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6,452 results for “Application”
Approaches for machine learning intermolecular interaction energies and application to energy components from symmetry adapted perturbation theory
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A hybrid approach to the small unannotated corpus-based language comparison and its application to the Old East Slavic charters - Supplementary material 2 (Modern East Slavic)
<h1>Modern East Slavic dialects (Belogornoje, Megra, Zialionka)</h1> <h2>General description</h2> <p>A set of modern East Slavic Belogornoje, Megra and Zialionka small territorial lects subcorpora. Megra is an autochthonous (Barannikova, 2005) Northern Russian small territorial lect (Kryuchkova and Goldin, 2011). Belogornoje is a late settlement (Barannikova, 2005) Central Russian small territorial lect (Kryuchkova and Goldin, 2011). Zialionka is an autochthonous Northern Belarusian lect, radically different from Belogornoje and Megra by most of the key isoglosses within the Eastern part of the Slavic continuum.</p> <h3>Sources</h3> <p>Both Megra and Belogornoje texts originate from the Saratov dialectological corpus (Kryuchkova and Goldin, 2011). These are manually transcribed interviews with dialect speakers, mostly on the slice-of-life, rarely touching the topic of religion, recorded during the field trips of Saratov State University from 1980 to 2019. They possess some tagging, but for the purpose of clear cross-evaluation, the experiments do not use this information. The transcription is phonemic, faithful to the dialect features, and remains untouched in the experiments.</p> <p><br>Zialionka texts are also phonemically transcribed and untouched in experiments, they come from the Polack ethnographic collection (Lobač, 2011). The main genre is folklore tales, collected by transcribing interviews with small territorial lects speakers during the field trips of Polack State University (Belarus) from 1992 to 2010 years. There are no traces of notable phonetic irregularities within the texts. Unfortunately, there is no way to reliably establish it, as there are no available original recordings.</p> <p>The data statement is available among the downloadable files.</p> <h2>How-to</h2> <p>This section contains the tutorials that allow to use this data with the intended pipelines.</p> <h3>Corpus-based distance measurement package</h3> <p>The source code for package is available <a href="https://doi.org/10.5281/zenodo.13958502" target="_blank" rel="noopener">here</a>, the manual is available in the <a href="https://github.com/The-One-Who-Speaks-and-Depicts/corpus_distance/blob/dev/README.md" target="_blank" rel="noopener">README</a> section of the repository.</p> <p>To use this dataset for the measurement of distance between Belogornoje, Megra and Zialionka lects, and their subsequent clusterisation, following steps should be completed:</p> <ol> <li>Download the <a href="https://github.com/The-One-Who-Speaks-and-Depicts/corpus_distance/blob/dev/example/Corpus_distance_tutorial.ipynb" target="_blank" rel="noopener">Jupyter notebook</a> that streamlines the package use.</li> <li>Download the dataset.</li> <li>Put the dataset into a selected folder on your computer (make sure there are no other files within this folder).</li> <li>Insert the path to the directory into <code>CONTENT_DIR</code> variable in the Jupyter notebook.</li> <li>Run the notebook, adjusting the parameters, if necessary.</li> </ol> <h2> </h2>
Effect of Drying Methods on the Properties of Ginger Powder and Its Application in Gummy Candy Production
<p>The data provides the parameters of the research process, with specific contents in the following sheets:</p> <p>The "Approximate Composition" sheet provides information on the basic composition of raw ginger.</p> <p>The "Color" sheet provides the color value of ginger powder.</p> <p>The "Physical" sheet provides the physical characteristics of ginger powder, such as bulk density, tap density, flowability, compressibility, solubility, hygroscopicity, and particle size.</p> <p>The "Biological Compounds" sheet provides information on the content of active ingredients such as total polyphenol, total flavonoid, gingerol, alkaloids, and tannins.</p> <p>The "Antioxidant Activity" sheet provides information on the antioxidant capacity of ginger powder with antioxidant values through DPPH and ABTS free radical scavenging ability and FRAP iron reducing ability.</p> <p>The "PCA" sheet shows the data obtained while performing principal component analysis.</p> <p>The "Pearson" sheet shows the data obtained during the Pearson correlation evaluation.</p> <p>The sheets with the names physical of ginger gummy candy, texture profile, and sensory evaluation show the results related to the properties of the ginger gummy candies.</p> <p>The data is currently available only to authors, editors, and reviewers and will be made public once the manuscript is accepted.</p> <div> </div>
Dataset related to publication "Active hyperspectral sensing (AHS) applications for mineral deposit exploration"
<p>Dataset related to publication "Active hyperspectral sensing (AHS) applications for mineral deposit exploration"</p>
Cohesion data of microservices of Spinnaker application
<p>This repository stores the cohesion data of the Spinnaker application.</p>
4C-seq/HiC distinguishes pathogenic from neutral duplications at the GPR101 locus: clinical application of chromatin conformation capture techniques to refine genetic diagnosis
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Supplementary Material for Mining Cost Awareness in the Infrastructure as Code Artifacts of Cloud-based Applications
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MACHINE LEARNING APPLICATIONS IN AGRICULTURE
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Demonstration of ClinGenNBL v2: A Model-based Application for the Management of Data associated with Neuroblastoma
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Fig. 5 in Distributional modeling of Mantophasmatodea (Insecta: Notoptera): a preliminary application and the need for future sampling
Fig. 5 Sampling priority for future surveys of Mantophasmatodea (Notoptera). a Thiessen polygons showing the density of samplings of Mantopasmathodea in southwestern Africa. b Priority areas for future generic surveys. c Priority areas for future species surveys. Both Karoo-Namib Zone and Cape Flore Kingdom are also depicted in b and c
Dataset: Applicability of open science practices to completed research projects from different disciplines and research paradigms
<p>This is the archived data for the reserach project "Applicability of open science practices to completed research projects from different disciplines and research paradigms". Data access is restricted.</p>
Fig. 4 in Exogenous application of polyamines alleviates water stress-induced oxidative stress of Rosa damascena Miller var. trigintipetala Dieck
Fig. 4. Effects of foliar application of spermine (Spm) and spermidine (Spd) on endogenous polyamines (A: putrescine, B: spermine and C: spermidine) contents of Rosa damascena Miller var. trigintipetala Dieck leaves grown under water stress (50% FC) or non-stress (100% FC) conditions. Columns had different letters are significantly differ from each other according to Duncan multiple range test at P = 0.05 (n = 8).
FIGURES 14–18. Bulk drying. 14 in A technique to dry mount Hymenoptera (Hexapoda) from alcohol in a few seconds, and its application to other insect orders
FIGURES 14–18. Bulk drying. 14, Petri dish with specimens pinned directly from alcohol, waiting for blow drying processing while once again fully immersed in alcohol. 15, Same specimens, arranged on polystyrene foam board and under direct air blow. 16–18, Specimens of Auplopus sp. (Pompilidae), Timulla sp. (Mutilidae), and Anoplius sp. (Pompilidae), respectively, immediately after a 40 seconds session of bulk air blowing. Figs 16–17 show good results. Fig. 18 shows the worst result, but note that position of appendages improved from original situation, in Fig. 14.
FIGURE 10 in Syngraph: An application for graphic display and interactive use of synonym lists
FIGURE 10. Geographical distribution of the sea anemone Heteractis aurora, its documented occurrences coded by the color used for the synonym unit applied to it in the Syngraph synonym list for the species (this image was captured when there were 73 records for the species; currently there are 84: http://hercules.kgs.ku.edu/Hexacoral/anemone2/ distribution.cfm?xmlsource=http%3A%2F%2Fhercules.kgs.ku.edu%2Fhexacoral%2Fdev%2Fxml%2Fhexmlscript.cfm %3Fseniorid%3D15%26type%3D&callingpage=species&speciessearched=Heteractis%20aurora).
FIGURE 3 in Syngraph: An application for graphic display and interactive use of synonym lists
FIGURE 3. Three entries of a synonym unit. The rectangle on which the first use of a name is imposed (the original description) is outlined in black. The genus name is annotated as having been misspelled – it was correctly rendered in the second citation. This example also illustrates an adjective, which is rendered as a two-branch relation using a solid green line to the right of the names. The name to which the adjective applies is linked to the name considered valid by the authority, indicated by a green box. Thus, the name listed third is a new combination, and the relation should be read: Carlgren (1949) placed the species described by Carlgren (1900) as Actinoides africana in the genus Anthopleura.
Engineering and functional characterization of a proton-driven β-lactam antibiotic translocation module for bionanotechnological applications
<p>Datasets to Stauffer et al. (2021) Sci. Rep. </p>
FIGURES 10–12 in A sketch-based system for modeling 3D objects: Applications to taxonomy
FIGURES 10–12. Examples of usages of our system. Fig. 10. Cactus: (a) coloring effect; (b) hatching effect; and (c) resulting 3D model. Fig. 11. Comparison of a bird reconstructed by our approach and previous works. (a-b) Results from similar drawings, modified from Li et al. (2017) and Ramos et al. (2018); (c) results from our system with multiple layers. Fig. 12. Flower: (a) illustration with overlapping contours; (b) our sketch done by selecting the contours in the original illustration with the Halo Effect; (c) layer coloring effect; and (d) resulting 3D model.
Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation
<p><strong>Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation</strong></p> <p>This repository contains data contributed and used in our MICCAI 2021 paper:<br> Fidon, L. et al. Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation.</p> <p>Our code is publicly available at<br> <a href="https://github.com/LucasFidon/fetal-brain-segmentation-partial-supervision-miccai21">https://github.com/LucasFidon/fetal-brain-segmentation-partial-supervision-miccai21</a></p> <p><strong>Files:</strong></p> <ul> <li>FeTA2021_Release1and2Corrected_v4.zip: contains 90 fetal brain T2w MRIs of the FeTA 2021 dataset with updated manual segmentations</li> <li>MICCAI21_partial_supervision_trained_models.zip: contains the weights of our pre-trained deep neural networks for fetal brain 3D MRI segmentation</li> </ul> <p>The FeTA 3D MRIs and segmentations contained in this repository shall be used only for research and education purposes.</p> <p>Segmentation labels:<br> 1: white matter (excluding corpus callosum; see below)<br> 2: intra-axial cerebrospinal fluid (CSF)<br> 3: cerebellum<br> 4: extra-axial CSF<br> 5: cortical gray matter<br> 6: deep gray matter<br> 7: brainstem<br> 8: corpus callosum</p> <p><strong>Updates:</strong></p> <p><strong>Version 4 (November 2022)</strong><br> 8 cases were forgotten by mistake when uploading version 3 (sub-feta004, sub-feta008, sub-feta017, sub-feta020, sub-feta021, sub-feta022, sub-feta023, sub-feta027). I have added them back in version 4, including the corpus callosum segmentations for those cases.<br> <br> Thank you to those who let me know about this. Please do not hesitate to reach out to me (lucas.fdon@gmail.com) if you think something is wrong.</p> <p><strong>Version 3 (July 2022)</strong></p> <p>Added some corpus callosum segmentations that were missing in version 2.</p> <p><strong>Version 2 (Avril 2022)</strong></p> <p>3D MRI and refined manual segmentation have been added for:<br> - the 10 fetal brain MRIs of the testing set of the FeTA data release 1 (sub-feta081 to sub-feta090).<br> - the 40 fetal brain MRIs new in the FeTA data release 2 (sub-041 to sub080).<br> The same pre-processing as in version 1 below has been performed.<br> The code used for the pre-processing is available at<br> https://github.com/LucasFidon/fetal-seg-preprocessing</p> <p>Some additional refinements of the manual segmentation have been performed for sub-feta001 to sub-feta040.</p> <p>See participant.tsv for more information about the 3D MRI studies.</p> <p>The pathology for each study has been diagnosed by two radiologists in our team and can be found in participant.tsv.<br> Those are not present in the original FeTA dataset release 2.1</p> <p><strong>Version 1</strong><br> Summary of the changes done on the original FeTA dataset (data release 1) [1].</p> <p>The only modifications we did are detailed below (pre-processing and manual segmentation).</p> <p>Pre-processing:<br> -registration to the neurotypical fetal brain atlas of Gholipour et al [2]<br> -resampling to 0.8mm isotropic<br> -automatic mask via label propagation using a neurotypical [2] and a spina bifida [3] fetal brain atlas.</p> <p>The code used for the pre-processing is available at<br> https://github.com/LucasFidon/fetal-seg-preprocessing</p> <p>Segmentation:<br> Manual corrections of all manual segmentations by Michael Aertsen, Lucas Fidon, and Philippe Demaerel for the 38 3D MRIs in the FeTA dataset release 1.<br> For 2 cases, the segmentations were not corrected manually (see /Excluded).</p> <p>[1] 10.5281/zenodo.4541605<br> [2] http://crl.med.harvard.edu/research/fetal_brain_atlas/<br> [3] 10.7303/syn25887675</p> <p><strong>How to cite:</strong></p> <p>If you use the deep neural network weights please cite:</p> <ul> <li>Fidon, L., Aertsen, M., Emam, D., et al. Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation. MICCAI (2021).</li> </ul> <p>if you use the FeTA data (release 1 and 2) and the corrected segmentations that we contributed please cite:</p> <ul> <li>Payette, K., de Dumast, P., Kebiri, H. et al. An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset. Sci Data 8, 167 (2021). https://doi.org/10.1038/s41597-021-00946-3</li> <li>Fidon, L., Aertsen, M., Emam, D., et al. Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation. MICCAI (2021).</li> </ul> <p><strong>Terms of use - FeTA 3D MRIs and segmentations:</strong></p> <p>This is an agreement (“Agreement”) between you the downloader (“Downloader”) and the owner of the materials (“User”) governing the use of the Fetal Tissue Annotation and Segmentation Dataset to be downloaded.</p> <p>I. Acceptance of this Agreement</p> <p>By downloading or otherwise accessing the Fetal Tissue Annotation and Segmentation Dataset, the Downloader represents his/her acceptance of the terms of this Agreement.</p> <p>II. Data ownership</p> <p>The owner of the Fetal Tissue Annotation and Segmentation Dataset is the University Children’s Hospital Zurich.</p> <p>III. Use of the Materials</p> <p>Fetal Tissue Annotation and Segmentation Dataset is used only for research and education. Any other kind of use you will lead to the recall of all datasets, stop of collaboration, and legal consequences. This Agreement represents the entire agreement between Downloader and User with respect to the downloading and use of the Materials and supersedes all prior or contemporaneous communications and proposals (whether oral, written, or electronic) between Downloader and User with respect to downloading or using the Materials. </p> <p> </p>
Input data: Evaluating HPX and Kokkos on RISC-V using an astrophysics application Octo-Tiger
<p>Silo input files to reproduce the performance measurements.</p>
Pomegranate Flower Extract—The Health-Promoting Properties Optimized by Application of the Box–Behnken Design
<p>Data includes source data and the publication to which it is related</p>
ScienceDex guides
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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.