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1,037 results for “large-scale”
Data from: Connectivity explains local ant community structure in a Neotropical forest canopy: a large-scale experimental approach
Understanding how habitat structure and resource availability affect local species distributions is a key goal of community ecology. Where habitats occur as a mosaic, variation in connectivity among patches influences both local species richness and composition, and connectivity is a key conservation concern in fragmented landscapes. Similarly, availability of limiting resources frequently determines species co-existence or exclusion. For primarily cursorial arthropods like ants, gaps between neighboring trees are a significant barrier to movement through the forest canopy. Competition for limited resources such as nest sites also promotes antagonistic interactions. Lianas (woody vines) connect normally isolated neighboring tree crowns and often have hollow stems inhabited by ants. We used two large-scale liana removal experiments to determine how connectivity and nest site availability provided by lianas affect arboreal ant species richness, species composition, and β-diversity in a lowland tropical forest in Panama. Removing lianas from a tree crown reduced ant species richness up to 35%, and disproportionately affected species that require large foraging areas. Adding artificial connectivity to trees mitigated the effects of liana removal. Ant colonization of artificial nests was higher (73% occupied) in trees without lianas vs. trees with lianas (28% occupied). However, artificial nests typically were colonized by existing polydomous, resident ant species. As a result, nest addition did not affect ant community structure. Collectively, these results indicate that lianas are important to the maintenance of arboreal ant diversity specifically by providing connectivity among neighboring tree crowns. Anticipated increases in liana abundance in this forest could increase the local (tree-level) species richness of arboreal ants, with a compositional bias toward elevating the density of broad-ranging specialist predators.
Data from: A degradation debt? large-scale shifts in community composition and loss of biomass in a tropical forest fragment after 40 years of isolation
Habitat loss and fragmentation are among the biggest threats to tropical biodiversity and associated ecosystem services. We examined forest dynamics in a mid-elevation 365-ha fragment in southern Costa Rica. The fragment was isolated in the mid-1970s and belongs to the Las Cruces Biological Station. A 2.25-ha permanent plot was established in the center of the old-growth forest (>400 m to nearest edge boundary) and all plants >5 cm DBH were censused, mapped, and identified to species in two surveys taken ~5–6 years apart (>3,000 stems/survey). Although the reserve maintains high species richness (>200 spp.), with many rare species represented by only one individual, we document a strong shift in composition with a two-fold increase in the number of soft-wooded pioneer individuals. The dominant late-successional understory tree species, Chrysochlamys glauca (Clusiaceae), and most species in the Lauraceae, declined dramatically. Turnover was high: 22.9% of stems in the first survey were lost, and 27.8% of stems in the second survey represented new recruits. Mean tree diameter decreased significantly and there was a 10% decrease in overall biomass. Such alteration has been documented previously but only in smaller fragments or within ~100 m of an edge boundary. Further penetration into this fragment was perhaps driven by a progressive invasion of disturbance-adapted species into the fragment's core over time; the loss of once-dominant late successional species could be a contributing factor. The pattern found is of particular concern given that such fragments represent a substantial portion of today's remaining tropical habitat; further studies in similar-sized fragments that have been isolated for similar prolonged periods are called for.
Data from: Forest plant community as a driver of soil biodiversity: experimental evidence from collembolan assemblages through large-scale and long-term removal of oak canopy trees Quercus petraea
Plant–soil interactions are increasingly recognized to play a major role in terrestrial ecosystems functioning. However, few studies to date have focused on slow dynamic ecosystems such as forests. As they are vertically stratified by multiple vegetation strata, canopy tree removal by thinning operations could alter forest plant community through tree canopy opening. Very little is known about cascading effects on soil biodiversity. We conducted a large-scale, multi-site assessment of collembolan assemblage response to long-term canopy tree removal in sessile oak Quercus petraea temperate forests. A total of 33 experimental plots were studied covering a large gradient of canopy tree basal area, stand age and local abiotic contexts. Collembolan abundance strongly declined with canopy tree removal in early forest successional stage and this was mediated by negative effect of understory plant community composition changes, i.e. shift from moss and forb to tree seedling, fern, shrub and grass species. Negative effect of this composition shift on collembolan species richness was largely offset by positive effect of the increase in understory plant species richness. This gives support to both the plant mass-ratio and functional diversity hypotheses. Collembolan functional groups had contrasting response patterns, which were mediated by different ecological factors. Epedaphic (r-strategist) abundance and species richness increased with canopy tree removal in relation with the increase in understory plant species richness. In contrast, euedaphic (K-strategist) abundance and species richness declined with canopy tree removal in early forest successional stage in relation with changes in understory plant community composition and species richness, as well as microclimatic conditions. Overall, our study provides experimental evidence that forest plant community can be a strong driver of collembolan assemblages. It also emphasizes the role of trees as foundation species of forest ecosystems that can shape soil biodiversity through their regulation of understory plant community and ecosystem abiotic conditions.
Data from: Short-term microbial effects of a large-scale mine-tailing storage facility collapse on the local natural environment
We investigated the impacts of the Mount Polley tailings impoundment failure on chemical, physical, and microbial properties of substrates within the affected watershed, comprised of 70 hectares of riparian wetlands and 40 km of stream and lake shore. We established a biomonitoring network in October of 2014, two months following the disturbance, and evaluated riparian and wetland substrates for microbial community composition and function via 16S and full metagenome sequencing. A total of 234 samples were collected from substrates at 3 depths and 1,650,752 sequences were recorded in a geodatabase framework. These data revealed a wealth of information regarding watershed-scale distribution of microbial community members, as well as community composition, structure, and response to disturbance. Substrates associated with the impact zone were distinct chemically as indicated by elevated pH, nitrate, and sulphate. The microbial community exhibited elevated metabolic capacity for selenate and sulfate reduction and an abundance of chemolithoautotrophs in the Thiobacillus thiophilus/T. denitrificans/T. thioparus clade that may contribute to nitrate attenuation within the affected watershed. The most impacted area (a 6km stream connecting two lakes) exhibited 30% lower microbial diversity relative to the remaining sites. The tailings impoundment failure at Mount Polley Mine has provided a unique opportunity to evaluate functional and compositional diversity soon after a major catastrophic disturbance to assess metabolic potential for ecosystem recovery.
Data from: Studying long-term, large-scale grassland restoration outcomes to improve seeding methods and reveal knowledge gaps
Studies are increasingly investigating effects of large-scale management activities on grassland restoration outcomes. These studies are providing useful comparisons among currently used management strategies, but not the novel strategies needed to rapidly improve restoration efforts. Here we illustrate how managing restoration projects adaptively can allow promising management innovations to be identified and tested. We studied 327 Great Plains fields seeded after coal mining. We modelled plant responses to management strategies to identify the most effective previously used strategies for constraining weeds and establishing desired plants. Then, we used the model to predict responses to new strategies our analysis identified as potentially more effective. Where established, the weed crested wheatgrass (Agropyron cristatum L.) increased through time, indicating a need to manage establishment of this grass. Seeding particular grasses reduced annual weed cover, and because these grasses appeared to become similarly abundant whether sown at low or high rates, low rates could likely be safely used to reduce seeding costs. More importantly, lower than average grass seed rates increased cover of shrubs, the plants most difficult to restore to many grassland ecosystems. After identifying grass seed rates as a driver, we formulated model predictions for rates below the range managers typically use. These predictions require testing but indicated atypically low grass seed rates would further increase shrubs without hindering long-term grass stand development. Synthesis and applications. Designing management around empirically based predictions is a logical next step towards improving ecological restoration efforts. Our predictions are that reducing grass seed rates to atypically low levels will boost shrubs without compromising grasses. Because these predictions derive from the fitted model, they represent quantitative hypotheses based on current understanding of the system. Generating data needed to test and update these hypotheses will require monitoring responses to shifts in management, specifically shifts to lower grass seed rates. A paucity of data for confronting hypotheses has been a major sticking point hindering adaptive management of most natural resources, but this need not be the case with degraded grasslands, because ongoing restoration efforts around the globe are providing continuous opportunities to monitor and manage processes regulating grassland restoration outcomes.
Data from: Evidence of large-scale range shift in the distribution of a Palaearctic migrant in Africa
Aim: Long-distance Palaearctic migrant birds are declining at a faster rate than short-distance migrant or resident species. This is often attributed to changes on their non-breeding grounds and along their migratory routes. The European Honey-buzzard (Pernis apivorus) is a scarce migrant in southern Africa that is declining globally. This study assessed the distribution and abundance of honey-buzzards in southern Africa over the past four decades and compared it to trends in the East African population to examine possible drivers of population expansion in southern Africa. Location Southern and East Africa Methods European Honey-buzzard reporting data were collected from a variety of sources including citizen science databases (1983-2017). In addition, records of all other southern African vagrants (including ten other regularly occurring species) were gathered to account for changes in birdwatching effort in the sub-region. To assess the effect of forest loss on honey-buzzard abundance, rolling correlations were performed using forest cover in East Africa and number of honey-buzzard records in both sub-regions. Results European Honey-buzzard records in southern Africa have increased over five times more than other regularly occurring vagrant species and almost 40 times more than honey-buzzard in Tanzania, where the population has remained stable. Loss of forested area in East Africa was correlated with an increase in European Honey-buzzard records in southern Africa. Main conclusions We suggest that the European Honey-buzzard shift in wintering range may be driven by a decline in suitable habitat further north in Africa amongst other possible reasons. This effect may have been amplified by an increase in appropriate habitat across southern Africa brought about by anthropogenic changes to vegetation such as increased tree cover in urban areas. This study further highlights the importance of using African distributional data banks to understand the effects of global change on Palaearctic migrant bird species.
Data from: How biological attention mechanisms improve task performance in a large-scale visual system model
How does attentional modulation of neural activity enhance performance? Here we use a deep convolutional neural network as a large-scale model of the visual system to address this question. We model the feature similarity gain model of attention, in which attentional modulation is applied according to neural stimulus tuning. Using a variety of visual tasks, we show that neural modulations of the kind and magnitude observed experimentally lead to performance changes of the kind and magnitude observed experimentally. We find that, at earlier layers, attention applied according to tuning does not successfully propagate through the network, and has a weaker impact on performance than attention applied according to values computed for optimally modulating higher areas. This raises the question of whether biological attention might be applied at least in part to optimize function rather than strictly according to tuning. We suggest a simple experiment to distinguish these alternatives.
Study the feasibility of large-scale implementation of electric buses in the city of São Paulo
<p>The transport segment is one of the main factors responsible for global CO 2 emissions, due to the majority use of combustion engines. One of the solutions proposed by experts is the replacement of combustion-powered vehicles with battery- powered electric vehicles (BEVs). Despite this, there are some variables that hinder the popularization of BEVs, including the advancement of technologies and cost, which often make users opt for traditional models, powered by combustion. This article focuses on studying the feasibility of large-scale implementation of electric buses in the city of São Paulo.</p>
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>
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>
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>
Large-scale annotation dataset for cell/tissue segmentation in H&E-stained images : anti-αSMA (smooth muscle cells / cancer associated firbroblasts)
<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-panCK (epithelial 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>
Spin-split collinear antiferromagnets: a large-scale ab-initio study
<p>Data set associated with our work "Spin-split collinear antiferromagnets: a large-scale ab-initio study".</p> <p>It contains input files for ab-initio calculations for over sixty compounds, resulting band structures, as well as tools used to generate two-band models.</p>
Large-scale movement patterns in a social vulture are influenced by seasonality, sex, and breeding region
<p>Dataset (in RData format) and Scripts corresponding to each of the analyses carried out for the manuscript entitled "Large-scale movement patterns in a social vulture are influenced by seasonality, sex, and breeding region".</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.