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73 results for “artificial dataset”
Dataset of images SfM - FRM - Lighting and Artificial texture - JPG
<p>This collection of images was employed to examine the impact of various configurations in the 3D modeling process for short-distance environments. This analysis established settings to achieve submillimeter accuracy in the RMSE values of the analyzed points. </p> <p>Set of images used to evaluate the use of light aids (softboxes) and artificial textures in a short-distance environment. This set was used to evaluate the configurations and the possibility of using the SfM technique in the 3D modeling of structural tests. Test specimens used (Concrete, Metal and Wood)—artificial texture in white Chalk (on Concrete and Wood) and red marker (on metal).<br>Texture patterns were drawn in a checkerboard fashion (T1) and a more closed checkerboard shape (T2).</p> <p>Images in JPG without compression.</p>
Semi-artificial datasets as a resource for validation of bioinformatics pipelines for plant virus detection
Open the record for dataset details and reuse information.
Umbilical cord blood artificial mixtures validation dataset: An Enhanced DNA Methylation Library for Deconvoluting Peripheral Blood
GEO Series GSE180970. Homo sapiens. 12 samples. Type: Methylation profiling by array.
12-cell-type artificial mixtures validation dataset: An Enhanced DNA Methylation Library for Deconvoluting Peripheral Blood
GEO Series GSE182379. Homo sapiens. 12 samples. Type: Methylation profiling by genome tiling array.
Atmospheric forcing dataset for Numerical study of the seasonal thermal and gas regimes of the large artificial lake in Western Europe using LAKE2.0
<p>Input dataset for the LAKE2.0 model (http://tesla.parallel.ru/Viktor/LAKE/wikis/LAKE-model) that was used for the experiments in the "Numerical study of the seasonal thermal and gas regimes of the large artificial lake in Western Europe using LAKE2.0" article. Archive contains the atmospheric forcing file itself, file with the header description, and the setup and driver files for the LAKE2.0 model.</p> <p>Source code of the current version of the LAKE2.0 model used in the work mentioned above, pre-compiled exec of the FLake model as well as its source code (freely available under the terms of the MIT license) are also provided as separate archive files.</p> <p> </p>
Dataset for: Thermofluidic heat exchangers for actuation of transcription in artificial tissues
<p>Dataset for:</p> <p>Thermofluidic heat exchangers for actuation of transcription in artificial tissues</p> <p>Daniel C. Corbett1,2, Wesley B. Fabyan1,2, Bagrat Grigoryan3, Colleen E. O’Connor1,2,<br> Fredrik Johansson1,2, Ivan Batalov1,2, Mary C. Regier1,2, Cole A. DeForest1,2,4,<br> Jordan S. Miller3, Kelly R. Stevens1,2,5,6*</p> <p>1Department of Bioengineering, University of Washington, Seattle, WA 98195, USA. 2Institute for Stem Cell and Regenerative Medicine, Seattle, WA 98195, USA. 3Department<br> of Bioengineering, Rice University, Houston, TX 77005, USA. 4Department of<br> Chemical Engineering, University of Washington, Seattle, WA 98195, USA. 5Department<br> of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98195,<br> USA. 6Brotman Baty Institute, University of Washington, Seattle, WA 98195, USA.<br> *Corresponding author. Email: ksteve@uw.edu</p> <p><br> Spatial patterns of gene expression in living organisms orchestrate cell decisions in development, homeostasis, and disease. However, most methods for reconstructing gene patterning in 3D cell culture and artificial tissues are restricted by patterning depth and scale. We introduce a depth- and scale-flexible method to direct volumetric gene expression patterning in 3D artificial tissues, which we call “heat exchangers for actuation of transcription” (HEAT). This approach leverages fluid-based heat transfer from printed networks in the tissues to activate heat-inducible transgenes expressed by embedded cells. We show that gene expression patterning can be tuned both spatially and dynamically by varying channel network architecture, fluid temperature, fluid flow direction, and stimulation timing in a user-defined manner and maintained in vivo. We apply this approach to activate the 3D positional expression of Wnt ligands and Wnt/-catenin pathway regulators, which are major regulators of development, homeostasis, regeneration, and cancer throughout the animal kingdom.</p>
Geomagnetic datasets of BJI station reconstructed through Artificial Neural Network improved by Genetic Algorithm in 2021
<p>Beijing station established in 1954 is one of the oldest geomagnetic observatories in China, which plays an important role in data exchange, and further provide data or standardization for satellite observation and geomagnetic model construction. With the development of urbanization, the observed data are greatly disturbed by subways, and data disturbed are almost unavailable. The dataset was reconstructed through Artificial Neural Network improved by Genetic Algorithm, including minutely data of three components (D, H and Z) in 2021. This reconstruction method has been proved to be effective.</p>
The Detectability of Organic Biosignatures in Artificially Matured Iron-rich Mars Analogues [Dataset]
<p>Dataset comprising py-GC-MS spectra from artifically matured circumneutral iron-rich deposits. This data were presented in Tan et al. in "The Detectability of Organic Biosignatures in Artificially Matured Iron-rich Mars Analogues".</p>
Validation of an Artificial Intelligence Enabled Diagnostic Support Software (ArtiQ.Spiro) in Primary Care Spirometry Datasets - a Retrospective Analysis
ClinicalTrials.gov study NCT05648227. IPD Sharing: NO. Countries: 1. Publications: 0.
Dataset related to the article "Feasibility of late gadolinium enhancement (LGE) in ischemic cardiomyopathy using 2D-multisegment LGE combined with artificial intelligence reconstruction deep learning noise reduction algorithm"
<p>This record contains raw data related to the article “Feasibility of late gadolinium enhancement (LGE) in ischemic cardiomyopathy using 2D-multisegment LGE combined with artificial intelligence reconstruction deep learning noise reduction algorithm”</p> <p>Abstract</p> <p><strong>Background: </strong>Despite the low spatial resolution of 2D-multisegment late gadolinium enhancement (2D-MSLGE) sequences, it may be useful in uncooperative patients instead of standard 2D single segmented inversion recovery gradient echo late gadolinium enhancement sequences (2D-SSLGE). The aim of the study is to assess the feasibility and comparison of 2D-MSLGE reconstructed with artificial intelligence reconstruction deep learning noise reduction (NR) algorithm compared to standard 2D-SSLGE in consecutive patients with ischemic cardiomyopathy (ICM).</p> <p><strong>Methods: </strong>Fifty-seven patients with known ICM referred for a clinically indicated CMR were enrolled in this study. 2D-MSLGE were reconstructed using a growing level of NR (0%,25%,50%,75%and 100%). Subjective image quality, signal to noise ratio (SNR) and contrast to noise ratio (CNR) were evaluated in each dataset and compared to standard 2D-SSLGE. Moreover, diagnostic accuracy, LGE mass and scan time were compared between 2D-MSLGE with NR and 2D-SSLGE.</p> <p><strong>Results: </strong>The application of NR reconstruction ≥50% to 2D-MSLGE provided better subjective image quality, CNR and SNR compared to 2D-SSLGE (p < 0.01). The best compromise in terms of subjective and objective image quality was observed for values of 2D-MSLGE 75%, while no differences were found in terms of LGE quantification between 2D-MSLGE versus 2D-SSLGE, regardless the NR applied. The sensitivity, specificity, negative predictive value, positive predictive value and accuracy of 2D-MSLGE NR 75% were 87.77%,96.27%,96.13%,88.16% and 94.22%, respectively. Time of acquisition of 2D-MSLGE was significantly shorter compared to 2D-SSLGE (p < 0.01).</p> <p><strong>Conclusion: </strong>When compared to standard 2D-SSLGE, the application of NR reconstruction to 2D-MSLGE provides superior image quality with similar diagnostic accuracy.</p>
Dataset related to article "Artificial intelligence processing electronic health records to identify commonalities and comorbidities cluster at Immuno Center Humanitas"
<p>This record contains raw data related to article “Artificial intelligence processing electronic health records to identify commonalities and comorbidities cluster at Immuno Center Humanitas”.</p> <p> </p> <p><strong>Background: </strong>Comorbidities are common in chronic inflammatory conditions, requiring multidisciplinary treatment approach. Understanding the link between a single disease and its comorbidities is important for appropriate treatment and management. We evaluate the ability of an NLP-based process for knowledge discovery to detect information about pathologies, patients' phenotype, doctors' prescriptions and commonalities in electronic medical records, by extracting information from free narrative text written by clinicians during medical visits, resulting in the extraction of valuable information and enriching real world evidence data from a multidisciplinary setting.</p> <p><strong>Methods: </strong>We collected clinical notes from the Allergy Department of Humanitas Research Hospital written in the last 3 years and used it to look for diseases that cluster together as comorbidities associated to the main pathology of our patients, and for the extent of prescription of systemic corticosteroids, thus evaluating the ability of NLP-based tools for knowledge discovery to extract structured information from free text.</p> <p><strong>Results: </strong>We found that the 3 most frequent comorbidities to appear in our clusters were asthma, rhinitis, and urticaria, and that 991 (of 2057) patients suffered from at least one of these comorbidities. The clusters which co-occur particularly often are oral allergy syndrome and urticaria (131 patients), angioedema and urticaria (105 patients), rhinitis and asthma (227 patients). With regards to systemic corticosteroid prescription volume by our clinicians, we found it was lower when compared to the therapy the patients followed before coming to our attention, with the exception of two diseases: Chronic obstructive pulmonary disease and Angioedema.</p> <p><strong>Conclusions: </strong>This analysis seems to be valid and is confirmed by the data from the literature. This means that NLP tools could have significant role in many other research fields of medicine, as it may help identify other important, and possibly previously neglected clusters of patients with comorbidities and commonalities. Another potential benefit of this approach lies in its potential ability to foster a multidisciplinary approach, using the same drugs to treat pathologies normally treated by physicians in different branches of medicine, thus saving resources and improving the pharmacological management of patients.</p>
Dataset related to the article "Cardiovascular magnetic resonance images with susceptibility artifacts: artificial intelligence with spatial-attention for ventricular volumes and mass assessment"
<p>This record contains raw data related to the article "Cardiovascular magnetic resonance images with susceptibility artifacts: artificial intelligence with spatial-attention for ventricular volumes and mass assessment"</p> <p>Abstract</p> <p>Background</p> <p>Segmentation of cardiovascular magnetic resonance (CMR) images is an essential step for evaluating dimensional and functional ventricular parameters as ejection fraction (EF) but may be limited by artifacts, which represent the major challenge to automatically derive clinical information. The aim of this study is to investigate the accuracy of a deep learning (DL) approach for automatic segmentation of cardiac structures from CMR images characterized by magnetic susceptibility artifact in patient with cardiac implanted electronic devices (CIED).</p> <p>Methods</p> <p>In this retrospective study, 230 patients (100 with CIED) who underwent clinically indicated CMR were used to developed and test a DL model. A novel convolutional neural network was proposed to extract the left ventricle (LV) and right (RV) ventricle endocardium and LV epicardium. In order to perform a successful segmentation, it is important the network learns to identify salient image regions even during local magnetic field inhomogeneities. The proposed network takes advantage from a spatial attention module to selectively process the most relevant information and focus on the structures of interest. To improve segmentation, especially for images with artifacts, multiple loss functions were minimized in unison. Segmentation results were assessed against manual tracings and commercial CMR analysis software cvi<sup>42</sup>(Circle Cardiovascular Imaging, Calgary, Alberta, Canada). An external dataset of 56 patients with CIED was used to assess model generalizability.</p> <p>Results</p> <p>In the internal datasets, on image with artifacts, the median Dice coefficients for end-diastolic LV cavity, LV myocardium and RV cavity, were 0.93, 0.77 and 0.87 and 0.91, 0.82, and 0.83 in end-systole, respectively. The proposed method reached higher segmentation accuracy than commercial software, with performance comparable to expert inter-observer variability (bias ± 95%LoA): LVEF 1 ± 8% vs 3 ± 9%, RVEF − 2 ± 15% vs 3 ± 21%. In the external cohort, EF well correlated with manual tracing (intraclass correlation coefficient: LVEF 0.98, RVEF 0.93). The automatic approach was significant faster than manual segmentation in providing cardiac parameters (approximately 1.5 s vs 450 s).</p> <p>Conclusions</p> <p>Experimental results show that the proposed method reached promising performance in cardiac segmentation from CMR images with susceptibility artifacts and alleviates time consuming expert physician contour segmentation.</p>
Dataset related to the article "AI-SCoRE (artificial intelligence-SARS CoV2 risk evaluation): a fast, objective and fully automated platform to predict the outcome in COVID-19 patients"
<p>This record contains partial raw data related to the article “AI-SCoRE (artificial intelligence-SARS CoV2 risk evaluation): a fast, objective and fully automated platform to predict the outcome in COVID-19 patients”</p> <p><strong>Purpose: </strong>To develop and validate an effective and user-friendly AI platform based on a few unbiased clinical variables integrated with advanced CT automatic analysis for COVID-19 patients' risk stratification.</p> <p><strong>Material and methods: </strong>In total, 1575 consecutive COVID-19 adults admitted to 16 hospitals during wave 1 (February 16-April 29, 2020), submitted to chest CT within 72 h from admission, were retrospectively enrolled. In total, 107 variables were initially collected; 64 extracted from CT. The outcome was survival. A rigorous AI model selection framework was adopted for models selection and automatic CT data extraction. Model performances were compared in terms of AUC. A web-mobile interface was developed using Microsoft PowerApps environment. The platform was externally validated on 213 COVID-19 adults prospectively enrolled during wave 2 (October 14-December 31, 2020).</p> <p><strong>Results: </strong>The final cohort included 1125 patients (292 non-survivors, 26%) and 24 variables. Logistic showed the best performance on the complete set of variables (AUC = 0.839 ± 0.009) as in models including a limited set of 13 and 5 variables (AUC = 0.840 ± 0.0093 and AUC = 0.834 ± 0.007). For non-inferior performance, the 5 variables model (age, sex, saturation, well-aerated lung parenchyma and cardiothoracic vascular calcium) was selected as the final model and the extraction of CT-derived parameters was fully automatized. The fully automatic model showed AUC = 0.842 (95% CI: 0.816-0.867) on wave 1 and was used to build a 0-100 scale risk score (AI-SCoRE). The predictive performance was confirmed on wave 2 (AUC 0.808; 95% CI: 0.7402-0.8766).</p> <p><strong>Conclusions: </strong>AI-SCoRE is an effective and reliable platform for automatic risk stratification of COVID-19 patients based on a few unbiased clinical data and CT automatic analysis.</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.