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831
datasets available to search
ShareScore release 0.9.0
Dataset results
831 results for “automaticity”
Dataset of the Automatic Unit Test Generation for Programming Assignments Using Large Language Models
Open the record for dataset details and reuse information.
Jind-Multi: An extended framework to leverage multiple labeled datasets for the automatic annotation of single-cell RNA and ATAC data
Open the record for dataset details and reuse information.
Image dataset from Automatic analysis of Trypanosoma cruzi: A machine learning approach for the detection of blood trypomastigotes in low resolution images
<p>Image files of mice blood smear containing <em>T. cruzi</em> trypomastigotes. The images were used to train and test the classification model of the objects (nuclei and/or kinetoplasts) present in the images.</p>
Figure 7 from: Brecko J, Mathys A, Dekoninck W, Leponce M, VandenSpiegel D, Semal P (2014) Focus stacking: Comparing commercial top-end set-ups with a semi-automatic low budget approach. A possible solution for mass digitization of type specimens. ZooKeys 464: 1-23. https://doi.org/10.3897/zookeys.464.8615
Figure 7 - Focus stacking in Zerene Stacker. The small image in the upper corner provides a detailed close-up of 518 × 345 pix of the image at 100%. A Stack of 77 pictures, aligned and combined with PMax B Stack of 44 pictures, aligned and combined with PMax The individual pictures of both stacks are made with the Leica Z6 APO with DFC290 and Manfrotto led light system.
Figure 9 from: Brecko J, Mathys A, Dekoninck W, Leponce M, VandenSpiegel D, Semal P (2014) Focus stacking: Comparing commercial top-end set-ups with a semi-automatic low budget approach. A possible solution for mass digitization of type specimens. ZooKeys 464: 1-23. https://doi.org/10.3897/zookeys.464.8615
Figure 9 - A 3D model of a Dicranorrhina sp. beetle. The left pictures (A, C) represent the 3D model with its texture, while the right pictures (B, D) are from the model with the mesh only and show the level of detail of the 3D model made in Agisoft Photoscan.
Figure 8 from: Brecko J, Mathys A, Dekoninck W, Leponce M, VandenSpiegel D, Semal P (2014) Focus stacking: Comparing commercial top-end set-ups with a semi-automatic low budget approach. A possible solution for mass digitization of type specimens. ZooKeys 464: 1-23. https://doi.org/10.3897/zookeys.464.8615
Figure 8 - Focus stacking in Zerene Stacker. The small image in the upper corner provides a detailed close-up of 518 × 345 pix of the image at 100%. A Stack of 74 pictures, aligned and combined with PMax B Stack of 41 pictures, aligned and combined with PMax. The individual pictures of both stacks are made with the Canon-Cognisys set-up and double flash lights.
Figure 5 from: Brecko J, Mathys A, Dekoninck W, Leponce M, VandenSpiegel D, Semal P (2014) Focus stacking: Comparing commercial top-end set-ups with a semi-automatic low budget approach. A possible solution for mass digitization of type specimens. ZooKeys 464: 1-23. https://doi.org/10.3897/zookeys.464.8615
Figure 5 - The results of the different stacking methods in Zerene Stacker, each with a detail of a hairy leg and a part of the head with hairs. A, B details of the stack pictures by the PMax method C, D details of the stacked picture with the DMap method.
Figure 4 from: Brecko J, Mathys A, Dekoninck W, Leponce M, VandenSpiegel D, Semal P (2014) Focus stacking: Comparing commercial top-end set-ups with a semi-automatic low budget approach. A possible solution for mass digitization of type specimens. ZooKeys 464: 1-23. https://doi.org/10.3897/zookeys.464.8615
Figure 4 - The results of the different stacking methods in Helicon Focus: each stacking option is provided with a detail of a hairy leg and a part of the head with hairs. A, B result of Method A (average) C, D result of Method B (depth) E, F result of Method C (pyramid).
Figure 6 from: Brecko J, Mathys A, Dekoninck W, Leponce M, VandenSpiegel D, Semal P (2014) Focus stacking: Comparing commercial top-end set-ups with a semi-automatic low budget approach. A possible solution for mass digitization of type specimens. ZooKeys 464: 1-23. https://doi.org/10.3897/zookeys.464.8615
Figure 6 - Focus stacking in Zerene Stacker. The small image in the upper corner provides a detailed close-up of 518 × 345 pix of the image at 100%. A Stack of 70 pictures, aligned and combined with PMax B Stack of 41 pictures, aligned and combined with PMax. The individual pictures of both stacks are made with the Leica MZ16A with DFC500 camera and Leica KL 1500 LCD lights.
Figure 3 from: Brecko J, Mathys A, Dekoninck W, Leponce M, VandenSpiegel D, Semal P (2014) Focus stacking: Comparing commercial top-end set-ups with a semi-automatic low budget approach. A possible solution for mass digitization of type specimens. ZooKeys 464: 1-23. https://doi.org/10.3897/zookeys.464.8615
Figure 3 - The results of the different stacking methods in CombineZP: each stacking option is provided with a detail of a hairy leg and a part of the head with hairs. A, B details of the stack pictures by the Do Stack method C, D details of the stacked picture with the Do Soft Stack E, F details of the Do Weighted Stack method G, H details of the combined pictured with the Pyramid Weighted method I, J details of the stacked image with the Pyramid Do stack method K, L Details of the Stacked image with the Pyramoid Maximum Contrast method.
Figure 1 from: Brecko J, Mathys A, Dekoninck W, Leponce M, VandenSpiegel D, Semal P (2014) Focus stacking: Comparing commercial top-end set-ups with a semi-automatic low budget approach. A possible solution for mass digitization of type specimens. ZooKeys 464: 1-23. https://doi.org/10.3897/zookeys.464.8615
Figure 1 - The Canon-Cognisys set-up at the RBINS. The Canon 600D Camera, equipped with a Canon MP-E 65 mm 1:2.8 1–5× Macro Photo Lens, is mounted on the vertically positioned StackShot (Cognisys inc.). In the Ikea closet, at the bottom, you see the two Yongnuo YN560II speedlites positioned frontally and above is the plexiglas fig covered with paper to position the specimen.
Figure 2 from: Brecko J, Mathys A, Dekoninck W, Leponce M, VandenSpiegel D, Semal P (2014) Focus stacking: Comparing commercial top-end set-ups with a semi-automatic low budget approach. A possible solution for mass digitization of type specimens. ZooKeys 464: 1-23. https://doi.org/10.3897/zookeys.464.8615
Figure 2 - The results of the different stacking methods in Auto-Montage: each stacking option is provided with a single picture which is reduced in size by the Auto-Montage software itself and imprinted with watermarks. A represents the Blended Stacking option B picture composed by the Compound Weighted method C picture stacked by the Exponentially Weighted option D picture composed by the Fixed method and E picture stacked by the Weighted method.
Drones, automatic counting tools and artificial neural networks in wildlife population censusing
<p>1. The use of a drone to count the flock sizes of 33 species of waterbirds during the breeding and non-breeding periods was investigated.</p> <p>2. In 96% of 343 cases, drone counting was successful. 18.8% of non-breeding birds and 3.6% of breeding birds exhibited adverse reactions: the former birds were flushed, whereas the latter attempted to attack the drone.</p> <p>3. The automatic counting of birds was best done with ImageJ/Fiji microbiology software – the average counting rate was 100 birds in 64 seconds.</p> <p>4. Machine learning using neural network algorithms proved to be an effective and quick way of counting birds – 100 birds in 7 seconds. However, the preparation of images and machine learning time is time-consuming, so this method is recommended only for large data sets and large bird assemblages.</p> <p>5. The responsible study of wildlife using a drone should only be carried out by persons experienced in the biology and behaviour of the target animals.</p>
Dataset of "Towards Automatic Feature Extraction and Sample Generation of Grain Structure by Variational Autoencoder"
<p><em>No description.</em></p>
Video - Assembly Task Modelling Strategy for Automatic Robot Program Generation
<p>Video - Assembly Task Modelling Strategy for Automatic Robot Program Generation</p>
Meta-data of published work titled 'Effect of selection bias on Automatic Colonoscopy Polyp Detection'
<p>Meta-data of published work titled 'Effect of selection bias on Automatic Colonoscopy Polyp Detection' includes:</p> <p>1. Gastrolab-Polyp test dataset </p> <p>2. App release of the developed real-time polyp detector </p> <p>3. Meta-data prepared during test set analysis of the developed app and Polyp detector app on Google Store for five existing datasets like Polypgen, Kvasir, and CVC-Clinic, etc. </p> <p>For more details : https://www.sciencedirect.com/science/article/abs/pii/S1746809423003488</p> <p> </p>
Exploring the Automatic Recommendation of Composite Refactorings
<p>Dataset of the survey with developers to explore a recommender of refactoring. </p>
Aster: Automatic Speech Recognition System Accessibility Testing for Stutterers
<p>Test Cases for Aster: Automatic Speech Recognition System Accessibility Testing for Stutterers</p>
Supplementary data to the paper: Automatic Extraction of Anthropometric Features for the Individualization of the Pinna-Related Transfer Function in the Median Plane
<p>Supplementary research data to the paper (rejected):</p> <blockquote> <p>Davide Fantini, Federico Avanzini, Stavros Ntalampiras and Giorgio Presti (2023) "Automatic Extraction of Anthropometric Features for the Individualization of the Pinna-Related Transfer Function in the Median Plane"</p> </blockquote> <p>The repository includes the research data generated and analyzed in the abovementioned paper describing a method for PRTF individualization. In particular, the following data are included:</p> <ul> <li><a href="../api/files/bf20ab4d-c913-4cd0-9357-3b4039f1affb/README.md">README.md</a>: instructions for the data</li> <li><a href="../api/files/bf20ab4d-c913-4cd0-9357-3b4039f1affb/pinna_range_img.mat">pinna_range_img.mat</a>: pinna range images extracted from the 3D head meshes of the <a href="https://depositonce.tu-berlin.de/items/dc2a3076-a291-417e-97f0-7697e332c960">HUTUBS dataset</a></li> <li><a href="../api/files/bf20ab4d-c913-4cd0-9357-3b4039f1affb/landmarks.mat">landmarks.mat</a>: landmarks coordinates both manually annotated and automatically placed with ASM</li> <li><a href="../api/files/bf20ab4d-c913-4cd0-9357-3b4039f1affb/anthropometry.mat">anthropometry.mat</a>: anthropometric parameters automatically extracted from both manually annotated and ASM-fitted landmarks</li> <li><a href="../api/files/bf20ab4d-c913-4cd0-9357-3b4039f1affb/img_features.mat">img_features.mat</a>: image features pinna cavities extracted from both manually annotated and ASM-fitted landmarks</li> <li><a href="../api/files/bf20ab4d-c913-4cd0-9357-3b4039f1affb/grnn_models.mat">grnn_models.mat</a>: Generalized Regression Neural Network (GRNN) models trained from both HUTUBS anthropometry and the proposed pinna features</li> <li><a href="../api/files/bf20ab4d-c913-4cd0-9357-3b4039f1affb/predicted_dtf.mat">predicted_dtf.mat</a>: Directional Transfer Function (DTF) sets predicted from both HUTUBS anthropometry and the proposed pinna features</li> <li><a href="../api/files/bf20ab4d-c913-4cd0-9357-3b4039f1affb/anthropometry_documentation.pdf">anthropometry_documentation.pdf</a>: documentation of the pinna anthropometric parameters</li> <li><a href="../api/files/bf20ab4d-c913-4cd0-9357-3b4039f1affb/auditory_model_complete_elevation_range.pdf">auditory_model_complete_elevation_range.pdf</a>: auditory model evaluation in the complete elevation range</li> </ul> <p>The data are provided in the Matlab file format MAT. Nevertheless, the MAT files can be read with other programming languages, such as Python (<a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html">scipy.io.loadmat</a>).</p> <p>A GitHub repository to automatically extract the pinna landmarks and features as described in the paper is available <a href="https://github.com/DavideFantini/pinna-landmarks-fitting-and-anthropometry-extraction" target="_blank" rel="noopener">here</a>.</p>
Automatic Prosthetic Foot Stiffness Modulation to Improve Balance
ClinicalTrials.gov study NCT06711588. IPD Sharing: YES. Countries: 1. Publications: 0.
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.