 2022, to May 21, 2022)</li> <li>Filename: TweetIDs_Part2.txt (No. of Tweet IDs: 17705, Date Range of the associated Tweet IDs: May 21, 2022, to May 27, 2022)</li> <li>Filename: TweetIDs_Part3.txt (No. of Tweet IDs: 17585, Date Range of the associated Tweet IDs: May 27, 2022, to June 5, 2022)</li> <li>Filename: TweetIDs_Part4.txt (No. of Tweet IDs: 19718, Date Range of the associated Tweet IDs: June 5, 2022, to June 11, 2022)</li> <li>Filename: TweetIDs_Part5.txt (No. of Tweet IDs: 46718, Date Range of the associated Tweet IDs: June 12, 2022, to June 30, 2022)</li> <li>Filename: TweetIDs_Part6.txt (No. of Tweet IDs: 138711, Date Range of the associated Tweet IDs: July 1, 2022, to July 23, 2022)</li> <li>Filename: TweetIDs_Part7.txt (No. of Tweet IDs: 105890, Date Range of the associated Tweet IDs: July 24, 2022, to July 31, 2022)</li> <li>Filename: TweetIDs_Part8.txt (No. of Tweet IDs: 93959, Date Range of the associated Tweet IDs: August 1, 2022, to August 9, 2022)</li> <li>Filename: TweetIDs_Part9.txt (No. of Tweet IDs: 50832, Date Range of the associated Tweet IDs: August 10, 2022, to August 24, 2022)</li> <li>Filename: TweetIDs_Part10.txt (No. of Tweet IDs: 39042, Date Range of the associated Tweet IDs: August 25, 2022, to September 19, 2022)</li> <li>Filename: TweetIDs_Part11.txt (No. of Tweet IDs: 12341, Date Range of the associated Tweet IDs: September 20, 2022, to October 9, 2022)</li> <li>Filename: TweetIDs_Part12.txt (No. of Tweet IDs: 15404, Date Range of the associated Tweet IDs: October 10, 2022, to November 11, 2022) </li> </ul> <p>Please note: The dataset contains only Tweet IDs in compliance with the terms and conditions mentioned in the privacy policy, developer agreement, and guidelines for content redistribution of Twitter. The Tweet IDs need to be hydrated to be used. For hydrating this dataset, the <a href="https://github.com/DocNow/hydrator/releases">Hydrator application</a> may be used (a step-by-step process on how to use Hydrator to hydrate this dataset is explained in the above-mentioned paper). </p>
Large-scale Instance-diverse Synthetic COVID-19 CT Dataset
<p><strong>Dataset Description</strong></p> <p>This dataset consists of 367000 synthetic COVID-19 CT images generated from a new GAN algorithm known as the stacked residual dropout GAN (sRD-GAN) [1]. The 367 input images are acquired from the iCTCF dataset [2] and are stored in 512 × 512 × 3 in .jpg format. The input images [2] and their corresponding synthetic images are included in this dataset. </p> <p> </p> <p><strong>Stacked Residual Dropout GAN (sRD-GAN)</strong></p> <p>The sRD-GAN utilizes a regularization-based strategy in an Image-to-Image (I2I) translation setting to facilitate instance-level diversity. In this study, we show that the stacked dropout regularization in the generator model can induce significant latent-space stochasticity which generates perceptually significant structural dissimilarity in the output space. </p> <p>Paper: <a href="https://www.mdpi.com/2306-5354/9/11/698">Diverse COVID-19 CT Image-to-Image Translation with Stacked Residual Dropout</a></p> <p>DOI: <a href="https://doi.org/10.3390/bioengineering9110698">10.3390/bioengineering9110698</a></p> <p> </p> <p><strong>Advantages</strong></p> <p>1) High-resolution, diverse patterns of synthetic ground-glass-opacities presented in chest CT images with different anatomy. </p> <p>2) Generalize across GAN-based models since the stacked residual dropout mechanism is not task- or dataset-specific. </p> <p>3) Does not require any auxiliary condition to generate diverse outputs. </p> <p>4) Does not require any non-trivial modification on the model's architectures. </p> <p> </p> <p><strong>Disadvantages</strong></p> <p>1) Diversity is not presented in large perceptual differences, and it focuses only on fine-grained details. </p> <p>2) Since sRD-GAN is trained in an unsupervised image-to-image setting, and the synthesis process does not require any auxiliary condition, thus, the magnitude of the style attributes (GGO features) cannot be manipulated. </p> <p> </p> <p><strong>Acknowledgments: </strong></p> <p>If you use this dataset in your research, please credit the author: </p> <p>[1] Lee, K.W.; Chin, R.K.Y. Diverse COVID-19 CT Image-to-Image Translation with Stacked Residual Dropout. <em>Bioengineering</em> <strong>2022</strong>, <em>9</em>, 698. https://doi.org/10.3390/bioengineering9110698</p> <p> </p> <p><strong>References: </strong></p> <p>[2] Ning, W.; Lei, S.; Yang, J.; Cao, Y.; Jiang, P.; Yang, Q.; Zhang, J.; Wang, X.; Chen, F.; Geng, Z.; et al. Open resource of clinical data from patients with pneumonia for the prediction of COVID-19 outcomes via Deep Learning. Nat. Biomed. Eng. <strong>2020</strong>, 4, 1197–1207.</p>
Scaling of activity space in marine organisms across latitudinal gradients
<p>Unifying models have shown that the amount of space used by animals (e.g., activity space, home range) scales allometrically with body mass for terrestrial taxa; however, such relationships are far less clear for marine species. We compiled movement data from 1,596 individuals across 79 taxa collected using a continental passive acoustic telemetry network of acoustic receivers to assess allometric scaling of activity space. We found that ectothermic marine taxa do exhibit allometric scaling for activity space, with an overall scaling exponent of 0.64. However, body mass alone explained only 35% of the variation with the remaining variation best explained by trophic position for teleosts and latitude for sharks, rays, and marine reptiles. Taxon-specific allometric relationships highlighted weaker scaling exponents among teleost fish species (0.07) than sharks (0.96), rays (0.55), and marine reptiles (0.57). The allometric scaling relationship and scaling exponents for the marine taxonomic groups examined were lower than those reported from studies that had collated both marine and terrestrial species data derived using various tracking methods. We propose that these disparities arise because previous work integrated summarised data across many studies that used differing methods for collecting and quantifying activity space, introducing considerable uncertainty into slope estimates. Our findings highlight the benefit of using large-scale, coordinated animal biotelemetry networks to address cross-taxa evolutionary and ecological questions.</p>
Pore-scale modeling and investigation on thermal-hydro-mechanical-chemical coupled rock dissolution and fracturing process
<p>Attached files include the executable file of the pore-scale multi-field coupled LBM-DEM program written by C language, post-processing programs to record the reactive surface area and reactive temperature written by MATLAB, and the simulation results of rock acid fracturing with 20MPa at the injection hole.</p>
Data and code from: The functional trait distinctiveness of plant species is scale dependent
<p>Beyond the local abundance of species, their functional trait distinctiveness is now recognized as a key driver of community dynamics and ecosystem functioning. Yet, since the functional distinctiveness of a species is always relative to a given species pool, a species distinct at the regional scale might not necessarily be distinct at the local or community scale, and reciprocally. To assess the importance of scale (i.e the definition of a species pool) when quantifying the functional distinctiveness of species, and how it might distort the ecological conclusions derived from it, we quantified trait distinctiveness of 1,350 plant species at regional, local, and community scales over ca. 88 000 grassland plots in France. We measured differences in functional distinctiveness of species between regional, local and community scales and tested the influence of environmental predictors (climate and nitrogen input) and contexts (environmental distinctiveness, frequency, and heterogeneity) on these variations. In line with theoretical expectations, we found large variations of functional distinctiveness (in particular between regional and community scales) for many species, with a general tendency of lower distinctiveness at smaller scales. We also showed that nitrogen input – a key aspect of high land use intensity – and environmental frequency partly explained the differences between local and regional scales only. These results suggest the role played by environmental filtering on species' distinctiveness at the local scale, but the determinant of distinctiveness variations at the community scale still needs to be elucidated. Our study provides robust empirical evidence that measures of ecological originality are strongly scale-dependent. We urge ecologists to carefully consider the scale at which they measure distinctiveness, as ignoring scale dependencies could lead to biased (or even entirely wrong) conclusions when not considered at the scale of interest for the respective research question.</p>
Figure 7 in Integrative taxonomic analysis of new collections from the central Angolan highlands resolves the taxonomy of African pipistrelloid bats on a continental scale
Figure 7. Lateral photograph of the alcohol-preserved head and anterior portion of the forearm and thumb of the holotype of N. hlandzeni, DM 8423 (left) and close-up photographs of the thumb and proximal forearm of the holotype, DM 8423 (centre) and paratype, DM 8422 (right) showing a distinct white marking at the base to the thumb. The thumb (encircled) is slightly longer than that of a typical pipistrelloid bat.
Figure 6 in Integrative taxonomic analysis of new collections from the central Angolan highlands resolves the taxonomy of African pipistrelloid bats on a continental scale
Figure 6. Dorsal, ventral, lateral cranial and lateral mandibular views of DM 8423 (holotype) and DM 8422 (paratype) of N. hlandzeni sp. nov. Scale bar represents 1 mm.
Figure 5 in Integrative taxonomic analysis of new collections from the central Angolan highlands resolves the taxonomy of African pipistrelloid bats on a continental scale
Figure 5. Photographs and drawings of bacula of N. hlandzeni sp. nov. from Eswatini (DM 8423, 8422), KwaZulu-Natal Province, South Africa (DM 2269, 8787, 5353, 5358), together with N. anchietae from Botswana (TM 48489), Angola (FWC 7521), and Zambia (NHM 70.2632; from Hill & Harrison, 1987). In the top row, D=dorsal, L=lateral and V=ventral view. Images in the lower row are of the ventral view, except for the far right image which is of the lateral view. Abbreviations of museums as follows: DM = Durban Natural Science Museum; TM = Ditsong National Museum of Natural History; NHM = The Natural History Museum, London. FWC indicates the field number of Fenton (Woody) Cotterill. The vertical scale bar on the bottom right indicates 1 mm.
Figure 4 in Integrative taxonomic analysis of new collections from the central Angolan highlands resolves the taxonomy of African pipistrelloid bats on a continental scale
Figure 4. PCA of 12 log-transformed cranial and dental length variables (a) and plots of forearm length and mass (b) and forearm and condylobasal skull length (c) in Angolan small vespertilionid bats with anterior premolar absent (open squares: N. zuluensis; dots: L. capensis). Species identification based on DNA barcoding of the cytb, CO1 and 12S genes.
Figure 3 in Integrative taxonomic analysis of new collections from the central Angolan highlands resolves the taxonomy of African pipistrelloid bats on a continental scale
Figure 3. PCA of components 1 and 2 (a) and components 1 and 3 (b) from 12 log-transformed cranial and dental variables in DNA barcoded Angolan small vespertilionid bats with anterior upper premolar present, in comparison with positively identified samples from South Africa and Eswatini. Species identification based on DNA barcoding of the cytb, CO1 and 12S genes for P. rusticus (open squares) and N. anchietae (dots) from Angola and on either sequences or baculum photographs for N. hlandzeni (open circles) from South Africa and Eswatini and P. hesperidus (crosses) from Eswatini and South Africa.
Figure 1. Maps showing A, 19 in Integrative taxonomic analysis of new collections from the central Angolan highlands resolves the taxonomy of African pipistrelloid bats on a continental scale
Figure 1. Maps showing A, 19 localities in southern Africa of all pipistrelloid bat specimens examined by this study (numbers correspond with locality numbers given in Supporting Information, Table S1; duplicate numbers represent closely spaced GPS points classified as the same locality) and B, seven localities of six species of pipistrelloid bats collected in central Angola by the National Geographic Okavango Wilderness project (in the area demarcated by the dashed lines) between 2016 and 2019. Species identifications based on CO1, 12S RNA and cytb genes. Shaded regions represent elevations of 1200 m a.s.l. and greater. The star represents the holotype locality of N. anchietae in the western Angolan highlands.
Climate and geology overwrite land use effects on soil organic nitrogen cycling on a continental scale
<p><strong>Abstract.</strong> Soil fertility and plant productivity are globally constrained by N availability. Proteins are the largest N reservoir in soils and the cleavage of proteins into small peptides and amino acids has been shown to be the rate limiting step in the terrestrial N cycle. However, we are still lacking a profound understanding of the environmental controls of this process. Here we show that integrated effects of climate and soil geochemistry drive protein cleavage across large scales. We measured gross protein depolymerization rates in mineral and organic soils sampled across a 4000-km-long European transect covering a wide range of climates, geologies and land uses. Based on structural equation models we identified that soil organic N cycling was strongly controlled by substrate availability, e.g. by soil protein content. Soil geochemistry was a secondary predictor, by controlling protein stabilization mechanisms and protein availability. Precipitation was identified as the main climatic control on protein depolymerization, by affecting soil weathering and soil organic matter accumulation. In contrast, land use was a poor predictor of protein depolymerization. Our results highlight the need to consider geology and precipitation effects on soil geochemistry when estimating and predicting soil N cycling at large scales.</p>
Large-scale annotation dataset for cell/tissue segmentation in H&E-stained images : anti-CD3/CD20 (lymphocytes)
<p><strong>LICENSE</strong></p> <p>This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (<strong>CC-BY-NC-SA 4.0</strong>)</p> <p>For non-commercial use, please use the dataset under CC-BY-NC-SA.<br> If you would like to use the dataset for commercial purposes, please contact us (ishum-prm@m.u-tokyo.ac.jp).</p> <p>A Tar.gz file contains the following files:</p> <p>- HE image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_HE.png</p> <p>- Mask image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_mask.png</p> <p>Each image file is 984x984 px.</p> <p>posX and posY are the leftmost position in WSI coordinate.</p> <p>Mask files store binary segmentation mask (background : 0, target : 1)</p> <p> </p> <p>A csv file contains the following information:</p> <p>antigen : Antibodies for this antigen were used to create the segmentation mask.</p> <p>filename: filename of image or mask file.</p> <p>train_val_test : train, validation, or test sample in the paper.</p> <p> </p> <p><strong>Citation</strong></p> <p>If you use this dataset for your research, please cite our paper.</p> <p>Daisuke Komura, Takumi Onoyama, Koki Shinbo, Hiroto Odaka, Minako Hayakawa, Mieko Ochi, Ranny Rahaningrum Herdiantoputri, Haruya Endo, Hiroto Katoh, Tohru Ikeda, Tetsuo Ushiku, Shumpei Ishikawa,<br> Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists, Patterns, Volume 4, Issue 2, 2023, 100688, https://doi.org/10.1016/j.patter.2023.100688.</p>
Large-scale annotation dataset for cell/tissue segmentation in H&E-stained images : anti-ERG (endothelial cells)
<p><strong>LICENSE</strong></p> <p>This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (<strong>CC-BY-NC-SA 4.0</strong>)</p> <p>For non-commercial use, please use the dataset under CC-BY-NC-SA.<br> If you would like to use the dataset for commercial purposes, please contact us (ishum-prm@m.u-tokyo.ac.jp).</p> <p>A Tar.gz file contains the following files:</p> <p>- HE image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_HE.png</p> <p>- Mask image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_mask.png</p> <p>Each image file is 984x984 px.</p> <p>posX and posY are the leftmost position in WSI coordinate.</p> <p>Mask files store binary segmentation mask (background : 0, target : 1)</p> <p> </p> <p>A csv file contains the following information:</p> <p>antigen : Antibodies for this antigen were used to create the segmentation mask.</p> <p>filename: filename of image or mask file.</p> <p>train_val_test : train, validation, or test sample in the paper.</p> <p> </p> <p><strong>Citation</strong></p> <p>If you use this dataset for your research, please cite our paper.</p> <p>Daisuke Komura, Takumi Onoyama, Koki Shinbo, Hiroto Odaka, Minako Hayakawa, Mieko Ochi, Ranny Rahaningrum Herdiantoputri, Haruya Endo, Hiroto Katoh, Tohru Ikeda, Tetsuo Ushiku, Shumpei Ishikawa,<br> Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists, Patterns, Volume 4, Issue 2, 2023, 100688, https://doi.org/10.1016/j.patter.2023.100688.</p>
Large-scale annotation dataset for cell/tissue segmentation in H&E-stained images : anti-MNDA (myeloid cells)
<p><strong>LICENSE</strong></p> <p>This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (<strong>CC-BY-NC-SA 4.0</strong>)</p> <p>For non-commercial use, please use the dataset under CC-BY-NC-SA.<br> If you would like to use the dataset for commercial purposes, please contact us (ishum-prm@m.u-tokyo.ac.jp).</p> <p>A Tar.gz file contains the following files:</p> <p>- HE image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_HE.png</p> <p>- Mask image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_mask.png</p> <p>Each image file is 984x984 px.</p> <p>posX and posY are the leftmost position in WSI coordinate.</p> <p>Mask files store binary segmentation mask (background : 0, target : 1)</p> <p> </p> <p>A csv file contains the following information:</p> <p>antigen : Antibodies for this antigen were used to create the segmentation mask.</p> <p>filename: filename of image or mask file.</p> <p>train_val_test : train, validation, or test sample in the paper.</p> <p> </p> <p><strong>Citation</strong></p> <p>If you use this dataset for your research, please cite our paper.</p> <p>Daisuke Komura, Takumi Onoyama, Koki Shinbo, Hiroto Odaka, Minako Hayakawa, Mieko Ochi, Ranny Rahaningrum Herdiantoputri, Haruya Endo, Hiroto Katoh, Tohru Ikeda, Tetsuo Ushiku, Shumpei Ishikawa,<br> Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists, Patterns, Volume 4, Issue 2, 2023, 100688, https://doi.org/10.1016/j.patter.2023.100688.</p>
Large-scale annotation dataset for cell/tissue segmentation in H&E-stained images : anti-CD235a (red blood cells)
<p><strong>LICENSE</strong></p> <p>This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (<strong>CC-BY-NC-SA 4.0</strong>)</p> <p>For non-commercial use, please use the dataset under CC-BY-NC-SA.<br> If you would like to use the dataset for commercial purposes, please contact us (ishum-prm@m.u-tokyo.ac.jp).</p> <p>A Tar.gz file contains the following files:</p> <p>- HE image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_HE.png</p> <p>- Mask image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_mask.png</p> <p>Each image file is 984x984 px.</p> <p>posX and posY are the leftmost position in WSI coordinate.</p> <p>Mask files store binary segmentation mask (background : 0, target : 1)</p> <p> </p> <p>A csv file contains the following information:</p> <p>antigen : Antibodies for this antigen were used to create the segmentation mask.</p> <p>filename: filename of image or mask file.</p> <p>train_val_test : train, validation, or test sample in the paper.</p> <p> </p> <p><strong>Citation</strong></p> <p>If you use this dataset for your research, please cite our paper.</p> <p>Daisuke Komura, Takumi Onoyama, Koki Shinbo, Hiroto Odaka, Minako Hayakawa, Mieko Ochi, Ranny Rahaningrum Herdiantoputri, Haruya Endo, Hiroto Katoh, Tohru Ikeda, Tetsuo Ushiku, Shumpei Ishikawa,<br> Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists, Patterns, Volume 4, Issue 2, 2023, 100688, https://doi.org/10.1016/j.patter.2023.100688.</p>
Large-scale annotation dataset for cell/tissue segmentation in H&E-stained images : anti-CD45RB (leukocytes)
<p><strong>LICENSE</strong></p> <p>This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (<strong>CC-BY-NC-SA 4.0</strong>)</p> <p>For non-commercial use, please use the dataset under CC-BY-NC-SA.<br> If you would like to use the dataset for commercial purposes, please contact us (ishum-prm@m.u-tokyo.ac.jp).</p> <p>A Tar.gz file contains the following files:</p> <p>- HE image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_HE.png</p> <p>- Mask image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_mask.png</p> <p>Each image file is 984x984 px.</p> <p>posX and posY are the leftmost position in WSI coordinate.</p> <p>Mask files store binary segmentation mask (background : 0, target : 1)</p> <p> </p> <p>A csv file contains the following information:</p> <p>antigen : Antibodies for this antigen were used to create the segmentation mask.</p> <p>filename: filename of image or mask file.</p> <p>train_val_test : train, validation, or test sample in the paper.</p> <p> </p> <p><strong>Citation</strong></p> <p>If you use this dataset for your research, please cite our paper.</p> <p>Daisuke Komura, Takumi Onoyama, Koki Shinbo, Hiroto Odaka, Minako Hayakawa, Mieko Ochi, Ranny Rahaningrum Herdiantoputri, Haruya Endo, Hiroto Katoh, Tohru Ikeda, Tetsuo Ushiku, Shumpei Ishikawa,<br> Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists, Patterns, Volume 4, Issue 2, 2023, 100688, https://doi.org/10.1016/j.patter.2023.100688.</p>
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.