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.
3,036
datasets available to search
ShareScore release 0.9.0
Dataset results
3,036 results for “ct”
X-ray CT and MAPS data of Hot-dry rock
<p>These multiple scanning experiment image can be used to construct multi-scale multi component digital rock.</p>
Chest CT Computerized Aided Quantification of PNEUMONIA Lesions in COVID-19 Infection: A Comparison among Three Commercial Software.
<p>We uploaded the complete database of the manuscript: Grassi R, Cappabianca S, Urraro F, Feragalli B, Montanelli A, Patelli G, Granata V, Giacobbe G, Russo GM, Grillo A, De Lisio A, Paura C, Clemente A, Gagliardi G, Magliocchetti S, Cozzi D, Fusco R, Belfiore MP, Grassi R, Miele V. Chest CT Computerized Aided Quantification of PNEUMONIA Lesions in COVID-19 Infection: A Comparison among Three Commercial Software. Int J Environ Res Public Health. 2020 Sep 22;17(18):6914. doi: 10.3390/ijerph17186914. PMID: 32971756; PMCID: PMC7558768.</p>
CT data and 3D models associated with: Palaeoneurology of the Early Cretaceous iguanodont Proa valdearinnoensis and its bearing on the parallel developments of cognitive abilities in theropod and ornithopod dinosaurs
<p><i>Proa valdearinnoensis </i>is a relatively large-headed and stocky iguanodontian dinosaur from the latest Early Cretaceous of Spain. Its braincase is known from three specimens. Similar to that of other dinosaurs, it shows a mosaic ossification pattern in which most of the bones seem to have fused together indistinguishably while a few bones (frontoparietal, basioccipital) might have remained loosely attached. The endocasts of the three specimens are described based on CT data and digital reconstructions. They show unmistakable morphological similarities with the endocast of closely related taxa, such as <i>Sirindhorna khoratensis </i>(which is close in age but from Thailand). This supports a high conservatism of the endocranial cavity. The issue of volumetric correspondence between endocranial cavity and brain in dinosaurs is analysed. Although a brain-to-endocranial cavity (BEC) index of 0.50 has been traditionally used, we employ instead 0.73. This is indeed the mid-value between the situation in adults of <i>Alligator mississippiensis</i> and <i>Gallus gallus</i>, which are members of the extant bracketing taxa of dinosaurs (Crocodilia and Aves). We thence gauge the level of encephalisation of <i>Proa valdearinnoensis</i> by the calculation of the Encephalisation Quotient (EQ), which remains valuable as a metric for assessing the degree of cognitive function in extinct taxa, especially those with fully ossified braincases like dinosaurs and other archosaurs. The EQ obtained for <i>Proa valdearinnoensis</i> (3.611) suggests that this species was significantly more encephalised than most if not all extant non-avian, non-mammalian amniotes. Our work adds to the growing body of data concerning theoretical cognitive capabilities in dinosaurs and supports the idea that increasing encephalisations were fostered not only once in theropods but also in parallel in the shorter-lived lineage of ornithopods. <i>Proa valdearinnoensis</i> was ill-equipped to respond to theropod dinosaurs and possibly lived in groups as a strategy to mitigate the risk of being predated upon. We hypothesize that group-living and protracted caring of juveniles in this and possibly many other iguanodontian ornithopods favoured a degree of encephalisation that was outstanding by reptile standards.</p>
Micro-CT data from: Maternal care in mid-Cretaceous lagonomegopid spiders
<p>This dataset contains the raw Micro-CT data and a 3D CT reconstruction of a piece of Burmese amber CNU009432 from "Guo, X., Selden, P. and Ren, D. (2021) Maternal care in mid-Cretaceous lagonomegopid spiders. Proc. R. Soc. B.". These data shows the maternal care behaviour of an adult female lagonomegopid spider guarding her egg sac. </p> <p>Maternal care benefits the survival and fitness of offspring often at a cost to the mother's future reproduction and has evolved repeatedly throughout the animal kingdom. In extant spider species, this behaviour is very common and has different levels and diverse forms. However, evidence of maternal care in fossil spiders is quite rare. In this study,we describe four Mid-Cretaceous (approx. 99 Ma) amber specimens from northern Myanmar with an adult female, part of an egg sac and some spiderlings of the extinct family Lagonomegopidae preserved, which suggest that adult lagonomegopid females probably built and then guarded egg sacs in their retreats or nests, and the hatched spiderlings may have stayed together with their mother for some time. The newfossils represent early evidence of maternal care in fossil spiders, and enhance our understanding of the evolution of this behaviour.</p>
Dataset for effects of shear zone thickness in root-reinforced soils: Fallow P X-ray CT scans and digital volume correlation data
<p>Dataset for effects of shear zone thickness in root-reinforced soils: Fallow P X-ray CT scans and digital volume correlation data</p>
Dataset for effects of shear zone thickness in root-reinforced soils: Gorse A X-ray CT scans and digital volume correlation data
<p>Dataset for effects of shear zone thickness in root-reinforced soils: Gorse A X-ray CT scans and digital volume correlation data</p>
Dataset for effects of shear zone thickness in root-reinforced soils: Willow C X-ray CT scans and digital volume correlation data
<p>Dataset for effects of shear zone thickness in root-reinforced soils: Willow C X-ray CT scans and digital volume correlation data</p>
Dataset for effects of shear zone thickness in root-reinforced soils: Gorse G X-ray CT scans and digital volume correlation data
<p>Dataset for effects of shear zone thickness in root-reinforced soils: Gorse G X-ray CT scans and digital volume correlation data</p>
Dataset for effects of shear zone thickness in root-reinforced soils: Fallow D X-ray CT scans and digital volume correlation data
<p>Dataset for effects of shear zone thickness in root-reinforced soils: Fallow D X-ray CT scans and digital volume correlation data</p>
CT-OCR-2022 (scroll004)
<p><strong>CT-OCR-2022 dataset</strong></p> <p>CT-OCR-2022 dataset contains optically scanned images for source paper document, X-ray projections, CT-reconstructed cross-sections and segmentation markups for model objects.</p> <p>Description of the structure of the dataset, contacts and information about the reference to the dataset see at <a href="https://doi.org/10.5281/zenodo.7123495">https://doi.org/10.5281/zenodo.7123495</a>.</p>
CT-OCR-2022 (folded001, part1)
<p><strong>CT-OCR-2022 dataset</strong></p> <p>CT-OCR-2022 dataset contains optically scanned images for source paper document, X-ray projections, CT-reconstructed cross-sections and segmentation markups for model objects.</p> <p>Description of the structure of the dataset, contacts and information about the reference to the dataset see at <a href="https://doi.org/10.5281/zenodo.7123495">https://doi.org/10.5281/zenodo.7123495</a>.</p>
CT-OCR-2022 (scroll003)
<p><strong>CT-OCR-2022 dataset</strong></p> <p>CT-OCR-2022 dataset contains optically scanned images for source paper document, X-ray projections, CT-reconstructed cross-sections and segmentation markups for model objects.</p> <p>Description of the structure of the dataset, contacts and information about the reference to the dataset see at <a href="https://doi.org/10.5281/zenodo.7123495">https://doi.org/10.5281/zenodo.7123495</a>.</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>
Weakly-Supervised Learning Significantly Reduces the Number of Labels Required for Intracranial Hemorrhage Detection on Head CT
<p>Modern machine learning pipelines, in particular those based on deep learning (DL) models, require large amounts of labeled data. For classification problems, the most common learning paradigm consists of presenting labeled examples during training, thus providing \emph{strong supervision} by directly presenting examples from the different classes, e.g. positive and negative samples. As a result, the adequate training of these models demands the curation of large datasets with high-quality labels. This constitutes a major obstacle for the development of DL models in radiology---in particular for cross-sectional imaging (e.g., computed tomography [CT] scans)---where labels must come from manual annotations by expert radiologists at the image or slice-level (as opposed to the examination level, such as could be obtained using natural language processing of radiology reports). <br> This work studies the question of what kind of labels should be collected for the problem of intracranial hemorrhage detection in brain CT. We investigate whether image-level annotations should be preferred to examination-level ones. By framing this task as a Multiple Instance Learning (MIL) problem, and employing modern attention-based DL architectures, we analyze the degree to which different levels of supervision improve the detection performance. We find that strong supervision (learning with local image-level annotations) and weak supervision (learning with only global examination-level labels) achieve comparable performance in both examination- and image-level hemorrhage detection, as well as in hemorrhage localization at the pixel-level (explainability). Furthermore, we study this behavior as a function of the number of labels available during training. Our results suggest that local labels may not be necessary at all, drastically reducing the time and cost involved in collecting and curating datasets.</p>
Fossil Dorylinae CT data
<p>Among social insects, army ants are exceptional in their voracious coordinated predation and nomadic life history: the synthesis of these remarkable ecological traits is referred to as the army ant syndrome. Despite molecular evidence that the army ant syndrome evolved twice during the mid-Cenozoic, once in the Neotropics and once in the Afrotropics, fossil army ants are markedly scarce, comprising a single known species from the Caribbean 16 Ma. Here we report the oldest army ant fossil and the first from the Eastern Hemisphere (EH), <em>Dissimulodorylus</em> perseus, preserved in Baltic amber dated to the Eocene. Using a combined morphological and molecular Ultra Conserved Elements dataset spanning doryline lineages, we find that <em>D</em>. <em>perseus</em> is nested among extant EH army ants with affinities to <em>Dorylus</em> "driver ants." Army ants are characterized by limited extant diversification throughout most of the Cenozoic; the discovery of <em>D</em>. <em>perseus</em> suggests an unexpected diversity of now-extinct army ant lineages in the Cenozoic, some of which were present in Continental Europe. </p>
Supplementary material 2 from: Tran TT, Pham AV, Le MD, Nguyen NH, Ziegler T, Pham CT (2023) A new species of Gracixalus (Anura, Rhacophoridae) from northwestern Vietnam. ZooKeys 1153: 15-35. https://doi.org/10.3897/zookeys.1153.93566
Uncorrected ("p") distance matrix showing average percentage pairwise genetic divergences (%) for the 16SrRNA gene between members of the genus Gracixalus
Supplementary material 3 from: Tran TT, Pham AV, Le MD, Nguyen NH, Ziegler T, Pham CT (2023) A new species of Gracixalus (Anura, Rhacophoridae) from northwestern Vietnam. ZooKeys 1153: 15-35. https://doi.org/10.3897/zookeys.1153.93566
Uncorrected ("p") distance matrix showing average percentage pairwise genetic divergences (%) for the Rag1 gene between members of the genus Gracixalus
Results of the CT competition at the 12th International Workshop on Combinatorial Testing
<p>These are the models and raw results achieved by participating tools in the CT competition at the 12th International Workshop on Combinatorial Testing (IWCT 2023).</p> <p>The file ct-comp-iwct2023-models.tar.gz contains the input files in CTWedge and ACTS format.</p> <p>The file ct-comp-iwct2023-output-and-timing.tar.gz contains one folder per tool and strength. In each of these folders, files conform to the following syntax:[toolname]_[model_filename]_[strength]_[iteration].[suffix]<br> where "toolname" is identical to the current folder,<br> "model_filename" is the exact name of the model file (including suffix),<br> "strength" is the combinatorial strength of the resulting CA (between 2 and 5),<br> "iteration" takes values between 1 and 3 and refers to the round of execution (at lower strengths, tools were tested using 3 iterations; at higher strengths, some tools required too much execution time and were only run once),<br> "suffix" is one of the following: "time" for the output of GNU time in verbose mode or "out" for the resulting CA (in headerless CSV form).</p> <p>The competing tools are (in alphabetical order):</p> <ul> <li>ACTS</li> <li>CAgen (in three versions; the default uses one thread less than the number of cores, _n uses exactly the number of available cores, and _np1 uses one additional thread)</li> <li>caopt</li> <li>KALI</li> <li>MEDICI</li> <li>pMEDICI</li> </ul> <p>The file validation-results.zip contains the results of the CA validation of all output files.</p> <p>All tools were executed in Docker containers based on the official OpenJDK image, which is itself based on Debian Buster (Docker identifier: openjdk:14-buster). The experiments were performed on a machine using a Intel(R) Xeon(R) CPU E5-2620 v4 @ 2.10GHz (16 physical cores, 32 logical cores) with 256 GB RAM.</p>
Lung CT COVID-19 batch 1
<p>This data set is part of the public development data for the <a href="http://auc23.grand-challenge.org/">2023 Automated Universal Classification Challenge</a> (AUC23). The data set concerns COVID-19 RT-PCR outcome prediction and prediction of severe COVID-19, defined as death or intubation after one month, from computed tomography (CT). The data set was previously introduced and described by <a href="https://pubs.rsna.org/doi/full/10.1148/radiol.2021210384">Revel, M. et al (2021)</a>. Data was restructured in compliance with the <a href="https://auc23.grand-challenge.org/">AUC23</a> challenge format. The STOIC project collected CT images of 10,735 individuals suspected of being infected with SARS-COV-2 during the first wave of the pandemic in France, from March to April 2020. For each patient in the training set, the dataset contains binary labels for COVID-19 presence based on RT-PCR test results, and COVID-19 severity, defined as intubation or death within one month from the acquisition of the CT scan. This data set contains the training sample of the STOIC dataset as used in the <a href="https://stoic2021.grand-challenge.org/">STOIC2021</a> challenge.</p> <p>Images are 3D tensors:</p> <ul> <li>0: 3D CT scan</li> </ul> <p>Classification labels:</p> <ul> <li>COVID-19: <ul> <li>0: Negative RT-PCR</li> <li>1: Positive RT-PCR<br> </li> </ul> </li> <li>Severe COVID-19: <ul> <li>0: Alive and no intubation after one month</li> <li>1: Death or intubation after one month</li> </ul> </li> </ul> <p>imagesTr (root folder with all patients and studies)<br> ├── covid19severity_6_0000.mha (3D CT for study 6)<br> ├── covid19severity_17_0000.mha (3D CT for study 17)<br> ├── ...</p> <p>Please cite the following article if you are using the <a href="https://registry.opendata.aws/stoic2021-training/">STOIC2021 training dataset</a>:</p> <pre><code>STOIC2021 Training was accessed on DATE from https://registry.opendata.aws/stoic2021-training. STOIC2021 Training was documented in Thoracic CT in COVID-19: The STOIC Project, Revel, Marie-Pierre, et al. Radiology, 2021, https://doi.org/10.1148/radiol.2021210384.</code></pre> <p>Due to upload size limits, the data set was split into six batches.</p> <p>Batch 2: <a href="https://zenodo.org/record/8042589">https://zenodo.org/record/8042589</a></p> <p>Batch 3: <a href="https://zenodo.org/record/8042817">https://zenodo.org/record/8042817</a></p> <p>Batch 4: <a href="https://zenodo.org/record/8043089">https://zenodo.org/record/8043089</a></p> <p>Batch 5: <a href="https://zenodo.org/record/8043216">https://zenodo.org/record/8043216</a></p> <p>Batch 6: <a href="https://zenodo.org/record/8043218">https://zenodo.org/record/8043218</a></p>
Supplementary material S1. Henoticus groehni sp. nov., holotype, No. 5087 [GPIH], X-ray micro-CT volume rendering of the habitus.
<p>Supplementary material S1 in paper: Bukejs A., Lyubarsky G.Yu., Alekseev V.I. The oldest fossil species of the genus <em>Henoticus</em> Thomson (Coleoptera: Cryptophagidae) from Eocene Baltic amber. <em>Historical Biology</em>.</p> <p> </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.